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Achieving comprehensive and high-quality employment is essential to achieving new levels of people’s well-being. The advancement of Internet technology not only affect the massiveness of employment, but also the quality of that. On the basis of constructing an employment quality evaluation index system, this article uses CLDS (China Labor-force Dynamics Survey) data to explore the impact of Internet use on the employment quality of workers and its underlying mechanisms. The results reveal that Internet use has a significant positive impact on improving the employment quality of workers. As the quantile of employment quality increases, internet use has a greater impact on workers with a lower employment quality quantile. In addition, the use of Internet has a more significant promoting effect on the employment quality of rural and female workers. From the perspective of mechanism, Internet use can increase workers’ social capital and influence their employment quality through the accumulation of social capital. Based on this, countermeasures and suggestions are put forward from the aspects of increasing investment and construction of Internet infrastructure, further perfecting the reform of household registration system, promoting human capital investment and social capital construction.

Shanghai is susceptible to land subsidence due to its unique geological environment and frequent human activities. Traditional leveling techniques are not sufficient for monitoring large areas of land subsidence due to the time-consuming, labor-intensive, and expensive nature of the process. Furthermore, the results of conventional methods may not be timely, rendering them ineffective for monitoring purposes. Interferometric Synthetic Aperture Radar (InSAR) technology is a widely used method for monitoring ground subsidence due to its low cost, high efficiency, and ability to cover large areas. To monitor the surface sink condition of Shanghai over the past 2 years, monitoring data were obtained through the technical processing of 24 images from Sentinel-1A data covering Shanghai from 2019 to 2020 using the Persistent Scatterer (PS-InSAR) and Small Baseline Subset (SBAS-InSAR) technique. The ground subsidence (GS) results were extracted via PS and SBAS interferometry processing, while Shuttle Radar Topography Mission data were used to correct the residual phase. According to PS and SBAS methods, the maximum ground subsidence in the study area reached 99.8 mm and 47.2 mm, respectively. The subsidence rate and the accumulated amount of subsidence derived from the monitoring results revealed the urban area in Shanghai to be principally characterized by uneven GS, with multiple settlement funnels being found to be distributed across the main urban area. Moreover, when compared with the historical subsidence data, geological data, and urban construction distribution data, the individual settlement funnels were observed to correspond to those data concerning the historical surface settlement funnel in Shanghai. By randomly selecting GS time-series data regarding three feature points, it was determined that the morphological variables of the GS remained largely consistent at all time points and that their change trends exhibited a high degree of consistency, which verified the reliability of the PS-InSAR and SBAS-InSAR monitoring method. The results can provide data support for decision making in terms of geological disaster prevention and control in Shanghai.

The construction of the national water network optimizes water resource allocation but also causes a redistribution of pollutants due to water quality differences, posing urgent challenges for water pollution management. Taking the South-to-North Water Diversion Project and Yangtze-to-Hanjiang River Water Diversion Project (YHRWDP) as an example and considering the pressure brought by the redistribution of pollutants to the polluting enterprises (PE) and water diversion enterprises (WDE) situated in areas where pollution is aggravated, a differential game model of pollution control among the central government, water-supply area (WSA), PE and WDE is constructed based on the cost-sharing mechanism. The results show that: (1) The pressure coefficient has an apparent negative correlation with the effort level of the affected subject. (2) An increase in the amount of pollution elimination per unit of pollution control effort of WSA and PE will lead to their own investment in more efforts to improve the ecology of the basin, thus benefiting all participants in the system. (3) The cost-sharing rate of the central government on the WSA of the YHRWDP, the PE, and the WDE is influenced by several factors, which shows a stable monotonically increasing or decreasing relation. The results are an useful supplement to the research on natural river pollution control, and can also provide a reference for designing pollution control mechanisms of national water network.

Aerosol optical depth (AOD) is one of essential atmosphere parameters for climate change assessment as well as for total ecological situation study. This study presents long-term data (2000–2017) on time-space distribution and trends in AOD over various ecological regions of China, received from Moderate Resolution Imaging Spectroradiometer (MODIS) (combined Dark Target and Deep Blue) and Multi-angle Imaging Spectroradiometer (MISR), based on satellite Terra. Ground-based stations Aerosol Robotic Network (AERONET) were used to validate the data obtained. AOD data, obtained from two spectroradiometers, demonstrate the significant positive correlation relationships (r = 0.747), indicating that 55% of all data illustrate relationship among the parameters under study. Comparison of results, obtained with MODIS/MISR Terra and AERONET, demonstrate high relation (r = 0.869 - 0.905), while over 60% of the entire sampling fall within the range of the expected tolerance, established by MODIS and MISR over earth (±0.05 ± 0.15 × AOD AERONET and 0.05 ± 0.2 × AOD AERONET ) with root-mean-square error (RMSE) of 0.097–0.302 and 0.067–0.149, as well as low mean absolute error (MAE) of 0.068–0.18 and 0.067–0.149, respectively. The MODIS search results were overestimated for AERONET stations with an average overestimation ranging from 14 to 17%, while there was an underestimate of the search results using MISR from 8 to 22%.

Change vector analysis in posterior probability space (CVAPS) is an effective change detection (CD) framework that does not require sound radiometric correction and is robust against accumulated classification errors. Based on training samples within target images, CVAPS can generate a uniformly scaled change-magnitude map that is suitable for a global threshold. However, vigorous user intervention is required to achieve optimal performance. Therefore, to eliminate user intervention and retain the merit of CVAPS, an unsupervised CVAPS (UCVAPS) CD method, RFCC, which does not require rigorous user training, is proposed in this study. In the RFCC, we propose an unsupervised remote sensing image segmentation algorithm based on the Mamba model, i.e., RVMamba differentiable feature clustering, which introduces two loss functions as constraints to ensure that RVMamba achieves accurate segmentation results and to supply the CSBN module with high-quality training samples. In the CD module, the fuzzy C-means clustering (FCM) algorithm decomposes mixed pixels into multiple signal classes, thereby alleviating cumulative clustering errors. Then, a context-sensitive Bayesian network (CSBN) model is introduced to incorporate spatial information at the pixel level to estimate the corresponding posterior probability vector. Thus, it is suitable for high-resolution remote sensing (HRRS) imagery. Finally, the UCVAPS framework can generate a uniformly scaled change-magnitude map that is suitable for the global threshold and can produce accurate CD results. The experimental results on seven change detection datasets confirmed that the proposed method outperforms five state-of-the-art competitive CD methods.

Workplace learning is prevalent in corporate environments. However, unreasonable workplace learning can induce stress among employees. Based on the Job Demands-Resources (JD-R) theory, this study aims to explore whether the resulting stress constitutes a hindrance job demand when learning tasks in the workplace are overly burdensome and examinations are excessively difficult and whether this stress compromises employees’ sleep quality and induces shortcut motivation, thereby reducing safety performance. Data from 723 employees was collected, confirm that both sleep quality and shortcut motivation mediate the impact of workplace learning examination stress on safety performance. The study also introduces occupational calling as a personal resource that mitigates the negative effects of workplace learning examination stress. Our research enriches our understanding of the impact of workplace learning and occupational calling on safety performance, with significant implications for employee safety and health.

The urban functional zone, serving as a bridge to understanding the complex interactions between human spatial activities and surface thermal environmental changes, explores the driving force information of its internal temperature changes, which is crucial for improving the urban thermal environment. However, the impacts of the current urban functional zones on the thermal environment, based on the delineation of human activities, have yet to be sufficiently investigated. To address the issue, we constructed a two-factor weighted dominant function vector model of “population heat—land use scale” to identify urban functional zones. This model is based on multisource data and considers the perspective of urban functional supply and demand matching. We then analyzed the spatial differentiation and driving factors of the relationship between urban functional zones and the surface thermal environment using the random forest algorithm, bivariate spatial autocorrelation, geographical detectors, and geographically weighted regression models. The results showed that there are significant differences in the Land Surface Temperature among different urban functional zones in the central urban area of Lanzhou. Among these, the life service zone has the greatest impact on the surface thermal environment, followed by the industrial zone and catering service zone, while the green space zone has the least impact. The surface thermal environment exhibits high-high clusters in localized spatial clustering patterns with life service, industrial, catering service, and residential zones. In contrast, it tends to exhibit low-high clusters with green spaces. Significant spatial clustering and dependence exist between various functional zones and the surface thermal environment. The land cover types characterized by the Normalized Difference Bare Land and Building Index, the vegetation coverage represented by the Fraction of Vegetation Cover, and the density of industrial activities indicated by the Industrial POI Kernel Density Index are the main drivers of the surface thermal environment in the various functional zones of the central urban area of Lanzhou, and all exhibit significant spatial heterogeneity.

Objective Prolonged hospital stay in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) significantly impacts patient outcomes and healthcare resource allocation. This study aimed to develop and validate an interpretable machine learning (ML) model for forecasting prolonged hospital stays of the AECOPD population. Methods A retrospective analysis was performed utilizing data from the MIMIC-IV database on patients diagnosed with AECOPD. The dataset was split into a training set (80%) and a validation set (20%). Feature selection was executed through LASSO regression and the Boruta algorithm. Logistic regression (LR), random forest (RF), neural network (NN), gradient boosting machine (GBM), naive Bayes (NB), as well as K-nearest neighbors (KNN) models were constructed based on the selected features. Model performance was evaluated via receiver operating characteristic (ROC), calibration and decision curves. The interpretability of the optimal model was enhanced through SHapley Additive exPlanations (SHAP) analysis. Results 7,373 AECOPD patients were encompassed. The final model incorporated the following features: hemoglobin, platelet count, anion gap, blood urea nitrogen(BUN), potassium, chloride, antibiotic use, invasive ventilation, vasopressor use, SOFA, body temperature(Tb), heart rate(HR), acute kidney injury (AKI), as well as sepsis. The RF model demonstrated the best predictive performance, with an area of the ROC curve (AUC) of 0.817 in the training set and 0.715 in the validation set, along with good calibration and clinical utility. SHAP analysis further enhanced the interpretability of the model, providing valuable clinical decision support. Conclusion The RF model developed in our study exhibited excellent performance in predicting prolonged hospital stays in AECOPD patients. The incorporation of interpretability analysis improved the transparency and reliability of its clinical application. Clinical trial number Not applicable.

Background The impact of insulin resistance (IR) on chronic obstructive pulmonary disease (COPD) has caught increasing attention, and the triglyceride-glucose (TyG) index and related indices are deemed reliable indicators for evaluating IR. Nevertheless, the potential associations of TyG and obesity-related indexes with COPD are currently understudied. Hence, this paper was to inspect the links of TyG and obesity-related indices with COPD. Methods This was a cross-sectional study based on data from the NHANES 2013–2018. Weighted logistic regression (WLR), restricted cubic sample (RCS), and receiver operating characteristic (ROC) curves were leveraged to examine the links of the TyG index and obesity indices with COPD. The stability of the correlations was also assessed via subgroup analyses. Results Data from 6383 participants were finally included, including 583 patients with COPD. WLR discovered positive associations of TyG, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), and TyG-waist height ratio (TyG-WHtR) with COPD regardless of covariate adjustment ( p -value < 0.05, p for trend test < 0.05). After adjusting for all confounders, RCS analysis signaled notable linear links of TyG and obesity-related indices with COPD ( p -value < 0.05, p for nonlinear > 0.05). TyG-WHtR showed the highest association with COPD among the indices tested, albeit with limited discriminative ability (AUC = 0.643, 95% CI: 0.619 ~ 0.665). Subgroup analyses further validated the stability and reliability of the results. Conclusion TyG and its combination with obesity-related indicators are associated with COPD. Among these, TyG-WHtR showed the strongest association with COPD, although causal relationships cannot be inferred and its discriminative ability remained modest. Further prospective studies are warranted to validate these findings. Clinical trial number Not applicable.

Urban parks have important impacts on urban ecosystems and in disaster prevention. They also have diverse social functions that are important to the living conditions and spatial structures of cities. Identifying and classifying the different types of urban parks are important for analyzing the sustainable development and the greening progress in cities. Existing studies have predominantly focused on the data extraction of urban green spaces as a whole, while there have been relatively few studies that have considered different categories of urban parks and their impact, which makes it difficult to characterize or predict the spatial distribution and structures of urban parks and limits further refinement of urban research. At present, the classification of urban parks relies on the physical features observed in remote sensing images, but these methods are limited when mapping the diverse functions and attributes of urban parks. Crowd-sourced geographic data may more accurately express the social functions of points of interest (POIs) in cities, and, therefore, employing open data sources may assist in data extraction and the classification of different types of urban parks. This paper proposed a multi-source data fusion approach for urban park classification including POI and urban functional zone (UFZ) data. First, the POI data were automatically reclassified using improved natural language processing (NLP) (i.e., text similarity measurements and topic modeling) to establish the links between urban park green-space types and POIs. The reclassified POI data as well as the UFZ data were then subjected to scene-based data fusion, and various types of urban parks were extracted using data attribute analysis and social attribute recognition for urban park mapping. Experimental analysis was conducted across Beijing and Hangzhou to verify the effectiveness of the proposed method, which had an overall classification accuracy of 82.8%. Finally, the urban park types of the two cities were compared and analyzed to obtain the characteristics of urban park types and structures in the two cities, which have different climates and urban structures.