International Journal of Research in Engineering Sciences and Management (IJRESM)

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Current Issue

          Welcome to the latest issue of the International Journal of Research in Engineering Sciences and Management (IJRESM). Our current issue, Volume [02], Issue [02], [July - December, 2025], features a diverse collection of cutting-edge research articles, reviews, and technical notes that reflect the latest advancements in various fields of engineering sciences and management. We are proud to present contributions from leading researchers and academics from around the world.

     Volume-2, Issue-2, July- December, 2025

                 Call for Papers

1. Biosynthesis of ZnO Nanoparticles Using Bombax ceiba flower petals’ extract and Investigation of their Photocatalytic and Biological Activities  [Full Paper in pdf]
   Authors: Ravi Kant, Monika Chahar, Anuj Mittal, Seema
   

   Abstract: Plant extract–mediated synthesis of zinc oxide nanoparticles (ZnO NPs) is an eco-friendly method that utilizes phytochemicals as natural reducing and stabilizing agents, to produce biocompatible and stablenanoparticles with tunable properties. This study reports the synthesis of ZnO Nanoparticles utilizing anaqueous extract of Bombax ceiba flower petals (BC). The characterization of BC-ZnO NPs was carried out using UV–visible (UV-Vis.), Powder XRD, FTIR, and FESEM-EDS spectroscopic methods. PXRD pattern confirms highly crystalline ZnONPs with a wurtzite hexagonal phase.The FESEM images revealed the flake-like or petal-shaped appearance of the particles, giving a flower-like or agglomerated granular morphology. BC-ZnO NPs with an average size ~41.89 nm were obtained. BC-ZnO NPs exhibited ~90% photodegradation of MB dye during initial 40 min of UV light irradiation. Further, the BC-ZnO NPs were also examined for their biological activities: antimicrobial, antioxidant, and antidiabetic. BC-ZnO NPs showed excellent antioxidant capability and good antimicrobial potential against C. albicans, S. aureus, and E. coli. They showed better antidiabetic activity (α-amylase activity) than the standard, with an IC50 value of 119.95 µg/mL. While for α-glucosidase, the BC-ZnO NPs exhibited excellent inhibition activity achievingan IC50 value of 98.94 µg/mL.The lower IC50 value (46.34 µg/mL) of the synthesized NPs was represented as their excellent antioxidant capability.

2. Comprehensive Review of SiC MOSFET and Parks Transformation-Based Control Strategies for Active Power Filters in Renewable Energy Applications  [Full Paper in pdf]
   Authors: Pramod, NeelashettyKashappa
   

   Abstract: The increasing penetration of renewable energy sources (RES) like solar and wind has introduced significant power quality issues, primarily due to harmonic distortions. Active Power Filters (APFs) have emerged as effective solutions for mitigating these challenges. This review explores the integration of Silicon Carbide (SiC) MOSFET technology with Park’s Transformation-based d–q control strategies in APF systems. SiC MOSFETs offer superior electrical characteristics, including high switching frequency, thermal efficiency, and voltage blocking capability, making them highly suitable for modern APFs. Park’s Transformation facilitates effective decoupling of active and reactive power components, enabling precise harmonic compensation. Various control methods—ranging from conventional PI to advanced predictive and AI-based controllers—are analyzed for their suitability in d–q reference frames. Case studies demonstrate significant Total Harmonic Distortion (THD) reduction using SiC-based APFs. The paper also highlights technical challenges such as EMI, thermal management, and cost, proposing research directions to enhance system performance, scalability, and cost-effectiveness in future renewable-integrated power systems.

3. CYBER THREAT PREDICTIVE ANALYTICS FOR IMPROVING CYBER SUPPLY CHAIN SECURITY  [Full Paper in pdf]
   Authors: A. Mithil, K.Madhavan,S Pavithra,A.Mallikarjuna
   

   Abstract: The Cyber Supply Chain (CSC) system is intricate, comprising various subsystems tasked with different functions. Securing this supply chain is challenging due to inherent vulnerabilities, which can be exploited anywhere within it, posing a significant risk to business continuity. Therefore, it's crucial to comprehend and anticipate potential threats to implement adequate security measures. Cyber Threat Intelligence (CTI) offers insights into identifying threats, utilizing factors such as threat actor skills, motivation, Tactics, Techniques, and Procedures (TTPs), and Indicators of Compromise (IoCs). This study aims to analyze and anticipate threats to enhance cyber supply chain security by leveraging CTI alongside Machine Learning (ML) techniques. By doing so, inherent vulnerabilities in the CSC can be pinpointed, enabling organizations to take appropriate control measures for overall cybersecurity enhancement.to validate our approach, CTI data was collected and several ML algorithms, including Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Cat Boost, and Gradient Boost, were employed using the Microsoft Malware Prediction dataset. The experiment focused on input parameters such as attack and TTP, with vulnerabilities and Indicators of Compromise (IoC) as output parameters. Results from the predictive analytics indicated that Spyware/Ransomware and spear phishing were the most foreseeable threats within the CSC. Additionally, we suggested relevant controls to mitigate these threats. We advocate the utilization of CTI data for ML-based predictive modeling to bolster overall CSC cybersecurity.