Search Results (3,095)

The failure of large gravity dams during an earthquake could lead to calamitous flooding, severe infrastructural damage, and massive environmental destruction. This paper aims to demonstrate reliable methods for evaluating dam performance after a seismic event. The work included a seismic hazard analysis and nonlinear finite element modelling of concrete cracking for two large dams (D1 and D2, of 35 and 90 m in height, respectively) in Eastern Canada. Dam D1 is located in Montreal, and Dam D2 is located in La Malbaie, Quebec. The modelling approach was validated using the Koyna Dam, which was subjected to the 1967 Mw 6.5 earthquake. This paper reports tensile cracks of D1 and D2 under combined hydrostatic and seismic loading. The latter was generated from ground motion records from 11 sites during the 1988 Mw 5.9 Saguenay earthquake. These records were each scaled to two times the design level. It is shown that D1 remained stable, with minor localised cracking, whereas D2 experienced widespread tensile damage, particularly at the crest and base under high-energy and transverse inputs. These findings highlight the influence of dam geometry and frequency characteristics on seismic performance. The analysis and modelling procedures reported can be adopted for seismic risk classification and safety compliance verification of other dams and for recommendations such as monitoring and upgrading. Full article

Soil moisture is one of the key hydrologic components indicating the performance of landfill final covers. Conventional compacted clay (CC) covers and evapotranspiration (ET) covers often suffer from moisture-induced stresses, such as desiccation cracking and irreversible hydraulic conductivity. Engineered turf (EnT) cover systems have been introduced recently as an alternative; however, their field-scale moisture distribution behavior remains unexplored. This study investigates and compares the soil moisture distribution characteristics of EnT, ET, and CC landfill covers at a shallow depth using one year of field-monitored data in a humid subtropical region. Three full-scale test Sections (3 m × 3 m × 1.2 m) were constructed side by side and instrumented with moisture sensors at a depth of 0.3 m. Distributional characteristics of moisture were evaluated with descriptive statistics, goodness-of-fit tests such as Shapiro–Wilk (SW) and Anderson–Darling (AD), Gaussian probability density functions, Q–Q plots, and standard-normal transformations. Results revealed that Shapiro–Wilk (W = 0.75–0.92, p < 0.001) and Anderson–Darling $(A^2=1.63\times {10}^3\mathrm{to}6.31\times {10}^3,p<0.001)$ tests rejected normality for every cover, while Levene’s test showed unequal variances between EnT and the other covers $(F>5.4\times {10}^4,$$p<0.001)$ but equivalence between CC and ET (F = 0.23, p = 0.628). EnT cover exhibited the narrowest moisture envelope $(95\%\mathrm{range}=0.156\mathrm{to}0.240{\mathrm{m}}^3/{\mathrm{m}}^3;\mathrm{CV}=10.6\%)$, whereas ET and CC covers showed markedly broader distributions (CV = 38.6 % and 33.3 %, respectively). These findings demonstrated that EnT cover maintains a more stable shallow soil moisture profile under dynamic weather conditions. Full article

As a high-frequency and essential type of special electromechanical equipment, a vertical elevator has a significant societal implication for their safe operation. The load-weighing module, serving as the core component for overload warning, is susceptible to precision degradation due to the nonlinear deformation of rubber buffers installed at the base of the elevator car. This deformation arises from the coupled effects of environmental factors such as temperature, humidity, and material aging, leading to potential safety risks including missed overload alarms and false empty status detections. To address the issue of accuracy deterioration in elevator load-weighing systems, this study proposes an online self-calibration method based on multimodal information fusion. A reference detection model is first constructed to map the relationship between applied load and the corresponding relative compression of the rubber buffers. Subsequently, displacement data from a draw-wire sensor are integrated with target detection model outputs, enabling real-time extraction of dynamic rubber buffers’ deformation characteristics under empty conditions. Based on the above, a displacement-based compensation term is derived to enhance the accuracy of load estimation. This is further supported by a dynamic error compensation mechanism and an online computation framework, allowing the system to self-calibrate without manual intervention. The proposed approach eliminates the dependency on manual tuning inherent in traditional methods and forms a highly robust solution for load monitoring. Field experiments demonstrate the effectiveness of the proposed method and the stability of the prototype system. The results confirm that the synergistic integration of multimodal perception and adaptive calibration technologies effectively resolves the challenge of load-weighing precision degradation under complex operating conditions, offering a novel technical paradigm for elevator safety monitoring. Full article

Accurate and non-invasive assessment of fat and muscle composition is crucial for biomedical monitoring to track health conditions in humans and pets, as well as for classifying meats in the meat industry. This study introduces a cost-effective, multifunctional ultra-wideband microwave system operating from 2.4 to 4.4 GHz, designed for rapid and non-destructive quantification of fat thickness, muscle thickness, and fat-to-muscle ratio in diverse ex vivo samples, including pork, beef, and oil–water mixtures. The compact handheld device integrates essential RF components such as a frequency synthesizer, directional coupler, logarithmic power detector, and a dual-polarized Vivaldi antenna. Bluetooth telemetry enables seamless real-time data transmission to mobile- or PC-based platforms, with each measurement completed in a few seconds. To enhance signal quality, a two-stage denoising pipeline combining low-pass filtering and Savitzky–Golay smoothing was applied, effectively suppressing noise while preserving key spectral features. Using a random forest regression model trained on resonance frequency and signal-loss features, the system demonstrates high predictive performance even under limited sample conditions. Correlation coefficients for fat thickness, muscle thickness, and fat-to-muscle ratio consistently exceeded 0.90 across all sample types, while mean absolute errors remained below 3.5 mm. The highest prediction accuracy was achieved in homogeneous oil–water samples, whereas biologically complex tissues like pork and beef introduced greater variability, particularly in muscle-related measurements. The proposed microwave system is highlighted as a highly portable and time-efficient solution, with measurements completed within seconds. Its low cost, ability to analyze multiple tissue types using a single device, and non-invasive nature without the need for sample pre-treatment or anesthesia make it well suited for applications in agri-food quality control, point-of-care diagnostics, and broader biomedical fields. Full article

Space weather exerts a significant influence on the Earth’s atmosphere, driving a variety of physical processes, including the production of cosmogenic radionuclides. Among these, ⁷Be is a naturally occurring radionuclide formed through spallation reactions induced by cosmic-ray showers interacting with atmospheric constituents, primarily oxygen and nitrogen. Over long timescales, the atmospheric concentration of ⁷Be exhibits a direct correlation with the cosmic-ray flux reaching the Earth and an inverse correlation with solar activity, which modulates this flux via variations of the heliosphere. The large availability of ⁷Be concentration data, resulting from its use as a natural tracer employed in atmospheric transport studies and in monitoring the fallout from radiological incidents such as the Chernobyl disaster, can also be exploited to investigate the impact of space weather conditions on the terrestrial atmosphere and related geophysical processes. The present study analyzes a long-term dataset of monthly ⁷Be activity concentrations in air samples collected at ground level since 1987 at the ENEA Casaccia Research Center in Rome, Italy. In particular, the linear correlation of this time series with the galactic cosmic ray flux on Earth and solar activity have been investigated. Data from a ground-based neutron monitor and sunspot numbers have been used as proxies for galactic cosmic rays and solar activity, respectively. A centered running-mean low-pass filter was applied to the monthly ⁷Be time series to extract its low-frequency component associated with cosmic drivers, which is partially hidden by high-frequency modulations induced by atmospheric dynamics. For Solar Cycles 22, 23, 24, and partially 25, the analysis shows that a substantial portion of the relationship between stratospheric ⁷Be concentrations and cosmic drivers is captured by linear correlation. Within a statistically consistent framework, the evidence supports a correlation between ⁷Be and cosmic drivers consistent with solar-cycle variability. The ⁷Be radionuclide can therefore be regarded as a reliable atmospheric tracer of cosmic-ray variability and, indirectly, of solar modulation. Full article

Ice nucleation temperatures and associated ice growth rates are critical parameters in defining the initial ice morphology template, which governs dry layer resistance during sublimation and therefore impacts primary drying kinetics and overall process time. In this study, we developed a through-vial impedance spectroscopy (TVIS) method to determine both ice nucleation temperature and average ice growth rate, from which future estimation of average ice crystal size may be possible. Whereas previous TVIS applications were limited to solutions containing simple, uncharged solutes such as sugars, our adapted approach enables the analysis of conductive solutions (5% sucrose with 0%, 0.26%, and 0.55% NaCl), covering osmolarities below and above isotonicity. We established that the real part capacitance at low and high frequencies—either side of the dielectric relaxation of ice—provides the following: (i) a temperature-sensitive parameter for detecting the onset of ice formation, and (ii) a temperature-insensitive parameter for determining the end of the ice growth phase (unaffected by temperature changes in the frozen solution). This expanded capability demonstrates the potential of TVIS as a process analytical technology (PAT) for non-invasive, in situ monitoring of freezing dynamics in pharmaceutical freeze-drying. Full article

Karst rocky desertification on the Qinghai–Tibet Plateau poses a severe threat to the region’s fragile ecosystem. Accordingly, the rapid and accurate delineation of plateau karst landforms is essential for monitoring ecological degradation and guiding restoration strategies. However, automatic recognition of these landforms in remote sensing imagery is hindered by challenges such as blurred boundaries, fragmented targets, and poor intra-region consistency. To address these issues, we propose the Region Affinity Propagation Network (RAP-Net). This framework enhances intra-region consistency, edge sensitivity, and multi-scale context fusion through its core modules: Region Affinity Propagation (RAP), High-Frequency Multi-Scale Attention (HFMSA), and Global–Local Cross Attention (GLCA). In addition, we constructed the Plateau Karst Landform Dataset (PKLD), a high-resolution remote sensing dataset specifically tailored for this task, which provides a standardized benchmark for future studies. On the PKLD, RAP-Net surpasses eight state-of-the-art methods, achieving 3.69–10.31% higher IoU and 3.88–14.28% higher Recall, thereby demonstrating significant improvements in boundary delineation and structural completeness. Moreover, in a cross-regional generalization test on the Mount Genyen area, RAP-Net—trained solely on PKLD without fine-tuning—achieved 2.38% and 1.94% higher IoU and F1-scores, respectively, than the Swin Transformer, confirming its robustness and generalizability in complex, unseen environments. Full article

Background: Rett syndrome (RS) is a rare neurodevelopmental disorder primarily affecting females, characterized by severe neurological impairment and complex comorbidities, including epilepsy, scoliosis, and respiratory dysfunction. Respiratory complications, such as recurrent infections and sleep-disordered breathing (SDB), are increasingly recognized as significant contributors to morbidity. This study aimed to evaluate the prevalence, severity, and management of major comorbidities—including epilepsy, scoliosis, respiratory infections, and SDB—in a pediatric cohort with genetically confirmed RS. Methods: We conducted a retrospective review of medical records from 23 female patients under 18 years of age with MECP2 mutations, referred to our tertiary care center from 2021 to 2025. Data on epilepsy, scoliosis, respiratory infections, and nutritional status were collected. The presence of SDB was assessed through overnight home polygraphy (oPG) and transcutaneous carbon dioxide monitoring in selected cases. Results: Epilepsy affected 65% of patients, all with good seizure control. Scoliosis was present in 52%, with two patients requiring spinal surgery. At least one episode of lower respiratory tract infection (LRTI) was presented in 39% of our girls. LRTIs positively correlated with the number of hospitalization and antibiotic treatment. Among patients undergoing oPG, 67% presented obstructive sleep apnea, with its severity positively correlating with the frequency of lower respiratory infections. Severe nocturnal hypercapnia was documented in three patients, leading to non-invasive or invasive ventilation. Conclusions: Our findings highlight the high prevalence of sleep-related respiratory disorders and their association with respiratory infections in children with RS. Systematic respiratory assessment, including sleep studies, and early implementation of airway clearance techniques and ventilatory support are crucial to improving clinical outcomes in this vulnerable population. Full article

Sea level is an important variable for studying water cycle and coastal hazards under global warming. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as a relatively new technique for monitoring sea level variations, leveraging signals from GNSS constellations. However, dynamic height errors, primarily caused by non-stationary sea surfaces, compromise the precision of GNSS-IR sea level retrievals and necessitate robust correction. In this study, we propose a new method to correct the dynamic height error by integrating the commonly used tidal analysis method and the cubic spline fitting method. The proposed method is applied to the GNSS-IR sea level retrievals from multiple systems and multiple frequency bands at two coastal GNSS stations, MAYG and HKQT. At MAYG, the results show that our method significantly reduces the Root Mean Square Error (RMSE) of the GNSS-IR sea level retrievals by 42.1% (11.4 cm) to 15.7 cm, performing better than the single tidal analysis method (16.5 cm) and the cubic spline fitting method (21.4 cm). At HKQT, our method improves the accuracy by 21.5% (3.1 cm) to 10.3 cm, which is still better than that of the tidal analysis method (11.3 cm) and the cubic spline fitting method (12.4 cm). Compared to the tidal analysis method and the cubic spline fitting method, our method maintains high retrieval retention while enhancing precision. The effectiveness of our method is further validated in the two storm surge events caused by Typhoon Hato and Typhoon Mangkhut in Hong Kong. Full article

Objectives: Virtual reality (VR) has emerged as a promising tool for assessing anxiety-related disorders through immersive exposure and physiological monitoring. This study aimed to evaluate whether multimodal data, including heart rate variability (HRV), skin conductance response (SCR), and self-reported anxiety, collected during VR exposure could classify patients with panic disorder and agoraphobia using machine learning models. Methods: Seventy-six participants (38 patients with panic disorder and agoraphobia, 38 healthy controls) completed 295 total VR exposure sessions. Each session involved two road and two supermarket scenarios designed to induce anxiety. Inside the sessions, self-reported anxiety was measured along with physiological signals recorded by photoplethysmography and SCR sensors. HRV measures of heart rate, standard deviation of normal-to-normal intervals, and low-frequency to high-frequency ratio were extracted along with SCR peak frequency and average amplitude. These features were analyzed using Gaussian Naïve Bayes (GNB), k-Nearest Neighbors (k-NN), Logistic Ridge Regression (LRR), C-Support Vector Machine (SVC), Random Forest (RF), and Stochastic Gradient Boosting (SGB) classifiers. Results: The best model achieved an accuracy of 0.83. Most models showed specificity and precision ≥0.80, while sensitivity varied across models, with several reaching ≥0.82. Performance was stable across major hyperparameters, VR-stimulus settings, and medication status. The patients reported higher subjective anxiety but exhibited blunted physiological responses, particularly in SCR amplitude. Self-reported anxiety demonstrated higher feature importance scores compared to other physiological properties. Conclusion: VR exposure with self-reported anxiety and physiological measures may serve as a feasible diagnostic aid for panic disorder and agoraphobia. Further refinement is needed to improve sensitivity and clinical applicability. Full article

Fourier transform-based frequency sweeping interferometry (FT-FSI) is an interferometric technique that enables absolute distance measurement by detecting the beat frequencies from the interference of reflected signals. This method allows robust, simultaneous distance measurements to multiple targets and is largely immune to variations in the reflected optical signal intensity. As a result, FT-FSI maintains accuracy even when measuring reflectors with low reflectance. FT-FSI has recently been integrated into the full remote alignment system (FRAS) developed for the High-Luminosity Large Hadron Collider (HL-LHC) project at CERN. Designed to operate in harsh environments with electromagnetic interference, ionizing radiation and cryogenic temperatures, FRAS employs FT-FSI for the precise monitoring of the alignment of accelerator components. The system includes specialized interferometers and a range of sensors, including inclinometers, distance sensors, and leveling sensors. This paper presents a comprehensive review of the challenges associated with remote measurement and monitoring systems in harsh environments such as those of particle accelerators. It details the development and validation of the FT-FSI-based measurement system, emphasizing its critical role in enabling micrometric alignment accuracy. The developments and results presented in this work can be readily translated to other demanding metrology applications in harsh environments. Full article

The implementation of autonomous haulage trucks in open-pit mines represents a progressive advancement in the mining industry, but it poses potential safety risks that require thorough assessment. This study proposes an integrated model that combines discrete-event simulation (DES) with a risk matrix to assess collisions associated with three different operational scenarios, including non-autonomous, hybrid, and fully autonomous truck operations. To achieve these objectives, a comprehensive dataset was collected and analyzed using statistical models and natural language processing (NLP) techniques. Multiple scenarios were then developed and simulated to compare the risks of collision and evaluate the impact of eliminating human intervention in hauling operations. A risk matrix was designed to assess the collision likelihood and risk severity of collisions in each scenario, emphasizing the impact on both human safety and project operations. The results revealed an inverse relationship between the number of autonomous trucks and the frequency of collisions, underscoring the potential safety advantages of fully autonomous operations. The collision probabilities show an improvement of approximately 91.7% and 90.7% in the third scenario compared to the first and second scenarios, respectively. Furthermore, high-risk areas were identified at intersections with high traffic. These findings offer valuable insights into enhancing safety protocols and integrating advanced monitoring technologies in open-pit mining operations, particularly those utilizing autonomous haulage truck fleets. Full article

High sugar intake from commercial beverages is a public health concern, motivating rapid, user-friendly tools for sugar quantification. We present a compact planar microwave metamaterial sensor that estimates sugar concentration by monitoring resonant frequency shifts induced by dielectric loading. Tests with aqueous glucose solutions demonstrated a wide dynamic range (0 to 12,000 mg/dL), perfect linearity (R² = 1), and high repeatability. Validation on two commercial beverages showed sensor-predicted sugar contents consistent with their nutrition labels. The method is reagent-free, tolerates opaque samples, and operates under ambient conditions, making it suitable for on-site consumer use as well as regulatory inspection and quality-control applications. Full article

Large underwater vehicles, designed for multiple cruising speeds, are required to operate under diverse conditions such as full speed, surfacing, diving, and hovering. This demands that the axial flow pumps used in these applications have a broad operational range, typically functioning efficiently from 0.1 times rated flow to 1.5 times rated flow. In the process of adjusting operational conditions, axial flow pumps may experience rotating stall phenomena. Importantly, the presence of tip leakage vortices within the pump markedly influences the internal flow dynamics. To assess the impact of tip leakage vortices on the internal flow field under varied operational states, this study delves into the inherent link between tip leakage vortices and pressure pulsation across three specific scenarios: optimal, critical stall, and deep stall conditions. Analyzing from the perspective of the vorticity transport equation, it is found that the compression–expansion term dictates the core strength of tip leakage vortices, while the viscous dissipation factor determines the frequency of pressure pulsation. With an increase in the core strength of tip leakage vortices, a gradual rise in pressure pulsation is observed; in optimal scenarios, the core of tip leakage vortices progressively shifts toward the interior of the clearance, keeping the pulsation amplitude at each monitoring point within the blade tip clearance at integer multiples of the blade passing frequency. During critical stall and deep stall scenarios, the viscous dissipation effect of tip leakage vortices contributes to the emergence of high-frequency harmonic components within pressure pulsation. Full article

Accurate extraction of water bodies in remote sensing images is crucial for natural disaster prediction, aquatic ecosystem monitoring, and resource management. However, most existing deep-learning-based methods primarily operate in the raw pixel space of images and fail to leverage the frequency characteristics of remote sensing images, resulting in an inability to fully exploit the representational power of deep models when predicting mask images. This paper proposes a Frequency-Band Decoupling Detail Enhancement Network (FDEN) to achieve high-precision water body extraction. The FDEN begins with an initial decoupling and enhancement stage for frequency information. Based on this multi-frequency representation, we further propose a Multi-Band Detail-Aware Module (MDAM), designed to adaptively enhance salient structural cues for water bodies across frequency bands while effectively suppressing irrelevant or noisy components. Extensive experiments demonstrate that the FDEN model outperforms state-of-the-art methods in terms of its segmentation accuracy and robustness. Full article