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Background: The application of posttransplant predictive models is limited by their poor statistical performance. Neglecting the dynamic evolution of demographics and medical practice over time may be a key issue. Objectives: Our objective was to develop and validate era‐specific predictive models to assess whether these models could improve risk stratification compared to non–era‐specific models. Methods: We analyzed the United Network for Organ Sharing (UNOS) database including first noncombined heart transplantations (2001–2018, divided into four transplant eras: 2001–2005, 2006–2010, 2011–2015, 2016–2018). The endpoint was death or retransplantation during the 1st‐year posttransplant. We analyzed the dynamic evolution of major predictive variables over time and developed era‐specific models using logistic regression. We then performed a multiparametric evaluation of the statistical performance of era‐specific models and compared them to non–era‐specific models in 1000 bootstrap samples (derivation set, 2/3; test set, 1/3). Results: A total of 34 738 patients were included, 3670 patients (10.5%) met the composite endpoint. We found a significant impact of transplant era on baseline characteristics of donors and recipients, medical practice, and posttransplant predictive models, including significant interaction between transplant year and major predictive variables (total serum bilirubin, recipient age, recipient diabetes, previous cardiac surgery). Although the discrimination of all models remained low, era‐specific models significantly outperformed the statistical performance of non–era‐specific models in most samples, particularly concerning discrimination and calibration. Conclusions: Era‐specific models achieved better statistical performance than non–era‐specific models. A regular update of predictive models may be considered if they were to be applied for clinical decision‐making and allograft allocation. [ABSTRACT FROM AUTHOR]

[Objective] In order to help China’s iron and steel industry to enter the carbon emission trading market, previous studies have mainly allocated carbon quotas for iron and steel enterprises from the perspective of the impact of cost. However, the iron and steel industry has large carbon emissions and complex production processes, and overemphasizing the impact of cost will easily reduce the benefits of emission reduction, which is not conducive to promoting the low-carbon transformation of iron and steel enterprises. Based on the carbon emission reduction efficiency and output efficiency, this study aimed to design a carbon quota allocation scheme that conforms to the current development situation of China’s iron and steel industry. [Methods] This study reviewed the production steps of different iron and steel production processes, took carbon quota as the necessary raw material for continuous production of each steps, and reconstructed the iron and steel production processes. By superposing carbon quota, an input-output stochastic frontier model was constructed, and the estimation formula of production efficiency and carbon quota efficiency of the iron and steel industry was obtained. Comparing the impact of different carbon quota reference values on the output rate of the iron and steel production processes, and taking efficiency improvement rate as the selection standard, this study determined the cross-regional carbon quota baselines of China’s iron and steel industry. [Results] Compared with the historical emission method, benchmarking is more suitable for the carbon quota allocation of China’s iron and steel industry. The carbon quota baselines of China’s iron and steel industry should be set in the top [30%, 50%] range of the industry, and the optimal baseline should be set in the top 40% of the industry. When the carbon emission is greater than the top 50% of the industry, the production rate of the iron and steel industry decreases with the increase of the carbon emission baseline. Incorporating the iron and steel industry into China’s carbon emission trading scheme by the benchmarking method will help this industry to both save carbon tariffs and obtain a carbon market share. [Conclusion] Allocating carbon quotas based on output efficiency can help improve the efficiency of emission reduction in the iron and steel industry, and stimulate the potential of independent emission reduction in this industry. Extending the quota allocation method of this study will help the whole process of decarbonization in China’s iron and steel industry.

Motivated by the challenge of allocating scarce resources from the federal government to different states during the COVID‐19 pandemic, this paper studies optimal schemes for allocating scarce resources to agents with private demand information under different favoritism structures. Through an investigation of a mechanism design model that aims to induce agents to report their demands truthfully, we find the following results. First, when the principal purely cares about social welfare and when the principal has sufficient resources to satisfy all agents' demands, we find that the optimal allocation scheme is efficient in the sense that it is identical to the optimal scheme for the "benchmark" case when favoritism differentials and information asymmetry are both absent. Second, when rationing is needed due to resource scarcity, we show that heterogeneity in "event‐independent" favoritism across agents will cause the principal to allocate more resources to agents with larger favoritism and less resources to others, resulting in inefficient allocations. Third, when agents possess heterogeneous "event‐specific" favoritism due to the existence of outside options, the resulting allocation may boost all agents' expected utilities, including those agents who do not have any outside option. Finally, we show that the "allocation distortion" caused by both information asymmetry and heterogeneous favoritism can be reduced when "positive externality" is present (i.e., allocating resources to one agent can benefit other agents). [ABSTRACT FROM AUTHOR]

Background: In the United Network of Organ Sharing (UNOS) allocation scheme prior to October 18, 2018, heart transplant (HTx) candidates with extracorporeal membrane oxygenation (ECMO), temporary mechanical circulatory support (MCS), or pulmonary artery (PA) catheter inotropic support all received Status 1A priority. In revised scheme, patients with PA catheter and inotropic support are Status 3 after those on ECMO (Status 1) or temporary MCS (Status 2). We examined the impact of the allocation change on HTx candidates listed Status 1A versus Status 3 at a high‐volume transplant center. Methods: Between January 2017 and January 2021, 75 patients were listed with a PA catheter and inotropic support prior to the allocation change (Era 1) and 48 were listed after (Era 2). Clinical characteristics and outcomes were compared for these 123 patients. Results: Heart transplant (HTx) candidates in Era 2 had higher median inotrope doses at listing. There was no significant difference in inpatient wait list days (12 vs. 20 days, P =.15), transition to temporary MCS (33.3% vs. 22.7%, P =.15), or wait list mortality (6.3% vs. 4.0%, P =.68). There was also no significant difference in survival to transplantation (91.7% vs. 94.7%, P =.71). There were no differences in post‐transplant outcomes including 1‐year survival (88.6% vs. 93.0%, P =.38). Conclusion: At a high‐volume transplant center, the UNOS allocation change did not result in increased wait list time, use of temporary MCS, or mortality on the waitlist or post‐transplant for candidates on inotropic support with continuous hemodynamic monitoring. [ABSTRACT FROM AUTHOR]

In the paper, we consider the generalized allocation scheme and study the asymptotic behaviors of the extreme η(N−s), where η(N−s) denotes the (n − s)th-order statistics from the allocation scheme. We establish asymptotic representations and large and moderate deviations for η(N−s). [ABSTRACT FROM AUTHOR]

This scheme generalizes the classical scheme of allocation of particles to cells (see [[5]]) and hence gets its name. Law of large numbers, local limit theorem, Allocation scheme, power series distribution, almost sure central limit theorem, moderate deviation principle Keywords: Allocation scheme; power series distribution; law of large numbers; almost sure central limit theorem; moderate deviation principle; local limit theorem EN Allocation scheme power series distribution law of large numbers almost sure central limit theorem moderate deviation principle local limit theorem 193 205 13 06/17/22 20220601 NES 220601 Note: Originally published in Diskretnaya Matematika (2022) 34,No1, 88-102 (in Russian). [Extracted from the article]

Aiming at the target allocation of multi-aircraft cooperative air combat, an improved artificial immune algorithm is proposed based on the modeling for comprehensive superiority function. Firstly, the random generation method and artificial construction method are used to create two initial populations, which ensure the diversity of the initial populations; following is to evolve the populations by adopting two different selection, crossover and mutation operations; then, the designed new immigration operator is used to exchange information among the populations, which further increases the diversity of the populations and improves the search efficiency. Finally, the comparison of the improved artificial immune algorithm with the traditional artificial immune algorithm had been made. Simulation results show that the improved artificial immune algorithm can effectively improve the premature convergence problem, and the search efficiency, the optimal allocation scheme is obtained, which is suitable for target allocation problem of multi-aircraft cooperative air combat and meet the actual operational requirements.

Establishing a fair and reasonable initial allocation scheme for carbon emission rights is a prerequisite for the efficient and stable operation of the carbon market and an important guarantee for China to achieve its emission reduction target. This study considers the historical transfer of carbon emissions and measures the cumulative net carbon emissions of China's provinces based on the principle of consumer responsibility. It draws up an initial allocation plan for carbon emission rights in China's provinces in 2020, based on the principles of equity, efficiency, and sustainability. The carbon emission and carbon intensity efficiencies of the provinces under this distribution scheme were also evaluated. The results show that the historical net carbon export area mainly consists of energy-intensive provinces and economically underdeveloped poor and remote provinces. Additionally, the historical net carbon import area mainly consists of energy-starved and economically developed provinces. Provinces with high-value ecological services (Xinjiang, Heilongjiang, and Inner Mongolia) or with large economies and dense populations (Shandong, Guangdong, and Sichuan) will receive higher carbon emission rights, while those with developed economies (Shanghai, Beijing, and Tianjin) will receive lower carbon emission rights. Provinces with a good ecological environment (Inner Mongolia, Heilongjiang, and Xinjiang) as well as certain economically underdeveloped western provinces (Chongqing, Sichuan, and Yunnan) will have abundant carbon space in the future. Provinces with rapid economic development (Beijing, Shanghai, and Jiangsu) and resource-based provinces (Shanxi and Liaoning) with a high historical carbon output have little carbon space. Considering the historical carbon transfer, the carbon emission rights allocation system will benefit resource-based industrial provinces (Shanxi, Inner Mongolia, and Liaoning) and less developed western provinces (Shaanxi, Ningxia, and Xinjiang) in improving their carbon emission efficiency. This study provides a new research idea for formulating a more equitable and efficient allocation scheme for carbon emission rights.

In order to find an optimal target allocation method to meet the requirements of multi-aircraft air combat and improve the air combat efficiency, a multi-aircraft air combat target allocation method based on cooperative co-evolutionary is proposed. Firstly, this method establishes a multi-aircraft cooperative air combat superiority evaluation index system based on single-aircraft air combat superiority. Secondly, the cooperative correlation between aircraft is analyzed and calculated, and a multi-aircraft cooperative air combat target allocation model is established. On the basis of variable length chromosome genetic algorithm (GA), an improved cooperative co-evolutionary algorithm based on crossover, grafting, splitting and splicing operators is designed, which improves the evolution efficiency of the model. Finally, experiments are designed to validate the effectiveness of the superiority evaluation index system, static examples, dynamic examples and large-scale unmanned combat aerial vehicles (UCAV) examples. The results of the two models and four algorithms are compared with the experimental results. The simulation results show that the improved cooperative co-evolutionary algorithm is suitable for the calculation of this model. The convergence is stable and the affinity value is significantly improved. So the target allocation scheme can be optimized and it has certain application significance in air combat. [ABSTRACT FROM AUTHOR]

We consider an allocation scheme of distinguishable particles by different cells under the condition than each cell contains an even number of particles. We show that this scheme is a general allocation scheme defined by the random variable with the distribution . Let be a number of cells from the first cells that contain particles. We prove that under some types of convergence of to infinity converges in distribution to the Poisson random variable. The limit Poisson random variable is described. [ABSTRACT FROM AUTHOR]

Groundwater in arid and semi-arid coastal aquifers is vulnerable to seawater intrusion and quality deterioration despite being one of the most reliable sources of water supply due to the increasing number of development plans and competition between water consumers. A multi-dimensional groundwater management framework is developed to trade-off between groundwater abstraction, allocation equity, groundwater quality, and energy considerations in the reverse osmosis (RO) filtration process in the fresh groundwater lens of Kish Island, Iran. An arid island confined in the Persian Gulf is modeled using 3D simulation and three well-occupied multi-objective evolutionary optimization algorithms. Four objectives include: maximizing the groundwater abstraction, minimizing the Gini coefficient (allocation inequity), minimizing the total energy required to pass saline water through the RO membrane to reach the standard total dissolved solids (TDS), and minimizing the average TDS concentration of water abstraction positions from 11 management zones have been considered over a 50-year management horizon. Solutions obtained in the simulation-based constrained multi-objective optimization framework allow managers to choose from 587 Pareto optimal solutions. They provide an abstraction scheme with a range of 1.44 to 4.53 MCM/yr, a Gini coefficient of 0 to 0.98, filtration energy of 988,562 to 1,935,760 kWh/yr, and an average TDS of 19,663 to 21,351 mg/L. The Pareto optimal solutions can help decision-makers decide on the multi-dimensional problems of sustainable coastal groundwater management and show patterns among different objectives. [Display omitted] • A simulation-based constrained multi-objective optimization framework is used to solve a real-world multi-dimensional coastal groundwater management problem. • A holistic management model for Kish Island lens with four objectives and their tradeoffs is developed. • The ideal solution maximizes groundwater abstraction and allocation equity while minimizing TDS concentration and RO filtration energy. • Non-dominated solutions combination of three multi-objective evolutionary algorithms resulted in obtaining the best solutions. • Desalination effluents are considered, and their effects on groundwater are investigated. [ABSTRACT FROM AUTHOR]

Recently developed methods of longitudinal discriminant analysis allow for classification of subjects into prespecified prognostic groups using longitudinal history of both continuous and discrete biomarkers. The classification uses Bayesian estimates of the group membership probabilities for each prognostic group. These estimates are derived from a multivariate generalised linear mixed model of the biomarker's longitudinal evolution in each of the groups and can be updated each time new data is available for a patient, providing a dynamic (over time) allocation scheme. However, the precision of the estimated group probabilities differs for each patient and also over time. This precision can be assessed by looking at credible intervals for the group membership probabilities. In this paper, we propose a new allocation rule that incorporates credible intervals for use in context of a dynamic longitudinal discriminant analysis and show that this can decrease the number of false positives in a prognostic test, improving the positive predictive value. We also establish that by leaving some patients unclassified for a certain period, the classification accuracy of those patients who are classified can be improved, giving increased confidence to clinicians in their decision making. Finally, we show that determining a stopping rule dynamically can be more accurate than specifying a set time point at which to decide on a patient's status. We illustrate our methodology using data from patients with epilepsy and show how patients who fail to achieve adequate seizure control are more accurately identified using credible intervals compared to existing methods. [ABSTRACT FROM AUTHOR]

Significant international collaboration is required to limit global temperature increase to below 2 °C above pre-industrial levels. Equity is the foundation of cooperation, and therefore, this study proposed a new dynamic carbon permit allocation scheme based on four principles: equality, historical responsibility, capability, and future development opportunities. Decision makers could have different preferences for allocating carbon permits, therefore, four equity rules or indicators (equality, responsibility, capacity, and sovereignty) were assigned different weights. Based on the global carbon budget of the 2 °C target, emission permits were calculated and relevant economic implications analyzed using the Global Change Assessment Model. Results indicated that developed countries should reduce emissions immediately, while allowances for developing regions could permit an initial increase in emissions until peaking. Applying different weights to the indicators resulted in multifarious regional allowances. Developed regions would benefit from the 'preferring sovereignty' scenario and most developing countries would benefit under the 'preferring responsibility' and 'preferring capacity' scenarios. Compared with the Intended Nationally Determined Contributions submitted to the United Nations Framework Convention on Climate Change, this study found that in the short term, developed countries might insist on sovereignty as the preferred indicator. However, preferring sovereignty would place substantial mitigation pressures on developing countries in the long term. Therefore, in addressing global climate change, a dynamic choice in the weighting distribution for different indicators might be conducive to international agreement. Furthermore, a market-based trading instrument could help all participants both mitigate global climate change by reducing regional and global costs and facilitate mitigation capital flow from developed to less developed regions. [ABSTRACT FROM AUTHOR]

In this research, a hybrid nonlinear grey-prediction and quota allocation model (HNGP-QAM) was developed for supporting optimal planning of China's carbon intensity reduction at both departmental and provincial levels in 2020. At such dual levels, HNGP-QAM can not only help forecast carbon intensity and its fluctuations over the concerned period, but also facilitate the identification of China's carbon intensity reduction target in 2020 and the corresponding quotas for minimizing the total abatement cost. Two scenarios were developed based on multiple governmental policies and allocation schemes among provinces and departments. The results showed that the total abatement cost would be 92.07 and 98.93 × 10 9 , as well as 180.57 and 194.19 × 10 9 RMB (It is another shortname for China Yuan) for provincial and departmental allocation schemes under the reduction ratios of 40 and 45%, respectively. Furthermore, the west, the east, and the central China would be allocated the emission assignments that would be accounting for 48.53, 28.26, and 23.21% of the total national emission reduction, respectively. The obtained results were particularly useful for multi-level governments in providing information to identify the carbon intensity reduction target, conducting emission reduction assignments among provinces and departments, as well as supporting relevant policy-making. The results also suggested that the developed HNGP-QAM be applicable to similar engineering and planning problems. [ABSTRACT FROM AUTHOR]

The determination of allocable water is one of the most important stages during the process of formulating water allocation scheme. The selection of appropriate allocable water is the prerequisite and fundament for developing a reasonable water allocation scheme. Based on the summary and analysis of international water allocation practice, this paper has distilled three attention-worth issues concerning the determination of allocable water. In addition, this paper has also proposed a general method for the determination of allocable water and illustrated this method in detail. It is hoped that through providing a general and feasible method for determining allocable water, a fundament could be established for the consultation of water allocation process among different stakeholders, promoting the integrated management of river basins. [ABSTRACT FROM AUTHOR]

We consider a scheme of equiprobable allocation of particles into cells by sets. An Edgeworth-type asymptotic expansion in the local central limit theorem for the number of empty cells left after allocation of all sets of particles is derived. [ABSTRACT FROM AUTHOR]

The paper introduces a refinement of the notion of population monotonic allocation scheme, called regular population monotonic allocation scheme (regular pmas). This refinement is based on economic situations in which players may have to select new partners from a set of potential players and in which there exist certain capacity constraints. A sufficient condition for the existence of a regular pmas is given. For the class of games with regular pmas, we prove that the core coincides with the Davis and Maschler and the Mas-Colell bargaining sets. [ABSTRACT FROM AUTHOR]