International Journal of Engineering Science & Humanities
Title: International Journal of Engineering, Science & Humanities ISSN: 2250-3552 (Online) Frequency: Quarterly (4 issues) Publisher: Publication and Advance Research Welfare Society Publication Place: Hisar, Haryana Publisher Email: ijeshjournal@gmail.com Editor-in-Chief: Dr. Anjali Khokhar Copyright: Remain Journal Starting Year: 2011 Main Subject: Multidisciplinary Language: English,Hindi Publication Format: Online (Open Access) Website: https://www.ijesh.com Impact Factor: 8.3
Publication and Advance Research Welfare Society
THE IMPACT OF JUDICIAL REVIEW ON DEMOCRATIC GOVERNANCE
Judicial review—the power of courts to examine the constitutionality of legislative and executive actions—is widely regarded as a cornerstone of modern constitutional democracies. By enabling an independent judiciary to invalidate laws and governmental measures that violate constitutional norms, judicial review seeks to protect fundamental rights, uphold the rule of law, and maintain the balance of powers among state institutions. This paper examines the conceptual foundations, historical evolution, and practical impact of judicial review on democratic governance, with particular reference to comparative experience and the Indian constitutional framework. It argues that judicial review functions as both a constraint and a support for democracy: it constrains majoritarian power when it threatens constitutional values, yet it also reinforces democratic legitimacy by ensuring that state action remains within the bounds of a higher law. At the same time, the paper engages critically with concerns about judicial activism, counter-majoritarian difficulty, and institutional overreach, proposing criteria for principled and democracy-enhancing exercise of review powers.
Copyright (c) 2026 International Journal of Engineering Science & Humanities https://creativecommons.org/licenses/by/4.0
Assessing the performance of the Public Distribution System in Haryana: A District-Level Study
This paper presents an empirical evaluation of food security status in Haryana through the lens of socio-demographic characteristics and Public Distribution System (PDS) outreach. Using a primary sample of 480 households across four districts of Haryana (Charkhi Dadri, Hisar, Nuh, Panchkula), the study explores the effectiveness of ration card distribution, PDS accessibility, and the socio-economic vulnerabilities affecting food sufficiency. The analysis integrates with percentage and frequency distributions, categorized by age, gender, income, caste, and other demographic traits. Findings reveal that while PDS coverage is nearly universal, nearly 20% of households still face hunger and insufficient meals, emphasizing the need for enhanced entitlements and nutritional diversity. The paper concludes with policy suggestions aimed at improving targeting efficiency, household-level adequacy and improve transparency in the delivery of rations etc.
INTEGRATING INDIAN KNOWLEDGE SYSTEMS IN EDUCATION: PERSPECTIVES FROM NEP 2020
The Indian Knowledge System (IKS) represents a rich intellectual tradition that has evolved in India over centuries. Rooted in ancient philosophical thought, cultural practices, and indigenous scientific understanding, it offers valuable perspectives for holistic learning and sustainable development. In recent years, there has been growing interest in integrating traditional knowledge with contemporary education, particularly with the introduction of the National Education Policy (NEP) 2020, which emphasizes the inclusion of Indian knowledge traditions in curricula and pedagogical practices. The integration of IKS in modern education can promote value-based learning, critical thinking, environmental awareness, and cultural understanding among learners. This paper examines the conceptual foundations of the Indian Knowledge System and its relevance in contemporary education while highlighting the role of NEP-2020 in encouraging its integration within the educational framework. The discussion also emphasizes that the incorporation of Indian Knowledge Systems in education can contribute to holistic learning by promoting interdisciplinary thinking, ethical values, and awareness of sustainable living practices. Furthermore, the paper highlights the importance of curriculum development, teacher preparation, and institutional support for the effective integration of traditional knowledge systems in modern education.
An Empirical Analysis of Leadership Practices in FMCG Evidence from Hindustan Unilever and Colgate-Palmolive
Paper emphasises that leadership practices in the FMCG sector significantly influence organisational performance, innovation, and employee engagement, forming the central focus of this study. The research provides an empirical analysis of leadership practices in Hindustan Unilever Limited and Colgate-Palmolive using secondary data from academic literature, corporate reports, and industry sources published after 2015. The study adopts a comparative approach to examine leadership styles, strategic orientation, and their impact on organisational outcomes. Findings indicate that Hindustan Unilever employs a decentralised and diversification-driven leadership model, whereas Colgate-Palmolive adopts a more centralised and product-focused approach. Both organisations demonstrate strong alignment between leadership practices and business strategy, contributing to sustained competitive advantage. The study highlights the role of transformational and strategic leadership in navigating market complexities and driving long-term growth in the FMCG sector.
Awareness-Driven Determinants of Methane Mitigation Practices in Rice–Livestock Farming Systems of Central India: Implications for Sub-National Climate Governance
Background: Agricultural methane emissions represent a critical yet understudied component of India's greenhouse gas inventory, with limited empirical evidence linking farmer awareness to actual mitigation behaviors at district scales. This study examines the role of farmer awareness and attitudes as determinants of methane emission practices across rice cultivation, livestock rearing, and agricultural waste management in Narmadapuram district, Madhya Pradesh. Methods: A cross-sectional mixed-methods design was employed with 600 farmers across three development blocks (Pipariya, Bankhedi, Sohagpur). Validated scales (Cronbach's α: 0.76–0.83) quantified methane-related practices. Awareness and attitudes were measured using a 40-item Likert instrument. One-way ANOVA and multiple linear regression analyzed relationships between awareness levels and practice scores. Institutional perspectives were gathered from 43 officials and analyzed thematically. Results: Overall, 51%, 56%, and 62% of farmers adopted high methane-emitting practices in rice cultivation, livestock rearing, and waste management, respectively. Farmer awareness was low in 63% of respondents, with only 8% demonstrating high awareness. ANOVA revealed significant differences in practice scores across awareness categories for all three domains (p<0.001). Regression analysis demonstrated that livestock practices were the strongest predictor of awareness (β=0.574), followed by waste management (β=0.289) and rice cultivation (β=0.125), with the model explaining 77% of variance (R²=0.77). Block-wise analysis identified Bankhedi as the highest emission block and Sohagpur as the lowest. Institutional assessment revealed weak coordination (60%), low technical capacity (59%), and irregular monitoring (63%). Conclusion: Farmer awareness is a powerful determinant of methane mitigation practice adoption, with livestock management serving as the most influential pathway for enhancing climate literacy. The strong awareness–practice linkage underscores the need for integrated capacity-building interventions. Block-specific emission patterns and institutional fragmentation necessitate spatially targeted, coordinated governance responses aligned with India's Nationally Determined Contributions and State Action Plan on Climate Change.
“An Analysis of Farmers’ Awareness of Financial Schemes in the Agricultural Sector: Evidence from Haryana”
This study examines the level of awareness among farmers in Haryana regarding financial schemes in the agricultural sector and evaluates the effectiveness of existing outreach and financial inclusion initiatives. Agriculture remains a vital component of the Indian economy, and access to institutional financial support is essential for enhancing productivity and ensuring sustainable rural development. The study is based on primary data collected from 588 farmers using a structured questionnaire and employs descriptive statistics along with the Chi-Square test for analysis. The findings reveal that while a significant proportion of farmers possess basic awareness of financial schemes such as agricultural loans and credit facilities, their understanding of key aspects like eligibility criteria, application procedures, and benefits remains partial and inconsistent. A considerable number of farmers fall under the category of moderate or incomplete awareness, highlighting gaps in financial literacy and information dissemination. Additionally, structural challenges such as bureaucratic delays, complex documentation, and collateral requirements further restrict access to these schemes. The results also indicate that awareness levels are not uniformly distributed and are influenced by socio-economic and informational factors. The study concludes that improving awareness through targeted education programs, simplified procedures, and effective communication strategies is crucial for enhancing the utilization of agricultural financial schemes and achieving inclusive rural development.
Development of Physical and Digital Resources in Medical College Libraries of Indore
This study provides a comprehensive investigation into the growth and development of medical college libraries in Indore, Madhya Pradesh, during the period from 2011 to 2020. The primary objective is to evaluate how these libraries have transformed in response to the evolving academic and informational needs of students, faculty, and researchers in the medical field. The study emphasizes the changes in infrastructure, library resources, and services offered over the decade, highlighting how advancements in technology and shifts in educational practices have influenced library operations. a key focus is the expansion of library collections, both physical and digital, and how effectively these collections support academic and clinical learning. The role of Information and Communication Technology (ICT) is critically analyzed, particularly its integration into library services such as online databases, e-journals, digital catalogs, and remote access platforms. The adoption of ICT has significantly altered the traditional functions of libraries, making them more dynamic and accessible.the research also assesses current infrastructure, including physical spaces, reading facilities, internet connectivity, and the availability of trained library staff. It investigates whether these facilities align with the needs of a modern medical education environment. Moreover, the study explores the information-seeking behavior of library users—how they access and utilize resources, the challenges they face in finding relevant information, and their expectations regarding library improvements. by understanding user behavior and needs, the study offers insights into how libraries can become more user-centric. The analysis also projects future requirements to ensure medical college libraries remain relevant and effective. These include upgrading digital infrastructure, expanding access to international databases, and improving staff training. The findings serve as a valuable guide for library administrators, academic institutions, and policymakers committed to strengthening library systems to support medical education and research excellence in Indore.
From Bookshelves to Cloud Systems: The Evolution of Libraries in the Information Age
In this study, the author is investigating the changing of the libraries as a traditional physical repository to current cloud based and digital information system during the information age. The broad aim is to take a look at how technological advancements have transformed libraries in terms of their structure, services, and accessibility. It highlights how the physical library and manual cataloging system is being changed to cloud storage, integrated digital orders, and the remote access system where the user can continuously access information wherever they are at any time. The research utilizes the qualitative approach to research, i.e. a critical analysis of literature, discussion of existing models of digital libraries and case studies of organizations that have successfully applied the use of cloud-based systems. Also, the surveys and secondary data sources are considered to be able to understand the user behavior and technological acceptance in libraries. As per the study, information technology has also enhanced efficiency, accessibility, and scalability of libraries to a great extent. Also, cloud computing will complement these benefits by allowing shared access, providing data security, providing real-time updates and offering low-expenses storage facilities. Nevertheless, there are still the problems of the digital divide, privacy issues, and the necessity of appropriate technological infrastructure, which remain significant. Change of the old-fashioned usage of libraries to the cloud-driven library can be considered as a revolutionary development in the process of managing information. Days of libraries as mere physical space are long gone; they have now become a computerized space. The further development should be aimed at facilitating the services offered, enhancing cybersecurity and overall sustaining technological preparedness to address the changing needs of the users.
Vision-Based Smart Meter Reading and Energy Consumption Analytics Using AI
In this work, a deep learning-based energy consumption predictor with Long Short-Term Memory (LSTM) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) network models are applied to time-series data. This is aimed at enhancing the accuracy of prediction by ensuring that both short-term variation and the temporal dependencies of electricity demand are in the model. Kaggle provides public datasets such as hourly energy consumption data, which are analyzed. The cleaning, chronological sorting, Min-Max scaling, and sliding window sequence generation (24-time steps) preprocess the data. To extract temporal patterns, including trends and seasonality, feature engineering and exploratory data analysis are used. Training of models is done with the help of 80: 20 time based split to avoid the data leakages and it is also evaluated with the help of Mean Squared error (MSE), root mean squared error (RMSE) and loss curves. Experimental ablation reveals that LSTM has an ultimate training loss of approximately 0.000175 and validation loss of approximately 0.000060 whereas CNN-LSTM has 0.000176 and 0.000058 accordingly. CNN-LSTM model has a little better performance because it has an enhanced feature extraction and noise reduction. Both models demonstrate stable convergence, low overfitting, and good generalization. Additionally, 24 hours recursive forecasting creates smooth and realistic forecasts that are consistent with the historical trends. On the whole, the suggested CNN-LSTM method is a more appropriate solution to short-term energy consumption forecasting due to its higher accuracy and strength, which is why it can be used in smart grid and energy management.
Deep Learning Framework for ECG and EEG Signal Classification in Smart Healthcare Systems
Biomedical signal analysis is a key component of current healthcare systems for the diagnosis of cardiovascular and neurological diseases through Electrocardiogram (ECG) and Electroencephalogram (EEG) signals. This study aims to build an automatic deep learning-based classification system for ECG and EEG signals to facilitate the early diagnosis of cardiovascular and neurological disorders and to enable real-time monitoring in health care. Our approach employs two publicly accessible biomedical datasets, the MIT-BIH Arrhythmia dataset for ECG signals and the Epileptic Seizure Recognition dataset for EEG signals. Data preprocessing includes data cleaning, normalization, reshaping, exploratory data analysis, and data splitting into training, validation, and testing sets. We have developed multiple deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Hybrid CNN-LSTM for biomedical signal classification. Evaluation metrics of accuracy, precision, recall and F1-score were used to assess model performance. The comparison of the results demonstrate that the CNN model has the highest accuracy of 0.98, Hybrid CNN-LSTM has 0.9767 accuracy, LSTM has 0.9751 accuracy and the RNN has 0.85 accuracy. The outcomes indicate that deep learning models can classify ECG and EEG signals and can be used for real-time embedded systems in health monitoring.
Use of photosensitive compounds in solar cells for generation of electrical energy
This need to ensure that more people can utilize the renewable energy has increased research into effective conversion technologies of the solar energy. This paper focuses on the use of the photosensitive compounds in dye-sensitized solar cell (DSSC) in electrical energy generation. Three types of photosensitive materials that were used to measure the photovoltaic activity on which the experimental research design was adopted are as follows: the non-metallic anthocyanin dyes, organic dye sensitizers and the metal complex ruthenium-based dyes. The fluorine-doped tin oxide glass substrates were applied to produce dye-sensitized solar cells using the application of nanostructured TiO 2 photoanode and iodide/triiodide electrolyte. The photovoltaic features were measured under standard conditions of illumination (AM 1.5, 100mW/cm 2). The parameters were open-circuit voltage, short-circuit current density, fill factor, power conversion efficiency and others. The result showed that ruthenium based dye had the highest value of light harvesting efficiency of 86 and power conversion efficacy of 8.7 at which organic dye had 6.9 percent efficiency, and lastly, natural dye had a 4.1 percent efficiency. The other feature that reflected that ruthenium dyes were stable enough to retain up to 92 percent of efficacy remained despite being exposed to light (120 hours) was the stability analysis. It offers the worth of the photosensitive materials in improving the efficiency of the solar cells and completes the design of the better-performing dyesensitiveolar technologies.
Power Quality Improvement Performance of PV and Grid Connected System
This paper presents the design and simulation of a hybrid renewable energy system integrating solar photovoltaic (PV), battery storage, and fuel cell subsystems for improved power quality and reliable energy supply. The proposed system is developed in the MATLAB/Simulink environment, where the PV array generates DC power that is regulated using a DC/DC boost converter with Maximum Power Point Tracking (MPPT) to ensure maximum energy extraction. The battery subsystem, connected through a bidirectional DC–DC converter, enables efficient energy storage and management by supporting both charging and discharging operations based on system demand. Additionally, a fuel cell subsystem is incorporated as a backup power source to enhance system reliability during low renewable generation conditions. All energy sources are integrated through a common DC-link, which acts as a voltage stabilization unit. The DC-link output is then converted into AC using a voltage source inverter (VSI) and supplied to the load or grid through appropriate filtering components to reduce harmonics and improve power quality. Advanced control strategies are implemented to regulate DC-link voltage, manage energy flow, and ensure stable system operation under varying conditions. The simulation results demonstrate that the proposed system achieves stable voltage regulation, efficient energy management, reduced harmonic distortion, and improved dynamic response, making it suitable for modern grid-connected and standalone renewable energy applications.
Enhancing Fault Protection in Motor-Generator Pair Systems for Renewable Energy Applications
Renewable energy systems, particularly those integrating motor-generator pairs, play a pivotal role in the transition towards sustainable energy solutions. However, the reliability of these systems is often compromised by faults and disturbances in the electrical grid. This study presents a comprehensive analysis and implementation of fault ride-through (FRT) protection for motor-generator pair systems within renewable energy frameworks. The proposed protection scheme aims to enhance system stability and continuity of operation during grid faults, thereby minimizing downtime and potential damage to equipment. The methodology involves the development of advanced control algorithms that detect and respond to voltage sags, swells, and other transient disturbances. These algorithms are embedded within a real-time simulation environment to validate their effectiveness under various fault conditions. Key performance indicators, such as voltage stability, current harmonics, and system response time, are meticulously analyzed to ensure robust protection. Experimental results demonstrate that the implemented FRT protection scheme significantly improves the resilience of motor-generator pair systems. The system maintains operational integrity during fault conditions, with minimal disruption to the overall power generation process. Additionally, the adaptive nature of the control strategy allows for seamless integration with existing renewable energy infrastructures, making it a viable solution for enhancing grid reliability. This research contributes to the field of renewable energy by providing a scalable and efficient approach to fault management, promoting the widespread adoption of motor-generator pair systems in modern energy grids. Future work will focus on the integration of machine learning techniques to further optimize the protection mechanisms and adapt to evolving grid conditions
Impact of Heartfulness Meditation on Stress Response and Decision-Making Ability among Higher Education Students: An Empirical study
In the contemporary academic milieu, students are increasingly subjected to multifaceted stressors that significantly impair their cognitive functioning and decision-making competencies. The present study endeavors to examine the efficacy of Heartfulness Meditation as a psychophysiological intervention in modulating stress responses and enhancing decision-making ability among higher education students. Employing a quantitative research design, data were collected from a sample of 120 university students using standardized scales. The findings reveal a statistically significant reduction in perceived stress levels and a marked improvement in rational decision-making post-intervention. The study underscores the transformative potential of meditative practices in fostering cognitive clarity and emotional regulation within academic environments.
Sushant Jha, Ms. Manisha Chouhan, Dr. Samir Thakur
Design and Development of a Solar-Powered SMPS Battery Charger with MPPT for Efficient Energy Extraction under Variable Environmental Conditions
This paper presents the design and development of a solar-powered switched-mode power supply (SMPS) battery charger integrated with Maximum Power Point Tracking (MPPT) for efficient energy extraction under variable environmental conditions. The proposed system architecture incorporates a photovoltaic (PV) module as the primary energy source, supported by environmental sensing units that monitor solar irradiance and temperature in real time. These inputs enable the MPPT controller, implemented using Perturb and Observe (P&O) and Incremental Conductance algorithms, to continuously track and operate the PV system at its maximum power point, thereby maximizing energy utilization. A microcontroller-based control unit processes sensor data and generates pulse-width modulation (PWM) signals to regulate a DC–DC converter configured as an SMPS. This ensures efficient voltage conversion and stable power delivery to the battery charging unit. The system employs a constant current–constant voltage (CC–CV) charging technique to achieve safe, reliable, and optimized battery charging while extending battery lifespan. Voltage and current sensors provide continuous feedback for closed-loop control, enhancing system responsiveness and accuracy. The harvested energy is stored in rechargeable batteries, which supply power to low-power loads such as portable electronics and IoT devices during periods of low or no solar availability. Additionally, a monitoring and data logging module records system performance metrics, including efficiency, response time, and tracking accuracy, enabling detailed analysis and optimization. Experimental evaluation under varying irradiance and temperature conditions demonstrates that the proposed system achieves improved energy conversion efficiency, faster MPPT response, and enhanced reliability compared to conventional solar charging systems. Overall, the proposed architecture offers a cost-effective, scalable, and energy-efficient solution for sustainable low-power applications.
Prevalence of Attention Deficit Hyperactivity Disorder - ADHD among Young Individuals - An Observational Study
Background: Attention deficit hyperactivity disorder-ADHD is a developmental nervous behavioural disorder mostly diagnosed in childhood and rarely in adults. Characteristic clinical features in adults are mainly, hyperactivity, attention deficit and impulsiveness instability in interpersonal relations and alcohol abuse which can cause poor occupational, academic and social performance in future. As ADHD is usually undiagnosed in adults because of its poor knowledge of the symptoms. Need of study is to see the prevalence of ADHD among individuals, as ADHD youth are at high risk of a wide range of adverse psychiatric outcomes. Methodology: This observational study was performed in 307 young female & male of all the fields aged between 18-24, according to Newman's classification. The subjects were screened for ADHD by Adult ADHD Self- Report Scale (ASRS) which has two parts- inattentive and hyperactive/impulsive. This questionnaire was sent through Google Form. Results: Statistical analysis was done using Microsoft Excel l0th version. According to ASR Scale among 307, 5.53 % were highly likely to have inattentiveness and 6.64% were highly likely to have hyper activeness and impulsiveness. In 207 females 6.28%, 5.79% highly likely to be inattentive, hyperactive /impulsive and in 100 males 9%,4%were highly likely to be hyperactive/impulsive, inattentive. Conclusion: Total prevalence of Adult ADHD is 11.4% Among all the participants, prevalence of ADHD for Part-A (Inattentive) is 5.53% and Part-B (Hyperactive/Impulsive) is 6.64% Out of total, 0.977% got mixed symptoms of ADHD.
Efficient Noise-Aware Image Compression using DWT and Multi-Level Block Coding Technique
Efficient image compression in the presence of noise remains a significant challenge in modern image processing applications, particularly in medical imaging, remote sensing, and multimedia transmission. This paper proposes a noise-aware image compression framework that integrates Discrete Wavelet Transform (DWT) with a multi-level block coding strategy to achieve high compression efficiency while preserving image quality. The proposed method first applies DWT to decompose the input image into multiple frequency sub-bands, enabling effective separation of noise components from essential image features. A noise estimation and adaptive thresholding mechanism is then employed to suppress noise in high-frequency coefficients without significantly affecting important structural details. Subsequently, a multi-level block coding technique is introduced, where the transformed coefficients are partitioned into variable-sized blocks based on their energy distribution and perceptual importance. High-energy blocks, containing critical image information, are encoded with higher precision, while low-energy blocks are aggressively compressed to reduce redundancy. This adaptive block-level encoding improves compression ratio while maintaining visual fidelity. The proposed approach also incorporates entropy coding to further enhance compression performance. Experimental results demonstrate that the proposed method achieves superior performance in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and compression ratio compared to conventional DWT-based compression techniques. The algorithm shows robustness against different types of noise, including Gaussian and impulse noise, making it suitable for real-world applications. Overall, the integration of DWT with multi-level block coding provides an efficient and scalable solution for noise-aware image compression with improved reconstruction quality and reduced storage requirements.
Therapeutic and Pharmacological Potential of Azadirachta indica
Azadirachta indica (neem) is a widely recognized medicinal plant with extensive use in traditional systems such as Ayurveda. The present study highlights the therapeutic and pharmacological potential of neem based on its rich composition of bioactive compounds, including azadirachtin, nimbin, nimbidin, and quercetin. Various parts of the plant—leaves, bark, seeds, and oil—exhibit significant biological activities such as antimicrobial, anti-inflammatory, antioxidant, antidiabetic, anticancer, and immunomodulatory effects. Neem has demonstrated effectiveness against a broad spectrum of pathogens, supports blood glucose regulation, and protects against oxidative stress–induced cellular damage. Additionally, its role in promoting skin health, wound healing, and oral hygiene has been well documented. Despite its promising pharmacological profile, concerns regarding toxicity at higher doses necessitate controlled usage and further clinical validation. Overall, Azadirachta indica represents a valuable natural resource with significant potential for the development of novel therapeutic agents in modern medicine.
Arun Ahirwar, Abhay Suryawanshi, Mohd Danish Sheikh, Ms. Neelam Yadav, Dr. Vaishali Rathi
Minimum THD of Bidirectional PFC Rectifier with Vehicle to Grid Application
In Vehicle-to-Grid (V2G) applications, maintaining high power quality during bidirectional energy transfer is a critical requirement, particularly in terms of minimizing Total Harmonic Distortion (THD) and achieving near-unity power factor. This paper presents a bidirectional Power Factor Correction (PFC) rectifier integrated with an H-bridge inverter to achieve minimum THD in both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. The proposed system utilizes an input inductor for current shaping and a DC-link capacitor for voltage stabilization, while the H-bridge inverter enables controlled bidirectional power flow between the electric vehicle battery and the utility grid. The control strategy is designed to ensure that the input/output current remains sinusoidal and synchronized with the grid voltage, thereby significantly reducing harmonic distortion. Advanced PWM techniques, along with feedback control (such as PI or intelligent controllers), are employed to regulate switching actions and maintain system stability under varying load and grid conditions. The use of the H-bridge inverter enhances waveform quality and provides better control over voltage and current profiles, leading to improved efficiency and reduced switching losses. Simulation and experimental results demonstrate that the proposed topology achieves a substantial reduction in THD compared to conventional rectifier systems, while maintaining high efficiency and robust dynamic performance. The system also ensures compliance with grid standards, making it suitable for practical V2G implementation. Overall, the integration of a bidirectional PFC rectifier with an H-bridge inverter offers an effective solution for achieving low THD, improved power quality, and reliable energy exchange in next-generation smart grid and electric vehicle applications.
Real Time Object Detection Classification Using Cnn Algorithm
Real-time object detection and classification have become essential in various applications such as surveillance, autonomous vehicles, and smart systems. This paper presents a robust approach for real-time object detection and classification using Convolutional Neural Networks (CNN). The proposed method leverages deep learning techniques to automatically extract spatial features from input images and accurately identify objects within dynamic environments. A pre-trained CNN model is fine-tuned and integrated with a detection framework to achieve high accuracy and low latency. The system is evaluated on standard datasets, demonstrating improved performance in terms of precision, recall, and processing speed compared to traditional methods. The results indicate that CNN-based models provide an efficient and scalable solution for real-time object detection tasks in complex scenarios.
Prashant Kumar Tripathi, Ms. Namrata Sahayam
This paper looks at the dynamic nature of risk management in digital banking and how technological changes have transformed the old banking risks besides creating new weaknesses. The study is a qualitative and literature-based approach in order to examine the major risk domains such as cybersecurity risk, operational risk, credit risk, and systemic risk in digital banking scenarios. The results have shown that although digital banking leads to improved efficiency, accessibility and financial inclusion, it also puts users more at risk of cyber threats, data hacking, and technology reliance. The paper also mentions the increasing importance of artificial intelligence, big data analytics and regulatory measures in risk detection and reduction. But issues like algorithmic bias, regulatory loopholes and system failures still happen. The study highlights the importance of integrating and adaptability in risk management actions that are technology-oriented and in which innovation and security balance in a manner that brings stability in terms of finances in the long run.
Development of an Integrated Decision Support Framework for Sustainable Municipal Solid Waste Management using Multi Attribute Decision-Making Techniques
The exponential growth of urban populations has transformed Municipal Solid Waste Management (MSWM) into a multidimensional challenge that necessitates the reconciliation of conflicting technical, economic, and environmental objectives. This research presents the development of an integrated decision-support framework designed to facilitate the selection of sustainable waste management strategies through the application of Multi-Attribute Decision-Making (MADM) techniques. The study utilizes a hybrid methodology, employing the Analytical Hierarchy Process (AHP) to establish the relative importance of sustainability criteria and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank four primary management alternatives: Baseline Landfilling (A1), Material Recovery and Composting (A2), Waste-to-Energy (A3), and an Integrated Sustainable Resource Management system (A4). Evaluated across five core dimensions—Technical Reliability, Economic Viability, Environmental Impact, Social Acceptance, and Resource Recovery Potential—the framework incorporates high-resolution waste characterization data and longitudinal life-cycle cost simulations. Results indicate that the Integrated Sustainable Resource Management system (A4) emerged as the most resilient alternative, achieving a closeness coefficient of 0.821, primarily due to its superior balance between energy recovery and greenhouse gas mitigation. The study reveals that environmental impact remains the most critical decision-making attribute, carrying a priority weight of 34.2%. Furthermore, the integrated approach demonstrated a potential 81.2% reduction in Global Warming Potential compared to conventional landfilling. This research provides municipal authorities and urban planners with a transparent, mathematically rigorous tool to "future-proof" waste infrastructure, bridging the gap between theoretical decision science and the practical requirements of the circular economy.
Machine Learning-Based Energy Optimization in Wireless Sensor Networks for Smart Cities
Wireless Sensor Networks (WSNs) form the foundational infrastructure for smart city applications, enabling real-time data collection and monitoring across urban environments. However, the energy-constrained nature of sensor nodes presents significant challenges to network longevity and operational sustainability. This paper presents a comprehensive analysis of machine learning (ML) approaches for energy optimization in WSNs tailored for smart city applications. Through systematic review of contemporary literature and comparative analysis of ML techniques including Reinforcement Learning (RL), Supervised Learning, and Swarm Intelligence, we identify optimal strategies for energy-efficient routing, cluster head selection, and data aggregation. Our findings demonstrate that hybrid ML models achieve 30-45% improvement in network lifetime compared to traditional protocols, with Reinforcement Learning-based approaches showing particular promise for dynamic smart city environments. The paper contributes a taxonomy of ML-enabled energy optimization techniques, quantitative performance comparisons, and practical implementation guidelines for smart city WSN deployments.
Relationship Between Home Environment and Preferred Learning Styles of Secondary School Students
The home environment serves as the primary context for learning during childhood and adolescence, shaping not only academic outcomes but also the ways in which students prefer to learn. This research paper examines the relationship between various dimensions of home environment—including parental involvement, socioeconomic status, physical learning space, and family emotional climate—and the preferred learning styles of secondary school students. Drawing upon Bronfenbrenner's ecological systems theory and existing empirical literature, the paper argues that home environment factors significantly influence the development of visual, auditory, kinaesthetic, and multimodal learning preferences among adolescents. Key findings indicate that parental engagement and provision of diverse learning resources correlate with more flexible, multimodal learning styles, while restrictive or resource-poor home environments tend to Favor more narrowly defined learning preferences. The paper identifies research gaps, including the need for longitudinal studies and cross-cultural investigations, and concludes with implications for educators, parents, and policymakers. Recommendations emphasize the importance of fostering home environments that recognize and accommodate diverse learning styles to support adolescent academic development.
India’s Maritime Strategy in Response to China’s Expanding Footprint in the Indian Ocean Region.
This paper looks at the maritime strategy India uses in reacting to the increased presence of China in the Indian Ocean Region (IOR). The increasing influence of China in the form of port development, naval positions, and Belt and Road Initiative has changed the regional geopolitical landscape and brought security and influence issues. The study is based on a qualitative methodology that employs secondary data to examine strategic expansion of China and the multidimensional reaction of India such as modernization of its navy, its strategic affairs, and its Diplomacy including SAGAR and the Indo-Pacific framework. The analysis indicates that China is far ahead in terms of maritime advantages, but India is also making strides to be at the forefront by forming alliances and developing national interests. The study concludes that the IOR is transforming into a sphere of strategic competition where there is the need to maintain equilibrium between cooperation and competition to provide stability and security.
An Evaluation of Technical, Economic and Environmental Performance of Bitumen Emulsion as A Sustainable Alternative to Cutback Bitumen for Prime and Tack Coats in Road Construction
The global push for sustainable infrastructure has necessitated a critical re-evaluation of traditional road construction materials, particularly the transition from solvent-based cutback bitumen to aqueous bitumen emulsions. This research provides a comprehensive comparative analysis of the technical, environmental, and economic performance of these binders when utilized for prime and tack coat applications. Utilizing a hybrid methodology, the study integrates laboratory-based experimental testing—specifically focusing on penetration depth and interface shear strength (ISS)—with life-cycle cost analysis (LCCA) and volatile organic compound (VOC) emission modeling. The technical results indicate that while cutback bitumen (MC-30) maintains a marginal 29% advantage in penetration depth within low-porosity granular bases, cationic bitumen emulsions (CSS-1h and CRS-2) demonstrate superior performance in curing efficiency and mechanical bonding. Specifically, bitumen emulsions achieved traffic-ready states in approximately 3.5 hours compared to the 24-hour window required for cutbacks, while interface shear strength was found to be up to 28% higher in emulsion-treated samples. From an environmental perspective, the adoption of emulsions resulted in a 97.4% reduction in VOC emissions and an 85.6% decrease in application energy requirements. Economic modeling further supports this transition, revealing a 33.9% reduction in total unit costs per metric tonne when accounting for material procurement, heating, and construction delay variables. The study concludes that bitumen emulsion is not merely an eco-friendly alternative but a superior engineering binder that enhances pavement durability while meeting stringent modern sustainability mandates.
Suman Kumar Shrestha, Mr. Saumitr Sharma
Future-Proofing Adaptive Frameworks: Integrating Green Metrics and Risk-Mitigation with Balancing Structural Integrity and Eco-Efficiency in Unpredictable Soils
Throughout the history of human civilization, tunnels have served as a vital component for advancement and sustainability, evolving from primitive shelters and water management systems to sophisticated modern networks for transportation, mining, and urban infrastructure. Despite these technological leaps, tunneling remains a high-risk engineering endeavor due to the inherent unpredictability of subsurface geological formations, which dictate the design, construction methodology, and total project expenditure. Because tunnel linings provide the primary structural support against these varying geological loads, they represent a substantial portion of the overall construction cost, particularly in long or large-diameter projects. This research explores innovative engineering strategies aimed at optimizing lining design to achieve significant material and cost savings through four primary technical contributions. The first strategy focuses on geometric optimization by implementing variable lining thickness that aligns with bending moment distribution, ensuring structural volume is utilized only where stress is highest. This is combined with the integration of pressure relief valves to mitigate hydrostatic forces, thereby reducing reinforcement requirements. The second contribution addresses large-diameter tunnels where rock loads are the dominant design factor; here, the research utilizes temporary supports and strategically placed rock anchors to break the structural span of walls and slabs, significantly lowering the steel intensity required for stability. Furthermore, the thesis investigates the economic feasibility of temporary tunnels in various shapes—including D-shaped, horseshoe, and circular profiles—by designing them without traditional reinforcement and relying instead on advanced instrumentation and real-time monitoring systems. Finally, the research addresses the complexities of Tunnel Boring Machine (TBM) operations in urban environments. By developing high-fidelity finite element models to predict soil-structure interaction and pile settlement, the study demonstrates how real-time monitoring can effectively replace expensive and time-consuming ground improvement techniques like jet grouting or micro-piling. Collectively, these methodologies provide a comprehensive framework for reducing concrete and reinforcement volumes while maintaining the highest standards of structural safety and serviceability.
Investors' Perception Towards Post Office Life Insurance Schemes - An Analytical Study (With Special Reference To Indore District)
Despite the expansion of private companies in the life insurance sector, post office insurance schemes still remain a safe and reliable option for a specific class. This study attempts to understand how investors view these schemes, what are their main reasons behind choosing the scheme, and what features they prefer. The objective of the present paper is to analyze the investors' perception towards post office life insurance schemes (Postal Life Insurance - PLI and Rural Postal Life Insurance - RPLI) in Indore district. The research collected responses of 210 investors and analyzed them with the help of statistical tools. The study found that a large number of investors prefer these schemes due to reasons such as security, government guarantee and minimum premium. However, many respondents identified quality of service, lack of publicity and limitations of digital access as weaknesses of the scheme. Data analysis shows that awareness of post office insurance schemes is mainly spread through post office employees, while the role of online mediums is relatively limited. Based on the satisfaction level, it was seen that about three-fourths of the investors are satisfied with the schemes, while the rest expect service improvement. This research suggests that to make post office life insurance schemes more effective, innovation is needed in the promotional strategy, as well as the reach of digital platforms should be strengthened. If modern technological measures are adopted and service quality is improved, then the reach and effectiveness of these schemes can be increased significantly. This study can be helpful in strategic planning for future expansion of post office insurance schemes and can become a useful reference for policy makers, scheme managers and researchers.
Performance Optimization of Francis Turbines in Small Hydro Power Plants Using CFD and Experimental Validation
This study presents the performance optimization of Francis turbines used in small hydro power plants through an integrated approach combining Computational Fluid Dynamics and experimental validation. The baseline turbine exhibited limitations such as non-uniform flow distribution, pressure fluctuations, higher cavitation risk, and reduced efficiency, particularly under part-load conditions. To address these issues, a CFD-based design optimization was carried out by modifying key parameters including runner blade geometry, guide vane angles, and draft tube configuration. The optimized turbine demonstrated a significant improvement in hydraulic efficiency, increasing from 82–86% to 88–92%, while experimental results confirmed efficiency values in the range of 87–91%, showing strong agreement with simulation outcomes. Power output was enhanced with more stable performance under rated conditions. Flow uniformity at the runner inlet improved considerably, leading to reduced hydraulic losses and smoother operation. Pressure distribution across the turbine blades became more uniform, minimizing mechanical stress and improving structural reliability. Furtherore, the optimized design reduced cavitation zones, thereby lowering the cavitation index and enhancing turbine durability. Torque stability was improved, resulting in reduced mechanical vibrations and better operational consistency. The efficiency at part-load conditions increased from 70–75% to 78–85%, with experimental validation indicating values between 76–83%, highlighting improved adaptability under varying flow conditions. In addition, draft tube losses were minimized, contributing to better energy recovery, while optimized guide vane performance enabled improved flow control and regulation. Overall, the results indicate that the proposed optimization approach significantly enhances turbine performance, reliability, and operational lifespan. The close correlation between CFD predictions and experimental findings validates the effectiveness of the methodology, making it a practical and efficient solution for improving the performance of Francis turbines in small hydro power applications.
An Enhanced Ensemble Machine Learning Framework with Explainable AI for Cyber Threat and Network Outlier Detection
Intrusion detection systems (IDS) are a cornerstone of modern cybersecurity infrastructure. Traditional machine learning approaches for IDS—including artificial neural networks (ANNs)—often suffer from class imbalance, limited generalization, and insufficient interpretability. This paper presents an enhanced ensemble machine learning framework that integrates Random Forest (RF), XGBoost, LightGBM, and a Multi-Layer Perceptron (MLP) deep learning classifier with a comprehensive preprocessing pipeline on the CICIDS2017 benchmark dataset. The preprocessing pipeline incorporates duplicate removal, IQR-based outlier clipping, Yeo-Johnson power transformation, standard scaling, and Principal Component Analysis (PCA) retaining 95% of explained variance. Experimental results demonstrate that LightGBM achieves the highest accuracy of 99.81% with an AUC-ROC of 0.9998, Matthews Correlation Coefficient (MCC) of 0.9832, and a training time of only 4.7 seconds. To address model transparency, Local Interpretable Model-agnostic Explanations (LIME) are applied to the best model to provide feature-level decision explanations. Comparative evaluation against a baseline ANN (92% accuracy) and prior state-of-the-art methods confirms the superiority of the proposed framework. These results demonstrate that ensemble methods with principled preprocessing and explainability mechanisms can significantly advance the effectiveness and trustworthiness of cyber threat detection.
A Comprehensive Review of Surface Defect Detection in Manufacturing Using Convolutional Neural Networks
Surface defect detection is a critical component of quality assurance in modern manufacturing, directly influencing product reliability and operational efficiency. With the rise of Industry 4.0, traditional inspection methods have been increasingly replaced by intelligent vision-based systems powered by Convolutional Neural Networks (CNNs). This paper presents a comprehensive review of CNN-based approaches for detecting surface defects across various manufacturing domains, including metal, textile, and electronics industries. It examines key architectures, detection strategies such as classification, object detection, and segmentation, and widely used industrial datasets. The review also highlights recent advancements, including transfer learning, attention mechanisms, and hybrid CNN-Transformer models, which enhance detection accuracy and robustness. Furthermore, it discusses major challenges such as data imbalance, small defect detection, and real-time deployment constraints, while outlining future research directions for scalable and explainable solutions.
A Systematic Review of Machine Learning-Based Disease Diagnosis in Modern Healthcare Systems
Machine learning (ML) has emerged as a transformative approach in modern healthcare systems, significantly enhancing the accuracy and efficiency of disease diagnosis. This systematic review synthesizes recent advancements in ML-based diagnostic models, focusing on their applications across various diseases, including cancer, cardiovascular disorders, diabetes, and neurological conditions. The study evaluates different machine learning techniques such as supervised, unsupervised, and deep learning algorithms, along with their performance metrics, data sources, and clinical applicability. Furthermore, it examines the benefits of ML, including early disease detection, improved decision-making, and cost reduction, while also addressing critical challenges such as data privacy, model interpretability, and lack of standardized datasets. By identifying key research gaps and emerging trends, this review provides valuable insights into the future integration of ML technologies in healthcare, aiming to support researchers and practitioners in developing robust and reliable diagnostic systems.
Brain Tumor Classification from MRI Using InceptionV3 with Squeeze-and-Excitation Attention and Multi-Scale Pooling
Brain tumor classification from Magnetic Resonance Imaging (MRI) is a clinically critical yet challenging task due to substantial intra-class variability and morphological similarity between tumor types. This paper presents IV3-SE-BTCNet, a novel hybrid deep learning architecture that integrates InceptionV3 transfer learning with Squeeze-and-Excitation (SE) attention blocks and multi-scale pooling for robust four-class brain tumor classification (Glioma, Meningioma, Pituitary, and No Tumor). A comprehensive 10-step domain-specific preprocessing pipeline — including ROI cropping, skull stripping, CLAHE contrast enhancement, hybrid denoising, and histogram equalization — is applied to a balanced 7,200-image MRI dataset. The model is trained using a two-phase strategy: Phase 1 (frozen backbone feature extraction, 80 epochs) followed by Phase 2 (selective fine-tuning, 80 epochs). The proposed model achieves a test accuracy of 96.76%, precision of 96.80%, recall of 96.76%, and F1-score of 96.77%, substantially outperforming CNN baselines reported in comparative literature: Custom CNN (92.72%), MobileNetV2 (89.12%), VGG16 (82.28%), and EfficientNetB0 (27.45%). AUC scores exceed 0.993 across all four tumor classes on the test set. These results demonstrate that attention-augmented transfer learning combined with domain-specific preprocessing provides a highly effective and reliable decision-support tool for radiological brain tumor diagnosis.
Optimization of Hybrid Wind–Solar Energy System with Battery Storage for Large-Scale Grid Integration
The increasing penetration of renewable energy sources in modern power systems necessitates efficient and reliable solutions for large-scale grid integration. Hybrid wind–solar energy systems have emerged as a promising approach due to the complementary nature of wind and solar resources. However, their intermittent and variable output poses significant challenges in maintaining grid stability and ensuring continuous power supply. To address these issues, the integration of battery energy storage systems (BESS) along with optimized system design has become essential. This study presents an optimization framework for a hybrid wind–solar energy system integrated with battery storage aimed at large-scale grid applications. The proposed approach focuses on optimal sizing and configuration of system components, including wind turbines, photovoltaic arrays, and battery storage, to achieve maximum efficiency, reliability, and cost-effectiveness. Advanced optimization techniques such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are employed to minimize power fluctuations, reduce operational costs, and enhance system performance. Furthermore, the study incorporates intelligent energy management strategies to ensure effective coordination between generation, storage, and load demand. Simulation results demonstrate that the optimized hybrid system significantly improves power quality, reduces energy curtailment, and enhances grid stability compared to conventional standalone renewable systems. The findings highlight the critical role of optimization and battery storage in enabling the large-scale deployment of hybrid renewable energy systems, contributing to a sustainable and resilient power infrastructure
Climateizing Diplomats Strategy: A Case Study of India – Maternal Women & Children Health
Climate change has become an urgent international issue with extensive consequences not only to the environment but also to human health and international systems of policies. This paper explores the notion of Climateizing Diplomats Strategy with a particular emphasis on India, which brings up the issue of the intersection between climate diplomacy and maternal, women and children health. The study examines the effects of climate change on health both directly and indirectly through the following pathways, which are extreme weather phenomena, food insecurity, water scarcity and spread of infectious diseases. It highlights that women and children are part of the most vulnerable groups based on socio-economic inequalities and unavailability of healthcare services. The research paper also examines how India is evolving in its climate diplomacy, where India is balancing its development agenda and its global climate commitments. It emphasizes the necessity to incorporate health issues, especially maternal and child health, into climate policies and diplomacy. Through the analysis of the climate-health nexus, the study finds that the biggest challenges are inadequate healthcare infrastructure, policy gaps and the absence of inter-sectoral coordination. The paper further suggests strategic policy recommendations such as climate resilient health systems, better nutrition security and better international cooperation. On the whole, the paper presents the argument that health as a component of climate diplomacy is necessary to attain sustainable development and safeguard vulnerable populations. It finds that India has much potential to be a leader in this field with the adoption of inclusive, equitable and health-oriented climate strategie.
Role of Charging/Discharging utilize Batteries to Intermittent Wind-Solar Energy Sources
The intermittent nature of renewable energy sources such as wind and solar presents significant challenges in maintaining a stable and reliable power supply. This study explores the critical role of battery energy storage systems (BESS) in managing the charging and discharging processes to effectively utilize fluctuating wind–solar energy. Batteries act as a buffer by storing excess energy generated during peak production periods and supplying power during low-generation or high-demand conditions, thereby ensuring continuity and stability of the energy system. The paper discusses various charging and discharging strategies, including state-of-charge (SoC) management, depth-of-discharge (DoD) control, and intelligent energy management systems, which optimize battery performance and lifespan. Additionally, advanced control techniques, such as predictive algorithms and machine learning-based optimization, are highlighted for their ability to enhance energy efficiency, reduce power losses, and improve grid integration. The integration of batteries with hybrid wind–solar systems significantly enhances system reliability, power quality, and load balancing, making it suitable for both grid-connected and off-grid applications. Despite challenges such as battery degradation, cost, and thermal management, ongoing advancements in battery technologies and control strategies are expected to further improve system performance. This study concludes that efficient charging and discharging mechanisms are essential for maximizing the utilization of renewable energy and enabling a sustainable and resilient energy infrastructure.
Deep Learning Based Indian Traffic Sign Detection And Recognition System
Traffic sign detection and recognition is a critical component of intelligent transportation systems and autonomous driving, particularly in complex and unstructured environments such as Indian road networks. This study proposes a deep learning-based framework for robust detection and classification of Indian traffic signs using a combination of object detection and Convolution Neural Network (CNN) models. The methodology integrates a real-time detection model to localize traffic signs in input images, followed by a CNN-based classifier to accurately categorize the detected signs into predefined classes such as mandatory, cautionary, and informatory signs. A comprehensive dataset comprising diverse Indian traffic scenarios is utilized, incorporating variations in illumination, occlusion, weather conditions, and sign degradation. Data preprocessing techniques, including normalization and augmentation, are employed to enhance model generalization. The system is implemented on a cloud-based platform using GPU acceleration, enabling efficient training and scalability. Transfer learning strategies are further incorporated to improve performance and reduce training time. Experimental results demonstrate that the proposed model achieves high detection accuracy and classification performance, outperforming conventional methods under challenging real-world conditions. The framework also exhibits real-time processing capability, making it suitable for deployment in advanced driver assistance systems and smart mobility solutions. The proposed approach contributes to improving road safety and supports the development of intelligent, automated traffic monitoring systems in developing countries.
Gopal Krishna Samdariya, Devendra Kumar Meda
CFD Analysis of Solar Still with Different Inclination Angle
The increasing demand for clean and sustainable freshwater resources has intensified research on solar desalination systems, particularly solar stills. This study presents a Computational Fluid Dynamics (CFD) analysis and optimization of a single-slope solar still to improve its thermal performance and freshwater productivity. Three-dimensional CAD models of the solar still were developed using ANSYS Design Modular software. The CFD analysis was performed using a Multiphase Volume of Fluid (VOF) model incorporating air, liquid water, and water vapor phases. Evaporation-condensation mechanisms and solar radiation effects were included to simulate realistic operating conditions for the geographical location of Bhopal, India (Longitude 77.40°E, Latitude 23.25990°N). The study investigated the influence of key parameters such as slope angle and glass thickness on mass flow rate, basin temperature, and distillate productivity. Taguchi optimization techniques were employed to determine the optimal operating configuration. The results demonstrated that slope angle significantly affects mass flow rate, while glass thickness has a major impact on productivity enhancement. The optimized configuration, consisting of a 30° slope angle and 0.03 m glass thickness, showed superior thermal performance compared to the conventional design. Under optimized conditions, productivity increased from 221.73 ml/hr to 293.52 ml/hr, indicating a substantial improvement in freshwater yield. The findings confirm that proper optimization of geometrical and operational parameters can considerably enhance solar still efficiency without increasing system complexity.
Performance optimization of counter flow double pipe heat exchanger using different nano fluids
Counter-flow heat exchangers are widely used in thermal systems due to their superior heat transfer performance and efficient energy utilization. This study aims to evaluate the effectiveness of a counter-flow heat exchanger under varying operating conditions and to identify the key parameters influencing heat transfer efficiency. A systematic analysis is carried out by varying critical operating variables, including hot and cold fluid flow rates, inlet temperatures, and effective heat transfer area. The effectiveness–NTU and energy balance approaches are employed to quantify thermal performance and to assess the impact of each parameter on exchanger effectiveness. Further, the study focuses on optimizing the outlet temperature of the hot fluid to maximize heat transfer while ensuring efficient energy utilization and avoiding unnecessary thermal losses. Simultaneously, the outlet temperature of the cold fluid is optimized to enhance overall thermal performance and improve heat recovery potential. Optimization techniques are applied to determine the best combination of operating parameters that achieve the desired outlet temperature conditions with maximum effectiveness. Additionally, the effect of different nanofluids on heat exchanger effectiveness was also tested.
Corrosion Inhibition Study of Thio-Urea Based Schiff Base (BDHNPMTU) on Mild Steel in Acidic Medium
A new thiourea-based Schiff base, N,N′-bis[(3,4-dihydroxy-5- nitrophenyl) methylidene] thiourea (BDHNPMTU), was synthesized by condensation of 3,4- dihydroxy-5-nitro-benzaldehyde with thiourea under acidic conditions. The synthesized compound was characterized using elemental analysis, FT-IR, and UV–Visible spectroscopy. The anti-corrosive effect of BDHNPMTU was evaluated on steel corrosion in 0.5 M HCl using the mass-loss method. The inhibition efficiency was found to increase with inhibitor concentration, showing a maximum efficiency of approximately 96.5 % at 5 × 10⁻⁵ M concentration. The adsorption of the inhibitor molecules on the mild steel surface obeyed the Langmuir adsorption isotherm, indicating monolayer adsorption. The results suggest that BDHNPMTU acts as an efficient, stable, and environmentally benign inhibitor for steel in acidic medium.
Sustainable Management of Temple Waste: Challenges and Opportunities
The rapid increase in religious activities and pilgrimages has led to a significant rise in temple waste generation, particularly in developing countries. Temple waste primarily consists of biodegradable materials such as flowers, garlands, food offerings, leaves, and incense residues, which are often disposed of in rivers or open landfills, causing environmental pollution and ecological imbalance. This paper presents a comprehensive review of sustainable management practices for temple waste, focusing on both the challenges and opportunities associated with its effective utilization. The study examines key challenges, including lack of proper waste segregation, inadequate infrastructure, limited public awareness, and absence of standardized management policies. Additionally, seasonal variations and large-scale waste generation during festivals further complicate waste handling and disposal processes. Despite these challenges, temple waste offers significant opportunities for resource recovery due to its high organic content. Various sustainable approaches such as composting, vermicomposting, biogas generation, and the production of value-added products like incense sticks, natural dyes, and organic fertilizers are explored. Furthermore, the paper highlights the role of circular economy principles, community participation, and government initiatives in promoting efficient waste management systems. Emerging technologies, including IoT-based monitoring and small-scale recycling industries, are also discussed as potential solutions for improving waste collection, segregation, and processing. The review emphasizes that adopting sustainable temple waste management practices can not only reduce environmental pollution but also create economic opportunities and support green development. Overall, the study provides insights into transforming temple waste from an environmental burden into a valuable resource through innovative and eco-friendly strategies.
Dr. Shilpi Shrivastava, Dr. Priyanka Garg, Dr. Richa Sahu
Analyse user behavior, preferences, and interaction patterns using Machine Learning algorithms in order to provide personalized content recommendations and enhanced user experiences.
The rapid growth of intelligent web applications and digital platforms has increased the demand for personalized recommendation systems capable of analyzing user behavior, preferences, and interaction patterns to improve user experiences and content relevance. Traditional recommendation systems often suffer from lower prediction accuracy, slower response time, limited personalization capability, and reduced user engagement. To address these challenges, this study proposes an AI-driven intelligent recommendation framework using Machine Learning and Deep Learning algorithms to provide personalized content recommendations and enhanced user experiences within modern web environments. The proposed system analyzes user browsing history, clickstream records, search activities, ratings, purchase behavior, session interactions, reviews, and feedback data to identify user interests and behavioral patterns. Machine Learning algorithms such as Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and K-Nearest Neighbor were integrated with collaborative filtering, content-based filtering, and hybrid recommendation techniques to improve recommendation quality and adaptive personalization. Deep Learning models including Artificial Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory networks were also implemented to analyze sequential interaction patterns and improve real-time recommendation accuracy. Experimental analysis was conducted using real-world user interaction datasets collected from intelligent web applications. The proposed AI-driven intelligent recommendation system demonstrated significant improvements compared to traditional recommendation systems across multiple performance metrics. The system achieved an accuracy of 96.20%, precision of 95.35%, recall of 94.60%, and F1-score of 94.97%, compared to 81.45%, 79.80%, 78.25%, and 79.01% respectively in traditional systems. The Mean Squared Error was reduced from 0.192 to 0.038, confirming improved prediction capability and recommendation reliability. Recommendation accuracy improved from 76.40% to 95.10%, while the Click-Through Rate increased from 61.35% to 89.80%. User engagement score improved from 68.20% to 93.75%, and user satisfaction level increased from 72.45% to 96.30%. Personalized content efficiency reached 95.60%, while browsing time optimization improved from 58.90% to 88.75%, demonstrating faster and more relevant recommendation delivery. The recommendation response time decreased significantly from 4.5 seconds to 1.4 seconds, while data processing speed improved from 3.8 seconds to 1.2 seconds. Purchase prediction accuracy increased to 93.95%, customer retention rate improved to 91.80%, and adaptive learning accuracy reached 94.25%. The integration of Natural Language Processing techniques also enhanced sentiment analysis accuracy from 70.25% to 94.10%, enabling better understanding of user opinions and feedback. The proposed recommendation framework demonstrated high scalability, improved resource utilization efficiency, and enhanced overall system reliability through cloud computing integration and real-time adaptive learning mechanisms. The intelligent recommendation system successfully optimized personalized content delivery, recommendation diversity, user retention, and real-time recommendation performance. The study confirms that analyzing user behavior, preferences, and interaction patterns using Machine Learning algorithms significantly improves personalized recommendation quality, operational efficiency, user engagement, and customer satisfaction within intelligent web applications. The proposed AI-driven recommendation framework provides a scalable, adaptive, and intelligent solution suitable for e-commerce, digital marketing, healthcare, education, entertainment, and modern online service platforms.
Lightweight Materials for Electric Vehicle
The rapid adoption of electric vehicles (EVs) relies on overcoming challenges in driving range, cost, and energy efficiency, with lightweight materials playing a crucial role. Reducing vehicle mass directly improves performance, increasing driving range by about 8–10% for every 10% weight reduction. A multi-material approach combining Advanced High-Strength Steels (AHSS), aluminum alloys, magnesium alloys, and polymer composites offers the best results. Aluminum is widely used due to its balance of strength and cost, while Carbon Fiber Reinforced Polymers (CFRP) provide greater weight savings but are expensive and difficult to recycle. Although light weighting enhances range, performance, and enables battery downsizing, challenges such as high material costs, manufacturing complexity, and recycling issues remain.
COMPARATIVE ANALYSIS OF JUVENILE JUSTICE LAWS IN INDIA AND OTHER COUNTRIES
The way a nation responds to its youngest offenders reveals its deepest moral compass—its stance on punishment, its faith in rehabilitation, and its perception of childhood itself. This research undertakes a comparative exploration of juvenile justice in India, situating it alongside the frameworks of the United States and the United Kingdom. The central concern is not to declare one system superior, but to examine how each country negotiates the delicate balance between accountability and the possibility of reform. By tracing historical developments, analyzing legislative principles, and reviewing responses to heinous crimes, the study uncovers both challenges and innovations. India’s trajectory reflects a post-colonial struggle to reconcile welfare-oriented traditions with contemporary demands for public safety. The U.S. system demonstrates dramatic shifts between rehabilitative ideals and punitive policies, often intertwined with racial and social inequalities. The UK, particularly in England and Wales, has adopted a structured, managerial approach that emphasizes prevention through a blend of support and accountability.[1] Ultimately, the paper argues that an effective juvenile justice system must not simply punish or indulge, but must build pathways—connecting young people to education, psychological support, and meaningful opportunities—while ensuring they confront the consequences of their actions. [1] Barry Goldson and John Muncie (eds.), Youth Crime and Justice (Sage Publications, 2015).
Integrate machine learning and deep learning techniques, including transformer-based NLP architectures such as BERT and GPT, in order to improve dialogue management, contextual understanding, and response generation capabilities of the chatbot system.
The development of intelligent chatbot systems using Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) has become an important research area for improving human-computer interaction. Traditional chatbot systems often face limitations such as weak contextual understanding, poor dialogue continuity, and inaccurate response generation. To address these challenges, this research proposes a context-aware chatbot system integrating machine learning, deep learning, and transformer-based NLP architectures such as BERT and GPT to enhance dialogue management, contextual understanding, and conversational response generation. The proposed framework combines contextual memory management, sentiment analysis, and Retrieval-Augmented Generation (RAG) to improve semantic interpretation and conversational adaptability. BERT is utilized for bidirectional contextual understanding and intent recognition, while GPT is employed for dynamic and coherent response generation. Experimental evaluation is conducted using multiple performance metrics including accuracy, precision, recall, F1-score, perplexity, semantic similarity, contextual relevance score, dialogue success rate, BLEU score, and ROUGE score. The proposed Hybrid BERT-GPT model achieved superior performance with 96.48% accuracy, 95.72% precision, 95.30% recall, and 95.51% F1-score, along with improved contextual relevance and dialogue success rates. The results demonstrate that integrating transformer-based NLP models significantly improves conversational intelligence, contextual reasoning, emotional adaptability, and user satisfaction in chatbot systems.