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Nitrate/nitrite-dependent anaerobic methane oxidation (n-DAMO) coupling to Anaerobic ammonium oxidation (Anammox) provides an opportunity for simultaneous nitrogen removal and methane emissions mitigation from wastewater. However, to achieve high nitrogen removal rate in such a process remains a critical challenge in practical application. This work investigated the interactions between n-DAMO and Anammox in membrane biofilm reactor (MBfR) and then developed operational strategies of MBfR for high rate nitrogen removal from landfill leachate. Initially, influent containing nitrate and ammonium facilitated the development of n-DAMO and Anammox microorganisms in MBfR, but nitrogen removal performance is hard to be further improved even deteriorated. Detailed investigations of interactions among n-DAMO and Anammox microorganisms confirmed that extra addition of nitrite into MBfR fed with nitrate and ammonium not only stimulated the activities of Anammox bacteria, but also enhanced the activities of n-DAMO archaea from 172.3 to 356.9 mg NO3−-N L−1 d−1. Functional gene analysis also indicated that mcrA and hzsA genes increased after nitrite addition. Based on this finding, influent containing NO3−, NO2− and NH4+ enabled nitrogen removal rates of MBfR increase from 224.9 to 888.2 mg N L−1 d−1. Finally, nitrate in the influent was gradually replaced with nitrite to mimic the effluent from partial nitriation of landfill leachate, but maintain the nitrate availability for n-DAMO archaea through increasing nitrate production from Anammox. These operation strategies enabled MBfR achieve the steady state with a nitrogen removal rate of 6.1 kg N m−3 d−1. Microbial community analysis revealed n-DAMO archaea, n-DAMO bacteria and Anammox bacteria jointly dominated the biofilm, and their relative abundance dynamically shifted with feeding regime. This work provides promising operational strategies for high rate of nitrogen removal from landfill leachate through integrating n-DAMO and Anammox process.

The optimization of an integrated coal gangue system of mining, dressing, and backfilling in deep underground mining is a multi-objective and complex decision-making process, and the factors such as spatial layout, node location, and transportation equipment need to be considered comprehensively. In order to realize the intellectualized location of the nodes for the logistics and transportation system of underground mining and dressing coal and gangue, this paper establishes the model of the logistics and transportation system of underground mining and dressing coal gangue, and analyzes the key factors of the intellectualized location for the logistics and transportation system of coal and gangue, and the objective function of the node transportation model is deduced. The PSO–QNMs algorithm is proposed for the solution of the objective function, which improves the accuracy and stability of the location selection and effectively avoids the shortcomings of the PSO algorithm with its poor local detailed search ability and the quasi-Newton algorithm with its sensitivity to the initial value. Comparison of the particle swarm and PSO–QNMs algorithm outputs for the specific conditions of the New Julong coal mine, as an example, shows that the PSO–QNMs algorithm reduces the complexity of the calculation, increases the calculation efficiency by eight times, saves 42.8% of the cost value, and improves the efficiency of the node selection of mining–dressing–backfilling systems in a complex underground mining environment. The results confirm that the method has high convergence speed and solution accuracy, and provides a fundamental basis for optimizing the underground coal mine logistics system. Based on the research results, a node siting system for an integrated underground mining, dressing, and backfilling system in coal mines (referred to as MSBPS) was developed.

In this study, white ginseng was used as the raw material, which was fermented with Paecilomyces hepiali through solid culture medium, to produce ginsenosides with modified chemical composition. The characteristic chemical markers of the products thus produced were investigated using rapid resolution liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (RRLC–QTOF–MS). Chemical profiling data were obtained, which were then subjected to multivariate statistical analysis for the systematic comparison of active ingredients in white ginseng and fermented ginseng to understand the beneficial properties of ginsenoside metabolites. In addition, the effects of these components on biological activity were investigated to understand the improvements in the phagocytic function of macrophages in zebrafish. According to the established RRLC–QTOF–MS chemical profiling, the contents in ginsenosides of high molecular weight, especially malonylated protopanaxadiol ginsenosides, were slightly reduced due to the fermentation, which were hydrolyzed into rare and minor ginsenosides. Moreover, the facilitation of macrophage phagocytic function in zebrafish following treatment with different ginseng extracts confirmed that the fermented ginseng is superior to white ginseng. Our results prove that there is a profound change in chemical constituents of ginsenosides during the fermentation process, which has a significant effect on the biological activity of these compounds.

Conventional traffic crash analyzing methods focus on identifying the relationship between traffic crash outcomes and impact risk factors and explaining the effects of risk factors, which ignore the changes of roadway systems and can lead to inaccurate results in traffic crash predictions. To address this issue, an innovative two-step method is proposed and a support vector regression (SVR) model is formulated into state-space model (SSM) framework for traffic crash prediction. The SSM was developed in the first step to identify the dynamic evolution process of the roadway systems that are caused by the changes of traffic flow and predict the changes of impact factors in roadway systems. Using the predicted impact factors, the SVR model was incorporated in the second step to perform the traffic crash prediction. A five-year dataset that obtained from 1152 roadway segments in Tennessee was employed to validate the model effectiveness. The proposed models result in an average prediction MAPE of 7.59%, a MAE of 0.11, and a RMSD of 0.32. For the performance comparison, a SVR model and a multivariate negative binomial (MVNB) model were developed to do the same task. The results show that the proposed model has superior performances in terms of prediction accuracy compared to the SVR and MVNB models. Compared to the SVR and MVNB models, the benefit of incorporating a state-space model to identify the changes of roadway systems is significant evident in the proposed models for all crash types, and the prediction accuracy that measured by MAPE can be improved by 4.360% and 6.445% on average, respectively. Apart from accuracy improvement, the proposed models are more robust and the predictions can retain a smoother pattern. Furthermore, the results show that the proposed model has a more precise and synchronized response behavior to the high variations of the observed data, especially for the phenomenon of extra zeros.

Background and Context: Computational thinking (CT) has been increasingly added to K-12 curricula, prompting teachers to grade more and more CT artifacts. This has led to a rise in automated CT assessment tools. Objective: This study examines the scope and characteristics of publications that use machine learning (ML) approaches to assess students' CT competencies from four perspectives: the educational context in which the assessments were implemented, the data used to train and validate ML algorithms, the specific ML algorithms used, and the aspects of CT assessed. Method: The PRISMA approach and Arksey and O'Malley's methodological framework for scoping reviews were adopted to search and screen studies. Findings: ML algorithms have been increasingly used to assess CT competencies. However, this study identified several research gaps in the literature: existing studies were mostly conducted in the context of programming or other learning activities related to computing science; datasets used by the ML algorithms were generally small; the most frequently used algorithms were regression techniques, naive Bayes, neural networks, clustering, and natural language processing, whereas no studies used reinforcement learning; and CT competencies were not comprehensively assessed. Implications: The applications of ML in CT assessments have the potential to enable personalized learning, improve assessment validity, reduce the workload of graders, and gain insights from large datasets by uncovering complex and subtle patterns.

In this paper, a single-phase five-level rectifier with coupled inductors is studied. First, a discrete mathematical model of a single-phase five-level rectifier is created in two-phase(αβ). A traditional single vector finite control set model predictive control (FCS-MPC) algorithm is improved to overcome the problems of a varying switching frequency, the large amount of time needed for calculation, and the inaccurate setpoint of current loop tracking. Then, the objective function of the system is established, and a simple objective function is used instead of an iterative optimization of the traditional FCS-MPC algorithm. At the same time, to eliminate the delay error and to reduce harmonic distortion, deadbeat control technology is introduced. Finally, the simulation and experimental results show that the improved model predictive current control algorithm not only retains the fast response of traditional model predictive control, but also has the advantages of fixed switching frequency, small calculation time, and small current steady-state error.

Air pollution shows a generally decreasing trend from the north to the south in China since 2013 (GB3095-2012, the current standard for monitoring). However, an opposite observation was recorded in 2017, especially in winter. In this study, we collected monitoring data of six air pollutants in 2016 and 2017, from a northern city (Beijing) and a southern city (Nanjing) for comparison. As air pollution was highly dependent upon meteorological conditions, we further analyzed their relationships to explain this abnormal phenomenon. Seasonal averaged PM2.5, PM10, SO2, CO, and NO2 were negatively correlated with wind scale (WS) while 8-h O3 exhibited an opposite relationship. Relative humidity (RH) has opposite effects on the concentrations of different pollutants in Beijing and Nanjing. The 8-h O3 showed the closest positive correlation with temperature (T), which is due to its formation mechanism. In Beijing, decreased RH, together with more wind from northwest in winter, resulted in an improved air quality in 2017. In Nanjing, WS, RH, T, and wind direction fluctuated within a narrow range in each season, leading to relatively stable pollutant concentrations. These results suggest that meteorological conditions are important factors to evaluate the air quality and implement control measures.

We summarize our work of the optoelectronic devices based on Germanium-tin (GeSn) alloys assisted with the Si3N4 liner stressor in mid-infrared (MIR) domains. The device characteristics are thoroughly analyzed by the strain distribution, band structure, and absorption characteristics. Numerical and analytical methods show that with optimal structural parameters, the device performance can be further improved and the wavelength application range can be extended to 2~5 μm in the mid-infrared spectra. It is demonstrated that this proposed strategy provides an effective technique for the strained-GeSn devices in future optical designs, which will be competitive for the optoelectronics applications in mid-infrared wavelength.

Background: Ginseng (the roots of Panax ginseng Meyer) is a well-known traditional Oriental medicine and is now widely used as a health food. It contains several types of ginsenosides, which are considered the major active medicinal components of ginseng. It has recently been reported that the qualitative and quantitative properties of ginsenosides found in ginseng may differ, depending on cultivation regions, ages, species, and so on. Therefore, it is necessary to study these variations with respect to cultivation ages and regions. Methods: In this study, 3–6-yr-old roots of P. ginseng were collected from three different cultivation regions. The contents of five ginsenosides (Rb1, Rd, Rc, Re, and Rgl) were measured by rapid resolution liquid chromatography coupled with quadruple–time-of-flight mass spectrometry. The Kruskal–Wallis Rank sum test and multiple t test were used for comparative analysis of the data to evaluate the dynamic changes in the accumulation of these ginsenosides affected by cultivation regions and ages. Results: The content and composition of ginsenosides varied significantly among specimens collected from different cultivation regions and having different cultivation ages. For all samples, the content of Rg1 and Re ginsenosides increases with age and this rate of increase is different for each sample. The contents of Rb1, Rc, and Rd varied with cultivation ages in samples from different cultivation regions; especially, Rb1 from a 6-yr-old root showed approximately twofold variation among the samples from three cultivation regions. Furthermore, the content of Rb1 highly correlated with that of Rd (r = 0.89 across all locations and ages). Conclusion: In our study, only the contents of ginsenosides Rg1 and Re were affected by the root age. Ginsenosides Rb1, Rc, and Rd varied widely with ages in samples from different cultivation regions.

Model species continue to underpin groundbreaking plant science research. At the same time, the phylogenetic resolution of the land plant Tree of Life continues to improve. The intersection of these two research paths creates a unique opportunity to further extend the usefulness of model species across larger taxonomic groups. Here we promote the utility of the Arabidopsis thaliana model species, especially the ability to connect its genetic and functional resources, to species across the entire Brassicales order. We focus on the utility of using genomics and phylogenomics to bridge the evolution and diversification of several traits across the Brassicales to the resources in Arabidopsis, thereby extending scope from a model species by establishing a model clade. These Brassicales-wide traits are discussed in the context of both the model species Arabidopsis thaliana and the family Brassicaceae. We promote the utility of such a model clade and make suggestions for building global networks to support future studies in the model order Brassicales.

Computational thinking (CT) skills of pre-service teachers have been explored extensively, but the effectiveness of CT training has yielded mixed results in previous studies. Thus, it is necessary to identify patterns in the relationships between predictors of CT and CT skills to further support CT development. This study developed an online CT training environment as well as compared and contrasted the predictive capacity of four supervised machine learning algorithms in classifying the CT skills of pre-service teachers using log data and survey data. First, the results show that Decision Tree outperformed K-Nearest Neighbors, Logistic Regression, and Naive Bayes in predicting pre-service teachers' CT skills. Second, the participants' time spent on CT training, prior CT skills, and perceptions of difficulty regarding the learning content were the top three important predictors in this model.