Fair Allocation Research Articles

Wearable sensors in patient acuity assessment in critical care.

Acuity assessments are vital for timely interventions and fair resource allocation in critical care settings. Conventional acuity scoring systems heavily depend on subjective patient assessments, leaving room for implicit bias and errors. These assessments are often manual, time-consuming, intermittent, and challenging to interpret accurately, especially for healthcare providers. This risk of bias and error is likely most pronounced in time-constrained and high-stakes environments, such as critical care settings. Furthermore, such scores do not incorporate other information, such as patients' mobility level, which can indicate recovery or deterioration in the intensive care unit (ICU), especially at a granular level. We hypothesized that wearable sensor data could assist in assessing patient acuity granularly, especially in conjunction with clinical data from electronic health records (EHR). In this prospective study, we evaluated the impact of integrating mobility data collected from wrist-worn accelerometers with clinical data obtained from EHR for estimating acuity. Accelerometry data were collected from 87 patients wearing accelerometers on their wrists in an academic hospital setting. The data was evaluated using five deep neural network models: VGG, ResNet, MobileNet, SqueezeNet, and a custom Transformer network. These models outperformed a rule-based clinical score (Sequential Organ Failure Assessment, SOFA) used as a baseline when predicting acuity state (for ground truth we labeled as unstable patients if they needed life-supporting therapies, and as stable otherwise), particularly regarding the precision, sensitivity, and F1 score. The results demonstrate that integrating accelerometer data with demographics and clinical variables improves predictive performance compared to traditional scoring systems in healthcare. Deep learning models consistently outperformed the SOFA score baseline across various scenarios, showing notable enhancements in metrics such as the area under the receiver operating characteristic (ROC) Curve (AUC), precision, sensitivity, specificity, and F1 score. The most comprehensive scenario, leveraging accelerometer, demographics, and clinical data, achieved the highest AUC of 0.73, compared to 0.53 when using SOFA score as the baseline, with significant improvements in precision (0.80 vs. 0.23), specificity (0.79 vs. 0.73), and F1 score (0.77 vs. 0.66). This study demonstrates a novel approach beyond the simplistic differentiation between stable and unstable conditions. By incorporating mobility and comprehensive patient information, we distinguish between these states in critically ill patients and capture essential nuances in physiology and functional status. Unlike rudimentary definitions, such as equating low blood pressure with instability, our methodology delves deeper, offering a more holistic understanding and potentially valuable insights for acuity assessment.

Reporting the successful timing of pre-construction services in the New Zealand construction industry - development of a visualisation tool

The use of Pre-Construction Services (PCS), also known as Early Contractor Involvement (ECI) in construction projects has become common internationally due to the various advantages it offers in complex projects. What remains an open question to researchers and government organisations is defining the optimal time to engage in it. This paper develops a methodology that reports on the successful timing of PCS use in New Zealand. This methodology and findings can assist the industry in determining when to appropriately engage a contractor for PCS in New Zealand. Reporting on the successful timing of PCS can help the industry avoid costly pitfalls when engaging in PCS too early or too late. This approach could be extended to an international audience. The timing of PCS and project complexity are closely related. For this paper data was collected in two rounds. Firstly, an online survey was used to determine weights for complexity factors, then a visualization tool was developed and refined using 29 case study projects derived from semi-structured interviews. The results show that there is a relationship between the timing of PCS, the project complexity, and the scale of a project. Additionally, findings show that PCS is not always related to the procurement method. For example, Design-Bid-Build (DBB) projects can still receive valuable PCS to make them successful through fair risk allocation. PCS can have an impact on the contract terms. Each project was given a complexity ranking based on the information given by the interviewees during semi-structured interviews. While the semi-structured interviews were extensive, the information is only as accurate as the recollection of the participant. This risk was minimised by the prequalification of the participants. The findings of this research will assist clients in determining when the best time is to engage PCS. Independent of the procurement model, and will assist clients in maximising the value that the contractor can bring to construction projects.

How to promote data sharing among enterprises based on dynamic evolutionary game analysis

ABSTRACT In traditional contexts, enterprises often hesitate to engage in data sharing due to the associated heightened risks and transaction costs. Blockchain technology, leveraging its encryption algorithms and smart contracts, effectively alleviates these challenges, thereby encouraging enterprises to actively participate in data sharing. The adoption of reasonable reward and penalty mechanisms can prevent opportunistic behaviours and stimulate data sharing among businesses. It is noteworthy that blockchain technology and data alliance have a mutually substitutive effect in terms of risk and communication costs, while they complement each other in regulatory and transaction costs. The integration of blockchain technology with data alliance can enhance collaborative revenue and reduce various costs. When distributing collaborative revenue, it is crucial to adhere to the guiding principle of ‘prioritizing efficiency while considering fairness’. Collaboration becomes a reality when all parties collectively achieve a substantial overall revenue threshold. Subsequently, in-depth exploration of fair resource allocation principles is crucial to ensure the equitable distribution of resources.

Artificial intelligence (AI) inspired an innovative approach for real-time flow measurements – water distributions at outlets, in Punjab province Pakistan

ABSTRACT Accurate water metering is essential for effective water management, especially for measured and equitable distribution of canal flows, most imperative in Pakistan. The inflows distributed through outlets must adhere to pre-designated levels, defined by Punjab Irrigation Department from head to tail. With recent channels lining, the outlets sanctioned flow are found changed requiring adjustments. Furthermore, there is lack of reliable mechanisms for continuous flow regulations, resulting in unmanaged distributions. The focus of the reported work was on precise and real-time discharge monitoring at outlets using a newly developed device utilizing Artificial Intelligence. During validation training, strong agreement was observed, and all evaluation parameters supported the device accuracy for measurements. In detailed studies of canal operations, the sanctioned discharges of individual outlets along selected distributaries were found significantly inequitable. Out of 162 outlets, 42 were suspended for being unmanaged, with tail users receiving ≤ 50% of their fair intakes. Additionally, there were instances of massive irrational supplies exceeding 200% of allocated discharges. The developed device has potential to accurately measure real-time discharges for managed distributions. Conclusively, the challenging task of fair and equity-based discharge allocations, in conjunction with Warabandi system, necessitates the reevaluation of the allocated flow quantity at each outlet.

Railway crew planning with fairness over time

Passenger railway operators typically employ large numbers of drivers and guards, and are interested in providing them with fair and attractive working conditions. At Netherlands Railways (NS), the largest passenger railway operator in The Netherlands, this challenge is addressed through the use of Sharing-Sweet-and-Sour rules, which specify a fair allocation of sweet (attractive) and sour (unattractive) work over the different crew bases. While these rules are currently implemented at the crew base level and in the tactical planning phase, NS is considering formulating these rules at the individual level, in the operational planning phase, and with respect to a given planning period. This gives rise to a new problem, which we call the railway crew planning problem with fairness over time. We propose a rolling horizon approach with a penalty-based feedback mechanism and a column generation heuristic to solve this problem. On several real-life instances from NS, including up to 572 unique guards, this method is able to satisfy the individual rules for on average 95.2% of the employees.

Tracing agricultural land carbon loss through transregional food consumption for fairness of responsibility allocation

Agricultural land carbon loss (ALCL) has been increasingly triggered by transregional food consumption in China. Understanding land carbon burden from consumption side is then recognized to be essential for facilitating regional fairness. To this end, we combined physical food-orient multi-regional input-output table and ALCL inventory for 26 kinds of foods in 31 provinces to trace China's ALCL through cross-regional consumption. Results show that ALCL embodied in inter-provincial trade was up to 42 % of the national total ALCL. Food consumption in Guangdong caused the largest ALCL, while Tibet and Qinghai stood out from per capita perspective. Furthermore, consuming foods produced in areas with higher production ALCL intensity would elevate national ALCL, thus shrinking the adverse flows and controlling agricultural land expansion in high-intensity areas may help lessen national ALCL. This study contributes to equitable allocation of responsibility for land carbon loss, and enlightens future land carbon loss risks and restoration opportunities.

Global Ethical Principles in Healthcare Networks, Including Debates on Euthanasia and Abortion.

In today's ever-evolving healthcare landscape, the focus is shifting towards integrated care, inter-organizational cooperation, and healthcare networks (HCNs) as alternatives to traditional healthcare institutions. This transformation is driven by factors such as an aging population and increasing healthcare costs, necessitating a reevaluation of ethical considerations to ensure the well-being of patients remains central. This review provides a narrative overview of ethics within HCNs, with a focus on patient-centered medical ethics. It elaborates on the four fundamental ethical principles, namely justice, beneficence, nonmaleficence, and autonomy. The principle of justice underscores HCNs' ethical obligation to provide equitable and transparent access to all patients, ensuring fairness in resource allocation. The principle of nonmaleficence underscores the responsibility to prioritize patient safety, while beneficence obliges HCNs to ensure continuity of care across all dimensions. Furthermore, the principle of autonomy is redefined as a commitment to actively promote and respect patient choices. HCNs that do not adhere to these ethical principles raise concerns and lack ethical justification. Additionally, the review delves into the legal aspects of euthanasia and abortion, both of which present complex ethical challenges in healthcare systems globally. A comparative analysis is provided, examining their legal status in Islamic countries, European nations, and the United States. This study sheds light on the evolving ethical landscape in HCNs and the diverse global perspectives on contentious issues. Therefore, harmonizing legislation and defining appropriate boundaries are crucial steps toward upholding ethical standards in healthcare practices on a global scale.

Optimal operation of multi-microgrid systems considering multi-level energy-certificate-carbon coupling trading

Under the background of sustainable development, the microgrid (MG) has become an important method for energy saving, emission reduction and meeting multiple energy demand. With the continuous promotion of China's carbon emission right market and green certificate market, to further promote the low-carbon development of MG, a multi-level trading optimization strategy is proposed for multi-microgrids (MMG) that considers the coupling of energy-certificate-carbon market. First, a MMG trading framework including a two-level certification-carbon and a three-level electric-thermal coupled trading is proposed. Second, based on the game theory pricing strategy and the elastic price mechanism of the supply-demand ratio, the MMG operation optimization strategy model is constructed considering multi-level energy-certification-carbon coupled trading. Finally, based on the principle of fair benefit allocation, the Shapley value method is utilized to redistribute the comprehensive income among the alliance members. The simulation results show that compared with the MMG baseline scenario, the overall economic efficiency of the proposed trading model is improved by 3.94 % and the carbon emissions are reduced by 11.71 %. In this way, it can effectively release the potential of MMG resource sharing and mutual benefit, so as to optimise the trade-off strategy for joint participation in the energy-certificate-carbon market.

Fair and effective vaccine allocation during a pandemic

This paper presents a novel model for the Vaccine Allocation Problem (VAP), which aims to allocate the available vaccines to population locations over multiple periods during a pandemic. We model the disease progression and the impact of vaccination on the spread of the disease and mortality to minimise total expected mortality and location inequity in terms of mortality ratios under total vaccine supply and hospital and vaccination centre capacity limitations at the locations. The spread of the disease is modelled through an extension of the well-established Susceptible–Infected–Recovered (SIR) epidemiological model that accounts for multiple vaccine doses. The VAP is modelled as a nonlinear mixed-integer programming model and solved to optimality using the Gurobi solver. A set of scenarios with parameters regarding the COVID-19 pandemic in the UK over 12 weeks are constructed using a hypercube experimental design on varying disease spread, vaccine availability, hospital capacity, and vaccination capacity factors. The results indicate the statistical significance of vaccine availability and the parameters regarding the spread of the disease.

A Granular Approach to Optimal and Fair Patient Placement in Hospital Emergency Departments

Prolonged emergency department (ED) length of stay (LOS) is associated with detrimental effects on patient care quality and outcomes. There is evidence that certain groups of patients experience longer LOS based on their gender or race, especially with regard to the part of LOS that is attributable to waiting to be seen by a clinician. This work tackles the patient prioritization and placement aspects of ED operations with the goal of improving throughput and wait time in a fair, equitable way. We present a novel Mixed Integer Linear Programming (MILP) predictive-prescriptive formulation that incorporates a breakdown of predicted patient ED LOS into actionable pieces. We incorporate considerations for fairness and reformulate the MILP formulation into a compact and computationally tractable formulation that can be solved efficiently in real time. To deal with uncertainty, we propose a sampling-based solution, and provide provable guarantees regarding its convergence, stability and sample complexity. The proposed solution increases the throughput of the ED by [Formula: see text] and decreases the average wait time by [Formula: see text] compared to current hospital practice. In addition, the method is near-optimal in terms of throughput, and produces high-quality solutions in terms of average wait time compared to a clairvoyant oracle. Our proposed approach demonstrates desirable properties when it comes to fairness in patient prioritization, illustrating a path for addressing hidden biases in patient ED wait times and hospital operations as a whole. This work was conducted in collaboration with a large US academic medical center. Data from more than 40,000 patient visits were used to shape and evaluate the predictive-prescriptive models. An important practical contribution is translating a complex algorithm’s output into recommendations that can be operationalized in the context of existing processes in the ED. Specifically, we develop an interpretable metamodel that is trained to mimic the predictive-prescriptive algorithm’s decisions and provides a transparent set of rules for patient placement. The method will be used by the hospital to improve patient flow and quality of care as well as to support more fair and consistent bed allocation decisions.

Urban life and sunshine: Equitable sunlight resource allocation among different consumer groups?

Owing to the significant role of sunlight hours in human survival, it is crucial to assess the fairness of sunlight resource allocation among different socioeconomic classes, which is essential for the overall harmonious development of a society. This study focused on selected residential communities in Fuzhou, China, and conducted a simulation analysis of sunlight hours using a Solar Grid Model. Additionally, based on the transaction prices of residential properties in the area, this study employs regression analysis to discuss the current supply and demand of sunlight hours for residences with different price ranges. This study found that properties belonging to the lower-spending group may experience excessive sunlight hours during summer. An increase of one hour in sunlight hours on the summer solstice is associated with a decrease of approximately 45,000 RMB in the total property price. In contrast, the higher-spending group is more likely to face insufficient sunlight during winter, and an increase of one hour in sunlight hours on the winter solstice is associated with an approximate 28,000 RMB rise in the total property price. Furthermore, the study found that controlling the size of the property area for medium- to low-priced residential properties within a range of 113 m2 enables better utilization of sunlight resources. In contrast, high-priced residences, if designed with large protrusions, are prone to wasting sunlight resources. This study employed a novel approach to simulate and replicate the current sunlight conditions in urban architectural clusters. For the first time, this study systematically analyzed variations in the demand for sunlight hours across different socioeconomic groups, using property prices as references. Finally, this study summarizes the typical characteristics of sunlight design issues related to residential sample variations and proposes corresponding solutions.

Measuring Panic as a Facet of Anxiety on Students’ Oral and Written Assessment in The University of Bamenda, Cameroon

Aim: The growing concern about the declining academic performance of students in Cameroon schools and colleges provokes varied reflections on the possible factors. One, but less obvious issue is anxiety, manifested in forms like agitation, excessive worrying, restlessness, and panic. While these variants are all critical in appreciating the connections with classroom assessment, this paper primarily isolates and focuses on panic, given its common visible expression among students. Specifically, the paper centers on how panic affects the performance of students in oral and written classroom assessments at the University of Bamenda.
 Methods: Systematic random and purposive sampling techniques were used to select 327 second-year students from the Faculty of Arts, Higher Teacher Training College, and the Faculty of Science to participate in the study. A sequential explanatory research design aided by a structured questionnaire, interview, and observation was used to collect quantitative and qualitative data. The qualitative data were analyzed using descriptive statistics, while the likelihood ratio test was used to test the hypothesis. 
 Results: The findings showed that panic has a high significant effect on students’ performance in assessment (Chi-square = 232.054, df = 60, P = 0.000, far <0.05).
 Conclusion: The findings indicated, among others, that when students exhibit panic during assessments, they lose concentration, forget what they studied, easily make errors, and experience disorganized speech, especially during oral assessments.
 Recommendation: To reduce this type of anxiety during assessment, there is a need for intensive rehearsals before assessment, the avoidance of panic triggers, especially from invigilators, and fairness in time allocation for both oral and written assessments.

Fair and efficient resource allocation via vehicle-edge cooperation in 5G-V2X networks

To cope with safety risks and operational efficiency problems, it is of paramount importance to ensure high data rates and meet the latency requirements in Connected and Autonomous Vehicles. The problem in such environments is two-fold: 1. Heavy load on the network due to increasing demands; 2) Resource imbalance, due to variations in the vehicular traffic density in certain regions. The consequences of these two phenomena may lead to service disruptions, as well as the fairness of resource allocation across vehicles. In this work, we propose a resource allocation method that distributes high workload among edge nodes and allocates network resources efficiently and fairly. Performance of the proposed method is evaluated under realistic scenarios, and compared to the state-of-the-art approaches in the literature. Behavior and performance of all methods in overload conditions in certain regions were analyzed. Simulation results exhibit a 38% improvement in the successful demand rate and a 48% improvement in capacity usage compared to state-of-the-art approaches.

Allocating the fixed cost as a complementary input in two-stage system: A DEA approach

The existing research on fixed cost allocation (FCA) in two-stage system data envelopment analysis (DEA) models typically regards the fixed cost as an additional input for each decision-making unit (DMU). However, these models overlook the prevalent real-world scenario where fixed costs act as complementary inputs in actual production processes. This study proposes a general two-stage network DEA model that incorporates the fixed cost as a complementary input to optimize the allocation scheme. First, we construct a functional relationship between the efficiency scores of DMUs and their allocated fixed costsusing a modified super-efficiency DEA model, which effectively classifies DMUs and solves infeasible solutions within the variable returns to scale (VRS) framework. Then, we propose a fair and unique allocation model based on fairness and efficiency maximization principles. Specifically, we allocate fixed costs based on the operational scale for inelastic DMUs while equitably increasing the efficiency scores for elastic DMUs. Finally, a numerical example and an empirical study on subsidy allocation among 30 provinces demonstrate the rationality and acceptability of our approach.

Quantifying fair income distribution in Thailand.

Given a vast concern about high income inequality in Thailand as opposed to empirical findings around the world showing people's preference for fair income inequality over unfair income equality, it is therefore important to examine whether inequality in income distribution in Thailand over the past three decades is fair, and what fair inequality in income distribution in Thailand should be. To quantitatively measure fair income distribution, this study employs the fairness benchmarks that are derived from the distributions of athletes' salaries in professional sports which satisfy the concepts of distributive justice and procedural justice, the no-envy principle of fair allocation, and the general consensus or the international norm criterion of a meaningful benchmark. By using the data on quintile income shares and the income Gini index of Thailand from the National Social and Economic Development Council, this study finds that, throughout the period from 1988 to 2021, the Thai income earners in the bottom 20%, the second 20%, and the top 20% receive income shares more than the fair shares whereas those in the third 20% and the fourth 20% receive income shares less than the fair shares. Provided that there are infinite combinations of quintile income shares that can have the same value of income Gini index but only one of them is regarded as fair, this study demonstrates the use of fairness benchmarks as a practical guideline for designing policies with an aim to achieve fair income distribution in Thailand. Moreover, a comparative analysis is conducted by employing the method for estimating optimal (fair) income distribution representing feasible income equality in order to provide an alternative recommendation on what optimal (fair) income distribution characterizing feasible income equality in Thailand should be.

Organizational justice, supervisor-provided resources and duty orientation: lessons from the mining sector

PurposeThe study analysed the influence of organizational justice on the duty orientation of employees in the mining sector of Ghana. Also, it examined the mediating role of supervisor-provided resources in the relationship between organizational justice and duty orientation.Design/methodology/approachThe study obtained data through a self-administered questionnaire from 291 employees of a mining firm. The data were analysed and interpreted in light of the hypotheses using the partial least squares structural equation modelling technique.FindingsThe findings revealed that organizational justice had a significant positive relationship with duty orientation and supervisor-provided resources. The results again established that supervisor-provided resources had a significant positive relationship with duty orientation. The study finally documented that supervisor-provided resources partially mediate the relationship between organizational justice and duty orientation.Practical implicationsWe recommended that the management of the mining companies devote resources to developing organizational justice policies based on fairness in resource allocation, clear roles, employee feedback and effective information dissemination. Furthermore, supervisors should place priority on acquiring and dispensing resources as employees demonstrate their willingness to improve duty orientation.Originality/valueThe study contributes to knowledge in a novel research area. It adds to empirical evidence by highlighting the possible variables that may influence employees to engage in duty orientation.

Study of distribution network loss allocation with high photovoltaic penetration ratio

With the rapid development of renewable energy, the penetration ratio of photovoltaic (PV) in the distribution network is getting higher and higher. The fair and reasonable allocation of high loss caused by high PV penetration in the distribution network is of great significance to the healthy development of distribution systems. In order to study the loss allocation in distribution networks with high PV penetration, based on the cooperative game theory, combined with the power flow algorithm, the power flow and loss of the 72-node active distribution network in agricultural and pastoral areas were analyzed. Three scenarios were included: no PV, low PV penetration, and high PV penetration. The allocated loss of the distribution network with high PV penetration and multiple distributed generators was computed. The results show that the loss allocation method, which takes into account the power as well as the network topology, is applicable to distribution networks with multiple distributed generators. In distribution networks with high PV penetration, distributed generators are the main cause of high loss. If the network contains the extreme reverse power flow concerned in this paper, the loss allocated to DGs accounts for more than 95% of the total network power loss.

A fair bed allocation during COVID-19 pandemic using TOPSIS technique based on correlation coefficient for interval-valued pythagorean fuzzy hypersoft set

The relationship between two variables is an essential factor in statistics, and the accuracy of the results depends on the data collected. However, the data collected for statistical analysis can be unclear and difficult to interpret. One way to predict how one variable will change about another is by using the correlation coefficient (CC), but this method is not commonly used in interval-valued Pythagorean fuzzy hypersoft set (IVPFHSS). The IVPFHSS is a more advanced and generalized form of the Pythagorean fuzzy hypersoft set (PFHSS), which allows for more precise and accurate analysis. In this research, we introduce the correlation coefficient (CC) and weighted correlation coefficient (WCC) for IVPFHSS and their essential properties. To demonstrate the applicability of these measures, we use the COVID-19 pandemic as an example and establish a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) model. The technique is used to study the problem of optimizing the allocation of hospital beds during the pandemic. This study provides insights into the importance of utilizing correlation measures for decision-making in uncertain and complex situations like the COVID-19 pandemic. It is a robust multi-attribute decision-making (MADM) methodology with significant importance. Subsequently, it is planned to increase a dynamic bed allocation algorithm based on biogeography to accomplish the superlative decision-making system. Moreover, numerical investigations deliberate the best decision structures and deliver sensitivity analyses. The efficiency of our encouraged algorithm is more consistent than prevalent models, and it can effectively control and determine the optimal configurations for the study.

“Smile even if your burdens are heavy”: Smallholder challenges and the Sabah sustainable palm oil initiative

AbstractSabah, Malaysia's biggest palm oil‐producing state, has made Roundtable Sustainable Palm Oil (RSPO) certification mandatory under Sabah's Jurisdictional Certified Sustainable Palm Oil (JCSPO) initiative. Few studies have examined the independent native smallholders' challenges in adopting the JCSPO initiative. To address this knowledge gap, we engaged with oil palm smallholders via various qualitative methodological approaches over a 14 month period. We identified a range of challenges currently faced by the smallholder; these include RSPO compliance, land tenure issue, low financial and knowledge capacities, increasing food insecurity, and unstable market price of certified crude palm oil. In our article, we argue that the statewide certification policy is consolidating the inequalities in resource access and capacity for growth amongst smallholder groups in different districts. Fair resource allocation to different geographical areas is needed to provide new empowerment mechanisms and remedies for marginalized groups.

The Reflection Principle and the Ex-Ante Pareto Principle in Anna Mahtani’s Objects of Credence

Abstract First, Mahtani argues that both in the game The Mug and in the Sleeping Beauty we should not defer to a trusted person under a particular designation if they do not self-identify under this designation. This invites a more complex Reflection Principle. I respond that there are more parsimonious ways to avoid the challenges posed to the Reflection Principle. Second, Mahtani argues that preferences create a hyperintensional context, which poses a challenge to the Ex-Ante Pareto Principle that can be averted by supervaluation. I respond that such an appeal to supervaluation would block randomization as a fair allocation device.