Optimal Resource Utilization Research Articles

Global HSE regulatory frameworks and their impact on operational efficiency in the energy sector

The global energy sector operates within a complex regulatory environment shaped by Health, Safety, and Environmental (HSE) frameworks that aim to protect both human health and the environment while enhancing operational efficiency. This study examines the key HSE regulatory frameworks implemented across various regions and their impact on operational practices in the energy sector. By analyzing regulations such as the European Union’s REACH (Registration, Evaluation, Authorisation, and Restriction of Chemicals) and the United States' OSHA (Occupational Safety and Health Administration) standards, alongside other national and international policies, the paper identifies the critical aspects of HSE regulations and their influence on risk management, safety protocols, environmental sustainability, and business performance. The findings highlight that while stringent HSE regulations contribute to reducing operational risks and enhancing corporate social responsibility, they may also increase compliance costs and operational complexities. The paper argues that a well-structured HSE framework can lead to improved long-term operational efficiency by reducing accidents, optimizing resource utilization, and enhancing sustainability practices. However, the balance between compliance and cost-effectiveness remains a significant challenge for energy companies operating in diverse regulatory environments. Recommendations are provided for harmonizing regulations and fostering innovation in compliance strategies to support sustainable development and enhanced operational efficiency.

Can section 45Q tax credit foster decarbonization? A case study of geologic carbon storage at Acid Gas Injection wells in the Permian Basin

Carbon capture, utilization, and storage (CCUS) is an important pathway for meeting climate mitigation goals. While the economic viability of CCUS is well understood, previous studies do not evaluate the economic feasibility of carbon capture and storage (CCS) in the Permian Basin specifically regarding the new Section 45Q tax credits. We developed a technoeconomic analysis method, evaluated the economic feasibility of CCS at the acid gas injection (AGI) wells, and assessed the implication of Section 45Q tax credits for CCS at the AGIs. We find that the compressors, well depth, and the permit and monitoring costs drive the facility costs. Compressors are the predominant contributors to capital and operating expenditure driving the levelized cost of CO2 storage. Strategic cost reduction measures identified include 1) sourcing of low-cost electricity and 2) optimizing operational efficiency in well operations. In evaluating the impact of the tax credits on CCS projects, facility scale proved decisive. We found that facilities with an annual injection rate exceeding 10,000 MT storage capacity demonstrate economic viability contingent upon the procurement of inputs at the least cost. The new construction of AGI wells were found to be economically viable at a storage capacity of 100,000 MT. The basin is heavily focused on CCUS (tax credit – $65/MT CO2), which overshadows CCS ($85/MT CO2) opportunities. Balancing the dual objectives of CCS and CCUS requires planning and coordination for optimal resource and pore space utilization to attain the basin's decarbonization potential. We also found that CCS on AGI is a lower cost CCS option as compared to CCS on other industries.

Exploring the Opportunities of Applying RFID Technology in Smart Agriculture

This paper explores the opportunities of applying RFID (radio-frequency identification) technology in smart agriculture. With the projected increase in global population and the resulting higher demand for food production, the agricultural industry is under pressure to find more efficient and sustainable ways to meet these needs. Radio-frequency identification (RFID) technology has been recently adopted in the agriculture industry as a means of improving efficiency and productivity towards achieving smart agriculture. RFID technology is revolutionizing smart agriculture, offering automation, real-time monitoring and data-driven decision-making. Integrating RFID tags with plants and soil samples optimizes resource utilization, crop health management, traceability and quality assurance. RFID in smart agriculture offers opportunities for resource optimization, crop health management, traceability and automation. Leveraging RFID technology, farmers can elevate productivity, reduce costs, improve sustainability and meet global demands. Adopting RFID in agriculture revolutionizes traditional practices, enabling efficient and sustainable farming. RFID-enabled traceability enhances food safety, quality assurance and transparency in the food supply chain. The objective of this paper is to explore the potential of RFID technology in smart agriculture and highlight the significance of effective RFID antenna design for optimizing food production, addressing global challenges and fostering a more efficient, sustainable and resilient agricultural sector.

Overview of Microgrids and Blockchain Technology

This paper addresses the vital role of microgrids in the efficient utilization of renewable energy within the context of the evolving energy internet and electricity market reforms. In this work, we review the fundamental concepts of microgrids, their historical progression, energy storage technologies, and advanced control and scheduling techniques. We focus on the benefits of flexible interconnection in hybrid AC/DC microgrids and propose a decentralized energy trading system utilizing blockchain technology. Our findings demonstrate that implementing blockchain not only enhances trading efficiency and reduces costs but also facilitates secure peer-to-peer transactions between consumers and producers. This innovative approach resolves prevalent issues in traditional energy trading frameworks and fosters greater market dynamics. The significance of this research lies in its potential to inform the commercialization and sustainable development of microgrids, ultimately promoting optimal allocation and utilization of energy resources. By providing actionable insights, this work contributes to advancing the field of energy management and supports the transition towards a more resilient and decentralized energy landscape.

AI at the Edge: Enhancing Cybersecurity and Real-Time Decision-Making in Healthcare IoT Systems

The integration of Artificial Intelligence (AI) with edge computing and Internet of Things (IoT) systems has revolutionized healthcare, enabling real-time decision-making and personalized care delivery. However, these advancements also introduce significant cybersecurity challenges that threaten patient privacy and system integrity. This research explores the convergence of AI, edge computing, and IoT in healthcare, focusing on enhancing real-time decision-making while mitigating cybersecurity risks. By leveraging edge computing’s decentralized architecture, this study aims to reduce latency, optimize resource utilization, and ensure data security during transmission and processing. Machine learning models at the edge facilitate real-time analytics and decision-making, while robust cybersecurity frameworks, including encryption, anomaly detection, and secure communication protocols, are proposed to safeguard sensitive healthcare data. The research also examines the ethical implications and scalability of deploying secure AI-driven IoT systems in healthcare. By addressing these challenges, the study aims to create a resilient infrastructure for delivering efficient, secure, and reliable healthcare solutions.

Scalable Data Integration for Real-Time Patient Care: A Compliance and Cybersecurity Perspective

In the evolving landscape of modern healthcare, real-time data integration has emerged as a critical component for delivering optimal patient care while maintaining robust security and regulatory compliance. This comprehensive article explores the implementation strategies, challenges, and outcomes of healthcare data integration systems across diverse clinical settings. The article examines the impact of integrated systems on clinical workflows, patient outcomes, and operational efficiency while addressing the complex requirements of data security and regulatory compliance. Through detailed case studies of large hospital networks and rural healthcare providers, this article demonstrates how strategic implementation of integrated healthcare systems can transform care delivery, enhance patient outcomes, and optimize resource utilization. The findings provide valuable insights for healthcare organizations at various stages of their digital transformation journey, offering evidence-based strategies for successful system implementation and optimization.

Commercial operation mode of shared energy storage system considering power transaction satisfaction of renewable energy power plants

The sharing economy mode can promote an optimal allocation and utilization of resources, and its integration with the energy storage and renewable energy can improve their utilization rate and reduce the dispatching deviation. In order to reduce the renewable energy dispatching deviation and improve profits of shared energy storage, this paper proposes a shared energy storage commercial operation mode considering the power transaction satisfaction of renewable energy plants. Firstly, in order to promote the participation of renewable energy plants in the commercial mode, a power transaction satisfaction mode is proposed, and a multilateral bidding transaction mode based on power transaction satisfaction is established. Secondly, in order to further compensate the dispatching deviation, a deviation elimination model through jointing multiple shared energy storages and renewable energy plant clusters is established, and the advantages of renting multiple shared energy storages are analyzed. Thirdly, in order to quantify the profits of each participant in the commercial mode, a shared profit and settlement cost model is developed. Finally, the correctness of the above proposed model is verified by analyzing the case study.

Save A Life Maps-Traffic Clearance System for Emergency Services

ABSTRACT The Save a Life (SAL) Maps addresses the challenge of traffic congestion affecting emergency services and provides swift response during life-critical situations. A path clearance algorithm is integrated into an application, which identifies the most optimal routes enabling emergency services to navigate efficiently. The algorithm incorporates a mechanism that uses micro services to facilitate path clearance for emergency services, provide fastest routes, assist general public in accessing emergency services, and notify the traffic police during high traffic congestion to assist by manually clearing out the path. Docker is employed to containerize the micro services, allowing for efficient deployment and management on the Google Kubernetes Engine platform. This micro services-based algorithm achieves enhanced efficiency, optimal resource utilization, real-time decision-making, scalability, adaptability and offers a reliable solution which highlight the importance of adopting path clearance algorithms and micro services architecture for ambulances enabling a more agile and responsive system.

Sample Size and Statistical Power Calculation in Multivariable Analyses: Development and Implementation of "SampleSizeMulti" Packages in R

This paper presents advanced methodological approaches and practical tools for sample size calculation in epidemiological studies involving multivariable analyses. Traditional sample size calculation methods often fail to account for the complexity of modern statistical analyses, particularly regarding the correlation between covariates in multivariable models. We introduce a series of R packages (SampleSizeMulti) designed to address these limitations. These packages offer two distinct calculation approaches: one based on the multiple correlation coefficient between covariates (rho-based method) and another utilizing standard errors from previous studies (SE-based method). These complementary approaches provide comprehensive solutions for different association measures commonly used in epidemiological research: prevalence ratios, odds ratios, risk ratios, and hazard ratios. The rho-based method innovatively incorporates the explicit consideration of the multiple correlation coefficient between covariates, significantly impacting required sample sizes in multivariable analyses. The SE-based method leverages information from previous studies through their confidence intervals, offering an alternative when correlation estimates are unavailable but published results exist. Furthermore, both approaches integrate crucial logistical considerations, including rejection rates, eligibility criteria, and expected losses to follow-up, providing researchers with realistic estimates of recruitment requirements and timelines. Seven detailed case studies covering various epidemiological study designs and analytical scenarios demonstrate the practical application of these methods. These examples illustrate how correlation values, standard errors, and logistical factors influence sample size calculations and study planning. The implementation in R ensures accessibility and reproducibility, while the incorporation of logistical planning tools bridges the gap between theoretical calculations and practical research requirements. These methods represent a significant advancement in study design methodology, potentially improving the quality and efficiency of epidemiological research by ensuring adequate statistical power while optimizing resource utilization.

Coordinated control strategy for improving frequency security of LCC[sbnd]HVDC sending-end system with large-scale wind power

To improve the frequency security of the LCCHVDC sending-end system with high penetration of wind power system and achieve optimal utilization of frequency regulation resources, a cooperative control strategy involving wind power and LCCHVDC in frequency regulation is proposed. Firstly, under a variety of scenarios, the power support requirements of LCCHVDC sending-end system are analyzed according to the power regulation margin of various frequency regulation resources and the disturbance power, and a multi-time scale frequency regulation control strategy for DFIG based wind farms is proposed, which combines the advantages of variable speed control and variable pitch angel control. And a cooperative control strategy of DFIG and LCCHVDC considering the frequency regulation deadband is proposed. Then, based on the maximum power regulation margin of synchronous generators and wind farms, the key control parameters of relative control links are designed to enable them autonomously adjust the active power according to various scenarios to involve in inertia response and primary frequency regulation task. Finally, the effectiveness of the control strategy is verified by the simulation model of LCCHVDC sending-end system with large-scale wind power, which significantly improves the frequency security and stability.

An Ontology for In‐Depth Description of User Situations in Connected Environments

ABSTRACTContext‐awareness is increasingly recognised as a fundamental principle in the development of ubiquitous computing and ambient intelligence. By leveraging contextual data about users and their environments, systems can gain a deeper understanding of the evolving user situation. This empowers them to dynamically adapt their operations, leading to optimised resource utilisation, enhanced decision‐making, and ultimately, greater user satisfaction. However, a critical challenge lies in effectively representing user situations with a high degree of expressiveness. While ontology‐based data models have emerged as a promising approach due to their ability to handle the inherent heterogeneity of context information, existing ontologies have limitations in terms of information coverage, data heterogeneity and uncertainties consideration, and reusability across various application domains. This paper addresses these limitations by proposing uCSN, an ontology that builds upon and extends the Data Privacy Vocabulary (DPV), Semantic Sensor Network (SSN) and W3C Uncertainty ontologies, to provide a rich and expressive vocabulary for representing diverse user situations. We evaluate uCSN based on its consistency, accuracy, clarity and performance.

Adaptive Dual-Layer Resource Allocation for Maximizing Spectral Efficiency in 5G Using Hybrid NOMA-RSMA Techniques

The unprecedented growth in data demands for 5G communication systems necessitates advanced techniques to maximize spectral efficiency while ensuring user fairness and low latency. This study proposes Adaptive Dual-Layer Resource Allocation (ADLRA), a novel hybrid technique combining Non-Orthogonal Multiple Access (NOMA) and Rate-Splitting Multiple Access (RSMA). The ADLRA framework introduces dynamic user pairing, hierarchical beamforming, and adaptive power and rate allocation strategies to optimize resource utilization. Key features include dynamic user pairing, leveraging machine learning algorithms for efficient group formation based on channel conditions, and hierarchical beamforming, which prioritizes high-priority users in the NOMA layer while effectively managing shared resources in the RSMA layer. Interference mitigation is achieved through spatial filtering and multi-user diversity techniques, ensuring minimal intra-cell and inter-cell interference. Simulation results demonstrate significant performance gains Spectral efficiency improved by 32%, compared to traditional NOMA. Latency reduced by 18%, ensuring seamless communication for ultra-reliable low-latency applications. Achieved a 94% fairness index, reflecting equitable resource allocation among users. Enhanced throughput, with an average gain of 28%, compared to RSMA-only systems. These results highlight the potential of ADLRA to meet the stringent requirements of next-generation 5G systems, offering a scalable and efficient solution for diverse communication scenarios. The proposed method sets a foundation for future hybrid access strategies in wireless communication networks.

Soil Health Metric: IoT-based Agriculture Monitoring System

Agriculture has been an essential component of human civilization, experiencing considerable evolution over the centuries. In contemporary society, technological advancements, particularly the Internet of Things (IoT), have emerged as transformative forces, facilitating precision farming and enhancing resource management efficiency. This research examines the development and implementation of an IoT-based agricultural monitoring system. By utilizing components such as sensors, wireless networks, and data analytics, this system offers real-time insights into soil moisture, temperature, humidity, and water levels. The automation will be integrated into optimal resource utilization, minimization of waste, and increased productivity. This paper outlining the operation, parts, and benefits of IoT-based farming systems with a perspective on its transformation of agriculture and the problem of sustainability and its relation to the environment.

A Case Study on the Application of Evolutionary Algorithms for Solving Non-linear Programming Problems in Water Resource Management

Evolutionary algorithms have been proposed and used in this case study for the solution to non-linear programming problems related to water resources. Heuristic, a type of optimization algorithm characterized by population-based search as well as using the genetic algorithms, are used to model and determine the optimal distribution and utilization of aquatic resources based on environmental and demand factors. These materials include examples based on water distribution networks, managing a reservoir, and forecasting demands of customers, demonstrating the effectiveness of the aforementioned algorithms in solving non-linear constraint problems and in dealing with multiple objectives. The comparative analysis is based on a number of simulation experiments to examine the efficiency of such microevolutionary algorithms as Genetic Algorithm, Particle Swarm Optimization, and Differential Evolution in contrast to the traditional optimization algorithms. These results show that there is significant value in using evolutionary algorithms as a basis for managing and modelling water resources as it offers a pragmatic approach and has the potential to meet the demands of the water industry in terms of sustainability, cost and resource requirements. There seems to be considerable scope for extension of these algorithms to a wide range of natural resources management and administration.

Pemetaan Bibliometrik Peran Kewirausahaan dalam Mewujudkan Ekonomi Biru Berkelanjutan

The blue economy has emerged as an essential paradigm in sustainable development. Through the role of entrepreneurship, optimal utilization of marine resources can improve welfare, environmental conservation, and economic growth. This study aims to map the development of research on the role of entrepreneurship in realizing a sustainable blue economy through bibliometric analysis. Data were obtained from the Scopus database for the period 2019-2024. The results of the study show that there has been a significant increase in the number of publications during the observation period, dominated by the United States, the United Kingdom, and China. The results of the network visualization analysis show that there are two clusters, namely the red cluster and the green cluster. The red cluster reflects the conceptual and practical foundations of the blue economy and sustainable development, while the green cluster shows the role of entrepreneurship and education in realizing the vision of a sustainable blue economy. Entrepreneurship is integral to the blue economy, linking innovation, sustainability, and economic growth. Further research is needed to understand the interactive mechanisms and develop innovative solutions to provide economic opportunities and environmental sustainability..

CHALLENGES AND OPPORTUNITIES FOR THE DEVELOPMENT OF LOGISTICS HUBS IN UKRAINE

Logistics, as a key element of modern supply chains, is increasingly becoming the subject of deep engineering research and development. The construction of logistics complexes in today's world is a critical aspect for the development and functioning of global economic systems, as it allows optimizing supply chains and organizing logistics processes to meet the high standards of consumers and business partners. Logistics complexes play a crucial role in global trade by ensuring the fast and efficient delivery of goods. The growth of e-commerce volumes enhances their significance, requiring more space for the storage and processing of goods. These complexes are strategically important for enterprises as they contribute to efficiency improvement, delivery time reduction, and cost optimization. Logistic infrastructures provide flexibility to respond to changing market conditions and support competitiveness at the international level. Accordingly, the construction of logistics complexes plays a strategic role in economic development and supports global business. Objective: The work aims to assess, analyze, and justify organizational-technical factors in optimizing the construction of logistics complexes. Research Object: Construction of logistics complexes as part of modern supply chains and their role in global economic systems. Research Subject: Organizational-technical factors influencing the process of construction of logistics complexes, as well as methods for their optimization to enhance the efficiency and competitiveness of logistic systems. Presentation of Material: To achieve the research objective, the following tasks were performed: analyzed the current state of warehouse logistics in Ukraine and the role of logistic hubs; analyzed the impact of innovative and sustainable materials on the construction of logistics complexes; studied the provision of flexibility and adaptability of logistics complexes to changing market conditions; analyzed resource utilization during construction. Research Results: The research results focus on improving organizational and technological aspects of the construction of logistics complexes through effective management of construction processes, implementation of innovative technologies, and optimization of resource utilization. Such measures contribute to increased productivity and competitiveness of logistic systems, as well as sustainable development and enhanced efficiency of the supply chain.

Minimal data poisoning attack in federated learning for medical image classification: An attacker perspective

The privacy-sensitive nature of medical image data is often bounded by strict data sharing regulations that necessitate the need for novel modeling and analysis techniques. Federated learning (FL) enables multiple medical institutions to collectively train a deep neural network without sharing sensitive patient information. In addition, FL uses its collaborative approach to address challenges related to the scarcity and non-uniform distribution of heterogeneous medical domain data. Nevertheless, the data-opaque nature and distributed setup make FL susceptible to data poisoning attacks. There are diverse FL data poisoning attacks for classification models on natural image data in the literature. But their primary focus is on the impact of the attack and they do not consider the attack budget and attack visibility. The attack budget is essential for adversaries to optimize resource utilization in real-world scenarios, which determines the number of manipulations or perturbations they can apply. Simultaneously, attack visibility is crucial to ensure covert execution, allowing attackers to achieve their objectives without triggering detection mechanisms. Generally, an attacker’s aim is to create maximum attack impact with minimal resources and low visibility. So, considering these three entities can effectively comprehend the adversary’s perspective in designing an attack for real-world scenarios. Further, data poisoning attacks on medical images are challenging compared to natural images due to the subjective nature of medical data. Hence, we develop an attack with a low budget, low visibility, and high impact for medical image classification in FL. We propose a federated learning attention guided minimal attack (FL-AGMA), that uses class attention maps to identify specific medical image regions for perturbation. We introduce image distortion degree (IDD) as a metric to assess the attack budget. Also, we develop a feedback mechanism to regulate the attack coefficient for low attack visibility. Later, we optimize the attack budget by adaptively changing the IDD based on attack visibility. We extensively evaluate three large-scale datasets, namely, Covid-chestxray, Camelyon17, and HAM10000, covering three different data modalities. We observe that our FL-AGMA method has resulted in 44.49% less test accuracy with only 24% of IDD attack budget and lower attack visibility compared to the other attacks.

Development of a Scoring Model to Predict Severe Dengue in Children at Admission in the Emergency Care: An Observational Study.

Dengue has emerged as the most widely spread mosquito-borne disease, hyperendemic in India. Although severe dengue occurs only in a small proportion of cases, delays in the recognition and management can result in significant morbidity and mortality. Risk stratification of children in the emergency care and identifying those at risk for worsening during hospital stay facilitates optimum utilization of health care resources. The objective of our study was to develop and validate a scoring model to predict the development of severe dengue in hospitalized children by identifying risk factors present in them at the time of admission in emergency department. A retrospective study of consecutive children aged 1 month to 18 years admitted with serologically confirmed dengue from January 2019 through December 2021 was conducted. Analysis of clinical and laboratory parameters of children resulted in the development of a comprehensive predictive scoring model. This model was internally validated prospectively on 107 children presenting with nonsevere dengue in the emergency care. A total of 743 children with confirmed dengue were studied out of which 216 (29.1%) had severe dengue. Presence of third spacing (odds ratio [OR] 3.74, 95% confidence interval [CI] 1.088-7.42, P < 0.001), tender hepatomegaly (OR 1.62, 95% CI 1.04-2.52, P < 0.032), respiratory distress (OR 3.50, 95% CI 1.89-6.51, P < 0.001), and moderate (OR 3.51, 95% CI 1.82-6.74, P < 0.001) to severe (OR 4.76, 95% CI 2.59-8.76, P < 0.001) elevation of aspartate aminotransferase were independent risk factors found to be associated with development of severe dengue. A score ≥7 had a specificity of 87%, negative predictive value of 86%, and overall diagnostic accuracy of 78.5% for predicting severe dengue. The dengue severity scoring model was found to have reasonable diagnostic accuracy in predicting severe disease prior to hospitalization. However, more studies are required to validate the score in different emergency care settings with varied pediatric populations.

Adaptive load balancing strategies in service composition for improved system performance

Service composition enables the integration of multiple services to create new functionalities, optimizing resource utilization and supporting diverse applications in critical domains such as safety-critical systems, telecommunications, and business operations. This paper addresses the challenges in comparing load-balancing algorithms within service composition environments and proposes a novel dynamic load-balancing algorithm designed specifically for these systems. The proposed algorithm aims to improve response times, enhance system efficiency, and optimize overall performance. Through a simulated service composition environment, the algorithm was validated, demonstrating its effectiveness in managing the computational load of a BMI calculator web service. This dynamic algorithm provides real-time monitoring of critical system parameters and supports system optimization. In future work, the algorithm will be refined and tested across a broader range of scenarios to further evaluate its scalability and adaptability. By bridging theoretical insights with practical applications, this research contributes to the advancement of dynamic load balancing in service composition, offering practical implications for high-tech system performance.

Stochastic Differential Game of Sustainable Allocation Strategy for Idle Emergency Supplies in Post-Disaster Management

This study aims to explore allocation strategies for idle emergency supplies in a “demander–platform–supplier” supply chain system along with government regulation during the post-disaster recovery period. Allocation of emergency supplies is a complex task that encompasses resource allocation before and after disasters. It is essential to reduce losses in disaster-stricken areas and support development during post-disaster recovery. However, there is often an excessive supply of emergency materials and a mismatch between supply and demand sides in downstream supply chains, which may lead to severe waste and difficulties in recovering surplus materials. This paper takes idle emergency resource sharing level and corporate social responsibility goodwill as endogenous variables. The allocation approaches are dynamically evaluated by incorporating random elements that influence the endogenous variables. Three stochastic differential games are introduced to examine the interactions between the players. The centralized decision-making satisfies the consistency of overall and individual rationalities at any time in the emergency material allocation process, promoting the optimal sharing levels of emergency materials and overall profits. The decentralized decision-making with cost-sharing contracts achieves local optima and increases the dual marginal effect of the emergency industry chain. This paper incorporates the sharing economy into emergency management, showing how technology-driven sharing platforms can optimize resource utilization. The results suggest introducing cost-sharing contracts between demanders and suppliers can enhance collaboration and effort, leading to better resource allocation and increased efficiency. It contributes to sustainability by promoting efficient resource utilization through idle emergency resource sharing. By optimizing allocation strategies and enhancing corporate social responsibility, the study fosters the long-term viability and resilience of the supply chain system in post-disaster management.