Significant progress in clinical care has extended human life expectancy to unprecedented levels. However, this trend has been parallelled by a rise in years lived with poor health, posing profound challenges not only to individual quality of life, but also to substantial medical and socioeconomic burdens at the population level. This underscores the urgent need for strategies that extend healthspan alongside lifespan. In this regard, nicotinamide adenine dinucleotide (NAD⁺) has emerged as a central metabolic cofactor and signaling molecule that regulates processes fundamental to health and longevity, including energy metabolism, mitochondrial function, inflammation, and DNA repair. Importantly, intracellular NAD⁺ levels decline with age across multiple tissues and organ systems, and restoring NAD⁺ content has been shown to reinstate cellular and physiological function in various model systems. Among the strategies to augment NAD⁺, supplementation with its precursors, namely nicotinic acid/niacin, nicotinamide, nicotinamide riboside, and nicotinamide mononucleotide, represents the most practical and extensively studied approach. Over the past two decades, preclinical research and an increasing number of clinical trials have investigated the therapeutic potential of these precursors in preventing or reversing age-associated decline and pathologies. In this review, we synthesize recent clinical advances, critically evaluate the promise and limitations of NAD⁺ precursor supplementation, and discuss future directions for leveraging NAD⁺ metabolism to improve healthspan in a rapidly aging global population.

The growing complexity of construction projects demands decision processes that can integrate diverse professional perspectives across design, cost control, construction, and acceptance. Traditional sequential management approaches often leave conflicts unresolved until late stages, causing delays, cost overruns, and rework. To address this issue, this study develops a cross-stage workflow for construction projects that embeds artificial intelligence into collaborative decision-making. The workflow employs digital agents representing different roles, which engage in structured debates supported by evidence from building information modeling models, engineering drawings, cost records, and technical standards. Through iterative exchanges, alternative solutions are proposed, challenged, and refined, with the process producing decisions that are transparent, traceable, and adaptable to changing project conditions. Results from four representative scenarios confirm significant improvements over conventional practice: unresolved design clashes were reduced by more than 40%, cost forecast deviations narrowed from around 12% to below 5%, schedule variance under disruption decreased by about 18%, and first-pass acceptance rates increased from 74% to 88% with fewer reworks. A longer-term case study further demonstrated reductions in cost variance and project duration, together with higher stakeholder satisfaction. By coupling AI-enabled debate mechanisms with established digital construction environments, the workflow turns disciplinary conflicts into structured reasoning that enhances decision quality. The findings highlight how intelligent and collaborative approaches can deliver measurable gains in cost, time, and quality, while offering a practical pathway for advancing smart construction practices.

Effective scheduling in construction projects increasingly depends on allocating scarce multi-skilled labour under strict precedence and capacity constraints. Reinforcement learning (RL) techniques have presented outstanding performances in addressing Resource Constrained Project Scheduling Problem (RCPSP) instances. However, studies are lacking that utilise RL algorithms in solving extended RCPSP like the Multi-Skilled RCPSP (MSRCPSP). MSRCPSP is a nondeterministic polynomial time (NP)-hard problem that enables activity start times and allocation of multi-skilled resources to be determined simultaneously. Unlike the classical RCPSP, each activity specifies explicit skill requirements rather than anonymous capacity units. This study formulates scheduling as a Markov decision process and compares five optimisation agents, including the genetic algorithm, particle swarm optimisation, black-winged kite algorithm, deep Q-Network (DQN), and proximal policy optimisation (PPO), on benchmark instances from the MSLIB library. The results showed that solutions produced by PPO and DQN concentrate near the reference optima. PPO, in particular, attains the largest number of optimal or near-optimal schedules and the shortest makespans. In several instances, the deep reinforcement learning (DRL) agents also outperform the benchmark solutions, plausibly because CPLEX, constrained by a 60-second time limit, returns suboptimal solutions. Overall, the comparative results provide a practical benchmark of widely used heuristics and metaheuristics against DRL baselines, offering guidance for future algorithm selection and hybridisation for MSRCPSP.

Aims: Unique in the broader category of drug-resistant cells, persister cancer cells (PSs) acquire their tolerance to compounds through reversible, chromatin-mediated changes, allowing them to ‘persist’ in the face of cancer therapeutic agents. PSs are implicated in minimal residual disease from which cancer relapse occurs, and given their established sensitivity to ferroptosis, PSs present a critical point through which identification and targeting of drug-resistant cancers may be possible. Ferroptosis sensitivity in drug-resistant cancers may be caused by the attainment of the persister state, or it may merely be correlative with this state and due instead to extended inhibition of oncogenic signaling or the induction of chemotherapy stress. Nonetheless, ferroptosis sensitivity has emerged as a common phenotype across multiple PS and drug-resistant cancer cell types. Identifying biomarkers for and drivers of ferroptosis sensitivity in drug-resistant and PS cells is therefore a high priority.

Methods: We derived PS cells from the lung carcinoma cell line PC9 (PS^PC9), performed transcriptomic analysis, and subsequently lipidomics on the PC9/PS^PC9 system. Additionally, we reverted PS^PC9 cells to the ferroptosis-resistant parental state (PC9^{PS -> PC9}) and assessed the resulting lipid changes. We generated two additional PS-like cell models: PS-like prostate carcinoma (PS^LNCaP) from LNCaP cells and PS-like fibrosarcoma (PS^HT1080) from HT1080 cells, with lipidomics analysis. Finally, we performed a mitochondrial elimination assay and assessed its effect on ferroptosis sensitivity.

Results: We observed enrichment of lipid and sugar metabolism gene expression in PS^PC9; lipidomics revealed enrichment within PS^PC9 for ferroptosis-driving diPUFA phospholipids (diPUFA-PL), as well as polyunsaturated free fatty acids (PUFA FFAs). Upon PS^PC9 reversion to the ferroptosis-resistant parental state (PC9^{PS -> PC9}), this lipid signature reverted. The LNCaP and HT1080 PS-like models individually showed features consistent with PS, including an increased labile-iron pool, reversibility, and enhanced ferroptosis sensitivity, and had lipid features consistent with those in PS^PC9. Finally, mitochondrial elimination partially abrogated ferroptosis sensitivity and altered the PS lipid profile.

Conclusion: In summary, lipidomic changes dependent on the presence of mitochondria are key to the ferroptosis sensitivity of drug-tolerant persister cancer cells.

The biology of aging is increasingly understood through geroscience frameworks integrating molecular, cellular, physiological, and social hallmarks. Recently, we introduced psychosocial factors including mental illness as an important hallmark of aging. Indeed, exposome-centered approaches reveal complex interactions among socioeconomic, environmental, behavioral, and genomic factors. Precision Geromedicine aims to target all these determinants in a holistic fashion to improve aging trajectories and extend healthspan.

Buildings account for a substantial proportion of urban energy demand, making it essential to understand the interrelationships between the built environment, urban heat island (UHI) effects, and energy demand. This study investigates the impacts of UHI on urban building energy demand in Mumbai, India, using a multi-scale framework. First, UHI intensity is assessed by generating and analyzing Land Surface Temperature maps and calculating the Urban Thermal Comfort and Vulnerability Index. This assessment identifies UHI hotspots and regions with potential thermal discomfort. Subsequently, energy demand modelling is conducted across two locations with distinct thermal comfort conditions, spanning from the individual building to the urban scale. Detailed building information and site-specific climate data from the two contrasting locations are collected to support urban building energy modelling (UBEM). Results reveal that UHI increases cooling energy demand by 7.31% at the individual building level; however, its impact on urban-scale energy demand is significantly mitigated by building geometry and surrounding structures. Specifically, variations in building height and inter-building shading outweigh the influence of UHI, leading to a substantial 15.9% reduction in the mean cooling energy demand intensity. These findings highlight the critical role of urban morphology and density in shaping cooling energy demand under UHI conditions. By extending beyond individual building-level assessments to UBEM, the study contributes new evidence regarding UHI-energy interactions in a year-round warm tropical context such as Mumbai. Furthermore, the results position urban-scale cooling energy demand assessments as a transferable framework to integrate UHI research with energy policy, ultimately supporting climate-responsive planning.

Aims: This study examined the speed of approach-avoidance actions in virtual reality (VR) as an indicator of psychological “readiness” to interact with social avatars.

Aims: This study examined the speed of approach-avoidance actions in virtual reality (VR) as an indicator of psychological “readiness” to interact with social avatars.

Methods: Given that fast response is a key psychological factor reflecting a user’s interest, motivation, and willingness to engage, we analyzed the response time of pulling or pushing inputs, typical actions showing approach-avoidance tendency, via bare-hand interaction in VR. We specifically investigated how the response time varied according to participants’ social resources, particularly the richness of their social lives characterized by broader networks of friends, social groups, and frequent interactions.

Results: Results showed that participants with richer social lives exhibited faster pulling (vs. pushing) actions toward both same- and opposite-sex avatars. These effects remained significant regardless of participants’ gender, age, and prior VR experience. Notably, the observed effects were specific to social stimuli (i.e., avatars) and were not revealed with non-social stimuli (i.e., a flag). Additionally, the effects did not occur with other indirect interactions (i.e., a mouse wheel or a virtual joystick).

Conclusion: The findings suggest that social resources may facilitate approach-oriented bodily affordances in VR environments.

In recent years, the terms “gerosuppressors” and “gerogenes” have been introduced to describe factors that respectively delay or accelerate aging. These factors are present across various cell types. Specific proteins, such as tau predominantly expressed in neurons, may act as neuron-specific gerosuppressors or gerogenes. Tau exhibits a dual role influenced by its post-translational modifications, particularly phosphorylation. In this review, we discuss relevant examples of tau isoforms that demonstrate both roles, underscoring its dual influence on neuronal aging.

Growth Differentiation Factor 15 (GDF15), a member of the transforming growth factor-beta superfamily, is highly expressed in response to cellular stress, ageing, and various pathological conditions. As a key component of the senescence-associated secretory phenotype, it plays important roles in modulating inflammation, mitochondrial dysfunction, energy metabolism, and appetite. It signals through the glial-derived neurotrophic factor receptor alpha-like receptor in the brainstem to suppress appetite and modulate energy balance. Increasing evidence supports that GDF15 exhibits dual and context-dependent functions in cancer, contributing to both tumor suppression and progression through its regulation of cellular proliferation, metastasis, and interactions within the tumor microenvironment. Elevated GDF15 levels have been observed in cardiovascular diseases, metabolic disorders, neurodegenerative diseases, and numerous malignancies, making it a potential biomarker and therapeutic target for a spectrum of age-related and pathological conditions, including cancer. Emerging therapeutic strategies targeting GDF15 encompass the use of agonists for obesity and antagonists for cachexia, either alone or in combination with immunotherapy, reflecting its complex role in disease. A comprehensive understanding of its context-dependent roles may shed light on fundamental disease mechanisms, offering a foundation for the development of innovative and personalized therapeutic approaches.

Stiffness of the primary longitudinal rubber joints is critical for both the high-speed stability and curve trafficability of the train. However, existing joints with fixed stiffness inevitably generate large wheel-rail lateral forces and wear on curves. To address this issue, a fail-safe rubber joint incorporating magnetorheological fluid (MRF) technology is proposed, enabling bidirectional variable stiffness. First, the structure and working principle of MRF rubber joint are described in detail. Subsequently, prototypes of the MRF damper and the MRF rubber joint are assembled and tested, yielding satisfactory variable damping and stiffness performance. Moreover, dynamic simulations further demonstrate the joint’s effectiveness in improving both stability and curve trafficability. A control algorithm based on track curvature identification is also proposed. The co-simulation results validate the potential of the MRF rubber joint on reducing wheel-rail wear and enhancing safety of trains.

The construction industry is at a critical juncture, facing both unprecedented opportunities and challenges driven by emerging technologies like BIMS-GPT, which combines Building Information Models (BIMs) with Generative Pre-trained Transformer (GPT) language models. This study investigates the drivers and barriers to the adoption of BIMS-GPT in the construction industry through empirical research using questionnaires and expert interviews. The results indicate that the most significant drivers are BIMS-GPT’s ability to automate tasks, enhance decision-making, improve safety, and optimize processes across the entire building lifecycle. The main barriers include high development and training costs, lack of legal frameworks, data security concerns, and resistance to change among employees. Furthermore, the study analyzes differences in perceptions among respondents based on their years of experience and departmental roles. Those with 6-10 years of experience show the highest interest in adopting BIMS-GPT, while management and technical departments prioritize different aspects of the technology. The findings provide valuable insights for construction companies looking to implement BIMS-GPT and establish a solid foundation for its promotion and implementation. By understanding stakeholders’ attitudes and addressing the identified drivers and barriers, the industry can fully leverage the potential of this transformative technology, paving the way for a smarter, more efficient, and sustainable future in construction.

Net Zero Energy Buildings (NZEBs) offer a transformative pathway for decarbonizing the built environment by integrating energy-efficient design, renewable energy systems, and smart grid interaction. This review positions NZEBs as critical enablers of low-carbon cities, highlighting their ability to balance annual energy demand through both passive strategies and active technologies. Evidence from the literature shows that advanced envelope materials can reduce heating and cooling loads by up to 18.2%, window retrofits lower thermal loads by 15.5%, and rooftop photovoltaic systems can supply up to 70% of household energy demand in certain regions. The review traces the evolution of NZEBs from early solar integration to contemporary climate-responsive designs aligned with global sustainability frameworks. It also identifies persistent challenges, including high upfront costs, climate-dependent performance variability, and retrofitting difficulties in dense urban contexts. Future directions are suggested in the areas of advanced materials (e.g., aerogels, phase-change composites), urban-scale microgrids for energy sharing, and policy harmonization to strengthen grid resilience. Successful deployment of NZEBs will additionally require interdisciplinary collaboration, standardized international codes, and financial incentives to overcome existing barriers.

Aims: This scoping review systematically maps the existing literature at the intersection of virtual reality (VR), synthetic speech, and affective computing. As immersive and voice-based technologies gain traction in education, mental health, and entertainment, it is critical to understand how synthetic speech shapes emotional experiences in VR environments. The review clarifies how these concepts are defined, how they contribute to empathic computing, and identifies common applications, methodological approaches, research gaps, and ethical considerations.

Methods: A comprehensive search across multiple databases (e.g., ACM Digital Library, IEEE Xplore, ScienceDirect) was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) framework. Eligible studies investigated synthetic or computer-generated speech in VR or comparable immersive 3D settings and assessed emotional responses or related outcomes. Data were extracted on study characteristics, applied technologies, affect-related measures, and reported effects.

Results: The findings reveal a growing interdisciplinary body of research at the convergence of synthetic speech technologies, embodied virtual agents, and affective data processing in immersive environments. Interest in this area has accelerated with the development of advanced text-to-speech models, suggesting increasing relevance and expansion of this topic.

Conclusion: This review underscores a rapidly expanding yet fragmented research landscape. It highlights conceptual and methodological gaps, stressing the need for clearer definitions, standardized evaluation measures, and ethically informed design of synthetic speech in VR. The results provide a foundation for advancing research and applications in emotionally responsive virtual environments.

Electrocatalytic oxidation of alcohols and aldehydes, known as alcohol oxidation reactions (AOR), provides a sustainable and efficient route for converting low-value feedstocks such as ethanol, glycerol, and 5-hydroxymethylfurfural into high-value chemicals, including organic acids and aldehydes, in line with the chemical industry’s transition toward carbon neutrality. This review synthesizes recent advancements in electrocatalytic AOR, emphasizing advances in catalyst design and detailed reaction mechanisms. A broad spectrum of catalysts is explored, ranging from noble metal-based (e.g., Pt, Pd, Au) to cost-effective non-noble metal-based (e.g., Ni, Cu, Co) materials, with attention to advanced strategies such as heteroatom doping, vacancy engineering, and alloying for fine-tuning electronic structures and optimizing intermediate adsorption. The review also delves into mechanistic insights, elucidating rate-determining steps, adsorption geometries, and electron-transfer pathways that govern AOR performance, supported by density functional theory analyses. Special emphasis is placed on the interplay between catalyst electronic structure and reaction kinetics, offering fresh perspectives for improving yield, selectivity, and Faradaic efficiency. Finally, current challenges, including catalyst stability, product selectivity, and scalability, are critically evaluated, and future directions such as in situ characterization and the development of non-noble metal catalysts are proposed to advance AOR toward large-scale, sustainable chemical synthesis.

Sustainability in the construction industry entails measures to reduce the environmental impact of construction projects, achieve economic viability, and ensure a livable environment before, during, and after construction. However, construction activities generate substantial pollution, including dust and noise, and given their large scale, long duration, and involvement of multiple public service departments, considerable information is produced throughout each project. Traditional approaches to pollution control have proven largely ineffective, whereas platform-based models have demonstrated efficiency and cost-effectiveness in many sectors. This study therefore explores the digital transformation of public service governance for construction pollution through a platform-based approach. Timed Petri nets are employed to model and analyze both traditional and platform governance modes, providing a theoretical foundation for the digital upgrading of the construction industry using public data (e.g., construction pollution data). Results indicate that the platform governance mode reduces governance time by over 40% and substantially enhances information exchange efficiency compared with the traditional mode. By contrasting time delays between the two approaches, the superior efficiency of the platform model is highlighted. The paper further summarizes key findings, offers recommendations for institutional support and future research, and validates the proposed governance model through an empirical case study.

This review examines recent advances and applications of three-dimensional (3D) printing technology in orthopedic fracture management, with a particular focus on its transformative role in personalized treatment strategies. The introduction of patient-specific 3D-printed implants and fracture plates has markedly improved surgical outcomes by reducing operative time, enhancing anatomical alignment, and promoting bone healing. By enabling the fabrication of customized implants, 3D printing provides an innovative approach for managing complex fractures and bone defects, particularly in cases where conventional methods are inadequate. Key benefits discussed include the development of tailored fracture plates, bone scaffolds, and bioactive materials that support bone regeneration. The review also explores the potential of emerging technologies such as four-dimensional printing and bioprinting, which allow for the creation of dynamic implants capable of adapting to biological changes and facilitating tissue regeneration. In addition, the integration of artificial intelligence into preoperative planning and implant design is highlighted for its contribution to improving surgical precision and individualized treatment. This review consolidates the latest advancements while also addressing challenges, including high production costs and regulatory barriers, that must be overcome for widespread clinical adoption. In conclusion, the future of orthopedic fracture management is expected to be significantly reshaped by the continuous evolution of 3D printing technologies, offering more personalized, effective, and efficient solutions for patients. As these innovations progress, 3D printing is anticipated to play a pivotal role in advancing orthopedic surgery and ultimately improving patient outcomes.