Search Results (5,093)

With the acceleration of urbanization and the intensification of climate change, wind conditions have become a critical factor in architectural design. They not only affect a building’s wind resistance but also influence ventilation, pollutant dispersion, pedestrian comfort, and energy consumption. Traditional computational fluid dynamics (CFD) simulations are costly. Although the application of machine learning for CFD prediction has become a relatively mature technology, machine learning models still face challenges in actual architectural design workflows. Building upon recent advancements in the field, it proposes two core technologies: a method for predicting outdoor wind environments in buildings based on the Site-Specific Training for Design Tasks (SST-DT) strategy, and an automated machine learning workflow. These innovations improve upon existing wind environment analysis methods and systems, establishing a fully automated working framework that is easy for architects to learn and use. Within this framework, dataset acquisition and model training are performed automatically. Finally, this framework was validated across various prediction tasks with different objectives. It significantly lowers the barrier to entry for architects adopting machine learning, advances the performance-driven design paradigm, and facilitates the deep integration of machine learning technologies into architectural wind engineering. Full article

This research proposes a conceptual framework that employs generative artificial intelligence (AI) to automatically generate dynamic biomimetic façade designs for reducing building carbon emissions. Biomimetic façades show strong carbon-reduction potential; however, their application remains limited by interdisciplinary requirements and time-intensive optimization processes. Existing studies primarily rely on traditional multi-objective optimization for energy performance, while machine learning integration and carbon-oriented evaluation remain limited in biomimetic façade research. To address this gap, this study proposes an AI system for biomimetic façade generation in tropical climates by combining reinforcement learning–based multi-objective optimization with deep learning–based parameter prediction models. A carbon payback assessment method integrating operational and embodied carbon is further proposed to evaluate carbon reduction performance. Preliminary validation through pilot experiments and K-fold cross-validation achieved an average RMSE of 8.7% and an average R² value of 0.547, while façade parameter prediction for new building conditions could be completed within approximately 10 s. Simulated cases also indicated that the generated façade strategies generally remained within predefined carbon payback thresholds under different material configurations. The framework supports carbon-oriented biomimetic façade design and early-stage low-carbon design decision-making. Full article

The rapidly aging global population is placing immense pressure on healthcare systems, which are struggling to meet the needs of older adults. Information and communication technologies (ICTs) are considered a key driver in supporting the development of age-friendly healthcare models. This scoping review aims to map and structure the multifaceted applications of ICTs in age-friendly healthcare, focusing on their design, benefits, challenges, and implementation in different contexts. We followed the PRISMA-ScR guidelines and conducted a systematic search of five major databases (Web of Science, Scopus, PubMed, ScienceDirect, and IEEE Xplore), supplemented with backward citation chaining to improve the robustness of literature identification. The results show that ICTs can help older adults by improving their access to healthcare information, enhancing their care coordination, supporting their independent living, and personalizing their health management. Key challenges include user experience issues for older adults, data privacy and security concerns, and implementation barriers related to resources and professional support. Effective implementation of ICTs requires greater emphasis on age-centered design, robust data governance, and scalable integration with existing healthcare systems. We further propose a Technology Design–Scenario Application–Effect Evaluation (TD-SA-EE) analytical framework for ICT application in age-friendly healthcare; the framework is grounded in sociotechnical systems theory to provide explanatory insights beyond descriptive classification. This research provides insights into optimizing age-friendly healthcare through ICTs and contributes to fully leveraging ICTs in building sustainable and equitable age-friendly healthcare systems. Full article

Supramolecular inclusion complexes are widely used in drug delivery and other fields, with the advantages of controllable structures, high stability, excellent biocompatibility, and the ability to improve drug solubility and achieve controlled release. However, traditional screening methods rely on experimental trial and error, which suffer from long cycles, high costs, and low throughput, limiting research and development efficiency. In recent years, the development of artificial intelligence has provided new solutions for the screening of inclusion complexes. This paper systematically reviewed the core technological system of AI in the screening of inclusion complexes, focusing on two aspects: prediction and optimization of key properties and rational design of host molecules, summarizing their specific application progress. At the same time, we analyzed the current core challenges, including data scarcity, insufficient model interpretability, and limited generalization capabilities, and propose future development directions such as building standardized databases, integrating physicochemical principles (e.g., molecular mechanics and thermodynamics), and establishing closed-loop research and development platforms. This review aims to provide a systematic reference for the in-depth application of artificial intelligence in the field of supramolecular inclusion complexes. Full article

Industrial heritage adaptive reuse occupies a structurally privileged position for degrowth: heritage listing already institutionalises material sufficiency as a regulatory obligation, mandating low intervention and resisting the demolish-and-replace logic of resource-intensive development. Yet this regulatory floor imposes no ceiling on how protected structures are programmed or who benefits; the same statutory instrument can produce different schemes depending entirely on governance. This paper demonstrates that gap through two contrasting UK gasholder adaptive reuse projects: King’s Cross Gasholders in London (private-led, luxury residential) and Granton Gasholder in Edinburgh (council-led community park). Applying De Castro Mazarro et al.’s multi-scalar degrowth framework across building, neighbourhood, and city scales through document analysis and site observations, we identify structural mechanisms explaining why building-scale alignment fails to propagate upward. The findings indicate three governance conditions are necessary to convert the structural degrowth potential of industrial heritage into substantive outcomes: public control over development decisions, community participation extended to strategic priorities rather than design preferences, and explicit integration of degrowth values into upstream planning frameworks. Industrial heritage adaptive reuse is not inherently a degrowth practice, but it is one of the few urban development contexts where the regulatory preconditions for degrowth alignment are already in place. Realising that potential requires governance structures that treat sufficiency and collective wellbeing as binding objectives, not rhetorical claims. Full article

Climate change necessitates a global transition toward green and low-carbon development, underscoring the critical importance of energy efficiency. Buildings account for a substantial portion of urban energy consumption and carbon emissions, with central air-conditioning systems representing the largest energy-consuming component. This study focuses on optimizing equipment selection—including chillers, pumps, and cooling towers—for the refrigeration plant of a commercial complex in Xiamen. Following theoretical optimization, the operational performance of the implemented system was empirically analyzed using long-term monitoring data from 2024 to 2025. The results demonstrate an energy efficiency ratio (EER) of 5.44 in 2024 and 5.28 in 2025, surpassing the Grade I efficiency threshold (5.2) stipulated by the Chinese standard T/CRAAS 1039-2023. Monthly EER values consistently remained above 5.06 throughout the cooling season. Detailed performance analysis of individual equipment further confirmed that actual operational performance of chillers, pumps, and cooling towers closely matched or even exceeded rated performance metrics, with chiller efficiency deviations controlled within 5%. This study integrates optimized equipment selection at the design stage with empirical performance analysis based on actual operation, providing a validated approach for improving the energy efficiency of refrigeration plants in commercial buildings and offering valuable references for the revision of relevant energy efficiency standards. Full article

The shift from fossil fuels to renewable energy is a key component in achieving global sustainability and dealing with climate change. Natural resources, such as sunlight, air, water, and biomass, have tremendous potential to create clean energy; however, exploiting these resources in an efficient, stable, and large-scale integration manner is difficult due to their variable and distributed nature. Artificial intelligence (AI) approaches that mimic human learning and decision-making have recently become viable approaches to solving renewable energy problems. This study mainly examines the current landscape of AI applications across solar, wind, hydro, geothermal, ocean, hydrogen, bioenergy, and hybrid energy systems. AI enhances renewable energy forecasting, improves power system frequency analysis and stability assessments, and optimizes dispatch and distribution networks. Beyond technical optimization, AI also contributes to broader sustainability goals, including energy efficiency improvements, intelligent smart grid management, and enabling mechanisms such as carbon trading and circular economy practices to reduce exposure to climate extremes. Drawing on evidence from a range of renewable energy areas, this review highlights the importance of AI in bridging the link between technological innovation and sustainable energy management. This paper discusses potential future research avenues, such as building sophisticated AI designs, energy-efficient chips, and data communication networks. Ultimately, the synergy between AI and renewable energy systems represents not only a technological advancement but also a transformative pathway toward a resilient, low-carbon future. Full article

Access to freshwater is one of the major global challenges, driven by population growth, industrial development, climate change, and increasing water stress, particularly in economically constrained regions. In this context, this study designs, builds, and experimentally and numerically evaluates an indirect solar concentration desalination system (ICST) composed of a humidification–dehumidification (HDH) subsystem thermally powered by a Linear Fresnel Concentrator (LFC) under the appropriate technology paradigm. The methodology integrates an experimental campaign conducted under real climatic conditions in Bucaramanga, Colombia, mathematical modeling based on mass and energy balances, and the implementation of a TRNSYS simulation model validated through qualitative and quantitative analyses using absolute and relative errors. Results showed close agreement between experimental and simulated data, with daily freshwater production deviations of 0.53 and 0.65 L/day in tests 04 and 05, respectively, while mean relative errors remained below 5% for the main thermal and productivity variables. Experimentally, an average freshwater production of 1.13 L/h was achieved, with a production gain ratio (GOR) of 0.32 and a recovery ratio (RR) of 0.021, while maintaining total dissolved solids below 500 mg/L. Economic assessment estimated a production cost of $0.065/L, demonstrating the technical and economic feasibility of the system for decentralized small-scale applications in regions with high solar irradiance throughout the year. Full article

Driven by global energy transformation and the progress of artificial intelligence technology, traditional automotive engineering is undergoing profound changes. Transportation is rapidly advancing toward electrification and intelligence. Against this background, this paper identifies three emerging paradigms for the development of electric vehicles: Heart Revolution, Brain Evolution, and Network Integration. This paper points out that automobiles are evolving from traditional one-way energy consumers to dynamic energy nodes in smart grids. With the support of artificial intelligence technology, the role of automobiles is also shifting from a simple means of transportation to an intelligent mobile terminal. At the same time, this paper focuses on analyzing the application of the integration theory of “Four Networks and Four Flows” in automobile upgrading. The theory does not focus on the optimization of a single node unit but emphasizes a systematic perspective to improve overall performance and support sustainable development. This paper suggests that the development of the automobile industry must be deeply integrated with the humanity world, information world and physical world. By building a five-in-one architecture of “Human–Vehicle–Road–Cloud–Satellite”, the automobile industry could follow a practical pathway toward coordinated development. At the same time, breakthroughs in core technologies such as solid-state batteries and wide-bandgap semiconductors are also imminent. This paper aims to provide a sustainable and high-performance automobile development path and integrate the concept of human-oriented design into it. Meanwhile, China’s new energy vehicle industry is used as a representative context to illustrate its engineering and industrial implementation. Full article

Automated compliance checking (ACC) integrated with Building Information Modeling (BIM) requires regulatory texts that can be translated into machine-executable rules. Existing studies have largely focused on rule extraction techniques and ontology-based modeling within single jurisdictions, leaving the upstream question of regulatory readiness underexplored. This study introduces a clause-level framework for assessing the formalizability of building regulations and applies it to four documents covering accessibility and fire safety in the United Kingdom and Kazakhstan. The corpus was decomposed into 2361 enforceable clauses, classified using a ten-category semantic taxonomy, and evaluated against four formalizability criteria: explicit scope, measurable requirement, deterministic outcome, and design-stage data availability. Clauses were classified as formalizable only when satisfying all four criteria simultaneously. UK documents reached 85% formalizability for accessibility and 90% for fire safety, compared with 77% and 51% for the corresponding Kazakh standards. The largest gap was observed in fire safety, where the Kazakh corpus contained fewer BIM-oriented and spatially explicit checks and a higher share of clauses lacking evidential specification. The proposed framework supports clause-level diagnosis of regulatory automation readiness, and a four-stage roadmap links linguistic structure to digital maturity in both jurisdictions. Full article

The study proposes and investigates a seismic retrofitting strategy based on an external reinforced concrete exoskeleton, grounded in the analysis of the actual force transfer mechanisms between the existing structure and the added system. The three-dimensional numerical model was developed in ETABS, employing linear response spectrum analysis in accordance with EN 1998-1 and P100-1/2013. The internal forces transmitted at the structural interface were determined using the Section Cut method, enabling the identification of integrated resultants and the prioritization of critical connections. Three types of connections are examined—slab-to-slab, column-to-wall, and beam-to-joint—while the distribution of stresses within the anchor groups is assessed based on an elastic model under combined axial force and bending action. The results indicate that the global structural response is governed by diaphragm coupling, whereas the vertical interfaces ensure kinematic compatibility and the redistribution of axial and bending effects. The proposed methodology provides a coherent framework for the rational design of interface connections in retrofit interventions carried out without interrupting building operation. Full article

Driven by Emerging Engineering Education and basic education reform, cultivating engineering literacy in pre-service teachers is vital for nurturing innovative talent. This qualitative multiple-case study examines current practices in nine leading Chinese normal universities, primarily through document analysis of institutional policies and curricula, supplemented by faculty interviews and a pre-service teacher survey in a subsample of institutions. Thematic analysis reveals prominent predicaments: a fragmented curriculum, monolithic training models, misaligned resources, and low student motivation. These issues stem from ambiguous conceptual positioning, weak institutional design, and a shortage of specialized faculty and platforms. To address these challenges, this paper proposes a systematic Four-in-One breakthrough framework encompassing Top-Level Design, Platform Foundation, Faculty Empowerment, and Project-Centric Cultivation. Central to this framework is a dual-track drive model, which integrates hands-on engineering practice with pedagogical application, enabling future teachers to develop engineering thinking and the competency to translate it into effective classroom teaching. While the proposed framework requires further empirical validation, this approach offers a theoretical and practical pathway for reconstructing teacher education and building a high-quality teaching workforce. Full article

Physicians routinely operate in environments that require the rapid processing of complex and dynamic visual information to diagnose patient conditions, communicate effectively, and make informed decisions. Despite the central role of visual attention in clinical practice, these processes are rarely conceptualized or systematically measured in medical education research. In other professional domains, such abilities are described as professional vision (PV)—the situated capacity to selectively attend to relevant cues and interpret them considering domain-specific knowledge. Although the term professional vision foregrounds visual attention, we use it here to cover the multimodal clinical perception in which visual cues are typically embedded—predominantly visual, but in many tasks also auditory and verbal—with visual attention as the analytic anchor. This paper introduces a cognitive process model of professional vision for medical education (PV-CP) that specifies the perceptual and cognitive subprocesses underlying how physicians perceive and interpret clinically relevant information. Building on this model, we propose a theory-driven framework for the measurement of professional vision using multimodal indicators. Central to our argument is the assumption that professional vision represents a latent, temporally unfolding construct that cannot be validly captured through single behavioral metrics or outcome measures. Instead, robust measurement requires the coordinated analysis of gaze-based indicators of visual attention and cognitive indicators of reasoning, each reflecting distinct subprocesses of professional vision. By systematically linking families of indicators to specific subprocesses and clarifying their respective inferential strengths and limitations, the PV-CP model advances a process-oriented approach to studying professional vision in medical education. The framework provides a conceptual basis for integrating multimodal data sources and supports more precise interpretations of gaze and reasoning data in expertise research. In doing so, the model contributes to the theoretical refinement of professional vision and offers a structured foundation for future empirical research and the design of learning environments aimed at fostering clinically relevant perceptual–cognitive skills. Full article

Against the backdrop of the global advancement of carbon neutrality goals and the energy transition in the building sector, zero-carbon buildings have emerged as pivotal enablers for achieving carbon neutrality in the construction industry. The rule-based scheduling of energy storage systems (ESS) is critical to enhancing energy efficiency and economic performance of buildings. This study takes the Jinan Zero-Carbon Operation Center Project in Shandong Province as the research object, developing a comprehensive technical framework covering the entire process from design to operation, and investigates the rule-based design and ESS scheduling strategies in response to Shandong’s newly implemented seasonal time-of-use (TOU) electricity pricing policy. First, core performance indicators are defined in accordance with national evaluation standards for zero-carbon buildings. Hourly building energy loads and photovoltaic (PV) generation profiles are simulated over a full year, which serves as the basis for determining the optimal PV installed capacity and ESS sizing. Second, an ESS scheduling strategy integrating PV generation forecasting and the seasonal TOU electricity price structure is formulated, with clear charging and discharging logic defined. Finally, the operational and economic performance of different scheduling modes are evaluated and compared through case studies. The results show that the annual PV generation ratio reaches 101.38%, with a self-consumption rate of 73% and a self-sufficiency rate of 72%, all meeting the core requirements for zero-carbon buildings. Compared with the conventional real-time scheduling mode (Mode 1), the proposed optimized mode (Mode 2) that incorporates TOU pricing and PV forecasting achieves an annual operational cost saving of 367,349 CNY, corresponding to a reduction of 47.02%. Distinct seasonal variations in core indicators are also observed: the PV generation ratio is lower in summer and winter but the self-consumption rate is higher, with the opposite trend in spring and autumn. The proposed technical framework and scheduling strategy provide practical guidance for the design and operational optimization of zero-carbon buildings and offer decision-making support for ESS operation under TOU electricity pricing policies. Full article

Lower-limb amputation remains a significant clinical and socio-economic challenge, while the high cost of microprocessor-controlled prostheses (MPKs) limits their widespread accessibility. This paper presents the design and preliminary laboratory-scale evaluation of a low-cost microprocessor-controlled ankle prosthesis intended as a feasibility-oriented alternative platform for future active prosthetic system development. Building upon the previously developed V1 mechanical architecture, an updated CAD model was created in the SolidWorks 2024 environment, and the kinematic configuration was refined using a ball-screw transmission (SFU1204-300) driven by a NEMA 17 stepper motor. The electronic control system integrates an ESP32 microcontroller, an MPU9250 inertial measurement unit (IMU), a limit switch for initial-position detection, and a WiFi-based REST API interface for communication and control. Laboratory no-load experiments demonstrated controlled positional behavior, repeatable angular response, and successful operation of the homing procedure within a motion range of 0–4200 motor steps. The prototype actively generated dorsiflexion–plantar flexion motion in the sagittal plane, while a passive inversion–eversion mechanism was incorporated and intended to improve structural adaptability. IMU-based measurements enabled preliminary monitoring of angular displacement and positional behavior during the experiments. The presented prototype represents an initial engineering feasibility study of a low-cost active ankle actuation architecture and provides a foundation for future investigations involving load-bearing experiments, biomechanical gait analysis, and closed-loop control implementation. Full article