Search Results (196)

The article establishes a measurement and modeling framework to quantify carbon sequestration in pocket forests as part of a multi-year research program. Pocket forests are multi-layered native planting initiatives inspired by the Miyawaki method of afforestation, adapted for small-scale regenerative applications in urban and peri-urban contexts. In this study, a pocket forest is treated as a repeatable 10 m² unit that can be distributed across small parcels and scaled through a network. The project examines how species composition and diversity affect above- and below-ground carbon storage under controlled field conditions. Twenty-one experimental plots were established with consistent soil preparation, planting density, plot geometry, and environmental exposure, while species diversity was varied from full capacity to reduced mixes and low-diversity reference conditions. The setup allows comparison of carbon-related performance across diversity levels and supports the development of a modeling framework linking proxy indicators and carbon sequestration potential. The initial phase focuses on system architecture, design criteria, baseline characterization, indicator selection, measurement integrity, sampling regime, and key parameter definition. Future phases will report temporal data and modeled outcomes to guide adaptive engineering of carbon-positive, self-sustaining landscapes. Full article

Urban residents spend an estimated 80–90% of their time indoors, yet urban indoor air quality (IAQ) science remains fragmented across pollutant types, settings, mitigation strategies, and governance contexts, contributing to an estimated 6.7 million deaths annually from indoor air pollution worldwide. This review asks the following question: what are the critical, multi-dimensional research and governance gaps in urban IAQ, and how can they be systematically derived and organized into a reference framework for future research and policy? A PRISMA 2020-aligned hybrid systematic evidence synthesis, combining bibliometric science mapping and structured thematic synthesis, was conducted across 105 records, primarily published between 2011 and 2026, with three pre-2011 foundational records retained, spanning 15 national contexts. A seven-stage hybrid deductive–inductive derivation procedure was applied to the coded corpus to produce the Multi-Dimensional Gap Identification Framework (MGIF), organizing research gaps across five dimensions: knowledge, methodological, technological, policy and implementation, and equity and urban context. Recurrent gaps include the absence of multi-pollutant mixture assessment in monitoring frameworks, the lack of standardized measurement protocols limiting cross-study comparability, a systematic gap between laboratory-validated and field-measured intervention performance, the near-total absence of enforceable indoor air quality standards across most jurisdictions, and the severe underrepresentation of Global South populations in both primary evidence and regulatory design. Building on the MGIF output, the Urban Indoor Air Quality Nexus (UIAQN) is proposed as a four-level conceptual organizing architecture linking pollutant dynamics, building systems, personal exposure, and governance mechanisms. Both frameworks are grounded in the coded corpus, have not been subjected to external validation, and are designed as structured reference architectures for future research investment, standard harmonization, and equity-centered policy design rather than as empirically validated predictive models. Full article

Spatial clustering does not show that specific built-form characteristics lead to the concentration of terrorist incidents, but it is common for incidents to be clustered in urban areas. This is an exploratory study that examines 7960 incidents of ISIS terrorism recorded in the GTD from 2012 to 19. Geocoded incident coordinates and Haversine great-circle distance were used to implement the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). An epsilon radius of 50 km and MinPts = 15 were chosen for the primary model, and 15 parameter combinations were analysed to investigate the robustness of the results. The main cluster found was 18 clusters with 7403 incidents (93.00%), and noise was the other cluster (557 incidents, 7.00%). The bulk of incidents (6118) were in Iraq, while the second largest cluster was in Syria with 675 incidents. The results show high geographical concentration, which is mainly due to the operational geography of ISIS, the intensity of the conflicts, the levels of exposure, and reporting. The analysis does not directly measure architectural morphology, sight lines, surveillance, permeability, crowding, and emergency egress. Based on this, the study suggests an initial multi-scalar urban design assessment framework that includes hotspot analysis as a first step, followed by site-specific assessment using the urban security, CPTED and crowd safety, and evacuation principles. The contribution is methodological and involves showing how the large-scale incident data can help inform, but not supplant, detailed architectural evaluation. Full article

With the acceleration of global urbanization and the large-scale development of underground spaces, underground engineering faces extremely complex and variable geological environments and high-risk construction disturbances. The widespread application of the Internet of Things and novel sensing technologies has given rise to structural health monitoring data increasingly characterized by massive volume, high dimensionality, multi-source heterogeneity, and strong spatiotemporal coupling. Traditional data processing methods based on mechanical analysis, empirical formulas, or numerical simulation have increasingly exposed limitations of insufficient accuracy, lengthy computation times, and weak generalization capability when confronted with such engineering big data. Machine learning and deep learning technologies, by virtue of their superior nonlinear mapping capability, advantages in feature extraction from massive data, and flexible architectural design, provide solutions for efficient knowledge extraction and intelligent assessment of underground engineering monitoring data. This paper reviews the current application status and frontier advances of machine learning technologies in the field of underground engineering monitoring data processing in recent years. First, the development trajectory of analytical algorithms evolving from classical shallow machine learning, through temporal and spatial deep learning, to physics-data dual-driven approaches is delineated. Second, targeting the critical challenges of missing field data and sparse sensor deployment, spatiotemporal fusion imputation techniques and spatial reconstruction methods incorporating mechanical prior knowledge are thoroughly evaluated, elucidating the paradigm shift in monitoring philosophy from discrete point-based alarming to inference-augmented sparse sensing that approximates full-field state awareness through model-dependent estimation rather than direct measurement. Third, the applications of machine learning in underground structural deformation mechanism interpretation, key influencing factor identification based on explainable artificial intelligence (AI), and rapid back-analysis of geomechanical parameters are summarized. Finally, composite network architectures and physics-constrained guidance strategies for non-stationary deformation time series prediction under complex and variable working conditions are discussed. A methodological audit of the 73 included studies—of which 33 enter the quantitative comparison tables—reveals that 26 of the 33 audited studies (78.8%) validate exclusively on single-project data, only 1 study conducts rigorous out-of-distribution generalization testing, and none of the 33 studies (0%) provides uncertainty quantification. These findings highlight cross-project generalization and probabilistic prediction as important methodological challenges. This paper aims to provide theoretical references and methodological guidance for safety early warning, intelligent construction, and full life-cycle health management of underground engineering. Full article

This paper presents a hardware–software co-design framework for real-time super-resolution (SR) of low-quality video on resource-constrained edge platforms. At its core is a compact residual network obtained by once-for-all (OFA) neural architecture search over the Residual Channel Attention Network (RCAN) design space, trained conventionally and then optimized with quantization-aware training (QAT) for deployment on an integer-only deep-learning processing unit (DPU). Loop tiling and data-flow scheduling are applied within a custom high-level synthesis (HLS) pre-processing pipeline that feeds the DPU, and a per-directive ablation isolates the contribution of each optimization to post-route resource usage and timing. Deployed on a Kria KV260 board with a $128\times 128$ network input, the INT8 network sustains $96.37$ FPS at the ×2 scale at a measured board power of $5.38$ W, corresponding to $6.32$ Mpixel/s of reconstructed output at $1.17$ Mpixel/J, within 63.2% of the device LUT budget and with timing closed at 275 MHz. Relative to the FP32 model, INT8 quantization costs $0.274$ dB of peak signal-to-noise ratio (PSNR) on Set5, $0.172$ dB on Set14, $0.116$ dB on B100, and $0.146$ dB on Urban100, a loss dominated (81–90%) by activation rather than weight quantization. On a held-out UAV subset drawn from VisDrone2019, which is the operating domain the system targets, the network reconstructs at $25.94$ dB and $0.748$ SSIM. These results show that a twenty-three-layer residual SR network can be deployed within a 5.38 W envelope on a low-cost integer-only edge FPGA, making the approach suitable for autonomous systems, robotics, and airborne surveillance. Full article

Low-emission zones (LEZs) require cities to define policy rules, predict effects before deployment, and verify outcomes afterwards, yet mobility, emissions, meteorology, exposure, data governance, and public communication are commonly handled in separate systems. This paper presents envair360, a Physical Intelligence architecture and a four-stage, evidence-gated LEZ methodology connecting project definition, baseline feasibility, digital-twin design, deployment, and verified impact closure. A design-science method is combined with an operational case study of Cartagena, Spain, because the research object is both a socio-technical artefact and a context-dependent municipal deployment. The technology chain is selected to bridge complementary scales and functions: SUMO for link- and vehicle-level traffic, WRF and CHIMERE for meteorology and regional chemistry, MUNICH and street-canyon parameterisation for computationally tractable street resolution, model-output calibration anchored to measurements, and FIWARE/NGSI-LD for governed context exchange. The manuscript distinguishes city observations, peer-reviewed component validation, demonstrated platform capabilities, and policy or engineering targets. A Murcia component study reports lower hourly than daily agreement after deep-learning calibration (NO₂: $r=0.79$ hourly and $0.94$ daily; O₃: $r=0.85$ hourly and $0.97$ daily), illustrating the importance of temporal aggregation and transfer limits. Digitisation of the prior Madrid ozone-density figure indicates modal shifts of approximately +32.0 and +27.8 source-axis units at two stations; the supplied source does not permit a numerical NO_x bias estimate. A separate six-city export audit covers 24,384 records and 4064 street segments and demonstrates a common model-output schema, not predictive validation. In Cartagena, project documentation reports elevated PM₁₀/PM_2.5, urban heat and solar-radiation stress, and a plausible role for dry-climate dust resuspension, supporting a superblock-oriented LEZ proposal with a long-term 30% vehicular CO₂ reduction target. The paper’s specific contribution is the governed orchestration, evidence taxonomy, quality gates, reproducible lineage, explicit policy-scenario representation, and portable city-onboarding protocol; it does not claim that the individual scientific models, the Cartagena deployment, or the cited project targets originated in this manuscript. Full article

Forests play an important role in timber and fiber production, carbon storage, biodiversity conservation, and various other ecosystem services, necessitating accurate and scalable inventory methods. Recent advances in remote sensing have enabled large-scale forest monitoring; however, challenges remain in extracting reliable information across varying spatial, temporal, and environmental conditions. Deep learning has emerged as a promising tool for addressing these limitations by learning complex patterns from diverse remote sensing data sources. This review synthesizes deep learning applications in forest inventory methods across three tasks: tree counting and localization, tree species identification, and tree measurement. In total, we evaluated 122 unique primary studies (37 for tree counting and localization, 57 for species identification, and 29 for tree measurement, with one study contributing to both the counting/localization and measurement tasks) spanning terrestrial, unmanned aerial vehicle (UAV), airborne, and satellite platforms, with a primary focus on optical imagery, Light Detection and Ranging (LiDAR) data, and their fusion. Across these studies, deep learning models frequently outperformed conventional machine learning and statistical baselines, with reported gains including up to 18% improvements in biomass estimation accuracy from data fusion and individual-tree species classification accuracies exceeding 90% for select architectures. However, performance differences were influenced strongly by forest structure, species complexity, sensor capability, and validation design. Counting and localization were generally more reliable in plantations than in complex natural or urban forests, while LiDAR was particularly valuable in dense, multilayer canopies. Species-identification accuracy was highest in studies with small, distinctive species sets, whereas mixed stands with many species showed lower accuracy. Only about a third of the reviewed studies (42 of 122) were externally validated on data or sites independent of model training, and reference data for tree measurement tasks were rarely based on direct destructive sampling. External validation often revealed lower performance than within-study testing, suggesting that reported accuracies may overestimate performance in new locations or conditions. Major advances are evident in the growing use of high-resolution UAV and smartphone-based imagery for tree-level analysis, the continued value of LiDAR for structural characterization, and the increasing integration of multimodal data fusion to improve detection, classification, and measurement accuracy. Persistent challenges include the limited availability of high-quality reference data, class imbalance and inconsistent species coverage, and weak model transferability across forest types, environmental conditions, and geographic regions. Future progress will likely depend on three priorities: development of larger and more standardized labeled datasets, stronger integration of structural, spectral, and phenological information, and the design of more transferable and application-oriented deep learning frameworks. Overall, this review provides a comprehensive, quantitatively grounded overview of deep learning-driven forest inventory methods and outlines future directions for improving scalability and applicability in forest monitoring and management. Full article

Vehicle theft and vandalism remain significant urban security challenges commonly addressed through reactive, post-incident forensic measures. This paper proposes a proactive, real-time computer vision system designed to detect potentially suspicious behavior around parked vehicles, with a specific focus on unauthorized proximity and loitering. The proposed architecture integrates state-of-the-art object detection using YOLOv11 (You Only Look Once version 11), multi-object tracking via a lightweight custom association tracker inspired by the ByteTrack/StrongSORT/OC-SORT paradigm, and monocular depth estimation based on the Intel DPT-Large framework.A key contribution is the identification and mitigation of the Perspective Challenge: the two-dimensional (2D) scale ambiguity that causes distant background pedestrians to appear falsely proximate to foreground vehicles in monocular camera feeds. To address this, three spatial analysis strategies are implemented and evaluated: (A) fixed Euclidean thresholding, (B) adaptive perspective thresholding, and (C) three-dimensional (3D) depth injection. Experimental results on real-world urban surveillance footage (27,000 annotated frames across two datasets) demonstrate that Strategy C achieves the highest precision (0.95) with an F1-score of 0.92, while Strategy B provides the best balance between accuracy (precision 0.88, recall 0.91, F1 0.89) and computational efficiency (32.7 frames per second, FPS). Compared to naive 2D thresholding (Strategy A), Strategy B reduces false alarms by approximately 80%, while Strategy C further improves precision to 0.95 through depth-plane verification. The system maintains real-time performance exceeding 30 FPS under Strategy B, making it a strong candidate for practical urban vehicle monitoring, subject to further large-scale validation across diverse environments. Full article

Outdoor public spaces are important built-environment settings that support health, social interaction, psychological restoration, and everyday urban life. In cold-region cities, however, winter conditions such as low temperature, snow and ice, short daylight, reduced vegetation, low solar altitude, and declining outdoor activity substantially weaken their usability, attractiveness, and vitality. Existing studies have mainly addressed these problems through thermal comfort, microclimate adaptation, snow safety, and physical environmental optimization, but they provide limited explanation of how users visually perceive winter spaces, allocate attention, and form subsequent spatial interpretations, perceptual evaluations, and behavioral intentions. To address this gap, this conceptual article develops a built-environment framework for explaining winter visual perception mechanisms and proposes an agenda for future eye-tracking-based validation. Through conceptual synthesis across cold-region public-space research, outdoor thermal comfort, environmental psychology, landscape visual perception, eye-tracking studies, public-space behavior, and architectural and built-environment design, the study conceptualizes winter public spaces as seasonal perceptual environments. It identifies five categories of winter visual stimuli: surface-related, vegetation-related, building interface-related, lighting-related, and activity-related stimuli. The framework clarifies how visual attention may serve as an observable mediating process between winter visual stimuli and inferred spatial interpretation, perceptual evaluation, and behavioral intention. Rather than empirically confirming these relationships, the article formulates a testable conceptual model and future validation agenda that should be examined through eye-tracking, behavioral observation, subjective evaluation, and environmental measurements. For architectural and built-environment research, the framework provides a theoretical basis for evaluating and optimizing façades, ground-floor interfaces, entrances, canopies, semi-outdoor spaces, path boundaries, lighting systems, vegetation configuration, and winter activity nodes. Full article

Individual differences in outdoor thermal comfort (OTC) are critical to the healthy use of urban public spaces, yet whether thermal preference can shape OTC independently of demographic characteristics remains largely unexamined. Using residential outdoor spaces in Fuzhou, a representative hot-humid city in China, as a case, this study combines field measurements and questionnaire data from 296 respondents (72.6% aged 60 or above) with explainable machine learning and K-Modes clustering to examine how thermal preference drives individual differences in OTC. Three stable preference profiles were identified—heat-sensitive (56.4%), wind-seeking (20.3%), and heat-tolerant (23.3%)—which exhibit markedly different thermal responses. The neutral globe temperature ranges from 29.90 °C for the heat-sensitive profile to 35.85 °C for the heat-tolerant profile, a difference of 5.95 °C, whereas the comfort bandwidth is widest for the heat-sensitive profile (9.03 °C) and narrowest for the heat-tolerant profile (4.13 °C), the former being 2.2 times the latter. The profiles are independent of sex and BMI and only weakly correlated with age, yet their explanatory power for the variance in thermal comfort vote (TCV) (η² = 0.254) is 4.9 to 23.1 times that of the demographic variables. The thermal environment contributes far more to TCV than the visual environment (74.4% versus 25.6%), with globe temperature (Tg) as the strongest single factor. Overall, differentiated design that adopts the most heat-sensitive profile as the constraint boundary covers the comfort needs of a broad population more effectively than demographic stratification. The novelty of this study lies in introducing psychologically grounded thermal-preference profiles as an operational stratification dimension for architectural design, offering age-friendly hot-humid residential environments a preference-oriented pathway toward refined, human-centered outdoor space design. Full article

The increasing pace of urbanization and climate change necessitate a holistic assessment of building energy performance during the early design phase. This study proposes an Evolutionary Field Optimization (EFO)-based multi-input multi-output artificial neural network (MIMO-ANN) model to simultaneously predict the heating load, cooling load, CO₂ emissions, and lighting energy consumption of smart buildings. The model’s dataset consists of 7963 observations generated via EnergyPlus building energy simulations of standardized TOKİ residential units constructed post-earthquake in Türkiye. No operational or physically measured building energy consumption data were used in the model development process. For the validation setting, the simulation-generated dataset was split into training (60%), validation (10%), and test (30%) subsets. The EFO algorithm was employed to automatically optimize the ANN architecture by dynamically determining the optimal number of hidden layers and neurons. The optimization process demonstrated strong global search capability and fast convergence, reducing the objective function by approximately 86% within 10 iterations. Experimental results on the test subset showed exceptional predictive accuracy for simulation data, with test R² values ranging from 0.9996 to 0.9998 across all four outputs, indicating that the optimized network topology effectively avoided overfitting. While the model’s performance under real-world operational uncertainties and varying occupant behaviors remains to be fully investigated, the proposed EFO-ANN framework provides a computationally efficient and highly accurate analytical core for early-stage design. It serves as a strategic decision-support tool intended for architects and engineers designing post-disaster housing, public authorities forming national energy efficiency policies, and developers building predictive engines for digital twin-enabled smart building systems. Full article

Urban parks are widely regarded as healthy and sustainable urban infrastructures, yet their respiratory benefits depend on the coupling of design, behaviour, and perception rather than ambient PM concentrations alone. A multi-season daytime fair-weather panel study across five urban park space types integrated in situ PM monitoring, SOPARC-based behavior mapping (9173 users), dwell-time surveys, and inhalation-rate libraries to estimate per capita inhaled doses, while an on-site survey (n = 837) assessed perceived PM and its influence on space choice. Understory and water spaces exhibited the highest PM₁₀ and TSP concentrations, whereas waterfront areas were the cleanest; winter concentrations were elevated but preserved the same space-type ranking. Sports spaces had the most intense activity profiles (61.9% moderate-to-extreme), and understory and sports spaces supported the longest stays, with little seasonal change in either intensity or duration. Consequently, per capita PM exposure was highest in sports and understory spaces and lowest in water and waterfront spaces. Spaces that attracted more users also delivered higher per capita doses, indicating an overlap between popularity and high-dose micro-environments. Perceptually, 94.9% of users rated PM as low or relatively low in water spaces, whereas squares had the highest share of “moderate or worse” ratings (26.4%); 78.1% chose locations based on perceived air quality, despite weak or even negative correlations with measured PM. These findings reveal a design-driven exposure architecture in which space configuration organizes both PM concentrations and user behavior, while misperception can steer visitors, especially in winter, toward the very park micro-environments that deliver the highest inhaled doses. This study provides evidence for exposure-aware park design and management that can reduce respiratory risk while supporting sustainable outdoor recreation and healthier urban living. Full article

Urbanization in Northern Nigeria has accelerated the adoption of modern architectural forms that often neglect climatic responsiveness and indigenous cultural values. This study examines how vernacular architectural principles can be integrated into contemporary urban development to enhance climate resilience. Focusing on Sokoto, Katsina, and Kano, the research adopts a mixed-methods approach combining field measurements, simulation modeling, structured interviews, and policy analysis. Findings indicate that traditional Hausa architecture achieves indoor temperature reductions of 4–6 °C compared to ambient outdoor conditions. This is primarily due to courtyard configurations, high thermal mass, and passive ventilation strategies associated with the architecture. Life-cycle assessment reveals that vernacular materials exhibit 60–75% lower embodied energy than modern buildings. Despite these advantages, policy frameworks inadequately support their integration. This study proposes an integrative model combining vernacular strategies with modern technologies and policy reforms. The findings contribute to sustainable urban design discourse and align with global sustainability goals. Full article

Sustainable EV charging infrastructure is fragmented by proprietary applications, vendor lock-in, and weakly time-differentiated pricing, blunting its contribution to urban-mobility decarbonisation. This paper asks whether an open-protocol, super-app-mediated photovoltaic–storage charging architecture can jointly resolve these three fragmentations under deployed field conditions and what its sustainability profile then looks like. We report a campus photovoltaic–storage microgrid integrating heterogeneous EV chargers under an open, vendor-neutral charging-control protocol with super-app authentication and payment replacing dedicated charging applications and a time-differentiated tariff aligned at the meter-interval level with the underlying utility wholesale rate; the deployment is exercised through a researcher-scheduled commissioning campaign of 13 sessions designed to establish functional correctness across the operating envelope rather than to measure user behaviour. Three results emerge across cross-vendor compatibility, onboarding friction, and grid alignment. First, basic message-level OCPP compatibility is sustained across two charger vendors under a single cloud management system—in sequential single-vendor sessions—including the full charging profile up to near-rated DC peak power. Second, the super-app-mediated workflow, which requires no charging-specific application installation and no new charger-operator account, structurally eliminates the dedicated application installation and the email/SMS/credit-card verification round-trips of conventional onboarding, compressing measured first-use end-to-end interaction to 31 s; relative to reconstructed commercial-operator baselines, this is, to the best of the authors’ knowledge, an order-of-magnitude reduction rather than a controlled benchmark. Third, mid-day energy delivery aligns incidentally with the utility off-peak window, not user-driven demand shifting, while PV-displacement and BESS-discharge contributions to charging are bracketed by scenario rather than being separately metered. The paper’s contribution is therefore a replicable, policy-embedded sustainable charging architecture validated at field scale within the New Taipei Net-Zero Carbon Demonstration Site Programme, with no claim of global novelty; the same architecture is structurally positioned to convert the observed incidental grid-friendliness into a deliberate, user-facing benefit via a hardware-free mid-day-discount redesign. Full article

Accurate distance measurement in outdoor environments remains a challenging problem for mobile augmented reality (AR) systems due to sensor noise, environmental variability, and the limitations of single-modality approaches. Existing consumer AR solutions often prioritize usability over metric robustness, leading to performance degradation in large-scale or heterogeneous outdoor scenarios. This work presents EfMAR, an adaptive framework for outdoor mobile AR-based geospatial measurements that integrates multiple sensing modalities through a structured sensor fusion architecture. EfMAR combines visual SLAM, inertial sensing, depth information, and global positioning cues to improve robustness and consistency in distance estimation across diverse outdoor conditions. Beyond implementation, the framework formalizes a reusable architectural model for adaptive multi-sensor fusion, supporting reproducibility and future comparative research. A dedicated dataset is described, comprising 584 unique real-world evaluation instances collected across representative outdoor scenarios. External literature-derived data were utilized strictly as calibration baselines for modeled operational degradation profiles, maintaining methodological transparency. Performance evaluation focuses on analyzing relative behavior, stability, and variability across sensing approaches rather than establishing absolute accuracy benchmarks. Comparative results across multiple distance ranges and environments indicate that hybrid sensor fusion strategies exhibit more stable and consistent performance trends compared to single-modality solutions, particularly in challenging urban contexts. Dispersion analysis further highlights the influence of environmental factors such as lighting conditions and spatial scale on measurement variability. Overall, the results position EfMAR as a flexible and adaptive framework designed to enhance robustness in outdoor AR-based geospatial measurement tasks. By emphasizing consistency, transparency, and architectural generalization, this work contributes a practical foundation for future research and development in mobile AR sensing for real-world outdoor applications. Full article