Search Results (2,226)

The eddy dissipation rate (EDR, or turbulence dissipation rate) is a crucial parameter in the study of the atmospheric boundary layer (ABL). However, the existing Doppler lidar-based estimates of EDR seldom offer long-term comparisons that span the entire ABL. Building upon prior research utilizing Doppler lidar wind-field data, we optimized the EDR retrieval algorithm using a genetic adaptive approach. The newly developed algorithm demonstrates enhanced accuracy in EDR estimation. The daily evolution of EDR reveals a distinct diurnal pattern in its variation. A detailed four consecutive days study of turbulence generated via low-level jets (LLJs) indicated that EDR driven by heat flux (~10⁻² m²/s³) is significantly stronger than that produced through wind shear (~10⁻³ m²/s³). Subsequently, we examined seasonal variations in EDR at different mixing layer heights (MLH, Zi): elevated EDR values in summer (~7 × 10⁻³ m²/s³ at 0.1Zi) contrasted with reduced levels in winter (~6 × 10⁻⁴ m²/s³ at 0.1Zi). In the early morning, EDR decreases with height for 1 magnitude, while in later stages, it remains relatively stable within 0.1 order of magnitude across 0.1Zi to 0.9Zi. Notably, the EDR during DJF exceeds that of MAM and SON in the afternoon. This suggests that ML turbulence is not solely dependent on surface fluxes (SHF + LHF) but may also be influenced by MLH. A lower MLH (smaller volume), even with reduced surface fluxes, could potentially result in a stronger EDR. Finally, we compared the evolution of the EDR and MLH in the boundary layer using Doppler lidar data from ARM sites and the PBL (Planetary Boundary Layer) Moving Active Profiling System (PBLMAPS) Airborne Doppler Lidar (ADL). The results show that the vertical wind data exhibit strong consistency (R = 0.96) when the ADL is positioned near ARM Southern Great Plains (SGP) sites C1 or E37. The ADL’s mobility and flexibility provide significant advantages for future field experiments, particularly in challenging environments such as mountainous or complex terrains. This study not only highlights the potential of utilizing Doppler lidar alone for EDR calculations but also extensively explores the development patterns of EDR within the ABL. Full article

In the context of climate change and ecological degradation, enhancing cropland productivity in Northeast China is essential for ensuring national food security. This study adopted an integrated framework combining the optimal parameter-based geographical detector (OPGD) and SHapley Additive exPlanations (SHAP) to identify key drivers of average and total cropland productivity at the county level from 2001 to 2020. Growing-season-based cropland Net Primary Productivity (NPP) was estimated using the CASA model to represent cropland productivity. Results indicated that natural and ecological factors significantly dominated the spatial variation of cropland productivity, with their interactions amplified through dual-factor or nonlinear enhancements. Various machine learning models were fine-tuned and compared, and optimal models were selected for subsequent SHAP analysis. The findings revealed that erosion intensity exhibited the most significant impact on cropland productivity, whereas the effect of precipitation shifted from negative to positive, with a clear threshold of around 400 mm—matching the boundary between China’s semi-arid and semi-humid regions. Low-elevation plains (<300 m) and gentle slopes (<0.5°) predominately promoted total cropland productivity. Interactions between erosion and fertilizer intensity highlighted the need for moderate fertilization to prevent ecological degradation in severely eroded counties. These findings provide scientific support for targeted cropland management aimed at achieving sustainable agriculture in Northeast China. Full article

Financial fraud detection is an important field in financial technology, and strong and effective machine learning (ML) models are needed to detect fraudulent transactions with high accuracy and reliability. Conventional fraud detection models, like probabilistic, instance-based, and tree-based models, tend to have high error rates, class imbalance problems, and poor adaptability to changing fraud patterns. These issues call for sophisticated methods that improve predictive accuracy while being computationally efficient. To overcome these limitations, this research introduces the Voted Perceptron (VP) model, which utilizes an iterative learning process to dynamically adapt decision boundaries based on misclassified examples. In contrast to traditional models with static decision rules, the VP model constantly updates its weight parameters, thus providing better fraud detection abilities. The evaluation compares VP with state-of-the-art machine learning models, such as Average One Dependency Estimator (A1DE), K-nearest Neighbor (KNN), Naïve Bayes (NB), Random Tree (RT), and Functional Tree (FT), by using important performance metrics, like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), True Positive Rate (TPR), recall, and accuracy. Experimental results show that VP outperforms its rivals significantly, yielding better fraud detection performance with low error rates and high recall. Furthermore, an ablation study confirms the influence of essential VP model elements on general classification performance. These results demonstrate VP to be an extremely effective model for detecting financial fraud, with enhanced flexibility towards evolving fraud patterns, and confirm the necessity for intelligent fraud detection mechanisms within financial organizations. Full article

Optimizing building equipment control is crucial for enhancing energy efficiency. This article presents a predictive control applied to a commercial building heated by a hydronic system, comparing its performance to a traditional heating curve-based strategy. The approach is developed and validated using TRNSYS18 modeling, which allows for comparison of the control methods under the same weather boundary conditions. The proposed strategy balances energy consumption and indoor thermal comfort. It aims to optimize the control of the secondary heating circuit’s water setpoint temperature, so it is not the boiler supply water temperature that is optimized, but rather the temperature of the water that feeds the radiators. Limited data poses challenges for capturing system dynamics, addressed through a black-box approach combining two machine learning models: an artificial neural network predicts indoor temperature, while a support vector machine estimates gas consumption. Incorporating weather forecasts, occupancy scenarios, and comfort requirements, a genetic algorithm identifies optimal hourly setpoints. This work demonstrates the possibility of creating sufficiently accurate models for this type of application using limited data. It offers a simplified and efficient optimization approach to heat control in such buildings. The case study results show energy savings up to 30% compared to a traditional control method. Full article

As an important carrier for the application of digital technologies, digital infrastructure plays a crucial role in promoting the digital transformation of the grain system and ensuring food security in the current era. This study utilizes panel data from 31 provinces (municipalities) in China, spanning the years from 2006 to 2022, and constructs a comprehensive evaluation index system for grain system resilience, grounded in its core components of resistance, recovery, and transformation. The grain system resilience index is measured using the entropy method. A spatial Durbin model is employed to estimate the impact of digital infrastructure on grain system resilience, and a panel threshold model is used to analyze the nonlinear effects of digital infrastructure on grain system resilience. The research findings are as follows: (1) Both the direct and spatial spillover effects of digital infrastructure on grain system resilience are significantly positive, but considerable regional heterogeneity is observed. Due to differences in economic development levels, digital infrastructure investments, and policy priorities, the indirect and total effects of digital infrastructure on food system resilience are more pronounced in the southeast region, whereas the direct effects are more significant in the northwest region. (2) The threshold regression results show that when market integration is below the threshold value, the estimated coefficient of digital infrastructure is 0.2242, which is significant at the 1% significance level. When market integration is above the threshold value, the estimated coefficient of digital infrastructure is 0.0790, which is also significant at the 1% significance level. However, its regression coefficient significantly decreases, indicating that the impact of digital infrastructure on food system resilience will weaken as the degree of market integration increases. (3) The analysis of the attenuation boundary of spatial spillover effects shows that within a distance of 225 km, the estimated coefficients of the indirect effects of digital infrastructure on grain system resilience are positive and statistically significant at least at the 10% significance level. However, beyond 225 km, the regression coefficients become negative and insignificant, indicating that the effective boundary of the spillover effect of digital infrastructure on grain system resilience is 225 km, after which the spillover effect gradually diminishes. Based on these findings, it is recommended that the southeast region further strengthen regional digital governance collaboration to maximize spillover effects, whereas the northwest region should prioritize improving digital infrastructure and introduce digital technologies through models such as an “enclave economy” to bridge the digital divide. This study reveals the impact of digital infrastructure on grain system resilience and provides a new perspective for scientifically evaluating the spatial spillover effects of digital infrastructure. Full article

Anemone altaica Fisch. ex C. A. Mey., a component of the tertiary boreo-nemoral vegetation complex, exhibits a disjunct distribution from European Russia to Central China. The Khamar-Daban Ridge, extending along Lake Baikal’s southern coast, has served as a refugium preserving mesophilic forest remnants in South Siberia since the Pleistocene. This study aimed to elucidate the phylogenetic relationships and historical biogeography of A. altaica within the Khamar-Daban refugium using plastid DNA markers (trnL + trnL-trnF). Phylogenetic and mismatch distribution analysis revealed polyphyly (more specifically diphyly) among A. altaica lineages, suggesting past hybridization events with related species followed by backcrossing. Estimation of isolation by distance effect, spatial autocorrelation analysis, PCoA, and AMOVA indicated a clear spatial genetic structure for A. altaica on the Khamar-Daban Ridge. The most reliable geographical model suggests that during periods of Pleistocene cooling, A. altaica persisted in at least six microrefugia within the ridge. Populations associated with these microrefugia formed western, central, and eastern genetic supergroups with limited gene flow among them. Gene flow likely occurred more easily during glaciations or early interglacials when the subalpine zone shifted closer to Lake Baikal due to the depression of the snow boundary, allowing adjacent populations to intermingle along the glacial edges and terminal moraines in mountain forest belt. Full article

This article presents a method for the approximate calculation of fractional Caputo derivatives, including a crucial aspect of the ability to handle arbitrary—even variable—terminals and order. The proposed method involves rearranging the fractional operator as a series of higher-order derivatives considered at a specific point. We demonstrate the effect of the number of terms included in the series expansion on the solution accuracy and error analysis. The advantage of the method is its simplicity and ease of implementation. Additionally, the method allows for a quick estimation of the fractional derivative by using a few first terms of the expansion. The elaborated algorithm is tested against a comprehensive series of illustrative examples, providing very good agreement with the exact/reference solutions. Furthermore, the application of the proposed method to fractional boundary/initial value problems is included. Full article

Using artificial neural networks (ANN), numerous models were developed for predicting the ultimate shear strength of reinforced concrete deep beams. Many experimental result databases from earlier research were carefully gathered for this study. Two hundred fifty-three findings from experiments were included in this database. The ultimate shear strength was the output parameter, while ten factors were determined as input parameters for the ANN model based on the completed literature research. The required model was constructed using a back propagation neural network. The model of the neural networks was determined using the trial-and-error method. It was discovered that, inside the range of the input boundaries considered, the ANN model could accurately estimate the ultimate shear strength of deep beams. The measured shear strength and the shear strength predicted by the ANN model have a high correlation coefficient of 0.97, indicating a strong relationship between the predicted and actual values. The results show that, given the range of input parameters, ANN offers an excellent agreement of interest as a practical technique for estimating the ultimate shear strength. A parametric investigation was performed using the trained neural network model to assess how the input parameters affected the shear strength capacity of deep beams. Full article

A simple design model, able to link test results of small concrete samples to failures of large structures, is desirable for fracture analysis of concrete structures, particularly if the model has no special requirements on small samples, e.g., size, notched or un-notched. The linear Boundary Effect Model ($BEM$, which has evolved over the past 20 years, is able to provide the link between small samples and large structures with fairly reliable predictions and a built-in function of statistical analysis. $BEM$ enables researchers and engineers to model the quasi-brittle fracture behavior of concrete and the associated size effects by focusing on the fracture process zone ($FPZ$) at the notch tip or at the specimen boundary (for an un-notched case). $FPZ$ and quasi-brittle fracture of concrete are directly influenced by the average aggregate size ($d_{av}$), but few models mathematically show such critical aggregate influence, except $BEM$. The aggregate size used in $BEM$ can be accurately estimated separately before fracture experiments. A comprehensive dataset of concrete fracture results from the existing literature, along with a new experimental dataset from three-point bending (3-P-B) tests, involving 138 specimens with varying notch depths (un-notched, $1\mathrm{m}\mathrm{m}$ shallow-notched, and $6\mathrm{m}\mathrm{m}$ deep-notched) was analyzed. The specimens, which present inconsistent dimensions ($160\mathrm{m}\mathrm{m}$ span, approximately $40\mathrm{m}\mathrm{m}$ thickness, and $38\mathrm{m}\mathrm{m}$ width/height), were used to estimate $FPZ$ at peak fracture loads and investigate their interactions with structural boundaries. Statistical analyses were integrated into $BEM$, allowing the model to account for the experimental scatter, thus improving its reliability as a predictive tool for maximum fracture loads of concrete structures. This study confirmed again that the linear $BEM$ is easy to use and provides fairly accurate predictions across concrete specimens and structures of various sizes. Full article

After entering deep mining, coal mines often experience various intense dynamic load phenomena due to increasingly complex geological conditions, which can lead to secondary disasters, where it is urgent to identify their sources and analyze their disaster-causing effects. This article takes the 3310 working face in Gu Cheng Coal Mine as the engineering background, and uses theoretical analysis, numerical simulation, on-site monitoring, and other methods to analyze the spatial and temporal distribution of dynamic load events during the mining period of this face. The study classifies dynamic load events based on this background into roof-type, fault-type, and coal pillar-type classes, revealing the differences in the spectra, waveforms, and disaster-causing effects of each class. The results show that the strong dynamic load events are mainly concentrated in the working face roof and fault zone areas. The first principal frequency of the three classes has an estimated boundary between 30 and 60 Hz. The waveform decay coefficients of the roof-type, coal pillar-type, and fault-type strong dynamic load events have average values of 4.53, 1.57, and 1.41, respectively. By adopting the above research methods, a theoretical basis can be provided for the source of dynamic loads, thereby achieving source-based prevention and control of rock burst. Full article

The temperature and humidity profiles within the planetary boundary layer (PBL) are crucial for Earth’s climate research. The Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST) measures downward thermal radiation in the atmosphere with high temporal and spectral resolution continuously during day and night. The physics-based retrieval method, utilizing iterative optimization, can obtain solutions that align with the true atmospheric state. However, the retrieval is typically an ill-posed problem and is affected by noise, necessitating the introduction of regularization. To achieve high-precision detection, a systematic evaluation was conducted on the retrieval performance of temperature and humidity profiles using ASSIST by regularization methods based on the Gauss–Newton framework, which include Fixed regularization factor (FR), L-Curve (LC), Generalized Cross-Validation (GCV), Maximum Likelihood Estimation (MLE), and Iterative Regularized Gauss–Newton (IRGN) methods, and the Levenberg–Marquardt (LM) method based on a damping least squares strategy. A five-day validation experiment was conducted under clear-sky conditions at the Anqing radiosonde station in China. The results indicate that for temperature profile retrieval, the IRGN method demonstrates superior performance, particularly below 1.5 $\mathrm{k}\mathrm{m}$ altitude, where the mean BIAS, mean RMSE, mean Degrees of Freedom for Signal (DFS), and mean residual reach 0.42 $\mathrm{K}$, 0.80 $\mathrm{K}$, 3.37, and $3.01\times {10}^{-13}$ $\left(\mathrm{W}/{\mathrm{c}\mathrm{m}}^2 \mathrm{s}\mathrm{r} {\mathrm{c}\mathrm{m}}^{-1}\right)$, respectively. In contrast, other regularization methods exhibit over-regularization, leading to degraded information content. For humidity profile retrieval, below 1.5 $\mathrm{k}\mathrm{m}$ altitude, the LM method outperforms all regularization-based methods, with the mean BIAS, mean RMSE, mean DFS, and mean residual of 3.65$\%$, 5.62$\%$, 2.05, and $4.36\times {10}^{-12}$ $\left(\mathrm{W}/{\mathrm{c}\mathrm{m}}^2 \mathrm{s}\mathrm{r} {\mathrm{c}\mathrm{m}}^{-1}\right)$, respectively. Conversely, other regularization methods exhibit strong prior dependence, causing retrieval to converge results toward the initial guess. Full article

GAMs were implemented to evaluate the spatial variation in concentrations of 33 elements in PM₁₀, in their water-soluble and insoluble fractions used as tracers for different emission sources. Data were collected during monitoring campaigns (November 2016–February 2018) in the Terni basin (an urban and industrial hotspot of Central Italy), using an innovative experimental approach based on high-spatial-resolution (23 sites, approximately 1 km apart) monthly samplings and the chemical characterization of PM₁₀. For each element, a model was developed using monthly mean concentrations as the response variable. As covariates, the temporal predictors included meteorological parameters (temperature, relative humidity, wind speed and direction, irradiance, precipitation, planet boundary layer height), while the spatial predictors encompassed distances from major sources, road length, building heights, land use variables, imperviousness, and population. A stepwise procedure was followed to determine the model with the optimal set of covariates. A leave-one-out cross-validation method was used to estimate the prediction error. Statistical indicators (Adjusted R-Squared, RMSE, FAC2, FB) were used to evaluate the performance of the GAMs. The spatial distribution of the fitted values of PM₁₀ and its elemental components, weighted over all sampling periods, was mapped at a resolution of 100 m. Full article

Understanding fine-scale spatial ecology is essential for defining effective conservation priorities, particularly at the range margins of vulnerable species. Here, we investigate the spatial ecology and habitat associations of the Persian squirrel (Sciurus anomalus) on Lesvos Island, Greece, representing the species’ westernmost distribution. Using a randomized grid-based survey, we recorded 424 presence records across the island and applied a suite of spatial analyses, including Kernel Density Estimation, Getis-Ord Gi*, and Anselin Local Moran’s I, to detect hotspots, coldspots, and spatial outliers. Binomial Logistic Regression, supported by Principal Component Analysis, identified key ecological drivers of habitat use, while spatial regression models (Spatial Lag and Spatial Error Models) quantified the influence of land-use characteristics and spatial dependencies on hotspot intensity and clustering dynamics. Our results showed that hotspots were primarily associated with olive-dominated and broadleaved landscapes, while coldspots and Low–Low clusters were concentrated in fragmented or degraded habitats, often outside protected areas. Spatial outliers revealed fine-scale deviations from broader patterns, indicating local habitat disruptions and emerging conservation risks not captured by existing Natura 2000 boundaries. Spatial regression confirmed that both hotspot intensity and clustering patterns were shaped by specific land-use features and spatially structured processes. Collectively, our findings underscore the fragmented nature of suitable habitats and the absence of cohesive population cores, reinforcing the need for connectivity-focused, landscape-scale conservation. Full article

Gradient damage processes in cementitious materials are generally produced by chemical and/or physical processes that travel from outside to inside. Depending on the type of damage, it can cause different effects such as decreased porosity, cracking, or steel corrosion in the case of carbonation, or increased porosity, micro-cracks, expansion, and spalling (also present in thermal damage) in the case of external attack by sulphates or acid attack. Therefore, estimating the boundaries of this damage is an essential task for concrete quality assessment. The first objective of this work was to use neural networks (NNs) for ultrasound tomographic reconstruction of concrete samples in order to estimate the advance front in gradient damage. Unlike the usual X-ray tomography, ultrasound tomography is affected by diffraction, among other factors. NNs can learn to compensate for these effects; however, they require a large amount of training data to achieve accurate results. In the case of cement-based materials, obtaining and measuring a real training database could be complicated, expensive, and time-consuming. For this purpose, a training process using simulated measurements was carried out. The second objective of this work was to demonstrate the feasibility of training neural networks through simulations, which reduces costs. Finally, the trained neural network for tomographic reconstruction was evaluated using real cylindrical concrete specimens. Each specimen consisted of an outer cylinder, representing externally exposed cement, and an inner cylinder, simulating the unaffected core. The Structural Similarity Index (SSIM) was used as a metric to assess the reconstruction accuracy, achieving values of 0.95 for simulated signals and up to 0.82 for real signals. Full article

The influence of annealing temperature on the mechanical properties, microstructural evolution, and recrystallization behavior of AA5083 cold-rolled sheets with and without Sc/Zr microalloying was studied utilizing hardness tests, optical microscopy, electron backscatter diffraction, and transmission electron microscopy. The results show that a minor addition of Sc/Zr to the Al-Mg-Mn alloy can significantly improve the alloy strength and recrystallization resistance. Adding 0.1 wt.% Sc and 0.08 wt.% Zr raised the recrystallization temperature of heavily deformed sheets to 500 °C, which is 250 °C higher than for the Sc-free base alloy. The higher recrystallization resistance of the Sc-bearing alloy was mainly attributed to the presence of Al₃(Sc,Zr) nanoparticles, which enhanced the Zener drag pressure and delayed recrystallization. Grain boundary strengthening effects at various annealing temperatures were estimated using a constitutive equation. This work revealed that grain structure change and the corresponding boundary strengthening effect are key factors governing alloy strength evolution during annealing. Full article