Search Results (278)

The number of Fritillaria species native to the Alps has long been debated, and observational biases due to the short flowering periods and the scattered distributions of endemic Fritillaria populations along the mountain range have probably made the task of botanists more complicated. Moreover, previous phylogenetic studies in Fritillaria have considered alpine taxa only marginally. To test species boundaries within the F. tubaeformis species complex and to study their phylogenetic relationships, intra- and inter-specific genetic variability of sixteen samples belonging to four Fritillaria species was carried out in different localities of the Maritime and Ligurian Alps, with extensions to the rest of the Alpine arc. The combined use of five plastid DNA markers (matK, ndhF, rpl16, rpoC1, and petA-psbJ) and nrITS showed that F. tubaeformis and F. burnatii are phylogenetically independent taxa, fully confirming morphological and morphometric divergences and, that F. burnatii is not related phylogenetically to the central European F. meleagris. Our phylogenetic study also supports the separation of F. tubaeformis from F. moggridgei, pointing to environment/ecological constraints or reproductive barriers as possible causes of their distinct evolutionary status. Our analysis also showed that the mountain endemic F. involucrata is not closely related to F. tubaeformis, contrasting with previous studies. The phylogenetic analysis of the nrITS region supports a close relationship between F. burnatii and F. moggridgei, but with low statistical support. Full article

Impact models used in water, ecology, and agriculture require accurate climatic data to simulate observed impacts. Some of these models emphasize the distribution of precipitation within a month or season rather than the overall amount. To meet this requirement, a study applied three bias correction techniques—scaled distribution mapping (SDM), quantile distribution mapping (QDM), and QDM with a separate treatment for precipitation below and above the 95th percentile threshold (QDM95)—to daily precipitation data from eleven Coupled Model Intercomparison Project Phase 6 (CMIP6) models, using the Climate Hazards Group Infrared Precipitation with Station version 2 (CHIRPS) as a reference. This study evaluated the performance of all bias-corrected CMIP6 models over Southern Africa from 1982 to 2014 in replicating the spatial and temporal patterns of precipitation across the region against three observational datasets, CHIRPS, the Climatic Research Unit (CRU), and the Global Precipitation Climatology Centre (GPCC), using standard statistical metrics. The results indicate that all bias-corrected precipitation generally performs better than native model precipitation in replicating the observed December–February (DJF) mean and seasonal cycle. The probability density function (PDF) of the bias-corrected regional precipitation indicates that bias correction enhances model performance, particularly for precipitation in the range of 3–35 mm/day. However, both corrected and uncorrected models underestimate higher extremes. The pattern correlations of the bias-corrected precipitation with CHIRPS, the GPCC, and the CRU, as compared to the correlations of native precipitation with the three datasets, have improved from 0.76–0.89 to 0.97–0.99, 0.73–0.87 to 0.94–0.97, and 0.74–0.89 to 0.97–0.99, respectively. Additionally, the Taylor skill scores of the models for replicating the CHIRPS, GPCC, and CRU precipitation spatial patterns over Southern Africa have improved from 0.57–0.80 to 0.79–0.95, 0.55–0.76 to 0.80–0.91, and 0.54–0.75 to 0.81–0.91, respectively. Overall, among the three bias correction techniques, QDM consistently demonstrated better performance than both QDM95 and SDM across various metrics. The implementation of distribution-based bias correction resulted in a significant reduction in bias and improved the spatial consistency between models and observations over the region. Full article

In connection with the International Year of Quantum Science and Technology, a review of joint works of the Lebedev Institute and the Mexican research group at UNAM is presented, especially related to solving the old problem of the state description, not only by wave functions but also by conventional probability distributions analogous to quasiprobability distributions, like the Wigner function. Also, explicit expressions of tomographic representations describing the quantum states of particles moving in known potential wells are obtained and briefly discussed. In particular, we present the examples of the tomographic distributions for the free evolution, finite and infinite potential wells, and the Morse potential. Additional to this, an extension of the Peres–Horodecki separability criteria for momentum probability distributions is presented in the case of bipartite, asymmetrical, real states. Full article

Agriculture plays a dual role in shaping biodiversity, providing secondary habitats while posing significant threats to ecological systems through habitat fragmentation and land-use intensification. This study aims to assess the relationship between bird species composition and land-use types in Apulia, Italy. Specifically, we investigate how different agricultural and semi-natural landscapes influence avian biodiversity and which agricultural models can have a positive impact on biodiversity. Biodiversity indices were calculated for each bird community observed. The abundance curves showed a geometric series pattern for the AGR communities, indicative of ecosystems at an early stage of ecological succession, and a lognormal distribution for the MIX and NAT communities, typical of mature communities with a more even distribution of species. Analysis of variance showed significant differences in richness and diversity between AGR and NAT sites, but not between NAT and MIX, which had the highest values. Logistic regression estimated the probability of sites belonging to the three ecosystem categories as a function of biodiversity, confirming a strong similarity between NAT and MIX. Finally, linear discriminant analysis confirmed a clear separation from AGR areas, as evidenced by the canonical components. The results highlight the importance of integrating high-diversity landscape elements and appropriate agricultural practices to mitigate biodiversity loss. Even a small increase in the naturalness of agricultural land would be sufficient to convert it from the AGR to the MIX ecosystem category, with significant biodiversity benefits. Full article

Highly resolved and accurate daily precipitation data are required for impact models to perform adequately and correctly measure the impacts of high-risk events. In order to produce such data, bias correction is often needed. Most of those statistical methods correct the probability distributions of daily precipitation by modeling them with either empirical or parametric distributions. A recent semi-parametric model based on a penalized Berk–Jones (BJ) statistical test, which allows for automatic and personalized splicing of parametric and non-parametric distributions, has been developed. This method, called the Stitch-BJ model, was found to be able to model daily precipitation correctly and showed interesting potential in a bias correction setting. In the present study, we will consolidate these results by taking into account the seasonal properties of daily precipitation in an out-of-sample context and by considering dry days probabilities in our methodology. We evaluate the performance of the Stitch-BJ method in this seasonal bias correction setting against more classical models such as the Gamma, Exponentiated Weibull (ExpW), Extended Generalized Pareto (EGP) or empirical distributions. Results show that a seasonal separation of data is necessary in order to account for intra-annual non-stationarity. Moreover, the Stitch-BJ distribution was able to consistently perform as well as or better than all the other considered models over the validation set, including the empirical distribution, which is often used due to its robustness. Finally, while methods for correcting dry day probabilities can be easily applied, their relevance can be discussed as temporal and spatial correlations are often neglected. Full article

The phase-sensitive optical time domain reflectometry (Φ-OTDR) technique offers a method for distributed acoustic sensing (DAS) systems to detect external acoustic fluctuations and mechanical vibrations. By accurately identifying vibration events, DAS systems provide a non-invasive solution for security monitoring. However, limitations in temporal signal analysis and the lack of spatial features significantly impact classification accuracy in event recognition. To address these challenges, this paper proposes a network model for vibration-event recognition that integrates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), and attention mechanisms, referred to as CNN-BiGRU-Attention (CBA). First, the CBA model processes spatiotemporal matrices converted from raw signals, extracting low-level features through convolution and pooling. Subsequently, features are further extracted and separated along both the temporal and spatial dimensions. In the spatial-dimension branch, horizontal convolution and pooling generate enhanced spatial feature maps. In the temporal-dimension branch, vertical convolution and pooling are followed by BiGRU processing to capture dynamic changes in vibration events from both past and future contexts. Additionally, the attention mechanism focuses on extracted features in both dimensions. The features from the two dimensions are then fused using two cross-attention mechanisms. Finally, classification probabilities are output through a fully connected layer and a softmax activation function. In the experimental simulation section, the model is validated using real-world data. A comparison with four other typical models demonstrates that the proposed CBA model offers significant advantages in both recognition accuracy and robustness. Full article

Numerical modeling was conducted to study the capture of particles diffusing in porous media with traps. The pores are cylindrical in shape, and the traps are randomly distributed along the cylindrical surfaces of the pores. The dynamics of particle capture by the traps, as well as the filling of the traps, were investigated. In general, the decrease in the number of particles follows an exponential trend, with a characteristic time determined by the trap concentration. However, at longer times, extended plateaus emerge in the particle distribution function. Additionally, the dynamics of the interface boundary corresponding to the median trap filling (M = 0.5) were examined. This interface separates regions where traps are filled with a probability greater than 0.5 from regions where traps are filled with a probability less than 0.5. The motion of the interface over time was found to follow a logarithmic dependence. The influence of the radius of the pore on the capture on traps, which are placed on the internal surface of the cylinders, was investigated. The different dependencies of the extinction time on the number of traps were found at different radii of pores the first time. Full article

This paper presents an ensemble of observed safety margins for aircraft deviations due to convective weather in European airspace. Leveraging historical high-resolution traffic and weather radar data from the FABEC and UK-Ireland FAB regions, meaningful lateral margins are determined based on composite reflectivity and echo top data. These margins enable the estimation of probability distribution for safety distances, supporting both deviation discrimination and lateral separation assessment. Cross-validated results compared against standard binary classifiers and deterministic baseline models indicate that the model effectively distinguishes deviations from non-deviations and accurately estimates lateral margins. This framework enhances understanding of pilot decision-making, contributing to more informed air traffic management and aviation safety strategies. Full article

Urban flooding is typically caused by multiple factors, with extreme rainfall and rising water levels in receiving bodies both contributing to increased flood risks. This study focuses on assessing urban flood risks in Jinhua City, Zhejiang Province, China, considering the combined effects of extreme rainfall and high river water levels. Using historical data from Jinhua station (2005–2022), the study constructed a joint probability distribution of rainfall and water levels via a copula function. The findings show that the risk probability of combined rainfall and high water levels is significantly higher than considering each factor separately, indicating that ignoring their interaction could greatly underestimate flood risks. Scenario simulations using the Infoworks ICM model demonstrate that flood areas range from 0.67% to 5.39% under the baseline scenario but increase to 8.98–12.80% when combined with a 50a return period water level. High river water levels play a critical role in increasing both the extent and depth of flooding, especially when low rainfall coincides with high water levels. These findings highlight the importance of considering compound disaster-causing factors in flood risk assessment and can serve as a reference for urban drainage and flood control planning and risk management. Full article

The lifetime performance index is widely used in the manufacturing industry to assess the capability and effectiveness of production processes. A new overall lifetime performance index is proposed when multiple parts of products are produced in multiple dependent production lines. Each individual lifetime performance index for a single production line is connected to the overall lifetime performance index for multiple independent or dependent production lines. The overall lifetime performance index increases with the overall process yield. We analyze the maximum likelihood estimators for the individual lifetime performance indices using progressively type I interval-censored samples while the lifetime of the ith part of products follows a Gompertz distribution for either independent or dependent cases. To determine whether the overall lifetime performance index meets the desired target value, the maximum likelihood estimator for the individual index is utilized separately to conduct the testing procedures about the overall lifetime performance index for either independent or dependent cases. Power analysis of the multiple testing procedure is illustrated with figures, and key findings are summarized. A simulation study is conducted for the test powers. Lastly, a practical example involving products with two parts is presented to demonstrate the application of the proposed testing algorithm. Given the asymmetry of the lifetime distribution, this research aligns with the study of asymmetric probability distributions and their diverse applications across various fields. Full article

Drifter observations can provide high-resolution surface velocity data (Lagrangian data), commonly used to reconstruct Eulerian velocity fields. Gaussian Process Regression (GPR), a machine learning method based on Gaussian probability distributions, has been widely applied for velocity field interpolation due to its ability to provide interpolation error estimates and handle separations between particles. However, its evaluation has primarily relied on cross-validation, which approximates temporal and spatial correlations but does not fully capture their dependencies, limiting the comprehensiveness of performance assessment. Moreover, GPR has not been rigorously tested on model datasets with reference velocity fields to evaluate its overall accuracy and the reliability of the error estimate. This study addresses these gaps by (1) assessing the accuracy of GPR-reconstructed fields and their error estimates, (2) evaluating GPR performance across temporal and spatial dimensions, and (3) analyzing the relationship between training data density and prediction accuracy. Using six metrics, GPR predictions are evaluated on a double-gyre model and a Navy Coastal Ocean Model (NCOM). Results show that GPR achieves high accuracy, contingent on sampling density and velocity magnitude, while validating the posterior covariance matrix as a reliable error predictor. These findings provide critical insights into the strengths and limitations of GPR in oceanographic applications. Full article

This article investigates the effects of roof opening and closure conditions on the mean and fluctuating wind pressure coefficient of the roof surface through rigid model wind tunnel tests and further explores the non-Gaussian characteristics of wind pressure (skewness, kurtosis, and wind pressure probability density) under the two conditions. Then, based on the non-Gaussian characteristics under two working conditions, this paper constructs a Hermite moment model to solve the peak factor of the roof surface to evaluate the impact of roof opening and closure on the most unfavorable extreme wind pressure. The research results show that under the two working conditions of roof opening and closure, the windward leading edge’s mean and fluctuating wind pressure coefficients change most significantly, leading to an increase in the degree of flow separation at the windward leading edge. This causes the skewness, kurtosis, and probability density function of the wind pressure at the windward leading edge of the roof to deviate significantly from the standard Gaussian distribution, exhibiting strong non-Gaussian characteristics. Meanwhile, based on the Hermite moment model, it is found that the peak factor of most measuring points is concentrated between 3.5 and 5.0 under both roof opening and closure conditions, significantly higher than the recommended value of 2.5 in GB 50009-2012. In addition, under roof opening, the most unfavorable negative pressure coefficient is −4.54, and the absolute value of its most unfavorable negative pressure extreme is 1.3% higher than the roof opening closure condition. Full article

With the advancement of polarimetric synthetic aperture radar (PolSAR) imaging technology and the growing demand for image interpretation, extracting meaningful land cover information from PolSAR images has become a key research focus. To address the segmentation challenge, we propose an innovative method. First, features from co-polarization and cross-polarization channels are separately used as dual inputs, and a cross-attention mechanism effectively fuses these features to capture correlations between different polarization information. Second, a diffusion framework is employed to jointly model target features and class probabilities, aiming to improve segmentation accuracy by learning and fitting the probabilistic distribution of target labels. Finally, experimental results demonstrate that the proposed method achieves superior performance in PolSAR image segmentation, effectively managing complex polarization relationships while offering robustness and broad application potential. Full article

Predictive maintenance, recognized as an effective health management strategy for extending the lifetime of devices, has emerged as a hot research topic in recent years. A general method is to execute two separate steps: data-driven remaining useful life (RUL) prediction and a maintenance strategy. However, among the numerous studies that conducted maintenance and replacement activities based on the results of RUL prediction, little attention has been paid to the impact of preventive maintenance on sensor-based monitoring data, which further affects the RUL for repairable degrading devices. In this paper, an adaptive RUL prediction method is proposed for repairable degrading devices in order to improve the accuracy of prediction results and achieve adaptability to future degradation processes. Firstly, a phased degradation model based on an adaptive Wiener process is established, taking into account the impact of imperfect maintenance. Meanwhile, integrating the impact of maintenance activities on the degradation rate and state, the probability distribution of RUL can be derived based on the concept of first hitting time (FHT). Secondly, a method is proposed for model parameter identification and updating that incorporates the individual variation among devices, integrating maximum likelihood estimation and Bayesian inference. Finally, the effectiveness of the RUL prediction method is ultimately validated through numerical simulation and its application to repairable gyroscope degradation data. Full article

Purpose: The impact of hybrid quantum-classical neural network (NN) architectures with multiple backbones and quantum transformation as a data augmentation (DA) technique on image classification tasks was investigated using the CIFAR-10 and MedMNIST (DermaMNIST) datasets. These datasets were chosen for their relevance in general-purpose and medical-specific small-scale image classification, respectively. Methods: A series of quanvolutional transformations, utilizing random quantum circuits based on single-qubit rotation quantum gates (Y-axis, X-axis, and combined XY-axis transformations), were applied to create multiple quantum channels (QC) for input augmentation. By integrating these QCs with baseline convolutional NN architectures (LCNet050) and scalable hybrid NN architectures with multiple (n) backbones and separate QC (n) inputs (HNN-QCn), the scalability and performance enhancements offered by quantum-inspired data augmentation were evaluated. The proposed cross-validation workflow ensured reproducibility and systematic performance evaluation of hybrid models by mean and standard deviation values of metrics (such as accuracy and area under the curve (AUC) for the receiver operating characteristic). Results: The results demonstrated consistent performance improvements by AUC and accuracy in HNN-QCn models with the number n (where $n\in \{4,5,9,10,17,18\}$) of backbones and QC inputs across both datasets. The different improvement rates were observed for the smaller increase in AUC and the larger increase in accuracy as input complexity (number of backbones and QCs inputs) increases. It is assumed that the prediction probability distribution is becoming sharpened with the addition of backbones and QC inputs, leading to larger improvements in accuracy. At the same time, AUC reflects these changes more slowly unless the model’s ranking ability improves substantially. Conclusion: The findings highlight the scalability, robustness, and adaptability of HNN-QCn architectures, with superior performance by AUC (micro and macro) and accuracy across diverse datasets and potential for applications in high-stakes domains like medical imaging. These results underscore the utility of quantum transformations as a form of DA, paving the way for further exploration into the scalability and efficiency of hybrid architectures in complex datasets and real-world scenarios. Full article