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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.

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.

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.

The construction industry’s pursuit of sustainable and high-performance materials has led to the exploration of alternative aggregates and innovative additives. This paper investigates the combined influence of recycled aggregates (RA) and nano TiO₂ particles on M20 and M30 concretes, addressing ecological concerns and seeking to improve material properties. RA, sourced from construction and demolition waste, presents a sustainable solution to alleviate the environmental impact associated with traditional virgin aggregates (VA). However, challenges related to the mechanical strength and durability often hinder the widespread use of RA in concrete. This study aims to bridge this gap by exploring the reinforcing potential of nano TiO₂ particles.

Nanomaterial such as TiO₂ is known for its photocatalytic properties and reinforcement capabilities and has emerged as a promising additive in construction materials. The investigation herein involves the incorporation of nano TiO₂ at percentages of 0.5% and 1% in both VA- and RA-based M20 and M30 concretes. Comprehensive series of tests on mechanical, durability and microstructural properties are conducted for each concrete mix.

Results unequivocally indicate that the addition of TiO₂ significantly improves the properties of concrete, with RA-based concrete exhibiting performance comparable to that of VA-based counterparts. This breakthrough suggests a viable application of RA with TiO₂ in construction projects, promoting sustainability without compromising performance. Following experimental analyses, linear regression and multiple linear regression analyses are used to establish predictive equations correlating interfacial transition zone (ITZ) thickness with TiO₂ percentage, compressive strength, tensile strength, flexural strength and chloride penetration.

These equations serve as valuable tools for predicting ITZ thickness in future concrete formulations based on specified parameter quantities, thereby contributing to informed decision-making in sustainable construction practices. The findings of this study have the potential to contribute to the improvement of environmentally conscious construction methods while also improving the performance and durability of concrete structures.

In the practice of live streaming e-commerce, the consumer demand is usually uncertain, and the inventory and prices can be decided by brand owners or streamers. To this end, this study examines the inventory and pricing decisions of the brand owner and streamer in a live streaming e-commerce supply chain under demand uncertainty.

In this study, four scenarios are considered, i.e. the brand owner determines the inventory and price (Scenario BB), the brand owner determines the inventory and the streamer determines the price (Scenario BS), the streamer determines the inventory and the brand owner determines the price (Scenario SB), and the streamer determines the inventory and price (Scenario SS).

The results show that the inventory and prices, as well as the profits of the brand owner and streamer increase with the consumer sensitivity to streamer’s sales effort level under the four scenarios. The inventory (price) is the highest under Scenario SS (SB), while that is the lowest under Scenario BB (BS). In addition, when the sensitivity is low, the brand owner’s profit is the highest under Scenario BB, otherwise, the profit is the highest under Scenario SS. Regardless of the sensitivity, the streamer’s profit is always the highest under Scenario SS.

Few studies focused on the inventory and pricing decisions of brand owners and streamers in live streaming e-commerce supply chains under demand uncertainty, while this work bridges the research gap. This study can provide theoretical basis and decision support for brand owners and streamers.

In this study, solid formaldehyde, benzoguanamine and butanol were used to synthesize butylated benzo-amino resin by one-step-two-stage method.

This research first examined the influence of solid formaldehyde content on the hydroxymethylation phase. Subsequently, the effects of butanol content, etherification time and hydrochloric acid content on the formation of benzo-amino resin during the etherification stage were studied in detail. In addition, the reaction process was further analyzed through interval sampling withdrawing during the hydroxymethylation and etherification stages. Finally, the synthesized benzo-amino resins were used in the production of high solid content polyester and acrylic coatings and the properties of that were also evaluated.

Based on the experimental findings, the authors have successfully determined the optimal process conditions for the one-step-two-stage method in this study. The hydroxymethylation stage demonstrated the most favorable outcomes at a reaction temperature of 60°C and a pH of 8.5. Similarly, for the etherification stage, the optimal conditions were achieved at a temperature of 45°C and a pH of 4.5. Furthermore, the investigation revealed that a ratio of benzoguanamine to solid formaldehyde to n-butanol, specifically at 1:5.2:15, produced the best results. The performance of the resulting etherified benzo-amino resin was thoroughly evaluated in high solid content coatings, and it exhibited promising characteristics. Notably, there was a significant enhancement in the water resistance, solvent resistance and glossiness of canned iron printing varnish coatings.

Amino resin, a versatile chemical compound widely used in various industries, presents challenges in terms of sustainability and operational efficiency when synthesized using conventional methods, primarily relying on a 37% formaldehyde solution. To address these challenges, the authors propose a novel approach in this study that combines the advantages of the solid formaldehyde with a two-stage catalytic one-step synthesis process. The primary objective of this research is to minimize the environmental impact associated with amino resin synthesis, optimize resource utilization and enhance the economic feasibility for its industrial implementation. By adopting this alternative approach, the authors aim to contribute toward a more sustainable and efficient production of amino resin.