Volume 13 | Issue 1

 Abstract

India's population expansion and urbanization have created a huge solid waste management problem, with legacy dumpsites continuing to be a major cause of pollution and environmental damage. This study focuses on the thorough remediation of the Ekla Bandha legacy site in Gorakhpur, where biomining was used to methodically treat 1,96,000 MT of legacy waste. The study demonstrates how a hazardous dumpsite has been converted into a green urban asset through resource recovery and sustainable approaches, such as turning excavated materials into useable resources. The study examines the remediation operations' effects on the environment, society, and economy, noting major reductions in methane emissions, enhanced soil and water quality, and the establishment of an urban forest with 50,000 planted trees. It also presents a conceptual framework for coordinating waste rehabilitation efforts with India's Green Credit Program, a ground-breaking program that was introduced in 2023 to encourage environmental sustainability. Gorakhpur exemplifies how cities may combine economic incentives and climate action by utilizing green credits for initiatives like solid waste management and afforestation. By reintegrating waste stream outputs into value chains, Gorakhpur is positioned as a leader in the shift to a circular economy, which lessens reliance on new resources. The study emphasizes the value of community involvement, public-private partnerships, and scalable approaches for other cities dealing with legacy waste issues. This study provides a reproducible methodology for attaining sustainable urban development, increasing India's net-zero promises, and building resilience in urban ecosystems by coordinating remediation with the Green Credit Program.

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dumpsite, remediation, bio-mining, green credit, circular economy, sustainability, plantation.

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Background & Objectives Tuberculosis (TB) is an ancient disease, with evidence of skeletal TB found in mummies in both the Old and New World. The causative agent is Mycobacterium tuberculosis, a fastidious, aerobic, acid-fast bacillus. In the wake of human immunodeficiency virus (HIV) infection, the number of children and adults afflicted with TB has escalated tremendously worldwide in the past 25 years. Control of TB in children often has been neglected because children are ineffective transmitters of the bacillus and frequently escape the attention of TB control programs. However, much of the morbidity and mortality of TB occurs in childhood, and acquisition of TB infection during childhood contributes to the future reservoir of cases. Risk factor based screening of children for TB infection, appropriate implementation of chemoprophylaxis. Aim of the study is to find "Effectiveness of a Self Instructional Module (SIM) On Knowledge Regarding Prevention and Management of Child Hood Tuberculosis among Final Year B Sc Nursing Students. Methods An evaluative approach with pre-experimental one group pre-test post-test design was used with purposive sampling technique to select the sample (N=40). A structured knowledge questionnaire was used to assess the knowledge and SIM was administered to find its effectiveness. The collected data was analyzed by using descriptive & inferential statistics. Results The mean percentage of post-test knowledge score (56.19%) was higher than the mean percentage of pre-test knowledge score (32.87%). The calculated't' value (43.18) is greater than the table value (0.05, 39df) = 2.57. It showed a significant difference between mean pre and post- test knowledge scores. Calculated ? 2 values is more than the table value with respect age and gender hence it showed significant association between age, and gender with post-test knowledge scores. Interpretation & Conclusion The findings of the study showed that there was a deficit in knowledge of final year students before administration of SIM. The results indicated that the SIM is effective in increasing the knowledge of final year students regarding the prevention and management child hood TB.

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 Keywords

Effectiveness; SIM; Knowledge; Tuberculosis Prevention; Management; Students

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Psychosis refers to a collection of symptoms that affect the mind, where there has been some loss of contact with reality. During an episode of psychosis, a person's thoughts and perceptions are disrupted and they may have difficulty recognizing what is real and what is not. This case highlights the importance of psychosis, especially in cases where mood disorders and false paranormal believes have complicate the clinical picture. It also stresses on the need for early intervention in patients with untreated psychiatric disorders to prevent the onset of severe psychotic symptoms.

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Psychosis, Homoeopathy, Psychotic disorder, Psychiatric, Paranoid, behaviour

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The number of cars on the road is increasing rapidly, causing more traffic problems. The "Brainy Parking System" project offers a brainy, autonomous parking solution with AI and IoT technology. It aims to handle problems in metro cities as well as rural areas. One major issue with current systems is the difficulty in tracking parking fees, which can change based on the time and parking space. This new system allows people to easily locate available parking spots. It uses the same entrance and exit gate, counting vehicles in reverse when they leave. The project combines RFID technology with IoT to enhance efficiency. The smart parking system not only reduces fuel consumption but also lowers urban pollution, contributing positively to the country's economy.

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Internet of Things, Brainy Parking System, RFID, Sensors

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Disease prediction systems based on symptoms are a revolutionary way to deal with the important problem of early identification and treatment, especially in places with limited resources. Accurate and timely detection of diseases is a key part of better healthcare results, lowering the number of preventable illnesses, and boosting public health around the world.

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Disease Prediction, Symptoms, Dataset, Neural Network, Normalization, Early Stopping, Epoch.

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The platform helps detect early signs of autism with AI assistance and tailors programs to fit every core area of development that has been identified, which may include communication skills, daily living skills and adaptive learning through interactive modules. The integrated chatbot gives conversational support, engagement, answering questions and assisting both users and caregivers in getting the most out of what is available. This AI also helps in detecting autism by analyzing language patterns and styles of interaction for early ASD markers. With data privacy and ethical use at its forefront, this platform gives a complete, supportive ecosystem to all people with autism, aiding them in the development of necessary skills and increasing their independence. The potential for the platform is to address the diverse needs of the autism community with a model of continuous, adaptive, and compassionate support that fits with personal growth as well as social integration for people with autism.

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HSV COLOR SPACE, COMPUTER VISION, GESTURE RECOGNITION, HUMAN-COMPUTER INTERACTION (HCI)

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Abstract - Digitalization in all aspects has become profound and inevitable in our private and work lives. Technology creates opportunities for new business models and lifestyles. In the same capacity technology also has become an eminent part of CSR initiatives. Recently, the term "Corporate Digital Responsibility" has come into existence which summarizes the Business world responsibilities towards digitalization-related impacts, risks, challenges, and opportunities. Out of Many societal issues which are being delt as part of CSR initiatives such as education, sanitation, ecological initiatives, sex education and gender sensitization has emerged as an inevitable social issue which needs to be delt while keeping pace with this fast digital world. The paper at hand reviews the role of CDR using a multi-step approach towards gender sensitization in Indian digital society. This paper also flashes light on key gender issues, challenges and key roles of corporations for joining hands in bringing gender sensitization in our society in digital era. Key words: Corporate digital responsibility, Gender Sensitization, Sex Education, Digital

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Corporate Digital responsibility, Digital Gender sensitisation, Se

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Political protests have been a defining feature of India's democratic journey, influencing significant policy shifts and legislative reforms. This paper explores how collective dissent, mass mobilization, and grassroots activism have shaped policy changes in India. It examines key instances, such as the Chipko Movement, the Anti-Corruption Movement, and recent farmer protests, highlighting the strategies, outcomes, and challenges faced by protest movements. Furthermore, the study delves into the interplay between protest dynamics, public opinion, and governmental responses, providing insights into how protests shape the policy-making process. The analysis underscores the critical role of citizen engagement in a vibrant democracy like India. ________________________________________

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public protest, politics, policies

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The integration of drone technology with the Internet of Things (IoT) has given rise to the Drone of Things (DoT), a paradigm that revolutionizes data collection, monitoring, and delivery across diverse domains such as agriculture, logistics, environmental monitoring, and emergency response. This paper examines the potential of edge computing and fog computing to enhance data transfer and processing in DoT systems. By enabling localized data processing on drones and intermediary fog nodes, these technologies address critical challenges such as latency, bandwidth limitations, and data privacy concerns. Edge computing allows drones to process and analyze data in real time, reducing dependency on central servers, while fog computing provides a distributed infrastructure for aggregating and pre-processing data from multiple drones before forwarding essential information to central systems. This paper also explores the benefits of these approaches, including improved efficiency, scalability, and reliability, alongside strategies to address challenges such as interoperability, resource management, and cybersecurity risks. By leveraging edge and fog computing, the DoT paradigm can achieve robust, secure, and scalable operations, paving the way for its widespread adoption in future technological landscapes.

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Drone, edge computing, fog computing, adoption,implementation

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Underwater Wireless Sensor Networks (UWSNs) are vital for monitoring and managing disaster-prone environments, including flood zones and wildfire-affected areas. However, the extreme conditions in such environments--water corrosion, sediment accumulation, heat damage, and mechanical wear--pose significant challenges to the reliability and operational lifespan of UWSN devices. This work introduces a predictive maintenance framework designed to anticipate hardware failures in UWSN nodes by leveraging machine learning algorithms. The proposed approach is tailored to unique disaster-zone conditions and is optimized for low-power devices, ensuring compatibility with the constrained resources of UWSN deployments. By analyzing usage patterns and environmental stressors in real time, the system provides early failure predictions and actionable maintenance alerts to field operators. The novelty lies in its environment-specific modeling, lightweight implementation for edge devices, and integration of predictive analytics with disaster response strategies. This framework enhances network reliability, minimizes downtime, and ensures sustained operation during critical disaster management activities.

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Predictive ,UWSNs, Machine Learning

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Skin cancer, one of the most prevalent cancers worldwide, requires timely and accurate detection for better pa- tient outcomes. This project investigates the use of deep learning methods for automatically classifying skin cancer from dermoscopic pictures. Specifically, convolutional neural networks (CNNs) and hybrid models incorporating transfer learning are employed to enhance classification accuracy. To tackle issues including unequal class distribution, noise in images, and restricted data accessibility, preprocessing methods like data augmentation, denoising, and feature extraction are implemented. The project explores novel model architectures, including compact transformers and dual autoencoder models, aiming to improve the model's performance. Additionally, established benchmark datasets such as HAM10000 and ISIC-2019 are used for training and evaluation. The effective- ness of the developed model is assessed using key metrics, includ- ing accuracy, sensitivity, and specificity. The results highlight the potential of deep learning in accurately classifying skin lesions as malignant or benign, offering valuable support for clinicians in early skin cancer diagnosis, especially in resource-limited settings.By highlighting current trends, obstacles, and potential avenues for future research in the field, this work advances automated diagnostic systems for skin cancer

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Skin Cancer Classification, Deep Learning, Convolutional Neural Networks (CNNs), Compact Transformers, Radiomics, Patient Metadata, Transfer Learning, HAM10000 Dataset, ISIC-2019 Dataset, Class Imbalance

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Breast cancer, a significant health concern among women, requires prompt and accurate diagnosis to improve treatment outcomes and survival rates. Traditionally, specialized doctors perform the diagnosis; however, advancements in machine learning algorithms are enabling supportive diagnostic tools. In this study, we employ a hybrid approach combining Convolutional Neural Networks (CNNs), OpenCV, and Random Forest algorithms to classify breast cancer cases as malignant or benign. The dataset, sourced from the University of Wisconsin, includes 357 malignant and 212 benign tumors, with clinically relevant features extracted using feature engineering techniques. OpenCV is utilized for image preprocessing, ensuring standardized input quality for model analysis, particularly in image-based features. Following data normalization and preprocessing, the dataset is divided into training and testing sets. CNNs are applied to image data for in-depth feature extraction, identifying patterns indicative of malignancy.

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Random Forest Algorithm, OpenCV, CNN algorithm, Machine learning, Breast Cancer Prediction.

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Machine Learning ,Job Prediction ,Employment Forecasting ,Classification Models, Data Preprocessing ,Feature Engineering, Random Forest, Neural Networks ,Logistic Regression, Model Evaluation

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The present study assessed the effectiveness of a planned teaching programme (PTP) on the knowledge of pre-university (PU) college students regarding mental hygiene in selected colleges in Bidar. The study revealed that examinations and grades were the primary stressors, followed by long working hours, criticism from supervisors, and fear of unemployment. A paired t-test analysis of pre-test and post-test knowledge scores showed a significant improvement, with a t value of 45.11 (p < 0.001) and a percentage mean difference of 49.76%. Key findings included a substantial increase in knowledge regarding the meaning, characteristics, and ways to restore mental hygiene, as well as awareness of risk factors and red-flag symptoms. The results highlight the need for interventional approaches, self-learning initiatives, and mass education to promote mental health awareness and minimize substance abuse among students.

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Mental hygiene, planned teaching programme, knowledge, mental health, effectiveness

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India is amongst few countries that does not consider marital rape as crime and continues to exempt husbands from being charges for rape. This article will examine the reason for exemption of marital rape from being considered crime in Bhartiya Nyaya Sanhita. The exemption stems from the belief in Indian culture that sexual consent is implied and irrefutable upon marriage. This depicts the patriarchal nature of the society which developed upon the notion that wife's body is a property of her husband thus undermining her liberty. The discussion on marital rape in India basically surrounded by intersection of marriage, consent and crime. Historically speaking the marriage in India is sacred concept with the deep-rooted beliefs that women should always fulfil the sexual needs of her partner. These notions clearly violate fundamental rights of women and stand in contradiction with constitutional values of gender equality, dignity and bodily integrity. The key issue relating marital rape is of consent. The exemption that consent is given automatically once marriage is solemnised which disregards the essential elements of consent which is consent should be free from any pressure and is dynamic and ongoing process which can be revoked at any time. The absence of legalisation of marital rape deprives women of right to bodily autonomy and personal security which are enshrined in constitution under Article 14 (equality before law), Article 19 (freedom of expression) and article 21 (right to life and personal liberty). This paper will also compare laws relating to marital rapes in other countries with Indian law.

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Marital rape, Implied consent, criminalised, Constitution of India, Indian Penal Code, human dignity.

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Rapid population growth and urbanization have limited accessible high-quality land, leading to construction on weak soils like black cotton soil. Despite being fertile, black cotton soil is unsuitable for foundations due to its expansive nature, which causes shrink-swell behavior with moisture changes. Cracks form during dry seasons, and even minimal rainfall can make these soils impassable. In India, 20% of the area is covered by black soil, posing challenges for highway engineers. Weak subgrade s in road construction can cause pavement failure. This study explores improving black cotton soil's geotechnical properties using geopolymer stabilization, an eco-friendly alternative to energy-intensive Portland cement. Fly ash (5-20%) is activated with sodium hydroxide, significantly enhancing soil strength and load-bearing capacity through geopolymerization.

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Black cotton soil, Fly ash, Sodium hydroxide and Geo polymer

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This research focuses on the modeling of energy demand predictability, along with the integration of renewable sources of energy, such as wind and solar through hybrid approaches. This study seeks to develop a new hybrid predictive model for energy demand forecasting with precise and effective predictions through the integration of traditional energy forecasting techniques and renewable energy sources, including wind and solar. The model will address the challenges posed by the intermittent and erratic nature of renewable energy generation, assessing how hybrid strategies can help stabilize the grid while optimizing energy distribution. The study seeks to improve the accuracy and adaptability of energy demand forecasts using advanced artificial intelligence and machine learning techniques, enabling more reliable integration of renewable energy sources. Moreover, the study will examine the economic and environmental impacts of integrating energy storage systems with smart grid technology and hybrid energy models. This will be conducted to improve resource optimization while minimizing reliance on fossil fuels. The entire study will be based on an all-inclusive framework for the implementation of hybrids forecasting models using different approaches such as statistical, machine learning, and physical methodologies to improve the reliability, sustainability, and efficiency of energy systems. In summary, the study focuses on developing sustainable, resilient, and economically feasible energy systems, which are part of the shift toward renewable sources but ensure stability on the grid as well as environmentally friendly effects. A framework with data-based considerations to aid the transition to more sustainable and secure energy systems.

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Forecasting Accuracy, Hybrid Renewable Energy System (HRES), Solar, Wind Power, Wind-Solar Hybrid System, Machine Learning (ML).

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This study examines how well wet scavenging works to reduce particulate matter (PM) concentrations and how that affects the Air Quality Index (AQI) in different parts of Agra. One important natural method for purging the air of contaminants is wet scavenging, in which precipitation collects and removes atmospheric particles. In the urban and peri-urban regions of Agra, a city notorious for its severe air pollution problems, mostly brought on by industrial activity, construction dust, and vehicle emissions, the study attempts to measure the efficacy of this procedure. The research offers a thorough evaluation of the ways in which wet scavenging affects PM concentrations of various sizes, especially PM2.5 and PM10, by gathering and evaluating PM data before to, during, and following rainstorm events at several monitoring stations located around the city. High-precision sensors and weather stations are used in the study technique to continuously monitor air quality metrics and record rainfall events. PM levels and precipitation duration and severity are correlated using sophisticated statistical techniques and atmospheric models. Additionally, by contrasting data from heavily populated metropolitan centers, industrial zones, and less-trafficked residential regions, the spatial heterogeneity of wet scavenging efficiency is investigated. The study also examines the temporal component, emphasizing the rate and duration of post-rainfall improvements in air quality.

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AQI, wet scavenging, PM2.5

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 Abstract

Shanti Ko Badava Dene Me Siksha Ki Bhumika

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Shanti Ko Badava Dene Me Siksha Ki Bhumika

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Nanotechnology represents a groundbreaking field focused on manipulating materials at the nanoscale. Its integration with biology and medicine, termed nanobiotechnology, has transformed healthcare by enabling precise drug delivery, advanced diagnostics, and innovative therapeutic approaches. This review highlights the applications of nanotechnology in medicine, particularly in cancer therapy, cardiovascular diseases, and infectious diseases. It explores various drug delivery systems, such as liposomes, micelles, and dendrimers, which improve bioavailability, stability, and targeted drug efficacy. Furthermore, the challenges of scalability, regulatory issues, and potential toxicity are discussed. The future of nanotechnology lies in its potential for personalized medicine and vaccine development, positioning it as a cornerstone for advanced healthcare solutions.

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Nanomedicine,Nanobiotechnology,Nanocarriers,Liposomes,Targeted Therapy,Biocompatibility

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