Optimal Resource Utilization Research Articles

Optimizing resource utilization for large scale problems through architecture aware scheduling

Rapid development realms of parallel architectures and its heterogeneity have inspired researchers to invent new scheduling strategies to efficiently distribute workloads among these architectures in a way that may lead to better performance. This paper presents a comprehensive study on optimizing resource utilization for large-scale problems by employing architecture-aware scheduling techniques. We conducted a series of experiments to measure the execution times of various architectures with different problem sizes. These experiments have been conducted multiple times to minimize measurement variance. The findings from these experiments are utilized to develop a scheduling strategy that enables faster completion of larger data-parallel problems while maximizing resource utilization. The proposed approach makes performance enhancement with 16.7% for large data size. It has a significant impact on enhancing computational efficiency and reducing costs in high-performance computing environments.

Assessing the impact of financial resources on Sorghum yield in Al-Qadarif State, Sudan

The sorghum agricultural sector in Al-Qadarif State, Sudan, plays a pivotal role in ensuring regional food security. However, its productivity is hindered by significant constraints. Substantial improvements in sorghum yield are impeded by the intricate complexities that characterize this sector's financial resources, and agricultural practices. This study in Al-Qadarif State, Sudan, explores the relationship between financial resources and sorghum agrarian productivity. Data were collected from 200 sorghum farmers using standardized questionnaires during the 2022 season, and regression analysis was employed to investigate the association between financial factors and sorghum productivity. The findings indicate that financial accessibility considerations significantly influence sorghum productivity. This emphasizes the importance of improved financial resources for farmers to adopt innovative farming technologies and practices, leading to higher sorghum productivity levels. The study recommends the integration of digital financial services and precision agriculture technologies as a creative solution to enhance financial accessibility and sorghum productivity. By leveraging mobile money platforms and digital credit services, farmers can conveniently access financial resources, enabling them to invest in modern agricultural inputs and technologies. Additionally, the adoption of precision agriculture techniques, such as soil moisture sensors, drone-based crop monitoring, and variable rate application of inputs, can optimize resource utilization, increase yield, and reduce production costs. The combination of these innovative approaches has the potential to bridge the financial and knowledge gaps, promoting the widespread adoption of sustainable agricultural practices. This synergistic approach could significantly improve sorghum productivity, enhance farmer livelihoods, and contribute to regional food security.

Cost minimization analysis of chronic kidney disease management: Evaluating economic strategies for early intervention and treatment optimization

Chronic kidney disease is a common global health condition characterized by progressive loss of kidney function and significant cost burden on the healthcare system worldwide. The disease often coexists with conditions such as diabetes and hypertension, further complicates its management and increasing associated costs. Effective economic management, particularly in the early stages of CKD, is crucial for reducing long-term healthcare expenses and improving patient outcomes. This review examines the utility of cost minimization analysis (CMA) as a strategic tool for evaluating economic options in CKD management. CMA is an economic evaluation method that compares the costs of different therapeutic interventions that have demonstrated equivalent clinical effectiveness, thereby aiding healthcare providers and policymakers in identifying the most cost-efficient options to optimize resource utilization. This review highlights the economic impact of CKD, focusing on the increasing costs linked to advanced disease management and the essential role of early diagnosis and intervention in reducing these costs and explores various cost-effective strategies for CKD management, including pharmacological treatments, lifestyle modifications, and patient education, to identify approaches that are both clinically and economically advantageous. Additionally, the broader implications of cost minimization for healthcare policy, clinical decision-making, and resource allocation are discussed for their significance in a resource-limited healthcare setting. Evidence indicates that prioritizing cost-effective CKD management through early intervention and preventive care can significantly lower healthcare expenditures, slow the disease progression, and enhance patient’s quality of life. By adopting such strategies, healthcare systems can achieve better clinical outcomes and ensure financial sustainability.

Optimizing Resource Utilization and Improving Performance in Cloud Computing Through PSO-Based Scheduling and ACO-Based Load Balancing

Optimizing Resource Utilization and Improving Performance in Cloud Computing Through PSO-Based Scheduling and ACO-Based Load Balancing

Integrating Artificial Intelligence into ESG Practices: Opportunities, Challenges, and Strategic Solutions for Corporate Sustainability

Environmental, social, and governance (ESG) practices have become increasingly important in corporate strategy in recent years, while the rapid development of artificial intelligence (AI) has created new opportunities and challenges for corporate sustainability. AI technology driving companies for ESG time is getting more and more attention. This study examines the application of AI technologies in environmental management, social responsibility, and corporate governance, demonstrating their potential to optimize resource utilization, reduce carbon emissions, improve recruitment fairness, and prevent fraud. However, integrating AI with ESG faces many challenges, including technological complexity, high costs, data privacy and ethical issues, and organizational and cultural resistance. To address these challenges, this study proposes solutions to reduce financial burdens, secure data, and enhance cultural buy-in through strategies such as technology partnerships, open-source tools, and employee training. By delving into the convergence of AI and ESG, this study provides companies with a guiding direction to fully utilize the potential of AI while maintaining long-term sustainability.

Unlocking Innovation in Crop Resilience and Productivity: Breakthroughs in Biotechnology and Sustainable Farming

Recent advancements in agricultural biotechnology and sustainable practices are revolutionizing crop production and resilience, addressing global food security challenges. This review highlights cutting-edge innovations in plant sciences, including genetic engineering, genome editing technologies, and biotechnological interventions that enhance crop resistance to biotic and abiotic stresses. It discusses the integration of precision agriculture and digital farming tools that optimize resource use and boost productivity, providing data-driven insights for better crop management. Additionally, sustainable agricultural practices such as agroecology, organic farming, and integrated pest management are examined for their potential to reduce environmental impact while maintaining high yield. The review investigates the key practices like conservation tillage, crop rotation, and biological pest control, emphasizing their roles in enhancing soil health, biodiversity, and agricultural resilience. Significant contributions of biotechnological interventions, including genetic engineering and genome editing, are explored for developing stress-resistant and high-yield crop varieties. Moreover, the integration of precision agriculture and digital farming technologies is discussed for their potential to optimize resource utilization and improve crop management through advanced data analytics.

2.M. Scientific session: Economics of Mental Health in the interplay between health systems and public health

Abstract Mental health systems and services are essential components of the public health infrastructure, serving as frontline for prevention and intervention for health overall. Despite this, access to mental health services remains largely inadequate, with the OECD calculating that only about one in three people with mental health problems has access to quality treatment across 19 EU Member States. The economics of mental health play an important role in determining the allocation of resources, the accessibility of services and the overall effectiveness of interventions. Effective planning requires an understanding of population needs, service gaps and utilization patterns, in order to ensure equity and health improvement. In fact, the conceptualisation of the economic dimensions of mental health enables policymakers, practitioners, and stakeholders to make informed decisions that optimize resource utilization and maximize societal benefits. The proposed workshop will build on previous activities of the EUPHA Section on Public Health Economics at EPH, including the recent establishment of a working group on the economics of mental health. The aim of the meeting is to promote the adoption of best practices and methodologies in the conduct of economic studies on mental health. As such, the workshop will provide an opportunity to discuss the unique challenges of economic evaluation in this field and to present relevant ongoing activities by members of the Section and other invited experts. Key messages • Mental health, its distribution and at-risk populations influence resource allocation and policy decisions within mental health systems. • Tackling inequalities in mental health is essential for promoting health equity and social justice, requiring evidence-based interventions and policy initiatives to empower marginalized communities.

Digital services as alternative for in-person visits among clients with long-term illnesses

Abstract Background For individuals managing long-term illnesses and often requiring complex care across health and social care sectors, the transition to digital platforms holds considerable potential to facilitate management of one’s health. This study examined factors among people with long-term illnesses associated with their perception that digital services had replaced in-person visits in health and social care. Methods A total of 11,012 Finnish residents with long-term illnesses necessitating regular care (56% female, mean age 56.9, SE .23) responded to a nationwide population survey between September 2022 and March 2023. The Inverse Probability Weighting method was used to correct for bias. Complex samples binary logistic regression was used to examine whether factors related to self-rated health, distress, digital skills and perceived benefits of digital services were associated with the replacement of in-person visits by digital services. The model was adjusted for age, gender, education, and urbanicity. Results Approximately one third (35.8%) of the respondents indicated that the use of digital services had replaced in-person visits within the past 12 months. Respondents in poor or moderate health (OR 1.17, 95% CI 1.02-1.34) had greater odds of reporting the replacement compared to those in good health. Those with proficient digital skills (OR 1.84, 1.57-2.14) and those perceiving more benefits in digital services (OR 1.56, 1.47-1.74) had greater odds of reporting of the replacement compared to their counterparts. Conclusions Transition to digital services could facilitate access to care for individuals with long-term illnesses perceiving their health poorer. Promoting digital skills and perceptions of benefits of digital services can significantly increase the use of digital services as substitutes of in-person visits and contribute to more efficient allocation of health and social care resources. Key messages • Especially those reporting poorer health seem to use digital services as replacements of in-person visits, offering the potential to optimize resource utilization in health and social services. • Promotion of digital skills and perceptions of the benefits of digital services could increase the use of digital services as replacements for traditional visits with health and social professionals.

An efficient Protocol for Enhanced Flynet Communication in Presence of Partitioning

This paper presents an adaptive fault-tolerant resource allocation protocol tailored for FlyNet, a dynamic aerial network characterized by its mobility and three-dimensional operational space. Addressing the challenges of network partitioning and resource allocation in aerial networks, the proposed algorithm dynamically adjusts to the network's changing topology, ensuring consistent and efficient resource management. Through robust fault tolerance mechanisms, the protocol enhances FlyNet's reliability, maintaining seamless communication and optimal resource utilization even amid node mobility and disruptions. Simulation results demonstrate the algorithm's effectiveness in adapting to dynamic environments, maximizing resource utilization, and minimizing communication delays.

Smart Cities and Education in the Digital Economy New Research Agenda for Operations Management in the Digital Economy

Smart Cities represent a transformative approach to urbanization, leveraging technology to enhance sustainability, efficiency, and quality of life for inhabitants. By integrating advanced data analytics, Io T (Internet of Things) devices, and interconnected infrastructure, Smart Cities aim to enhance urban living through integrated technology, promoting economic growth, sustainability, and community engagement to optimize resource utilization and enhance urban mobility, foster economic growth, and promote citizen engagement. This abstract captures the essence of how technology can revolutionize urban living, enhancing the responsiveness of cities, resilient, and adaptable to the needs of their residents.

Serverless Computing: Optimizing Resource Utilization and Cost Efficiency

Serverless computing has emerged as a transformative paradigm in cloud infrastructure, offering organizations the ability to scale their applications dynamically without the burden of managing underlying servers. By abstracting away the provisioning and scaling of infrastructure, serverless computing enables developers to focus on building and deploying their applications, while the cloud provider handles the auto- scaling, load balancing, and fault tolerance. This paper examines the key benefits and challenges of serverless computing, with a particular emphasis on optimizing resource utilization and cost efficiency. The findings suggest that serverless computing can lead to significant improvements in resource utilization and cost savings, but organizations must also address challenges related to cold starts, vendor lock-in, and monitoring complexity to fully realize the potential of this cloud computing paradigm.

Trend Analysis of Cross-Border Electricity Trading in Pan-European Network

The long-term global target of facilitating energy security and optimizing resource utilization while supporting the transition to sustainable energy systems has led the pathway towards regional cooperation in the context of energy infrastructures and trading. Recent trends highlight the development of transnational energy grids, pipelines, and renewable energy projects, facilitating efficient distribution of electricity, gas, and other energy forms. These advancements offer benefits such as increased reliability of energy supply, cost savings through shared resources, and strengthened relationships between countries. However, challenges persist, including regulatory differences, geopolitical tensions, alignment of market rules as well as substantial investment requirements in infrastructure and technology. Addressing these challenges necessitates harmonized policies, robust legal frameworks, and cooperative international governance, which are crucial for effective cross-border energy trading. The aim of the work is to present the current landscape as well as analyze the latest trends and developments in the dynamic field of electricity interconnectors, providing insights into its trajectory and implications for the European internal energy market.

On Optimizing Resources for Real‐Time End‐to‐End Machine Learning in Heterogeneous Edges

ABSTRACTDeploying end‐to‐end ML applications on edge resources becomes a viable solution to achieve performance and data regulations. With the microservice architecture, these applications can scale dynamically, improving service availability under dynamic workloads. However, orchestrating multiple end‐to‐end ML applications within heterogeneous edge environments must deal with numerous challenges while sharing computing resources. Prevalent orchestration tools/frameworks supporting edge ML serving are inefficient in provisioning methods due to constrained resources, diverse resource demands and utilization patterns. In this work, we present a provisioning method to optimize resource utilization for end‐to‐end ML applications on a heterogeneous edge. By profiling all microservices within the application, we estimate scales and allocate them on desired hardware platforms with sufficient resources when considering their runtime utilization patterns. We also provide several practical analyses on runtime monitoring metrics to detect and mitigate resource contentions, guaranteeing performance. The experiments with three real‐world ML applications demonstrate the practicality of our method on a heterogeneous edge cluster of Raspberry Pis and Jetson Developer Kits.

Clinical utility of EBV DNA testing of blood in immunocompetent and immunocompromised patients: A single-center experience.

Epstein-Barr virus (EBV) causes different clinical presentations in immunocompetent and immunocompromised persons, and thus indications for testing vary between these populations. We reviewed our institution's EBV DNA testing across these populations to understand its clinical utility and appropriateness. We conducted a retrospective chart review of adult patients with positive EBV nucleic acid amplification (NAAT) testing from November 2022 to 2023. We recorded demographics, indications for testing, EBV-related diagnosis, laboratory results, and whether testing influenced patient treatment. Of 3560 EBV NAAT tests, 187 (5.3%) were positive, representing 124 unique adult patients (51 immunocompetent and 73 immunocompromised). Reactivation of EBV was the most common diagnosis in both populations, followed by acute EBV infection in the immunocompetent and non-posttransplant lymphoproliferative disorder malignancies in the immunocompromised. In immunocompromised patients, positive tests led to treatment changes more frequently than in immunocompetent patients (27.4% vs 11.7%, P=.06). Testing affected clinical management in 0% to 2% of cases when sent for sepsis/shock and nonspecific viral syndromes compared to 37% to 49% of cases when sent for posttransplant and malignancy screening/monitoring. EBV NAAT testinginfrequently influenced clinical management in immunocompetent individuals but had a notable impact in immunocompromised patients, particularly those undergoing posttransplant or malignancy screening. This underscores the need for targeted testing to optimize resource utilization and cost-effectiveness.

IPv6 addressing strategy with improved secure duplicate address detection to overcome denial of service and reconnaissance attacks

With technology development, the growing self-communicating devices in IoT networks require specific naming and identification, mainly provided by IPv6 addresses. The IPv6 address in the IoT network is generated by using the stateless auto address configuration (SLAAC) mechanism, and its uniqueness is ensured by the DAD protocol. Recent research suggests that IPv6 deployment can be a risky decision due to the existing SLAAC-based addressing scheme and the DAD protocol being prone to reconnaissance and denial of service (DoS) attacks. This research paper proposes a new IPv6 generation scheme with an improved secure DAD mechanism to address these problems. The proposed addressing scheme generates IPv6 addresses by taking a hybrid approach based on vendor id of medium access control (MAC) address, physical location, and arbitrary random numbers, which mitigates reconnaissance attacks by malicious nodes. To prevent the DAD process from DoS attacks, hybrid values of interface identifier (IID) are multicast instead of actual values. The proposed scheme is evaluated under reconnaissance and DoS attacks in the presence of malicious nodes. The evaluation results demonstrate that the proposed method effectively mitigates reconnaissance and DoS attacks, outperforming the EUI-64 and SEUI-64 schemes in terms of address success rate (ASR), energy consumption, and communication overhead. Specifically, the proposed method significantly reduces the average probing rate for scanning the existence of an IPv6 address, with only a 1% probing rate compared to SEUI-64’s 5% and EUI-64’s 100%. Furthermore, the additional communication overhead introduced by the proposed method is less than 13% and 11% compared to EUI-64 and SEUI-64, respectively. Additionally, the energy consumption required to assign an IPv6 address using the proposed method is lower by 12% and 5% when compared to EUI-64 and SEUI-64, respectively. These findings highlight the effectiveness of the proposed method in enhancing security and optimizing resource utilization in IPv6 addressing.

Marketing-Driven Supply Chain Innovation: A Framework for FMCG SME Sustainability

In an increasingly competitive and sustainability-conscious market, small and medium-sized enterprises (SMEs) in the Fast-Moving Consumer Goods (FMCG) sector face unique challenges in achieving long-term sustainability. This review explores the concept of marketing-driven supply chain innovation as a strategic framework for enhancing SME sustainability. By integrating consumer-centric marketing strategies with supply chain operations, SMEs can align their product offerings, sourcing practices, and logistics with growing consumer demand for environmentally and socially responsible products. The framework outlined in this study includes key components such as sustainable product design, green logistics, ethical sourcing, and the incorporation of circular economy principles. Additionally, technology-driven innovation, such as data analytics and digital platforms, is emphasized as a tool to improve supply chain transparency, reduce waste, and optimize resource utilization. Through these collaborative efforts, SMEs can foster stronger consumer trust, enhance brand loyalty, and build a competitive edge in the market. Despite the potential benefits, the review also discusses the challenges SMEs may face, including limited financial resources, resistance to organizational change, and balancing profitability with sustainability goals. However, by leveraging green financing options, cross-sector partnerships, and phased implementation strategies, SMEs can overcome these barriers. Ultimately, the integration of marketing-driven supply chain innovation is positioned as a critical path for FMCG SMEs to achieve long-term sustainability, profitability, and market competitiveness in an evolving global economy. Keywords: Marketing-Driven, Supply Chain, FMCG SME, Review.

Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (Brassica rapa subsp. Pekinensis) Plants

The exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage (Brassica rapa subsp. Pekinensis) plants offers important theoretical reference value in balancing the accuracy of plant identification with work efficiency. This study focuses on Chinese cabbage plants during the rosette stage; RGB images were obtained by drones at different flight heights (20 m, 30 m, 40 m, 50 m, 60 m, and 70 m). Spectral sampling analysis was conducted on different ground backgrounds to assess their separability. Based on the four commonly used vegetation indices for crop recognition, the Excess Green Index (ExG), Red Green Ratio Index (RGRI), Green Leaf Index (GLI), and Excess Green Minus Excess Red Index (ExG-ExR), the optimal index was selected for extraction. Image processing methods such as frequency domain filtering, threshold segmentation, and morphological filtering were used to reduce the impact of weed and mulch noise on recognition accuracy. The recognition results were vectorized and combined with field data for the statistical verification of accuracy. The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. The method proposed in this paper is beneficial for agricultural growers and managers in carrying out precision planting management and planting structure optimization analysis and can aid in the timely adjustment of planting density or layout to improve land use efficiency and optimize resource utilization.

Outcomes of percutaneous coronary intervention in patients with chronic total occlusion (CTO): a single-center study evaluating a new dedicated CTO program in Egypt

PurposeThe conundrum of limited resources in developing countries leads to the abonnement and underutilization of percutaneous coronary intervention for chronic total occlusion, in addition to lack of expertise, limited supplies, and success rates. We embraced this challenge by initiating a dedicated program in a tertiary academic center in Egypt, aiming for optimum utilization of resources, limit cost, and gaining experience. We present our outcomes and the proficiency in establishing the program.MethodsWe enrolled all patients undergoing percutaneous coronary intervention for chronic total occlusion during the first year of initiating our dedicated program. Analysis of clinical data, angiographic findings, and procedural features were evaluated, aiming to evaluate outcomes. Our primary outcome was successful recanalization achievement, utilization of available resources and cost. Secondary outcomes such as peri-procedural myocardial infarction, coronary perforation, urgent need of coronary artery bypass grafting, cerebrovascular stroke, and mortality were assessed.ResultsOur study enrolled 64 patients electively admitted to the Critical Care Department, Cairo University, undergoing percutaneous coronary intervention for chronic total occlusion. The median J-CTO score was 2 (0–5). We achieved successful recanalization in 47 patients (73%). The mean procedural time was 123 ± 48.8 min, mean contrast volume was 359.03 ± 151.9 ml, and mean radiation dose was 9.4 ± 4.2 Gy. The mean number of wires per patient was 2.69 ± 1.32, and the most successful crossing strategy was antegrade wiring (56.3%). The commonest cause of failure was failure of re-entry (46.7%). One patient had peri-procedural MI (1.6%), and one patient required urgent CABG. There was no in-hospital mortality or cerebrovascular stroke. The cost ranged from 6520 Egyptian Pounds (EP) ($415) to 63,720 EP ($4058), with a mean of 26,213 EP ($1670).ConclusionsEstablishing a dedicated program for percutaneous coronary intervention for chronic total occlusion proved to be feasible and proficient, achieving high success rates with limited complications in view of limited resources, rationalized utilization of equipment, and limitation of hospital costs.

Revolutionizing Textile Manufacturing: Sustainable and Profitable Production by Integrating Industry 4.0, Activity-Based Costing, and the Theory of Constraints

The textile industry, a cornerstone of daily life and a highly globalized sector, faces significant environmental challenges due to its high water and energy consumption and extensive chemical usage. This study proposes a comprehensive green production planning and control model integrating Industry 4.0 concepts, activity-based costing (ABC), and the theory of constraints (TOC). The model utilizes mathematical programming to optimize product mix, maximize profitability, and minimize environmental impact. It leverages real-time sensing technologies and ERP systems to facilitate waste recovery, reduce carbon emissions, and achieve energy savings. Various carbon emission cost models, including continuous and discontinuous tax functions, are explored to balance corporate profitability with environmental sustainability. The findings demonstrate the model’s potential in optimizing resource utilization, reducing the environmental footprint, and enhancing profitability.

Artificial Intelligence in Logistics Optimization with Sustainable Criteria: A Review

In recent years, the integration of artificial intelligence (AI) into logistics optimization has gained significant attention, particularly concerning sustainability criteria. This article provides an overview of the diverse AI models and algorithms employed in logistics optimization, with a focus on sustainable practices. The discussion covers several techniques, including generative models, machine learning methods, metaheuristic algorithms, and their synergistic combinations with traditional optimization and simulation methods. By employing AI capabilities, logistics stakeholders can enhance decision-making processes, optimize resource utilization, and minimize environmental impacts. Moreover, this paper identifies and analyzes prominent challenges within sustainable logistics, such as reducing carbon emissions, minimizing waste generation, and optimizing transportation routes while considering ecological factors. Furthermore, the paper explores emerging trends in AI-driven logistics optimization, such as the integration of real-time data analytics, blockchain technology, and autonomous systems, which hold immense potential for enhancing efficiency and sustainability. Finally, the paper outlines future research directions, emphasizing the need for further exploration of hybrid AI approaches, robust optimization frameworks, and scalable solutions that accommodate dynamic and uncertain logistics environments.