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Abstract Community-acquired pneumonia (CAP) is associated with high mortality rates and often results in prolonged hospital stays. The potential of machine learning to enhance prediction accuracy in this context is significant, yet clinicians often lack the programming skills required for effective data mining. This study aimed to assess the effectiveness of a low-code approach for assisting clinicians with data mining for mortality and length of stay (LOS) prediction in patients with CAP. A retrospective study was conducted using a low-code platform and the PyCaret library in Google Colab on data from patients with community-acquired pneumonia (CAP) admitted between January 2013 and December 2021 to two medical facilities. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) for mortality prediction and the R2 score for LOS prediction, with benchmarks set at AUC > 0.9 and R2 > 0.5. The Shapley Additive Explanations (SHAP) method was used for interpreting individual predictions. A total of 669 CAP patients were enrolled in the analysis.Fifteen models were evaluated for mortality prediction, and nineteen models were evaluated for LOS prediction utilizing the PyCaret library. The Light Gradient Boosting Machine model yielded the highest AUC (0.963) for mortality prediction. In predicting LOS, the Extratrees Regressor model achieved the highest R2 score of 0.585. Factors such as the severity of pneumonia and the Charlson Comorbidity Index (CCI) were significant factors influencing mortality. For the LOS, the CCI score, activities of daily living, and social support were significant predictors. The low-code approach enables medical professionals with limited technical expertise to effectively employ data science in their clinical decision-making process. This approach proved to be a valuable tool in the analysis of CAP patient data.

Abstract Background Hericium coralloides is a traditional edible and medicinal mushroom. Light is a key factor in forming fruiting bodies of fungi; however, the effects of different light on the yield and morphogenesis of H. coralloides are still unknown. Therefore, the morphology, yield, and transcriptome of H. coralloides under blue, red, and white light conditions were investigated. Result Fruiting bodies under blue light exhibited superior morphological traits, such as milky white color, larger size, elongated stalks, and higher spine count, leading to higher yields. Different light treatments led to dramatic transcriptome changes ranging from 10,827 differentially expressed genes (DEGs) induced by blue light in Blue-4d to 11,375 DEGs induced by red light in Red-4d and accounted for 64.56% to 67.81% of all expressed genes. This massive amount of light-responsive genes has never been reported in fungi. Gene Ontology analysis showed that light affected nearly all aspects of life in H. coralloides; suggesting that the influence of light on fungi may have been underestimated. Blue light-induced yield increment may be achieved by specifically upregulating the growth-related processes such as DNA replication, chromosomes, and cell division. Conclusions This study offers preliminary insights into the potential role of blue light in modulating gene expression and yield stimulation in H. coralloides, potentially improving cultivation practice.

Abstract Recently, a novel low‐cost coding digital receiving array based on machine learning (ML‐CDRA) has been proposed to reduce the required radio frequency channels in modern wireless systems. The spatial sensitivity of ML‐CDRA is studied which describes the spatial accumulation gain in different directions. It is demonstrated that the spatial sensitivity is determined by the encoding network, decoding network, and beamforming criterion. To obtain the desired spatial sensitivity, a spatial sensitivity synthesis method is proposed based on the alternate projection by optimising the encoding network with the constraint of amplitude‐phase quantisation. Simulation results show that the proposed method can significantly improve the spatial sensitivity of ML‐CDRA. Furthermore, in the directions of interest, the spatial accumulation gain of ML‐CDRA can exceed the full‐channel digital receiving array.

The present paper investigates the dynamic behaviors of a bubble restricted by two parallel plates near an elliptical wall. The typical experimental phenomena of the bubble are recorded employing the high-speed photography and a theoretical Kelvin impulse model is established. The impacts of the spatial position and the curvature of the wall on the bubble collapse behaviors are quantitatively investigated through the theoretical model and verified against the experimental results. The Kelvin impulse intensity and the direction during the bubble collapse process are compared and discussed for different elliptical-shaped walls. The main conclusions include: (1) During the bubble collapse process, the phenomenon of the bubble uneven splitting is discovered. (2) At different spatial positions and wall curvatures, the bubble collapse jet angle, movement distance, and velocity are in good agreement with the theoretical Kelvin impulse predictions. (3) As the short-to-long axis ratio increases, the differences in the distributions of the Kelvin impulse intensity and the direction near the elliptical wall gradually become larger, and the range of the influence of the impulse intensity expands.

The deep neural network has achieved remarkable recognition performance in synthetic aperture radar (SAR) automatic target recognition (ATR) by extracting the discriminative features from massive SAR images. Due to the sensitivity of SAR image to the observation aspect, the multiview ATR method could enhance the robustness of feature representation and improve the recognition performance. However, existing multiview ATR methods suffer from increasing complex structure and heavy computation when the number of input images grows. An adaptive multiview fusion network based on image attention (IA-AMF-Net) compatible with variable number of input images is proposed for SAR ATR in this article. In IA-AMF-Net, first, the depthwise separable convolution is employed to extract the classification features from multiple SAR input images in parallel with the lightweight attribute. Second, the channel feature weight vector of each image is generated and concatenated by applying the squeeze-and-excitation operation to the extracted features. The image attention weights for feature fusion are calculated through softmax normalizing the concatenated channel feature weights of input images. At last, the extracted features from multiview SAR images are fused by the obtained image attention weights. The dimension of fused feature keeps constant regardless of the number of input images, and the attention to the classification features of interested images is enhanced. Experimental results on the moving and stationary target acquisition and recognition dataset show that IA-AMF-Net achieves superior recognition performance under various operating conditions with fewer parameters and lower computational load compared to the other networks.

Abstract Cognitive dysfunction increases as menopause progresses. We previously found that estrogen receptors (ERs) contribute to dyslipidemia, but the specific relationship between ERs, dyslipidemia and cognitive dysfunction remains poorly understood. In the present study, we analyzed sequencing data from female hippocampus and normal breast aspirate samples from normal and Alzheimer’s disease (AD) women, and the results suggest that abnormal ERs signaling is associated with dyslipidemia and cognitive dysfunction. We replicated a mouse model of dyslipidemia and postmenopausal status in LDLR−/− mice and treated them with β-estradiol or simvastatin, and found that ovariectomy in LDLR−/− mice led to an exacerbation of dyslipidemia and increased hippocampal apoptosis and cognitive impairment, which were associated with reduced estradiol levels and ERα, ERβ and GPER expression. In vitro, a lipid overload model of SH-SY-5Y cells was established and treated with inhibitors of ERs. β-estradiol or simvastatin effectively attenuated dyslipidemia-induced neuronal apoptosis via upregulation of ERs, whereas ERα, ERβ and GPER inhibitors together abolished the protective effect of simvastatin on lipid-induced neuronal apoptosis. We conclude that decreased estrogen and its receptor function in the postmenopausal stage promote neuronal damage and cognitive impairment by exacerbating dyslipidemia, and that estrogen supplementation or lipid lowering is an effective way to ameliorate hippocampal damage and cognitive dysfunction via upregulation of ERs. Graphical Abstract

Abstract Objectives In clinical practice, digestive symptoms such as nausea, vomiting are frequently observed in COVID-19 patients. However, the causal relationship between COVID-19 and digestive diseases remains unclear. Methods We extracted single nucleotide polymorphisms associated with the severity of COVID-19 from summary data of genome-wide association studies. Summary statistics of common digestive diseases were primarily obtained from the UK Biobank study and the FinnGen study. Two-sample Mendelian randomization analyses were then conducted using the inverse variance-weighted (IVW), Mendelian randomization-Egger regression (MR Egger), weighted median estimation, weighted mode, and simple mode methods. IVW served as the primary analysis method, and Multivariable Mendelian randomization analysis was employed to explore the mediating effect of body mass index (BMI) and type 2 diabetes. Results MR analysis showed that a causal association between SARS-CoV-2 infection (OR = 1.09, 95% CI 1.01–1.18, P = 0.03), severe COVID-19 (OR = 1.02, 95% CI 1.00–1.04, P = 0.02), and COVID-19 hospitalization (OR = 1.04, 95% CI 1.01–1.06, P = 0.01) with gastroesophageal reflux disease (GERD). Mediation analysis indicated that body mass index (BMI) served as the primary mediating variable in the causal relationship between SARS-CoV-2 infection and GERD, with BMI mediating 36% (95% CI 20–53%) of the effect. Conclusions We found a causal relationship between SARS-CoV-2 infection and gastroesophageal reflux disease. Furthermore, we found that the causal relationship between SARS-CoV-2 infection and GERD is mainly mediated by BMI.

In the increasingly complex electromagnetic environment, main lobe jamming significantly degrades the performance of a wideband radar system. To mitigate this issue, this paper developed a wideband main lobe jamming suppression algorithm based on a distributed array radar. Firstly, this algorithm utilizes eigen-projection matrix processing in the main array to cancel out the main lobe jamming for main lobe maintenance, and then suppresses side lobe jamming through null constraint beamforming. Subsequently, the large aperture of the full array is leveraged to form a narrow beam directed toward the main lobe interference. Finally, joint beamforming using the minimum mean square error criterion is employed. In scenarios where both main lobe jamming and side lobe jamming exist, this algorithm can adaptively cancel main lobe wideband jamming, suppress side lobe wideband jamming, and effectively control the significant loss of the desired wideband signals caused by main lobe jamming within a smaller angular range. Simulation results validate the effectiveness of the algorithm.

Natural speech plays a pivotal role in communication and interactions between human beings. The prosody of natural speech, due to its high ecological validity and sensitivity, has been acoustically analyzed and more recently utilized in machine learning to identify individuals with autism spectrum disorders (ASDs). In this meta-analysis, we evaluated the findings of empirical studies on acoustic analysis and machine learning techniques to provide statistically supporting evidence for adopting natural speech prosody for ASD detection. Using a random-effects model, the results observed moderate-to-large pooled effect sizes for pitch-related parameters in distinguishing individuals with ASD from their typically developing (TD) counterparts. Specifically, the standardized mean difference (SMD) values for pitch mean, pitch range, pitch standard deviation, and pitch variability were 0.3528, 0.6744, 0.5735, and 0.5137, respectively. However, the differences between the two groups in temporal features could be unreliable, as the SMD values for duration and speech rate were only 0.0738 and −0.0547. Moderator analysis indicated task types were unlikely to influence the final results, whereas age groups showed a moderating role in pooling pitch range differences. Furthermore, promising accuracy rates on ASD identification were shown in our analysis of multivariate machine learning studies, indicating averaged sensitivity and specificity of 75.51% and 80.31%, respectively. In conclusion, these findings shed light on the efficacy of natural prosody in identifying ASD and offer insights for future investigations in this line of research.

The plant rhizosphere underlies the crosstalk between plant and soil and has a crucial role in plant growth and development under various environments. We examined the effect of temperature rise on the rhizosphere environment of soybean roots to clarify the rhizosphere crosstalk between roots and soil in response to warm temperature rises in a global warming background. The in situ results of root enzyme activity revealed that soybean roots secrete β-glucosidase, and enzyme spectrum imaging demonstrated different enzymatic activities under different temperature environments. The soil enzyme kinetics results showed that soil enzymatic activity increased with increasing temperature, and soybean rhizosphere soil enzymatic activity was higher than that of non-rhizosphere soil. Rhizosphere soil and non-rhizosphere soil showed that the dominant bacterial phylum in soybean rhizosphere soil was Acidobacteria, and the dominant bacterial genus was JG30-KF-AS9. Compared with non-rhizosphere soil, rhizosphere soil was more nutrient-rich, and root secretions provided abundant carbon sources and other nutrients for soil microorganisms in the rhizosphere. Rhizosphere microorganisms affect plant growth by influencing the decomposition of soil organic carbon. The organic carbon content of rhizosphere soil was higher than that of non-rhizosphere soil under high temperatures.

When the satellite is in orbit, the thruster will experience drastic temperature changes (100–1000 K) under solar radiation, which will affect the rarefied gas flow state in the micro-nozzle structure of the cold gas micro-thruster. In this study, the effect of different wall temperatures on the rarefied flow and heat transfer in the micro-nozzle is investigated based on the DSMC method. The micro-nozzle structure in this paper has a micro-channel with a large length-to-diameter ratio of 10 and a micro-scale needle valve displacement (maximum needle valve displacement up to 4 μm). This leads to more pronounced multiscale flow characteristics in the micro-nozzle, which is more influenced by the change in wall temperature. At wall temperatures ranging from 100 K to 1000 K, the spatial distribution of local Kn distribution, slip velocity distribution, temperature, and wall heat flux distribution in the micro-nozzle were calculated. The slip flow region is located in the flow channel and transforms into transition flow as the slip velocity reaches approximately 50 m/s. The spatial distribution of the flow pattern is dominated by the wall temperature at small needle valve opening ratios. The higher the wall temperature, the smaller the temperature drop ratio in the low-temperature region inside the micro-nozzle. The results of the study provide a reference for the design of temperature control of micro-nozzles in cold gas micro-thrusters.

ObjectivesThis study aims to explore the interaction of different pathogens in Hand, foot and mouth disease (HFMD) by using a mathematical epidemiological model and the reported data in five regions of China.MethodsA cross-regional dataset of reported HFMD cases was built from four provinces (Fujian Province, Jiangsu province, Hunan Province, and Jilin Province) and one municipality (Chongqing Municipality) in China. The subtypes of the pathogens of HFMD, including Coxsackievirus A16 (CV-A16), enteroviruses A71 (EV-A71), and other enteroviruses (Others), were included in the data. A mathematical model was developed to fit the data. The effective reproduction number (Reff) was calculated to quantify the transmissibility of the pathogens.ResultsIn total, 3,336,482 HFMD cases were collected in the five regions. In Fujian Province, the Reff between CV-A16 and EV-A71&CV-A16, and between CV-A16 and CV-A16&Others showed statistically significant differences (P < 0.05). In Jiangsu Province, there was a significant difference in Reff (P < 0.05) between the CV-A16 and Total. In Hunan Province, the Reff between CV-A16 and EV-A71&CV-A16, between CV-A16 and Total were significant (P < 0.05). In Chongqing Municipality, we found significant differences of the Reff (P < 0.05) between CV-A16 and CV-A16&Others, and between Others and CV-A16&Others. In Jilin Province, significant differences of the Reff (P < 0.05) were found between EV-A71 and Total, and between Others and Total.ConclusionThe major pathogens of HFMD have changed annually, and the incidence of HFMD caused by others and CV-A16 has surpassed that of EV-A71 in recent years. Cross-regional differences were observed in the interactions between the pathogens.

The needle valve, serving as the flow control unit of the thruster system, is a crucial component of the entire thruster. Its performance directly impacts the flow state of the rarefied gas in the micro-nozzle structure of the cold gas micro-thruster, thereby exerting a significant influence on the high precision and stability of the propulsion system as a whole. This study examines the impact of different needle valve structures on the flow and thrust in micro-nozzles using the DSMC method. The analysis includes discussions on the spatial distribution, Kn distribution, slip velocity distribution, and pressure distribution of the micro-nozzle’s flow mechanism. Notably, increased curvature of the needle valve enhances the flow velocity in the throat and expansion section. The magnitude of the curvature directly affects the flow velocity, with larger curvatures resulting in higher velocities. Comparing different spool shapes, the conical spool shape minimizes the velocity gradient in the high-speed region at the junction between the spool area and the outlet pipe, particularly with a wide opening. Increasing the curvature of the spool leads to a higher velocity in the expansion section. Consequently, an arc-shaped spool valve maximizes the nitrogen flow at the nozzle during wide openings, thereby enhancing thrust. These research findings serve as a valuable reference for the structural design of the needle valve in the micro-nozzle of the cold gas micro-thruster.

Dispersed soil is highly sensitive to water and can easily disperse in low-salt water, leading to weakened structures and engineering issues. To investigate the freeze–thaw effects on the mechanical properties and microstructure of dispersed soils in western Jilin Province, we simulated 0, 1, 3, 5, 7, 9, 12, and 15 freeze–thaw cycles. Qualitative and quantitative analyses were performed using numerous methods, including soluble salt determination, density and water content determination, particle size analysis, dispersion identification tests, and scanning electron microscopy (SEM), to investigate the mechanism of deterioration in soil mechanical properties from various perspectives. The research findings indicate that the unconfined compressive strength (UCS) decreased from 156.843 kPa in the unfrozen state to 76.961 kPa and then stabilized. The freeze–thaw action resulted in particle fragmentation, increased soil porosity, and elevated crack content, thereby contributing to soil structure deterioration and strength reduction. Furthermore, the cohesion value (c-value) gradually decreased from 22.196 kPa in the unfrozen state to 7.997 kPa and then stabilized. The angle of internal friction (φ-value) started at 7.514°, peaked at 9.514°, and gradually declined. This comprehensive study provides valuable insights into the variations in soil mechanical properties under freeze–thaw cycles from multiple perspectives.

The dynamics of a cavitation bubble within a droplet is one of the hot research topics at present. The present paper summarizes the research progress of bubble collapse within droplets and associated droplet splash. Firstly, three typical structures of bubble collapse are introduced, together with the collapsing shock waves and the distribution of collapsing forces. Secondly, several typical forms of droplet splash are shown in terms of splash speed, splash direction, and dynamic mechanisms. Finally, the unsolved problems in the field of cavitation bubbles within droplets are proposed with perspectives.

The cavitation bubble within a droplet is one of the frontier topics in bubble dynamics, with applications in many industrial fields. In the present paper, the dynamics of the cavitation bubble wall and the droplet surface, with different radius ratios, are compared and analyzed. The relationship between cavitation bubble collapse and droplet splash is disclosed. Research shows that, firstly, under the same type of splash, there is a positive correlation between the radius ratio with the displacement of the feature point and the distance between the two ends of the X-axis. It shows that the splash becomes more prominent with the radius ratio increase. Secondly, under different splash cases, the radius ratio also shows a significant impact on the trend of interface displacement and the splash dynamics. In addition, as the radius ratio increases the modification coefficient of the collapse time being smaller.

The optimum placement of receiving telescope antennas is a central topic for designing radio interferometric arrays, and this determines the performance of the obtained information. A variety of arrays are designed for different purposes, and they perform poorly in scalability. In this paper, we consider a subclass of structured sparse arrays, namely nested arrays, and examine the important role of “coarray” in interferometric synthesis imaging, which is utilized to design nested array configurations for a complete uniform Fourier plane coverage in both supersynthesis and instantaneous modes. Both nested arrays and the theory of the coarray have rich research achievements, and we apply them to astronomy to design arrays with good scalability and imaging performance. Simulated celestial source image retrieval performance validates the effectiveness of nested interferometric arrays.

The outflow of the rural labor force has a great impact on the location distribution of agricultural production, which has not attracted enough attention in existing studies. This article describes the mechanism of agricultural production location adjustment and further studies the influence of a regional difference in labor endowment on agricultural production location adjustment. Based on commodity and province-level panel data calculation, the results show that the agricultural production location in China has been adjusted from the East to the Central and then to the West with accelerating transfer speed. Furthermore, it is found that the regional differences in labor endowment are the main driving force for the shift of low alternative crop production. The more labor-intensive the crop varieties are, the more obvious the influence of the regional differences in labor endowment is on the crop production location adjustment. Moreover, regional differences in labor endowment have little effect on the location adjustment of vegetable production, as expanding the vegetable market demand may offset the effect of labor supply shortage on the adjustment of vegetable production location. Therefore, it is necessary to formulate a regional industrial development plan consistent with the trend of agricultural production location adjustment, as well as promote the construction of agricultural circulation facilities and socialized services in less developed areas.

With the wideband beamforming approaches, the synthetic aperture radar (SAR) could achieve high azimuth resolution and wide swath. However, the performance of conventional adaptive wideband time-domain beamforming is severely affected as the received signal snapshots are insufficient for adaptive approaches. In this paper, a wideband beamformer using convolutional neural network (CNN) method, namely, frequency constraint wideband beamforming prediction network (WBPNet), is proposed to obtain a satisfactory performance in the circumstances of scanty snapshots. The proposed WBPNet successfully estimates the direction of arrival of interference with scanty snapshots and obtains the optimal weights with effectively null for the interference by utilizing the uniqueness of CNN to extract potential nonlinear features of input information. Meanwhile, the novel beamformer has an undistorted response to the wideband signal of interest. Compared with the conventional time-domain wideband beamforming algorithm, the proposed method can fast obtain adaptive weights because of using few snapshots. Moreover, the proposed WBPNet has a satisfactory performance on wideband beamforming with low computational complexity because it avoids the inverse operation of covariance matrix. Simulation results show the meliority and feasibility of the proposed approach.

Based on early experiences and current conditions, a future time perspective influences college students’ behaviors, while psychological violence critically threatens college students’ health. This study explored the relationship between a future time perspective and the psychological violence of perpetrators based on an online investigation of 1424 college students (87.1% women) aged 17 to 31 in China. The results showed that a future time perspective is significantly positively associated with psychological violence. Positive future orientation is negatively associated with psychological violence. Negative and confused future orientations are positively associated with psychological violence. These findings support the need to introduce an intervention regarding a future time perspective to reduce psychological violence among college students.

During the working process of the ADN-based thruster, continuously, heat generated by the chemical reaction in the combustion chamber will transfer along the upstream capillary, the propellant in the capillary continuously absorbs heat under the effect of heat transfer from the wall and undergoes a phase change when the saturation temperature is reached. In this study, effects of the downstream heating temperature (623 K to 923 K) on mass flow rate and pressure change in the capillary were investigated based on the established test platform. Simultaneously, the VOF (volume of fraction) model, and the Lee phase transition model coupled with the Navier–Stokes method was utilized to simulate the spatial distribution of the gas-liquid propellant in the capillary. The results show that the ADN-based propellant firstly formed bubbles on the inner wall surface near the exit of the capillary, and these vapor bubbles moved and grew upstream along the capillary. Due to the cooling effect of the ADN-based propellant inflow, the temperature distribution of the front chamber and capillary gradually reached equilibrium. Bubbles were constantly generated in the capillary, and as the heat reflux intensified, the total volume of bubbles in the capillary continued increasing. Single-phase flow, annular flow, wave flow, and segment plug flow appeared sequentially along the axial direction of the capillary, and the proportion of gas phase volume fraction at the capillary outlet section gradually increased.