Search Results (3,038)

With accelerating urbanisation, extreme rainfall events have become increasingly frequent, leading to rising urban flooding risks that threaten city operation and infrastructure safety. The rapid expansion of impervious surfaces reduces infiltration capacity and accelerates runoff responses, making cities more vulnerable to short-duration, high-intensity storms. Although the SWMM is widely used for urban stormwater simulation, its application is often constrained by the lack of detailed drainage network data, such as pipe diameters, slopes, and node connectivity. To address this limitation, this study focuses on the main built-up area within the Second Ring Expressway of Chengdu, Sichuan Province, in southwestern China. As a regional core city, Chengdu frequently experiences intense short-duration rainfall during the rainy season, and the coexistence of rapid urbanisation with ageing drainage infrastructure further elevates flood risk. Accordingly, a technical framework of “open road data substitution–automated modelling–SWMM-based assessment” is proposed. Leveraging the spatial correspondence between road layouts and drainage pathways, open road data are used to construct a virtual drainage system. Combined with DEM and land-use data, Python-based automation enables sub-catchment delineation, parameter extraction, and network topology generation, achieving efficient large-scale modelling. Design storms of multiple return periods are generated based on Chengdu’s revised rainfall intensity formula, while socioeconomic indicators such as population density and infrastructure exposure are normalised and weighted using the entropy method to develop a comprehensive flood-risk assessment. Results indicate that the virtual drainage network effectively compensates for missing pipe data at the macro scale, and high-risk zones are mainly concentrated in densely populated and highly urbanised older districts. Overall, the proposed method successfully captures urban flood-risk patterns under data-scarce conditions and provides a practical approach for large-city flood-risk management. Full article

This paper proposes an emergency power supply restoration strategy for a distribution network that considers the operational risk of an islanded microgrid in response to the issues of voltage exceeding limits and power imbalance faced during their operation. Firstly, a distribution network emergency power supply restoration model supported by a generalized dynamic islanded microgrid is constructed. By equating the alternate tie line with a virtual distributed generator (DG), the integrated power supply restoration problem of distribution network is transformed into a generalized island power distribution network division problem based on DGs. Then, the risk of islanded microgrid operation is considered and restricted by chance constraints. Finally, simulation results based on the improved IEEE-33 node distribution network show that, compared to the generalized island partitioning strategy which ignores operational risks, the proposed strategy increases the power supply restoration rate from 83.4% to 97.8% while successfully ensuring the stability of all islanded microgrids under the specified confidence level for operational risk. Full article

As renewable energy integration accelerates, the displacement of synchronous generators by inverter-based resources (IBRs) necessitates advanced grid-forming (GFM) control strategies to maintain system stability. While techniques such as Droop control, Virtual Synchronous Generator (VSG), and Dispatchable Virtual Oscillator Control (dVOC) are well-established, their comparative performance under coordinated cyber-physical stress remains underexplored. This paper presents a comprehensive Controller Hardware-in-the-Loop (CHIL) assessment of these three GFM strategies within a networked microgrid environment. Utilizing a co-simulation framework that integrates an OPAL-RT real-time simulator with the EXata CPS network emulator, we evaluate the dynamic resilience of each controller under islanded, parallel, and fault-induced reconfiguration scenarios. Experimental results demonstrate that the VSG strategy offers superior transient performance, characterized by faster settling times and enhanced fault-ride-through capabilities compared to the Droop and dVOC strategies. Furthermore, recognizing the vulnerability of connected microgrids to cyber threats, this study investigates the impact of False Data Injection (FDI) attacks on the control layer. To address this, a model-reference resilience layer is proposed and validated on a TI C2000 DSP. The results confirm that this protection mechanism effectively detects and mitigates attacks on control references and feedback measurements, ensuring stable operation despite cyber-physical disturbances. Full article

This paper proposes the development of an IEC 61850-compliant platform that is readily programmable and deployable for future digital twin applications. Given the compatibility between IEC-61850 and digital twin concepts, a focused case study was conducted involving the robust development of a Raspberry Pi platform with protection relay functionality using the open-source libIEC61850 library. Leveraging IEC-61850’s object-oriented data modelling, the relay can be represented by fully consistent virtual and physical models, providing an essential foundation for accurate digital twin instantiation. The relay implementation supports high-speed Sampled Value (SV) subscription, real-time RMS calculations, IEC Standard Inverse overcurrent trip behaviour according to IEC-60255, and Generic Object-Oriented Substation Event (GOOSE) publishing. Further integration includes setting group functionality for dynamic parameter switching, report control blocks for MMS client–server monitoring, and GOOSE subscription to simulate backup relay protection behaviour with peer trip messages. A staged development methodology was used to iteratively develop features from simple to complex. At the end of each stage, the functionality of the added features was verified before proceeding to the next stage. The integration of the Raspberry Pi into Curtin’s IEC = 61,850 digital substation was undertaken to verify interoperability between IEDs, a key outcome relevant to large-scale digital twin systems. The experimental results confirm GOOSE transmission times below 4 ms, tight adherence to trip-time curves, and performance under higher network traffic. Such measured RMS and trip-time errors fall well within industry and IEC limits, confirming the reliability of the relay logic. The takeaways from this case study establish a high-performing, standardised foundation for a digital twin system that requires fast, bidirectional communication between a virtual and a physical system. Full article

This study analyzes the ICT infrastructure in teaching practice centers from the perspective of students enrolled in early childhood and primary education degree programs at a Spanish university during the 2024–2025 academic year. A quantitative, cross-sectional design was employed. A questionnaire was distributed to all first- to fourth-year students via the university platform, with a sample of 556 participants. The data collection instrument consisted of an ad hoc adaptation and extension of the validated EdSocEval_V2 questionnaire, ensuring factorial validity. It was used to examine the availability of technological resources for communication and digital management, together with personal and contextual variables to support data classification. Results indicate high availability of basic digital resources, including projectors, Wi-Fi, interactive whiteboards, printers, alongside limited access to robotics, digital tablets, and classrooms of the future. High homogeneity was observed in communication and digital management resources, such as websites, virtual learning environments and corporate email. MANOVA analyses revealed that students perceive ICT infrastructure to be more integrated at higher levels of primary education, with no significant differences based on school ownership. Binary logistic regressions showed that school ownership predicts the availability of certain ICT resources, with private schools exhibiting lower network presence. Full article

This article presents the design and implementation of a Virtual Cyber-Physical Testbed (VCPT) for transportation systems, featuring an automated level-crossing process. The proposed design improves network fidelity while keeping the platform lightweight. Key components include the Programmable Logic Controller (PLC), sensors, actuators, the Supervisory Control and Data Acquisition (SCADA) system, and OPNsense. Guided by NIST SP 800-115, penetration testing revealed several vulnerabilities and weaknesses that can be exploited and mitigated. Six attack scenarios—enumeration, brute force, remote code execution, ARP poisoning, DoS, and command injection—were executed, demonstrating realistic impacts on process safety and availability. Mitigation strategies using custom firewall and Intrusion Detection and Prevention System (IDPS) rules contributed to improving the security posture of VCPT. Educational evaluation with 41 cybersecurity students showed a 24% increase in average scores and a significant rise in top performers, further supported by positive feedback on engagement and realism. These results validate the VCPT as an effective platform for cybersecurity research, training, and experiential learning. Full article

Recognizing emotions accurately in virtual reality (VR) enables adaptive and personalized experiences across gaming, therapy, and other domains. However, most existing facial emotion recognition models rely on acted expressions collected under controlled settings, which differ substantially from the spontaneous and subtle emotions that arise during real VR experiences. To address this challenge, the objective of this study is to develop and evaluate generalizable emotion recognition models that jointly learn from both acted and natural facial expressions in virtual reality. We integrate two complementary datasets collected using the Meta Quest Pro headset, one capturing natural emotional reactions and another containing acted expressions. We evaluate multiple model architectures, including convolutional and domain-adversarial networks, and a mixture-of-experts model that separates natural and acted expressions. Our experiments show that models trained jointly on acted and natural data achieve stronger cross-domain generalization. In particular, the domain-adversarial and mixture-of-experts configurations yield the highest accuracy on natural and mixed-emotion evaluations. Analysis of facial action units (AUs) reveals that natural and acted emotions rely on partially distinct AU patterns, while generalizable models learn a shared representation that integrates salient AUs from both domains. These findings demonstrate that bridging acted and natural expression domains can enable more accurate and robust VR emotion recognition systems. Full article

High-penetration integration of distributed photovoltaics (PV) into distribution networks introduces significant challenges regarding voltage limit violations and fluctuations. To address these issues, this manuscript proposes a hierarchical coordinated voltage control strategy for medium- and low-voltage distribution networks utilizing a cloud-edge collaboration architecture. The research methodology involves constructing a multi-objective optimization model at the cloud layer to minimize network losses and voltage deviations, solved via an improved Honey Badger Algorithm (HBA). Simultaneously, at the edge layer, a multi-mode coordinated control strategy incorporating Virtual Synchronous Generator (VSG) technology is developed to provide fast reactive power support and inertial response. Through simulation analysis on an IEEE 33-node test system, the findings demonstrate that the proposed strategy significantly mitigates voltage fluctuations and enhances the hosting capacity of distributed energy resources. The study concludes that the cloud-edge framework effectively decouples control time-scales, ensuring both global economic operation and local transient stability. These results are significant for advancing the resilient operation of active distribution networks with high renewable penetration. Full article

Extended reality (XR), encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), is rapidly reshaping the landscape of digital interaction and immersive communication. As XR evolves toward ultra-realistic, real-time, and interactive experiences, it places unprecedented demands on wireless communication systems in terms of bandwidth, latency, and reliability. Conventional RF-based networks, constrained by limited spectrum and interference, struggle to meet these stringent requirements. In contrast, visible light communication (VLC) offers a compelling alternative by exploiting the vast unregulated visible spectrum to deliver high-speed, low-latency, and interference-free data transmission—making it particularly suitable for future XR environments. This paper presents a comprehensive survey on VLC-enabled XR communication systems. We first analyze XR technologies and their diverse quality-of-service (QoS) and quality-of-experience (QoE) requirements, identifying the unique challenges posed to existing wireless infrastructures. Building upon this, we explore the fundamentals, characteristics, and opportunities of VLC systems in supporting immersive XR applications. Furthermore, we elaborate on the key enabling techniques that empower VLC to fulfill XR’s stringent demands, including high-speed transmission technologies, hybrid VLC-RF architectures, dynamic beam control, and visible light sensing capabilities. Finally, we discuss future research directions, emphasizing AI-assisted network intelligence, cross-layer optimization, and collaborative multi-element transmission frameworks as vital enablers for the next-generation VLC–XR ecosystem. Full article

Operational expenditure in wind farms is heavily influenced by unplanned maintenance, much of which stems from undetected rotor system faults. Although many fault-detection methods have been proposed, most remain confined to laboratory test. Blade-root bending-moment measurements are among the few techniques applied in the field, yet their reliability is limited by strong sensitivity to varying operational and environmental conditions. This study presents a data-driven rotor health-monitoring framework that enhances the diagnostic value of blade bending-moments. Assuming that the wind speed profile remains approximately stationary over short intervals (e.g., 20 s), a machine-learning model is trained on bending-moment data from healthy blades to predict the incident wind-speed profile under a wide range of conditions. During operation, real-time bending-moment signals from each blade are independently processed by the trained model. A healthy rotor yields consistent wind-speed profile predictions across all three blades, whereas deviations for an individual blade indicate rotor asymmetry. In this study, the methodology is verified using high-fidelity OpenFAST simulations with controlled blade pitch misalignment as a representative fault case, providing simulation-based verification of the proposed framework. Results demonstrate that the proposed inverse-modeling and cross-blade consistency framework enables sensitive and robust detection and localization of pitch-related rotor faults. While only pitch misalignment is explicitly investigated here, the approach is inherently applicable to other rotor asymmetry mechanisms such as mass imbalance or aerodynamic degradation, supporting reliable condition monitoring and earlier maintenance interventions. Using OpenFAST simulations, the proposed framework reconstructs height-resolved wind profiles with RMSE below 0.15 m/s (R² > 0.997) under healthy conditions, and achieves up to 100% detection accuracy for moderate-to-severe pitch misalignment faults. Full article

Stroke continues to impose an enormous morbidity and mortality burden worldwide. Stroke survivors often incur debilitating consequences that impair motor function, independence in activities of daily living and quality of life. Rehabilitation is a pivotal intervention to minimize disability and promote functional recovery following a stroke. The Internet of Medical Things, a network of connected medical devices, software and health systems that collect, store and analyze health data over the internet, is an emerging resource in neurorehabilitation for stroke survivors. Technologies such as asynchronous transmission to handle intermittent connectivity, edge computing to conserve bandwidth and lengthen device life, functional interoperability across platforms, security mechanisms scalable to resource constraints, and hybrid architectures that combine local processing with cloud synchronization help bridge the digital divide and infrastructure limitations in low-resource environments. This manuscript reviews emerging rehabilitation technologies such as robotic devices, virtual reality, brain–computer interfaces and telerehabilitation in the setting of neurorehabilitation for stroke patients. Full article

Wee1-like protein kinase 2 (WEE2) is an oocyte-specific kinase that regulates meiotic arrest and fertilization. Its largely restricted expression in female germ cells and absence in somatic tissues make it a highly selective target for reproductive health interventions. Despite its central role in human fertility, no clinically approved WEE2 modulator is available. In this study, we employed an integrated in silico approach that combines structure-based virtual screening, molecular dynamics (MD) simulations, and MM-PBSA free-energy calculations to identify repurposed drug candidates with potential WEE2 inhibitory activity. Screening of ~3800 DrugBank compounds against the WEE2 catalytic domain yielded ten high-affinity hits, from which Midostaurin and Nilotinib emerged as the most mechanistically relevant based on kinase-targeting properties and pharmacological profiles. Docking analyses revealed strong binding affinities (−11.5 and −11.3 kcal/mol) and interaction fingerprints highly similar to the reference inhibitor MK1775, including key contacts with hinge-region residues Val220, Tyr291, and Cys292. All-atom MD simulations for 300 ns demonstrated that both compounds induce stable protein–ligand complexes with minimal conformational drift, decreased residual flexibility, preserved compactness, and stable intramolecular hydrogen-bond networks. Principal component and free-energy landscape analyses further indicate restricted conformational sampling of WEE2 upon ligand binding, supporting ligand-induced stabilization of the catalytic domain. MM-PBSA calculations confirmed favorable binding free energies for Midostaurin (−18.78 ± 2.23 kJ/mol) and Nilotinib (−17.47 ± 2.95 kJ/mol), exceeding that of MK1775. To increase the translational prioritization of candidate hits, we place our structure-based pipeline in the context of modern machine learning (ML) and deep learning (DL)-enabled virtual screening workflows. ML/DL rescoring and graph-based molecular property predictors can rapidly re-rank docking hits and estimate absorption, distribution, metabolism, excretion, and toxicity (ADMET) liabilities before in vitro evaluation. Full article

The increasing complexity of modern electrical networks poses significant challenges in terms of monitoring, maintenance, and operational efficiency. However, current planning approaches often lack a unified integration of real-time data and predictive modeling. In this context, Digital Twins (DTs) emerge as a promising solution, as they enable the creation of virtual replicas of physical assets. This research addresses the lack of standardized technical frameworks by proposing a novel mathematical optimization model for grid planning based on DTs. The proposed methodology integrates comprehensive architecture (frontend/backend), specific data standards (IEC 61850), and a linear optimization formulation to minimize operational costs and enhance reliability. Case studies such as DTEK Grids and American Electric Power are analyzed to validate the approach. The results demonstrate that the proposed framework can reduce planning errors by approximately 15% and improve fault prediction accuracy to 99%, validating the DTs as a key tool for the digital transformation of the energy sector towards Industry 5.0. Full article

With the development of the Internet of Things (IoT) and smart industry, the demand for high-precision indoor positioning is becoming increasingly urgent. Ultra-ideband (UWB) technology has become a research hotspot due to its centimeter-level ranging accuracy, good penetration, and high multipath resolution. However, in complex environments, it still faces problems such as high cost of anchor node layout, gross errors in observation data, and difficulty in eliminating systematic errors such as electronic time delay. To address the aforementioned problems, this paper proposes a comprehensive UWB indoor positioning scheme. By constructing virtual reference stations to enhance the observation network, the geometric structure is optimized and the dependence on physical anchors is reduced. Combined with a gross error elimination method under short-baseline constraints and a double-difference positioning model including virtual observations, it systematically suppresses systematic errors such as electronic delay. Additionally, a quality control strategy with velocity constraints is introduced to improve trajectory smoothness and reliability. Static experimental results show that the proposed double-difference model can effectively eliminate systematic errors. For example, the positioning deviation in the Xdirection is reduced from approximately 2.88 cm to 0.84 cm, while the positioning accuracy in the Ydirection slightly decreases. Overall, the positioning accuracy is improved. The gross error elimination method achieves an identification efficiency of over 85% and an accuracy of higher than 99%, providing high-quality observation data for subsequent calculations. Dynamic experimental results show that the positioning trajectory after geometric enhancement of virtual reference stations and velocity-constrained quality control is highly consistent with the reference trajectory, with significantly improved trajectory smoothness and reliability. In summary, this study constructs a complete technical chain from data preprocessing to result quality control, effectively improving the accuracy and robustness of UWB positioning in complex indoor environments, and exhibits promising engineering application potential. Full article

In the context of high-quality urbanization, a key challenge for urban agglomerations is the structural mismatch between economic linkages and rapidly expanding information interactions, which may constrain the performance of coupled systems under shocks. Taking the Beijing–Tianjin–Hebei (BTH) urban agglomeration as a case, we construct an inter-city economic network from cross-city corporate investment ties and an information network from online attention flows, and further derive an economic–information coupled network using a coupling-coordination framework. Using social network analysis and resilience assessment (hierarchy, assortativity, clustering, and disruption simulations), we compare network structures in 2013 and 2023 and evaluate how the structural gap shapes coupled resilience. Results show that (i) economic ties strengthen steadily but moderately, whereas the information network expands faster and becomes more inclusive, widening the structural gap between “virtual” and “material” flows; (ii) despite a persistently high correlation between the two layers, coordination declines, indicating increasing local divergence in link organization; and (iii) resilience improves overall, but differentiation remains: the information network gains robustness through decentralization and redundancy, while the economic network is more sensitive to targeted removals of core nodes, and the coupled network exhibits intermediate performance. These findings suggest that enhancing BTH resilience requires strengthening cross-jurisdictional redundant links and reducing excessive dependence on core corridors to better translate information interactions into balanced economic connectivity. Full article