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Natural disasters profoundly influence agricultural trade sustainability. This study investigates the effects of natural disasters on agricultural production imports in China within 2002 and 2018. This exploration estimates the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations.

This investigation uses Probit, Logit, Cloglog and Ordinary Least Squares (OLS) models.

The results confirm the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations in China. According to the findings, natural disasters in trading partners heighten the risk to the agricultural imports. This risk raises, if disasters damage overall agricultural yield or transportation infrastructure. Moreover, governments’ effective response or diplomatic ties with China mitigate the risk. Finally, the effect of disasters varies by the developmental status of the country involved, with events in developed nations posing a greater risk to China’s imports than those in developing nations.

China should devise an early warning system to protect its agricultural imports by using advanced technologies such as data analytics, remote sensing and artificial intelligence. In addition, it can leverage this system by improving its collaboration with trading partners, involvement in international forums and agreement for mutual support in crisis.

The study aims to address a critical gap in existing healthcare payment schemes and care service pricing by recognizing the influential role of patients' decisions on self-management efforts. These decisions not only impact health outcomes but also shape the demand for care, subsequently influencing care costs. Despite the significance of this interplay, current payment schemes often overlook these dynamics. The research focuses on investigating the implications of a novel behavior-based payment scheme, designed to align incentives and establish a direct connection between patients' decisions and care costs. The primary objective is to comprehensively understand whether and how this innovative payment scheme structure influences key stakeholders, including patients, care providers, insurers and overall social welfare.

In this paper, we propose a game-theoretical model to incorporate the performance of self-management with the demand for healthcare service, compare the patient's effort decision for self-management and provider's price decision for healthcare service under a behavior-based scheme with that under two implemented widely payment schemes, that is, co-payment scheme and co-insurance scheme.

Our findings confirm that the behavior-based scheme incentives patient self-management more than current schemes while reducing their possibility of seeking healthcare service, which indirectly induces the provider to lower the price of the service. The stakeholders' utility under various payment schemes is sensitive to the cost of treatment and the perceived health utility of patients. Especially, patient health awareness is not always benefited provider profit, as it motivates patient self-management while diminishing the demand for care.

We provide a novel framework for characterizing behavior-based payment schemes. Our results confirm the need for modification of the current payment scheme to incentivize patient self-management.

This paper explores the effect of natural disasters on the profitability and solvency of US banks.

Employing a sample of 187 large-scale natural disasters that occurred in the United States between 2000 and 2014 and a sample of 2,891 banks, we examine whether and how disaster-related damages affect various measures of bank profitability and bank solvency. We differentiate between different types of banks (with local, regional and national operations) based on a breakdown of their state-level deposits and explore the reaction of these banks to damages weighted by the GDP of the states they operate in.

We find that natural disasters have a pronounced effect on the net-income-to-assets and the net-income-to-equity ratio of banks, as well as the banks' impaired loans and return on average assets. We also observe significant effects on the equity ratio and the tier-1 capital ratio (two solvency measures). Interestingly, the latter are positive for regional banks which appear to benefit from increased customer deposits related to safekeeping, government payments for post-disaster recovery, insurance payouts and decreased withdrawals, while they are significantly negative for banks that operate locally or nationally.

We contribute to the literature by offering various new insights regarding the effects natural disasters have on financial institutions. With climate change-driven natural disasters widely expected to increase both in terms of frequency and severity, their economic fallout is likely to impose an increasing burden on financial institutions. Large, nationally operating banks tend to be well diversified both geographically and in terms of their product offerings. Small, locally operating banks, however, are increasingly at risk – particularly if they operate in disaster-prone areas. Current banking regulations generally do not factor natural disaster risks into their capital requirements. To avoid the next big financial crisis, regulators may want to adjust their reserve requirements by taking this growing risk exposure into consideration.

According to the survey and measurement on rural housing in the Northeast severe cold regions of China, this paper analyzed the existing situation and problems of current rural housing in terms of integral development, functional layout, envelop structure, interior thermal environment, heating system and energy utilization etc.. Based on the climatic features of severe cold regions, as well as rural financial and technical conditions, living and production mode, residential construction characteristics and existing resource status etc., the feasible approaches of achieving building energy saving has been proposed, thus acting as a guidance for new rural housing design in severe cold regions.

This study aims to identify the counterproductive knowledge behavior (CKB) of volunteers in nonprofit organizations and its influencing factors, based on the theories of planned behavior and well-being.

An online survey was used to collect 496 valid responses. A structural equation model was constructed, and the relationships among the constructs were estimated via the maximum likelihood method. To analyze the direct and indirect effects, 2,000 bootstrapping runs were conducted. A Kruskal-Wallis test was also conducted to analyze the relationship between the variables.

A combination of organizational factors and individual attitudes and perceptions can be used to explain CKB. Insecurity about knowledge sharing had the greatest impact on CKB. A competitive organizational norm induced CKB while a knowledge-sharing organizational norm did not have a significant impact. Further, the more self-determined the volunteer activity was, the more the CKB was suppressed. However, well-being did not have a significant direct effect. Volunteers with high levels of well-being and self-determination had significantly lower levels of insecurity about knowledge sharing compared to those who did not.

Well-being arising from volunteering did not directly suppress CKB. To improve organizational efficiency by reducing CKB, nonprofit organization managers should provide intrinsically motivating tasks and interact with the volunteers.

There is a lack of empirical research on CKB in volunteer organizations; therefore, the authors propose a new approach to knowledge management in volunteer activities.

Drawing on social capital theory, this study investigated the effects of structural, cognitive and relational family social capital on employees' career advancement through the mechanism of family-to-work enrichment (FWE), taking perceived organizational politics (POP) as a moderator.

Survey data were collected from 252 full-time employees working in public institutions and government departments in China, a collectivist cultural context. Hierarchical regression and path analysis were conducted to test the hypotheses.

FWE significantly mediated the positive relationships between the three subtypes of family social capital and career advancement. The effects of structural and cognitive family social capital, but not relational family social capital (RFSC), on FWE were stronger when POP was low (vs high).

FWE is arguably a promising mechanism for explaining the links between family social capital and career outcomes. However, due to the cross-sectional nature of the data, conclusions regarding causality remain limited.

Family social capital may enrich the careers of employees in collectivist cultures. Managers should mitigate their organization's political climate to promote employees' career advancement.

This study contributes to career research by linking family social capital to career outcomes through the lens of FWE for the first time and by identifying organizational politics as an important moderator that can influence the dynamics of resource enrichment in a collectivist culture.

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

The purpose of this paper is to present a new method and model for constructing a new decision-making paradigm of Medicare, which can not only satisfy the needs of the sick people but also reduce the possibility of people slipping back to poverty due to diseases under the policy of Targeted Poverty Alleviation (TPA) of China.

This paper uses the traditional supply chain theory to analyze the Medicare of impoverished people with the policy of TPA of China and transforms it into a multi-layer supply chain optimization decision-making problem. First, a nonlinear integer programming model for poor people’s Medicare decision with opportunity constraints is constructed. To facilitate the solution of the optimal decision scheme, the abovementioned model is transformed into a linear integer programming model with opportunity constraints by using the Newsvendor model for reference. Meanwhile, the scope of the inventory model is discussed, for it can be combined with the construction of the medical insurance system better. Second, the theoretical model is applied to the practical problem. Finally, based on the results of the theoretical model applying the practical problem, we give further improvement and modification of the theoretical model applies it to the actual situation further.

This paper presents a theoretical model about determine the optimal the inventory, under the framework of traditional supply chain decision-making, for it can be combined with the construction of the medical insurance system better. The theoretical model is applied to the practical problem of the fight against poverty in XX County, China. By using the actual data and MATLAB, optimal decision scheme is obtained.

There are two aspects of value. On the one hand, this paper provides a new way to construct a Medicare system of impoverished people with TPA of China. On the other hand, this paper tries making a new way to handle the storage of medicines and related medical devices at basic standard clinics decision-making problems based on above mentioned Medicare system.

User-generated content (UGC) refers to semantic and behavioral traces created by users on various social media platforms. While several waves of platforms have come and gone, the long-term sustainability of UGC activities has become a critical question that bears significance for theoretical understanding and social media practices.

Based on a large and lengthy dataset of both blogging and microblogging activities of the same set of users, a multistate survival analysis was applied to explore the patterns of users' staying, switching and multiplatforming behaviors, as well as the underlying driving factors.

UGC activities are generally unsustainable in the long run, and natural attrition is the primary reason, rather than competitive switching to new platforms. The availability of leisure time, expected gratification and previous experiences drive users' sustainability.

The authors adopted actual behavioral data from two generations of platforms instead of survey data on users' switching intentions. Four types of users are defined: loyal, switcher, multiplatformer and dropout. As measured by the transitions among the four states, the different sustainability behaviors are thereby studied via an integrated framework. These two originalities bridge gaps in the literature and offer new insights into exploring user sustainability in social media.

The purpose of this paper is to propose a new velocity prediction navigation algorithm to develop a conflict-free path for robots in dynamic crowded environments. The algorithm BP-prediction and reciprocal velocity obstacle (PRVO) combines the BP neural network for velocity PRVO to accomplish dynamic collision avoidance.

This presented method exhibits innovation by anticipating ahead velocities using BP neural networks to reconstruct the velocity obstacle region; determining the optimized velocity corresponding to the robot’s scalable radius range from the error generated by the non-holonomic robot tracking the desired trajectory; and considering acceleration constraints, determining the set of multi-step reachable velocities of non-holonomic robot in the space of velocity variations.

The method is validated using three commonly used metrics of collision rate, travel time and average distance in a comparison between simulation experiments including multiple differential drive robots and physical experiments using the Turtkebot3 robot. The experimental results show that our method outperforms other RVO extension methods on the three metrics.

In this paper, the authors propose navigation algorithms capable of adaptively selecting the optimal speed for a multi-robot system to avoid robot collisions during dynamic crowded interactions.