Pre-crisis management involves the optimal selection of relief and rescue centers to minimize vulnerability. Iran is particularly vulnerable due to its location on the Alpine-Himalaya seismic belt, resulting in an average death rate six times higher than the global average during earthquakes. Therefore, selecting appropriate relief and rescue centers is crucial to Iran’s disaster preparedness. When selecting the placement of rescue centers, accessibility and the appropriateness of the land should be taken into account as well as the distance from high-risk areas. The location of these centers does not require any particular combinations. To address this issue, a study was conducted utilizing GIS, artificial neural networks, fuzzy logic, and mathematical models to determine the optimal placement based on 12 indicators within two clusters: natural and human. To examine the information layers of the initial stage, a spatial data repository concerning the variables impacting the placement of these centers was established using ARCGIS. Using functions and algorithms such as Fuzzy Logic in IDRISI, TOPSIS, and VIKOR software, the layers were assessed for weightage before being overlaid. The study’s analysis of the models used revealed that the positioning priority limits of the areas differed across all four models. Notably, the areas with high desirability varied to a greater extent: the fuzzy model varied by 9.3%, neural network by 12.4%, VIKOR by 4.5%, and TOPSIS by 16.2%. The variance in results can be attributed to the differing levels of risk acceptance and non-acceptance in each model. Additionally, the study yielded other significant findings such as the correlation between study area size and model accuracy. Specifically, smaller study areas exhibited higher model accuracy. The research also demonstrated that both fuzzy and VIKOR models achieved greater accuracy. As a result, employing these models in crisis management planning, particularly in pre-crisis management for identifying rescue center locations, would be highly advantageous and increase the precision of these endeavors.

In the context of low carbon, this paper discussed the impact of the carbon cap-and-trade policy on the fresh-keeping decision-making of two-echelon fresh agricultural product supply chains under different dominance, and designed cost-sharing contracts to coordinate the supply chain of fresh agricultural products dominated by suppliers and retailers respectively. The results showed that: dominance has no effect on the fresh-keeping decision and total revenue of fresh agricultural product supply chain, but it affects the internal income distribution, and dominance does not always bring more benefits; the implementation of carbon cap-and-trade reduces the fresh-keeping decision-making of fresh agricultural products supply chain and reduces the free-rider income of followers; the role of higher carbon trading price is twofold, which not only brings about the speculation of leading enterprises, but also promotes the application of low-carbon technologies; consumers’ high preference for freshness, low-cost and high-efficiency low-carbon technology are all conducive to improving the fresh-keeping efforts and benefits of the supply chain; cost-sharing contracts can coordinate the supply chain of fresh agricultural products.

CEO compensation stickiness represents an important indicator to measure the effectiveness of compensation contracts. This study uses CEO career experience data and compensation stickiness data from Shanghai and Shenzhen A-share listed companies from 2015 to 2020 to investigate the compensation contracts’ effectiveness of CEOs with diverse career experiences. The findings are as follows: 1) Compensation stickiness is more pronounced for CEOs with diverse career experiences. According to the mechanism test, these CEOs with diverse career experiences can obtain compensation incentives by reducing corporate uncertainty perception and improving total factor productivity. This approach leads to increased compensation stickiness and the effectiveness of compensation contracts. CEOs with diverse career experiences may receive excess compensation by raising agency costs, which intensifies compensation stickiness and weakens the effectiveness of compensation contracts. 2) Compensation stickiness of CEOs with diverse career experiences is more significant in companies with lower investor protection, which brings about less effective compensation contracts. In contrast, in companies with higher diversification, the compensation stickiness of CEOs with diverse career experiences is more significant, which delivers more effective compensation contracts. The conclusions deepen the research of CEO compensation contracts and provide a helpful reference for CEO compensation management practices.

Surging demand and reduced capacity in the ride-hailing industry have prompted numerous ride-hailing platforms to build their own car-rental services catering to drivers who do not possess private vehicles. However, the trade-off between the ride-hailing service and the car-rental service is an important issue that is still unclear in theory. Moreover, ride-hailing platforms are transitioning towards all-electric fleets in the context of Carbon Neutrality goals and government regulations. This paper considers a ride-hailing system comprising a monopolist ride-hailing platform, an electric vehicle (EV) rental firm, and a gasoline vehicle (GV) rental firm. Furthermore, we build a stylized model to study the sequential pricing of the system. The equilibrium outcomes show the significant impact of the ride-hailing platform’s decision to continue or withdraw offering EV rental services on EV and GV drivers’ net earnings, rental prices, and wages. The ride-hailing platform providing EV rental services increases EV drivers’ net earnings but decreases the GV driver wages and rental prices. However, the EV rental service offered by the ride-hailing platform does not necessarily lead to an increased total profit for the system. The improved profitability of the system by the ride-hailing platform providing EV rental services is contingent upon lower rider prices and higher fuel costs. The ride-hailing platform’s EV rental services provision also effectively fosters the ride-hailing fleet’s electrification. Furthermore, as the number of riders increases, the ride-hailing platform should reduce the commission rate for EV drivers to maintain competitiveness and profitability.

Nowadays, data are more and more used for intelligent modeling and prediction, and the comprehensive evaluation of data quality is getting more and more attention as a necessary means to measure whether the data are usable or not. However, the comprehensive evaluation method of data quality mostly contains the subjective factors of the evaluator, so how to comprehensively and objectively evaluate the data has become a bottleneck that needs to be solved in the research of comprehensive evaluation method. In order to evaluate the data more comprehensively, objectively and differentially, a novel comprehensive evaluation method based on particle swarm optimization (PSO) and grey correlation analysis (GCA) is presented in this paper. At first, an improved GCA evaluation model based on the technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. Then, an objective function model of maximum difference of the comprehensive evaluation values is built, and the PSO algorithm is used to optimize the weights of the improved GCA evaluation model based on the objective function model. Finally, the performance of the proposed method is investigated through parameter analysis. A performance comparison of traffic flow data is carried out, and the simulation results show that the maximum average difference between the evaluation results and its mean value (MDR) of the proposed comprehensive evaluation method is 33.24% higher than that of TOPSIS-GCA, and 6.86% higher than that of GCA. The proposed method has better differentiation than other methods, which means that it objectively and comprehensively evaluates the data from both the relevance and differentiation of the data, and the results more effectively reflect the differences in data quality, which will provide more effective data support for intelligent modeling, prediction and other applications.