A data-driven approach to predicting breast cancer recurrence with hybrid machine learning models
breast cancer; logistic regression; machine learning; random forest; recurrence
Breast cancer recurrence is one of the most significant medical concern, and accurate recurrence models can assist in early intervention and treatment planning. Breast cancer recurrent remains as one of the most critical concern for patients prognosis and treatment planning. Accuracy Predicting individual recurrence risk is crucial for the development of precise therapy, specialy for those patients with high-risk profiles. In the study proposes a hybrid machine learning approach that uses the computational modeling and the medical information to predict the recurrence of breast cancer in a patient. The dataset contains the medical and patient information like the age, tumor size, lymph node involvement, malignancy degree, location, irradiation status and recurrence class. This proposed approach begins with the process of data processing, handling the missing data values, features normalization and encoding of categorical variable into numerical format. The dataset is divided into two parts the training set and the testing set and the two selected models random forest and logistic regression models are trained independently. The predictions form both the model is stacked and a logistic regression meta-model is trained on these combined predictions. The evaluation of the model was conducted using the metrics such as accuracy, precision, recall, and F1 score. The designed hybrid model was able to achieve the accuracy of 97.66% with the precision, recall and F1 score all reaching around 98.15%. This study highlights the potential of hybrid machine learning techniques, improving the accuracy and reliability of machine learning models for breast cancer recurrence prediction. This development model can serve as a valuable tool for the medical industry to support decision making and assist in personalized treatment decisions, offering early detection of recurrence. This can enhance the treatment of a patient by supporting early detection and patients outcomes through targeted therapy. Copyright 2026 Techno-Press
Deepa, B.G.; Velmurugan, R.; Narender, M.; Suhaas, K.P.
Advances in Computational Design;Volume;11;Issue;1;pp.79-96
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.12989/acd.2026.11.1.079 https://www.scopus.com/pages/publications/105031064136?origin=resultslist
Deepa B.G., Department of Computer Science, Christ University, Bangalore, India; Velmurugan R., Department of Computer Science, Kristu Jayanti (Deemed to be University), Bangalore, India; Narender M., Department of Computer Science and Engineering, The National Institute of Engineering, Mysore, India; Suhaas K.P., Department of Computer Science and Engineering, The National Institute of Engineering, Mysore, India
Strain energy based frequency independent impedances of soil for interaction effect on structure
microtremor test; natural frequency; parabolic antenna; soil impedance; soilstructure interaction; strain energy principle
The present study proposes a strain energybased method for evaluating soilstructure interaction (SSI) effects for ground-mounted parabolic antenna systems founded on layered soils. Conventional frequency-independent impedance formulations, such as those recommended in ASCE 4-16, are primarily based on homogeneous soil assumptions and may not adequately capture the dynamic behavior of layered soil profiles. In this work, a strain energy equivalence approach is developed to estimate translational and rotational soil impedances by accounting for depth-wise variations in soil stiffness. The proposed method is validated using three-dimensional finite element modeling and in-situ microtremor measurements. The results demonstrate improved prediction of natural frequencies and modal mass participation factors compared to standard impedance-based approaches, indicating that the proposed method provides a more realistic representation of SSI effects for precision-sensitive antenna structures. 2026 Techno-Press, Ltd.
Gullapalli, Venkata Lakshmi; Kumar, Raghunandan R.; Reddy, G.R.
Geomechanics and Engineering;Volume;44;Issue;5;pp.697-720
https://doi.org/10.12989/gae.2026.44.5.697 https://www.scopus.com/pages/publications/105033602436?origin=resultslist
Gullapalli V.L., Department of Civil Engineering, CHRIST University, Karnataka, Bangalore, India; Kumar R.R., Department of Civil Engineering, CHRIST University, Karnataka, Bangalore, India; Reddy G.R., Department of Structural Engineering, VNIT, Maharashtra, Nagpur, India
Novel Approach for Osteoporosis Classification Using X-ray Images
BMD; Osteoporosis; Pix2Pix Segmentation; ViT Classification; X-ray Images
This research delves into the technical advancements of image segmentation and classification models, specifically the refined Pix2Pix and Vision Transformer (ViT) architectures, for the crucial task of osteoporosis detection using X-ray images. The improved Pix2Pix model demonstrates noteworthy strides in image segmentation, achieving a specificity of 97.24% and excelling in the reduction of false positives. Simultaneously, the modified ViT models, especially the MViT-B/16 variant, exhibit superior accuracy at 96.01% in classifying osteoporosis cases, showcasing their proficiency in identifying critical medical conditions. These models are poised to revolutionize osteoporosis diagnosis, providing clinicians with accurate tools for early detection and intervention. The synergies between the Pix2Pix and ViT models open avenues for nuanced approaches in automated diagnostic systems, with the potential to significantly improve clinical results and contribute to the broader landscape of medical image analysis. As osteoporosis remains a prevalent and often undiagnosed condition, the technical insights from this study hold substantial importance in advancing the field, emphasizing the critical role of accurate diagnostic tools in improving patient care and health outcomes. 2025 Oriental Scientific Publishing Company. All rights reserved.
Dodamani, Pooja Shivanand; Palanisamy, Kanmani; Danti, Ajit
Biomedical and Pharmacology Journal;Volume;18;pp.203-216
https://doi.org/10.13005/bpj/3082 https://www.scopus.com/pages/publications/105001193909?origin=resultslist
Dodamani P.S., Computer Science and Engineering, Christ University, Bangalore, India; Palanisamy K., Christ University, Computer Science and Engineering, CSE, Bangalore, India; Danti A., Computer Science and Engineering, Christ University, Bangalore, India
Polycystic Ovary Syndrome Diagnosis: The Promise of Artificial Intelligence for Improved Clinical Accuracy
Artificial Intelligence; Endocrine disorders; Hyperandrogenism; Infertility; Polycystic ovaries; Rotterdam Criteria
PCOS is an endocrine illness that affects 610% of women worldwide. It can cause a variety of symptoms, including irregular menstruation periods, ovarian cysts, and hyperandrogenism. Its lack of defined biomarkers, overlapping symptoms, and heterogeneity make diagnosis difficult. By studying hormone profiles, identifying patterns difficult to see with conventional approaches, and offering great precision and accuracy, AI and ML techniques are transforming diagnostic difficulties. Hybrid models in the list include SWISS-AdaBoost and ensemble learning techniques that have accuracies up to 98% enabling early diagnosis along with appropriate treatment strategies. Early detection by technologies such as AI will prevent significant health complications that are PCOS-related, such as infertility, type II diabetes, or cardiovascular diseases. This study depicts the transformative role of the application of AI in diagnosing cases of PCOS and highlights the possibility of facilitating clinical decision-making, reducing potential diagnostic delays, and enhancing improvements in patient outcomes. Future research should hence be directed towards the establishment of AI within healthcare with consideration of validation, reliability, and ethical considerations to maximize its use in clinical practice. 2025 Oriental Scientific Publishing Company. All rights reserved.
Upreti, Kamal; George, Jossy; Upreti, Shitiz; Mahajan, Shubham
Biomedical and Pharmacology Journal;Volume;18;Issue;1;pp.353-372
https://doi.org/10.13005/bpj/3093 https://www.scopus.com/pages/publications/105002833893?origin=resultslist
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; George J., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Upreti S., Department of IT & Management, Maharishi Markandeshwar (Deemed to be University), Haryana, Mullana, Ambala, India; Mahajan S., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India
Automated Brain Tumor Segmentation in MRI Using AI for Improved Neurodiagnostics
Classification; Diagnosis; Early Detection; Machine Learning; MRI; Neuro-images
Early and accurate classification of brain tumors plays a pivotal role in clinical decision-making and treatment planning. Manual methods are time-intensive and prone to variability, creating a need for robust automated solutions. This study aims to classify brain tumors from MRI scans using artificial intelligence techniques, specifically Logistic Regression (LR) and Support Vector Machines (SVM) with Radial Basis Function (RBF) kernels. The dataset, sourced from The Cancer Imaging Archive (TCIA), includes four classes: Meningioma, Glioma, Hypothalamic tumor, and No tumor. Preprocessing involved dimensionality reduction using Principal Component Analysis (PCA) to retain dominant features. Models were trained on an 80:20 train-test split, with LR achieving 99.83% training and 78.91% testing accuracy, while SVM performed better with 93.85% training and 81.88% testing accuracy. Error analysis revealed 104 misclassified samples, primarily due to structural similarity among tumor types. The findings suggest that SVM offers superior classification performance, and the study recommends further enhancement through deep learning models like Convolutional Neural Networks (CNNs) for improved diagnostic accuracy. 2025 Oriental Scientific Publishing Company. All rights reserved.
Upreti, Kamal; George, Jossy; Malik, Khushboo
Biomedical and Pharmacology Journal;Volume;18;Issue;2;pp.1257-1271
https://doi.org/10.13005/bpj/3167 https://www.scopus.com/pages/publications/105011041787?origin=resultslist
Upreti K., Department of Computer Science and Engineering, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; George J., Department of Computer Science and Engineering, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; Malik K., Department of Law, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India
Optimized Hybrid Prognostics Using Hynetreg Model for Infertility Prediction
Alpha-Feto-Protein (AFP); Anti-Mlerian Hormone (AMH); Blood Urea Nitrogen (BUN); Follicle Stimulating Hormone (FSH); Idiopathic Female Infertility (IFI); Luteinizing Hormone (LH); multi-layer perceptron (MLP); poor ovarian response (POR); recurrent reproductive failure (RRF); total motile sperm count (TMSC)
This paper develops an optimized hybrid approach to predict infertility with the HyNetReg Model. The HyNetReg Model combines deep feature extraction by using neural networks with logistic regression with regularization. It uses both hormonal and demographic information of 100 participants to clarify intricate interlinkages between demographic factors and salient hormonal levels, such as Luteinizing Hormone, Follicle Stimulating Hormone, Anti-Mlerian Hormone, and Prolactin, and the ability of these same factors to affect fertility outcomes. It applies heavy data pre-processing including normalization, missing values imputation, and class imbalance handling through oversampling techniques. A multi-layer neural network is utilized to extract features for the reduction of complex, non-linear interaction among the input variables. Then, regularized logistic regression is applied for classification on the same features. Performance evaluation metrics, including accuracy, precision, recall, F1-score, and ROC curve analysis, demonstrate the superiority of the HyNetReg Model over traditional logistic regression. The ROC curve was specifically utilized to assess the models discrimination ability between infertile and fertile cases by plotting the true positive rate (sensitivity) against the false positive rate (1-specificity). A higher Area Under the Curve indicated that the model effectively distinguished infertility risks based on hormonal and demographic features. The results indicate that the model can recover very slight interdependencies of hormones and influences of demographics, making it suitable for modeling multi-factorial determinants of infertility and holding significant implications for clinical decision-making. 2025 Oriental Scientific Publishing Company. All rights reserved.
Upreti, Kamal; George, Jossy; Hundekari, Sheela; Alam, Mohammad Shabbir
Biomedical and Pharmacology Journal;Volume;18;Issue;2;pp.1272-1288
https://doi.org/10.13005/bpj/3168 https://www.scopus.com/pages/publications/105010881684?origin=resultslist
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; George J., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Hundekari S., Department of Computer Science and Engineering, Pimpri Chinchwad University, Maval Talegaon, Maharashtra, Pune, India; Alam M.S., Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan, Saudi Arabia
Gene Expression Data-Based Interpretable Machine Learning Framework for Classifying Brain Cancer Subtypes
Biomarker Identification; Brain Cancer; Cell Lines; Gene-expression; Machine Learning; Microarray Data
Early detection, therapeutic stratification, and precision medicine all rely on the precise classification of brain cancer subtypes. To categorize brain tumor subtypes, we examine the application of ensemble machine learning modelsRandom Forest, XGBoost, and LightGBMusing high-dimensional gene expression data from the GSE50161 dataset (CuMiDa). The top 1000 genes were selected using variance thresholding, and models were then trained and evaluated on a stratified split of the dataset. Despite the availability of models achieving similar accuracies (~9596%) in existing works, our framework integrates SHAP-based interpretability to identify biologically significant genes, such as CDK4, EGFR, and TP53, offering dual benefits of high predictive power and explainability. The use of SHAP (SHapley Additive exPlanations) values to assess model predictions and identify physiologically important gene features revealed that key gene probes, including as CDK4, EGFR, and TP53, were significant across different tumor subtypes. This study demonstrates how SHAP and interpretable ensemble learning may be used to diagnose brain tumors with excellent classification accuracy and physiologically meaningful gene identification. Published by Oriental Scientific Publishing Company 2025.
Kushwah, Virendra Singh; Krishnan, Sivaneasan Bala; Upreti, Kamal; Kshirsagar, Pravin; Kumar, Manoj; Shankar, Uma; Radhakrishnan, Ganesh Veluswamy
Biomedical and Pharmacology Journal;Volume;18;Issue;3;pp.2014-2023
https://doi.org/10.13005/bpj/3233 https://www.scopus.com/pages/publications/105018641122?origin=resultslist
Kushwah V.S., Department of CSE and AI, VIT Bhopal University, Madhya Pradesh, Sehore, India; Krishnan S.B., Department. of Electrical and Electronics Engineering, Singapore Institute of Technology, Singapore; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Kshirsagar P., Department of Electronics and Telecommunication Engineering, J D College of Engineering and Management, Nagpur, India; Kumar M., Department of Mathematics and Statistics, Gurukula Kangri University, Uttarakhand, Haridwar, India; Shankar U., Department of Management and Social Sciences, Qaiwan International University, Kurdistan, Sulaymaniyah, Iraq; Radhakrishnan G.V., Department of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India
The Impact of Helicobacter Pylori Infection on Renal and Hepatic Function Parameters in Saudi Arabia, A Case-Control Study
Gastric infection; Helicobacter pylori; Kidney function test; Liver function test; Non-alcoholic fatty liver disease
Helicobacter pylori (H. pylori) is a common gastric pathogen that is linked to peptic ulcers, gastric cancer and systemic disorders. There is emerging evidence of an association between infection with H. pylori and chronic kidney disease and non-alcoholic hepatic steatosis, and possible renal and hepatic involvement. This study was conducted to assess the relationship between H. pylori and kidney and liver function changes in Saudi Arabian people. Blood samples were collected from 82 participants divided into two groups, namely H. pylori-infected and non-infected. Biochemical analyses were done to determine kidney (creatinine, urea, sodium and potassium) and liver function (ALT, AST, ALP, total protein and albumin). A total of 82 participants were included in the study, of whom 42 (51.2%) tested positive for Helicobacter pylori infection, while 40 (48.8%) were non-infected and served as the control group. Liver enzyme levels (ALP (64.00), AST (21.40), ALT (21.26) and GGT (20.78)) were normal and did not differ significantly between groups (p > 0.05). Regression analysis showed a significant relationship between H. pylori infection and high creatinine (OR = 6.38, p = 0.018). Borderline correlations were found for GGT (OR = 1.179, p = 0.092), uric acid (OR = 2.661, p = 0.061), and the electrolytes and lipid profiles showed no significant variation. H. pylori infection was significantly related to elevated creatinine levels, and may be affecting renal function. Most liver and electrolyte parameters were not affected. Further studies with larger cohorts are required in order to confirm these findings. Oriental Scientific Publishing Company 2026.
Mahmoud, Madyha Hassan; Upreti, Kamal; Jeraiby, Mohammed Abdullah; Jain, Rituraj; Mjery, Yahia; Alnajai, Mahdi Mohammed; Otaif, Fozia Siddiq; Alam, Mohammad Shane
Biomedical and Pharmacology Journal;Volume;19;Issue;1;pp.683-693
https://doi.org/10.13005/bpj/3385 https://www.scopus.com/pages/publications/105035071706?origin=resultslist
Mahmoud M.H., Department of Medical Laboratory Technology, College of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Jeraiby M.A., Department of Basic Medical Science, College of Medicine, Jazan University, Jazan, Saudi Arabia; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India; Mjery Y., Department of Medical Laboratory Technology, College of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia; Alnajai M.M., Laboratory Department, Jazan University Hospital, Jazan University, Jazan, Saudi Arabia; Otaif F.S., Department of Medical Laboratory Technology, College of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia; Alam M.S., Department of Medical Laboratory Technology, College of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia
An Efficient Detection of Suspicious Objects from Dynamic Video Surveillance by Fusion-based Multiview Deep Learning Techniques
Deep learning; fusion; object detection; video surveillance; YOLO
Real-time detections of suspicious objects are needed to identify for finding criminal activities and are used in immediate alert systems for public safety applications. Video surveillance systems use live, closed-circuit televisions (live CCTVs) for dynamic video capturing of objects. Finding criminal activities over the dynamic video data is an emerging surveillance problem. The deep learning techniques are tedious for detecting suspicious movable objects and criminal activities. YOLO (You Only Look Once) gives more prominent movable video object detection accuracy than conventional deep models, like Convolutional Neural Network (CNN), 3D CNN, and Convolutional LSTM. State-of-the-art YOLO models, YOLOv8n, YOLOv8s, and YOLOv8l, are emphasized for extracting and detecting object motion detection from the dynamic video. YOLO models use single-view deep learning to classify or detect objects. These models limit the accuracy of the detection of complex and dynamic objects of dynamic video data. This paper presents the Fusion-based Multiview deep learning techniques to overcome this issue. The experimental study demonstrates that the proposed methodology efficiently detects suspicious data objects more than the single-view deep models. 2025 River Publishers.
Poonia, Ramesh Chandra; Upreti, Kamal; Singh, Nidhi; Kesarwani, Jyoti; Alam, Mohammad Shabbir
Journal of Mobile Multimedia;Volume;21;Issue;1;pp.1-26
https://doi.org/10.13052/jmm1550-4646.2111 https://www.scopus.com/pages/publications/105003927796?origin=resultslist
Poonia R.C., Department of Computer Science, Christ University, NCR, Delhi, India; Upreti K., Department of Computer Science, Christ University, NCR, Delhi, India; Singh N., G. D. Goenka University, Haryana, Gurgaon, India; Kesarwani J., United College of Engineering and Research, Uttar Pradesh, Prayagraj, India; Alam M.S., Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
Emotion Recognition Through Facial Expressions: A Machine Learning Perspective in Mobile Multimedia
advanced machine learning; Emotion detection; facial expressions; feature extraction; human-computer interaction
Facial expression-based emotion detection is very attractive because of the possibilities in security systems, mental health monitoring, and human-computer interaction. Even with the progress in accuracy in real-world settings, issues such as the lack of balanced datasets and the inability to differentiate between faint or superimposed emotions continue to plague it. This study aims to bridge these constraints by developing a CNN-based model that would be able to recognize face emotions reliably and be utilized in real-time situations, such as webcam integration. The Affect Net dataset, which is a comprehensive collection of over a million facial photos labeled with the seven major emotions of anger, disgust, fear, happiness, neutrality, sadness, and surprise, was used to train the proposed model. Other pre-processing data techniques used include grayscale conversion, normalization, scaling, and data supplementation to increase the robustness of the model. Using metrics like accuracy and loss trends for evaluation, the model demonstrated efficiency stability at around the 30th training phase. When the model is compared to existing models, this proposed model can attain the competitive level of accuracy up to approximately 60%. It also has the potential to run in real applications through its webcam integration. While the model can differentiate between various clear-cut emotions, it becomes ineffective at identifying subtle emotions, which include Fear and Neutral majorly because of unbalanced data and the subtleness of these expressions. 2025 River Publishers.
Ahmad, Akram; Singh, Vaishali; Upreti, Kamal
Journal of Mobile Multimedia;Volume;21;Issue;1;pp.87-112
https://doi.org/10.13052/jmm1550-4646.2114 https://www.scopus.com/pages/publications/105003940451?origin=resultslist
Ahmad A., Department of Computer Science, Maharishi University of Information Technology, Lucknow, India; Singh V., Department of Computer Science, Maharishi University of Information Technology, Lucknow, India; Upreti K., Christ University, Delhi NCR Campus, Ghaziabad, India
Decentralized Data Integrity: Integrating MySQL with Blockchain for Resilient Healthcare Systems
blockchain; data integration; data secrity; healthcare information systems; MySQL
A transformational solution to the problems created by healthcare data management is presented by the integration of MySQL and blockchain technology, centered around security, scalability, and efficiency. This paper presents MBHA MySQL-Blockchain Healthcare Architecture combining thestructured data storage, querying capabilities of MySQL with the decentralized, tamper-proof framework of blockchain. The system shows impressive performance metrics with an average API response time of 1.54 seconds for user registration and 841 milliseconds for login. The database queries and data retrieval or insertion took less than 1 millisecond, and JWT tokens were generated for authentication in less than 50 milliseconds. Conclusion Results indicate an efficient real-time system to accomplish tasks with integrity in terms of data but also with safety in operations. This architectural model, discussed above, is issues regarding data security and access with a need to provide care-collaboration needs. Scalability would then be optimized while keeping down computational overhead; in fact, work toward readiness for adoption is mainly towards being more regulatory compliant. 2025 River Publishers
Uprety, Deepak Chandra; Upreti, Kamal; Kshirsagar, Pravin R.; Tak, Tan Kuan; Upreti, Shitiz; Radhakrishnan, Ganesh V.; Kumar, Ajay
Journal of Mobile Multimedia;Volume;21;Issue;2;pp.275-306
https://doi.org/10.13052/jmm1550-4646.2124 https://www.scopus.com/pages/publications/105014256518?origin=resultslist
Uprety D.C., Dept. of Computer Science and Engineering, Noida Institute of Engineering and Technology, Greater Noida, India; Upreti K., Dept. of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Kshirsagar P.R., Dept. of Electronics & Telecommunication, J D College of Engineering & Management, Maharashtra, Nagpur, India; Tak T.K., Engineering Cluster, Singapore Institute of Technology, Singapore; Upreti S., School of Computer Science & Engineering (SCSE), IILM University, (UP), Greater Noida, India; Radhakrishnan G.V., Dept. of Economics and Finance, KIIT School of Management (KSOM), KIIT University, Bhubaneswar, India; Kumar A., Inderprastha Engineering College, Ghaziabad, India
Whale Optimization and AutoML for Precise Phishing Detection
AutoML framework; AutoML H2O; optimization algorithm; Phishing attack; random forest algorithm; regression analysis; whale optimization algorithm
Online fraud and social engineering tactics frequently use phishing websites as platforms. Phishers often modify the source code of the web pages they exploit in their attacks to create the illusion that alterations were made to authentic websites. A solitary response is insufficient to mitigate phishing due to the many methods employed in its execution. This study examines machine learning algorithms and evaluates their efficacy when trained on datasets including attributes that differentiate secure websites from phishing sites. Automated algorithms facilitate real-time fraud protection by swiftly detecting suspicious URLs, domain names, and website content. This study aims to identify the optimal method for detecting a prevalent category of cyberattacks. This would enhance the security and privacy of all internet users by facilitating the identification and blocking of malicious websites. Nonetheless, there is an urgent desire for automated models that provide rapid and precise detection. This research introduces a regression-based assessment method for phishing detection to address this demand. Our approach employs a whale optimization algorithm for feature selection. An AutoML framework subsequently utilizes the selected feature subsets as input. The model showed good accuracy in its predictions with very small errors on the test data, shown by an RMSE of 0.1079, an MSE of 0.0116, and an R2 value of 0.9534. These results demonstrate the reliability of our feature selection and modeling methods. 2025 River Publishers
Singhal, Divya; Verma, Ankit; Radhakrishnan, Ganesh V.; Parashar, Jyoti; Date, Saroj S.; Upreti, Kamal
Journal of Mobile Multimedia;Volume;21;Issue;5;pp.855-880
https://doi.org/10.13052/jmm1550-4646.2153 https://www.scopus.com/pages/publications/105020064783?origin=resultslist
Singhal D., Department of Computer Science, Noida Institute of Engineering & Technology, Greater Noida, India; Verma A., Department of Computer Applications, KIET Group of Institutions, Delhi-NCR, Ghaziabad, India; Radhakrishnan G.V., Department of Economics and Finance, KIIT School of Management (KSOM), KIIT University, Bhubaneswar, India; Parashar J., Department of Computer Applications, Panipat Institute of Engineering & Technology College, Haryana, Panipat, India; Date S.S., Department of Artificial Intelligence and Data Science, Chh.Shahu College of Engineering, Kanchanwadi, Paithan Road, Chhatrapati Sambhajinagar, MS, Aurangabad, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India
Landholding size, indebtedness, and crop insurance in India: A macro-level quantitative assessment
Actuarial performance; crop insurance; farmer indebtedness; landholding size; regression analysis
Keerthikumara SM, Saikia B, Hiremath C. 2025. Landholding size, indebtedness, and crop insurance in India: A macro-level quantitative assessment. Asian J Agric 9: 377-390. Indian farmers continue to face structural distress driven by low income, high indebtedness, and inadequate risk protection. This study investigates the interrelationship between landholding size, agricultural credit, indebtedness, and crop insurance uptake using state-level secondary data from 2016 to 2023, drawn from Agricultural Statistics at a Glance, PMFBY/RWBCIS dashboards, and NCRB reports. Using descriptive statistics, linear regression, and paired t-tests, we identify key macro-level trends across 20 major Indian states. Results show that marginal and small farmers (less than 2 hectares) account for over 62.7% of all indebted farm households, but receive only 38.5% of total institutional agricultural credit. A bivariate regression reveals that a ?1,000 increase in monthly farm income is associated with a reduction of 1,314 indebted households (?=-0.445, p=0.016). Despite substantial credit disbursal in high-debt states like Andhra Pradesh and Telangana, farmer debt remains elevated, underscoring that credit alone does not reduce vulnerability. Crop insurance enrollment increased after the 2020 policy shift from mandatory to optional participation among loanee farmers, yet the change was not statistically significant (p=0.099). Actuarial analysis reveals that in several states, claim settlement ratios remain below 50%, with high premiums and delayed payouts fueling distrust. The study recommends fully subsidized premiums for marginal farmers, region-specific pricing, improved claim transparency, and financial literacy integration with agricultural extension. Effective risk mitigation in agriculture must go beyond insurance and integrate income and credit reforms to ensure equitable protection for Indias most vulnerable farmers. 2025, Smujo International. All rights reserved.
Keerthikumara, S.M.; Saikia, Bondita; Hiremath, Channaveerayya
Asian Journal of Agriculture;Volume;9;Issue;2;pp.377-390
https://doi.org/10.13057/asianjagric/g090205 https://www.scopus.com/pages/publications/105018127663?origin=resultslist
Keerthikumara S.M., School of Commerce, Christ University, Karnataka, Bengaluru, 560029, India; Saikia B., Centre for Economic Studies and Policy, Institute for Social and Economic Change, Karnataka, Bengaluru, 560072, India; Hiremath C., School of Commerce and Management, Garden City University, Karnataka, Bengaluru, 560049, India
Fire Safety Challenges in Electrical Shafts: A Case of Fire Accident at High Rise Residential Building in Bengaluru, India
ASET; Evacuation; Fire Safety; High-Rise Buildings; NBC-2016; Performance-Based Design; RSET
Fire safety in high-rise residential buildings is a complex issue, especially when it comes to electrical shaft fires. These enclosed vertical shafts help the rapid spread of smoke, toxic gases and heat across different floors and pose a high risk to the occupants and affect the evacuation process. However, the actual life fire outbreaks show that the existing measures are still wanting as far as evacuation safety is concerned. These issues are addressed in this research through the use of performance-based fire design (PBFD) to assess fire behaviour and evacuation patterns in high-rise buildings. The study then simulates using PyroSim and Pathfinder simulation software, the visibility of smoke, reduction of visibility, CO and CO2 levels, temperature changes, and congestion of the evacuation corridors. Unlike other studies, this paper combines both fire development and occupants response to give a holistic approach to the issue of evacuation challenges. One of the findings of this research is the analysis of the ASET and RSET whereby it was found that the current provisions in fire safety do not ensure a safe exit. It was established that RSET is much higher than ASET, which points to a severe lack in current fire safety solutions. Simulations incorporating NBC-2016 (National Building Code-2016) provisionssuch as fire-rated doors, sprinklers, and mechanical ventilationdemonstrate improved outcomes, reducing RSET from 1,272 seconds to 746 seconds. However, persistent challenges such as congestion, bottlenecks, and hazardous gas levels highlight the need for enhanced fire safety strategies. This research offers practical recommendations to improve evacuation effectiveness through better ventilation, compartmentalization, and advanced suppression systems. The findings contribute to risk mitigation strategies, expand the knowledge base for fire safety, and provide a foundation for improving fire safety regulations in high-rise buildings. 2025 by authors, all rights reserved.
Civil Engineering and Architecture;Volume;13;Issue;3;pp.2036-2065
https://doi.org/10.13189/cea.2025.130342 https://www.scopus.com/pages/publications/105005150971?origin=resultslist
Mamatha N., School of Architecture, Christ Deemed to be University, Karnataka, Bengaluru, India; Ajai Chandran C.K., School of Architecture, Christ Deemed to be University, Karnataka, Bengaluru, India
Examining the impact of maternal experiences of domestic violence on the mental health of their adolescent children in India
Background Domestic violence (DV) is experienced by one in three women in India and is linked to poor mental health outcomes. We hypothesize that maternal experiences of DV can have negative impacts on the mental health of their children. Previous studies have demonstrated this link in Western countries, however culturally specific manifestations of DV and mental health disorders and socio-cultural differences in parent-child relationships and home environments necessitate deeper understanding of the impacts of maternal experiences of DV on children in the Indian context. Methods This study presents a secondary analysis of data collected from a seven-center study in urban and rural India examining mental health disorders among adolescents aged 1217 years and psychological, physical, and sexual abuse affecting their mothers. The Indian Family Violence and Control Scale (IFVCS) was used to examine experiences of DV among mothers and the Mini International Neuropsychiatric InterviewKid (MINI-Kid) was used to examine mental health outcomes among adolescents. Multivariate analyses examined the associations between maternal DV and adolescent mental disorders. Results Data from 2,784 adolescent-mother pairs were analyzed. In bivariate analyses, maternal experiences of physical, psychological, and sexual abuse were significantly associated with adolescent common mental disorders including anxiety and depression (p < 0.05). After adjusting for adolescent gender, site, and education status in the multivariate analysis, physical, sexual, and any DV were significantly associated with adolescent anxiety disorders and common mental disorders. Physical abuse was significantly associated with adolescent depressive disorders. Conclusions These results suggest that exposure to maternal DV significantly impacts adolescent mental health in India and underscore the need to develop trauma-informed school programs and enhance DV prevention for women in India. 2025 Gourisankar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Gourisankar, Amritha; Ravi, Preethi; Kalokhe, Ameeta; Hall, Rachel Waford; Vaidya, Nilakshi; Sharma, Eesha; Holla, Bharath; Basu, Debasish; Bharath, Rose Dawn; Chakrabarti, Amit; Desrivieres, Sylvane; Hickman, Matthew; Kartik, Kamakshi; Ghattu, Krishnaveni; Kalyanaraman, Kumaran; Krishna, Murali; Kuriyan, Rebecca; Murthy, Pratima; Orfanos, Dimitri Papadopoulos; Purushottam, Meera; Kurpad, Sunita Simon; Singh, Rajkumar Lenin; Singh, Roshan Lourembam; Subodh, Bhagyalakshmi Nanjayya; Toledano, Mireille B.; Benegal, Vivek; Schumann, Gunter; Kalyanram, Kartik; Varghese, Mathew; Kandavel, Thennarasu; Urvakhsh, Metha; Girimaji, Satish; Jacob, Preeti; Jayarajan, Deepak; Kumar, Keshav; Narayanan, Gitanjali; Khullar, Madhu; Khandelwal, Niranjan; Ghosh, Abhishek; Amit; Joshi, Nainesh; Chanu, Ningthoujam Debala; Fujica, M.C.; Ph, Victoria; Phurailatpam, Celina; Bhattacharya, Debangana; Haque, Bidisha; Nagraj, Alisha; Ghosh, Arpita; Basu, Anirban; Pandit, Mriganka; Das, Subhadip; Yadav, Anupa; Das, Surajit; Roy, Sanjit; Maurya, Pawan; Rani, Geetha T.; Sujatha, B.; Rangaswamy, Madhavi; Fall, Caroline; Kiran, Kn; Ramya, Mc; Urs, Chaitra; Santhosh, N.; Somashekhara, R.; Divyashree, K.; Rao, Arathi; Poornima, R.; Tripathy, Saswathika; Parashar, Neha; Nayana, K.B.; Seshadri, Ashwini; Kumar, Sathish; Baligar, Suneela; Arumugam, Thamodharan; Safai, Apoorva; Cyril, Anthony; Rashmitha; Roy, Ashika; Dhanalakshmi, D.; Shivamma, D.; Bhavana, B.R.
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1371/journal.pone.0304936 https://www.scopus.com/pages/publications/105006726457?origin=resultslist
Gourisankar A., Emory University School of Medicine, Atlanta, United States, Emory Rollins School of Public Health, Atlanta, United States; Ravi P., Emory University School of Medicine, Atlanta, United States, Emory Rollins School of Public Health, Atlanta, United States; Kalokhe A., Emory University School of Medicine, Atlanta, United States, Emory Rollins School of Public Health, Atlanta, United States; Hall R.W., Emory Rollins School of Public Health, Atlanta, United States; Vaidya N., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India, Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Neuroscience, CharitUniversitsmedizin Berlin, Berlin, Germany; Sharma E., Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Holla B., Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Basu D., Department of Psychiatry, Regional Institute of Medical Sciences, Manipur, Imphal, India; Bharath R.D., Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India; Chakrabarti A., Centre for Ageing and Mental Health, Indian Council of Medical Research (ICMR), Kolkata, India; Desrivieres S., Centre for Population Neuroscience and Precision Medicine, MRC Social, Genetic, Developmental Psychiatry Centre, Institute of Psychology, Psychiatry & Neuroscience, Kings College London, London, United Kingdom; Hickman M., Bristol Medical School, University of Bristol, Bristol, United Kingdom; Kartik K., Rishi Valley Rural Health Center, Madanapalle, India; Ghattu K., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Kalyanaraman K., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Krishna M., Institute of Public Health, Bangalore, India; Kuriyan R., NeuroSpin, CEA, UniversitParis-Saclay, Paris, France; Murthy P., Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Orfanos D.P., Molecular Genetics Laboratory, National Institute of Mental Health and Neurosciences, Bangalore, India; Purushottam M., Division of Nutrition, St. Johns Research Institute, Bangalore, India; Kurpad S.S., Department of Psychiatry, Postgraduate Institute of Medical Education and Research, Chandigarh, India; Singh R.L., Department of Psychiatry, Regional Institute of Medical Sciences, Manipur, Imphal, India; Singh R.L., Department of Psychiatry, Regional Institute of Medical Sciences, Manipur, Imphal, India; Subodh B.N., Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom; Toledano M.B., Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China; Benegal V., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Schumann G., Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Neuroscience, CharitUniversitsmedizin Berlin, Berlin, Germany, Department of Psychiatry, Department of Medical Ethics, St. Johns Medical College and Hospital, Bangalore, India; Kalyanram K., Rishi Valley Rural Health Center, Madanapalle, India; Varghese M., Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Kandavel T., Department of Biostatistics, National Institute of Mental Health and Neurosciences, Bangalore, India; Urvakhsh M., Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Girimaji S., Department of Child & Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Jacob P., Department of Child & Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Jayarajan D., Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Kumar K., Department of Mental Health and Clinical Psychology, National Institute of Mental Health and Neurosciences, Bangalore, India; Narayanan G., Department of Mental Health and Clinical Psychology, National Institute of Mental Health and Neurosciences, Bangalore, India; Khullar M., Department of Experimental Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India; Khandelwal N., Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research, Chandigarh, India; Ghosh A., Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh, India; Amit, Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research, Chandigarh, India; Joshi N., Department of Experimental Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India; Chanu N.D., Regional Institute of Medical Sciences, Manipur, Imphal, India; Fujica M.C., Regional Institute of Medical Sciences, Manipur, Imphal, India; Ph V., Regional Institute of Medical Sciences, Manipur, Imphal, India; Phurailatpam C., Regional Institute of Medical Sciences, Manipur, Imphal, India; Bhattacharya D., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Haque B., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Nagraj A., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Ghosh A., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Basu A., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Pandit M., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Das S., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Yadav A., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Das S., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Roy S., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Maurya P., Indian Council for Medical Research, Centre for Ageing and Mental Health, Kolkata, India; Rani G.T., Rishi Valley Rural Health Centre, Andhra Pradesh, India; Sujatha B., Rishi Valley Rural Health Centre, Andhra Pradesh, India; Rangaswamy M., Department of Psychology, Christ University, Bangalore, India; Fall C., MRC Lifecourse Epidemiology Unit, University of Southampton, United Kingdom; Kiran K., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Ramya M., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Urs C., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Santhosh N., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Somashekhara R., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Divyashree K., Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India; Rao A., Division of Nutrition, St. Johns Research Institute, Bangalore, India; Poornima R., Division of Nutrition, St. Johns Research Institute, Bangalore, India; Tripathy S., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Parashar N., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Nayana K.B., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Seshadri A., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Kumar S., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Baligar S., Molecular Genetics Laboratory, National Institute of Mental Health and Neurosciences, Bangalore, India; Arumugam T., Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India; Safai A., Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India; Cyril A., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Rashmitha, Molecular Genetics Laboratory, National Institute of Mental Health and Neurosciences, Bangalore, India; Roy A., Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India; Dhanalakshmi D., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Shivamma D., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India; Bhavana B.R., Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
Curiosity in calamity: How personal schadenfreude shapes disaster-tourism intentions
Tourism to sites of recent disaster, a form of dark tourism has raised questions about whether visitors are driven by typical travel motivations or by morbid impulses. This study examines how conventional tourist motives and the personality trait of benign schadenfreude (pleasure at others misfortune) jointly influence peoples intentions to visit a recent disaster site. By surveying 438 tourists to Kerala, four months after the July 2024 Wayanad landslides, we measured four common travel motives (novelty seeking, fun/entertainment, knowledge/learning, and relationship bonding) alongside a benign schadenfreude scale and visit intention. Partial least squares structural equation modelling (PLS-SEM) was employed for modeling. The model explained 58.80 percent of the variance in visit intention. Three motives viz., novelty, knowledge, and relationship had significant positive associations with intention, whereas the fun motive showed a negative effect. Schadenfreude emerged as the strongest predictor of disaster-site visit intention. Moreover, schadenfreude significantly moderated the influence of novelty seeking: respondents high in schadenfreude exhibited especially strong curiosity-driven intent to visit. These findings suggest that interest in post-disaster tourism often stems from ordinary travel drivers (curiosity, learning, social bonding), but a disposition to enjoy others misfortune can intensify the appeal when novel experiences are involved. The research highlights the need for ethical considerations to be followed by the destination managers and authorities in managing dark tourism destinations. Key limitations include the use of a cross-sectional data, region-specific sample and the focus on benign dimension (versus malicious) of schadenfreude. Future research should validate these results in other cultural and disaster contexts, establish causal relationships, and examine additional personal factors as well as dimensions of schadenfreude. 2026 Joseph et al.
Joseph, Joshin; Hasaballah, Mustafa M.; Abdelwahab, Mahmoud M.; Elbatal, Ibrahim; Gillariose, Jiju
https://doi.org/10.1371/journal.pone.0347535 https://www.scopus.com/pages/publications/105040540406?origin=resultslist
Joseph J., School of Commerce and Professional Studies, Marian College Kuttikkanam, Kerala, Peermade, India; Hasaballah M.M., Department of Basic Sciences, Marg Higher Institute of Engineering and Modern Technology, Cairo, Egypt; Abdelwahab M.M., Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia; Elbatal I., Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia; Gillariose J., Department of Statistics and Data Science, Christ University, Karnataka, Bangalore, India
Design and experimental validation of multi-section directional coupler with arbitrary coupling and high directivity for sub-6 GHz UWB applications
This work presents a geometrically simple topology for developing an ultra-wideband directional coupler with improved coupling and directivity. A short-ended coupled-line structure is used to achieve an ultra-wideband, tightly coupled symmetric three-section coupler using the microstrip line technology. The proposed design demonstrates an explicit improvement of approximately 1.2 dB in coupling compared to conventional multi-section directional couplers. Calculated, simulated, and measured responses validate the effectiveness of the proposed configuration in terms of low-ripple coupling bandwidth, low insertion loss, and improved directivity performance compared to respective responses of the conventional structure. Couplers featuring a higher number of sections to implement different bandwidths and couplings can be fabricated using the presented structure due to its transmission line-based approach. A prototype of the three-section directional coupler with coupling of 7.6 dB, 8.1 dB, and 8.3 dB and corresponding bandwidths of 104%, 123% and 133% is designed, fabricated, and measured. The experimental results confirm that the coupler can reliably achieve higher coupling with ultra-wideband response from 0.75 GHz to 3.75 GHz (5:1) with 8.3 1.4 dB (ripple). Additionally, the design yields promising performance with return loss > 16 dB, isolation > 20 dB, a phase difference of 90 4, and directivity > 30 dB, and the maximum circuit size is 0.067?02. This work aligns with SDG 9: Industry, Innovation and Infrastructure by advancing high-performance microwave components that support efficient, reliable, and scalable communication infrastructure. 2026 Patel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Patel, Shrawan K.; Barik, Rusan Kumar; Dewangan, Niraj Kumar; Koziel, Slawomir
https://doi.org/10.1371/journal.pone.0347748 https://www.scopus.com/pages/publications/105038253731?origin=resultslist
Patel S.K., Department of Electronics and Communication Engineering, School of Engineering and Technology, GGV Bilaspur, Chhattisgarh, India; Barik R.K., Department of Electronics and Communication Engineering, School of Engineering and Technology, CHRIST University, Bangalore, India; Dewangan N.K., Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India; Koziel S., Engineering Optimization and Modeling Center, Reykjavik University, Reykjavik, Iceland, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland
Decoding impermanent narratives: A study of transient migrants as digital influencers on YouTube
celebrity culture; digital content creator; identity; Indian international students; international education; migration studies; short-term migration; social media
Students migrate from India annually for higher education in large numbers. Social media has become an essential network for disseminating information related to aspects of migration like student visas, college applications, residence and finances. YouTube engages vigorously in this dispersion of information. Many times, the sources of these kinds of information are found to be transient migrants themselves. YouTubers and influencers like Tushar Bareja, Nidhi Nagori, Gursahib Singh, Bani Singh and Saloni Verma, among others, have made a niche, creating content and sharing information about the experience of being a transient migrant. Much like the status of being transient, creating ones brand on social media is both dynamic and fleeting, which cannot be defined in a sense of permanence. The analysis of content created by YouTube influencers enables an insight into the definition of transient migrant identity. The topics that are covered in the content showcase the particular components of international student life that add to the concept of a transient migrant identity. The article attempts to ask the question of how the YouTube videos made by student migrants end up contributing to the transient migrant identity. It also attempts to decipher how the transient identity itself is packaged as a commodity to be monetized by these student migrant influencers on YouTube. Using theoretical frameworks of influencer culture, social media and migration, the article attempts to unravel the workings of YouTube in commodifying the transient migrant experience. 2025 Intellect Ltd.
Transitions: Journal of Transient Migration;Volume;9;Issue;2;pp.193-214
https://doi.org/10.1386/tjtm_00069_1 https://www.scopus.com/pages/publications/105025766465?origin=resultslist
Gupta G., Christ University, Bannerghatta Road Campus, Pai Layout, HulimavuKarnataka, Bengaluru, 560076, India; Banerjee T., Jamia Millia Islamia, Jamia Nagar, Okhla, New Delhi, 110025, India
Biochemical and Rapid Paper Sensory Detection of Heavy Metals in Milk Based on Biosynthesized Silver Nanoparticles
Colorimetric sensor; Green synthesis; Heavy metals; Milk; Silver nanoparticles
Milk is an emulsion of proteins and fats in water that contributes to a nutritious diet and enhances our immune system. However, contamination of heavy metals in milk due to an increase in industrialization and urbanization can be a serious threat to human health. This study focused on the rapid detection of heavy metals particularly lead and mercury in milk using biochemical assays as well as paper-based colorimetric sensor based on green synthesized silver nanoparticles (AgNPs) from leaf extract of Hemigraphis colorata. Biochemical assays such as the lead chromate test and sodium hydroxide test were employed to detect lead and mercury in milk samples. The biogenic AgNPs were characterized by UVVis spectroscopy, scanning electron microscope, Fourier transform infrared spectroscopy, energy dispersive X-ray analysis (EDX) and X-ray diffraction. The unique properties of silver nanoparticles (AgNPs) like surface plasma resonance (SPR), large surface area and visible colour change upon aggregation when metal ions interact, enable them to detect heavy metals. This is a portable and affordable method of detection that ensures safer milk consumption and sustainable environmental practices. 2025 Asian Publication Corporation. All rights reserved.
Tom, Mia; Pradeep, Sheethal; Tomson, Majesh
Asian Journal of Chemistry;Volume;37;Issue;5;pp.1183-1190
https://doi.org/10.14233/ajchem.2025.33466 https://www.scopus.com/pages/publications/105014354685?origin=resultslist
Tom M., Department of Life Siences, School of Sciences, Christ University, Bengaluru, 560029, India; Pradeep S., Department of Life Siences, School of Sciences, Christ University, Bengaluru, 560029, India; Tomson M., Department of Life Siences, School of Sciences, Christ University, Bengaluru, 560029, India
Exploring the Pharmaceutical Potential of Meretrix casta (Gmelin, 1791) (Mollusca: Bivalvia)
Medicinal marine mollusks; Meretrix casta; Natural antioxidants; Therapeutic marine resources
Meretrix casta, a marine mollusk, has been recognized traditionally for its nutritional and medicinal properties. This study aims to investigate the pharmacological potential of M. casta extracts, specifically focusing on the antioxidant, hemolytic, anti-inflammatory and antimicrobial activities. The antioxidant activity, assessed via hydrogen peroxide scavenging and phosphomolybdate assays, revealed concentration-dependent inhibition, with the methanol extract showing 61.04% inhibition at 100 g/mL compared to 61.19% for ethyl acetate, while the standard ascorbic acid exhibited 87.80%. Anti-inflammatory activity was evaluated using heat-induced hemolysis, hypotonicity-induced hemolysis and protein denaturation assays. Both extracts demonstrated significant anti-inflammatory effects, with the ethyl acetate extract achieving 85.40% inhibition of hemolysis, closely matching acetylsalicylic acids 90.50% and methanol extract showing 87.60% at 100 g/mL. Antibacterial and antifungal assays demonstrated significant inhibitory effects against pathogenic bacteria and fungi, with the methanolic extract frequently exhibiting higher efficacy. These findings highlight the therapeutic potential of M. casta extracts as natural bioactive agents. Future investigations should aim to isolate and characterize the specific bioactive compounds underlying these pharmacological effects and to explore their mechanisms of action in detail. Such studies could pave the way for new therapies derived from marine biodiversity, addressing various health challenges. 2025 Asian Publication Corporation. All rights reserved.
Thippesh, Deepak; Velayudhannair, Krishnakumar
Asian Journal of Chemistry;Volume;37;Issue;6;pp.1469-1476
https://doi.org/10.14233/ajchem.2025.33827 https://www.scopus.com/pages/publications/105012758639?origin=resultslist
Thippesh D., Department of Life Sciences, Christ University, Dharmaram College Post, Hosur Road,, Bengaluru, 560029, India; Velayudhannair K., Department of Life Sciences, Christ University, Dharmaram College Post, Hosur Road,, Bengaluru, 560029, India