Search Results (498)

The bulk of the LiNa₅Mo₉O₃₀ (LNM) crystals were successfully grown in the [010] and [001] directions without internal inclusions and cracks, using the Czochralski method with a low temperature gradient. The crystal grown in the [010] direction showed a tendency to twinning. The crystal grown in the [001] direction demonstrated high structural perfection (FWHM = 13″) for the (001) plane and high optical quality Δn ≈ 2 × 10⁻⁵. The laser-induced damage threshold was measured along a, b and c axes and was 12.2, 27.0 and 27.5 J/cm², respectively. The thermo-optical coefficient dn/dT was measured for the main crystallographic axes, which was −5.75 × 10⁻⁶, −20.2 × 10⁻⁶ and 3.65 × 10⁻⁶ K⁻¹ along the a, b and c axes, respectively. The second harmonic generation (SHG) was conducted in the crystalline LNM sample. The maximum efficiency value of 3.5% at a pump power of 12 W was achieved. Full article

Due to the influence of water and sediment conditions, engineering projects, channel erosion and siltation, river-related factors, and human activities (such as adjustments in floodplain production structures and village construction), there have been significant variations in the transverse gradient of the floodplain in the lower Yellow River. An irrational transverse gradient can lead to the rapid conversion of gravitational potential energy into kinetic energy during the flood evolution process, resulting in increased flow velocity and inundated areas. Exploring reasonable transverse gradients can provide technical support for floodplain management. Using “flood risk assessment” as a keyword, research papers from the Web of Science core database and CNKI published in the past five years were collected. Through a VOS viewer analysis of indicators, a flood risk assessment model based on the “Source–Path–Receptor–Consequence–Resilience” framework was established. A two-dimensional water and sediment model was used to simulate flood inundation scenarios with different transverse gradients in the same flood event, evaluate flood risks in the floodplain, and determine the optimal transverse gradient based on flood risk levels. The results indicate that, compared to low transverse gradients, moderate and high transverse gradients have a more significant driving effect on flood inundation, increasing flood risk opportunities for floodplains. Lower transverse gradients (i.e., TG = 10LG = 1.25‰) are the most favorable for flood protection in the floodplain after flood inundation. Full article

Dockless bike-sharing mobility brings considerable benefits to building low-carbon transportation. However, the operators often rush to seize the market and regulate the services without a good knowledge of this new mobility option, which results in unreasonable layout and management of shared bicycles. Therefore, it is meaningful to explore the relationship between the built environment and bike-sharing ridership. This study proposes a novel framework integrated with the extreme gradient boosting tree model to evaluate the impacts and threshold effects of the built environment on the origin–destination bike-sharing ridership. The results show that most built environment features have strong nonlinear effects on the bike-sharing ridership. The bus density, the industrial ratio, the local population density, and the subway density are the key explanatory variables impacting the bike-sharing ridership. The threshold effects of the built environment are explored based on partial dependence plots, which could improve the bike-sharing system and provide policy implications for green travel and sustainable transportation. Full article

Scientific knowledge of image-based crack detection methods is limited in understanding their performance across diverse crack sizes, types, and environmental conditions. Builders and engineers often face difficulties with image resolution, detecting fine cracks, and differentiating between structural and non-structural issues. Enhanced algorithms and analysis techniques are needed for more accurate assessments. Hence, this research aims to generate an intelligent scheme that can recognize the presence of cracks and visualize the percentage of cracks from an image along with an explanation. The proposed method fuses features from concrete surface images through a ResNet-50 convolutional neural network (CNN) and curvelet transform handcrafted (HC) method, optimized by linear discriminant analysis (LDA), and the eXtreme gradient boosting (XGB) classifier then uses these features to recognize cracks. This study evaluates several CNN models, including VGG-16, VGG-19, Inception-V3, and ResNet-50, and various HC techniques, such as wavelet transform, counterlet transform, and curvelet transform for feature extraction. Principal component analysis (PCA) and LDA are assessed for feature optimization. For classification, XGB, random forest (RF), adaptive boosting (AdaBoost), and category boosting (CatBoost) are tested. To isolate and quantify the crack region, this research combines image thresholding, morphological operations, and contour detection with the convex hulls method and forms a novel algorithm. Two explainable AI (XAI) tools, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping++ (Grad-CAM++) are integrated with the proposed method to enhance result clarity. This research introduces a novel feature fusion approach that enhances crack detection accuracy and interpretability. The method demonstrates superior performance by achieving 99.93% and 99.69% accuracy on two existing datasets, outperforming state-of-the-art methods. Additionally, the development of an algorithm for isolating and quantifying crack regions represents a significant advancement in image processing for structural analysis. The proposed approach provides a robust and reliable tool for real-time crack detection and assessment in concrete structures, facilitating timely maintenance and improving structural safety. By offering detailed explanations of the model’s decisions, the research addresses the critical need for transparency in AI applications, thus increasing trust and adoption in engineering practice. Full article

Differential privacy techniques have shown excellent performance in protecting sensitive information during GAN model training. However, with the increasing attention to data privacy issues, ensuring high-quality output of generative models and the efficiency of federated learning while protecting privacy has become a pressing challenge. To address these issues, this paper proposes a One-shot Federated Personalized Protection–Generative Adversarial Network (OFPP-GAN). Firstly, this scheme employs dual personalized differential privacy to achieve privacy protection. It adjusts the noise scale and clipping threshold based on the gradient changes during model training in a personalized manner, thereby enhancing the performance of the generative model while protecting privacy. Additionally, the scheme adopts the one-shot federated learning paradigm, where each client uploads their local model containing private information only once throughout the training process. This approach not only reduces the risk of privacy leakage but also decreases the communication overhead of the entire system. Finally, we validate the effectiveness of the proposed method through theoretical analysis and experiments. Compared with existing methods, the generative model trained with OFPP-GAN demonstrates superior security, efficiency, and robustness. Full article

Biodiversity response-based diagnostic tools are nonlinear approaches that simultaneously consider complex environmental stressors. Such approaches have been used to quantify biological responses to environmental changes. This study identified the major environmental stressors of community turnover and corresponding thresholds by applying diagnostic tools that use multiple biological assemblages in a large river with artificially controlled flow. Four Gradient Forest models were constructed using the relationships between stream biological assemblage and 66 parameters over 12 years. The multi-stressors that caused community turnover and their thresholds differed depending on the biological assemblage, even under the same environmental conditions. Specifically, they showed that operation of weirs has increased the importance of certain species (e.g., non-native species). In addition, specific-taxon response to multi-stressors analysis identified the ecological or management thresholds of endangered species, Korean endemic species, non-native species, and legal pollution indicator species, which must be managed from a biodiversity perspective. These thresholds are significant as the first reference points presented in similar ecological environments and can be used as guidelines for species over the long term. We propose that ‘true’ threshold identification requires efforts to recognize and improve the limitations of GF techniques confirmed in this study. This may ultimately enable a sustainable aquatic ecosystems maintenance and biodiversity preservation. Full article

Karst yellow soil is one of the most important cultivated soils in southwest China. At present, only a few studies have dealt with rainfall erosivity and erosive rainfall thresholds in the karst yellow soil region. This paper utilizes statistical methods to identify erosive rainfall thresholds and slope erosion-prone areas in the Qianzhong region. This analysis is based on long-term experimental data from 10 experimental stations and 69 experimental plots within the region in 2006 to 2022. The findings show the following: The rainfall amount threshold was 12.66 mm for woodland plots, 10.57 mm for grassland plots, 9.94 mm for farmland plots, and 8.93 mm for fallow plots. Soil and water conservation measures in forestry and grassland effectively increase the rainfall amount thresholds. Compared to farmland, the rainfall threshold increased by 27.32% for woodland and 6.32% for grassland. Bare land and farmland are erosion-prone areas in the karst yellow soil region. The erosive rainfall thresholds for farmland plots with slopes of 13°, 15°, 20°, 23°, and 25° were 10.41 mm, 10.28 mm, 9.66 mm, 9.52 mm, and 9.15 mm, respectively. With the increase in the 13–25° slope gradient of farmland, the initial rainfall required for runoff generation leads to a reduction. The wrong selection indices (WSI) of all landcover plots were less than 10%, and the efficiency indices (EFF) were between 80.43% and 90.25%. The relative error index (REI) of the erosive rainfall thresholds for all landcover runoff plots was less than 0.50%, very close to 0, indicating that these thresholds have small errors and high accuracy. This study gained a better understanding of natural rainfall-induced erosion characteristics in the study area, determined rainfall thresholds for distinguishing erosive rainfall events from non-erosive across different landcover types, and reduced the workload of calculating rainfall erosivity while enhancing the accuracy of soil erosion forecasting and simulation in the karst mountain yellow soil area. Full article

The temperature sensitivity (Q₁₀) of soil respiration (Rs) plays a crucial role in evaluating the carbon budget of terrestrial ecosystems under global warming. However, the variability in Q₁₀ along soil moisture gradients remains a subject of debate, and the associated underlying causes are poorly understood. This study aims to investigate the characteristics of Q₁₀ changes along soil moisture gradients throughout the whole growing season and to assess the factors influencing Q₁₀ variability. Changes in soil respiration (measured by the dynamic chamber method) and soil properties were analyzed in a poplar plantation located in the suburban area of Beijing, China. The results were as follows: (1) Q₁₀ increased with the increasing soil water content up to a certain threshold, and then decreased, (2) the threshold was 75% to 80% of the field capacity (i.e., the moisture content at capillary rupture) rather than the field water-holding capacity, and (3) the dominant influence shifted from soil solid-phase properties to microbes with increasing soil moisture. Our results are important for understanding the relationship between the temperature sensitivity of soil respiration and soil moisture in sandy soil, and for the refinement of the modeling of carbon cycling in terrestrial ecosystems. Full article

The HZ 26-B buried hill reservoir is located in the eastern part of the South China Sea. This reservoir is characterized by the development of natural fractures, a high density, and a complex geological structure, featuring an upper condensate gas layer and a lower volatile oil layer. These characteristics present significant challenges for oilfield exploration. To address these challenges, this study employed advanced embedded discrete fracture methods to conduct comprehensive numerical simulations of the fractured buried hill reservoirs. By meticulously characterizing the flow mechanisms within these reservoirs, the study not only reveals their unique characteristics but also establishes an embedded discrete fracture numerical model at the oilfield scale. Furthermore, a combination of single-factor sensitivity analysis and the Pearson correlation coefficient method was used to identify the primary controlling factors affecting the development of complex condensate reservoirs in ancient buried hills. The results indicate that the main factors influencing the production capacity are the matrix permeability, geomechanical effects, and natural fracture length. In contrast, the impact of the threshold pressure gradient and bottomhole flow pressure is relatively weak. This study’s findings provide a scientific basis for the efficient development of the HZ 26-B oilfield and offer valuable references and insights for the exploration and development of similar fractured buried hill reservoirs. Full article

In optical remote sensing image object detection, discontinuous boundaries often limit detection accuracy, particularly at high Intersection over Union (IoU) thresholds. This paper addresses this issue by proposing the Spatial Adaptive Angle-Aware (${\mathrm{SA}}^3$) Network. The ${\mathrm{SA}}^3$ Network employs a hierarchical refinement approach, consisting of coarse regression, fine regression, and precise tuning, to optimize the angle parameters of rotated bounding boxes. It adapts to specific task scenarios using either class-aware or class-agnostic strategies. Experimental results demonstrate its effectiveness in significantly improving detection accuracy at high IoU thresholds. Additionally, we introduce a Gaussian transform-based IoU factor during angle regression loss calculation, leading to the development of Edge-aware Skewed Bounding Box Loss (EAS Loss). The EAS loss enhances the loss gradient at the final stage of angle regression for bounding boxes, addressing the challenge of further learning when the predicted box angle closely aligns with the real target box angle. This results in increased training efficiency and better alignment between training and evaluation metrics. Experimental results show that the proposed method substantially enhances the detection accuracy of ReDet and ReBiDet models. The ${\mathrm{SA}}^3$ Network and EAS loss not only elevate the mAP of the ReBiDet model on DOTA-v1.5 to 78.85% but also effectively improve the model’s mAP under high IoU threshold conditions. Full article

The fuel consumption and greenhouse gas (GHG) emission patterns of taxis are in accordance with the urban structure and daily travel footprints of residents. With taxi trajectory data from the intelligent transportation system in Xi’an, China, this study excludes trajectories from electric taxis to accurately estimate GHG emissions of taxis. A gradient boosting decision tree (GBDT) model is employed to examine the nonlinear influence of the built environment (BE) on the GHG emissions of taxis on weekdays and weekends in various urban areas. The research findings indicate that the GHG emissions of taxis within the research area exhibit peak levels during the time intervals of 7:00–9:00, 12:00–14:00, and 23:00–0:00, with notably higher emission factors on weekends than on weekdays. Moreover, a clear nonlinear association exists between BE elements and GHG emissions, with a distinct impact threshold. In the different urban areas, the factors that influence emissions exhibit spatial and temporal heterogeneity. Metro/bus/taxi stops density, residential density, and road network density are the most influential BE elements impacting GHG emissions. Road network density has both positive and negative influences on the GHG emissions in various urban areas. Increasing the road network density in subcentral urban areas and increasing the mixed degree of urban functions in newly developed urban centers to 1.85 or higher can help reduce GHG emissions. These findings provide valuable insights for reducing emissions in urban transportation and promoting sustainable urban development by adjusting urban functional areas. Full article

Understanding the relationship between the demand for public transportation and land use is critical to promoting public-transportation-oriented urban development. Taking Beijing as an example, we took the Public Transportation Index (PTI) during the working day’s early peak hours as the dependent variable. And 15 land use and built environment variables were selected as the independent variables according to the “7D” built environment dimensions. According to the Modifiable Areal Unit Problem (MAUP), the size and shape of the spatial units will affect the aggregation results of the dependent variable and the independent variables. To find the ideal spatial unit division method, we assess how well the nonlinear model fits several spatial units. Extreme Gradient Boosting (XGBoost) was utilized to investigate the nonlinear effects of the built environment on PTI and threshold effects based on the ideal spatial unit. The results show that (1) the best spatial unit division method is based on traffic analysis zones (TAZs); (2) the top four explanatory variables affecting PTI are, in order: mean travel distance, residential density, subway station density, and public services density; (3) there are nonlinear relationships and significant threshold effects between the land use variables and PTI. The priority regeneration TAZs were identified according to the intersection analysis of the low PTI TAZs set and the PTI-sensitive TAZs set based on different land use variables. Prioritized urban regeneration TAZs require targeted strategies, and the results of the study may provide a scientific basis for proposing strategies to renew land use to increase PTI. Full article

Existing studies have limited evidence about the complex nonlinear impact mechanism of road network topology and built environment on bike-sharing systems’ greenhouse gas (GHG) emission reduction benefits. To fill this gap, we examine the nonlinear effects of road network topological attributes and built environment elements on the potential GHG emission reduction of dockless bike-sharing (DBS) trips in Shenzhen, China. Various methods are employed in the research framework of this study, including a GHG emission reduction estimation model, spatial design network analysis (sDNA), gradient boosting decision tree (GBDT), and partial dependence plots (PDPs). Results show that road network topological variables have the leading role in determining the potential GHG emission reduction of DBS trips, followed by land use variables and transit-related variables. Moreover, the nonlinear impacts of road network topological variables and built environment variables show certain threshold intervals for the potential GHG emission reduction of DBS trips. Furthermore, the impact of built environment on the potential GHG emission reduction of DBS trips is moderated by road network topological indicators (closeness and betweenness). Compared with betweenness, closeness has a greater moderating effect on built environment variables. These findings provide empirical evidence for guiding bike-sharing system planning, bike-sharing rebalancing strategy optimization, and low-carbon travel policy formulation. Full article

Background: A better understanding of the peritumoral stroma changes due to tumour invasion using non-invasive diagnostic methods may improve the differentiation between benign and malignant breast lesions. This study aimed to assess the correlation between breast lesion differentiation and intra- and peritumoral shear-wave elastography (SWE) gradients. Methods: A total of 135 patients with newly diagnosed breast lesions were included. Intratumoral, subsurface, and three consecutive peritumoral SWE value measurements (with three repetitions) were performed. Intratumoral, interface, and peritumoral gradients (Gradient 1 and Gradient 2) were calculated using averaged SWE values. Statistical analysis included descriptive statistics and an ordinary one-way ANOVA to compare overall and individual gradients among Breast Imaging-Reporting and Data System (BI-RADS) 2, 3, and 5 groups. Results: Malignant tumours showed higher average SWE velocity values at the tumour centre (BI-RADS 2/3: 4.1 ± 1.8 m/s vs. BI-RADS 5: 4.9 ± 2.0 m/s, p = 0.04) and the first peritumoral area (BI-RADS 2/3: 3.4 ± 1.8 m/s vs. BI-RADS 5: 4.3 ± 1.8 m/s, p = 0.003). No significant difference was found between intratumoral gradients (0.03 ± 0.32 m/s vs. 0.0 ± 0.28 m/s; p > 0.999) or gradients across the tumour–tissue interface (−0.17 ± 0.18 m/s vs. −0.13 ± 0.35 m/s; p = 0.202). However, the first peritumoral gradient (−0.16 ± 0.24 m/s vs. −0.35 ± 0.31 m/s; p < 0.0001) and the second peritumoral gradient (−0.11 ± 0.18 m/s vs. −0.22 ± 0.28 m/s; p = 0.037) were significantly steeper in malignant tumours. The AUC was best for PTG1 (0.7358) and PTG2 (0.7039). A threshold value for peritumoral SWI PT1 above 3.76 m/s and for PTG1 below −0.238 m/s·mm⁻¹ indicated malignancy in 90.6% of cases. Conclusions: Evaluating the peritumoral SWE gradient may improve the diagnostic pre-test probability, as malignant tumours showed a significantly steeper curve of the elasticity values in the peritumoral stroma compared to the linear regression with a relatively flat curve of benign lesions. Full article

As urbanization accelerates, the prevalence of fire incidents leads to significant hazards. Enhancing the accuracy of remote fire detection systems while reducing computation complexity and power consumption in edge hardware are crucial. Therefore, this paper investigates an innovative lightweight Convolutional Spiking Neural Network (CSNN) method for fire detection based on acoustics. In this model, Poisson encoder and convolution encoder strategies are considered and compared. Additionally, the study investigates the impact of observation time steps, surrogate gradient functions, and the threshold and decay rate of membrane potential on network performance. A comparison is made between the classification metrics of the traditional Convolutional Neural Network (CNN) approaches and the proposed lightweight CSNN method. To assess the generalization performance of the proposed lightweight method, publicly available datasets are merged with our experimental data for training, which results in a high accuracy of 99.02%, a precision of 99.37%, a recall of 98.75%, and an $F_1$ score of 99.06% on the test datasets. Full article