The rapid pace of urbanization and increasing social complexity have made the efficiency of Emergency Medical Services (EMS) crucial for public safety and societal stability. Most existing studies analyze rescue station locations, ambulance deployment, and vehicle dispatch separately. However, these studies mainly focus on deterministic factors, such as travel time and rescue demand, without addressing uncertainties in real-world transportation networks and resource constraints. To overcome these limitations, this paper proposes a three-stage optimization model. The first two stages aim to determine the optimal layout of rescue stations and the number of vehicles to deploy, considering effective travel time instead of traditional Euclidean distance. The concepts of “contribution” and “fairness” are introduced to maximize vehicle service efficiency. The third stage applies a multi-objective scheduling strategy that integrates road network data. This study proposes an exact algorithm based on the greedy (EABG) approach. The results show significant improvements in demand coverage and response efficiency, despite limited resources. Sensitivity analysis confirms that fuzzy logic effectively handles demand uncertainty. These results offer actionable insights for EMS resource allocation and policy design, advancing emergency response strategies in complex urban environments. In addition, the proposed algorithm significantly outperforms GA in terms of solution speed and quality.

The traffic sensor location problem determines where sensors should be installed so that full link flow observability can be achieved while sensing quality remains reliable under uncertainty. The problem becomes more difficult in cyclic networks, where local flow inference may fail because of unresolved cycles, and under uncertain sensing conditions, where sensor type and error exposure affect allocation decisions. This paper addresses the traffic sensor location problem in cyclic networks through a two-stage framework. Stage~1 selects observed links with a cycle-breaking model. Stage~2 allocates sensor types on those links by distributionally robust optimization (DRO) and sample average approximation (SAA) using error-propagation scores. The numerical study moves from a 12-link cyclic testbed to a medium-scale Eastern Massachusetts case and the large Anaheim network. On the 12-link testbed, Stage~1 attains the rank-based lower bound of 8 observed links, whereas one-third of node-priority orders under order-sensitive inference require a ninth sensor. On the 254-link Eastern case, IFOS selects 200 observed links, preserves an invertible unobserved subsystem, and yields lower noisy same-sparsity reconstruction error than greedy-rank. On Anaheim, the global inverse-based score becomes uniform, whereas a localized score differentiates links and changes upgrade priorities. Taken together, the results support a unified two-stage framework that connects cycle-breaking screening with uncertainty-aware sensor allocation in cyclic traffic networks.

Accurate traffic flow prediction is a cornerstone of intelligent transportation systems (ITSs), yet achieving high precision remains a critical challenge due to the complex and nonlinear spatiotemporal dynamics of real-world traffic. Conventional graph convolutional approaches often fall short as they primarily model pairwise spatial dependencies, failing to capture the higher-order interactions inherent in large-scale road networks. To address this limitation and provide a more comprehensive understanding of traffic evolution, this paper proposes the Spatiotemporal Multi-Hypergraph Neural Network (STMHGNN), a novel framework for network-wide traffic flow prediction. STMHGNN explicitly captures higher-order spatial correlations through three purpose-built hypergraphs: (1) an upstream-downstream adjacency hypergraph that encodes directional interactions between connected road segments; (2) a regional hypergraph that reflects shared traffic patterns within geographic neighborhoods; and (3) a historical pattern hypergraph that links road segments exhibiting similar temporal dynamics. These hypergraph representations are integrated using HGNNs in the spatial module, while a long short-term memory network is employed to model temporal dependencies. Experiments conducted on real-world freeway traffic data from Los Angeles demonstrate that STMHGNN consistently outperforms state-of-the-art benchmarks across all evaluation metrics, achieving a root mean square error (RMSE) of 27.257, a mean absolute error (MAE) of 17.019, and a mean absolute percentage error (MAPE) of 6.21 %. By effectively capturing complex multi-way spatial relationships and temporal dynamics, this study not only provides a superior predictive tool but also offers valuable theoretical insights for advancing ITS, ultimately supporting more proactive traffic management and sustainable urban mobility.

Under the novel pattern of "Internet + Pharmaceutical" distribution and the promotion of IoT e-commerce, the pharmaceutical cold chain logistics sector is rapidly expanding. This paper explores the present development status and prospective potential of the pharmaceutical cold chain logistics industry in China. It focuses on the location-routing problem of pharmaceutical cold chain logistics under a mixed fleet of oil and electric vehicles (MFLRPPCCL). The study proposes a multi-objective optimization framework under various uncertain conditions, with a focus on factors such as carbon emission, fuzzy pharmaceutical demand, travel time, and energy consumption, to optimize the economic influence and service quality of PCCL. To solve the proposed model, the NSGA-II algorithm is applied. The results indicate that the routes of refrigerated vehicles and the associated costs significantly change when various uncertainties are considered. Specifically, changes in the age structure of demand points may cause vehicles to violate time windows to prioritize high-demand customers. Additionally, the introduction of temporary distribution centers can significantly reduce total transportation costs and carbon emissions. For example, when both travel time reliability (TTR) and energy consumption reliability (ECR) are set as 0.99, establishing a temporary distribution center reduces transportation costs by 15.18 % and lowers carbon emissions compared to scenarios without a temporary distribution center. When both are set to be 0.51, the strategy of using a mixed fleet of oil and electric vehicles effectively reduces time window penalty costs and improves delivery efficiency. Furthermore, numerical results at different reliability levels demonstrate that a combination of lower TTR and higher ECR can yield better economic benefits. Moreover, the proposed model and algorithm's correctness and effectiveness are validated through ample experimental cases. These findings provide critical support for pharmaceutical companies to enhance their competitiveness in the global market, emphasizing the importance and practical value of green, low-carbon logistics.

In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links.

The linear complementary problem (LCP) is a unified formulation for linear and quadratic programming problems. Therefore, it has many applications in practical problems like bimatrix game. We prove that it makes sense to look for sparse LCP solutions. A l2−l1 regularization technique transforms the original sparse optimization problem into an unconstrained one. Thereafter, a linearized ADMM (for alternating direction method of multipliers) is designed to solve the regularization model. Then, using a penalty function approach, we propose an efficient sequential linearized ADMM to find the sparse LCP solutions. Finally, numerical experiments prove that the sparse solution of LCPs can be solved efficiently, and is competitive with other state-of-the-art algorithms. A practical application in bimatrix game is also reported.

The collection and transportation of medical waste (MW) is critical because of environmental and public safety implications. However, the variability in travel times of waste transport vehicles, influenced by various factors, complicates this process. This article proposes a multi-objective optimization model to enhance travel time reliability (TTR) while minimizing vehicle transportation costs and penalties for time window violations. Given the NP-hard nature of the problem, a genetic algorithm enhanced by an improved harmony search genetic algorithm (IHSGA) is utilized. The model and algorithm are evaluated in N City, China. The results highlight the significance of TTR in optimizing MW collection operations. Sensitivity analysis of key parameters validates the model's robustness. Furthermore, comparative analysis demonstrates the superiority of IHSGA over traditional methods. These findings underscore the importance of incorporating additional costs associated with travel time variability in decision making, and advocate for strategies to mitigate such uncertainties.

This paper presents a new path-finding problem to ensure reliable energy consumption for electric vehicles (EVs) under rainfall conditions. The objective function of the proposed model aims to find a reliable path that minimises energy consumption while ensuring a certain probability of completing the trip without exhausting a given battery energy budget. By considering the influence of adverse weather conditions at different periods, the existing model is expanded. To address the non-additivity and non-linearity characteristics of the optimisation model, an enhanced heuristic algorithm is proposed, incorporating inequality techniques, the K-shortest algorithm, and path-updating strategies. Lastly, the proposed algorithm is validated using Hong Kong's grid-based road network as a case study, which demonstrates the correctness and effectiveness of the algorithm. The results indicate that by considering adverse weather conditions, the estimation of energy consumption can be significantly improved in terms of accuracy, achieving more efficient and reliable optimal path recommendations.

The linear complementary problem (LCP) is a unified formulation for linear and quadratic programming problems. Therefore, it has many applications in practical problems like bimatrix game. We prove that it makes sense to look for sparse LCP solutions. A l 2 − l 1 regularization technique transforms the original sparse optimization problem into an unconstrained one. Thereafter, a linearized ADMM (for alternating direction method of multipliers) is designed to solve the regularization model. Then, using a penalty function approach, we propose an efficient sequential linearized ADMM to find the sparse LCP solutions. Finally, numerical experiments prove that the sparse solution of LCPs can be solved efficiently, and is competitive with other state-of-the-art algorithms. A practical application in bimatrix game is also reported.

Transporting medical waste (MW) generated by medical institutions (MIs) is a process that poses potential threats to the environment and public safety. Therefore, it is vital to find a safe and efficient way to transport this type of waste to disposal centers (DCs). However, there are challenges in the transportation of MW due to random factors such as the generation of waste in an unpredictable manner and unforeseen travel times. In this paper, we propose a bi-level optimization model to minimize site selection costs, transportation costs, time-window penalties costs, and transportation risks under uncertainties. The concepts of “loading reliability”, “travel time reliability” and “transportation risk” are adopted in the proposed optimization model. The simulated annealing algorithm (SA) is employed to address the optimal location problem (Upper level), which involves minimizing the construction cost, transportation cost (DC-CP-DC), and transportation risk (estimated using Bayesian method). To tackle the capacity-constrained vehicle routing problem considering transportation risk and time window penalties (Lower level), we propose an improved genetic algorithm named harmony search algorithm (IHSGA). Subsequently, the results from the lower level are looped back to the upper level, fostering mutual influence between the two stages. We demonstrate the effectiveness and correctness of the proposed model and algorithm using S city in China as an illustrative example. Furthermore, a series of sensitivity analyses were conducted to examine the impact of various factors. The findings highlight the pivotal roles of both travel time reliability and loading reliability in designing the medical waste recycling network. In comparison to the general Genetic Algorithm (GA) and CPLEX solver, the modified IHSGA presented in this paper exhibits superior performance.

Water conservancy project scheduling is an extension to the classic resource-constrained project scheduling problem (RCPSP). It is limited by special time constraints called "forbidden time windows" during which certain activities cannot be executed. To address this issue, a specific RCPSP model is proposed, and an approach is designated for it which incorporates both a priority rule-based heuristic algorithm to obtain an acceptable solution, and a hybrid genetic algorithm to further improve the quality of the solution. In the genetic algorithm, we introduce a new crossover operator for the forbidden time window and adopt double justification and elitism strategies. Finally, we conduct simulated experiments on a project scheduling problem library to compare the proposed algorithm with other priority-rule based heuristics, and the results demonstrate the superiority of our algorithm.

Objectives This study evaluated the impact of the Urban and Rural Residents’ Basic Medical Insurance scheme on hospitalisation expenses of rural patients in eastern China, which unified separate healthcare systems for urban and rural residents. Design Monthly hospitalisation data from municipal and county hospitals were collected from the local Medicare Fund Database, covering the period from January 2018 to December 2021. The unification of insurance between urban and rural patients was implemented at different times for county and municipal hospitals. An interrupted time series analysis was used to assess the immediate and gradual effects of the integrated policy on the total medical expenses, out-of-pocket (OOP) expenses and effective reimbursement rate (ERR) among rural patients. Setting and participants This study included 636 155 rural inpatients over 4 years in Xuzhou City, Jiangsu Province, China. Results In January 2020, the policy of urban and rural medical insurance was initially integrated in county hospitals, after which the ERR decreased at a monthly rate of 0.23% (p=0.002, 95% CI −0.37% to −0.09%) compared with the preintervention period. After the insurance systems were unified in municipal hospitals in January 2021, OOP expenses decreased by ¥63.54 (p=0.002, 95% CI −102.48 to −24.61) and the ERR increased at a monthly rate of 0.24% (p=0.029, 95% CI 0.03% to 0.045%). Conclusions Our results suggest that the unification of urban and rural medical insurance systems was an effective intervention to reduce the financial burden of illness for rural inpatients, especially OOP expenses for hospitalisation in municipal hospitals.

目的 了解徐州市区老年人照护服务的需求现状、照护费用支出情况及其影响因素,为健全老年人照护服务体系提供理论支持与对策建议.方法 采用分层随机整群抽样方法,对徐州市区60岁及以上老年人进行问卷调查,了解老年人照护服务的需求及费用支出情况,运用单因素x2检验和Tobit回归模型分析影响照护费用支出的影响因素.结果 调查1 623名老年人中,32.4％(526名)近1年内有过1 d以上且生活不能自理的卧床经历;卧床老年人中,74.7％(393名)的老年人接受过专业机构提供的照护服务,服务内容集中在医疗护理(79.4％、312名)和康复护理(24.2％、95名).调查对象中有1 489名(91.7％)在过去的1年内照护费用支出为0元,96名(6.0％)支出为1～4 999元,17名(1.0％)支出为5 000～9 999元,21名(1.3％)支出为10 000元及以上.单因素分析结果显示,配偶情况、年龄、医疗费用支出、生活满意度等因素影响老年人照护费用支出(均P＜0.05).Tobit回归模型结果显示,月收入较高(Me=3.114)、年龄较高(Me=3.458)、知道照护险(Me=7.647)、失能(Me=10.998)、无配偶(Me=14.387)、失智(Me=20.473)的老年人照护费用支出高.结论 徐州市区老年人照护服务需求存在需要高但利用率低的问题,照护费用负担大;应重点关注失能失智、高龄老年人群的照护需求,照护服务的利用以医疗护理和康复护理为主,应完善老年人照护服务,推动与老年人照护需求以及费用负担相适宜的老年人照护服务体系和照护保障制度建设.

Objective:To analyze the referral status and its influencing factors of patients in compact county medical commu-nity in Xinyi city of Jiangsu province,so as to provide suggestions for improving the referral system of medical community.Meth-ods:The stratified cluster sampling method was utilized to select 1068 patients from 20 grassroots medical institutions in Xinyi county medical community via the questionnaire survey.The random forest model and logistic regression were employed to analyze the influencing factors of referral behavior of patients in the medical community.Results:The referral rate of patients in the Xinyi county medical community was 12.6%.The results of random forest model showed that the out-of-bag estimation error rate reached the lowest when the number of variables was 11.The top 11 variables in order of ranking included medical expenses,the awareness about medical community,literacy,type of medical facility preferred for treating common illnesses,time taken to reach the nearest general hospital,age,time taken to reach the grassroots medical institutions,knowledge of the health insurance system,choice of medical facility for this visit,and impact of illness on life.The results of logistic regression analysis showed that the patients who had the average and more awareness level of the medical community(OR=2.235,OR=12.579),the 3001-5000 yuan and more than 5001 yuan of medical expenses(OR=2.371,OR=4.786),as well as the general and significant perceived impact level of ill-ness on daily life(OR=1.834,OR =3.043),made more use of referral services.Conclusion:The referral rate of patients within the Xinyi county medical community is not high,and the factors affecting patient referral include the awareness about medical com-munity,the medical expenses and the impact of illness on life.It is recommended to enhance the recognition of scientific referrals among patients through innovating the ways of policy advocacy and improving the awareness about medical communities.To achieve the orderly referral and scientific diversion of patients within the medical communities,it is suggested to establish the cooperative referral mechanism and to encourage the divisional collaboration of medical institutions.

The ongoing global coronavirus pandemic (COVID-19) has significantly increased urban medical waste. Such waste often contains pathogenic microorganisms, harmful chemicals, and even radioactive and defective sub-stances, hence imposing disease transmission and public health risks. Nevertheless, due to multiple factors, the amount of medical waste produced in medical institutions is stochastic. This paper proposed an optimization model for a waste recycling network consideration of loading reliability to minimize the collective cost of location, vehicle usage, and transportation. A modified ant colony algorithm combined with the K-means clus-tering method based on a genetic algorithm is then proposed (MACO-GKA) to solve the optimal location problem and the vehicle routing problem (LP-VRP). The numerical examples are then conducted in Xuzhou City, China to evaluate the performance of the proposed model. Taking the loading reliability level & theta; = 0.9 as an example, the results show that the total cost will reach $100546.53 when collection points are not set, but decrease to $86,907.11 when they are set. In the latter case, the total cost was reduced by 13.57%. The detailed results indicate that the selection and establishment of medical waste collection points are essential factors in designing an urban medical waste recycling network. The proposed MACO-GKA algorithm also outperforms the CPLEX solver.

Traffic assignment model (TAM) is an important research issue of urban traffic design and planning. Most of the existing studies are conducted under deterministic conditions. In reality, the link travel time and waiting time at signalized intersections are stochastic due to many uncertain factors in transportation networks. Under this circumstance, this paper proposes a new travel time reliability-based user equilibrium (TRUE) traffic assignment model with consideration of link travel time correlations and waiting time at signalized intersections in stochastic traffic networks. Under the assumption that link travel times and waiting times at signalized intersections follow normal distributions, the proposed model is transformed into a variational inequality (VI) model. It is rigorously proven that there is at least one solution for the VI problem, and the method of successive average (MSA) is employed to solve the proposed model. The numerical experiments are used to illustrate the applications and effectiveness of the proposed model.

Consideration of the travel time variation for rescue vehicles is significant in the field of emergency management research. Because of uncertain factors, such as the weather or OD (origin-destination) variations caused by traffic accidents, travel time is a random variable. In emergency situations, it is particularly necessary to determine the optimal reliable route of rescue vehicles from the perspective of uncertainty. This paper first proposes an optimal reliable path finding (ORPF) model for rescue vehicles, which considers the uncertainties of travel time, and link correlations. On this basis, it investigates how to optimize rescue vehicle allocation to minimize rescue time, taking into account travel time reliability under uncertain conditions. Because of the non-additive property of the objective function, this paper adopts a heuristic algorithm based on the K-shortest path algorithm, and inequality techniques to tackle the proposed modified integer programming model. Finally, the numerical experiments are presented to verify the accuracy and effectiveness of the proposed model and algorithm. The results show that ignoring travel time reliability may lead to an over- or under-estimation of the effective travel time of rescue vehicles on a particular path, and thereby an incorrect allocation scheme.

本文在交通流量、行程时间、受影响的人数等不确定条件下,研究了危险品运输车辆的可靠性和安全性双目标路径规划模型.对于可靠性目标,考虑了路段的随机通行时间、路口等待时间以及路段和路口通行时间相关性的3个因素的可靠路径搜索问题.对于安全性目标,不仅考虑危险品运输车辆所经过路段附近受影响的人数,还考虑了事故发生后该路段上的车辆中受影响的人数以及其相关性.由于目标函数的不可加性,本文提出了一个新的基于不等式放缩技巧和K短路算法的启发式算法,对双目标函数进行求解,节约了计算量,并给出了搜索非支配解(non-dominated solution)的方法.数值算例的结果说明与传统的模型相比,若忽略信号交叉口的随机延迟和相关性会导致寻找可靠最短路径和最优安全路径的结果存在偏差,导致路径选择不准确,有可能造成严重的经济损失和环境危害.最后,通过数值演示了该算法在现实道路交通网络中的潜在应用前景.

ObjectiveEpidemiological characteristics of COVID-19 outbreak in Yangzhou city caused by the highly contagious Delta variant strain of SARS-CoV-2 virus were investigated in this retrospective descriptive study to provide prevention and control guidelines for outbreaks in the future.MethodsAll the epidemiological data used in this study were collected manually from the official website of the Yangzhou Municipal Health Committee from 28 July to 26 August 2021, and then were analyzed systematically and statistically in this study.ResultsA total of 570 COVID-19 cases were reported during the short-term outbreak in Yangzhou City. The ages of infected individuals ranged from 1 to 90 years with the average age at 49.47 ± 22.69 years. As for gender distributions, the ratio of male- to-female patients was 1:1.36 (242:328). Geographic analysis showed that 377 patients (66.1%) were in Hanjiang District while 188 patients (33.0%) were in Guangling District. Clinical diagnosis showed that 175 people (30.7%) had mild symptoms, 385 people were in moderate conditions (67.5%), and 10 people were in severe situations (1.8%). Significant age differences were found among the three groups (P < 0.001). However, no significant difference was identified in terms of gender ratio (P > 0.05). Based on the transmission chain formed by 6 generations of infected persons with a clear transmission relationship, the age showed a gradually decreasing trend, while the median time of diagnosis in 2 adjacent generations was 3 days. In addition, the estimated basic reproduction number R0 of the Delta variant was 3.3651 by the classical Susceptible, Infectious, and/or Recovered (SIR) model.ConclusionThe Delta variant of SARS-CoV-2 was highly infectious and has obvious clustering characteristics during the Yangzhou outbreak in China.

Solving the absolute value equation (AVE) is a nondifferentiable NP-hard and continuous optimization problem with a wide range of applications. Because its solutions have different forms, it is challenging to design the most efficient algorithm that can solve different AVEs without using overcomplicated technical improvement and problem-dependent objectives. Hence, this paper proposed an improved glowworm swarm optimization (GSO) algorithm with an adaptive step size strategy based on the sigmoid function (SIGGSO) that solves the AVEs. Seven test AVEs, including multisolution and high-dimensional AVEs, are selected for testing and compared with seven metaheuristic algorithms. The experimental results show that the proposed SIGGSO algorithm has higher solution accuracy and stability when seeking multiple solution of AVEs compared to the basic GSO. Moreover, it obtains competitive advantages on multisolution and high-dimensional AVEs compared with other metaheuristic algorithms and provides an effective method for engineering and scientific calculations.