Coordinated Resource Allocation Research Articles

Implementing active surveillance for TB: A descriptive survey of healthcare workers in the Eastern Cape, South Africa.

South Africa is a tuberculosis (TB) high-burden country. In the Eastern Cape (EC), community health worker (CHW) teams implement active surveillance for TB to curb spread in disadvantaged communities. However, achieving the goals of the End-TB strategy require coordinated efforts that implement policy and strengthen health systems. This survey described views of healthcare workers (HCWs) in primary care facilities on factors that influence implementation of active surveillance for TB. This survey was conducted across two districts, among healthcare workers working in TB rooms at primary health facilities. A cross-sectional survey of HCW in the EC. The survey included 37 clinics in the OR Tambo Health District (ORTHD) and 44 clinics in the Nelson Mandela Bay Health District (NMBHD). Routine screening at primary care facilities (88.2%) and contact tracing initiatives (80.8%) were the common modes of TB screening. Tuberculosis screening services in the community were only provided by CHWs in 67.3% of instances. Although CHWs were adequately trained and motivated; the lack of transport, limited availability of outreach team leaders (OTLs) and poor security limited implementation of TB screening services in the community. Comparison between both districts revealed TB screening was limited by lack of transport in the rural district and poor security in the urban context. Community engagement provided a platform for improving acceptability. Community-based TB screening was limited. Inadequate coordination of services between stakeholders in the community has limited reach. Further research should describe that coordinating resource allocation and community empowerment could improve the implementation of active surveillance for TB.Contribution:This study highlights the views of TB room HCWs who believe the opportunity for community-level TB screening is improved with effective leadership and community engagement for acceptability of these services.

A multi-criteria simulation-optimization coupling approach for effective emergency response in marine oil spill accidents

Effective marine oil spill responses are vital to reduce environmental, societal, and economic damage. This study developed a Multi-Criteria Emergency Response System (MC-ERS) to comprehensively evaluate response efficiency, operational costs, and environmental losses. The proposed system integrates dynamic multiphase simulation of oil weathering and oil cleanup processes and further provides effective planning for multi-stage resource allocation through system optimization. The developed weight-sum model improved the performance of response operations by reducing the complexity of multi-criteria decision-making. Particle Swarm Optimization (PSO) was chosen as the foundational optimization algorithm due to its efficiency in rapid convergence and suitability for complex problems. From extensive comparisons of PSO variants across benchmark functions and inertia strategies, the C-PSO algorithm was developed, demonstrating enhanced optimization performance for MC-ERS. The developed modelling system performance was demonstrated and evaluated through a representative case study. The optimization plan coordinated resource allocation from onshore warehouses to harbors and spill sites, balancing oil recovery efficiency, costs, and ecological losses. Optimized results indicate an oil recovery of up to 76.50% in five days. Additionally, the system cuts costs by 3.45% and environmental losses by 15.75%. The findings enhance the efficiency of marine oil spill emergency response and provide support for such incidents.

Differences in Urban Development in China from the Perspective of Point of Interest Spatial Co-Occurrence Patterns

An imbalance in urban development in China has become a contradiction. Points of Interest (POIs) serve as representations of the spatial distribution of urban functions. Analyzing POI spatial co-occurrence patterns can reveal the agglomeration patterns of urban functions across cities at different levels, providing insights into imbalances in urban development. Using POI data from 297 cities in China, the Word2vec model was employed to model the POI spatial co-occurrence patterns, allowing for the quantification of fine-granular urban functionality. Subsequently, the cities were clustered into five tiers representing different levels of development. An urban hierarchical disparity index and graph were introduced to examine variations in urban functions across different tiers. A significant correlation between POI spatial co-occurrence patterns and the GDP of cities at different levels was demonstrated. This study revealed a notable polarization trend characterized by the development of top-tier cities and lagging tail-end cities. Top-tier cities exhibit advantages in terms of their commercial environments, such as international banks, companies, and transportation facilities. Conversely, tail-end cities face deficiencies in urban infrastructure. It is crucial to coordinate resource allocation and establish sustainable development strategies that foster mutual support between the top-tier and tail-end cities.

Enhancing Data Freshness in Air-Ground Collaborative Heterogeneous Networks through Contract Theory and Generative Diffusion-Based Mobile Edge Computing.

Mobile edge computing is critical for improving the user experience of latency-sensitive and freshness-based applications. This paper provides insights into the potential of non-orthogonal multiple access (NOMA) convergence with heterogeneous air-ground collaborative networks to improve system throughput and spectral efficiency. Coordinated resource allocation between UAVs and MEC servers, especially in the NOMA framework, is addressed as a key challenge. Under the unrealistic assumption that edge nodes contribute resources indiscriminately, we introduce a two-stage incentive mechanism. The model is based on contract theory and aims at optimizing the utility of the service provider (SP) under the constraints of individual rationality (IR) and incentive compatibility (IC) of the mobile user. The block coordinate descent method is used to refine the contract design and complemented by a generative diffusion model to improve the efficiency of searching for contracts. During the deployment process, the study emphasizes the positioning of UAVs to maximize SP effectiveness. An improved differential evolutionary algorithm is introduced to optimize the positioning of UAVs. Extensive evaluation shows our approach has excellent effectiveness and robustness in deterministic and unpredictable scenarios.

Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum

Modern computing environments, thanks to the advent of enabling technologies such as Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary network of computing resources that extend from the Edge of the network to the Cloud, which enables a dynamic and adaptive service fabric. Efficiently coordinating resource allocation, exploitation, and management in the Cloud Continuum represents quite a challenge, which has stimulated researchers to investigate innovative solutions based on smart techniques such as Reinforcement Learning and Computational Intelligence. In this paper, we make a comparison of different optimization algorithms and a first investigation of how they can perform in this kind of scenario. Specifically, this comparison included the Deep Q-Network, Proximal Policy Optimization, Genetic Algorithms, Particle Swarm Optimization, Quantum-inspired Particle Swarm Optimization, Multi-Swarm Particle Optimization, and the Grey-Wolf Optimizer. We demonstrate how all approaches can solve the service management problem with similar performance—with a different sample efficiency—if a high number of samples can be evaluated for training and optimization. Finally, we show that, if the scenario conditions change, Deep-Reinforcement-Learning-based approaches can exploit the experience built during training to adapt service allocation according to the modified conditions.

Optimal resource allocation method and fault-tolerant control for redundant robots

Abstract. Resource coordination and allocation strategies are proposed to reduce the probability of failure by aiming at the problem that the robot cannot continue to work after joint failure. Firstly, the principal component analysis method under unsupervised branches in machine learning is used to analyze the reliability function and various indexes of the robot to obtain the comprehensive evaluation function. Then, based on the fault-tolerant-control inverse-kinematics optimal algorithm, each joint can be scheduled by weighted processing. Finally, the comprehensive evaluation function is used as an index to evaluate the probability of fault occurrence, and the weight is defined to realize the coordinated resource allocation of redundant robots. Taking the planar four revolute joints (4R) redundant robot as an example, the algorithm control is compared. Based on reasonable modeling and physical verification, the results show that the method of optimal resource coordination and allocation is effective.

A multi-agency coordination resource allocation and routing decision-making problem: A coordinated truck-and-drone DSS for improved wildfire detection coverage

This study proposes a novel coordinated truck-drone system as a decision support system to improve fire suppression operations. The problem in this study is formulated as a bi-objective mathematical model to minimize total monitoring cost and time, considering trucks as mobile drone depots and that drones can fly at various altitudes and access hard-to-reach areas. In addition, time and cost parameters in the mathematical model are deemed uncertain, rendering the model more realistic. Accelerated Benders' decomposition (ABD) is utilized to solve the model rapidly. Furthermore, a column-and-constraint generation (CCG) algorithm is employed, which is an effective method for solving scenario-based models under uncertainty. Two criteria are subsequently used to evaluate and compare proposed robust optimization approaches (risk averse and min-max relative regret). The results indicate that the proposed system can assist firefighters in determining the optimal number of drones and trucks to patrol and the time and cost required to visit all areas. Moreover, the findings demonstrate that the min-max relative regret approach can outperform other methods during the hot season when the risk of wildfire is elevated. In contrast, when the fire risk is lower during the cold season, time and cost can be effectively managed, and a risk-averse strategy can be implemented. Finally, the proposed solution framework can facilitate optimal strategic organizational decision planning.

Node Essentiality Assessment and Distributed Collaborative Virtual Network Embedding in Datacenters

Network virtualization (NV) has extensive and significant applications in cloud computing and parallel and distributed systems. Virtual network embedding (VNE) is a key issue in NV, which is an effective means to advance systems' performance. While existing VNE research lacks resource allocation coordination between mappings of different virtual network requests, resulting in insufficient resource utilization and high overhead. In this paper, we propose a novel node essentiality evaluation model for data center networks (DCNs), and design an efficient distributed collaborative virtual network embedding. Firstly, we propose a node essentiality evaluation scheme based on dynamic model, which combines the characteristics of network topology and nodes to make the evaluation results more comprehensive. Secondly, we establish the two-stage node importance evaluation criteria for the deviation mean of the data center dynamic model and the variance based on the deviation mean. Furthermore, we investigate a nodal importance assessment method based on the data center dynamic model for perturbation testing. Finally, we design a distributed coordinated VNE algorithm ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CNI-VNE</b> ) which calculates the importance index of physical nodes through topology awareness. The proposed algorithm can increase the coordination between different request mappings, thereby reducing the mapping cost of physical node resources and minimizing the cost of VNE. We use the real Fat-tree DCN of 128 servers and 80 switches as testbed, and evaluate them from indicators such as average reliability, average bandwidth consumption, average energy consumption, and average mapping time. Massive simulation results in different scenarios show that our algorithm achieves the best performance on most indicators compared with the existing state-of-the-art proposals, mapping acceptance and average revenue increased by 19.4% and 21.3%, respectively, and DCN reduced bandwidth consumption by about 30%.

Cross-domain coordination of resource allocation and route planning for the edge computing-enabled multi-connected vehicles

Multi-access edge computing (MEC) has unique interests in processing intensive and delay-critical computation tasks for vehicular application through computation offloading. However, due to the spatial inhomogeneity and dynamic mobility of connected vehicles (CVs), the edge servers (ESs) must dynamically adjust their resource allocation schemes to effectively provide computation offloading services for CVs. In this case, we propose a MEC framework supporting the collaboration of CVs, and incorporate digital twins (DTs) into wireless network to mitigate the unreliable long-distance communication between CVs and ESs. To solve the contradiction between the task change requirements of CVs and ES resources, we proactively balance the computation resources load of ESs by appropriately cooperative route planning of CVs, and achieve cross-domain load balancing between traffic flow and edge cloud resources domains. Furthermore, we jointly formulate route planning and resource allocation to balance the travel and service time delay by considering the mobility of CVs, distributed resources of ESs and the deadline sensitive vehicular tasks comprehensively. Besides, considering the coupled relationship between route planning and resource allocation, an alternating optimization algorithm is proposed to solve the formulated problem. we decompose it into two sub-problems. Firstly, a reinforcement learning method is used to optimize the route planning of CVs with fixed resource allocation. Then, an online learning and iterative algorithm is used to optimize the resource allocation strategy of edge cloud with fixed route selection. In order to demonstrate that our suggested scheme is more effective than other comparison schemes, a comprehensive series of experiments are conducted.

Online Coordinated NFV Resource Allocation via Novel Machine Learning Techniques

Thanks to Network Function Virtualization (NFV), Internet Service Providers (ISPs) can improve network resource utilization with significantly reduced capital and operational expenditures. To dig deeper into the potential of NFV, an important challenge is the resource allocation problem in NFV (NFV-RA), which can be divided into three stages: VNFs chain composition, VNF forwarding graph embedding, and VNFs scheduling. The key to the NFV-RA problem is to design an effective and coordinated resource allocation algorithm for the three stages. Besides, the NFV-RA problem has been proved to be NP-Hard, and thus most existing approaches focus on heuristic and meta-heuristic algorithms. In this paper, we propose an NFV online coordinated resource allocation framework (OCRA) that completes the three stages simultaneously in a coordinated manner by combining parallel Multi-Agent Deep Reinforcement Learning with novel neural networks and RL training techniques. The extensive experimental results show that compared with the state-of-the-art solutions, OCRA is highly-efficient in terms of time, with up to 50% and 10.8% improvement on resource overhead and acceptance ratio, respectively.

Production Strategy Optimization of Integrated Exploitation for Multiple Deposits Considering Carbon Quota

Nowadays, the mining industry actively advocates and practices the concept of green and integrated exploitation to realize the sustainable development of resources with low-carbon emissions. The certain carbon quota for mining companies limits the production capacity and resource utilization efficiency. The integrated exploitation of multiple deposits could coordinate resource allocation and operation facilities, which would reduce capital expenditure and operating costs for the mining company from a systematic perspective. In this condition, some deposits located nearby could be treated as one entity to make plans and optimize. An optimization framework is proposed based on the analysis of the characteristics and advantages of integrated exploitation. A new mathematical programming model is presented to optimize production capacity and extracted ore grade for each deposit considering constraints of maximum and minimum mining capacity, extracted ore grade and concentrated ore grade requirement, and metal output target, which has a significant influence on the economic benefit and resource utilization rate for a mining company. The model is verified using the data collected from three deposits of a gold mining company in China to demonstrate its ability to optimize the allocation of production capacity and improve the technical and economic effect of mining under the limitation of carbon quota. The sensitivity analysis of some key parameters is carried out to generate a series of integrated exploitation schemes under different production and operation conditions, which is useful for the mining company to make decisions in different situations.

Coordinated parallel resource allocation for integrated access and backhaul networks

The architecture of integrated access and backhaul (IAB) endows 5G and beyond networks with the capabilities of both centralized and distributed resource management: a centralized node, IAB-donor, can efficiently coordinate resources with a network-wide view, and other distributed nodes, IAB-nodes, can quickly react to local traffic bursts multiple hops away from the IAB-donor. However, existing efforts on IAB networking mostly focus on either the centralized or distributed capability of IAB networks and lack the studies on handling bursty traffic. In this paper, a Coordinated Parallel Resource allocation scheme (CPReal) is developed to handle both bursty and non-bursty traffic. Its key feature is that it consists of both a centralized scheme at the IAB-donor for efficient coordination of resource allocation and a distributed scheme at each IAB-node for quick response to short-term bursty traffic. CPReal is standard-compliant and considers the strict parent-to-child relation between IAB-nodes specified in 5G standards. In CPReal, with the assistance from their parent nodes, IAB-nodes can quickly reach an agreement on resource allocation in a distributed and conflict-free way for traffic bursts. Extensive simulations show that CPReal outperforms existing schemes in throughput by 67.3% under bursty traffic and in end-to-end delay by up to 34.4% under various scenarios.

FDSA-STG: Fully Dynamic Self-Attention Spatio-Temporal Graph Networks for Intelligent Traffic Flow Prediction

With the development of transportation and the ever-improving of vehicular technology, Artificial Intelligence (AI) has been popularized in Intelligent Transportation Systems (ITS), especially in Traffic Flow Prediction (TFP). TFP plays an increasingly important role in alleviating traffic pressure caused by regional emergencies and coordinating resource allocation in advance to deployment decisions. However, existing research can hardly model the original intricate structural relationships of the transportation network (TN) due to the lack of in-depth consideration of the dynamic relevance of spatial, temporal, and periodic characteristics. Motivated by this and combined with deep learning (DL), we propose a novel framework entitled <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>F</u>ully <u>D</u>ynamic <u>S</u>elf-<u>A</u>ttention <u>S</u>patio-<u>T</u>emporal <u>G</u>raph Networks</i> (FDSA-STG) by improving the attention mechanism using Graph Attention Networks (GATs). In particular, to dynamically integrate the correlations of spatial dimension, time dimension, and periodic characteristics for highly-accurate prediction, we correspondingly devised three components including the spatial graph attention component (SGAT), the temporal graph attention component (TGAT), and the fusion layer. In this case, three groups of similar structures are designed to extract the flow characteristics of recent periodicity, daily periodicity, and weekly periodicity. Extensive evaluation results show the superiority of FDSA-STG from perspectives of prediction accuracy and prediction stability improvements, which also testifies high model adaptability to the dynamic characteristics of the actual observed traffic flow (TF).

Coordinated resource allocation to plant growth-defense tradeoffs.

Plant resource allocation patterns often reveal tradeoffs that favor growth (G) over defense (D), or vice versa. Ecologists most often explain G-D tradeoffs through principles of economic optimality, in which negative trait correlations are attributed to the reconciliation of fitness costs. Recently, researchers in molecular biology have developed 'big data' resources including multi-omic (e.g. transcriptomic, proteomic and metabolomic) studies that describe the cellular processes controlling gene expression in model species. In this synthesis, we bridge ecological theory with discoveries in multi-omics biology to better understand how selection has shaped the mechanisms of G-D tradeoffs. Multi-omic studies reveal strategically coordinated patterns in resource allocation that are enabled by phytohormone crosstalk and transcriptional signal cascades. Coordinated resource allocation justifies the framework of optimality theory, while providing mechanistic insight into the feedbacks and control hubs that calibrate G-D tradeoff commitments. We use the existing literature to describe the coordinated resource allocation hypothesis (CoRAH) that accounts for balanced cellular controls during the expression of G-D tradeoffs, while sustaining stored resource pools to buffer the impacts of future stresses. The integrative mechanisms of the CoRAH unify the supply- and demand-side perspectives of previous G-D tradeoff theories.

Agent based coordination protocol for system of cyber-physical systems

Nowadays, society and business rely heavily on Information and Communication Technology (ICT) systems, which are progressing faster than ever. To stay on pace with them, focus is shifted towards integration of individual ICT systems into complex systems, which offers more functionality than simply the sum of individual systems. In this regard, Cyber-Physical Systems (CPSs) have gained significant importance and System-of-Systems (SoS) approach has been suggested for modeling complex CPSs to achieve a higher level goal, by dynamically building a large system with existing autonomous, and heterogeneous constituent systems (CSs). An important challenge in a system of Cyber-Physical Systems (SoCPSs) is to develop seamless collaboration between autonomous constituent-CPSs (CCPSs) to coordinate their operations. In this paper, we propose an agent based coordination mechanism to coordinate resource allocation and demand in SoCPSs. The approach models each CCPS as an agent and describes how multiple autonomous CCPSs, i.e., Virtual Power Plant (VPP), Commercial Greenhouse Growers (CGGs), communicate and collaborate with each other asynchronously through negotiation and how potential conflicts between CCPSs with conflicting goals are resolved. The efficacy of the proposed mechanism is validated through simulation of different real-world acyclic SoCPSs topologies. The results show that proposed approach is able to balance the individual requirements of multiple connected CPSs while achieving SoCPSs’ mission.

Beef supply chains and the impact of the COVID-19 pandemic in the United States.

COVID-19 has revealed both strengths and weaknesses in beef supply chains. It has also revealed much about market economics. Under normal, stable market conditions, markets coordinate resource and product allocation with such efficiency and subtly as to be largely unrecognized. Only in the face of abrupt and unexpected shocks are the reactions of markets to rebalance and restore equilibrium revealed. Freely operating markets react with dramatic, sometimes surprising, and confusing responses to massive and unprecedented shock such as COVID-19. Consumers, producers, companies, and policymakers all learned much about how beef supply chains and the market-based economy works as a result of COVID-19.

COVID-19, palliative care and public health

The lack of integration between public health approaches, cancer care and palliative and end-of-life care in the majority of health systems globally became strikingly evident in the context of the coronavirus disease 2019 (COVID-19) pandemic. At the same time, the collapse of the boundaries between these domains imposed by the pandemic created unique opportunities for intersectoral planning and collaboration. While the challenge of integration is not unique to oncology, the organisation of cancer care and its linkages to palliative care and to global health may allow it to be a demonstration model for how the problem of integration can be addressed. Before the pandemic, the large majority of individuals with cancer in need of palliative care in low- and middle-income countries and the poor or marginalised in high-income countries were denied access. This inequity was highlighted by the COVID-19 pandemic, as individuals in impoverished or population-dense settings with weak health systems have been more likely to become infected and to have less access to medical care and to palliative and end-of-life care. Such inequities deserve attention by government, financial institutions and decision makers in health care. However, there has been no framework in most countries for integrated decision-making that takes into account the requirements of public health, clinical medicine and palliative and end-of-life care. Integrated planning across these domains at all levels would allow for more coordinated resource allocation and better preparedness for the inevitability of future systemic threats to population health.

Coordinated Resource Allocation-Based Integrated Visible Light Communication and Positioning Systems for Indoor IoT

With the rapid development of Internet of Things (IoT) in the smart city, smart grid and smart industry, indoor communication and positioning are important fields of applications for indoor IoT. This paper presents an integrated visible light communication and positioning (VLCP) system for indoor IoT, in order to provide the high-speed data rate and high-accuracy positioning for IoT devices. where the filter bank multicarrier-based subcarrier multiplexing (FBMC-SCM) technique is exploited to effectively reduce the out-of-band interference (OOBI) on both adjacent communication and positioning subcarriers. After that, we propose a coordinated resource allocation approach for the system with the purpose of maximizing the sum rate while guaranteeing the minimum data rates and positioning accuracy requirements of devices. To this end, we solve the optimization problem by decomposing it into two subproblems, where a low-complexity suboptimal subcarrier allocation approach is proposed and the sequential quadratic programming (SQP) method is adopted to solve the non-linearly constrained power allocation optimization problem. Numerical results verify the superiority in performance of the presented integrated VLCP system for indoor IoT, and the results also reveal that the proposed coordinated resource allocation approach can effectively improve the sum rate and the positioning accuracy compared with other resource allocation approaches.

Scaling relationships among functional traits are similar across individuals, species, and communities

AbstractQuestionBivariate relationships among functional traits reflect how plants adjust to environments through the allocation of limiting resources. Bivariate relationships are well studied across species, but whether the nature of these trait relationships changes across organizational levels (individual, species, community), and whether processes driving these relationships vary across these levels, is seldom explored.LocationThe tropical cloud forests of the Bawangling Nature Reserve, Jianfengling Nature Reserve and Limushan Nature Reserve on Hainan Island, Southern China.MethodsWe measured leaf mass per area (LMA), plant height (H) and wood density (WD) for 4,748 individual trees, 174 species and 48 communities in three tropical cloud forests, and recorded five soil characteristics that are important for plant growth. We evaluated bivariate relationships between these traits across the three organizational levels, and assessed the effects of soil conditions on these trait relationships.ResultsLMA versus H, WD versus H, and LMA versus WD were all positively and disproportionately related, suggesting differential carbon investment between leaves and stem, as well as between stem height and stem density. The slopes of these relationships did not differ significantly across the three levels, suggesting a similar allocation strategy operating at different hierarchical levels. Soil phosphorus had a significant effect on the scaling exponents across all three organizational levels, indicating that phosphorus limitation in cloud forests is a principal driver of resource allocation patterns in trees.conclusionsWe conclude that tropical cloud forest trees have relatively consistent scaling relationships between three primary functional traits across the individual, species, and community levels. The coordinated resource allocation strategies in plants are most likely driven by the prevailing environmental constraints.

Resource and Power Allocation for OFDM-Based Device-to-Device Communications in a Multicell Environment

We investigate the optimal resource and power allocation for device-to-device (D2D) communications in a multicell environment. When D2D links reuse the cellular radio resources, each D2D user will interfere with a cellular link and other D2D links, in its own cell as well as in adjacent cells. We first develop a distributed resource allocation scheme that is performed by each base station independently. Then, we propose a coordinated resource allocation scheme that can handle the intercell interferences as well as the intracell interference. For a given resource allocation, we also formulate a power optimization problem and present an algorithm to find the optimal solution. The resource and power allocation algorithms are designed to maximize the achievable rate of the D2D link, while limiting the interference generated to the cellular link. The performance of the proposed algorithms is evaluated through simulations in a multicell environment. Numerical results are presented to verify the coordination gain in the resource and power allocation.