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The digital teaching model of Civics at the current stage still has deficiencies and does not achieve the expected effect. This paper utilizes data analysis and mining methods starting from the digital classroom platform to assess students’ knowledge and skill mastery through the CDA static cognitive analysis model. Then, students’ learning behaviors were analyzed using K-Means, and an evaluation model of interactive behavioral characteristics was constructed to analyze students’ Civics learning characteristics in combination with the above three aspects. Relying on the digital classroom platform, a corresponding teaching model for Civics in colleges and universities was developed. Our analysis of the impact of teachers’ and students’ interaction behaviors in Civics teaching and the realistic results of the Civics teaching model designed in this study is aided by the selection of empirical samples. The results show that the optimized students’ interactive behavior rate of Civics learning is more than 50%, and the interaction time increases to 19 minutes, which has a significant improvement effect. Analyzing the digital classroom teaching model of Civics, 28 students in Case 2 in the course comparison had greater interest in Civics learning, meanwhile, only 6 students had low interest in Civics learning, and the digital teaching model of Civics effectively enhanced students’ knowledge and literacy.

Aqueous rechargeable batteries are the promising energy storge technology due to their safety, low cost, and environmental friendliness. Ammonium ion (NH4+) is an ideal charge carrier for such batteries because of its small hydration radius and low molar mass. In this study, VO2·xH2O with rich oxygen defects (d-HVO) is designed and synthesized, and it exhibits unique nanoarray structure and good electrochemical performances for NH4+ storge. Experimental and calculation results indicate that oxygen defects in d-HVO can enhance the conductivity and diffusion rate of NH4+, leading to improved electrochemical performances. The most significant improvement is observed in d-HVO with 2 mmol thiourea (d-HVO-2) (220 mAh·g−1 at 0.1 A·g−1), which has a moderate defect content. A full cell is assembled using d-HVO-2 as the anode and polyaniline (PANI) as the cathode, which shows excellent cycling stability with a capacity retention rate of 80% after 1000 cycles and outstanding power density up to 4540 W·kg−1. Moreover, the flexible d-HVO-2∥PANI battery, based on quasi-solid electrolyte, shows excellent flexibility under different bending conditions. This study provides a new approach for designing and developing high-performance NH4+ storage electrode materials. [ABSTRACT FROM AUTHOR]

Objectives To build a predictive model for central lymph node metastasis (CLNM) in unifocal papillary thyroid carcinoma (UPTC) using a combination of clinical features and multimodal ultrasound (MUS). Methods This retrospective study, included 390 UPTC patients who underwent MUS between January 2017 and October 2022 and were divided into a training cohort (n  = 300) and a validation cohort (n  = 90) based on a cut-off date of June 2022. Independent indicators for constructing the predictive nomogram models were identified using multivariate regression analysis. The diagnostic yield of the 3 predictive models was also assessed using the area under the receiver operating characteristic curve (AUC). Results Both clinical factors (age, diameter) and MUS findings (microcalcification, virtual touch imaging score, maximal value of virtual touch tissue imaging and quantification) were significantly associated with the presence of CLNM in the training cohort (all P  < .05). A predictive model (MUS + Clin), incorporating both clinical and MUS characteristics, demonstrated favourable diagnostic accuracy in both the training cohort (AUC = 0.80) and the validation cohort (AUC = 0.77). The MUS + Clin model exhibited superior predictive performance in terms of AUCs over the other models (training cohort 0.80 vs 0.72, validation cohort 0.77 vs 0.65, P  < .01). In the validation cohort, the MUS + Clin model exhibited higher sensitivity compared to the CLNM model for ultrasound diagnosis (81.2% vs 21.6%, P  < .001), while maintaining comparable specificity to the Clin model alone (62.3% vs 47.2%, P  = .06). The MUS + Clin model demonstrated good calibration and clinical utility across both cohorts. Conclusion Our nomogram combining non-invasive features, including MUS and clinical characteristics, could be a reliable preoperative tool to predict CLNM treatment of UPTC. Advances in knowledge Our study established a nomogram based on MUS and clinical features for predicting CLNM in UPTC, facilitating informed preoperative clinical management and diagnosis. [ABSTRACT FROM AUTHOR]