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This paper aims to explore the impact of cycle superposition on bank liquidity risk under different levels of financial openness so that banks can better manage their liquidity risk. Meanwhile, it can also provide some ideas for banks in other emerging economies to better cope with the shocks of the global financial cycle.

Employing the monthly data of 16 commercial banks in China from 2005 to 2021 and based on the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model, the authors first examine whether the cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. Subsequently, the authors investigate the impact of different levels of financial openness on cycle superposition amplification. Finally, the shock of the financial cycle of the world's major economies on the liquidity risk of Chinese banks is also empirically analyzed.

Cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. However, there are significant differences under different levels of financial openness. Compared with low financial openness, in the period of high financial openness, the magnifying effect of cycle superposition is strengthened in the short term but obviously weakened in the long run. In addition, the authors' findings also demonstrate that although the United States is the main shock country, the influence of other developed economies, such as Japan and Eurozone countries, cannot be ignored.

Firstly, the cycle superposition index is constructed. Secondly, the authors supplement the literature by providing evidence that the association between cycle superposition and bank liquidity risk also depends on financial openness. Finally, the dominant countries of the global financial cycle have been rejudged.

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 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.

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 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.

Introduces a method of food sensory evaluation employing artificial neural networks. The process of food sensory evaluation can be viewed as a multi‐input and multi‐output (MIMO) system in which food composition serves as the input and human food evaluation as the output. It has proved to be very difficult to establish a mathematical model of this system; however, a series of samples have been obtained through experiments, each of which comprises input and output data. On the basis of these sample data, applies the back‐propagation algorithm (BP algorithm) to “train” a three‐layer feed‐forward network. The result is a neural network that can successfully imitate the food sensory evaluation of the evaluation panel. This method can also be applied in other fields such as food composition optimizing, new product development and market evaluation and investigation.

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.

The purpose of this paper is to examine village heads’ information seeking and decision making in 2014 Kaohsiung Blast and to analyze if the current disaster trainings help those leaders to enhance disaster risk deduction in an unprecedented disaster.

This study adopts after-action review and information seeking and decision making literature both from communication research and disaster research. Document analysis, and in-depth interviews with 13 village heads, 1 district officer, and 15 residents from the affected areas are conducted.

This study finds that the village heads have received trainings of regular types of disasters; however, most of them act like lay people in Kaohsiung Blast. In the beginning of the gas leaking, village heads slack off when first respondents arrive. After the Blast, most of them wait for authority orders and cannot launch minimum self-help and community help which they learned from the trainings.

This study confirms that the leadership research should take different categories and levels of leaders into consideration to distinguish public leaders from non-public leaders, professional disaster risk reduction leaders from non-professional leaders, and higher authorities from basic levels of government. The findings from this study provide a basis for the rational design of the job descriptions of village leaders.

This study is the first empirical research to investigate first-level but non-professional disaster management staffs’ information seeking and decision making after an unprecedented disaster in Taiwan.

The paper investigates the market performance of strategic acquisitions for growth in the fifth and sixth merger waves and outlines the major determinants that affect the performance of acquiring companies in these most complex and most challenging corporate transactions.

To perform the quantitative analysis a unique data sample was built out of acquisitions performed in the 5th and 6th merger waves with an only single purpose – strategic growth. Their performance was first analyzed using the method of market-based event study. In addition, the impact of several non-accounting determinants identified through a thorough literature review was tested using univariate/multivariate regression analysis.

The new findings of the study state that strategic acquisitions for growth created more value for acquiring companies if they were completed internationally and involved an acquisition of a middle-sized company. Moreover, the acquisition of targets in the less related industries (2-SIC) led to stronger performance of acquirers, especially in the international settings.

The study suggests additional directions for future research. The future analysis can investigate the post-merger acquisition performance of strategic acquirers and can focus on additional financial (accounting) determinants in the evaluation of performance. This perspective can not only address the limitations imposed by the assumption of efficient capital markets but also provide additional insights.

The results of current study have important implications for executives performing M&A for growth. They show that the market reaction to M&A announcement can be at least partially anticipated and help managers to plan their strategic moves based on a defined set of variables.

The study contributes to the sustainable, value-creating growth dynamics and encourages Executives to “lead for value.”

(1) In contrast to the existing studies that do not differentiate between the transaction rationale in their analysis, this paper focuses explicitly only on those acquisitions that have strategic growth as their primary objective and responses therefore, to the problem stated by Halpern (1983). This approach helps to mitigate the distortion of results and make a reliable assessment of the strategic move. (2) The results of quantitative analysis also outline that acquisition of mid-sized targets and larger degree of diversification (2-SIC, international focus) code were associated with higher value creation.

本文旨在研究於第五和第六波的併購浪潮中為增長而作出的策略性收購的市場表現;本文亦概述在這些極其複雜的和極具挑戰性的公司交易中影響作收購公司的表現的主要因素.

為了能進行定量分析,研究人員收集在第五及第六波的併購浪潮中以策略性增長為唯一目的的收購個案、建立一個獨特的數據樣本。研究人員首先以基於市場的事件研究法分析那些進行了收購的公司的表現,並以單變量/多變項迴歸分析法去試驗那幾個透過深入的文獻研究而找到的非會計的決定因素的影響.

研究得出的新發現是、如果以增長為目的的策略性收購是於國際間完成及涉及收購中型公司的話,則這收購行動會給進行收購的公司帶來更多價值。而且、如果收購目標的產業與作收購公司的不太相關的話 (2-標準產業分類),收購行動會為進行收購的公司帶來更強的表現、特別是在國際環境下進行這收購行動.

(1) 有別於現時其它於其分析中不區分交易理由的研究，本文明確地表示只集中探討那些以帶來策略性增長為主要目標的收購；因此、本研究對 (哈爾彭，1983年)(Halpern, 1983) 陳述的問題作出了回應。本研究的理念有助於減輕我們對收購結果的曲解，從而讓我們對策略性行動能作出可靠的評估. (2) 定量分析的結果、亦概述了以中型公司為目標的收購及更大程度的多樣化 (2－標準產業分類、以國際為焦點) 代碼與創造更大價值是有關聯的。

本研究的結果對學術界及管理人員均具吸引力，亦為策略規劃提供一個額外的工具.

本研究可帶來可持續的及可創造價值的增長動力，又可鼓勵行政主管採用以價值為本的領導方針.