Search Results (1,436)

We analyse pebble RFID tracing observations to investigate sediment transport dynamics in gravel-bed rivers using statistical modelling. This study examines a dataset of nearly 3500 tracer displacement measurements collected during 27 sediment-mobilizing events in a pre-Alpine reach in Italy. Our analysis follows three main steps, addressing tracer mobility patterns, event-scale transport dynamics, and reach-scale bedload inference. First, using Markov Chain analysis of state transitions on typical and high-magnitude transport events, we demonstrate that pebbles tend to maintain their mobility state between events, characterizing the between-event intermittency of bedload transport. A subsequent analysis of flow characteristics reveals that consecutive floods of similar magnitude exhibit increasing movement probability while maintaining similar virtual velocities. Finally, we train Gradient Boosting regression models to estimate distributions of pebble displacements and virtual velocities (defined, following common usage, as the ratio between the distance a tracer travels during a mobilising event and the duration of that event). Together with Monte Carlo propagation, these models are used to derive reach-scale volume estimates. The models identify flow rate and event duration as primary controls, while grain size has minimal influence within the sampled range of tracer dimensions. To strengthen our approach, we implement an extensive multi-stage validation process aimed at both single-tracer predictions and overall basin-scale movement estimates. The results indicate that high-magnitude transport events (12% of observations) contribute similar bedload volumes as typical events (88% of observations), highlighting the significant role of extreme events in total sediment transport. Model predictions yield bedload volume estimates that align well with independent measurements from a downstream sediment retention basin. Full article

Reconfigurable intelligent surfaces (RISs) have recently attracted attention as a potential solution for improving the reliability of optical wireless communication links, especially when direct transmission (DT) becomes severely degraded due to dynamic channel conditions. In this study, an RIS-assisted architecture based on a modulating retroreflector is proposed for underwater optical wireless communications (MRR-UOWC). In the considered system, both the DT path and the RIS-assisted path transmit the same information simultaneously at the same data rate. The propagation channels are modeled by taking into account propagation loss, Gamma–Gamma turbulence, and pointing error effects. At the receiver, the signals arriving through the direct path and the RIS-reflected path are coherently combined. To evaluate the effectiveness of this configuration, two diversity combining techniques, namely selection combining (SC) and maximum ratio combining (MRC), are investigated. Closed-form analytical expressions for the outage probability (${\mathrm{P}}_{\mathrm{out}}$), average bit-error rate (BER), and ergodic capacity ($\overline{C}$) are derived using the probability density function (PDF), cumulative distribution function (CDF), and moment-generating function (MGF) of the end-to-end signal-to-noise ratio (SNR). The analysis indicates that jointly exploiting the DT and RIS-assisted links can provide noticeable performance gains by leveraging the complementary characteristics of the two propagation paths. Full article

Skewness (S) and kurtosis (K) are statistical measures that provide insights into the characteristics of turbulence. This paper investigates the effects of adverse pressure gradients (APG) on S and K for mean and fluctuating velocities in the turbulent boundary layer (TBL), using the probability distribution function (PDF) and cumulative distribution function (CDF). The velocity distributions in the TBL are obtained experimentally. The experiments are conducted at Re ~ 1.12 × 10⁵. According to the Clauser criterion, the APG parameter is β = 0.62. Two test sections are examined: a straight duct (zero pressure gradient) and a straight diffuser with a divergence angle of 6° and a cross-sectional area ratio of 1:4. Measurements are performed at five streamwise stations (x/c = 1, 1.5, 2, 3, and 4, where c = 100 mm). The results show that the APG does not influence the maximum or minimum values of the PDFs for mean and fluctuating velocities. Compared to the third and fourth moments, variations in the first and second moments are minimal. It is found that S values for the straight duct are lower than those for the straight diffuser. The largest difference is observed in the fourth moment of the PDF, i.e., K. Additionally, four PDF curve-fitting equations are presented for the mean velocity and velocity fluctuations in the TBL for both the straight duct and the straight diffuser. Differential entropy analysis indicates that the decrease in entropy resulting from wall shear and the turbulent boundary layer in the straight channel is more pronounced than the reduction in mean velocity entropy caused by the APG in the diffuser channel. Full article

To address the challenges of severe surface undulation in mountainous mining areas, significant InSAR geometric distortion, and the inability to directly calculate three-dimensional (3D) displacement from single-track Line-of-Sight (LOS) data, this paper proposes a 3D deformation reconstruction method that integrates Distributed Scatterer Interferometric Synthetic Aperture Radar (DS-InSAR) with an improved Probability Integral Model (PIM) considering topographic sliding effects. The traditional Probability Integral Method (PIM) ignores the additional sliding caused by topographic slope, leading to significant deviations when applied in mountainous areas. This study introduces a nonlinear sliding influence function and constructs a topographic correction model incorporating sliding intensity, position offset, and morphological attenuation parameters to quantitatively describe surface movement patterns under the coupling effect of mining and topography. Based on this, a model parameter-driven single-track InSAR observation equation is established, and the Adaptive Genetic Algorithm (AGA) is employed to invert the complete set of model parameters using high-density LOS deformation obtained from DS-InSAR as constraints, thereby resolving the full-basin 3D displacement field. Experimental results from a typical mountainous coal mine in the Taihang Mountain area of China demonstrate that this method effectively overcomes the ill-posedness of 3D displacement inversion from single-track InSAR data. The maximum vertical subsidence is 1050 mm, and the maximum horizontal displacement was 370 mm, consistent with leveling measurements (vertical RMSE: 75.1 mm; horizontal RMSE: 27.2 mm). Compared with traditional PIM methods without topographic correction, the proposed model reduces 3D displacement RMSE by approximately 35%, significantly improving calculation accuracy in mountainous areas with topographic undulation. Validation against leveling measurement points distributed along strike and dip directions confirms the reliability of reconstructed 3D displacement fields. This method not only restores the physical characteristics of topographic sliding but also provides a low-cost, high-precision solution for mining damage monitoring in complex terrain. Full article

This study investigates the selection of suitable statistical models for speckle reflections from the underlying surface under low-altitude sensing conditions. A parametric approach to modeling speckle images of terrain fragments typical of synthetic aperture radar (SAR) is presented. We use a phenomenological model of speckle formation during radio wave interference, taking into account the spectrum of fluctuations, the roughness of the reflecting surface, the angle of incidence, and other radar parameters. We investigate the influence of the properties of the reflecting surface and the probing parameters on the nature of speckle images. The values of the sample cumulative coefficients for various multiplicative models of the reflection distribution are obtained. The properties and characteristics of various classes of distributions for describing the intensity and amplitude of speckles are considered: the gamma distribution, the K-distribution, and the classes of non-Gaussian probability densities G and G⁰. A generalized Gaussian (GG) distribution is used to model the complex components of reflected signals. We compare the obtained model characteristics with the sample characteristics of real terrain fragments in synthesized speckle images obtained by the on-board radar system. Based on a comparative analysis of cumulants, this paper examines methods for modeling amplitude and intensity speckle images using several classes of backscatter probability densities. Limitations in specific applications have been identified, and a modeling method using quadrature components has been developed in cases of extremely rough reflections. Full article

Background and Objectives: Chronic headache after craniotomy is common and may include neuropathic subtypes (scar neuroma pain, occipital neuralgia). However, no large series has quantified these phenotypes. We conducted a single-center retrospective review (n = 5624 adult craniotomy patients) to estimate the prevalence of post-craniotomy neuropathic pain and to describe its characteristics. Materials and Methods: Institutional records were screened to identify craniotomy patients referred to a multidisciplinary pain clinic (n = 272). Eligible cases were reviewed in tiers: (1) exclusion of primary headache and noncranial pain; (2) identification of “probable neuropathic cranial pain” based on documented neuropathic features (lancinating/scalp pain, trigger tenderness, dermatomal distribution); and (3) subgroup categorization into occipital neuralgia-like, supraorbital/supratrochlear neuralgia-like, and scar-site neuropathic pain phenotypes. The supraorbital/supratrochlear subgroup was defined by frontal or frontotemporal postoperative pain in the supraorbital region, local tenderness or Tinel-like hypersensitivity over the supraorbital/supratrochlear course, and/or response to supraorbital–supratrochlear nerve block. Data extracted included demographics, timing (surgery to pain referral), pain characteristics, and treatments (blocks, radiofrequency, medications). Results: Of 5624 craniotomy patients, 272 (4.8%) had pain clinic encounters. The initial review identified 124 cases with chronic post-craniotomy headache requiring follow-up; after detailed chart classification, probable neuropathic cranial pain was present in 111 cases (2% of the cohort). Among the 111 probable neuropathic cranial pain cases, the dominant regional phenotype was occipital neuralgia-like pain. In addition, eight patients (7.2%) demonstrated a supraorbital/supratrochlear neuralgia-like phenotype, predominantly after frontal or frontotemporal craniotomies. Scar-site neuropathic pain frequently coexisted with both regional phenotypes, supporting a partially overlapping spectrum rather than mutually exclusive categories. The median time from surgery to pain referral was several months (≈12–18 months). Management commonly included occipital nerve blocks (±steroid); some patients received pulsed radiofrequency ablation of the occipital nerves, and most were trialed on neuropathic analgesics (gabapentinoids, SNRIs, etc., according to neuropathic pain guidelines). Conclusions: A clinically meaningful subset of post-craniotomy patients develops chronic neuropathic cranial pain, most commonly with occipital, supraorbital/supratrochlear, or scar-related features. Because most postoperative headaches are managed through neurosurgical follow-up and improve without pain clinic referral, the present cohort likely underestimates the true burden of neuropathic post-craniotomy pain while enriching for its most refractory neuralgic presentations. This is nevertheless the subgroup that must be recognized, discussed with patients, studied prospectively, and targeted in future prevention strategies. Full article

Armenia is located within the central segment of the Arabia–Eurasia continental collision zone and is exposed to significant seismic hazard. This study presents an updated probabilistic seismic hazard assessment (PSHA) for Armenia based on an integrated seismotectonic framework incorporating active fault data, paleoseismological evidence, and historical and instrumental seismicity. A hybrid seismic source model was developed by combining fault-based characteristic earthquake sources with distributed background seismicity. Hazard calculations were performed using the OpenQuake engine within a logic-tree framework to account for epistemic uncertainties in earthquake occurrence and ground-motion prediction. Ground motion was estimated using a weighted set of ground motion prediction equations (GMPEs). Peak ground acceleration (PGA) hazard maps were computed for several return periods, with emphasis on the 475-year return period (10% probability of exceedance in 50 years). The results indicate PGA values across Armenia ranging from approximately 0.2 g to 0.5 g, with the highest hazard levels in northwestern Armenia along the Pambak–Sevan–Syunik Fault System. Hazard deaggregation shows that seismic hazard in major Armenian cities is primarily controlled by shallow earthquakes with magnitudes Mw 6.8–7.4 occurring within ~30 km of urban centers. The results provide a scientific basis for seismic hazard assessment, zonation, and earthquake risk mitigation in Armenia. Full article

A coordinated optimization method for emergency repair scheduling and system operation restoration is proposed to address large-scale transmission line outages caused by extreme weather events such as ice and snow disasters. First, an active outage scenario for transmission lines is constructed based on the Jones icing thickness model and an exponential failure probability model, while incorporating the spatial distribution characteristics of ice disasters. Subsequently, a bi-level optimization model with repair resource constraints is developed. The upper-level model determines the transmission line repair schedule with the objective of minimizing the total repair time while taking system power supply restoration efficiency into account. Based on the completion times of line repairs, the lower-level model optimizes the system restoration process by considering power flow constraints, generator start-up processes, and load restoration characteristics. To address the challenges posed by discrete operational states and strongly coupled bi-level constraints that are difficult to solve using conventional approaches, a logic-based Integer L-shaped coordinated solution method is proposed. Finally, the effectiveness of the proposed method is validated through case studies based on the IEEE New England 10-unit 39-bus system. The results demonstrate that the proposed method can significantly improve system load restoration levels while maintaining high repair efficiency. Full article

To address the problems of unreasonable task allocation and low target search efficiency in the collaborative search of multiple unmanned undersea vehicles (UUVs) in complex marine environments, this paper proposes a search-information-driven collaborative task planning method for multi-UUV systems, and constructs a systematic and integrated multi-UUV collaborative task planning framework. Considering the spatial characteristics of the complex underwater environment and sonar detection rules, an underwater task environment grid model and an active sonar instantaneous detection model are constructed as the environmental and detection foundation of the framework. Within the framework, the Gaussian Mixture Model (GMM) is adopted to realize dynamic division of task regions, and reasonable resource allocation among multiple UUVs is achieved by defining scientific area allocation indicators. A search information map consisting of target probability distribution and environmental uncertainty is established, and a receding horizon planning framework is introduced to balance short-term detection effectiveness and long-term search value. Furthermore, a motion-coded Grey Wolf Optimization (GWO) algorithm is proposed to generate continuous UUV paths, which avoids path discontinuity caused by discrete grids and ensures the convergence efficiency of the algorithm. Simulation results verify that compared with traditional methods, the proposed method improves the total probability benefit by 19.87% and the number of discovered targets by 18.29%, demonstrating better search performance and environmental adaptability. Full article

Background/Objectives: Operational DNA databases traditionally rely on static locus-count thresholds to determine search eligibility and report matches. While computationally straightforward, these rigid criteria routinely discard high-value investigative leads from degraded forensic profiles while simultaneously permitting adventitious matches when common alleles are involved. To overcome the limitations of static rules, this study introduces an automated framework for dynamic likelihood ratio (LR) thresholding. Methods: Utilizing a Fast Fourier Transform (FFT) algorithm, the system calculates the Probability Mass Function (PMF) for any specific combination of shared loci in real-time, natively incorporating the Balding–Nichols model to account for population substructure. Instead of applying an arbitrary locus count or fixed LR cutoff, the framework defines admissibility based on a user-defined maximum upper bound of acceptable false positives at a specified confidence (probability) level (e.g., 95%). Results: This empowers database custodians to precisely predict and adapt their search criteria to match an acceptable administrative workload, dynamically adjusting the required LR threshold to the exact size of the searched database. This approach was validated through massive-scale empirical simulations across five reference population groups. Receiver Operating Characteristic (ROC) and Poisson distribution analyses reveal that static thresholds inevitably collapse under the multiplicity effect of large-scale comparisons; for instance, a static locus rule that maintains safety within a small DNA database yields an unmanageable false positive risk when scaled to larger DNA databases or international networks like the Prüm DNA Exchange. Conclusions: By explicitly coupling the decision threshold to the database size and the genetic rarity of the evidence, this dynamic framework provides a mathematically rigorous and scalable solution. Most notably, it identifies rare, low-locus matches that static rules typically discard, offering a method to maintain a predefined expected false positive rate. Full article

Background: To explore the correlation between spatial dose distribution and post-implant urinary toxicity, aiming to assist decision making in low-dose-rate (LDR) treatment planning, thereby improving patient outcomes. Methods: Eighty-five prostate LDR patients with >12-month follow-up were included. Patient-reported urinary toxicity was collected prospectively using the International Prostate Symptom Score (IPSS) questionnaire, from before implant (baseline) to post-implant follow-up. Patients were then grouped into those whose symptom scores returned to ≤2 points above baseline by 12 months (no long-term toxicity) vs. those who did not (long-term toxicity). A total of 106 features were extracted for each patient, including principal components of dose-volume histograms (DVHs) from multiple prostate subzones, the whole prostate and urethra, along with baseline IPSS, implantation characteristics, and additional DVH indicators for the prostate and the urethra. A machine learning (ML) model incorporating backward feature selection algorithm was developed to predict long-term toxicity status, using a shuffle-and-split validation strategy for model evaluation during feature selection. A univariate statistical analysis was conducted on the model’s selected features. Results: Out of 85 patients, 41 (48%) had long-term urinary toxicity. Seven features were selected during model training, including baseline IPSS and six dosimetric features from several prostate subzones primarily located in the posterior prostate. The model achieved a high mean area under the receiver operating characteristic curve (AUC) of 0.81, with a balanced sensitivity and specificity of 0.78 by adjusting the probability threshold. In univariate analysis, only baseline IPSS and one selected dose feature were significantly correlated with long-term toxicity with AUC < 0.71. Conclusions: The proposed ML model, integrating baseline IPSS and spatial dosimetric features, effectively predicts long-term urinary toxicity after prostate LDR. This approach offers a practical method for risk stratification, allowing clinicians to identify patients at elevated risk and prioritize them for targeted preventative measures and closer follow-up. Full article

This paper presents a lightweight method for assessing the resilience of power distribution systems that integrates climate and infrastructure data through impact chains and a probabilistic approach, while minimizing data integration and implementation complexity. The method is demonstrated for heatwave hazards by combining network characteristics, failure probabilities of heat-sensitive components (e.g., medium-voltage cable joints), and location-specific climate projections to generate spatial maps of failure risk and network resilience. These maps support the identification and prioritization of critical components requiring intervention. Critical segments are then further analyzed using probabilistic resilience metrics to compare alternative adaptation strategies. Overall, this work contributes a practically applicable, low-complexity methodology for identifying the weakest portions of distribution networks, along with a more in-depth probabilistic approach for assessing their climate resilience. The comprehensive framework is illustrated through a case study of a representative portion of the Italian electricity distribution system in the urban area of Rome. It is implemented in a test environment that reflects realistic distribution network data structures and automatically integrates climate data from established online repositories. Full article

Addressing the lack of high-fidelity test cycles for fuel cell light-duty trucks, this paper proposes an automated driving cycle construction method that integrates unsupervised clustering and reinforcement learning. Firstly, based on large-sample real-world driving data, four libraries of typical driving pattern segments are extracted through dimensionality reduction via Principal Component Analysis (PCA) and K-means clustering. Subsequently, the cycle construction process is formulated as a sequential decision-making problem, and a framework based on the Proximal Policy Optimization (PPO) algorithm, incorporating an action masking mechanism, is designed. This framework innovatively injects macro-level time budget allocation as a hard constraint into the agent’s policy space via action masking, while utilizing micro-level Markov transition probabilities as a soft guide. This dual approach drives the agent to learn an optimal segment concatenation strategy, thereby simultaneously ensuring both the macro-level statistical representativeness and the micro-level driving logic coherence of the synthesized cycle. Validation results demonstrate that the cycle constructed by the proposed method achieves an average relative error of only 7.53% in key characteristic parameters, and its joint speed-acceleration distribution exhibits a similarity as high as 0.9886 with the original data, significantly outperforming traditional methods such as the clustering method, the Markov chain method, and standard driving cycles. This study provides an effective tool for generating high-fidelity driving cycles and testing energy management strategies for fuel cell commercial vehicles. Full article

Harmonic reducers exhibit non-stationary and phase-dependent degradation behavior during long-term service, challenging the ability of classical stochastic degradation models to accurately assess reliability. To address phase-dependent differences in degradation behavior, this paper proposes a reliability assessment model based on a two-phase hybrid stochastic degradation process. In the proposed framework, the Wiener process is employed to characterize early-phase gradual degradation dominated by stochastic fluctuations, while the Inverse Gaussian process is used to describe later-phase monotonically accelerated degradation driven by cumulative damage. The framework allows for sample-level variability in transition times to more realistically capture individual degradation behavior. The Schwarz Information Criterion is also adopted to detect change points. Maximum likelihood estimation is performed for model parameter inference, and analytical expressions for the reliability function, cumulative distribution function, and probability density function are derived. Numerical results indicate that a change point exists for each tested product and that the proposed model achieves the best goodness of fit among the considered candidates, demonstrating its superiority in capturing phase-dependent characteristics of harmonic reducer degradation. In terms of reliability assessment bias, the proposed model (0.06%) significantly outperforms the Wiener degradation model (32%) and the IG degradation model (9.9%). These results further confirm that, under an identical failure threshold, the proposed approach yields more accurate and realistic reliability assessment outcomes. Full article

We investigate the potential to adopt waveguide-integrated GeSn single-photon avalanche detectors (SPADs) over a wideband wavelength range from very-near-infrared to telecommunication wavelengths based on an Si-rich SiN waveguide platform via an end-fire coupling approach. Electrical properties of GeSn SPAD heterodiodes are investigated, including their I–V characteristics, electric field distribution, charge sheet doping variation, avalanche triggering probabilities, dark count rate, and afterpulsing probability, to identify the appropriate critical parameters and to reliably benchmark against previous related simulation works. Notably, to enable a waveguide-integrated GeSn SPAD for the entire wavelength of interest, this paper finds that, among several potentially important parameters, the coupling efficiency between the input waveguide and the GeSn SPAD plays a very critical role in determining the single-photon detection efficiency (SPDE) performance, and a suitable GeSn absorber thickness should be carefully considered according to the chosen Sn content. Interestingly, although the coupling efficiency and SPDE are significantly varied between the longer wavelengths of 1310 and 1550 nm and the shorter wavelengths of 700 and 900 nm, an acceptable SPDE performance can be maintained for all wavelengths of interest for both close end-fire coupling (no gap between the amorphous Si-rich SiN waveguide and the GeSn SPAD) and a 50 nm gap assumption for simpler fabrication. Full article