Search Results (247)

Indoor environmental comfort is essential in performing arts spaces due to its influence on both audience engagement and performers’ experience during live events. This paper primarily investigates the acoustic response of the Goldoni Theatre, with an additional assessment of its thermo-hygrometric and lighting conditions across audience areas. Acoustic measurements were conducted under unoccupied conditions to evaluate the sound field in accordance with standard requirements. The results indicate favourable listening conditions for both music and speech. Complementary surveys were carried out during the summer season to assess thermal comfort while the HVAC system was not in operation, as well as illuminance levels. The findings show that air temperature falls within the optimal comfort range when unconditioned, fluctuating between 24.6 °C and 26.2 °C, whereas illuminance levels remain below the recommended lower limit, specifically between 156 and 190 lux. Overall, this study provides an acoustically focused evaluation of the theatre in Bagnacavallo, complemented by an assessment of its thermal and lighting conditions, which reflects the candlelight practices as typical of similar historical opera houses. Full article

Automated three-dimensional (3D) mapping with a survey-grade terrestrial laser scanner (TLS) is the canonical instance of robotic stop-and-scan light detection and ranging (LiDAR) mapping: the mobile platform must remain stationary for minutes per scan, so the environment must be covered with as few scans as possible. Where to stop hinges on a coverage model predicting what a scan pose will observe. The conventional isotropic-disk model counts every free cell within sensing range as covered—including cells behind walls—and, therefore, skips scans that genuine observation requires. We replace the disk with a ray-cast visibility region computed on the robot’s live two-dimensional (2D) occupancy map, admitting only cells in direct line of sight; define a line-of-sight (LOS) coverage metric over mapped free space and drive a marginal-gain scan/skip rule embedded in frontier exploration. The planner thus performs 2D scan-station placement for subsequent 3D mapping. In a controlled paired ablation in simulation, the visibility rule raises LOS coverage from about 77% to 84–85% for one to two additional scans, and it also outperforms a disk baseline governed by the identical marginal-gain rule, isolating the coverage model itself as the decisive factor. In a fully autonomous on-hardware comparison in an industrial test room, with each rule driving a mobile platform carrying a Leica BLK360 G1, the visibility rule raised LOS coverage from 77.6% to 92.1%; every visibility run’s scans registered offline at survey grade (4–5 mm bundle error, 84–88% overlap), whereas disk runs yielded at best a single-link network and once a single unregistrable scan. The results indicate that, for the room-scale indoor environments studied, occlusion-aware visibility is a sounder basis than Euclidean proximity for stop-and-scan placement. Full article

Biophilic design connects people to nature through direct, indirect, and spatial experiences, yet how systematically LEED v5 and WELL v2 represent these dimensions remains insufficiently understood. This study examines how and to what extent LEED v5 BD+C and WELL v2 optional scoring components represent biophilic design, using 14 Patterns of Biophilic Design as a common analytical framework. Optional LEED v5 credits and WELL v2 optimization features were screened for biophilic relevance, mapped to a primary pattern, and scored for alignment strength on a 0–3 scale. Representation was limited on every measure. Only 9 of 34 LEED v5 optional credits (26.5%) and 11 of 84 WELL v2 optimization features (13.1%) qualified as biophilically relevant, and only 3 credits (8.8%) and 4 features (4.8%) showed strong and direct alignment. LEED v5 addressed 7 of the 14 patterns and WELL v2 only 5; the remaining 7, including Mystery, Risk/Peril, Biomorphic Forms and Patterns, and Complexity and Order, received no primary representation in either system. Nature in the Space accounted for 17 of the 20 qualifying items (85%). LEED v5 showed a more site- and ecology-oriented profile, particularly through Connection with Natural Systems, while WELL v2 concentrated more on occupant-centered indoor environmental conditions, especially lighting, daylight, airflow, and thermal comfort. These findings suggest that, within the optional components analyzed, biophilic design is more readily represented where it overlaps with measurable and documentable criteria. Experiential, symbolic, and psychological–spatial dimensions are largely absent from the primary pattern assignments. The study contributes a transparent procedure for screening, mapping, and scoring biophilic content across rating systems. Given the small number of qualifying items and the interpretive nature of pattern scoring, the findings are presented as a documentation-based, pattern-level critique rather than a statistical ranking of the two systems. Full article

Underground heritage spaces represent a distinctive yet underexplored asset of the built environment, characterized by stable microclimatic conditions, limited natural lighting, strong material identity and complex perceptual dynamics. Despite their potential for sustainable regeneration, existing adaptive reuse approaches often fail to establish explicit links between environmental diagnosis, heritage conservation and user-centered design. This study proposes an integrated human-centered framework for the sustainable adaptive reuse of hypogeal heritage spaces, structured into three sequential phases: multisensory environmental diagnosis, reversible architectural design and virtual reality (VR)-based perceptual validation. The framework is applied to a disused historic underground complex in Dugenta (southern Italy), where an underground cavity is being repurposed within a hospitality-oriented redevelopment project. The diagnostic phase combines LiDAR-based geometric acquisition, indoor environmental monitoring and acoustic analysis, translating measured parameters into sensory indicators and a diagnostic criticality matrix. These outputs inform the development of a reversible liner/shell architectural system designed to improve environmental quality while preserving the integrity of the heritage fabric. Finally, alternative multisensory design scenarios are assessed through an exploratory VR-based evaluation focused on perceived restorativeness and spatial experience. The results demonstrate how environmental data can be systematically transformed into traceable design decisions, supporting conservation, user well-being and sustainable heritage regeneration. The proposed framework contributes a replicable methodology for integrating environmental performance, heritage preservation and human-centered design into the adaptive reuse of underground built heritage. Full article

Vertical farming (VF) offers a smart way to grow crops in stacked layers inside controlled indoor environments. By doing so, it uses far less land and water than traditional open-field agriculture, making it a promising solution for cities with limited space and resources. In recent years, artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) technologies have begun to transform vertical farming. These tools are moving the industry away from rigid, rule-based systems toward more flexible, data-driven operations that can adapt in real time. This paper presents a systematic review of 208 peer-reviewed studies from 2015 to 2025. It explores how AI, ML, and IoT are applied across the VF ecosystem, focusing on key areas such as computer vision for disease detection, crop growth and yield prediction, smart climate control, and precision nutrient and irrigation management. This review examines the performance of different algorithms, including Convolutional Neural Networks (CNNs), Random Forest, XGBoost, and LSTMs across hydroponic, aeroponic, and aquaponic systems. The review also covers IoT setups with multi-sensor networks, edge-cloud computing, and automated control systems. Commercial farms have shown real gains in resource efficiency and shorter supply chains. However, challenges remain: high energy use (especially from LED lighting, which makes up 40–60% of costs), expensive setup, scattered datasets, and limited real-world testing. Many high-accuracy claims (>95%) come from lab conditions and need better validation in actual farms. Overall, AI-powered vertical farming has strong potential to support resilient urban food systems. Future work should focus on lightweight edge AI models, improved data standards, explainable AI, and robust life cycle assessments to ensure the benefits outweigh the environmental and economic costs. Full article

Contemporary lifestyles lead the elderly to spend up to 80% of their time indoors. Research underscores the unique visual requirements of different types of spaces to enhance accessible design for aging populations. This paper aims to evaluate the daylight conditions of the interior spaces of a senior living facility in Algeria. Interviews and on-site illuminance measurements were conducted to investigate perceptions and preferences regarding these spaces. A geometric model was then created in Rhino 8 and Grasshopper to simulate the interior daylight environment over an entire year. The study reveals significant daylighting deficiencies in the investigated spaces, including insufficient illuminance uniformity, localized overexposure near window zones with a potential glare risk, and some underlit activity areas. Additionally, annual daylight simulations revealed marked differences between the two shading scenarios. With the existing shading devices, the north-west-facing bedroom (R1NW) achieved UDI of 87.09%, whereas the south-east activity room (AR1SE) reached only 13.78%; removing the shading increased acceptable UDI in all investigated spaces. However, annual glare analysis showed that this improvement in daylight availability was accompanied by a reduction in glare autonomy. These results support orientation-specific daylighting strategies that include optimized or adaptable shading devices, supplementary task lighting for underlit functional areas, and increased interior finish reflectance to improve daylight distribution while reducing glare. Full article

Indoor badminton venues are common mass-fitness spaces in China, but their acoustic environment remains underexamined relative to lighting, thermal comfort, and functional facilities. This study uses grounded theory to examine how users perceive acoustic conditions within the broader experience of indoor badminton venues. A total of 4721 raw online reviews for seven purposively selected venues in five Chinese cities were collected, and 3937 valid reviews remained after preprocessing. A hybrid text-processing procedure combining DeepSeek-V3-assisted term pre-screening and Python (3.11) jieba segmentation identified 74 core high-frequency terms; all grounded theory coding was conducted manually in NVivo 15. Open, axial, and selective coding generated 32 initial categories, six main categories, and an Indoor Badminton Venue User Experience Perception Model. Acoustic-related categories were then extracted to construct an Acoustic Environment Perception Mechanism Sub-Model. The results show that noise level was directly mentioned in only 45 reviews but was indirectly embedded in sport atmosphere, time-based flow, and user experience, indicating a latent perceptual role. Moderate sound may be interpreted as a vibrant sport atmosphere, whereas crowd overload and reverberant spatial conditions may shift perception toward chaotic noise. The findings provide qualitative evidence for integrating user-centered acoustic considerations into the design and operation of mass-leisure sports venues. Full article

Highway service-area buildings are characterized by long operating hours, diverse functional spaces, and considerable energy consumption, resulting in significant life-cycle carbon emissions. This study quantifies life-cycle carbon emissions of the buildings in highway service areas. A life-cycle accounting framework was established, and net emissions were further evaluated by considering the contributions of photovoltaic (PV) electricity and vegetation carbon sinks. Five representative service areas covering hot-summer/cold-winter, severe-cold, cold, temperate, and hot-summer/warm-winter zones were investigated through field surveys and indoor thermal environment measurements to obtain envelope properties, equipment configurations, and operating profiles. Results revealed that life-cycle carbon emissions vary substantially across climatic regions, ranging from 4.31 × 10³ to 3.06 × 10⁴ tCO₂e. The operational stage accounts for the largest share of total emissions, approximately 61–84%. Heating demand dominates operational emissions in severe-cold and cold regions, whereas cooling and lighting loads become increasingly important in warm and temperate climates. The orthogonal analysis reveals significant differences in the sensitivity of design parameters across climatic regions. After implementing climate-adaptive optimization measures, life-cycle carbon emissions are reduced by 40.35–87.94% in four service areas. In the hot-summer/warm-winter region, the combined effects of PV electricity generation and vegetation carbon sinks maintain a net-negative carbon balance. The findings provide evidence for sustainable highway service-area design by linking life-cycle accounting, climate-specific design priorities, and renewable-energy substitution. Full article

Reliable selective picking of long-staple cotton remains challenging because dense dwarf canopies restrict robot operating space and increase boll occlusion, resulting in reduced target visibility and potential fiber damage during picking. To address these challenges, a mobile dual-arm robotic picking system integrating hierarchical depth perception, cotton-boll recognition, optimized motion planning, and three-finger flexible end-effectors was developed for autonomous picking in Xinjiang long-staple cotton fields. The proposed YOLOv7-DCN-SENet model reached 95.75% precision, 92.65% recall, and 97.19% mAP@0.5 on the test set, while the onboard computing platform operated at 101 FPS under the experimental configuration. Indoor and field experiments were conducted on directly visible upper-canopy open cotton bolls. The dual-arm robot achieved parallel picking success rates of 74.6% and 57.6%, with average picking cycles of 28.2 s and 34.9 s, respectively. Field performance was mainly limited by strong-light overexposure, depth-information loss, occlusion-induced localization errors, arm interference within narrow canopy spaces, and incomplete fiber separation during boll detachment. These results demonstrate the feasibility of autonomous dual-arm selective picking for long-staple cotton under dense planting conditions and provide a basis for further improvements in robotic cotton-picking systems. Full article

Visible light positioning (VLP) based on received signal strength (RSS) offers a low-cost solution for indoor localization, being easily implemented in a warehouse based on existing infrastructure. However, RSS-based VLP remains challenging in 3D and yields subpar performance compared to 2D due to the larger localization space, as well as the presence of dark spots where many LEDs are not bright enough. This limits the practical use cases of RSS-based VLP in industrial applications. We study the performance of RSS-based VLP on a 3D simulated environment by training various machine learning models, including Gaussian processes and Kolmogorov–Arnold networks on different representations of RSS data. Our findings show that the use of Gaussian processes for predicting distances to LEDs coupled with a logarithmic transformation and multilateration leads to both high-accuracy and high-precision predictions under thermal noise (p95 localization error of 10 cm under 50 dB SNR). With this technique, RSS-based VLP reaches levels of accuracy in our simulated 3D environment that are comparable to those reported for 2D applications, supporting the extension of RSS-based VLP to height-varying industrial use cases. Full article

Accurate and stable indoor occupancy information is essential for occupant-based intelligent ventilation control. Under a single-camera setting, existing indoor occupancy detection methods commonly suffer from missed detections caused by occlusion and blind zones, false detections caused by people outside the room, and cumulative entry–exit errors that are difficult to correct. These problems lead to false fluctuations in detected occupancy, affect control performance, and may further reduce indoor comfort or cause unnecessary energy use. To address the practical situation in which indoor spaces are commonly equipped with a single security camera, this study proposes an indoor occupancy detection method by fusing field-of-view information and entry–exit events with a single camera. The study covers method development, multi-scenario validation, parameter analysis, and a ventilation control application. The proposed method uses YOLOv8x and DeepSORT as front-end models and performs post-processing on their outputs to extract field-of-view occupancy information, entry–exit events, and blind-zone events. An occupancy confirmation and correction module is then constructed. The blind-zone event mechanism reduces the influence of missed entry–exit events and camera blind zones on occupancy judgment. The correction module integrates frame-by-frame ID counts, historical outputs, and multiple event signals to verify and suppress false occupancy changes caused by false detections, missed detections, and blind zones, thereby producing more stable indoor occupancy results. Experimental results show that the proposed method outperforms the baseline methods based on front-end object detection and tracking in terms of score, RMSE, and F1 score in three typical scenarios: an office, a home, and a classroom. In the office scenario, the proposed method achieved a score of 99.36%, an RMSE of 0.081, and an F1 score of 0.781. The detection stability was also improved in the home and classroom scenarios. In the high-density and strongly occluded classroom scenario, the absolute detection performance of the fusion-based detection method was limited by the front-end models, indicating that the method still has certain applicability boundaries in complex high-density scenes. Parameter sensitivity analysis shows that key parameters, including the entry–exit area depth, confidence threshold, and time threshold, affect the detection results of the fusion-based detection method. Under the test conditions of this study, the method performs well when the entry–exit area depth is approximately 1.5d, the YOLOv8x confidence threshold is 40%, and the time threshold is 5 × FPS. These results can provide a reference for initial parameter setting and on-site calibration in similar scenarios. Using the office scenario as a case study, the method was further applied to occupant-based ventilation control. The average CO₂ concentration during occupied periods under the proposed method was 622.43 ppm, which was closest to the result under ground-truth occupancy control, with a deviation of only 0.9 ppm. This indicates that the method can help improve indoor air quality. Compared with conventional schedule-based control, occupant-based ventilation control driven by the proposed fusion method reduced cumulative fan energy consumption by approximately 65.2%, showing good energy-saving potential at the ventilation-control level. In summary, the proposed method can effectively improve the accuracy and stability of indoor occupancy detection under a single-camera setting and provide more reliable input for occupant-based ventilation control. The framework is modular, and the front-end object detection and tracking models can be replaced according to actual deployment needs. However, the validation in this study is still mainly based on scenarios where existing security cameras can cover the main activity areas and all entry–exit passages. The applicability of the method under more complex camera arrangements, lighting variations, and automatic region configuration requires further investigation. Full article

Background: Indoor Environmental Quality (IEQ) in housing plays a critical role in supporting health, comfort, and daily well-being, yet research and practice often address thermal, visual, acoustic, and air quality conditions in isolation. Objective: This systematic review synthesizes findings from peer-reviewed studies to examine how residential IEQ is experienced and shaped through interactions among physical building factors, environmental conditions, occupant behaviors, and socio-economic contexts. Methods: A systematic literature review was conducted following PRISMA guidelines, including 110 peer-reviewed studies published between 2015 and 2025. Data were extracted and coded from 10,838 quotations and corresponding measured environmental parameters, enabling cross-domain thematic synthesis across eight IEQ domains and four analytical themes. Results: The results show persistent perception-to-measurement gaps, particularly in ventilation usability, low-frequency noise, nighttime thermal conditions, and moisture control. Demographic factors, including age, life stage, health sensitivity, and housing tenure, influence how IEQ conditions are perceived. Integrated IEQ assessments indicate that sleep-critical spaces, moisture robustness, and simple, quiet control systems exert disproportionate influence on overall environmental satisfaction. Conclusions: The findings highlight the need to prioritize preventive design strategies addressing moisture, thermal comfort, acoustics, and lighting, while improving usability of environmental controls. Future research should expand longitudinal and cross-context studies, particularly in low-income communities, and strengthen links between IEQ performance and health outcomes. Healthy residential environments require understanding IEQ not only as a technical performance metric but as a spatial and social condition. Full article

The COVID-19 pandemic highlighted the need for effective disinfection strategies in order to minimize human exposure and reduce the risk of contagion in indoor environments. Ultraviolet-C (UV-C) irradiation has proven to be an effective solution for inactivating a wide range of pathogens. However, traditional fixed UV-C systems suffer from limited coverage and lack operational flexibility. To address these limitations, this paper proposes an augmented reality (AR)-based teleoperation framework for an omnidirectional mobile robot equipped with a UV-C disinfection light. Unlike traditional toolchain integrations, our framework synergizes immersive spatial visualization of a reconstructed environment, operator-guided waypoint-based remote navigation, and real-time interaction with the disinfection payload in a single operational workflow. The system is implemented using a ROSMASTER X3 Plus robotic platform, which generates a three-dimensional representation of the environment through visual simultaneous localization and mapping using RTAB-Map. The result is a 3D map that is imported into the Unity game engine and deployed to a Meta Quest 3 head-mounted display, enabling immersive visualization and interaction. Communication between the AR interface and the robotic system is achieved via the ROS-TCP-Connection, allowing real-time data exchange and remote robot control. Through the AR interface, the operator can navigate the robot within the scanned environment and activate the UV-C light. Experimental validation conducted in a classroom demonstrates the feasibility of the proposed approach and shows measurable reductions in surface microbial load. These results indicate that our system-level integration of AR-assisted teleoperation with mobile UV-C robotics represents a feasible proof-of-concept for flexible, operator-guided disinfection of indoor spaces. Full article

When is an unknown, confined environment traversable for a specific ground robot using only touch? We answer by (i) giving an environment-anchored definition of traversability, expressed through the max-min value $T^{★}(E;A)={sup}_{\pi \in {\Pi }_{S\to G}}{inf}_{s\in [0,1]}\varphi \left(\pi \left(s\right)\right)$, where the bottleneck margin $\varphi$ aggregates the clearance, curvature ($\rho \ge R_{min}$), slope/step, and friction constraints, and (ii) introducing an on-policy, tactile certificate (TC) that maintains a conservative, monotone lower bound $T_t$ using partial contact histories. The TC fuses pessimistic free-space from contacts and the body envelope, the M3 decaying contact memory as a risk prior, and local bend/FSR proxies; a certificate is issued when $T_t>0$ and the explored corridor graph connects S to G. Relative to Papers 1–2 (tactile traversal; offline software assurance), this work formalizes traversability itself and provides a tactile-only, online certificate computable during runs. In a retrospective analysis of 660 trials across Indoor/Outdoor/Dark lighting environments, (H1) the early TC margin predicts success and traversal time better than contact/dwell heuristics (higher AUC/$R^2$), (H2) the TC predictivity is lighting-invariant, and (H3) speed-gating M3 by a TC margin recovers part of the CB-V speed gap without degrading success. Artifacts include the TC implementation, explored-corridor graphs, and per-trial TC time series added to the Paper-1 log bundle; these materials are available from the corresponding author upon reasonable request. Full article

Climate change is a critical global challenge, and the building sector accounts for nearly 30% of global greenhouse gas (GHG) emissions, remaining a key target for mitigation. Indoor environments contribute significantly to GHG emissions, primarily through heating, cooling, lighting, and occupant-driven energy use. Indoor mapping, serving as the foundation for Digital Twins (DTs), provides a spatiotemporal framework that integrates sensor data with Building Information Modelling (BIM), Geographic Information Systems (GIS), and Internet of Things (IoT) to support energy-efficient, low-carbon building operations. This review examined the role of indoor mapping in understanding, modelling, and reducing GHG emissions in buildings. It synthesized current advancements in indoor spatial data acquisition, ranging from Light Detection And Ranging (LiDAR) and Simultaneous Localization and Mapping (SLAM) to deep learning-based floor plan extraction, and evaluated their contribution to improved indoor environmental analysis. The review highlighted emerging techniques, challenges, and gaps, particularly the limited integration of physical indoor spaces with virtual layers representing assets, occupants, and equipment. Addressing this gap requires embedding spatial modelling as an intermediate analytical layer that structures and contextualizes sensor data to support spatiotemporal decision-making. Overall, this review demonstrated that indoor mapping plays a critical role in transforming spatial information into actionable insights, enabling more accurate energy modelling, enhanced real-time building management, and stronger data-driven strategies for GHG mitigation in the built environment. Full article