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Abstract Evaluating Community-Acquired Pneumonia (CAP) is crucial for determining appropriate treatment methods. In this study, we established a machine learning model using radiomics and clinical features to rapidly and accurately identify Severe Community-Acquired Pneumonia (SCAP). A total of 174 CAP patients were included in the study, with 64 cases classified as SCAP. Radiomic features were extracted from chest CT scans using radiomics techniques, and screened to remove irrelevant features. Additionally, clinical indicators of patients were similarly screened and constituted the clinical feature set. Subsequently, eight common machine learning models were employed to complete the SCAP identification task. Specifically, interpretability analysis was conducted on the models. In the end, we screened out 15 radiomic features (such as LeastAxisLength, Maximum2DDiameterColumn and ZonePercentage) and two clinical features: Lymphocyte (p = 0.041) and Albumin (p = 0.044). Using radiomic features as inputs in model predictions yielded the highest AUC of 0.85 on the test set. When using the clinical feature set as model inputs, the AUC was 0.82. Combining the two sets of features as model inputs, Ada Boost achieved the best performance with an AUC of 0.89. Our study demonstrates that combining radiomics and clinical data using machine learning methods can more accurately identify SCAP patients.

Study region: The Yangtze−Huang−Huai−Hai River basin (3.19 million km2) in China, which is vital for China's economic development. Study focus: We employed the Eulerian method to identify the primary water vapor channels from 2005 to 2020, and the water vapor net budget over the basin was calculated. Additionally, the Lagrangian method was utilized to simulate water vapor transport trajectories across four seasons, with the aim of investigating the influence of changes in water vapor on precipitation. New hydrological insights for the region: Utilizing both Eulerian and Lagrangian methods, the findings indicate that: (1) Precipitation in the basin experienced a downward trend of −3.5 mm·a−1 during 2005−2020, mainly in spring (−1.4 mm·a−1) and summer (−1.7 mm·a−1); (2) The water vapor net budget has a substantial downward trend (−0.9 kg·m−1·s−1·a−1), mainly in spring (−0.4 kg·m−1·s−1·a−1) and summer (−0.3 kg·m−1·s−1·a−1), which aligns with the declining trend of precipitation. (3) The Hybrid Single Particle Lagrangian Integrated Trajectory Model demonstrates that the decrease in long−distance water vapor transport trajectories impacts the basin’s water vapor channels. The decline in spring is predominantly influenced by the water vapor transport trajectories from Asia, Europe, and Africa−Arctic Ocean (−0.7 %·a−1), while the summer is mainly driven by the water vapor transport trajectory from the Indian Ocean−South China Sea (−0.3 %·a−1). This study improves the existing understanding of the hydrological cycle in the context of the basin, and offers a crucial scientific basis for water resource management and regulation.

Abstract Climate warming leads to frequent extreme precipitation events, which is a prominent manifestation of the variation of the global water cycle. In this study, data from 1842 meteorological stations in the Huang–Huai–Hai–Yangtze River Basin and 7 climate models of CMIP6 were used to obtain the historical and future precipitation data using the Anusplin interpolation, BMA method, and a non-stationary deviation correction technique. The temporal and spatial variations of extreme precipitation in the four basins were analysed from 1960 to 2100. The correlation between extreme precipitation indices and their relationship with geographical factors was also analysed. The result of the study indicates that: (1) in the historical period, CDD and R99pTOT showed an upward trend, with growth rates of 14.14% and 4.78%, respectively. PRCPTOT showed a downward trend, with a decreasing rate of 9.72%. Other indices showed minimal change. (2) Based on SSP1-2.6, the intensity, frequency, and duration of extreme precipitation changed by approximately 5% at SSP3-7.0 and 10% at SSP5-8.5. The sensitivity to climate change was found to be highest in spring and autumn. The drought risk decreased, while the flood risk increased in spring. The drought risk increased in autumn and winter, and the flood risk increased in the alpine climate area of the plateau in summer. (3) Extreme precipitation index is significantly correlated with PRCPTOT in the future period. Different atmospheric circulation factors significantly affected different extreme precipitation indices of FMB. (4) CDD, CWD, R95pD, R99pD, and PRCPTOT are affected by latitude. On the other hand, RX1day and RX5day are affected by longitude. The extreme precipitation index is significantly correlated with geographical factors, and areas above 3000 m above sea level are more sensitive to climate change.

Abstract The drilling and blasting method is widely used in tunnel engineering. The accompanying airblast may damage structures and annoy nearby occupants. The prediction of airblast overpressure (p oa) outside the tunnel is necessary to improve the safety of blasting works. A study of propagation characteristics of airblasts induced by tunnel blasting was carried out through experimental and numerical studies. The results indicate that the distributions of the p oa outside the tunnel were anisotropic, which does not conform to the decay law of an explosion in free-field. The propagation of airblasts induced by tunnel blasting is related to the airblast shape. The phenomenon that the p oa along the axial direction of the tunnel was higher than along other directions can be explained by the numerical results of the process of airblasts. The airblasts outside the tunnel traveled as a spherical wave, but the pressure was not uniformly distributed. After an airblast plane wave with high speed and high pressure inside the tunnel was transmitted out of the tunnel, its inertia strengthened the pressure in the axial direction. The airblast outside the tunnel is related to the propagation distance R out, the angle from the measurement to the tunnel axis α, and the pressure intensity p 0 at the tunnel portal. Subsequently, an ellipsoidal contour curve of the p oa outside the tunnel was plotted, and therefore a new prediction equation was validated by numerical results and field data. Finally, the newly proposed methodology guided the blast design.

The existence of defects in the enclosure structure is the primary cause of water and sand leakage in foundation pits, as well as being a significant source of danger in pit construction, but current research lacks an in-depth investigation of the generation mechanism and gestation process. In this paper, which comprehensively considers the microscopic particles and macroscopic level, the development mechanism of a water and sand leakage disaster in a foundation pit with a water-rich sand layer was studied using the principle of computational fluid dynamics and discrete element method coupled analysis (CFD–DEM); moreover, based on the anisotropy of the particle force and fluid energy analysis, the deformation of the stratum and ground stress field were analyzed. The results show that the stress field will produce a plugging effect at a certain distance from the defect, and the strata exhibit a dominant displacement tendency in the vertical direction, resulting in the emergence of a gradually concave stress relaxation zone and an elliptical contour in the strata displacement map near the defect. The fluid energy describes the displacement of the sand layer very well, and it is separated into the sand layer’s centralized loss region and the major loss area based on the high and low levels of the fluid energy class. The impact of fluid at the defect reaches the maximum kinetic energy, which penetrates the structural weakness and causes the loss of sand particles, and the cross-section of the water influx near the defect gradually expands with the loss of particles, indicating that there is a danger of further expansion of the defect under the impact of water flow. These results have technical implications for the management of water and sand leakage disasters in foundation pit engineering.

Study region: Beijing–Tianjin–Hebei Region, China Study focus: Understanding factors driving water demand is crucial for water resource planning and management. However, traditional models fail to capture the complex nonlinear factors that drive real-world water demand. While machine learning models can capture nonlinear relationships, comprehending the complex mechanisms underpinning the models is difficult. Therefore, we combined machine learning with explainable artificial intelligence to analyze the factors driving water demand in the study region. New hydrological insights for the region: A water demand forecasting framework is proposed for analyzing the factors driving water demand. Results show that the main driving factors differ across city types. Population is the most crucial factor influencing water demand, with an effect size of 50.30%, 39.72%, and 31.79% in service-based, industrial, and agricultural cities, respectively. The second- and third-most important factors in service-based cities are the added value of secondary industry (AVSI) and irrigated area (IA), respectively. In industrial and agricultural cities, the second- and third-most-important factors are AVSI and temperature and temperature and IA, respectively. By quantifying the nonlinear relationships between water demand and driving factors, we identify the critical points associated with changes in correlation structure, such as urbanization rate (70%) and per capita disposable income (25,000 CNY per annum). Thus, this study can serve as a valuable reference for developing accurate models to forecast water demand.

Chongqing is a mountain city in western China with a complex geological environment, which brings many difficulties to the construction of large sections of underground projects. In order to avoid serious problems in the construction of Guobo Station with large sections, intensive coring and laboratory tests were carried out to obtain the rock mass properties in detail. The boreholes reveal that the lithology of the rock mass in the project area consists mainly of mudstone and a small area of sandstone. The uniaxial compressive strength (UCS) and the Brazilian tensile strength (BTS) of mudstone are highly scattered, which are 4.43~42.43 MPa and 0.66~4.30 Mpa, respectively, but they all show a lognormal distribution. The average UCS of sandstone is 65.31 MPa, and the BTS is 3.06 MPa. Meanwhile, the softening coefficients of mudstone and sandstone are 0.33 and 0.73, which indicate that the water content has a great influence on the mechanical properties of the rock. For the in situ stress field, the relationship between the three principal stresses is as follows: the maximum horizontal principal stress > minimum horizontal principal stress > vertical principal stress, and the lateral pressure coefficient is 2.11~5.71. Based on the present experimental results, it is proposed that the potential risks of excavation include: (1) bias pressure (2) high in situ stress; (3) surrounding rock deterioration.

Underground structures in earthquake-prone zones should be designed to withstand both static overburden pressures and earthquake shocks. This paper presents a case study on a large-span powerhouse cavern. With seismic data selected from past earthquake records, lab and in situ tests of the cavern’s geological and geomechanical conditions were performed to analyze the static and dynamic stability of the cavern through the continuum modeling approach. Performance analyses via both 2D and 3D modeling were carried out under seismic conditions and for selected design ground motions. The dynamic response of the surrounding rock mass and of the reinforcement system was detected. The results obtained will facilitate future stability analyses of large underground caverns constructed in the past on the basis of seismic design analyses and input data from post-earthquake records that were recently made available on the site.

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Nowadays, there is no precedent for building a high-speed railway in diatomite area. Due to the complex structure and poor mechanical properties of diatomite as well as the lack of relevant engineering experience, more attention has been paid to the proper constitutive model of the tunnel in diatomite layer using the numerical calculation method, while the traditional Elastoplastic calculation model is the most used yet. Therefore, relying on the Feifengshan tunnel, through FLAC3D software as well as the on-site monitoring, the analysis of the settlement law about tunnelling in diatomite stratum is carried out based on different constitutive models. The research results show that diatomite has obvious strain-softening characteristics. The calculated surface settlement and vault settlement based on the Strain Softening model is greater than that based on the Mohr Coulomb model. When compared with the on-site monitoring data, it is found that the Strain Softening model would more accurately show the settlement law of the tunnel in diatomite and has better applicability in the diatomite area. The above-mentioned research results may provide some references for the construction and design of tunnels in similar strata in the future. [ABSTRACT FROM AUTHOR]

Objective To investigate the nutritional status of patients undergoing concurrent chemoradiotherapy for malignant tumor and its influencing factors. Methods A self-designed questionnaire was used to collect related clinical data of the patients who underwent concurrent chemoradiotherapy for malignant tumor in Beijing Cancer Hospital from March to May, 2019. Patient-generated Subjective Global Assessment (PG-SGA) was used to investigate the nutritional status of these patients and related influencing factors. Results A total of 150 usable questionnaires were collected. The median PG-SGA score was 6.0 (3.0,11.0) for all 150 patients. Among these 150 patients, 56 (37.3%) had severe malnutrition and 49 (32.7%) had moderate malnutrition. The univariate analysis showed that old age, esophageal cancer or head and neck cancer, stage Ⅳ tumor, more than 15 sessions of radiotherapy, more than 4 sessions of chemotherapy, and paclitaxel+platinum regimen or platinum monotherapy were influencing factors for malnutrition in these patients (Z=-3.839--2.087,P