Resource Allocation In Systems Research Articles

Optimization of Urban Fire Emergency Resource Allocation Based on Pre-Allocated Swarm Algorithm

As a high-frequency disaster with potentially devastating consequences, urban fires not only threaten the lives of city residents but can also lead to severe property losses, especially for hazardous chemical leaking scenarios. Quick and scientific decision-making regarding resource allocation during urban fire emergency responses is crucial for reducing disaster damages. Based on several key factors such as the number of trapped individuals and hazardous chemical leaks during the early stages of an incident, an emergency weight system for resource allocation is proposed to effectively address complex situations. In addition, a multi-objective optimization model is built to achieve the shortest response time for emergency rescue teams and the lowest cost for material transportation. Additionally, a pre-allocated bee swarm algorithm is introduced to mitigate the issue of local incident points being unable to participate in rescue due to low weights, and a comparison of traditional genetic algorithms and particle swarm optimization algorithms is conducted. Experiments conducted in a virtual urban fire scenario validate the effectiveness of the proposed model. The results demonstrate that the proposed model can effectively achieve the dual goals of minimizing transportation time and costs. Furthermore, the bee swarm algorithm exhibits advantages in convergence speed, allowing for the faster identification of ideal solutions, thereby providing a scientific basis for the rapid allocation of resources in urban fire emergency rescues.

Multi-objective optimization of urban logistics land: a gradient-based method approach with Wuhan city as an example

ABSTRACT Effective planning of logistics land is crucial for mitigating urban freight congestion, fostering economic activities, and achieving environmental equilibrium. However, the dual challenge of mismatched logistics supply and demand, along with conflicts in land use functions, can lead to inefficiencies in resource allocation and urban freight system performance. To tackle this issue, our study integrates truck GPS trajectory data with urban land use datasets to formulate a multi-objective optimization model. By utilizing the gradient descent algorithm, which effectively handles large-scale datasets, we can navigate the complexities of logistics land planning with precision. The application of this model in the Wuhan Urban Development Area reveals that: (1) across various scenarios, the model balances the utilization of multiple optimization objectives and demonstrates high solution efficiency; (2) in both the equal weight scenario and the economic preference scenario, the areas of logistics land change are characterized by high economic output, relatively good traffic conditions, greater distance from residential zones, and comparatively low land prices; and (3) based on urban development goals, the model can determine the upper bounds of the optimization objectives through manual supervision of the selection of ideal points.

Identifying people with post-COVID condition using linked, population-based administrative health data from Manitoba, Canada: prevalence and predictors in a cohort of COVID-positive individuals

ObjectiveMany individuals exposed to SARS-CoV-2 experience long-term symptoms as part of a syndrome called post-COVID condition (PCC). Research on PCC is still emerging but is urgently needed to support diagnosis,...

Task Offloading and Resource Allocation in UAV-Assisted Vehicle Platoon System

Task Offloading and Resource Allocation in UAV-Assisted Vehicle Platoon System

Cloud Technologies Revolutionizing Global Education: A Technical Case Study

The article offers a thorough examination of how cloud technologies, by offering strong answers to persistent problems in the distribution of educational information, have radically changed the face of global education. Through the strategic application of cutting-edge technologies like serverless architectures, dynamic resource allocation systems, and content delivery networks, educational institutions have successfully developed accessible and scalable learning environments that cater to a wide range of international audiences. It painstakingly details how these technology deployments have produced notable advancements in several areas, such as improved system performance, cost-effective cost structures, and improved user experiences. The article describes how educational institutions have successfully integrated cloud infrastructure, emphasizing their capacity to effectively handle growing user demands while continuously preserving remarkable levels of system availability and performance metrics. Beyond the obvious operational advantages, the article offers a thorough roadmap for educational institutions looking to use cloud technology by identifying important factors for future deployments and delivering priceless insights into technical implementation tactics. It also highlights the transformational potential of cloud technology in contemporary education by highlighting how careful cloud adoption has improved the overall quality of educational delivery in addition to resolving technical issues. The article's conclusions are especially important because they offer practical advice for educational establishments around the globe, showing how strategic technology adoption can both solve operational issues and further learning goals in a setting that is becoming more and more digital.

Blockchain-Based Security Deployment and Resource Allocation in SDN-Enabled MEC System

Blockchain-Based Security Deployment and Resource Allocation in SDN-Enabled MEC System

Framework and Outlooks of Multi-Source–Grid–Load Coordinated Low-Carbon Operational Systems Considering Demand-Side Hierarchical Response

In the context of advancing new power systems, a multi-source–grid–load interactive operation framework considering low-carbon demand hierarchical response is developed to further explore the support value of the multi-source–grid–load interaction mechanism for the low-carbon economic operation of the power system. The framework analyzes the support mechanisms of carbon tracking and load-side demand response for the low-carbon economic dispatch of the system and derives the carbon flow calculation method based on the network node correlation matrix, laying the foundation for developing low-carbon demand response strategies. Meanwhile, considering the marginal contribution of each load-side node to the system carbon emissions, a combined Shapley–Topsis low-carbon demand hierarchical response mechanism is designed to guide load nodes in implementing accurate low-carbon hierarchical responses, thereby ensuring the optimal allocation and efficient utilization of system resources. Finally, based on the proposed framework, promising future research perspectives are proposed to provide critical insights for constructing a low-carbon and reliable new energy system.

Optimization of High-Density Planting Configurations for Poovan Banana (Musa spp.) in Coconut-Based Agroforestry Systems of the Cauvery Delta Zone

Bananas (Musa spp.) are a vital global agricultural commodity and an essential crop in tropical agricultural systems. The Poovan cultivar, known for its high productivity and adaptability, is particularly effective in intercropping within coconut-based agroforestry systems. This study investigates the impact of planting geometries on crop performance in the Cauvery Delta Zone by evaluating five spatial configurations, ranging from 2.1×2.1m to 0.9×0.9m, with a focus on morphological, physiological, and economic parameters. The results indicate that wider spacing configurations, especially 2.1×2.1m, significantly improve leaf morphological traits, including maximum leaf length (148.17 cm), breadth (77.75 cm), and leaf area index (2.61m²/plant). Additionally, these configurations enhance key fruit quality characteristics, such as increased bunch weight (16kg), improved fruit dimensions (20cm length), higher sugar content (22°Brix), and greater fruit firmness (4.5kg/cm²). The economic analysis suggests that a 1.5×1.5m spacing provides the most favorable cost-benefit ratio (1.14). This study offers valuable insights into the complex relationships between planting density, resource allocation, and productivity in tropical farming systems, providing evidence-based recommendations for optimizing both agricultural performance and economic viability in integrated farming systems.

Time-varying distributed fuzzy resource allocation for multi-agent system over switching topology

Time-varying distributed fuzzy resource allocation for multi-agent system over switching topology

Multi-airport system management strategies considering air-rail intermodality and social welfare

The resource allocation and utilization efficiency in multi-airport systems can be significantly enhanced through the implementation of effective management strategies, thereby enhancing transportation efficiency in these critical transportation hubs. This study proposes a decision-making model for governments, airports, and air carriers operating within multi-airport systems, incorporating network structures that encompass both air-rail intermodal connectivity and direct route options. A backward induction method is employed to conduct a comparative analysis of social welfare between group management and localized management strategies for multi-airport systems. The results demonstrate that the group management strategy represents the Nash equilibrium, as it ensures the maximization of social welfare while maintaining alignment with public welfare objectives. Further examination of high-speed rail unit operating costs reveals a potential Prisoner’s Dilemma that the management departments may encounter. When the high-speed rail unit operating costs fall below a critical threshold, total social welfare under the localized management strategy surpasses that of the group management strategy for multi-airport systems. Moreover, higher airline revenue proportions derived from air-rail intermodal services may mitigate the likelihood of encountering a Prisoner’s Dilemma, as the critical value of high-speed rail unit operating cost increases with the increase of airline revenue proportions. These findings provide valuable insights for policymakers and stakeholders in designing effective multi-airport system management strategies, integrating both operational efficiency and social welfare maximization.

Advanced Queueing Model of a Multiprocessor Computing System

This paper presents an advanced queueing model for a multiprocessor computing system, where tasks require a random number of processors and are subject to constraints on waiting times in the queue. Unlike classical multi-server queueing systems, this model accounts for both resource requirements and queue waiting time restrictions, making it more suitable for real-world computing environments. By incorporating the probabilistic behavior of task arrival, service, and waiting constraints, the expressions are derived for key performance metrics, including the probabilities of task rejection and failure, system throughput, and resource utilization. An algorithm for determining the optimal queue length is also developed to enhance system efficiency by minimizing the probability of task losses. The proposed model provides a framework for analyzing and optimizing resource allocation in multiprocessor systems, improving their capability to handle dynamic and complex workloads.

Optimizing urban bike-sharing systems: a stochastic mathematical model for infrastructure planning

AbstractThis paper addresses the optimization of resource allocation and infrastructure planning in bike-sharing systems, particularly inspired by dynamic demand patterns as observed during the COVID-19 pandemic. We introduce a stochastic mathematical model that considers varying demand scenarios to enhance system performance and resource utilization. The research objectives are to fulfill the total travel demand across scenarios and compute the network's capacity to satisfy demand, thereby enhancing the system's efficiency and meeting users' diverse travel needs. The main contributions of this paper include presenting a stochastic mathematical model for bike-sharing station allocation and path network design, which optimizes resource allocation and infrastructure planning. Through a case study on the Vienna bike-sharing system, the model demonstrates practical applicability and effectiveness, offering insights for improving efficiency and service quality. The sensitivity analysis reveals that as costs for bicycle docks and station building increase, fulfilled demand decreases, emphasizing the crucial role of cost management in meeting demand efficiently.

Altair: Resource-efficient optimization and deployment for data plane programs

As network applications are increasingly offloaded to the programmable switches, program fitting problems come to the fore, which means mapping the programming entities (e.g., tables and metadata) into the hardware resources. However, existing works fail to achieve resource-efficient deployment in an acceptable time. In this paper, we present Altair, a high-performance compiler system for program optimization and resource allocation. In addition to proactively reducing program redundancy, Altair designs a two-layer framework to generate the near-optimal mapping solutions with respect to all the hardware constraints while minimizing resource usage. We also propose a novel feedback mechanism between the two layers to make the best trade-offs among bottleneck resources and accelerate the solving process. Altair not only converts the network program into a functionally identical and compilable codes, but also uses up to 30% fewer hardware resources with nearly 10X shorter execution time than state-of-the-art.

American Society of Clinical Oncology guideline update on palliative care for patients with cancer: Addressing the reality gap.

Plain Language Summary We commend the American Society of Clinical Oncology for their sweeping recommendations and advocacy for integrated palliative care as part of oncologic care. The literature that exists is heterogeneous and at times difficult to interpret, and the panel has clearly described their systemic review process and subsequent rationale for their recommendations. Although we agree with the overall recommendations, there are significant practical limitations to their application that renders them aspirational. We worry that the guidelines may be impossible for most programs to fully achieve due to a variety of factors including specialty palliative care workforce shortages, limited health care system resource allocation to integrate palliative care widely in cancer care, and lack of financial support for the research necessary to better answer outstanding clinical and implementation questions. We outline briefly some practical ways in which these recommendations can be applied by existing cancer centers and health care systems. Ultimately, both financial and quality incentives will be necessary to push forward the expansion and integration of palliative care within our national health care system as a whole.

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.

Water Use Attribution Analysis and Prediction Based on the VIKOR Method and Grey Neural Network Model: A Case Study of Zhangye City

Water consumption forecasting is a critical aspect of the increasingly strained water resources and sustainable water management processes. It is essential to explore the current status of water use patterns and future development directions in Zhangye City. In this study, 17 factors affecting water consumption in Zhangye City were selected to analyze changes in water consumption and to predict values from 2003 to 2022, utilizing the entropy weight–VIKOR model and the grey neural network model. The results indicate that agricultural water consumption and annual rainfall are the factors with the largest weights among the social and natural attribute indicators, respectively, significantly influencing water consumption in Zhangye City. As the proportions of water consumption for forestry, animal husbandry, fishery, livestock, urban public use, and ecological environment increase, while agricultural water consumption continues to decline, the overall water consumption trend in Zhangye City from 2003 to 2022 shows a positive trajectory. Each water consumption factor is tending toward greater balance, and the relationship between water supply and distribution is improving. The multi-year average relative error of the water consumption predictions for Zhangye City from 2003 to 2022 using the grey neural network model was 4.28%. Furthermore, the relative error values for annual predictions ranged from 0.60% to 5.00%, achieving an accuracy rate of 80.00%. This indicates a strong predictive performance. Ultimately, the model was used to predict a water consumption of 20.18 × 108 m3 in Zhangye City in 2027. The model can serve as a theoretical reference for short-term water consumption forecasting and for establishing a basin water resource allocation system in Zhangye City.

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.