Advancing age substantially increases cancer risk, primarily due to progressive biological alterations over time. With the global population aging rapidly, the incidence of cancer is also rising. In cancer immunotherapy, patient age is emerging as a critical determinant for both identifying and predicting responses to immune checkpoint inhibitors. Aging is accompanied by significant changes in the immune system, notably a decline in T-cell function and a reduction in tumor-infiltrating lymphocytes, which collectively reshape the tumor microenvironment and weaken antitumor immunity. Immune senescence compromises the ability to recruit and maintain functional TIL activity, thereby limiting the benefits of immune checkpoint inhibitors therapy. Furthermore, senescent tumor cells influence their surroundings by releasing a broad spectrum of pro-inflammatory cytokines and chemokines, a phenomenon termed the senescence-associated secretory phenotype, while simultaneously upregulating immune inhibitory markers such as PD-L1. In addition, age-related immune dysregulation exacerbates cellular exhaustion, leading to abnormal expression of key biomarkers that govern immune checkpoint inhibitors efficacy and ultimately attenuating antitumor immune responses. This perspective discusses the mechanisms through which aging alters systemic immunity and the tumor microenvironment, thereby reducing immunotherapy effectiveness. By integrating current mechanistic insights into the interplay between aging and cancer immunobiology, we highlight potential aging-related biomarkers that may improve therapeutic strategies in geriatric oncology. A deeper understanding of these interactions is essential for developing personalized immunotherapeutic approaches tailored to the unique needs of elderly cancer patients.

The treatment of early invasive breast cancer in older patients poses unique challenges due to the distinct biological, clinical, and psychosocial complexities associated with aging. As the population of breast cancer patients aged 70 years and older continues to grow, their persistent underrepresentation in clinical trials remains a major obstacle to evidence-based treatment decision-making. To support the development of a more effective, personalized, and patient-centered approach to systemic therapy, this review outlines the biological features of breast cancer in older women, synthesizes current evidence on neoadjuvant and adjuvant systemic therapies, and discusses strategies for individualized treatment decision-making. Key recommendations include the use of hormonal therapy as the standard of care for hormone receptor positive breast cancer, neoadjuvant therapy primarily when tumor downstaging is desired, and chemotherapy or anti-human epidermal growth factor receptor 2 therapy for relatively fit older patients with high-risk subtypes. Additionally, bisphosphonates may help preserve bone health and reduce recurrence risk. Novel targeted therapies such as cyclin-dependent kinase 4 and 6 inhibitors and immune checkpoint inhibitors show promise, though further studies are needed to confirm their safety and efficacy in older populations. Comprehensive geriatric assessments are essential for identifying patient frailty and vulnerabilities, while predictive tools such as the Cancer and Aging Research Group Breast Cancer model can help assess toxicity risk. In this population, competing risks of non-cancer-related mortality may reduce the absolute benefit of systemic treatment. For patients with elevated risks of other-cause mortality, the potential survival benefit of cancer therapy may be negligible. Predictive models that account for competing mortality, such as the PORTRET tool, can facilitate personalized and shared decision-making.

Aims: This study proposes a multimodal emotion recognition framework that combines a private-shared disentangled multimodal variational autoencoder (DMMVAE) with a long short-term memory (LSTM) network, herein referred to as DMMVAE-LSTM. The primary objective is to improve the robustness and generalizability of emotion recognition by effectively leveraging the complementary features of electroencephalogram (EEG) signals and facial expression data.

Methods: We first trained a variational autoencoder using a ResNet-101 architecture on a large-scale facial dataset to develop a robust and generalizable facial feature extractor. This pre-trained model was then integrated into the DMMVAE framework, together with a convolutional neural network-based encoder and decoder for EEG data. The DMMVAE model was trained to disentangle shared and modality-specific latent representations across both EEG and facial data. Following this, the outputs of the encoders were concatenated and fed into a LSTM classifier for emotion recognition.

Results : Two sets of experiments were conducted. First, we trained and evaluated our model on the full dataset, comparing its performance with state-of-the-art methods and a baseline LSTM model employing a late fusion strategy to combine EEG and facial features. Second, to assess robustness, we tested the DMMVAE-LSTM framework under data-limited and modality dropout conditions by training with partial data and simulating missing modalities. The results demonstrate that the DMMVAE-LSTM framework consistently outperforms the baseline, especially in scenarios with limited data, indicating its capacity to learn structured and resilient latent representations.

Conclusion : Our findings underscore the benefits of multimodal generative modeling for emotion recognition, particularly in enhancing classification performance when training data are scarce or partially missing. By effectively learning both shared and private representations, DMMVAE-LSTM framework facilitates more reliable emotion classification and presents a promising solution for real-world applications where acquiring large labeled datasets is challenging.

Aims: Extended reality (XR) has been widely recognized for its ability to evoke empathetic responses by immersing users in virtual scenarios and promoting perspective-taking. However, to fully realize the empathic potential of XR, it is necessary to move beyond the concept of XR as a unidirectional “empathy machine.” This study proposes a bidirectional “empathy-enabled XR” framework, wherein XR systems not only elicit empathy but also demonstrate empathetic behaviors by sensing, interpreting, and adapting to users’ affective and cognitive states.

Methods: Two complementary frameworks are introduced. The first, the Empathic Large Language Model (EmLLM) framework, integrates multimodal user sensing (e.g., voice, facial expressions, physiological signals, and behavior) with large language models (LLMs) to enable bidirectional empathic communication. The second, the Matrix framework, leverages multimodal user and environmental inputs alongside multimodal LLMs to generate context-aware 3D objects within XR environments. This study presents the design and evaluation of two prototypes based on these frameworks: a physiology-driven EmLLM chatbot for stress management, and a Matrix-based mixed reality (MR) application that dynamically generates everyday 3D objects.

Results: The EmLLM-based chatbot achieved 85% accuracy in stress detection, with participants reporting strong therapeutic alliance scores. In the Matrix framework, the use of a pre-generated 3D model repository significantly reduced graphics processing unit utilization and improved system responsiveness, enabling real-time scene augmentation on resource-constrained XR devices.

Conclusion: By integrating EmLLM and Matrix, this research establishes a foundation for empathy-enabled XR systems that dynamically adapt to users’ needs, affective and cognitive states, and situational contexts through real-time 3D content generation. The findings demonstrate the potential of such systems in diverse applications, including mental health support and collaborative training, thereby opening new avenues for immersive, human-centered XR experiences.

Construction and demolition waste management remains a critical challenge in China, where low recycling rates and fragmented stakeholder coordination impede the transition toward a circular economy. This study develops a tripartite evolutionary game model involving government regulators, construction enterprises, and recycling enterprises. By integrating replicator dynamics with MATLAB-based simulations and incorporating phased subsidies, penalties, and firm-level cost parameters, the model analyzes the strategic evolution of stakeholder behaviors. Simulation results show that a stable cooperative equilibrium emerges when subsidies for construction and recycling enterprises are set at E₁ = 50 and E₂ = 48, and the penalty H is at least 50. Under these conditions, the probability of adopting proactive strategies exceeds 0.9 within 50 iterations. While penalties remain consistently effective across all levels of market maturity, the marginal utility of subsidies declines sharply as the resource utilization rate (q) approaches 0.5. Increasing the initial cooperation probability of construction enterprises from 0.5 to 0.8 reduces convergence time by approximately 35%. High sorting costs (F > 45) and low resale revenues (I < 40) are identified as key barriers to sustained cooperation. Based on these findings, a three-phase policy strategy is proposed: subsidies should be deployed in the early stage to lower entry barriers; penalties should be prioritized during the transition phase; and transaction cost reduction and long-term revenue mechanisms should be emphasized in the mature stage. The study provides both theoretical and practical insights into sustainable governance of construction and demolition waste.

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.

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.

Bone defects represent a significant orthopedic challenge, with associated disorders continue to pose clinical difficulties. In the biomedical field, advancements in three-dimensional (3D) printing technology have established bone tissue engineering (BTE) scaffolds as a promising approach for effective treatment. These scaffolds not only provide structural support for cells but also serve as templates to guide bone tissue regeneration. In recent years, owing to their exceptional physicochemical properties, two-dimensional nanomaterials (2D NMs) have garnered increasing attention and have been widely explored as additives in the fabrication of BTE scaffolds. This review centers on the most recent developments in the combination of 2D NMs and 3D printing for BTE applications. It begins with a concise summary of the common synthesis and surface modification methods of 2D NMs. Then, it offers a comprehensive overview of recent advancements in their use within BTE. Finally, it discusses current challenges and future perspectives regarding the application of 2D NMs-based 3D-printed scaffolds in bone tissue regeneration.

The development of high-performance narrowband red organic light-emitting diodes (OLEDs) has garnered significant attention, offering both exciting opportunities and formidable challenges. In this study, we report the synthesis of a novel red thermally activated delayed fluorescence (TADF) molecule, 10,10',10''-([1,2,4]triazolo[1,5-a][1,3,5]triazine-2,5,7-triyltris(benzene-4,1-diyl))tris(10H-phenoxazine) (TPXZ-TAZTRZ), which integrates a highly electron-deficient triazolotriazine unit as the acceptor and three strongly electron-donating phenoxazine (PXZ) moieties as donors. TPXZ-TAZTRZ exhibits a small singlet-triplet energy gap, enabling a rapid reverse intersystem crossing rate. Additionally, it shows a broad emission spectrum peaking at 634 nm, spanning the yellow-to-red region. These features render TPXZ-TAZTRZ as an ideal TADF sensitizer for narrowband red fluorescent OLEDs. Accordingly, TPXZ-TAZTRZ was employed to sensitize the conventional fluorescent emitter DBP. The resulting TADF-sensitized fluorescence OLEDs (TSF-OLEDs) demonstrated efficient energy transfer from the TADF sensitizer to the emitter, effectively addressing the limitations previous encountered with TADF systems. The devices achieved high-performance pure red emission, with Commission International de l'Éclairage (CIE) coordinates of [0.67, 0.33], an emission peak at 612 nm, a narrow full width at half maximum (FWHM) of 27 nm, and a maximum external quantum efficiency of 16.2%.

Aims: This study aims to enhance the design of augmented reality (AR) technologies for remote collaboration by examining the complex relationships among individual factors (user characteristics), psychological and physiological states during AR-mediated remote collaboration, and outcomes within an Input-Mediator-Output (IMO) model. The goal is to evaluate how individual characteristics influence psychological and physiological experiences, as well as task performance, in AR-mediated collaboration.

Methods: We hypothesize and evaluate an IMO model and use correlation analyses to examine the relationships among person-related input variables (e.g., predispositions, traits, attitudes, states, and contextual factors), psychological and physiological emergent states, and performance-related output variables.

Results: Our results demonstrate that individual characteristics significantly influence subjective experiences, physiological responses, and task performance, emphasizing the critical role of individual differences, alongside task- and technology-related factors, in shaping collaboration experiences and performance. These findings highlight the importance of considering individual characteristics in the design of AR tools to optimize user well-being and performance outcomes.

Conclusion: Our study provides a foundational framework for understanding the interplay between individuals, tasks, and technology, underscoring the need for AR tools that align with user characteristics. It also lays the groundwork for future IMO research in AR-mediated remote collaboration, contributing to the development of more effective and health-promoting AR technologies.

High-performance and long-lifetime blue organic light-emitting diodes (OLEDs) are crucial for meeting the demands of advanced display and lighting technologies. Despite high device efficiency has been achieved in blue OLEDs, development of high-performance and long-lifetime blue OLEDs still lag far behind their red/green counterparts due to the presence of long-lived high-energy triplet excitons and polarons. Given the critical role of charge and exciton management in both the emission and degradation processes of OLEDs, this review systematically summarizes strategies for suppressing charge leakage and exciton quenching, as well as for enhancing exciton utilization in blue fluorescent, phosphorescent, and thermally activated delayed fluorescent (TADF) OLEDs. In this context, we further discuss the roles of conventional fluorescent hosts, triplet-triplet annihilation/hot exciton hosts, TADF assistant hosts, phosphorescent assistant hosts, and exciplex/electroplex hosts in regulating charge and exciton dynamics in blue OLEDs. Additionally, the modification of emitting layer materials is highlighted as a key strategy for managing charge and exciton processes in efficient and stable solution-processed blue OLEDs. Based on current insights into the efficiency and operational stability of blue OLEDs, this review proposes feasible charge and exciton management strategies to address the current challenges.

Neuropeptides are crucial signaling molecules that regulate diverse physiological processes spanning growth, social behavior, learning, memory, metabolism, homeostasis, reproduction, and neural differentiation across both nervous and peripheral systems. Dysregulation of neuropeptides signaling is closely linked to various pathological conditions, such as neurological disorders, metabolic diseases, cardiovascular conditions, and even cancer, positioning them as potential therapeutic agents or targets for intervention. In recent years, research into neuropeptides has accelerated, with vast amounts of data continuously accumulating in multiple databases. However, the study of neuropeptides is often impeded by the need for extensive and time-consuming experimental investigations. As a result, computational tools have become essential for the rapid, large-scale identification of neuropeptides. This review systematically discusses neuropeptide-related databases and computational tools. These databases organize extensive data on neuropeptide sequences, structures, and functions. Among these, NeuroPep2.0, with 11,417 neuropeptide entries, is currently the most widely used dataset for neuropeptide prediction. Additionally, this review explores the application of computational approaches in neuropeptide prediction. While early methods predominantly relied on homologous sequence alignment and biochemical feature statistics, recent advances in machine learning have significantly enhanced prediction accuracy and efficiency. Tools such as NeuroPred-PLM and DeepNeuropePred, developed by our research group using protein language models, have substantially improved prediction performance. In conclusion, this review provides a comprehensive overview of current neuropeptide databases and computational tools, offering researchers a thorough survey of available resources and analytical methods, and emphasizing the necessity of continuous optimization to advance neuropeptide research and its therapeutic applications.

The architecture, engineering and construction (AEC) industry is currently encountering numerous challenges and actively pursuing industrial upgrades through digital transformation. Digital twin (DT) technology aligns well with the industry's evolving needs due to its capabilities in data integration, intelligent decision-making, and effective management of project progress, efficiency, and quality. This study conducts a comprehensive literature review to analyze the strengths, weaknesses, opportunities, and threats (SWOT) associated with DT applications in the AEC sector. Additionally, field visits and semi-structured interviews were conducted on a selected construction project where DT was implemented, allowing for an in-depth examination of both its significant benefits and potential limitations. To further evaluate and prioritize the identified SWOT factors, a SWOT-AHP analysis was performed. The results indicate that the AEC industry has a strong interest in DT’s advantages, particularly its potential to enhance resource allocation and process efficiency. Furthermore, the analysis reveals that the strategic quadrilateral occupies the largest area in the fourth quadrant (0.8554), with the strategic center of gravity located at (0.2321, -0.04135), suggesting that Strengths-Threats (ST) strategies should be implemented to support the future development and adoption of DT. Based on this strategic framework, the study formulates actionable development strategies to facilitate the effective integration and widespread adoption of DT in the AEC industry.

In pursuit of proactive safety management in construction, accurate and real-time recognition of workers’ cognitive states is essential for crafting targeted interventions to minimize safety risks. Unlike conventional manual and qualitative methods, electroencephalogram (EEG) offers a reliable and objective solution for cognitive state recognition. Notably, efforts that integrate EEG with deep learning have exhibited significant advancements. Nevertheless, certain constraints, such as experimental costs and limited participants, have caused a scarcity of high-quality data, which has impeded performance in recognition tasks. Although transfer learning demonstrates its capability in addressing this challenge, the lack of relevant explorations into EEG-based cognitive state recognition remains a significant research gap. Therefore, customizing pre-trained deep learning models for these tasks would be beneficial. This study aims to develop pre-trained models to advance future studies in this domain through transfer learning, encompassing: 1) extracting extensive and accurately labeled EEG data from the DEAP dataset, 2) selecting appropriate network architectures and implementing pre-trained model development, and 3) collecting EEG data for mental fatigue recognition and evaluating model effectiveness. Rigorous evaluation suggests a substantial improvement in model performance, with test accuracy increasing from 63.19% to 92.29% by leveraging the pre-trained convolutional neural networks (CNN)—long short-term memory (LSTM) model. In conclusion, this study significantly contributes to enhancing safety management for construction workers through EEG by providing validated pre-trained models to address challenges of data scarcity. Future research may advance by exploring additional network architectures, increasing sample size, and considering more specific recognition tasks for model evaluation.

This paper explores two virtual child simulators, BabyX and the VR Baby Training Tool, which provide immersive, interactive platforms for child-focused research and training. These technologies address key ethical and practical constraints, enabling the systematic study of caregiver-infant interactions and the development of professional relational skills with young children. We examine their design, features, and applications, as well as their future trajectories, highlighting their potential to advance research and improve training methodologies.

This study investigates the integration of Building Information Modeling (BIM) and the Internet of Things (IoT) in construction projects to enhance efficiency, safety, and lifecycle monitoring. Despite the significant potential of BIM and IoT to facilitate the transformation of the construction industry, existing research lacks a comprehensive framework that addresses its interoperability challenges, data security concerns, and real-world implementation barriers. To address this gap, a multiple case study approach was employed, incorporating semi-structured interviews, document analysis, and real-world project evaluations. The results find that standardized protocols, data encryption, and modular IoT devices are essential for effective BIM-IoT adoption across different construction phases. The proposed framework offers a structured approach for construction teams to effectively utilize BIM and IoT, facilitating smarter and more sustainable project management. The contribution of this paper resides in the detailed analysis of the BIM-IoT applications, clearly demonstrating its advantages, such as improving building productivity and safety, and its potential to align with China’s sustainable urban development goals. The study also provides a forward-looking prediction of the future development trend of BIM-IoT in China, offering valuable insights for construction project teams and decision makers. By demonstrating the structured framework and its practical application, this study provides guidance for the industry’s transition to smarter and safer construction practices.

Digital twin (DT) technology is revolutionizing the architecture, engineering, construction, and operation (AECO) industry, driven by the advancements of Construction 4.0 (C4.0). Despite this, adopting DT in the AECO sector remains limited due to various barriers. This study, driven by four research questions (RQ), aims to advance DT adoption in the AECO industry under C4.0 using an integrated approach. The degrees of influence and importance ranking of barriers are assessed through the decision-making trial and evaluation laboratory (DEMATEL). The hierarchy and causal relationships among barriers are revealed through interpretative structural modeling (ISM). Then, barriers are divided into four clusters employing the cross-impact matrix multiplication applied to classification (MICMAC) method. The paper reveals that "lack of government financial and policy support" is the most critical barrier, "lack of trust and long-term perspective in DT" is the most significant direct-influence barrier, and "immature 3D engine technology" is the most fundamental barrier. By exploring interrelationships and prioritizing barriers, the study provides insights to enhance adopting DT in the AECO industry in the context of C4.0.