Volume 12 | Issue 2 | February-2024

 Abstract

Various Coupling reactions have been studied by using modified Y-Zeolite (Mg, Ca, Cu, Ni, etc.) heterogeneous catalysts. The structural, morphological and textural properties of catalysts were inspected by various physico-chemical procedures, for example ICP-OES, XRD, CO2-TPD, NH3-TPD, H2-TPR, N2sorption, Electron Microscopy (SEM, FE-SEM, TEM and HR-TEM), XPS, Raman Spectroscopy, FT-IR spectroscopy, NMR Spectroscopy, CHNS elemental analysis, etc. The study suggested that acidic as well as basic attributes of catalysts in addition to their strength (weak, moderate, moderately-strong, strong) have a vital role in coupling reaction performance and product selectivities . These modified Y-zeolite catalysts were explored for various organic coupling reactions to attain valuable desired products. The used catalysts were further analyzed by different techniques to understand insights into the materials stability and recyclability. Specifically, more attention is given to the acidity, basicity, and pore structure of the modified catalyst. And also, emphasis is placed on the acid-base properties of the catalyst for control of selectivity, rate of conversion and stability.

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Insights into the Acidic and Basic Characteristics of modified Y- Zeolite in a Coupling Reactions

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The Optimized Path Planning for Last-Meter Drone Delivery Using Deep Q-Networks system is an intelligent autonomous delivery framework designed to improve the efficiency, speed, and reliability of last-meter logistics. Traditional delivery methods are often hampered by traffic congestion, high fuel consumption, increasing labour costs and delays in delivery, especially in congested urban areas and remote areas. To overcome these limitations, the proposed system employs Unmanned Aerial Vehicles (UAVs) with Deep Q-Network (DQN)-based reinforcement learning to achieve optimal path planning and autonomous navigation. The system combines GPS navigation, real-time obstacle detection sensors, flight controllers, wireless communication technologies (RF/Wi-Fi/4G/5G), and a web-based management platform to execute safe and efficient package delivery. Unlike traditional shortest path algorithms, the DQN agent learns the optimal delivery route on the fly through interaction with the environment, taking into account dynamic obstacles, battery constraints, weather conditions, no-fly zones, and delivery priorities. This adaptive learning enables the drone to achieve minimal travel distance, energy consumption, and delivery time, whilst maximising delivery success and operational safety. The proposed architecture is composed of hardware, software and communication layers and modules for user management, order processing, route optimisation, autonomous flight control, payload management, real-time tracking and monitoring. The system is implemented using Python, Flask, HTML, CSS and JavaScript and simulations are used to validate the effectiveness of the DQN based navigation strategy before implementation. Experimental evaluation demonstrates that the proposed approach significantly improves route efficiency, reduces operational costs, decreases human intervention, and enhances delivery reliability compared to traditional ground-based logistics systems. Furthermore, the system is particularly suitable for medical supply transportation, emergency response, food delivery, grocery distribution, and e-commerce logistics, where rapid and contactless delivery is essential. The proposed framework is a scalable, eco-friendly and intelligent solution for future autonomous drone-based logistics, paving the way to smart last-meter delivery systems in modern cities and remote areas.

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 Keywords

Deep Q-Network (DQN), Last-Meter Drone Delivery, Path Planning, Reinforcement Learning, Unmanned Aerial Vehicle (UAV).

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: As the use of autonomous and smart vehicles in the ground transportation systems increases, new security issues emerge. That shift from human-run operations to computer-run operations allows bad people to attack more easily. With Internet of Things (IoT) becoming more common in cars, they are constantly making and sharing a lot of data. Sophisticated attackers can exploit the weaknesses of this trend with Advanced Persistent Threats (APT) for example. It is very important to be able to find the APTs in the vehicles that have the IoT technology . We have to do better at detecting these threats. Traditional centralised Machine Learning (ML) techniques are hindered by the critical demand of data privacy in vehicles. In addition, there are no publicly available APT datasets in the vehicle domain, which makes it difficult to develop and test models. That's a big problem for cybersecurity in this ever-changing area. To address these issues, the study introduces a novel Federated Deep Neural Network (FDNN) framework with a privacy-preserving technique. The paper discusses the main challenges in detecting APTs and describes its unique contribution to the field. It discusses the research questions that are driving the study. The datasets of UNSW-NB15, Edge-IIoTset and CSE-CIC-IDS2018 depict different phases of an APT attack. The framework constructed with these datasets is examined and evaluated. The framework without the privacy-preserving technique achieves the APT detection accuracies of 97.32%, 96.81%, and 98.06%, respectively, for these datasets. With the privacy-preserving technique, the accuracies of the framework are 95.62%, 96.11%, and 95.63%, respectively. There are tables with all results and other evaluation metrics like Precision, False Positive Rate and F1 Score. We apply "Shapley Additive Explanations (SHAP)" analysis to our framework to identify the most important features for detecting APTs. This work confirms the effectiveness of a novel framework for APT detection in distributed vehicular environments. The framework works well because it reduces the data and features. This was demonstrated by extensive testing with several benchmark datasets. Future work will investigate the framework's ability to identify APTs in other domains.

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 Keywords

APT, Connected Vehicles Security, Explainable AI, Federated Learning, Privacy-Preserving Deep

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The controversial, but to a great extent understudied, "Nellie Massacre" stands as a dark reminder of the deep-seated ethnic and communal tensions that plagued, and continue to affect, the socio-political setting of Assam. The present study delves into the systemic factors surrounding this tragic episode, highlighting the complex socio-political landscape that culminated in unparalleled violence during the height of the Assam Agitation. The massacre, which occurred on February 18, 1983, across several villages in the Nellie region, resulted in the brutal deaths of thousands, primarily from the minority East Bengal origin Muslim community. Factors such as historical grievances over land alienations, shifting demographic profiles, immigration fears, and deliberate political manipulation contributed to the sudden eruption of the violence. The aftermath saw a distinct lack of judicial accountability for the victims, with the provisions of subsequent political accords granting effective amnesty to perpetrators. This paper aims to provide a critical overview of the massacre, shedding light on a historically complex problem of genocide in the post-colonial history of the state that continues to resonate with the contemporary issues of legal identity, communal harmony, and human rights in Northeast India.

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Assam Agitation, Nellie Massacre, Communal Violence, Ethnic Identity, Political Marginalization, Land Alienation.

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The present study was conducted to examine the effect of cyberbullying on psychological abnormality among school students. In the digital age, school students are increasingly exposed to online communication through smartphones, social media, messaging applications, online games and digital learning platforms. Cyberbullying in the form of abusive messages, threatening comments, fake accounts, online exclusion, trolling, spreading rumours, body shaming and digital humiliation has emerged as a serious psychological and educational problem. The study was based on descriptive survey research design and included a sample of 600 secondary and senior secondary school students from Dehradun district. Cyberbullying was taken as the independent variable, while psychological abnormality was taken as the dependent variable. Gender, locality and school type were considered as demographic variables. Data were collected with the help of the Cyberbullying Scale, Psychological Abnormality Scale and Personal Information Schedule developed for the study. The data were analysed through percentage, mean, standard deviation, t-test, Pearson product moment correlation and regression analysis. The findings revealed that the majority of school students were found at the moderate level of cyberbullying and psychological abnormality. Boys showed significantly higher cyberbullying than girls, whereas girls showed significantly higher psychological abnormality than boys. Urban students and private school students showed higher cyberbullying and psychological abnormality than rural and government school students. A significant positive relationship was found between cyberbullying and psychological abnormality, with a correlation value of 0.68. Regression analysis further showed that cyberbullying explained 46.2% variance in psychological abnormality, indicating that cyberbullying is an important predictor of psychological abnormality among school students. The study concludes that cyberbullying is a serious risk factor for anxiety, depression, stress, aggression, withdrawal, low self-esteem and poor adjustment among students. Therefore, cyber-safety education, digital citizenship, parental monitoring, teacher awareness, school counselling and strong anti-cyberbullying policies are urgently required to protect the psychological health of school students.

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 Keywords

Cyberbullying, Psychological Abnormality, School Students, Digital Safety, Mental Health

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Indian painting has historically maintained a profound relationship with mythology, functioning as a visual language through which religious, philosophical, and cultural ideas are communicated. Mythological elements--derived from texts such as the Ramayana, Mahabharata, and Puranas--have been continuously interpreted and reinterpreted across different periods of Indian art. This paper examines the transformation of mythological imagery in Indian painting from ancient mural traditions to contemporary artistic practices. It analyses stylistic, iconographic, and conceptual changes while situating them within broader socio-cultural, political, and aesthetic contexts. The study argues that mythological elements in Indian painting are not static but dynamic constructs that evolve in response to changing historical conditions, artistic innovations, and ideological shifts.

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 Keywords

mythological elements, Indian Painting, symbolic, visual representation, metaphysical, painting traditions,

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Today, the whole world has become like a small box, each and every corner of the world is well known to all. Sea had a main role to connect the places and the people around the world from primitive days. The United Nations Convention on the Law of the Sea, 1982 (UNCLOS) constitutes the foundational legal framework governing the use and division of maritime spaces. Often described as the "Constitution of the Oceans," the Convention establishes a comprehensive zoning system that balances coastal State sovereignty with the principle of freedom of the seas. This article examines the legal division of the sea as codified under UNCLOS, focusing on the nature, extent, and legal consequences of each maritime zone. It further analyses maritime boundary delimitation, dispute settlement mechanisms, and the contemporary relevance of UNCLOS in addressing emerging challenges in ocean governance.

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 Keywords

Law of the Sea; UNCLOS 1982; Maritime Zones; Territorial Sea; Exclusive Economic Zone; Continental Shelf; High Seas

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A government budget is an instrument for fulfilling the state's obligations and a political statement of the priorities set by the government in allocating resources. It is generally perceived as gender neutral. However, the budgetary policies have differential impacts on men and women, and more often than not, the resource allocation tends to be partial. Despite the numerous attempts at narrowing the gender disparity, women continue to experience continuous vulnerabilities, and their opportunity gaps endure in all spheres. It is anticipated that gender budgeting would provide the mechanism to close the gender gap. The paper provides an overview of the concept of gender budgeting, its historical development and its current status in Mizoram. It is descriptive and is based on secondary sources. It explores the issues and posits that capacity building, leadership commitment, access to gender-disaggregated data and the participation of the civil society, among others, would facilitate the institutionalisation of gender budgeting in the state.

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 Keywords

Gender, gender disparity, women, gender budgeting, equality.

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Section 113 of India's new Bharatiya Nyaya Sanhita (BNS) embeds the definition of "terrorist act" directly into the ordinary criminal code, shifting it from special laws like the UAPA. This integration normalizes terrorism charges, making them appear routine alongside common crimes. Critics argue this perilous embedding carries severe risks: the overbroad definition (including acts threatening security, unity, or causing disruption "by any means") and harsh penalties enable potential misuse against dissent, protests, minorities, and journalists. It blurs crucial distinctions between ordinary crime and national security threats, erodes due process, creates a chilling effect on fundamental freedoms, and risks mainstreaming controversial anti-terror frameworks. This article mainly critically analyses the newly inserted provision. The Paper briefly analyses the historical development of counter terrorism laws in India from TADA to POTA to the current primary counter terrorism statute, the Unlawful Activities (Prevention) Act (UAPA).Further the possible perils of normalization of terrorism law under section 113 BNS is also analyzed. Furthermore a comparative analysis of the Indian law with the international legal standard is also discussed briefly. And lastly the actions in order to mitigate the profound perils posed by section 113 are also discussed as suggestions.

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Terrorism, Counter Terrorism laws, Section 113 BNS.

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Chhau dance of Purulia has had the history of performing arts based on traditional culture of Bengal. It is a popular dance culture performed on traditional Hindu religious themes. It is originated in ancient time and developed on Hindu cultural values and religious traditions. The tradition of Chhau dance was evolved through different stages. However wearing mask at the time of performance is quite normal and it is the tradition which has separated them from other dance culture. From time immemorial Chhau dance has got importance in rural Bengal but in contemporary society it becomes a popular folk culture which attracts people of different regions. In the present study I have tried to find out problems and challenges of Chhau dancer of rural Chhau artists of Purulia and their socio-economic conditions. Qualitative methodology has been applied in the study for in-depth understanding of the subject. Various tools and techniques of qualitative research have been applied throughout the research as and when necessary. Study reveals that Chhau dancers are coming from lower stratum of the society and struggling as artists due to their economic conditions, illiteracy and backwardness.

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 Keywords

Chhau Dancer, Hindu religion, Folk Culture, Social Backwardness, Art, Primitive culture, Agility, Indigenous, Tribal Dance.

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Creative Commons Attribution 4.0 and The Open Definition