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This article investigates the mechanism of the direct and indirect effects of epidemics on agricultural production and projects the impact of COVID-19 on agricultural output in China.

This article first adopts a dynamic panel model and spatial Durbin model to estimate the direct and indirect effects, followed by a growth accounting method to identify the channels by which epidemics affect agriculture; finally, it projects the overall impact of COVID-19 on agriculture.

The incidence rate of epidemics in a province has a negative impact on that province's own agricultural productivity, but the increase in the input factors (land, fertilizer and machinery) can make up for the loss and thus lead to insignificant direct effects. However, this “input-offset-productivity” mechanism fails to radiate to the surrounding provinces and therefore leads to significant indirect/spillover effects. It is projected that COVID-19 will lower China's agricultural growth rate by 0.4%–2.0% in 2020 under different scenarios.

It is crucial to establish a timely disclosure and sharing system of epidemic information across provinces, improve the support and resilience of agricultural production in the short run and accelerate the process of agricultural modernization in the long run.

Considering the infectivity of epidemics, this article evaluates the mechanism of the direct and indirect effects by introducing a spatial dynamic model into the growth accounting framework. Moreover, besides the impact on input portfolio and productivity, this article also investigates whether epidemics reshape agricultural production processes due to panic effects and control measures.

The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data.

A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail.

MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance.

The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.

The purpose of this paper is to determine the corrosion fatigue behavior and mechanism of 6005A aluminum alloy and welded joint.

Electron back-scattered diffraction (EBSD) were adopted to characterize the microstructure of 6005A aluminum alloy and welded joint. Through potentiodynamic polarization, electrochemical impedance spectroscopy (EIS) and corrosion fatigue experiments, the corrosion fatigue behavior and mechanism of 6005A aluminum alloy base metal and welded joint were studied.

The results show that the corrosion fatigue crack initiation of 6005A aluminum alloy base metal and welded joint is mainly caused by the preferential anodic dissolution and hydrogen concentration in the areas with inclusions and welding defects.

The research is an originality study on the corrosion fatigue behavior and mechanism of 6005A aluminum alloy and welded joint.

As an unhealthy dependence on social media platforms, social media addiction (SMA) has become increasingly commonplace in the digital era. The purpose of this paper is to provide a general overview of SMA research and develop a theoretical model that explains how different types of factors contribute to SMA.

Considering the nascent nature of this research area, this study conducted a systematic review to synthesize the burgeoning literature examining influencing factors of SMA. Based on a comprehensive literature search and screening process, 84 articles were included in the final sample.

Analyses showed that antecedents of SMA can be classified into three conceptual levels: individual, environmental and platform. The authors further proposed a theoretical framework to explain the underlying mechanisms behind the relationships amongst different types of variables.

The contributions of this review are two-fold. First, it used a systematic and rigorous approach to summarize the empirical landscape of SMA research, providing theoretical insights and future research directions in this area. Second, the findings could help social media service providers and health professionals propose relevant intervention strategies to mitigate SMA.

China's stock market, which serves as an example of emerging markets, is steadily maturing in the context of globalization. In order to analyze the pricing mechanism of China's stock market, this paper builds a six-factor model to address the market features that are structurally efficient but not entirely efficient.

This study updates the Fama–French factor model's construction process to account for the unique features of China's stock market before creating a model that incorporates size, volume, value, profitability, and profit-income factors based on institutional investors' trading behavior and research preferences. The SWS three-tier sector stock index's monthly and quarterly data for the years 2016–2021 are used as samples for this study.

The results imply that China's stock market is structurally efficient and exhibits high levels of rationality in the region dominated by institutional investors. Specifically, big-size and high-volume portfolios that perform well in terms of liquidity can receive trading premiums. Growth-style sectors prevail at present, and investing in sectors with strong profitability and reliable financial reporting data can produce better returns.

The research on China's stock market can be extended to improve the understanding of the development process of similar emerging markets, thereby promoting their improvement. To enhance the development of emerging markets, the regulators should attach great importance to the role of local institutional investors in driving the market to maturity. It is crucial to adopt a structured approach to examine the market pricing mechanism throughout the middle stage of the transition from developing to mature markets.

This study offers a structured viewpoint on asset pricing in growing emerging markets by combining the multi-factor pricing model approach with behavioral finance theories.

The paper aims to identify the emergent themes of hotel guests’ satisfaction, to compare the distribution of the attributes of the themes between Chinese and North American guests and to compare the importance of the themes for different satisfaction levels between Chinese and North American guests from a cross-cultural perspective.

By adopting Python (a computer language), the word-frequency method was used to identify emergent themes of hotel guests’ satisfaction. Topic modeling was adopted to compare the attributes distribution of each theme and the features of satisfaction between Chinese and North American guests.

First, three themes were identified including functionality, staff and price. Functionality can be further categorized into five subthemes, namely, room, travel, food, environment and hotel facility. Second, the distribution of the attributes of the themes between Chinese and North American guests was compared from a cross-cultural perspective. Chinese guests tend to mention both lifestyles- and social norms–related attributes and expect personalized service, while North American guests mainly prefer to describe lifestyle-related attributes and prefer standardized service. Third, the study compared the changing importance of the themes (functionality, staff and price) for different satisfaction levels between Chinese and North American guests. As the satisfaction level decreases, the importance of functionality decreases, that of staff increases and that of price remain stable for Chinese guests. In contrast, the importance of each theme has fluctuated mildly from the high to the low satisfaction level for North American guests.

Proposed managerial implications are to highlight lifestyle- and social norms-related attributes, as well as personalized service for Chinese guests. However, lifestyle-related attributes and standardized service should be facilitated for North American guests. Specific suggestions were made to help improve hotel performance such as the good performance of functional-related attributes, which could enhance satisfaction and better staff performance, which would reduce dissatisfaction.

By mining big data, this study investigated hotel guests’ satisfaction from a dynamic instead of a static perspective. This study provides some rare insights into differences in key attributes influencing satisfaction levels of Chinese versus North American guests staying in luxury hotels in China. This study also takes a novel approach to examine the dynamics of the importance of the various themes at different satisfaction levels, and contrast these dynamics between Chinese and North American guests. The findings offer valuable insight into market segmentation and management in the hospitality industry.

To meet the emerging demand for fine-grained annotation and semantic enrichment of cultural heritage images, this paper proposes a new approach that can transcend the boundary of information organization theory and Panofsky's iconography theory.

After a systematic review of semantic data models for organizing cultural heritage images and a comparative analysis of the concept and characteristics of deep semantic annotation (DSA) and indexing, an integrated DSA framework for cultural heritage images as well as its principles and process was designed. Two experiments were conducted on two mural images from the Mogao Caves to evaluate the DSA framework's validity based on four criteria: depth, breadth, granularity and relation.

Results showed the proposed DSA framework included not only image metadata but also represented the storyline contained in the images by integrating domain terminology, ontology, thesaurus, taxonomy and natural language description into a multilevel structure.

DSA can reveal the aboutness, ofness and isness information contained within images, which can thus meet the demand for semantic enrichment and retrieval of cultural heritage images at a fine-grained level. This method can also help contribute to building a novel infrastructure for the increasing scholarship of digital humanities.

The purpose of this paper is to study the dynamical group consensus of heterogeneous multi-agent systems with fixed topologies.

The tool used in this paper to model the topologies of multi-agent systems is algebraic graph theory. The matrix theory and stability theory are applied to research the group consensus of heterogeneous multi-agent systems with fixed topologies. The Laplace transform and Routh criterion are utilized to analyze the convergence properties of heterogeneous multi-agent systems.

It is discovered that the dynamical group consensus for heterogeneous multi-agent systems with first-order and second-order agents can be achieved under the reasonable hypothesizes. The group consensus condition is only relied on the nonzero eigenvalues of the graph Laplacian matrix.

The novelty of this paper is to investigate the dynamical group consensus of heterogeneous multi-agent systems with first-order and second-order agents and fixed topologies and obtain a sufficient group consensus condition.

Microblogs are communication platforms for companies and consumers that challenge companies' brand marketing strategies. This paper provides a theoretical basis for expanding microblog applications and a practical basis for improving the effectiveness of brand marketing.

The authors use factor analysis to extract the factors of microblog user influence and construct a structural equation model to reveal the interaction mechanism of the influencing factors. Additionally, the authors clarify the promotion and enhancement effects of these factors.

Microblog user influence can be converted into richness, interaction and value factors. The richness factor significantly affects the latter two, whereas the interaction factor does not affect the value factor.

First, the sample used is limited to media industry practitioners. To increase generalizability, diverse groups should be included in future studies. Second, this model's theoretical explanatory ability can be further developed by adding other meaningful factors beyond the existing ones.

This study analyzes the factors of microblog user influence in China and validates the relevant elements. As a result, it improves the influence research on social media users and benefits the practice of information recommendation and microblog marketing.

The purpose of this paper is to explore a semantics representation framework for narrative images, conforming to the image-interpretation process.

This paper explores the essential features of semantics evolution in the process of narrative images interpretation. It proposes a novel semantics representation framework, ESImage (evolution semantics of image) for narrative images. ESImage adopts a hierarchical architecture to progressively organize the semantic information in images, enabling the evolutionary interpretation under the support of a graph-based semantics data model. Also, the study shows the feasibility of this framework by addressing the issues of typical semantics representation with the scenario of the Dunhuang fresco.

The process of image interpretation mainly concerns three issues: bottom-up description, the multi-faceted semantics representation and the top-down semantics complementation. ESImage can provide a comprehensive solution for narrative image semantics representation by addressing the major issues based on the semantics evolution mechanisms of the graph-based semantics data model.

ESImage needs to be combined with machine learning to meet the requirements of automatic annotation and semantics interpretation of large-scale image resources.

This paper sorts out the characteristics of the gradual interpretation of narrative images and has discussed the major issues in its semantics representation. Also, it proposes the semantic framework ESImage which deploys a flexible and sound mechanism to represent the semantic information of narrative images.