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Abstract Purpose Previous studies suggest an association between chronic kidney disease (CKD) and cognitive impairment. The purpose of this study was to explore the association between the diverse stages of CKD and the cognitive performance of elderly American adults. Methods Data from the National Health and Nutrition Examination Survey (NHANES) 2011–2014 were used. Multivariate adjusted logistic regression, subgroup analysis, and the restricted cubic spline model were used to assess the associations of CKD stage and estimated glomerular filtration rate (eGFR) with cognitive performance. The measures used to evaluate cognitive function included the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) test, the Animal Fluency test, and the Digit Symbol Substitution test (DSST). Results This study included 2234 participants aged ≥ 60 years. According to the fully adjusted model, stages 3–5 CKD were significantly associated with the CERAD test score (OR = 0.70, 95% CI [0.51, 0.97], p = 0.033), the Animal Fluency test score (OR = 0.64, 95% CI [0.48, 0.85], p = 0.005), and the DSST score (OR = 0.60, 95% CI [0.41, 0.88], p = 0.013). In addition, the incidence of poor cognitive function increased with decreasing eGFR, especially for individuals with low and moderate eGFRs. Both the DSST score (p nonlinearity

Mandatory land expropriation, resettlement and welfare loss of landless peasants have received increasing research attention. However, in most previous analyses, the loss of psychological welfare of landless farmers is often neglected, which may lead to biased land requisition compensation and resettlement programs and incomplete policy recommendations. This study attempts to use a three-dimensional framework of place attachment to analyze the causes of psychological welfare loss. China’s land expropriation and centralized resettlement cut off the material and emotional ties between farmers and land. Farmers’ dependence on land, or their dependence on land-dependent farming lifestyle, has been neglected in the process of land acquisition and resettlement, resulting in the loss of farmers’ psychological welfare. Based on a comparative analysis of the existing methods, this paper chooses the contingent valuation method (CVM) to measure the loss of psychological welfare of landless peasants via monetization. The feasibility of the model has been tested at a small scale in nine cities. The results show that 1) farmers have strong place attachment on agricultural land, although centralized land expropriation and resettlement have neglected this dependence and caused psychological welfare losses to landless peasants; and 2) the loss of psychological welfare of landless peasants can be measured by the CVM and presented in monetary units. The monetization measurement method in this paper still has some shortcomings and its feasibility requires more empirical tests. However, as a rare monetization measurement study, this paper can direct academic and policy attention towards this psychological welfare loss.

Effective extraction of building edge information based on high-resolution remote sensing images is the basis for efficient urban 3D modeling. Existing building extraction methods still have some problems, such as an uncertain segmentation scale, effective feature selection, and sample selection. In this paper, we propose a practical building extraction method based on convolutional network edge-enhanced attention U2-Net (EA U2-Net) to accurately achieve multi-scale extraction of buildings from remote sensing imagery. First, the U2-Net is used as the backbone network for building extraction because each stage of the network is filled by residual U-block (RSU), and the network can better aggregate multi-scale features. Second, the building edge feature map is introduced into the generation network to compensate for the problems of insufficient extracted building edge features and loss of detail. Finally, the convolutional block attention module is used to achieve effective feature extraction of buildings. We performed the experiment on the WHU building dataset, and the experimental results showed that the EA U2-Net model has significantly improved the ability to extract buildings, with an accuracy of 96.30%, a recall rate of 94.91%, f1 of 95.26%, and iou of 91.57%. This proves that EA U 2 - Net can achieve better remote sensing image-building segmentation results. Finally, in view of the problem that the deep learning network relies on training samples, this study examined the influence of the number of building samples, sample purity, and sample resolution on the effect of building extraction. The results confirmed that reasonable sample parameter settings can improve the target extraction accuracy and the optimal sample parameter combination was verified in this experiment.

This work presents a novel method for the sensitive and selective detection of atmospheric hydroxyl radicals (OH). After activation by a hydrothermal method in the presence of sodium citrate, carbon fiber paper (CFP) with good conductivity and hydrophilicity was impregnated with salicylic acid (SA) and used as a detector to monitor OH. The impregnated SA can trap atmospheric OH, producing 2,5-dihydroxybenzoic acid (2,5-DHBA), an electroactive species. The concentration of 2,5-DHBA was quantified using a simple electrochemical method to estimate the number of OH radicals that have participated in the reaction. This method provided a linear correlation for 2,5-DHBA concentrations from 1.0 × 10−13 to 3.0 × 10−10 M with a detection limit of 2.09 × 10−14 M, which corresponds to an OH range from 1.0 × 105 to 3.0 × 108 molecules/cm3 with a detection limit of 2.1 × 104 molecules/cm3. This method has been used to measure the atmospheric OH levels in Yangzhou. Keywords: Salicylic acid, Impregnated, Carbon fiber paper, Hydroxyl radical, Electrochemical