Search Results (13,431)

The ongoing advancement of urbanization has significantly amplified its impacts on the water environment. Understanding the coupling relationships between urbanization and the water environment (UAWE) is crucial for Chinese policymakers aiming to promote sustainable urban development. In this study, a comprehensive UAWE evaluation model was developed to examine the development trajectories in Neijiang City from 2012 to 2022. Methodologically, a comprehensive evaluation approach was applied to assess urbanization and water resource trends over this period, followed by the development of a Coupling Coordination Degree Model (CCDM) to quantify their synergistic relationship. The results showed that the coupling between the comprehensive urbanization index and the water environment system evolved over time, as reflected in the following key findings: (1) Neijiang underwent three distinct stages from 2012 to 2022 in terms of coupling and coordination between urbanization and the water environment: Basic Coordination (2012–2015), Good Coordination (2016–2020), and Excellent Coordination (2020–2022). (2) Urbanization exerted varying impacts on subsystems of the water environment, with the pressure-response subsystems exhibiting marked volatility from 2012 to 2022. The impact intensity followed the order spatial urbanization > economic urbanization > social urbanization > population urbanization. These findings offer valuable theoretical and practical insights for aligning urban sustainability goals with effective water environment protection measures. This study provides essential guidance for policymakers in Neijiang and similar regions, enabling the development of tailored strategies for sustainable urbanization and enhanced water management. Full article

Morocco is facing a situation of alarming water stress, aggravated by climate change, overexploitation of resources, and unequal distribution of water, placing the country among the most vulnerable to water scarcity in the MENA region. This study aims to investigate the dynamic relationship between economic growth and water stress in Morocco while highlighting the importance of integrated water management and adaptive economic policies to enhance resilience to water scarcity. A mixed methodology, integrating both qualitative and quantitative methods, was adopted to overview the economic–environmental Moroccan context, and to empirically analyze the GDP (gross domestic product) and water stress in Morocco over the period 1975–2021 using an Autoregressive Distributed Lag (ARDL) approach. The empirical analysis is based on annual data sourced from the World Bank and FAO databases for GDP, agricultural value added, renewable internal freshwater resources, and water productivity. The results suggest that water productivity has a significant positive effect on economic growth, while the impacts of agricultural value added and renewable water resources are less significant and vary depending on the model specification. Diagnostic tests confirm the reliability of the ARDL model; however, the presence of outliers in certain years reflects the influence of exogenous shocks, such as severe droughts or policy changes, on the Moroccan economy. The key contribution of this study lies in the fact that it is the first to analyze the intrinsic link between economic growth and the environmental aspect of water in Morocco. According to our findings, it is imperative to continuously improve water productivity and adopt adaptive management, rooted in science and innovation, in order to ensure water security and support the sustainable economic development of Morocco. Full article

Background/Objectives: There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. While we use digital breast tomosynthesis (DBT) to show these effects, our results would be generally applicable to a wider range of other imaging modalities and applications. Methods: We examine factors in texture estimation methods, such as quantization, pixel distance offset, and region of interest (ROI) size, that influence the magnitudes of these readily computable and widely used image texture features (specifically Haralick’s gray level co-occurrence matrix (GLCM) textural features). Results: Our results indicate that quantization is the most influential of these parameters, as it controls the size of the GLCM and range of values. We propose a new multi-resolution normalization (by either fixing ROI size or pixel offset) that can significantly reduce quantization magnitude disparities. We show reduction in mean differences in feature values by orders of magnitude; for example, reducing it to 7.34% between quantizations of 8–128, while preserving trends. Conclusions: When combining images from multiple vendors in a common analysis, large variations in texture magnitudes can arise due to differences in post-processing methods like filters. We show that significant changes in GLCM magnitude variations may arise simply due to the filter type or strength. These trends can also vary based on estimation variables (like offset distance or ROI) that can further complicate analysis and robustness. We show pathways to reduce sensitivity to such variations due to estimation methods while increasing the desired sensitivity to patient-specific information such as breast density. Finally, we show that our results obtained from simulated DBT images are consistent with what we see when applied to clinical DBT images. Full article

This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. A comprehensive dataset of 482 scenarios was engineered based on literature-informed parameters, encompassing transport distance, processing energy intensity, initial moisture content, gradation adjustments, and regional electricity emission factors. Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. Feature importance analysis via SHapley Additive exPlanations (SHAP) provided interpretative insights, highlighting transport distance and energy intensity as dominant factors affecting unit cost, while moisture content and grid emission factor predominantly influenced CO₂ emissions. Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. The resulting Pareto front revealed a diverse solution space, with significant nonlinear trade-offs between economic efficiency and environmental performance, clearly identifying strategic inflection points. To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO₂ factor of 0.47 kg CO₂/kWh. This scenario offered a substantial reduction (45%) in CO₂ emissions relative to cost-minimized solutions, with a moderate increase (33%) in total cost, presenting a realistic and balanced pathway for sustainable infrastructure practices. Overall, this study introduces a robust, scalable, and interpretable optimization framework, providing valuable methodological advancements for sustainable decision making in infrastructure planning and circular economy initiatives. Full article

Medicago falcata is one of the most important perennial forage legumes in the Medicago genus. In this study, we reported the complete chloroplast genome of two M. falcata ecotypes grown in different regions, and compared them with those of Medicago truncatula and Medicago sativa. We found that the M. falcata chloroplast genome lacks a typical quadripartite structure, containing 78 protein-coding genes, 30 tRNA genes, and four ribosomal RNA genes. They shared high conservation in size, genome structure, gene order, gene number and GC content with those of M. truncatula and M. sativa. High nucleotide diversity occurred in the coding gene regions of rps16, rps3, and ycf4 genes. Meanwhile, mononucleotide repeats are the most abundant repeat type, followed by the di-, tri-, tetra-, and pentanucleotides, and forward repeats were more abundant than reverse and palindrome repeats for all these three Medicago species. Phylogenetic analyses using both coding sequences and complete chloroplast genomes revealed that M. falcata shares the closest phylogenetic relationship with M. hybrida and M. sativa. This study provided valuable information for further studies on the genetic relationship of the Medicago genus. Full article

Water science has always been a central part of modern scientific research. In this study, the viscosity and hydrogen bond structures of methanol aqueous solutions with different molar ratios were investigated via confocal microscopic Raman spectroscopy. The Raman spectra of methanol in the CH and CO stretching regions were measured in order to investigate the structure of water/methanol molecules. The points of transition were identified by observing changes in viscosity following changes in concentration, and the bands were assigned to the C-H bond vibration shifts where the molar ratios of methanol and water were 1:3 and 3:1. Furthermore, the large band shift of 19 cm⁻¹ between the methanol solutions with the lowest and highest concentrations contained three hydrogen bond network modes, affecting the viscosity of the solution. This study provides an explanation for the relationship between the microstructures and macroscopic properties of aqueous solutions. Full article

The use of the Soil and Water Assessment Tool (SWAT) model driven by China Meteorological Assimilation Driving Datasets (CMADS) for runoff simulation research is of great significance for regional flood prevention and control. Therefore, from the perspective of production-living-ecological space, this article combined multi-source data such as DEM, soil texture and land use type, in order to construct scenarios of territorial spatial change (TSC) across distinct periods. Based on the CMADS-L40 data and the SWAT model, it simulated the runoff dynamics in the Xitiaoxi River Basin, and analyzed the hydrological response characteristics under different TSCs. The results showed that The SWAT model, driven by CMADS-L40 data, demonstrated robust performance in monthly runoff simulation. The coefficient of determination (R²), Nash–Sutcliffe efficiency coefficient (NSE), and the absolute value of percentage bias (|PBIAS|) during the calibration and validation period all met the accuracy requirements of the model, which validated the applicability of CMADS-L40 data and the SWAT model for runoff simulation at the watershed scale. Changes in territorial spatial patterns are closely correlated with runoff variation. Changes in agricultural production space and forest ecological space show statistically significant negative correlation with runoff change, while industrial production space change exhibits a significant positive correlation with runoff change. The expansion of production space, particularly industrial production space, leads to increased runoff, whereas the enlargement of agricultural production space and forest ecological space can reduce runoff. This article contributes to highlighting the role of land use policy in hydrological regulation, providing a scientific basis for optimizing territorial spatial planning to mitigate flood risks and protect water resources. Full article

Facial expression recognition (FER) is a critical research direction in artificial intelligence, which is widely used in intelligent interaction, medical diagnosis, security monitoring, and other domains. These applications highlight its considerable practical value and social significance. Face expression recognition models often need to run efficiently on mobile devices or edge devices, so the research on lightweight face expression recognition is particularly important. However, feature extraction and classification methods of lightweight convolutional neural network expression recognition algorithms mostly used at present are not specifically and fully optimized for the characteristics of facial expression images, yet fail to make full use of the feature information in face expression images. To address the lack of facial expression recognition models that are both lightweight and effectively optimized for expression-specific feature extraction, this study proposes a novel network design tailored to the characteristics of facial expressions. In this paper, we refer to the backbone architecture of MobileNet V2 network, and redesign LightExNet, a lightweight convolutional neural network based on the fusion of deep and shallow layers, attention mechanism, and joint loss function, according to the characteristics of the facial expression features. In the network architecture of LightExNet, firstly, deep and shallow features are fused in order to fully extract the shallow features in the original image, reduce the loss of information, alleviate the problem of gradient disappearance when the number of convolutional layers increases, and achieve the effect of multi-scale feature fusion. The MobileNet V2 architecture has also been streamlined to seamlessly integrate deep and shallow networks. Secondly, by combining the own characteristics of face expression features, a new channel and spatial attention mechanism is proposed to obtain the feature information of different expression regions as much as possible for encoding. Thus improve the accuracy of expression recognition effectively. Finally, the improved center loss function is superimposed to further improve the accuracy of face expression classification results, and corresponding measures are taken to significantly reduce the computational volume of the joint loss function. In this paper, LightExNet is tested on the three mainstream face expression datasets: Fer2013, CK+ and RAF-DB, respectively, and the experimental results show that LightExNet has 3.27 M Parameters and 298.27 M Flops, and the accuracy on the three datasets is 69.17%, 97.37%, and 85.97%, respectively. The comprehensive performance of LightExNet is better than the current mainstream lightweight expression recognition algorithms such as MobileNet V2, IE-DBN, Self-Cure Net, Improved MobileViT, MFN, Ada-CM, Parallel CNN(Convolutional Neural Network), etc. Experimental results confirm that LightExNet effectively improves recognition accuracy and computational efficiency while reducing energy consumption and enhancing deployment flexibility. These advantages underscore its strong potential for real-world applications in lightweight facial expression recognition. Full article

Although the importance of digital transformation in contemporary port development has been widely acknowledged, there is little empirical research on the extent to which it promotes sustainable development by reducing costs and increasing efficiency. This study takes the digital transformation of one of the largest ports in northern China—Tangshan Port—as an example, as the application of digital technologies has greatly improved its operational efficiency. By using cargo throughput and container throughput data from Tangshan Port as the experimental group and from Qinhuangdao Port as the control group, difference-in-differences regression models with monthly data and port fixed effects were adopted to clarify the impact of digital transformation on sustainability for different types of cargo throughput, as well as the differential effects of policy impact on port production efficiency and economic performance in the short and long term, in order to examine the impact of digitalization on port operation performance. Our findings demonstrate that digital transformation has a significant positive impact on both port cargo and container throughput, with the long-term effect surpassing the short-term effect. Additionally, regional economic level positively moderates policy impact. These findings provide critical evidence that ports can balance economic growth and environmental sustainability within sustainable development frameworks. Full article

We propose a novel interpretability framework that integrates instance-wise feature selection with causal reasoning to explain decisions made by black-box image classifiers. Instead of relying on feature importance or mutual information, our method identifies input regions that exert the greatest causal influence on model predictions. Causal influence is formalized using a structural causal model and quantified via a conditional mutual information term. To optimize this objective efficiently, we employ continuous subset sampling and the matrix-based Rényi’s $\alpha$-order entropy functional. The resulting explanations are compact, semantically meaningful, and causally grounded. Experiments across multiple vision datasets demonstrate that our method outperforms existing baselines in terms of predictive fidelity. Full article

Reliable estimation of extreme waves is essential for offshore wind turbine system design; however, site-specific conditions limit the application of one-size-fits-all statistical methods. We analyzed extreme wave conditions at potential offshore wind farm sites in South Korea using high-resolution hindcast data (1979–2022) based on the Weather Research and Forecasting (WRF) model. While previous studies have typically relied on a limited combination of distribution types and parameter estimation methods, this study systematically applied various Weibull distribution models and parameter estimation techniques to the environmental contour (EC) method. The results show that the optimal statistical approach varied by site according to the tail characteristics of the wave height distribution. The inverse second-order reliability method (I-SORM) provided the highest accuracy in regions with rapidly decaying tails, achieving root mean square error (RMSE) values of 0.21 in Shinan (using the three-parameter Weibull distribution with maximum likelihood estimation, MLE) and 0.34 in Chujado (with the method of moments, MOM). In contrast, the inverse first-order reliability method (I-FORM) yielded superior performance in areas where the tail decays more gradually, such as Yokjido (RMSE = 0.47 with MLE using the exponentiated Weibull distribution) and Ulsan (RMSE = 0.29, with MLE using the exponentiated Weibull distribution). These findings underscore the importance of selecting site-specific combinations of statistical models and estimation techniques based on wave distribution characteristics, thereby improving the accuracy and reliability of extreme design wave predictions. The proposed framework can significantly contribute to the establishment of reliable design criteria for offshore wind turbine systems by reflecting region-specific marine environmental conditions. Full article

Bruising in ‘Korla’ pears represents a prevalent phenomenon that leads to progressive fruit decay and substantial economic losses. The detection of early-stage bruising proves challenging due to the absence of visible external characteristics, and existing deep learning models have limitations in weak feature extraction under complex optical interference. To address the postharvest latent damage detection challenges in ‘Korla’ pears, this study proposes a collaborative detection framework integrating structured-illumination reflectance imaging (SIRI) with multi-order gated attention mechanisms. Initially, an SIRI optical system was constructed, employing 150 cycles·m⁻¹ spatial frequency modulation and a three-phase demodulation algorithm to extract subtle interference signal variations, thereby generating RT (Relative Transmission) images with significantly enhanced contrast in subsurface damage regions. To improve the detection accuracy of latent damage areas, the MOGA-UNet model was developed with three key innovations: 1. Integrate the lightweight VGG16 encoder structure into the feature extraction network to improve computational efficiency while retaining details. 2. Add a multi-order gated aggregation module at the end of the encoder to realize the fusion of features at different scales through a special convolution method. 3. Embed the channel attention mechanism in the decoding stage to dynamically enhance the weight of feature channels related to damage. Experimental results demonstrate that the proposed model achieves 94.38% mean Intersection over Union (mIoU) and 97.02% Dice coefficient on RT images, outperforming the baseline UNet model by 2.80% with superior segmentation accuracy and boundary localization capabilities compared with mainstream models. This approach provides an efficient and reliable technical solution for intelligent postharvest agricultural product sorting. Full article

In order to fulfill the commitment to the “dual carbon goal” at an early date, China has implemented a series of carbon control policies. However, the actual impact of these policy combinations on green innovation in Chinese cities remains unknown. Taking the implementation of the low-carbon pilot policy (LCP) and the carbon emission trading pilot policy (CET) as the research opportunity, this paper uses panel data from 276 prefecture-level cities and a multiple-period difference-in-differences (DID) model to explore the impact of carbon control policy combination on green innovation in China and their mechanisms. The results indicate the following: A single LCP or CET can significantly boost green innovation. However, the impact of cross-tool carbon control policy combination on green innovation is notably greater than that of a single policy, with a trend of increasing effectiveness over time. Even after a series of robustness tests, this conclusion remains valid. Heterogeneity analysis shows that the promotion effect is more significant in the eastern region and high-level administrative cities. The policy combination incentivizes green innovation through fiscal technology expenditure and public environmental awareness, focusing more on fostering strategic green innovation. Consequently, the Chinese government should tailor policy combinations to specific contexts, expand their implementation judiciously, and consistently drive forward green innovation. Full article

The Copernicus European Regional Reanalysis (CERRA) was released in August 2022, providing a continental atmospheric reanalysis, and, in addition, the New European Wind Atlas (NEWA) is a recently released hindcast product that can be used to create a high temporal and spatial resolution wind resource atlas of Europe. In order to demonstrate the suitability of the NEWA and CERRA wind datasets for offshore wind energy applications, the accuracy of these datasets was assessed for the Mediterranean Sea, a basin with a high potential for the development of offshore wind projects. Long-term in situ measurements from 13 offshore locations along the basin were used in order to assess the performance of the CERRA and NEWA wind speed datasets in the hourly and seasonal time scales by using a variety of different evaluation tools. The results revealed that the CERRA dataset outperforms NEWA and is a reliable source for offshore wind energy assessment studies in the examined areas, although special attention should be paid to extreme value analysis of the wind speed. Full article

Replication timing (RT), the temporal order of DNA replication during S phase, influences regional mutation rates, yet the mechanistic basis for RT-associated mutagenesis remains incompletely defined. To identify drivers of RT-dependent mutation biases, we analyzed whole-genome sequencing data from cells with disruptions in DNA replication/repair genes or exposed to mutagenic compounds. Mutation distributions between early- and late-replicating regions were compared using bootstrapping and statistical modeling. We identified 14 genes that exhibit differential effects in early- or late-replicating regions, encompassing multiple DNA repair pathways, including mismatch repair (MLH1, MSH2, MSH6, PMS1, and PMS2), trans-lesion DNA synthesis (REV1) and double-strand break repair (DCLRE1A and PRKDC), DNA polymerases (POLB, POLE3, and POLE4), and other genes central to genomic instability (PARP1 and TP53). Similar analyses of mutagenic compounds revealed 19 compounds with differential effects on replication timing. These results establish replication timing as a critical modulator of mutagenesis, with distinct DNA repair pathways and exogenous agents exhibiting replication timing-specific effects on genomic instability. Our systematic bioinformatics approach identifies new DNA repair genes and mutagens that exhibit differential activity during the S phase. These findings pave the way for further investigation of factors that contribute to genome instability during cancer transformation. Full article