Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya

Abstract Continuous and sustainable aquaculture development is essential to provide affordable protein sources for the global population. Nevertheless, the intensification of fish farming at high stocking density led to chronic crowding stress that could impair the growth performance, immune system, and antioxidant capacity of an aquaculture species. Consequently, the prevalence of disease outbreaks increases, resulting in low aquaculture production. Various countries remain highly dependent on antibiotics and chemotherapies to overcome this problem. Recently, researchers have proposed and developed alternative solutions using environmentally-friendly feed additives, such as probiotics, phytobiotics, and synthetic immune stimulants, to mitigate crowding stress. The feed additives reportedly alleviate crowding stress, promote growth performance, enhance gut microbiota, immune system, and antioxidant capacity, and stimulate disease resistance, contributing to the overall improvement of the health status of aquaculture species and boosting aquaculture production. The application of feed additives in aquafeed has been investigated in various aquaculture species to explore their beneficial effects at optimum doses. The study outcomes illustrated that feed additives in aquafeed are promising in mitigating crowding stress among aquaculture species. Herein, this review paper discussed and summarised the impacts of crowding stress on the growth, immune system, and antioxidant capacity of aquatic animals. In addition, this review highlighted the potential of feed additives such as probiotics, phytobiotics, and synthetic immune stimulants in mitigating crowding stress in aquatic animals.

This study presents a bibliometric analysis of research on the relationship between Social Emotional Learning (SEL) and Second Language Acquisition (SLA). It explores publication trends, emerging research themes, influential authors, and geographical contributions to the growing intersection of emotional learning and language acquisition. A systematic search of Scopus, Web of Science, Google Scholar, and ERIC identified peer-reviewed journal articles and conference papers published between 2000 and 2025. The findings reveal a growing body of work emphasizing SEL’s role in enhancing SLA outcomes, particularly through emotional factors such as language anxiety, motivation, and learner engagement. Key contributors include the United States, United Kingdom, and China, with leading scholars such as Zoltán Dörnyei, Patricia A. Alexander, and Lauri R. MacIntyre. Keyword co-occurrence analysis highlights central themes including emotional intelligence, self-regulation, and classroom engagement. This analysis offers insights into how SEL strategies support emotional resilience and language learning while identifying directions for future research across diverse cultural and digital learning environments.

The swift advancement of cyberattacks, particularly zero-day vulnerabilities, presents considerable difficulties for intrusion detection systems (IDSs) within 5G/6G and IoT-enabled networks. Current deep learning-based Intrusion Detection Systems, while successful, frequently exhibit deficiencies in scalability, interpretability, and robustness within heterogeneous and resource-limited contexts. This research presents a federated ensemble Intrusion Detection System (IDS) that amalgamates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformers to concurrently record geographical, temporal, and contextual patterns in network traffic. Federated learning with aggregation algorithms is implemented to facilitate privacy-preserving training. Model transparency is improved using explainable AI (XAI) tools, such as SHAP, LIME, and counterfactual explanations, which offer interpretable decision assistance. Additionally, a GAN-based adversarial defense is integrated to enhance robustness against evasion attacks. The model undergoes thorough evaluation on benchmark datasets (NSL-KDD, UNSW-NB15, CICIDS2017, CSE-CIC-IDS2018, TON_IoT20, Edge-IIoTset) and is validated on a practical IoT edge testbed utilizing Raspberry Pi and Jetson Nano devices. Experimental findings indicate exceptional predictive performance (accuracy reaching 98.5%, AUC-ROC exceeding 0.99), enhanced adversarial resilience, and efficient inference on edge devices. The suggested methodology provides a scalable, interpretable, and robust IDS framework appropriate for advanced intelligent networks.

<div class="section abstract"><div class="htmlview paragraph">The transition towards sustainable transportation necessitates the development of advanced thermal management systems (TMS) for electric vehicles (EVs), hybrid electric vehicles (HEVs), hydrogen fuel cell vehicles (FCVs), and hydrogen internal combustion engine vehicles (HICEVs). Effective thermal control is crucial for passenger comfort and the performance, longevity, and safety of critical vehicle components. This paper presents a rigorous and comparative analysis of TMS strategies across these diverse powertrain technologies. It systematically examines the unique thermal challenges associated with each subsystem, including cabin HVAC, battery packs, fuel cell stacks, traction motors, and power electronics. For cabin HVAC, the paper explores methods for minimizing energy consumption while maintaining thermal comfort, considering factors such as ambient temperature, humidity, and occupant load. The critical importance of battery thermal management is emphasized, with a focus on preventing thermal runaway and maximizing battery lifespan through precise temperature regulation. The complexities of fuel cell stack thermal management are addressed, considering the electrochemical reactions and the need for uniform temperature distribution for optimal performance and durability. Furthermore, the paper investigates the thermal behavior of high-power traction motors and power electronics, analyzing different cooling techniques and their impact on efficiency and reliability. A comparative assessment of various cooling technologies, including forced air convection, liquid cooling, and emerging two-phase cooling methods, is provided. The paper also delves into integrating these individual TMS components, exploring opportunities for waste heat recovery and holistic system optimization. Finally, the paper identifies critical research gaps and outlines future directions in the development of intelligent and adaptive thermal management systems for next-generation electrified vehicles, emphasizing the need for robust control algorithms, advanced materials, and innovative cooling architectures.</div></div>

This study aims to develop novel spiro-pyrrolizidine-benzyloxy hybrids (RP1, RP2, and RP3) to reduce the dosage and mitigate the side effects of doxorubicin (DOX) while harnessing potential synergistic effects for enhanced anticancer efficacy. Spiro-pyrrolizidine-benzyloxy hybrids (RP1, RP2, and RP3) were synthesized using isatin, L-proline, and sub-chalcone. The anticancer potential of these compounds was evaluated against MDA-MB-231 breast cancer cells. Based on superior efficacy, RP1 was selected and compounded with doxorubicin in different ratios. The anticancer efficacy of these compounded formulations was assessed through cell viability assays and IC50 values. Among the synthesized hybrids, RP1 exhibited the highest anticancer efficacy against MDA-MB-231 cells. When RP1 was combined with doxorubicin, the combination showed reduced cell viability, with the most effective ratio being 23.50 µM (20:80) (RP1: dox), followed by 11.22 µM (50:50) and 8.82 µM (80:20). The compounded formulation resulted in a lower IC50 value compared to doxorubicin alone, indicating enhanced efficacy. Compounding RP1 with doxorubicin effectively enhances anticancer activity while potentially reducing the side effects associated with doxorubicin's quinone-hydroquinone moiety. The optimized formulation (80:20) presents a promising approach for improving breast cancer treatment outcomes.

Abstract Professor is always a buzz word among the very intelligent and highly educated individuals. It is not just because the societal status being enjoyed by the Professors, but also their contribution to the society by sculpting responsible citizens of the future. These academicians’ role is felt pivotal for development of any institution on the one side and making every individual as a complete man on the other side. It is such a tough task to travel between these two extremes and also focusing on their professional development simultaneously. The emotional intelligence is taken as study variable for conducting this study. This study focuses on emotional intelligence and its influence on the discretionary behaviour among the academicians. Primary data collected by way of circulating the structured questionnaire as google form among the faculty has been used in this study. Statistical Analysis Software SPSS 23 has been used for analysis of the data so collected. This quantitative research has found a significant relationship between emotional intelligence and the citizenship behaviour of the academicians.

Stroke rehabilitation plays a crucial role in restoring motor functions in patients suffering from neurological impairments. Accurate posture prediction and assessment during physiotherapy sessions are vital for tracking recovery and guiding therapeutic interventions. In this study, proposed StrokePoseNet(SPN) a novel temporal-spatial deep learning architecture designed to predict and classify rehabilitation postures based on joint coordinate sequences. Our approach combines 1D Convolutional Neural Networks (CNNs) to extract spatial features from individual frames and Long Short-Term Memory (LSTM) networks to capture temporal dependencies across sequences. Utilized the publicly available Toronto Rehabilitation Stroke Pose Dataset, which includes posture sequences of stroke patients performing various rehabilitation exercises. SPN was trained and evaluated using standard machine learning metrics, achieving a classification accuracy of 96.42% and a balanced F1-score of 95.99%. Comparative analysis with baseline models such as Random Forest, SVM, and standalone CNN or LSTM architectures confirmed StrokePoseNet’s superior performance. The outcomes reinforce that the model can be implemented in real-time scenarios in the clinical setting, proposing a data-based method to monitor stroke treatment through the form of intelligent physiotherapy frameworks.