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While performance demands in the natural world are varied, graded lattice structures reveal distinctive mechanical properties with tremendous engineering application potential. For biomechanical functions where mechanical qualities are required from supporting under external loading and permeability is crucial which affects bone tissue engineering, the geometric design in lattice structure for bone scaffolds in loading-bearing applications is necessary. However, when tweaking structural traits, these two factors frequently clash. For graded lattice structures, this study aims to develop a design-optimization strategy to attain improved attributes across different domains.

To handle diverse stress states, parametric modeling is used to produce strut-based lattice structures with spatially varied densities. The tailored initial gradients in lattice structure are subject to automatic property evaluation procedure that hinges on finite element method and computational fluid dynamics simulations. The geometric parameters of lattice structures with numerous objectives are then optimized using an iterative optimization process based on a non-dominated genetic algorithm.

The initial stress-based design of graded lattice structure with spatially variable densities is generated based on the stress conditions. The results from subsequent dual-objective optimization show a series of topologies with gradually improved trade-offs between mechanical properties and permeability.

In this study, a novel structural design-optimization methodology is proposed for mathematically optimizing strut-based graded lattice structures to achieve enhanced performance in multiple domains.

Many of the Chinese military theorists are famous for their military operations in ancient China and their thoughts and philosophies have been repeatedly translated, investigated and discussed throughout the world. Researches with thoughts that were originated from these philosophies were published, advocating enterprises, their businesses and management strategies. T’ai Kung’s Six Secret Teachings or The Six Strategies for War in Chinese Liu Tao was written by the brilliant strategist who helped establish the western Zhou dynasty about eleventh century BC. The three strategies of Huang Shik Kung or three strategies for war of Huang Shi Gong in Chinese Shan Lue was used as a standard reference for military officials in China. It deals with the three strategies, Superior Strategy, Middle Strategy and Inferior Strategy. This paper examines the philosophies of Liu Tao and Shan Lue for the purposes of development of management philosophies of the Powerhouse of Hong Kong Productivity Council, “Three strategies and six focuses”.

This study aims to take advantage of exporters’ product codes and examine the effects of government subsidization on corporate product strategies by focusing on the dimension of product differentiation.

This study uses harmonized system (HS) product codes to construct a novel measure of product differentiation among a sample of Chinese exporters during 2000–2012. It uses propensity score matching to construct a comparable sample of control firms for exporters receiving government subsidies, and then a difference-in-differences (DID) analysis is conducted.

This study finds that product differentiation decreases immediately upon receiving a government subsidy. This finding suggests that in an emerging market, firms use their subsidy to imitate competitors rather than increase innovation. Further analyses show that this effect is concentrated among wholly foreign-owned enterprises and firms that focus on general trade rather than processing trade. In addition, the authors find some evidence that government subsidization leads to an increase in the number of product lines and decreases in domestic value added and export product quality.

This study constructs a novel measure of product differentiation for a large sample of Chinese exporters and provides insights that government subsidization can affect corporate product strategies.

This study aims to systematically review the literature on how various factors influence investor sentiment and affect financial markets. This study also sought to present an overview of explored contexts and research foci, identifying gaps in the literature and setting an agenda for future research.

The systematic literature investigation yielded 555 journal articles, with few other exceptional inclusions. The data have been extracted from the two databases, i.e. Scopus and Web of Science. For bibliometric analysis, VOSviewer and Biblioshiny by R have been used. The period of investigation is from 1985 to July 2023.

This systematic literature review helped us identify factors influencing investor sentiment and financial markets. This study has broadly classified these factors into two categories: rational and irrational. Rational factors include – economics and monetary policy, exchange rate, interest rates, inflation, government mandatory regulations, earning announcements, stock-split, dividend decisions, audit quality, environmental, social and governance aspects and ratings. Irrational factors include – behavioural and psychological factors, social media and online talk, news and entertainment, geopolitical and war events, calendar anomalies, environmental, natural disasters, religious events and festivals, irrationality caused due to government/supervisory body regulations, and corporate events. Using these factors, this study has developed an investor sentiment model. In addition, this review identified research trends, methodology, data and techniques used by researchers.

This review comprehensively explains how various factors affect investor sentiment and the stock market using the investor sentiment model. It further proposes an extensive future research agenda. This study has implications for stock market participants.

Purpose – The advancement of multimedia technology has spurred the use of multimedia in business practice. The adoption of audio and visual data will accelerate as marketing scholars become more aware of the value of audio and visual data and the technologies required to reveal insights into marketing problems. This chapter aims to introduce marketing scholars into this field of research.Design/methodology/approach – This chapter reviews the current technology in audio and visual data analysis and discusses rewarding research opportunities in marketing using these data.Findings – Compared with traditional data like survey and scanner data, audio and visual data provides richer information and is easier to collect. Given these superiority, data availability, feasibility of storage, and increasing computational power, we believe that these data will contribute to better marketing practices with the help of marketing scholars in the near future.Practical implications: The adoption of audio and visual data in marketing practices will help practitioners to get better insights into marketing problems and thus make better decisions.Value/originality – This chapter makes first attempt in the marketing literature to review the current technology in audio and visual data analysis and proposes promising applications of such technology. We hope it will inspire scholars to utilize audio and visual data in marketing research.

The purpose of this research is to investigate the short-term capital markets' reactions to the public announcement first local detection of novel corona virus (COVID 19) cases in 12 major Asian capital markets.

Using the constant mean return model and the market model, an event study methodology has been implied to determine the cumulative abnormal returns (CARs) of 10 pre and post-event trading days. The statistical significance of the data was assessed using both parametric and nonparametric test statistics.

First discovery of local COVID 19 cases had a substantial impact on all 12 Asian markets on the event day, as shown by statistically significant negative average abnormal return (AAR) and cumulative average abnormal return (CAAR). The single factor ANOVA result has also demonstrated that there is no variability among 12 regional markets in terms of short-term market responses. Furthermore, there is little evidence that these major Asian stock market indices differ significantly from the FTSE All-World Index which might suggest possible spillover impact and co-integration among the major Asian capital markets. The study further discovers that market capitalization and liquidity did not have any significant impact on market reaction to announcement.

The study's contribution might have been compromised by the absence of socio-demographic, technical, financial and other significant policy factors from the analysis.

These findings will be considerably helpful in tackling this unprecedented epidemic issue for personal and institutional investors, industrial and economic experts, government and policymakers in assessing the market in special circumstances, diversifying risk and developing financial and monetary policy proposals.

This paper is the first to examine the effects of local COVID 19 detection announcement on major Asian capital markets. This study will add to the literature by investigating unusual market returns generated by infectious illness outbreaks and the overall market efficiency and investors' behavioral pattern of major Asian capital markets.

Due to the large and fast growing sentiment analysis (SA) area recently, many new concepts and different nomenclatures have emerged without the desired organization. This confusion in the research field makes the understandability of the concepts hard and also hampers the comparison of different approaches. Thus, this paper aims to propose a hierarchical taxonomy to help the consolidation of SA area. The taxonomy aims at covering the addressed problems and methods in the SA field.

This taxonomy is a filtered union of various classifications found in the literature with a proposal of nomenclatures standardization. As instance, a case study is presented with 20 SA-related articles classified according to the proposed taxonomy.

This taxonomy is very expressive because it covers many concepts and is also effective once it allows the distinction and categorization of the previous SA works.

To the best of the authors’ knowledge, the literature does not present such an expressive and effective classification as proposed in this paper. This new taxonomy allows for the navigation between the existing concepts in the SA field as well as, facilitates the search, comparison and indexing of papers already published.

The purpose of this paper is to investigate an idea of producing an assistive and augmentative communication (AAC) tool that uses natural human computer interfacing to accommodate the disabilities of children with cerebral palsy (CP) and assist them in their daily communication.

The authors developed a prototype that recognizes the real time detected emotions display on the face and send alerts to the caretakers through Short Messaging System (SMS) or loud speaker.

The evaluation result shows that the proposed prototype recognizes real time facial expressions from the children with CP with an average of 79.4 per cent, and a maximum of 88.3 per cent (standard deviation of 7.4 per cent) on ten children with CP. Evaluations were also conducted to investigate the effectiveness of the prototype to deliver critical expression messages to their caretakers. The result showed that 98.5 per cent of SMS was sent successfully to the caretakers (pre‐defined mobile phone number) with an average waiting time of 8.3 seconds.

The paper demonstrates the potential of the proposed prototype to assist children with CP to communicate with their caretakers in real time.

Content‐based image retrieval (CBIR) is an important research area for automatically retrieving images of user interest from a large database. Due to many potential applications, facial image retrieval has received much attention in recent years. Similar to face recognition, finding appropriate image representation is a vital step for a successful facial image retrieval system. Recently, many efficient image feature descriptors have been proposed and some of them have been applied to face recognition. It is valuable to have comparative studies of different feature descriptors in facial image retrieval. And more importantly, how to fuse multiple features is a significant task which can have a substantial impact on the overall performance of the CBIR system. The purpose of this paper is to propose an efficient face image retrieval strategy.

In this paper, three different feature description methods have been investigated for facial image retrieval, including local binary pattern, curvelet transform and pyramid histogram of oriented gradient. The problem of large dimensionalities of the extracted features is addressed by employing a manifold learning method called spectral regression. A decision level fusion scheme fuzzy aggregation is applied by combining the distance metrics from the respective dimension reduced feature spaces.

Empirical evaluations on several face databases illustrate that dimension reduced features are more efficient for facial retrieval and the fuzzy aggregation fusion scheme can offer much enhanced performance. A 98 per cent rank 1 retrieval accuracy was obtained for the AR faces and 91 per cent for the FERET faces, showing that the method is robust against different variations like pose and occlusion.

The proposed method for facial image retrieval has a promising potential of designing a real‐world system for many applications, particularly in forensics and biometrics.

The purpose of this paper is to explore the joint effects of different types of IT resources and top management support. Especially, the authors attempt to mainly examine a negative synergy or substitution relationship between IT infrastructure resources and CEO support, and a positive synergy or complementary relationship between IT human resources and CEO support among the large-sized enterprises.

A research model that integrates IT infrastructure resources, IT human resources, CEO support and the degree of usage of IT for business objectives (i.e. IT business spanning capability) is developed. Based on a sample of 112 large-sized enterprises, partial least squares is used to analyze the research model.

Whereas the positive moderating role of CEO support in the effectiveness of IT human resources is insignificant, CEO support and IT infrastructure resources have a substitution relationship in predicting IT business spanning capability. Furthermore, the results can explain under which conditions IT infrastructure resources insignificantly or significantly affect IT business spanning capability in large-sized enterprises. Specially, IT infrastructure resources significantly affect IT business spanning capability only when CEO support is low. Thus, in the presence of high CEO support, IT executives in large-sized enterprises should prioritize developing highly effective IT resources, such as IT human resources.

This paper highlights the joint effects of two critical IT resource types (i.e. IT infrastructure and IT human resources) and CEO support in the IT assimilation process among the large-sized enterprises, ultimately contributing to information systems theories and practices.