The Value and Realization Strategy of Teacher Ethics Construction in Colleges and Universities under the Perspective of“Cultivating People by Virtue”

“Cultivating people by virtue”has become the foundation of China’s education,and is the top priority of college education.“Only the learned can become teachers,and only those who behave properly can become role models”.The construction of teachers’ethics in colleges and universities is the premise of realizing the fundamental task of moral education,the need of teachers,the key to realizing the great rejuvenation of the Chinese nation,and it is also the way to correct the bad atmosphere in colleges and universities.In the construction of teachers’ethics,teachers should understand their roles and improve their professional ethics,schools should improve the guarantee system of teachers’ethics construction,meanwhile,improve the status of teachers and create a social environment of respecting teachers and valuing teachers.

4种植物源性成分多重real-time PCR检测方法的建立及其在食用淀粉中的应用

建立一种可同时快速检测红薯、木薯、马铃薯、玉米源性成分的多重实时聚合酶链式反应(real-time polymerase chain reaction,real-time PCR)方法。分别以红薯g3pdh基因、木薯g3pdh基因、马铃薯UGPase基因、玉米zSSIIb基因为靶基因设计特异性引物和TaqMan探针,以18S rRNA基因为内参基因,建立多重real-time PCR方法,开展方法学验证,并对不同掺入比例模拟样品和实际淀粉样品进行检测。结果显示,该方法具有高通量、特异性强、灵敏度高等优点。与15种非目标源性均无交叉反应;对目标DNA的检测灵敏度可达到3×10^(-3) ng/μL,且具有良好的线性关系和扩增效率;对淀粉样品的检出限可达0.1%,对50份实际样品进行检测,结果与参比方法一致,说明建立的多重real-time PCR法可用于食用淀粉种类掺假鉴别检测。

血-迷路屏障通透性与突聋发病机制及预后的关系:在3D real IR的发现

目的分析突聋患者的内耳钆造影MRI三维真实重建反转恢复(three dimensional real inversion recovery,3D real IR)成像上的表现,探讨血-迷路屏障的通透性与突聋发病机制及其预后的关系。方法对41例单侧突聋患者行内耳钆造影MRI,测量患耳和健耳的耳蜗信号强度,并测量延髓信号强度,分别计算出耳蜗/延髓比值(cochlear/medulla ratio,CM ratio),以CM比值作为血-迷路屏障通透性的标志物,分析突聋患者患耳、健耳CM比值的不对称程度与疗效之间的关系。结果41例患者中,33例(80.48%)患耳的CM比值高于健耳,差异有统计学意义(P<0.05);患耳CM比值为健耳的1.5倍以下者18例,治疗有效率为77.78%(14/18);患侧CM比值不高于健侧者8例,治疗有效率为100%;达到健耳的1.5倍至1.75倍之间者7例,治疗有效率为100%(7/7);达到健耳的1.75倍至2.0倍之间者2例,治疗有效率为50%(1/2);达到健耳的2.0倍以上者14例,治疗有效率为14.28%(12/14);差异有统计学意义(P<0.05)。结论内耳3D Real IR可显示突聋患者血-迷路屏障通透性的改变,80.48%的突聋患者患侧耳蜗出现高信号,患耳CM比值达健耳的1.75倍以上者多数预后不良。

基于WGAN-GP和Mean Teacher的WiFi使能跨域人体行为识别

人体行为识别(Human Activity Recognition,HAR)是当前众多研究工作的基石,对于推动人机交互和智能数字化转型具有巨大潜力。由于目标域样本较难采集,现有方法在跨域识别方面表现不佳。为解决这一问题,提出一种新的WiFi使能跨域HAR方法,从WiFi信号中获取信道状态信息(Channel State Information,CSI)并转化为图像,在基于Wasserstein距离和梯度的生成对抗网络(Wasserstein Generative Adversarial Network with Gradient Penalty,WGAN-GP)中引入双判别器,通过与源域样本和单目标域样本特征联合对抗,生成同时带有双域特征的虚拟样本。该方法还结合基于Mean Teacher的半监督学习设计识别分类(Recognition and Classification,RC)模块,通过对有标记样本与无标记样本分别构造损失函数,进行整体一致性损失的评估,实现对目标域样本的识别。实验结果证明了所提方法能够在减轻目标域样本采集压力的同时,实现较高的检测精度,在手势与动作的数据集上测试准确率分别达到92.71%和86.65%。

Polish Teachers in Changing Reality

Authors of the text present Polish context of teachers’ professional promotion and teachers’ competencies. The text also has its empirical dimension-a research about teachers’ sense of life quality has been done. There are teachers’ expressions, collected and quoted during qualitative research. Teachers’ thoughts are concerned about the sense of life quality in the context of working at lower secondary school.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

Teachers' Transformation and the Way to Realize the Construction of Entrepreneurial Universities

The fundamental problem of transforming traditional universities into entrepreneurship universities lies in the transformation of teachers. The development of entrepreneurial universities has an important impact on the global higher education system, especially on the role of University teachers. Therefore, the author carries out research on the path of teachers' transformation and the construction of entrepreneurial universities. The study found that the traditional university teacher role has emerged as a new feature. How to realize the transformation of the entrepreneurial university teacher role, ease the identity crisis of the teacher role, and make it better adapt to the professional development and the construction of the entrepreneurial university. An important research topic. In order to realize the transformation of traditional teachers to entrepreneurial teachers, it is necessary to change the employment standards and establish a school-level achievement transformation platform.

Teachers'Transformation and the Way to Realize the Construction of Entrepreneurial Universities

The fundamental problem of transforming traditional universities into entrepreneurship universities lies in the transformation of teachers.The development of entrepreneurial universities has an important impact on the global higher education system,especially on the role of University teachers.Therefore,the author carries out research on the path of teachers'transformation and the construction of entrepreneurial universities.The study found that the traditional university teacher role has emerged as a new feature.How to realize the transformation of the entrepreneurial university teacher role,ease the identity crisis of the teacher role,and make it better adapt to the professional development and the construction of the entrepreneurial university.An important research topic.In order to realize the transformation of traditional teachers to entrepreneurial teachers,it is necessary to change the employment standards and establish a school-level achievement transformation platform.

The Realistic Dilemma, Problem Tracking and Mitigating Strategies of Primary School Science Teachers’ Professional Happiness

The Science teachers in primary schools are the key group in developing science education, and they play a vital role in realizing the dream of strengthening the country through education. Their professional happiness deserves special attention. Whether this group is happy not only affects their own working conditions and quality of life, but also affects the work vitality of other teachers. Improving the professional happiness of science teachers in primary schools has become an important topic in the construction of teachers’ team in the new period. At present, the intensity of primary school science teachers’ own work is high, their non-own work is increasing, their physical and mental health is difficult to guarantee, and their professional happiness is reduced. The main causes of the problems are poor individual psychological quality, poor school management style and improper social cognitive evaluation. In order to solve this problem, we have explored strategies to improve the professional happiness of science teachers in primary schools through reshaping teachers’ work, democratic management of schools and national policy guarantee.

基于Mean Teacher-BA模型的钢材表面缺陷半监督检测方法

钢材表面缺陷是影响钢材质量的因素之一。现有的缺陷检测方法难以在提供少量标注样本的前提下实现钢材缺陷的准确检测。针对这一问题,提出了一种改进的缺陷半监督检测方法Mean Teacher-BA。首先,提出了一种双级注意力机制(bi-level attention module,BAM),以增加模型对复杂缺陷的关注和检测能力;其次,对主干网络Resnet50提取的特征图像应用BAM模块,进一步提升对缺陷图像的特征提取能力;最后,提出了一种全局自适应损失策略,以改善在训练期间生成的伪标注质量。实验结果表明,Mean Teacher-BA在5%、10%、20%、100%的标注比例条件下比原模型Mean Teacher分别提升了1.0%、1.2%、1.5%和0.9%,相比较于其他全监督和半监督网络性能更好,更适合于工业生产的部署应用。

Real eigenvalues determined by recursion of eigenstates

Quantum physics is primarily concerned with real eigenvalues,stemming from the unitarity of time evolutions.With the introduction of PT symmetry,a widely accepted consensus is that,even if the Hamiltonian of the system is not Hermitian,the eigenvalues can still be purely real under specific symmetry.Hence,great enthusiasm has been devoted to exploring the eigenvalue problem of non-Hermitian systems.In this work,from a distinct perspective,we demonstrate that real eigenvalues can also emerge under the appropriate recursive condition of eigenstates.Consequently,our findings provide another path to extract the real energy spectrum of non-Hermitian systems,which guarantees the conservation of probability and stimulates future experimental observations.

Systematizing Teacher Development:A Review of Foreign Language Teacher Learning

Foreign language teaching practice is developing rapidly,but research on foreign language teacher learning is currently relatively fragmented and unstructured.The book Foreign Language Teacher Learning,written by Professor Kang Yan from Capital Normal University,published in September 2022,makes a systematic introduction to foreign language teacher learning,which to some extent makes up for this shortcoming.Her book presents the lineage of foreign language teacher learning research at home and abroad,analyzes both theoretical and practical aspects,reviews the cuttingedge research results,and foresees the future development trend,painting a complete research picture for researchers in the field of foreign language teaching and teacher education as well as front-line teachers interested in foreign language teacher learning.This is an important inspiration for conducting foreign language teacher learning research in the future.And this paper makes a review of the book from aspects such as its content,major characteristics,contributions and limitations.

Development and validation of a novel questionnaire regarding vision screening among preschool teachers in Malaysia

AIM:To develop and evaluate the validity and reliability of a knowledge,attitude,and practice questionnaire related to vision screening(KAP-VST)among preschool teachers in Malaysia.METHODS:The questionnaire was developed through a literature review and discussions with experts.Content and face validation were conducted by a panel of experts(n=10)and preschool teachers(n=10),respectively.A pilot study was conducted for construct validation(n=161)and test-retest reliability(n=60)of the newly developed questionnaire.RESULTS:Based on the content and face validation,71 items were generated,and 68 items were selected after exploratory factor analysis.The content validity index for items(I-CVI)score ranged from 0.8-1.0,and the content validity index for scale(S-CVI)/Ave was 0.99.Internal consistency was KR^(2)0=0.93 for knowledge,Cronbach’s alpha=0.758 for attitude,and Cronbach’s alpha=0.856 for practice.CONCLUSION:The KAP-VST is a valid and reliable instrument for assessing knowledge,attitude,and practice in relation to vision screening among preschool teachers in Malaysia.

Prevalence and Factors Associated with Overweight and Obesity among Public Secondary School Teachers in Parakou, Benin, in 2021

Introduction: Overweight and obesity represent a public health problem in Africa due to the epidemiological transition. The objectives of this work were to determine the prevalence of overweight and obesity and to identify associated factors among public secondary school teachers in Parakou, Benin in 2021. Methods: We conducted a descriptive and analytical cross-sectional study. Teachers working in public secondary schools in Parakou during the 2020-2021 academic year, present at work and who gave their written informed consent, were included. A two-stage random sampling was carried out. Data were collected during an individual interview using a questionnaire followed by the measurement of anthropometric parameters and blood pressure. Overweight and obesity were defined by a body mass index ≥ 25 kg/m2. Multivariable logistic regression was performed to identify associated factors. Results: A sample of 325 teachers, including 88.6% of men, was recruited with an average age of 36.2 ± 6.8 years. The prevalence of overweight and obesity was 43.7% (95% CI [38.1%-44.8%]). It increased significantly with income (p Conclusion: The results show a high prevalence of overweight and obesity. Actions are necessary to prevent overweight and obesity among secondary school teachers in Parakou, in Benin.

基于Real ESRGAN的视频修复系统研究

图像修复和视频修复是计算机视觉的一项重要任务,其中图像修复又是视频修复的基础。为此,如何有效提升图像质量是实现视频质量提升的关键。传统的图像修复算法主要以样本信息为基础,通过对样本内容的扩撒来实现对破损区域的修复;由于这种方式对于图像样本有一定要求,从而制约传统图像修复技术的发展。为此,以生成新图像内容为基础的神经网络如GAN的出现,为图像修复技术转向深度学习提供方向。该课题主要以Real ESRGAN网络的图像修复技术为基础,通过对音频视频数据的隔离处理以及相同帧数据的优化和标记,构建视频修复处理流程。通过对随机视频样本的测试,并通过对单帧图片质量和视频数据流畅性与协调性的评估,该视频处理方法表现出较好的系统性能。

Rich Connotation,Realistic Dilemma,and Practical Path of Teachers’Digital Literacy in the Era of Digital Intelligence

With the rapid development of information technology and the popularity of the Internet,digital education has become an important direction of modern education reform.Digital education provides a new way and tool for education and teaching,so that teachers and students can make better use of the application of information technology in the teaching process.Teachers’digital literacy has become the key to realize digital education.In the digital age,the level of teachers’digital literacy directly affects teachers’teaching effect and students’learning results.Therefore,improving teachers’digital literacy has become an urgent task of education reform.

A Stochastic Model to Assess the Epidemiological Impact of Vaccine Booster Doses on COVID-19 and Viral Hepatitis B Co-Dynamics with Real Data

A patient co-infected with COVID-19 and viral hepatitis B can be atmore risk of severe complications than the one infected with a single infection.This study develops a comprehensive stochastic model to assess the epidemiological impact of vaccine booster doses on the co-dynamics of viral hepatitis B and COVID-19.The model is fitted to real COVID-19 data from Pakistan.The proposed model incorporates logistic growth and saturated incidence functions.Rigorous analyses using the tools of stochastic calculus,are performed to study appropriate conditions for the existence of unique global solutions,stationary distribution in the sense of ergodicity and disease extinction.The stochastic threshold estimated from the data fitting is given by:R_(0)^(S)=3.0651.Numerical assessments are implemented to illustrate the impact of double-dose vaccination and saturated incidence functions on the dynamics of both diseases.The effects of stochastic white noise intensities are also highlighted.

The Influence of Preschool Teachers’Social Skills on Job Burnout:A Moderated Mediation Model

Background:Teacher burnout is a serious issue in the field of education,particularly in early childhood education,where teachers face high levels of work stress and emotional labor,leading to emotional exhaustion and job burnout.However,past research has not sufficiently explored the mechanisms of social skills,empathy,and mindfulness in mitigating teacher burnout.Therefore,this study aims to investigate the relationship between preschool teachers’social skills,empathy,and mindfulness with job burnout,in order to provide theoretical basis and practical guidance for reducing teacher burnout.Methods:This research utilized a convenience sampling approach to target preschool teachers for a questionnaire survey.A total of 1109 questionnaires were collected.To ensure the quality of the data,we excluded questionnaires that were not carefully filled out in terms of lie scale questions,those with abnormal demographic variables,and outliers identified based on response time.Ultimately,901 valid questionnaires were obtained,achieving a valid response rate of 81.2%.Participants’levels of social skills,empathy,mindfulness,and job burnout were assessed using the Social Skills Scale(SKS),Empathy Scale(Measure of Empathy,ME),Mindful Attention Awareness Scale(MAAS),and the Maslach Burnout Inventory-Educators Survey(MBI-ES),respectively.Data analysis was conducted using SPSS.Results:After controlling for gender,age,teaching experience,educational level,grade taught,and location of the kindergarten,the study found:(1)There is a negative correlation between preschool teachers’social skills and the level of job burnout(r=−0.238);(2)Empathy has a dual-track effect on job burnout,where cognitive empathy negatively affects job burnout(r=−0.245),while emotional empathy has a positive effect(r=0.045);(3)Cognitive empathy partially mediates the relationship between social skills and job burnout(β=−0.124);(4)Mindfulness significantly impacts social skills,cognitive empathy,and job burnout(r=0.278;r=0.286;r=−0.539),and plays a moderating role in the mediation model(β=0.003;β=−0.023).Conclusion:These findings provide theoretical support for the development of burnout prevention and intervention strategies targeted at preschool teachers.They also point out new directions for future research and potential intervention targets,suggesting that enhancing preschool teachers’social skills and cognitive empathy,as well as increasing their mindfulness level,can help them cope with work-related stress and emotional labor,thereby alleviating job burnout.

An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.

LDAS&ET-AD:Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation

Adversarial distillation(AD)has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training.However,fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation.Additionally,the reliability of guidance from static teachers diminishes as target models become more robust.This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation(LDAS&ET-AD).Firstly,a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation.A strategy model is introduced to produce attack strategies that enable adversarial examples(AEs)to be created in areas where the target model significantly diverges from the teachers by competing with the target model in minimizing or maximizing the AD loss.Secondly,a teacher evolution strategy is introduced to enhance the reliability and effectiveness of knowledge in improving the generalization performance of the target model.By calculating the experimentally updated target model’s validation performance on both clean samples and AEs,the impact of distillation from each training sample and AE on the target model’s generalization and robustness abilities is assessed to serve as feedback to fine-tune standard and robust teachers accordingly.Experiments evaluate the performance of LDAS&ET-AD against different adversarial attacks on the CIFAR-10 and CIFAR-100 datasets.The experimental results demonstrate that the proposed method achieves a robust precision of 45.39%and 42.63%against AutoAttack(AA)on the CIFAR-10 dataset for ResNet-18 and MobileNet-V2,respectively,marking an improvement of 2.31%and 3.49%over the baseline method.In comparison to state-of-the-art adversarial defense techniques,our method surpasses Introspective Adversarial Distillation,the top-performing method in terms of robustness under AA attack for the CIFAR-10 dataset,with enhancements of 1.40%and 1.43%for ResNet-18 and MobileNet-V2,respectively.These findings demonstrate the effectiveness of our proposed method in enhancing the robustness of deep learning networks(DNNs)against prevalent adversarial attacks when compared to other competing methods.In conclusion,LDAS&ET-AD provides reliable and informative soft labels to one of the most promising defense methods,AT,alleviating the limitations of untrusted teachers and unsuitable AEs in existing AD techniques.We hope this paper promotes the development of DNNs in real-world trust-sensitive fields and helps ensure a more secure and dependable future for artificial intelligence systems.