Search Results (132)

Mineral waters from two tectonically active mountain systems within the Alpine-Himalayan orogenic belt, the Pamir and the Greater Caucasus (Elbrus region), were analyzed for ²²²Rn activity and ²³⁸U concentrations to establish correlations with geological conditions, physicochemical characteristics of water, and to assess the potential health risk associated with ²³⁸U and ²²²Rn. It was found that in mineral waters of the Pamir, the concentrations of ²³⁸U (0.004–13.3 µg/L) and activity of ²²²Rn (8–130 Bq/L) are higher than in the Elbrus area: 0.04–3.74 µg/L and 6–33 Bq/L, respectively. Results indicate that uranium mobility in water is strongly influenced by T, pH, and Eh, but is less affected by the age of host rocks or springs′ elevation, whereas radon activity in waters depends on the age of rocks, spring elevation, ²³⁸U content, and values of δ¹⁸O and δ²H in water. This study reveals fundamental geological distinctions governing uranium and radon sources in the mineral waters of these regions. Isotopic evidence (²²²Rn and ³He/⁴He) demonstrates crustal radon sources prevail in Pamir, whereas the Elbrus system suggests mantle-derived components. The U concentrations do not exceed 30 µg/L, and most water samples (94%) showed ²²²Rn activities below 100 Bq/L, complying with the drinking water exposure limits recommended by the World Health Organization and European Union Directive. However, in intermountain depressions of the Pamirs, at low absolute elevations (~2300 m), radon concentrations in water can increase significantly, which requires special attention and study. Full article

Biodegradable mulch film (BDM) is regarded as a key solution to combat plastic mulch film pollution due to its ability to degrade completely into CO₂ and H₂O through environmentally friendly microorganisms. However, commercial BDM often fails to degrade fully after use, leading to the accumulation of BDM residues in soil and their transformation into microplastics (MPs) via various processes, posing a threat to the soil ecosystem. Given these discrepancies between the theoretical and practical degradation performance of BDM, there is an urgent need to understand the impacts of BDM residues on plant growth and soil health. This research conducted pot experiments spanning the entire growth cycle of Chinese cabbage to evaluate the impact of PBST-BDM raw material (R), PBST-BDM residues (M), and PBST-BDM composting product (P) on crop growth and soil quality. The findings revealed that R treatments had a slight effect on Chinese cabbage growth (e.g., a 5.80% increase in emergence rate in R 1% treatment, p < 0.05), while M treatments significantly hindered the emergence rate, plant height, leaf area, and biomass accumulation of Chinese cabbage by 30.4% (p < 0.05), 2.71 cm (p < 0.05), 39.0% (p < 0.05), and 1.86 g (p < 0.05) in the M 1% treatment compared to the control group (CK). In contrast, P treatments enhanced Chinese cabbage growth, with greater improvements at higher weight ratios, resulting in increases of 8.89% (p < 0.05), 4.96 cm (p < 0.05), 36.3% (p < 0.05), and 2.31 g (p < 0.05) in the P 1% treatment. Microbial network topology in the M 1% treatment is highly variable, with the increased proportion of positive correlations in the P 1% treatment hinting at stronger symbiotic interactions between species (p < 0.05). Analysis results of PCoA and PLS-DA showed significant differences in microbial community and soil metabolites between M 1% treatment and CK (p < 0.05). These findings suggest that, although composting post-use BDM may reduce their negative ecological effects, possibly via accelerating the early breakdown of residues, the feasibility and scalability of this approach require further validation under real-world field conditions. Full article

Climate changes and coastal development pose growing risks to coastal roadways constructed on flat and low-elevation terrains near retreating shorelines. Although GIS has been widely used for shoreline change analysis and roadway management, significant limitations remain in accurately analyzing shoreline changes relative to roadways and integrating the analysis results into roadway spatial databases in the Geographic Information System (GIS). In this regard, this study proposes a novel geospatial approach that integrates the linear referencing system (LRS) with the vector-offset based analysis method for shoreline change. The LRS, implemented in GIS, defines the specific positions of a roadway using relative distances from predefined referents. Vector offsets, representing the shortest distance and direction from historical shorelines to the roadway, are then employed to analyze shoreline changes. The proposed approach was applied to a coastal roadway experiencing significant shoreline changes driven by climate change and the construction of coastal infrastructure. The results demonstrate the effectiveness of the proposed approach in analyzing shoreline retreat caused by coastal infrastructure development, as well as shoreline accretion following the installation of erosion control structures. These results, which closely reflect the actual erosion pattern, indicate that the proposed approach can effectively support planning for roadway maintenance and reinforcement. Full article

The increasing urbanisation and technological advancements have driven the global adoption of smart city initiatives, yet regional differences persist due to economic, social, and technological disparities. Despite the numerous studies on smart cities, there remains a research gap in comprehensive global analyses exploring regional differentiations in smart city development. This study aims to examine how smart cities differentiate, especially through associations between regions and smart city dimensions. This study utilises data from the IMD Smart City Index 2023 and applies a multi-step methodology based on the United Nations’ geographic regions, employing geographical and statistical analyses. The findings reveal distinct regional differentiations, highlighting a clear Global North–South divide and notable subregional differentiations, including the North–South divide in the Americas and the East–West divide in Asia. The correlation analysis demonstrates significant relationships between smart city dimensions, with smart mobility and smart living exhibiting the highest association. The correspondence analysis further identifies four major regional concentration groups, notably the Global North, with equi-distant associations with all dimensions, and Asia, which is closely linked to smart governance. The findings confirm that smart city development is not uniform and is shaped by regional socio-economic and technological conditions and emphasises the need for context-dependent regional policies. Full article

The Yancheng coastal wetlands serve as a crucial stopover site along the East Asian–Australasian Flyway. The rapid expansion of aquaculture has led to a significant decline in natural wetlands, impacting both the distribution and quality of waterbird habitats. Following the designation of the region as a World Natural Heritage site in 2019, the local government has prioritized the protection of waterbird habitats, leading to the large-scale withdrawal of aquaculture from the region. Nevertheless, the impact of the fishing withdrawal on waterbird habitat selection and the ecological benefits it brought remain unknown. In this study, based on the identification of fishing withdrawal zones in the Yancheng coastal area, six waterbird groups, Anatidae, Ardeidae, Charadriiformes, Laridae, Gruidae and Ciconiidae, were selected to construct an evaluation index system for habitat suitability. The Biomod2 ensemble model was employed to analyze the spatial differences of suitable habitats for waterbirds within the fishing withdrawal zones. The result revealed the following: (1) As of 2022, the area of fishing withdrawal zones had reached 2.23 × 10⁴ ha, primarily distributed in Beihuan and Nanhuan. Among these, the area of fishing withdrawal zones in Nanhuan was the largest, reaching 6.78 × 10³ ha. (2) Unsuitable area for waterbirds was largest in the fishing withdrawal zones, with a proportion of 60% and 58% for Gruidae and Ciconiidae, respectively. (3) The rich nutrients, high coverage and tall stature of emergent vegetation in the fishing withdrawal zones led to a reduction in water surface area, resulting in significant adverse effects on the suitable habitats for Charadriiformes and Gruidae. Therefore, the results suggest that most areas after fishing withdrawal were still not suitable habitats for waterbirds. The implementation of scientific fishing withdrawal practices, along with ecological restoration and management, is crucial for improving the habitat suitability in fishing withdrawal zones. This study provides valuable insights for more purposeful selection of fishing withdrawal sites, and more scientific management and restoration of these areas to enhance their ecological benefits. Full article

Existing network intrusion detection models effectively capture relationships between nodes and extract key features. However, they often struggle to accurately represent node characteristics, particularly in modeling the spatiotemporal dynamics and topological structures with sufficient granularity. To address these limitations, we propose the dynamic interaction multi-channel graph convolutional network (DIMK-GCN), which integrates three key components: a spatiotemporal feature weighting module, an interactive graph feature fusion module, and a temporal feature learning module. The spatiotemporal feature weighting module constructs a dynamic graph structure that incorporates both nodes and edges, leveraging self-attention mechanisms to enhance critical feature representations. The interactive graph feature fusion module employs graph attention networks (GATs) to refine node relationships while integrating a multi-channel graph convolutional network (GCN) to extract multi-perspective features, thereby enhancing model depth and robustness. The temporal feature learning module utilizes gated recurrent units (GRUs) to effectively capture long-term dependencies and address challenges posed by non-stationary time series data. Experimental results on the CIC-IDS2017, CIC-IDS2018, and Edge-IIoTSet datasets demonstrate that DIMK-GCN significantly outperforms existing models in key performance metrics, including detection accuracy, recall, and F1-score. Notably, on the Edge-IIoTSet dataset, DIMK-GCN achieves an accuracy of 97.31%, verifying its effectiveness and robustness in detecting various types of network attacks. Full article

The distributive environmental justice approach focuses on analysing the spatial patterns of environmental effects and identifying situations of inequality between different social groups. These inequalities may be related to specific impacts or be the result of accumulated benefits or harms to certain populations, the latter aspect being less investigated globally and, in particular, in Latin America. This work aims to analyse the existence of environmental injustices in the city of Montevideo (Uruguay). For this purpose, an environmental quality index (EQI) composed of five subindices is proposed: pollution, exposure to risks, health impacts, habitat quality and availability of green spaces, and their relationship with the level of poverty is evaluated through descriptive analyses and spatial regression models. The results reveal an inverse relationship between the cumulative environmental quality and poverty level, this being especially marked in the subindices of environmental pollution and habitat quality. In contrast, the availability of green spaces presents a more favourable situation for the poorest groups of the population. This study highlights the importance of analysing environmental injustices through multiple indicator-based approaches and highlights the need to incorporate these perspectives into the study of cities with high levels of segregation. Full article

Heavy metal ion pollution in aquatic environments is a critical global issue, damaging ecosystems and threatening human health via bioaccumulation in the food chain. Despite promising progress with biosorbents, the development of environmentally friendly and stable heavy metal adsorbents requires further exploration. In this study, we present an algae-loaded alginate hydrogel as a composite adsorbent for heavy metals. The incorporation of algae enhanced the hydrogel’s adsorption capacity by 38.0%, 20.6%, and 27.1% for Cu²⁺, Cr³⁺, and Co²⁺, respectively. Additionally, the composite hydrogel demonstrated excellent stability and recyclability after adsorption, reducing the ecological risks associated with algae biomass usage. This algae-loaded alginate hydrogel offers an efficient and eco-friendly strategy for removing heavy metal ions from aquatic systems, highlighting its potential for environmental remediation applications. Full article

Preeclampsia (PE) is a major cause of maternal mortality and morbidity, affecting 3–6% of pregnancies worldwide and ranking among the top six causes of maternal deaths in the U.S. PE typically develops after 20 weeks of gestation and is characterized by new-onset hypertension and/or end-organ dysfunction, with or without proteinuria. Current management strategies for PE emphasize early diagnosis, blood pressure control, and timely delivery. For prevention, low-dose aspirin (81 mg/day) is recommended for high-risk women between 12 and 28 weeks of gestation. Magnesium sulfate is also advised to prevent seizures in preeclamptic women at risk of eclampsia. Emerging management approaches include antiangiogenic therapies, hypoxia-inducible factor suppression, statins, and supplementation with CoQ10, nitric oxide, and hydrogen sulfide donors. Black women are at particularly high risk for PE, potentially due to higher rates of hypertension and cholesterol, compounded by healthcare disparities and possible genetic factors, such as the APOL1 gene. This review explores current and emerging strategies for managing PE and addresses the underlying causes of health disparities, offering potential solutions to improve outcomes. Full article

How to prevent the influence of precipitation’s localized and sudden characteristics is the most formidable challenge in the quality control (QC) of precipitation observations. However, with sufficiently high spatiotemporal resolution in observational data, nuanced information can aid us in accurately distinguishing between intense, localized precipitation events, and anomalies in precipitation data. China has deployed over 70,000 automatic weather stations (AWSs) that provide high spatiotemporal resolution surface meteorological observations. This study developed a new method for performing QC of precipitation data based on the high spatiotemporal resolution characteristics of observations from surface AWSs in China. The proposed QC algorithm uses the cumulative average method to standardize the probability distribution characteristics of precipitation data and further uses the empirical orthogonal function (EOF) decomposition method to effectively identify the small-scale spatial structure of precipitation data. Leveraging the spatial correlation characteristics of precipitation, partitioned EOF detection with a 0.5° spatial coverage effectively minimizes the influence of local precipitation on quality control. Analysis of precipitation probability distribution reveals that reconstruction based on the first three EOF modes can accurately capture the organized structural features of precipitation within the detection area. Thereby, based on the randomness characteristics of the residuals, when the residual of a certain observation is greater than 2.5 times the standard deviation calculated from all residuals in the region, it can be determined that the data are erroneous. Although the quality control is primarily aimed at accumulated precipitation, the randomness of erroneous data indicates that 84 continuous instances of error data in accumulated precipitation can effectively trace back to erroneous hourly precipitation observations. This ultimately enables the QC of hourly precipitation data from surface AWSs. Analysis of the QC of precipitation data from 2530 AWSs in Jiangxi Province (China) revealed that the new method can effectively identify incorrect precipitation data under the conditions of extreme weather and complex terrain, with an average rejection rate of about 5%. The EOF-based QC method can accurately detect strong precipitation events resulting from small-scale weather disturbances, thereby preventing local heavy rainfall from being incorrectly classified as erroneous data. Comparison with the quality control results in the Tianqing System, an operational QC system of the China Meteorological Administration, revealed that the proposed method has advantages in handling extreme and scattered outliers, and that the precipitation observation data, following quality control procedures, exhibits enhanced similarity with the CMAPS merged precipitation data. The novel quality control approach not only elevates the average spatial correlation coefficient between the two datasets by 0.01 but also diminishes the root mean square error by 1 mm. Full article

This study introduces AgroTec 4.0, which is a smart farming system designed to revolutionize strawberry cultivation in greenhouses through the integration of edge computing technology in the Andean region of Ecuador. The primary objective has been to enhance cultivation efficiency by comparing results from strawberry crops with and without the system, under identical greenhouse conditions. Given the low educational and economic status of local farmers, AgroTec 4.0 was engineered to be user-friendly, easy to operate, and cost-effective, empowering producers with data-driven decision-making capabilities. Key findings underscore the potential of AgroTec 4.0 and agricultural data, including a 15% increase in strawberry yield, from 5.0 kg/m² in the control greenhouse to 5.75 kg/m² with AgroTec 4.0, highlighting the system’s ability to maximize productivity. There has also been a significant 20% reduction in water usage, decreasing from 80 L/m² in the control greenhouse to 64 L/m² with the system, showcasing AgroTec 4.0’s efficiency in resource management. Furthermore, there were significant improvements in fruit quality, with an 11.8% increase in the Brix index (from 8.5 to 9.5) and a 16.7% increase in average fruit weight (from 30 to 35 g), demonstrating the system’s capacity to enhance product quality. Finally, there has been an impressive 103.03% return on investment (ROI) with AgroTec 4.0, compared to no change in ROI in the control greenhouse, emphasizing the economic value of implementing this technology. These results underscore the transformative potential of AgroTec 4.0 in precision agriculture, offering a scalable and sustainable approach for small-scale producers in Ecuador. The system’s modularity and real-time data analysis capabilities allow for flexible adaptation to various needs, providing farmers with an intuitive interface for managing crops and optimizing resource use. This study demonstrates the feasibility of leveraging agricultural data and edge computing to improve cultivation practices and enhance productivity, contributing efficiently to the sustainability of agriculture in challenging environments. Full article

The surface texture of objects in industrial scenes is complex and diverse, and the characteristics of surface defects are often very similar to the surrounding environment and texture background, so it is difficult to accurately detect the defect area. However, when deep learning technology is used to detect complex texture surface defects, the detection accuracy is not high, due to the lack of large-scale pixel-level label datasets. Therefore, a defect detection model Siamese-RCNet for complex texture surface with a small number of annotations is proposed. The Cascade R-CNN target detection network is used as the basic framework, making full use of unlabeled image feature information, and fusing the nonlinear relationship learning ability of Siamese network and the feature extraction ability of the Res2Net backbone network to more effectively capture the subtle features of complex texture surface defects. The image difference measurement method is used to calculate the similarity between different images, and the attention module is constructed to weight the feature map of the feature extraction pyramid, so that the model can focus more on the defect area and suppress the influence of complex background texture area, so as to improve the accuracy of detection. To verify the effectiveness of the Siamese-RCNet model, a series of experiments were carried out on the DAGM2007 dataset of weakly supervised learning texture surface defects for industrial optical inspection. The results show that even if only 20% of the labeled datasets are used, the mAP@0.5 of the Siamese-RCNet model can still reach 96.9%. Compared with the traditional Cascade R-CNN and Faster R-CNN target detection networks, the Siamese-RCNet model has high accuracy, can reduce the workload of manual labeling, and provides strong support for practical applications. Full article

Synchronized variable frequency soft-switching is analyzed and implemented in a 3-phase bidirectional grid-tied inverter. The common-mode connected topology and control allow for independent analysis of a single phase leg before six are combined into two interleaved, 3-phase inverters. Effective operation is enabled by discretizing the variable switching frequencies before synchronizing them with a control signal. The resulting inverter can operate at any power factor at power levels up to 50 kVA while maintaining zero-voltage switching (ZVS) throughout the grid cycle. Formal conditions for soft-switching and methods for achieving ZVS while maintaining global synchronization are presented. These conditions are then verified in a simulation. Finally, results for different power factors with and without interleaving are demonstrated in a prototype that achieves >98.1% efficiency when converting all real power. Full article

To evaluate the effectiveness of the low-carbon innovation development of enterprises, this paper proposes a neutral dynamic network cross-efficiency model and introduces the bootstrap sampling method to correct the model. The model categorizes the low-carbon green innovation R&D activities of enterprises into two distinct stages, as follows: the green R&D stage and the results transformation stage. It then assesses the efficiency of each stage and provides an overall efficiency rating. The model has been applied to a sample of listed Chinese iron and steel enterprises (CISES). The results of the study show that the overall efficiency of low-carbon innovation and development of CISES is on the low side, with the highest efficiency achieved in the green R&D stage, which is less than the lowest efficiency attained in the transformation stage, and most of the enterprises are in the stage of high green R&D and low transformation of the results. The ability of marketization of the R&D results still needs to be strengthened. Full article

Plants are important ecological factors and food resources, which can significantly affect the occurrence and distribution of insects. The metabolites in host plants can affect the feeding, spawning, and avoidance behaviors of herbivorous insects. Galeruca daurica (Joannis) is a phytophagous pest that has seriously occurred in the desert steppe of Inner Mongolia in recent years, only infesting the leaves of Allium plants. In order to clarify the effects of plant metabolites on the gene expression in G. daurica larvae at the transcriptome level, we fed the larvae of G. daurica with Allium tuberosum leaves soaked in 10% DMSO solutions containing d-galactose, β-d-glucopyranose, l-rhamnose, isoquercitrin, isoflavone, and rutin, respectively, used the larvae fed on A. tuberosum leaves soaked in a 10% DMSO solution as the control, and screened out the differentially expressed genes (DEGs) by performing high-throughput transcriptome sequencing. The results showed that a total of 291 DEGs were identified compared to the solvent control (DMSO), including 130, 34, 29, 21, 72, and 97 in the isoquercitrin, isoflavone, rutin, d-galactose, β-d-glucopyranose, and l-rhamnose treatment groups, respectively. GO and KEGG enrichment analysis showed that most DEGs were enriched in various metabolic pathways, implying that these six main primary and secondary metabolites in Allium plants may affect various metabolic processes in the larvae of G. daurica. Full article