Allocation Process Research Articles

A scoping review of the design and characteristics of e-bike financial incentives

ABSTRACT E-bikes are recognised as a sustainable mode of transportation with an unmet potential for widespread adoption. However, despite a decade of global implementation, research gaps persist regarding the design and characteristics of incentive programmes for e-bikes and their effectiveness. This review examines different design elements of implemented financial incentive programmes for e-bike uptake in OECD countries. The findings reveal three main components common to these schemes: (1) target cohort, with the majority of programmes focusing on the local population; (2) eligible e-bike types, with regular e-bikes being the most frequently chosen; and (3) financial incentive structures aimed at maximising uptake among the target cohort, with post-purchase rebates being the most prevalent. Another significant aspect identified was the allocation process, predominantly following a “first come, first served” structure. Very few (n = 4) studies included in this review assessed the effectiveness of financial incentives to purchase an e-bike. The variety of designs in these schemes, coupled with a lack of effectiveness assessments and limited evidence, highlights challenges in determining optimal transport policies. In addition to highlighting the knowledge and research gaps, this review synthesises global insights on design elements of financial incentive schemes to boost e-bike uptake, providing a guide of available components of such schemes for programme administrators in designing executive programmes.

A comprehensive simultaneous allocation algorithm of charging stations and vehicle to grid operation in radial networks

The widespread adoption of electric vehicles (EVs) helps improve air quality by minimizing pollutants and promoting sustainable transportation practices. The integration of a substantial fleet of EVs leads to an increase in the installation of charging stations. The unplanned allocation of these loads impacts the distribution systems, such as increasing power loss. This paper introduces an algorithm that proposes an optimal allocation strategy for charging stations (CSs) in a distribution system. The allocation process aims to minimize the total apparent energy losses and ensure that the system voltage profile remains within limits. Load profile, charging and discharging profiles of CSs are considered. Vehicle-to-Grid (V2G) mode is one of the merits of integrating EVs in the grid, so this feature has been used to maintain system voltage stability and minimize energy loss. Power rating and locations of V2G mode are optimally determined to guarantee power quality indices. A hybrid algorithm of genetic algorithm (GA) and Self-Adaptive Multi-Population Elitist JAYA (SAMPE-JAYA) is developed to simultaneously allocate CSs and V2G in the systems. The proposed algorithm is verified on standard systems, IEEE 33, and 69-bus systems. The proposed algorithm is verified on standard IEEE 33-bus and 69-bus systems. V2G integration with CSs results in a reduction of energy losses by 6.33% and 22.25%, respectively, and voltage deviation improvements to 7.61% and 7.88% for the two systems.

Variation Across Organ Procurement Organizations in Deceased-Donor Kidney Offer Notification Practices.

How offer notifications are distributed early in the kidney allocation timeline, including how widely they are offered, is unclear. A better understanding of offer notification practices across organ procurement organizations (OPOs) may identify opportunities for more efficient allocation. We merged the Scientific Registry of Transplant Recipients potential transplant recipient file with additional offer notification time stamps to identify 54631 deceased-donor kidney match runs from 2017 to 2023. Offer notifications for a given match run are sent to candidates/centers in "batches." We quantified the number of offers in the initial batch-which theoretically reflects the OPO's initial estimate of how widely a kidney should be offered-and compared this metric across OPOs. Kidneys were offered to a median of 14 candidates (IQR 9-38) in the first batch of notifications, and this varied across OPOs from 3 to 746 candidates per initial batch. Batch size at the OPO-level did not correlate with rank at kidney placement or OPO nonuse rate. OPOs in the highest quartile of batch size sent more offers (median 100) than presumably necessary to place kidneys (median rank at placement 21), and OPOs in the lowest quartile of batch size sent fewer offers (6) than needed to place kidneys (rank at placement 19). Offer notification practices vary widely across OPOs, and many OPOs offer kidneys far more widely than necessary for placement. Optimization of offer notification practices may reduce unnecessary communications. Further research into allocation processes is needed to identify opportunities to improve efficiency of allocation for OPOs and transplant centers.

NARA: Network-Aware Resource Allocation mechanism for minimizing quality-of-service impact while dealing with energy consumption in volunteer networks

A large-scale volunteer computing system is a type of distributed system in which contributors volunteer their computing resources, such as personal computers or mobile devices, to contribute to a larger computing effort. Volunteer resources are connected over the Internet and together form a powerful computing system capable of providing a service without depending on a service provider. Volunteer network resource allocation is the process of assigning computing tasks or services to a network of volunteer resources. The allocation process includes identifying the needed resources, selecting appropriate volunteers, and assigning tasks or services based on their capabilities. Volunteer computing systems consist of a large number of heterogeneous resources - in terms of processing power, storage, and availability - belonging to different authorities - users or organizations - and exhibiting uncertain behavior in terms of connection, disconnection, capacity, and failure. All of this makes resource allocation a challenging task in terms of ensuring a minimum quality of service, requiring complex algorithms and optimization techniques to ensure that services are efficiently allocated while respecting the constraints of the available resource. This paper introduces the Network-Aware Resource Allocation mechanism, which leverages the location, connectivity, and network latency of volunteer nodes to minimize the time a service runs with degraded quality of service and aims to deal with the energy consumption resulting from data replication requirements. This resource allocation mechanism applies to both the initial deployment of the service in the network and to the reallocation of nodes in the event that one of the allocated nodes fails or becomes unavailable. Our method has been validated in a simulation environment of a realistic volunteer system. The analysis of the results shows how our mechanism meets the quality requirements of users while minimizing the synchronization and replication times of service data replicas, as well as the time that services run with degraded quality of service, reducing the times by more than 70% in the service deployment phase and by more than 60% in the service execution phase. It also helps to reduce overall energy consumption.

Confiscated property from illegality to common good: requirements for a necessary resignification

The property confiscated from organised crime is the outcome of a particular communing process. Italian law provides for a primary compensatory function, implemented in the final destination of their use for collective, institutional, or social purposes. Despite the critical management issues, which prolong the allocation process, the strong symbolic value is enriched by the significance of compensation for the mafia oppression that certain territories suffer. In addition, confiscated properties constitute a potential resource for the territory from an economic point of view, capable of supporting job opportunities and enhancing useful services and activities for the community involved. The resignification of confiscated property requires specific focus on the various phases of the interventions. It is not just a question of a mere change of legal status or intended use but of a profound reorganisation of the architectural, material, technological, environmental, and managerial characteristics. This is consequent and consistent with the interweaving of legal, social, cultural and environmental aspects triggered by confiscation.

Stratification risk computation of primary graft gysfunction in heart transplant recipients on a smartphone application

Abstract Introduction There are no standardized guidelines for helping graft allocation in heart transplant (HTx) and accepting transplants from marginal donors and donors after circulatory arrest make decision making even more complex and time consuming than ever. The organ allocation process should include a risk analysis for primary graft dysfunction (PGD) that represents the most dreadful complication of HTx. There are 3 score systems validated to predict PGD but none has clear superior predictive capability compared to the others and the complexity of score computation potentially discourage their clinical use. A score integrating all the variables presented in a user-friendly format should improve efficacy and safeness of the organ allocation process. Methods Severe PGD defined as need for temporary mechanical circulatory support (ECMO) in the first 24 hours following HTx was considered the end-point for risk stratification computation. As a derivation cohort we used meta-analytic approach for identifying variables that are independently associated to severe PGD. The overall prevalence of each variable was determined to define the absolute risk increase value in presence of each factor. We then created a scoring system applying multivariate logistic regression model. As a validation cohort we studied the performance of the score in a retospective series of HTx performed at our institution from 2013 to 2022. We used C statistic equivalent to the area under a receiver-operating characteristic curve for dichotomous outcomes. Values above 0.7 were considered acceptable. We eventually developed an application for smartphone for risk computation. Results On 64 papers screened, 5 studies were considered eligible for the derivation cohort definition. Cumulative number of patients considered was 10’175 with an incidence of severe PGD of 7.1% (CI 5.3 - 10.4) and a 30-day mortality of 41%. 13 variables were identified (table 1). The ROC curve predicted the use of ECMO with the specificity and the sensitivity of 0,81 and 0,92 respectively. We have included in the validation cohort 128 out of 150 transplanted patients, excluding pediatric and double organs transplants. The mean donor age was 45 +/- 12,4 years, 72 (54%) were men. The mean recipient age was 57 +/- 9,3; 92 were men; 32 recipients (25%) needed ECMO support within 24 hours post-transplant; 75% of the donors in the ECMO group were > 40 y.o.; 30 days mortality was 3.1% and 6,2% in the non-ECMO and ECMO group respectively. We validated the model on our cohort defying a binary outcome (ECMO yes/no). Conclusion Clinicians fill donor, recipient and surgical parameters in user-friendly smartphone application identifying patients that are most likely to need ECMO support post HTx. The decision-making process during organ allocation evolves from what was once little more than personal experience and intuition to a reliable evidence-based method based on personalized risk prediction.Table 1

Neurocognitive dynamics of preparatory and adaptive cognitive control: Insights from mass-univariate and multivariate pattern analysis of EEG data.

Cognitive control refers to humans' ability to willingly align thoughts and actions with internally represented goals. Research indicates that cognitive control is not one-dimensional but rather integrates multiple sub-processes to cope with task demands successfully. In particular, the dynamic interplay between preparatory (i.e., prior to goal-relevant events) and adaptive (i.e., in response to unexpected demands) recruitment of neural resources is believed to facilitate successful behavioural performance. However, whether preparatory and adaptive processes draw from independent or shared neural resources, and how these align in the information processing stream, remains unclear. To address these issues, we recorded electroencephalographic data from 52 subjects while they performed a computerised task. Using a combination of mass-univariate and multivariate pattern analysis procedures, we found that different types of control triggered distinct sequences of brain activation patterns, and that the order and temporal extent of these patterns were dictated by the type of control used by the participants. Stimuli that fostered preparatory recruitment of control evoked a sequence of transient occipital-parietal, sustained central-parietal, and sustained fronto-central responses. In contrast, stimuli that indicated the need for quick behavioural adjustments triggered a sequence of transient occipital-parietal, fronto-central, and central parietal responses. There was also a considerable degree of overlap in the temporal evolution of these brain activation patterns, with behavioural performance being mainly related to the magnitude of the central-parietal and fronto-central responses. Our results demonstrate how different neurocognitive mechanisms, such as early attentional allocation and subsequent behavioural selection processes, are likely to contribute to cognitive control. Moreover, our findings extend prior work by showing that these mechanisms are engaged (at least partly) in parallel, rather than independently of each other.

Cooperation and Profit Allocation Mechanism of Traditional and New Energy Complementary Power Generation: A Framework for Renewable Portfolio Standards

To boost the sustainable development of energy and the environment, a new power system with clean energy sources has been proposed by the Chinese government and traditional coal-fired power units are being transformed into regulation service providers for this new energy power system. Then, in this study, complementary power generation cooperation between traditional coal-fired power and new energy power producers is analyzed and discussed, and the energy quota agents, power sellers, are also included. Based on the cooperation game idea, different decision-making models of the tripartite power entities are elaborately constructed. Then, according to the price linkage mechanism between new energy and traditional thermal power, the profit of all power subjects is calculated and the profit allocation process is also analyzed. The conclusions show that the similarity of the two wholesale power price coefficients verifies the symmetry of the cooperative status of power producers. For BPC and SPC quota patterns, for example, BPC is bundled with new energy power and green certificates, whereas SPC is separate. Under the SPC pattern, there is a critical value for effective cooperation between the two power producers in the price range of traditional thermal power or new energy, which can achieve a win–win situation of increasing economic benefits and the consumption scale. Under the BPC pattern, the dynamic benefit compensation mechanism, which is the corrected Shapley value based on the RPS quota ratio, can solve the compressed profit of traditional coal-fired power producers. In contrast, the overall effect of profit allocation using the nucleolar method is not ideal. This study aims to give full play to the elastic induction effect of RPS to promote the sustainable transformation of traditional thermal power energy, especially combining the market mechanism to encourage traditional coal-fired power units to improve green technology to advance the construction of the green power market in China.

Customization in Standard Hierarchy Report for Improving Activity Based Costing Process

Activity based costing process is a process to accurately manage the cost allocation process for aproduct. With the development of technology, the activity-based costing process has been integratedwith the ERP system so that it can relate to all business processes as a whole and can reduce expensesto manage the flow of information systems within the company. It is necessary to determine a fastbusiness process for the allocation of these costs so that a product can be produced. A standardhierarchy is needed which serves as the center of the business process for determining the activitybased costing process. This process is still done manually so that it slows down the businessdetermination process in the activity-based costing process. This research will implement the searchfunction in the ABC system by creating a reporting system that can search business processes in thestandard hierarchy in SAP S/4HANA. This research will use SEEM embed with SDLC method wherethis method pays attention from the perspective of the user and the company to produce an efficientABC process with the company's objectives.

Evaluation system of vocational education construction in China based on linked TOPSIS analysis

In response to the task of vocational education reform in the new era, in order to cultivate innovative skilled talents, it is necessary to improve traditional evaluation methods to enhance their efficiency. This study proposes an evaluation model for vocational education construction that combines grey relational analysis and approximate ideal point method (TOPSIS). By selecting indicators and combining subjective analytic hierarchy process and objective entropy weight method for weight allocation, the weights of the indicators are determined using subjective analytic hierarchy process and objective entropy weight method. Through experimental analysis, the results showed that among the 11 analysis indicators, the weight of policy environment indicators was the highest at 0.124, while the weight of employment rate and contract signing rate indicators was the lowest at 0.050. The above results indicate that the TOPSIS comprehensive evaluation system based on grey correlation analysis has better application effects in practical scenarios and is more suitable for the development requirements of vocational education.

How to optimize government R&D budget allocation? A review of recent studies and research gaps

Purpose Research and Development (R&D) activities are important for technological innovation and present opportunities for entrepreneurship. These activities depend on the flow of funding. This paper aims to review approaches used in R&D project selection and budget allocation. Design/methodology/approach This study conducts a systematic review, examining the content of 60 relevant papers (spanning 2000–2022) concerning public R&D budget allocation. The analysis focuses on allocation methodology, R&D output evaluation, budget allocation efficiency and the management of uncertainty in the allocation process. Findings The systematic review reveals different methods proposed for allocating government R&D budgets. These methods range from classical optimization, multi-criteria analysis and hierarchical analysis to techniques such as balanced scorecard, data envelopment analysis and analytic hierarchy process, including fuzzy approaches. Recent trends indicate an increase in the use of advanced optimization, integration and simulation algorithms. Performance indicators for reflecting R&D project outputs or goals can be categorized into four main groups: output (e.g. publications, patents, graduates), outcome, productivity (e.g. citations, patent references, articles and patents per capita) and sector-specific metrics. Practical implications Future research directions in government R&D budget allocation may include optimizing allocation to maximize social, economic and political benefits, developing ranking models, decision-making frameworks, simulations and evaluations of factors influencing allocation type and strategy. Additionally, there is a growing interest in novel budget allocation algorithms leveraging artificial intelligence and self-adjusting meta-heuristic algorithms. Originality/value The systematic review showed that some important research gaps in (government) R&D budget allocation could be considered in future studies; for example, long-term social, economic and political benefits in budget allocation optimization models, comprehensiveness of allocating government R&D budgets to universities, higher education and research institutes, R&D budget allocation to strategic technology development, e.g. renewable energy sector, supply chain issues and renewable energy value chain; new budget allocation algorithms based on artificial intelligence and self-adjusting meta-heuristic algorithms; methods for optimizing the structures of government budget allocation to R&D, considering executive and regulatory conflicts.

Analytical Hierarchy Process for Construction Safety Management and Resource Allocation

The construction industry plays a crucial role in contributing to the economy and developing sustainable infrastructures. However, it is known as one of the most dangerous industrial domains. Over the years, special attention has been paid to developing models for managing and planning construction safety. Many research studies have been carried out to analyze the root causes of fatal accidents in construction sites to develop models for preventing them and mitigating their consequences. Root cause identification and analysis are essential for effective risk mitigation. However, implementing mitigation activities is usually limited to the project’s safety budget. The construction sector suffers from a lack of allocation of appropriate safety resources triggered by a dynamic and complex project environment. This study aims to address the gap in safety resource allocation through a comprehensive root cause analysis of construction work accidents. In this paper, we present a comprehensive review of work accident-related research, categorized according to the 5M model into five root factors: medium, mission, man, management, and machinery. A novel methodology for construction safety resource allocation is proposed to mitigate risks analyzed by the 5M model with the aid of advanced technological solutions. Safety resource allocation alternatives are formulated, and their priorities are established based on an analysis of structured criteria that integrate both risk and cost considerations. The Analytical Hierarchy Process (AHP) is employed to select the optimal alternative for safety resource allocation, with the objective of effective risk mitigation. The proposed model underwent validation through two different case studies. The findings indicate that risk aversion is a critical factor in the optimal allocation of safety resources. Furthermore, the results suggest that regulatory measures should prioritize the stimulation of risk motivation in the safety decision-making processes of construction firms.

The Value Attribution Solution: A Novel Approach to Financial Allocation and Performance Evaluation in Organizations

This paper introduces the Value Attribution Solution , a novel approach to financial allocation and performance tracking that enables organizations to monitor and allocate both costs and revenues at an unprecedented level of detail. By leveraging advanced data integration techniques and multi-dimensional analysis, the framework provides organizations with real-time insights into value creation and resource utilization across various operational dimensions. This research presents the theoretical foundations of the solution, details its methodology and implementation process, and discusses its potential impact on organizational performance and decision-making. Through a series of case studies and empirical analyses, we demonstrate the solution's effectiveness in enhancing financial performance, operational efficiency, and strategic planning across diverse industries. The solution represents a significant advancement in management accounting and financial control systems, offering a powerful tool for organizations seeking to optimize their resource allocation and value creation processes in an increasingly complex and dynamic business environment.

Identifying optimal funding allocations for public water infrastructure improvements under uncertainty

Diversified and locally managed funding strategies bring substantial benefits to water infrastructure investments across financial, social, institutional, and political spheres. However, these strategies often occupy a secondary role in the public sector's capital allocation process. We outline optimal funding strategies for water infrastructure by zeroing in on communities' specific needs and navigating two types of uncertainty—historical and priority uncertainty—across both long-run (multi-decade) and short-run (recent years) timeframes. Employing Modern Portfolio Theory (MPT), our approach pinpoints optimal funding distribution both across and within categories (namely, individual counties) based on their funding needs. By comparing these distributions with Tennessee's Clean Water State Revolving Fund's historical funding approach, we benchmark how risk and socioeconomic factors influenced past decisions. This analysis yields optimal allocations that inform which counties should be given priority by public funding agencies, moving toward portfolios that adeptly balance risk with funding necessity. Our methodology is executed in two stages, each bearing distinct implications. The initial stage seeks to prioritize groups of counties based on their funding needs, considering both immediate and long-term contexts, in line with what government agencies might aim to achieve. The subsequent stage refines the equilibrium between risk and need at a more detailed level within these groups, facilitating risk diversification that effectively addresses both short- and long-term funding requirements.

Impact of early continuous positive airway pressure in the delivery room (DR-CPAP) on neonates < 1500 g in a low-resource setting: a protocol for a pilot feasibility and acceptability randomized controlled trial

BackgroundPreterm birth is the leading cause of childhood mortality, and respiratory distress syndrome is the predominant cause of these deaths. Early continuous positive airway pressure is effective in high-resource settings, reducing the rate of continuous positive airway pressure failure, and the need for mechanical ventilation and surfactant. However, most deaths in preterm infants occur in low-resource settings without access to mechanical ventilation or surfactant. We hypothesize that in such settings, early continuous positive airway pressure will reduce the rate of failure and therefore preterm mortality.MethodsThis is a mixed methods feasibility and acceptability, single-center pilot randomized control trial of early continuous positive airway pressure among infants with birthweight 800–1500 g. There are two parallel arms: (i) application of continuous positive airway pressure; with optional oxygen when indicated; applied in the delivery room within 15 min of birth; transitioning to bubble continuous positive airway pressure after admission to the neonatal unit if Downes Score ≥ 4 (intervention), (ii) supplementary oxygen at delivery when indicated; transitioning to bubble continuous positive airways pressure after admission to the neonatal unit if Downes Score ≥ 4 (control). A two-stage consent process (verbal consent during labor, followed by full written consent within 24 h of birth) and a low-cost third-party allocation process for randomization will be piloted.We will use focus group discussions and key informant interviews to explore the acceptability of the intervention, two-stage consent process, and trial design. We will interview healthcare workers, mothers, and caregivers of preterm infants. Feasibility will be assessed by the proportion of infants randomized within 15 min of delivery; the proportion of infants in the intervention arm receiving CPAP within 15 min of delivery; and the proportion of infants with primary and secondary outcomes measured successfully.DiscussionThis pilot trial will enhance our understanding of methods and techniques that can enable emergency neonatal research to be carried out effectively, affordably, and acceptably in low-resource settings. This mixed-methods approach will allow a comprehensive exploration of parental and healthcare worker perceptions, experiences, and acceptance of the intervention and trial design.Trial registrationThe study is registered on the Pan African Clinical Trials Registry (PACTR) PACTR202208462613789. Registered 08 August 2022. https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=23888.

Resource Allocation Approach of Avionics System in SPO Mode Based on Proximal Policy Optimization

Single-Pilot Operations (SPO) mode is set to reshape the decision-making process between human-machine and air-ground operations. However, the limited on-board computing resources impose greater demands on the organization of performance parameters and the optimization of process efficiency in SPO mode. To address this challenge, this paper first investigates the flexible requirements of avionics systems arising from changes in SPO operational scenarios, then analyzes the architecture of Reconfigurable Integrated Modular Avionics (RIMA) and its resource allocation framework in the context of scarcity and configurability. A “mission-function-resource” mapping relationship is established between the reconfiguration service elements of SPO mode and avionics resources. Subsequently, the Proximal Policy Optimization (PPO) algorithm is introduced to simulate the resource allocation process of IMA reconfiguration in SPO mode. The objective optimization process is transformed into a sequential decision-making problem by considering constraints and optimization criteria such as load, latency, and power consumption within the feasible domain of avionics system resources. Finally, the resource allocation scheme for avionics system reconfiguration is determined by controlling the probability of action selection during the interaction between the agent and the environment. The experimental results show that the resource allocation scheme based on the PPO algorithm can effectively reduce power consumption and latency, and the DRL model has strong anti-interference and generalization. This enables avionics resources to respond dynamically to the capabilities required in SPO mode and enhances their ability to support the aircraft mission at all stages.

Multi-objective optimal allocation of construction project risks, ant colony optimization algorithm

PurposeThe purpose of this paper is to introduce a new approach for finding the most appropriate risk allocation among construction contract parties. Although risk allocation is a strategic decision that can greatly affect the cost and time of the project, it is often made based on the personal judgments of employers. That is why owners usually find it a very time-consuming and expensive decision process, while most contractors feel that risk sharing is not done fairly. In this paper, a quantitative model for risk allocation is introduced to fulfill clients’ conflicting expectations in the risk allocation process.Design/methodology/approachBy defining conflicting expectation of owners in the form of three quantitative objectives (lowest cost, maximum reliability and minimum risk exposure), a multi-objective optimization algorithm was developed to select the most appropriate risk allocation. Using experts’ knowledge through fuzzy set theory, a multi-objective decision-making model is developed based on an ant colony optimization algorithm. The proposed model is able to find the optimum risk allocation at the lowest cost and highest reliability while protecting the client against risk exposure within multiple parties projects.FindingsThe proposed model has the ability to select the most appropriate risk allocation in multi-parties projects (such as public–private partnerships) and quantitatively measure the impact of each employer’s choice on project results in the form of cost and time. The results of implementing the proposed model in a case study project revealed that optimum risk allocation requires a balanced attitude, and the transfer of all risks to the other parties will not necessarily lead to the lowest cost. The client should bear more responsibilities in risk management to avoid extreme time delay and cost overrun.Research limitations/implicationsThe proposed model can be implemented in multi-parties projects (such as public–private partnership), while other methods introduced in previous studies can only be used for projects with two party (client and contractor).Practical implicationsBy implementing the proposed model in a real project in this article and comparing its results with previous works, it has been shown that the proposed model has a good performance. Using this model can help clients drastically reduce cost and time and ultimately successfully conclude risk allocation negotiations (which is the most difficult part of any contract negotiation).Originality/valueIn this paper, the risk allocation process is modeled in the form of a multi-objective decision problem. This method helps employers to measure their conflicting goals and choose the most appropriate risk allocation according to their objectives. Also, in this article, the conflicting expectations of the employer in the process of contract negotiations are introduced as three measurable goals, which gives the decision-maker the ability to balance his expectations and not miss an objective. In the final step, an optimization model is developed to select the best option, which can choose the most appropriate party to bear the risk in a short time and among an unlimited number of participants.

Temporal and Spatial Dynamics of Preschool Education Resource Allocation in China From 2011 to 2019: Based on Statistical and Spatial Analysis Methods

Balancing the allocation of preschool education resources is a crucial objective within China’s preschool education reform. Our analysis focused on investigating the disparities between regions and urban-rural areas in China from 2011 to 2019, employing the technique for order preference by similarity to ideal solution (TOPSIS), spatial analysis, and Markov chains. The results demonstrate a gradual reduction in the gaps between regions, and a rapid decrease in urban-rural gaps. Notably, Sichuan stands out with noticeably higher urban-rural differences compared to other provinces. The dynamic evolution revealed by Markov chains illustrates a limited level of fluidity in the observed changes within preschool education resource allocation levels from 2011 to 2019, thereby indicating progression toward a higher-quality allocation process. Additionally, the steady-state formation predicted by Markov chains highlights that if the current investment strategy remains unaltered, the rural areas in middle China will become the least developed in terms of preschool education by 2025. This situation is projected to deteriorate further by 2030. Urgent attention and policy adjustments are therefore imperative to address new trends.

Research and Application of Emergency Logistics Resource Allocation Algorithm based on Supply Chain Network

The ”Fuzzy-Enhanced for Emergency Logistics Resource Allocation in Supply Chain Networks (FEM-ELRAS)” presents a novel approach to optimizing emergency logistics and resource allocation in supply chain networks, especially during critical disaster response scenarios. This research integrates fuzzy logic with the improve Multi Agent Genetic Algorithm (MAGA), creating a more adaptive and efficient framework capable of handling the uncertainties and complexities inherent in emergency situations. FEM-ELRAS employs fuzzy decision variables to represent ambiguous and fluctuating parameters like demand at disaster sites, supply availability, and variable transportation conditions. It incorporates a fuzzy inference system, utilizing expert-derived rules to guide the allocation process amidst uncertain and rapidly changing conditions. The algorithm’s evaluation mechanism is enhanced with fuzzy logic, offering a refined assessment of solution effectiveness, balancing multiple logistical objectives such as minimizing response time, optimizing costs, and maximizing resource utilization and delivery precision. Moreover, fuzzy logic principles are integrated into the genetic algorithm’s operators, enabling more context-sensitive and flexible solution adaptations. FEM-ELRAS is particularly designed to navigate the trade-offs between different logistical goals in emergency scenarios, making it a robust tool for decision-makers in disaster management. Its application promises significant improvements in emergency response efficiency, showcasing a step forward in the field of emergency logistics and supply chain management.

Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor-Recipient Matching.

(1) Background: Globally, the kidney donor shortage has made the allocation process critical for patients awaiting a kidney transplant. Adopting Machine Learning (ML) models for donor-recipient matching can potentially improve kidney allocation processes when compared with traditional points-based systems. (2) Methods: This study developed an ML-based approach for donor-recipient matching. A comprehensive evaluation was conducted using ten widely used classifiers (logistic regression, decision tree, random forest, support vector machine, gradient boosting, boost, CatBoost, LightGBM, naive Bayes, and neural networks) across three experimental scenarios to ensure a robust approach. The first scenario used the original dataset, the second used a merged version of the dataset, and the last scenario used a hierarchical architecture model. Additionally, a custom ranking algorithm was designed to identify the most suitable recipients. Finally, the ML-based donor-recipient matching model was integrated into a web-based platform called Nephron. (3) Results: The gradient boost model was the top performer, achieving a remarkable and consistent accuracy rate of 98% across the three experimental scenarios. Furthermore, the custom ranking algorithm outperformed the conventional cosine and Jaccard similarity methods in identifying the most suitable recipients. Importantly, the platform not only facilitated efficient patient selection and prioritisation for kidney allocation but can be flexibly adapted for other solid organ allocation systems built on similar criteria. (4) Conclusions: This study proposes an ML-based approach to optimize donor-recipient matching within the kidney allocation process. Successful implementation of this methodology demonstrates significant potential to enhance both efficiency and fairness in kidney transplantation.