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Understanding the restorative potential of built environments is essential for promoting mental well-being. However, existing studies often rely on static image-based methods, which are inherently limited in capturing the temporal and spatial dynamics of environmental perception. These methods frequently introduce biases, such as selective framing and abrupt transitions, failing to reflect natural viewing behavior. Addressing these limitations, this study investigates the restorative qualities of campus environments using dynamic VR stimuli and eye-tracking technology. By providing continuous temporal and spatial information, dynamic VR stimuli offer a more immersive and ecologically valid approach to understanding how specific environmental features contribute to psychological restoration.

This study investigates the restorative qualities of campus environments using VR eye-tracking technology and dynamic stimuli. Campus environments were filmed through walking sequences and paired with PRS audio prompts. About 40 university students participated in the experiment, with eye-tracking data processed using computer vision-based semantic segmentation and the concept of relative areas of interest, followed by correlation analysis with restorative quality scores.

The results revealed that natural elements such as “sky,” “tree,” “waterscape” and “landscape corridor” were significantly positively correlated with the being-away and fascination dimensions, indicating their role in capturing attention and supporting psychological recovery. Conversely, architectural elements like “architectural corridor” and “building facade” were negatively correlated with the extent dimension, while “architectural open space” positively correlated, enhancing spatial perception and exploration.

These findings underscore the importance of natural elements and open spaces in built environments while also revealing the complex influence of architectural features. The study provides valuable insights for optimizing campus design to support students’ mental health and well-being.

This study aims to explore the environmental and easy-care benefits of diacetate fiber blended textiles, emphasizing their potential in enhancing sustainability and reducing carbon emissions in the textile industry. It addresses the pressing need for innovative materials that combine functional advantages with reduced environmental impacts.

A comprehensive series of experiments was conducted to assess the easy-care properties of fabrics blended with diacetate fibers. These properties include stain resistance, wash dimensional stability and antistatic performance, using standardized textile testing methods. The experimental setup involved a variety of fabric blends tested under simulated conditions that mimic real-world usage to evaluate the effectiveness of diacetate fibers in practical applications.

The inclusion of diacetate fibers significantly enhances several easy-care properties of the textiles. Fabrics containing these fibers showed improved stain resistance, particularly in blends with polyester and cotton, which also exhibited better dimensional stability after washing. Antistatic properties were notably better in diacetate-polyester blends compared to other fiber compositions. Furthermore, the research demonstrated that these fabrics require fewer wash cycles, effectively reducing water and energy consumption, thereby contributing to environmental sustainability.

This study is among the first to systematically quantify the multiple benefits of diacetate fiber blends in textiles, providing a dual focus on environmental impact and practical textile care. The findings offer new insights into the use of sustainable fiber technologies in reducing the ecological footprint of the textile industry while maintaining material performance, supporting the advancement toward a more sustainable fashion industry.

This study is a pilot study exploring the usefulness and ease of use of a prototype VR PetCPR system and discusses the possibility of using it to facilitate pet healthcare skills acquisition. The designed VR PetCPR training system aims to provide pet healthcare professionals with an inexpensive, accessible and reliable CPR training tool and refine their skills in a controlled and simulated environment.

The study was conducted in a one-day workshop. The workshop consisted of the morning section (Section A) and the afternoon section (Section B). Section A was the knowledge acquisition stage. Section B is the VR PetCPR stage. Trainees were then given 30 min to experience the VR PetCPR set. When trainees were ready, they were required to complete two trials of dog CPR practice. After the practice, trainees completed the questionnaire and reported their attitudes toward VR PetCPR practice.

Overall, trainees held positive attitudes toward the effectiveness and usefulness of the VR PetCPR. After practicing skills via VR CPR, over half of the trainees responded that the system is effective in helping them understand the essential knowledge (e.g. operation status, operation positions, etc.) of performing CPR skills on a 30-pound dog. A significantly positive attitude was reported on trainees’ perceptions toward the ease of use of practicing their chest compression skills with the PetCPR. The positive attitudes significantly outnumbered the negative attitudes on explicit instruction and guidance, accessibility, convenience in practice and straightforward interface.

From data collected from 16 animal hospitals in the United States, Europe and Australia with 709 cases, 147 dogs (28%) and 58 cats (30%) temporarily attained ROSC during CPR, and 14 dogs (3%) and four cats (2%) survived to hospital discharge. Training veterinary CPR techniques and implementing RECOVER guidelines still have a long way to go. However, recent virtual reality simulations for CPR training were mainly designed for human patients CPR (Issleib et al., 2021; Liu et al., 2022; Almousa et al., 2019; Wong et al., 2018). The VR PetCPR remains a missing puzzle in the current VR training designs.

This study aims to review recent advancements in solar energy generation and identify future research trends, with a focus on integrating energy storage systems to enhance the reliability and efficiency of solar power.

This study involved conducting a review of 313 articles published between 2014 and 2024 in the Scopus database. The research applied bibliometric analysis techniques, which are bibliographic coupling and co-occurrence analysis, to examine recent advancements and future trends in solar energy generation.

This research highlights recent field contributions through bibliographic coupling, indicating that solar PV is the most environmentally sustainable renewable energy source, with energy storage systems, particularly hydrogen, critical for stability. In addition, co-occurrence analysis on the future research avenue highlights optimizing green hydrogen production and hybrid storage systems while exploring innovative financing models to enhance grid stability and facilitate a sustainable energy transition.

This review recognizes some limitations in bibliometric analysis, such as relying solely on the Scopus database, subjective interpretations of themes and the challenge of distinguishing self-citations from external ones, which can give a misleading impression of a publication’s influence.

This study focuses on exploring how energy storage systems can be integrated with solar energy generation. Although there has been extensive research on renewable energy, this specific intersection has been largely overlooked. By using bibliometric analysis, this study aims to highlight recent developments and future directions in this critical area, ultimately providing valuable insights that can help shape both academic research and practical policy decisions.

The research investigates how green bonds and Fintech contribute to advancing sustainable energy adoption in India while addressing the intricate investment risks associated with green initiatives.

This study employs a stringent approach, conducting an extensive examination of data to analyze the interplay among green bonds, Fintech, and the renewable energy industry in India.

The study unveils Fintech’s capacity to optimize financing for renewable projects in India by leveraging blockchain technology and digital platforms, enhancing accessibility and investor confidence. Additionally, it underscores the role of green bonds in fostering the development of eco-friendly energy sources.

This research offers novel insights into the dynamic relationship among green bonds, Fintech, and India’s renewable energy sector. It emphasizes the importance of adaptable regulatory frameworks in facilitating sustainability efforts and provides valuable guidance for stakeholders navigating environmental initiatives.

Compared to traditional (domestic) e-commerce consumers, cross-border electronic commerce (CBEC) consumers may face greater information asymmetry in the CBEC purchase process. Given this background, however, the literature has paid limited attention to the informational antecedents that influence consumers' perceptions of transaction costs and their CBEC purchase intentions. To fill this gap, this study integrates the elaboration likelihood model (ELM) and transaction cost theory (TCT) to develop a model for exploring how product (website informativeness, product diagnosticity and website interactivity as the central route) and external (country brand, website policy and vendor reputation as the peripheral route) informational antecedents affect consumers’ evaluations of transaction costs in terms of uncertainty and asset specificity and their CBEC purchase intentions.

This study employs a survey approach to validate the model with 766 Generation Z CBEC consumers based on judgment sampling. The partial least squares (PLS) technique is adopted for data analysis.

The results show that all the proposed central and peripheral informational antecedents reduce consumers’ perceptions of uncertainty and asset specificity, which in turn negatively influences their CBEC purchase intentions.

Through this investigation, this study increases our understanding of how product and external informational antecedents affect consumers’ evaluations of transaction costs, which subsequently determine their CBEC purchase decisions. This study offers theoretical contributions to existing CBEC research and has practical implications for CBEC organizations and managers.

It is found that previous studies only focus on how digital transformation contributes to individual firms’ green innovation performance while ignoring the important role that it plays in the spillover and diffusion of green innovations among peer firms. Therefore, this study aims to investigate the influence of focal firms’ digital transformation on the spillover of green innovation among peer firms in heavily polluting industries mediated by environmental, social and governance (ESG) performance and agency conflict. Further, this study is also expected to explore the effects of digital transformation’s green innovation spillover.

This study chooses 6,438 A-share heavily polluting listed firms in the stock exchanges based in Shanghai and Shenzhen in China during 2010–2020 as samples and tests the hypothesis with ordinary least squares (OLS) regression. Results prove to be robust to a battery of robustness analyses the authors performed to take care of endogeneity.

The results show that the focal firm’s digital transformation may trigger their peer firms’ green innovation spillover and prompt them to engage in green innovation activities actively. The mechanism test shows that peer firms’ ESG performance and agency conflict mediate the influence path between digital transformation and peer firms’ green innovation spillover. Finally, among heavily polluting firms with high industry competition and large scale, digital transformation’s green innovation spillover effects are more significant in conventional energy-based source control, end-of-pipe treatment and substantive green innovation.

This study is possible to provide a potential driving mechanism of green innovation spillovers. The findings lay a sound foundation for future research, providing important theoretical support and practical insights for digital transformation to empower heavily polluting industries to achieve green transformation and low-carbon development.

Homophily, a prominent phenomenon in social networking, profoundly shapes user behaviors on social media but has not been well studied in the livestream commerce context. This study aims to investigate its moderation role in leveraging the effects of key livestream commerce factors – perceived expertise of live streamers and perceived interaction during live streaming – on audience trust, a critical determinant of purchase intentions.

A survey was conducted among livestream shoppers on Taobao. A sample of 313 responses was analyzed. SPSS (version 29) was used for general statistical analysis. The partial least squares structural equation modeling approach with SmartPLS 4.1 software was used to assess the research model and hypotheses.

The results reveal noteworthy differential effects of homophily: it negatively moderates the expertise–trust association but positively moderates the interaction–trust relationship. When the audience perceives strong homophily with live streamers, their trust in these live streamers becomes increasingly contingent on the level of interaction, whereas the effect of perceived expertise diminishes.

The insights on the differential effects of homophily are novel to the literature. These findings extend theoretical understanding of the homophily effect and provide valuable guidance for live streamers, marketers and platforms seeking to reinforce audience trust and drive purchase intentions in livestream commerce.

This research focuses on developing a dual-nozzle slurry-based extrusion 3D printer capable of fabricating intricate zirconia structures. The designed 3D printer combines material extrusion and photopolymerization technologies to improve material diversity, precision and cost-effectiveness.

The 3D printer design incorporates ultraviolet curing to instantly cure extruded zirconia slurry thereby, eliminating the need for a step-wise curing procedure. Printing parameters were optimized to achieve high-quality prints, and supports made of polyethylene terephthalate glycol were used for intricate geometries. The printability and mechanical properties were evaluated for two different zirconia slurry compositions: 70 / 30 and 80 / 20 powder-to-resin weight percentages. The printed green body was subjected to a two-phase sintering process.

The 3D printer fabricated structures with features subtending angles greater than 50 degrees and a filling density above 80% without any supports. Shrinkage analysis showed the 80 / 20 composition resulted in higher density parts, with shrinkage ratios of 25.23%, 26.23% and 27.26% along the X, Y and Z axes, respectively. The sintered objects displayed hardness (1525 HV) and flexural strength (117 MPa), with minimal porosity.

This study demonstrates the development of a cost-effective dual-nozzle 3D printer that can effectively fabricate functional parts with complex material compositions and geometries that can cater to the futuristic requirements of high-end industries.

Based on the advice response theory perspective, this study aims to investigate the effects of human managers and artificial intelligence (AI) systems on customer-contact employees’ aversion to AI systems in the hospitality industry. It examined the mediating role of advice content characteristics (efficacy, feasibility and implementation limitations) and advice delivery (facework and comprehensibility) on customer-contact employees’ aversion to AI systems.

Two scenario-based experiments were conducted (Nexperiment 1 = 499 and Nexperiment 2 = 300). Experiment 1 compared the effects of different advisor types (human managers vs AI systems) on employees’ aversion to AI systems. Experiment 2 investigated the mediating role of advice content characteristics (efficacy, feasibility and implementation limitations) and advice delivery (facework and comprehensibility).

The results showed employees tended to prioritize advice from human managers over output from AI systems. Moreover, advice content characteristics (efficacy, feasibility and implementation limitations) and advice delivery (facework and comprehensibility) played mediating roles in the relationship between advisor type characteristics and employees’ aversion to AI systems.

These findings contribute to the understanding of AI system aversion and provide theoretical insights into management practices involving customer-contact employees who interact with AI technology in the hospitality industry.

The primary contribution of this study is that it enriches the literature on employee aversion to AI systems by exploring the dual mediators (advice content characteristics and advice delivery) through which advisor type characteristics affect AI system aversion.