Category Archives: Uncategorized

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

Authors: Raushan Kumar, Dr. Navneet Seth

Abstract: The rapid growth of social media platforms has significantly transformed consumer behavior and purchasing patterns across India. While extensive research has examined the influence of social media marketing in metropolitan regions, limited attention has been given to its impact on consumers residing in Tier-II cities. This study explores the effect of key social media marketing factors, namely influencer endorsements, online reviews, targeted advertisements, brand interaction, and user-generated content (UGC), on consumers' purchase intentions in Bathinda city. A quantitative research approach was adopted, and primary data were collected through structured questionnaires administered to 150 respondents, primarily college students aged between 18 and 35 years, who constitute one of the most active segments of social media users. The data were analyzed using multiple linear regression techniques to determine the relationship between social media marketing variables and purchase intention. The findings reveal that all five variables exert a significant positive influence on consumers' buying intentions, explaining a substantial proportion of the variation in purchase behavior (R² = 0.68, p < .001). Among the examined factors, influencer endorsements (β = 0.41) and online reviews (β = 0.33) emerged as the most influential predictors of purchase intention. Furthermore, reliability analysis demonstrated strong internal consistency across all measurement constructs, with Cronbach's alpha values exceeding 0.79. The study enriches the existing literature by providing empirical evidence from a Tier-II Indian city and highlights the growing importance of social media marketing in shaping consumer decisions beyond major urban centers. The findings offer valuable insights for marketers and businesses seeking to develop effective digital marketing strategies targeted at increasingly connected and socially engaged consumers.

Published by: vikaspatanker

Authors: Akash katheria, Dr Vinod Kumar

Abstract: Employment generation remains one of the most significant indicators of economic development in India. The country's workforce is distributed across both organised and unorganised sectors, each playing a distinct role in creating employment opportunities. While the organised sector offers formal employment, job security, social protection, and regulated working conditions, the unorganised sector continues to absorb a substantial share of the labour force, particularly among low-skilled and economically vulnerable populations. This study examines and compares the contribution of organised and unorganised sectors to employment generation in India. The research analyses trends in employment, sectoral distribution of workers, wage structures, job security, and the quality of employment opportunities available in both sectors. It also explores the challenges faced by workers, including issues related to income stability, social security benefits, and working conditions. The study is based on secondary data collected from government reports, labour surveys, and published literature. The findings reveal that although the organised sector contributes significantly to productivity and economic growth, the unorganized sector remains the largest source of employment in India. However, employment in the unorganised sector is often characterized by low wages, limited social protection, and higher job insecurity. The study highlights the need for policies that promote formalization, skill development, and social security coverage to improve the quality of employment across sectors

Published by: vikaspatanker

Authors: Ajay Malviya, Dr. Satnam Singh

Abstract: The present work focuses on the CFD-based evaluation of thermal performance and Nusselt number enhancement in a heat exchanger using modified twisted tape inserts. Twisted tape inserts are widely used passive methods for improving heat transfer in internal flow systems. In this study, four geometries were analyzed: Plain Twisted Tape (PTT), Double-Hole Perforated Twisted Tape (DHPTT), Curved-Slot Twisted Tape (CSTT), and Multi-Hole Perforated Twisted Tape (MHPTT). A circular pipe of 44 mm outer diameter, 42 mm inner diameter, and 400 mm length was modeled with a 1 mm thick twisted tape. The total twist angle was 1800°, forming five complete rotations with an 80 mm twist pitch. The CFD model was developed in ANSYS Fluent using a polyhedral mesh of 472,350 cells. Water was used as the working fluid, while the pipe and tape were modeled as aluminum. The inlet velocity and temperature were 0.6 m/s and 293 K, and the pipe wall temperature was 365 K. The standard k-epsilon model was used for turbulent flow analysis. Results showed that MHPTT achieved the highest outlet temperature of 345.99 K, temperature rise of 52.99 K, and 18.09% change. The Nusselt number also increased with Reynolds number. Overall, MHPTT gave the best thermal performance.

DOI: http://doi.org/10.5281/zenodo.20556413

Published by: vikaspatanker