Search Results (48,604)

Smart Mobility plays a key role in Smart Cities, given its ability to support the rollout of intelligent transport systems, allowing for more sustainable urban transportation and greater interoperability across diverse mobility modes. Furthermore, Smart Mobility is essential to maximize the quality of life for the community while advancing principles of sustainability, economic development, technological innovation, and collaborative governance. Real-Time Traffic Management (RTTM) emerges as a vital technology for optimizing traffic management in Smart Mobility. Using the PRISMA framework, the proposed systematic literature review examines 165 peer-reviewed publications related to RTTM research work published between 2019 and 2025. This review identified eleven application domains, with Urban Traffic Management Systems (36.97%) and Artificial Intelligence (AI) and Predictive Analytics (12.73%) representing the most prominent areas. A retrospective analysis of the literature on control architecture used in closed-loop feedback systems indicates that most studies (89%) have adopted a more dynamic control model, while 7.8% adopted a Digital Twin (DT)-based approach. However, several implementation barriers persist, including limited integration of online optimization and learning loops into RTTM systems, gaps in performance comparisons between simulation and reality, scalability issues due to heterogeneous environments, inconsistent data quality caused by various sensor types, and difficulties integrating sensors into a control system. In addition, this paper proposes a taxonomy of RTTM applications and control architectures, while outlining key practical barriers to implementation and charting future research directions for advancing Smart Mobility through robust RTTM. Full article

Transportation hub cities serve as pivotal nodes within integrated transport systems. This study reveals the corridor-oriented characteristics of comprehensive transportation system, confirms the progress of its transportation hub city development, and identifies future improvement directions based on diagnostic evaluation, taking Gansu Province, China as the research subject. To address hierarchical differentiation and structural constraints in the development of integrated transportation hubs, this study develops an evaluation framework integrating the entropy-weighted TOPSIS method, a coupling coordination model, and indicator-based diagnostic analysis. This framework was applied to 14 prefecture-level cities and autonomous prefectures in Gansu, classifying them into four hub tiers according to the comprehensive evaluation index. The results reveal a pronounced hierarchical and corridor-oriented spatial structure: Lanzhou is identified as the only Tier 1 core hub, five cities are classified as Tier 2 backbone hubs, seven cities and prefectures as Tier 3 general hubs, and Pingliang as Tier 4 terminal hub. Lanzhou exhibits the highest development level, with a comprehensive evaluation index of 0.9640, which is substantially higher than the provincial mean of 0.3867, but its radiation-driving capacity still needs to be strengthened. In terms of subsystem coordination, Lanzhou reaches the primary coordination stage with a coupling coordination degree of 0.532, while Jiuquan, Jiayuguan, and Tianshui are classified into the near-coordination stage with D values of 0.353, 0.351, and 0.321, respectively; the remaining ten units are classified as uncoordinated relatively. Based on the combined perspectives of development level and subsystem coordination, the study identifies future development directions for hub operational organization, multimodal transport integration, feeder connectivity, and industry-logistics coupling. The findings reveal the corridor-oriented characteristics and development progress of Gansu’s transportation hub system, highlight the analytical value of distinguishing hub development level from subsystem coordination, and provide empirical evidence for understanding hierarchical and functional differentiation in corridor-oriented inland regions. Full article

The rapid development of high-density cities has triggered severe ecological challenges, including habitat fragmentation, urban heat island (UHI) effects, and conflicting demands for public recreation. Traditional ecological networks (ENs) often focus only on “source” landscapes while neglecting degraded “sink” areas. This bias limits the ability of planners to resolve complex spatial conflicts. Therefore, the primary aim of this study is to develop a robust spatial planning framework that mitigates urban ecological conflicts and enhances regional resilience. To achieve this, we constructed a composite ecological network (CEN) for the high-density city of Guangzhou that harmonizes bird habitat conservation, thermal regulation, and cultural recreation. We combined the MaxEnt model, morphological spatial pattern analysis (MSPA), and circuit theory to identify functional “sources” and “sinks” across these three dimensions. Next, using complex network theory, we optimized the CEN and evaluated its structural robustness using low degree addition (LDA) and low betweenness addition (LBA) strategies. The results indicate the following: (1) The CEN effectively captured the complex mosaic landscape of the city. (2) Single-objective networks displayed distinct spatial differences—the recreational network formed a dispersed web of 242 corridors, while habitat and climate networks remained highly clustered. (3) The integrated CEN generated 1137 multi-layered corridors, creating a vital green skeleton to support species dispersal, mitigate UHI effects, and improve cultural access. (4) Optimization simulations verified that the LBA strategy provided the highest stability against targeted attacks by balancing network connectivity with local aggregation. Ultimately, this framework offers a highly adaptable planning tool for dense cities, providing precise spatial guidance to overcome ecological bottlenecks and harmonize urban growth with ecosystem resilience. Full article

Reducing carbon emissions from daily travel has become an important goal of 15-minute living-circle planning, yet it remains unclear which facility configurations are most supportive of green travel. Using 634 living circles and 20 million mobile-phone travel records and point-of-interest (POI) data, this study examines how facility layout within a 15-minute cycling circle influences residents’ walking and cycling travel behavior. Extreme Gradient Boosting (XGBoost) models and Shapley Additive Explanations (SHAP) suggest that low accessibility is generally associated with lower green travel shares, while moderate facility density promotes green travel, yet for some facility types, high density may show diminishing marginal benefits. Vegetable markets and primary schools emerge as key facilities, with education facilities driven mainly by accessibility, entertainment facilities by density, and commercial and healthcare facilities by both. K-means clustering identifies three types of low-green-travel-performing living circles—characterized by low density and poor accessibility—concentrated in peripheral and newly developed areas. The methodology is transferable, and the derived numerical ranges and living-circle typologies offer context-specific implications for Tangshan, and identified differences in facility importance and diminishing marginal benefits enrich 15-minute city theory. Full article

Clarifying the mutual feedback relationship and coordinated evolution characteristics between low-carbon transportation (LCT) and high-quality economic development (HQED) is of great significance for the green transformation of resource-based and ecologically fragile urban agglomerations. Taking 18 cities in the Yellow River Jiziwan Metropolitan Area as the research objects, this paper constructs an evaluation indicator system for LCT and HQED based on panel data from 2013 to 2022, and comprehensively applies the ISM-MICMAC model, a modified coupling coordination degree model, a gravity model, an obstacle degree model, and a combined GM-ARIMA forecasting model to analyze the interaction relationships, spatiotemporal evolution, spatial correlations, and scenario differences between the two systems. The results indicate that: (1) A hierarchical mutual feedback relationship exists between LCT and HQED, in which the relevant factors exhibit a hierarchical association within the system structure, extending from basic input, transportation supply, and economic operation to green and low-carbon outcomes. (2) During the study period, the comprehensive development levels of the two systems generally improved, with the mean coupling coordination degree rising from 0.4374 in 2013 to 0.4702 in 2022, remaining overall at a borderline coordination stage, while inter-city divergence was relatively pronounced. (3) The spatial connection network gradually exhibited multi-node linkage characteristics, yet strong connections remained concentrated in a few core cities. (4) Scenario predictions reveal that the synergistic development scenario is most conducive to enhancing the coupling coordination level, and the differences among scenarios gradually widen after 2026. Simultaneously advancing LCT and HQED is an important pathway to enhance the regional synergy level of the Yellow River Jiziwan Metropolitan Area. Full article

Housing is a crucial determinant of population migration. However, the mechanisms through which urban housing influences floating-population re-migration, as well as its role in guiding the efficient spatial allocation of populations, remain underexplored. This study investigated the impact of urban housing status on population re-migration based on the spatial equilibrium theory, and empirically tested this relationship using nearly 370,477 individual migration intentions records from the China Migrants Dynamic Survey (CMDS). The key findings are as follows. First, urban housing status is related to shaping population re-migration intentions. In particular, owner-occupied housing and government-provided low-rent housing are associated with lower re-migration intentions. Second, institutional constraints on migrant populations can vary somewhat depending on household registration status. Rural-registered floating populations may sometimes face somewhat more restrictions in accessing urban housing and public services. By contrast, high-wage areas has less re-migration intentions primarily through labor income gains, leading to heterogeneous housing status effects on migration intentions. Further analysis reveals spatial and individual heterogeneity in how urban housing status shapes population re-mobility. Floating populations residing in first-tier, second-tier, and provincial capital cities prioritize employment opportunities. In comparison, first-generation floating populations, those with local spouses, and individuals engaged in low-risk occupations exhibit stronger demand for stable residence. Full article

This study examines whether green finance promotes green development across Chinese prefecture-level cities from 2005 to 2019. We find a positive association between green finance and green development using panel regressions with city and year fixed effects. This result remains robust after accounting for potential endogeneity and implementing a series of robustness checks. Further heterogeneity analysis shows that this positive effect is stronger in regions characterized by high fiscal capacity and within the Yangtze River Economic Belt. Additionally, green finance drives regional green development by promoting green innovation. Environmental decentralization moderates the relationship, with a stronger positive effect at higher levels of decentralization. This study offers empirical evidence regarding how green finance shapes green development outcomes. Full article

Historic blue bricks are fundamental to Beijing’s architectural heritage, yet cross-site compositional data for guiding material-compatible restoration remain scarce. This study applies WD-XRF, XRD, SEM, thermal expansion measurement, and physical property testing to 21 blue brick specimens from four Beijing-area sites spanning the Tang through Qing dynasties, with PCA and K-means clustering used to explore compositional grouping structures. Within this exploratory dataset, a compositional distinction separates the Ming and Qing Great Wall bricks: CaO falls from 7.7 to 1.5 wt.% as anorthite gives way to albite, while Qing specimens are denser (1.79 vs. 1.65 g·cm⁻³) with lower water absorption (15.9% vs. 20.9%). Two Wanping City bricks are strongly sulfate-enriched (SO₃ up to 9.8%), and WP-SE3 additionally carries a heavy chloride load (Cl 2.1%), masking their original clay signatures and illustrating how unrecognized weathering can distort compositional grouping and source-related interpretation from bulk chemistry. K-means clustering yields compositional types that overlap only partially with site boundaries, capturing raw material variation rather than site-specific manufacturing fingerprints. Despite constraints in sample size and physical property coverage, the integrated dataset offers preliminary compositional benchmarks and limited performance data to inform period-specific brick replacement at these heritage sites. Full article

Background/Objectives: Ventilator-associated pneumonia (VAP) remains a major complication in patients requiring prolonged mechanical ventilation. The effect of tracheostomy on VAP risk remains controversial, particularly when differences in duration of mechanical ventilation are considered. This study evaluated the association between tracheostomy, VAP occurrence, and clinical outcomes in mechanically ventilated ICU patients. Methods: We conducted a retrospective matched exposed–unexposed cohort study in a tertiary-care ICU in Mexico City. Patients undergoing tracheostomy were compared with an age- and sex-matched subcohort of intubated patients receiving invasive mechanical ventilation for ≥48 h. VAP incidence was assessed using cumulative incidence, incidence density, and multivariable generalized linear models. Results: A total of 218 patients were included (55 tracheostomized and 163 intubated). VAP incidence density was similar between groups (31.5 vs. 30.3 per 1000 ventilator-days; RR 1.04, 95% CI 0.7–1.7), whereas cumulative incidence was higher among tracheostomized patients (61.8% vs. 22.7%; RR 2.7, 95% CI 1.9–3.9). Broad-spectrum antibiotics, mechanical ventilation ≥ 5 days, chronic pulmonary disease, and ICU stay remained associated with VAP occurrence in an exploratory multivariable model. Gram-negative microorganisms predominated, and antimicrobial resistance was more frequent among tracheostomized patients. Conclusions: Tracheostomy was associated with higher cumulative incidence of VAP, but a similar incidence density compared with endotracheal intubation. The crude association between tracheostomy and VAP disappeared after adjustment for confounding factors, suggesting that prolonged mechanical ventilation and ICU exposure are more important determinants of VAP risk than tracheostomy itself. Full article

Purpose-aware AI systems are increasingly deployed in safety-critical, multi-agent, and human-facing environments, where they must transform heterogeneous data into timely, explainable, and goal-aligned decisions under uncertainty. Existing architectures often couple perception, reasoning, communication, and security only at the pipeline level. This creates a research gap in unified semantic transformation, purpose-oriented judgment, bounded imperfection handling, and semantic self-protection. To address this gap, this paper proposes a DIKWP+BUG semantic–cognitive reference architecture for artificial-consciousness-oriented computing, without claiming definitive artificial consciousness. The architecture represents cognition through the Data–Information–Knowledge–Wisdom–Purpose (DIKWP) model and uses BUG theory to model bounded approximation, incomplete evidence, and confidence miscalibration in cross-dimensional reasoning. The model is mapped to an Artificial Consciousness Processing Unit (ACPU) reference substrate, an Artificial Consciousness Operating System (ACOS), a DIKWP semantic communication subsystem, and a concept–semantic fused security subsystem. The components are implemented through runtime emulation and evaluated in smart-city governance, autonomous-driving, and medical-triage simulations. Compared with selected baselines, the prototype increased cognitive throughput from $4.5\mathrm{k}$ to $7.8\mathrm{k}$ logged events, reduced perception–action latency from $340\mathrm{ms}$ to $120\mathrm{ms}$, reduced CPU utilization from $95\%$ to $68\%$, lowered smart-city congestion duration by $30\%$, improved emergency response time by approximately $40\%$, achieved 0 collisions versus approximately $2/10$ baseline IoV runs, and improved medical-triage accuracy from $85\%$ to $92\%$. These online-runtime results provide initial feasibility evidence under controlled simulation conditions; they do not include offline model-preparation costs and therefore should not be interpreted as end-to-end lifecycle speedups. Matched-compute ablation, statistical benchmarking, hardware prototyping, and real-world validation remain future work. Full article

The city of Bologna (Italy) boasts an outstanding hydraulic heritage linked to the development of the silk industry, embodied in an extensive and valuable canal network. These public works, such as the Canale di Reno and the Canale Navile, were fundamental to the urban and economic shaping of the city from the Middle Ages onwards; however, many were concealed or dismantled from the 19th century. This article analyses recent heritage engagement and dissemination strategies regarding Bologna’s historic canals and proposes new tools to overcome their spatial fragmentation and enhance their interpretation as a continuous network. The methodology combines analysis, fieldwork or valorisation of the hydraulic system, proposing two complementary promotion actions: the design of a mobile application and the development of a straightforward urban intervention consisting of linear pavement marking of the underground canals layout. The proposed operational hypotheses suggest that integrating digital resources with on-site signage brings invisible heritage to light, improves the spatial understanding of the hydraulic system, and fosters both community and tourist engagement. The study concludes that these strategies reinforce the territorial understanding and social awareness of civil engineering heritage, offering a transferable approach for the outreach of hydraulic networks. Full article

Background/Objectives: Limited research has evaluated the association between the triglyceride-to-high-density lipoprotein (TG/HDL) ratio and premature coronary artery disease (PCAD), particularly in Saudi Arabia. Therefore, this study aimed to investigate the association of the TG/HDL ratio with PCAD and to assess its sensitivity and specificity in a young Saudi population. Methods: This comparative retrospective case–control study utilized data collected from patients’ electronic medical records at King Saud University Medical City (KSUMC) between 2015 and 2023. The vessel score and Gensini score were used to evaluate the severity of coronary occlusion. The study population was divided into two groups: (1) a healthy control group consisting of blood bank donors, selected to exclude individuals with chronic diseases such as metabolic disorders and hypertension, with no evidence of coronary artery disease and aged ≤50 years (as confirmed by a cardiologist to rule out cardiovascular disease); and (2) patients with PCAD, aged ≤51 years, who underwent selective coronary angiography using the standard hospital procedure (right femoral artery approach). Coronary angiographic images were evaluated using right and left oblique views with cranial and caudal angulations. Results: A total of 898 subjects were included in the study, comprising 440 healthy controls and 458 patients with PCAD. Higher HbA1c levels were significantly associated with PCAD (adjusted OR = 13.03, 95% CI [7.32, 23.18], p < 0.001). Importantly, the TG/HDL ratio, the primary biomarker of interest, remained significantly associated with PCAD after full adjustment. Each unit increase in the TG/HDL ratio was associated with more than a threefold increase in the odds of PCAD (adjusted OR = 3.39, 95% CI [2.22, 5.16], p < 0.001), independent of age, sex, BMI, HbA1c, smoking, and total cholesterol levels. Among females, the TG/HDL ratio demonstrated an area under the curve (AUC) of 0.796, with an optimal cut-off value of 0.91, yielding 77.8% sensitivity and 71.4% specificity. Among males, the TG/HDL ratio yielded an AUC of 0.786, with a higher optimal cut-off value of 1.09 providing 73.4% sensitivity and 65.4% specificity. Conclusions: Our study indicates that the TG/HDL ratio and HbA1c are significantly associated with PCAD in young Saudi male and female populations, demonstrating good sensitivity and specificity. Females exhibited a lower cut-off value than males. Smoking and elevated cholesterol levels were also identified as prominent risk factors. However, the TG/HDL ratio did not distinguish between moderate and severe coronary stenosis, as assessed by the Gensini score. Full article

Detecting objects in drone-captured aerial imagery is particularly formidable due to challenges such as the prevalence of numerous small targets and their dense spatial distribution. To bridge this gap, this paper introduces YOLO-UTD (YOLO-UAV Traffic Detection), a dedicated small object detector tailored for drone traffic surveillance. Built upon the YOLOv8 framework, the proposed model incorporates three principal enhancements. First, a specialized small-object detection head replaces the original large-object head to increase the sensitivity to fine-grained features. Second, we introduce a shallow-augmented feature pyramid network (SFPN) into the neck module. The SFPN enriches the semantic content of high-resolution shallow features via dense multiscale interactions and CARAFE upsampling, boosting performance on small targets. Finally, a C2fA layer is integrated into the deep backbone stages to adaptively fuse spatial details and semantic context through a dual-path architecture and a cross-attention mechanism, thereby dynamically refining features critical for small objects. Extensive experiments on the VisDrone2019 dataset validate that YOLO-UTD achieves a 3.6% higher mean average precision (mAP) than YOLOv8 while preserving a low parameter footprint, with a particularly significant gain of 5.3% in vehicle detection accuracy. These findings confirm the model’s efficacy and strong potential for application in smart city drone surveillance. Full article

João António de Aguiar was one of the most prolific Portuguese architect-planners of the twentieth century, producing an extensive body of work within the framework of the 1934 legislative reform. He employed Urban Development Plans as a key scientific and technical instrument for territorial intervention, both in mainland Portugal and in the overseas territories. Despite his significance, Aguiar’s contribution remains relatively understudied, frequently overshadowed by the reformist ministry of Duarte Pacheco and by the dominant ideological narratives of the period. This article advances a critical analysis centred on urban composition and city design, with particular emphasis on the transformation of coastal urban structures and on Aguiar’s interventions in the Portuguese colonial context. Through a comparative and interpretative methodology, the study examines the formal and spatial principles underpinning his plans, while addressing the cultural challenges involved in adapting European urban models to non-European contexts. By shifting the focus from a merely descriptive inventory of planning instruments to a deeper investigation of urban form, this research offers a more nuanced reading of urban transformation processes in overseas coastal settlements. It contributes to a clearer and more structured understanding of Aguiar’s influence on African and Asian urbanism and on colonial planning practices more broadly. Full article

Accurate land use/land cover (LULC) mapping from remotely sensed data remains challenging in arid regions, particularly for spatial and seasonal generalization. This work proposes a novel exclude-one-city-out (EOCO) framework based on machine learning (ML) to achieve LULC generalization across summer and winter in arid environments. Four cities in Saudi Arabia witnessing rapid urban growth were selected: Riyadh, Madinah, Jeddah, and Dammam. The ML models were trained on three cities and tested on the unseen city. Sentinel-2 surface reflectance data for the visible (Blue, Green, and Red) and near-infrared bands (NIR, SWIR1, and SWIR2) were used. Spectral indices, texture features, and topographical data were used to form five feature sets, which were utilized as inputs for four ML algorithms: random forest, support vector machine, classification and regression trees, and K-nearest neighbors. Statistical tests (Friedman, Kendall’s W, and Wilcoxon signed rank) were conducted to assess differences across ML models, feature sets, and seasons. The random forest model consistently outperformed other models across the five feature sets, while the spectral texture and combined feature sets outperformed other feature combinations. Significant differences in feature importance were observed across cities and seasons for spectral texture during summer and winter (p-values: 1.25 × 10⁻⁴ and 9.2 × 10⁻⁵, respectively), with strong agreement (Kendall’s W = 0.9212 and 0.9424). The findings can support urban environmental monitoring in arid regions, contributing to sustainable urban development. Full article