Science Exploration Press

Diabetic foot ulcers (DFUs) are a serious complication of diabetes and often result in amputation. Traditional wound care methods have limitations in addressing the complex pathophysiology of DFUs. Hydrogel dressings, a type of biomaterial, have emerged as promising candidates for treating DFUs due to their biocompatibility, ability to retain moisture, and potential to incorporate therapeutic agents. Hydrogels create a moist environment, promote cell migration, and reduce inflammation, thereby supporting wound healing. Incorporating bioactive molecules, such as growth factors and anti-inflammatory agents, can further enhance the effectiveness of hydrogels. Additionally, stem cells can be loaded into hydrogels to improve tissue regeneration and help modulate the wound microenvironment. Recent advancements in hydrogel technology have also led to the development of smart hydrogels that can respond to changes in wound conditions, such as glucose levels and pH. These intelligent dressings offer personalized care by delivering targeted treatments based on real-time wound data. This review explores the mechanisms behind DFU development, the role of hydrogels in wound healing, and recent progress in hydrogel technologies for personalized DFU care.

This study integrates ChatGPT and complex network (CN) techniques into an accident analysis framework designed to reduce manual effort in accident causation analysis. The proposed framework supports construction stakeholders in extracting causal factors (CFs) from accident reports and identifying both critical CFs and key causal paths. A multistep research design was adopted to develop and validate this novel framework for analyzing crane-related construction accident reports using ChatGPT and CN techniques. First, ChatGPT was prompted to extract CFs from a database of crane-related accident reports. Second, evaluation metrics and an expert questionnaire survey were developed to assess ChatGPT’s performance in CF extraction. Finally, CN analysis was conducted to explore the relationships among CFs and to identify critical causal paths. A total of 95 crane-related accidents from Hong Kong (2011-2020) were analyzed using the proposed framework. The critical CFs identified included: “carelessness”, “operation error”, “crane unbalanced”, “machine failure”, “parts of a crane fall”, “object strike”, “worker fall”, “trapping”, “collapse of crane”, and “load drop”. The critical path identified was: “broken/failed rope” → “load drop” → “object strike”. The primary contribution of this study lies in developing an AI-driven framework that combines the contextual understanding of ChatGPT with the structural analysis capabilities of CN methods—offering a novel and scalable approach to accident causation analysis in the construction industry. Safety managers and practitioners can leverage this framework to improve the automation, consistency, and interpretability of construction accident reporting.

Photocatalytic reduction of CO₂ to produce valuable fuels or chemicals is a promising CO₂ utilization technology, which is of great significance for carbon emission reduction. The unique features of the UiO series of metal-organic frameworks (MOFs), such as the excellent water and chemical stability, notable structural tunability, broad and adjustable light-harvesting capacity, strong electron-hole separation ability, and high porosity and specific surface area, make them a class of photocatalysts with great potential for the CO₂ reduction reaction (CO₂RR). Significant progress has been made in the development of efficient UiO-based photocatalysts for CO₂RR. This paper provides a summary of recent research advances in UiO-MOFs for photocatalytic CO₂RR. The characteristics, synthesis methods, and modifications of UiO-based materials, along with their photocatalytic performance, are described. Various modification strategies for UiO-MOFs, including band-gap engineering, defect engineering, introduction of metal species, and construction of composite materials, are summarized and discussed. The challenges facing UiO-MOFs in photocatalytic CO₂RR and potential future development directions are also presented. This review is intended to provide insights into CO₂ photoreduction using UiO-based materials and to encourage further research and development in this promising field.

This study explores the adoption of Virtual Reality (VR) in the Nigerian construction industry, with a focus on its potential benefits and associated challenges. Purposive and snowball sampling techniques were employed to select 52 construction professionals from Benin, Edo State,an emerging urban center with extensive construction activity. Adopting a quantitative approach, the research utilized a five-point Likert scale survey to assess perceptions of the benefits and barriers to VR adoption. The survey was pretested for clarity and reliability, and data were collected via the Qualtrics platform. The findings indicate that the key benefits of VR include improved task-technology alignment, enhanced workplace safety through virtual training, and more effective remote collaboration. VR was also found to enrich user experience and learning engagement by simulating high-risk scenarios to aid hazard prevention. Nevertheless, the study identifies several critical barriers to adoption, such as uncertainty regarding learning outcomes, technical disruptions, and high implementation costs. Despite these limitations, VR holds considerable promise for transforming training and professional development in the construction sector. To maximize its impact, the study recommends the development of customized training modules, technological improvements to enhance system reliability, and government support to mitigate implementation costs. Overall, VR has the potential to significantly improve training effectiveness, safety standards, and operational efficiency in the Nigerian construction industry, provided that the identified barriers are adequately addressed.

Amid growing global attention to occupational health and safety, the construction industry faces critical challenges associated with engineered stone, which emits high concentrations of respirable crystalline silica during processing and has been linked to severe lung diseases. In response, Australia enacted a comprehensive nationwide ban on engineered stone in July 2024. Drawing on media framing theory, this study analyzes public discourse and sentiment surrounding the ban by examining 7,198 comments collected from Reddit and YouTube. Through Latent Dirichlet Allocation, three dominant themes emerged: health risks and safety concerns, economic impacts and industry transition, and regulatory implementation. Sentiment analysis revealed that 55.5% of the comments expressed negative sentiment, mainly centered on economic concerns, while 21.3% were positive, emphasizing health benefits, and 23.1% were neutral. Economic impact frames predominated among negative comments, whereas health risk frames were more common in positive ones. These findings suggest that future policy communications should more effectively integrate narratives around both health protection and economic transition. This study contributes to the methodological development of sentiment analysis and offers practical insights for policy formulation and implementation.

Aims: Storytelling has evolved alongside human culture, giving rise to new media such as social robots. While these robots employ modalities similar to those used by humans, they can also utilize non-biomimetic modalities, such as color, which are commonly associated with emotions. As research on the use of colored light in robotic storytelling remains limited, this study investigates its integration through three empirical studies.

Methods: We conducted three studies to explore the impact of colored light in robotic storytelling. The first study examined the effect of emotion-inducing colored lighting in romantic storytelling. The second study employed an online survey to determine appropriate light colors for specific emotions, based on images of the robot’s emotional expressions. The third study compared four lighting conditions in storytelling: emotion-driven colored lights, context-based colored lights, constant white light, and no additional lighting.

Results: The first study found that while colored lighting did not significantly influence storytelling experience or perception of the robot, it made recipients felt more serene. The second study showed improved recognition of amazement, rage, and neutral emotional states when colored light accompanied body language. The third study revealed no significant differences across lighting conditions in terms of storytelling experience, emotions, or robot perception; however, participants generally appreciated the use of colored lights. Emotion-driven lighting received slightly more favorable subjective evaluations.

Conclusion: Colored lighting can enhance the emotional expressiveness of robots. Both emotion- driven and context-based lighting strategies are appropriate for robotic storytelling. Through this series of studies, we contribute to the understanding of how colored lights are perceived in robotic communication, particularly within storytelling contexts.