The Scientific Temper publishes papers from Science and Engineering, Cognitive Neuroscience and Psychology, Pharmacy and nursing, and other related multidisciplinary dimensions after a peer-review process. Plagiarism-free manuscripts following all international-standard ethical guidelines by authors are highly recommended. Globally competitive findings and original and innovative ideas are the key factors for the acceptance of manuscripts for publication in The Scientific Temper.
Prem Narayan Tripathi for BABURAM AND SONS
Impact of a Targeted Training Module on Premenopausal Osteoporosis Care: A Pilot Evaluation
Osteoporosis is a major health concern in premenopausal women due to hormonal changes that accelerate bone loss. Early intervention through education and lifestyle modification is essential to reduce risk and progression. This pilot study evaluated the effectiveness of a structured training module for osteoporosis prevention and management in this population. Methods: A quasi-experimental pilot study was conducted among 30 purposively selected perimenopausal women at risk of or diagnosed with osteoporosis. Baseline knowledge, attitudes, and practices (KAP) were assessed using a validated questionnaire. Participants received a comprehensive training module covering bone health, nutrition, and physical activity. Post-intervention assessment was performed after four weeks (33 days). Data were analysed using repeated-measures ANOVA.Results: The instruments demonstrated high validity and reliability (I-CVI ≥ 0.80, S-CVI ≥ 0.90, CVR ≥ 0.86; Cronbach’s α = 0.916; KR-20 = 0.87; test–retest = 0.95). Repeated-measures ANOVA showed significant improvements (p < 0.001) across multiple domains: knowledge (7.6 ± 2.3 to 15.5 ± 1.4; η² = 0.596), physical functioning (21 ± 22.3 to 68.7 ± 8.3; η² = 0.649), energy/fatigue (14 ± 9.1 to 72.7 ± 10.2; η² = 0.896), and emotional well-being (15.5 ± 8.8 to 71.2 ± 8.4; η² = 0.878). Significant gains were also observed in social functioning (η² = 0.668), pain reduction (η² = 0.838), general health (η² = 0.785), and self-management practices (gain = 1.20 vs. 0.30, p < 0.001). Post-hoc analyses confirmed that improvements were sustained at follow-up. Baseline demographic equivalence enhanced internal validity. Conclusion: The structured training module effectively improved knowledge, self-care practices, and overall well-being among perimenopausal women, supporting its use for osteoporosis prevention and management.
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Multipath Relay (MPR) Node Selection for Collision Avoidance and Efficient Routing in Mobile Ad Hoc Network
Mobile Ad Hoc Network (MANET) are characterized by dynamic topologies, limited bandwidth, and the absence of centralized control, making reliable data transmission a significant challenge. The performance of the network is frequently deteriorated by collisions are concurrent transmissions and ineffective relay node selection. This research suggests a new method for choosing multipath relay nodes that minimises packet collisions and improves MANET overall routing efficiency.OS approach ensures optimal route diversity and low interference by dynamically assessing possible relay nodes using a composite measure that takes into account node mobility, residual energy, and link stability. The technique was compared to existing Multi Path Relay (MPR) selection algorithms through simulations using NS3.The experimental findings show that, under different network densities and mobility models, there is a significant decrease in end-to-end delay, an improvement in packet delivery ratio, and a greater throughput. These enhancements validate that the strategy is successful in preserving reliable and effective communication channels. The results possess significant implication for mission-critical and delay-sensitive MANET applications, including military communications and disaster recovery.
A Customized CNN-Based Framework for Learning Disability Detection Using Handwriting Image Classification
Handwriting analysis has been widely explored as a supportive computational approach for understanding writing patterns frequently associated with learning difficulties in children. While deep learning techniques, particularly transfer learning algorithms, have demonstrated strong performance in image-based classification tasks, their reliance on large pre-trained models often limits adaptability and computational efficiency. To address these limitations, this study proposes a custom Convolutional Neural Network (CNN) architecture specifically designed for the classification of children’s handwriting patterns from images. The model consists of five Conv2D layers to extract and learn significant spatial features, promoting high classification accuracy while maintaining simplicity and reduced computational complexity. A carefully curated dataset of handwriting samples classified as “correct,” “casual,” and “reverse” was used to evaluate the model. This work does not claim to provide a clinical diagnosis of learning disabilities; rather, it presents a handwriting pattern classification framework that may support educational analysis and assistive screening when combined with validated assessment procedures.
Bibliometrics Analysis of Economic and Political Weekly (EPW): A Study
This bibliometric analysis of the Economic and Political Weekly (EPW) examines its research output, authorship patterns, and citation trends over a 25-year period, revealing significant insights into its role in economics and political science. The study identifies a total of 8,373 authors contributing to the journal, with the most prolific author, Kumar A, leading with 34 publications. The analysis shows that articles and reviews constitute 27.6% and 34.1% of the total publications, respectively, indicating a diverse range of scholarly contributions. Citation analysis reveals that the EPW is frequently cited by other journals, reflecting its substantial influence; for instance, it has been cited over 1,200 times in the last five years alone. The findings highlight a notable increase in multi-author papers, suggesting a shift towards collaborative research, with 45% of articles published in the last decade being co-authored. While the study underscores the journal’s impact and interdisciplinary nature, it also acknowledges limitations, such as potential biases in citation practices and the predominance of English-language publications. This analysis serves as a crucial resource for understanding the Economic and Political Weekly’s contributions to academic discourse and its significance in shaping research agendas in the social sciences.
Application of Bradford’s Law for the Covid-19 Research Output Indexed in Web of Science during 2020 – 2025
The COVID-19 pandemic triggered an unprecedented surge in scientific publications across diverse disciplines, including medicine, virology, public health, and biomedical sciences. Understanding the distribution of this rapidly expanding body of literature is essential for identifying key sources of scholarly communication. This study applies Bradford’s Law of Journal Scattering to examine the distribution and concentration of COVID-19 research articles indexed in the Web of Science (WoS) database during the period 2020–2025. The primary objective is to identify the core (nucleus) journals contributing to the field and to verify the applicability of Bradford’s Law using established mathematical models. Bibliographic data were retrieved using the keyword “COVID-19,” yielding a total of 21,762 publications distributed across 3,733 journals. The analysis categorizes journals into Bradford zones to determine the concentration of research output and to assess the conformity of the observed distribution with theoretical expectations. The findings of this study provide valuable insights for researchers, librarians, and policymakers in identifying influential journals and understanding publication patterns in pandemic-related research. The collected data were categorized by document type, and only journal articles were selected for analysis, resulting in a dataset of 20,935 articles published across 3,294 journals. This study aims to identify the core journals and assess the applicability of Bradford’s Law using established mathematical and theoretical models. Bradford’s zoning technique was applied to classify journals into three distinct zones, each contributing approximately one-third of the total number of articles. The distribution was further validated using the Leimkuhler logarithmic model, resulting in the construction of a Bradford–Leimkuhler curve. In addition, Egghe’s theoretical formulation was employed to interpret the characteristics of the three Bradford zones. The study also analyzed key bibliometric indicators of the most productive journals, including impact factor, h-index, publication origin, and the countries of publication. The results demonstrate a strong conformity of COVID-19 research publications to Bradford’s Law, revealing a highly concentrated core (nucleus) of journals, followed by two successive zones characterized by exponentially increasing numbers of journals with decreasing productivity. Verification through the Leimkuhler model confirmed a logarithmic growth pattern, thereby supporting the applicability of Bradford’s Law. Egghe’s model further elucidated deviations observed in the peripheral zones as a natural outcome of inequalities in journal productivity. Overall, this bibliometric investigation provides valuable insights for researchers, librarians, and policymakers in identifying influential and essential journals within the domain of COVID-19 research.
Enhanced Positional Vigenère (EPV): A Confidentiality-Enabled Encryption Technique for Secure Cloud Storage
Cloud computing is an internet-based computing paradigm that provides various hosting and delivery services over the internet. It offers computational resources to users based on their demand. Data storage is one of the main benefits of cloud computing. It provides users with plenty of space to store their data neatly and access it easily, no fuss involved. More and more companies are jumping on cloud platforms that offer Storage as a Service (STaaS), helping them skip the hefty upfront costs and ongoing maintenance of their own servers. However, when organizational and enterprise data are moved to public cloud storage, ensuring data protection and security becomes a critical concern. If unencrypted data is transmitted to the public cloud, there is a possibility of data breaches during transmission. To address this issue, an efficient technique called Enhanced Positional Vigenère (EPV) is proposed in this work. Strengthening the ciphertext and improving data security are the goals of the suggested approach. The EPV technique is implemented in Java, and our experiments show it boosts performance, ramps up efficiency, and makes the ciphertext even more complex.
Hybrid Ayurveda using Machine Learning for Disease Prediction System using Dosha-Guided Feature Weighting
Ayurveda is focused on a personal diagnosis along with the balance of doshas, but it is not an easy task to convert the qualitative concepts of Ayurveda into the decision-support systems. In this study, an Ayurveda-based prediction system of disease is presented with the help of the Machine Learning techniques such as Linear Regression (LR), Decision Tree (DT) and Dosha-Guided Feature Weighting (DGFW). The proposed model combines clinical information, lifestyle parameters, the symptom profile and the answers of Ayurvedic questionnaires in order to create a structured dataset to classify the disease. Throughout the preprocessing, Ayurvedic specific elements like Prakriti, Agni, Nadi and signs of imbalance in dosha go through normalization and weighting depending on the contribution to a particular disease. A DT model is used to learn the non-linear decision rule, which depicts Ayurvedic diagnostic logic, whereas Linear Regression is used to capture the linear relationships between weighted features. Theoretical testing on a multi-class data set shows that DT model performs better than LR, reaching an accuracy of 92.6 and LR reaches an accuracy of 88.4. Dosha-guided feature weighting to a large extent enhances classification performance, especially on non-linear models. The analysis of a confusion matrix and performance measures prove a decrease in misclassification and equal precision, recall and F1-scores by disease classes. The findings confirm the usefulness of integrating Ayurvedic domain knowledge with machine learning to provide a clear, understandable and effective decision-support system to predict diseases early and provide personalized healthcare.
Bianchi Type-I Bulk Viscous String Cosmological Model with a Dynamical Cosmological Term
The Einstein field equations with a cosmological constant are analyzed for a Bianchi type-I universe in the presence of bulk viscosity. The analysis assumes that: the bulk viscosity coefficient is directly proportional to the expansion scalar ; the expansion scalar is directly proportional to the shear scalar and the cosmological constant is directly proportional to the Hubble parameter . The physical and cosmological implications of these solutions are presented along with a discussion.
IoT Aware Polynomial Regressive Ensemble Artificial Intelligence Model for Crop Yield Prediction in Cloud Computing Environment
Accurately estimating crop yields across large geographical regions is essential for ensuring food security and promoting sustainable development. The machine learning driven Artificial Intelligence (AI) based approaches have emerged for enhancing the precision of agricultural yield predictions. While many existing models have achieved higher accuracy by increasing their complexity, their ability to generalize remains limited due to variations in key features across different regions. To address this issue, IoT aware Polynomial Regressive Ensemble AI (IoT-PREAI) Model is introduced. IoT-PREAI technique is to perform several processes. In data harvesting process, the numbers of data samples are collected from the Crop Yield Prediction Dataset. In order to increase the size of input data samples, Adversarial scaling model is employed in IoT-PREAI technique. Data scrubbing is performed to clean the dataset by handling missing data using polynomial function and removing outliers. After that, feature selection is conducted using censored regression method by Roger and Tanimoto Distributed Feature Engineering. Following this, crop yield prediction is performed using the selected features through Adaptive Gradient weight preserved boosting technique. This method processes the data samples and generates the final crop yield prediction results while reducing the error using Nesterov Accelerated Gradient. Experimental evaluation considers several factors. The quantitatively analyzed results indicate that the proposed IoT-PREAI technique achieves higher crop yield prediction accuracy with minimal computation time, RMSE compared to conventional ensemble techniques.
India’s Digital Banking Era: Cyber Fraud Targeting Women and Senior Citizens from Legal and Socio-Economic Perspectives
The evolution of digital banking in India can be traced back to the early 2000s, particularly following the global concerns surrounding the Y2K crisis, which accelerated advancements in information technology and cybersecurity. Over time, experts have worked extensively to counter threats such as phishing, vishing, data breaches, and SQL-based attacks. While technological systems have become more stable and secure, cybercriminals have simultaneously developed increasingly sophisticated techniques to exploit financial systems. In recent years, digital payment platforms—especially Unified Payments Interface (UPI) and Aadhaar-enabled payment systems (AePS)—have significantly enhanced financial inclusion across India. Spearheaded by institutions such as the Government of India, the Reserve Bank of India (RBI), and the National Payments Corporation of India (NPCI), these systems have made financial services accessible to millions. However, this rapid digital expansion has also led to a surge in cyber fraud, disproportionately affecting vulnerable groups, particularly women and senior citizens. These groups are often targeted through deceptive practices such as phishing messages disguised as official communications, impersonation scams, and coercive tactics like “digital arrest” frauds. Such crimes exploit not only technological gaps but also socio-cultural vulnerabilities, including limited digital literacy and trust-based social behavior. Recent data highlights the severity of the issue. In 2025 alone, the National Cybercrime Reporting Portal recorded over 76,000 cybercrime incidents involving women. Similarly, senior citizens—especially those living in isolation—have suffered significant financial losses. Studies indicate that nearly half of individuals in these categories struggle to identify fraudulent schemes due to limited awareness and digital skills. This study adopts a doctrinal and socio-legal approach, examining statutory frameworks such as the Information Technology Act, 2000, along with recent reforms under the Bharatiya Nyaya Sanhita, 2023 and Bharatiya Nagarik Suraksha Sanhita, 2023. It identifies critical gaps in existing legal mechanisms and highlights the need for adaptive and responsive regulatory structures. To address these challenges, the paper proposes targeted interventions, including real-time fraud alerts within banking interfaces, enhanced cybercrime investigation infrastructure, improved coordination among enforcement agencies, and the introduction of compensation mechanisms for victims. These measures aim to shift the focus from reactive enforcement to proactive prevention. Additionally, the study draws upon ethical principles from Buddhist philosophy, emphasizing values such as non-violence (ahimsa) and mindfulness (sati), to frame cyber fraud not merely as a legal violation but as a broader moral concern.
Region Entropy–Based Histogram Equalization for Medical Image Contrast Enhancement
Contrast enhancement is applied in image processing and visualization as a preprocessing step to improve image clarity prior to visual inspection, object detection, and image segmentation. In medical imaging, contrast enhancement plays an important role in emphasizing regions of interest. However, existing algorithms often scatter pixel intensities in a histogram, leading to noise amplification, over-saturation, and poor human perception. To overcome these limitations, Region Entropy-based Histogram Equalization (REHE) was introduced as a preprocessing algorithm to enhance the local contrast while preserving structural integrity and texture information. The proposed algorithm is evaluated on publicly available multimodal medical images and benchmarked against multiple state-of-the-art enhancement algorithms. Results show that the proposed approach improves image quality and structural preservation, leading to better visual and diagnostic outcomes.
Climate Variability and Its Impact on Agricultural Productivity in Moradabad District, Uttar Pradesh (1990–2024)
Climate variability poses a serious threat to agricultural productivity in India, particularly in climate-sensitive regions such as western Uttar Pradesh. Understanding long-term changes in rainfall and temperature and their impacts on crop yields is essential for designing climate-resilient agricultural strategies. This study analyzes trends in rainfall and temperature from 1990 to 2024 and quantifies their impact on the productivity of major crops such as wheat, rice and sugarcane—in Moradabad district. Secondary data on annual rainfall and temperature were obtained from the Indian Meteorological Department (IMD), while crop yield data were collected from the Department of Agriculture, Government of Uttar Pradesh. Descriptive statistics, trend analysis, correlation analysis, and multiple linear regression models were employed. Results reveal a statistically significant increase in both maximum and minimum temperatures, accompanied by increasing rainfall variability. Regression results indicate that rising temperatures negatively affect wheat and rice yields, while sugarcane shows relative resilience to rainfall variability. The study highlights crop-specific climate sensitivity and underscores the urgency of climate-resilient agricultural planning at the district level.
Early Detection of Preeclampsia and Gestational Hypertension Using Machine Learning Techniques
Gestational hypertension is a maternal entanglement, manifested through increased blood pressure which may occur later than week 20 of gestation period and may result in severe snags viz. preeclampsia, preterm neonatal delivery and injury of obstetric organs. Early and precise prediction is important because early tracking and cure would protect the pregnant women and the fetus from great number of dangers. Computational classification models are crucial for forecasting hypertensive disorders in pregnant women with analysis of large volumes of clinical, demographic and physiological data. Among the different predictive characteristics, Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) are of particular concern, since the aberrant changes of SBP and DBP are considered to be directly related to the presence of gestational hypertension and preeclampsia. The classification models that have been employed in this study in the analysis are the Mutual Information based classification, Recursive feature elimination (RFE), Lasso Regression based classification and Tree based classification. Moreover, Hybrid Feature Selection strategy has been suggested which is a mixture of the Filter Method (Mutual Information) and an Embedded Tree-Based Model, which enhances the predictive accuracy. All the models were measured by the important characteristics like F1-score, precision, accuracy, specificity, and sensitivity. The findings show that the suggested hybrid method was the most successful and recorded the best accuracy of 98% in all the datasets and an acceptable result in the training and testing data. The paper represents various machine learning classification algorithms to forecast gestational hypertension and preeclampsia. These are optimized models that can help in diagnosing the issues at an early stage and also improve the results of the maternal healthcare by taking into consideration the variables of SBP, DBP and other clinically significant variables.
Dynamic Feature Driven Machine Learning Model for Accurate Anomaly Detection in Cloud Environments
Cloud environments generate large volumes of dynamic and heterogeneous data, making anomaly detection a challenging task. Traditional static feature-based models often fail to adapt to evolving attack patterns and workload variations. This paper proposes a dynamic feature-driven machine learning model designed to improve anomaly detection accuracy in cloud systems. The proposed model integrates adaptive feature selection with supervised learning algorithms to capture time-varying behavioral patterns. Initially, raw cloud monitoring data is pre-processed and transformed into structured feature sets. A dynamic feature selection mechanism is then applied to identify the most relevant attributes based on statistical significance and temporal variation. The selected features are used to train classification models for distinguishing normal and anomalous activities. Experimental evaluation demonstrates that the proposed approach improves detection accuracy, reduces false alarm rates, and adapts effectively to changing cloud conditions. The results indicate that dynamic feature selection plays a crucial role in enhancing anomaly detection performance in cloud environments.
Predictive Accuracy of the Osteoporosis Self-Assessment Tool (OSTA) for Hip Fracture in Premenopausal women’s
Perimenopausal women with type 2 diabetes mellitus (T2DM) face an elevated risk of osteoporotic hip fractures. The Osteoporosis Self-Assessment Tool for Asians (OSTA), which relies on age and body weight, is a simple screening method. However, its effectiveness in predicting fracture risk in this specific population remains unclear, particularly due to the complex relationship between T2DM and bone mineral density (BMD), as well as limited access to DXA scans. AIM - To assess the predictive ability of OSTA for hip fracture risk in perimenopausal women with T2DM. Methods: This retrospective study was conducted at Shri Mata Vaishno Devi Narayana Super Speciality Hospital between 2024 and 2025. The study included 182 women going through perimenopause. Of these, 62 had low-energy hip fractures, and 120 were controls, matched for age (within ±2 years), who had not had fractures. People with conditions affecting bone metabolism or significant spinal deformities were not included. OSTA scores were calculated using the standard formula [(weight in kg − age in years) × 0.2] and were classified as high risk (< −4), intermediate risk (−4 to −1), or low risk (> −1). Bone mineral density (BMD) was measured for all participants. The statistical methods included independent t-tests, analysis of covariance (ANCOVA), and receiver operating Characteristic (ROC) curve analysis. A p-value of less than 0.05 was used to determine statistical significance. Results: Women in the fracture group were younger (67 ± 9.09 vs. 70.5 ± 8.02 years, p = 0.0123), taller (172 ± 8.09 vs. 168 ± 5.99 cm, p = 0.0009), and heavier (73 ± 9.75 vs. 44.15 ± 9.8 kg, p < 0.001) compared to the control group. Mean OSTA scores were higher in the fracture group (0.9 ± 2.75 vs. 0.29 ± 2.7), but the difference was not statistically significant (p = 0.1591). ROC curve analysis demonstrated poor predictive performance of OSTA for hip fracture risk (AUC = 0.534). The prevalence of osteoporosis was higher in the fracture group (66.7%) than in controls (41.4%). Conclusion: The OSTA tool’s predictive capacity for hip fracture risk in perimenopausal women with T2DM appears constrained, potentially due to the elevated body weight characteristic of this demographic. Although its straightforward design renders it appropriate for environments with limited resources, additional refinement is essential to enhance its precision within this particular cohort Results: Women in the fracture group were younger (67 ± 9.09 vs. 70.5 ± 8.02 years, p = 0.0123), taller (172 ± 8.09 vs. 168 ± 5.99 cm, p = 0.0009), and heavier (73 ± 9.75 vs. 44.15 ± 9.8 kg, p < 0.001) compared to the control group. Mean OSTA scores were higher in the fracture group (0.9 ± 2.75 vs. 0.29 ± 2.7), but the difference was not statistically significant (p = 0.1591). ROC curve analysis demonstrated poor predictive performance of OSTA for hip fracture risk (AUC = 0.534). The prevalence of osteoporosis was higher in the fracture group (66.7%) than in controls (41.4%). Conclusion: The OSTA tool's predictive capacity for hip fracture risk in perimenopausal women with T2DM appears constrained, potentially due to the elevated body weight characteristic of this demographic. Although its straightforward design renders it appropriate for environments with limited resources, additional refinement is essential to enhance its precision within this particular cohort.
Deep Learning Approaches for Regional Rainfall Time Series Prediction Using ERA5 Dataset
Precise monthly rainfall forecasting is crucial for Gujarat, India, where agriculture depends heavily on monsoon patterns. This study compares three deep learning architectures—Convolutional Neural Network (CNN), Convolutional Long Short-Term Memory (Conv-LSTM), and hybrid Discrete Wavelet Transform-ConvLSTM (DWT-ConvLSTM)—using 10 years (2010-2020) of high-resolution ERA5 meteorological data processed via Google Earth Engine across approximately 14,600 grid points. The CNN baseline achieved a Mean Absolute Error (MAE) of 0.0471 and Root Mean Square Error (RMSE) of 0.0747. Conv-LSTM improved performance with MAE 0.0421 and RMSE 0.0723. The proposed DWT-ConvLSTM hybrid model excelled, attaining the lowest errors of MAE 0.0370 and RMSE 0.0570—a 12% improvement over Conv-LSTM. This superior performance stems from wavelet decomposition, which isolates complex climate signals into frequency components for enhanced pattern recognition. These findings demonstrate that integrating frequency-domain analysis with deep learning effectively uncovers hidden spatiotemporal climate patterns. Though computationally intensive, the hybrid model offers significant potential for agricultural planning, water resource management, and climate adaptation in monsoon-dependent regions.
Multi Linear Tensor and Graph Convoluted Attention Network Based Classifier for Fake News Detection
The growing acceptance of social media platforms has streamlined the news articles sharing that have induced the boom in fake news. With the appearance of fake news at a very swift rate, a pressing distress has brought out in our society due to extensive fake content propagation. The quality of the news content is uncertain and there prevails a requirement for the detection at an early stage. However detection of fake news in a swift and accurate manner is a laborious and cumbersome task. However, in terms of knowledge extraction, most existing methods lack the mining of both textual and feature knowledge hidden in the news content and discard the mutual correlation between them. To address on these gaps, in this work, a method called, Multi-linear Tensor and Graph Convoluted Attention Network-based Classifier (MT-GCANC) for fake news detection is designed. The MT-GCANC method is split into two sections, namely preprocessing, feature engineering and classification. In the preprocessing stage, Multi-linear Tensor function is applied to the raw dataset to generate computationally efficient preprocessed sample results. Second with the preprocessed sample results as input are subjected to Graph Convoluted Attention Network-based Knowledge-aware feature engineering. Here, both relevant features are extracted and then with the engineered features classification is performed for accurate and precise fake news detection. The performance of our proposed MT-GCANC method has been validated on Fake News dataset. Classification results have revealed that the proposed MT-GCANC method outperforms existing and relevant onsets for fake news detection and accomplished a precision and recall rate of 19% and 58%. These results have shown significant advancements over the existing state-of-the-art methods in the domain of fake news detection and state the probable utilization of the method for classifying fake news.
Study of Citation Pattern of Economic and Political Weekly (EPW)
This study conducts a comprehensive analysis of citation patterns within the Economic and Political Weekly (EPW) from 1978 to 2024, examining over 16,527 records to identify trends in authorship, collaboration, and thematic focus in the fields of economics and political science. The findings reveal a robust co-occurrence network of key terms such as socio-economics, health care, and public policy, highlighting the journal’s commitment to addressing pressing societal issues in India. Notable authors and their contributions are identified, alongside an exploration of collaboration networks that emphasize regional engagement, particularly among Indian scholars. The research also categorizes the types of documents published, noting a preference for original research and comprehensive analyses. Furthermore, the study observes an increasing trend in interdisciplinary references and discusses the implications of digital resource accessibility on citation practices. Overall, the analysis underscores the EPW’s pivotal role in shaping academic discourse and influencing socio-economic and political discussions in India.
A Comparative Study of Poetic Language and Aesthetic Thought in Medieval Indian and English Romantic Poetry
This scholarly inquiry pursues a cross-cultural analysis of poetic diction and aesthetic theory in English Romantic poetry and the Indian Medieval era, with special reference to the theoretical paradigm of Keshavadasa. Building on concepts like Rasa, i.e. aesthetic emotion, Bhava, i.e. emotive state or psychological condition, and the fourfold typology of poetry. This research investigates representative works of William Wordsworth, Keats, Blake, and Coleridge from an Indian aesthetic perspective. Adopting a qualitative textual methodology, the paper scrutinises stylistic elements and formal characteristics, figurative devices and foundational philosophical perspectives across the two traditions. The analysis demonstrates substantial similarities, affective articulation, depiction of nature and authorial intent, while simultaneously foregrounding foundational divergencies in metaphysical foundations and universal aesthetic principles. The paper advocates the relevance and utility of Indian aesthetic theory in intercultural literary analysis, thus contributing to the decolonisation of literary criticism and extending the horizons of comparative literary studies.
Chronic Kidney Disease Detection using Imputation-Aware Deep Neural Network
Chronic renal disease damages kidney function. Hypertension, diabetes, and cardiovascular disease are associated with chronic kidney disease. Risk factors for chronic kidney disease encompass age, genetic predisposition, hypertension, diabetes, obesity, proteinuria, and dyslipidemia. Tests, blood pressure measurements, and medical imaging can assist deep neural networks in diagnosing chronic renal disease. These models can identify minuscule patterns that are imperceptible to individuals with an accuracy of 97–100%. Artificial intelligence has progressed through deep neural networks, which are capable of processing intricate data and exhibit enhanced accuracy. Deep neural networks for tabular data reconstruct multi-layered connections among data points. Data, statistics, and machine learning experts employ this strategy to evaluate datasets and analyses containing missing data. Imputation substitutes missing data with alternative values. It infers each absent item, analyzes each completed dataset individually, and subsequently amalgamates the results after inputting various values. Imputation-aware deep neural networks effectively manage absent values. Managing real-world datasets with absent data is challenging. This is relevant to numerous enterprises, including the healthcare sector. To safeguard valuable data, these networks employ fundamental imputation to prevent users from removing missing rows or columns. This strategy preserves the sample size of the dataset to ensure the validity of statistical tests. It enables models to assimilate comprehensive data, reducing bias and improving precision.
Investigating Extended Kin as A Positive Psychology Amidst the Collectivistic Forces of Anita Desai’s Fasting, Feasting
Anita Desai’s formative work, Fasting Feasting is a commentary on both individual and collective system of values. It provides an emphasis on family as a group with a sense of interdependence and interconnectedness. But is this interdependence and interconnectedness a source of warmth, acceptance, comfortable personal space devoid of any conflict, recognizing the self not as selfish but as the necessary individual identity and autonomy. This paper is a study on how the individual priorities come face to face with those of the group such as family. How an individual is shaken to the core, loses one’s confidence and will power through a repetitive pattern of onslaught from a group psyche. However, the argument is extended when one is able to find the emotional support of the redeeming fulcrum from the extended kin such as uncle-aunts, cousins. The paper will look into the presence of such a collaborative stimulus that can offer a balance to the aversive control of familial-groups and manifest a coping mechanism for the victim. It helps provide the space which is personal and respected. Desai endorses this argument in the wavering moods across the moments of joy, confidence, assurance and self-discovery to insecure, self-doubting, lack of assurance, timid silences of the main cast Uma in the presence and absence of the extended kin. She delineates, meticulously the ideals of positive psychology by allowing positive interactions and promoting emotional well-being of one through these associations.The novel candidly via the wholesome presence of these elements unveils the idea in the experience and perceptions of Uma, her struggles and a sense of entrapment in her efforts to combat the external, oppressive, collectivist forces that come in the way to her own path of individuality and personal happiness and how the transient reliefs in the presence of the extended kin emerge as redemptive comforting enforcement and provide a balanced sustenance periodically.
Designing Employability-Oriented Curriculum in English Studies: A Skill-Based Approach
In today's competitive global job market, the value of a college degree goes beyond just imparting factual information; it also involves developing marketable abilities. English Studies, previously centered on literature and cultural knowledge acquisition, is now required to be aligned with industry requirements and skill acquisition. The purpose of this article is to investigate the incorporation of employability skills into the curriculum designed for English Studies programs. This study investigates the ways in which students might benefit from a literature-based education in terms of developing abilities that are essential in a variety of business sectors, including critical thinking, communication, creativity, emotional intelligence, teamwork, and problem-solving. The research proposes an employability-based English Studies program curriculum framework that incorporates literature-based knowledge acquisition and industry-based skill acquisition. This research has provided significant insights into how to bridge the gap between theoretical knowledge of literature and the development of practical abilities in the workplace. This has been accomplished by reframing English Studies as a field of study that is dynamic. The study has offered important suggestions for rethinking English Studies as a transformational curriculum framework that prioritises career development and lifelong learning, in addition to literature-based knowledge acquisition as cultural capital.
Unlocking Startup Capital in India’s Economy: How Banks Can Leverage Singapore’s IP-Backed Financing Model for New Intellectual Property Ventures
India’s startup ecosystem is often described as vibrant, yet a closer look shows a persistent funding gap estimated at around ₹2.5 lakh crore. A large part of the problem lies in how banks approach lending. They still lean heavily on tangible assets, and as a result, intellectual property (IP) rarely gets accepted as reliable collateral. For early-stage ventures, especially those built around innovation, this creates a real barrier. Singapore offers a different picture. Its IP Financing Scheme (IPFS), introduced in 2014, takes a more flexible view. Under this model, banks extend loans against intellectual property, with risk partially absorbed through a structured support mechanism. Over time, the scheme has facilitated funding exceeding S$100 million, even for relatively new and untested patents. This shift—from physical assets to knowledge-based valuation—has made a noticeable difference in how startups access capital. This paper draws on existing literature and uses doctrinal as well as comparative methods to examine whether a similar framework can work in India. It suggests that a carefully designed, RBI-led model could adapt key features of the Singapore system while remaining sensitive to domestic legal and financial realities. At the same time, the discussion does not ignore the practical side. Issues like valuation uncertainty, enforcement challenges, and institutional hesitation still need to be addressed. There is also a forward-looking dimension. With the growing role of AI in financial assessment, IP valuation may become more consistent and scalable, which could ease some of the current concerns. If implemented in a calibrated manner, such a framework has the potential to unlock nearly ₹50,000 crore annually in startup financing. It may also reduce equity dilution by around 30%, allowing founders to retain greater control. More broadly, by improving the commercialization of innovation, it could contribute to an estimated 15% growth in GDP linked to knowledge-driven sectors.
Food and Nutrition Recommendation based Therapy for T2DM using User-User Collaborative Filtering Model
A reliable food plan is essential for Type-2 Diabetes Mellitus (T2DM) in order to sustain ideal glucose control and prevent long-term issues. Individual inclinations, lifestyle habits, and peer-based behavioral likenesses are typically overlooked by traditional food planning approaches. In order to provide T2DM patients, a modified dietary advice model presents a food and nutrition recommendation therapy method that creates the use of a User-User Collaborative Filtering Algorithm (UUCFA). The proposed strategy values interpersonal harmony based on these clinical indicators, dietary consumption patterns, lifestyle choices, and demographics inputs. The method suggests nutrient-dense meals that satisfy diabetic dietary requirements based on dietary results and experiences. The collaborative filtering approach promotes relevancy while identifying individual issues that occur in traditional rule-based systems by using collective capacity. Here, recommendation systems based experimental examination employed using real-time datasets, revealed an improved dietary faithfulness, user satisfaction, and accuracy. Hence, UUCF algorithm can aid to improve beneficial outcomes and self-care by serving as a valuable decision-support tool in adapted dietary therapy in T2DM control.
Banking Frauds in India: A Critical Analysis of Regulatory Framework and Preventive Mechanisms
This paper is aiming the study of various Banking Frauds happened bank wise in India which have adversely impacted the performance of banks. After the turmoil of various banking frauds which have committed in the banking sector have shaken the roots of banks. The banking frauds comprising of various types of frauds i.e. Frauds in Deposits segment, Frauds in Advances segment, Frauds in Foreign Exchange Business and Frauds through Technology that is popularly known as Cybercrime frauds have adversely affected the health of the Banking in India which leads to increasing NPA (Non-Performing Assets) of the Bank. Reserve Bank of India (RBI) the Controller of Banks, who is responsible to frame the rules and issue the guidelines to control the Banks. Banks have to follow various International norms so that to maintain the good health Banks have to maintain Cash Reserve Ratio (CRR) and Statutory Liquidity Ratio (SLR), Capital Adequacy and also provision for NPA. Sometimes banks have to go in the market for various types of Bonds & shares even in call money market to maintain various ratios on higher rate of interest. The margin of Banks Assets & Liabilities have already squeezed to minimal level and the cost of funds have increased and if Bank have to make provision major on Non-Performing Assets (NPA) along with other provisions, which have turned Banks into Lossmaking banks and result are numbers of Public Sector banks have come down from 20 to 12 which may further go for consolidation.
A Scientometric Analysis of Scholarly Publications on COVID-19: A Study
The COVID-19 pandemic has generated an unprecedented volume of scholarly literature across multiple disciplines, necessitating systematic evaluation of research trends and impact. The present study conducts a comprehensive scientometric analysis of global scholarly publications on COVID-19 published during the period 2019–2025, with the aim of examining the growth, structure, and influence of research output related to the pandemic. Bibliographic data were retrieved from the Web of Science (WoS) database using relevant keywords such as COVID-19, Coronavirus, SARS-CoV-2, and Pandemic. Scientometric tools including HistCite and Microsoft Excel were employed for data processing, analysis, and visualization. The study analyses publication growth, author productivity, citation impact, journal performance, and keyword frequency using indicators such as Local Citation Score (LCS), Global Citation Score (GCS), and citation intensity measures (LCS/t and GCS/t). The findings reveal a rapid and substantial increase in COVID-19-related publications, confirming the topic as one of the most intensively researched areas in recent scientific history. A small group of authors accounts for a significant share of publications, while citation analysis indicates that scholarly impact is driven more by research quality and relevance than by publication volume alone. Journal analysis identifies a core set of productive and high-impact journals, highlighting the importance of interdisciplinary and high-visibility publication venues. Keyword analysis shows strong emphasis on virology, clinical care, public health interventions, vaccination, and analytical and review-based research, with notable geographical focus on India. Overall, the study demonstrates that COVID-19 research is characterized by rapid growth, concentrated authorship, evolving thematic focus, and high citation intensity. The scientometric insights offered by this study provide valuable guidance for researchers, policymakers, and funding agencies in understanding research dynamics and planning effective responses to future global health emergencies.
Reimagining Sita’s Voice in Select Retellings of the Ramayana
This paper examines the retelling of Sita in contemporary Indian English Fiction within the framework of revisionist mythmaking. In the narrative structure of the Ramayana, Sita has long been understood, within traditional and critical discourse, as an embodiment of virtue, endurance, and self-sacrifice, an ideal that has shaped cultural perceptions of womanhood across generations. As noted in feminist reading of the Ramayana, her interior voice remains only partially visible. Adopting a character-centred approach, the study engages with select contemporary retellings such as The Forest of Enchantments, The Liberation of Sita, Sita: Warrior of Mithila, and The Missing Queen to explore how narrative strategies reshape her presence. Across these retellings, Sita is no longer confined to relational roles. She begins to reflect, to question, to interpret her own experience. The shift is gradual, but it alters how she is encountered. Identity, in this context, is no longer simply assigned, it begins to emerge. These rewritings do more than offer alternative versions of a familiar story. They intervene in the ways meaning has been stabilised, opening the narrative to reconsideration. Myth, as a result, does not remain closed; it becomes dialogic, a space where tradition and reinterpretation exist together. Sita’s shifting representation signifies a transition in the interpretation of her character, developing from a static ideal to a more intricate and introspective persona. These retellings do not provide a singular, definitive portrayal of Sita; rather, they give several viewpoints that redefine her identity
Dr. Shailendra Hridayanand Pandey Pandey
Genetic Algorithm-Based Adaptive Pattern Mining for Customer Basket Analysis
Customer basket analysis aims to understand purchasing patterns and enhance marketing strategies by leveraging data mining techniques. To the best of our knowledge, this study is one of the few that applies low-support adaptive pattern mining in personalization, introducing a robust genetic algorithm for improving static transaction baskets.The proposed method uses GA integrated with sophisticated pattern mining methods that quickly finds complex associations as well as patterns from transaction data. By utilizing GAs, our model has the ability to adapt and evolve to fit this changing landscape of consumer purchasing behaviors. The GA-based method is compared to traditional data mining methods using a large dataset of retail transactions. Further it shows big improvements in both accuracy and speed. The results indicate that the adaptive nature of the proposed method not only helps uncover purchasing patterns but also supports retailers in making informed decisions on inventory control, promotional activities, and personalized marketing. The findings provide a promising insight into how implementing genetic algorithms in combination with pattern mining frameworks could yield more proactive and responsive customer analysis within the industry of retail.
Securing Smart IoT Networks from Cyber Threats Using Explainable Zero Channel Attention-aided Ghost Convolution Neural Network Framework
The rise of advancements driven by the Internet of Things (IoT), presents numerous benefits, such as improved efficiency and enhanced quality of life. However, this interconnectedness also exposes critical vulnerabilities, making robust cyber-attack detection essential. Hence, this manuscript emphasizes the innovative explainable deep learning (XAI-DL) model for detecting and classifying multiclass cyber threat attacks in Internet of Things (IoT) platforms. Initially, the raw data samples collected from the BCCC-CIC-IDS dataset are preprocessed by performing a Pareto Scaling Normalization (PSN) and one-hot encoding processto improve the data quality.After preprocessing, the Zero Channel Attention-aided Ghost Convolution Neural Network (ZCAtt-GCNN) is proposed to detect and classify the various cyber threat attacks like Denial of Service (DoS) Attacks, Distributed Denial of Service (DDoS) Attacks, Web Attacks, file transfer protocol (FTP) Attacks, and Botnet Attacks. Furthermore, three XAI models are investigated for enhanced visualizations over the cyberattack detection: Shapley additive explanations (SHAP), Partial Dependence Plot-Individual Conditional Expectation (PDP-ICE), and Permutation Feature Importance (PFI). The proposed method is simulated via the Python platform and various performance measures like G-mean, Accuracy, Matthews Correlation Coefficient (MCC), Negative Predictive Value (NPV), computation time (CT), and false positive rate (FPR) are scrutinized, and associated with other techniques. The overall accuracy of 99.48%, G-mean of 99.18%, and FPR of 0.322 are obtained by the suggested framework for identifying various IoT Cyberattacks.
Effect of Educational and Fitness Interventions on Obesity and Cardiovascular Risk among Adolescents
Adolescent obesity has become a pressing public health problem worldwide, largely because of its links to cardiovascular disease and metabolic dysfunction. Educational institutions provide an empirical framework for interventions that integrate health education with physical activity, thereby promoting healthier lifestyles and mitigating obesity-related risks among adolescents. We executed a randomized controlled trial involving 120 male students classified as obese, aged between 15 and 18 years. Participants were allocated randomly and in equal proportions to one of four distinct groups: an educational awareness initiative, a fitness training regimen, a hybrid educational and fitness program, or a designated control group. The duration of the intervention was established at 12 weeks. We conducted measurements of body mass index (BMI), waist-hip ratio (WHR), total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglycerides at both the pre- and post-intervention phases. Analysis of covariance was employed to evaluate comparative outcomes among the groups. In relation to the control group, all three experimental cohorts exhibited statistically significant enhancements in both anthropometric and biochemical parameters (p ≤ 0.05). The group receiving the combined intervention consistently demonstrated superior performance across all assessed variables. Effect sizes were quantified to range from 0.14 to 0.21, reflecting moderate yet practically significant effects of the intervention. Our results indicate that the amalgamation of educational awareness and physical fitness training surpasses the efficacy of either modality in isolation concerning the reduction of obesity and cardiovascular risk factors among adolescents. School-based programs that address multiple dimensions of health behavior may serve as important preventive measures to reduce chronic disease risk in this population.
Impact of Isolated Endurance and Concurrent Training on Aerobic Capacity (VO2max) and Speed Performance in field hockey players
An arbitrary cohort of 60 male participants of subject hockey was categorised into the following four coaching groups: staying power coaching (ET), energy coaching (ST), concurrent coaching (CT), and manipulate crew (CG). The cure used to be for ten weeks. A standardized discipline check used to be used to verify each VO2max and speed. Keep in thought that as we habits analyses of covariance (ANCOVA) to supply proof if there are tremendous variations in groups. This is additionally supported by way of the absolute best VO2max beneficial properties acquired in the ET group, whereas average upgrades had been found for concurrent training. A marginal enlarge in ET and an index of enchancment in CT used to be found in velocity performance. They determined persistence workout as the most high-quality technique of growing cardio performance; concurrent workout has a extra balanced however decrease degree of adaptation.