Search Results (2,498)

The digitisation of urban governance requires a context-sensitive method that balances operational efficiency, data security and transparency. This study proposes a context-sensitive QR code system as a conceptual framework for smart urban governance and real estate visualisation in Saudi Arabia, aligned with the strategic objectives of Vision 2030. Unlike traditional static QR code applications, the proposed system acts as a smart urban interface dynamically linking physical buildings to structured digital records and delivering role-specific information through a single scan. This system enables municipal authorities to retrieve compliance and regulatory data and allows emergency response teams to access real-time occupancy data with geographic coordinates. The proposed system enables visitors to explore curated heritage and site-based information, with each interface subject to policy-defined access rules. The proposed QR code system is evaluated by using a scenario-based computational simulation across three representative Saudi cities (Riyadh, Jeddah, and Dammam), and the results show that it significantly reduces service response time compared to manual processes while maintaining data integrity through role-based dynamic filtering. The proposed system enhances administrative efficiency and supports heritage preservation in sensitive areas such as the Al-Balad district in Jeddah city. By integrating governance, visualisation, and cultural sustainability within a simple, scalable and interactive model, the study provides an important framework for emerging smart cities in Saudi Arabia. Full article

Underground structures are becoming increasingly vital components of modern transportation networks and urban systems, making their structural integrity a critical factor for safety and operational reliability. However, despite considerable progress in Structural Health Monitoring (SHM), the application of data-driven and vibration-based strategies to underground infrastructures remains an open and under-explored field, often because of limited data availability. Population-Based Structural Health Monitoring (PBSHM) offers a promising pathway to overcome this challenge by leveraging transfer learning to share diagnostic knowledge among similar structures. This study investigates the feasibility of extending the PBSHM paradigm to underground infrastructures, with a particular focus on a metro tunnel application. Through dynamic finite element simulations, relevant vibration features are identified, and damage detection strategies based on transmissibilities and cross-correlation functions are evaluated. The numerical results show that transmissibility-based indicators enable accurate damage localisation along the tunnel lining, even under noisy conditions. In contrast, cross-correlation features exhibit more limited performance in some configurations. Building on this evidence, the transmissibility-based damage indicator is subsequently embedded within the PBSHM framework and used as a transferable feature between tunnel models, achieving reliable damage detection in a second tunnel with heterogeneous characteristics, with F1 scores exceeding 80% for all considered damage severities and above 94% for the most critical case, thereby highlighting the potential of knowledge transfer for large-scale underground networks. Full article

The conventional time-dependent perturbation theory in quantum mechanics is established within the framework of Hermitian Hamiltonians, applicable for describing quantum transitions and associated energy level responses in such systems. However, this theory has fundamental limitations when applied to non-Hermitian systems. Consequently, researchers have systematically extended time-dependent perturbation theory to non-Hermitian systems, establishing a corresponding mature framework. Building on this foundation, this study extends the theory to investigate the transition dynamics induced by non-Hermitian interactions in non-Hermitian Hamiltonian systems. We employ a biorthogonal basis representation for a three-level non-Hermitian system. This work investigates a system comprising an unperturbed static non-Hermitian Hamiltonian and a periodically driven time-dependent perturbation Hamiltonian. Taking the three-level system as a concrete example, we combine analytical methods with numerical simulations to solve and analyze its dynamical evolution equations. These complementary approaches reveal that when system parameters complete a full cycle around an exceptional point, the transitional behavior exhibits specific evolutionary patterns. In this system, quantum transition probabilities exhibit significant asymmetry and non-conservation that depend on the initial and final states, revealing inherent directional characteristics in the dynamical process. Furthermore, for a three-level, periodically driven non-Hermitian system with time-dependent perturbations, this asymmetry is even more pronounced, manifesting as a distinct disparity between forward and reverse transition probabilities. The periodic driving actively amplifies the asymmetry in the transition process. By designing the perturbation spectrum, selective manipulation of specific quantum states can be achieved. Moreover, transition probabilities can be significantly enhanced under resonance conditions, while non-Hermiticity further breaks the system’s inherent symmetry, leading to substantial amplification of transitions in a single direction. By precisely tuning the drive frequency, interactions between specific coupling channels can be selectively enhanced or suppressed. The amplification of channel asymmetry by non-Hermitian properties provides a novel mechanism for directional control of quantum states and opens new pathways for realizing related quantum technologies. Full article

Maneuver pattern classification is fundamental for understanding and predicting the dynamic behaviors of aerial vehicles operating in increasingly complex airspace environments. However, existing rule-based and data-driven approaches are constrained by the scarcity, imbalance, and limited maneuver diversity of real-world flight data, leading to a restricted generalization capability and a reduced robustness to noise. To address these challenges, we construct a standardized Maneuver Pattern Library, a curated dataset of simulated flight trajectories encompassing five representative maneuver primitives: climb, descent, left turn, right turn, and loiter. Trajectories are generated using the X-Plane 12 flight simulator under controlled conditions to ensure maneuver diversity and label consistency, refined through noise reduction and cubic spline interpolation, and rendered from synchronized top and side views with time-encoded color gradients to preserve temporal continuity. Building upon this dataset, we propose DualView-LiteNet, a lightweight Siamese convolutional network designed to jointly learn complementary spatial and temporal cues from dual-view trajectory representations through parameter sharing and feature fusion. In addition to comprehensive comparisons with multiple baseline models on the simulated benchmark, we further evaluate the trained model via direct inference on a real-world ADS-B dataset collected from ADS-B Exchange, without any fine-tuning. The consistent performance observed in this sim-to-real setting demonstrates the practical feasibility and generalization capability of the proposed approach. The experimental results show that DualView-LiteNet achieves an accuracy of 97.64%, with its precision, recall, and F1-score all reaching 0.98 on the benchmark dataset, validating its effectiveness for aerial maneuver pattern classification and establishing a reliable reference for future research. Full article

The exceptional mechanical strength and toughness of collagen arise from its well-defined hierarchical architecture. Conventional methods for obtaining collagen aggregates (CAs), such as direct extraction from native tissues or acid swelling followed by mechanical processing, offer limited control over dimensional uniformity and provide little insight into the underlying exfoliation mechanisms. To overcome these challenges, this study introduces a novel strategy that leverages insights into the hierarchical interactions within collagen. We employ the ionic liquid 1-allyl-3-methylimidazolium chloride ([AMIM]Cl) as an exfoliating agent to successfully isolate fibrous CAs from native bovine tendon. By precisely modulating temperature and processing time, we achieve CAs with tunable mesoscale dimensions (diameter 0.9–1.1 μm, length > 160 μm). Molecular dynamics simulations reveal that [AMIM]Cl disrupts the intramolecular hydrogen-bonding network within collagen, thereby facilitating controlled exfoliation. These exfoliated aggregates serve as fundamental building blocks for fabricating collagen films. The resulting materials exhibit robust mechanical integrity, high transparency, reversible pH-responsive behavior, and excellent biocompatibility as verified by cytotoxicity assays, which together underscore their potential as versatile biomaterial platforms. Furthermore, the integration of single-walled carbon nanotubes yields conductive composites with confirmed electrical functionality. This study thus presents an innovative pathway for the precision processing of collagen and advances the design of high-performance collagen-based biomaterials. Full article

Edge, fog, and cloud continuum architectures that interconnect resource-constrained devices, intermediate edge servers, and remote cloud data centers face persistent challenges in handling heterogeneous and latency-sensitive workloads while reducing energy consumption and improving resource utilization. Classical task offloading approaches either rely on static heuristics, which lack adaptability to dynamic conditions, or on metaheuristic optimizers, which often incur high computational overhead and centralized coordination. This paper proposes LITO, a lemur-inspired task offloading algorithm for edge, fog, and cloud continuum systems that models the infrastructure as a social system in which computing nodes assume distinct roles that mirror lemur social hierarchies. Building on an abstracted model of lemur group behavior, LITO incorporates two key lemur-inspired mechanisms: an energy-aware task assignment mechanism based on sun basking, a thermoregulation behavior in which lemurs seek favorable warm spots, mapped here to selecting energetically efficient execution nodes, and a cooperative scheduling policy based on huddling, group clustering under stress, mapped here to sharing load among overloaded nodes. These mechanisms are combined with a continual supervised policy-learning layer with contextual bandit feedback that refines offloading decisions from online feedback. The resulting multi-objective formulation jointly minimizes energy consumption and deadline violations while maximizing resource utilization and throughput under high-load conditions in the edge and fog segment of the continuum. Simulations under diverse workload regimes and task complexities show that LITO outperforms representative multi-objective offloading baselines in terms of energy consumption, resource utilization, latency, Service Level Agreement (SLA) violations, and throughput in congested scenarios. Full article

Historic buildings exhibit coupled response characteristics during long-term service, characterized by slowly varying global inclination evolution superimposed with local component-level deformation. Meanwhile, multi-source measurements are susceptible to environmental noise and structural non-integrality, which poses challenges to obtaining stable and physically interpretable inclination measurements. To address these issues, this study proposes a multi-source fusion monitoring method for global inclination and local deformation of historic buildings using an extended Kalman filter with fractional-order state modeling (FEKF). A state-space model incorporating global inclination, local component-level additional deformation, and their projection relationships is established, in which global inclination information derived from Global Navigation Satellite System (GNSS) and local observations obtained from inclinometers are formulated within a unified measurement framework. Fractional-order dynamics are introduced into the state evolution model to represent the long-memory and non-stationary characteristics of structural responses in historic buildings. By adopting a finite-memory approximation, the fractional-order model is embedded into the extended Kalman filtering framework, enabling joint estimation and physical decoupling of multi-source measurements. Numerical simulation results demonstrate that the proposed method can stably separate global inclination and local deformation components under noisy conditions, while improving the stability of global inclination estimation. Further validation using measured data from a historic building shows that the fusion results effectively suppress high-frequency disturbances in GNSS measurements and allow reliable reconstruction of local component-level inclination responses, indicating good stability and practical applicability. These results demonstrate that the proposed approach provides a physically consistent and robust solution for long-term posture and deformation monitoring of historic buildings. Full article

Multi-storey steel buildings may be susceptible to structural damage under seismic loading. Shear plate walls are often integrated in the structural system of this type of buildings in order to restrict the lateral response. This article aims, therefore, to propose a methodology for the automatic evaluation of failure on the shear plate walls of multi-storey steel buildings using computer vision. Physics-based non-linear dynamic finite element models have been developed and solved for a range of geometries, shear plate wall thicknesses and seismic loading from past events. Images depicting failure on shear plate walls given as equivalent plastic strain contour plots are included in the output data of the parametric simulations. Then, Convolutional Neural Networks (CNNs) are introduced, predicting the failure distribution on shear plate walls. The input parameters are the geometric properties of the buildings and the seismic event intensity, and the output parameters is the equivalent plastic strain images. This scheme was tested on random buildings with satisfactory accuracy. The proposed methodology can be adopted and used within structural digital twin solutions. Full article

Amid global climate change and rapid urbanization, megacities such as Shanghai confront prominent ecological challenges. A critical issue is the growing mismatch between the supply of and demand for urban green space (UGS) ecosystem services. This study aims to explore the supply–demand compatibility of Shanghai’s UGS ecosystem services and simulate future scenarios. Guided by the SSP1-2.6 scenario, it integrates the PLUS model, InVEST model, and nSFCA method to conduct dynamic analysis, quantifying supply–demand alignment and identifying imbalance areas. Results show a significant spatial mismatch: high demand but low supply in Shanghai’s inner ring and low demand but high supply in the outer ring. UGS attractiveness presents a core-concentrated and peripheral-diffused pattern by level. By 2030, a coordinated supply framework of “city-level dominance, community-level support, and neighborhood-level supplementation” will form, improving supply–demand alignment, though accessibility gaps persist. The study reveals that urbanization, planning policies, and population–spatial expansion asynchrony drive these patterns, providing scientific decision-making support for optimizing Shanghai’s green space planning and building an ecologically livable city. Full article

The evaluation of building energy performance under dynamic conditions requires reliable estimates of the thermophysical properties of envelope components. In existing buildings, however, the properties of multilayer walls are often unknown or uncertain, limiting the applicability of detailed physical models. To address this issue, this study proposes an inverse modeling framework for identifying the equivalent thermophysical parameters of a multilayer wall through a simplified homogeneous one-dimensional conduction model. The equivalent parameters are determined by matching the inner-side dynamic thermal response of the homogeneous model to that of the actual multilayer structure under the same external excitation. The approach explicitly accounts for the role of inner boundary conditions, which govern both the identifiability of the equivalent parameters and the formulation of the inverse problem. Adiabatic, isothermal, and more general inner boundary conditions are analyzed to determine how many independent parameters can be reliably identified and which response variables should be used in the objective function. Synthetic datasets, generated via numerical simulations driven by real weather data, are first employed to assess the method and to quantify the effect of transient initialization. The framework is then applied to experimental measurements collected from a full-scale test room. The results show that, under adiabatic conditions, the wall dynamics can be accurately reproduced by identifying a single equivalent thermal diffusivity, whereas isothermal and near-isothermal conditions require the simultaneous estimation of thermal conductivity and volumetric heat capacity. Moreover, the analysis demonstrates that inverse formulations based on inner heat flux are significantly more robust than temperature-based formulations, particularly when the inner-surface temperature is weakly varying or tightly controlled, as commonly occurs in real buildings. In a nearly isothermal experimental case, the inverse identification failed (${EF}_T=-5.76$) when based on the inner-surface temperature, while it resulted in a better match (${EF}_q=0.63$) when based on the inner heat flux. Overall, the proposed framework provides a physically consistent and practically robust methodology for the dynamic thermal characterization of multilayer building walls using equivalent homogeneous models. Full article

Southwest China’s karst region has developed a dam- and reservoir-dense pattern in which cascaded hydropower on mainstem rivers coexists with small hydropower on tributaries, forming a foundation for the region’s low-carbon energy supply. Under China’s “dual-carbon” targets and a strengthening ecological civilization agenda, it is urgent to clarify the mechanisms driving habitat quality (HQ) change under compound disturbances from cascaded hydropower, urbanization, and related pressures—especially the nonlinear pathway through which engineering disturbance propagates to ecological responses via land-use restructuring. To address this need, we develop a Cascade disturbance–Land restructuring–Habitat response chain framework and integrate an InVEST–IntPLUS–OPGD modeling approach to capture HQ dynamics in the Wujiang River Basin (1980–2020), attribute the interactive effects of coupled natural–social drivers, and project ecological responses under alternative 2035 scenarios. Results show that: (1) The basin maintained a stable ecological matrix, with forest land and cropland consistently >82.5% and forest cover near 50%, while construction land increased by 972.15 km² and water bodies by 354.23 km² (2) Mean HQ stayed high and declined by only 1.42%, with high and medium–high HQ dominating (>65%). HQ degradation is concentrated in urban expansion areas and reservoir shorelines, whereas most mountainous/forested regions remain stable; and (3) HQ spatial differentiation is mainly shaped by the synergy between forest structure and NDVI, while nonlinear urbanization edge effects impose stronger stress than hydropower development itself. Scenario simulations further indicate that a water protection pathway can enhance HQ by building integrated “water–forest” corridors that promote blue–green synergy. Overall, this study supports improved trade-off design between energy supply and ecological protection in vulnerable karst regions. Full article

Triptonide (TN), a natural bioactive compound derived from Tripterygium wilfordii with multiple antitumor activities, has a poorly defined exact mechanism in acute myeloid leukemia (AML)—a hematologic malignancy with limited treatment options. This study systematically clarifies TN’s mechanisms in AML through an integrative strategy combining network pharmacology, molecular docking, molecular dynamics simulation, and in vitro/in vivo experiment validation. Predicted TN targets using Swiss Target Prediction and PharmMapper, and AML-associated genes via GeneCards, OMIM, and CTD. Verall, O198 overlapping targets were mapped to build a PPI network using STRING and Cytoscape. Identified hub gene (AKT1, EGFR, HSP90AA1, HSP90AB1, and PIK3R1) using CytoNCA, MCODE, and CytoHubba algorithms. GO and KEGG enrichment analyses highlighted marked enrichment in the PI3K/AKT pathway. TN exhibited high affinity binding to AKT1 (−7.28 kcal/mol) and PIK3R1 (−7.33 kcal/mol), with stable interactions confirmed by molecular dynamics simulations. The GSEA of the DEGs from GEPIA 2 revealed prominent activation of the PI3K/AKT signaling pathway, indicating its key role as a regulator of AML pathogenesis. In vitro, TN dose-dependently suppressed proliferation of multiple AML cell lines induced apoptosis, and downregulated the expression of P-PI3K and P-AKT. The AKT activator SC79 reversed TN-induced suppression in AML cells, validating PI3K/AKT pathway dependency. In vivo, TN significantly inhibited tumor growth in xenograft models without causing organ toxicity in female nude mice. These findings reveal the therapeutic potential of TN against AML through inhibiting the PI3K/AKT axis. With no PI3K/AKT inhibitors targeting AML approved as first-line therapies, TN emerges as a promising candidate for AML treatment, offering a safer natural alternative. Full article

Agent-based modeling (ABM) is a powerful tool for simulating building users’ dynamic behavior in demand response (DR) programs. However, ABM faces several challenges, particularly in encoding building users’ natural language features and common sense into rules or mathematical equations. To overcome these limitations, this paper proposes an agent framework based on large language models (LLMs) to simulate building users’ air-conditioning setpoint adjustment behavior under DR. This framework leverages LLMs’ natural language processing capabilities to replicate building users’ reasoning and decision-making processes. It consists of five modules: persona, perception, decision, reflection, and memory. Agents are assigned diverse personas through natural language descriptions based on empirical survey data. LLMs drive agents to reason and make decisions based on incentive prices and historical experiences. The results show that the LLM-based agent has common sense derived from natural language-defined personas and exhibits human-like irrational characteristics. This demonstrates the feasibility of replacing rules with natural language in ABM. The LLM-based agent can more effectively model hard-to-parameterize human features and provide decision explanations through LLM outputs. The results show that the inclusion of reflection and memory modules enables the agent to learn from previous decisions and reduce unreasonable choices. Full article

Mast climbing work platforms (MCWPs) are designed to vertically access building facades and other structures to perform various construction tasks. The mast in an MCWP system is structurally considered “slender”, its anchorages to the building play an important role in maintaining its stability. Failure of anchorages can affect overall structural stability, potentially increasing the risk of the mast collapsing. The anchorages and their attachments to a construction structure are likely among the most critical components for the MCWPs. This study developed an instrumented anchorage using strain gauges to measure and understand the anchorage reaction forces and to identify the major factors for the measurement of those forces. In the experiment, a single mast work platform was used at a simulated work site. Besides the anchoring reaction forces, the vibration motions on the platform were also measured. The study found that the amount of the load on the platform, the position of the load on the platform, and the platform’s vertical position on the mast may all affect the reaction forces on the anchorages. Such effects varied with the specific anchorages installed at different heights of the mast. The dynamic forces on the anchorages were correlated to the platform vibrations. Full article

Cooperative operations in which a UAV advances ahead of a UGV to conduct forward reconnaissance are critical in disaster relief and urban inspection missions. Prevalent air–ground coordination methods operate under the assumption of ideal communication or treat connectivity as a secondary objective. However, obstacle occlusion, such as high-rise buildings in urban areas and mountainous terrain, results in Non-Line-of-Sight (NLOS) conditions, disrupting communication between the two platforms. To address these challenges, this paper introduces a cooperative control framework based on dynamically varying modulation matrices for both the UAV and the UGV. By evaluating and mapping occlusion risks in real time, the cooperative motions of the UAV and UGV are adaptively adjusted to maintain Line-of-Sight (LOS). An LOS assessment function is designed and mapped to the eigenvalues of the modulation matrices, enabling smooth and adaptive coordination under changing environmental conditions while avoiding the limitations of traditional discrete mode-switching strategies. Theoretical analysis and simulation results confirm that the proposed approach not only ensures stable LOS connectivity but also enhances trajectory smoothness, adaptability, and computational efficiency. Full article