Maharashtra State Board of Technical Education

Abstract This paper presents the complete technical documentation for CollegeConnect, an advanced academic management ecosystem designed to modernize institutional workflows within the MSBTE diploma framework. The system addresses the inherent inefficiencies and data latency found in legacy educational portals by implementing a serverless, event-driven architecture. Developed using React.js and Google Firebase, the application features real-time attendance synchronization, an automated MCQ assessment engine, and digital identity verification modules. Significant technical challenges, such as NoSQL document size limitations and network instability, were overcome through the implementation of a custom Base64 image compression utility and exponential backoff networking algorithms. The resulting platform offers a scalable, secure, and highly transparent digital environment for students, faculty, and administrators. Key Words: Serverless Architecture, Firebase Firestore, React Hooks, EdTech, NoSQL, Digital Identity.

The increasing complexity of modern power grids and the integration of renewable energy sources necessitate advanced big data-driven transformer health monitoring and predictive maintenance solutions. Traditional methods relying on periodic inspections are inadequate for real-time failure prevention. This study proposes a scalable AI-driven framework leveraging big data technologies, integrating Apache Kafka for real-time data streaming, Apache Spark for large-scale processing, and machine learning models (LSTM, ARIMA, XGBoost) for predictive analytics. The framework consolidates SCADA, SFRA, DGA, and historical maintenance data, dynamically computing the Transformer Health Index (THI) using Multi-Criteria Decision Making (MCDM) for adaptive asset management. Big data processing, deep learning-based feature extraction, and realtime analytics enhance fault detection, while interactive Power BI dashboards provide real-time monitoring and visualization. The proposed model improves failure prediction, optimizes resource allocation, and enhances grid reliability. Future work will explore edge computing and IoT-based big data analytics for further advancements in predictive maintenance and real-time decisionmaking.

Background: With the growing need for information safety and security rules in every corner of the globe. The biometric innovation has been used widely all over the world in daily life. In this view, the Multimodal-based biometric technique has gained popularity and interest due to its capability to resolve various problems associated with single-model biometric identification systems Methods:: In this research, an advanced multimodal-based biometric recognition system is introduced, which depends on multilayer perceptron (MLP), and it identifies humans using biometric features of Fingerprints and IRIS. To build an efficient model, the popular model called Rest- Net50 was used, the gradient method was used and the hinge technique was used as a loss function. For the technique of fusion, different techniques are used to show the effect of fusion in our model. The recognition rate of introduced systems was assessed by experimenting with various techniques on SDUMLA-HMT datasets based on the multimodal biometric datasets. The recognition rate showed that merging two biometric features in biometric recognition systems gives the best results compared to a single biometric feature. Results: The results obtained showed the superiority of our model with other existing techniques by obtaining a recognition rate of 99.79% with a feature-based fusion method. Conclusion: The introduced system used the MLP deep learning technique. The feature level fusion was applied to recognize the user using IRIS and fingerprints. To the best of our belief, this is the first research to incorporate MLP and multimodal-based biometric systems using IRIS and fingerprints.

This article examines the critical challenges and opportunities in optimizing supply chain networks for quality compliance in today's globalized business environment. It explores how organizations navigate complex quality management requirements across diverse geographical locations while integrating advanced technologies for enhanced performance. The article investigates multi-criteria decision frameworks, predictive analytics, IoT-enabled monitoring, and blockchain-based traceability solutions through comprehensive case studies in the pharmaceutical and food & beverage industries. Through the evaluation of implementation strategies and risk mitigation approaches, the study provides insights into successful quality management system deployment and offers practical recommendations for organizations seeking to enhance their supply chain quality capabilities.

The increasing use of electric vehicles (EVs) has led to significant concerns regarding power quality due to nonlinear loads, heavy load switching, and line faults. To address these issues, a three-phase bridge inverter, acting as a dynamic voltage restorer (DVR), is powered by a solar-powered DC-DC converter. The DVR operates in synchronization with the distribution system, effectively mitigating power quality problems. By injecting compensating voltage from the alternate source into the main grid, the DVR recovers voltage sags caused by EV charging and other disturbances. To maximize solar panel output, the system incorporates the Perturb and Observe (P&O) algorithm. The entire system is modeled and simulated using MATLAB/Simulink, with results demonstrating that the proposed system performs efficiently in restoring power quality during voltage sag events in EV-integrated distribution systems.

The increasing complexity of healthcare, coupled with an ageing population, poses significant challenges for decision-making in healthcare delivery. Implementing smart decision support systems can indeed alleviate some of these challenges by providing clinicians with timely and personalized insights. These systems can leverage vast amounts of patient data, advanced analytics, and predictive modeling to offer clinicians a comprehensive view of individual patient needs and potential outcomes. Currently, researchers and doctors need a faster solution for various diseases in health care. So they started to use the Machine Learning (ML) algorithms for better solution. ML is a sub field of Artificial Intelligence (AI) that provides a useful tool for data analysis, automatic process and others for healthcare system. The use of ML is increasing continuously in healthcare system due to its learning power. In this paper the following algorithms are used for the diagnosis of Diabetes and Kidney Disease such as: Gradient Boosting Classifier (GBC), Random Forest Classifier (RFC), Extra Trees Classifier (ETC), Support Vector Classifier (SVC) and Multilayer Perceptron (MNP) Neural Network, In our model, Gradient Boosting Classifier is used with repeated cross validation to develop our system for better results. The experiment analysis performed for both unbalanced and balanced dataset. The accuracy achieved in case of unbalanced and balanced datasets for GBC, ETC, RFC SVC, MLP & DTC are 75.7 & 92.2, 75.7 & 90.1, 74.4 & 80.0, 62.5 & 66.4, 58.3 & 63.0 and 59.4 & 74.5 respectively. On comparing these results, we found that GBC results are better than other algorithms.

The present work aimed to evaluate anti-oxidant and anti-bacterial activity of polyherbal formulation (gel) prepared using Tulsi, Aloe vera, Neem. It has been used for skin benefits since ancient times. Formulations were tested anti-oxidant activity by DPPH radical scavenging assay and anti-bacterial activity by disk diffusion Susceptibility Testing method. The anti-oxidant activity is exhibited in percentage inhibition. F1 formulation showed maximum percentage inhibition with increasing concentration as compared to standard ascorbic acid. The anti-bacterial activity is exhibited in zone of inhibition. The F5 formulation showed highest zone of inhibition (26.5 mm) against E. coli and zone of inhibition (26.0 mm) against Staphylococcus aureus which is an indication that F5 formulation is more effective on gram positive as well as negative bacteria.

This research examined the ground improvement technique for enhancing the load-bearing capacity and enhancing of weak or compressible soils. In this study, we focus on how we minimize the cost of ground improvement and make environment friendly solution for ground improvement. The research involves a review of the existing literature, including the Objectives, methodology, and case studies related to stone column implementation. Laboratory testing and field investigations are conducted to assess the geotechnical properties of the soil and the behavior of stone columns under different loading conditions. The project aims to provide insights into the geotechnical behavior of stone columns and their effectiveness in stabilizing weak soils. Furthermore, the study will explore the economic and environmental aspects of this ground improvement technique. The findings of this research are expected to contribute valuable knowledge to the field of geotechnical engineering, assisting engineers and construction professionals in making informed decisions regarding ground improvement methods. Keywords : Ground Improvement, Stone Column, Soil Sample, cost effective, environment friendly, Load Bearing Capacity, Black Cotton Soil, Soil Stabilization.

Organogels represent semi-solid systems where an organic liquid phase is entrapped within a three-dimensional network formed by self-assembled, crosslinked, or entangled gelator fibers. These versatile materials find applications in a wide range of fields, including chemistry, pharmaceuticals, cosmetics, biotechnology, and food technology. Notably, in pharmacology, they serve as valuable platforms for drug and vaccine delivery, facilitating the transport of active ingredients through various routes such as transdermal, oral, and parenteral. However, their previous utility as drug delivery systems was hindered by the toxicity associated with the organic solvents used. The pharmacokinetics of medications delivered via organogels are primarily influenced by the distinctive properties of these materials, specifically their "high permeability and poor aqueous solubility," which can impact the bioavailability of the drugs. Organogels can be employed topically or for the controlled release of medications through cutaneous administration and percutaneous absorption, expanding their scope of application beyond conventional drug delivery methods. Organogels hold significant promise as drug delivery vehicles due to their biocompatibility, non-irritating properties, and thermoremanent characteristics. They enable the formulation of diverse drug delivery systems by incorporating both hydrophilic and hydrophobic bioactive compounds within the gel matrix. This comprehensive review offers an overview of organogels, encompassing their nature, synthesis, characterization, and properties. Special attention is directed towards cutting-edge technologies employed in designing organogels as potential controlled delivery systems, with a focus on their emerging therapeutic applications.