Volume 14 | Issue 2 |

 Abstract

The advancement of autonomous systems has significantly enhanced modern defence technology, enabling safer and more efficient military operations. This project focuses on the design and implementation of an Unmanned Ground Vehicle (UGV) that can operate autonomously in defence and surveillance environments. The proposed system utilizes a Raspberry Pi 3 Model B+ as the central processing unit, integrating ultrasonic sensors, motor drivers, and a Pi camera to achieve real-time environmental monitoring and obstacle detection. Through the implementation of computer vision and image processing techniques using OpenCV, the UGV is capable of identifying paths, avoiding obstacles, and navigating autonomously in dynamic environments. The system also allows for live video streaming, making it suitable for border surveillance, reconnaissance missions, and hazardous area monitoring where human access is limited or risky. Furthermore, the proposed UGV architecture emphasizes modularity and ease of expansion, allowing additional sensors such as GPS, infrared, gas sensors, or weapon detection modules to be integrated in future enhancements. This work lays a strong foundation for further research into advanced artificial intelligence algorithms, swarm robotics, and secure communication systems, contributing toward the development of next-generation autonomous ground systems for defence and security applications.

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 Keywords

Artificial Intelligence, Arduino Microcontroller, Raspberry Pi, GSM, Suspension System

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 Abstract

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bstract: Institutional autonomy and academic freedom are widely regarded as essential foundations for the development of effective, innovative, and globally competitive higher education systems. Together, they create an enabling environment for intellectual inquiry, pedagogical innovation, and high-quality research, thereby contributing to academic excellence and societal progress. Institutional autonomy empowers universities and colleges to independently design academic programs, manage administrative processes, and allocate financial resources in alignment with their strategic priorities. Academic freedom, on the other hand, safeguards the rights of faculty and students to engage in critical inquiry, open dialogue, and independent research without fear of external interference or censorship. In recent decades, higher education systems worldwide have experienced growing regulatory oversight, performance-based evaluation, and market-oriented reforms, which have intensified debates regarding the balance between autonomy, accountability, and governance. While regulatory mechanisms are necessary to ensure quality assurance, transparency, and public accountability, excessive control can restrict institutional flexibility and weaken the creative potential of academic communities. This study critically examines the conceptual foundations, historical development, and contemporary policy frameworks related to institutional autonomy and academic freedom, with particular emphasis on the Indian higher education context and relevant global perspectives. Key words: Institutional Autonomy, Academic Freedom, Knowledge Innovation, University Excellence.

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Institutional Autonomy, Academic Freedom, Knowledge Innovation, University Excellence.

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With more than 85% of today's network traffic encrypted using TLS/SSL, traditional Intrusion Detection Systems (IDS) fail to inspect encrypted packets and detect hidden threats. Attackers increasingly hide malware, botnets, ransomware, and command-and-control traffic inside encrypted channels. This paper presents a Real-Time AI Threat Detection Framework using a hybrid 1D-CNN + BiLSTM deep learning model to identify malicious encrypted traffic without decrypting it. The model analyzes metadata features such as packet lengths, inter-arrival times, TLS handshake behavior, flow statistics, and directional patterns. Experimental results on CIC-IDS-2018 and ISCX VPN datasets show an accuracy of 98.3%, outperforming classical ML models. The proposed system is scalable, privacy-preserving, and suitable for Zero Trust networks.

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 Keywords

Encrypted Traffic Analysis, Deep Learning, Intrusion Detection, CNN-LSTM, Network Security, Real-Time Threat Detection.

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India aims to become 'Viksit Bharat' - a developed and self-reliant nation by 2047. It envisions creating an equitable and developed society where every individual has equal opportunities and accessibility to resources. By inclusivity, it promotes empowerment of marginalized communities, including women. Whereas water, which is fundamental for living, is still under threat, making effective water governance crucial for sustainable development. To address this, the right to access clean drinking water is protected under the broad framework of the Right to Life guaranteed under Article 21 of the Indian Constitution. Various government programs have been implemented to mitigate the water security challenges. Among them, the Jal Jeevan Mission, launched by the Government of India in 2019, aims to provide functional household tap water connections in every rural household. As there seems to be a gendered division of labour around water, mostly in rural settings which reinforces systemic inequities, making women remain marginalized. The mission not only focuses on the service delivery parameters, but also puts great emphasis on reducing the age-old drudgery of fetching water by women from the remotest villages of India. The initiative has urged women to participate in water governance and take active roles in managing the working of the mission at the community levels. This paper will assess the impact of this flagship programme on inclusive development, with particular emphasis on women empowerment. The study contributes in understanding how a government-led water policy has its impact on bridging gender disparities, which ultimately supports India's broader goal of becoming a developed and self-reliant nation by 2047.

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Water governance, women empowerment, inclusive development, Viksit Bharat.

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid proliferation of social networking sites (SNSs) has significantly transformed the daily lives of students, influencing not only their social interactions but also their overall well-being. This study examines the impact of social networking site usage on the mental, emotional, physical, and academic well-being of students, with a particular focus on patterns of usage and associated outcomes. A descriptive survey research design was employed, and data were collected from a sample of students using a structured questionnaire. The instrument measured frequency and duration of SNS usage, platform preference, and perceived effects across multiple well-being dimensions. Descriptive statistics and correlation analysis were used to analyze the data. The findings reveal that excessive and prolonged use of social networking sites is associated with increased levels of mental fatigue, emotional stress, and reduced physical activity among students. Moderate negative relationships were observed between time spent on SNSs and indicators of academic concentration and productivity. Conversely, limited and purposeful use of social networking platforms demonstrated some positive effects, such as improved peer communication and access to academic resources. The results suggest that the impact of SNS usage on student well-being is not uniform but varies according to intensity, purpose, and individual engagement patterns. This study contributes to the growing body of literature on digital media and student well-being by providing empirical evidence from a student population and by adopting a multidimensional approach to well-being assessment. The findings underscore the need for awareness programs, digital literacy initiatives, and institutional guidelines to promote healthy and balanced social media usage among students. Future research may extend this work by employing longitudinal designs and incorporating qualitative perspectives to better understand long-term effects

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Social networking sites; Student well-being; Mental health; Emotional well-being; Academic performance; Digital media use; Social media impact.

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The quality and safety of country-made liquors (Desi Sharab) remain a critical public health concern in India due to frequent adulteration and dilution. This study is particularly dedicated towards adulteration through dilution. In this study, country-made liquor samples sold by the name 'Desi Madira Mashala Sharab' in short (Desi Sharab) were collected and analyzed using pH measurement, pycnometry and UV-Visible spectrophotometry. The pH values ranged between 5.08 and 6.32, showing that most samples were slightly acidic in nature and pH values near neutral, indicating dilution. The results obtained from pycnometer revealed that only sample A which is directlly collected from the Bapuna Alcobrew Pvt. Ltd. distillery (42.72% v/v) matched close to the labelled ethanol content of 42.8%. In contrast to this, other examined samples, showed significantly reduced ethanol content in the range of 25.37-41.74% v/v (~40% dilution). UV-Visible spectroscopy shows that in the diluted 'Desi Sharab' samples (obtained in pycnometer analysis) the absorbance for caramelized sugar was higher than standard 'Desi Sharab' sample. These results establish that the combination of pycnometry and UV-Visible spectrophotometry provides reliable, cost-effectiv, and non-destructive approach for routine monitoring of liquor authenticity.

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Ethanol, Country-made liquors, Desi Sharab, Dilution, pycnometer, UV-Visible Spectrophotometry

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Abstract Rapid urbanization and population growth have significantly increased the volume of municipal solid waste, placing pressure on existing waste management systems [1], [13]. Efficient segregation and timely collection of wet and dry waste are essential for sustainable urban development. However, conventional waste management practices rely heavily on historical averages and manual estimation, leading to inefficiencies in collection planning and resource utilization. This paper proposes EcoForecast AI, an Artificial Intelligence-based predictive analytics framework designed to anticipate wet and dry waste generation patterns. The framework analyzes historical waste data, seasonal variations, population dynamics, and consumption behavior to forecast future waste quantities. By integrating machine learning models with predictive analytics, the proposed system enables proactive waste collection planning, optimized resource allocation, and improved segregation strategies. Furthermore, Power BI is utilized as a dynamic visualization and reporting tool within the framework. By creating interactive dashboards, Power BI allows municipal authorities to monitor real-time waste generation trends, identify high-risk areas, and evaluate the effectiveness of collection strategies. The visualizations also support scenario analysis, enabling stakeholders to simulate the impact of policy changes, population growth, or seasonal variations on waste generation. The combination of AI-based forecasting with Power BI's intuitive dashboards enhances transparency, informed decision-making, and citizen engagement in smart waste management initiatives. Experimental evaluation using simulated municipal datasets demonstrates improved prediction accuracy, reduced forecasting error, and enhanced decision-making capabilities. EcoForecast AI, complemented by Power BI's visualization capabilities, offers a scalable and intelligent solution for smart and sustainable urban waste management systems.

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Keywords: Predictive analytics, Artificial Intelligence, Waste management, Wet waste, Dry waste, Smart cities, Power BI, Data visualization, Decision support systems, Sustainable urban planning.

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Yoga, an age-old discipline based in Indian subcontinental wisdom, provides significant insights into promoting social cohesion. In addition to its well-known psychological and physical advantages, yoga has unrealized potential for fostering compassion, empathy, and a sense of community among people. The aim of this review is to examine the theoretical underpinnings, empirical evidence, and practical applications of the concept and Practices of Yoga Traditions in promoting social cooperation by synthesizing knowledge from classical yogic texts with current research. This text explores the theoretical foundations of yoga in fostering social collaboration, which include the yama and niyama, the interconnection between the mind and body, and the development of compassion and empathy. This study presents empirical evidence that demonstrates the effectiveness of yoga practices in improving social collaboration. It also discusses practical ways in which yoga may be used to promote social cooperation, including implementing yoga programmes in schools, organising community outreach projects, and using yoga as a tool for resolving interpersonal conflicts. This thorough analysis highlights the transformational potential of yoga as a catalyst for societal well-being and harmonious relationships by drawing on qualitative data and academic literature. The synthesis of traditional knowledge with modern outcome based scientific approach may be vital & beneficial for the entire society.

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Yoga, Social Cooperation, Social Behavior, Ancient Wisdom, Traditional Knowledge.

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Creative Commons Attribution 4.0 and The Open Definition

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The integration of technology into Human Resource Management (HRM) has revolutionized how organizations recruit, train, evaluate, and retain their workforce. This article, based on a detailed MBA research project conducted at Hajee A.P. Bava & Co. Constructions Pvt. Ltd. (HAPBCO), Hyderabad, explores how digital transformation enhances HR operations, improves efficiency, and reshapes employee engagement in the construction and engineering sector. Supported by extensive literature and practical findings, the study underscores how technology not only streamlines HR processes but also aligns them strategically with organizational goals.

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The Evolving Role of Technology in Human Resource Management: Insights from HAPBCO's HR Practices

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