Advanced Management System Research Articles

A Scalable and User-Friendly Framework Integrating IoT and Digital Twins for Home Energy Management Systems

The rise in electricity costs for households over the past year has driven significant changes in energy usage patterns, with many residents adopting smarter energy-efficient practices, such as improved indoor insulation and advanced home energy management systems powered by IoT and Digital Twin technologies. These measures not only mitigate rising bills but also ensure optimized thermal comfort and sustainability in typical residential settings. This paper proposes an innovative framework to facilitate the adoption of energy-efficient practices in households by leveraging the integration of Internet of Things technologies with Digital Twins. It introduces a novel approach that exploits standardized parametric 3D models, enabling the efficient simulation and optimization of home energy systems. This design significantly reduces deployment complexity, enhances scalability, and empowers users with real-time insights into energy consumption, indoor conditions, and actionable strategies for sustainable energy management. The results showcase that the proposed method significantly outperforms traditional approaches, achieving a 94% reduction in deployment time and a 98% decrease in memory usage through the use of standardized parametric models and plug-and-play IoT integration.

Heat transfer optimization using computational insights into nodal/saddle point flow patterns of tera-hybrid nanofluid containing microbes in a cylindrical shells

Tetra hybrid nanofluids enhance heat transfer efficiency in advanced thermal management systems, benefiting industries like electronics cooling, automotive, aerospace, and renewable energy. In this study, we examine the impact of magnetohydrodynamic tetra-hybrid nanofluid on nodal/saddle stagnation points in a rounded cylinder with a sinusoidal radius. The analysis focuses on optimizing energy and mass transfer rates around a circular cylinder with a sinusoidal surface, simulating thermal processes in biological systems. By utilizing similarity variables, a complex set of nonlinear partial differential equations is transformed into ordinary differential equations and solved numerically using MATLAB's bvp4c solver. The effects of several parameters are discussed graphically for the nodal stagnation point as well as numerically for both the nodal and saddle points. At R=4.5, the heat transfer rate for the tetra hybrid nanofluid shows a 1.36 % increase compared to the nanofluid, underscoring the enhanced thermal efficiency of hybrid nanofluids in radiative conditions. indicates that the application of a magnetic field, combined with variations in d, results in significant improvements in shear stress and heat transfer, reflecting enhanced velocity and thermal profiles compared to Madhukesh et al. (Gangadhar et al., 2024) [21]. The results indicate that increasing ϕ1 enhances the Nusselt number and improves heat transfer, while the accompanying rise in flow resistance typically leads to a decrease in mass transfer rate.

Dynamic inferences of crash risks in freeway merging zones: a spatio-temporal deep learning model

Freeway merging zones are critical for freeway operations and management due to potential crashes arising from complex vehicle merging behaviours. This paper investigates the application of a spatio-temporal deep learning model to infer crash risks in the zones. We first introduce a crash risk index based on Time-To-Collision and vehicle merging patterns. An innovation is that the developed spatio-temporal transformer model can analyze the evolving risk index. This model effectively captures dynamic risk features through a multi-head attention mechanism within its spatio-temporal learning components. Numerical experiments on nine inference tasks with varying spatial resolutions show improved performance of the model with lower resolution. Moreover, the ST-Transformer model is benchmarked against three advanced deep learning models, which consistently demonstrates its superiority in capturing spatio-temporal dependence in risk sequences. This investigation significantly contributes to a richer understanding of proactive traffic safety, providing valuable insights for advanced freeway management and driver assistance systems.

Developing a digital management system for museum collections using RFID and enhanced GIS technology.

In recent years, the integration of Radio Frequency Identification (RFID) technology with deep learning has revolutionized the Internet of Things (IoT), leading to significant advancements in object identification, management, and control. Museums, which rely heavily on the meticulous management of collections, require precise and efficient systems to monitor and oversee their valuable assets. Traditional methods for tracking and managing museum collections often fall short in providing real-time updates and ensuring optimal environmental conditions for preservation. These shortcomings place a considerable burden on museum staff, who must manually track, inspect, and maintain extensive collections. This study addresses these challenges by proposing an advanced electronic management system that leverages the synergy between RFID technology and Geographical Information Systems (GIS). By integrating an enhanced LANDMARC algorithm into our geoinformation framework, the system visually represents the real-time location of museum collections on custom electronic maps, significantly improving the accuracy and timeliness of environmental monitoring. Additionally, RFID technology is utilized to continuously identify the real-time location of museum staff, facilitating the evaluation of their inspection tasks. This dual approach not only enhances the operational efficiency of collection management but also supports the development of intelligent, automated systems for museums, advancing the application of RFID technology in item identification and location management.

Achieving Ultrahigh Efficiency of Triboelectric Nanogenerator Energy Harvesting Systems via Hybrid Electronic‐Spark Power Management

AbstractSignificant efforts are devoted to optimizing the efficiency of triboelectric energy harvesting systems, particularly through the design of an advanced power management system (PMS) for Triboelectric Nanoenerators (TENGs). A critical aspect of PMS is the design and control of switches. However, existing switches face significant limitations. For spark switches, precise control cannot be achieved, and electronic switches can only operate at voltages below several hundred volts which is limited by the risk of electrical breakdown. To address these limitations, a hybrid electronic‐spark switch power management system (HESS) is proposed. HESS changes the connection of capacitors from parallel to series by deploying a maximum voltage tracking switch components at the peak voltage point, resulting in a much‐elevated voltage level to activate the spark switch. This approach achieves precise control of the spark switch for the first time and significantly reduces the operating voltage of electronic switches. Through simulation and experimental verification, HESS achieves the control at a voltage level of 1.8 kV for spark switch, with an electrical component breakdown voltage of only 450 V. The power density of the HESS is 29.8 mW Hz−1 m−2, which is a new record for electronic switches.

Innovative management strategies for addressing paediatric medical staff shortages in underdeveloped cities in developing countries

BackgroundPaediatric professional scarcity and uneven distribution is acute in underdeveloped regions, exacerbated by COVID-19’s workload surge and burnout, highlighting the need for strengthened prevention and response measures.AimPropose an effective talent...

A Case Study on AI Integration for Global Services at a Bangalore-based Tech Company

This case study examines Justin's leadership in integrating AI technologies at a prominent Bangalore-based tech company to enhance global services and customer experiences. By deploying AI-powered chatbots, personalized recommendations, and predictive analytics, the company significantly improved customer satisfaction, operational efficiency, and scalability. However, challenges related to data management, employee resistance, and process alignment arose during integration, prompting the implementation of strategic solutions such as advanced data management systems and workforce upskilling to foster a collaborative environment.

Does smartphone use encourage farmers to participate in centralized household waste disposal?

The centralization of household waste disposal represents a significant stride toward achieving ecological viability in rural China. This initiative can substantially alleviate the grassroots government's burden of managing rural household waste. The proliferation and utilization of smartphones, a powerful tool that can expedite and reduce the cost of imparting environmental protection knowledge to producers, is a beacon of hope in the fight against waste. This article, utilizing Probit modeling and micro-survey data from 2126 agricultural households in China, examines the effect of smartphone usage on farmers' participation in centralized household waste disposal. The findings indicate that smartphone usage significantly enhances farmers' engagement in centralized domestic waste disposal, motivating them to participate actively. Notably, this finding persists even after robustness tests. Further heterogeneity analyses indicate that older and low-income populations exhibit a more pronounced level of engagement in centralized household waste disposal. This paper presents these findings and underscores the importance of the proposed policies to enhance farmers' consciousness regarding the environmental implications of smartphone usage. These policies are not just suggestions but urgent and necessary steps towards a more technologically advanced and efficient waste management system, and their implementation is crucial for the future of waste management.

A Review on Advanced Battery Thermal Management Systems for Fast Charging in Electric Vehicles

To protect the environment and reduce dependence on fossil fuels, the world is shifting towards electric vehicles (EVs) as a sustainable solution. The development of fast charging technologies for EVs to reduce charging time and increase operating range is essential to replace traditional internal combustion engine (ICE) vehicles. Lithium-ion batteries (LIBs) are efficient energy storage systems in EVs. However, the efficiency of LIBs depends significantly on their working temperature range. However, the huge amount of heat generated during fast charging increases battery temperature uncontrollably and may lead to thermal runaway, which poses serious hazards during the operation of EVs. In addition, fast charging with high current accelerates battery aging and seriously reduces battery capacity. Therefore, an effective and advanced battery thermal management system (BTMS) is essential to ensure the performance, lifetime, and safety of LIBs, particularly under extreme charging conditions. In this perspective, the current review presents the state-of-the-art thermal management strategies for LIBs during fast charging. The serious thermal problems owing to heat generated during fast charging and its impacts on LIBs are discussed. The core part of this review presents advanced cooling strategies such as indirect liquid cooling, immersion cooling, and hybrid cooling for the thermal management of batteries during fast charging based on recently published research studies in the period of 2019–2024 (5 years). Finally, the key findings and potential directions for next-generation BTMSs toward fast charging are proposed. This review offers an in-depth analysis by providing recommendations and potential solutions to develop reliable and efficient BTMSs for LIBs during fast charging.

Effective Energy Management System in Microgrid Employing Model Predictive Controller

The primary focus of this study is to develop an energy management system that regulates the energy transfers between the hybrid microgrid system and the loads connected to it, and the grid via MATLAB/Simulink so as to model the flow of energy. The secondary aim is to make recommendations aimed at the charging and discharging of what is referred to as the hybrid energy storage system (HESS). The results indicate that the proposed algorithm successfully carried out the required task of bridging the HESS charging to discharging ratio in relation to the different operating conditions as well as power management between the microgrid and the network. In this application, a stronger charging power might be employed on the HESS. It has been seen that the HESS is more likely to complete charging within a short time than the greater charging power. A more advanced and efficient energy management system is critical to the microgrid system so that the generation can keep pace with the requirements of the load profile. It is important to take account of load forecasting with regard to power planning and executing so as to know the most suitable action that should be taken. To achieve a general reduction in the cost of operation, in this paper, we propose the use of an advanced Energy Management System (EMS), Model Predictive Control (MPC), to effectively manage the allocation of power in the microgrid.

Green logistics: Transforming supply chains for a sustainable future

In the face of escalating climate change and the increasing significance of sustainability, companies are shifting towards green logistics solutions. This article explores the various sustainable practices within logistics, the implementation of green logistics solutions, and the strategies for reducing carbon footprints. The logistics sector, a significant contributor to global greenhouse gas emissions, is under increasing pressure to adopt environmentally friendly practices. By examining case studies and industry data, we provide a comprehensive analysis of the current trends and future directions in sustainable logistics. Our study highlights the importance of integrating eco-friendly transportation, energy-efficient warehousing, sustainable packaging, and advanced inventory management systems. Additionally, we discuss the challenges companies face in adopting green logistics, such as high initial costs, technological limitations, and regulatory compliance. Through this analysis, we aim to shed light on the transformative potential of green logistics in mitigating climate change and promoting long-term sustainability in the supply chain industry.

ANALYSIS AND EVALUATION OF THE EFFECTIVENESS OF INNOVATIVE METHODS IN ENERGY

The global energy landscape is undergoing a transformative shift as the demand for energy continues to grow due to industrialization, urbanization, and population expansion. Traditional energy sources, particularly fossil fuels, have long dominated the energy sector, contributing to significant environmental challenges such as climate change, air pollution, and resource depletion. As a result, there is an urgent need to develop and implement innovative energy technologies that can meet growing energy demands while minimizing environmental impact. This paper presents a comprehensive analysis of contemporary innovations in the energy sector, with a focus on the integration of renewable energy sources, optimization of energy conversion processes, and the deployment of advanced energy management systems. These innovations are assessed in terms of their potential to enhance energy efficiency, sustainability, and reliability. By synthesizing the latest research findings and practical implementations, this study aims to identify key trends, challenges, and opportunities in the ongoing transition towards a more sustainable energy future. Keywords: renewable energy sources, energy efficiency, integration, energy consumption.

Leveraging AI and ML for Next-Generation Cloud Security: Innovations in Risk-Based Access Management

In the increasing reliance on the cloud computing era, securing digital assets against sophisticated cyber threats has become a critical concern for organizations globally. Traditional security mechanisms, which often rely on static and pre-defined access control policies, must be revised to address these threats' dynamic and evolving nature. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing cloud security through the development of advanced Risk-Based Access Management (RBAM) systems. The primary objective is to evaluate how AI and ML can improve dynamic access control, threat prediction, and mitigation strategies within cloud environments. The research adopts a mixed-methods approach, combining quantitative analysis of RBAM system performance with qualitative insights from cybersecurity experts. AI/ML models were developed using extensive historical access log datasets and integrated into a cloud-based RBAM prototype. The system's performance was assessed based on its accuracy in threat detection, reduction in false positives, and effectiveness in dynamically adjusting access controls. Results indicate that the AI-enhanced RBAM system significantly outperforms traditional methods, achieving a 30% reduction in false positives and a 25% decrease in unauthorized access incidents. Additionally, AI-driven threat prediction models demonstrated high accuracy, enabling preemptive actions to mitigate potential security breaches. These findings highlight the transformative potential of AI and ML in cloud security, providing a more adaptive and proactive defense against emerging threats. The study concludes with recommendations for refining AI/ML models and exploring their application in other areas of cloud security, emphasizing the need for continued innovation to safeguard the increasingly complex digital landscapes that organizations operate within today.

The role of technology in modern industrial processes: a comparative analysis of automated, automatic and mechanical systems

In the modern technological sphere, systems are categorized into various types based on their level of complexity and functional capabilities. The primary types are mechanical, automatic, and automated systems. Each of these types has its unique characteristics and areas of application. Mechanical systems are the simplest of all. They consist primarily of physical components such as gears, levers, and springs, which interact to perform specific tasks. These systems typically lack built-in control or regulation mechanisms, and their operation is based on basic mechanical principles. Examples of mechanical systems include simple clock mechanisms or traditional measuring devices. Automatic systems represent an evolutionary step beyond mechanical systems, as they include elements for automatic control. These may be electrical or electronic components that enable the system to perform certain operations without direct human intervention. For example, automatic doors or thermostats that regulate room temperature are examples of such systems. They can respond to changing conditions and perform tasks based on predefined parameters. Automated systems, in turn, are the most complex and integrated. They combine automatic control with software and advanced management systems, allowing them to adapt to changing environmental conditions and make decisions independently. Automated systems may include elements of artificial intelligence and machine learning, enabling them to perform complex tasks such as data processing or optimizing production processes. Examples include industrial robotic lines or management systems in distribution networks. Thus, the main differences between these systems lie in their level of complexity and functional capabilities: mechanical systems are based on simple physical principles, automatic systems contain elements of automatic control, and automated systems provide a high level of integration and autonomy.

Development of an Intelligent Drone Management System for Integration into Smart City Transportation Networks

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditionally manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large number of drones expected to operate at low altitudes in complex urban environments. Therefore, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include the following: (i) developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; (ii) designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; (iii) improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and (iv) investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations.

Numerical investigation of synergistic effects of magnetic fields and bluff bodies on heat transfer enhancement and pressure drop reduction in microchannels

Efficient thermal management is essential for designing compact and high-performance heat sinks and heat exchangers. This study addresses the challenge of optimizing heat transfer while minimizing pressure losses in systems exposed to concentrated heat flux by proposing a novel approach that employs both active and passive vortex generators. Specifically, a uniform magnetic field generated by permanent magnets and a bluff body are utilized within a microchannel containing a 2 vol% ferrofluid. Numerical simulations were performed across Reynolds numbers ranging from 100 to 500 and magnetic field intensities up to 0.5 T to evaluate the system's performance. The results demonstrate that the combination of magnetic fields and a bluff body induces vortex generation, alters velocity distribution, and enhances flow mixing, resulting in a 30 % increase in heat transfer efficiency and an 11 % reduction in pressure drop under optimal conditions. Although the introduction of barriers led to a 3 % rise in pressure drop, the uniform magnetic field effectively mitigated friction by reducing flow separation and limiting surface contact. These findings highlight the potential of this method for improving the design of advanced thermal management systems.

Silicon porous disc featured parabolic trough collector functioned with paraffin: Thermal performance study

The advancement of renewable solar energy has potential for industrial heat exchanger applications, and its performance is enhanced by using nanofluid. Besides, the losses of heat during the energy transfer from solar to heat exchanger limit thermal performance and lead to reduced thermal efficiency. The conventional solar system functioning with nanofluid needs to be upgraded with an advanced thermal management system to limit energy loss and enrich the thermal performance of the overall system. The investigation aims to enrich the thermal performance of a parabolic trough solar collector (PTC), which features silicon porous discs and phase change material (PCM-paraffin). During the investigation, the flow of fluid ranged from 100 to 200 LPH with an interval of 50 LPH. Influences of silicon porous disc and phase change material melting phase on fluid temperature, temperature, energy stored, and thermal efficiency of the present system are experimentally investigated, and its values are related to conventional absorber performance without porous material and phase change material. The output results prove the significance of silicon porous and phase change material related to conventional absorber systems. The parabolic solar collector functioned with silicon porous material with phase change material phase on 200LPH spotted superior thermal performance including fluid temperature, PCM temperature, energy stored by phase change material, and average thermal & exergy efficiency of 84.2 °C, 98.5 °C 599.7 kJ, and 73.8 % & 15.1 %. These findings underscore the promising potential of porous medium absorbers in significantly elevating the efficiency of parabolic trough collector systems.

ENGINEERING CHALLENGES AND SOLUTIONS IN SMART GRID INTEGRATION WITH ELECTRIC VEHICLES

This paper explores the critical engineering challenges and innovative solutions for successfully integrating intelligent grids and electric vehicles (EVs), emphasizing the increasing need for a resilient and adaptive electrical grid as global EV adoption accelerates. It comprehensively examines the technological, infrastructural, and regulatory obstacles that must be addressed to ensure seamless integration, focusing on advanced energy management systems, grid stability amidst fluctuating demand, and incorporating renewable energy sources. The study delves into the infrastructural requirements, including the expansion of charging networks, upgrades to transmission and distribution systems, and the implementation of vehicle-to-grid (V2G) technologies, while also analyzing the necessary regulatory and policy frameworks, stressing the importance of clear standards, incentives, and public-private collaboration. The paper offers a forward-looking perspective on overcoming current challenges by reviewing recent advancements in innovative grid technology—such as high-capacity energy storage and artificial intelligence (AI) use for predictive maintenance and load balancing. It highlights the need for interdisciplinary collaboration among engineers, policymakers, and industry leaders to develop a cohesive strategy for future energy distribution while underscoring the role of AI in optimizing grid performance, predicting energy consumption patterns, and enhancing overall efficiency. Ultimately, the paper provides a comprehensive analysis of the current state of smart grid and EV integration, offering actionable insights for stakeholders and concluding with recommendations for future research and development priorities, with a strong emphasis on continued innovation and cooperation to achieve a sustainable and resilient energy future.

Integrated rooftop solar PV-based residential advanced energy management system: An economic involvement of energy systems for prosumers

The growing adoption of rooftop solar photovoltaic (PV) systems, coupled with the ability to sell surplus energy back to the national grid, presents a promising opportunity for residential energy management. This research introduces an innovative Advanced Energy Management System (AEMS) that integrates rooftop solar PV with energy-efficient appliances, offering a transformative approach to optimizing household energy consumption. By leveraging advanced demand-side management (DSM) techniques, the AEMS enables users to strategically shift energy usage away from peak hours, thereby reducing reliance on grid energy and minimizing costs. Empirical evaluations reveal that the AEMS significantly outperforms conventional energy management systems, achieving cost reductions of 28.59–35.48 %. The user-friendly interface and robust optimization strategies of the proposed model ensure operational efficiency, making it a valuable tool for maximizing energy savings and enhancing grid stability. Focusing on the specific context of Bangladesh, this study provides a comprehensive techno-economic analysis, demonstrating the practical applicability and long-term sustainability of suggested AEMS. The findings underscore the potential of the proposed model to revolutionize residential energy management, positioning it as a key enabler of both economic and environmental benefits for prosumers in emerging markets.

ADOPTION OF AI-BASED LIBRARY SERVICES: A NECESSITY FOR NIGERIAN UNIVERSITIES

The increasing demand for advanced information management systems has prompted universities to explore AI technologies to optimize library resources and improve user experiences. Therefore, this paper explores the need for a paradigm shift in Nigerian universities by integrating Artificial Intelligence (AI) into library services. The study examines the potential benefits, challenges, and ethical considerations associated with integrating AI in the academic library context. It highlights the potential advantages of AI, such as improved information retrieval, personalized recommendations, and streamlined administrative processes. However, challenges including limited infrastructure, financial constraints, and data privacy concerns are identified as potential hurdles that universities must navigate. In addressing these challenges, the paper emphasizes the importance of capacity building, stakeholder collaboration, and the establishment of ethical guidelines. It underscores the need for a balanced approach that preserves human involvement and upholds ethical standards, ensuring responsible AI integration in library services. Drawing on lessons from global AI implementations in academic libraries, this paper provides insights into the implications of AI for Nigerian universities, emphasizing the importance of strategic planning, continuous monitoring, and user education. Finally, the integration of AI in library services emerges as a transformative opportunity for Nigerian universities to enhance academic support, research endeavors, and user satisfaction in the ever-evolving digital landscape.