Volume 13 | Issue 12 |

 Abstract

"A Study of Emotional Intelligence of Prospective Teachers"

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"A Study of Emotional Intelligence of Prospective Teachers"

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A Study of The Emotional Intelligence of Prospective Teachers at The B.Ed. And D.L.Ed. Levels in Rural Areas."

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A Study of The Emotional Intelligence of Prospective Teachers at The B.Ed. And D.L.Ed. Levels in Rural Areas."

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Abstract Evidence shows that increasing physical activity and fitness may enhance academic performance, as well as setting aside time throughout the school day for recess, physical education class, and physical exercise in the classroom. According to available information, physical exercise has the greatest impact on mathematics and reading. These themes rely on effective executive function, which has been related to physical exercise and fitness. Academic achievement relies on executive function and brain health. Physical exercise and increased cardiovascular fitness improve basic cognitive skills such as attention and memory, which enable learning. Physical activity, both during single sessions and over time, improves cognitive function and brain health. Children who engage in vigorous or moderately intense physical exercise gain the most. Given the significance of focused time for learning, kids should be given periodic developmentally appropriate physical exercise breaks. Although currently understudied, physically active teachings in the classroom have the potential to enhance time on task and attention to task. Keywords: Physical Activity, Fitness, Physical Education, Academic Performance

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Keywords: Physical Activity, Fitness, Physical Education, Academic Performance

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The dynamics of world trade are rapidly transforming under the influence of digital innovation. Traditional trade systems, characterized by paper-based documentation, manual verification, and limited transparency, have long hindered efficiency and trust among global trading partners. Blockchain technology, a decentralized and tamper-proof digital ledger, offers an opportunity to address these long-standing inefficiencies. This research paper explores how blockchain redefines trust, traceability, and efficiency in global trade by automating documentation, strengthening transparency, and ensuring authenticity across supply chains. The study adopts a qualitative and analytical methodology using secondary data from the World Trade Organization (WTO), UNCTAD, OECD, and industry reports. Case studies such as TradeLens, we.trade, and VeChain are analyzed to assess blockchain's practical applications in customs, logistics, and trade finance. Findings reveal that blockchain enhances trade facilitation, reduces costs, prevents fraud, and improves compliance. However, adoption barriers such as regulatory fragmentation, interoperability challenges, and uneven digital infrastructure persist. The paper concludes with a five-dimensional strategic framework for integrating blockchain into world trade systems, emphasizing technological integration, policy harmonization, institutional collaboration, capacity building, and ethical governance. The study contributes to the discourse on digital trust infrastructure as the foundation for a sustainable, transparent, and inclusive global trade ecosystem.

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Blockchain, Trust, Traceability, Trade Facilitation, Transparency, Supply Chain Efficiency, Digital Innovation

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The proposed project is a Ransomware Detection System developed using Python, TensorFlow/Keras, Pandas, and NumPy. It employs lightweight deep learning models, specifically a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN), to accurately detect and classify Ransomware samples while remaining suitable for resource-limited environments. The MLP model captures nonlinear feature interactions, while the CNN model processes feature vectors as sequences to extract local patterns. Together, these architectures enable robust Ransomware detection with high accuracy and efficiency. To enhance performance, the system incorporates feature selection using mutual information, reducing input dimensionality while preserving critical information. The models are trained and evaluated on a balanced Ransomware dataset, with results measured using Accuracy, Precision, Recall, F1-score, and ROC-AUC. Additionally, a generalization test is performed using a hold-out subset of unseen Ransomware samples, demonstrating the models' ability to adapt to novel attack variants. By combining lightweight deep learning architectures with effective preprocessing, this proposed system provides practical and efficient solution for Ransomware detection in resource-constrained environments such as IoT devices, mobile systems, and embedded platforms.

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Ransomware Detection, Cyber security, Lightweight Deep Learning, Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), Feature Selection, Efficient Classification, Resource-Constrained Systems, Generalization, Binary Classification.

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The increasing reliance on Medical Internet of Things (MIoT) devices for patient monitoring and healthcare delivery has introduced significant security challenges, necessitating advanced threat detection mechanisms. This review provides a comprehensive analysis of deep learning approaches for MIoT threat detection, while also considering complementary techniques such as machine learning, anomaly detection, blockchain-based security, hybrid models, and cloud-edge integration. Traditional machine learning methods like SVM, Decision Trees, Random Forests, and k-NN are effective for structured data but struggle with complex healthcare datasets, whereas deep learning models such as CNNs, RNNs, and LSTMs excel in feature extraction and sequential data analysis, achieving higher accuracy in detecting malicious activities. Anomaly detection techniques, including autoencoders and clustering methods, offer the ability to identify zero-day attacks but often suffer from false-positive rates. Blockchain integration enhances trust, transparency, and data integrity, though at the cost of latency and energy efficiency. Hybrid frameworks that combine ML/DL with anomaly detection or blockchain provide a balanced approach to security, and cloud-edge computing synergy further improves scalability and real-time responsiveness. Collectively, these approaches highlight the growing importance of deep learning in enabling secure, reliable, and intelligent threat detection in Medical IoT environments.

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MIoT, Deep Learning, Accuracy, Security

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This study explores the historical evolution and cultural significance of masks by examining their roles across diverse civilizations and time periods. From early ritual practices to classical theatre, healing traditions, funerary customs, and modern artistic and protective uses, masks have served as powerful tools for expressing identity, mediating spiritual beliefs, and supporting social cohesion. Although extensive research exists on masks within individual cultures, comprehensive cross-cultural analyses remain limited. This paper addresses that gap by integrating perspectives from anthropology, performance studies, and material culture to trace how masks have transformed in form, function, and symbolic meaning. Through comparative historical analysis, the study reveals how masks embody themes of transformation, protection, and performance, and how they continue to influence contemporary expressions of identity and creativity. Ultimately, the paper argues that masks are more than ornamental objects they are dynamic cultural artefacts that reflect the evolving relationship between humans, society, and the sacred.

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Masks Cultural significance Cross-cultural analysis Identity Ritual and performance

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Abstract Women constitute an eternal and indispensable component in the growth and development of any nation. With the passage of time , the role of women in society and nation-building has been increasingly recognized and substantially expanded across the world. It is now widely acknowledged that national development cannot be achieved without ensuring the holistic development and empowerment of women. Among the various dimensions of empowerment, political empowerment holds a central position, as it enables women to participate directly in decision-making processes and democratic governance. In India, Panchayati Raj Institutions (PRIs) serve as a vital mechanism for promoting women's political empowerment at the grassroots level. The 73rd Constitutional Amendment Act, which mandates reservation for women in PRIs, has significantly enhanced their representation and participation in local self-governance. This study focuses on Himachal Pradesh, where PRIs have emerged as an important platform for strengthening women's leadership and political awareness.

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Keywords: Women, Empowerment, Panchayati Raj Institutions, Himachal Pradesh

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Natural indicators extracted from plant sources offer an economical, environmentally sustainable, and readily available alternative to synthetic chemical indicators in pharmaceutical and analytical applications. This project focuses on the systematic isolation and comprehensive evaluation of natural acid-base indicators from selected plant species including Hibiscus rosa-sinensis, Curcuma longa (turmeric), Lawsonia inermis (henna), and other flowering plants rich in anthocyanins and flavonoid pigments. The isolation was performed using aqueous and methanolic extraction techniques followed by evaluation through pH titration studies and colorimetric analysis. Results demonstrate that natural indicators exhibit sharp color transitions comparable to synthetic indicators (phenolphthalein and methyl orange) across various pH ranges, with minimal standard deviations. The extracted indicators successfully performed acid-base titrations involving strong acid-strong base, strong acid-weak base, weak acid-strong base, and weak acid- weak base combinations. Natural indicators represent a viable, cost-effective, pollution- free replacement for conventional synthetic indicators, offering superior stability and reliability for pharmaceutical quality control and academic laboratory applications.

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natural indicators, anthocyanins, plant extraction, acid-base titration, pH indicators, pharmaceutical applications

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In this era of a politically unstable world, there is a growing demand for the use of military robots to aid the soldiers to perform perilous missions. This paper focuses on the design and build of a semi-autonomous, unmanned robotic system used for various military and rescue operations. Dangerous tasks such as bomb disposal, enemy territory surveillance, search and rescue can be efficiently carried out by the Military Support and Rescue Robot(MSRR). This reduces the risk of losing the lives of both soldiers and civilians. With the help of live feed from the wireless camera and data analysis of environmental composition by various sensors of the area under surveillance the soldiers can better prepare for their missions. Using technology, the above-mentioned tasks can be achieved. The different sensors of the robots are connected to the Arduino UNO which in turn is connected to the bluetooth. Data transmission and receiving are through bluetooth technology. This prototype design overcomes the weakness of the existing models and thus provides better support for military operations.

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Arduino UNO, MSSR, EOD, Rescue Robot

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