It is difficult for the conventional image compression method to achieve good compression effect in the underwater acoustic image, because the UWAI has large amount of noise and low correlation between pixel points. In this paper, fractal coding is introduced into UWAI compression, and a fractal coding algorithm based on interest region is proposed according to the importance of different regions in the image. The application problems of traditional quadtree segmentation in UWAIs was solved by the range block segmentation method (...) in the coding process which segmented the interest region into small size and the noninterest region into large size and balanced the compression ratio and the decoded image quality. This paper applies the classification, reduction codebook, and correlation coefficient matching strategy to narrow the search range of the range block in order to solve the problem of the long encoding time and the calculation amount of encoding process is greatly reduced. The experimental results show that the proposed algorithm improves the compression ratio and encoding speed while ensuring the image quality of important regions in the UWAI. (shrink)

The effect of authentic leadership and leader competency on employee job performance has received growing attention in the past decades; however, few studies have simultaneously integrated these two leadership perspectives. We have thus developed a mediated moderation model to test the interactive effect of authentic leadership and competency on followers’ job performance through work engagement. Based on a sample of 248 subordinate–supervisor pairs, hierarchical regression analyses reveal that authentic leadership positively relates to followers’ task performance and organizational citizenship behavior ; (...) leader competency moderates the relationship between authentic leadership and OCB; and followers’ work engagement mediates the main effect of authentic leadership and the interactive effect of authentic leadership and competency on followers’ task performance and OCB. All the three results are consistent with our hypotheses. (shrink)

Based on ideology-infused psychological contract theory and cognitive evaluation theory, this study investigated the curvilinear relationship between ethical leadership and employee creativity. A curvilinear mediation model was proposed to explain the impact of ethical leadership on creativity, using employee intrinsic motivation as the mediator. Applying a two wave sampling design that consist 258 employees and their leaders, we found that employee creativity improved as ethical leadership increased from low to moderate levels. However, the employee creativity improvement was attenuated when ethical (...) leadership increased from moderate to high levels. Furthermore, the study also revealed that employee intrinsic motivation partially mediated this curvilinear relationship. The theoretical and managerial implications are discussed. (shrink)

We examine the impact of a government-initiated CSR project on corporate misconduct using the unique setting of China’s Targeted Poverty Alleviation (TPA) program. The difference-in-differences estimates show that firms participating in the TPA program engage in fewer misconduct activities than do their counterparts. This finding is robust to the parallel trends test, the placebo test, alternative regression specifications, alternative research designs, the reverse causality analysis, and the bivariate probit model with partial observability. Further analysis shows that TPA participation enhances the (...) ability of external financial professionals to monitor performance by stimulating the dissemination of TPA-related incremental information, thereby contributing to the decline in corporate misconduct. The cross-sectional heterogeneity analysis shows that the impact of TPA participation on corporate misconduct is more pronounced in firms with higher information asymmetry, with weaker political connections, and with weaker internal governance. Furthermore, we find evidence that TPA participation improves corporate financial performance but does not deteriorate information transparency. (shrink)

This study investigates how the explicitly anti-prosocial and anti-pro-environmental speech by political elites affects firms’ environmental and social (E&S) performance. Using the case of the Donald J. Trump (DJT) campaign for president in the United States when he gave out controversial and inflammatory remarks, including those regarding E&S issues, we find that local firms’ E&S performance decreased significantly in the year following DJT’s presidential campaign rally compared with firms in other geographic areas where there was no DJT’s presidential campaign event. (...) A further test indicates that the change in firms’ E&S performance can be driven by DJT remarks’ influence on local social norms shift. Furthermore, we show that the post-rally decrease of local firms’ E&S performance is more pronounced for local firms that operate primarily locally or local firms that are more sensitive to political uncertainty. Taken together, these findings indicate that political events such as political elites giving remarks can affect firms’ E&S performance. (shrink)

ABSTRACT Although unethical pro-organizational behavior (UPB) in the workplace has been widely researched, studies have focused on its antecedents rather than its outcomes. To fill this gap in the literature, we integrated moral licensing theory and the literature on leader gratitude expression to explore the ethical consequences of UPB. Using a sample of multi-source time-lagged surveys of 206 leader–employee dyads, we found that the pro-organizational nature of UPB fostered employees’ psychological entitlement and thereby increased their likelihood of engaging in subsequent (...) unethical behavior. Leaders’ expressions of gratitude for employee UPB strengthened the relationship between UPB and psychological entitlement, ultimately reinforcing employees’ unethical behavior. The theoretical and practical implications of the findings are discussed. (shrink)

Music education is one of human kind most universal forms of expression and communication, and it can be found in the daily lives of people of all ages and cultures all over the world. As university life is a time when students are exposed to a great deal of stress, it can have a negative impact on their mental health. Therefore, it is critical to intervene at this stage in their life so that they are prepared to deal with the (...) pressures they will face in the future. The aim of this study was to see how music education affects university students’ mental health, with emotional intelligence functioning as a moderator. The participants in this research were graduate students pursuing degrees in music education. Non probability convenience sampling technique was used to collect and evaluate the data from 265 students studying in different public and private Chinese universities. The data was gathered at a time, and therefore, the study is cross-sectional. The data was collected from January 2022 till the end of March 2022. Many universities have been closed because to COVID-19, therefore data was also gathered online through emails. The data was analyzed quantitatively using the partial least squares –structural equation modeling technique. The findings backed up the hypotheses. The results revealed that there is a significant effect of music education on student’s mental health. Also, emotional intelligence as a moderator significantly and positively moderates the relationship between music education and students’ mental health. Music has numerous physiological aspects, and listening to it on a daily basis may be beneficial to your general health and well-being. Furthermore, musicians and music students with a high level of emotional intelligence have a better chance of not just performing well in school, college and university or in the music industry, but also of maintaining mental health and improving it. (shrink)

According to upper echelon theory, this research seeks to explore how environmental leadership (EL) influences environmental performance through green intellectual capital (GIC), while also considering environmental climate as a moderator. We analyze the data from 317 Chinese manufacturing companies in two waves to test these hypotheses. The findings suggest that EL positively affects environmental performance via green structural capital and green relational capital (GRC). Moreover, environmental climate strengthens the impacts of EL on green human capital and GRC. This research enriches (...) the existing literature on EL and GIC by revealing the vital roles of EL in promoting environmental performance and GIC, as well as explaining the mechanism between these relationships. (shrink)

BackgroundMedical disputes, medical disturbances, verbal and physical violence against physicians, and burnout have reached epidemic levels. They may negatively impact both physicians and the healthcare system. The experience of medical disputes, medical disturbances, verbal, and physical violence, and burnout and the correlates in physicians working in public hospitals in China needed to be investigated.MethodsA nationwide cross-sectional survey study was conducted between 18 and 31 March 2019. An anonymous online questionnaire was administered. The questionnaire included the 22-item Maslach Burnout Inventory-Human Services (...) Survey. We also collected data on demographic and job-related factors, as well as physicians’ experiences of medical disputes, medical disturbances, verbal and physical violence from patients and the patients’ family members.FindingsIn total, 22,213 physicians from 144 tertiary public hospitals in all of China’s 31 provinces completed the survey. The overall burnout rate among the surveyed physicians was 31.28%. Moreover, 33.48% of physicians experienced disputes, 20.86% experienced disturbances, 48.52% experienced verbal violence, and 5.84% experienced physical violence in the past 12 months. Factors found to be significantly associated with burnout included younger age, being divorced or widowed, having a lower educational background, working in internal medicine departments, longer working hours per day, working in general hospitals, being in East China, as well as having experienced disputes, disturbances, and physical and verbal violence.InterpretationClose to a third of the Chinese doctors working in the tertiary hospitals reportedly experienced burnout, and the problem is related to the unsafe working environment caused by the worsening doctor-patient relationship. (shrink)

To improve the movie box office prediction accuracy, this paper proposes an adaptive attention with consumer sentinel for movie box office prediction. First, the influencing factors of the movie box office are analyzed. Tackling the problem of ignoring consumer groups in existing prediction models, we add consumer features and then quantitatively analyze and normalize the box office influence factors. Second, we establish an LSTM box office prediction model and inject the attention mechanism to construct an adaptive attention with consumer sentinel (...) for movie box office prediction. Finally, 10,398 pieces of movie box office dataset are used in the Kaggle competition to compare the prediction results with the LSTM-AACS model, LSTM-Attention model, and LSTM model. The results show that the relative error of LSTM-AACS prediction is 6.58%, which is lower than other models used in the experiment. (shrink)

Job shop scheduling problem is one of the most difficult optimization problems in manufacturing industry, and flexible job shop scheduling problem is an extension of the classical JSP, which further challenges the algorithm performance. In FJSP, a machine should be selected for each process from a given set, which introduces another decision element within the job path, making FJSP be more difficult than traditional JSP. In this paper, a variant of grasshopper optimization algorithm named dynamic opposite learning assisted GOA is (...) proposed to solve FJSP. The recently proposed dynamic opposite learning strategy adopts the asymmetric search space to improve the exploitation ability of the algorithm and increase the possibility of finding the global optimum. Various popular benchmarks from CEC 2014 and FJSP are used to evaluate the performance of DOLGOA. Numerical results with comparisons of other classic algorithms show that DOLGOA gets obvious improvement for solving global optimization problems and is well-performed when solving FJSP. (shrink)

The paper presents a Chinese philosophical point of view of AI, and presents a novel system of the AI machine. There are two basic relations or contradictions which drive computer developments forward. One is between software and hardware and the other is between data structure and system organization. It is suggested that a description of a future AI system should primarily start from these contradictions.

The problem of the dynamic of development is a fundamental problem over which materialist dialectics has been struggling with metaphysics for a long time. The metaphysical cant of the "gang of four," which frantically promoted the "philosophy of struggle," distorted and perverted this problem beyond recognition. In order to correct the confusion and accelerate the realization of the four modernizations, a thorough critique of the false doctrines of the "gang of four" must provide a correct response to this problem.

There has been no consensus on the neural dissociation between emotion-label and emotion-laden words, which remains one of the major concerns in affective neurolinguistics. The current study adopted dot-probe tasks to investigate the valence effect on attentional bias toward Chinese emotion-label and emotion-laden words. Behavioral data showed that emotional word type and valence interacted in attentional bias scores with an attentional bias toward positive emotion-laden words rather than positive emotion-label words and that this bias was derived from the disengagement difficulty (...) in positive emotion-laden words. In addition, an attentional bias toward negative emotion-label words relative to positive emotion-label words was observed. The event-related potential data demonstrated an interaction between emotional word type, valence, and hemisphere. A significant hemisphere effect was observed during the processing of positive emotion-laden word pairs rather than positive emotion-label, negative emotion-label, and negative emotion-laden word pairs, with positive emotion-laden word pairs eliciting an enhanced P1 in the right hemisphere as compared to the left hemisphere. Our results found a dynamic attentional bias toward positive emotion-laden words; individuals allocated more attention to positive emotion-laden words in the early processing stage and had difficulty disengaging attention from them in the late processing stage. (shrink)

Named entity recognition is a subtask in natural language processing, and its accuracy greatly affects the effectiveness of downstream tasks. Aiming at the problem of insufficient expression of potential Chinese features in named entity recognition tasks, this paper proposes a multifeature adaptive fusion Chinese named entity recognition model. The model uses bidirectional long short-term memory neural network to extract stroke and radical features and adopts a weighted concatenation method to fuse two sets of features adaptively. This method can better integrate (...) the two sets of features, thereby improving the model entity recognition ability. In order to fully test the entity recognition performance of this model, we compared the basic model and other mainstream models on Microsoft Research Asia and “China People’s Daily” dataset from January to June 1998. Experimental results show that this model is better than other models, with F1 values of 97.01% and 96.78%, respectively. (shrink)

Ethical voice involves individuals’ perceptions of what is right and what is wrong. Although prior research has investigated the impacts of individual-level prohibitive ethical expression on personal outcomes, there has been limited examination of ethical voice at the group level. Our study examines how and when different types of ethical voice influence group outcomes. Using data gathered from 363 participants from 61 groups in two Chinese companies, the results reveal that promotive and prohibitive ethical voices at the group level exert (...) opposing influences on group moral transitive motivation. Group moral transitive motivation serves as a mediating factor in the association between the two types of ethical voice and group ethical behaviors (GEB). In addition, group faultlines moderate the impact of promotive ethical voice and prohibitive ethical voice on group moral transitive motivation and on GEB (via group moral transitive motivation). Our study extends the focus of research from a single type of ethical voice at the individual level to multiple types at the group level. The findings contribute to the body of knowledge regarding group ethical voice (GEV) by articulating its mediating mechanism as well as its boundary condition. This research provides valuable insights for researchers and managers by offering a comprehensive understanding about the role of different types of GEV and how to reform GEV. (shrink)

This study focuses on examining the thematic landscape of the history of scholarly publication in business ethics. We analyze the titles, abstracts, full texts, and citation information of all research papers published in the field’s leading journal, the Journal of Business Ethics, from its inaugural issue in February 1982 until December 2016—a dataset that comprises 6308 articles and 42 million words. Our key method is a computational algorithm known as probabilistic topic modeling, which we use to examine objectively the field’s (...) latent thematic landscape based on the vast volume of scholarly texts. This “big-data” approach allows us not only to provide time-specific snapshots of various research topics, but also to track the dynamic evolution of each topic over time. We further examine the pattern of individual papers’ topic diversity and the influence of individual papers’ topic diversity on their impact over time. We conclude this study with our recommendation for future studies in business ethics research. (shrink)

Time is a necessary element in understanding AI art. Firstly, time reveals the historical process by which art-theoretical predicates move from the unmarked to the marked, which can thus be utilized as a defense for arguing the legitimacy of AI art as art. Furthermore, AI art should be seen as a “new” art that is temporally ahead of the descriptive forms of art theory. Secondly, time provides a unique interpretation of AI artworks’ characteristics and aesthetic experience. The absence of experience, (...) the de-depth of AI artworks, and the “short experience/short memory” aesthetic mode of the masses are closely linked to the scarcity of time in an age of acceleration. Finally, time reflects the loss of “aura” and the new end of art that AI art may bring about. Time provides a defense and explanation of AI art, as well as a perspective for reflecting on the development of art in contemporary times. (shrink)

The color of green product advertisements is an important factor affecting consumers’ preferences. Based on the theory of the self-control system, this paper explores the influence mechanism and boundary conditions of green product ad color on consumers’ preferences through three experiments. Experiment 1 tested the effect of advertisement color type on consumers’ preferences for green products. The results show that color ad can promote consumers’ preferences for green products compared with green ad. Experiment 1 also analyzed the mediating role of (...) the self-control system between advertisement color type and consumers’ preferences. Experiment 2 further clarified the boundary of the main effect. The effect of ad color on consumers’ preferences was only effective in the context of green products. Experiment 3 explored the moderating effect of green product type on the main effect. The results show that only when the green product type is altruistic, the ad color type can significantly affect consumers’ preferences. This study is the first to link the ad color of green products with consumers’ preferences. The findings confirm that the use of color ad for green products can elicit higher consumers’ preferences than pure green ad, which enriches the research on the color of green product advertisements. (shrink)

The ethical controversies of firms in the sharing economy (SE) have recently drawn attention and caused debates. Ridesharing firms violate laws in many countries, but how they become legitimised remains underexplored. We apply the co-evolutionary perspective to examine how ethical controversies, legitimation and institutionalisation co-evolve in the ridesharing segment in the dynamic and changing institutional environment of China. We conducted a case study on Didi using firm-institution dual-level analysis based on stakeholder salience theory (SST) and the Orders of Worth framework. (...) Our findings uncover ethical controversies among stakeholders with different levels of salience. We also reveal a dynamic interplay between the legitimacy work of Didi and the institutional-level market regulations on ridesharing firms. We develop a process model demonstrating the co-evolution among the changes in ethical controversies, new market category emergence and legitimation of ridesharing firms. These findings shed light on the application of the co-evolutionary perspective in analysing ethical controversies among multiple stakeholders’ evolving interests in the SE context. (shrink)

Competition for land is increasing as demand for multiple land uses and ecosystem services rises. Land regulation of the principles of landscape ecology is necessary to develop more sustainable approaches to land use planning. The research evaluated the present land patterns and determined best practices for its regulation of Dongwang Township in Quyang County, located in the Taihang Mountain area of Hebei Province, China. The research used the landscape ecology theory to construct an index system for landscape pattern analysis based (...) on the GIS and Fragstats 3.3 software. In this study, we examined the specific reasons that landscape ecology is superior to traditional methods in land consolidation planning and design, which is conducive to the comprehensive development of land ecological benefits. Landscape ecological planning can effectively reduce landscape fragmentation and improve intensive management. The result found that the descending order of the Shannon index was current landscape, landscape ecological planning, and traditional planning. Landscape ecological planning could protect the natural diversity than traditional planning. Landscape ecological planning enables the creation of long corridors, with higher densities and connectivity and lower average corridor widths than traditional planning. Besides, it can improve ecological service function values in the study area to varying degrees, thus discouraging residents from limiting themselves to grain production. This research has great potential to improve the visibility of ecosystem services in local land use planning and, thus, to improve the ecological functioning of future landscapes. (shrink)

Massive Open Online Course is a new online education model that provides new opportunities and challenges for the development and reform of teaching in colleges and universities. This paper first builds a MOOC-based model of influencing factors of blended learning adaptability. Through investigation and research, it is found that the six influencing factors all have different ways and different degrees of influence on learning adaptability. Among them, learning motivation has a direct and significant impact on learning adaptability, and learning self-efficacy (...) not only has a direct and significant impact on learning adaptability but also has an indirect impact on learning adaptability through learning motivation. Teacher teaching has indirect effects on learning adaptability through learning self-efficacy, learning motivation, and learning support. Learning support not only has a direct and significant impact on learning adaptability but also has an indirect impact on learning adaptability through learning motivation. MOOC platform has a direct and significant impact on learning adaptability. The quality of course content has a direct and significant impact on learning adaptability through the MOOC platform and learning motivation. The creative results of this research are mainly embodied in the combination of theory and practice, exploring and forming related results based on MOOC-based college students’ learning adaptability. According to the MOOC-based learning-adaptive intervention model, teaching practice is carried out to verify the effect of the intervention model from two aspects of student procedural performance and result performance. Finally, in view of the problems existing in traditional computer teaching in colleges and universities, combined with the design ideas and basic characteristics of MOOC, the teaching mode reform of computer courses is analyzed and discussed, and a MOOC-based computer engineering education teaching mode is proposed. (shrink)

The two-dimensional coupled map lattice model has been extensively employed as the basis component for designing various schemes in the cryptography system due to its complicated chaotic dynamic behavior. In this study, we analyze the chaotic characteristics of the 2D CML model, such as the Lyapunov exponent, synchronization stability, bifurcation, and ergodicity. We then show that the chaotic sequences generated by the 2D CML model are random according to the NIST testing. Furthermore, we propose an image encryption scheme based on (...) the 2D CML model and Singular Value Decomposition. In our scheme, the SVD method is used to reduce the image storage, and the Red, Green, and Blue channels of a color image will be encrypted through confusion and diffusion. The simulation results, as well as the results of the comparison with other schemes, demonstrate that our scheme possesses outstanding statistics, excellent encryption performance, and high security. It has great potential for ensuring the security of digital images in real applications. (shrink)

Computational social science, as an emerging interdisciplinary discipline, is a field ushered in by long-term development of traditional social science. It is committed to supplying data thinking, resources, and analytics to study human social behavior and social operation laws to accurately grasp and judge the developing path of the discipline, which is of great significance to promote the innovation and development of social sciences. This study is to conduct a systematic quantitative analysis from a bibliometric perspective, aiming to provide a (...) reference for scholars to explore the paths and changing rules in the field. We use the relevant literature in Web of Science as the dataset. After eliminating journal calls and irrelevant literature, R language and SciMAT tools are used to visualize and analyze the number of articles, keyword clustering, keyword cooccurrence network, and theme evolution, so as to summarize and sort out the paths of computational social science research. The study found that the annual volume of publications has been gradually increasing and will probably remain active in the next few years with high productivity. Subject themes in different periods are diversified, and the evolutionary relationship is found complex as well. Besides, as a cross discipline, scientific knowledge from different fields cross collides and couples with each other in the big data environment, changing the traditional concept of computational social science and forming a new development path. Recently, the emergence of “big data+” has promoted the rise of new subject areas, making the development of new disciplines a reality. (shrink)

At present, most of the research in the field of medical-assisted diagnosis is carried out based on image or electronic medical records. Although there is some research foundation, they lack the comprehensive consideration of comprehensive image and text modes. Based on this situation, this article proposes a fusion classification auxiliary diagnosis model based on GoogleNet model and Bi-LSTM model, uses GoogleNet to process brain computed tomographic images of ischemic stroke patients and extract CT image features, uses Bi-LSTM model to extract (...) the electronic medical record text, integrates the two features using the full connection layer network and Softmax classifier, and obtains a method that can assist the diagnosis from two modes. Experiments show that the proposed scheme on average improves 3.05% in accuracy compared to individual image or text modes, and the best performing GoogleNet + Bi-LSTM model achieves 96.61% accuracy; although slightly less in recall, it performs better on F1 values, and has provided feasible new ideas and new methods for research in the field of multi-model medical-assisted diagnosis. (shrink)

Recent theoretical advances describing consciousness from information and integration have highlighted the unique role of the thalamocortical system in leading to integrated information and thus, consciousness. Here, we examined the differential distributions of specific and nonspecific thalamocortical functional connections using resting-state fMRI in a group of healthy subjects and vegetative-state patients. We found that both thalamic systems were widely distributed, but they exhibited different patterns. Nonspecific connections were preferentially associated with brain regions involved in higher-order cognitive processing, self-awareness and introspective (...) mentalizing . In contrast, specific connections were prevalent in the ventral and posterior part of the prefrontal and precuneus, known involved in representing externally-directed attentions. Significant reductions of functional connectivity in both systems, especially the nonspecific system, were observed in VS. These data suggest that brain networks sustaining information and integration may be differentiated by the nature of their thalamic functional connectivity. (shrink)

ObjectiveThis study aims to investigate anxiety and its influencing factors in patients with drug-induced liver injury (DILI).Materials and MethodsNinety-four patients with DILI were enrolled and evaluated with a self-rating anxiety scale (SAS). According to the anxiety score, they were divided into four groups: the non-anxiety, mild anxiety, moderate anxiety, or severe anxiety groups, and the scores were analyzed based on demographic and biochemical indicators.ResultsOf the 94 patients with DILI, 63 did not have anxiety and 31 had anxiety (32.9%), of which (...) 27 had mild, 3 had moderate, and 1 had severe anxiety. There were no statistically significant differences in gender, age, occupation, and level of education between the groups (F= 1.42,H= 2.361,H= 6.751,H= 1.796, andP> 0.05); anxiety score and degree of anxiety between the types of drugs that led to the liver injury (H= 0.812,H= 1.712, andP> 0.05); anxiety score between the different degrees of liver injury (H= 2.836,H= 4.957,P> 0.05); or length of hospital stay or prognosis between the degrees of anxiety (F= 1.487,H= 0.761,P> 0.05). However, there were statistically significant differences in the degree of anxiety between different degree and types of liver injury (H= 7.981,H= 8.208,P< 0.05).ConclusionPatients with DILI may have anxiety, especially mild anxiety. The occurrence of anxiety in patients with DILI is not related to gender, age, occupation, or level of education but may be related to the degree and type of liver injury. Anxiety has no impact on the length of stay in hospital or the prognosis of the DILI. These findings may contribute to the development of management strategies for patients with DILI. (shrink)

Community search is a query-oriented variant of community detection problem, and the goal is to retrieve a single community from a given set of nodes. Most of the existing community search methods adopt handcrafted features, so there are some limitations in applications. Our idea is motivated by the recent advances of node embedding. Node embedding uses deep learning method to obtain feature representation of nodes directly from graph structure automatically and offers a new method to measure the distance between two (...) nodes. In this paper, we propose a two-stage community search algorithm with a minimum spanning tree strategy based on node embedding. At the first stage, we propose a node embedding model NEBRW and map nodes to the points in a low-dimensional vector space. At the second stage, we propose a new definition of community from the distance viewpoint, transform the problem of community search to a variant of minimum spanning tree problem, and uncover the target community with an improved Prim algorithm. We test our algorithm on both synthetic and real-world network datasets. The experimental results show that our algorithm is more effective for community search than baselines. (shrink)

Cross-shareholding is a new type of strategic means for capital operation and is an important component of corporate governance. With the increasing complexity of business motivation, the structure of a cross-shareholding network is becoming more intricate, and it exposes various important local patterns with different economic functions. The goal of this paper is to uncover investment mechanisms and economic functions implied in cross-shareholding networks by analyzing the local characteristic patterns of company interactions. In this paper, we construct the CSNs of (...) listed companies and extract the directed triadic motifs to reveal the evolutionary characteristics of local investment patterns at the company and industry levels. On the company level, we find that companies tend to form V-shaped structures with other companies, but bidirectional shareholding patterns and circular relationships in the triads are scarce. On the industry level, we identify the characteristic linking patterns of some industries with a role analysis of the industries. Furthermore, we detect the evolutionary characteristics of industry interrelationships in three implied patterns. Such a motif evolution analysis may provide valuable information for investors and supervisory departments that make decisions about investment portfolios and policy. Meanwhile, this study is also helpful for exploring the implied information in other empirical networks. (shrink)