International Journal of Engineering Technologies and Management Research

A COMPREHENSIVE STUDY OF BUSINESS OPERATIONS AND CONSUMER OUTREACH IN THE SUSTAINABLE RECYCLING INDUSTRY IN AHMEDABAD

Mr. Deepak Sharma, Dr. Sameer Kulkarni — Fri, 05 Dec 2025 00:00:00 +0000

India is one of the world’s largest producers of textile waste, generating over 5.2 million tonnes annually, of which less than 30% enters formal recycling streams. Ahmedabad, historically referred to as the ‘Manchester of India’, remains a major textile production centre and also one of the highest contributors to textile waste. This research examines operational practices, supply chain structures, and consumer behaviour within the sustainable textile recycling industry in Ahmedabad, with a detailed case analysis of ReVerse Green Clothing Pvt. Ltd.
A mixed-method research design was implemented incorporating surveys (N=200), field observations, and interviews with industry stakeholders. Findings reveal that although awareness of environmental issues is rising, behavioural conversion toward recycling remains limited due to infrastructural gaps, inconsistent waste inflow, lack of segregation, and low accessibility of collection points. Youth-driven sustainable fashion adoption is increasing, reflecting changing consumer values. The study recommends enhanced reverse logistics, digital transparency, decentralised collection points, and public–private partnerships to scale the circular textile economy.

PSYCHOLOGICAL SAFETY AS A STRATEGIC ASSET UNDER ADVERSE SHOCKS A COOPERATIVE GAME THEORETIC FRAMEWORK FOR SUPPLY CHAIN RESILIENCE

Mohammad Taleghani, Mohammadreza Jabreilzadeh Sola — Wed, 17 Dec 2025 00:00:00 +0000

Purpose – This paper develops the first cooperative game-theoretic model that treats psychological safety as an endogenous strategic asset under Lévy-jump adverse shocks.
Design/methodology/approach – A three-player stochastic game (workers, supervisors, suppliers) is calibrated to ILO global injury statistics (2019-2023). A closed-form Psychological-Safety Resilience Index (PSRI) is derived and validated via 5,000 Monte-Carlo paths.
Findings – A one-standard-deviation increase in safety climate reduces expected accident cost by 23 % (95 % CI: 21-25 %), an effect equivalent to a 14 % productivity gain. Low-cost behavioral interventions (cost < 1 % payroll) yield an NPV of +8.2 % within 12 months.
Originality/value – The PSRI converts intangible trust into a quantifiable dashboard metric, offering managers and policy-makers a scalable lever for supply-chain resilience without additional capital expenditure.

DESIGN AND CONSTRUCTION OF WITH WIND DRIVEN TURBO VENTILATOR

Abubakar R.A. — Wed, 17 Dec 2025 00:00:00 +0000

This study evaluates the performance of a greenhouse vegetable dryer integrated with a wind-driven turbo ventilator, developed to enhance natural convection and improve the drying process through renewable energy utilization. The design targets small-scale farmers in remote or off-grid are-as, where access to electricity and mechanical drying systems is limited or non-existent. The experi-mental setup involved drying freshly harvested leafy vegetables, which had an initial moisture content of approximately 80%. The test was conducted over a continuous 10-hour period under clear sunlight and moderate natural wind conditions. Key performance parameters—including internal and ambient temperatures, relative humidity, and vegetable moisture content—were systematically measured at two-hour intervals. The results revealed a consistent increase in internal temperature within the dryer, peaking at 49°C, which significantly exceeded the maximum ambient temperature of 33°C. This thermal gain was attributed to the greenhouse effect and the enhanced air circulation enabled by the wind-powered ventilator. Relative humidity within the drying chamber ranged from 55% to 62%, establishing an optimal environment for moisture evaporation. At the end of the drying cycle, the final moisture con-tent of the vegetables was reduced to 15%, marking a 65% total reduction. Compared to conventional passive solar dryers documented in the literature, this system demonstrated improved drying efficiency while maintaining simplicity and requiring no external power. Its performance aligns well with semi-passive systems and even rivals some electrically assisted dryers in efficiency. The ventilator played a key role by preventing internal heat saturation and promoting consistent airflow. Overall, the system offers a promising, cost-effective, and sustainable approach for post-harvest preservation of perishable crops in resource-limited settings.

PREDICTING MACHINE FAILURES AND SYSTEM SECURITY USING MACHINE LEARNING AND DEEP LEARNING ALGORITHMS

Ambreena Muneer, Dr. Vineet Mehan — Wed, 17 Dec 2025 00:00:00 +0000

The term “Internet of Things” (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, mobile system, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy. Industry 4.0 emphasizes real-time data analysis for understanding and optimizing physical processes. This study leverages a Predictive Maintenance Dataset from the UCI repository to predict machine failures and categorize them. This study covers two objectives namely, to compare the performance of machine learning algorithms in classifying machine failures, and to assess the effectiveness of deep learning techniques for improved prediction accuracy. The study explores various machine learning algorithms and finds the XG Boost Classifier to be the most effective among them. Long Short-Term Memory (LSTM), a deep learning algorithm, demonstrates its superior accuracy in predicting machine failures compared to both traditional machine learning and Artificial Neural Networks (ANN). The novelty of this study is the application and comparison of machine learning and deep learning models to an unbalanced dataset. Findings of this study hold significant implications for industrial management and research. The study demonstrates the effectiveness of machine learning and deep learning algorithms in predictive maintenance, enabling proactive maintenance interventions and resource optimization.

ALGORITHM APPROACHES FOR MINIMAL OPERATION BACKWARD ERROR RECOVERY IN DYNAMIC NETWORK TOPOLOGIES

Aasifa Arabi, Dr. Nilesh Bhosle — Wed, 17 Dec 2025 00:00:00 +0000

Network survivability is a crucial requirement in high-speed optical networks. Typical approaches of providing survivability have considered the failure of a single component such as a link or a node. In this paper, we consider a failure model in which any two links in the network may fail in an arbitrary order. Three loopback methods of recovering from double-link failures are presented. The first two methods require the identification of the failed links, while the third one does not. However, pre computing the backup paths for the third method is more difficult than for the first two. A heuristic algorithm that pre-computes backup paths for links is presented. Numerical results comparing the performance of our algorithm with other approaches suggests that it is possible to achieve recovery from double-link failures with a modest increase in backup capacity. Current means of providing loop-back recovery, which is widely used in SONET, rely on ring topologies, or on overlaying logical ring topologies upon physical meshes. Loop-back is desirable to provide rapid preplanned recovery of link or node failures in a bandwidth-efficient distributed manner. We introduce generalized loop-back, a novel scheme for performing loop-back in optical mesh networks. We present an algorithm to perform recovery for link failure and one to perform generalized loop-back recovery for node failure. We illustrate the operation of both algorithms, prove their validity, and present a network management protocol algorithm, which enables distributed operation for link or node failure. We present three different applications of generalized loop-back. First, we present heuristic algorithms for selecting recovery graphs, which maintain short maximum and average lengths of recovery paths. Second, we present WDM-based loop-back recovery for optical networks where wavelengths are used to back up other wavelengths. We compare, for WDM-based loop-back, the operation of generalized loop-back operation with known ring-based ways of providing loop- back recovery over mesh networks. Finally, we introduce the use of generalized loop-back to provide recovery in a way that allows dynamic choice of routes over preplanned directions.

GREEN ENERGY, LITERATURE REVIEW OF RENEWABLE ENERGY SOURCES IN INDIA

Dr. Nagaraju Kaja, Isha Borawake — Thu, 18 Dec 2025 00:00:00 +0000

In India, the main goals of the use of renewable energy are to reduce climate change, enhance economic development, and enhance energy security and its accessibility. Utilizing sustainable energy and ensuring that all residents have access to modern, affordable, dependable, and sustainable energy are necessary for sustainable development. India's renewable energy sector has grown tremendously in recent years, due to strong government support and a favourable economic climate. The country has set ambitious targets to increase its renewable energy capacity to 500 GW by 2030. Over a third of India's installed capacity and more than 40% of the country's power generation, including large-scale hydropower, are currently derived from renewable energy sources. People now have better access to electricity as a result of the creation of a unified national power system, and the growth of renewable energy has been crucial to this. However, there are still challenges that need to be addressed, such as the standardization of guidelines, development of stable grid and transmission networks, and steep fluctuations in solar project tariffs. This paper aims to study comprehensive information on the achievements, prospects, projections, and challenges of renewable energy in India.

TECHNO-ECONOMIC ASSESSMENT OF A CHICKEN-MANURE-FED BIOGAS GENERATOR SYSTEM IN A PHILIPPINE FARM

Elisa Santos, Ian Pagdato — Sat, 20 Dec 2025 00:00:00 +0000

Chicken manure is an abundant agricultural waste stream capable of supporting decentralized biogas power systems. This study presents a techno-economic assessment of a 160 kW biogas generator operating on chicken-manure-derived biogas in a tropical Philippine farm. Using realistic but generic technical parameters, the system consumes approximately 90 Nm³/hr of biogas at 55% methane content and generates an estimated 1.27 gigawatt-hours (GWh) of electricity annually. Methane mitigation totals roughly 260,000 kg/year, equivalent to 6,500 tons of carbon dioxide equivalent (CO₂eq) using a Global Warming Potential over 100 years (GWP100) of 25. Diesel displacement exceeds 380,000 liters annually. With a capital cost of PHP 6.5 million and annual operating expenses of PHP 1.4 million, the system achieves net annual savings of approximately PHP 15 million, yielding a payback period of about five months and a Return on Investment (ROI) exceeding 200%. Findings demonstrate that chicken-manure-fed biogas generator systems offer strong technical, environmental, and economic benefits suitable for rural energy applications.