Search Results (671)

The indoor microclimate of museums plays an essential role in preserving priceless cultural heritage for future generations and in ensuring visitors’ comfort and health. In this context, the present study aimed to evaluate indoor air quality, the degree of fungal contamination, and visitors’ perceptions in a museum environment through an integrated, interdependent approach. Measurements of the physicochemical parameters of air quality (temperature, relative humidity, CO₂, TVOC, HCHO, PM_2.5 and PM₁₀, negative and positive ions and brightness) were carried out in three exhibition halls within a museum in Oradea, Romania, during the period January–August 2024. Fungal contamination was assessed using surface and air samples, with classical isolation and microscopic identification methods. Visitors’ perceptions were analysed using a standardised questionnaire that focused on perceived comfort and visit duration. The results showed that the parameters defining indoor air quality generally fell within the limits set by the international standards in force, with occasional exceedances. These conditions are associated with the presence of fungi of the genera Cladosporium, Penicillium, and Aspergillus in the air and on museum exhibits, which pose risks to human health and the deterioration of the exhibited materials. The statistical decision-making model determined the critical thresholds above which visitor behaviour changed visibly. The results highlighted the importance of maintaining a stable microclimate in museum spaces, not only for the protection of exhibits, but also for optimising the cultural experience. Indoor air quality indicators and fungal microflora can only affect vulnerable people or those with pre-existing conditions. Occasional visitors do not present a significant risk of developing new conditions, considering the limited duration of exposure. Full article

Indoor space segmentation is essential for indoor navigation, 3D reconstruction, and Building Information Modeling (BIM). However, reliable segmentation from unstructured 3D point clouds remains challenging due to structural voids caused by occlusion and noise, as well as the difficulty of distinguishing permanent structural elements from dense non-structural clutter. To address these issues, this paper proposes a semantic-grid structural completion method for indoor space segmentation from 3D point clouds. The method first integrates RandLA-Net-based semantic segmentation with geometric similarity correction to improve structural consistency. Subsequently, a semantic-grid structural completion algorithm detects and fills structural voids under height constraints; this process employs dual-grid structural marking with a 2D semantic occupancy grid and a 3D voxel grid to identify missing observations and generates synthetic points with inherited semantic labels to restore structural integrity within the scene. A density-aware height difference filtering method is then applied to remove non-structural clutter and clearly separate structural elements from the rest of the scene. Finally, indoor spaces are delineated through connectivity-based segmentation and inverse distance-weighted label propagation. Experiments on public datasets, including S3DIS, UZH and Structured3D, demonstrate that the proposed method consistently outperforms existing approaches, achieving a mean F1 Score of 0.99, an Intersection over Union (IoU) of 0.98, and a Segmentation Error Rate (SER) of 0 in most scenarios, particularly in occlusion-affected and structurally complex indoor environments. Full article

LiDAR is widely used in robotics because it provides reliable range data for navigation and mapping. On a small embedded robot, however, there is a practical conflict between scan resolution and reaction speed. Dense scans provide better environmental detail, but they take too long for fast obstacle avoidance, whereas sparse scans are faster but can miss obstacles if the spacing between adjacent rays is too large. This paper presents an Interleaved Sparse–Dense Scanning method for a servo-actuated single-point time-of-flight LiDAR mounted on an embedded mobile robot. A dense nested pan–tilt sweep is used for three-dimensional mapping, while a sparse forward scan is inserted between dense rows for obstacle detection and motion control. A geometric model is derived to relate sensing range, beam spacing, and minimum detectable object width. That model is then linked to zone-based safety constraints and to the distance the robot can travel before the next obstacle update. For the robot used in this study, the resulting sparse configuration is a 7-point forward scan over a 180${}^{\circ }$ field of view. Experiments in a real indoor environment showed that this configuration reliably detected target blocking obstacles and reduced decision latency by 6.2 times compared with waiting for a complete dense scan before each navigation update. The proposed method provides a practical balance between reactive obstacle avoidance and useful 3D mapping on a low-cost embedded platform, while making the system’s timing and safety limits explicit. Full article

Urban nature and nature-based solutions are increasingly promoted to enhance public space experience and urban climate resilience. In Public and semi-public indoor settings, biophilic design is considered beneficial for stress reduction and mental health restoration through the introduction of natural elements such as plants. However, research focusing on the specific visual features of plants and the underlying mechanisms remains limited. Based on 200 indoor greenery images and their multi-dimensional feature vectors, and combined with questionnaire data from 253 valid participants, this study developed a quantitative framework of plant visual features and adopted a two-level analytical approach. At the image level, linear mixed-effects models (LMMs) were used to identify how plant features influenced immediate responses. At the group level, partial least squares structural equation modelling (PLS-SEM) was employed to examine how cumulative restorative experience translated into affective states, perceived restorativeness, and behavioural intention. The results showed that Green View Index (GVI) and species richness were the most stable positive features, while plant health status, certain planting modes, and spatial layer-related features also showed significant effects. Restorative experience influenced behavioural intention mainly through positive affect and perceived restorativeness. These findings provide evidence for biophilic design, offering quantitative support for incorporating indoor public space into broader urban nature and public space framework. Full article

Aging populations have intensified the demand for thermally comfortable and energy-efficient housing, particularly for elderly residents whose diminished thermoregulatory capacity renders them disproportionately vulnerable to indoor temperature fluctuations. Existing senior apartments in cold-climate regions frequently fail to meet age-specific thermal comfort standards, yet systematic retrofit optimization frameworks explicitly tailored to elderly occupants remain scarce. This study presents a data-driven multi-objective optimization framework for building envelope retrofitting, which is validated using on-site temperature measurements from a representative 1980s brick–concrete senior apartment building in Beijing. The framework integrates Latin Hypercube Sampling (LHS) for design space exploration, a Long Short-Term Memory (LSTM) surrogate model for simultaneous prediction of three performance objectives, and Non-dominated Sorting Genetic Algorithm II (NSGA-II) for Pareto-optimal solution generation, with final selection performed via a weighted Mahalanobis distance-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Optimization targets—annual energy consumption, indoor thermal discomfort hours, and retrofit cost—are parameterized using the age-sensitive comfort thresholds specified in GB 50340-2016. The LSTM surrogate achieved R² values of 0.91–0.93 across all objectives with training–testing differences below 0.02. The optimal retrofit package—Polyvinyl Chloride (PVC) Low Emissivity (Low-E) double-glazed windows (5 + 6A + 5), glass fiber roof insulation (65.25 mm), and Extruded Polystyrene (XPS) external wall insulation (65.39 mm)—reduces annual energy consumption by 47.1% (from 40,867 to 21,626 kWh) and annual thermal discomfort hours by 62.4% (from 2454 °C·h to 923 °C·h). SHapley Additive exPlanations (SHAP)-based sensitivity analysis further identifies wall U-value and roof thickness as the dominant performance drivers. A reproducible and computationally efficient pathway is provided by the proposed framework for evidence-based envelope retrofit decision-making in existing senior residential buildings. Full article

WiFi fingerprinting remains attractive for indoor localization because it reuses existing wireless infrastructure, yet RSSI fingerprints are high-dimensional, sparse, and often ambiguous across adjacent floors and building regions. This study develops a hybrid blended localization framework that combines multi-task learning with feature-space weighted k-nearest-neighbor refinement. A shared neural encoder predicts building labels, floor labels, and normalized coordinates from 520-dimensional WiFi fingerprints, and the learned embedding space is then used for semantically constrained WKNN correction. The final model is trained with AdamW, a learning rate of $8\times {10}^{-4}$, batch size 512, and a joint loss over building classification, floor classification, and coordinate regression, without a learning-rate scheduler. Experiments on a public WiFi fingerprint dataset show that the hybrid model achieves the strongest overall localization robustness among the evaluated non-ensemble methods. On the official validation split, it obtains a mean localization error of 9.01, a median error of 6.25, and an RMSE of 12.95 in the dataset coordinate units. On the internal semantic validation split, it reaches 94.81% floor classification accuracy and 97.62% building classification accuracy. Floor-wise and building–floor analyses further show that the largest errors are concentrated in a small number of difficult semantic regions, especially the highest floor and sparsely constrained partitions. Full article

Leakage identification in district heating networks is challenging because leakage-induced transient pressure waves often overlap with pressure disturbances triggered by routine operations such as valve regulation, pump speed variation, and emergency shut-off. In addition, the scarcity of high-quality labeled leakage samples limits the robustness of data-driven models under small-sample conditions. To address these issues, this study proposes a compact and moderately interpretable framework for multi-condition identification from transient pressure-wave signals, integrating signal preprocessing, handcrafted statistical feature extraction, multiclass ReliefF-based feature selection, and class-wise generative adversarial network augmentation in the selected feature space. A dataset containing four representative conditions, namely leakage, valve regulation, pump speed regulation, and emergency valve shut-off, was constructed using an integrated indoor district heating network testbed. After Hampel-based spike suppression and zero-phase Butterworth band-pass filtering within 0.5 to 300 Hz, time- and frequency-domain statistical features were extracted, and a compact subset was selected by multiclass ReliefF. A class-wise generative adversarial network was then used to augment the training set in feature space, while all evaluations were performed strictly on real samples. The results show that feature-space augmentation improves robustness and generalization under operational disturbances and noise. Using random forest as the representative classifier, Accuracy and Macro-F1 increased from 0.960 to 0.985, while leakage recall improved from 0.920 to 0.980. Further comparisons confirmed that the ReliefF-selected subset outperformed representative alternatives such as LASSO and mRMR. Overall, the proposed framework provides an effective solution for distinguishing leakage events from operational disturbances and offers practical support for online monitoring and intelligent operation of district heating networks. Full article

Residential interior spaces significantly contribute to material consumption, renovation waste, and indoor environmental exposure, yet sustainability at the interior scale is still commonly assessed through prescriptive design guidelines, rather than measurable performance. The existing literature lacks an empirically validated framework that operationalizes circular economy practices within residential interiors and links them to consumption-related behavior. To address this gap, this study develops and validates a multidimensional measurement model based on the 3R framework (Reduce, Reuse, Recycle) to evaluate interior sustainability through environmental, economic, and social indicators and examine its relationship with perceptions of overconsumption and continuous interior change. The model was empirically tested in Jerash, Jordan, using a structured survey of adult homeowners (N = 304). Reliability and construct validity were confirmed through exploratory and confirmatory analyses, followed by regression modeling. The results demonstrate that interior sustainability can be reliably quantified using coherent 3R-based constructs, with environmental, economic, and social indicators strongly associated with the three dimensions (r > 0.8). Engagement in reduce and Recycle practices showed significant associations, with more critical attitudes toward trend-driven renovation and excessive consumption, whereas reuse did not demonstrate a statistically significant effect. The model explained 43% of the variance in these perceptions (R² = 0.432, p < 0.001). The findings advance interior sustainability from prescriptive guidance toward analytical, behavior-based measurement and provide a transferable framework for assessing circular material practices in residential interiors. Full article

Fine particulate matter (PM)-induced pollution is one of the major causes of indoor air quality deterioration. Passive air purification technologies offer advantages of structural simplicity and low energy consumption, yet their spatiotemporal mass transfer characteristics remain poorly understood. This study presents a theoretical and experimental investigation of PM spatiotemporal mass transfer under the sink effect induced by an electro-convective passive air purifier. The apparent mass transfer coefficient (D_app) and PM concentration prediction models based on Fick’s second law were established, and then the space-and-time-dependent mass transfer coefficient (D_st) was determined by using the Boltzmann–Matano method. The results revealed that the absolute values of D_st quantified local migration intensity, while its sign provided directional information unattainable from conventional averaged parameters. The logarithmic values of D_app showed a consistent logarithmic relationship with distance at fixed time windows, and the validated prediction model maintained errors within ±15%, enabling accurate reconstruction of full-field concentration distributions from limited measurement points. The complementary nature of these two coefficients offers a comprehensive evaluation framework. This work advances both the theoretical understanding and practical application of passive air purification technology, offering new tools for indoor PM exposure control and purifier performance optimization. Full article

Children’s affinity for natural elements, or biophilic preferences, has gained increasing recognition as a cornerstone of family-centered sustainability. This study examines how parental factors, specifically environmental attitudes and in-home biophilic design plus guidance, directly shape children’s preference for nature-infused environments. A cross-sectional survey (N = 397) for parents collected data on household greenery, animal care, parental attitudes toward environmental responsibility, and the degree of child involvement with natural elements. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the analysis identified proactive parental mindsets and frequent biophilic home modifications as significant predictors of stronger child affinity for plants, water features, and other nature-inspired components. The findings highlight several key parental and environmental factors that contribute to the development of children’s biophilic preferences, underscoring the importance of coordinated efforts among families, communities, and policymakers to nurture children’s environmental consciousness. By highlighting how indoor greenery, small-scale animal care, and intentional parental support can foster early engagement with nature, this research offers fresh insights into the synergy between biophilic design and sustainable family practices. Emphasizing the potential role of home-based natural elements in enhancing children’s environmental awareness, the study concludes that nature-rich living spaces and holistic sustainability interventions are essential for empowering the next generation to shape a more sustainable future. Full article

Existing buildings with large glazing ratios within subtropical Mediterranean climates face substantial challenges for thermal and visual control of their indoor environment. Previous research by the same authors has already identified the potential of incorporating both solar–PDLC (polymer-dispersed liquid crystal) and SPD (suspended particle device) switchable films within facades exposed to high solar insolation to provide a wide dynamic range of visual transparencies. This paper identifies a novel application for switchable laminates within a dynamic external shading device that permits the casting of a shadow on demand onto existing fenestration. This study compares the degree of glare within an enclosed space attained by a conventional opaque overhang over a window to that achieved with glass shading overhangs incorporating two types of switchable films. Using a scale model in a field test setting, indoor illumination and glare measurements are investigated under different states of switchable films and compared to those provided by conventional static glazing, with and without ordinary external overhangs under identical field test conditions. Results show that switchable overhangs in their transparent/bleached state can allow the ingress of daylight without creating excessive glare, whereas in their translucent/tinted state, switchable shades can deliver a level of glare protection similar to that provided by an opaque shading overhang. Full article

During the interpretation of Borehole Radar (BHR) B-scan profiles, the accurate determination of the azimuth of geological targets in three-dimensional space is a critical issue for achieving precise anomaly localization and spatial structure inversion. However, existing directional BHR anomaly localization methods exhibit limited intelligence, insufficient adaptability to multi-site data, and weak generalization capability, rendering them inadequate for engineering applications under complex geological conditions. To address these challenges, a robust deep learning model, termed BSS-Pose-BHR, is developed based on YOLOv11n-pose for keypoint detection in directional BHR profiles. The model incorporates three key optimizations: Bi-Level Routing Attention (BRA) replaces Multi-Head Self-Attention (MHSA) in the backbone to improve computational efficiency; Conv_SAMWS enhances keypoint-related feature weighting in the backbone and neck; and Spatial and Channel Reconstruction Convolution (SCConv) is integrated into the detection head to reduce redundancy and strengthen local feature extraction, thereby improving suitability for keypoint detection tasks. In addition, a three-dimensional electromagnetic model of limestone containing a certain density of clay particles is established to construct a simulation dataset. On the simulated test set, compared with current mainstream deep learning approaches and conventional directional borehole radar anomaly localization algorithms, BSS-Pose-BHR achieves superior performance, with an mAP50(B) of 0.9686, an mAP50–95(B) of 0.7712, an mAP50(P) of 0.9951, and an mAP50–95(P) of 0.9952. Ablation experiments demonstrate that each proposed module contributes significantly to performance improvement. Compared with the baseline, BSS-Pose-BHR improves mAP50(B) by 5.39% and mAP50(P) by 0.86%, while increasing model weight by only 1.05 MB, thereby achieving a reasonable trade-off between detection accuracy and complexity. Furthermore, indoor physical model experiments validate the effectiveness of the method on measured data. Robustness experiments under different Peak Signal-to-Noise Ratio (PSNR) conditions and varying missing-trace rates indicate that BSS-Pose-BHR maintains high detection accuracy under moderate noise and data loss, demonstrating strong engineering applicability and practical value. Full article

Impulsive noise significantly degrades the performance of power line communication (PLC) systems due to their non-Gaussian amplitude distribution, burst clustering, and inherent temporal dependence. Conventional statistical and spectral models often describe marginal behavior but do not fully account for the underlying temporal organization of such noise processes. This paper introduces an ensemble-based non-linear dynamical framework for the short-window characterization of impulsive PLC noise using delay-embedded phase-space reconstruction (PSR). Rather than relying on extended stationary recordings, the analysis is conducted across multiple independent short-duration acquisition windows obtained from indoor low-voltage networks. For each realization, the delay parameter is selected using average mutual information, and the embedding dimension is determined through the false nearest neighbors (FNN) criterion. The reconstructed trajectories are then examined using correlation dimension estimation, largest Lyapunov exponent analysis, and recurrence quantification measures. The resulting non-linear descriptors reveal structured phase-space organization and low-dimensional dynamical characteristics that are not readily observable in the original time-domain representation. In addition, these findings show that short-window PLC data preserve meaningful dynamical characteristics and support the use of non-linear geometric descriptors for impulsive PLC noise analysis and future mitigation approaches. Full article

A classroom is an educational environment where teaching and intellectual engagement take place. It is designed specifically to integrate technological advances in education and improve student outcomes. This study examines the impact of various visual design elements on students’ academic performance across different indoor classroom configurations in Erbil. Furthermore, it aims to determine the most effective classroom design elements for optimizing educational results. The research employed a mixed-methods approach, integrating qualitative content analysis with illustrations, pictures, and graphs. The quantitative component comprises two methodologies: a researcher-developed closed-ended questionnaire along with ergonomic and visibility evaluations utilizing DepthMapX 10 simulation software. Research shows that Modularity, Visual diversity display, Legibility, Sightline, and easy Accessibility enhance the quality of learning. The study suggests that spatial and visual design elements can establish a dependable model, reinforcing the notion that classroom space design is a crucial component of the learning environment. Full article

In many non-tourism historical districts in China, property division has subdivided traditional dwellings into multi-household units. While such subdivision reshapes spatial sequences and connections, its consequences for everyday space use and circulation are rarely documented with continuous in situ evidence, partly because residential behaviour is temporally continuous and difficult to observe directly. This study examines two typical subdivision patterns in Subu Old Street: a longitudinal, single-axis serial dwelling (Case A) and a transversal, courtyard-centred dwelling (Case B). We formalize spatial units, connections, and operational nodes using a semantic ontology and map day-long Ultra-Wideband (UWB) trajectories to quantify occupancy and transition characteristics. Case A concentrates both staying and passing at the entrance-end kitchen, where activities overlap with through-movements and transition durations are short in most events but highly volatile with a long tail. Case B channels most transitions through the courtyard hub, keeping indoor rooms mainly for staying and producing longer but more stable transition durations. This study is positioned as a comparative exploratory case study of two representative subdivision patterns identified in Subu Old Street. Semantic ontology modelling, UWB-based behavioural tracking, and behavioural indicators are used together in a comparative analytical approach for examining how subdivision reorganises spatial structure and everyday residential behaviour. The results reveal pattern-specific differences in occupancy concentration, transition organisation, and movement duration. These findings are analytical observations derived from two representative cases. They provide a basis for spatial adjustment and micro-regeneration in still-inhabited subdivided traditional dwellings. Full article