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The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price fluctuations) in their decision-making. This research investigates the optimal dynamic pricing strategy of the content product developer in relation to their consideration of consumer fairness concerns to elucidate the impact of consumer fairness concerns on the dynamic pricing strategy of the developer.

This paper assumes that monopolistic content developers implement a dynamic pricing strategy for the content product. Through constructing a two-period dynamic pricing game model, this research investigates the optimal decisions of the content developer, contingent upon their consideration or disregard of consumer fairness concerns. In the extension section, the authors additionally account for the influence of myopic consumers on these optimal decisions.

Our findings reveal that the degree of consumer fairness concerns significantly influences the developer’s optimal dynamic pricing decision. When a developer offers content products with lower depth, there is a propensity for the developer to refrain from incorporating consumer fairness concerns into a dynamic pricing strategy. Conversely, in cases where the developer offers a high-depth content product, consumer fairness concerns benefit the developer. Furthermore, our analysis reveals a consistent benefit for the developer from the inclusion of myopic consumers.

Few studies have delved into the conjoined influence of consumer fairness concerns and strategic behavior on dynamic pricing strategy. Our findings indicate that consumer fairness concerns can enhance the efficiency of the value chain for content products under specific conditions. This paper not only enriches the existing literature on dynamic pricing by incorporating consumer fairness concerns theoretically but also offers practical insights. The outcomes of this research can guide content product developers in devising optimal dynamic pricing strategies.

This article aims to elaborate on the current relevant policies, research literature and technical applications of administrative intelligent decision-making in major countries in the world, to summarize the current focus areas of international competition in administrative intelligent decision-making and to provide references and insights for other countries.

The knowledge graph tool CiteSpace was used to conduct a quantitative analysis of the field of administrative intelligent decision-making, using the two dimensions of research literature and technology patents to analyze the basic status and evolution process of artificial intelligence applied to administrative decision-making.

Over the past 30 years, theoretical research on administrative intelligent decision-making has primarily centered on countries like the United States of America, China and Japan. The main themes include leveraging artificial intelligence (AI) technology to enable digital government transformation, enhance administrative efficiency and promote public value through service-oriented initiatives. Countries like South Korea, China and the United States of America have demonstrated significant advantages in technology development and patent applications, focusing on e-government systems, intelligent government auditing, addressing policy mismatches between supply and demand and integrating online and offline administrative services. The international competitive landscape in this field is shaped by three key factors: foundational theories, technological advancements and practical scenario applications.

Despite the rapid advancements in AI technology and the acceleration of enterprise digital transformation, discussions on the intelligent transformation of administrative decision-making leveraging next-generation information technology remain limited. This study summarizes the development trajectory of administrative intelligent decision-making in major countries worldwide, analyzes the international competitive landscape of its theoretical and application research and highlights key focus areas. These findings offer valuable insights for advancing the construction of administrative intelligent decision-making systems in other nations.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Community centers play a socio-economic and urban role of combining different communal necessities, that serve inhabitants, at different neighborhoods in cities. Their role emerged in importance as being a hub for improving and customizing quality of life experiences of the public. This research presents a code-based risk assessment tool for evaluating fire safety measures that can be adapted in the context of community centers. It also provides an exemplary case study to demonstrate its application.

The study identified the factors that render community centers as a high-risk type of facilities in fire events. Various fire codes and standards were reviewed to describe the relevant fire safety measures. A code-based fire risk assessment tool was developed and implemented, through a case study. A set of recommendations were developed to improve the fire safety conditions of the case study facility.

Several violations to fire safety were identified in the case study building. The findings led to identifying a set of recommendations to improve its fire safety conditions.

This research introduced a systematic approach to raise awareness about fire incidences and consequences in community centers, and provides facilities managers with a tool, to assess compliance based on international fire code requirements.

In fire events, community centers are considered as high-risk facilities that may lead to significant losses of human lives and damages to assets. It is significant to study the causes of fire, for ensuring effective prevention and safe operations.

This study aims to create and deform 3D garment apparel in an immersive virtual reality using head-mounted display and controllers. For this, adequate design methods for immersive virtual environment were explored and developed in order to confirm the suitability of the developed methods.

An immersive virtual environment was prepared using Unreal Engine (UE) version 5.1 and Meta Human Lexi to create template garment that corresponds to the sizes of a human model. Dual quaternion skinning was adopted for pose deformation. For size deformation, patches were constructed with the measurement lines defined on Lexi. This patch-based approach was adopted not only for automatic generation but also for flat pattern projection of the template garment.

The research found that garment-making process can be brought into immersive virtual reality. Free use of one's hands and body made apparel deformation in an immersive environment conform with the real garment draping process.

Simulating garment making in an immersive virtual reality has not previously been explored in detail. This research discovered, implemented and tested methods that best suit the environment where head-mounted display and controllers are essential in detail.

This study aims to explore the influence of science fiction on innovators and present a comprehensive model using the theory of planned behavior and social support theory to discuss the impact of science fiction on the intention of becoming an innovation worker.

Partial least squares structural equation modeling (PLS-SEM) was adopted in this study and responses were obtained from 244 Chinese innovators.

The results revealed the adequacy of the proposed model and the above-mentioned constructs in explaining innovation intention. Science fiction perception was found to influence the intention of becoming an innovation worker directly. Subjective norm, perceived behavioral control, and attitude directly influence the intention of becoming an innovation worker. Additionally, attitude is a mediator between science fiction perception and the intention of becoming an innovation worker. Moreover, social support network moderates the relationship between attitude and intention.

These results shed light on the mechanism by which science fiction influence innovators as well as provide critical managerial implications for policymakers and practitioners.

The purpose of the current study is to assess Omani teachers’ performance on tasks related to the stages of engineering design. To achieve this, data from an engineering design test was used, and demographic variables that are correlated with this performance were identified.

This descriptive study employed a cross-sectional design and the collection of quantitative data. A sample of preservice science teachers from Sultan Qaboos University (SQU) (n = 70) participated in this study.

Findings showed low and moderate levels of proficiency related to the stages of engineering design. Differences between males and females in terms of performance on engineering design tasks were found, with females scoring higher overall on the assessment. Biology preservice teachers scored higher than teachers from the other two majors (physics and chemistry) in two subscales. There were also differences between teachers studying in the Bachelor of Science (BSc) program and the teacher qualification diploma (TQD) program.

This study provides an overview, in an Arab setting, of preservice science teachers’ proficiency with engineering design process (EDP) tasks. It is hoped that the results may lead to improved instruction in science teacher training programs in similar contexts. Additionally, this research demonstrates how EDP competency relates to preservice teacher gender, major and preparation program. Findings from this study will contribute to the growing body of research investigating the strengths and shortcomings of teacher education programs in relation to science, technology, engineering and mathematics (STEM) education.

Laser point clouds are a 3D reconstruction method with wide range, high accuracy and strong adaptability. Therefore, the purpose is to discover a construction point cloud extraction method that can obtain complete information about the construction of rebar, facilitating construction quality inspection and tunnel data archiving, to reduce the cost and complexity of construction management.

Firstly, this paper analyzes the point cloud data of the tunnel during the construction phase, extracts the main features of the rebar data and proposes an M-E-L recognition method. Secondly, based on the actual conditions of the tunnel and the specifications of Chinese tunnel engineering, a rebar model experiment is designed to obtain experimental data. Finally, the feasibility and accuracy of the M-E-L recognition method are analyzed and tested based on the experimental data from the model.

Based on tunnel morphology characteristics, data preprocessing, Euclidean clustering and PCA shape extraction methods, a M-E-L identification algorithm is proposed for identifying secondary lining rebars in highway tunnel construction stages. The algorithm achieves 100% extraction of the first-layer rebars, allowing for the three-dimensional visualization of the on-site rebar situation. Subsequently, through data processing, rebar dimensions and spacings can be obtained. For the second-layer rebars, 55% extraction is achieved, providing information on the rebar skeleton and partial rebar details at the construction site. These extracted data can be further processed to verify compliance with construction requirements.

This paper introduces a laser point cloud method for double-layer rebar identification in tunnels. Current methods rely heavily on manual detection, lacking objectivity. Objective approaches for automatic rebar identification include image-based and LiDAR-based methods. Image-based methods are constrained by tunnel lighting conditions, while LiDAR focuses on straight rebar skeletons. Our research proposes a 3D point cloud recognition algorithm for tunnel lining rebar. This method can extract double-layer rebars and obtain construction rebar dimensions, enhancing management efficiency.

Agricultural science and technology (AST) often need the form of industry-university-research cooperation in successfully completing breakthrough agricultural technology innovations. Therefore, AST industry-university-research cooperation is not only the need of the AST development strategy in China but also the only way for the development of agricultural colleges and universities, agricultural scientific research institutions and agricultural enterprises. Among them, in the process of cooperative breakthrough agricultural technology innovation, the correct selection of partners is the basis for ensuring its effective operation.

Aiming at the time-series characteristics and information ambiguity of decision information in the dynamic selection process of AST industry-university-research partners, this research introduces the dynamic intuitionistic fuzzy multi-criteria decision-making method of time degree and orthogonal projection. On this basis, field theory is used to construct the cooperative innovation capability field model of the partners, and the threshold for the partners to enter or exit the system is designed to dynamically select and eliminate the partners.

Results show that this method fully considers the situation of cooperative innovation resources within the AST industry-university-research system and the resource complementarity of candidate partners.

Combined with examples from agricultural scientific research institutions, the applicability and superiority of the model are verified.

The purpose of this paper is the study of flexural properties of architected lattice beams composed of modified body-centered cubic (BCC) structures when such are additively manufactured with the liquid crystal display method. The BCC topology was modified by grading the dimensions of the cross-sections of the struts that compose them and their targeted distribution within the lattice beam.

Six gradations of strut cross-sections were proposed, and their effective stiffness was evaluated in compression finite element (FE) simulations. These were compared and categorized according to their stiffness. Then, these were distributed and arranged in a targeted manner, following two approaches: longitudinal and transversal. Experimental three-point bending tests and FE simulations were performed to characterize their effective flexural properties. The properties of targeted distributions were contrasted with those of uniform distributions.

Although the structures with longitudinal and transverse distribution presented the same relative density, they demonstrated different stiffness and strength. Beams with longitudinal distribution were 77% stiffer than those with transverse distribution. The method proposed here demonstrates how the effective mechanical properties and failure modes can be tailored by modifying the material arrangement in engineered structures while keeping the amount of material used constant.

The flexural properties of lattice beams with two types of grading and unit cell arrangements were studied. The literature has not deeply studied such a double degree of matter distribution and arrangement in structures.