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The electrochemical behavior of Li2O in KCl-LiCl molten salt has been studied by cyclic voltammetry and chronopotentiometry. The diffusion coefficient D=0.5×10-5 cm2/s of O2- in KCl-LiCl molten salt at 923 K has been calculated by convolutional voltammetry. Gd2O3, Nd2O3, Sm2O3 and Dy2O3 were used as cathodes to conduct electrolysis in KCl-LiCl-Li2O(w=1%) molten salt(constant voltage 3.40 V, electrolysis temperature 923 K, electrolysis time 25 h). By XRD analysis, it was confirmed that the rare earth oxides are partially reduced to metals, and the possible reactions during the electrolysis process were analyzed. At the same time, the PRS model was used to analyze the electrolytic reduction models of these rare earth oxides, and the optimal porosity(Popt) of Gd2O3, Nd2O3, Sm2O3 and Dy2O3 are 18.7%, 24.2%, 30.6%,16.7%, respectively. The minimum electrolysis time of them are 133, 157, 143, and 119 h, respectively, which are compared with the results of the electrolytic experiment, it is found that the porosity and electrolysis time of the cathode do not meet the requirements for complete reduction of metal oxides, and the corresponding explanation is given.

Pig welfare is closely related to the economical production of pig farms. With regard to pig welfare assessment, pig sounds are significant indicators, which can reflect the quality of the barn environment, the physical condition and the health of pigs. Therefore, pig sound analysis is of high priority and necessary. In this review, the relationship between pig sound and welfare was analyzed. Three kinds of pig sounds are closely related to pig welfare, including coughs, screams, and grunts. Subsequently, both wearable and non-contact sensors were briefly described in two aspects of advantages and disadvantages. Based on the advantages and feasibility of microphone sensors in contactless way, the existing techniques for processing pig sounds were elaborated and evaluated for further in-depth research from three aspects: sound recording and labeling, feature extraction, and sound classification. Finally, the challenges and opportunities of pig sound research were discussed for the ultimate purpose of precision livestock farming (PLF) in four ways: concerning sound monitoring technologies, individual pig welfare monitoring, commercial applications and pig farmers. In summary, it was found that most of the current researches on pig sound recognition tasks focused on the selection of classifiers and algorithm improvement, while fewer research was conducted on sound labeling and feature extraction. Meanwhile, pig sound recognition faces some challenging problems, involving the difficulty in obtaining the audio data from different pig growth stages and verifying the developed algorithms in a variety of pig farms. Overall, it is suggested that technologies involved in the automatic identification process should be explored in depth. In the future, strengthen cooperation among cross-disciplinary experts to promote the development and application of PLF is also nessary.

With the development of mobile networks and APPs,user generated contents (UGC) containing multi-source heterogeneous data such as evaluations,markings,scoring,images and videos are greatly valuable information for improving the quality of personalized services.The representation learning of fusion and vectorization on the multi-source heterogeneous UGC is the most critical issue for the successful application.Motivated by this,we propose a representation learning method for effectively fusing and vectorizing the comments and image data.We utilize the Doc2vec and LDA models to sufficiently extract the features of the multi-source comments.The images correlated with the comments are represented with deep convolutional network.A hybrid vectorized representation learning for fusing comments and a convolution strategy for integrating images and comments are presented.The feasibility and effectiveness of the proposed method is demonstrated by applying it to typical Amazon public data sets with heterogeneous UGC,in which the vectorized multi-source heterogeneous UGC is taken as the representation of each product and the classification accuracy of the products are compared.

BackgroundAlterations in thermal parameters directly affect the safety of reactors. Accurately predicting the trends of key thermal parameters under various working conditions can greatly improve reactor safety, thereby effectively preventing the occurrence of nuclear power plant accidents. The thermal and hydraulic characteristic parameters in the reactor are affected by many factors.PurposeThis study aims to explore the prediction method of core thermal-hydraulic parameters of fast reactor and determine the feasibility of neural network prediction.MethodsThe China experimental fast reactor (CEFR) was selected as the research object, and the maximum temperature of the fuel cladding surface and mass flow rate were used as predictor variables. After data samples were generated through the Subchannel code (named Subchanflow), two widely used adaptive neural networks were employed to perform the thermal parameter forecast analysis of CEFR fuel assembly under steady-state conditions, and CEFR 1/2 core was taken as subject to carry out single-step and continuous predictive analysis of thermal parameters under transient conditions.ResultsThe results show that, compared with adaptive BP neural network, the adaptive RBF neural network exhibits a better fitting ability and higher forecasting accuracy, and its maximum error under steady-state conditions is 0.5%. Under transient conditions, some local points have poor forecasting accuracy, however, the adaptive RBF neural network is generally excellent at predicting temperature and mass flow. The average relative error of temperature does not exceed 1%, and the average relative error of flow does not exceed 6%.ConclusionsThe adaptive RBF neural network model can provide real-time forecasting in a short time under unstable flow conditions, and its forecasting results have certain reference value.

Rhizosphere soil microorganisms affect the diversity and ecological distribution of aboveground plants and play an important role in the functions of terrestrial ecosystems. Using high-throughput sequencing, we have investigated the rhizosphere soil microbial communities of three species of Stipa from six plots, in the eastern steppe of Inner Mongolia, and analyzed the relationship between microbial diversity and vegetation diversity and soil physicochemical characteristics. The dominant bacteria phyla were: Actinobacteria, 31.73–44.73%; Proteobacteria, 15.79–23.85%; Acidobacteria, 9.42–15.14%; and Chloroflexi, 4.90–13.60%. While the dominant fungi phyla were: Ascomycota, 16.93–82.12%; Basidiomycota, 2.44–81.28%; and Zygomycota, 0.40–18.92%. Principal coordinates analysis (PCoA) indicated that the soil microbial community compositions in the Stipa baicalensis steppe in Hulunbuir and the Stipa krylovii steppe in the Xilingol plots, were significantly different from the others. The species characteristics of the plant communities were different in each plot for the vegetation diversity factors, the Shannon Weiner index, Simpson index, and the importance value indices of the Stipa and Leymus chinensis significantly affected the community structures of the bacteria and fungi. Among the physicochemical soil parameters, soil pH, ammonium nitrogen (AN), and sucrase (SC), significantly affected the community structures of the bacteria and fungi, and were inseparable from the interactions between the microorganisms and environmental factors. We found that microbial community diversity was complex in the rhizosphere soils of the Stipa, and that they were mainly affected by the vegetation diversity, soil pH, ammonium nitrogen, and soil sucrase activities. These results improve our understanding of the correlations between the Stipa rhizosphere soil microorganisms and the environmental factors, and provide guidance for future research on the distribution characteristics and growth of the Stipa.

A multimorbidity-focused approach may reflect common etiologic mechanisms and lead to better targeting of etiologic agents for broadly impactful public health interventions. Our aim was to identify clusters of chronic obesity-related, neurodevelopmental, and respiratory outcomes in children, and to examine associations between cluster membership and widely prevalent chemical exposures to demonstrate our epidemiologic approach. Early to middle childhood outcome data collected 2011-2022 for 1092 children were harmonized across the ECHO-PATHWAYS consortium of 3 prospective pregnancy cohorts in six U.S. cities. 15 outcomes included age 4-9 BMI, cognitive and behavioral assessment scores, speech problems, and learning disabilities, asthma, wheeze, and rhinitis. To form generalizable clusters across study sites, we performed k-means clustering on scaled residuals of each variable regressed on study site. Outcomes and demographic variables were summarized between resulting clusters. Logistic weighted quantile sum regressions with permutation test p-values associated odds of cluster membership with a mixture of 15 prenatal urinary phthalate metabolites in full-sample and sex-stratified models. Three clusters emerged, including a healthier Cluster 1 (n = 734) with low morbidity across outcomes; Cluster 2 (n = 192) with low IQ and higher levels of all outcomes, especially 0.4-1.8-standard deviation higher mean neurobehavioral outcomes; and Cluster 3 (n = 179) with the highest asthma (92 %), wheeze (53 %), and rhinitis (57 %) frequencies. We observed a significant positive, male-specific stratified association (odds ratio = 1.6; p = 0.01) between a phthalate mixture with high weights for MEP and MHPP and odds of membership in Cluster 3 versus Cluster 1. These results identified subpopulations of children with co-occurring elevated levels of BMI, neurodevelopmental, and respiratory outcomes that may reflect shared etiologic pathways. The observed association between phthalates and respiratory outcome cluster membership could inform policy efforts towards children with respiratory disease. Similar cluster-based epidemiology may identify environmental factors that impact multi-outcome prevalence and efficiently direct public policy efforts.

BackgroundA major challenge in epidemiology is knowing when an exposure effect is large enough to be clinically important, in particular how to interpret a difference in mean outcome in unexposed/exposed groups. Where it can be calculated, the proportion/percentage beyond a suitable cut-point is useful in defining individuals at high risk to give a more meaningful outcome. In this simulation study we compute differences in outcome means and proportions that arise from hypothetical small effects in vulnerable sub-populations.MethodsData from over 28,000 mother/child pairs belonging to the Environmental influences on Child Health Outcomes Program were used to examine the impact of hypothetical environmental exposures on mean birthweight, and low birthweight (LBW) (birthweight