Survey and analysis on the resource situation of primary health care institutions in rural China

BackgroundChina’s rural population is immense, and to ensure the well-being of rural residents through healthcare services, it is essential to analyze the resources of rural grassroots healthcare institutions in China. The objective is to examine the discrepancies and deficiencies in resources between rural grassroots healthcare institutions and the national average, providing a basis for future improvements and supplementation of rural healthcare resources.MethodologyThe study analyzed data from 2020 to 2022 on the number of healthcare establishments, the capacity of hospital beds, the number of healthcare professionals, and the number of physicians in both rural and national settings. Additionally, it examined the medical service conditions and ratios of township health centers in rural areas to assess the resource gap between rural areas and the national average.ResultsHealthcare establishments: On average, there were 2.2 fewer healthcare institutions per 10,000 persons in rural areas compared to the national average over three years. Hospital beds: On average, there were approximately 36 fewer hospital beds per 10,000 persons in rural areas compared to the national average over three years. Healthcare professionals and physicians: On average, there were about 48 fewer healthcare technical personnel and 10 fewer practicing (including assistant) physicians per 10,000 persons in rural areas compared to the national average over three years.ConclusionCompared to the national average, there are significant discrepancies and deficiencies in grassroots healthcare resources in rural China. This underscores the necessity of increasing funding to progressively enhance the number of healthcare institutions in rural areas, expand the number of healthcare personnel, and elevate medical standards to better align with national benchmarks. Improving rural healthcare resources will strategically equip these institutions to cater to rural communities and effectively handle public health emergencies. Ensuring that the rural population in China has equal access to healthcare services as the rest of the country is crucial for promoting the well-being of rural residents and achieving health equity

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Optimization of Synthesis of Biomorphic Cellular Iron Oxide from Wood Templates

International audienc

Textual Emotional Tone and Financial Crisis Identification in Chinese Companies: A Multi-Source Data Analysis Based on Machine Learning

Nowadays, China is faced with increasing downward pressure on its economy, along with an expanding business risk on listed companies in China. Listed companies, as the solid foundation of the national economy, once they face a financial crisis, will experience hazards from multiple perspectives. Therefore, the construction of an effective financial crisis early warning model can help listed companies predict, control and resolve their risks. Based on textual data, this paper proposes a web crawler and textual analysis, to assess the sentiment and tone of financial news texts and that of the management discussion and analysis (MD&A) section in annual financial reports of listed companies. The emotional tones of the two texts are used as external and internal information sources for listed companies, respectively, to measure whether they can improve the prediction accuracy of a financial crisis early warning model based on traditional financial indicators. By comparing the early warning effects of thirteen machine learning models, this paper finds that financial news, as external texts, can provide more incremental information for prediction models. In contrast, the emotional tone of MD&A, which can be easily modified by the management, will distort predictions. Comparing the early warning effect of machine learning models with different input feature variables, this paper also finds that DBGT, AdaBoost, random forest and Bagging models maintain stable and accurate sample recognition ability. This paper quantifies financial news texts, unraveling implied information hiding behind the surface, to further improve the accuracy of the financial crisis early warning model. Thus, it provides a new research perspective for related research in the field of financial crisis warnings for listed companies

Textual Emotional Tone and Financial Crisis Identification in Chinese Companies: A Multi-Source Data Analysis Based on Machine Learning

Nowadays, China is faced with increasing downward pressure on its economy, along with an expanding business risk on listed companies in China. Listed companies, as the solid foundation of the national economy, once they face a financial crisis, will experience hazards from multiple perspectives. Therefore, the construction of an effective financial crisis early warning model can help listed companies predict, control and resolve their risks. Based on textual data, this paper proposes a web crawler and textual analysis, to assess the sentiment and tone of financial news texts and that of the management discussion and analysis (MD&A) section in annual financial reports of listed companies. The emotional tones of the two texts are used as external and internal information sources for listed companies, respectively, to measure whether they can improve the prediction accuracy of a financial crisis early warning model based on traditional financial indicators. By comparing the early warning effects of thirteen machine learning models, this paper finds that financial news, as external texts, can provide more incremental information for prediction models. In contrast, the emotional tone of MD&A, which can be easily modified by the management, will distort predictions. Comparing the early warning effect of machine learning models with different input feature variables, this paper also finds that DBGT, AdaBoost, random forest and Bagging models maintain stable and accurate sample recognition ability. This paper quantifies financial news texts, unraveling implied information hiding behind the surface, to further improve the accuracy of the financial crisis early warning model. Thus, it provides a new research perspective for related research in the field of financial crisis warnings for listed companies

Gas Sensing Properties of Biotemplated Porous ZnO

International audienc

Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations

As air pollution becomes highly focused in China, the accurate identification of its influencing factors is critical for achieving effective control and targeted environmental governance. Land-use distribution is one of the key factors affecting air quality, and research on the impact of land-use distribution on air pollution has drawn wide attention. However, considerable studies have mostly used linear regression models, which fail to capture the nonlinear effects of land-use distribution on PM2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) and to show how impacts on PM2.5 vary with land-use magnitudes. In addition, related studies have generally focused on annual analyses, ignoring the seasonal variability of the impact of land-use distribution on PM2.5, thus leading to possible estimation biases for PM2.5. This study was designed to address these issues and assess the impacts of land-use distribution on PM2.5 in Weifang, China. A machine learning statistical model, the boosted regression tree (BRT), was applied to measure nonlinear effects of land-use distribution on PM2.5, capture how land-use magnitude impacts PM2.5 across different seasons, and explore the policy implications for urban planning. The main conclusions are that the air quality will significantly improve with an increase in grassland and forest area, especially below 8% and 20%, respectively. When the distribution of construction land is greater than around 10%, the PM2.5 pollution can be seriously substantially increased with the increment of their areas. The impact of gardens and farmland presents seasonal characteristics. It is noted that as the weather becomes colder, the inhibitory effect of vegetation distribution on the PM2.5 concentration gradually decreases, while the positive impacts of artificial surface distributions, such as construction land and roads, are aggravated because leaves drop off in autumn (September–November) and winter (December–February). According to the findings of this study, it is recommended that Weifang should strengthen pollution control in winter, for instance, expand the coverage areas of evergreen vegetation like Pinus bungeana Zucc. and Euonymus japonicus Thunb, and increase the width and numbers of branches connecting different main roads. The findings also provide quantitative and optimal land-use planning and strategies to minimize PM2.5 pollution, referring to the status of regional urbanization and greening construction

Hierarchically porous ZnO with high sensitivity and selectivity to H2S derived from biotemplates

International audienceHierarchical porous wood-templated ZnO has been successfully synthesized using Lauan and Fir woods as template through a simple hydrothermal bioinspired approach. The template type and calcination temperature in the preparation process have a large effect on the morphologies and porous structures of ZnO according to FESEM, TEM, mercury porosimetry and N2 adsorption investigations. The gas sensing performances of wood-templated and non-templated ZnO were investigated using H2, CO, H2S, NH3, Formaldehyde, Methanol, Ethanol, Acetone, and Isobutene. The article studies the effects of wood template, calcination temperature, and working temperature of gas flow on the gas sensitivity and selectivity in detail. It is revealed that wood-templated ZnO has excellent sensitivity and selectivity to H2S due to inheritance of wood's hierarchical porous structure. The sensing response to H2S of Fir-templated ZnO is about 5.1 times higher than that of non-templated ZnO. Fir-templated ZnO calcined at 600 °C, has the best sensing properties including the highest gas sensing response, the highest selectivity coefficients of H2S and the shortest response and recovery time. The selective sensing mechanism has been discussed from some key aspects, such as gas properties, gas–solid reactions, grain size and hierarchical porous microstructures

A Review of Radio Frequency Identification Sensing Systems for Structural Health Monitoring

Structural health monitoring (SHM) plays a critical role in ensuring the safety of large-scale structures during their operational lifespan, such as pipelines, railways and buildings. In the last few years, radio frequency identification (RFID) combined with sensors has attracted increasing interest in SHM for the advantages of being low cost, passive and maintenance-free. Numerous scientific papers have demonstrated the great potential of RFID sensing technology in SHM, e.g., RFID vibration and crack sensing systems. Although considerable progress has been made in RFID-based SHM, there are still numerous scientific challenges to be addressed, for example, multi-parameters detection and the low sampling rate of RFID sensing systems. This paper aims to promote the application of SHM based on RFID from laboratory testing or modelling to large-scale realistic structures. First, based on the analysis of the fundamentals of the RFID sensing system, various topologies that transform RFID into passive wireless sensors are analyzed with their working mechanism and novel applications in SHM. Then, the technical challenges and solutions are summarized based on the in-depth analysis. Lastly, future directions about printable flexible sensor tags and structural health prognostics are suggested. The detailed discussion will be instructive to promote the application of RFID in SHM