J-STAR: Journal of Social & Technological Advanced Research
J-STAR is an interdisciplinary journal exploring the integration of social sciences and technological research. It addresses contemporary challenges by encouraging studies that combine innovation, policy, and human development. The journal publishes original research, case studies, and reviews that contribute to global knowledge and problem-solving. By fostering collaboration between scholars, technologists, and policymakers, J-STAR creates a bridge between research and practice. It aims to inspire solutions that advance both society and technology for a better future.
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Digital Platforms and the Gig Economy: Socio-Economic Opportunities and Challenges
A digital environment has changed the nature of the labor market and the emergence of the gig economy- a new form of a flexible, technologically mediated labor model, that is typified by temporary contracts, freelance work, and jobs defined by tasks. This paper will discuss socio-economic opportunities and threats of incorporating digital platforms into labor markets in developed countries and developing regions across the globe. Using empirical research and theories, the article examines the impacts of gig work on income-making, labor rights, social protection, and autonomy of workers. The impact of platform algorithms, online marketplaces and mobile applications on providing labor flexibility and creating risks of job insecurity, inequality, and exploitation are also taken into account in the study. This study offers a subtle explanation of the transformative nature of the gig economy on employment and the society through the integration of technological, economic and sociological viewpoints.
Copyright (c) 2025 J-STAR: Journal of Social & Technological Advanced Research
Data-Driven Optimization Models and Performance Enhancement in Pakistan’s Supply Chain Networks
Demand of supply chain networks to incorporate data-driven optimization models has been brought about by the growing complexity of global and domestic markets in a bid to improve performance and competitiveness. The supply chains in Pakistan have structural inefficiencies, fluctuating demand, infrastructural bottlenecks, and a lack of digitalization. This paper will discuss how data-driven optimization models (including predictive analytics, big data analytics and algorithm-based decision systems) can be used to enhance the performance of supply chains in Pakistan manufacturing, logistics, and agro-industrial industries. Applying the analytical and empirical framework, the study measures the role played by data-enabled decision-making in cost efficiency, accuracy in demand forecasting, flexibility of operation, and resiliency of the network. The results emphasize that companies with data-driven optimization achieve a great enhancement of supply chain responsiveness and total performance, though issues connected with data characteristics, technological preparedness and skills deficiency remain. The research offers strategic recommendations to managers and policy makers interested in transforming the supply chain competitiveness of Pakistan by using analytics.
Cybercrime Awareness and Digital Safety Behavior Among Internet Users
Cybercrime has proliferated along with the rapid increase in the use of internet and digital connectivity in the world. As people conduct online activities which can be anything ranging from social networking to e-commerce to even telecommuting, it makes people more and more vulnerable to hacking, phishing, identity thefts, malware attack and various forms of cyber fraud. This research article has investigated cybercrime awareness and digital safety behavior relationship among the internet users, exploring the influence this knowledge of cyber threats has on safety practices such as secure password behaviours, antivirus software usage, use of privacy settings and cautious interaction online. Inspired by the interdisciplinary research of cybersecurity, psychology and the information systems, this study recommends the essential determinants of safe behaviour online and evaluates the Perverse socio-demographic aspects affecting cyber awareness users and cyber protection and measures. The results point to both potential opportunities to help build digital resilience, as well as potential barriers to helping make cyber safety practices work.
Cloud Computing Adoption, Cost Efficiency, and Organizational Performance in Pakistan’s IT Sector
Cloud computing has transformed the working environment of the information technology (IT) organizations globally and the firms have been in a position to optimize on cost, grow flexibility and general performance. The application of cloud technologies to the Pakistani IT industry is topical as one of the strategic requirements which allow organizing the efficiency of operations, reduction of costs, and competitive advantages. The paper investigates the relationship between the application of cloud computing, the cost-effectiveness and performance of the organizations of IT companies in Pakistan. The paper uses statistical modeling, including regression analysis and structural equation modeling to establish the impact that the adoption of clouds has on cost efficiency and, by extension, performance of firms. The findings reveal that a higher level of cloud adoption can positively impact the cost-efficiency and influence such performance indicators within the organization as the operational productivity, scalability, and financial performance. According to the paper, cloud technologies may be the secret of sustainable development of the Pakistani IT industry and its competitive power.
Automation, Robotics Adoption, and Cost–Performance Efficiency in Pakistan’s Industrial Sector
The industrial processes are changing fast, with the implementation of automation and the use of robotics technologies worldwide which promises industries more efficiency, cost-efficient activities, and better performance. The paper examines automation and robotics in the industrial sector of Pakistan and how it has affected the cost-performance efficiency. A quantitative research design was used whereby 250 industrial firms in the manufacturing, engineering, and processing industries were sampled to obtain the data. The correlation between automation adoption, efficiency in operations, and financial performance was tested by using applied statistical methods, such as regression and efficiency modeling. The results indicate that companies that combine robotics and automated solutions record high levels of efficiency in production processes, labor cost reduction, and quality of output. It can also be noted that there is also sectoral variation as the large-scale manufacturing firms realize better performance gains than smaller, less automated firms. The mediating factor between automation adoption and financial results is identified to be operational efficiency, with the strategic importance of technology in improving industrial competitiveness. The findings can guide managers and policymakers to promote automation-based productivity in the industrial sector in Pakistan.
Quantum Computing: Algorithms and Applications
Quantum computing is a modern new paradigm of computation similarly exploiting the standards of quantum mechanics like superposition, entanglement and quantum interference to finish computations a good way to be unfathomable for classical computers. This paper follows the trails to the theoretical foundations of quantum computing and offers with a few essential algorithms for quantum computing like Shor's set of rules for factorization, Grover's set of rules for search, Quantum Fourier Transform and Quantum Approximate Optimization Algorithm (QAOA). This paper specializes in how complicated the computation is, mathematical ideas underlying such algorithms and their feasible makes use of in cryptography, optimization and simulation in quantum. By sifting thru the interaction among quantum mechanics and algorithmic design, this paintings offers a few perception into the mechanisms that permit for quantum speedup, the cutting-edge obstacles due to hardware obstacles and mistakess charges and what may be carried out withinside the destiny each theoretically and empirically.
Smart Water Management Systems Using Sensor Networks
Rapid urbanization, populace boom and weather variability have positioned exceptional stress at the world's water resources, and green control of water is a essential precedence for sustainable development. Conventional water control structures are frequently depending on guide tracking and centralized manipulate mechanisms that lessen the cappotential to discover leakages, screen the water quality, and reply to the fluctuations in call for in actual time. Recent development in sensor community and clever era has brought about the introduction of clever water control structures that offer for ongoing tracking, computerized control and records-provided choice making. Smart water control structures are primarily based totally on wi-fi sensor networks, Internet of Things (IoT) technology and facts analytics that provide metric size of parameters together with water flow, stress, quality, and intake patterns. These structures assist growth operational efficiency, limit water losses and marketplace carrier reliability withinside the city and agricultural areas. This paper examines the function of sensor networks in clever water control structures, especially focusing at the structure of the system, the operational mechanism of the networks, and the overall performance results. By bearing on empirical studies findings and technological innovations, the studies record emphasised the position of sensor-primarily based totally structures in attaining water conservation, infrastructure resilience and sustainable useful resource control, suggesting getting to know for coverage makers and different stakeholders inclusive of software carriers to pan out modernized water governance frameworks.
Blockchain Applications for Secure Data Transmission in Engineering Systems
The excessive fee of digitalization of the engineering structures has significantly amplified the amount, speed, and sensitivity of the information despatched throughout the networks, making the difficulty of making sure the protection of statistics transmission extraordinarily important. Traditional protection structures that depend totally on a centralized structure have become greater at risk of cyberattacks, information breaches, and a unmarried factor of failure. The blockchain generation has proved to be a disruptive answer that has the capability to enhance the facts protection, integrity, transparency, and accept as true with withinside the allotted engineering environment. Using hash functions, decentralised facts and consensus mechanisms, blockchain allows the switch of tamper-resistant and verifiable statistics with out regarding centralized authorities. This paper will check out how blockchain can be used to make certain the protection of statistics transmission in engineering structures, which include commercial manage structures, clever grids, Internet of Things (IoT) infrastructure, and cyber-bodily structures. The paper discusses the makes use of of blockchain primarily based totally architectures in tackling a number of its principal safety troubles like records alterations, unauthorized access, and gadget weakness. Moreover, it additionally factors out the troubles of integration, scalability, and destiny research. The consequences suggest that blockchain generation has a excessive capacity to boost steady verbal exchange structures withinside the modern-day engineering structures further to facilitating resilience, transparency, and efficiency.
Intelligent Traffic Management Systems Using AI and IoT
Rapid urbanization and the exponential increase of vehicle ownership has led to increased traffic congestion, road accidents, high fuel consumption and environmental pollution in cities across the world. The traditional traffic management systems based entirely on fixed-time signals and manual observation have failed to cope with the dynamic and complicated traffic flows of the modern world. In the above context, the development of Intelligent Traffic Management Systems (ITMS) based on Artificial Intelligence (AI) and Internet of Things (IoT) has become a game-changing design for sustainable urban mobility. AI-driven algorithms help predict traffic in real-time, adaptive signal control, and anomaly detection. 04 Myriad data IoT-based devices like sensors, cameras, and vehicles, collect and communicate objects for continuous data gathering. 05 Concluding note While AI terrorism strategies provide many benefits to glean the field's positive aspects, they also demand specific conditions before they can be applied to any city. 06 conclusion AI terrorism strategies offer multiple advantages to benefit from the good parts of this field, they also require these sets of conditions prior to implementing them in any city. This paper focuses on the conceptual background, technological building blocks and operating mechanisms of AI- and IoT-based traffic management systems. It outlines the positive impact of using data-driven decision making to boost traffic efficiency, reduce congestion and improve road safety, and minimize environmental impacts. The study goes on to discuss several challenges around data privacy, infrastructure costs and scalability of the systems and stresses on the need for integrated policies plus smart city frameworks. The results indicate the growing need of AI and IoT based traffic systems to build resilient, efficient and intelligent traffic network systems for rapidly urbanizing areas.
Multi-Agent Control Systems in Autonomous Vehicles
Multi-Agent Control Systems (MACS) has become one of the fundamental platforms on the development of autonomous vehicle (AV) technologies because of its capability to facilitate decentralized coordination, cooperative intelligence, and adaptive decision-making. In this paper, the theoretical basis, architecture, communication models, and application of MACS in autonomous driving are studied. The paper examines the collaborative model of navigation, obstacle detection, path planning, and traffic coordination of distributed agents by relying on a systematic literature review and analytical synthesis. It is found that MACS provide a significant boost in scalability, fault tolerance, safety and real-time responsiveness in AV ecosystems. The study finds that the future intelligent transportation systems will require multi-agent control particularly when cities evolve into large-scale autonomous mobility networks.
Optimization of Microgrid Operations Using Metaheuristic Algorithms
The optimization of the work of microgrids (MGs) has become one of the most important issues of sustainable, cost-effective, and reliable power supply in the context of proliferation of distributed energy resources (DERs) and development of energy storage. The choice of approaches to the nonconvex, stochastic, multi-objective nature of the issues at hand in the day-ahead scheduling, real-time dispatch, economic dispatch, unit commitment, energy storage management, and resilience optimization related to micro grids has been to adopt metaheuristic algorithms (population-based and nature-inspired search strategies such as the Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Ant Colony Optimization (ACO) and even The paper presents the literature review and synthesis of research on metaheuristic optimization of microgrid operations, considers comparative trends in performance, provides an insight into the advantages and limitations of existing popular algorithms, and suggests the directions of future research (hybridization, surrogate-assisted search, multi-objective formulations, and integration of machine learning in forecasting). The comparison of the algorithms is conducted on recent surveys and studies on the domain (since 2019 to 2024), and representative case studies, which provides the analysis in terms of minimization of cost, reduction of emissions, reliability, and efficiency of the computation. Suggestions to investigators and practitioners regarding the application of both metaheuristic-based energy management systems (EMS) in grid-connected and isolated microgrids.
Wireless Sensor Networks for Structural Health Monitoring
Wireless sensor networks (WSNs) have revolutionized the field of structural health monitoring (SHM) due to the possibility of dense, low-cost and scalable deployment of sensing nodes in order to continuously monitor civil, aerospace and mechanical structures. WSN based SHM system, which can provide a high-resolution dataset for vibration based damage detection, modal analysis, strain monitoring and environment condition supervision etc. Installation simplicity and lifecycle costs are decreased comparing to wired systems. This article goes through a review of basic concepts, sensor technologies, communications protocols, data processing algorithms and damage detection algorithms, deployment strategies, power management strategies, and some real world study examples. Some of the key challenges -- such as time synchronization, reliable wireless communication, energy harvesting, sensor placement and data management -- as well as some recent technical advances in this field (edge computing, IoT connectivity, low power wide area networks, and machine learning) are some of the topics addressed. Finally, a set of research gaps and a series of practical recommendations for future WSN-based SHM implementations are given.
FPGA-Based Embedded System Design for Real-Time Applications
FPGAs have turned out to be an effective development environment in the real-time embedded system design because they are inherently parallel, reconfigurable, and deterministic in their execution. In contrast to the use of microcontrollers or processors, FPGA-based embedded systems can address very high real-time requirements needed in industrial automation, automotive control, aerospace, medical equipment and high-speed communication systems. This is a research paper discussing the application of FPGA based embedded systems in real-time system, in relation to system architecture, design methods, hardware software co-design and performance optimization strategies. The research paper examines literature, presents a systematic design approach and the performance indexes that include latency, throughput and determinism. The results establish that FPGA-based platforms are superior in system time-critical applications as compared to traditional embedded platforms besides providing flexibility and scalability. The article ends with the discussion of challenges, future trends and recommendations toward implementing FPGA-based real-time embedded systems in the modern engineering practice.
Energy Harvesting Technologies for Low-Power Devices
The rising exponential rate of low-power electronic devices, such as wireless sensors, wearable electronics, and IoT systems, has exacerbated the need of sustainable and autonomous sources of energy. Traditional battery-based power systems tend to have capacity constraints, lifetime constraints, and maintenance constraints, thus researchers have looked into energy harvesting (EH) technologies as an alternative power source. This paper survey recent developments in energy harvesting schemes- including solar, thermal, vibrational, piezoelectric and radio-frequency (RF) sources- and their viability to low-power devices. It focuses on the system integration, energy conversion efficiency, and scalability under operations in real world. The review indicates the promise of hybrid EH systems to increase the lifespan of the operation of devices coupled with a reduced environmental impact. Empirical experiments show that self-sustaining operation of a wide range of low-power applications can be facilitated by optimized EH solutions but that there are still issues associated with energy intermittency, storage, and resource constraints. The proposed study will offer a broad framework to researchers and practitioners who want to adopt EH technologies in next-generation low-power electronics.
Biodegradable Polymers for Sustainable Engineering Applications
The biodegradable polymers are currently gaining special importance as an essential material in the field of sustainable engineering, due to their possibility of being degraded by the natural environment as environmentally friendly products. These polymers are a good alternative to traditional plastics made of petroleum, which causes a significant contribution to ecological pollution of the environment and harm on the ecosystem on the long term. This research paper will discuss properties, synthesis, degradation, engineering uses and performance of biodegradable polymers in the environment. The method of systematic review and qualitative synthesis was selected in order to evaluate the condition of the existing research and identify challenges and opportunities to adopt this research in the industry. Results show that though polymers like polylactic acid (PLA) and polyhydroxyalkanoates (PHAs) are high in their mechanical performance and biodegradability there are still hurdles implicitly like high cost of production, inconsistent rate of degradation and lack of large scale infrastructure. The paper concludes that biodegradable polymers have the potential to be of great use in sustainable engineering through interdisciplinary innovation, industrial scale adoption and proper dumping techniques.
Application of Machine Learning in Structural Health Monitoring of Bridges
The necessity of structural health monitoring (SHM) for the safety, reliability and service life of bridge structures is well recognized. Traditional inspection techniques that are commonly manual and tedious and susceptible to human errors, now can be complemented or replaced by more advanced data driven methods. Machine Learning (ML) has powerful tools for processing massive sensor data, anomaly detection, predicting structural decay and promoting proactive maintenance decision making. In this paper, such ML techniques are reviewed in the context of applying them to bridge SHM including supervised and unsupervised learning intact damage detection/condition assessment algorithms along with those that estimate time till failure (or remaining service life).The review was conducted using secondary data derived from published literature, case studies and experimental reports and assessed the potential of each ML algorithm such as ANN, SVM, decision trees and CNN. The analysis shows accuracy in detection, early warning and action precision increase as benefit while challenges on data quality, sensor location, the interpretability of models and computation time are also considered. Results show that ML-based SHM could help improve the safety and reliability of bridges, reduce maintenance cost, and facilitate the transformation to smart infrastructure.
Optimization of Electric Vehicle Battery Performance Using Machine Learning Techniques
Electric vehicles (EVs) have become among the foundations of green transportation because of the fast global shift towards sustainable transportation. Nevertheless, the problems of short battery life, extended charge durations, and unpredictable performance in different conditions are impediments to the mass adoption of EVs. Machine learning (ML) has therefore come in to overcome these shortcomings to become a groundbreaking application in maximizing the battery performance of electric vehicles. ML algorithms will be able to capture the nonlinear relationships that are present in battery systems to predict the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) with great accuracy. In this paper, I will discuss how the efficiency, reliability, and sustainability of EV batteries can be improved using machine learning techniques, specifically neural network, support vectors machine (SVM), and reinforcement learning. In the research, the secondary data will be based on the literature available to study the predictive models and optimization strategies that enhance battery management systems (BMS). Results point to the fact that implementing ML in EV battery management leads to alleviations in battery degradation, adaptive charging, and longer battery life. The paper concludes that the optimization of the electric mobility solutions towards energy efficiency, cost-effectiveness, and intelligent usage, is majorly driven by ML, and in line with global sustainability imperatives.
Energy-Efficient IoT Architectures for Smart Cities
The Internet of Things (IoT) is a key to the development of smart cities as it gives an opportunity to interconnect the devices, sensors, and systems to enhance urban management and the quality of life. Nevertheless, the rapid increase in the number of IoT devices has come into conflict with energy consumption, sustainability, and scalability of the systems. This essay discusses smart cities with their energy-efficient IoT architecture with the focus on how the advanced communication protocol, edge computing, and artificial intelligence can help minimize power consumption and keep their performance high. The paper examines some design architectures that maximize energy by using dynamic data processing, data communication using low-power technologies, and smart network management. It also notes the performance versus energy savings trade-offs, and ways to strike a balance between the two by using adaptive resource allocation. The results imply that the development of smart urban ecosystems that require low power consumption of the Internet of Things is imperative to reduce carbon emissions and operational expenses and enable massive smart solutions that include intelligent transportation, smart grids, and waste management.
Blockchain-Based Framework of Cybersecurity in Cloud Data Protection using APA-style in-text citation
The rapid irrepressible evolution of cloud computing services has not only transformed the manner in which industrial organizations save, manipulate and disseminate their data but has also generated significant cybersecurity issues. The traditional security measures such as access controls, encryption and intrusion detection systems are not inclined towards providing comprehensive security against the new and emerging cyber threats such as data breach, unauthorized access and insider attacks. Blockchain technology is an emerging technology that is decentralized, immutable, and transparent, which is a good alternative to enhance cloud data security. The paper will explain how the blockchain can be adopted into the cloud-based cybersecurity systems to protect sensitive data and ensure the integrity of data and secure access control. The study presents the potential of deploying smart contracts, consensus mechanisms, and cryptographic protocols in clouds through an examination of the secondary data in the form of published literature, case studies, and in-depth simulation. The discussion shows that blockchain-based systems may offer greater data credibility, detectability, and verifiability. Other challenges that are also discussed in the study include scalability, latency, and energy consumption that interfere with the practical use of blockchain solutions on clouds. The results indicate that the blockchain-enhanced with the current cloud security systems can make data security and compliance much better and more reliable and enable the development of more resilient and trusted cloud computing systems.
The Future of Edge AI: Combining Artificial Intelligence with Edge Computing
The recent rapid evolution of the Artificial Intelligence (AI) and Edge Computing resulted in the advent of the new technological paradigm of the Edge AI, which brings the intelligence closer to the location of the data sources, which offers real-time analytics, enables making decisions in less time, and offers more privacy. Unlike the previous system of cloud based AI (relying on the central processing unit), Edge AI is decentralized and introduces machine learning into the peripheral devices, such as sensors, smartphones, and Internet of Things units. In this paper, the future of Edge AI, its application and issues associated with the current industries, such as healthcare, manufacturing, transportation, and smart cities will be discussed. The study shows that Edge AI reduces the time of latency, optimizes the use of bandwidth and generates greater autonomy in the system, and incorporates the concerns of privacy and power consumption. In addition, the 5G networks and neuromorphic computing, combined together, will make an Edge AI system implementation and operation faster. The findings show that Edge AI will play a significant role in designing intelligent ecosystems since it would offer sustainable and responsive infrastructures across various regions in accordance with the trends of worldwide digital transformation.