Search Results (4,456)

Global urbanization is driving a rising demand for sand and gravel, which has intensified riverbed mining. This threatens fluvial stability, flood safety, and ecological integrity. Although previous studies have documented localized geomorphic and hydrological impacts, systematic assessments that integrate long-term incision trends, embankment stability mechanisms, and resource optimization under multiple objectives remain limited. In this study, we investigate the Wengang section of the Fu River (Jiangxi, China), a sediment-deficient river reach subjected to decades of intensive mining. Through the application of hydrosediment analysis, hydrodynamic modeling, geotechnical–hydrological–mechanical (GHM) simulations, and a dynamic optimization model, the sustained impacts of mining are quantified, and science-based management strategies are proposed. The results indicate that extensive excavation has resulted in irreversible riverbed incision, with a net volume increase of 12.97 × 10⁶ m³ between 2003 and 2023, far exceeding the natural sediment deposition volume (0.853 × 10⁶ m³). Although the overall longitudinal profile remains stable, localized flow velocities in the primary mining area are increased by 0.22–0.39 m/s. A GHM analysis identifies a critical safe distance of 13–14 m between pit edge and embankment toe and demonstrates that wide-shallow pit morphology is associated with reduced stability risk compared to narrow-deep pits. Based on these constraints, an economic optimization model incorporating flood safety and market demand is developed, yielding an optimal extraction plan for 2024–2028 with a total volume of 4.4848 million tons and an estimated revenue of 50.03 million USD. This study provides an integrated framework for assessing mining impacts and offers actionable strategies to support sustainable sediment management in vulnerable river systems. Full article

Wind turbine (WT) misalignment fault diagnosis is constrained by critical signal processing challenges: weak fault features, intense background noise, and poor generalization. This study proposes a lightweight method for high-precision fault diagnosis. A fixed-threshold wavelet denoising method with the scene-specific pre-optimized parameter a (0 < a ≤ 1.3) is proposed: the parameter a is determined via offline grid search using the feature retention rate (FRR) as the objective function for typical wind farm operating scenarios. A multi-scale depthwise separable CNN (MS-DSCNN) captures multi-scale spatial features via 3 × 1 and 5 × 1 kernels, reducing computational complexity by 73.4% versus standard CNNs. An attention-based minimal peephole LSTM (AttMPLSTM) enhances temporal feature measurement, using minimal peephole connections for long-term dependencies and channel attention to weight fault-relevant signals. Joint L1–L2 regularization mitigates overfitting and environmental interference, improving model robustness. Validated on a WT test bench, the Adams simulation dataset, and the CWRU benchmark, the model achieves a 90.2 ± 1.4% feature retention rate (FRR) in signal processing, an over 98% F1-score for fault classification, and over 99% accuracy. With 2.5 s single-epoch training and a 12.8 ± 0.5 ms single-sample inference time, the reduced parameters enable real-time deployment in embedded systems, advancing signal processing for rotating machinery fault diagnosis. Full article

Background/Objectives: Pyrazole carboxamides are widely used as adaptable medicinal-chemistry scaffolds and have been explored as cholinesterase (ChE) inhibitor chemotypes. In this work, we prepared a new series of 4-arylazo-3,5-diamino-N-tosyl-1H-pyrazole-1-carboxamides 5(a–m) and evaluated their inhibitory activity against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), supported by structure-based computational analyses. Methods: Thirteen derivatives 5(a–m) were synthesized, fully characterized with analytical techniques (FT-IR, H NMR, and C NMR), and tested in vitro against AChE and BChE, with tacrine (THA) used as the reference inhibitor. Docking calculations were used to examine plausible binding modes. The top-ranked complexes (7XN1–5e and 4BDS–5i) were further examined by 100 ns explicit-solvent molecular dynamics (MD) simulations in Cresset Flare, followed by RMSD/RMSF analysis and contact-persistence profiling. Predicted ADME/Tox. properties were also assessed to identify potential developability issues. Results: The series showed strong ChE inhibition, and several compounds were more potent than THA. Compound 5e (4-nitro) was the most active AChE inhibitor (K_I = 20.86 ± 1.61 nM) compared with THA (K_I = 164.40 ± 20.84 nM). For BChE, the K_I values ranged from 31.21 to 87.07 nM and exceeded the reference compound’s activity. MD trajectories supported stable binding in both systems (10–100 ns mean backbone RMSD: 2.21 ± 0.17 Å for 7XN1–5e; 1.89 ± 0.11 Å for 4BDS–5i). Most fluctuations were confined to flexible regions, while key contacts remained in place, consistent with the docking models. ADME/Tox. predictions suggested moderate lipophilicity but generally low aqueous solubility; all compounds were predicted as non-BBB permeant, and selected liabilities were flagged (e.g., carcinogenicity for 5e/5g/5h/5i; nephrotoxicity for 5f/5g). Conclusions: The 4-arylazo-3,5-diamino-N-tosyl-1H-pyrazole-1-carboxamide scaffold delivers low-nanomolar ChE inhibition, with docking and MD supporting stable binding modes. Future optimization should prioritize solubility improvement and mitigation of predicted toxicities and metabolic liabilities, especially given the predicted lack of BBB permeability for CNS-directed applications. Full article

To meet the need for efficient and precise detection of people and obstacles in the actual operating environment of a gantry crane, a detection model based on an improved YOLOv5s was proposed which incorporates the parameter-free SimAM attention mechanism to enhance obstacle feature extraction capabilities, employs the EIoU loss function to optimize bounding box regression accuracy, and utilizes preprocessing techniques to improve input image quality. Training experiments on humans and simple simulated obstacles demonstrate that the improved model achieves significantly higher recognition accuracy and speed compared to the original YOLOv5 model. The improved model was applied to the recognition experiments of reducer obstacles under varying sizes, visibility levels, and distance conditions, and the comparative experiments were conducted with mainstream YOLO models, as well as different attention mechanisms and loss functions. The results show that the mAP@0.5 of the improved model achieves 0.884 with superior recognition performance and used lower computational resource requirements, providing a reliable solution for real-time obstacle detection in crane operation scenarios. Full article

Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeout (RTO); inaccurate RTT estimates may trigger unnecessary retransmissions or slow loss recovery. This paper proposes an Enhanced Regularized Extreme Learning Machine (RELM) for RTT estimation that improves generalization and efficiency by interleaving a bidirectional log-step heuristic to select the regularization constant C. Unlike manual tuning or fixed-range grid search, the proposed heuristic explores C on a logarithmic scale in both directions ($\times 10$ and /10) within a single loop and terminates using a tolerance–patience criterion, reducing redundant evaluations without requiring predefined bounds. A custom RTT dataset is generated using Mininet with a dumbbell topology under controlled delay injections (1–1000 ms), yielding 1000 supervised samples derived from 100,000 raw RTT measurements. Experiments follow a strict train/validation/test split (6:1:3) with training-only standardization/normalization and validation-only hyperparameter selection. On the controlled Mininet dataset, the best configuration (ReLU, 150 hidden neurons, $C={10}^2$) achieves $R^2=0.9999$, $MAPE=0.0018$, $MAE=966.04$, and $RMSE=1589.64$ on the test set, while maintaining millisecond-level runtime. Under the same evaluation pipeline, the proposed method demonstrates competitive performance compared to common regression baselines (SVR, GAM, Decision Tree, KNN, Random Forest, GBDT, and ELM), while maintaining lower computational overhead within the controlled simulation setting. To assess practical robustness, an additional evaluation on a public real-world WiFi RSS–RTT dataset shows near-meter accuracy in LOS and mixed LOS/NLOS scenarios, while performance degrades markedly under dominant NLOS conditions, reflecting physical-channel limitations rather than model instability. These results demonstrate the feasibility of the Enhanced RELM and motivate further validation on operational networks with packet loss, jitter, and path variability. Full article

Blood–brain barrier (BBB) breakdown is a hallmark of several neurological disorders, including multiple sclerosis (MS). NX210c, a novel therapeutic peptide, has shown promise in restoring BBB integrity, in both preclinical and clinical settings, offering potential for use in MS populations and across various central nervous system conditions with overlapping mechanisms. In this study, we evaluated the therapeutic potential of NX210c in patients with relapsing–remitting MS (RRMS) using a previous quantitative systems pharmacology (QSP) model currently redesigned to capture the dynamic interplay between BBB integrity and immune system activity. We validated the QSP model using both preclinical and clinical datasets, and generated virtual populations representing healthy individuals and RRMS patients for in silico testing. NX210c was assessed as both a monotherapy and in combination with established MS treatments. Simulations predicted time course changes in key BBB integrity markers, including tight junction protein (TJP) expression and transendothelial electrical resistance (TEER), under various dosing regimens. NX210c treatment was associated with a significant attenuation of BBB degradation compared to untreated controls (~7–8% higher TJP expression and BBB electrical resistance). Furthermore, we investigated the long-term impact of NX210c on clinical outcomes such as relapse rates. Both 5 and 10 mg/kg doses (single cycle [thrice-weekly for 4 weeks]) induced improvement in disease activity in RRMS patients, as well as a 10 mg/kg dose (single or repeated 4-week cycles every 6 months) in highly active patients. Particularly when administered alongside one of five commonly used MS therapies (interferon β-1a, teriflunomide, cladribine, natalizumab, ocrelizumab), in the highly active subpopulation, the model on average predicted a reduction in relapse frequency in the 10 mg NX210c-treated group versus untreated group from four to no relapses over two years. These findings suggest that NX210c may enhance therapeutic efficacy in RRMS by promoting BBB restoration and modulating immune responses, offering a promising avenue for combination treatment strategies. Full article

Human-in-the-loop (HIL) immersive simulators integrate a human operator into the simulation loop, enabling real-time interaction with virtual environments. To expose users to controlled acceleration fields, they employ parallel kinematic machines (PKMs), including reduced-degree-of-freedom (DoF) configurations when compact and cost-effective systems are required. These reduced-DoF platforms frequently exhibit kinematic crosstalk, whereby motion along one axis causes unintended displacements or rotations along others. Among compact PKMs, the four-legged, three-DoF platform is widely used, particularly in driving simulators. However, to the best of the authors’ knowledge, its kinematics have never been systematically analyzed in the literature. It is an over-actuated system with specific constraint conditions characterized by actuators that are not fully grounded. As a result, kinematic crosstalk accelerations are not fully determined by kinematic relationships. They also depend on friction at the constraints; thus, they are also determined by the dynamic behavior of the machine, which is difficult to predict during operation. To address this issue, this paper introduces a simplified modeling approach to estimate kinematic crosstalk whose usability is evaluated experimentally both with mono-harmonic, combined DoF tests and in a real-world engineering application on an actual driving simulator. Results show that kinematic crosstalk on the platform is likely to generate acceleration levels up to 4 $\mathrm{m}/{\mathrm{s}}^2$, exceeding the vestibular perception threshold of 0.17 $\mathrm{m}/{\mathrm{s}}^2$ defined by Reid and Nahon. This result is relevant with respect to enabling a comprehensive assessment of the acceleration field to which the user is actually subjected, which determines the actual quality and immersiveness of the simulation. Full article

Recent projections by the Intergovernmental Panel on Climate Change indicate that global warming will turn permanent and further intensify the severity and frequency of extreme weather events (heat waves, rain, and intense droughts), with coastal regions being the most vulnerable to extreme events. Therefore, the risk of natural disasters and the associated regional impacts on water, food, energy, social, and health security represents one of the world’s greatest challenges of this century. However, conventional methodologies for monitoring these regions during extreme events are usually not available to managers and decision-makers with the necessary urgency. The aim of this study was to present a framework concept for assessing extreme flood event impacts in coastal zones using a suite of field data combined with numerical (hydrological, meteorological, and hydrodynamic) and computational (flooding) models in a virtual environment that provides a replica of a natural environment—the Patos Lagoon Digital Twin. The study case was the extreme flood event that occurred in the southernmost region of Brazil in May 2024, considered the largest flooding event in 125 years of data. The hydrodynamic model calculated the water levels around Rio Grande City (MAE ± 0.18 m). These results fed the flooding model, which projected the water over the digital elevation model of the city and produced predictions of flooding conditions on every street (ranging from a few centimeters up to 1.5 m) days before the flooding happened. The results were further customized to attend specific demands from the security forces and municipal civil defense, who evaluated the best alternatives for evacuation strategies and infrastructure safety during the May 2024 extreme flood event. Flood Safety Maps were also generated for all the terminals in the Port of Rio Grande, indicating that the terminals were 0.05 to 2.5 m above the flood level. Overall, this study contributes to a better understanding of the strengths of digital twin models in simulating the impacts of extreme flood events in coastal areas and provides valuable insights into the potential impacts of future climate change in coastal regions, particularly in southern Brazil. This knowledge is crucial for developing targeted strategies to increase regional resilience and sustainability, ensuring that adaptation measures are effectively tailored to anticipated climate impacts. 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

High-temperature operation of AlGaN/GaN Heterojunction Field Effect Transistor embedded diaphragm-based MEMS pressure sensors have been investigated, which utilized their wide bandgap and piezo resistivity to perform stably at elevated temperatures. The performance of the pressure sensor was observed over a change in applied pressure of 35 kPa, which resulted in an experimentally measured change in drain–source resistance (ΔR_DS/R_DS(0)) of 0.32% at room temperature and 0.65% at 250 °C, respectively. Additionally, the COMSOL-based Finite Element (FE) Simulations, in conjunction with our developed theoretical model, was utilized to theoretically determine the change in drain–source resistance. This theoretically calculated ΔR_DS/R_DS(0) of 0.45% at room temperature closely aligns with the experimental observations. Moreover, the sensor exhibited a gate-bias-dependent tunability, with the enhancement of sensitivity under increasingly negative gate voltages. Furthermore, the sensors demonstrated a stable and repeatable sensing operation over multiple pressure cycles up to 300 °C, with a rapid response time of <10 ms, suggesting excellent potential for reliable, high-performance pressure sensing in harsh, high-temperature environments. Full article

This study developed a virtual reality (VR) simulator for training the inferior alveolar nerve block (IANB) procedure using Leap Motion-based hand tracking and the Unity engine, and evaluated its interaction performance, task-level outcomes within the simulator, and usability. Built on a 3D anatomical model, the system provides a pre-clinical practice environment for realistic syringe manipulation and visually guided needle insertion, enabling repeated rehearsal of the procedural workflow. Interaction stability was assessed using participant-level gesture recognition rates and input latency. Usability was evaluated via a questionnaire addressing ease of use, cognitive load, and perceived educational usefulness. The results indicated participant-level mean gesture recognition rates of 88.8–90.5% and mean response latencies of approximately 64–66 ms. In usability testing (n = 40), the item related to perceived procedural skill improvement received the highest score (4.25/5.0). Because this study did not include controlled comparisons with conventional training or objective measures of clinical competency transfer, the findings should be interpreted as preliminary evidence of technical feasibility and learner-perceived usefulness within a simulated setting. Controlled comparative studies using objective learning outcomes are warranted. Full article

Mucus plugs are airway-obstructing accumulations of inspissated secretions frequently observed in obstructive lung diseases (OLDs), including chronic obstructive pulmonary disease (COPD), severe asthma, and cystic fibrosis. Their presence on chest CT is strongly associated with airflow limitation, reduced lung function, and increased mortality, making them emerging imaging biomarkers of disease burden and treatment response. However, manual delineation of mucus plugs is labor-intensive, subjective, and impractical for large cohorts, leading most prior studies to rely on coarse segment-level scoring systems that overlook lesion-level characteristics such as size, extent, and location. Automated plug-level quantification remains challenging due to substantial heterogeneity in plug morphology, overlap in attenuation with adjacent vessels and airway walls on non-contrast CT, and pronounced size imbalance in clinical datasets, which are typically dominated by small distal plugs. To address these challenges, we developed and validated a simulation-driven, annotation-free deep learning framework for automated detection and segmentation of airway mucus plugs on non-contrast chest CT. A total of 200 COPD CT scans were analyzed (98 plug-positive, 83 plug-negative, and 19 uncertain). Synthetic mucus plugs were generated within segmented airways by transferring voxel-intensity statistics from adjacent intrapulmonary vessels, preserving realistic morphology and texture while enabling controlled sampling of plug phenotypes. An nnU-Net trained exclusively on synthetic data (S-Model) was evaluated on an independent, expert-annotated test set and compared with an nnU-Net trained on manual annotations using 10-fold cross-validation (M-Model). The S-Model achieved significantly higher detection performance than the M-Model (sensitivity 0.837 [95% CI: 0.818–0.854] vs. 0.757 [95% CI: 0.737–0.776]; 1.91 false positives per scan vs. 3.68; p < 0.001), with performance gains most pronounced for medium-to-large plugs (≥6 mm). This simulation-driven approach enables accurate, scalable quantification of mucus plugs without voxel-wise manual annotation in thin-slice (<1.5 mm) non-contrast chest CT scans. While the framework could, in principle, be extended to other annotation-limited medical imaging tasks, its generalizability beyond this COPD cohort and imaging protocol has not yet been established, and future work is required to validate performance across diverse populations and scanning conditions. Full article

To improve the dynamic response and steady-state frequency quality of a wind–storage coordinated system during primary frequency regulation, and to address the secondary frequency dip caused by rotor kinetic energy recovery when a doubly fed induction generator (DFIG)-based wind turbine (DFIG-WT) participates in frequency support, this paper proposes a coordinated wind–storage primary frequency regulation strategy. This strategy synergistically controls the wind turbine’s rotor kinetic energy recovery and exploits the advantages of hybrid energy storage system (HESS). During the DFIG-WT control stage, an adaptive weighted model is developed for the inertial and droop power contributions of the DFIG-WT based on the available rotor kinetic energy, enabling a rational distribution of primary frequency regulation power. In the control segment of HESS, an adaptive complementary filtering frequency division strategy is proposed. This approach integrates an adaptive adjustment method based on state of charge (SOC) to control both the battery energy storage system (BESS) and supercapacitor (SC). Additionally, the BESS assists in completing the rotor kinetic energy recovery process. Through simulation experiments, the results demonstrate that under operating conditions of 9 m/s wind speed and a 30 MW step disturbance, the proposed adaptive weight integrated inertia control elevates the frequency nadir to 49.84 Hz and reduces the secondary frequency dip to 0.0035 Hz. Under the control strategy where wind and storage coordinated participate in frequency regulation and BESS assist in rotor kinetic energy recovery, secondary frequency dips were eliminated, with steady-state frequency rising to 49.941 Hz. The applicability of this strategy was further validated under higher wind speeds and larger disturbance conditions. Full article

Heat waves (HWs), characterized by periods of unusually high temperature, would inevitably affect the soil microenvironment and then soil respiration (Rs), which is considered to be the most active part of the global carbon cycle. An in situ spring and summer HWs simulation experiment combined with a locally common human disturbance (mowing) was conducted to separate Rs into autotrophic respiration (Ra) and heterotrophic respiration (Rh) on a natural Eurasian meadow steppe in 2018 and 2019. HWs significantly affected grassland Rs, Rh and Ra (p < 0.01) and also interacted with mowing, but the effect of spring HW and summer HW were different. During the summer HWs, daily Rs of the non-mowed plots increased by 1.07 μmol m⁻² s⁻¹ (11.71%) and increased in the mowed plots by 2.15 μmol m⁻² s⁻¹ (23.81%). During the spring HWs, daily Rs of the non-mowed plots decreased by 0.13 μmol m⁻² s⁻¹ (2.36%) and decreased by 0.52 μmol m⁻² s⁻¹ (9.02%) in the mowed plots. Rs, Rh and Ra were inhibited by spring HWs, but promoted by summer HWs. Our results indicated that the occurring time change in HWs would cause widely divergent influences on the ecosystem, and mowing would decrease the anti-interference ability of the ecosystem, which acted as an enhancement on both the positive and negative effects of HWs. These findings have important implications for accurate model prediction and carbon budget assessment. Full article

As the most frequent dust event in the Tarim Basin (TB), persistent floating dust significantly impacts the regional weather and climate. Long-term analysis (2015–2024) showed that the occurrence of persistent floating dust is significantly associated with the presence of the nocturnal low-level jet (NLLJ). To investigate this potential linkage, the Weather Research and Forecasting model with Chemistry (WRF-Chem) was used to simulate the persistent floating dust event accompanied by the NLLJ in the TB from 29 to 31 July 2006. Results indicated that a typical NLLJ occurred during the event, with an easterly jet core (>12 m/s) near 850-hPa facilitating the westward dust transport and accumulation within the TB, as well as strong convergence and vertical uplift on its front side elevating the dust layer height (DLH). Quantification showed that the NLLJ enhanced dust column concentrations (mean maximum > 100 mg/m²) and DLH (mean maximum > 300 m) over the central and western TB, and the cumulative maximum increase in dust emissions exceeded 200 mg/m², in the NLLJ region. Furthermore, nocturnal dust radiative forcing intensified the NLLJ by up to 1 m/s, thereby establishing a positive feedback mechanism. These results reveal the crucial role of the NLLJ in persistent floating dust events and enrich our understanding of such events in the TB. Full article