Allocation System Research Articles

Dynamic Neural Network-Based Resource Allocation Framework for Latency-Aware and Energy-Efficient Optimization in Wireless Communication Systems

In wireless communication, efficiency is a crucial factor due to the inherent constraints of the radio frequency spectrum and the escalating demand for connectivity. With a limited spectrum available, efficient communication protocols are vital to maximize its utilization and accommodate the increasing number of devices. This study introduces a dynamic resource allocation system for wireless communication, encompassing essential components such as Base Stations (BS), Mobile Devices (MD), and a Centralized Controller (CC). BSs, serving as access points, establish wireless communication with MD within predetermined coverage areas. The CC acts as the neural centre of the system, collecting real-time data from both BSs and MDs to facilitate an adaptive resource allocation process. The comprehensive mechanism involves a scheduling algorithm for optimized data transmission, considering priority and Quality of Service (QoS) requirements, alongside power control mechanisms that dynamically adjust power levels to optimize energy efficiency while maintaining communication quality. MDs communicate with nearby BSs, relaying data transmission information to the CC, which strategically coordinates resources based on the collected data. This integrated system enhances resource utilization, minimizes latency, and improves energy efficiency, collectively contributing to the robustness and reliability of the wireless communication infrastructure.

Research on Dynamic Optimization Algorithm of Warehousing Location Layout Based on Nonlinear Optimization

The paper aims to improve the turnover rate and operation efficiency of goods that are shipped out and replenished in the warehouses of electric power enterprises through big data analysis and optimization algorithms.The data is distributed in diverse locations and data nonlinear optimization algorithms certainly helps to understand the patterns for effective management of warehouses.This article focuses on reducing the delay in the operational processes.A multi-objective optimization (MOO) has been proposed which is aiming at improving the efficiency of transition process of commodities, storage, and overall warehouse operations.The study helps in the optimization of the allocation of cargo spaces with the aid of big data analysis optimization technology which collects and manages data in a distributed environment. A multi-objective cargo space optimization algorithm is proposed along with consideration of dynamic constraints.The algorithm is based on the coefficient of variation adaptive differential evolution algorithm.Individual decoding is performed according to the real-time cargo space availability.The simulation results show that the convergence speed of the algorithm is greatly improved.Meanwhile, the efficiency of warehouse transition process, shelf stability and the classification of commodities are remarkably improved.In nutshell, the multi-objective decision-making with the integration of big data analysis optimization technology assists in the effective organization of warehouse allocation system by considering multiple factors and constraints.

Joint mode selection and resource allocation for cellular V2X communication using distributed deep reinforcement learning under 5G and beyond networks

Vehicle-to-everything (V2X) communication via cellular networks is a promising technique for 5G and beyond networks. The cars interact directly with one another, as well as with the infrastructure and various vehicles on the road, in this mode. It enables the interchange of time-sensitive and safety-critical data. Despite these benefits, unstable vehicle-to-vehicle (V2V) communications, insufficient channel status information, high transmission overhead, and the considerable communication cost of centralized resource allocation systems all pose challenges for defense applications. To address these difficulties, this study proposes a combined mode selection and resource allocation system based on distributed deep reinforcement learning (DRL) to optimize the overall network sum rate while maintaining the reliability and latency requirements of V2V pairs and the data rate of V2R connections. Because the optimization issue is non-convex and NP-hard, it cannot be solved directly. To tackle this problem, the defined problem is first translated into machine learning form using the Markov decision process (MDP) to construct the reward function and decide whether agent would conduct the action. Following that, the distributed coordinated duelling deep Q-network (DDQN) method based on prioritized sampling is employed to improve mode selection and resource allocation. This approach learns the action-value distribution by estimating both the state-value and action advantage functions using duelling deep networks. The results of the simulation show that the suggested scheme outperforms state-of-the-art decentralized systems in terms of sum rate and QoS satisfaction probability.

Dynamic multiobjective two-stage fuzzy stochastic strategy for optimal water allocation in inter-basin water division under changing environment: A case study of Hanjiang-to-Weihe water division in Shaanxi Province, China

Extensive environmental changes increase the complex uncertainty surrounding water allocation systems, and irrational water allocation further exacerbates competition among users, posing formidable challenges to achieving dynamic and sustainable water resource management in inter-basin water diversion (IBWD) projects. This study established a dynamic multi-objective water allocation framework to address environmental change, uncertainty of water systems, and coordination between water allocation and deficit risk. The proposed framework focuses on water allocation as a continuous, multi-period decision-making process and considers reasonable water allocation from the individual to the overall multidimensionality. The feasibility and practicality of the framework were verified by long-term water allocation planning of the Hanjiang-to-Weihe IBWD in Shaanxi Province, China. The framework integrates the Soil and Water Assessment Tool model, three classical scenarios of the Coupled Model Intercomparison Project Phase 6, and optimization models to realize future sustainable water allocation optimal planning. The study found that the framework enables sustainable water allocation management and provides decisions for decision-makers under different water deficit conditions. Global optimal water allocation and the maximization of system benefits were achieved using multi-objective dynamic modeling. The average satisfaction level exceeded 0.9, and the Gini coefficient was below 0.05, both of which have a synergistic effect, with the former enhancing satisfaction within each subarea, and the latter ensuring equity among the subareas. The proposed framework aims to manage water resources of IBWD sustainably and provide optimal water allocation schemes to decision-makers under changing environments, which will alleviate water scarcity and interregional competition to promote regional sustainable development.

Impact of age over 70 years in the new allocation system on the outcomes of heart transplantation in the US.

United Network for Organ Sharing (UNOS) allocation criteria changed in 2018 to accommodate the increased prevalence of patients on a ventricular assist device as a bridge to heart transplant and prioritize sicker people in anticipation of a heart graft. We aimed to assess the impact of patient age in the new allocation policy on mortality following heart transplantation. Secondary outcomes included the effect of age ≥70 on post-transplant events, including stroke, dialysis, pacemaker, and rejection requiring treatment. The UNOS Registry was queried to identify patients who underwent heart transplants alone in the US between 2000 and 2021. Patients were divided into groups according to their age (over 70 and under 70 years old). Patients aged over 70 were more likely to require dialysis during follow-up, but less likely to experience rejection requiring treatment, compared with patients aged <70. Age ≥70 in the new allocation system was a significant predictor of 1-year mortality (adjusted HR: 1.41; 95% CI: 1.05-1.91; p=.024), but its effect on 5-year mortality was not significant after adjusting for potential confounders (adjusted HR: 1.27; 95% CI:.97-1.66; p=.077). Undergoing transplantation under the new allocation policy vs the old allocation policy was not a significant predictor of mortality in patients over 70 years old. Age ≥70 is a significant predictor of 1-year mortality following heart transplantation, but not at 5 and 10 years; however, the new allocation does not seem to have changed the outcomes for this group of patients.

How do innovation factor allocation and institutional environment affect high-quality economic development? Evidence from China

An in-depth understanding of the relationship between innovation factor allocation, the institutional environment, and high-quality economic development can help us better comprehend how better productivity promotes high-quality, regionally coordinate, and balanced economic development. Using panel data of 30 provincial-level regions in China from 2011 to 2019, we explore the effect of innovation factor allocation on high-quality economic development and the moderating effects of different institutional environments. First, we construct a comprehensive indicator system for innovation factor allocation and a new model to calculate the weights of various indicators of innovation factors. Second, we apply TOPSIS and grey correlation to measure the level of innovation factor allocation, and construct a modified gravity model to generate a directed weighted spatial association network of this allocation as a spatial weight matrix. Finally, we use the spatial Durbin model to empirically test the spatial effects of innovation factor allocation on high-quality economic development under different institutional environments. We find that: First, innovation factor allocation significantly positively affects the high-quality development of the local economy, while it has negative spatial spillover effects on neighboring areas. The impact of different types of institutional environments on high-quality economic development exhibits an inverted “U” shape. China's institutional environment has not crossed the critical threshold of this “U" shaped relationship. Thus, different types of institutional environments can still effectively strengthen the role of innovation factor allocation in promoting high-quality economic development. In particular, the strengthening effect of online government is the strongest and its negative impact is the weakest. Overall, this study is of great significance for optimizing innovation factor allocation in various regions and promoting high-quality economic development.

(461) - Impact of the UNOS Donor Heart Allocation System in Adult Heart Transplant Recipients with Hypertrophic Cardiomyopathy in the United States

(461) - Impact of the UNOS Donor Heart Allocation System in Adult Heart Transplant Recipients with Hypertrophic Cardiomyopathy in the United States

(540) - Increased Incidence of Stroke After Heart Transplant in the New Allocation System Era

(540) - Increased Incidence of Stroke After Heart Transplant in the New Allocation System Era

(427) - Waitlist and Post-Transplant Outcomes of Patients Listed for Heart-Lung Transplantation After Implementation of the 2018 French Heart Allocation System

(427) - Waitlist and Post-Transplant Outcomes of Patients Listed for Heart-Lung Transplantation After Implementation of the 2018 French Heart Allocation System

(328) - Geographic Variation in Rank Priority Waiting Time in the United States Heart Allocation System

(328) - Geographic Variation in Rank Priority Waiting Time in the United States Heart Allocation System

(435) - Sex-Specific Trends in the Use of Temporary Mechanical Circulatory Support in Patients Listed for Orthotopic Heart Transplant Before and After The UNOS Allocation System Change

(435) - Sex-Specific Trends in the Use of Temporary Mechanical Circulatory Support in Patients Listed for Orthotopic Heart Transplant Before and After The UNOS Allocation System Change

(616) - Development of a Machine Learning-Based Lung Allocation System for the Maximum Benefit of Lung Transplantation

(616) - Development of a Machine Learning-Based Lung Allocation System for the Maximum Benefit of Lung Transplantation

(182) - Waitlist Mortality for Patients with Cardiac Allograft Vasculopathy Under the 2018 UNOS Donor Heart Allocation System

(182) - Waitlist Mortality for Patients with Cardiac Allograft Vasculopathy Under the 2018 UNOS Donor Heart Allocation System

(433) - Impact of the New 2018 US Donor Heart Allocation System on the Long-Term Outcomes in Patients with Hypertrophic Cardiomyopathy

(433) - Impact of the New 2018 US Donor Heart Allocation System on the Long-Term Outcomes in Patients with Hypertrophic Cardiomyopathy

(649) - Changes in Lung Transplant Waitlist Outcomes for Primary Pulmonary Hypertension (PPH) After Revision of Lung Allocation System: A UNOS Analysis

(649) - Changes in Lung Transplant Waitlist Outcomes for Primary Pulmonary Hypertension (PPH) After Revision of Lung Allocation System: A UNOS Analysis

(443) - Worse Risk-Adjusted Post-Transplant Survival for Women in the New Heart Transplant Allocation System

(443) - Worse Risk-Adjusted Post-Transplant Survival for Women in the New Heart Transplant Allocation System

A2/A2B to B kidney transplantation outcomes: A single center 7-year experience.

Data on long-term outcomes following A2/A2B to B kidney transplants since the 2014 kidney allocation system (KAS) changes are few. The primary aim of this study is to report our 7-year experience with A2/A2B to B kidney transplants and to compare post-transplant outcomes of A2/A2B to a concurrent group of B to B kidney transplants. Additionally, the study evaluates the impact of pre-transplant anti-A1 titers on survival outcomes in A2/A2B transplants. This retrospective, single-center analysis included all adults who received A2/A2B to B deceased donor kidney transplants from December 2014 to June 2021 compared to B to B recipients. The effects of pre-transplant IgM/IgG titers, stratified as ≤1:8 and ≥1:16, on death-censored, rejection-free, and overall graft survival were tested. Fifty-three A2/A2B and 114 B to B adults were included with a median follow-up time of 32 months. Overall graft survival, patient survival, and rejection-free graft survival did not differ between the two groups. There were no differences between the groups' overall kidney function values (p>.80) or their temporal trajectories (time by group interaction p>.11). Unadjusted death-censored graft survival was lower in A2/A2B to B compared to B recipients (p=.03), but the effect was not significant (p=.195) after adjusting for any readmissions (p=.96), rejection episodes (p<.001) or BK infection (p=.76). We did not detect an effect of pre-transplant titer group on death-censored (p=.59), rejection-free (p=.61), or overall graft survival (p=.26) CONCLUSIONS: A2/A2B to B kidney transplants have comparable overall patient and graft survival, rejection-free graft survival, and longitudinal renal function compared to B to B transplants at our center. Allograft survival outcomes were not significantly different between patients with low and high pre-transplant anti-A1 IgM/IgG titers.

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.

Impact of the Kidney Allocation Revision on Access to Kidney Transplantation and Outcomes in the United States.

The 2014 Kidney Allocation System (KAS) revision aimed to enhance equity in organ allocation and improve patient outcomes. This study assesses the impacts of the KAS revision on renal transplantation demographics and outcomes in the United States. We conducted a retrospective study utilizing the Organ Procurement and Transplantation Network/Scientific Registry of Transplant Recipients (OPTN/SRTR) database from 1998 to 2022. We compared recipient and donor characteristics, and outcomes (graft failure and recipient survival) pre- and post-KAS revision. Post-KAS, recipients were significantly older (53 vs 48, P < .001) with an increase in Medicaid beneficiaries (7.3% vs 5.5%, P < .001). Despite increased graft survival, HR = .91 (95% CI 0.80-.92, P < .001), overall recipient survival decreased, HR = 1.06 (95% CI 1.04-1.09, P < .001). KAS revision led to greater racial diversity among recipients and donors, enhancing equity in organ allocation. However, disparities persist in graft failure rates and recipient survival across racial groups. The 2014 Kidney Allocation System revision has led to important changes in the renal transplantation landscape. While progress has been made towards increasing racial equity in organ allocation, further refinements are needed to address ongoing disparities. Recognizing the changing patient profiles and socio-economic factors will be crucial in shaping future policy modifications.

Socio-Economic Indicators for Water Management in the South-West Europe Territory: Sectorial Water Productivity and Intensity in Employment

Given the need for water use to be a crucial consideration in sustainable development, an adequate water allocation system across economic sectors is essential, especially in the face of increasing seasonal and perennial water scarcity. In an attempt to facilitate a socially and economically efficient adaptation to the climate emergency, we propose a set of eleven socio-economic indicators to analyze the current water management. This set of indicators could help to quantify the interrelationship between water use and its economic perspective, as well as its social perspective through its impact on employment. Any demand for water not only includes the direct use of water but also its indirect use, referred to as virtual water. This is the water indirectly used through the other inputs in the production process (input–output methodology). These indicators are evaluated in the South-West Europe territory where, in light of increasing water scarcity, there is a need to orientate water allocation toward employment with less intensive water use, to more water productivity and to less environmental impacts. The results at river basin scales show that water use is more productive in the tertiary than in the secondary and primary sectors.