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Artificial Intelligence (AI) and Digital Twins (DTs) can support the digital and energy transition in the construction sector; however, their application to the built environment presents both opportunities and limitations. This study aims to give a critical perspective on the topic analyzing the related key challenges, including error assessment, model interpretability, data availability, cybersecurity risks, organizational constraints, and lifecycle costs. Where AI is nowadays developed as a context-dependent tool set, it is most effective when embedded within integrated socio-technical systems rather than adopted as a universal solution. Instead, DTs can be intended as an enabling framework, integrating AI, Internet of Things (IoT), Big Data, and Building Management Systems (BMS) to enhance energy performance, indoor environmental quality, safety, maintenance, and decision-making at both building and urban scales. The direction international research on these topics is facing is clear as evidenced by the wide number of research papers published. The future of these technologies moves towards a simulative approach oriented towards the sustainable and fair development goals and will bring a broad transformation of the building environment where they are ever more integrated into each social and technical aspect. The work is supported by a case study developed at Sapienza University of Rome founded by the Italian National Recovery and Resilience Plan within Flagship Project 2 (FP2), “Energy Transition and Digital Transition in Urban Regeneration and Construction,” of the Rome Technopole ecosystem. Full article

Against the global backdrop of carbon neutrality and the green transition of the construction sector, modern timber-framed buildings have emerged as a core enabler of sustainable construction. However, a systematic synthesis of research on indoor hygrothermal environments and thermal comfort in such buildings remains lacking, and the underlying coupling mechanisms—as well as pathways for performance optimization—are still insufficiently understood. To address these gaps, this study aims to systematically characterize and evaluate the performance features of indoor thermal and moisture environments in modern timber buildings, and to identify the key influencing factors and their underlying mechanisms. In accordance with the PRISMA 2020 guidelines for systematic reviews, this study identified and analyzed 203 high-quality peer-reviewed publications retrieved from three major academic databases, covering the period 2010–2025. Specifically, the literature search was conducted across the Web of Science, Scopus, and the China National Knowledge Infrastructure (CNKI), and visualization analysis was performed using VOSviewer 1.6.20 software. The results indicate that timber-framed buildings exhibit distinctive indoor hygrothermal characteristics: rapid temperature response, strong humidity buffering capacity, and superior thermal insulation performance compared with concrete structures, enabling indoor relative humidity to remain stably within the thermally comfortable range. Nevertheless, challenges persist, including summer overheating and elevated risks of mold growth under hot-humid conditions. Furthermore, the PMV model demonstrates significant predictive deviation for thermal comfort in timber-framed buildings; its application thus requires calibration incorporating both the hygrothermal properties of timber materials and occupants’ psychological adaptation. This study synthesizes the current state of research, identifies key influencing factors, and proposes climate-responsive optimization strategies to advance the development of robust thermal comfort models and support the low-energy, high-comfort design of timber-framed buildings. Full article

Soil salinization is one of the most severe forms of land degradation in arid and semi-arid regions, posing substantial threats to agroecosystem stability and food security. In this study, saline–alkali soil collected from the Wuding River Basin in Yulin, Shaanxi Province was used to construct a three-factor amendment system comprising superabsorbent polymers (SAP), biochar, and humic acid. A systematic assessment was conducted to elucidate their combined effects on soil water–salt transport and crop growth. Results from one-dimensional constant-head infiltration experiments using indoor soil columns demonstrated that the application of amendments significantly increased cumulative infiltration and improved the uniformity of wetting-front advancement. Specifically, the treatments regulated the redistribution of salts within the soil profile; while surface salinity reduction varied, the leaching efficiency was significantly enhanced in the A2B2C2 treatment. Soil bulk density (BD) showed dynamic fluctuations during the growth cycle, peaking at 1.628 cm⁻³ during the branching stage, while high-rate biochar (A3) reduced BD by up to 13.64% compared to the control by the initial flowering stage. Fitting results based on the Philip and Kostiakov models further indicated that the combined amendment strategy—particularly the A2B2C2 treatment (30 kg/ha SAP, 15,000 kg/ha biochar, and 600 kg/ha humic acid)—markedly enhanced both the initial infiltration rate and the steady infiltration capacity. Field experiments corroborated the indoor findings: plant height and dry biomass of Melilotus officinalis (L.)Lam. were significantly higher under amendment treatments than in the control, driven by improved water availability, mitigated salt stress, and enhanced soil structure. Single-factor and multi-factor interaction analyses revealed that SAP exerted pronounced effects during early growth stages, whereas biochar and humic acid contributed more substantially during the middle to late stages through sustained regulatory functions. Collectively, the results demonstrate that the combined application of SAP, biochar, and humic acid improves the water–salt regime of saline–alkali soils through a coupled “water–salt–structure–plant” mechanism, ultimately enhancing crop productivity. This study provides both theoretical insights and practical guidance for the amelioration of saline–alkali soils. Full article

Wet direct-seeded rice (WDSR) is a resource-efficient cultivation system gaining global popularity, but its sustainability is severely threatened by weedy rice (Oryza sativa f. spontanea). Due to the high genetic and physiological similarities between weedy and cultivated rice, selective chemical control remains a formidable challenge. This study evaluated an integrated chemical control strategy utilizing the safener metcamifen (applied as a seed coating) to protect cultivated rice from the pre-emergence herbicide pretilachlor in a simulated WDSR system. Indoor bioassays and outdoor mock-plot trials revealed that metcamifen seed coating alone (up to 560 mg a.i. kg⁻¹ seed) significantly promoted early seedling vigor in cultivated rice (‘Jia 67’) without exhibiting phytotoxicity. Conversely, soil application of pretilachlor at 375 g a.i. ha⁻¹ provided effective initial herbicidal activity, suppressing weedy rice emergence to merely 7.0%. Under this severe herbicide stress, metcamifen seed coating at an effective dose of 480 mg a.i. kg⁻¹ seed significantly mitigated phytotoxicity. However, this protection was partial; crop emergence was maintained at 63.8%, substantially preserving seedling biomass compared to the non-safened control (28.3%), but still reflecting a clear emergence penalty. We hypothesize that this moderate reduction in initial crop stand could potentially be compensated by proportionally increasing the initial seeding rate—a potential agronomic compromise that warrants future empirical validation in the field. In summary, this study provides a preliminary, controlled-environment evaluation demonstrating that the protective application of metcamifen with pretilachlor offers a potential framework for mitigating weedy rice infestations, subject to further field-scale verification. Full article

To mitigate the spatiotemporal mismatch between renewable energy supply and building heating demand, this study proposes a novel non-pressurized shell-and-tube latent heat storage (NP-LHS) device coupled with an air-source heat pump (ASHP) system. To overcome the inherent low thermal conductivity of organic phase change materials (PCMs), the thermal performances of plain, corrugated, and finned tubes were systematically compared using both computational fluid dynamics (CFD) simulations and full-scale experiments. Numerical results indicate that the optimal tube spacing ratio ranges from 1.0 to 1.5. Among the evaluated geometries, the finned tube configuration exhibited superior comprehensive performance. It achieved an exceptionally high PCM volume fraction of 92.5% and dramatically reduced the complete melting time to 180 min—significantly faster than both corrugated (280 min) and bare tubes—while attaining a higher terminal temperature. Full-cycle dynamic experiments further demonstrated that integrating the finned tube NP-LHS into the ASHP system yielded a peak-shaving power reduction rate of 98.0%, effectively maintaining indoor thermal comfort. These findings conclude that expanding the conductive surface area via fins is practically more effective than inducing fluid turbulence for low-conductivity PCMs in non-pressurized storage applications. Full article

Background: Indoor climbing is a rapidly growing sport; however, data on injury patterns and clinical outcomes remain limited. This study aimed to evaluate the injury severity, characteristics, and clinical outcomes of indoor climbing-related injuries and explore the clinical applicability of the UIAA MedCom Score. Methods: We conducted a 10-year retrospective analysis (2012–2021) of patients aged ≥16 years presenting with indoor climbing-related injuries to a Swiss level 1 emergency department. Cases were identified using predefined keywords in the E-care and Qualicare databases. Demographics, injury mechanisms and patterns, Injury Severity Score (ISS), UIAA MedCom Score, treatment strategies, and clinical outcomes were analysed. A multivariable logistic regression model was applied to explore factors associated with higher injury severity. Results: A total of 98 patients were included, with 50% aged 26–35 years. Injuries occurred with a similar frequency during climbing and bouldering (51.0% vs. 49.0%). The predominant mechanism was ground fall (68.4%). Lower-extremity injuries were most common, particularly affecting the ankle and foot (43%). Most injuries were of mild-to-moderate severity, with 46.9% classified as UIAA grade 2. Conservative treatment was sufficient in 83.7% of cases, while 16.3% required surgical intervention, and one fatality (1.0%) was recorded. Injury severity was significantly associated with clinical outcomes, including hospitalisation and resource utilisation. In addition, in a multivariable model, higher Injury Severity Score (ISS) was significantly associated with longer hospital length of stay. A strong association between the UIAA MedCom Score and ISS was observed (p < 0.001). Conclusions: The indoor climbing injuries of individuals presenting to the emergency department were predominantly mild to moderate and were generally associated with favourable short-term outcomes. These findings are supported by model-based analysis demonstrating an independent association between injury severity and hospital length of stay. These findings are based on a single-centre emergency department cohort and do not capture injuries managed outside the hospital setting. Therefore, conclusions regarding overall injury risk should be interpreted with caution. The observed association between the UIAA MedCom Score and ISS suggests that the UIAA classification may serve as a complementary tool for injury assessment, although further validation is required. Full article

The reliable identification of impairment relevant to safety-critical activities remains a significant challenge for public safety, motivating the exploration of unobtrusive and widely accessible sensing technologies. This study examines the viability of utilising inertial data acquired from consumer-grade smartphones to characterise gait disturbances associated with simulated visual impairment. The study simulates intoxication-related effects using alcohol-impairment goggles and does not involve the measurement of real alcohol intoxication. Two supervised experimental protocols were conducted in which participants traversed predefined walking routes under normal conditions and while wearing alcohol-impairment simulation goggles representing five manufacturer-declared blood alcohol concentration (BAC)-related goggle conditions plus a no-goggles control condition. An initial indoor trial, conducted in a structured corridor environment, yielded limited discrimination of gait dynamics due to strong spatial and visual stabilisation cues. To address this limitation, a subsequent outdoor experiment was conducted along a 100 m path lacking prominent visual reference points, resulting in motion patterns that more closely reflect unconstrained, real-world locomotion. Tri-axial accelerometer and gyroscope signals were recorded using smartphones, followed by artefact removal, segmentation, and standardisation to ensure inter-trial comparability. The resulting curated dataset comprises 290,919 multi-channel samples derived from 96 walking trials involving 16 participants and is released as an openly accessible resource to support further research in gait analysis and classification of gait alterations associated with simulated impairment. Model evaluation was performed using an 80/20 train–test split conducted within each traversal, with training and test windows originating from the same participant and walking session. Consequently, the reported results reflect within-subject performance instead of subject-independent generalisation. Multiple deep learning architectures combining convolutional feature extraction, bidirectional long short-term memory layers, and self-attention mechanisms were systematically evaluated. Using a subject-dependent evaluation protocol, the best-performing architecture achieved an accuracy of 71.4% and a weighted F1-score of 71.5% in distinguishing gait patterns associated with different levels of simulated visual impairment. The best-performing architectures yielded classification performance consistent with exploratory, low-stakes assessment of gait alterations associated with simulated visual impairment, using accelerometer data alone. These findings illustrate the feasibility of using smartphones as auxiliary tools for exploratory, low-stakes screening or educational applications and contribute a publicly released dataset and benchmark results to facilitate methodological advancement in inertial sensor-based gait impairment analysis. Full article

To mitigate health hazards from pathogenic bacteria (Escherichia coli, Staphylococcus aureus) and fungi (Aspergillus niger) as well as the coating mildew issue in high-humidity indoor environments, and to overcome the challenges of particle agglomeration and non-uniform distribution in conventional benzalkonium chloride (BAC)-clay composites, this study proposes a wet chemical strategy to prepare BAC-hectorite antimicrobial composites using synthetic hectorite as a high-performance carrier, which is superior to natural clays such as montmorillonite and kaolin in structural uniformity, ion-exchange efficiency, and dispersion stability. Characterization using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and Brunauer–Emmett–Teller (BET) analysis confirmed the successful intercalation of BAC cations into the hectorite interlayers through ion exchange. This resulted in a significant expansion of the interlayer spacing from 1.0–1.2 nm to 1.5–1.8 nm, a marked alleviation of particle agglomeration, and an optimized pore structure. A clear structure–activity relationship between preparation conditions, microstructure regulation, and antimicrobial performance is systematically established. Antibacterial tests revealed superior efficacy against Gram-positive bacteria; the composite exhibited an inhibition zone of 13.31 mm and a minimum inhibitory concentration (MIC) of 4 μg/mL against S. aureus, compared to 11.62 mm and 32 μg/mL against E. coli. Practical application tests demonstrated that at an ultralow addition level of 0.4%, incorporating this composite into latex paint achieved an antibacterial rate exceeding 99.9% against both pathogens. When added to putty powder, it yielded Grade 0 mold resistance with no observable growth. Furthermore, compounding with polypropylene (PP) increased the elongation at break to approximately 600%, simultaneously realizing antibacterial, antifungal, and toughening functions, thereby not only conferring antibacterial functionality but also significantly enhancing toughness—resolving the typical polymer embrittlement caused by traditional inorganic antibacterial fillers. Short-term evaluations confirm that this composite offers a stable structure, high-efficiency antimicrobial properties, and improved substrate mechanics at low loading levels. These findings provide technical support and experimental guidance for the functional upgrading of indoor decorative coatings, putties, and polymer materials used in high-humidity scenarios such as kitchens and bathrooms. Full article

Environmental monitoring systems are often constrained by high cost, limited portability, restricted pollutant coverage, and dependence on fixed infrastructure, which can limit their suitability for distributed real-time sensing. This study presents FRESH, an autonomous Internet of Things (IoT)-based platform for multi-parameter environmental monitoring and short-term forecasting. The system integrates sensors for air quality, thermal conditions, light, acoustics, and weather, together with GSM-based remote data transmission, onboard data logging, and hybrid battery–solar power management. FRESH was deployed across multiple indoor and outdoor locations in Coventry and at the University of Warwick, UK, and operated over a 10-month period to assess practical performance under varied environmental conditions. In addition to continuous environmental sensing, machine learning models were developed to predict short-term changes in selected environmental variables. Across the tested models, the best predictive performance was obtained for several key parameters, including particulate matter (R² = 0.93), volatile organic compounds (R² = 0.92), and ozone (R² = 0.98). The results suggest that FRESH has potential to support portable, multi-parameter environmental monitoring with integrated short-horizon forecasting, providing a basis for further development of distributed sensing and localised early-warning applications. Full article

Pipe networks are essential components of modern building infrastructure, including heating, ventilation, and air conditioning (HVAC) water systems, water distribution networks (WDNs), and district heating and cooling (DHC) systems. Leakage in these systems can lead to increased energy consumption, loss of thermal efficiency, and unstable system operation, thereby affecting indoor environmental quality and overall building performance. Despite differences in scale and application, similar leakage mechanisms are also observed in other pipe network systems, such as oil and gas pipelines and liquid cooling networks. These shared characteristics motivate a unified analytical perspective across different applications. This review provides a systematic analysis of leakage diagnosis methods, with a focus on machine learning (ML) approaches. The results indicate that ML methods have become a dominant research direction due to their ability to capture nonlinear relationships and process high-dimensional data. However, their effectiveness is often constrained by the limited availability of labeled leakage data, sensitivity to dynamic operating conditions, and insufficient physical interpretability. This review provides a structured framework for understanding ML-based leakage diagnosis and offers insights into the integration of data-driven and physics-based approaches for pipe network systems. In addition, the potential role of reinforcement learning (RL) is briefly discussed as an emerging direction for handling dynamic and adaptive scenarios. Compared with ML-based methods, RL has not yet been systematically explored in leakage diagnosis and remains at an early stage of development. This review synthesizes current methodologies, identifies key challenges, and outlines future research directions. Full article

The work environment in automated laboratories and industrial sites exposes workers to the risks associated with chemical gas and vapor leaks caused by unforeseen incidents. Such leaks may result in severe health hazards as well as damage to equipment or infrastructure at the leak site. Therefore, the development of systems capable of early detection and highly accurate localization of chemical leaks is of high importance for occupational safety. In this work, a low-cost, portable sensor node based on the Internet of Things (IoT) is proposed for the detection and localization of gas and chemical leaks in indoor environments. The sensor node features a modular design that enables flexible integration and replacement of gas and environmental sensors depending on the target application. In addition, the system includes an ultra-wideband (UWB)-based positioning and tracking unit, allowing operation across multiple indoor zones. The main contribution of this work lies in the combined integration of (i) multi-sensor-based environmental event detection and prediction and (ii) high-precision location within a dynamic multi-zone tracking architecture. The system automatically selects the most relevant anchors in each zone and applies trilateration and least-squares estimation, enhanced by Kalman filtering techniques. In particular, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are employed, with sensor fusion incorporating inertial measurement unit (IMU) data to mitigate the effects of on-line-of-sight (NLoS) conditions and signal degradation caused by obstacles. Experimental results demonstrate that both the EKF and UKF significantly reduce positioning errors and improve tracking stability compared to baseline methods under challenging indoor conditions. The UKF shows superior performance in highly nonlinear scenarios. A quantitative evaluation using manually surveyed reference points showed that the UKF achieved the best overall performance, with a mean error of 39.72 cm and an RMSE of 43.03 cm. These findings confirm the effectiveness of Kalman filter-based sensor fusion for reliable indoor positioning and highlight the suitability of the proposed system for real-time safety monitoring applications. Full article

Nowadays, people spend over 80% of their lives indoors, which makes indoor air quality (IAQ) research important. The paper presents, firstly, a structured overview of publicly available IAQ datasets suitable for machine learning (ML) research, secondly, a comparative analysis of the reviewed datasets, thirdly, an ML-oriented mapping between tasks and algorithms, to outline the algorithmic families that are most appropriate given the dataset structure and the prediction target, and fourthly, an investigation on IAQ–ML using custom-made solutions that include sensors for data acquisition. The methodology included an analysis of 1162 papers from the Web of Science, 1536 from Scopus, and 756 from IEEE Xplore, between 1 January 2020 and 31 December 2025, to capture recent trends in ML-based IAQ research. The findings show that linear regression (132 articles), Logistic regression (91), random forest—RF (77), Long Short-Term Memory—LSTM (77), Principal Component Analysis (63), and Elastic Net are the most popular among researchers. Most studies report accuracy over 90%, with maximum values of 99.37% for LSTM and 99.20% for RF. In the case of regression, the R² values range between 82% and 98%, especially for CO₂ and PM_2.5 prediction. eXtreme Gradient Boosting or hybrid RF-LSTM architectures achieve R² values of up to 99%. The IAQ public and private datasets analyzed for this study provide a strong foundation for transfer learning, but differences require careful preprocessing to ensure consistent comparisons and reliable conclusions. The distribution of articles by sensor type for IAQ parameters shows that linear regression remains the most widely used ML method (26 studies), followed by LSTM (19) and RF (18). The research results confirm that there is no universal algorithm for IAQ, and the quality and structure of the data contribute to the success of ML models. This study aims to be a foundation for the development of future intelligent IAQ monitoring systems. Full article

In architecture and construction, it is common practice to use acrylic products with a high flammable content, ranging from lacquers designed to improve the curing of concrete and mortar to resins that provide protection, sealing, flexibility, and elasticity. The drying process of the treated surface involves the formation of vapours of volatile organic compounds (VOCs); to prevent these from creating a potentially hazardous flammable atmosphere, a procedure is presented that establishes the maximum application rate for solvent-based products, providing equations that relate the maximum application area and the minimum drying time to the available air velocity in the work area. The results are provided for both indoor and outdoor applications. A maximum application rate is established to prevent the creation of areas classified as fire or explosion hazards: 1.5 m²/h indoors and 1 m²/h outdoors. When this is carried out at an ambient temperature of 20 °C, and from 40 °C upwards, it is not possible to apply the varnishes in practice without creating a flammable atmosphere. Full article

Sparse local feature matching has served as the cornerstone of numerous visual geometry tasks and attracted extensive attention. Although significant progress has been made in this area, improving the discriminative power of descriptors remains a key challenge. As far as we know, existing sparse feature matching methods only predict a single descriptor map for keypoints, which might restrict their potential in solving complex scenarios. This issue is particularly pronounced in real-time applications where most methods only learn descriptor maps at a reduced spatial resolution compared to the input image. Consequently, they require interpolating from the low resolution map for obtaining per-keypoint descriptors, which will introduce background contamination and reduce the discriminability of final descriptors. To address these issues, we propose an efficient novel complementary local feature description model. Specifically, the model simultaneously learns two descriptor maps using different loss functions within a single Convolutional Neural Network (CNN). An orthogonal loss is introduced to effectively coordinate the learning of the two branches, aiming to obtain decoupled and complementary descriptors. Extensive experiments across various visual geometry tasks, such as homography estimation, indoor and outdoor pose estimation, as well as visual localization, have demonstrated the superior performance of the proposed method. Full article

Recent studies have shown that common household activities produce characteristic patterns in indoor air pollutants, enabling activity inference using environmental measurements alone. However, pollutant-based approaches are usually formulated as flat multi-class classification problems, even though indoor environments are dominated by long baseline periods with no emission-generating activity, leading to false alarms and unstable predictions. This work proposes a gated hierarchical inference framework for recognizing activities from indoor air quality data. A first-stage gate detects whether a time window contains activity-induced pollutant dynamics, while a second-stage classifier conditionally identifies the specific activity only when activity relevance is detected. Multivariate time-series measurements of particulate matter, volatile organic compounds, nitrogen oxides, carbon dioxide, temperature and relative humidity were collected using a portable monitoring system during controlled household cooking and cleaning experiments. Temporal windows were processed using recurrent neural network models in both stages. By separating activity detection from activity identification, the proposed method aligns inference with the physical generation of indoor pollutant signals and improves robustness in baseline-dominated monitoring scenarios while maintaining reliable discrimination among activities. The framework supports unobtrusive activity recognition and enables applications in exposure-aware monitoring and intelligent indoor environmental management. Full article