VLOG-BASED EFL TEACHING MODEL FOR UNIVERSITY STUDENTS: IMPACT ON SPEAKING SKILLS AND ENGAGEMENT

This study aims to investigate the efficacy of a vlog-based teaching model in enhancing oral speaking skills and engagement among university students in an English as a Foreign Language (EFL) setting. With the digital technologies reshaping educational methodologies, this research integrates vlogs (video blog) into EFL teaching to build a more engaging learning path. The study was conducted over a semester with 30 university EFL learners in China, employing a mixed-methods approach to evaluate the impact of vlog-based learning. Pre-and-post oral speaking tests were administered to assess improvements in students' language proficiency, while questionnaires measured levels of engagement and motivation. Initial findings suggest that the vlog-based teaching model significantly improved students' speaking skills and heightened their engagement in learning English, indicating a promising avenue for digital media integration in language education. This paper contributes to the evolving field of digital media in EFL teaching, offering insights into the potential of vlogs to enrich language learning experiences and outcomes

VLOG-BASED EFL TEACHING MODEL FOR UNIVERSITY STUDENTS: IMPACT ON SPEAKING SKILLS AND ENGAGEMENT

This study aims to investigate the efficacy of a vlog-based teaching model in enhancing oral speaking skills and engagement among university students in an English as a Foreign Language (EFL) setting. With the digital technologies reshaping educational methodologies, this research integrates vlogs (video blog) into EFL teaching to build a more engaging learning path. The study was conducted over a semester with 30 university EFL learners in China, employing a mixed-methods approach to evaluate the impact of vlog-based learning. Pre-and-post oral speaking tests were administered to assess improvements in students' language proficiency, while questionnaires measured levels of engagement and motivation. Initial findings suggest that the vlog-based teaching model significantly improved students' speaking skills and heightened their engagement in learning English, indicating a promising avenue for digital media integration in language education. This paper contributes to the evolving field of digital media in EFL teaching, offering insights into the potential of vlogs to enrich language learning experiences and outcomes

A Study of Taxi Service Mode Choice Based on Evolutionary Game Theory

The emergence of online car-hailing service provides an innovative approach to vehicle booking but has negatively influenced the taxi industry in China. This paper modeled taxi service mode choice based on evolutionary game theory (EGT). The modes included the dispatching and online car-hailing modes. We constructed an EGT framework, including determining the strategies and the payoff matrix. We introduced different behaviors, including taxi company management, driver operation, and passenger choice. This allowed us to model the impact of these behaviors on the evolving process of service mode choice. The results show that adjustments in taxi company, driver, and passenger behaviors impact the evolutionary path and convergence speed of our evolutionary game model. However, it also reveals that, regardless of adjustments, the stable states in the game model remain unchanged. The conclusion provides a basis for studying taxi system operation and management. Document type: Articl

Microstructure evolution and surface cleaning of Cu nanoparticles during micro-fields activated sintering technology

For the purpose of extensive utilization of powder metallurgy to micro/nano- fabrication of materials, the micro gear was prepared by a novel method, named as micro- forming fields activated sintering technology (Micro-FAST). Surface-cleaning of particles, especially during the initial stage of sintering, is a crucial issue for the densification mechanism. However, up to date, the mechanism of surface-cleaning is too complicated to be known. In this paper, the process of surface-cleaning of Micro-FAST was studied, employing the high resolution transmission electron microscopy (HRTEM) for observation of microstructure of micro-particles. According to the evolution of the microstructure, surface-cleaning is mainly ascribed to the effect of electro-thermal focusing. The process of surface-cleaning is achieved through rearrangement of grains, formation of vacancy, migration of vacancy and enhancement of electro-thermal focusing

Yeast Dun1 Kinase Regulates Ribonucleotide Reductase Small Subunit Localization in Response to Iron Deficiency

Ribonucleotide reductase (RNR) is an essential iron-dependent enzyme that catalyzes deoxyribonucleotide synthesis in eukaryotes. Living organisms have developed multiple strategies to tightly modulate RNR function to avoid inadequate or unbalanced deoxyribonucleotide pools that cause DNA damage and genome instability. Yeast cells activate RNR in response to genotoxic stress and iron deficiency by facilitating redistribution of its small heterodimeric subunit Rnr2-Rnr4 from the nucleus to the cytoplasm, where it forms an active holoenzyme with large Rnr1 subunit. Dif1 protein inhibits RNR by promoting nuclear import of Rnr2-Rnr4. Upon DNA damage, Dif1 phosphorylation by the Dun1 checkpoint kinase and its subsequent degradation enhances RNR function. In this report, we demonstrate that Dun1 kinase triggers Rnr2-Rnr4 redistribution to the cytoplasm in response to iron deficiency. We show that Rnr2-Rnr4 relocalization by low iron requires Dun1 kinase activity and phosphorylation site Thr-380 in the Dun1 activation loop, but not the Dun1 forkhead-associated domain. By using different Dif1 mutant proteins, we uncover that Dun1 phosphorylates Dif1 Ser-104 and Thr-105 residues upon iron scarcity. We observe that the Dif1 phosphorylation pattern differs depending on the stimuli, which suggests different Dun1 activating pathways. Importantly, the Dif1-S104A/T105A mutant exhibits defects in nucleus-to-cytoplasm redistribution of Rnr2-Rnr4 by iron limitation. Taken together, these results reveal that, in response to iron starvation, Dun1 kinase phosphorylates Dif1 to stimulate Rnr2-Rnr4 relocalization to the cytoplasm and promote RNR function.This work has been supported by a predoctoral fellowship from “Conselleria d'Educació de la Generalitat Valenciana” (to N. S.), a predoctoral fellowship from the Spanish Ministry of Economy and Competitiveness (to A. M. R.), Spanish Ministry of Economy and Competitiveness Grants AGL2011-29099 and BIO2014-56298-P (to S. P.), and National Institutes of Health Grant CA125574 (to M. H.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.Peer reviewe

Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI

Image noise and motion artifacts greatly affect the quality of brain MRI and negatively influence downstream medical image analysis. Previous studies often focus on 2D methods that process each volumetric MR image slice-by-slice, thus losing important 3D anatomical information. Additionally, these studies generally treat image denoising and artifact correction as two standalone tasks, without considering their potential relationship, especially on low-quality images where severe noise and motion artifacts occur simultaneously. To address these issues, we propose a Joint image Denoising and motion Artifact Correction (JDAC) framework via iterative learning to handle noisy MRIs with motion artifacts, consisting of an adaptive denoising model and an anti-artifact model. In the adaptive denoising model, we first design a novel noise level estimation strategy, and then adaptively reduce the noise through a U-Net backbone with feature normalization conditioning on the estimated noise variance. The anti-artifact model employs another U-Net for eliminating motion artifacts, incorporating a novel gradient-based loss function designed to maintain the integrity of brain anatomy during the motion correction process. These two models are iteratively employed for joint image denoising and artifact correction through an iterative learning framework. An early stopping strategy depending on noise level estimation is applied to accelerate the iteration process. The denoising model is trained with 9,544 T1-weighted MRIs with manually added Gaussian noise as supervision. The anti-artifact model is trained on 552 T1-weighted MRIs with motion artifacts and paired motion-free images. Experimental results on a public dataset and a clinical study suggest the effectiveness of JDAC in both tasks of denoising and motion artifact correction, compared with several state-of-the-art methods

Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI

Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. To the best of our knowledge, this is among the first attempts to study the complex heterogeneous progression of LLD based on task-oriented and handcrafted MRI features. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks

SPE-UHPLC-FLD Method for the Simultaneous Determination of Five Anthraquinones in Human Urine Using Mixed-Mode Bis(tetraoxacalix[2]arene[2]triazine) Modified Silica as Sorbent

The five anthraquinones compounds (including aloe-emodin, emodin, physcion, chrysophanol, and rhein) are regarded as the main effective ingredients in rhubarb (Dahuang in Chinese, one of the commonly used Chinese herbal medicines). In this work, a simple and effective solid phase extraction (SPE) method based on bis(tetraoxacalix[2]arene[2]triazine) modified silica gel as adsorbent was developed. Coupled with UHPLC-FLD, the developed method was successfully applied for the measuring of main anthraquinones in human urine after oral administration of the extracts of rhubarb. To obtain the highest recoveries of the five anthraquinones in the SPE process, the main parameters which may affect extraction efficiency were optimized. The optimized sorbent amount, sample loading pH, sample loading rate, washing solution, and eluent condition were obtained. The developed method showed good linearity in 0.012–1.800 μg mL−1 for the five anthraquinones with correlation coefficients more than 0.9993. The investigated LOD values ranged from 3.9 to 5.7 ng mL−1, while the LOQs were between 12.0 and 18.2 ng mL−1. The recoveries of the method were also investigated, which were in the range of 94.8–106.6%. The application of the mixed-mode SPE materials in the proposed method was feasible and simple, and suitable for the enrichment of anthraquinones in urine samples

Fabrication of equiatomic FeCo alloy parts with high magnetic properties by fields activated sintering

Electrical field activated sintering technology combined with micro-forming (Micro-FAST), as a new rapid powder sintering/forming method, is used to fabricate FeCo alloy parts. The successfully prepared FeCo parts have a high saturation of 214.11 emu/g and a low coercivity of 16 Oe, and these values are 20% and 10% higher than that of commercially available FeCoV alloy parts on the saturation and coercivity respectively. During the sintering process, the high current application shortened the densification time and enhanced the uniformity of the microstructure significantly. The grain sizes of FeCo alloys were in a range of 5–6 mm, and good isotropy was also shown. The low angle grain boundary (LAGB) accounted for more than 30% and the low angle misorientation accounted for more than 30% of the sample parts. Furthermore, the formation of the nano B2 phase was promoted during the Micro-FAST, and the size of the B2 phase was about 5 nm. The coherent interface between a and B2 was conducive for reducing the coercivity. As a consequence, the outstanding microstructure formed by Micro-FAST makes the FeCo alloys have high saturation and low coercivity

A mechanochemical synthesis of submicron-sized Li2S and a mesoporous Li2S/C hybrid for high performance lithium/sulfur battery cathodes

Lithium sulfide, Li2S, is a promising cathode material for lithium–sulfur batteries (LSBs), with a high theoretical capacity of 1166 mA h g−1. However, it suffers from low cycling stability, low-rate capability and high initial activation potential. In addition, commercially available Li2S is of high cost and of large size, over ten microns, which further exacerbate its shortcomings as a sulfur cathode. Exploring new approaches to fabricate small-sized Li2S of low cost and to achieve Li2S cathodes of high electrochemical performance is highly desired. This work reports a novel mechanochemical method for synthesizing Li2S of high purity and submicron size by ball-milling LiH with sulfur in an Ar atmosphere at room temperature. By further milling the as-synthesized Li2S with polyacrylonitrile (PAN) followed by carbonization of PAN at 1000 °C, a Li2S/C hybrid with nano-sized Li2S embedded in a mesoporous carbon matrix is achieved. The hybrid with Li2S as high as 74 wt% shows a high initial capacity of 971 mA h g−1 at 0.1C and retains a capacity of 570 mA h g−1 after 200 cycles as a cathode material for LSBs. A capacity of 610 mA h g−1 is obtained at 1C. The synthesis method of Li2S is facile, environmentally benign, and of high output and low cost. The present work opens a new route for the scalable fabrication of submicron-sized Li2S and for the development of high performance Li2S-based cathodes