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This paper aims to review empirical research on the relationship between institutional ownership (IO) and board governance (85 studies).

Based on agency and upper echelons theory, the heterogeneous monitoring function of specific types and the nature of institutional investors on board composition, compensation and chief executive officer (CEO) characteristics will be focused.

The author found that most studies have referred to archival studies, analyzed the impact of board governance on IO, focused on CEO characteristics, neglected IO heterogeneity and advanced regression models to address endogeneity concerns. In line with the theoretical framework, the relationship between total IO and board governance is heterogeneous. However, specific types such as foreign, dedicated and pressure-resistant institutions represent active monitoring tools and push for increased board governance.

The author provided useful recommendations for future research from a content and methodological perspective, e.g. the need for analyzing the impact of IO on sustainable board governance and other characteristics of top management team members, e.g. the chief financial officer.

As many regulatory bodies implemented regulations to promote shareholder rights and board governance, this literature review highlights the connections of both corporate governance mechanisms. Managers should conduct a careful and timely investor analysis and change the composition and compensation of the board of directors in line with institutional investors’ preferences.

This analysis makes useful contributions to prior research by focusing on IO and board governance, whereas the author structured the heterogeneous variables and results within the structured literature review. The authors guides researchers, regulatory bodies and business practice in this corporate governance topic.

This study delves into the nuanced implications of short-sale constraints on stock prices within the context of stock market efficiency. While existing research has explored this relationship, inconsistencies persist in their findings. The purpose of this study is to conduct a comprehensive review of literature to elucidate the reasons behind these disparities.

A systematic review of existing theoretical and empirical studies was conducted following the PRISMA method. The analysis centered on discerning the factors contributing to the divergence in projected stock prices due to these constraints. Key areas explored included assumptions related to expectations homogeneity, revisions, information uncertainty, trading motivations and fluctuations in supply and demand of risky assets.

The review uncovered multifaceted reasons for the disparities in findings regarding the influence of short-sale constraints on stock prices. Variations in assumptions related to market expectations, coupled with fluctuations in perceived information uncertainty and trading motivations, were identified as pivotal factors contributing to differing projections. Empirical evidence disparities stemmed from the use of proxies for short-sale constraints, varied sample periods, market structure nuances, regulatory changes and the presence of option trading.

This study emphasizes the significance of not oversimplifying the impact of short-sale constraints on stock prices. It highlights the need to understand these effects within the broader context of market structure and methodological considerations. By delineating the intricate interplay of factors affecting stock prices under short-sale constraints, this review provides a nuanced perspective, contributing to a more comprehensive understanding in the field.

This chapter proposes a model for discovering suspicious financial operations such as money laundering. To achieve this, the authors reviewed research papers on money laundering and financial institutions’ cases and problems, especially those related to financial transfers. They also collected primary data through face-to-face semi-structured interviews with financial companies’ owners and experts in financial transfers to identify hypotheses that help discover suspicious transfers. The chapter discusses the six big data analysis cycle phases from problem discovery to model deployment to identify suspicious transfers. The chapter uses hypothetical data and models to discuss the results and focuses on exchange companies willing to analyze financial operations. The chapter proposes tools that exchange companies can use to monitor and prevent suspicious transfers including data visualization and machine learning algorithms.

This study aims to examine whether the presence of advisory directors affects firm discretionary accruals (DACC), a widely used proxy for financial reporting quality. The authors argue that the advisory director weakens the board monitoring role and impairs the firm financial reporting quality by increasing DACC.

The sample consists of listed firms on the Australian Stock Exchange from 2001 to 2015 using 7,649 firm-year observations. The authors perform descriptive statistics, regression and propensity score matching analyses to examine the research hypothesis.

The research evidence that firms with a higher presence of advisory directors have more DACC, indicating poor financial reporting quality. Furthermore, the authors categorize the DACC and find that the firm has higher income-increasing DACC in the presence of higher advisory directors. The findings are robust concerning endogeneity issues.

The research evidence that firms with a higher presence of advisory directors have more DACC, indicating poor financial reporting quality. Furthermore, the authors categorize the DACC and find that the firm has higher income-increasing DACC in the presence of higher advisory directors. The findings are robust concerning endogeneity issues.

The research contributes valuable insights for regulators and policymakers seeking to comprehend the implications of firms using more advisory directors. Additionally, the authors recognize the potential significance of the findings for the institution of directors, as they can provide a nuanced understanding of the specific roles played by advisory directors in organizational dynamics.

While the extensive body of literature on corporate governance and financial reporting quality has been well-established, a noticeable void exists in academic research delving into the relationship between advisory directors and DACC management. This study seeks to fill this gap, making a distinctive and original contribution to the existing literature on corporate governance.

This chapter analyzes how the macro-environment determines corporate dividend decisions. First, political factors including political uncertainty, economic policy uncertainty, political corruption, and democracy may have two opposite effects on dividend decisions. For example, firms learn democratic practices to improve their corporate governance, but dividend policy may be the outcome of strong corporate governance or the substitute for poor corporate governance. Second, firms in countries of high national income, low inflation, and highly developed stock markets tend to pay more dividends. A monetary restriction (expansion) reduces (increases) dividend payments, as economic shocks like financial crises and the COVID-19 may negatively affect corporate dividend policy through higher external financial constraint, economic uncertainty, and agency costs. On the other hand, they may positively influence corporate dividend policy through agency costs of debt, shareholders' bird-in-hand motive, substitution of weak corporate governance, and signaling motive. Third, social factors including national culture, religion, and language affect dividend decisions since they govern both managers' and shareholders' views and behaviors. Fourth, firms tend to reduce their dividends when they face stronger pressure to reduce pollution, produce environment-friendly products, or follow a green policy. Finally, firms have high levels of dividends when shareholders are strongly protected by laws. However, firms tend to pay more dividends in countries of weak creditor rights since dividend payments are a substitute for poor legal protection of creditors. Furthermore, corporate dividend policy changes when tax laws change the comparative tax rates on dividends and capital gains.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

The performance of oil-filled pressure cores is very much affected by the corrugated diaphragm and the oil filling volume. The purpose of this paper is to show the effects of different corrugated diaphragms, different oil filling volumes and different treatments of the corrugated diaphragms on the performance of pressure sensors.

Pressure-sensitive cores with different diaphragm diameters, different diaphragm ripple numbers and different oil filling volumes are produced, and thermal cycling is introduced to improve the diaphragm performance, and finally the performance of each pressure-sensitive core is tested and the test data are analyzed and compared.

The experimental results show that the larger the diameter of the corrugated diaphragm used for encapsulation, the better the performance. For pressure-sensitive cores using smaller diameter corrugated diaphragms, the performance of one corrugation is better than that of two corrugations. When the number of corrugations and the diameter are the same size, the performance of the outer ring of the diaphragm with concave corrugations is better than that with convex corrugations. At the same time, the diaphragm after thermal cycling treatment and appropriate reduction of encapsulated oil filling can improve the performance of the pressure-sensitive core.

By exploring the effects of corrugated diaphragm and oil filling volume on the performance of oil-filled pressure cores, the design of oil-filled pressure sensors can be guided to improve sensor performance.

This chapter analyzes how the internal environment determines corporate dividend decisions. First, dividend policy is influenced by strategic and financial issues. Corporate strategies are developed by top managers to achieve firms' missions, visions, and long-term goals while business strategies are designed by middle managers to maintain firms' competitive advantages. These strategies affect corporate dividend decisions through corporate performance and business operations. In addition, many financial characteristics are important determinants of dividend policy. Financial characteristics are classified into three groups, namely performance-related issues (e.g., firm profitability, free cash flow, and stock liquidity), leverage-related issues (e.g., debt ratio, asset tangibility, business risk, and firm size), and investment-related issues (e.g., investment opportunities and firm maturity). Firms with high profitability, free cash flow tends to pay more dividends. Stock liquidity may have a positive effect on dividend payments through lowering costs of equity; however, it may also have a negative effect through weakening the signaling motive. Moreover, firms with high debt ratio, low asset tangibility, high business risk, and small size face higher costs of external financing. Therefore, they have low incentives to pay dividends. When firms have more investment opportunities, they are more likely to restrict dividends and save cash for their investment projects and vice versa. Second, internal stakeholders may influence corporate dividend policy since their benefits are closely related to dividend decisions. Shareholders, directors, the chief executive officer, and employees have different characteristics, positions, and hold various proportions of shares. Therefore, they create pressures on dividend decisions to protect their wealth.

Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.

VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.

The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.

User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.