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Research on the hydrochemical characteristics of groundwater can help to elucidate its evolution and distribution, as well as other hydrogeological conditions. The present study investigated the current status of groundwater hydrochemistry and nitrogen contamination in Yinchuan area, Yinchuan basin of northwest China. Piper and Gibbs diagrams were constructed and multivariate statistical methods were used to explore the groundwater hydrochemical characteristics and its controlling factors, as well as nitrogen contamination of groundwater and its sources. The results demonstrated that phreatic water was moderately salty with a large variation of TDS and major ion concentrations, while the confined water was mainly fresh water with TDS less than 1000 mg/L. Phreatic water was mainly HCO3·Cl–Na·Mg and HCO3·SO4–Na·Mg types, while the confined water was HCO3·Cl–Na·Ca and HCO3·SO4–Na·Ca types. The major ion chemistry of the confined water is governed by water–rock interactions, while evaporation plays a key role in phreatic water chemistry formation. Furthermore, the concentration of NO3− in phreatic water is significantly higher than that of confined water, and the opposite was observed for NH4+. The concentrations of NO3− and NH4+ exhibited an increasing tendency in groundwater in the last 26 years. The nitrogen sources in phreatic water involved human activities like nitrogen fertilizer application in agriculture, and industrial and domestic sewage. In confined aquifer, the higher NH4+ concentration is comprehensively affected by hydrogeological conditions, leakage of phreatic water and an anaerobic environment. Overall, the results of the present study are helpful for the groundwater protection in Yinchuan area, and will contribute to the effective management and reasonable development of groundwater resources in Yinchuan basin.

With the explosive growth of false information on social media platforms, the automatic detection of multimodal false information has received increasing attention. Recent research has significantly contributed to multimodal information exchange and fusion, with many methods attempting to integrate unimodal features to generate multimodal news representations. However, they still need to fully explore the hierarchical and complex semantic correlations between different modal contents, severely limiting their performance detecting multimodal false information. This work proposes a two-stage detection framework for multimodal false information detection, called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency and inconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training (CLIP) model to learn the relationship between text and images through label awareness and train an image aesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity between the image and related images and use this similarity as a threshold to divide the multimodal correlation matrix into consistency and inconsistency matrices. Finally, the fusion module is designed to identify essential features for detecting multimodal false information. In extensive experiments on four datasets, the performance of the ASMFD is superior to state-of-the-art baseline methods.

Assessment of contaminated food through the dietary intake is essential for human health. To investigate the health risk of rare earth element (REE) exposure to fruits and vegetables in mining areas in China, we collected 288 fruit samples and 942 vegetable samples from four representative mining points (Bayan Obo in Inner Mongolia, Weishan in Shandong, Maoming in Guangdong, Longnan in Jiangxi) and their control areas. The content of REEs was determined by inductively coupled plasma-mass spectrometry (ICP-MS). The total REEs in fruits from mining and control areas were 12.90 μg kg −1 and 11.89 μg kg −1 , and in vegetables were 92.90 μg kg −1 and 62.38 μg kg −1 , and the difference was statistically significant in vegetables ( P = 0.048). The drupes had more REE concentration in fruits (68.41 μg kg −1 , 16.90 μg kg −1 in mining and control areas, respectively) ( P < 0.01), and the leafy vegetables had more REE concentration in vegetables (245.81 μg kg −1 , 123.51 μg kg −1 in mining and control areas, respectively) ( P < 0.01). With the enrichment of light rare earth elements (LREE), the REE distribution patterns coincided in mining and control areas and different types of fruits and vegetables. The health risk assessment indicated that the estimated daily intakes (0.02–0.06 μg kg −1 day −1 , 0.53–1.22 μg kg −1 day −1 for fruits and vegetables, respectively) were lower than the allowable daily intake value (60.4 μg kg −1 day −1 ). In mining areas, REEs obtained from fruits and vegetables were insufficient to cause health damage to human beings. However, sustained exposure to low REEs, especially for children, still needs attention.

To investigate the clinical characteristics of acute myeloid leukemia (AML) with biliary obstruction as the first manifestation and explore the treatment options. A retrospective analysis was performed on a case of AML with biliary obstruction as the first manifestation admitted to the First Affiliated Hospital of Jishou University (Jishou, China). The relevant laboratory examination, imaging, pathological results and treatment strategies were analyzed. The patient was a 44-year-old male with an initial manifestation of biliary obstruction. Combined with the results of laboratory tests and bone marrow aspiration, the patient was diagnosed with AML and was treated with an IA regimen (idarubicin 8 mg d1-3, cytarabine 0.2 d1-5). After 2 courses of treatment, complete response was achieved, the liver function returned to normal and the biliary obstruction disappeared. The initial symptoms of AML are varied, and always combine with multi-system organ damage. Early diagnosis and active treatment of primary diseases are the keys to improving the prognosis of these patients.

Since the 2008 global financial crisis, the Basel Committee on Banking Supervision Committee implemented Basel III, a new set of regulatory standards on the banking industry. The main objective of Basel III is to strengthen the international banking system and improve market resilience. China has committed to upgrade the Chinese banking regulatory system and align it with international standards. After issuing a series of regulatory policies, Basel III in China has been considered as complete by Basel III Committee. Using a panel dataset of 3400 publicly traded firms’ financial figures from 2006 to 2015, this paper investigates the impact of Basel III on Chinese firms’ access to credit. The hypothesis of this paper is that the implementation of Basel III will have negative effects on firms’ accessibility to credit but the effect is heterogeneous, varies across firm ownership types and firm industries. Using the fixed effect model, this paper found China experienced a dramatic credit increase at the corporate level over the same time period. However, this is mainly driven by government policies to stimulate Chinese economy after the 2008 financial crisis.

Background Promoting rapid wound healing with functional recovery of all skin appendages is the main goal of regenerative medicine. So far current methodologies, including the commonly used back excisional wound model (BEWM) and paw skin scald wound model, are focused on assessing the regeneration of either hair follicles (HFs) or sweat glands (SwGs). How to achieve de novo appendage regeneration by synchronized evaluation of HFs, SwGs and sebaceous glands (SeGs) is still challenging. Here, we developed a volar skin excisional wound model (VEWM) that is suitable for examining cutaneous wound healing with multiple-appendage restoration, as well as innervation, providing a new research paradigm for the perfect regeneration of skin wounds. Methods Macroscopic observation, iodine–starch test, morphological staining and qRT-PCR analysis were used to detect the existence of HFs, SwGs, SeGs and distribution of nerve fibres in the volar skin. Wound healing process monitoring, HE/Masson staining, fractal analysis and behavioral response assessment were performed to verify that VEWM could mimic the pathological process and outcomes of human scar formation and sensory function impairment. Results HFs are limited to the inter-footpads. SwGs are densely distributed in the footpads, scattered in the IFPs. The volar skin is richly innervated. The wound area of the VEWM at 1, 3, 7 and 10 days after the operation is respectively 89.17% ± 2.52%, 71.72% ± 3.79%, 55.09 % ± 4.94% and 35.74% ± 4.05%, and the final scar area accounts for 47.80% ± 6.22% of the initial wound. While the wound area of BEWM at 1, 3, 7 and 10 days after the operation are respectively 61.94% ± 5.34%, 51.26% ± 4.89%, 12.63% ± 2.86% and 6.14% ± 2.84%, and the final scar area accounts for 4.33% ± 2.67% of the initial wound. Fractal analysis of the post-traumatic repair site for VEWM vs human was performed: lacunarity values, 0.040 ± 0.012 vs 0.038 ± 0.014; fractal dimension values, 1.870 ± 0.237 vs 1.903 ± 0.163. Sensory nerve function of normal skin vs post-traumatic repair site was assessed: mechanical threshold, 1.05 ± 0.52 vs 4.90 g ± 0.80; response rate to pinprick, 100% vs 71.67% ± 19.92%, and temperature threshold, 50.34°C ± 3.11°C vs 52.13°C ± 3.54°C. Conclusions VEWM closely reflects the pathological features of human wound healing and can be applied for skin multiple-appendages regeneration and innervation evaluation.

Strategic transformation of regionalization for agricultural comprehensive development （ACD） was presented by the Ministry of Finance of the People＇s Republic of China （MOF） in 2014. The regionalization is the premise and basis of the sustainable development and improved competitiveness for agriculture. Based on the environmental resources related to agriculture, such as cropland, climate, water resources, terrain, geomorphology, patterns of the ACD projects, distribution of ecological planning, etc., we devised 13 indices using the geographic comprehensive regionalization method. The indices took into account a combination of dynamic and static, qualitative and quantitative, as well as agricultural and ecological factors. The strategic transformation of regionalization for the ACD in Ningxia Hui Autonomous Region of China was performed; seven types were included： prioritized regions, prioritized and restricted regions, protected regions, protected and restricted regions, restricted and prioritized regions, restricted and protected regions, and restricted regions. A further 24 subtypes were used based on locations and ecological zones. The regionalization results showed that prioritized regions were mainly in northern Ningxia, the most suitable area for agriculture. The protected and restricted regions were in central and southern Ningxia. In the central part, drought was the limiting factor for agriculture, and water conservation projects there should be supported. The ecological environment is fragile in southern Ningxia, so there is a need for ecologically sound agriculture to be developed in this region. Such regionalization could achieve two goals, namely agricultural conservation and eco-environmental protection. It was performed following the requirement for scientific regionalization to include three types of regions （prioritized regions, protected regions, and restricted regions）, and was applied at the township scale in a provincial or autonomous region for the first time. The results provide both guidance for the strategic transformation of the ACD in Ningxia, and a reference for similar work in other provinces.

Soil texture and soil particle size fractions (PSFs) play an increasing role in physical, chemical, and hydrological processes. Many previous studies have used machine-learning and log-ratio transformation methods for soil texture classification and soil PSF interpolation to improve the prediction accuracy. However, few reports have systematically compared their performance with respect to both classification and interpolation. Here, five machine-learning models – K-nearest neighbour (KNN), multilayer perceptron neural network (MLP), random forest (RF), support vector machines (SVM), and extreme gradient boosting (XGB) – combined with the original data and three log-ratio transformation methods – additive log ratio (ALR), centred log ratio (CLR), and isometric log ratio (ILR) – were applied to evaluate soil texture and PSFs using both raw and log-ratio-transformed data from 640 soil samples in the Heihe River basin (HRB) in China. The results demonstrated that the log-ratio transformations decreased the skewness of soil PSF data. For soil texture classification, RF and XGB showed better performance with a higher overall accuracy and kappa coefficient. They were also recommended to evaluate the classification capacity of imbalanced data according to the area under the precision–recall curve (AUPRC). For soil PSF interpolation, RF delivered the best performance among five machine-learning models with the lowest root-mean-square error (RMSE; sand had a RMSE of 15.09 %, silt was 13.86 %, and clay was 6.31 %), mean absolute error (MAE; sand had a MAD of 10.65 %, silt was 9.99 %, and clay was 5.00 %), Aitchison distance (AD; 0.84), and standardized residual sum of squares (STRESS; 0.61), and the highest Spearman rank correlation coefficient (RCC; sand was 0.69, silt was 0.67, and clay was 0.69). STRESS was improved by using log-ratio methods, especially for CLR and ILR. Prediction maps from both direct and indirect classification were similar in the middle and upper reaches of the HRB. However, indirect classification maps using log-ratio-transformed data provided more detailed information in the lower reaches of the HRB. There was a pronounced improvement of 21.3 % in the kappa coefficient when using indirect methods for soil texture classification compared with direct methods. RF was recommended as the best strategy among the five machine-learning models, based on the accuracy evaluation of the soil PSF interpolation and soil texture classification, and ILR was recommended for component-wise machine-learning models without multivariate treatment, considering the constrained nature of compositional data. In addition, XGB was preferred over other models when the trade-off between the accuracy and runtime was considered. Our findings provide a reference for future works with respect to the spatial prediction of soil PSFs and texture using machine-learning models with skewed distributions of soil PSF data over a large area.

Substituting hydrazine oxidation reaction for oxygen evolution reaction can result in greatly reduced energy consumption for hydrogen production, however, the mechanism and the electrochemical utilization rate of hydrazine oxidation reaction remain ambiguous. Herein, a bimetallic and hetero-structured phosphide catalyst has been fabricated to catalyze both hydrazine oxidation and hydrogen evolution reactions, and a new reaction path of nitrogen-nitrogen single bond breakage has been proposed and confirmed in hydrazine oxidation reaction. The high electro-catalytic performance is attributed to the instantaneous recovery of metal phosphide active site by hydrazine and the lowered energy barrier, which enable the constructed electrolyzer using bimetallic phosphide catalyst at both sides to reach 500 mA cm −2 for hydrogen production at 0.498 V, and offer an enhanced hydrazine electrochemical utilization rate of 93%. Such an electrolyzer can be powered by a bimetallic phosphide anode-equipped direct hydrazine fuel cell, achieving self-powered hydrogen production at a rate of 19.6 mol h −1  m −2 . Detailed mechanistic study is challenging but meanwhile benefits rational design of electrocatalysts. Here the authors report bimetallic phosphide for bifunctional hydrazine oxidation and hydrogen evolution reaction and reveal hydrazine oxidation pathways as well as the recovery of metal phosphide active site by N 2 H 4 molecules.

PurposeTo evaluate the capabilities and incapabilities of a GPT-4V(ision)-based chatbot in interpreting ocular multimodal images.MethodsWe developed a digital ophthalmologist app using GPT-4V and evaluated its performance with a dataset (60 images, 60 ophthalmic conditions, 6 modalities) that included slit-lamp, scanning laser ophthalmoscopy, fundus photography of the posterior pole (FPP), optical coherence tomography, fundus fluorescein angiography and ocular ultrasound images. The chatbot was tested with ten open-ended questions per image, covering examination identification, lesion detection, diagnosis and decision support. The responses were manually assessed for accuracy, usability, safety and diagnosis repeatability. Auto-evaluation was performed using sentence similarity and GPT-4-based auto-evaluation.ResultsOut of 600 responses, 30.6% were accurate, 21.5% were highly usable and 55.6% were deemed as no harm. GPT-4V performed best with slit-lamp images, with 42.0%, 38.5% and 68.5% of the responses being accurate, highly usable and no harm, respectively. However, its performance was weaker in FPP images, with only 13.7%, 3.7% and 38.5% in the same categories. GPT-4V correctly identified 95.6% of the imaging modalities and showed varying accuracies in lesion identification (25.6%), diagnosis (16.1%) and decision support (24.0%). The overall repeatability of GPT-4V in diagnosing ocular images was 63.3% (38/60). The overall sentence similarity between responses generated by GPT-4V and human answers is 55.5%, with Spearman correlations of 0.569 for accuracy and 0.576 for usability.ConclusionGPT-4V currently is not yet suitable for clinical decision-making in ophthalmology. Our study serves as a benchmark for enhancing ophthalmic multimodal models.