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Vocabulary serves as the cornerstone of Japanese translation instruction, with the identification of high-frequency terms within educational texts yielding significant pedagogical insights. This study employs the KH-Coder software to extract feature words frequently used in Japanese translation teaching materials. Through a combination of covariate network analysis, sentiment analysis, and hierarchical clustering, the paper delves into the intrinsic textual value of these educational resources. Furthermore, it integrates both generative and discriminative approaches to conduct dependency parsing of Japanese translation. By incorporating probabilistic functions, the methodology enhances contextual understanding, ensuring that each annotation unit within a sentence is optimally tagged. Analysis of the CGTT-35 corpus through KH-Coder reveals a list of the top 50 high-frequency words in Japanese translation teaching, each appearing over 100 times. This enables students to use these frequent words and sentence structures as cues to master common phrases and familiarize themselves with core content. The approach has proven effective in enhancing students’ receptivity to Japanese translation instruction and their comprehension capabilities, offering valuable implications for teaching strategies.

Heterogeneous advanced oxidation processes (AOPs) based on non-radical reactive species are considered as a powerful technology for wastewater purification due to their long half-lives and high adaptation in a wide pH range. Herein, we fabricate surface Co defect-rich spinel ZnCo2O4 porous nanosheets, which can generate ≡CoIV=O and 1O2 over a wide pH range of 3.81–10.96 by the formation of amphoteric ≡Zn(OH)2 in peroxymonosulfate (PMS) activation process. Density functional theory (DFT) calculations show Co defect-rich ZnCo2O4 possesses much stronger adsorption ability and more electron transfer to PMS. Moreover, the adsorption mode changes from terminal oxygen Co–O–Co to Co–O, accelerating the polarization of adjacent oxygen, which is beneficial to the generation of ≡CoIV=O and 1O2. Co defect-rich ZnCo2O4 porous nanosheets exhibit highly active PMS activation activity and stability in p-nitrophenol (PNP) degradation, whose toxicity of degradation intermediates is significant reduction. The Co defect-rich ZnCo2O4 nanosheet catalyst sponge/PMS system achieved stable and efficient removal of PNP with a removal efficiency higher than 93% over 10 h. This work highlights the development of functional catalyst and provides an atomic-level understanding into non-radical PMS activation process in wastewater treatment. [ABSTRACT FROM AUTHOR]

Metal–organic frameworks (MOFs) have received widespread attention for their large specific surface area, porous structure, and tunable particle size. MOFs can be utilized as precursors to prepare structurally diverse bimetallic oxides, which exhibit outstanding electrochemical properties as anode materials for lithium-ion batteries (LIBs). This paper introduces the study of MOF-derived bimetallic oxides as anode materials for LIBs, focusing on the application of MOF-derived manganese-, cobalt-, iron-, and vanadium-based bimetallic oxides in LIBs. MOF-derived bimetallic oxides, with the advantages of tunable compositions and nano-structures, are capable of further contributing to the enhancement of electrochemical performances. Finally, the problems of these derived materials in the application of LIBs are prospected, and reliable solutions and future development prospects are proposed. [ABSTRACT FROM AUTHOR]

Data-driven individualized decision making has recently received increasing research interest. However, most existing methods rely on the assumption of no unmeasured confounding, which cannot be ensured in practice especially in observational studies. Motivated by the recently proposed proximal causal inference, we develop several proximal learning methods to estimate optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. Explicitly, in terms of two types of proxy variables, we are able to establish several identification results for different classes of ITRs respectively, exhibiting the tradeoff between the risk of making untestable assumptions and the potential improvement of the value function in decision making. Based on these identification results, we propose several classification-based approaches to finding a variety of restricted in-class optimal ITRs and establish their theoretical properties. The appealing numerical performance of our proposed methods is demonstrated via extensive simulation experiments and a real data application. for this article are available online. [ABSTRACT FROM AUTHOR]

The combat domain of modern warfare is becoming increasingly multidimensional. It is important to evaluate the resilience of the air-ground cooperative network for defending against attack threats and recovery performance. First, a resilience analysis model was proposed to effectively analyze and evaluate the resilience of the air-ground cooperative network. Then, considering the available resources, three dynamic reconfiguration strategies were given from the global perspective to help the air-ground cooperative network quickly recover performance and enhance combat capabilities. Finally, a typical 50-node network was taken as an example to prove the effectiveness and feasibility of the proposed model. The proposed method can provide scientific guidance for improving the air-ground cooperative network combat capabilities. [ABSTRACT FROM AUTHOR]

Objective: Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC. Methods: A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model. Results: In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM. Conclusion: The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans. [ABSTRACT FROM AUTHOR]

In the field of electric aircraft, the axial force of the conical motor is used to offset propeller thrust and reduce bearing wear. For the problem of accurate calculation of the no‐load axial force of the conical motor, combined with the characteristics that the rotor magnetic bridge of the hybrid conical inner‐mounted permanent magnet synchronous motor (HC‐IPMSM) is easy to be saturated and difficult to be calculated analytically, an equivalent analytical calculation method is proposed. According to the principle of constant air‐gap magnetic flux, the HC‐IPMSM is equivalent to many cylindrical surface‐mounted permanent magnet synchronous motors (CY‐SPMSM), and an analytical model is gained. Combining with the scalar magnetic potential Laplace equation and Poisson equation, the air‐gap flux density distribution on the rotor surface is calculated. And finally, the no‐load axial force of the motor is obtained by using the cone angle and the Maxwell stress method. Simulation and experiments verify the correctness and validity of the analytical model. [ABSTRACT FROM AUTHOR]

The aims of this study were to explore whether student suicide reporting is consistent with media recommendations for suicide reporting; analyze public opinion and sentiments toward student suicide reports. A keyword search was performed on the WeiboReach platform. This study included 113 student suicide report posts and 176,262 readers’ comments on suicide news reports. Hierarchical generalized linear modeling was used to analyze the relationships between adherence to reporting recommendations and negative emotions in readers’ comments. None of the media reporting of student suicide was consistent with all of the media recommendations for suicide reporting. Netizens were less likely to post negative comments when the reports describe the suicide method used (OR 1.169, 95% CI 1.022∼1.337), and not specifying the cause of suicide was a protective factor for public negative emotion (OR 0.799, 95% CI 0.707, 0.905). The findings suggest improving responsible media reporting on student suicide to reduce negative public emotion. [ABSTRACT FROM AUTHOR]