Resource Allocation Mechanism Research Articles

Uneven primary healthcare supply of rural doctors and medical equipment in remote China: community impact and the moderating effect of policy intervention

BackgroundUnequal access to primary healthcare (PHC) has become a critical issue in global health inequalities, requiring governments to implement policies tailored to communities’ needs and abilities. However, the place-based facility dimension of PHCs is oversimplified in current healthcare literature, and formulating the equity-oriented PHC spatial planning remains challenging without understanding the multiple impacts of community socio-spatial dynamics, particularly in remote areas. This study aims to push the boundary of PHC studies one step further by presenting a nuanced and dynamic understanding of the impact of community environments on the uneven primary healthcare supply.MethodsFocusing on Shuicheng, a remote rural area in southwestern China, multiple data are included in this village-based study, i.e., the facility-level healthcare statistics data (2016–2019), the statistical yearbooks, WorldPop, and Chinese GDP’s spatial distribution data. We evaluate villages’ PHC service capacity using the number of doctors and essential equipment per capita, which are the major components of China’s PHC delivery. The indicators describing community environments are selected based on extant literature and China’s planning paradigms, including town- and village-level factors. Gini coefficients and local spatial autocorrelation analysis are used to present the divergences of PHC capacity, and multilevel regression model and (heterogeneous) difference in difference model are used to examine the driving role of community environments and the dynamics under the policy intervention.ResultsDespite the general improvement, PHC inequalities remain significant in remote rural areas. The village’s location, aging, topography, ethnic autonomy, and economic conditions significantly influence village-level PHC capacity, while demographic characteristics and healthcare delivery at the town level are also important. Although it may improve the hardware setting in village clinics (coef. = 0.350), the recent equity-oriented policy attempts may accelerate the loss of rural doctors (coef. = − 0.517). Notably, the associations between PHC and community environments are affected inconsistently by this round of policy intervention. The town healthcare centers with higher inpatient service capacity (coef. = − 0.514) and more licensed doctors (coef. = − 0.587) and nurses (coef. = − 0.344) may indicate more detrimental policy effects that reduced the number of rural doctors, while the centers with more professional equipment (coef. = 0.504) and nurses (coef. = 0.184) are beneficial for the improvement of hardware setting in clinics.ConclusionsThe findings suggest that the PHC inequalities are increasingly a result of joint social, economic, and institutional forces in recent years, underlining the increased complexity of the PHC resource allocation mechanism. Therefore, we claim the necessity to incorporate a broader understanding of community orientation in PHC delivery, particularly the interdisciplinary knowledge of the spatial lens of community, to support its sustainable development. Our findings also provide timely policy insights for ongoing primary healthcare reform in China.

Fiscal Devolution and Healthcare Accessibility A Case Study of Homa Bay County

Fiscal devolution has gained prominence as a policy approach in many developing countries, aiming to enhance healthcare accessibility. This study focused on the influence of Fiscal devolution on healthcare accessibility in Homabay County. The study employed a mixed-methods approach to assess the impact of fiscal devolution on public sector efficiency, combining quantitative data analysis and qualitative observations. With an emphasis on metrics like service quality, punctuality, and accessibility, quantitative data were gathered via questionnaires and administrative records. Key stakeholders, such as representatives of the local government, prominent members of the community, and recipients of services, were interviewed in-depth and participated in focus groups to get qualitative data. The data were analyzed using appropriate SPSS version 27 statistical techniques and thematic analysis to identify patterns and relationships. Results showed that fiscal devolution had both positive as well as negative effects on healthcare accessibility in Homabay County, underlining the importance of effective local government and resource allocation processes. Preliminary data indicate that fiscal devolution in Homabay County has had a mixed effect on healthcare accessibility. On one hand, decentralization has empowered local authorities to make decisions tailored to local needs, resulting in improved service responsiveness and community engagement. On the other hand, challenges related to inadequate financial resources, capacity constraints, and intergovernmental coordination have hindered the efficient delivery of public services. Effectiveness of fiscal devolution largely depends on the presence of effective local governance structures, transparent resource allocation mechanisms, and capacity building initiatives.

Truthful Auction-Based Resource Allocation Mechanisms With Flexible Task Offloading in Mobile Edge Computing

Truthful Auction-Based Resource Allocation Mechanisms With Flexible Task Offloading in Mobile Edge Computing

Trophic Niche Differentiation in Two Sympatric Nuthatch Species (Sitta yunnanensis and Sitta nagaensis).

Sympatric closely related species may experience interspecific trophic competition due to ecological similarity; they may isolate in terms of diet or habitat use as a strategy to avoid competition. The body tissues of consumers contain stable isotope signatures information that can be applied to infer their dietary information. In this study, δ13C and δ15N stable isotopes were analyzed to determine the dietary information and trophic niches of sympatric coexisting Sitta yunnanensis and Sitta nagaensis. The results showed that the food sources of S. yunnanensis and S. nagaensis were from six orders, including Orthoptera, and the cumulative contribution rate was 99.97%, with the two species eating similar diets but at different rates. The larger δ13C of S. yunnanensis indicates that it had a wider range of habitats for feeding, while the difference in δ15N values was not significant (p > 0.05), indicating that both species feed on similar nutrient levels. As determined by Bayesian ellipses, the isotopic niches of S. yunnanensis and S. nagaensis were differentiated; the isotopic niche width of S. yunnanensis is 2.69‱2, which was larger than that of S. nagaensis (0.73‱2), indicates that differentiation between the two species in diet or habitat use reduced competition. Trophic niche differentiation and differences in foraging proportions may be the principal resource allocation mechanisms behind S. yunnanensis and S. nagaensis coexistence.

An ML‐based task clustering and placement using hybrid Jaya‐gray wolf optimization in fog‐cloud ecosystem

SummaryThe rapid expansion of IoT systems has caused network congestion and delays in task placement and resource provisioning as usually the tasks are executed at a far location in the cloud. Fog computing reduces the computing burden of cloud data centers as well as the communication burden of the internet as fog resources are placed near the data generation points. Within Fog computing, an important challenge is the optimal task placement which is an NP‐class problem. This work applies machine learning for task clustering and addresses the task placement problem in a fog computing environment using a hybrid of two recent metaheuristics; Jaya and gray wolf optimization (GWO). The hybrid method considers optimizing the total number of active fog nodes, load balancing in fog nodes, and average response time of the tasks. The performance of the proposed method is evaluated on a real‐time LCG dataset and is compared with reinforcement learning fog scheduling (RLFS), genetic algorithm (GA), dynamic resource allocation mechanism (DRAM), load balancing and scheduling algorithm (LBSSA), and particle swarm optimization with simulated annealing (PSO‐SA) algorithms. The results demonstrate the superiority of the suggested method over the baseline techniques in terms of average improvement of 51.04% in load balance variance, 30.25% in average response time, 24.16% in execution time, and 47.10% in the number of devices used.

A non-cooperative game approach on isolated water-energy microgrids

The integration of renewable energies into water systems emerges as a crucial challenge and an opportunity within the water-energy nexus, particularly in isolated communities, emphasizing the importance of developing sustainable and self-sufficient isolated water-energy microgrids (IWEMGs). This study addresses a significant gap in existing research by proposing a comprehensive model that simultaneously considers operation and dispatch in IWEMGs. Our work introduces a framework based on game theory to model and optimize resource management in IWEMGs, where actors, including consumers, producers, or prosumers, engage in a non-cooperative game (Cournot competition), enabling an effective resource allocation mechanism for water and energy that considers strategic interactions among participants. The application of this model to a case study in a rural community, known as Ranchería, in La Guajira, Colombia, demonstrates the practicality and effectiveness of IWEMGs in meeting the water and energy demands of users. The model is implemented and solved using the GAMS software. Our quantitative results show optimal performance in resource management, highlighting the model’s ability to facilitate informed decision-making. Through a detailed analysis of water and energy transactions and the net benefits achieved, the study illustrates the potential of game theory to enhance the operational performance of IWEMGs. Furthermore, this research contributes to the broader discourse on sustainable resource management, offering insights that could guide the development of policies and strategies for implementing IWEMGs in similar contexts globally.

Wi‐Fi 6‐based home area network for demand response in smart grid

SummaryThe past decade has brought prolific developments to power systems that range from large‐scale renewable integration to digital twins. Modern power systems consist of highly variable generation clusters. Hence, monitoring and prediction of power generation has become a vital part of today's power systems. On the other hand, detailed real‐time information of power usage is also a key parameter for the control algorithms used for the demand side management. With this information flow, power systems become smart grids which offer improved service and enable many advanced operations. In this regard, the home area network (HAN), which collects the power consumption/generation of household devices, plays a key role. So far, a number of technologies such as ZigBee, Z‐Wave, and Power Line Communication (PLC) have been considered for the HAN. Yet, widely used wireless local area network (WLAN) technology, Wi‐Fi, was not identified as a prominent candidate due to the limitations in its latency and reliability. However, with the introduction of the IEEE 802.11ax standard, Wi‐Fi 6 was developed, and it demonstrates guaranteed latency and reliability for its users. Thus, with its widespread deployment in households, Wi‐Fi is becoming a natural candidate for HAN. In this article, we present a comprehensive HAN protocol based on Wi‐Fi 6 which can achieve guaranteed latency and reliability for HAN users. The results show that when an efficient resource allocation mechanism is in place, using Wi‐Fi for HAN does not adversely affect the other Wi‐Fi users in the normal WLAN.

An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC–MCC environments

Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment, effectively tackling performance, environmental, and security constraints. However, the proliferation of clouds and applications introduces complexities in offloading decisions. Mobile Edge Computing (MEC) has emerged as a solution to bolster MCC performance, capitalizing on the proximity to edge devices. However, optimizing computation offloading remains paramount for heterogeneous smart (mobile phones, wearables, and IoT) devices, necessitating efficient utilization of network resources and computational offloading. To address these challenges, this paper introduces NSANNOM (Network and Device Resources Utilization through Smart ANN-based Offloading Mechanism), an expert system designed for optimal computational offloading decision-making and efficient network resource allocation mechanisms. NSANNOM employs Artificial Neural Networks (ANN) for precise decision-making by validating real-world datasets, underscoring ANN’s superiority over existing algorithms, and showcasing enhanced energy savings, cost efficiency, and latency response. Experimental evaluations demonstrate that the proposed ANN model for the offloading decision-making algorithm achieves a training accuracy of 97% and a validation accuracy of 99%. The system consumes minimal energy (10 MJ) for task scheduling and exhibits remarkable accuracy in resource utilization across multiple tasks (10–50) ranging in size (from 1 to 16 GB). Additionally, it minimizes time delays (in milliseconds) during the offloading process.

The Perceived Inequalities in Resource Allocation in Multi-Campus Universities in Ghana: The Case of University for Development Studies and Presbyterian University of Ghana

This paper examined and compared the resource allocation mechanisms in two multi-campus universities (MCUs) in Ghana. They are University for Development Studies (UDS) and Presbyterian University College, Ghana (PUCG), now known as Presbyterian University, Ghana (PUG). The main objective of the study was to investigate the perceived inequalities in the allocation of resources among the various campuses of the two Universities. The qualitative method, comparative case study approach and in-depth interview techniques were used to conduct the research. In all, eight senior management members (Pro Vice-Chancellors; Registrars; and Campus Heads) of the two Universities were purposively selected and interviewed. The findings revealed that these Universities do not have an established formula for the allocation of infrastructural and human resources for their respective campuses. It was also found that they use some percentages/ratios-based formula for the allocation of funds to the campuses. The paper recommends that physical infrastructure and human resources should be allocated based on deficits identified thorough need assessments. Secondly, to ensure a fair allocation of funds to the various campuses of the two universities, the deployment of a standardised mathematical formula is recommended.

The rise of common state ownership and corporate environmental performance

This study assesses the effect of common state ownership on corporate environmental performance. Using a large sample of Chinese listed firms, we find that state-owned common ownership leads to significantly enhanced corporate environmental performance. Our mechanism analysis indicates that state-owned common owners promote environmental-friendly practices through resource allocation mechanisms that alleviate corporate financial constraints. In addition, these owners play a leadership role in fostering corporate green innovation and enhancing the overall performance of the industry. Specifically, common state ownership leads to higher industry's green total factor productivity and profitability. Moreover, we observe that the positive relationship between common state ownership and corporate environmental performance is more pronounced in firms without politically connected CEOs/chairpersons and in privately owned firms.

REVIEW OF EVOLVING CLOUD COMPUTING PARADIGMS: SECURITY, EFFICIENCY, AND INNOVATIONS

The paper provides a comprehensive analysis of the dynamic landscape of cloud computing, with a focus on security, efficiency, and innovative trends. It delves into the evolving paradigms that shape the current state of cloud computing, offering insights into the challenges and opportunities that arise as the technology continues to advance. Security is a central theme in the review, addressing the critical concerns associated with data privacy, integrity, and access control in cloud environments. The study explores the latest security measures and protocols implemented to safeguard sensitive information, emphasizing the importance of robust security frameworks in an era marked by cyber threats and vulnerabilities. Efficiency is another key aspect under scrutiny, with the review evaluating performance optimization strategies, resource allocation mechanisms, and scalability solutions. The authors examine how cloud providers are adapting to meet the growing demand for efficient and sustainable computing resources, highlighting best practices and emerging trends that contribute to enhanced performance. Furthermore, the review sheds light on the innovative trends shaping the future of cloud computing. It explores cutting-edge technologies, such as edge computing, serverless architectures, and containerization, and their impact on reshaping the cloud landscape. The paper concludes by summarizing the key takeaways and emphasizing the importance of understanding and adapting to the evolving paradigms in cloud computing to ensure a secure, efficient, and innovative digital future.
 Keywords: Cloud Computing, Information Technology, Computing, Security, Innovations, Review.

高质量发展背景下普惠性幼儿园面临的挑战及其发展策略

Under the background of high-quality development, the construction and development of inclusive kindergartens are particularly important. This is not only about educational equity, but also an important embodiment of social progress and population policy. Inclusive kindergartens aim to narrow the education gap between urban and rural areas and between regions and provide equal educational opportunities, especially for children from low- and middle-income families. The main challenges include the unbalanced distribution of resources between urban and rural areas, the structural imbalance of financial input, and the significant differences in the management system of parks. The key to solving these challenges lies in the fine allocation of resources, the establishment of early warning and cooperation mechanism between demand and supply, the optimization of fair and reasonable resource allocation mechanism, and the use of policy tools to build a sound incentive mechanism. In addition, it is essential to strengthen quality assessment and content development, and improve the level of education and childcare services.

Phytophagy impacts the quality and quantity of plant carbon resources acquired by mutualistic arbuscular mycorrhizal fungi

Arbuscular mycorrhizal (AM) fungi associate with the roots of many plant species, enhancing their hosts access to soil nutrients whilst obtaining their carbon supply directly as photosynthates. AM fungi often face competition for plant carbon from other organisms. The mechanisms by which plants prioritise carbon allocation to mutualistic AM fungi over parasitic symbionts remain poorly understood. Here, we show that host potato plants (Solanum tuberosum cv. Désirée) selectively allocate carbon resources to tissues interacting with AM fungi rather than those interacting with phytophagous parasites (the nematode Globodera pallida). We found that plants reduce the supply of hexoses but maintain the flow of plant-derived fatty acids to AM fungi when concurrently interacting with parasites. Transcriptomic analysis suggest that plants prioritise carbon transfer to AM fungi by maintaining expression of fatty acid biosynthesis and transportation pathways, whilst decreasing the expression of mycorrhizal-induced hexose transporters. We also report similar findings from a different plant host species (Medicago truncatula) and phytophagous pest (the aphid Myzus persicae). These findings suggest a general mechanism of plant-driven resource allocation in scenarios involving multiple symbionts.

Collaborative resource allocation-based differential evolution for solving numerical optimization problems

Differential evolution (DE) is an efficient and powerful population-based search algorithm for solving numerical optimization problems in continuous spaces. It has been proven that multi-strategy DE algorithms are more effective than single-strategy DE algorithms in addressing benchmark and real-world problems. However, most multi-strategy DE variants focus on maintaining population diversity and balancing exploitation and exploration, ignoring the dynamic allocation of computational resources. Moreover, the success of these algorithms often depends on additional designed techniques, leading to increased computational complexity. In this paper, the Collaborative Resource Allocation-based Differential Evolution (CRADE) is introduced. It involves a collaborative resource allocation mechanism that utilizes the historical performance ranking of three mutation strategies to automatically allocate computational resources to various subpopulations during the search process. The parameter adaptation technique is used to adjust the associated control parameters of different mutation strategies. As a result, the most efficient mutation strategy consumes the majority of computational resources at different search stages to mitigate inefficient search under constrained resources. The performance of CRADE is evaluated on the well-known CEC2013 benchmark function set. The paper also investigates its application in the parameter identification of photovoltaic solar cells and modules. The overall results show that CRADE exhibits superior and competitive performance compared to other state-of-the-art algorithms. Consequently, CRADE has emerged as a novel and effective approach for addressing numerical optimization problems, distinguished by its excellence, practicality, and unwavering reliability.

A mechanism for network resource allocation and task offloading in mobile edge computing and network engineering

AbstractAt present, most of the resource allocation methods in mobile edge computing allocate computing resources according to the time order in which task requests are calculated and unloaded, without considering the priority of tasks in practical applications. According to the computing requirements in such cases, a priority task‐oriented resource allocation method is proposed. According to the average processing time of the task execution, the corresponding priority for task is given. The tasks with different priorities are weighted to allocate computing resources, which not only ensures that the high‐priority tasks obtain sufficient computing resources, but also reduces the total time and energy consumption to complete the calculation of all tasks, thus improving the quality of service. The experimental results show that the proposed method can achieve better performance.

Game-Theoretic Resource Allocation and Dynamic Pricing Mechanism in Fog Computing

Game-Theoretic Resource Allocation and Dynamic Pricing Mechanism in Fog Computing

Research on allocation mechanism of rural financial resources in China

Financial benefit is the center of rural financial activities, and also the foothold of further allocation of financial resources. There are many problems encountered in the process of rural financial resource allocation. Based on the analysis of existing problems one by one, this paper reveals the reasons affecting the existing problems in China's rural resource allocation, sort out the ideas to improve the allocation of rural financial resources, and put forward corresponding solutions, in order to provide beneficial reference for the construction of the current rural financial resource allocation mechanism in China.

Cross-Layer Video Synthesizing and Antenna Allocation Scheme for Multi-View Video Provisioning Under Massive MIMO Networks

Due to the growing need for bandwidth starving Multi-View Videos (MVV) in virtual reality, TV, and education, effectively allocating the resources of next-generation wireless technologies for MVV streams becomes increasingly crucial. To achieve high utility for MVV users, this paper proposes a cross-layer resource allocation mechanism to leverage video synthesizing schemes (such as Depth-Image-Based Rendering (DIBR) for efficient MVV streaming with massive MIMO). First, we formulate a new problem, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">antenna allocation with video synthesis</i> (AAVS), and prove its NP-hardness. Then, we design an approximation algorithm named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Utility-based Multi-View Synthesis</i> (UMVS) with the analytical performance provided, and dynamic scenarios are addressed by augmenting UMVS with deep reinforcement learning. Data-driven simulation results show that UMVSoutperforms existing antenna allocation schemes by at least 10%, and the DRL extension provides an additional 6% improvement in system utility under congested scenarios.

Blockchain-Based Resource Allocation Mechanism for the Internet of Vehicles: Balancing Efficiency and Security

Blockchain-Based Resource Allocation Mechanism for the Internet of Vehicles: Balancing Efficiency and Security

Lowest revenue limit-based truthful auction mechanism for cloud resource allocation

Lowest revenue limit-based truthful auction mechanism for cloud resource allocation