Resource Allocation In Systems Research Articles

Application of Distributed Collaborative Optimization in Building Multi-Energy Complementary Energy Systems

This article investigates the application and physical mechanism exploration of distributed collaborative optimization algorithms in building multi-energy complementary energy systems, in response to the difficulties in coordinating various subsystems and insufficient dynamic control strategies. On the basis of modeling each subsystem, the Dual Decomposition algorithm is used to decompose the global optimization problem of the system into several independent sub problems, achieving independent optimization of each subsystem. Through an adaptive dynamic scheduling strategy, real-time data and predictive information are continuously updated and controlled, effectively allocating system resources. The experimental results show that compared to the original system before optimization, the improved algorithm in this paper reduces the total energy consumption of the system by 6.9% and 2.8% on typical summer and winter days, respectively. The conclusion shows that the algorithm proposed in this paper can effectively solve the problem of system coordination difficulties, improve system resource allocation and overall operation level, and provide a new perspective for the optimization design and operation control of energy systems.

The Impact of Health Administration on Healthcare Quality: A Systematic Review of Effective Management Practices

Health administration plays a crucial role in shaping the quality of healthcare delivery by implementing effective management practices that enhance patient outcomes and operational efficiency. This systematic review aims to examine the impact of key administrative practices on healthcare quality, focusing on strategies that improve patient satisfaction, reduce errors, and optimize resource utilization. A comprehensive literature search was conducted across databases including PubMed, Scopus, and Google Scholar, reviewing studies published from 2016 onwards. Inclusion criteria targeted research on management practices such as resource allocation, leadership models, operational efficiency, and quality assurance systems. Findings indicate that health administration practices, particularly in leadership and operational efficiency, have a significant positive effect on quality metrics, such as reduced patient wait times, lower readmission rates, and increased patient satisfaction. However, challenges like limited resources and resistance to change often hinder implementation. This review underscores the need for adopting evidence-based administrative strategies and suggests directions for future research to further enhance healthcare quality through effective management practices.

Configuration of liveness-enforcing initial marking with the minimum resources for resource allocation systems

The enforcement of liveness is crucial for Petri nets models of resource allocation systems (RASs). It is interesting yet very challenging to establish an initial marking for Petri net plants so that the net is live. Such an initial marking is referred to as liveness-enforcing initial marking (LIM). Despite existing literature presenting various LIMs, no studies have addressed the issue of minimizing the number of resources in an LIM. This work focuses on designing an LIM with the minimum resources (LIM-MR) for a class of Petri nets called systems of sequential systems with shared resources (S4PRs) by assigning a token capacity to each resource place, such that the sum of all involved resources is minimized. This work first establishes a kind of necessary and sufficient liveness condition for S4PR, which is then encoded into a series of variables and constraints in a mixed-integer programming (MIP) formulation. Although LIM-MR may not be unique, solving the proposed MIP formulation can obtain at least one LIM-MR for S4PR under consideration. The experimental results show the solvability of this approach for S4PRs.

Graph Attention-Based MADRL for Access Control and Resource Allocation in Wireless Networked Control Systems

Graph Attention-Based MADRL for Access Control and Resource Allocation in Wireless Networked Control Systems

A Retrospective Analysis of Jordan's National COVID-19 Call Center: Operations, Effectiveness, and Lessons Learned.

Contact tracing has been a cornerstone of non-pharmaceutical interventions to control the COVID-19 epidemic, with highly mixed effectiveness internationally. In Jordan, the Ministry of Health (MOH) collaborated with the Jordan Nurses and Midwives Council and the USAID Local Health System Sustainability Project to set up a call center for contact tracing of COVID-19. This study described the operation and assessed the effectiveness of Jordan's COVID-19 call center activities in reaching COVID-19 cases and their contacts. A retrospective observational design was conducted using data from all calls made by the COVID-19 call center cases between November 2020 and April 2022. Data were collected from initial and follow-up calls to PCR-confirmed COVID-19 cases and their contacts. Data on socio-demographics, symptoms, and contact tracing activities were recorded. The study focused on key outcomes, including call success rates, the number of cases and contacts reached, and the role of different detection modes in identifying cases. During the study period, the call center attempted to contact 1,027,911 COVID-19 cases, successfully reaching 802,525 cases (78.1%). Follow-up calls were made to 1,126,334 cases, with a success rate of 74%. The call center appeared particularly valuable during the initial period of the pandemic until it was overwhelmed by the significantly more transmissible Omicron variant of the virus. Two weaknesses were identified: gaps in reaching non-Jordanian citizen cases and difficulty in keeping up with case volume during the Omicron wave of February-March 2022, when reported cases peaked at over 20,000 per day. One-third of all reached cases said that they had been referred for testing through contact tracing. Contact tracing activities led by the MOH were instrumental in identifying new cases, optimizing resource allocation, improving surveillance and data systems, targeting vulnerable population, and supporting mitigation strategies to combat the COVID-19 pandemic in Jordan.

Hypersonic High Speed Strike Weapons: Design, Research and Development

Hypersonic High-Speed Strike Weapons are a part of advanced weapon technology used in military engagements, designed to travel and sustain speeds of at least Mach 5, which is five times the speed of sound. These specially designed weapons are capable of maneuvering and changing directions unpredictably. In this paper, we explore the advantages of using such weapons, the research behind them, and how their implementation could enhance military engagements. By employing this system of weapons, we can expect quick weapon system activation times and precise deployment regarding targets. This study examines the design, overview, technical aspects, feasibility, advantages, and disadvantages of deploying this weapon technology in real-time use. However, several challenges must be addressed to successfully deploy these weapons systems. The primary challenges associated with the deployment of hypersonic weapons include temperature management, propulsion technology, material science (which is critical due to the high-speed demands), safety and reliability, strategic and ethical considerations, financial and time constraints, testing facilities, and the guidance and control of these systems. Overcoming these challenges through advanced technology and innovative ideas is essential for the successful implementation of Hypersonic High-Speed Strike Weapons (HHSSW) systems. The HHSSW systems hold promise for advancing the current weapons technology that humanity possesses. With proper research and resource allocation for HHSSW systems, we can anticipate their development for applications such as asteroid or comet deflection and, if necessary, space warfare.

Energy-water-carbon system management in response to climate change in China under uncertainties (2026–2050)

Climate change has seriously affected the resource utilization of the energy-water-carbon system, while the system carbon emissions continuously act on climate change. Given the interaction between the two, there is an urgent need to facilitate the rational allocation of system resources to realize the positive feedback between climate change and energy-water-carbon system development. In this study, an interval stochastic fuzzy integer programming (ISFIP) model is developed to support resource planning and management of the energy-water-carbon system in response to climate change in China. The developed model has advantages in characterizing the complexity and uncertainty of the energy-water-carbon system and providing optimal system decision-making options under multiple climate change scenarios. The results show that the SSP126-NZE scenario power system would reduce carbon emissions by 16.23 %, air pollutant emissions by 19.31 %, water consumption by 30.96 %, and increase economic benefits by 10.97 %, respectively, compared to the SSP585-STEPS baseline scenario.

Joint Trajectory Design and Resource Allocation in UAV-Enabled Heterogeneous MEC Systems

Joint Trajectory Design and Resource Allocation in UAV-Enabled Heterogeneous MEC Systems

Bi-level electricity and heat sharing strategy for cross-border integrated energy system based on Nash game

As global energy demands continue to rise and the need for sustainable development becomes more urgent, Cross-Border Integrated Energy Systems (CBIES) offer a crucial pathway to improving energy efficiency and security through international collaboration. This paper proposes a bi-level electricity and heat sharing model based on Nash game theory, which optimizes resource allocation and management in cross-border energy systems. The model addresses two sub-problems: maximizing the overall benefits of the alliance and ensuring fair redistribution of those benefits within the alliance. To tackle the complexities of these problems, the Alternating Direction Method of Multipliers (ADMM) is employed in a distributed manner. Additionally, the ARIMA-GARCH model is introduced to handle the uncertainties related to exchange rate volatility in cross-border energy transactions. Carbon capture and storage (CCS) and power-to-gas (P2G) technologies are also integrated into the cogeneration system to promote low-carbon energy production, aligning with environmental sustainability goals. The results show that the upper-level electricity sharing sub-problem reduced the total operational cost from 1,173,330 CNY to 875,582 CNY after 55 iterations, while the lower-level heat sharing sub-problem further optimized the cost to 687,356 CNY after 14 iterations. Post-cooperation cost comparisons showed that the costs for Country B and Country C were reduced by 207,828 CNY and 390,672 CNY, respectively, demonstrating the model’s effectiveness.

Leveraging machine learning to optimize renewable energy integration in developing economies

The integration of renewable energy sources into power grids is a critical challenge for developing economies, where infrastructure limitations, unpredictable energy demand, and policy gaps hinder effective energy transitions. Machine learning (ML) offers transformative potential in addressing these challenges, enabling more efficient and reliable energy systems through advanced data analytics, predictive modeling, and real-time decision-making. This review explores how ML can optimize renewable energy integration by improving forecasting accuracy, enhancing grid stability, and optimizing resource allocation in solar, wind, and hydropower systems. Machine learning algorithms are particularly effective in predicting energy demand and renewable resource availability, allowing for better alignment between energy supply and consumption patterns. By leveraging ML-based predictive models, grid operators can mitigate the risks of energy shortages or oversupply, improve grid stability, and reduce operational costs. Furthermore, the application of ML in renewable energy systems provides opportunities for developing economies to leapfrog traditional energy infrastructure limitations by adopting smart grids that integrate real-time data to enhance decision-making and efficiency. This review also reviews case studies from Africa and Latin America, highlighting successful implementations of ML in renewable energy systems. These examples underscore the potential for ML to accelerate the deployment of sustainable energy solutions, while also addressing technical, economic, and policy barriers that exist in developing contexts. With continued advancements in machine learning, combined with supportive regulatory frameworks and investment in digital infrastructure, developing economies have the potential to realize substantial gains in renewable energy integration. This review concludes by discussing future trends, challenges, and opportunities for leveraging machine learning to optimize renewable energy integration, ultimately contributing to sustainable development and energy security in emerging markets.

Epidemiological characteristics, diagnosis and treatment effect of rifampicin-resistant pulmonary tuberculosis (RR-PTB) in Guizhou Province

BackgroundRifampicin-resistant pulmonary tuberculosis (RR-PTB) presents a significant threat to global public health security. China bears a substantial burden of RR-PTB cases globally, with Guizhou Province experiencing particularly alarming trends, marked by a continual increase in patient numbers. Understanding the population characteristics and treatment modalities for RR-PTB is crucial for mitigating morbidity and mortality associated with this disease.MethodsWe gathered epidemiological, diagnostic, and treatment data of all RR-PTB cases recorded in Guizhou Province from January 1, 2017 to December 31, 2023. Utilizing composition ratios as the analytical metric, we employed Chi-square tests to examine the spatiotemporal distribution patterns of RR-PTB patients and the evolving trends among different patient classifications over the study period.ResultsIn our study, 3396 cases of RR-PTB were analyzed, with an average age of 45 years. The number of RR-PTB patients rose significantly from 176 in 2017 to 960 in 2023, peaking notably among individuals aged 23–28 and 44–54, with a rising proportion in the 51–80 age group (P < 0.001). Since 2021, there has been a notable increase in the proportion of female patients. While individuals of Han ethnic group comprised the largest group, their proportion decreased over time (P < 0.001). Conversely, the Miao ethnicity showed an increasing trend (P < 0.05). The majority of patients were farmers, with their proportion showing an upward trajectory (P < 0.001), while students represented 4.33% of the cases. Geographically, most patients were registered in Guiyang and Zunyi, with a declining trend (P < 0.001), yet household addresses primarily clustered in Bijie, Tongren, and Zunyi. The proportion of floating population patients gradually decreased, alongside an increase in newly treated patients and those without prior anti-tuberculosis therapy. Additionally, there was a notable rise in molecular biological diagnostic drug sensitivity (real-time PCR and melting curve analysis) (P < 0.001). However, the cure rate declined, coupled with an increasing proportion of RR-PTB patients lost to follow-up and untreated (P < 0.05).ConclusionsEnhanced surveillance is crucial for detecting tuberculosis patients aged 23–28 and 44–54 years. The distribution of cases varies among nationalities and occupations, potentially influenced by cultural and environmental factors. Regional patterns in RR-PTB incidence suggest tailored prevention and control strategies are necessary. Despite molecular tests advances, challenges persist with low cure rates and high loss to follow-up. Strengthening long-term management, resource allocation, and social support systems for RR-PTB patients is essential.

Multi-agent reinforcement learning for task offloading with hybrid decision space in multi-access edge computing

Multi-access Edge Computing (MEC) has become a significant technology for supporting the computation-intensive and time-sensitive applications on the Internet of Things (IoT) devices. However, it is challenging to jointly optimize task offloading and resource allocation in the dynamic wireless environment with constrained edge resource. In this paper, we investigate a multi-user and multi-MEC servers system with varying task request and stochastic channel condition. Our purpose is to minimize the total energy consumption and time delay by optimizing the offloading decision, offloading ratio and computing resource allocation simultaneously. As the users are geographically distributed within an area, we formulate the problem of task offloading and resource allocation in MEC system as a partially observable Markov decision process (POMDP) and propose a novel multi-agent deep reinforcement learning (MADRL) -based algorithm to solve it. In particular, two aspects have been modified for performance enhancement: (1) To make fine-grained control, we design a novel neural network structure to effectively handle the hybrid action space arisen by the heterogeneous variables. (2) An adaptive reward mechanism is proposed to reasonably evaluate the infeasible actions and to mitigate the instability caused by manual configuration. Simulation results show the proposed method can achieve 7.12%−20.97% performance enhancements compared with the existing approaches.

Aflatoxins in Cameroon: Occurrence, Potential Health Risks and the Way Forward in Implementing the Partnership for Aflatoxin Control in Africa (PACA) Country-Led Model

Aflatoxin hazard is present among the main food dangers in Cameroon. This paper gives insight on how affected crops and commodities such as maize and groundnuts are likely to be contaminated with aflatoxin. Possible negative effects of aflatoxin include hepatic injury, cancer risk, kwashiorkor, and marasmus kwashiorkor is also discussed. Therefore, this report recommends using the Partnership for Aflatoxin Control in Africa (PACA) method for Cameroon. This policy also targets aflatoxin control to be a country-led, multi-sectoral approach. The aims of the study propose useful strategies for guiding the PACA approach; these include raising awareness of the health risks of aflatoxins, enhancing the laws on food safety, supporting adequate agricultural practices, and improving proper disposal and utilization procedures of post-harvest storage and processing systems. Government authorities and research institutes must collaborate to successfully execute aflatoxin control measures. The report emphasizes the need of capacity building, resource allocation, and monitoring systems in ensuring the efficacy of these initiatives. Thus, Cameroon can apply the PACA concept and the mentioned measures to decrease the level of aflatoxin contamination. Therefore, enhance the conditions for food safety and decrease the level of health risks. Consequently, this research provides useful information and suggestions to policymakers, researchers, and any party interested in Cameroon’s aflatoxin-related programs. This paper emphasizes the need for capacity enhancement, resource mobilization, and the proper monitoring and implementation of the strategies. Through the application of the PACA approach and the above-outlined strategies, Cameroon can reduce the incidence of aflatoxin contamination and its disturbing impacts on consumer health and food security. In conclusion, the findings and recommendations of this paper are highly informative to policymakers, researchers, and other interested stakeholders in Cameroon’s aflatoxin management processes.

W-band photonic-aided reconfigurable multichannel vector mm-wave transmission enabled by the digital subcarrier multiplexing technique.

As multimedia service demands rise, radio-over-fiber (RoF) systems necessitate a flexible wireless resource allocation in multi-user scenarios. Optical wavelength division multiplexing (WDM) can meet multi-user application scenarios but has the problems of high hardware cost and complexity. In this Letter, we propose a photonic-aided reconfigurable multichannel vector millimeter-wave (mm-wave) transmission scheme in the RoF system enabled by the digital subcarrier multiplexing (DSCM) technique. The scheme can flexibly adjust the number and frequency spacing of subcarriers as well as the bandwidth and modulation format allocated to each subcarrier, and it can be applied to multi-user scenarios without increasing the cost and complexity of hardware implementation. The off-line experiments verify the system transmission performance with different numbers, bandwidth, and frequency spacing of subcarriers, fully verifying the feasibility of the scheme. Furthermore, the Letter delves into the effects of the subcarrier number, bandwidth, and frequency spacing on overall system performance, highlighting the potential of this scheme for flexible and cost-effective wireless resource allocation in RoF systems.

E-CARGO-based dynamic weight offload strategy with resource contention mitigation for edge networks

With the widespread use of Mobile Edge Computing (MEC) in smart manufacturing systems in Industrial Internet of Things (IIoT) and 5G networks, determining how to efficiently offload computing tasks has become a hot research area. The Role-Based Collaboration (RBC) Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is introduced to comprehensively manage MEC servers and user computation tasks in edge network environments, thereby improving the effectiveness and performance of task offloading in smart manufacturing systems. To begin with, latency and energy consumption are important indicators for evaluating the offloading effect. A pre-allocation algorithm based on user latency tolerance is proposed to dynamically adjust the latency-energy consumption weighting factor to optimize system resource allocation for real-time adjustment of offloading decisions. Second, the Group Role Assignment of Agent Role Conflicts (GRACAR) model based on E-CARGO is extended, along with a dynamic weighting of the GRACAR (GRACAR-DW) model and formal modeling. By introducing resource contention constraints, the resource contention caused by excessive task data offloading to the same MEC server is proactively mitigated. Finally, a Gurobi solution based on Mixed-Integer Linear Programming (MILP) is developed to help validate and synthesize the proposed model. Simulation results show that the strategy considerably enhances the MEC system's overall performance in terms of latency and energy consumption while also providing new ideas and technological support for offloading decisions in edge networks.

Resource Allocation and Security Threat in Cloud Computing: A Survey

Cloud computing is a cutting-edge technology with enormous commercial and enterprise potential. Apps and related data can be accessed from any location thanks to clouds. Companies can drastically lower the cost of their infrastructure by renting resources from the cloud for storage and other processing needs. They can also use pay-as-you-go application access available to the entire firm. Therefore, obtaining licenses for specific products is not necessary. However, allocating resources as efficiently as possible is one of the main challenges in cloud computing. Because the model is unique, resource allocation is done to lower its expenses. Meeting application and customer requirements and consumer needs presents additional issues in resource allocation. This study provides a detailed discussion of several resource allocation systems, security threats, and the difficulties they face. This work is anticipated to help researchers and cloud users overcome obstacles.

Dynamic resource allocation and scheduling for appointment-based systems with walk-ins

Effectively managing constrained and perishable capacity amid fluctuating demands is a common challenge in service industries, often addressed through appointment-based systems that incorporate walk-ins. Balancing appointments and walk-ins is crucial for enhancing system value and maintaining stability. In this article, we study an integrated decision problem for appointment scheduling and resource allocation across multiple facilities, considering stochastic demands from three segments: priority customers, walk-in customers, and appointment customers. We formulate the problem as a sequential scheduling-allocation challenge using stochastic dynamic programming. Leveraging the anti-multimodularity of the value function, we fully characterize the optimal dynamic resource allocation policy and its monotone structural properties under any appointment schedule. Given the complexity and intractability of optimal scheduling with dynamic resource allocation, we introduce two suboptimal scheduling policies—vertical scheduling and horizontal scheduling—and a heuristic allocation policy. Additionally, we create an Upper Bound (UB) for the original problem as a surrogate, establishing an asymptotically optimal UB for the scaled problem through its objective value and a corresponding lower bound through its solution. This UB solution serves as an efficient and effective heuristic, demonstrating superior performance compared with the combined performance of suboptimal scheduling policies with optimal dynamic resource allocation, especially as problem size increases. It also holds significant practical implications for integrated pooling systems.

Joint Resource Allocation and Intrusion Prevention System Deployment for Edge Computing

Joint Resource Allocation and Intrusion Prevention System Deployment for Edge Computing

Elevating optical networks: Machine learning approach for optimal resource scheduling and performance boost

SummaryThe increasing demand for massaging networks that are stable and quick needs reevaluations of standard optical networking administration strategies. To improve the efficacy of optical networks by integrating machine learning (ML) approach for the best resource scheduling, this research presents an innovative dynamic block widow optimized random forest (DBWO‐RF) strategy. To implement the DBWO‐driven resource allocation method in accordance with the categorization and clustering findings, the RF method is incorporated with the software defined optical to achieve channel quality assessment after successfully clustering employs the RF approach to achieve channel quality assessment after successfully clustering traffic patterns using the fuzzy C‐means (FCM) algorithm. To lessen the likelihood of blocking, the fragmentation‐function‐fit (FFF) algorithm was provided and the findings indicate that this approach possesses a reduced blocking risk. Using multiple approaches to modulation for various channel quality, the suggested resource allocation system leverages the DBWO approach to distribute the necessary resources based on various “traffic flow (TF)” clustering findings. The examination's outcomes demonstrate that, compared to other techniques under various given load levels, the present study has a reduced blocking risk, a sufficient complexity degree and greater effectiveness in the utilization of spectrum resources.

Revolutionising waste management with the impact of Long Short-Term Memory networks on recycling rate predictions

This study explores the efficacy of Long Short-Term Memory (LSTM) networks in predicting recycling rates and enhancing resource allocation in waste management systems. It addresses the limitations of traditional statistical models and machine learning algorithms that struggle with sequential data and temporal dependencies. The methodology comprised collecting extensive datasets from public repositories, configuring the LSTM network architecture, training the model with historical data, and testing various activation functions and hyperparameters. The model’s performance was rigorously compared to traditional models and alternative machine learning algorithms using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2). The findings demonstrate that the LSTM model significantly outperformed traditional approaches, achieving an MAE of 3.5%, an RMSE of 2.8%, and an R2 of 0.92. These results underscore the superior capability of LSTM networks to capture complex temporal patterns in recycling data, offering substantial improvements in predictive accuracy and reliability. Consequently, the study highlights the potential of LSTM networks to revolutionize waste management strategies, contributing to more effective and sustainable practices.