International Journal of Engineering and Management Research
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Powder Metallurgy Processed W-Cu Composites: A Microstructural Study
Full text linkThis study assesses the microstructure and elemental concentration of W–Cu composites with fixed ratios of tungsten and copper (W80Cu20, W70Cu30, W60Cu40) using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS). Backscatter electron mapping through SEM shows the dispersion and characteristics of the tungsten and copper-phase carried in the matrix, whereas EDS characterizes semiquantitatively and identifies the W, Cu, C, and O content in the compositions. The distribution of which portrays an increased Cu content as well as reduced W content with increased Cu ratio, with minor carbon and oxygen present, possibly pre-process contamination. The results obtained here are in line with previous works established from this research that SEM and EDS are suitable methods of analyzing phase distribution, homogeneity and elemental composition in W–Cu composites for enhancing the processing parameters and mechanical and functional properties. Thus, the combination of SEM and EDS allows a deep investigation of the influence of the microstructure on the properties of W–Cu composites and accomplishments of their intended aims and tasks in the frame of modern science and engineering
A Study on Fundamental Analysis of Top 10 Gainers on BSE (Last Fiscal Year 2023-24)
No full textThis study aims to evaluate the financial health and investment potential of the top 10 gainers listed on the Bombay Stock Exchange (BSE) for the fiscal year 2023–2024 through fundamental analysis. Using secondary data from company financial statements, BSE reports, and public disclosures, key metrics such as EPS, P/E ratio, ROE, and debt-equity ratios are analyzed. The results provide insights into whether the stock price growth of these companies is supported by solid fundamentals or driven by market speculation. The paper concludes with sector-specific recommendations for value and growth investors
Hybrid Metaheuristic Framework for Multi‑Objective Job Shop Scheduling: Balancing Makespan and Resource Utilization
Full text linkIn manufacturing and service industries, job shop scheduling problems (JSSP) are central to efficient production planning. Traditional studies often focus solely on minimizing the makespan – the total time required to complete all jobs – without explicitly considering how effectively resources (machines, buffers, energy) are utilized. To address the dual challenge of throughput and resource efficiency, we propose a hybrid metaheuristic framework that simultaneously optimizes makespan and resource utilization. The proposed approach combines a multi‑objective genetic algorithm (MOGA) with a particle swarm optimization (PSO) based refinement to dynamically allocate resources and adjust job sequences. Experiments on benchmark datasets and simulated shop‑floor scenarios demonstrate that the hybrid model yields Pareto‑optimal solutions that reduce makespan and idle time while increasing machine utilization, outperforming conventional single‑objective heuristics
Comparative Analysis of Induction Machine Performance Under Balanced/Unbalanced Conditions Using MATLAB Simulink
Full text linkThis study investigates the dynamic performance of a three-phase induction motor across stationary, rotor, and synchronous reference frames. It evaluates torque-speed characteristics under balanced and unbalanced supply voltage conditions to identify the most appropriate reference frame for simulation in scenarios involving voltage imbalances. The analysis employs park\u27s transformation to simplify the modelling process, with simulations conducted in MATLAB/Simulink. Flux linkage equations derived from the motor parametric voltage equations are integrated into an embedded MATLAB functions to model the motor\u27s dynamic behaviour. Results reveal that the synchronous reference frame is optimal for balanced conditions, the rotor frame for unbalanced stator voltages, and the stationary frame for unbalanced rotor voltages. Torque-speed profiles remain consistent across frames under balanced voltages, highlighting the robustness of the d-q transformation modelling approach
Functional Freeze in Urban India: An Integrative Literature Review of Stress, Environment and Workplace Experience
Full text linkStress is a physiological and psychological response to perceived threats in the environment, through which the body and mind attempt to protect themselves. Among the recognised stress responses: fight, flight, fawn, and freeze, this study focuses on freeze, specifically its contemporary subtype, functional freeze, within the Indian urban context. Freeze has been understood as an acute response to life-threatening situations; emerging research in clinical psychology suggests that persistent, non-life-threatening stressors can evoke a state of functional freeze. In this state, individuals meet everyday demands outwardly while experiencing internal disengagement, emotional flattening, and a reduced capacity to process stress.
In urban India, sensory overload, crowding, limited access to restorative natural spaces, fast-paced routines, and increasing dependence on digital devices for work and education can disrupt normal stress-recovery cycles. These conditions create a context in which functional freeze may emerge and persist.
This study synthesises perspectives from behavioural neuroscience, urban environmental research, and self-regulation to develop a systemic understanding of functional freeze. The review extends into organisational settings, examining how functional freeze may manifest in workplaces through patterns such as presenteeism, decision inertia, and mental withdrawal, despite apparent productivity. From the standpoints of human resource management, organisational behaviour and transformation, functional freeze recognition has propositions for employee wellbeing, sustainable performance, and workplace culture
A Review of Literature on Sustainable Supply Chain Management: Challenges and Solutions
Full text linkSustainability in supply chain management is crucial approach that combine environmental social and the economic factor into supply chain operation. it increasingly identified as a essential component for achieving environmental, economic and social sustainability within organization. This review strengthens the research insights organised the challenges, factors affecting environment and future trends in sustainability in supply chain management adoption. This paper explores various dimension of sustainability in supply chain management and highlighting the above Research Insights that Shape its implementation. Additionally the route to achieving sustainability are highlighted through innovative structure, technological advancement and policy based solution. The key difficulties such as organizational barrier, financial constraints are analysed alongside possibly presented by industry 4.o, circular economy model and green logistics. Besides, policy and regulatory issue plays a vital role in molding the landscape of sustainability in supply chain management in many zones inadequate government support under vague regulations hinder companies efforts to accept sustainable practices. The paper is structured into several key sections: challenges and barriers of sustainability in supply chain management, environmental factors of sustainability in supply chain management, future trends in sustainability in supply chain management, achieving sustainability in supply chain management. The each section combine existing research and offers insights how these elements connect to Forster sustainability in supply chain
Quantum-Inspired Resource Allocation in Cloud-IoT Networks Using Hybrid Classical-Quantum Algorithms
Full text linkThe rapid expansion of Cloud-IoT networks has created significant challenges in resource allocation, requiring advanced optimization techniques to efficiently manage computational power, storage, and bandwidth. The increasing demand for low-latency, high-efficiency allocation mechanisms necessitates adaptive and scalable solutions. Traditional resource management techniques, including heuristic-based algorithms and machine learning approaches, often struggle to handle dynamic workloads, heterogeneous IoT devices, and unpredictable traffic fluctuations. These conventional models suffer from limited adaptability, slower convergence rates, and suboptimal resource utilization, leading to higher operational costs and resource wastage. To address these limitations, this research introduces a hybrid classical-quantum model integrating the Quantum Approximate Optimization Algorithm (QAOA) to enhance real-time resource allocation. The proposed model combines classical computing for handling routine data processing with quantum-inspired optimization to solve complex allocation problems more efficiently. This approach ensures dynamic adaptability, minimizing latency and maximizing energy efficiency. The experimental evaluation was conducted using dynamic IoT workload scenarios, where key performance metrics such as accuracy, convergence speed, adaptation latency, energy efficiency, and operational cost reduction were analyzed. The results show that QAOA achieves 97.8% accuracy, significantly outperforming WOA (87.5%), HHO (85.2%), MPA (83.1%), and AHA (82.4%). Additionally, it reduces latency from 105 ms to 85 ms, increases energy efficiency from 1.82 to 2.48, and lowers resource wastage from 6.5% to 3.8%, demonstrating superior optimization capabilities. These findings confirm that the proposed hybrid model is highly effective in addressing resource allocation complexities, significantly improving cost efficiency, scalability, and computational performance in Cloud-IoT networks
IOT-Based Accident Prevention System: A Model Experiment for U-Turn Curves
Full text linkIn today’s world, the combination of high population density and the widespread use of vehicles has led to a serious concern: the increasing number of road accidents. Every year, thousands of people lose their lives or suffer serious injuries in such incidents. In developing countries like India, road accidents remain one of the leading causes of death. National highways, as well as mountain and hill areas, have dangerous roads and curves that are narrow and single-lane. Accidents at U-turns commonly occur due to limited sight distance, especially on curved or hilly roads, where drivers cannot see oncoming traffic in time to react safely. Inadequate road signage, poor lighting, and lack of dedicated turning lanes further increase the risk. Additionally, high vehicle speeds, misjudgment of gaps in traffic, and sudden or illegal U-turns made without proper signaling often lead to collisions. In areas with high traffic volume or narrow roads, the risk multiplies as vehicles may not have sufficient space or time to complete a U-turn safely. Addressing these risks requires a combination of improved road design, warning systems, enforcement of traffic laws, and driver awareness. At these curved sections, drivers are often unable to see oncoming vehicles or obstacles, and if their vehicle is not in good condition, it becomes difficult to control, increasing the risk of accidents. To minimize such accidents, we propose a project aimed at preventing collisions at U-turns by alerting drivers to oncoming vehicles. This is done by keeping an ultra sound sonic sensor on both sides of the U-turn and so that if vehicle comes from one end of the curve, then sensor senses and it gives signal to Arduino and Arduino gives command to LED lights of the other side in order to alert the driver
Digital Transformation in Marketing: Strategies, Challenges, and Future Trends
No full textDigital transformation has revolutionized marketing, redefining how businesses engage with consumers. The shift from traditional marketing to digital strategies has been driven by technological advancements, data analytics, artificial intelligence, and automation. This paper explores the impact of digital transformation on marketing, examining key trends, challenges, and future directions. Through a review of literature and empirical data, the study highlights the role of digital tools, social media, and personalized marketing in enhancing customer experience and business growth
Geospatial Clustering of Psychotropic Substances Crime Locations
Full text linkThe worldwide prevalence of drug overdose and the misconception on psychotropic substances lead to the increased incidents of drug use disorders, drug offences and environmental harms along with financial burden on local and federal government for drug control and prevention. As a small step to reduce drug-related offences, we analyze the data sets consisting of drug- or alcohol-related crime incidents to discover temporal and seasonal patterns of such crimes. More importantly, we employ a density-based clustering algorithm to find a natural grouping of the geographic locations of crime incidents based on their longitude and latitude information. By visualizing such clusters with major crime types for each cluster, we allow residents and public safety officers to easily identify hot spots of drug-related crimes and hence develop new prevention plans to cope with drug-related crimes