Search Results (17,059)

Although smart city investments have surged in fields such as healthcare, infrastructure and waste management, very little is known about the quality of evidence or its association with urban economic development. This systematic review, based on PRISMA 2020 guidelines, searched four major academic databases for peer-reviewed literature published between January 2011 and April 2025. 58 included studies were appraised independently by two reviewers using MMAT 2018 (mean score 76.5%, Cohen’s κ 0.738) and were divided into four thematic clusters: Healthcare AI (n = 14), Smart Infrastructure (n = 12), Waste Management AI (n = 11) and Smart Cities—Saudi/GCC (n = 21). CNNs dominate the image-based waste classification; LSTM and ensemble methods each outperform in different time-series and tabular waste forecasting tasks. Quantified benefits include a 0.773 AUC-ROC across 78 disease prediction tasks, a 16% decrease in travel time, and a 35% decrease in waste collection frequency. Direct empirical linkage between the service efficiency generated by the ANN and economic development at the zone level remains largely unestablished in the reviewed literature-an issue that is particularly salient in the GCC context. The dual-stage ANN mediation model is suggested as the main research priority in this review. Full article

Accurate prediction of building energy consumption is critical for sustainable urbanization, while dynamic occupant interactions with the building envelope remain a major source of uncertainty. Prevailing cross-scale building energy models often rely on simplified or uniform behavioral assumptions that cannot adequately represent the spatial and temporal heterogeneity of occupant actions such as window opening and curtain use. This study proposes an AI-augmented framework that integrates field-observed occupant behavior, machine-learning prediction, and physics-based building energy simulation. Time-series observations of window and curtain states were collected from 1609 rooms across 12 residential buildings in a high-density neighborhood in Hong Kong. Environmental and contextual associations were first examined at the aggregated behavioral level, after which Random Forest models were used to generate dynamic behavior schedules. These schedules were subsequently integrated into EnergyPlus to evaluate how different levels of occupant behavior representation affect simulated cooling energy demand and computational cost. Compared with the Detailed Scenario, the Template Scenario produced 20.5% higher simulated cooling energy, while the Average Scenario showed an 8.7% relative difference with 20.6% less simulation time. An additional controlled analysis showed that removing surrounding buildings increased simulated cooling energy by 1.7–9.3%, demonstrating the direct physical influence of inter-building shading. Overall, the proposed framework provides a practical pathway for incorporating context-dependent occupant behavior into urban building energy modeling and for evaluating the trade-offs between behavioral modeling detail and computational efficiency. Full article

The primary goal in designing classical music concert halls is to create acoustic and spatial conditions that foster deep listener immersion, a phenomenon often termed acoustic envelopment or “spaciousness”. International standards recognize this phenomenon with the parameters of Lateral Energy Fraction and Late Lateral Gain. The present research applies a cross-disciplinary methodology to investigate whether a unified index can be formulated to describe holistically the perception of a listener being immersed in and surrounded by the acoustic sound field within a concert hall, hereafter termed Periichisi. The research question is examined through three specific components: the exploration of the acoustic, psychoacoustic, and possible neurological mechanisms underlying this perceptual condition; the identification of architectural and musical factors that contribute to its formation; and the evaluation of selected acoustic parameters, including G, C80, and BR, in supporting the proposed index. The research methodology uses an ex post facto analysis of psychoacoustic data collected at the AuroLab chamber in the School of Architecture, NTU Athens. To promote objectivity and inclusivity, the dataset comprises 130 participants, intentionally including non-expert listeners without formal musical training. Key contributions include the introduction of the term Periíchisi into the Hellenic technical lexicon and the development of the Periíchisi Index for quantifying listener envelopment. Findings suggest future directions, including EEG-based studies of brain activity during Periíchisi and the strategic optimization of G, C80, and BR to enhance immersive acoustic experiences. Full article

Interferometric synthetic aperture radar (InSAR) maps ground motion in permafrost regions, but deformation does not uniquely identify the underlying process. This systematic review assessed how far the field has progressed from deformation detection toward validated process inference. A Web of Science Core Collection search on 16 July 2026 returned 409 records; 262 publications met the core scope, and 175 contributed 372 coherent scientific results and 727 companion metrics. Research expanded rapidly after 2020 and diversified from seasonal mapping toward active-layer thickness (ALT), ground ice, hydrology, infrastructure, and predictive modelling. Evidence maturity nevertheless declined along the inference chain. Descriptive deformation/quality and model/algorithm results comprised 58.3% of the evidence, whereas direct validation against field observations, global navigation satellite system (GNSS) measurements, or levelling comprised 4.6%. Only 12.4% of results reported a defensible analytic sample size and 33.9% provided usable uncertainty. ALT evaluations using probing, ground-penetrating radar (GPR), or model references estimated non-equivalent quantities and could not support one pooled accuracy measure. The radar line-of-sight (LOS) displacement is also a projected, non-unique response whose process interpretation depends on motion geometry and thermal, hydrological, and mechanical assumptions. No evidence family met the prespecified requirements for global meta-analysis. Progress toward cumulative inference requires explicit estimands, matched independent validation, uncertainty propagation, and transparent dataset dependence. Full article

Hangzhou Bay is an estuarine ecosystem on the Yangtze River Delta and a key wintering habitat for waterbirds. However, suitable waterbird habitats have been degrading and fracturing. Therefore, we aimed to assess waterbird habitat suitability and identify the principal factors affecting quality in Hangzhou Bay National Wetland Park. Habitat suitability index (HSI) models were constructed for wading and swimming birds using water source conditions, disturbance levels, concealment, and food abundance. The models adopted consistent factor weights but differentiated the grading criteria for the waterbird groups. Applicability was validated using bird survey data collected in 2025, and habitat suitability was quantitatively assessed using field and literature data. Food abundance and water source conditions were dominant factors influencing habitat suitability; concealment and disturbance levels had minor effects. The abundances of wading and swimming birds increased with HSI values, although limited validation sites constrained these relationships. Positive trends indicate that the HSI models may capture the spatial variation in habitat use. Grade III or higher areas comprised 96.67% and 74.21% of the total area for wading and swimming birds, respectively; no Grade I habitats were identified. Overall, the study area provided favorable habitats for waterbirds, although habitat suitability differed between the groups. These findings may provide a quantitative reference for the targeted conservation and restoration of waterbird habitats in the Hangzhou Bay wetland. Full article

The renewal of historic districts requires a balance between heritage conservation, spatial quality improvement, and contemporary urban use. Yet the links between perceptual experience and measurable spatial characteristics remain insufficiently clarified. Using the Yantai Mountain Historic District in Fuzhou, China, as an empirical case, this research develops an integrated evaluation framework that combines perceptual assessment, street-view image analysis, and interpretable machine learning. Street view images, field investigations, and questionnaires were used to construct a subjective-objective evaluation system. A total of 1408 sampling points were established, and 1272 valid street view images were collected. Perceptual scores were provided by 15 local residents and 15 professionals. Objective indicators, including enclosure, greenery, openness, motorization, and non-motorization, were extracted through semantic segmentation. Multiple linear regression and random forest models were then used to examine the relationships between spatial indicators and perceptual experience. The findings reveal nonlinear associations and multi-factor interactions between objective spatial characteristics and subjective perception. Safety and cultural perception scores are higher when the enclosure ranges from 0.30 to 0.60, and the non-motorization ranges from 0.10 to 0.30. Comfort and visual pleasantness scores are higher when non-motorization ranges from 0.10 to 0.20 and motorization from 0.05 to 0.15. SHAP analysis further indicates that openness and motorization primarily influence perceived safety, whereas non-motorization and enclosure contribute more strongly to cultural perception, comfort, and visual pleasantness. The proposed framework provides a quantitative and interpretable basis for refined spatial renewal in historic districts. Full article

Although educational robotics is widely recognised as an effective approach to supporting curriculum-aligned learning in primary education, comparatively little attention has been paid to how teachers develop the learning resources needed for meaningful classroom activities. This descriptive qualitative case study, situated within the interpretive research paradigm, examined primary school teachers’ experiences in collaboratively designing curriculum-aligned learning resources during a three-hour practical workshop focused on integrating the Bee-Bot^® robot into formal instructional settings. Data collection methods included participant observation, field notes, semi-structured interviews, photographic documentation, and participant-produced learning resources, all analysed using Reflexive Thematic Analysis. The findings indicate that participants generally found Bee-Bot^® easy to programme and recognised its educational potential for fostering pupil engagement and collaboration in interdisciplinary learning environments. However, significant challenges emerged during the collaborative design of curriculum-based learning resources, particularly in translating curriculum objectives into pedagogically meaningful and technically feasible Bee-Bot mats. The study concludes that professional development in educational robotics should extend beyond technical familiarisation and prioritise supporting teachers as designers of curriculum-aligned educational activities that actively engage pupils as co-creators of robotics-enhanced learning experiences. Full article

Drought is a major abiotic constraint on sugarcane production, but systematic multitrait evaluation of drought tolerance remains limited in genetically complex advanced backcross progenies derived from sugarcane × Tripidium arundinaceum. In this study, 100 genotypes, including 93 BC₄F₁ or BC₅F₁ progenies and seven parental or control genotypes, were evaluated under a 20 d progressive drought treatment in pots at the seven-leaf stage. Sixteen traits related to root architecture, leaf water status, gas exchange, PSII photochemistry, antioxidant activity, and lipid peroxidation were measured. Drought significantly reduced TRL, RSA, RV, RWC, Pn, Gs, Tr, ΦPSII, and ETR and significantly increased SOD activity and MDA content. Twelve traits with relatively clear and biologically interpretable relationships with drought tolerance were integrated using membership functions and trait weights to calculate an integrated D value. D values ranged from 0.078 to 0.516, and 19, 45, and 36 genotypes were classified into groups with relatively high, moderate, and low drought tolerance within this germplasm collection, respectively. Genotypes 55-109, YCE07-65, 55-43, 211-172, and 91-74 had the highest integrated D values. Within the integrated evaluation framework, higher D values were most strongly associated with RSA, RV, TRL, Pn, ΦPSII, and ETR, while PC1 was strongly correlated with the D value (r = 0.893). Maintenance of root growth and photosynthetic function therefore represented major phenotypic features associated with overall drought tolerance under the present experimental conditions. These features may provide useful criteria for screening drought-tolerant germplasm, but their utility requires validation in independent drought experiments and field environments. Full article

Urban snow can serve as a passive sampler of atmospheric inputs during a defined accumulation period. This data descriptor presents georeferenced chemical element concentrations in snow collected during two discrete six-day accumulation events in Jelgava City, Latvia: 4–10 January 2024 and 16–22 February 2025. A fixed network of 59 urban monitoring sites and one rural reference site was sampled, with three independent field replicates collected and analysed separately at each location. After melting, the samples were acidified with 1% (v/v) HNO₃ for 72 h and filtered through Whatman Grade 541 filter paper with a normal 22 µm particle-retention rating. The reported values therefore represent an operationally defined acid-leachable filtrate fraction rather than total or conventionally dissolved concentrations. Al, Si, Cr, Mn, Fe, Ni, Cu, Zn, As, Mo, Cd, Ba, W, and Pb were measured by inductively coupled plasma mass spectrometry (ICP-MS). Two separate Mendeley Data records each contain one year-specific Excel workbook. The 2024 workbook retains left-censored results as “<0.05” or “<0.01”. In the revised 2025 workbook, results below element-specific LoDs are reported as “<LoD”; 334 of 2520 replicate results (13.3%) are left-censored. Direct comparisons of substantially censored elements require appropriate censored-data methods. The datasets support event-specific spatial assessment and cautious comparison between the two monitoring events, but they do not represent seasonal or annual conditions and cannot be used to calculate deposition loads or fluxes without snow-water-equivalent data. Potential uses include identifying locations with elevated element concentrations during each monitoring period and integration with meteorological, traffic, and land-use data. Full article

Temperate native grasslands in southeastern Australia have been extensively cleared for agriculture, and the remaining patches are under growing pressure from further land use change, climate variability, and invasive species. Mapping and monitoring their distribution and the cover of native and exotic grasses are critical for their conservation and management. Field-based methods are not always scalable or time-effective, and this study aimed to develop a scalable method to map and monitor the fractional cover-class maps of native C3 and native C4 grass cover as a component of remnant native grasslands on the western outskirts of Melbourne, Victoria, Australia. Field-based reference data for training and validation of random forest machine learning models were collected across multiple sites in 2021. Sentinel-2 optical spectral bands and vegetation indices were used as the primary input data, and Sentinel-1 Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture metrics were assessed for their capacity to improve the model. Results show that random forest models trained on Sentinel-2 data without GLCM texture information derived from Sentinel-1 SAR data provided a moderate overall accuracy (C3: 59.1%, C4: 78.1%). Class-specific metrics showed that reliability was highest for better represented lower-cover classes, particularly the 6–25% native C3 class and the 0–5% native C4 class, while higher-cover classes were less reliable because of the limited number of training and validation samples. Grass cover fractions were modelled well for sparse to moderate grass cover, but dense grass cover was not modelled accurately, probably due to limited high-cover samples in the training dataset. Model performance was not improved by the inclusion of Sentinel-1 SAR-derived GLCM texture metrics, indicating that C-band VH-polarised SAR is not sensitive to the fine-scale structural heterogeneity that characterises native grassland ecosystems. Sparse native C3 and C4 grasses could be mapped most reliably in the lower-cover classes as a component of grasslands with optical remote sensing, and the method developed here can now be applied to enable evidence-based management of grasslands, biodiversity conservation and the monitoring of grassland composition in the WGR and elsewhere. Higher-resolution structural datasets and more sophisticated machine learning approaches may be required to accurately predict native C3 and C4 grass cover fractions in denser grasslands. Full article

Bridge surface cracks are important early indicators of structural performance degradation. However, affected by complex environmental interferences and irregular morphologies, existing models still fall short in micro-crack recognition, accurate bounding-box localization, and lightweight. To address these challenges, this study proposes a multi-module collaborative lightweight model (MCL-YOLO) based on YOLOv12. Specifically, the existing ADown module from YOLOv9 is incorporated into the YOLOv12 architecture to reduce computational complexity while preserving critical information during feature downsampling. To enhance the representation of slender, curved, and branched crack patterns, a C3k2-RFAConv module is designed by integrating a receptive-field attention mechanism. Furthermore, an iEMA module is embedded before the high-resolution detection branch to strengthen the semantic response to weak-texture cracks. A bridge crack dataset containing 4029 images was constructed to evaluate the proposed model. Experimental results show that MCL-YOLO achieves Precision, Recall, mAP@50, and mAP@50:95 values of 0.891, 0.757, 0.844, and 0.675, with 5.5 GFLOPs, 2.240 M parameters, and a model-file size of 4.689 M. Compared with the YOLOv12n baseline, MCL-YOLO improves the four detection metrics by 2.30%, 4.56%, 4.07%, and 3.21%, while reducing GFLOPs and parameter count (Params) by 12.70% and 12.77%, respectively. Ablation experiments, model version comparisons, attention mechanism comparisons, and qualitative detection results collectively verify the effectiveness of the integrated architectural modifications. Full article

The diagnosis of Bluetongue Virus (BTV) in Argentina is primarily conducted via RT-qPCR and faces challenges due to the limited number of diagnostic laboratories, many of which are located in regions where BTV is exotic and far from primary sample collection areas. As a result, blood samples must be transported over considerable distances for analysis. This poses significant logistical challenges due to the specific transport conditions that are required. Consequently, the development of methodologies that optimize sample collection, preservation, and safe transport is of vital importance. The objective of this study was to standardize a sampling protocol for the detection of BTV using filter paper cards. This protocol was developed to ensure adequate preservation and transport of blood samples for diagnosis. Hydration with TE at 37 °C resulted in the lowest Cq among the tested treatments, with ~10 TCID₅₀/mL representing the lowest viral concentration that was consistently detected under the experimental conditions. The stability of the sample on the cards was evaluated through a series of storage trials at temperatures of 25 °C, 4 °C, −20 °C, and −80 °C for 1, 7, 30, 60, 120, and 360 days. Stability was verified up to 120 days at all temperatures. Furthermore, the absence of infectious material in the card eluate was confirmed. A preliminary field evaluation was conducted, in which blood samples from cattle and sheep were collected in tubes and on cards. According to the Cohen’s Kappa index, the agreement between both methods was nearly perfect. The standardized methodology signifies a substantial advancement in BTV diagnosis, as it facilitates the transportation of samples without the necessity of refrigeration and with adequate biosafety conditions without losing diagnostic capacity. Full article

Rural courtyard dwellings in cold regions are commonly characterized by low winter indoor temperatures, intermittent heating, solar heat gain, pronounced thermal fluctuations, and strong occupant adaptation. This study aimed to compare short-term winter indoor thermal conditions among three rural courtyard dwelling cases in Qingcheng Ancient Town, Gansu Province, Northwest China, and to examine the associations between operative temperature and multidimensional subjective thermal responses under the monitored conditions. Synchronized indoor–outdoor environmental measurements and 216 paired questionnaire records were collected during two winter survey days. To account for shared environmental exposure, the questionnaire records were organized into 66 dwelling–time clusters. The results showed that: (1) outdoor air temperature ranged from −13.34 °C to 13.24 °C, with a mean of −3.91 °C, while the mean operative temperature during the questionnaire periods was 11.31 °C and the mean thermal sensation vote (TSV) was −0.97, indicating an overall sensation close to slightly cool; (2) the dwelling with an added sunspace had the highest mean operative temperature, the ordinary brick–concrete dwelling exhibited the greatest temperature fluctuation, and the earth–wood dwelling remained cooler but relatively stable; (3) each 1 °C increase in operative temperature was associated with 75% higher odds of reporting a warmer TSV category (OR = 1.75, 95% CI: 1.53–2.00), while the two-way fixed-effects model estimated a 0.176-unit increase in mean TSV; and (4) higher operative temperature was associated with greater broad and clear thermal acceptability and a lower likelihood of preferring warmer conditions, with the study-specific neutral operative temperature estimated at 16.75 °C and a block-bootstrap 95% confidence interval of 15.66–18.40 °C. These findings provide field-based evidence of the associations between operative temperature and multidimensional winter thermal responses and offer an empirical basis for climate-responsive and heritage-sensitive thermal improvement strategies for rural courtyard dwellings. Full article

This work describes the formation of electrically charged nanobubbles and collective electrodynamical ordering in liquid water based upon the framework of the quantum electrodynamical theories of Del Giudice, Preparata, Vitiello and their co-workers. Nanobubbles with experimentally observed negative zeta potentials are predicted to generate interfacial electric fields on the order of 10⁵–10⁶ V m⁻¹, comparable to field strengths previously associated with collective vibrational coupling in electrically stressed water. The model addresses magnetic stabilization of the electrically induced vibronically coupled interfacial state, while the observed changes in nanobubble size and number are discussed within the broader framework, including a hypothesized preconditioning effect of the dynamically varying magnetic field on nanobubble formation. Under these conditions, regions of enhanced collective coupling of vibronic modes around a nanobubble with characteristic thicknesses of approximately 9.6–52.5 nm become physically plausible. Furthermore, a phenomenological Landau-type free-energy model is used to investigate the influence of external magnetic fields on the process. We suggest that magnetic fields primarily couple to the low-energy protonic and vibronic modes within this shell. These theoretical predictions are qualitatively consistent with recent experimental observations showing stronger negative zeta potentials, and higher nanobubble concentrations under the influence of magnetic fields, together with smaller characteristic nanobubble radii under an alternating field configuration. Our results support the interpretation that magnetic fields stabilize electrically induced mesoscopic coupling of vibronic modes that emerge transiently during cavitation-driven nanobubble formation. Full article

Atmospheric turbulence is a key multi-scale motion affecting numerical weather prediction, aviation safety, and atmospheric mass-energy exchange. Thorpe analysis is a classic method for turbulence retrieval, but traditional observations are limited by low spatiotemporal resolution, the absence of a stratospheric turbulence inversion framework, and insufficient cross-layer comparisons between northern and southern China, restricting the understanding of turbulence modulation mechanisms. Based on the domestic round-trip intelligent sounding system, atmospheric observations at 12 stations in Guangdong and Inner Mongolia from December 2022 to March 2023 are collected, with ascending-phase profiles used in the turbulence analysis and synergistically analyzed with ERA5 reanalysis data, which provide the background wind fields for westerly jet identification and precipitation data for environmental modulation assessment. Results show that turbulence in the study area shows significant layered differentiation and a latitudinal contrast between the southern and northern stations: the troposphere is the main turbulent layer, southern tropospheric turbulence is mainly associated with solar radiation, and northern tropospheric turbulence is mainly related to large-scale dynamic processes; stratospheric turbulence occurs sporadically only at northern stations under westerly jet-induced wind shear and is nearly absent in the south. Precipitation modulates southern tropospheric turbulence, while the westerly jet acts as a key dynamic factor for northern stratospheric turbulence. By integrating in situ radiosonde profiling with reanalysis data, this work supports refined turbulence detection and parameterization model optimization, and provides a scientific basis for weather forecast improvement and aviation route planning. Full article