Allocation Policy Research Articles

Advancing physical activity management in nursing: a systematic literature review on educational interventions and technology integration

Introduction: This systematic literature review (SLR) aims to critically examine the role of nurses in promoting physical activity through educational interventions and the integration of technology. Method: This systematic review was conducted across multiple databases, including PubMed, CINAHL, Scopus, Cochrane Library, Web of Science, and Google Scholar. Result: The SLR finds that behavior change interventions, such as the use of pedometers and self-liberation techniques, along with simulation-based learning and scenario-based approaches, significantly increase patient activity levels, self-efficacy, and knowledge of lifestyle modification among patients with chronic condition. Conclusion: The review highlights the importance of systemic changes like better resource allocation, streamlined workflows, and supportive policies to empower nurses to prioritize physical activity education in their responsibilities, ultimately enhancing patient care and outcomes.

Abstract 4137135: Trends in Mechanical Circulatory Support After the New Heart Allocation Policy

Background: In October 2018 a new heart allocation policy (NHAP) was implemented, which resulted in patients with Intra-aortic balloon pumps (IABP) being upgraded to status-two. We sought to determine practice pattern changes in mechanical circulatory support (MCS) use as a result of the NHAP. Hypothesis: Changes in the new heart allocation policy may lead to shifts in choice of mechanical circulatory support. Methods: A retrospective analysis was conducted using the National Inpatient Sample (NIS) between 2017–2020. Hospitalizations for cardiogenic shock were identified, and stratified based on whether the patients were admitted before or after the NHAP. Baseline characteristics were compared between these cohorts, and the primary outcome of interest was the use of MCS and transplant before and after the NHAP. Subgroup analyses included patients only in transplant centers, non-transplant centers, and those who eventually underwent transplant. Results: A total of 689,935 hospitalizations were included in the final analysis, of which 279,230 (40.5%) were before the NHAP, and 410,705 (59.5%) were after. In allcomers, hospitalizations after the NHAP were less likely to undergo IABP (Adjusted odds ratio [OR] 0.81, p<0.01) and LVAD (Adjusted OR 0.73, p<0.01). These findings were consistent in most subgroup analyses. Patients who underwent transplant after the NHAP however were more than twice as likely to have received a IABP compared to those admitted prior to the NHAP (adjusted OR 2.58; p<0.01). Conclusion: IABP placement after the NHAP increased in patients who underwent transplant, as opposed to allcomers or patients who did not undergo transplant. This change in utilization of MCS may be reflective of perceived benefits and biases in regards to transplantation candidacy.

Optimal Bundling Cooperation Strategies for Online Paid Membership Programs

ABSTRACTPaid membership programs are popular in Internet firms, and “bundling cooperation for paid memberships” is trendy in their online operations. Unlike traditional product bundling problems, membership bundling between Internet firms presents new characteristics such as member sharing, which brings challenges to profit allocation and partner selection. Despite being a commonly seen business practice, little is known about the optimal bundling strategies and the cooperation mechanisms regarding firms' membership status. To fill this gap, we develop a stylized model that considers the potential added‐value income of membership to analyze the bundling problem between two firms. We show that the optimal bundling strategy depends on firms' original member bases and their ability to mine member value (i.e., value‐monetization ability). We discuss the allocation policy with Nash bargaining and find that the firm may entirely waive its bundled membership fee income and even compensate its partner to achieve cooperation. Surprisingly, a start‐up partner with no member base may be the optimal option. Nevertheless, if there is no such a partner in the market, forming a bundle with a well‐established partner is the right choice. Our findings provide novel managerial insights into bundled membership operations of Internet firms in practice.

Meta-learning and proximal policy optimization driven two-stage emergency allocation strategy for multi-energy system against typhoon disasters

To achieve resilience improvement of the multi-energy system against typhoon disasters, this study designs a novel two-stage optimization framework that considers the emergency allocation of distributed resources under typhoon disasters to fully exploit the potential of distributed resources for resilience enhancement. Firstly, we formulate the emergency allocation of distributed resources as a Markov decision process. Then, a meta-learning-driven proximal policy optimization method is utilized to solve it. Different from that the existing reinforcement learning methods always ignore the unpredictable change caused by typhoon and keep multi-energy system dynamics invariant, limiting its control performance. The proposed method embeds meta-learning to fine-tune the pre-trained allocation policy to new tasks with high adaptability and few interactions. Finally, comparison results with other benchmark methods are carried out and shows that the proposed method can learn the appropriate resource allocation policy for multi-energy system and achieve better resilience enhancement, yielding fast application efficiency and good generalization ability for emergency fault conditions.

A simheuristic for project portfolio optimization combining individual project risk, scheduling effects, interruptions, and project risk correlations

This paper introduces a simheuristic method to the Project Portfolio Selection Problem, designed to maximize the net present value of the portfolio while considering uncertain costs, schedules, interruptions, and inter-project risk correlations. The novel approach combines techniques from Monte Carlo simulation, critical path analysis, queuing theory, and optimization, integrating baseline schedules, project-level uncertainties, budgetary constraints, and risk correlations in a single model. A computational experiment is conducted on a realistic set of ten candidate projects and validated respect to the deterministic version of the problem, demonstrating its ability to select near optimal portfolio proposals with varying combinations of risk and net present value. The findings highlight the significant impact of factors such as contingency reserve allocation policies, operational interruptions, and project risk correlations on portfolio decisions, constituting a helpful framework for the decision-makers at portfolio level.

Navigating the Creator Economy: An Analysis of Content Promotion and View Allocation Policies on Digital Content Platforms

Navigating the Creator Economy: An Analysis of Content Promotion and View Allocation Policies on Digital Content Platforms

Optimization and Protection of Kidney Health in Liver Transplant Recipients: Intra- and Postoperative Approaches.

Postoperative acute kidney injury after liver transplant (LT) has long-term implications for kidney health. LT recipients are at risk of acute kidney injury due to a number of factors related to the donor liver, intraoperative factors including surgical technique, as well as recipient factors, such as pre-LT kidney function and postoperative complications. This review discusses these factors in detail and their impact on posttransplant kidney function. Long-term risk factors such as calcineurin inhibitors have also been discussed. Additionally, the impact of liver allocation policies on pre- and post-LT kidney health is discussed.

Optimal Control Allocation for 2D Reaction‐Diffusion Equations With Multiple Locally Distributed Inputs

ABSTRACTIn this article, the problem of stabilization of a 2D unstable parabolic equation with multiple distributed inputs is addressed using a spectral decomposition approach. Furthermore the underlying redundancy of the actuation arrangement is exploited and actively used by introducing a suitable control allocation architecture. In particular, two optimal allocation policies have been considered: gradient descent and linear quadratic allocation. A simulation study supports and illustrates the theoretical findings.

GRRIS: A Real-time Intrasite Observation Scheduling Scheme for Distributed Survey Telescope Arrays

The distributed telescope array offers promise for conducting large-sky-area, high-frequency time-domain surveys. Multiple telescopes can be deployed at each observation site, so intrasite observation task scheduling is crucial for enhancing observation efficiency and quality. Efficient use of observable time and rapid response to special situations are critical to maximize scientific discovery in time-domain surveys. Besides, the competing scientific priorities, time-varying observation conditions, and capabilities of observation equipment, lead to a vast search space of the scheduling. So with the increasing number of telescopes and observation fields, balancing computational time with solution quality in observation scheduling poses a significant challenge. Informed by the seminal contributions of earlier studies on a multilevel scheduling model and global scheduler for a time-domain telescope array, this study is devoted to further exploring the site scheduler. Formulating the observation scheduling of multiple telescopes at the site as a cooperative decision-making problem, this paper proposes GRRIS, a real-time intrasite observation scheduling scheme for the telescope array using graph and reinforcement learning (RL). It employs a graph neural network to learn node features that can embed the spatial structure of the observation scheduling. An algorithm based on multi-agent RL is designed to efficiently learn the optimum allocation policy of telescope agents to field nodes. Through numerical simulations with real-world scenarios, GRRIS can achieve up to a 22% solution improvement over the most competitive scheme. It offers better scalability and subsecond decision speed, meeting the needs of observation scheduling control for future distributed telescope arrays.

Promotion of Knowledge and Trust Surrounding Scarce Resource Allocation Policies

The COVID-19 pandemic prompted rapid development of scarce resource allocation policies (SRAPs) in case demand for critical health services eclipsed capacity. To test whether a brief educational video could improve knowledge of how the University of California Health's SRAP would be implemented and trust in health systems to implement such policies in accordance with ethical principles during the pandemic. This randomized clinical trial used an educational video intervention embedded in a longitudinal web-based survey and was conducted between May and December 2020 and analyzed during March 2024. A total of 1971 adult participants were enrolled, of whom 939 completed follow-up; participants with matched baseline and follow-up responses were analyzed. California residents were randomized to view the intervention (n = 345) or not (n = 353) and stratified by age, sex, education, racial identity, and self-reported health care worker status. Non-California residents were allocated to the control group (n = 241). A brief (6-minute) "explainer" video that provided an overview of mechanics and ethical principles underpinning the University of California Health SRAP, subtitled in 6 languages. Self-reported survey assessment of knowledge of components of SRAP, graded as correct vs incorrect, and trust graded on a 10-point Likert scale. Anxiety about such policies was graded on a 10-point Likert scale with an a priori noninferiority margin of half of a standard deviation. Participants answered items at baseline and follow-up (approximately 10 weeks after baseline), with randomization occurring between administrations. Of 770 randomized participants with responses at both points, 566 (73.5%) were female, and the median (IQR) age was 43.5 (36-57) years. Intervention participants demonstrated improvement of 5.6 (95% CI, 4.8-6.4; P < .001) more correct knowledge items of 20 vs controls, as well as significant improvements in reported trust in fairness/consistency and honesty/transparency about SRAP implementation. There was no significant change in reported anxiety surrounding SRAP in either treatment or control groups. The trial found that a brief educational video is sufficient to explain complex ethical tenets and mechanics of SRAP and improved knowledge of such policies and trust in health systems to implement them equitably while not exacerbating anxiety about potential policy implications. This informs practice by providing a framework for educating people about the use of these policies during future situations necessitating crisis standards of care. ClinicalTrials.gov Identifier: NCT04373135.

Determinants Of The Proportion Of Tabarru' Funds In Indonesian Sharia General And Life Insurance For The 2018-2022 Period

This study examines the impact of claim expense ratio, commission expense ratio, total assets, and investment profit sharing income ratio on the proportion of tabarru' funds in general and life Islamic insurance in Indonesia, both sharia and full-fledged business units. The aim is to recognise the factors that influence the allocation of tabarru' funds and how it affects the financial performance of the company. Using panel data regression analysis, this study found that the claims expense ratio has a significant positive impact on full-fledged insurers, while the commission expense ratio has a significant positive effect on the combined model and Islamic business units. In contrast, total assets negatively affect the proportion of tabarru' funds significantly in the combined model and Islamic business units, but not significantly in full-fledged insurers. The investment profit-sharing income ratio does not show a significant effect in all models. These results provide useful insights for the financial management of Islamic insurance companies in designing effective tabarru' fund allocation policies, especially during the transition period from Islamic business units to full-fledged Islamic insurance companies.

A simulation optimization approach for weight valuation in analytic hierarchy process

The analytic hierarchy process (AHP) is a structured technique used to analyze complex decision-making situations such as resource allocation, benchmarking, and quality management. In the weight valuation step of using AHP to select the best design, pairwise comparison matrices are used to calculate the local priorities for designs that have contentious and unresolved criticisms. In this study, we propose a Bayesian approach using a Dirichlet-multinomial model to estimate local priorities during weight valuation. Experts are only asked to select the best design with respect to predetermined criterion. Subsequently, local priorities are estimated without pairwise comparison matrices. To improve the efficiency of the AHP, we propose two expert allocation policies (AHP-KG and AHP-AKG) based on the ranking and selection procedures. Our numerical results show that the proposed AHP-KG and AHP-AKG policies outperform pure exploration and proportional allocation policies.

Associations Among Circle-Based Kidney Allocation, Center Waiting Time, and Likelihood of Deceased-Donor Kidney Transplantation

The kidney allocation system (KAS250), using circle-based distribution, attempts to address geographic disparities through broader sharing of deceased-donor kidney allografts. This study sought to evaluate the association between KAS250 and likelihood of deceased-donor kidney transplantation (DDKT) among waitlisted candidates, and whether the policy has differentially affected centers with shorter vs. longer waiting time. Retrospective cohort study. & Participants: 160,941 candidates waitlisted at 176 transplant centers between 3/2017-3/2024. KAS250 allocation policy. Rate of DDKT. Multivariable Cox regression, modeling KAS250 as a time-dependent variable. KAS250 was not independently associated with likelihood of DDKT overall (HR=1.01 vs. pre-KAS250, 95% C.I. 0.97-1.04). KAS250's association with likelihood of DDKT varied across centers from HR=0.18 (DDKT less likely after KAS250) to HR=17.12 (DDKT more likely) and varied even among neighboring centers. KAS250 was associated with decreased DDKT at 25.6% and increased DDKT at 18.2% of centers. Centers with previously long median waiting times (57+ months) experienced increased likelihood of DDKT after KAS250 (HR=1.20, 95% C.I. 1.15-1.26), whereas centers with previously short median waiting times (6-24mo.; HR=0.88, 0.84-0.92) experienced decreased likelihood of DDKT. Retrospective study of allocation policy changes, confounded by multiple changes over the study timeframe. Association between KAS250 and DDKT varied across centers. For one-in-four centers, DDKT was less likely after KAS250 relative to pre-KAS250 trends. Candidates at centers with previously long waiting times experienced increased likelihood of DDKT after KAS250. Thus, broader distribution of kidneys may be associated with improved equity in access to DDKT, but additional strategies may be needed to minimize disparities between centers.

Current Burden of and Geographic Disparities in Liver Mortality and Access to Liver Transplant

Liver disease is a leading cause of mortality in the US. Liver transplant can be a lifesaving procedure for patients with severe liver disease. To assess temporal trends and geographic variance in liver-related mortality (LRM) and liver transplant in the US. In this cohort study, the frequencies and geographic variance of LRM in 2018 and 2021 were obtained in a cross-sectional analysis of the Underlying Cause of Death data available on the Centers for Disease Control and Prevention's Epidemiologic Research database. The number of livers donated and transplanted according to the state of residence of each donor and recipient were obtained from the United Network for Organ Sharing. Liver-related mortality in 2018 and 2021, overall and by state, as well as the liver transplant rate according to state of residence of recipient and donor. Overall LRM in the US was 93 418 in 2021, with a crude rate of 28.1 per 100 000 individuals, an increase of 19.1% compared with rates seen immediately prior to the COVID-19 pandemic in 2018 (77 282 [23.6 per 100 000 individuals]). Liver-related mortality in 2021 varied several-fold between states, from 18.4 per 100 000 individuals per year in Utah to 65.9 per 100 000 individuals per year in New Mexico. The mean number of liver-related deaths per transplant from all donor sources (in state and out of state) was 7.2 in the lowest LRM quintile compared with 21.5 in the highest (95% CI, 12.1-16.6; SE, 1.1; P < .001). Ten states had no liver transplant center. Paradoxically, residents of states with the highest LRM had a much lower rate of liver transplant (at any location) from organs procured from in-state residents than states with the lowest LRM quintile (13.0% vs 35.2% in-state donors; 95% CI, 14.1%-30.3%; SE, 3.9%; P < .001). This study suggests that rates of LRM have increased dramatically since the COVID-19 pandemic and vary several-fold between states. Rates of liver transplant are paradoxically lowest among residents living in states with the highest LRM. These findings highlight apparent geographic disparities in access to liver transplant that allocation policy cannot address.

A Stackelberg game framework for promoting battery-powered construction machinery through government subsidies

Diesel-powered construction machinery is a major source of harmful pollutants and carbon emissions. Technological advancements have made battery-powered construction machines (BPCMs) increasingly viable in operations. However, the high purchase prices of BPCMs represent a huge obstacle for contractors to embrace these low-emission alternatives. To facilitate the sustainable development of the construction industry, it is imperative for the government to implement effective subsidy allocation policies to promote the electrification of construction machinery. This paper proposes a Stackelberg game framework for optimal subsidy allocation. In the proposed framework, the government decides the subsidy amount offered to each type of BPCMs to minimize both pollutant emissions and carbon emissions. The contractors can then observe the government’s decision and make their optimal decisions regarding the purchase, operation, and replacement of construction machines accordingly to minimize their total costs. Contractors’ decisions in turn influence the government’s decision. Such an intricate framework has many appealing properties, which are analyzed in depth to provide useful managerial insights. Additionally, the design of effective subsidy policies by the government depends on a precise prediction of the contractors’ demand for construction machinery. To this end, a random forest machine learning model is developed. Real data were collected for model construction and testing. Statistical results and industrial comments show the high quality of our predictions.

Commentator Discussion: Impact of lung allocation policy change on Hispanic lung transplant outcomes: Addressing disparities and improving access

Commentator Discussion: Impact of lung allocation policy change on Hispanic lung transplant outcomes: Addressing disparities and improving access

The Year in Cardiothoracic Transplant Anesthesia: Selected Highlights From 2022 Part II: Cardiac Transplantation

These highlights focus on the research that has been published in the year 2022 and is divided into preoperative, intraoperative, and postoperative sections. The preoperative section includes research pertaining to the assessment and optimization of candidates for HTX, donor optimization and the use of extended donors, organ protection systems, and donation after circulatory death (DCD) allografts, recipient factors including cannabis use, sex, race, and comorbidities such as obesity, diabetes mellitus, and peripartum cardiomyopathy, the effects of the 2018 heart allocation policy change on waitlist and postoperative mortality, updates on HTX in patients with Covid-19, in pediatric patients, and those who require a bridge to transplant. The intraoperative section includes the use of a multidisciplinary team, a proposed transfusion algorithm, bench surgery on the allograft, and size matching. The postoperative section focuses on the research pertaining to the development and management of tricuspid regurgitation, echocardiography, arrhythmia management, and finally xenotransplantation.

End-to-End Resource Allocation Management Model in Next-Generation Network: Survey

Network communication has grown rapidly with massive demands of services. Moreover, resource allocation in networking is a fundamental and crucial issue that cannot ensure the network's stability and efficiency with the myriad requirements of different services. Various vertical businesses may seek varied network services, particularly in the Fifth-Generation networks and Beyond (5G+). The pros of Fifth-Generation communication networks are to outperform 4G in performance by having higher bandwidth, minimum latency, more capacity, and QoS (Quality of Service). Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two technologies that are combined in the next generation cellular network to provide improved network management. The primary idea behind resource allocation (RA) in the next generation network is the concept of network slicing where the network resources are virtually partitioning into many separate networks. Each separated network must satisfy the unique needs of the required service to achieve the required QoS. In this survey, we focus on resource management issues related to network slicing and tackling the biggest obstacles in this field while offering a thorough and up-to-date overview of this field. Thus, thorough analysis of the allocation of resources on the access side and core side of the network communication was sought. Also, demonstrates how revolutionary techniques that are used to support the management of sliced networks which are based on Machine Learning (ML) and Artificial Intelligence (AI). Importantly, use appropriate ML techniques such as deep learning for predicting the network condition and Reinforcement learning to learn optimal allocation policy without depending on prior knowledge and other techniques such as classification and clustering to aggregate the similar needs of users into separate slices. This could help to enhance resource utilization by allocating a sufficient amount of resource as needed based on ML algorithms and optimal utilization of resources and reducing operational costs by real-time adjustment of it based on user demands and network conditions

Interaction between product strategy selection and the carbon quota allocation policy

The interaction between the government’s carbon reduction policy and a firm’s product strategy has not been well studied in the literature. This paper considers the government’s two different carbon quota allocation policies for the cap-and-trade scheme and the firm’s two product strategies and investigates the interaction between them by establishing a theoretical model and deriving the optimal decisions. This paper first examines the firms’ selection of low-carbon products or ordinary product strategies under the government’s two carbon quota allocation policies and then studies the government’s optimal carbon quota allocation policy for overall social welfare, which is based on the firm’s two product strategies. Our analysis reveals that (i) when the government allocates carbon quotas aimed at reducing the firm’s total carbon emissions, the firm will choose the low-carbon product strategy. When the government allocates a carbon quota aimed at optimal total social welfare, the firm’s decision depends on the impact of total carbon emissions. (ii) To achieve optimal social welfare, the government will formulate different carbon quota allocation policies on the basis of firms’ different product strategies.

Comprehensive Brand Analysis of the Telecommunication Industry in Delhi and Delhi NCR

The telecommunication sector in Delhi and the National Capital Region (NCR) is a crucial component of India's digital landscape, catering to a population exceeding 30 million and driving technological advancements in one of the country's most economically vibrant regions. In this dynamic environment, understanding the intricacies of brand strategies, market dynamics, and consumer behavior is paramount for industry stakeholders seeking to thrive amidst fierce competition and rapid technological evolution. This in-depth brand analysis embarks on a journey to unravel the complexities of the telecommunication sector in Delhi and the NCR. Through a meticulous blend of qualitative and quantitative methodologies, the research endeavors to shed light on the diverse array of factors shaping the regional telecommunications landscape. From the strategies adopted by industry contenders to the regulatory frameworks governing operations, this exploration aims to provide a comprehensive understanding of the opportunities and challenges inherent in the Delhi-NCR telecommunication market. The study delves into the strategies deployed by key players to navigate the competitive landscape. It illuminates how brands position themselves to capture market share, differentiate their offerings, and respond to evolving consumer demands. Additionally, the analysis dissects the regulatory context shaping operations within Delhi and the NCR, including licensing requirements, spectrum allocation policies, and consumer protection regulations. By examining the interplay between industry dynamics and regulatory frameworks, the research provides insights into the governance of the telecommunication sector in the region. As the telecommunication sector in Delhi-NCR continues to evolve, informed decision-making becomes imperative for industry participants, policymakers, and investors alike. By synthesizing insights gleaned from the analysis of market dynamics, consumer preferences, and regulatory frameworks, this examination serves as a strategic resource for navigating the complexities of the regional telecommunication market. Armed with actionable intelligence and a nuanced understanding of the telecommunication landscape, stakeholders can capitalize on emerging opportunities, address challenges, and contribute to the continued growth and innovation of the sector in Delhi and the NCR.