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A physics-guided neural network (PGNN) is proposed to predict the fatigue life of materials. In order to reduce the complexity of fatigue life prediction and reduce the data required for network training, the PGNN only predicts the fatigue performance parameters under a specific loading environment, and calculates the fatigue life by substituting the load into the fatigue performance parameters. The advantage of this is that the network does not need to evaluate the effect of numerical changes in the load on fatigue life. The load only needs to participate in the error verification, which reduces the dimension of the function that the neural network needs to approximate. The performance of the PGNN is verified using published data. Due to the reduction in the complexity of the problem, the PGNN can use fewer training samples to obtain more accurate fatigue life prediction results and has a certain extrapolation ability for the changes in trained loading environment parameters. The prediction process of the PGNN for fatigue life is not completely a black box, and the prediction results are helpful for scholars to further study the fatigue phenomenon.

At present, bridge crane accidents occur frequently, resulting in significant losses and casualties; to ensure the safe use of in-service bridge cranes, it is necessary to predict the residual fatigue life of in-service bridge cranes. Firstly, a static analysis of the most dangerous working conditions of in-service bridge cranes is carried out to find the fatigue failure point. Subsequently, a three-parameter Weibull distribution model is established for the characteristic parameters affecting the acquisition of the stress spectrum. Latin hypercubic sampling is applied to randomly sample the characteristic parameters to produce a random sample set of characteristic parameters for use in obtaining the stress–time history. The amplitude and mean values of the stress spectra are obtained by cycle counting using the rainflow counting method. Finally, Forman’s formula and Miner’s continuous damage accumulation theory were used to derive the remaining life prediction equations for constant and variable amplitude loads. Comparing the remaining life obtained from the simulation and test, the error is about 9.145%, which proves that the remaining life obtained from the simulation is more accurate. The results show that the combined method of simulation and testing is feasible and can predict the remaining life more accurately. In the past, the prediction of residual life was performed with either testing or simulation, which is long and costly. Simulation is low-cost and takes a short time, but the accuracy is not high. In this paper, the combination method of testing and simulation improves the efficiency of production and reduces the cost of use.

High-speed trains operating in windy areas may accelerate and decelerate frequently to maintain safe travel, especially when passing curves. During acceleration and deceleration, the role of inter-vehicle suspension (IVS) cannot be ignored. The present study aims to evaluate the effect of IVS on the variable speed curve running of trains under crosswinds. To achieve this purpose, a multibody model of a China Railways High-speed 2 (CRH2) high-speed train considering the IVS is established. By inputting the crosswind loads and traction or braking forces to the model and setting curved tracks with different radii and the unloading factor set as safety criterion, the safe running speeds of the train under different crosswind speeds and different track radii were obtained. The difference in the vehicle dynamics considering the IVS and the fixed connection under traction and braking conditions is analyzed. The radius of the curve track significantly affects the safety characteristics of a train under crosswinds, but its impact diminishes for radii greater than 7000 m. The lateral acceleration, movement angle, unloading factor, and derailment coefficient in both acceleration and deceleration cases of car bodies are affected by the IVS. As a consequence, the IVS will lead to lower safe speeds than fixed connections, but it will also convey more realistic and credible train dynamics.

Background: Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods. Objectives: This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets. Methods: Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts. Results: Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10−5). ROC curves analysis indicated that C-score could distinguish “high-risk chemicals” from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts. Conclusion: TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.

Lead toxicity poses a big hurdle for the commercialization of perovskite optoelectronics, hence reducing the environmental impact holds the answer for its future application. To tackle this challenge, the authors utilize germanium to reduce the lead content, enabling highly luminescent eco-friendly compound for LEDs.

Abstract Background Two studies have suggested that severe prolonged nausea and vomiting during pregnancy is associated with emotional and behavioral problems in offspring, with smaller sample size and short-term follow-up. Moreover, little information is available on the role of the brain structure in the associations. Methods In a US-based cohort, the association was investigated between severe prolonged nausea and vomiting in pregnancy (extending after the second trimester and termed SNVP), psychiatric and cognitive problems, and brain morphology, from the Adolescent Brain Cognitive Development (ABCD) study, from 10,710 children aged 9–11 years. We validated the emotional including psychiatric findings using the Danish National Cohort Study with 2,092,897 participants. Results SNVP was significantly associated with emotional and psychiatric problems (t = 8.89, Cohen’s d = 0.172, p = 6.9 × 10−19) and reduced global cognitive performance (t = − 4.34, d = − 0.085, p = 1.4 × 10−5) in children. SNVP was associated with low cortical area and volume, especially in the cingulate cortex, precuneus, and superior medial prefrontal cortex. These lower cortical areas and volumes significantly mediated the relation between SNVP and the psychiatric and cognitive problems in children. In the Danish National Cohort, severe nausea and vomiting in pregnancy were significantly associated with increased risks of behavioral and emotional disorders in children (hazard ratio, 1.24; 95% confidence interval, 1.16–1.33). Conclusions SNVP is strongly associated with psychiatric and cognitive problems in children, with mediation by brain structure. These associations highlight the clinical importance and potential benefits of the treatment of SNVP, which could reduce the risk of psychiatric disorder in the next generation.

Electrocatalytic reduction of carbon dioxide is attractive for obtaining multicarbon products, but conversion efficiency is low. Here the authors use copper complex materials for electrochemical reduction of carbon dioxide to ethanol and acetic acid with high efficiencies and activities.

Although several reports have studied the types of magnetic exchange interactions, the origin of the ferrimagnetism or ferromagnetism, and crystal structure in double perovskite La2CrMnO6, there are still many controversies because of the complexity of structures, valence states and synthesized conditions in this material. To further explore the structure, magnetism, resistivity and exchange interactions, the X-ray diffraction, X-ray photoelectron spectroscopy, AC and DC magnetic measurements, and the resistivity as a function of temperature are performed in this study. The O-type orthorhombic structure (b < c√2

While the conversion of CO2 to valuable, storable chemicals is attractive, there are few inexpensive and abundant catalysts that are also active and selective for liquid fuels. Here, the authors study copper selenide as a high-performing and efficient electrocatalyst for CO2 conversion to methanol.

Bovine endometrial stromal cells (bESCs) are exposed to a complex environment of bacteria and viruses due to the rupture of epithelial cells after delivery. Inflammatory responses are elicited by the activation of host pattern recognition receptors through pathogen-related molecules such as lipopolysaccharides (LPS) on the cell membrane. Forsythoside A (FTA) is a major active constituent of Forsythia suspensa (Thunb.) Vahl. is a flowering plant widely employed as a traditional Chinese herbal medicine to treat various inflammatory diseases such as nephritis, eye swelling, scabies, ulcers, and mastitis; however, the molecular mechanisms underlying its therapeutic effects on bovine endometritis are still unclear. The aim of this study was to explore the role of miRNA and the mechanisms underlying the protective activity of FTA on the inflammation of bovine endometrial stromal cells induced by LPS. Based on previous research, we isolated and cultured bESCs in vitro and categorized them into LPS and LPS+FTA groups with three replicates. Upon reaching 80% confluence, the bESCs were treated with 0.5 μg/mL of LPS or 0.5 μg/mL of LPS + 100 μg/mL of FTA. We, then, performed high-throughput sequencing (RNA-Seq) to investigate the effects of FTA on LPS-stimulated primary bESCs and their underlying mechanisms. We identified 167 miRNAs differentially expressed in the LPS groups; 72 miRNAs were up-regulated, and 95 were down-regulated. Gene ontology enrichment analysis revealed that differentially expressed microRNA (DEGs) were most enriched during the cellular metabolic process; they were mostly located intracellularly and participated in protein, enzyme, and ion binding. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that the DEGs were most enriched in the mitogen-activated protein kinase, tumor necrosis factor, and Interleukin-17 signaling pathways. These results reveal the complex molecular mechanism involved in the FTA and provide a basis for future studies of bovine endometritis treatment with traditional Chinese medicine monomer.

Endometritis adversely affects the ability of cattle to reproduce and significantly reduces milk production. The is mainly composed of epithelial and stromal cells, and they produce the first immune response to invading pathogens. However, most of the epithelial cells are disrupted, and stromal cells are exposed to an inflammatory environment when endometritis occurs, especially postpartum. Many bacteria and toxins start attacking stromal cell due to loss of epithelium, which stimulates Toll-like receptor (TLRs) on stromal cells and causes upregulated expression of cytokines. Understanding the genome-wide characterization of bovine endometritis will be beneficial for prevention and treatment of endometritis. In this study, whole-transcriptomic gene changes in bovine endometrial stromal cells (BESCs) treated with LPS were compared with those treated with PBS (control group) and were analyzed by RNA sequencing. Compared with the control group, a total of 366 differentially expressed genes (DEGs) were identified in the LPS-induced group (234 upregulated and 132 downregulated genes), with an adjusted P < 0.05 by DESeq. Gene Ontology (GO) enrichment analysis revealed that DEGs were most enriched in interleukin-1 receptor binding, regulation of cell activation, and lymphocyte-activated interleukin-12 production. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed DEGs were most enriched in the TNF signaling pathway, Toll-like receptor signaling pathway, cytokine–cytokine receptor interaction, NF-κB signaling pathway, and chemokine signaling pathway. The results of this study unraveled BESCs affected with LPS transcriptome profile alterations, which may have a significant effect on treatment inflammation by comprehending molecular mechanisms and authenticating unique genes related to endometritis.

Electroreduction of carbon dioxide (CO2) to value-added chemicals and fuels is a promising approach for sustainable energy conversion and storage. Many electrocatalysts have been designed for this purpose and studied extensively. The role of the electrolyte is particularly interesting and is pivotal for designing electrochemical devices by taking advantage of the synergy between electrolyte and catalyst. Recently, ionic liquids as electrolytes have received much attention due to their high CO2 adsorption capacity, high selectivity, and low energy consumption. In this review, we present a comprehensive overview of the recent progress in CO2 electroreduction in ionic liquid-based electrolytes, especially in the performance of different catalysts, the electrolyte effect, as well as mechanism studies to understand the reaction pathway. Perspectives on this interesting area are also discussed for the construction of novel electrochemical systems.

Medium- and high-Mn steels have excellent properties but are very difficult to be commercially produced because of the high content of some alloy elements. To enhance the understanding of the reaction between medium/high-Mn steel and refining slag which is significantly different from the conventional steels, steel and slag composition and the inclusions were investigated by equilibrium reaction between Fe-xMn (x = 5, 10, 15, and 20 mass pct) and CaO-SiO2-Al2O3-MgO top slag at 1873 K in the laboratory. Furthermore, the effect of Mn content on inclusion transformation and steel cleanliness was also explored. After slag–steel reaction, both contents of MnO in slag and Si in steel increased. Most MnO inclusions in master steel transformed to MnO-SiO2 and MnO-Al2O3-MgO. With the increase in Mn content, the amount share of MnO type inclusions decreased and that of MnO-Al2O3-MgO type increased. In addition, both the number density of observed inclusions and the calculated oxygen content in inclusions increased. Thermodynamic analysis indicates that the composition change of steel and slag and the transformation of inclusions are mainly the consequence of the reaction between Mn in molten steel and SiO2 and MgO in top slag. The dissolved Mn in medium/high-Mn steel presents a strong reactivity.

As environmental and energy issues are becoming more and more serious, China has seen the birth of a number of electric vehicle start-ups (EVSUs). Although there has been a dozen or so studies about consumers’ purchase intention of battery electric vehicles, few of them were about electric vehicle start-ups. Therefore, it is necessary to explore the factors that influence consumers’ purchase intention of electric vehicles from Chinese electric vehicle start-ups (CEVSUs). This study establishes a theoretical model of the factors that influence consumers’ purchase intentions of electric vehicles from CEVSUs through brand identity, brand image, brand awareness, and perceived risk. The analysis results of 332 valid questionnaires show that the factors that affect consumers’ purchase intention of electric vehicles from CEVSUs include brand identity, brand image, and perceived risk. Brand image will be affected by brand recognition, brand awareness, and perceived risk. The perceived risk will be affected by brand awareness and brand identity. Brand awareness will be affected by brand identity, but brand awareness will not directly affect purchase decisions. The conclusions of this study can be used as a reference for the government, consumers, or practitioners in the electric vehicle industry to promote the sales of electric vehicles to achieve low-carbon transportation, and also to help Chinese start up brands to take a larger share of the domestic market.

Double perovskite La2NiMnO6 is an attractive spintronic material with magnetocapacitance and magnetoresistance effects. These outstanding properties are associated with the biphasic characteristic and anti-site defects. To study the biphasic nature of La2NiMnO6, the spontaneous shear strains, crystal structures, microstructures, element distribution, and magnetic properties were investigated. The first principles density functional theory calculations within the Perdew-Burke-Ernzerhof functional were first used to calculate the static formation enthalpies and crystal structures of La2NiMnO6 at different hydrostatic pressures. The coexistence is most likely related to static formation enthalpies and/or spontaneous strains and partly due to the inhomogeneous characteristics of the sample.

A novel Cu and boron doped graphitic carbon nitride catalyst (Cu-CNB) was synthesized using cheap precursors and systematically characterized. The selective oxidation of toluene proceeded very smoothly over the catalyst at 70 °C using tert-butyl hydroperoxide (TBHP) as the oxidant to exclusively afford benzaldehyde. The catalyst can be used for at least five cycles without decrease in activity and selectivity.

Electroreduction of CO² is a sustainable approach to produce syngas with tunable CO/H² ratios, which are required as specific reactants for the optimization of desired products. Herein, ZnO-d and ZnO-n nanomaterials were derived from zeolitic imidazolate framework-8 (ZIF-8) precursors with different morphologies, which exhibit good performance for CO² electroreduction to syngas when using 1-butyl- 3-methylimidazolium hexafluorophosphate ([Bmim]PF6, 30 wt%)/acetonitrile/water (H²O, 5 wt%) as the supporting electrolyte. Regulation of the number of oxygen vacancies in different ZnO samples and the applied potentials during electrolysis can obviously affect the CO/H² ratios in syngas, which would be changed from 1/3 to 8.5/1. Meanwhile, ZnO-n with abundant oxygen vacancies displays the highest CO faradaic efficiency (FE) of 73.2% with the total current density of 9.8 mA cm-2 at -1.9 V vs. Ag/Ag+. More oxygen vacancies and higher electrochemical specific surface area of ZnO-n can provide more active sites and facilitate the adsorption of CO2 and its intermediates. The smaller charge transfer impedance of ZnO-n can accelerate the electron and proton transfer, and thus improve the catalytic activity and the selectivity of products. In addition, the good synergistic effect between the [Bmim]PF6- containing electrolyte and ZnO-n can enhance the formation of syngas. [ABSTRACT FROM AUTHOR]