Resource Allocation In Systems Research Articles

Joint User Association, Resource Allocation, and Beamforming in RIS-Assisted Multi-Server MEC Systems

Joint User Association, Resource Allocation, and Beamforming in RIS-Assisted Multi-Server MEC Systems

Mental Health Prediction Using Catboost Algorithm

Abstract: This study investigates the application of the CatBoost algorithm in predicting mental health outcomes using Python programming language. Mental health prediction is a critical area of research due to its significant impact on individuals and society. Traditional predictive modeling techniques often encounter challenges in handling complex and highdimensional data inherent in mental health datasets. CatBoost , a state- of-the-art gradient boosting algorithm, has shown promise in effectively addressing these challenges by handling categorical variables seamlessly and exhibiting robust performance in various domains. Leveraging its powerful capabilities, this study aims to develop predictive models for mental health outcomes utilizing a comprehensive dataset encompassing diverse socio- demographic, behavioural , and clinical factors. The predictive performance of the CatBoost algorithm will be evaluated and compared against other commonly used machine learning algorithms, demonstrating its effectiveness in accurately predicting mental health outcomes. This research contributes to the advancement of predictive modeling in mental health research and holds potential implications for personalized interventions and resource allocation in mental healthcare systems

Resource allocation in healthcare entrepreneurial ecosystems: the strategic role of entrepreneurial support organizations

PurposeInnovation management in the healthcare sector has undergone significant evolutions over the last decades. These evolutions have been investigated from a variety of perspectives: clusters, ecosystems of innovation, digital ecosystems and regional ecosystems, but the dynamics of networks have seldom been analyzed under the lenses of entrepreneurial ecosystems (EEs). As identified by Cao and Shi (2020), the literature is silent about the organization of resource allocation systems for network orchestration in EEs. This article investigates these elements in the healthcare sector. It discusses the strategic role played by entrepreneurial support organizations (ESOs) in resource allocation and elaborates on the distinction between sponsored and nonsponsored ESOs in EEs. ESOs are active in network orchestration. The literature explains that ESOs lift organizational, institutional and cultural barriers, and support entrepreneurs' access to cognitive and technological resources. However, allocation models are not yet discussed. Therefore, our research questions are as follows: What is the resource allocation model in healthcare-related EEs? What is the role played by sponsored and nonsponsored ESOs as regards resource allocation to support the emergence and development of EEs in the healthcare sector?Design/methodology/approachThe article offers an explanatory, exploratory, and theory-building investigation. The research design offers an abductive research protocol and multi-level analysis of seven (sponsored and nonsponsored) ESOs active in French healthcare ecosystems. Field research elaborates on semi-structured interviews collected between 2016 and 2022.FindingsThis article shows explicit complementarities between top-down and bottom-up resource allocation approaches supported by ESOs in the healthcare sector. Despite explicit originalities in each approach, no network orchestration model prevails. Multi-polar coordination is the rule. Entrepreneurs' access to critical technological and cognitive resources is based on resource allocation modalities that differ for sponsored versus nonsponsored ESOs. Emerging from field research, this research also shows that sponsored and nonsponsored ESOs manage their roles in different ways because they confront original issues about organizational legitimacy.Originality/valueBeyond the results listed above, the main originalities of the paper relate to the instantiation of multi-level analysis operated during field research and to the confrontation between sponsored versus nonsponsored ESOs in the domain of healthcare-related innovation management. This research shows that ESOs have practical relevance because they build original routes for resource allocation and network orchestration in EEs. Each ESO category (sponsored versus nonsponsored) provides original support for resource allocation. The ESO's legitimacy is inferred either from the sponsor or the services delivered to end-users. This research leads to propositions for future research and recommendations for practitioners: ESO managers, entrepreneurs, and policymakers.

Dynamic task scheduling in edge cloud systems using deep recurrent neural networks and environment learning approaches

The rapid growth of the cloud computing landscape has created significant challenges in managing the escalating volume of data and diverse resources within the cloud environment, catering to a broad spectrum of users ranging from individuals to large corporations. Ineffectual resource allocation in cloud systems poses a threat to overall performance, necessitating the equitable distribution of resources among stakeholders to ensure profitability and customer satisfaction. This paper addresses the critical issue of resource management in cloud computing through the introduction of a Dynamic Task Scheduling with Virtual Machine allocation (DTS-VM) strategy, incorporating Edge-Cloud computing for the Internet of Things (IoT). The proposed approach begins by employing a Recurrent Neural Network (RNN) algorithm to classify user tasks into Low Priority, Mid Priority, and High Priority categories. Tasks are then assigned to Edge nodes based on their priority, optimizing efficiency through the application of the Spotted Hyena Optimization (SHO) algorithm for selecting the most suitable edge node. To address potential overloads on the edge, a Fuzzy approach evaluates offloading decisions using multiple metrics. Finally, optimal Virtual Machine allocation is achieved through the application of the Stable Matching algorithm. The seamless integration of these components ensures a dynamic and efficient allocation of resources, preventing the prolonged withholding of customer requests due to the absence of essential resources. The proposed system aims to enhance overall cloud system performance and user satisfaction while maintaining organizational profitability. The effectiveness of the DTS-VM strategy is validated through comprehensive testing and evaluation, showcasing its potential to address the challenges posed by the diverse and expanding cloud computing landscape.

Optimizing the resource allocation in cyber physical energy systems based on cloud storage and IoT infrastructure

Given the prohibited operating zones, losses, and valve point effects in power systems, energy optimization analysis in such systems includes numerous non-convex and non-smooth parameters, such as economic dispatch problems. In addition, in this paper, to include all possible scenarios in economic dispatch problems, multi-fuel generators, and transmission losses are considered. However, these features make economic dispatch problems more complex from a non-convexity standpoint. In order to solve economic dispatch problems as an important consideration in power systems, this paper presents a modified robust, and effective optimization algorithm. Here, some modifications are carried out to tackle such a sophisticated problem and find the best solution, considering multiple fuels, valve point effect, large-scale systems, prohibited operating zones, and transmission losses. Moreover, a few complicated power systems including 6, 13, and 40 generators which are fed by one type of fuel, 10 generators with multiple fuels, and two large-scale cases comprised of 80 and 120 generators are analyzed by the proposed optimization algorithm. The effectiveness of the proposed method, in terms of accuracy, robustness, and convergence speed is evaluated, as well. Furthermore, this paper explores the integration of cloud storage and internet of things (IoT) to augment the adaptability of monitoring capabilities of the proposed method in handling non-convex energy resource management and allocation problems across various generator quantities and constraints. The results show the capability of the proposed algorithm for solving non-convex energy resource management and allocation problems irrespective of the number of generators and constraints. Based on the obtained results, the proposed method provides good results for both small and large systems. The proposed method, for example, always yields the best results for the system of 6 power plants with and without losses, which are $15,276.894 and $15,443.7967. Moreover, the improvements made in the proposed method have allowed the economic dispatch problem regarding multi-fuel power plants to be solved not only with optimal results ($623.83) but also in less than 35 iterations. Lastly, the difference between the best-obtained results ($121,412) and the worst-obtained results ($121,316.1992) for the system of 40 power plants is only about $4 which is quite acceptable.

Research on the Dynamic Model of Emergency Rescue Resource-Allocation Systems for Mine-Fire Accidents, Taking Liquid CO2 Transportation as an Example

After a mine-fire accident occurs, a large number of emergency resources need to be allocated to rescue those involved in the mine-fire accident. The allocation of emergency resources for mine-fire accidents has the characteristic of being a complex system with strong uncertainty. To investigate the impact of various variables on the allocation of emergency resources in mine-fire situations, this paper analyzes the relevant factors that influence the process of allocating emergency resources during mine fires. It defines the variables of the mine-fire emergency resource-allocation system based on relevant assumptions. Causal loop and stock flow diagrams are drawn to illustrate the relationships between the variables and the system dynamics equation. Finally, a system dynamics model for mine-fire emergency resource allocation is established. The Vensim software was used to simulate the model of a mine-fire emergency rescue. The simulation produced curves for the evolution rate of the fire, the arrival rate, the demand for emergency resources, in-transit resources, arrival, and the usage of resources during the emergency. The results indicate a positive correlation between the quantity of emergency resources in-transit and the arrival rate of emergency resources: they are positively correlated with the amount of emergency-management investment. Additionally, the duration of the maximum quantity of emergency resources in-transit is positively correlated with the length of the emergency resource-allocation route. On the other hand, the evolution rate of the mine fire and the arrival rate of its emergency resources are negatively correlated with the level of emergency management. The evolution rate of the mine fire becomes larger and the damage caused by the mine-fire accident is greater when the decision-making ability of commanders is at a low level.

Health Expenditure, Institutional Quality, and Under-Five Mortality in Sub-Saharan African Countries.

The aim of this study is to examine the relationship between health expenditure, institutional quality, and under-five mortality rates in sub-Saharan African countries. Specifically, the study seeks to explore the mediating role of institutional quality in this relationship, focusing on understanding how variations in healthcare spending and institutional frameworks impact child health outcomes. By examining these dynamics, the study aims to provide valuable insights that can inform evidence-based policy interventions to reduce under-five mortality and improve child health outcomes in the region. Utilizing data spanning the years 2000 to 2021 from 46 sub-Saharan African countries, this study employs a systems GMM model to explore the intricate relationship between health expenditure and under-five mortality rates (U5MRs), with a particular focus on the mediating role of institutional quality. The findings reveal that the quality of institutions significantly influences the impact of health expenditures on the U5MR. Strong institutional quality enhances the effectiveness of health expenditure in improving child health outcomes, particularly concerning the allocation of external health funds. Conversely, poor institutional quality amplifies the positive impact of domestic private and out-of-pocket health expenditures on the U5MR, as these serve as coping mechanisms in the absence of robust public healthcare systems. This research emphasizes the need for strategies that increase health expenditure and prioritize institutional strengthening to ensure efficient resource allocation and healthcare system management, thereby reducing under-five mortality rates. Furthermore, it underscores the importance of policies that minimize reliance on private and out-of-pocket health expenditures, which can lead to financial burdens and worsened health outcomes. Sub-Saharan African countries can make significant strides toward improving child survival and overall public health by addressing these issues.

Data-driven allocation of smart grid-connected system based on ant colony optimization algorithm

In the era of advanced technology, integrating and distributing data are crucial in smart grid-connected systems. However, as energy loads continue to increase, practical implementation of these systems faces challenges in resource allocation and lacks efficient data collaboration. In this study, the ant colony optimization algorithm is further investigated for stochastic crossover systems and cluster nodes in intelligent path planning management. To improve the pheromone setting method in smart grid-connected systems, we propose an adaptive intelligent ant colony optimization algorithm called the Group Allocation Optimization Algorithm (GAOA). This algorithm expands the pheromone transmission rate of network nodes, establishes a multi-constrained adaptive model with data mining as the pheromone target, and analyzes the accuracy of resource allocation to import the optimal scheme for smart grid-connected systems. Through experimental results, we demonstrate that the optimized adaptive ant colony algorithm leads to effective improvements in grid-connected systems, pheromone evaluation, data throughput, convergence speed, and data load distribution. These findings provide evidence that the optimized ant colony algorithm is both feasible and effective for resource allocation in smart grid-connected systems.

Efficient Trajectory Planning and Dynamic Resource Allocation for UAV-Enabled MEC System

Efficient Trajectory Planning and Dynamic Resource Allocation for UAV-Enabled MEC System

Assessment of Nursing Practice Environment and Its Influencing Factors: A Cross-Sectional Study at Shandong Province, Jinan.

To investigate the current nursing practice environment in Jinan, Shandong Province, and to identify the factors influencing the practice environment. This study is a cross-sectional study for nurses. From October to December 2022, using the clustering and stratified sampling methods, 2426 nurses from internal Medicine, Surgery, Obstetrics and Gynecology, Outpatient Department and Intensive Care Department of the Provincial Hospital of Shandong Medical University were selected and then investigated and analyzed using the revised Nurse Practice Environment Assessment Scale. The overall mean evaluation of the practice environment scored 75.13±19.87, with a minimum value of 59.74 and a maximum value of 95.82. The items with higher scores were "the hospital has systematic training for new nurses", "the work system is perfect", and "the hospital can provide continuing education for nurses in accordance with the needs of their positions". The items with lower scores were "nurses enjoy legal benefits", "nurses have the opportunity to participate in hospital management decisions", and "nurses have the opportunity to participate in hospital internal management". The results of the multiple linear regression analysis of the factors influencing nurses' practice environment showed that gender, education, position, and years of work were independent influences on nurses' practice environment scores (p < 0.05), and they explained 48.127% of the variation in the total scores of the nurses' practice environment scale. The estimated values (β) of sex, education, cheif nurse, nurses staff, work experience (year), and whether the only child variables were 3.141, 3.237, 2.713, 5.471, 2.074 and 0.732, respectively. The nurse practice environment still needs to be improved, mainly in terms of hospital management participation, human resource allocation and salary distribution system.

Can the allocation of primary health care system resources affect efficiency? A spatial Dubin model study in China

BackgroundThe primary health care (PHC) system plays an important role in China’s health care system, but there are challenges such as irrational allocation of health resources and inefficient operation, which need to be improved. The purpose of this study was to explore the impact of resource allocation on the efficiency of the PHC system in China.MethodsThe data in 31 provinces were collected from the China Statistical Yearbook 2017–2021 and the China Health Statistical Yearbook 2017–2021. The comprehensive health resource density index (CHRDI) was constructed based on the entropy method and the health resource density index (HRDI), which was used to analyze the allocation of primary health resources in each province. The adjusted efficiency of the PHC system in each province was calculated by the bootstrap data envelopment analysis (DEA). Finally, the spatial Dubin model was used to explore the effect of the CHRDI on efficiency.ResultsFrom 2016 to 2020, the allocation of primary health resources in 31 provinces showed an increasing trend, and the average efficiency after correction showed a decreasing state year by year. The spatial direct effect and spatial spillover effect coefficients of CHRDI were 0.820 and 1.471, which positively affect the efficiency. Per capita Gross Domestic Product (GDP), urbanization rate, and the proportion of the elderly were the factors affecting the efficiency of the PHC system.ConclusionsThe allocation of primary health resources in all provinces in China has improved each year, but there are still great differences, and efficiency must be further improved. Pay attention to the spatial spillover effect of the level of resource allocation and formulate differentiated measures for different regions. Attention should also be paid to the impact of population aging and economic development on the utilization of primary health resources by increasing health needs and choices.

Delay-sensitive resource allocation for IoT systems in 5G O-RAN networks

The rapid advancement in sensors and communications has led to the expansion of the Internet of Things (IoT) services, where many devices need access to the transport network using fixed or wireless access technologies and mobile Radio Access Networks (RAN). However, supporting IoT in RAN is challenging as IoT services may produce many short and variable sessions, impacting the performance of mobile users sharing the same RAN. To address this issue, network slicing is a promising solution to support heterogeneous service segments sharing the same RAN, which is a crucial requirement of the upcoming fifth-generation (5G) mobile network. This paper proposes a two-level network slicing mechanism for enhanced mobile broadband (eMBB) and Ultra-Reliable and Low Latency communications (URLLC) in order to provide end-to-end slicing at the core and edge of the network with the aim of reducing latency for IoT services and mobile users sharing the same core and RAN using the O-RAN architecture. The problem is modeled at both levels as a Markov decision process (MDP) and solved using hierarchical reinforcement learning. At a high level, an SDN controller using an agent that has been trained by a Double Deep Q-network (DDQN) allocates radio resources to gNodeBs (next-generation NodeB, a 5G base station) based on the requirements of eMBB and URLLC services. At a low level, each gNodeB using an agent that has been trained by a DDQN allocates its pre-allocated resources to its end-users. The proposed approach has been demonstrated and validated through a real testbed. Notably, it surpasses the prevalent approaches in terms of end-to-end latency.

Advancing 6G-IoT networks: Willow catkin packet transmission scheduling with AI and bayesian game-theoretic approach-based resource allocation.

The rapid expansion of mobile broadband networks and the proliferation of Internet of Things (IoT) applications have substantially increased data transmission and processing demands. However, the application domains of IoT-enabled models often face resource limitations, requiring rapid responses, low latency, and large bandwidth, surpassing their inherent capabilities. To address these challenges, we propose a fishnet approach-based packet scheduling and resource allocation system, termed Fishnet-6G, to optimize network resource allocation in the proposed 6G networks. Initially, we constructed a Sierpinski Triangle-based network in a 6G-IoT environment, enhancing device connectivity. We utilize the Quantum Density Peak Clustering (QDPC) algorithm to perform clustering for IoT devices, establishing Cluster Head (CH) and Substitute CH (SUB CH) based on actual metrics. Furthermore, traffic prediction is achieved through two processes, grouping, and fair queue status, using the Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm with a variable sampling rate, resulting in well-organized packet scheduling. Subsequently, we perform optimal packet scheduling by employing the Willow Catkin Optimization (WCO) algorithm, and the scheduled packets are managed within a Fishing Net Topology to reduce energy consumption and system complexity. Finally, we allocate the scheduled packets to the desired resource blocks using the Bayesian Game-Theoretic Approach (BGTA). The proposed approach is implemented using Network Simulator-3.26, and the performance of the Fishnet-6G model is evaluated based on time, transmission rate, energy efficiency, average throughput, latency, and Packet loss rate. Numerical analysis demonstrates that Fishnet-6G outperforms existing approaches across these metrics, showcasing its effectiveness in addressing the challenges of 6G-IoT networks.

Optimal resource allocation and routing in robotic mobile fulfillment systems

This paper addresses a combinatorial optimization problem in the context of a robotic mobile fulfillment system deployed in a warehouse that consists of movable racks, a picker, and a fleet of mobile robots. The objective is to efficiently prepare a set of orders with specified due dates by bringing the racks to a picking station in a sequential manner, where the picker selects the required products for the open orders, namely the orders that are being processed simultaneously. The picking station has a limited capacity for processing of open orders. Our goal is to minimize the total travel times of the robots and the delays in fulfilling orders. To achieve this, several decision variables need to be determined, including order sequencing, rack allocation, rack scheduling, and mobile robot routing. We formulate the problem as a mixed integer programming model and devise a heuristic algorithm grounded in decomposition principles to address the problem across two distinct phases. Additionally, we introduce two variable neighborhood search (VNS) algorithms tailored to resolve each respective phase within the aforementioned heuristic framework. The performance of the proposed solution approaches is evaluated and compared using two sets of randomly generated test instances, encompassing small-size and large-size instances. Furthermore, we conduct an analysis to examine the influence of key parameters on the objective function value.

Governing Integrated Health and Social Care: An Analysis of Experiences in Three European Countries.

Achieving greater health and social care integration is a policy priority in many countries, but challenges remain. We focused on governance and accountability for integrated care and explored arrangements that shape more integrated delivery models or systems in Italy, the Netherlands and Scotland. We also examined how the COVID-19 pandemic affected existing governance arrangements. A case study approach involving document review and semi-structured interviews with 35 stakeholders in 10 study sites between February 2021 and April 2022. We used the Transparency, Accountability, Participation, Integrity and Capability (TAPIC) framework to guide our analytical enquiry. Study sites ranged from bottom-up voluntary agreements in the Netherlands to top-down mandated integration in Scotland. Interviews identified seven themes that were seen to have helped or hindered integration efforts locally. Participants described a disconnect between what national or regional governments aspire to achieve and their own efforts to implement this vision. This resulted in blurred, and sometimes contradictory, lines of accountability between the centre and local sites. Flexibility and time to allow for national policies to be adapted to local contexts, and engaged local leaders, were seen to be key to delivering the integration agenda. Health care, and in particular acute hospital care, was reported to dominate social care in terms of policies, resource allocation and national monitoring systems, thereby undermining better collaboration locally. The pandemic highlighted and exacerbated existing strengths and weaknesses but was not seen as a major disruptor to the overall vision for the health and social care system. We included a relatively small number of interviews per study site, limiting our ability to explore complexities within sites. This study highlights that governance is relatively neglected as a focus of attention in this context but addressing governance challenges is key for successful collaboration.

Enhancing Traffic Management in Smart Cities: A Cyber-Physical Approach

The challenges of managing traffic in rapidly urbanizing cities with increasing populations. It highlights the need for advanced technologies and frameworks in smart cities to develop intelligent traffic management systems. The proposed approach is a cyber-physical framework that integrates physical infrastructure with digital infrastructure for real-time data collection, analysis, and decision-making. By using sensors, IoT devices, and data analytics, the system captures and processes traffic-related information to predict traffic flow, optimize signal timings, and recommend alternate routes. The system also communicates with connected vehicles to provide real-time updates and alerts, improving driver safety and reducing congestion. The cyber-physical approach allows seamless integration with other smart city systems for efficient coordination and resource allocation. Implementing this system offers benefits such as reduced travel times, improved air quality, enhanced road safety, and increased overall efficiency in urban transportation. It can adapt and evolve by learning from new data inputs and incorporating emerging technologies, ensuring sustainability and scalability. Overall, this paper presents a comprehensive framework for enhancing traffic management in smart cities, transforming urban transportation, and improving residents' quality of life while making cities more sustainable and resilient.

The Impact of Socioeconomic Factors on Educational Management: Comparative Analysis of International Policies and Practices

This study investigates the influence of socioeconomic factors on educational management through a comparative analysis of international policies and practices. Utilizing qualitative methods, including literature review and library research, the study examines the correlation between socioeconomic status and educational outcomes across diverse contexts. The analysis encompasses various dimensions of educational management, including resource allocation, curriculum development, teacher training, and student support systems. By exploring the policies and practices implemented in different countries, the study aims to identify common trends and unique approaches in addressing the impact of socioeconomic factors on educational management. Findings reveal that socioeconomic disparities significantly affect educational opportunities and outcomes, influencing access to quality education, student performance, and educational equity. Moreover, the study highlights the importance of tailored interventions and policy reforms to mitigate the negative effects of socioeconomic factors on educational management. The comparative analysis underscores the need for context-specific strategies that consider the socioeconomic landscape, cultural norms, and institutional capacities of each country. The study concludes by advocating for collaborative efforts among policymakers, educators, and stakeholders to develop inclusive educational policies and practices that foster equitable learning environments and enhance educational outcomes for all students.

The Correlation between Economic Convergence and Health Indices in Developed Countries.

Economic convergence signifies diminishing income disparities among global or regional economies and their eventual disappearance. It is also linked to economic growth and key health indicators. We aimed to assess the association between economic convergence and key health indicators in developed countries called G7 (USA, UK, Germany, France, Italy, Japan, and Canada). We examined G7 health and economic indicators from 2000 to 2021 using panel data analysis. We compared balanced and unbalanced panel datasets to address missing data and applied suitable methods to handle missing health indicators. Little's MCAR test (X 2 = 3.2872, P - value = 0.3494) confirmed random missing data in the unbalanced panel, enabling us to impute missing values as missing observations were below 5%. Unit root tests on balanced and unbalanced panel data validated the health convergence hypothesis, showing no unit roots in economic growth rate, current health expenditure, and female and male population indicators (P<0.05). Interestingly, the hypothesis for hospital bed counts in the unbalanced panel, differing from the balanced panel, offers new insights into addressing incomplete health data. While G7 have economic similarities, their health indicators diverge (excluding hospital bed counts). Variations in health indicators stem from healthcare system structures, funding mechanisms, resource allocation, and health investments, even among economies of similar size. Therefore, G7 member states should develop tailored national health policies based on their specific circumstances and priorities, utilizing economic convergence data for effective health resource planning.

A digital twin-driven multi-resource constrained location system for resource allocation

A digital twin-driven multi-resource constrained location system for resource allocation

Large-Scale Cooperative Task Offloading and Resource Allocation in Heterogeneous MEC Systems via Multiagent Reinforcement Learning

Large-Scale Cooperative Task Offloading and Resource Allocation in Heterogeneous MEC Systems via Multiagent Reinforcement Learning