Nagpur Institute of Technology

Supercapacitors, often referred to as Ultracapacitors, are electrochemical energy storage devices with high power density and moderate energy storage which serves as a bridge between the conventional capacitors and batteries. While conventional capacitors offer extreme power but negligible energy and batteries provide high energy density for long-term storage, Supercapacitors fill the gap between these technologies. By combining the rapid charge-discharge characteristics of conventional capacitors with comparatively enhanced energy storage capability associated with electrochemical systems, supercapacitors become highly suitable for applications that require substantial power delivery over short time interval. These distinct abilities of supercapacitors offer exceptional cycle stability, operational reliability and long service durability. The review focuses on the use of supercapacitors in power density and long cycle life applications, such as quick acceleration and regenerative braking in HEVs, power management in HESS, and battery-less electric vehicle technology based on supercapacitors with ultra-fast charging capabilities. In this paper, a comprehensive review of supercapacitor technology is presented, including the working principle, classification, materials, characteristics, and latest developments in supercapacitor technology. Moreover, the review critically discusses the applications of supercapacitors, such as RBS and HESS, along with the current challenges, limitations, and future research avenues for the betterment of supercapacitor technology. Keywords— Supercapacitors; High Power Density; Hybrid Energy Storage System (HESS); Regenerative Braking System (RBS); Electric Vehicles; Energy Management

Abstract This research paper presents the design and development of an automated agricultural system combining robotic seed sowing, fertilizer spraying, and IoT-based monitoring. The proposed system aims to reduce manual labor, improve agricultural efficiency, ensure precise seed placement, and optimize fertilizer usage. IoT integration enables real-time monitoring of soil moisture, seed dispensing status, fertilizer levels, and overall field conditions. This work demonstrates how automation and IoT can support modern precision agriculture. Keywords Seed Sowing Robot, Fertilizer Sprayer, IoT Monitoring, Precision Agriculture, Automation, Smart Farming

Advancements in human–computer interaction have led to the development of alternative input mechanisms that reduce reliance on conventional devices such as keyboards and mice. This paper proposes a Virtual PC Controller, a software-based system that enables remote and gesture-based control of a personal computer. The system utilizes computer vision techniques to recognize hand gestures in real time and maps them to corresponding system-level actions such as cursor movement, clicking, and application control. Additionally, a network communication module allows secure remote interaction with the target system. The proposed architecture is modular, cost-effective, and hardware-independent, requiring only a standard camera and network connectivity. Experimental evaluation indicates satisfactory accuracy, responsiveness, and usability under typical operating conditions. The results demonstrate that the proposed system provides an efficient and intuitive solution for touchless and remote computer interaction, with potential applications in remote work, smart environments, and assistive technologies.

Plant diseases threaten food security, especially in pulse crops essential for sustainable agriculture. This study explores Convolutional Neural Networks (CNNs) for rapid detection of major diseases such as Ascochyta blight, Fusarium wilt, and phytoplasma infections, which cause severe losses in countries like Ethiopia, India, and Taiwan. Using advanced imaging, we propose a web-based diagnostic tool to empower farmers with timely, accurate disease identification, enabling effective management decisions. This approach offers not just technological advancement but also economic sustainability for smallholder farmers by improving crop health and yields through early intervention and precision agriculture practices.

In today’s competitive job market, a well-structured and professional resume plays a crucial role in securing employment opportunities. However, many candidates face challenges in designing resumes due to lack of technical knowledge and formatting skills. This project proposes ResumeGen – an Online Resume Generator using Flask, which enables users to create professional resumes easily through an interactive web interface. The system allows users to input personal, educational, and professional details, select customizable templates, preview the resume in real-time, and download it in PDF format. The application is developed using Python Flask framework with HTML, CSS, and JavaScript for front-end development. The system aims to simplify resume creation by providing automation, user-friendly templates, and multilingual support. This tool significantly reduces time and effort required in resume building while ensuring accuracy and professional presentation.

Mpox and buffalopox are zoonotic viral infections which demonstrate distinct clinical and immune profile. This study examines the clinical presentation with genomic and immunological characteristics among three confirmed cases of Mpox Clade IIb-A.2.1 and Clade Ib infections and a buffalopox from Malappuram, Kerala, India. Immunophenotyping via flow cytometry was used to assess T, B, Natural Killer (NK) cells, and monocytes. The viral DNA, Anti-Mpox IgG/IgM, and anti-BPXV IgG were detected using real time PCR and ELISA respectively. Genomic characterization was performed using next generation sequencing. MAFFT alignment and IQ-TREE2 with maximum likelihood method was used for phylogenetic analysis. Clinically, vesiculo-pustular lesions detected in Mpoxv infection were more severe in Clade Ib patient along with penile edema and gangrenous eschar. The face and hands were primarily affected in BPXV infection. Immunological analysis demonstrated notable differences where Mpoxv infection exhibited major increase in lymphocytes and NK cells and differential activation patterns of immune T-cell population. During BPXV infection, B-cell numbers rose with expansion of mucosal-homing B-cells. IgM and IgG responses differed among infections, where Mpoxv Clade Ib generated early but transient IgM and low IgG titer, while Clade IIb-A.2.1 and BPXV exhibited prolonged IgG response. Genomic analysis revealed deletion in diagnostic region in Clade Ib, albeit Clade IIb showed host-derived mutations. The multiple genomic alignments revealed distinct immunological responses and vaccine development capacity of orthopoxviruses. The findings of the study emphasizes the need for extensive genomic surveillance and immunological research. This will help in understanding the pattern of virus evolution and host immune responses in orthopoxvirus infections.

Student drop out of the art education programs in the academic institutions is a major problem because the students tend to drop out of the programs due to a complex interplay of creative, behavioral, psychological, and the socio-economic factors and not as a result of their work performance. This research paper introduces a predictive dropout analysis model that suits the field of art education management, and which can be used to predict potential at-risk students at an early stage and effectively implement data-driven, time-based response. The framework combines institutional data that is heterogeneous, such as attendance data, studio and portfolio submissions, assessment data, and traces of use of digital tools with psycho-creative data, such as creativity indices, portfolio development rates, and qualitative feedback on critique. The advanced feature engineering methods are used to extract the measures of engagement-trajectories, skill-growth-slopes, and creative-consistency indicators of the longitudinal dynamics of learning specific to art-based programs. Several machine learning models, which include logistic regression, random forest, support vectors machines, artificial neural networks, and gradient boosting are trained and tested through a structured training-validation pipeline through hyperparameter optimization. The accuracy, area under the ROC curve, F1-score, and the precision-recall are the measures of model performance that are evaluated to guarantee that the model can be robust under class imbalance conditions. The experimental findings show that ensemble and non-linear models have a higher performance compared to the traditional baselines and show the predictive power of creative interaction and behavioral characteristics as well as academic indicators.

Self healing polymers are a new type of intelligent material that has the potential to repair mechanical damage and restore functional activity independently. Mechanical degradation (microcracking, puncture formation and fatigue failure) in contemporary packaging system greatly decreases the efficiency of the barrier and increases the spoilage of the product and environmental degradation. The integration of self healing properties in to the polymer based packaging materials presents a great initiative in improving durability, increased sustainability and less use of resources. This paper is a critical analysis of the scientific principles, material design principles, production techniques as well as the performance attributes of self healing polymers in next generation packaging applications. Intrinsic systems of healing which are founded on reversible covalent bonding or supramolecular interactions and extrinsic systems founded on encapsulated agents of healing are both considered. Recent developments in biodegradable and recyclable self healing polymer networks are brought up in the context of the goals of the circular economy. There is a presentation of experimental techniques of investigating the efficiency of healing, mechanical recovery, and barrier performance. The findings suggest that dynamic polymer networks which are intrinsic have better repeatability and structural stability than capsule based systems. Nonetheless, the issues of scalability, cost effectiveness and regulatory approvals are still the major obstacles to commercialization. The research concludes that self healing polymers have great potential in changing the packaging materials, as long as material optimization and strategies of industrial integration are developed.

Equus kiang is a robust equid and the only odd-toed wild ungulate endemic to the Tibetan Plateau. It belongs to the order Perissodactyla, the family Equidae, and the genus Equus. In India, it is found exclusively in the Ladakh region within the Changthang Wildlife Sanctuary. In our research, we employed a non-invasive genetic approach to unravel the genetic diversity and to resolve the phylogeny of species by using partial mtDNA D-loop sequence information. A total of 33 samples were amplified and sequenced, and a final 27 D-loop partial sequences of ~230 bp were used for further analysis. The E. kiang population from Changthang Wildlife Sanctuary harbours moderate genetic diversity, displaying haplotype diversity of 0.501 with the discovery of two haplotypes. The phylogenetic tree constructed indicated the monophyletic clade of the two haplotypes with other E. kiang sequences. The median-joining network analysis revealed a gene flow across the border where the haplotypes from Ladakh displayed haplotype sharing with the Southern Tibet haplotype. Based on our results, we can conclude that the E. kiang population in the Changthang Wildlife Sanctuary showcases a moderate genetic diversity, supporting the IUCN Least-Concern status of the species, and demonstrates gene flow with the nearby regions of Tibet.