EKSPLORIUM-BULETIN PUSAT TEKNOLOGI BAHAN GALIAN NUKLIR
EKSPLORIUM – Buletin Pusat Teknologi Bahan Galian Nuklir is a peer-reviewed scientific journal. Established to support the advancement and dissemination of research in the field of nuclear materials and related technologies, EKSPLORIUM serves as a key platform for scientists, engineers, academics, and practitioners engaged in the exploration, mining, processing, and application of nuclear mineral resources. Scope EKSPLORIUM focuses on the following research areas: Exploration and evaluation of nuclear mineral resources Geology and geochemistry of uranium and thorium Mining and mineral processing technologies Nuclear material characterization and analysis Environmental management and safety in nuclear resource development Nuclear energy policy and technology innovation in mineral resource sectors The journal welcomes original research articles, review papers, technical notes, and case studies relevant to both fundamental and applied aspects of nuclear material science and engineering. Publication Frequency The journal is published twice a year (biannually), in June and December. Peer Review and Ethics All submissions undergo a rigorous double-blind peer review process to ensure the quality, originality, and relevance of published content. The journal adheres to the highest standards of academic integrity and publication ethics, following guidelines from the Committee on Publication Ethics (COPE). Open Access Policy EKSPLORIUM is an open-access journal. All articles are freely available to readers and researchers worldwide without subscription or payment, in line with the journal's commitment to the global exchange of scientific knowledge. Indexing and Abstracting The journal is indexed in various national and international databases (list specific indexes if known), enhancing its visibility and accessibility. Contact For inquiries, submissions, or further information, please contact us editor@eksplorium.com
Enhancing Supply Chain Sustainability Using AI for Carbon Footprint Analysis and Optimization.
The global push for sustainable operations has compelled organizations to re-evaluate their supply chains to reduce environmental impacts. Among the most pressing challenges is the accurate measurement and reduction of carbon footprints across complex, multi-tiered supply networks. Artificial Intelligence (AI) has emerged as a transformative tool in enabling organizations to enhance supply chain sustainability through precise carbon footprint analysis and optimization. This paper explores how AI-driven technologies such as machine learning, deep learning, natural language processing, and predictive analytics can be integrated into supply chain management systems to identify emission hotspots, simulate low-carbon alternatives, and implement optimization strategies. We review the role of AI in gathering and interpreting emissions data from diverse sources, providing real-time visibility into Scope 1, 2, and 3 emissions. Case studies are presented to illustrate how leading firms are leveraging AI to reduce emissions, optimize routes, and transition to sustainable logistics models. The paper also discusses the challenges of data availability, ethical considerations, and technological infrastructure, offering recommendations for businesses and policymakers. Through this exploration, the research underscores the potential of AI not only in reducing environmental impact but also in aligning with global sustainability frameworks such as the UN Sustainable Development Goals (SDGs) and Science Based Targets initiative (SBTi). The study concludes with future directions, emphasizing the need for interdisciplinary collaboration and regulatory support to fully harness AI's potential for a greener global supply chain.
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Design of an Intelligent Power Management System for IoT Devices Using Machine Learning.
The rapid expansion of Internet of Things (IoT) ecosystems has transformed how devices interact with each other and the environment. However, the exponential increase in connected devices has led to serious concerns over energy consumption, particularly in resource-constrained and battery-powered systems. Conventional power management techniques often employ static thresholds or rule-based heuristics, which fail to adapt to the dynamic and context-sensitive nature of IoT environments. This paper presents the design and development of an intelligent power management system for IoT devices using machine learning (ML). The proposed system employs time-series forecasting and supervised learning algorithms to predict workload patterns, environmental factors, and device usage. Based on these predictions, the system dynamically adjusts energy consumption through intelligent scheduling, adaptive sensor sampling, and communication frequency control. We trained and validated our models using real-world sensor datasets from environmental monitoring nodes. The experimental results show that our ML-based power optimization system achieves up to 35% energy savings while maintaining performance metrics such as latency and data fidelity. Furthermore, this system demonstrates adaptability across various IoT domains including agriculture, healthcare, and smart homes. The modular architecture ensures scalability and compatibility with modern microcontrollers. This research underscores the potential of machine learning in driving energy-aware intelligence in future IoT networks and paves the way for more sustainable and autonomous device ecosystems.
Enhanced Mathematical Framework for Non-Integer Order Functional Integro-Differential Equations through Advanced Contractivity Conditions and Dhage’s Sophisticated Fixed Point Theorem
This investigation develops advanced methodologies for analyzing solution existence and asymptotic convergence in non-integer order functional integro-differential equations within sophisticated algebraic frameworks. Our methodology employs enhanced contractivity conditions, advanced function characteristics, and asymptotic convergence principles. The primary theoretical advancement materializes through refined multipoint asymptotic convergence techniques established by Dhage’s innovative approach. Our mathematical structure provides a comprehensive foundation for studying sophisticated non-integer order equations exhibiting memory characteristics and functional interdependencies.
Navigating Crisis Communication in Southeast Asia: A Comparative Analysis of Media Framing during Public Health Emergencies
In the increasingly interconnected media environment of Southeast Asia, public health emergencies such as pandemics and viral outbreaks challenge governments and media systems to communicate information effectively and manage public sentiment responsibly. This paper investigates the strategies and nuances of crisis communication by analyzing how news media in selected Southeast Asian countries framed public health emergencies. Using a comparative lens, the study examines media coverage from Singapore, Indonesia, Vietnam, and the Philippines during major health crises—including the COVID-19 pandemic, the dengue fever outbreak, and the avian influenza scare. The research applies a qualitative content analysis of mainstream media articles and broadcast content from 2020 to 2023, identifying keyframing devices such as responsibility attribution, fear appeal, reassurance messaging, and expert reliance. It further integrates theoretical perspectives from framing theory, agenda-setting, and cultural communication to explore how sociopolitical structures and media freedom indices influence message construction. The findings reveal that while Singapore employed a technocratic, top-down communication approach with a strong emphasis on governmental expertise and scientific framing, Indonesia and the Philippines exhibited a more fragmented media landscape with narratives often oscillating between populism, blame assignment, and grassroots storytelling. Vietnam, with its state-controlled media, predominantly adopted a consensus-driven model that emphasized collective action and minimized panic. Crucially, the analysis uncovers significant differences in audience engagement, misinformation management, and media trust levels across the countries studied. In particular, the extent of media freedom and digital literacy emerged as pivotal variables affecting both the clarity and credibility of crisis narratives. The paper argues that effective crisis communication in Southeast Asia depends not only on timely and transparent information but also on the cultural adaptability of framing strategies and trust-building mechanisms embedded in national media systems. By synthesizing media sociology with comparative communication frameworks, this study offers valuable insights for policymakers, journalists, and public health communicators aiming to improve future crisis response and public resilience. It contributes to the growing field of regional media studies by highlighting the intricate interplay between communication styles, governance models, and public health imperatives in one of the world's most diverse and dynamic regions.
A Deep Learning Approach to Generate Thermal Images Synthetically for Emotion Recognition Applications
Thermal imaging is widely used in emotion recognition for its ability to capture physiological responses such as temperature variations in facial regions. However, collecting large-scale thermal datasets is challenging due to cost, privacy concerns, and limited availability. This presents a deep learning approach to synthetically generate thermal images from RGB images for emotion recognition applications. A Generative Adversarial Network (GAN)-based model is trained to make it learn the mapping between visible and thermal domains, ensuring realistic thermal image synthesis. The proposed method enhances existing emotion recognition systems by providing augmented thermal datasets, reducing dependency on expensive thermal cameras. Experimental results shows that using synthetic thermal images enhances the accuracy of deep learning-based emotion recognition models.
A Comprehensive Study on AI and ML Techniques in Healthcare Diagnostics Challenges and Future Directions
Artificial intelligence is a field of computer science which focuses on creating machines which performs task based on human intelligence. The process consists of learning from which information can be gathered, reasoning is done to reach the output by processing rules finally correction is done for future processing. On other hand the concept of robotics involves phases such as designing, building and performing operation. Despite the advancements there is a critical research gap in understanding the challenges of AI adoption in healthcare diagnostics particularly with regard to infrastructure limitations, cost considerations, ethical issues, and the disparity in acceptance between urban and rural areas. This disparity also includes the scant investigation of AI models tailored to the provision of individualized equal healthcare. Designing phases involves building a blue print. Construction is used for Incorporating AI components according to the needs of user. Operations involves working of a robot similar to that of human in performing tasks which produces more number of efficiency. The main ideology of incorporating AI and robotics in healthcare is to offer enhanced outcomes with better accuracy rate and efficiency for health care ecosystem. The review article gives you an overview about advanced technologies of AI and robotics applications and mechanism for medical diagnosis. The latest algorithms can be used in field of diagnosis and personalised medical treatment In this article cancer data set has been trained using ML model and comparison has been made identifying best performing model. To obtain this the data set should be pre-processed at the first stage. After the first stage future extraction has been performed and finally classification algorithms for machine learning have been added. The topic of context has been extracted using data set. Support vector machine, naïve bayes and entropy has been utilised for context extraction. Key findings demonstrate that Support Vector Machines provide a reliable method for AI-driven diagnostics by outperforming previous models in terms of accuracy, recall, and precision time. This study also suggests practical approaches to get beyond adoption hurdles, such as cost-cutting measures, ethical frameworks, and scalable infrastructure solutions.
Advancing Speech Processing with Efficient, Robust, and Data-Scarce Neural Network Architectures
Speech processing has become a cornerstone of modern technology, enabling applications like virtual assistants, real-time transcription, and language translation. However, despite advancements, significant challenges remain that hinder the effectiveness and scalability of these systems. One primary issue is the high computational demand of current neural network models, which limits their deployment on resource-constrained devices such as mobile phones and edge computing systems. These models require substantial processing power, making real-time speech processing challenging in many practical scenarios. Another critical issue is the linguistic bias inherent in many speech processing models. Most current systems are trained predominantly on high-resource languages, leading to poor performance in underrepresented languages and dialects. This creates a digital divide, leaving a significant portion of the global population without access to reliable speech technologies. Additionally, speech processing systems often struggle with robustness in noisy environments, where background noise and overlapping speech degrade system accuracy, further limiting their real-world applicability. Furthermore, neural network-based speech models require vast amounts of labeled data to achieve high performance, which is often unavailable for low-resource languages. This data scarcity presents a barrier to developing inclusive systems that cater to diverse linguistic contexts. This research aims to address these challenges by developing efficient neural network architectures, enhancing robustness in noisy conditions, and exploring data-efficient training strategies. By improving performance in resource-constrained settings and enhancing linguistic inclusivity, this work seeks to advance speech processing technologies, making them more reliable and accessible to a global user base.
Strategic Leadership in the Digital Age: Leveraging Technology for Sustainable Business Growth
In the contemporary business environment, marked by rapid technological disruption and volatile global markets, the role of strategic leadership has become more crucial than ever. This research explores how modern leaders can harness digital technologies not merely as tools for operational efficiency but as strategic assets to drive sustainable business growth. The paper investigates the convergence of digital innovation and strategic decision-making, analyzing how transformative technologies—such as artificial intelligence, big data analytics, cloud computing, and the Internet of Things (IoT)—are reshaping leadership practices across industries. The study adopts a multidisciplinary approach, synthesizing concepts from strategic management, digital transformation, and sustainability. Through a combination of empirical data, case studies, and current industry reports, it identifies how digital-savvy leaders are influencing organizational culture, stakeholder engagement, and long-term value creation. It also delves into the competencies required of today’s strategic leaders—such as agility, technological literacy, innovation orientation, and ethical responsibility—to navigate and lead in digitally dynamic environments. Furthermore, this research highlights the challenges leaders face in achieving alignment between digital initiatives and broader organizational goals. These include issues related to change resistance, talent acquisition, data governance, and the ethical use of technology. The paper argues that sustainable digital transformation is not purely technological but deeply human-centric, requiring inclusive leadership and a clear vision to guide teams through complexity and change. Findings from the study reveal that organizations with proactive, visionary leaders tend to outperform competitors in terms of innovation, market responsiveness, and sustainability metrics. The paper concludes by proposing a strategic leadership model tailored to the digital age—emphasizing adaptability, continuous learning, and purpose-driven transformation—as a blueprint for business resilience and sustainable growth. This research contributes to both academic literature and practical leadership paradigms by articulating a roadmap for how leaders can strategically position digital capabilities as core drivers of long-term organizational success. It invites further exploration into the evolving nature of leadership in a technology-driven world and encourages future scholars to investigate the interplay between leadership, innovation, and sustainability in various regional and industrial contexts.
Novo-NEX: A Tailored Self-Attention Deep Convolutional Neural Network for the Detection of Disparate Morbidity
COVID-19 also known as Severe Acute Respiratory Syndrome Corona virus-2 is a contagious disease that is released from tiny droplets containing saliva or mucus from respiratory system of a diseased person who talks, sneeze, or cough. It spreads rapidly through close contact with somebody who is infected or tapping or holding a virus contaminated objects and surfaces. Another infectious illness known as Pneumonia is often caused by infection due to a bacterium in the alveoli of lungs. When an infected tissue of the lungs has inflammation, it builds-up pus in it. To find out if the patient has these diseases, experts conduct physical exams and diagnose their patients through Chest X-ray, ultrasound, or biopsy of lungs. Misdiagnosis, inaccurate treatment, and if the disease is ignored will lead to the patient's loss of life. The progression of Deep Learning contributes to aid in the decision-making process of experts to diagnose patients with these diseases. The study employs a flexible and efficient approach of deep learning applying the Novo-NEX model in predicting and detecting a patient unaffected and affected with the disease employing a chest X-ray image. The study utilized a collected dataset of 7,181 images using a 256 x 256 image resolution with 32 batch size is applied to prove the performance of the Novo-NEX model being trained. The trained-model produced an accuracy rate of 92% during the performance training. Based on the result of testing conducted, the research study can detect and predict COVID-19, bacterial, and viral-pneumonia diseases based on chest X-ray images.
A Hybrid Machine Learning Framework for Financial Fraud Detection in Corporate Management Systems
Financial fraud is still a very serious problem in the operation of the enterprise, the conventional detection mode can not keep pace for changes in the fraud model, and the interpretability in the enterprise audit is lack. To improve financial anomalies detection in enterprise from a machine learning perspective, we propose an innovative hybrid machine learning approach named as EHRN-GMM, which combines a Heterogeneous Recurrent Network (HRN) and Gaussian Mixture Model (GMM). The HRN consists of GRU-LSTM fused layers with temporal embeddings and attention, which is able to learn short and long term dependencies in company sequential transaction logs. Its results are given to a GMM to estimate the distribution of valid behaviors, and detect anomalies in a probabilistic image. Furthermore, a SHAP-based interpretability layer is added which helps construct auditor-friendly explanations and enhances the transparency and trustworthiness of the model’s decisions. The proposed methodology is evaluated on synthetic ERP datasets, real-world credit card fraud data (e.g., Vesta), and simulated audit trails, where it achieves an average AUC of 0.96, outperforming competing methods such as XGBoost and CNNs. Furthermore, the model exhibits strong concept drift adaptability facilitating by GMM updates at intervals. This paper presents a scalable, interpretable, high-performing architecture for enterprise-wide fraud surveillance that helps to fill the void between automated anomaly detection and responsible corporate governance.
“Design And Develop Computational Models to Reduce the Maintenance Cost at Deployment Level”
Software maintenance at the deployment stage remains a significant contributor to the total cost of ownership (TCO) in software engineering. Despite advanced development practices, many organizations experience escalated post-deployment maintenance due to unpredictable failures, inefficient resource utilization, and lack of intelligent monitoring systems. This paper presents the design and implementation of computational models that leverage machine learning and statistical methods to predict maintenance risks, automate diagnostics, and optimize resource allocation. Experimental results demonstrate a substantial reduction in maintenance efforts and costs when applied to real-world deployment environments. The proposed models offer scalable and intelligent solutions for enhancing software maintainability in production systems.
Incentive Policies of the State to Attract Investment for Foreign Investors in Vietnam
This paper delves into the analysis of incentives policies implemented by Vietnam to attract foreign direct investment (FDI). In the context of deep international economic integration, FDI plays a crucial role in promoting socio-economic development by contributing to GDP growth, facilitating technology transfer, creating jobs, and enhancing national competitiveness. To achieve this goal, Vietnam has developed a legal framework offering attractive incentives in taxation, land access, administrative procedures, and capital support. The paper explores each specific policy, analysing its significance, practical impact, achievements, and limitations. Based on this analysis, the paper proposes recommendations to improve the legal framework and enhance the investment environment in Vietnam, with the aim of attracting high-quality FDI.
Awareness and Adoption of Eco‑Friendly Packaging among Youth: Perceptions, Influencing Factors, and Adoption Intentions
The paper examines how young people are aware and embrace eco-friendly packaging and whether it is a perceived idea and what influences willingness to participate in green practice. As the consequence of the rising environmental awareness, the study aims at explaining the meaning of eco-oriented packages according to young people and the role of information sources and relevance to behaviour. Structured questionnaire was used on a heterogeneous sample (n = 342) of youth. Data were analysed using statistical methods such as, descriptive statistics, one sample t-test, chi-square tests, ANOVA, Spearman correlation and logistic regression. The findings reveal high levels of awareness (M = 4.12) and positive perception (M = 4.05) which are both very far beyond neutral level (p < 0.001). The chi-square analysis and ANOVA test prove that information sources and personal relevance have a meaningful impact with respect to awareness (p = 0.001 and p < 0.001 correspondingly). Logistic regression further proves again that the likelihood of adopting eco-friendly packaging triples with increased awareness (OR = 2.14). Environmental responsibility and cost sensitivity are traced as a motivation through thematic analysis. These perceptions aid specific awareness programs that will promote sustainable consumption.
Ethical Governance in the Age of AI: A Cross-Disciplinary Study of Technology, Policy, and Civic Education
With the artificial intelligence (AI) developing at a high pace, it is providing disruptive power to practically every sphere of society, covering governance, education, industry, and civic life. These positive outcomes, however, come hand-in-hand with serious ethical issues that include algorithm bias, excessive surveillance, degradation of democracy and lack of civic responsibility. It is a cross-disciplinary study of this paper, which focuses on the stochastic modeling procedure, ethical theories, and policy-making to allow proposing a model of ethical governance in the era of AI. We investigate interplay between non-linear AI system dynamics and human decisions with the focus being on the implications of enormous swings in the performance of the AI when subjected to socio-political pressures. We simulate the relationship between governance and ethical instability, that is, amplified noise and feedback loops on ethical instability via stochastic differential equations and bifurcation theory. The examples would be a case study of algorithmic criminal justice institutions, automated welfare delivery, and educational devices to show the reflection of real-life implication of unregulated AI systems. It is also in the paper that the researchers emphasize the roles of civic education and policy literacy in laying out a participatory approach to the governance of AI. Our findings are that a top-notch governance has necessitated more that regulation frameworks and embracive communication among technical, policy, and civic players. We end this editorial with a proposal of a multi-objective optimization framework that can find a common ground between ethical integrity, technical robustness, and social accountability in the deployment of AI.
Profitability and Profit Efficiency in Wheat Farming: A Stochastic Frontier Analysis in Nineveh Governorate
This research examines the capacity of contemporary technologies to improve both profitability and profit efficiency for wheat farmers in the Nineveh Governorate. Utilizing the stochastic profit frontier model through Frontier 4.1 software, we assess the profit frontier alongside the inefficiency function. The maximum likelihood estimation highlights the influence of technological and managerial elements on the economic performance of agriculture. Key production inputs analyzed include output prices, land rental costs, pesticide expenditures, labor hours (both manual and mechanical), water consumption, seed costs, and fertilizer expenses. The results indicate that profitability is positively affected by higher output prices, land rents, labor input, and water consumption, whereas rising prices for pesticides and seeds negatively impact profitability. Additionally, the study investigates the influence of managerial factors—such as family size, education, irrigation experience, and the age of the farmer—on production inefficiency. Findings reveal that larger family sizes and more extensive farming experience contribute to a reduction in inefficiency. In contrast, increased levels of education, particular irrigation experience, and an older age of the farmer correlate with heightened inefficiency. This underscores the importance of integrating modern technologies with effective human resource management to optimize profitability. In conclusion, the study confirms that adopting modern technologies alongside improved agricultural management practices is crucial for sustainable profitability. We recommend reassessing production volumes, optimizing land use, strategically organizing labor with technology, and embracing advanced irrigation systems. Additionally, the study emphasizes the need for credit support and training programs to equip farmers with the skills and tools necessary to manage rising production costs and enhance resource management, ultimately contributing to improved economic performance in the agricultural sector. There are studies estimating technical efficiency for different crops and measuring the impact of modern irrigation systems on economic and production efficiency.
The Role of GPX4 rs713041 Polymorphism in Hypertension Susceptibility Among Iraqi Patients
Hypertension, a global health challenge and major contributor to cardiovascular disease, is influenced by both environmental and genetic factors. Among genetic regulators, the glutathione peroxidase 4 (GPX4) gene plays a critical role in antioxidant defense mechanisms, especially in lipid peroxidation and ferroptosis regulation. A functional single nucleotide polymorphism (SNP) in the 3′ untranslated region (3′UTR) of GPX4, rs713041 (C>T), has been linked to altered enzyme activity and increased oxidative stress. This study evaluated the association between GPX4 rs713041 polymorphism and hypertension in an Iraqi cohort, with emphasis on sex-specific effects and genetic models. A total of 45 hypertensive patients and 35 healthy controls aged 40–70 years were genotyped using TaqMan PCR assays. Results revealed that the TT and CT genotypes were more prevalent among hypertensive individuals, while the CC genotype was significantly more common in the control group. The T allele was associated with increased hypertension risk (63.33% in patients vs. 38.57% in controls; P = 0.001). Males with the TT genotype had a fivefold increased risk, while CC was protective. Under a dominant genetic model, individuals carrying at least one T allele (CT+TT) were nearly five times more likely to develop hypertension (OR = 4.9, P = 0.004). Additionally, GPX4 serum levels were significantly reduced in hypertensive patients and appeared to correlate with genotype. These findings suggest that the GPX4 rs713041 T allele is a genetic risk factor for hypertension in the Iraqi population, particularly among males. GPX4 polymorphism genotyping may serve as a potential biomarker for early identification of individuals at elevated risk.
Omnidirectional Wind Turbines: A Solution for Harnessing Energy from Unpredictable Wind Patterns
This study presents experimental and numerical investigations of vertical-axis H-type wind turbines (VAWTs), focusing on the impact of blade pitch angles and innovative design enhancements. A modified NACA0015 airfoil profile was tested in wind tunnels at varying Reynolds numbers, and performance metrics, including power coefficients and tip speed ratios (TSRs), were evaluated. The research highlights the role of guiding devices, such as omni-directional nozzles and passive pitch mechanisms, in improving efficiency by managing airflow and reducing wake effects. Computational simulations using tools like Q blade and Ansys Fluent confirmed the findings, showing that optimal blade thickness, curvature, and deflector geometry significantly enhance performance. Variable blade designs, passive control techniques, and counter-rotating configurations were identified as promising solutions to boost efficiency and self-starting capabilities, positioning VAWTs as viable alternatives to horizontal-axis turbines in turbulent and low-speed environments.
The Influence of Personal, Social, and Psychological Factors on Body Image Perception Among Women in Kerala: A Structural Equation Modelling Approach
The study examines the influence of health, social, and psychological factors on the body image perception (BIP) of women in Kerala, utilizing Structural Equation Modelling (SEM). It states that personal factors, such as health behaviour and individual experiences, influence body image, particularly the importance of physical health. Social factors, such as media portrayals and peer group influence, contribute to the norms, where idealized media images of the body increase body dissatisfaction. Factors such as self-esteem, anxiety, and emotional well-being strongly influence body image perception, especially those with low self-esteem. It highlights the complex and multifaceted concepts of body image and advocates for solutions that extend beyond the physical to encompass socio-psychological dimensions, promoting improvement in body image.
Synthesis And Characterization of Nio /V2o5 /Ag2o Nanocomposites and Application in Dye-Sensitized Solar
In this study, a simple chemical method was used. A nanocomposite of nickel oxide and vanadium oxide was prepared, and silver oxide was added to this composite. The compounds were characterized using X-ray diffraction to show additional diffraction peaks in the spectrum of nickel oxide, vanadium oxide, and silver oxide for the second compound. Comparing these spectra with the spectrum of the oxides proved the correctness of the preparation of the compounds. The microstructure of the resulting compounds was examined via (FESEM). Differences between the synthesized compounds were also identified using FE-SEM, TEM, AFM, BET, and FTIR techniques. The prepared nanoparticles were used as electrodes in solar cells using a natural dye (green). The results showed that the efficiency of the prepared nanocatalyst NiO/ V2O5, was higher than that of the catalyst NiO/ V2O5 /Ag2O , as calculated by the conversion efficiency (η) of the cells under study. This is due to several factors, including the energy gap and the difference in surface area.
Assessing the Impact of Digitalization on Impulsive Buying Behavior: An Empirical Study
This study explores the impact of digitalization on impulsive buying behavior among consumers, focusing on how digital platforms influence purchasing decisions and identifying key factors driving impulsivity in the digital shopping environment. Using a survey-based approach, data was collected from 500 respondents. Statistical analyses, including descriptive statistics, correlation analysis, regression analysis, and structural equation modeling (SEM), were employed to test the hypotheses. The results reveal significant correlations between digital engagement and impulsive buying tendencies, highlighting that frequent online shopping, exposure to online advertisements, and extensive use of shopping apps are strong predictors of impulsive buying behavior. This study contributes to the understanding of consumer behavior in the digital age and provides practical insights for marketers aiming to leverage digital platforms effectively.
Impact of Glucose Variability on Hospital Mortality in Non-Diabetic Septic Patients: A Novel Insight from Original Research
Background Severe sepsis is a potentially fatal illness that necessitates critical care due to organ failure. It has been determined that glucose variability (GV) may play a role in hospital mortality, especially in non-diabetic septic patients. In non-diabetic septic patients, our study examines the relationship between hospital mortality rates and glucose fluctuation. Materials & Methods In the Department of General Medicine at Sree Balaji Medical College and Hospital in Kancheepuram, Tamil Nadu, India, a prospective observational study was carried out , over an 18-month period beginning in November 2022. We enrolled 100 randomized patients, with 50 patients presenting with sepsis and 50 non-septic patients as controls. Glucose variability was assessed using blood glucose measurements, and its association with mortality was analysed using logistic regression, adjusting for potential confounders including organ failure, glucose testing, and medication. Results According to our research, non-diabetic septic patients with high glucose fluctuation had higher hospital mortality rates. Compared to patients with more stable glucose levels, individuals with greater variations in their levels of glucose also had higher mortality rates. The relationship between glucose variability and mortality was unaffected by hypoglycaemia, organ failure, frequency of glucose testing, or medication use.
"Exploring The Interrelationship: Serum Creatinine, Blood Urea Nitrogen, And Blood Pressure in Hypertensive Crisis-Induced Renal Dysfunction"
Hypertensive crises, comprising hypertensive urgency and emergency, pose significant risks to organ systems, particularly the kidneys. This study explores the correlation between parameters of renal dysfunction—serum creatinine and blood urea nitrogen (BUN)—and blood pressure levels in patients experiencing hypertensive crises. Data from 150 patients admitted with hypertensive crises were analyzed, revealing significant positive correlations between elevated blood pressure and increased levels of serum creatinine and BUN. These findings underscore the importance of monitoring renal parameters in managing hypertensive crises.
Electrocardiographic and Echocardiographic Alterations in Patients with Acute Cerebrovascular Stroke
Background: Stroke, a leading cause of morbidity and mortality, is often accompanied by significant cardiovascular changes, which may complicate management and affect patient outcomes. This study explores electrocardiographic (ECG) and echocardiographic changes in patients during the acute phase of stroke, focusing on the prevalence, types, and implications of these findings. Methods: This hospital-based, cross-sectional study was conducted at ESIC Medical College & PGIMSR, enrolling 76 patients with acute ischemic or hemorrhagic stroke. Each patient underwent 12-lead ECG, cardiac troponin-I testing, and echocardiographic evaluation, including assessments for left ventricular ejection fraction, left ventricular hypertrophy (LVH), and regional wall motion abnormalities (RWMA). Data were analyzed to identify the prevalence and types of ECG and echocardiographic changes and their association with patient demographics and stroke type. Results: The study population’s largest age group was 71–80 years (30.3%), with 61.8% male participants. Most patients (81.6%) had hypertension, 42.1% were smokers, 32.9% had type 2 diabetes, and 78.9% had ischemic stroke. Common ECG findings included prolonged QTC (46%), T-wave abnormalities (42.1%), and ST-segment depression (28.9%). Atrial fibrillation was the most common arrhythmia (13.2%), followed by sinus arrhythmia and sinus bradycardia (each 6.6%). Echocardiographic findings showed LVH in 25% of patients, RWMA in 19.7%, and left atrial thrombus in 6.6%. Patients with hemorrhagic stroke demonstrated higher rates of prolonged QTC and T-wave abnormalities compared to ischemic stroke patients. Conclusion: This study highlights a high prevalence of ECG and echocardiographic changes in acute stroke patients, particularly in those with hemorrhagic stroke. These findings underscore the importance of routine cardiovascular evaluation in stroke patients to detect and manage cardiac complications early. Further studies are warranted to explore the implications of these cardiac abnormalities on stroke outcomes and recovery.
Impact of Sexual Abstinence Period on Lipid Profiles and Dna Fragmentation Index for Oligozoospermia Patients
Background: Oligozoospermia is a common cause of male infertility, and although the duration of sexual abstinence is known to impact the quality of semen, its correlation with biochemical indicators such DNA fragmentation and lipid levels is still being investigated. Objective: This study examines the effects of different durations of sexual abstinence on sperm DNA fragmentation index, lipid profile markers, and important semen features in men with oligozoospermia. Methods: Forty-four men who were infertile and had aberrant semen profiles were divided into two groups according to how long they had been abstinent: two to three days (n = 21) and four to seven. Standard procedures were followed to quantify the characteristics of semen, and blood tests were performed to measure DNA fragmentation index (DFI), low-density lipoprotein (LDL), triglycerides (TG), and total cholesterol (CHO). Results: Higher round cell count (P=0.017) and significantly superior sperm morphology (36.38% vs. 31.13%, P=0.013) were linked to shorter abstinence (2–3 days). There was an increase in semen volume with longer abstinence (4–7 days) (P=0.035). There were no discernible variations between the groups in terms of DNA fragmentation, lipid markers (TG, CHO, LDL), sperm concentration, or motility (P>0.05). Conclusion: In oligozoospermic men, the length of sexual abstinence does not seem to substantially change lipid metabolism or sperm DNA integrity, while
Insect Classification Using Deep Learning and Machine Learning: A Comparative Study for Agricultural Applications
Insect pests pose a significant threat to global agricultural productivity, necessitating rapid and accurate identification methods. Traditional taxonomic approaches are labor-intensive and require expert knowledge, making automated solutions increasingly vital. This study explores the application of machine learning (ML) and deep learning (DL) techniques for insect classification, comparing their performance on a custom dataset of field-captured insect images. We implement traditional ML algorithms (e.g., Support Vector Machines, Random Forests) and DL models (e.g., Convolutional Neural Networks) to classify 10 common agricultural pests. Results demonstrate that DL models, particularly a fine-tuned ResNet-50, achieve superior accuracy (94.5%) compared to ML methods (85.2% with SVM). We discuss the implications for smart pest management, including real-time monitoring and reduced pesticide use, while highlighting challenges such as dataset size and computational requirements.
Constraints Affecting Destination Choice of Disabled Individuals within the Scope of Accessible Tourism, Social Appearance Anxiety, Intention to be a Tourist and Recommendation Relationship
Tourism offers significant benefits to people with disabilities, not only by providing opportunities for rest and recreation but also by improving personal and social well-being. It supports the development of physical, psychological, and emotional health while enhancing social skills, broadening horizons, encouraging intercultural exchange, and facilitating learning. "Accessible tourism" refers to the ability of individuals with disabilities to travel and vacation with the same freedom and equality as everyone else. Accessible infrastructure and services help people with disabilities increase their self-confidence and independence, reduce concerns about their appearance, and have a more enjoyable vacation experience. As a result, individuals with disabilities can engage more freely in society and socialize with others. Ensuring access to accessible tourism is important for promoting equality and inclusivity, advancing efforts in the field, and creating solutions to improve the tourism experiences of people with disabilities. This study aims to examine the relationship between social appearance anxiety, tourist intention, and recommendation intention, focusing on the barriers that individuals with disabilities face in participating in accessible tourism. The research was conducted in Konya, with a sample of 486 individuals with disabilities selected from the districts of Karatay and Selçuklu using purposive sampling. A quantitative research method was used, and participants completed a personal information form. Data collection tools included the Social Appearance Anxiety Scale [15], the Tourism Constraints and Tourist Intention Scale [27] and the Recommendation Intention Scale [1]. The data were analyzed using SPSS, and the results were presented in terms of numbers, percentages, means, and standard deviations. For normally distributed data, the Kolmogorov-Smirnov test was used, while the Mann-Whitney U test, Kruskal-Wallis test, and Spearman Correlation analysis were used for non-normally distributed data. Demographic characteristics were taken into account in each analysis. The results showed a moderate negative relationship between social appearance anxiety and tourist intention (r: -0.536; p<0.05), indicating that as social appearance anxiety increased, tourist intention decreased. Additionally, a weak positive relationship was found between social appearance anxiety and recommendation intention (r: 0.253; p<0.05), meaning that higher levels of social appearance anxiety were associated with a greater likelihood of recommending tourism.
Enhancing Data Security in the Cloud with Double Encryption: Strengths, Weaknesses and Best Practices
As Cloud computing adoption increases rapidly due to the benefits of scalability, cost-saving and flexibility, ensuring data security at rest and in transit within a cloud has become a serious concern. Double encryption, a method involving two layers of encryption using separate keys, offers a resilient solution for enhancing data security. This article summarizes the concept of double encryption, elaborates on its application, highlights its strengths and weaknesses, and discusses best practices for safeguarding data with optimal security measures.
“Hey Friend Buy Green”: Influence of Green Blogging on Green Purchase Intention Through Green Trust and Perceptions of Information Usefulness
Grounded in the Stimulus–Organism–Response (SOR) framework, Social Learning Theory, and the Technology Acceptance Model (TAM), this study investigates how social media usage affects green purchase intention (GPI) through the sequential and parallel mediating roles of green blogging, perceptions of information usefulness, and green trust. Drawing on data from 293 urban consumers in eastern China, the structural equation modeling results validate all seven proposed hypotheses. Social media usage significantly enhances green blogging, perceptions of information usefulness, and green trust. Green blogging further reinforces both perceptions of information usefulness and green trust. In turn, both perceptions of information usefulness and green trust positively influence GPI. Mediation analysis confirms that green trust and perceptions of information usefulness partially mediate the relationship between social media usage and GPI, with green trust emerging as the stronger mediator. Multi-group comparisons reveal that gender, age, education, and income significantly moderate the mediation pathways, with the effects more pronounced among women, individuals with higher education and income, and those with greater exposure to social media. These findings extend digital green marketing theory and offer actionable insights for platform managers, sustainability advocates, and policy developers aiming to foster environmentally responsible consumer behavior through digital channels.
The Socio-Legal Challenges of Biobanking and Genetic Data Privacy: View from India’s Digital Personal Data Protection Act, 2023
Biobanking—the systematic collection and storage of biological samples and associated data has become a cornerstone of modern biomedical research and precision medicine. However, as it involves sensitive genetic and health-related information, the practice raises critical socio-legal concerns, especially around data privacy, consent, ownership, and potential misuse. In India, the enactment of the Digital Personal Data Protection Act, 2023 (DPDPA) marks a new legal era of data governance. This paper investigates the legal and ethical implications of biobanking through the lens of the DPDPA, examining the balance between scientific advancement and individual rights. It explores issues such as consent, data ownership, cross-border data sharing, and the potential for genetic discrimination. Drawing from constitutional jurisprudence, global ethical frameworks, and Indian regulatory gaps, the article proposes reforms to ensure a robust, transparent, and rights-based approach to biobanking in India.
Green Synthesis and Characterization of ZrO₂ Nanoparticles: In Vitro Efficacy Against Echinococcus Granulosus Protoscolices
Background: Cystic echinococcosis (CE), caused by the parasite Echinococcus granulosus, represents a significant global health challenge, necessitating the development of innovative and effective therapeutic strategies. This study aimed, for the first time, to biosynthesize zirconium dioxide nanoparticles (ZrO₂ NPs) via a green route using an aqueous fruit extract of Tribulus terrestris, characterize them, and evaluate their in vitro scolicidal efficacy against E. granulosus protoscolices. Methods: The synthesized ZrO₂ NPs were comprehensively characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), field emission scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (FESEM-EDX), zeta potential analysis, and UV-visible spectroscopy (UV-Vis). The scolicidal activity was assessed by exposing protoscolices to various concentrations of ZrO₂ NPs (50–200 µg/mL) for different time intervals (5–60 min). Albendazole (100 µg/mL) and phosphate-buffered saline (PBS) were used as positive and negative controls, respectively. Results: XRD analysis confirmed the crystalline nature of the ZrO₂ NPs in a cubic fluorite structure with an average crystallite size of ~47.7 nm. FESEM images revealed quasi-spherical particles with a mean diameter of ~48 nm, while FTIR and EDX analyses indicated their successful capping with bioactive compounds from the plant extract. The ZrO₂ NPs exhibited significant concentration- and time-dependent scolicidal activity (P < 0.001), with the 200 µg/mL concentration achieving 100% mortality after 60 min. The median lethal concentration (LC₅₀) was 67.8 µg/mL after 60 min. The scolicidal efficacy of ZrO₂ NPs (at ≥150 µg/mL) was significantly higher than that of albendazole (P < 0.001). Conclusion: This study is the first to report the use of T. terrestris extract for the biosynthesis of ZrO₂ NPs and to evaluate their scolicidal activity against E. granulosus. The results indicate that biosynthesized ZrO₂ NPs possess promising in vitro therapeutic potential, positioning them as a viable candidate for developing alternative or adjunctive therapies for CE. Further in vivo studies are warranted to assess their clinical efficacy and safety.
The Role of AI in Enhancing Decision-Making in Management Accounting
Artificial Intelligence (AI) is revolutionizing the field of management accounting by enhancing the quality, speed, and accuracy of decision-making processes. The integration of AI tools such as machine learning, natural language processing, and predictive analytics enables management accountants to move beyond traditional reporting functions to strategic decision support. AI facilitates real-time data analysis, identification of patterns, and generation of insights that support budgeting, forecasting, performance measurement, and cost control. By automating routine tasks, AI allows accountants to focus on value-added activities, thereby improving strategic planning and organizational efficiency. This paper explores the transformative role of AI in management accounting and its implications for better-informed, data-driven managerial decisions.
E- Customer Satisfaction in Online Purchase of Laptops: A Thematic Analysis Approach
Purpose: Online purchasing and selling, technology-driven corporate processes, information transmission, and customer service are the four main focuses of e-commerce leading to online customer satisfaction. Objectives: The goals of the current study involve finding correlation patterns in consumer buying preferences in branded laptops (HP, Dell, Asus & Lenovo) assess the expectations of the buyers from the online shopping giant like Amazon, understand the sentiments of buyers’ feedback & analysing the customer satisfaction through their feedback with reference to the hybrid working arrangement across corporate sectors post pandemic. Methodology: 80 online customer reviews of the branded laptops were collected through secondary research in the form of texts from Amazon India applying deductive method of research within a study period of June 2023 through January 2024. Thematic Analysis was applied using tools like Code Co-Occurrence Analysis, Code–Document Analysis, Sentiment Analysis, Word Cloud & Semantic Network through Atlas.ti V23 software. Key Findings: The Quality, Battery life and Reliability of the product are the three key factors in determining the online customer satisfaction through when purchased from E-Commerce giant, Amazon India. Customer Experience, Satisfaction & Product features are the key themes of Online customer satisfaction. Implications: Online merchants in luring in fresh customers for online shopping and inspiring the generated lead customers into loyal customers. They can raise customer satisfaction after a transaction and subsequently attract more online shoppers through enormous measures by addressing the complaints expressed by the customers.
Atherosclerosis And Hearing Loss: A New Understanding of the Relationship Between Cardiology and Otolaryngology
Atherosclerosis, characterized by the deposition of lipids and fibrous tissue in the walls of the arteries, is one of the leading causes of cardiovascular diseases. In recent years, there has been a growing interest in the relationship between atherosclerosis and hearing loss, which highlights the importance of an interdisciplinary approach combining cardiology and otolaryngology. Impaired blood circulation in the microcirculatory system of the inner ear caused by atherosclerotic changes can lead to hypoxia and degeneration of the auditory receptors, which increases the risk of developing sensorineural hearing loss. Current research shows that patients with atherosclerosis have a higher prevalence of hearing impairment compared to the general population. In addition, systemic inflammation, oxidative stress, and endothelial dysfunction, which play a key role in the pathogenesis of atherosclerosis, can also affect the functional state of the hearing aid. This work summarizes current data on the pathophysiological mechanisms linking atherosclerosis and hearing loss, and also suggests promising areas for further research. Particular attention is paid to potential prevention and treatment strategies based on correcting atherosclerosis risk factors and improving microcirculation in the inner ear.
Influence of Covid-19 on the Course and Tactics of Therapy of Chronic Non-Communicable Diseases (On the Example of Arterial Hypertension and Type 2 Diabetes Mellitus).
The aim of the study was to evaluate the influence of COVID-19 on the course of arterial hypertension and type 2 diabetes mellitus and to determine the necessity of therapy correction. The study included 100 patients evenly divided into two groups. After COVID-19, the majority of patients showed worsening of blood pressure and glycemic control. Hypertensives had increased mean BP values, and diabetics had increased HbA1c and fasting glucose levels. More than 20% of patients required intensification of therapy. The results confirm the necessity of active surveillance and early adjustment of treatment after coronavirus infection.
The Effectiveness of Non-Drug Methods in the Rehabilitation of Patients with Severe Covid-19
A prospective controlled trial was conducted involving 100 patients in the Russian Federation who were discharged after severe coronavirus pneumonia. The patients were randomly divided into the main group (50 people), who received an 8-week course of non-drug rehabilitation (respiratory gymnastics, physical therapy, physiotherapy, etc.), and the control group (50 people), who received standard outpatient follow-up without specialized rehabilitation. The assessment included a six–minute walking test (6MWT), lung function indicators (forced lung capacity - LVEF) and quality of life (SF-36) before and after the course. Non-drug rehabilitation measures after severe COVID-19 lead to a significant restoration of exercise tolerance, respiratory function, and quality of life compared to standard treatment. The results confirm the expediency of widespread implementation of medical rehabilitation programs for patients who have suffered severe COVID-19.
A Systematic Review of AI Techniques for ECG Analysis in Acute Coronary Syndrome Detection
Electrocardiography (ECG) is an essential cardiovascular medicine tool, offering non-invasive information regarding cardiac electrical activity to diagnose many conditions. ECG interpretation, though, can be intricate, laborious, and variable across human interpreters. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has become a game-changer with the potential to enhance the precision, efficiency, and availability of ECG analysis. This systematic review synthesizes the evidence on the diagnostic performance of AI models in ECG interpretation for acute coronary syndrome (ACS), comparing AI’s effectiveness with that of human experts and traditional methods. The methodology followed established systematic review guidelines, such as PRISMA, with comprehensive searches across databases like PubMed and Scopus. Included studies utilized high-level ML/DL architectures, e.g., CNNs, and were risk of bias assessed using tools such as QUADAS-2. Diagnostic accuracy measures, e.g., sensitivity, specificity, and Area Under the Curve (AUC), were estimated through bivariate random-effects models. Results show that AI models exhibit high diagnostic accuracy in ACS conditions. For Acute Coronary Syndromes (ACS), AI models recorded AUROCs of as much as 0.997, outperforming human experts in speed and accuracy. Even with these numbers, issues persist, such as AI-associated misdiagnosis, performance variability, and data-related issues like quality and imbalance. Patient privacy and algorithmic bias ethical issues also present challenges. The practical implementation of AI in clinical practice relies on continuous improvement, verification, and coordination among AI vendors, clinicians, and policymakers to overcome the technical and practical challenges.
World Trade Organization and the Indian Economy: An Analytical Perspective on Globalization
The global economy has undergone a profound transformation marked by accelerated globalization, driven primarily by transnational economic integration through trade, investment flows, and the strategic operations of multinational corporations. The twin forces of privatization in many market-oriented economies and the liberalization of trade and investment—initially under the General Agreement on Tariffs and Trade (GATT) and subsequently institutionalized through the World Trade Organization (WTO)—have significantly intensified the momentum of globalization. Since the late 20th century, the world has witnessed widespread economic liberalization, making globalization an increasingly irreversible phenomenon. The global economy has evolved into a complex, interdependent, and transnational system characterized by diminishing trade barriers and the enhanced mobility of goods, services, and capital. Cross-border trade and investment have consistently outpaced the growth of global output, indicating a deepening integration of national economies within a unified global framework. Developing countries are increasingly aligning with the economic structures of advanced economies, participating more actively in global trade networks. In the Indian context, the WTO has played a pivotal role in shaping the country’s engagement with the global economy. As a WTO member, India enjoys Most-Favoured-Nation (MFN) status in its trade relations with all other member countries. The WTO's influence has been instrumental in embedding globalization trends within India’s economic framework, catalyzing a significant economic transformation and ushering in a new era of global connectivity and competitiveness.
Microbial Contamination of Toothbrushes Used After Dental Extraction: a Comparative Study of Decontamination Methods
Background: Toothbrushes used after dental extractions are prone to microbial contamination, which can compromise wound healing. This study investigates and compares the efficacy of three decontamination methods in reducing microbial load on toothbrushes post-extraction. Methods: A total of 75 patients undergoing dental extraction were randomly assigned to three groups (n=25 each). Group A used toothbrushes without any decontamination (control), Group B rinsed brushes daily with 0.12% chlorhexidine, and Group C immersed brushes in 60°C hot water for 5 minutes daily. After 7 days, toothbrushes were cultured, and colony-forming units (CFU/mL) were measured. Statistical analysis was conducted using ANOVA and Tukey's post-hoc test. Results: Group A (control) showed the highest mean CFU count (1,500,000 ± 300,000 CFU/mL), followed by Group D (600,000 ± 120,000 CFU/mL), and Group B (250,000 ± 80,000 CFU/mL). ANOVA showed significant differences among groups (p < 0.001). Tukey’s test confirmed chlorhexidine was significantly more effective than hot water and control. Conclusion: Daily rinsing of toothbrushes with 0.12% chlorhexidine significantly reduces microbial contamination post-extraction and is more effective than hot water immersion. Toothbrush disinfection should be recommended as part of post-operative oral hygiene.
Exploring the Impact of Virtual Destination Element on Tourist Behavior in Metaverse Tourism: A Flow Theory Perspective
Based on the stimulus-organism-response (SOR) framework and flow theory, this study investigates the effects of the characteristics of the metaverse (interactivity, storytelling, gamification, soundscape, authenticity of virtual destinations) - flow, memory, nostalgia, and attachment to destination - tourists' actual visit intention. A total of 502 samples from different regions of China were collected through a questionnaire survey, and the data were analyzed using the smart partial least squares method (SmartPLS). The results show interactivity, storytelling, gamification, soundscape and authenticity of the virtual destination jointly positively affect flow, and then flow has an enhancing effect on memory and attachment to destination. Then memory positively affects nostalgia and actual visit intention, nostalgia can also positively affect actual visit intention, Finally, attachment to destination has a positive impact on actual visit intention. This study provides a theoretical basis for metaverse tourism and provides practical guidance for destination marketers.
Sustainable Supply Chain Management in the Mekong Delta: A Case Study of Agricultural Processing Firms
This study examines the factors influencing sustainable supply chain management practices among agricultural processing firms in the Mekong Delta region of Vietnam. The research used a survey method, distributing approximately 220 questionnaires to processing firms and receiving 170 responses. The analysis identified factors affecting sustainable supply chain management, including mandatory pressure, mimetic pressure, standardization pressure, strategic orientation toward sustainable development, and humanistic culture. Based on these findings, the authors provide recommendations for agricultural processing firms in the Mekong Delta to enhance and strengthen sustainable agricultural processing supply chains.
The Role of SLC30A8 Rs13266634 Polymorphism with Type 2diabetes Mellitus-Related Hypertension in a Population of Wasit Province-Iraq
This case-control study was conducted from October 1, 2024, to January 30, 2025, and included 80 participants: 45 patients with type 2 diabetes mellitus (T2DM)-related hypertension (23 males, 22 females) and 35 healthy controls (18 males, 17 females). Genotyping of the SLC30A8 rs13266634 polymorphism was performed using the TaqMan SNP Genotyping Assay, which revealed three genotypes: CC, CT, and TT. Among T2DM patients, genotype frequencies were CC: 26%, CT: 16%, and TT: 58%. In the control group, frequencies were CC: 34%, CT: 26%, and TT: 40%. Genotype and allele distributions conformed to Hardy–Weinberg equilibrium (χ² = 2.602, P = 0.272; χ² = 2.902, P = 0.088). The T allele was more prevalent in patients (66.28%) than in controls (52.86%), suggesting a possible risk association.Odds ratio analysis indicated that the TT genotype conferred an elevated, though non-significant, risk of T2DM (OR = 2.083, 95% CI: 0.840–5.165, P = 0.113). The CT and CC genotypes showed potential protective effects (OR = 0.561, P = 0.308; OR = 0.658, P = 0.403, respectively). In females, both TT and CC genotypes were associated with increased risk (OR = 2.851 and 1.578), while the CT genotype was protective (OR = 0.339, P = 0.11). Among males, the TT genotype showed a moderate risk (OR = 2.187), CC showed a strong association (OR = 17.903, P = 0.050), and CT was significantly protective (OR = 0.038, P = 0.003).Genetic model analysis revealed no statistically significant associations under dominant (CC+CT vs. TT, OR = 0.48, P = 0.113), recessive (TT+CT vs. CC, OR = 1.517, P = 0.40), or over-dominant (TT+CC vs. CT, OR = 1.780, P = 0.30) inheritance patterns. However, the TT genotype was consistently associated with increased disease risk trends.In conclusion, the SLC30A8 rs13266634 polymorphism—particularly the TT genotype—may serve as a potential genetic marker for increased susceptibility to T2DM-related hypertension, especially in specific subgroups such as males. Further studies with larger cohorts are warranted to validate these findings and explore their clinical utility in personalized risk assessment.
Brain Image Vessel Skeletonization Analysis Using Deep Learning Salient Vascular Candidate Extraction Method
Image processing plays a crucial role in modern medical imaging, enhancing the ability to analyze and interpret complex medical data. By applying advanced algorithms to medical images such as X-rays, MRIs, CT scans, and ultrasounds, image processing techniques allow for the extraction of valuable diagnostic information, improving the accuracy and efficiency of disease detection and treatment planning. Key methods in medical image processing include image enhancement, segmentation, registration, and feature extraction, which assist clinicians in identifying anomalies such as tumors, lesions, and organ abnormalities. Recent advancements in deep learning have markedly enhanced the accuracy and efficiency of brain image analysis. By making it possible to analyze complicated neuroimaging data more accurately and efficiently, the use of deep learning techniques in brain image processing has greatly advanced the area of neuro science. For the clinical evaluation of intracranial vascular disorders, brain blood vessel extraction is a crucial concern. This work formulates the problem of vessel extraction as a connected region classification problem. A post-processing step is added to the image processing process to collect salient vessel candidate extraction method (SVCEM), and an enhanced multi-scale filtering method is used to increase vessel connection. A neural network classifier is then trained using the features that are computed after SVCEM is broken down to connected regions. The trained neural network is used to examine each connected region separately for extraction, taking to the values of nearby voxels that are part of the connection. The extraction results demonstrate the validity of the proposed approach.
Targeted Drug Delivery Systems for Cancer Management: Advancements, Challenges, and Future Directions
Background: Cancer malignancy management remained a critical challenge despite therapeutic advancements attributed to systemic toxicity, non-specificity &drug targeting issues, reduced drug bioavailability and drug resistance problems associated with available chemotherapeutic approaches. Current limitations underline prime requirement for development of highly efficacious patient oriented drug delivery systems. Objective: Current review is focused to discuss cancer-specific targeted drug delivery systems (TDDS), its types and formulation modifications brought about to shape cancer therapeutics with ultimate benefited therapeutic outcomes and precise controlled-release with minimal possible side effects. Methods: Current review discussed the development, possible mode/mechanism of action, and effectiveness of various TDDS, including nanoparticles, liposomes, dendrimers, antibody-drug conjugates, ligand-based biomimetic and, stimuli-responsive drug delivery systems thereby improving targeted drug release phenomenon and ultimate benefited clinical outcomes however certain limitations including tumor heterogeneity, immune system clearance, scalability and manufacturing cost, together with limited penetration to solid tumors and unpredictable Bio distribution ultimately acquire innovative drug delivery platforms for global. Current review is also focused on recent advancements in TDDS over the past decade together with evaluation of its limitations and ascertaining potential future directions for renovating cancer management. Results: Management of cancer malignancies has been remarkably reformed via TDDS, allowing the potential delivery of anticancer medicaments to the targeted site and preventing systemic side effects. Liposome’s, polymeric nanoparticles, dendrimers, Exosomes, stimuli-responsive systems and antibody-drug conjugates presenting significant advancements together with promising clinical outcomes. Currently precision nano-medicine, CRISPR-based carriers, AI integration, and microenvironment-responsive systems are under consideration for efficacious management of cancer malignancies. Conclusion: TDDS provide a breakthrough approach for cancer management by augmenting treatment efficacy and meanwhile reducing drug induced toxicity. Continued research and interdisciplinary collaboration is required to address intervening hurdles and completely analyzing the potential of TDDS in clinical oncology.
Rewilding the Human: Ecological Consciousness and the Ethics of Survival in Contemporary Fiction
By analyzing how it is portrayed in the writings of Margaret Atwood and Amitav Ghosh, this study investigates the developing idea of "Rewilding the human" as a critical paradigm in eco-critical discourse. Rewilding is situated in the nexus of post-humanist ethics and environmental philosophy, emphasizing the restoration of multispecies interdependence, a return to ecological balance, and a decentering of anthropocentric cognition. Examining how modern literature addresses the climatic issue, ecological degradation, and the pressing need to reevaluate human identity and responsibilities within the planetary biosphere is made easier by Atwood's MaddAddam (2013) trilogy and Ghosh's Gun Island (2019), Nutmeg Curse (2021), and the Hungry Tide (2004). This study probes that both writers emphasize ecological consciousness as a lived ethic of survival that is influenced by fortitude, spiritual rejuvenation, and opposition to colonial and capitalist forms of exploitation rather than as an ideal. In order to restore lost forms of kinship with nature, the study uses close textual analysis to show how their stories foster an imaginative reorientation toward the non-human world. The article argues that Atwood and Ghosh not only identify the anthropocenic catastrophe but also imagine ecologically conscious futures based on humility, empathy, and care by placing literary storytelling inside the framework of environmental justice and planetary ethics.
Ayurvedic Medicine: A Complementary Path in Modern Sports Medicine
Emphasizing it’s possible to improve athletic performance, encourage recovery, and avoid injuries, this research investigates how Ayurvedic medicine may be included into contemporary sports medicine. Ayurvedic techniques include herbal medicines (e.g., Ashwagandha, Turmeric), Panchakarma therapy, and yoga have been demonstrated to lower inflammation, increase flexibility, and hasten muscle healing. Presenting a more complete and individualized approach to athlete care, the report emphasizes the advantages of combining these standard therapies with physiotherapy and cryotherapy. The absence of consistent methods and the necessity of more thorough clinical research make the integration difficult even with the favorable results. Large-scale clinical investigations should be the main emphasis of further research to confirm the effectiveness of Ayurvedic therapies in sports environments. This combination gives players the chance to reach ideal physical and mental health, therefore opening the path for a more balanced and environmentally friendly sporting career.
AI Adoption in Small and Medium Enterprises (SMEs): Opportunities and Challenges
Artificial Intelligence (AI) is rapidly transforming business operations across industries, offering significant potential for innovation, efficiency, and competitiveness. For Small and Medium Enterprises (SMEs), AI presents unique opportunities to streamline processes, enhance customer experiences, improve decision-making, and compete with larger enterprises in the digital economy. From automated customer service and intelligent supply chain management to predictive analytics and personalized marketing, AI applications can empower SMEs to operate more efficiently and scale effectively. However, the adoption of AI in SMEs is not without challenges. Limited financial resources, lack of technical expertise, data scarcity, and concerns about integration and cybersecurity pose significant barriers. This paper explores both the opportunities and constraints faced by SMEs in adopting AI technologies. It further examines policy support, capacity-building strategies, and practical frameworks needed to foster inclusive and sustainable AI adoption among small businesses. By addressing these issues, SMEs can harness the power of AI to drive long-term growth and innovation.
Application of Artificial Intelligence in Fraud Detection in the Banking Sector
The rapid digitization of banking services has increased the susceptibility of financial systems to fraudulent activities. In response, Artificial Intelligence (AI) has emerged as a powerful tool in detecting and preventing fraud in the banking sector. This paper explores the various AI-driven techniques such as machine learning algorithms, neural networks, and natural language processing used to analyze vast volumes of transactional data in real-time. These technologies enable the identification of suspicious patterns, anomaly detection, and predictive risk assessment, significantly enhancing the speed and accuracy of fraud detection systems. Furthermore, AI models continuously learn from evolving fraud tactics, making them more adaptive and robust than traditional rule-based systems. Despite its advantages, the adoption of AI also presents challenges related to data privacy, algorithmic bias, and regulatory compliance. This study highlights the current applications, benefits, limitations, and prospects of AI in fraud detection, aiming to contribute to the development of more secure and intelligent banking ecosystems.
Strength and Durability Modelling of Lateritic Soil Treated with Sustainable Pozzolanic Additives
This study explores the effectiveness of Rice Husk Ash (RHA) and Cement Kiln Dust (CKD) as sustainable stabilizing agents for improving the geotechnical properties of lateritic soil from Guwahati, Assam. Laboratory tests were conducted to evaluate changes in shear strength, permeability, consolidation characteristics, and pH across different RHA-CKD combinations and curing periods (7, 14, and 28 days). The results demonstrated significant improvements in shear strength (up to 95 kN/m²) and substantial reductions in permeability (to as low as 1.69 × 10⁻⁵ cm/s), particularly with the 15% RHA and 9% CKD mix. The pozzolanic interaction between RHA and CKD led to the formation of cementitious compounds, which improved particle bonding and pore structure. pH measurements confirmed a shift from acidic to optimal alkaline conditions conducive to long-term stabilization. Durability tests, including wet-dry and freeze-thaw cycles, showed excellent retention of mass and strength, indicating the stabilized soil’s resilience under cyclic environmental loading. Comparative analysis with conventional stabilizers highlighted RHA and CKD’s advantages in terms of environmental impact, waste valorization, and contribution to the circular economy. The study concludes that RHA and CKD can serve as efficient, low-cost, and eco-friendly alternatives to cement and lime, with promising potential for application in sustainable infrastructure projects.
Stability and Chaos Analysis of Nonlinear Fluid Flows Using AI-Accelerated Computational Techniques
Nonlinear fluid dynamics often exhibit rich and complex behavior marked by transitions from stability to chaos. In this study, we investigate such transitions using AI-accelerated computational frameworks tailored for fluid flows governed by the Navier-Stokes equations. Our approach integrates physics-informed neural networks (PINNs) with chaos quantification techniques to detect bifurcations, strange attractors, and sensitivity to initial conditions in both laminar and transitional regimes. We model benchmark systems including the Lorenz flow and 2D Rayleigh–Bénard convection to demonstrate the accuracy of AI models in capturing spatiotemporal dynamics. Lyapunov exponents, Poincaré sections, and entropy measures are used to quantify chaos levels, and results are validated against traditional numerical solvers such as finite volume and finite element methods. The AI-based methods showed significant speed-ups (5–10x) without compromising accuracy. Our findings provide scalable alternatives for simulating turbulent systems in engineering and geophysical contexts.
Integrating IoT and BIM for Sustainable Construction Management: A Data-Driven Civil Engineering Model
The construction sector is quickly changing and adopting digital technologies that support the good use of the environment, cost-effectivity, and smart decision-making. The Internet of Things (IoT) and Building Information Modeling (BIM), which are some of these innovations, have come out as revolutionary in contemporary civil engineering. The paper submits a data driven model that harnesses the synergy between IoT and BIM to play a greater role in the management of sustainable construction. Constructed IoT sensors present in building constructions sites produce real-time information about energy consumption, the health of structures, environmental conditions, and the functionality of equipment, all which are dynamically passed to BIM systems. The integration can support predictive analytics in alerts, and project workflow, and eventually proactive resource allocation, minimizing environmental impact, and project safety. The adoption of a hybrid approach based on field-level sensors systems and 3D modelling and remote analytics reveals that via interactive tools implementing smart monitoring and BIM visualization, one can achieve measurable sustainability results. The simulated case analysis gives a practical demonstration of the IoT-BIM model during the enhancement of operational transparency, achievement of carbon reduction objectives, and interdisciplinary cooperation in multifaceted projects. The findings highlight the importance of digital twins as a key way of supporting smart construction and green development goals.
AI-Powered Urban Infrastructure: A Multidisciplinary Approach to Smart City Development and Social Inclusion
Cities are fast changing, as the population grows to exert pressure on resources and subjected to climatic pressures. Artificial Intelligence (AI) is coming up as a disruptive technology that can help to optimize the urban infrastructure in a way that could enhance the social inclusivity in smart cities. The proposed multidisciplinary model of AI, city planning, environmental monitoring, and social analytics provides a medium through which the capacity of AI-driven infrastructure can achieve efficiency and sustainable growth. Our team employs a mixed approach to generating probable data and uses both geospatial mappings and on-the-ground surveys in three major cities of India (Mumbai, Bengaluru, and Ahmedabad) to estimate the potential number of vulnerable women. It was found that transportation and energy systems with AI integration may play an efficient role in cutting commute time by 18 percent, efficient energy use by 22 percent, and citizen involvement in the city services by 30 percent in the high-need areas. Another block to equity access also found in the study is digital inequality, which suggests that a multi-stakeholder approach to open smart cities can solve this problem. The results highlight how AI can change cities into socially conscientious and sustainable ecosystems.
Circular Economy and Green Finance: An Interdisciplinary Pathway to Sustainable Business Models
Environmental degradation, resource scarcity, and climate change are all burning issues of the global community that require re-evaluation of linear economic models. The circular economy (CE) is an opportunity to provide a regenerative option by decoupling economic growth and resource consumption through recycling, reuse, and extension of products lifecycle. At the same time, the so-called green finance has taken the form of a driver tool to direct sustainable investment and reduce the risk of environment-related innovations. The current article examines the interconnection between CE practices and green finance tools, suggesting the interdisciplinary direction of developing resilient and sustainable business models. Relying on empirical case studies and policy frameworks, the research defines the most visible financial mechanisms, which are green bonds, sustainability-linked loans, and ESG funds, as a means of transitioning to circularity. The intersection of circular principles and new financial models not only benefits the environment, but also returns economic and competitive benefits to companies. The paper ends with a policy-research-business partnership roadmap that would scale sustainable business models to be in line with the Sustainable Development Goals (SDGs).
Effectiveness of the Guided Discovery Method in Enhancing Critical Thinking Skills in Physics at the Higher Secondary Level
The present study attempts to Enhancing Critical thinking skills in physics at Higher Secondary Level. The study is concerned with finding out the Effectiveness of the Guided Discovery Method in Enhancing Critical Thinking Skills in Physics at Higher Secondary Level. Samples of 106 students were selected for the study. The analysis of data for examining the significance of hypotheses formulated in this context was performed using appropriate statistical techniques. Findings from this study revealed that there is significant difference between the control and experimental groups with respect to the post test scores for Critical thinking skills in physics at Higher Secondary Level. It finds that Effectiveness of the Guided Discovery Method in Enhancing Critical Thinking Skills in Physics at Higher Secondary Level and the method currently being practiced in the higher secondary schools of Kerala.
Enhancing Employability: Comparing Curriculum Adequacy Vs Need for Skill Development Programme
Purpose: The main purpose of this study is to find out the intensity of relationship between curriculum and employability of the students. Design/methodology/approach: Qualitative approach was used in our study, adopting descriptive research design and purposive sampling method was used to select 100 sampling units from the population of final year under graduate and post graduate. Findings: In Principal Factor Analysis the main factors extracted affecting the relationship of curriculum and the employability are Team development skill, personal development skill and professional development skill. In CFA using structural equation model it’s been confirmed with Personal development skill Practical implications: In our economy stake holders like parents are willing to get the maximum benefit from academic curriculum itself, the time and cost are crucial factors for deciding upon the individual skill development programs. In our study we have identified in the curriculum skill-oriented courses are contributing maximum for the employability. Originality/value: We have adopted empirical study for our research. Using questionnaire given to the respondents.
Comparative Efficacy of Different Antiviral Therapy Regimens in Covid-19
The present study comparatively analyzed the efficacy of different antiviral therapy regimens used in the treatment of COVID-19 in clinical practice. The aim of the study was to identify the most effective approaches based on the results of therapy of patients with a confirmed diagnosis of a new coronavirus infection. Based on the collected clinical data, we evaluated the dynamics of symptoms, viral load, length of hospitalization, complication rates and mortality with different antiviral drugs, including remdesivir, favipiravir and combinations with immunomodulators. The results obtained show that there are differences in therapeutic efficacy between the considered treatment regimens, which allows us to justify the preference for the use of some of them depending on the clinical situation. The work has implications for improving therapeutic strategies and optimizing clinical protocols for the management of COVID-19 patients.
OWA-Adam: Accelerating Convergence through Ordered Weighted Averaging in Adaptive Optimization
This paper introduces OWA-Adam, a novel optimization algorithm addressing the challenges of efficient and adaptive optimization in deep learning. By incorporating the advantages of adaptive learning rate algorithms, gradient moment estimators, and ordered weighted average parameter updates, OWA-Adam aims to enhance convergence speed and generalization performance in deep learning models. Unlike traditional Adam which uses fixed exponential decay for moment estimation, OWA-Adam employs adaptive gradient aggregation strategies that consider the relative importance of historical gradients. Through comprehensive experimental validation across multiple independent trials, we demonstrate that OWA-Adam with exponential decay weighting achieves faster convergence compared to standard Adam while maintaining identical final performance across all evaluation metrics. Statistical significance testing across 10 independent trials confirms the robustness and reliability of these improvements. These findings underscore the potential of OWA-Adam to significantly improve the training process and overall performance of deep learning models. The implications of the proposed algorithm extend to both the research community and academia, offering advancements in optimization techniques for deep learning.
Exploring Neurological Phytochemical Chemical Constituents Using Aqueous Extraction of Benincasa Hispida Fruit: Molecular Bombardment Study of Electrospray Ionisation by Liquid Chromatography-Mass Spectroscopy Method
The Benincasa hispida, also known as budkumbalikai and belong to the family Cucurbitaceae. Fruit of this used by native people for the treatment central nerve system. The aim of the study was to identify the phenolic compound from the fruit of Benincasa hispida by using LC-MS and GC-MS-based targeted and untargeted analysis. LC-MS untargeted chromatogram peak showed flavonoid molecules in graph. In GC-MS untargeted analysis showed the 48 bioactive phytochemical constitutes from different classes were annotated there are 48 bioactive molecules including phenolic acids, glycoside, organic acids, flavonoids and other compounds. The Benincasa hispida has excellent potential as edible vegetable fruit-based food due to its phytochemical profile and high phenolic content.
Evaluating Bert Variants For Disaster-Related Information Classification: Identifying Situational Vs Non-Situational Data
Natural language processing (NLP) plays a critical role in disaster management, particularly in distinguishing between situational and non-situational information shared during emergencies. This paper presents an evaluation of various BERT (Bidirectional Encoder Representations from Transformers) variants for classifying disaster-related information. The study focuses on identifying the most effective approach for accurately categorizing data into situational (relevant to disaster response) and non-situational (irrelevant or secondary) information. Several BERT models, including BERT-base, RoBERTa, and DistilBERT, are fine-tuned on a dataset containing real-world disaster communications such as social media posts, news articles, and emergency updates. We compare their performance based on metrics such as accuracy and processing time. Our findings highlight the strengths and limitations of each variant, with a particular focus on how each model handles the nuances of situational information during crises. The results demonstrate that RoBERTa outperforms other models in accuracy, while DistilBERT offers a faster alternative with minimal trade-offs in performance. This comparative analysis provides insights into selecting the optimal BERT model for real-time disaster response systems, ultimately contributing to faster and more accurate decision-making during emergencies.
Telemedicine in the Fight Against Pandemic Covid-19 Effectiveness, Risks and Prospects
The COVID-19 pandemic posed a global challenge to healthcare systems, revealing their vulnerability in the face of mass infection and resource scarcity. One of the key solutions was the introduction of telemedicine, which provided remote interaction between doctors and patients, while preserving access to care. The aim of this study is to analyse the effectiveness of telemedicine technologies during the pandemic, assess potential risks and determine the prospects for the development of digital medicine in the post-pandemic era. Based on a comparative analysis of data on the number of referrals, hospitalisations and patient satisfaction, it was found that telemedicine allowed not only to reduce the burden on hospitals, but also to maintain continuity of treatment of chronic diseases. At the same time, risks associated with limited access to technology and reduced quality of diagnostics in the absence of face-to-face examination was identified. Prospects for telemedicine include its further integration into national health care systems, subject to regulatory and technical improvements.
The Influence of Knee Muscle Strength on Balance and Mobility in Young Recreational Football Players with and Without Generalized Joint Hypermobility
Background: Generalized Joint Hypermobility (GJH) is a musculoskeletal condition characterized by increased joint laxity, potentially leading to greater injury risk in athletes. Understanding its influence on knee muscle strength, balance, and mobility is critical for injury prevention and performance optimization. Purpose: This study aimed to assess the impact of GJH on knee strength, balance, and mobility among young recreational football players and explore the associated risk of injury. Methods: Twenty male recreational football players from a Saudi Arabian university participated. They were categorized into GJH and non-GJH groups using the Beighton Score. Knee muscle strength was evaluated using an isokinetic dynamometer, while knee range of motion (ROM) and balance were assessed through MSBET. Mobility was tested via three functional measures: the Six-Minute Walk Test (6MWT), Stair Climb Test (SCT) and Timed Up and Go (TUG) test. Statistical comparisons between groups were conducted using t-tests and ANOVA. Participants also self-reported their physical activity levels. Results: The GJH group showed significantly reduced knee strength, balance, and mobility compared to the non-GJH group (p < 0.05), along with higher injury incidence. Conclusion: GJH negatively affects knee function and increases injury risk. Targeted strength and balance training may help mitigate these risks in affected athletes.
Measuring the Effectiveness of the Active Ingredient in the Drug Histadine by Ultraviolet Spectroscopy and Comparing the Results with Previous Studies
An accurate, simple and sensitive spectroscopic method was developed to measure the percentage of the effect of the active substance loratadine, which is included in the compositions of most antihistamine pharmaceutical compounds, including the drug histamine, which is used to treat seasonal allergies and colds in the form of runny nose and sneezing, and also to relieve and calm the symptoms of urticaria, or what is known as skin urticaria, resulting from contact with irritating substances or exposure to different temperatures, in its pure form and in pharmaceutical preparations. ، The effectiveness of loratadine was measured by replacing the drug tablets after converting them into powder using( ) First: Ethanol and acetone alcohol in a ratio of 1:1. The ultraviolet spectrometer recorded a decline in determining the calibration curve and the curve did not reach the zero percentage, which made it impossible to measure the effectiveness of the drug. Second: Using 100% distilled water, the pure substance was replaced, as well as in its pharmaceutical form. The device recorded a calibration curve and the percentage of loratadine effectiveness and its solubility in distilled water. The percentage ranged between 1-300 nanometers. The results indicated that there were no interferences of excipients in the process of measuring the effectiveness of loratadine. The method was successfully applied to measure the dissolution rate and effectiveness of the drug compound in pharmaceutical preparations.
Effects of Step Count Monitoring Smartphone Health App in Promoting Walking Behaviour among the Young Adults
Objectives: Physical inactivity poses a significant global health threat, particularly among young adults, contributing to conditions like obesity, cardiovascular disease, and metabolic disorders. Mobile health apps such as Google Fit (GFSH) aim to combat this issue through features like step tracking, reminders, and feedback. This study evaluates GFSH's efficacy in promoting walking and reducing sedentary time among 18-26-year-olds, assessing both mental and physical outcomes and user engagement. Methodology: The study utilized a five-week, two-time-point longitudinal design with 61 participants in each group: an intervention group using GFSH and a control group following general physical activity guidelines. Criteria included owning a compatible smartphone and being within the specified age range; participants with incompatible devices or app issues were excluded. Physical activity data, including daily steps and app interaction, were collected. The intervention followed the COM-B model, emphasizing goal setting, progress tracking, and inactivity alerts. Statistical analyses (ANOVA, Expectation Maximization) compared group differences. Results: Among 136 participants (56.6% male, 43.4% female; mean age = 22.4 ± 2.31 years), both groups showed increased physical activity. The GFSH group saw greater step count improvement (+2436 steps vs. +1209 steps). While between-group differences were not significant (P = 0.15), within-group improvements were notable (GFSH: P < 0.001; Control: P = 0.009), suggesting positive impacts of the app. Conclusion: GFSH shows promise in enhancing physical activity levels among young adults, yet further research with longer durations and enhanced engagement strategies is necessary to affirm sustained effects on sedentary behaviour and physical activity patterns.
Implementing Behavior Analysis to Detect Cryptojacking using Machine Learning
The rise of cryptojacking, a surreptitious form of crypto currency mining that exploits computing resources without user consent, poses a significant threat to cyber security. This abstract proposes a novel approach to counter this menace by leveraging machine learning techniques for behavior analysis. The implementation involves the deployment of sophisticated algorithms [5] to scrutinize patterns and anomalies in system behavior, aiming to identify indicators associated with cryptojacking activities. By training the machine learning model on historical data, it becomes adept at distinguishing normal system behavior from cryptojacking instances. The proposed solution enhances traditional cybersecurity measures by providing a proactive [12] and adaptive defense mechanism against evolving cryptojacking techniques. This research represents a crucial step in the ongoing efforts to safeguard digital ecosystems from the growing threat of unauthorized cryptocurrency mining activities.
Ulam - Hyers Stability of Second Order Difference Equations using Laplace Transforms
This article explores the Ulam-Hyers stability of both homogeneous and non-homogeneous second-order difference equations, which are widely applied in control systems and digital signal processing. Using the Laplace transform, we analyze the convergence of these newly defined difference equations. Additionally, we extend our study to the Ulam-Hyers stability of discrete values (difference equations) within an open subinterval by providing a detailed analysis of their properties through Laplace transformation. To enrich our theoretical findings, numerical examples are stated and these examples demonstrates and extend the applications of our theory.
Pathogenicity Study of the Local and Commercial EPNS Isolates Against the Third Instar Larvae of Cotton Leaf Worm Spodoptera Littoralis in Baghdad, Iraq
Spodoptera littoralis larvae were collected from tomato fields in al-usefiya region , Baghdad, Iraq. Bioassay was done to test the pathogenicity of EPNs isolates against the third instar larvae of cotton leaf worm using three concentrations 100,150 ,200 ijs/ larvae. Results showed that best concentration was 200 ijs/ larvae . At 200 ijs/larvae higher mortality rates were recorded after 5 days of treatment table for native isolates Oscheius tipulae ,Oscheius myriophilis, Heterorhabditis bacteriophora reached 93.00 , 92. 50 , 93.50 % respectively. While Cezar pesticide overcome on all isolates and recorded the highest mortality rate at 99.50% after 5 days of treatment. Oscheius tipulae isolate recorded less LC50 value reached 82.10 ijs/ larvae, While the commercial isolate Steinernema carpocapsae had LC50 value reached 125.77 ijs/larvae .
Evaluation of Women-Centric Programming on Community Radio with a Case Focus on Jayoti Vani
This paper is an assessment of the success and community relevance of female programming at Jayoti Vani, a community radio station at Rajasthan. As it becomes an evident phenomenon that localized media can be very important in empowering the marginalized groups especially females in rural and semi urban societies, the study will address some critical areas in terms of listening patterns, awareness of the programmes, relevance of the content, sense of empowerment, community participation and barriers to access such as financial ability. An appropriate sample of 250 respondents (mostly females) with different socio-demographic backgrounds was also surveyed by the use of structured questionnaire. The information gathered was through self-administration and queries because this made it inclusive particularly to the illiterates. Descriptive statistical analysis, Pearson correlation, and regression indicated a close relationship between being a listener and feeling empowered as an individual, knowing the content and being an active member of the community, and inverse correlation between the barriers of access and listening frequency. The results indicate that the programming of Jayoti Vani is not only well-known and context-specific but also serves as a way of promoting behavioral changes including health seeking behaviors, gender rights de-bates in a family and financial decision making. Nevertheless, there are still some long-lasting issues that adversely affect optimal engagement, including poor access to technologies, as well as sociocultural resistance. It can therefore be concluded that Jayoti Vani has achieved a lot in ensuring media to be used as a tool of empowerment and participation among the women in India but there is a definite necessity to overcome structural obstacles of access. Respondents suggested improvements like use of local language, addition of success stories that the audience could relate with and the active training of women as writers of the content. This research leads to the wider building of debate in gender issues with the media or grass-root development, and it is of importance to the policymaker in their effort of working together with NGOs and community broadcasters in the quest to utilize radio as a resource to enhance an inclusive social change.
Dual-Plant Phytoremediation Strategy for Lead Decontamination Using Helianthus Annuus and Ocimum Sanctum
Lead (Pb) contamination in soil poses a serious threat to the environmental and human health due to its non-biodegradable and highly toxic nature. Phytoremediation, an eco-friendly and cost-effective technique, utilizes plants to extract, immobilize, or stabilize the heavy metals from contaminated environments. In this work, the phytoremediation ability of Ocimum sanctum (tulsi) and Helianthus annuus (sunflower) in lead-spiked soils is assessed and contrasted.A controlled pot experiment was conducted over the course of 90 days under consistent climatic conditions, utilising three different lead concentrations (100, 200, and 400 mg/kg) along with a control. Atomic Absorption Spectrophotometry was used to quantify the Pb concentration in root and shoot tissues as well as plant biomass according to standard procedures.The performance of phytoremediation was analysed employing quantitative indicators like the Bioaccumulation Factor, Translocation Factor, and Remediation Efficiency. Because of its increased translocation ability (Translocation Factor (TF) > 1) and shoot Pb accumulation, the study's findings suggested that Helianthus annuus (H. annuus) offers a significant potential for phytoextraction. However, Ocimum sanctum (O. sanctum) retained higher Pb in its roots (TF < 1), making it suitable for phytostabilization. Compared to H. annuus, O. sanctum had a more pronounced decline in biomass as Pb stress increased, suggesting a less effective tolerance.The results suggest that a dual-plant strategy using Helianthus annuus for active Pb removal and O. sanctum for root-zone root-zone stabilization. This comparative analysis supports species specific phytoremediation technique and provides the promising framework for environmentally sustainable lead decontamination.
Optimization of Process Design Time in a Distributed Multi-Factory Environment Using Genetic Algorithms to Organize the Process and Support the Development of Technical Designs for Part Production Based on Information Available in the Production Database
Today, with the increase in the power of computers in storing and processing data, as well as the advances made in the field of information technologies, especially artificial intelligence, the facilities and capabilities of CAD systems have increased significantly. In the fundamental approach, the process design is created based on the information available in the production database and process mining. In this approach, the process design system operates in the form of knowledge-based systems and artificial intelligence, and in some cases as a DSS system, in order to automate the design, and by receiving information about the details of the desired part, the types of production operations available and their capabilities in terms of accuracy and tolerance, experience related to previous parts, etc., it designs the appropriate process for the part. These systems have been created for the production process of parts that were previously carried out by specialists in manufacturing methods. These systems are very important in terms of integration. The outputs of a process design system include: selecting the appropriate operations and determining the sequence of these operations on the part, selecting the necessary machines to perform the operations, determining the tools and fixtures, as well as the operating instructions for adjusting the machine, the path of the tools, the operation parameters such as speed, duration, load, etc. Of course, it should be noted that since the planning and design of the parts manufacturing process is very dependent on the experience and judgment of the planners, automating all of the aforementioned activities is a very difficult task and most of the existing process design systems are not capable of performing all of the above activities, but in most cases they can only provide decision support services. In a distributed industrial environment, different factories with different machines and tools in different geographical locations are often combined to achieve the highest production efficiency. Process designs may differ due to different resource constraints. Therefore, obtaining an optimal or near-optimal process design seems important. In other words, it is necessary and essential to determine which factory and with which machines and tools each product should be produced. For this purpose, it is necessary to choose a design from different designs that minimizes the cost of producing products while being possible. In this research, a genetic algorithm is introduced that can quickly search for the optimal process design for a single manufacturing system as well as a distributed manufacturing system according to predetermined criteria such as minimizing the process time. This research presents an emerging genetic algorithm for process design with the help of data obtained from process mining that has a successful application in a traditional and distributed multi-factory environment. The application of genetic algorithm for a distributed multi-factory environment in distributed manufacturing systems was developed based on the geographical distribution of manufacturing machines and tools. It can produce an optimal or near-optimal process design compared to other main approaches for single-product manufacturing. The most suitable manufacturing plant can be established when the distributed manufacturing problems are considered. Furthermore, this approach is able to perform multiple optimization objectives based on the lowest production cost or the shortest production time. Based on the selected objectives, near-optimal solutions can be obtained by using genetic algorithm. Through experiments, it has been shown that the developed techniques are better or comparable to other systems in a distributed multi-factory environment.
Deep Learning Framework for Person Classification in Video Streams Using Yolo8, EfficientNet-B7, GRU, and Attention Mechanism
This paper introduces an end-to-end deep learning framework designed to analyze student behavior in classroom environments using automated video processing. The system begins by segmenting classroom video into individual frames, which are then filtered using image hashing techniques to eliminate redundant frames. A GRU-based module subsequently preserves temporal coherence among the selected frames. Human subjects are detected within these frames using YOLOv8, with cropped person images used to create a labeled dataset. For feature extraction, the framework utilizes EfficientNet-B7, a pre-trained CNN known for its high accuracy and computational efficiency. Temporal dependencies are modeled using GRU layers, while an attention mechanism emphasizes critical behavioral sequences. These modules are integrated into a unified classification network. Experimental results, conducted on the Techno CS dataset, demonstrate the model’s ability to classify student behavior into four distinct categories with 95% validation accuracy, indicating the robustness of the architecture and its potential for real-time implementation in smart classroom settings.
Artificial Intelligence and Organizational Learning — Enhancing Workplace Efficiency through Smart Collaboration
This research paper explores how AI, rather than being a mere automation tool is now in- creasingly seen upon as a collaborative partner. It lays emphasis on how AI is shaping the present-day world in terms of organizational environments, in the context of collaboration, cre- ativity and learning. This paper done a qualitative study of AI tools in organizational learning with original survey data gathered from professionals working in creative and design intensive roles. To understand real work implications, a survey was conducted among 35 professionals in diverse creative industries with a substantial number being product designers, UI/UX design- ers and product managers. Respondents varied in their level of experience and used different AI tools on a regular basis. Furthermore, respondents found AI tools as easy to adopt, with many reeling on online tutorials rather than formal training. Even though it shows the gap in institutional support for AI education, it represents the initiative among individuals. The study even sheds light upon the potential risks. While some participants raised concerns about the over dependence on AI which could lead to tenuous critical thinking skills and originality, other pointed out the issued related to data privacy, lack of formal training or even the skill gap in adopting these new tools. However, these concerns are outweighed by the perceived benefits of AI. These analyses are supported by including interviews with five early-career professionals working in design-oriented roles. Their views aligned in the same direction- AI tools were seen as effective in improving productivity and helped streamline routine tasks. The study aims at a clear conclusion about AI and its role in transforming creative and organizational work environments. Moreover, the challenges faced by the employees are also discussed. Ultimately the study positions AI as not a replacement for humans but as a helping hand to create an efficient, collaborative and smarter work environment.
Safety Concerns and Future Possibilities of AI Integration in Autonomous Vehicles
In 2024, Autonomous vehicles (AVs) are no longer a thing of the future, they are already navigating our streets, transforming the way we think about transportation. With advancements in artificial intelligence and machine learning these vehicles promise to enhance road safety, reduce traffic and much more. However, it is crucial to explore the various safety issues with AI in autonomous vehicles, understand the risk and whether it is ethical or not.
A Comparative Study of the Three Principles of NPT From the Perspective of Imami Jurisprudence and International Nuclear Law
This article has been prepared using a descriptive-analytical method and based on the use of primary and secondary sources, referring to numerous books, sources and documents. The advantages and high potential of using nuclear energy increase the enthusiasm of governments to employ this technology in various domestic and international arenas. The problems become apparent when the use of this energy by countries in non-peaceful fields, especially the production of nuclear weapons and their use against other countries, poses a serious threat to international peace and security. This has caused a sharp disagreement between nuclear and non-nuclear countries in the use of this energy to the extent that it has faced restrictions on its peaceful use. To solve this problem and in order to organize and control the nuclear activities of countries; numerous international agreements and treaties have been concluded so far, the most important of which is the Treaty on the Non-Proliferation of Nuclear Weapons (NPT). This treaty is based on three principles; Non-proliferation of nuclear weapons, peaceful use of nuclear energy, and general and complete nuclear disarmament have been concluded. In this study, an attempt is made to examine the Imamiyyah jurisprudence, which includes jurisprudential rulings and evidence, including Quranic verses, jurisprudential rules, and the narrations of religious leaders, regarding the use of energies used by the human race, especially nuclear energy, and the three principles governing the NPT on the one hand, and the recent international treaties and decisions that have been concluded as an interpretation of the NPT and have been defined under the general title of NPT Practic makers on the other hand. Based on the findings of this study, it seems that the foundations of the NPT principles and the Imamiyyah jurisprudential system are in most cases fully compatible, but the NPT Practic makers have acted in most cases in conflict with the principles of the aforementioned treaty.
Examining the Clothing Design Process and the Application of Artificial Intelligence in it
In today’s world, fashion design has been influenced by digital transformations. These developments have not only affected the design process but also education and learning in this field. Recent developments in the field of artificial intelligence, especially the development of intelligent language models, have provided a new context for rethinking the fashion design process. The fashion and apparel design industry, one of the most dynamic and creative industries in the world, has undergone dramatic changes in recent decades. One of the most important factors in this transformation has been the introduction of new technologies such as artificial intelligence (AI), digital design tools, and robotics into the field of clothing design, production, and distribution. These technologies have not only transformed traditional clothing production processes, but have also redefined the concept of creativity, design processes, consumer behavior, business models, and even the concept of “fashion” itself.This article examines the fashion design process and the application of AI science to it, and analyzes the various tools and technologies that this technology brings. By providing machine learning algorithms, design software, personalized platforms, and chatbots, AI allows designers and brands to improve the design, production, and marketing process and respond to customer needs more effectively. This article also examines the future of AI in the fashion and fashion design industry, emphasizing the importance of managing the challenges and limitations. Ultimately, by understanding and exploiting the potential of AI, the fashion industry can move towards a more creative and sustainable future.
Genotyping of Enterobius Vermicularis in Patients of Northern Baghdad
Background: Enterobius vermicularis is a common intestinal parasite with a global distribution. Accurate molecular identification is essential for understanding its genetic variation and epidemiology. Aim: This study aimed to detect and genetically characterize Enterobius vermicularis in stool samples using molecular techniques targeting the cox1 gene. Methods: A total of 85 stool samples were collected from individuals suspected of E. vermicularis infection. DNA was extracted and purified, and concentrations were measured using a Nanodrop device. PCR was employed to amplify the cytochrome c oxidase subunit 1 (cox1) gene. Amplified products (407 bp) were visualized using gel electrophoresis. Selected PCR products were sequenced via the Sanger method using the ABI-310 Genetic Analyzer (Macrogen, Korea). Sequences were aligned and compared with entries in the GenBank database (NCBI) to confirm species identity and analyze genetic variation. Results: E. vermicularis DNA was confirmed in 35 samples. DNA concentrations ranged from 9.6 to 19.5 ng/μl. Sequence alignment revealed that all positive samples belonged to the E. vermicularis species. Samples 28 and 31 showed 100% identity with the Japanese strain (accession number NC_056632.1), while sample 12 showed the lowest identity at 88%. Genetic variation included both transition (purine-to-purine or pyrimidine-to-pyrimidine substitutions) and transversion mutations (purine-to-pyrimidine or vice versa). Conclusion: PCR and sequencing of the cox1 gene effectively identified E. vermicularis in stool samples and revealed notable genetic diversity among isolates. These findings underscore the importance of molecular methods in parasitological diagnostics and epidemiological studies.
Filling The Policy-Law Gap in Indian Sports Governance: A Critical Legal Review
The current article seeks to understand the need for a Sports Law in India by tracing the available literature especially in India in the area of sports. There have been several scholars who have addressed the various aspects of sports such as the politicisation, age-fraud, corruption, selection procedure and the like issues that are plaguing sports in India. A similar pattern is shaping the development of sports law in India. It looks into the various aspects covering sports and the ultimate decision making that must be made by the various sportspersons. The codification of the area of sports is being sought as a precursor to the existing legal scenario in which the various sports disputes are being understood as means for espousing the cause of sports and the future of sports in India. There has been ample literature for understanding the lacunae prevalent in sports which must be addressed by a carefully crafted sports law that must cover the areas of governance, regulation, competition policies, agendas, Arbitration techniques and the future of sports in India. The present paper seeks to delve on various aspects of sports and their outcomes in bringing about the much needed shift in sports in India. The setting up of a sports regulatory body and setting up of specific committees for looking into various aspects of sports in India has meant that the sports need a proper channel to address their disputes and manage the working of various sports bodies which have been exerting their influence and governing sports since a long time. It is the need of the hour to look into the monopolistic structure of these organizations and develop a mechanism to deal with these concerns of sports.
Intrusion Detection using Anomaly Detection and Isolation Forest Method for Implementation and SHAP for Interpretability.
The rapid proliferation of Internet of Things (IoT) devices has increased the risk of cyber intrusions, necessitating robust and intelligent Intrusion Detection Systems (IDS). Traditional IDS methods struggle with the dynamic nature of IoT networks and the growing sophistication of cyberattacks. In this study, we propose a novel framework for anomaly detection in IDS using the Isolation Forest (IF) method on the RT-IoT2022 dataset. The framework leverages unsupervised learning to identify anomalous network behavior and potential cyber threats in real-time. To enhance interpretability, we integrate SHapley Additive Explanations (SHAP) to provide explainable AI insights into the model’s decision-making process. The SHAP technique helps in understanding the contribution of individual features towards anomaly detection, thereby improving transparency and trust in the IDS. Our evaluation metrics, including precision, recall, F1-score, and confusion matrix analysis, demonstrate the efficiency of the proposed model in detecting malicious activities. The experimental results validate the effectiveness of our approach in identifying anomalies while ensuring model interpretability, making it a promising solution for securing IoT networks against evolving cyber threats.
A Discriminative AI-Based Sequential Classification Model for Fake News Detection using BERT
In today’s world, there is a widespread flow of data and information due to immense usage of web applications. The usage of information available on web is difficult to be categorised and identified as true or Fake for a normal user. This creates a sort of doubt and confusion in the mind of normal users as how to verify and authenticate the news in the social media. This is essential in today’s modern-day scenario as regulatory bodies do wants and enforce that the true news or information should be further circulated or propagated rather than fake news which at times is not known to common user and he may be mis leaded by fake news. Public confidence in widely disseminated news has been undermined by the ease with which disinformation may now reach large audiences due to immense growth of social media and online platforms. To overcome such situation, we need a well-defined system which ensure the identification of fake and true news with highest accuracy. The authors proposed a text-based sequential classification model for detecting fake news using Bidirectional Encoder Representations from Transformers (BERT). The model proposed is able to clearly classify the news as fake or true with enhanced accuracy and precision. The model will be able to accurately identify the Fake news and will benefit the society by helping to clearly distinguish between fake and true news. The model is built on usage of transformer model which has ability to learn from complex relationship in label data and enhances the performance of predicted model with faster training time due to parallel processing.
Types of Melanin Pigments and their Applications
Melanin is a multifunctional pigment in many different organism and its special properties caused rising interest for research. Of its microbial sources, one promising producer is Streptomyces. After being initially discovered in this genus its melanin biosynthesis was extensively studied, which eventually unfolded the great versatility of melanins. Importantly, melanin from Streptomyces shows other biological activities such as antioxidant, antimicrobial and anti cancerativas. Its antioxidant properties can help to scavenge free radicals and combat oxidative stress, thus qualifying it for use as medicinal ingredient. It also helps with antimicrobial effects and it becomes an excellent choice for natural preservation. With its role in the lives of medical science, it also found benefits to a diverse group with different industrial applications for Streptomyces melanin.. Its pigmentation properties have been exploited for dyes, cosmetics, and food colorings. Furthermore, its biocompatibility and stability have opened avenues for its use in materials science and environmental remediation. Current study focuses on optimizing melanin production, characterizing its structural variations, and elucidating the mechanisms underlying its biological activities. the potential applications of Streptomyces melanin are expected to grow, making it a valuable resource across multiple fields.
Evaluation of the Energy Effect from Heating Blast Furnace Gas before Top-pressure Recovery Turbines at Metallurgical Plants
The article examines a circuit for heating blast furnace gas before gas turbines at metallurgical plants. A methodology is presented for assessing the complex energy effect from the use of fuel energy for generating electricity, taking into account the effect of heating on gas turbines and on the efficiency of the steam-turbine cycle. It is shown that gas heating allows increasing the power of gas turbines and also reducing fuel consumption in the steam-turbine cycle, which will reduce the specific fuel consumption for electricity generation.
Multidimensional Insights into the Role of Stress, Nutrition and Physical Activity in Modulating Dysmenorrheal Pathophysiology
Background: Dysmenorrhea, a common gynaecological condition, affects the quality of life among young women, with its severity influenced by various lifestyle factors such as stress, diet, physical activity, and hydration. Materials and methods: A cross-sectional study involving 608 female students from various academic disciplines was carried out to examine lifestyle factors associated with dysmenorrhea. Validated questionnaires were used to evaluate dietary habits, physical activity, and stress levels. Statistical analyses, including chi-square tests, were employed to identify significant correlations between these variables and the severity of dysmenorrhea. Results: The study found that pain was highest in students aged 18-22 years (53.45%, p = 0.409), while stress worsened symptoms in 65.46% (p = 0.041), mainly due to exams (18.09%) and personal issues (30.10%). Exercise reduced pain (p = 0.039), especially cardio (21.38%) and flexibility training (18.09%), with short workouts (<15 min) providing the most relief (46.05%, p = 0.035). Whole grains (46.76%, p = 0.043), dairy (56.87%, p = 0.041), and hydration (>2L/day, 23.03%, p = 0.038) helped alleviate symptoms, while high sugar intake worsened pain (39.47%, p = 0.037). Conclusion: The research has provided compelling evidence on the role of lifestyle factors in dysmenorrhoea severity, particularly stressing diet, hydration, and physical activity. Interventions such as stress management, sufficient water intake, and regular moderate exercise may help alleviate symptoms. Incorporating physical activity, balanced nutrition, and effective stress-reduction strategies could serve as practical approaches for managing dysmenorrhea among college students.
Assessing the Role of Crisis Communication in Power Outage Response During Natural Disasters, Enhancing System Resilience through Renewable Integration
Effectively managing power outages during natural disasters is essential to protect the well-being and safety of impacted populations. This research investigates how crisis communication strategies can reduce the adverse consequences of electrical service disruptions, while also examining how renewable energy sources contribute to enhancing grid resilience. Adopting a mixed-methods approach, the study analyzes recent case studies and empirical evidence from high-risk regions affected by power failures. The findings underscore the critical role of timely and coordinated communication, along with the deployment of renewable technologies, in reinforcing the robustness of energy infrastructure. The article concludes with actionable recommendations aimed at strengthening emergency preparedness and energy response strategies..
Awareness of Tobacco-Related Health Hazards among Medical, Nursing, and Paramedical Students and Paramedical staff in two Tertiary Care Hospitals
Background: Tobacco use remains a major public health concern, and healthcare professionals play a pivotal role in tobacco cessation efforts. Assessing awareness among medical, nursing, and paramedical personnel is essential to strengthen preventive strategies and patient counseling. Objectives: To evaluate the awareness of tobacco-related health hazards among medical, nursing, and paramedical students and paramedical staff in two tertiary care hospitals. Methods: A cross-sectional study was conducted among 420 participants comprising MBBS students, nursing students, and paramedical staff. A structured questionnaire assessed knowledge across four domains: tobacco-related diseases, passive smoking risks, national tobacco control laws, and role in cessation counseling. Awareness scores were compared using appropriate statistical tests. Results: The mean age of participants was 22.1 ± 2.3 years; 58% were female. Tobacco use prevalence was highest among paramedical staff (12%), followed by MBBS (6%) and nursing students (4%). MBBS students demonstrated the highest awareness across all domains: Tobacco-related diseases: 84.2% (MBBS) vs. 78.6% (Nursing) vs. 71.3% (Paramedical), p = 0.01 Passive smoking risks: 80.5% vs. 74.1% vs. 68.9%, p = 0.02 National tobacco control laws: 68.4% vs. 61.2% vs. 55.7%, p = 0.03 Role in cessation counseling: 75.6% vs. 70.3% vs. 62.5%, p = 0.04 Only 38% of all participants felt confident in advising patients on tobacco cessation. Conclusion: While MBBS students exhibited relatively higher awareness, significant gaps were observed among paramedical staff, particularly in policy knowledge and counseling roles. These findings underscore the need for targeted educational interventions and integration of tobacco control modules across all healthcare training programs.
Discussions around the Principles of Soviet Art of the 1920s -1930s and the Addition of Painting “Socialist Realism”
In this article, the author examines the problem of the formation of ideological and artistic programs of painting “social realism” in the period of 1920-1930-ies... Author proves that the artistic trends of the Soviet avant-garde initially grew on the basis of the revolutionary ideology and philosophy of nihilism, which actively rejected a thousand-year-old Christian culture and art as alien to the ideology of Soviet communism. This approach, which contained aggressive revolutionary features in the early Soviet avant-garde, was later “softened” by reliance on the traditions of Russian realism XIX He became less aggressive in painting socialist realism.
Studies of Geology, Mineralisation and Fluid Involved in Lead and Zinc Indice of the Dareh Qileh (Northeast of Aligudarz(
The tectono-magmatic evolution of NW Iran records the Paleozoic–early Mesozoic formation and consumption of the Neo-Tethys Ocean. Debate focus on how the regionally-distributed basement exposures and their diverse evolution setting are connected in the division of Iranian tectonic basins. The oldest sediments that are exposed in the study area are the Upper Triassic-Jurassic sediments and sandstones of the region have been altered by the Late Cimmerian phase to the level of the greenschist facies (biotite zone). The structural elements present in the area include faults, folds, joints, and plates. The Upper Triassic-Jurassic sediments and sandstones of the region have been altered by the Late Cimmerian phase to the level of the greenschist facies (biotite zone). The degree of alteration of the rocks in question, based on microscopic studies, is at the level of the albite, epidote-hornfels facies. The mineralization in this deposit has formed in the form of veins and lenses with layering and along the quartz veins. The mineralogy of the mineral includes sphalerite, galena, pyrite and to a very small extent chalcopyrite. In studies of the fluids involved, the homogenization temperature of the fluid inclusions varied in the temperature range between 115-215 degrees Celsius, and based on studies conducted on the samples, the salinity of the fluids involved is equal to 10 to 22 percent by weight equivalent to common salt. The density of the fluids also varies from 0.8 to more than 0.9. According to microthermometry studies, the evolution of the mineralizing fluid was due to mixing with surface waters in the mineralizing fluid, which led to the deposition of the mineral. It can be said that the mineralizing fluid shows a range of characteristics of basin brines and atmospheric fluids. In general, studies show that the deposition of metallic minerals occurred simultaneously from the hydrothermal fluid transported to the region and deposition along the vein quartz left by these fluids. According to the results of the studies and based on the tectonic environment, the type of host rock, the homogenization temperature and the salinity of the fluid interlayers, the deposit can be considered similar to the Irish-type zinc-lead deposits. Our study provides an evolution model that integrates the various complexes of NW Iran into a common Late Cambrian to Late Triassic evolution of the neo-Tethys.
Attitude of MBBS and Nursing Students Toward Problem -Based Learning Versus Traditional Teaching
Background: Problem-Based Learning (PBL) has emerged as a transformative pedagogical approach in medical and nursing education, emphasizing active learning, clinical reasoning, and teamwork. Traditional Teaching-Learning (TTL) methods, while foundational, are often criticized for promoting passive knowledge acquisition. Objectives: To compare the attitudes of MBBS students/interns and nursing students toward PBL and TTL using a 15-point Likert scale questionnaire. Methods: A cross-sectional study was conducted with 100 MBBS students/interns and 100 nursing students from two renowned universities of India. Participants completed a 15-item Likert scale questionnaire assessing engagement, understanding, critical thinking, and other educational dimensions. Data were analyzed using descriptive statistics and visualized through pie, line, box, and scatter charts. Results: Both groups showed a strong preference for PBL over TTL. Nursing students rated PBL higher across all dimensions, especially in teamwork, self-directed learning, and clinical relevance. MBBS interns appreciated PBL for its practical application and problem-solving focus. Conclusion: PBL is favored over TTL by both MBBS and nursing students, suggesting a need for curriculum reform that integrates student-centered learning strategies.
Attitude Toward Interprofessional Education and Collaborative Learning between Medical and Nursing Students
Background: Interprofessional education (IPE) is essential for fostering collaboration among healthcare professionals. In India, where hierarchical structures and discipline-specific training dominate, understanding attitudes toward IPE is critical for curriculum reform. Objectives: To assess attitudes toward IPE among MBBS students, nursing students, and nurses. To compare perceptions across two medical colleges in Rajasthan and Uttar Pradesh. To identify barriers and facilitators to collaborative learning. Methods: A cross-sectional study was conducted with 250 participants: 100 MBBS students (including interns), 100 nursing students, and 50 nurses. A 15-item Likert scale questionnaire was administered. Data were analyzed using descriptive statistics and visualized through bar, line, box, scatter, and pie charts. Results: 85% of participants agreed that IPE improves patient care. 78% supported joint training sessions. 62% of MBBS students expressed concern about role ambiguity. Nurses emphasized the importance of mutual respect and communication. Conclusion: Interprofessional education is well-received among medical and nursing students and practicing nurses. However, tailored interventions are needed to address role ambiguity and foster collaborative competencies.
Superconductivity and the Role of Zero Resistance in Modern Physics
Superconductivity, first discovered in 1911 by Heike Kamerlingh Onnes, represents one of the most significant breakthroughs in condensed matter physics. It is characterized by two fundamental properties: the complete disappearance of electrical resistance and the expulsion of magnetic fields (Meissner effect). The phenomenon of zero resistance allows superconductors to conduct current indefinitely without energy dissipation, holding transformative implications for modern physics and technology. This paper explores the theoretical foundation of superconductivity, its connection to zero resistance, and its implications for modern physics, ranging from quantum field theories to real-world applications such as magnetic levitation, quantum computing, and energy transmission.
The Connection between Artificial Intelligence and Blockchain on Accounting and Auditing
With the development of new technologies, blockchain, as one of the most advanced digital tools, has a significant impact on the field of accounting and financial reporting. This technology, with its unique features such as transparency, data security, immutability and transaction traceability, allows for increased accuracy and reliability of companies' financial reports. This research paper focuses on the impact of artificial intelligence and blockchain on the efficiency and effectiveness of various accounting professions and the audit process. Various analyses and studies have shown that artificial intelligence plays an important role in facilitating the proper functioning of companies and that efficient and effective artificial intelligence can significantly improve the audit process through audit automation. Artificial intelligence also plays an important role in accounting, which leads to improved procedures and decision-making processes in various business areas.This paper uses a qualitative method to provide a good overview of the impact of AI on the audit field and the accounting process. It also addresses research gaps in the area of ​​shortcomings that have not been highlighted by other researchers and provides directions for future studies on the limitations of AI in the audit process and the accounting profession.
Position Control of a Permanent Magnet Brushless DC Motor using a Model Predictive Control Method with Laguerre Functions
This article focuses on reducing the high computational load in the MPC method with long prediction horizons, by using orthogonal basis functions (Laguerre functions) for controlling the position of a BLDC motor. MPC is one of the controllers that can be used to achieve the desired and optimal dynamics of the BLDC motor. One of the main challenges in the practical implementation of MPC, which limits its application, is the computational volume, especially with high prediction horizons. In fast systems and systems with complex dynamics and small sampling times, the computational load increases exponentially, which leads to a decrease in real-time execution speed and requires shorter prediction horizons. This high computational load can be approximated using discrete orthogonal basis functions, such as Laguerre polynomials. The main advantage of this approach is the optimization of coefficients with fewer Laguerre functions instead of optimizing the control trajectory itself, making it possible to choose a longer prediction horizon. Simulation and implementation result clearly demonstrate that the Laguerre-based MPC method benefits from reduced computation time, indicating a significant reduction in computational load.
Attitude Towards Use of Social Media for Medical Education and Professional Networking
Background: Social media platforms are increasingly used for medical education and professional networking. Understanding attitudes across health professional trainees and staff can guide formal educational strategies and policy. Objective: To evaluate attitudes toward use of social media for medical education and professional networking among MBBS students/interns, nursing students, and paramedical staff. Methods: A cross-sectional survey was conducted among 250 participants (100 MBBS students/interns, 50 nursing students, 100 paramedical staff). A structured questionnaire captured demographics, platform usage, purposes (education, networking, patient education, personal use), perceived benefits and risks, professionalism concerns, and preferred institutional policies. Primary outcomes: proportion reporting social media use for medical education and for professional networking; mean attitude scores (5-point Likert) for perceived usefulness, trustworthiness, and professionalism risk. Statistical tests: chi-square for categorical comparisons, ANOVA/Kruskal-Wallis for Likert scores, and logistic regression adjusting for age, role, and prior formal training. Results: (Illustrative simulated example — clearly labeled) Among 250 respondents, 80% reported using social media daily, 68% used it for medical education, and 45% used it for professional networking. MBBS students/interns reported higher educational use (78%) compared with nursing students (66%) and paramedical staff (58%). Mean perceived usefulness score for education was 3.9/5 (SD 0.8). Major concerns were misinformation (74%) and breach of patient confidentiality (61%). A majority (67%) supported institutional guidance/training on professional social media use. Conclusions: Social media is widely used among health trainees and staff for educational purposes; however, concerns about misinformation and professionalism persist. Institutional training and clear policies are recommended to harness benefits while reducing risks.
A Narrative Review of the Impact of Virtual Reality (VR) on Clinical Skills Development in Nursing Students
Background: The integration of virtual reality (VR) within nursing education has advanced rapidly over the past six years, driven by the mounting need for innovative approaches to clinical training, challenges in traditional education models, and the demands created by global health crises. Objective: This narrative review analyzes recent evidence regarding the role and effectiveness of VR in developing clinical skills among nursing students worldwide. Methods: Relevant literature from 2019 to 2025 was identified through comprehensive database searches (PubMed, Scopus, Web of Science, Google Scholar) using keywords relating to VR, nursing education, and clinical skills. Studies selected for inclusion consisted of experimental, mixed-methods, systematic reviews, and narrative syntheses, focusing specifically on VR applications in undergraduate and postgraduate nursing programs. Results: VR significantly enhances students’ technical, cognitive, and communication competencies, as well as self-efficacy and readiness for clinical practice. Its benefits are best seen in improvement of practical skills, decision-making, reduction of anxiety and errors, and increased engagement. Despite persistent barriers such as costs, lack of faculty expertise, and infrastructure requirements, VR’s pedagogical value is now widely recognized. Future directions include large-scale implementation, integration with artificial intelligence, and research on long-term outcomes. Conclusion: VR is a transformative agent in the evolution of nursing education, enabling flexible, standardized, and immersive skills training. Systematic investment, curricular adaptation, and ongoing research are essential to maximize its impact and sustainability.
Using Finite Element Process Simulation to Design and Simulate an Optimal Preform Mold with Neural Networks and Continuous Genetic Algorithm
In the forging process, if the shape of the final part is complex, the raw material cannot be transformed into the shape of the final part in one forging step; therefore, the use of preform molds is essential. An optimal preform mold is a mold that can meet the design criteria. Design criteria include producing a defect-free part with the least volume of raw material, the least plastic strain, the least force required to perform the process, and also completely filling the final mold. In this research, using the ability of the continuous genetic algorithm to generate Cartesian paths, first several different preform mold shapes are produced using mathematical functions. Then, using finite element simulation of the process, the optimal mold selection criteria are calculated. From the information obtained from the simulation, the artificial neural network is trained so that it can predict the results of the simulation process. This network and design criteria are used for the objective function in the continuous genetic algorithm. Finally, the best shape of the preform mold is calculated by the continuous genetic algorithm, which is a mathematical function and is obtained by plotting this function in the Cartesian coordinates of the mold shape. Next, this method is used for a part with an H-shaped cross-section to examine its efficiency. The optimal preform mold for the part is calculated and its forging results are extracted by the continuous genetic algorithm. Also, finite element simulation is performed for the optimal mold to compare its results with the results obtained from the continuous genetic algorithm. Finally, the success of using this method was proven.
Toxoplasma gondii Discovery: History, Research, and Future Papers
Toxoplasma gondii is a parasitic protozoan of great scientific interest due to its wide distribution and complex life cycle. It is best known for its ability to infect all warm-blooded animals, including humans, with significant biological and environmental implications. This article focuses on the historical development of T. gondii research, identifies key players, assesses the impact of the parasite, and considers future directions for research and control. By presenting different perspectives and a small review, this article aims to shed light on the complexity of T. gondii plays a major role in parasitology and public health.
Research and Evidence-Based Medical Practice: Attitude of MBBS & Nursing Students and Technical Staff Towards it.
Background Evidence-Based Medical Practice (EBMP) integrates scientific evidence, clinical expertise, and patient values to optimize healthcare outcomes. However, the successful implementation of EBMP depends on healthcare professionals’ awareness, attitudes, and skills. Objective To assess and compare the attitudes and awareness toward EBMP among MBBS students, nursing students, and technical hospital staff (including nurses and technicians). Methods A cross-sectional descriptive study was conducted with 250 participants: 100 MBBS students, 50 nursing students, and 100 technical staff from two tertiary care hospitals. A prevalidated 15-item Likert scale questionnaire assessed awareness, attitude, and perceived barriers. Data were analyzed using SPSS v25, with p < 0.05 considered significant. Results MBBS students demonstrated the highest awareness (mean score 4.3 ± 0.5) and favorable attitude (4.4 ± 0.6), followed by nursing students (3.8 ± 0.6) and technical staff (3.3 ± 0.7). Major barriers included lack of time (63%), limited access to research (58%), and insufficient training (55%). Statistical analysis (ANOVA) showed significant differences among groups (p < 0.01). Conclusion Although EBMP is positively perceived across groups, notable gaps exist—especially among technical staff. Curriculum integration, continuous workshops, and institutional support are vital to foster a culture of evidence-based healthcare.
Integrated Pest Management Strategies for Sustainable Control of Major Corn Pests
The production of corn is continually threatened by various pests, including fall armyworms, aphids, grasshoppers, and beetles that affect yield and quality of the crop. The study presented here provides a total pest management plan to control these major corn pests using several avenues of pest management, including mechanical pest management, biological pest management, and botanical pest management. Fall armyworms were controlled using hand picking, applying sand in whorls, pheromone traps, and deep tillage. Biological methods considered for controlling fall armyworm included Trichogramma species parasitoids, Bacillus thuringiensis, Nomuraea rileyi. Botanical method used for control of fall armyworm included neem oil, garlic extract sprays, and intercropping with repellent crops or trap crops. Aphids were managed with hand removal, natural predators such as lady beetles and lacewing, and biopesticides including horticultural oils (mineral oil), pyrethrin (pyrethroids), and neem oil-based insecticides. Grasshoppers were managed using physical barriers (exclusion), tillage, and planting earlier in the season and biological agents (Nosema locustae) as well as predators (spiders and beetles). Less conventional botanical tactics such as repellent botanical insecticide sprays and spraying flour as a powder dust to control grasshoppers were also implemented. Beetles were managed using traps, hand picking, and natural enemies (for example parasitoids wasps), using neem and pyrethrum based insecticides. The following strategies can be incorporated into an Integrated Pest Management (IPM) program to allow for sustainable and environmentally responsible corn pest control. Using the various IPM methods to lessen reliance on synthetic insecticides is in keeping with the ways of fostering long-term protection for crops. In addition, methods that do not depend on chemicals work in harmony with agricultural and ecological environments in corn production.
Chitosan-Based Nanoemulgel for Enhanced Co-Delivery and Sustained Release of Lipophilic Cynaropicrin and Hydrophilic Salicin: Formulation, Physicochemical Characterization, and Stability
The co-delivery of therapeutic agents with differing physicochemical properties presents significant formulation challenges. This study aimed to develop and characterize a chitosan-based nanoemulgel for the simultaneous encapsulation and sustained delivery of the lipophilic anticancer agent cynaropicrin and the hydrophilic anti-inflammatory/anticancer agent salicin. Nanoemulsions were prepared by integrating an oil phase containing cynaropicrin (Medium-Chain Triglyceride oil) with an aqueous phase of chitosan and salicin, followed by high-shear homogenization and sonication, and subsequently incorporated into a Carbopol gel. Five distinct formulations (F1-F5) were systematically developed and characterized for pH, viscosity, spreadability, particle size, polydispersity index (PDI), zeta potential, encapsulation efficiency (EE%), and morphology via TEM. Optimized nanoemulgels exhibited desirable particle sizes ranging from 81.63 ± 0.47 nm to 106.13 ± 0.75 nm, low PDI values (0.249 ± 0.002 to 0.337 ± 0.003), and strong negative zeta potentials (–25.2 ± 0.2 mV to –40.33 ± 0.21 mV), indicating good colloidal stability. Encapsulation efficiencies were notable, ranging from 80.94-91.80% for cynaropicrin and 64.52-81.40% for salicin. TEM analysis confirmed the presence of uniform, spherical nano-droplets. Crucially, in vitro release studies demonstrated a sustained release profile for both encapsulated drugs over a 24-hour period. These findings collectively suggest that the developed chitosan-based nanoemulgel system is a promising and effective platform for the co-delivery and controlled release of cynaropicrin and salicin.
Comparative Performance Analysis of BLDC Motor Speed Control Using CPLD-Based Implementation
The widespread usage of brushless motors in today's technological trend necessitates the occasional development of novel control techniques. This study compares the mathematical design of speed control for a brushless direct current (BLDC) motor using a complex programmable logic device (CPLD) with the implementation of speed control without CPLD modeling. A PWM technique is used to mathematically construct speed control by applying different duty cycles to a 3-ф inverter. The rotor position is determined via Hall sensors, which act as references to synchronize the PWM control signals. Verilog Hardware Description Language (VHDL) is used to write the control model, which is then validated by simulation of with CPLD. To evaluate the BLDC motor's speed performance under PWM control, an experimental setup is constructed. A 500W BLDC motor with a 48V rating is equipped with CPLD Max II devices to implement the control algorithm. This system generates PWM with a configurable duty cycle for a working frequency of roughly 20 kHz and commutates consecutively up to 400 RPM using a six-step commutation.
MBBS & Nursing Students and Paramedical Staff Views Regarding Artificial Intelligence in Diagnosis and Treatment.
Background: Artificial Intelligence (AI) has emerged as a transformative tool in modern healthcare, aiding in diagnosis, treatment planning, and administrative efficiency. However, the perception and acceptance of AI among future and current healthcare providers are crucial for its successful integration. Objective: To assess the awareness, perception, and attitudes of MBBS students and interns, nursing students, and paramedical staff toward the use of AI in diagnosis and treatment. Methods: A cross-sectional descriptive study was conducted among 250 participants, comprising 100 MBBS students and interns, 50 nursing students, and 100 paramedical staff (including nurses and technicians). A semi-structured questionnaire was distributed to assess knowledge, attitude, and perceived impact of AI in clinical practice. Data were analyzed using descriptive statistics and chi-square tests for comparison between groups. Results: Among all participants, 78% were aware of AI applications in healthcare. MBBS students and interns demonstrated the highest awareness (92%), followed by nursing students (70%) and paramedical staff (65%). A positive attitude toward AI integration in diagnosis and treatment was seen in 81% of respondents, though 57% expressed concerns about over-reliance on technology and job displacement. While 68% believed AI could improve diagnostic accuracy, only 41% trusted AI-driven decisions without human verification. Conclusion: Most healthcare students and staff hold favorable views regarding AI’s role in diagnosis and treatment, though concerns persist about its ethical implications and reliability. There is a clear need for structured AI education within medical and allied health curricula to promote responsible use and understanding of this evolving technology.
Impact of Chronic Pancreatitis on Work Productivity and Daily Functioning: A Cross-Sectional Study of Patients from Three Tertiary Care Hospitals in India
Background Chronic pancreatitis (CP) is a progressive inflammatory disease of the pancreas that leads to persistent pain, exocrine and endocrine dysfunction, and significant impairment in quality of life. While clinical manifestations are well-documented, the impact of CP on work productivity and daily functioning—particularly in the Indian context—remains underexplored. Objectives To evaluate the effects of chronic pancreatitis on absenteeism, fatigue, and routine activities among patients from three tertiary care hospitals in India. Methods A cross-sectional observational study was conducted involving 20 adult patients diagnosed with CP for at least six months. Participants were recruited from three tertiary care centers across India. Data were collected using a 15-item structured questionnaire, the Work Productivity and Activity Impairment (WPAI) tool, Fatigue Severity Scale (FSS), and Daily Functioning Index (DFI). Descriptive statistics and visual analytics (pie chart, bar chart, line plot, box plot, scatter plot) were used to interpret findings. Results Employment Status: 60% employed, 40% unemployed. Absenteeism: Average of 6.2 days/month missed due to CP. Fatigue: Mean FSS score of 5.8, indicating moderate to severe fatigue. Daily Functioning: 85% reported difficulty in household chores; 40% required assistance with routine tasks. Visuals: Charts illustrated correlations between fatigue and absenteeism, and disparities in functioning between employed and unemployed patients. Conclusion Chronic pancreatitis significantly impairs work productivity and daily functioning in Indian patients. High absenteeism, fatigue, and reduced routine capacity highlight the need for integrated care strategies, including occupational support and fatigue management.
Effectiveness of Hand Hygiene Training Modules: Feedback from Medical and Paramedical Students and Paramedical Staff in Three Tertiary-Level Hospitals of India
Background: Hand hygiene is a critical component of infection control in healthcare settings. Despite established guidelines, compliance remains inconsistent among healthcare workers. Objective: To evaluate the effectiveness of hand hygiene training modules and gather feedback from MBBS students, nursing and paramedical students, and paramedical staff across three tertiary-level hospitals in India. Methods: A cross-sectional interventional study was conducted involving 100 MBBS students, 50 nursing and paramedical students, and 100 paramedical staff. Participants underwent a standardized training module based on WHO guidelines. Pre- and post-training questionnaires assessed knowledge and practices, while feedback forms evaluated training quality. Results: Post-training scores showed significant improvement across all groups, with paramedical staff demonstrating the highest behavioral change. Feedback indicated high satisfaction with the training content and delivery. Conclusion: Structured hand hygiene training modules effectively enhance knowledge and compliance among diverse healthcare personnel. Regular reinforcement and role-specific customization are recommended for sustained impact.
Awareness and Usage of Clinical Decision Support Systems (CDSS) in Antimicrobial Prescribing: A Cross-Sectional Study in Four Tertiary Hospitals and a Nursing School
Background: Clinical Decision Support Systems (CDSS) play a vital role in optimizing antimicrobial prescribing, reducing errors, and supporting antimicrobial stewardship (AMS). Limited data exist on the awareness and usage of CDSS among healthcare trainees and paramedical staff in India. Objectives: To assess the awareness, usage patterns, perceived usefulness, and perceived barriers related to CDSS in antimicrobial prescribing among MBBS students, nursing students, BDS students, and paramedical staff across four tertiary care hospitals and a nursing school. Methods: A descriptive cross-sectional study was conducted among 100 MBBS students, 100 nursing students, 50 BDS students, and 100 paramedical staff (including nurses and technicians). A validated, self-administered questionnaire assessing awareness, usage frequency, perceived benefits, and barriers was used. Descriptive and comparative statistics were applied. Results: Awareness of CDSS was highest among MBBS students (78%), followed by BDS students (62%), nursing students (55%), and paramedical staff (38%). Regular usage of CDSS in antimicrobial prescribing was reported by only 21% of participants overall, with MBBS students being the most frequent users (29%). Major perceived benefits included improved prescribing accuracy (72%) and reduced antimicrobial resistance (65%). Key barriers included lack of training (68%), limited system access (54%), and poor integration into workflow (49%). Conclusion: Despite moderate awareness, CDSS usage remains low across all groups. Enhanced training, improved accessibility, and institutional integration are strongly recommended to promote CDSS adoption and strengthen antimicrobial stewardship.
Evaluation of Stress and Burnout Levels Among Health Care Students and Healthcare Professionals in Medical Colleges and Hospital Wards
Background: Stress and burnout are increasingly recognized as major challenges in healthcare education and practice. Medical students, nursing students, dental students, and healthcare workers often face high workloads, emotional strain, and academic pressures. Objective: To evaluate stress and burnout levels among healthcare students and healthcare workers in tertiary-level hospitals in India. Methods: A cross-sectional study was conducted among 350 participants: 100 MBBS students and interns, 100 nursing students, 50 BDS students, and 100 healthcare workers (including nurses and technicians) across four tertiary hospitals in India. Standardized questionnaires — the Perceived Stress Scale (PSS) and Maslach Burnout Inventory (MBI) — were administered. Results: High stress levels were reported among MBBS students (68%) and nursing students (72%), compared to BDS students (54%) and healthcare workers (61%). Burnout prevalence was highest among healthcare workers (65%), followed by nursing students (62%), MBBS students (58%), and BDS students (46%). Emotional exhaustion was the most prominent burnout dimension across all groups. Conclusion: Stress and burnout are prevalent among both healthcare students and workers, with nursing students and healthcare workers being particularly vulnerable. Institutional interventions focusing on mental health support, workload management, and resilience training are urgently needed.
An Integrated Framework for Demolition Optimization and Material Recycling: A Strategy for Environmental Protection and Construction Sustainability
The construction industry is under increasing pressure to adopt sustainable practices for Demolition Waste Management (DWM) from a life-cycle perspective. Traditional approaches are failing, necessitating a sophisticated assessment framework to handle multifaceted sustainability criteria. This paper proposes an integrated framework based on Building Information Modeling (BIM) designed to facilitate comprehensive Life Cycle Assessment (LCA). The system functions by coupling an enriched data model with hybrid Multi-Criteria Decision-Aiding (MCDA) methods. A pilot study validating this framework yielded definitive results: the optimal DWM scenario, characterized by the highest material recycling rate, achieved a sustainability score of 91.63. In stark contrast, a "status quo" scenario, reflecting current industry practices, registered a score of only 8.37. This demonstrates a clear correlation between recycling rates and quantifiable sustainability. However, the analysis also reveals a flattening "growth curve," suggesting that increasing recycling rates beyond a certain threshold yields diminishing returns in sustainability scores, pointing toward an optimum point for cost-efficiency. This research establishes a clear path for integrating digital technologies like BIM with carbon strategies and phase-based planning. Adopting such a framework is a critical step toward accelerating the industry's transition to a circular economy, thereby supporting key global objectives such as SDG 11 (Sustainable Cities) and SDG 12 (Responsible Consumption and Production).
Perceptions of Healthcare Professionals on Continuing Medical Education and Skill Development: A Cross-Sectional Study in Four Tertiary Care Hospitals of India
Background: Continuing medical education (CME) and skill development are essential for maintaining professional competence and ensuring high-quality patient care. Objective: To assess the perceptions of healthcare professionals—including MBBS students and interns, BDS students, and paramedical staff—towards CME and skill development in tertiary care hospitals of India. Methods: A cross-sectional study was conducted among 250 participants (100 MBBS students/interns, 50 BDS students, and 100 paramedical staff) across four tertiary care hospitals. A structured questionnaire assessed awareness, attitudes, perceived benefits, barriers, and preferred modes of CME. Results: Majority of participants (82%) recognized CME as vital for professional growth. MBBS students and interns emphasized its role in clinical decision-making, while paramedical staff highlighted skill development for patient safety. Common barriers included time constraints (64%), limited institutional support (48%), and financial challenges (32%). Online CME modules were preferred by 56% of respondents, while 44% favored hands-on workshops. Conclusion: Healthcare professionals across disciplines perceive CME and skill development as crucial, but systemic barriers hinder participation. Institutional policies should prioritize accessible, affordable, and blended learning approaches to strengthen continuous professional development.
Attitudes of Medical, Dental, Nursing Students and Healthcare Workers Towards Vaccination Policies in Hospitals: A Study in Four Tertiary Hospitals of India
Background: Vaccination policies in hospitals are vital for infection control; however, attitudes toward these policies vary across healthcare professional groups. This study aimed to assess and compare the attitudes of MBBS students, dental students, nursing students, and healthcare workers toward hospital vaccination policies in four tertiary hospitals in India. Methods: A cross-sectional survey was conducted among 250 participants (100 MBBS students, 50 dental students, 50 nursing students, 50 healthcare workers). Data collection utilized a 15-item Likert-scale questionnaire evaluating knowledge, perception, and willingness to comply. Descriptive and inferential statistics analyzed attitudes across groups. Results: Healthcare workers exhibited the highest positive attitude (85%), followed by MBBS students (78%), nursing students (68%), and dental students (62%). Significant differences were observed among the groups (p<0.05). Motivation for vaccination was strongly correlated with perceived risk reduction (r=0.72). Conclusion Attitudes toward vaccination policies are generally positive but vary significantly among different professional groups. Tailored educational programs could enhance vaccine acceptance, especially among dental and nursing students. Implementing targeted interventions may improve adherence to hospital vaccination protocols.
Parabolic Applications in Suspension Bridge Design: Mathematical Modeling, Structural Analysis, and Computational Verification
Suspension bridges are the classic example of application of geometry in civil and structural engineering. This paper examines the mathematical foundation of the parabolic shape formed by the main cables when they carry a uniform load. By using mathematical derivation and computational modelling in python this paper shows how ideal equations of parabola can be used in real life application.