South Asian Journal of Engineering and Technology

SUSTAINABLE LACCASE-MEDIATED ANTIMICROBIAL FINISH FOR HERBAL-DYED COTTON TEXTILES

Mr. M. Venkat Prasath — 2025-09-16

The increasing need for sustainable and functional textiles has led to the exploration of herbal dyes and bio-enzymatic finishes. This research examines the application of a fungal laccase enzyme as a functional finish on cotton fabric dyed with turmeric (Curcuma longa) and neem (Azadirachta indica) extracts. These herbs possess potent antimicrobial compounds, but their durability decreases after repeated washing. Using a pad–dry–cure method, laccase-treated fabric was tested for antimicrobial activity (AATCC 100) against Staphylococcus aureus and Escherichia coli, color fastness, and tensile strength. The laccase finish achieved 99% bacterial reduction initially and 96% even after 20 laundering cycles, with minimal loss in color and mechanical strength. This study confirms that laccase-mediated phenolic cross-linking provides a wash-durable, eco-friendly antimicrobial finish.

DEVELOPMENT OF CALOTROPIS GIGANTEA–COTTON BLENDED FIBERS FOR HOME TEXTILE APPLICATIONS

Mrs. S. N. Priyadharshini — 2025-09-22

The need for eco-friendly textile materials has spurred more study into using unusual natural fibers. Often referred to as milkweed or erukkam, Calotropis gigantea is an underutilized plant fiber with special qualities like hollow shape, lightweight structure, and biodegradability. This study investigates the creation of novel home textile uses by combining Calotropis fibers with cotton. The study looks into yarn development, fabric properties, fiber properties, and the viability of blending. According to the results, mixes of cotton and calotropis have improved softness, thermal insulation, and environmentally friendly qualities while retaining enough tensile strength for use in home furnishings. According to the results, Calotropis has the potential to be a sustainable substitute for creating useful and valuable textiles.

A Novel Single-Stage Buck-Boost Transformerless Inverter with Fuzzy Control for Single Phase Grid-Connected Solar PV Systems

Bhavana G — 2025-09-30

This work presents a novel single-stage buck–boost transformerless inverter (BBTI) suitable for single-phase grid-connected photovoltaic (PV) systems. In the proposed structure, the PV source is directly referenced to the grid neutral, thereby eliminating leakage currents and enhancing operational safety. Unlike conventional inverter designs, the topology offers wide operational flexibility through its inherent buck–boost capability, ensuring reliable maximum power point tracking (MPPT) under varying solar input conditions. The inverter operates solely with switched capacitors and requires no inductors, which guarantees symmetrical performance during both positive and negative half cycles while minimizing overall system size and cost. A simple sinusoidal–triangular pulse-width modulation (SPWM) scheme is employed to regulate switching states, and fuzzy logic control is incorporated to improve dynamic response. The proposed configuration has been validated through MATLAB/Simulink simulations, demonstrating reliable operation, improved efficiency, and strong suitability for grid integration of PV sources.

A Versatile Non-Isolated Multiport Converter for Efficient Integration of Solar PV, Battery and Ultracapacitor in Electric Vehicles.

SUDARSHAN D K — 2025-09-30

The DC-DC converter crucial in modern power electronic systems. They are extensively used in renewable energy integration, hybrid electric vehicles (HEVs) and portable electronic devices. For the electric vehicles (EVs) with a hybrid energy storage system (HESS) that includes both battery and ultracapacitor (UC) efficient power management is the key feature for improving system performance, reliability, and the lifespan of the storage units. This paper mainly focusses on improving how energy is managed in electric vehicles (EVs) by the help of hybrid energy storage system (HESS) that includes-ultra capacitor, solar photovoltaic (PV) and battery input. Hear to control the flow of power which from the sources to EV load, I design a versatile non isolated multiport converter for integration of solar PV, battery and ultracapacitor and getting a single output. This paper suggests a non-isolated multi-input, single-output multiport converter design. This architecture aims to offer flexibility in the power flow while adjusting to the changing load demands of EVs. The DC-DC converter manages varying power demands by selecting the most appropriate energy source and channeling power through the proper paths depending on whether high or low current are required. It also supports bidirectional power transfer, enabling sources in both directions. Moreover, the converter allows single- stage power conversion, thereby improving the overall efficiency of the system.

Optimized Load Distribution in Cloud Computing Using Random Opposition-Based Coati Optimization Algorithm (RO-COA)

Sathish R — 2025-09-30

Cloud computing has become an integral part of modern IT infrastructure, providing scalable, on-demand resources to users worldwide. Efficient task scheduling and load balancing are critical challenges in cloud environments, as uneven distribution of workloads can lead to underutilized virtual machines, overloaded servers, increased energy consumption, and reduced Quality of Service. Traditional heuristic and metaheuristic optimization methods often struggle to find global optima due to premature convergence or limited exploration capabilities. This paper introduces the Random Opposition-Based Coati Optimization Algorithm (RO-COA), a bio-inspired optimization technique designed specifically for multi-objective cloud resource scheduling. The algorithm leverages the cooperative foraging behavior of coatis and incorporates a random opposition-based learning mechanism to improve both exploration and exploitation during the search process. RO-COA evaluates each candidate solution alongside its dynamically generated opposite, ensuring diversity in the search space and preventing the algorithm from being trapped in local minima. Experimental simulations demonstrate that RO-COA effectively balances CPU and memory utilization, minimizes task makespan, reduces energy consumption, and improves overall system performance compared to conventional optimization techniques. The approach offers an adaptive, robust, and scalable solution for cloud resource management, making it suitable for both homogeneous and heterogeneous cloud infrastructures.

An Innovative Approach to Design a Sensor for Environmental Hazardous Waste Management Using L-Alanine reinforced with Diglycine Picrate (LADGP) Crystal

R. SUGANTHI — 2025-09-30

L-Alanine Diglycine Picrate (LADGP) single crystals were successfully grown at room temperature using the slow evaporation solution growth technique. The optical properties of the grown crystals were studied using UV–Vis–NIR spectroscopy, revealing good optical transparency in the visible region. The electrical properties were examined through dielectric and conductivity measurements, indicating favorable dielectric behavior. The laser damage threshold (LDT) study demonstrated that the LADGP crystal possesses a high resistance to laser induced damage, suggesting its suitability for laser-related and optoelectronic applications. In addition, picrate ions are known for their strong affinity toward binding organic compounds and heavy metal ions, and the crystalline structure of diglycine picrate provides a stable matrix with a relatively high surface area advantageous for adsorption processes. By incorporating L-alanine into the diglycine picrate lattice and optimizing the growth conditions at different pH values, the material’s adsorption capacity, stability, and selectivity can be enhanced. These characteristics indicate that LADGP crystals have potential not only in optical applications but also as promising candidates for hazardous waste treatment and environmental remediation.

Machine Learning-Based framework for battery Remaining Useful Life Prediction

Munhamoh M.T Cusack — 2025-09-30

Lithium-ion batteries find great use in critical power requirement in electric vehicles, renewable energy storage systems, aerospace and aviation, medical devices and substation DC systems. These systems require reliable operation and higher safety considerations thereby preventing catastrophic failures. Real time monitoring of critical battery parameters such as capacity, voltage, current and temperature is paramount for predictive maintenance. Several Machine Learning based techniques such as Decision Tree Regression,Random Forest, Support Vector Regression, Gaussian Process Regression and Long-Term Short Memory can be used to predict the Remaining useful life (RUL) of batteries. In this study, three machine-learning models are considered. These are the Random Forest (RF), which represents the ensemble methods class of machine learning. Support Vector Regression (SVR), representing the classical regression models class and the Long-Term Short Memory (LSTM) representing the deep learning/sequence models class. The selected models are on the basis of being good representative of each class of Machine – Learning models. The methodology used include downloading and loading in MATLAB the online NASA data set. Exploratory data analysis in MATLAB, preparing the Data for Machine Learning, Implementing the three Machine Learning Models, Comparing the Models and making Remaining Useful Life (RUL) predictions. The performance parameters such as the Root Mean Square Error (RMSE) and the Statistical Correlation Coefficient ? ? are analysed to find the Model performance in predicting RUL. The LSTM performed better than Random Forest in accuracy and long term prediction. The technique is complex and slower to train. However, the SVR model performs better with hyper- parameter tuning. The study contributes to the increasing body of Machine Learning techniques in predictive maintenance. Routine checking done practically usually requires work force. Predictive maintenance, allow for real time monitoring according to the model developed and corrective action taken. The study provides techniques for predictive maintenance, for batteries and other system with measurable raw operational data. Further research may be required on the integration of different Machine Learning based techniques in predicting the RUL. This improves the prediction accuracy, robustness and adaptability.