Authors: Dr. Rahul Arya, Aashish Kumar, Dr. Rishabh Bhardwaj

Abstract: Green synthesis of inorganic nanomaterials has emerged as an environmentally sustainable alternative to conventional chemical and physical synthesis methods. In the present study, zinc oxide (ZnO), copper oxide (CuO), and iron oxide (Fe₂O₃) nanoparticles were synthesized using aqueous plant extracts of Neem, Tulsi, Orange peel, and Tea leaves. These plant extracts served as natural reducing and stabilizing agents. Nanoparticle formation was confirmed through visible color change and UV–Visible spectrophotometric analysis. The characteristic absorption peaks confirmed successful nanoparticle synthesis. The influence of pH, conductivity, viscosity, and reaction time on nanoparticle formation was systematically studied using basic laboratory instrumentation including UV–Visible spectrophotometer, pH meter, conductivity meter, Redwood viscometer, hot plate, and flame photometer. Results demonstrated that near-neutral to slightly alkaline pH favored stable nanoparticle formation. Conductivity decreased during the reaction, confirming metal ion reduction. Viscosity measurements suggested interaction between biomolecules and nanoparticles, enhancing colloidal stability. The study establishes that meaningful nanoparticle synthesis and characterization can be achieved.

DOI: http://doi.org/10.5281/zenodo.20574267

Published by: vikaspatanker

Authors: Meenal Maan, Mayank Chauhan, Dr. Rishabh Bhardwaj

Abstract: Food contamination has emerged as a major global concern due to increasing industrialization, intensive agricultural practices, environmental pollution and improper food handling procedures. Contaminants such as pesticide residues, antibiotic residues, heavy metals and naturally occurring toxins frequently enter the food chain and may pose significant risks to human health. Chronic exposure to these contaminants has been associated with cancer, neurological disorders, endocrine disruption, organ toxicity and antimicrobial resistance. The present study evaluates major food contaminants commonly detected in fruits, vegetables, dairy products, meat, fish, cereals and processed foods. Food samples collected from agriculturally intensive regions of western Uttar Pradesh were analyzed using advanced analytical techniques including Gas Chromatography–Mass Spectrometry (GC–MS), High Performance Liquid Chromatography (HPLC), Enzyme-Linked Immunosorbent Assay (ELISA) and Atomic Absorption Spectroscopy (AAS). The occurrence, sources, detection methodologies and public health implications of contaminants were investigated. Results revealed detectable levels of pesticide residues, antibiotic residues, heavy metals and mycotoxins in several food samples, with certain samples approaching or exceeding recommended safety limits. The findings highlight the importance of routine monitoring, regulatory compliance and sustainable agricultural practices for ensuring food safety and consumer protection.

DOI: http://doi.org/10.5281/zenodo.20574084

Published by: vikaspatanker

Authors: Dr. Rahul Arya, Aditya Kumar, Dr. Rishabh Bhardwaj

Abstract: Environmental toxicity has emerged as a major global concern due to rapid industrialization, extensive use of hazardous chemicals, and unsustainable manufacturing practices. Conventional chemical processes often depend on toxic raw materials, energy-intensive methods, and non-renewable resources, leading to hazardous waste generation and environmental pollution. Green chemistry has emerged as a sustainable approach that focuses on preventing pollution at its source rather than managing it after generation. This research examines the role of green chemistry in controlling environmental toxicity by analyzing its principles, applications, and effectiveness across various industrial sectors. The study is based on qualitative review, secondary data analysis, and case studies comparing conventional chemical practices with green chemistry alternatives. The findings indicate that green chemistry significantly reduces hazardous waste generation, toxic emissions, and energy consumption while improving resource efficiency and process safety. Applications in pharmaceuticals, agriculture, polymers, and advanced materials demonstrate that green chemistry can support environmental protection without compromising industrial productivity or economic viability.

DOI: http://doi.org/10.5281/zenodo.20572364

Published by: vikaspatanker

Authors: Meenal Maan, Ritika Saharawat, Dr. Rishabh Bhardwaj

Abstract: The Kali River, originating from Antwada village in Muzaffarnagar district, Uttar Pradesh, is one of the most polluted river systems in northern India. Rapid industrialization, urbanization, and intensive agricultural practices have significantly increased the discharge of organic pollutants into the river. The present study reviews the occurrence, sources, environmental impacts, and remediation strategies associated with organic pollutants in the Kali River of the Muzaffarnagar region. Major pollution sources include untreated industrial effluents from sugar mills, paper mills, distilleries, textile units, domestic sewage, and agricultural runoff. Elevated levels of Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), pesticides, phenolic compounds, surfactants, and other organic contaminants have severely degraded water quality. The study highlights ecological consequences such as dissolved oxygen depletion, biodiversity loss, eutrophication, and contamination of agricultural soils and groundwater. Advanced remediation approaches including wastewater treatment technologies, bioremediation, phytoremediation, constructed wetlands, and integrated river basin management are discussed. Sustainable implementation of these measures is essential for restoring the ecological integrity of the Kali River and safeguarding public health.

DOI: http://doi.org/10.5281/zenodo.20572306

Published by: vikaspatanker

Authors: Jai Raghuveera Samarth, Punith Gowda Y N, Akhilesh Gowda J, Mr. Sudeep Kumar K S Assistant Professor

Abstract: Majority of braking systems work on the principle of dissipation of kinetic energy to heat energy. This method has its own drawbacks and must be replaced with a more reliable braking system that is quick in response, doesn’t heat up and is maintenance free. In this project the design of an electro-magnetic braking system and optimization for various operational parameters has been done and the advantage of using the electromagnetic braking system in automobile is studied. These parameters have been previously iterated in cited projects and papers and also in the simulation models and are to be cross-checked with the experimental setup. An Electromagnetic Braking system uses Magnetic force to engage the brake, but the power required for braking is transmitted manually. The wheel is connected to a shaft and the electromagnet braking unit is attached to one side of the wheel. Here the braking unit consists of a hollow circular steel plate and a stator which has 3 spokes made of iron wounded with copper wire (or) magnetic wire. Here the round steel plate which is attached to the wheel rotates when wheel rotates with the help of motor. when current is supplied to the stator the spokes gets magnetized and creates an magnetic field which tries to attract or oppose the motion of rotating circular plate with the help of magnetic field created. In this brakes there is no contact between the electro-magnetic coils and rotating circular plate (i.e 2 mm gap between coil and circular plate) so this is also called as contactless braking system which is a main advantage in using these brakes. These brakes can be incorporated in heavy vehicles as an auxiliary brake. The electromagnetic brakes can be used in commercial vehicles by controlling the current supplied to produce the magnetic flux. Making some improvements in the brakes it can be used in automobiles in future.

Published by: vikaspatanker

Authors: Alice Lydia Immanuel

Abstract: Ensuring the security and structural integrity of aircraft doors and windows is critical for safe flight operations. This paper proposes a vibration-based monitoring system to verify that aircraft doors and windows are properly secured before and during flight. The proposed method identifies and measures characteristic vibration signatures associated with the aircraft structure, door assembly, and fastening components such as bolts and nuts. Any deviation from the expected vibration pattern, which may indicate a loose or improperly secured fastener, is detected through a comparison algorithm. The system then generates a warning indication on the cockpit display panel, enabling timely corrective action. Furthermore, an Artificial Intelligence (AI)-based analysis approach is incorporated to improve detection accuracy, minimize false alarms, and provide reliable, error-resistant assessments. The proposed solution enhances aircraft safety by offering continuous, real-time monitoring of door and window security.

DOI: http://doi.org/10.5281/zenodo.20569405

Published by: vikaspatanker

Authors: Amandeep Kaur

Abstract: Due to the expanding applications of Unmanned Aerial Vehicles (UAVs) for surveillance, security, disaster response and urban monitoring in recent past years, Human Action Recognition (HAR) in aerial videos has also multiplied with an outstanding courtesy. Ground-level videos are moderately easy enough to analyse but HAR in aerial videos also comes with exclusive contests. These races include low resolution, dynamic backgrounds, camera motion, occlusions and varying scales, viewpoints and low lighting. This review paper is an attempt to cover a comprehensive analysis of the modern techniques developed in past few years to address these challenges. The paper provides a categorization of already existing techniques which are based on the strategies to represent the features such as handcrafted features, deep learning-based representations and also some hybrid approaches. It gives a deep overview of various classification models which includes older algorithms of machine learning and recently developed Deep Neural Networks (DNNs). Furthermore, encroachments in multi-modal data fusion, spatiotemporal modeling and silhouette-based action recognition tailored for aerial perspectives are also covered in depth. The paper also evaluates a number of benchmark datasets, highlights performance metrics and compares the effectiveness and limitations of various techniques. The main intention of writing this review paper is to facilitate the researchers with valuable insights and a consolidated understanding of the current landscape in aerial HAR which will be further helpful in this emerging field.

DOI: http://doi.org/10.5281/zenodo.20568845

Published by: vikaspatanker

Authors: Ravikumar PC, Bhuvan A H, Yashwanth KH, Manjunath SH Professor

Abstract: Unmanned Aerial Vehicles (UAVs), commonly known as drones, have gained significant importance in various fields such as surveillance, agriculture, disaster management, environmental monitoring, and military operations. The design and development of UAVs require lightweight structures, high strength, aerodynamic efficiency, and cost-effective manufacturing techniques. Additive Manufacturing (AM), also known as 3D printing, has emerged as a revolutionary technology that enables rapid prototyping, complex geometrical designs, reduced material waste, and faster production cycles. This study focuses on the design and development of an Unmanned Aerial Vehicle using Additive Manufacturing techniques. The UAV components are designed using Computer-Aided Design (CAD) software and fabricated through 3D printing technology. The use of additive manufacturing allows the production of lightweight and customized parts while maintaining structural integrity and reducing overall manufacturing costs. The study evaluates the design process, material selection, fabrication methods, assembly procedures, and performance characteristics of the developed UAV. The results demonstrate that additive manufacturing significantly improves design flexibility, reduces production time, and enhances the efficiency of UAV development. Furthermore, the fabricated UAV exhibits satisfactory flight performance, stability, and durability for various operational applications. The research highlights the potential of additive manufacturing as an effective solution for next-generation UAV production and aerospace innovation.

Published by: vikaspatanker

Authors: Harshitha T N, Soujanya R

Abstract: Oral cancer is a critical and life-threatening disease, where early detection plays a vital role in improving patient survival rates. However, traditional diagnostic approaches rely heavily on manual clinical examination and biopsy, which are time-consuming, invasive, and often lead to delayed diagnosis. To address these limitations, this paper proposes a deep learning framework for automated oral cancer detection using medical image analysis and lesion-focused classification techniques. The proposed system integrates image preprocessing, lesion segmentation, and deep convolutional neural networks (CNNs) for accurate classification. Preprocessing techniques such as contrast enhancement and noise reduction are applied to improve image quality. Lesion regions are extracted using Otsu thresholding and contour-based segmentation to isolate regions of interest (ROI), which enhances feature learning. Multiple deep learning architectures, including Baseline CNN and EfficientNet-B0 are evaluated for performance comparison. In addition, the proposed framework integrates lesion segmen-tation and deep feature extraction to improve classification robustness and diagnostic performance. To enhance model interpretability, Grad-CAM is employed to visualize the regions contributing to predictions, making the system more transpar-ent for medical applications. Experimental results demonstrate that the proposed EfficientNet-B0 based model achieves superior performance compared to baseline approaches, with improved accuracy and F1-score on the test dataset. The proposed framework provides an efficient, scalable, and interpretable solution for early-stage oral cancer detection, supporting clinical decision-making and reducing diagnostic delays.

Published by: vikaspatanker

Authors: Dr Nilani Sammuarachchi

Abstract: Type 2 Diabetes Mellitus (T2DM) constitutes a major and escalating global and national public health challenge, characterised by rising prevalence, substantial complication burden, and profound impacts on physical, psychological, and social wellbeing. Despite the availability of effective pharmacological treatments, evidence-based clinical guidelines, and structured diabetes education programmes, a significant proportion of individuals continue to experience suboptimal glycaemic control and diminished quality of life. These persistent gaps highlight the need for integrative approaches that extend beyond biomedical management to address behavioural, emotional, and contextual influences on diabetes self-management. This doctoral research applied the TAP-IT mixed-methods framework to examine the interrelationships between clinical indicators, self-care behaviours, emotional experiences, and lived realities of adults managing T2DM. A convergent mixed-methods design was employed, involving 150 adults diagnosed with T2DM who participated in quantitative surveys, clinical assessments, and in-depth qualitative interviews. Quantitative analyses demonstrated high levels of medication adherence (80%), moderate dietary adherence (65%), and comparatively low engagement in physical activity and psychological support behaviours. Significant associations were identified between self-care behaviours and key clinical indicators, including glycated haemoglobin (HbA1c), body mass index (BMI), and blood pressure, underscoring the central role of lifestyle and behavioural factors in glycaemic control and cardiometabolic risk. Qualitative thematic analysis revealed diabetes-related distress, cultural expectations, family and caregiving responsibilities, limited motivation, and time constraints as major barriers to sustained self-management, while strong family support, culturally responsive healthcare, and positive clinician–patient relationships emerged as critical facilitators. Triangulation of quantitative, qualitative, and clinical data generated a comprehensive and integrated understanding of how emotional burden and contextual constraints shape behavioural patterns and metabolic outcomes in T2DM. The TAP-IT framework proved effective in identifying misalignments between clinical recommendations and the lived experiences of individuals managing diabetes in everyday contexts. The findings emphasise the necessity of person-centred and culturally responsive care models that integrate emotional support, tailored health education, and community-based interventions alongside clinical management. This study contributes novel evidence demonstrating that effective T2DM management requires coordinated, multidimensional strategies addressing biological, behavioural, psychological, and sociocultural determinants simultaneously, with particular relevance for Māori, Pasifika, and South Asian populations in Aotearoa New Zealand.

DOI: http://doi.org/10.5281/zenodo.20560629

Published by: vikaspatanker