PAD Class教学模式对英语专业学生写作焦虑影响的实证研究

本研究以英语专业大一120名学生为实验对象,进行了为期16周的实证研究,以探讨PAD class教学模式对英语专业学生写作焦虑的影响。基于《二语写作焦虑量表》调查问卷结果表明:英语专业学生总体焦虑处于中等焦虑水平,PAD Class能有效降低学生的写作总体焦虑,其中课堂焦虑、构思焦虑、自信焦虑明显降低,但回避焦虑没有达到显著水平;PAD Class模式对高中低不同写作水平学生的写作焦虑都有影响,对中、低水平学生的写作焦虑缓解比对高水平学生的影响效果更明显。

Hybrid treatment of varied orthodontic appliances for a patient with skeletal class II and temporomandibular joint disorders:A case report and review of literature

BACKGROUND The relation between orthodontic treatment and temporomandibular disorders(TMDs)is under debate;the management of TMD during orthodontic treatment has always been a challenge.If TMD symptoms occur during orthodontic treatment,an immediate pause of orthodontic adjustments is recommended;the treatment can resume when the symptoms are managed and stabilized.CASE SUMMARY This case report presents a patient(26-year-old,female)with angle class I,skeletal class II and TMDs.The treatment was a hybrid of clear aligners,fixed appliances and temporary anchorage devices(TADs).After 3 mo resting and treatment on her TMD,the patient’s TMD symptom alleviated,but her anterior occlusion displayed deep overbite.Therefore,the fixed appliances with TAD were used to correct the anterior deep-bite and level maxillary and mandibular deep curves.After the levelling,the patient showed dual bite with centric relation and maximum intercuspation discrepancy on her occlusion.After careful examination of temporomandibular joints(TMJ)position,the stable bite splint and Invisible Mandibular Advancement appliance were used to reconstruct her occlusion.Eventually,the improved facial appearance and relatively stable occlusion were achieved.The 1-year follow-up records showed there was no obvious change in TMJ morphology,and her occlusion was stable.CONCLUSION TMD screening and monitoring is of great clinical importance in the TMD susceptible patients.Hybrid treatment with clear aligners and fixed appliances and TADs is an effective treatment modality for the complex cases.

高中物理课堂师生互动的形成路径研究——基于组态视角的fsQCA方法

师生互动作为落实物理核心素养、培育科学思维的有效手段,探析其高质量的形成路径具有重要的意义.研究借助CLASS课堂观察工具,编制高中物理课堂师生互动观察量表,利用该量表对50节高中物理课堂实录(包含25节优质课、25节常规课)进行评分,采用模糊集定性比较分析(fsQCA)方法探析高中物理课堂高质量师生互动的形成路径.结果表明,高中物理课堂师生互动共形成了四条路径:课堂组织型路径、情感-教学支持型路径、组织-教学支持型路径、全面发展型路径.通过对每条路径典型特征分析、对比优质课与常规课的差异,提出促进师生高质量互动的相关建议.

幼儿园师幼互动质量的现状、类别及其影响因素:基于潜在剖面分析

师幼互动质量是学前教育过程性质量的关键要素。本研究使用分层抽样法,从广东省三个地区共选取了45所幼儿园135个班级,使用课堂评估评分系统(简称CLASS)对幼儿园师幼互动质量进行观察评估。结果表明,广东省幼儿园师幼互动质量整体处于中等水平,其中,教育支持领域得分最低。同时,潜在剖面分析结果表明,广东省幼儿园师幼互动质量存在四种潜在类别,即高师幼互动质量组(8.1%)、中等偏高师幼互动质量组(37%)、中等师幼互动质量组(47.4%)和低师幼互动质量组(7.4%)。进一步的方差分析结果发现,不同剖面幼儿园师幼互动质量存在极其显著差异。多元线性回归分析结果表明,幼儿教师资格证、最高学历和编制能够显著预测幼儿园师幼互动质量。研究进一步提出相应的对策与建议:重视幼儿园师幼互动质量,尤为关注教育支持领域;开展师幼互动专题培训,提升幼儿教师师幼互动技能;提升幼儿教师入职门槛,积极给予政策扶持。

基于改进Vision Transformer的蝴蝶品种分类

蝴蝶作为一种品类繁多且相似度极高的生物,具有重要的生态环境感知功能。不同品类蝴蝶对环境变化的敏感程度各不相同,因此在农学与生物学研究方向上对蝴蝶的研究具有十分重要的意义。近年来,计算机视觉技术的飞速发展为快速识别蝴蝶品类提供了强有力的技术支持。然而,传统的Vision Transformer模型存在着一些问题,例如缺乏卷积所具有的归纳偏置、局部信息提取能力不足、容易过拟合以及在小数据集上训练缓慢等。针对这些问题,提出了一种基于Vision Transformer改进的蝴蝶分类算法。引入VanillaNet卷积结构,并通过全局注意力机制改进了Class token的更新方式。实验结果显示,在100类蝴蝶数据集上,改进后的Vision Transformer模型的Top-1准确率达到了94.87%,比改进前提升了28.9%。在使用改进的Class token后,算法的Top-1准确率进一步提升至96.64%,相比改进前提升了30.44%。与原网络模型相比,改进后的模型更适用于蝴蝶品种分类任务。

Rock Mass Quality Rating Based on the Multi-Criteria Grey Metric Space

Assessment of rock mass quality significantly impacts the design and construction of underground and open-pit mines from the point of stability and economy.This study develops the novel Gromov-Hausdorff distance for rock quality(GHDQR)methodology for rock mass quality rating based on multi-criteria grey metric space.It usually presents the quality of surrounding rock by classes(metric spaces)with specified properties and adequate interval-grey numbers.Measuring the distance between surrounding rock sample characteristics and existing classes represents the core of this study.The Gromov-Hausdorff distance is an especially useful discriminant function,i.e.,a classifier to calculate these distances,and assess the quality of the surrounding rock.The efficiency of the developed methodology is analyzed using the Mean Absolute Percentage Error(MAPE)technique.Seven existing methods,such as the Gaussian cloud method,Discriminant method,Mutation series method,Artificial neural network(ANN),Support vector machine(SVM),Grey wolf optimizer and Support vector classification method(GWO-SVC)and Rock mass rating method(RMR)are used for comparison with the proposed GHDQR method.The share of the highly accurate category of 85.71%clearly indicates compliance with actual values obtained by the compared methods.The results of comparisons showed that the model enables objective,efficient,and reliable assessment of rock mass quality.

BLS-identification:A device fingerprint classification mechanism based on broad learning for Internet of Things

The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security risks.As a network-level security authentication method,device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases.However,flexible and diversified IoT devices with limited resources increase dif-ficulty of the device fingerprint authentication method executed in IoT,because it needs to retrain the model network to deal with incremental features or types.To address this problem,a device fingerprinting mechanism based on a Broad Learning System(BLS)is proposed in this paper.The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices,and extracts feature parameters of the traffic packets.A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset.The complexity of the dataset is reduced using Principal Component Analysis(PCA)and the device type is identified by training weights using BLS.The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.

Scientific Elegance in NIDS: Unveiling Cardinality Reduction, Box-Cox Transformation, and ADASYN for Enhanced Intrusion Detection

The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’private information.Many intruders actively seek such private data either for sale or other inappropriate purposes.Similarly,national and international organizations have country-level and company-level private information that could be accessed by different network attacks.Therefore,the need for a Network Intruder Detection System(NIDS)becomes essential for protecting these networks and organizations.In the evolution of NIDS,Artificial Intelligence(AI)assisted tools and methods have been widely adopted to provide effective solutions.However,the development of NIDS still faces challenges at the dataset and machine learning levels,such as large deviations in numeric features,the presence of numerous irrelevant categorical features resulting in reduced cardinality,and class imbalance in multiclass-level data.To address these challenges and offer a unified solution to NIDS development,this study proposes a novel framework that preprocesses datasets and applies a box-cox transformation to linearly transform the numeric features and bring them into closer alignment.Cardinality reduction was applied to categorical features through the binning method.Subsequently,the class imbalance dataset was addressed using the adaptive synthetic sampling data generation method.Finally,the preprocessed,refined,and oversampled feature set was divided into training and test sets with an 80–20 ratio,and two experiments were conducted.In Experiment 1,the binary classification was executed using four machine learning classifiers,with the extra trees classifier achieving the highest accuracy of 97.23%and an AUC of 0.9961.In Experiment 2,multiclass classification was performed,and the extra trees classifier emerged as the most effective,achieving an accuracy of 81.27%and an AUC of 0.97.The results were evaluated based on training,testing,and total time,and a comparative analysis with state-of-the-art studies proved the robustness and significance of the applied methods in developing a timely and precision-efficient solution to NIDS.

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.

THE BOUNDEDNESS OF OPERATORS ON WEIGHTED MULTI-PARAMETER LOCAL HARDY SPACES

Though atomic decomposition is a very useful tool for studying the boundedness on Hardy spaces for some sublinear operators,untill now,the boundedness of operators on weighted Hardy spaces in a multi-parameter setting has been established only by almost orthogonality estimates.In this paper,we mainly establish the boundedness on weighted multi-parameter local Hardy spaces via atomic decomposition.

Maximal operators of pseudo-differential operators with rough symbols

Consider a pseudo-differential operator T_(a)f(x)=∫_(R^(n))e^(ix,ζ)a(x,ζ)f(ζ)dζwhere the symbol a is in the rough Hormander class L^(∞)S_(ρ)^(m)with m∈R andρ∈[0,1].In this note,when 1≤p≤2,if n(ρ-1)/p and a∈L^(∞)S_(ρ)^(m),then for any f∈S(R^(n))and x∈R^(n),we prove that M(T_(a)f)(x)≤C(M(|f|^(p))(x))^(1/p) where M is the Hardy-Littlewood maximal operator.Our theorem improves the known results and the bound on m is sharp,in the sense that n(ρ-1)/p can not be replaced by a larger constant.

Extensive numerical simulations on competitive growth between the Edwards–Wilkinson and Kardar–Parisi–Zhang universality classes

Extensive numerical simulations and scaling analysis are performed to investigate competitive growth between the linear and nonlinear stochastic dynamic growth systems, which belong to the Edwards–Wilkinson(EW) and Kardar–Parisi–Zhang(KPZ) universality classes, respectively. The linear growth systems include the EW equation and the model of random deposition with surface relaxation(RDSR), the nonlinear growth systems involve the KPZ equation and typical discrete models including ballistic deposition(BD), etching, and restricted solid on solid(RSOS). The scaling exponents are obtained in both the(1 + 1)-and(2 + 1)-dimensional competitive growth with the nonlinear growth probability p and the linear proportion 1-p. Our results show that, when p changes from 0 to 1, there exist non-trivial crossover effects from EW to KPZ universality classes based on different competitive growth rules. Furthermore, the growth rate and the porosity are also estimated within various linear and nonlinear growths of cooperation and competition.

Differential response of injured and healthy retinas to syngeneic and allogeneic transplantation of a clonal cell line of immortalized olfactory ensheathing glia:a double-edged sword

Olfactory ensheathing glia promote axonal regeneration in the mammalian central nervous system,including retinal ganglion cell axonal growth through the injured optic nerve.Still,it is unknown whether olfactory ensheathing glia also have neuroprotective properties.Olfactory ensheathing glia express brain-derived neurotrophic factor,one of the best neuroprotectants for axotomized retinal ganglion cells.Therefore,we aimed to investigate the neuroprotective capacity of olfactory ensheating glia after optic nerve crush.Olfactory ensheathing glia cells from an established rat immortalized clonal cell line,TEG3,were intravitreally injected in intact and axotomized retinas in syngeneic and allogeneic mode with or without microglial inhibition or immunosuppressive treatments.Anatomical and gene expression analyses were performed.Olfactory bulb-derived primary olfactory ensheathing glia and TEG3 express major histocompatibility complex classⅡmolecules.Allogeneically and syngenically transplanted TEG3 cells survived in the vitreous for up to 21 days,forming an epimembrane.In axotomized retinas,only the allogeneic TEG3 transplant rescued retinal ganglion cells at 7 days but not at 21 days.In these retinas,microglial anatomical activation was higher than after optic nerve crush alone.In intact retinas,both transplants activated microglial cells and caused retinal ganglion cell death at 21 days,a loss that was higher after allotransplantation,triggered by pyroptosis and partially rescued by microglial inhibition or immunosuppression.However,neuroprotection of axotomized retinal ganglion cells did not improve with these treatments.The different neuroprotective properties,different toxic effects,and different responses to microglial inhibitory treatments of olfactory ensheathing glia in the retina depending on the type of transplant highlight the importance of thorough preclinical studies to explore these variables.

Lean body mass index is a marker of advanced tumor features in patients with hepatocellular carcinoma

BACKGROUND Obesity is an independent risk factor for the development of hepatocellular carcinoma(HCC)and may influence its outcomes.However,after diagnosis of HCC,like other malignancies,the obesity paradox may exist where higher body mass index(BMI)may in fact confer a survival benefit.This is frequently observed in patients with advanced HCC and cirrhosis,who often present late with advanced tumor features and cancer related weight loss.AIM To explore the relationship between BMI and survival in patients with cirrhosis and HCC.METHODS This is a retrospective cohort study of over 2500 patients diagnosed with HCC between 2009-2019 at two United States academic medical centers.Patient and tumor characteristics were extracted manually from medical records of each institutions'cancer registries.Patients were stratified according to BMI classes:<25 kg/m^(2)(lean),25-29.9 kg/m^(2)(overweight),and>30 kg/m^(2)(obese).Patient and tumor characteristics were compared according to BMI classification.We performed an overall survival analysis using Kaplan Meier by the three BMI classes and after adjusting for Milan criteria.A multivariable Cox regression model was then used to assess known risk factors for survival in patients with cirrhosis and HCC.RESULTS A total of 2548 patients with HCC were included in the analysis of which 11.2%(n=286)were classified as noncirrhotic.The three main BMI categories:Lean(n=754),overweight(n=861),and obese(n=933)represented 29.6%,33.8%,and 36.6%of the total population overall.Within each BMI class,the non-cirrhotic patients accounted for 15%(n=100),12%(n=94),and 11%(n=92),respectively.Underweight patients with a BMI<18.5 kg/m^(2)(n=52)were included in the lean cohort.Of the obese cohort,42%(n=396)had a BMI≥35 kg/m^(2).Out of 2262 patients with cirrhosis and HCC,654(29%)were lean,767(34%)were overweight,and 841(37%)were obese.The three BMI classes did not differ by age,MELD,or Child-Pugh class.Chronic hepatitis C was the dominant etiology in lean compared to the overweight and obese patients(71%,62%,49%,P<0.001).Lean patients had significantly larger tumors compared to the other two BMI classes(5.1 vs 4.2 vs 4.2 cm,P<0.001),were more likely outside Milan(56%vs 48%vs 47%,P<0.001),and less likely to undergo transplantation(9%vs 18%vs 18%,P<0.001).While both tumor size(P<0.0001)and elevated alpha fetoprotein(P<0.0001)were associated with worse survival by regression analysis,lean BMI was not(P=0.36).CONCLUSION Lean patients with cirrhosis and HCC present with larger tumors and are more often outside Milan criteria,reflecting cancer related cachexia from delayed diagnosis.Access to care for hepatitis C virus therapy and liver transplantation confer a survival benefit,but not overweight or obese BMI classifications.

World Class Elite Breaststrokers Downward Kick EMG Muscle Activity of the Gluteus Maximus, Quadriceps Femoris, Hamstring, Gastrocnemius Medialis, Rectus Abdominal, and Erector Spinae

This study analyzed the difference between using a downward breaststroke kick and a horizontal breaststroke kick in a sample of world class elite swimmers.We compared average muscle activity of the gluteus maximus,quadriceps femoris(vastus medialis and rectus femoris),hamstring/long head of the biceps femoris,gastrocnemius medialis,rectus abdominal,and erector spinae when using the downward breaststroke kick technique.We find that when this sample of swimmers utilized the downward breaststroke kick,max speed and velocity per stroke increased,measured by 12,788 EMG samples,where the results are highly correlated to duration of the aerodynamic buoyant force in breaststroke kick technique.The increases in performance observed from measuring the world class elite swimmers is highly correlated to the duration of the kick aerodynamic buoyant force.Among this sample of elite swimmers,the longer a swimmer demonstrates a buoyant force breaststroke kick,the lower the time in a 100 breaststroke.

Rice University Rule to Determine the Number of Bins

This study aims to establish a rationale for the Rice University rule in determining the number of bins in a histogram. It is grounded in the Scott and Freedman-Diaconis rules. Additionally, the accuracy of the empirical histogram in reproducing the shape of the distribution is assessed with respect to three factors: the rule for determining the number of bins (square root, Sturges, Doane, Scott, Freedman-Diaconis, and Rice University), sample size, and distribution type. Three measures are utilized: the average distance between empirical and theoretical histograms, the level of recognition by an expert judge, and the accuracy index, which is composed of the two aforementioned measures. Mean comparisons are conducted with aligned rank transformation analysis of variance for three fixed-effects factors: sample size (20, 35, 50, 100, 200, 500, and 1000), distribution type (10 types), and empirical rule to determine the number of bins (6 rules). From the accuracy index, Rice’s rule improves with increasing sample size and is independent of distribution type. It outperforms the Friedman-Diaconis rule but falls short of Scott’s rule, except with the arcsine distribution. Its profile of means resembles the square root rule concerning distributions and Doane’s rule concerning sample sizes. These profiles differ from those of the Scott and Friedman-Diaconis rules, which resemble each other. Among the seven rules, Scott’s rule stands out in terms of accuracy, except for the arcsine distribution, and the square root rule is the least accurate.

Analysis of the Energy and Environmental Sustainability of a Built Space System: The Case of Patte d’Oie University Campus in Ouagadougou

The aim of this study was to carry out a dynamic simulation of the energy and environmental performance of a built space system, with a view to assessing its energy and environmental class. The use of a simulation and modeling tool, supported by various methodological references, formed the basis of our approach. Adopting a systemic perspective, we described the structural and functional aspects of the systems making up built spaces, as well as the associated energy flows. Our approach was also based on a typology, taking into account typical days, structural and functional configurations at different scales and angles of observation. The analysis tool we developed in Java was applied to the built space system of the Patte d’Oie university campus in Ouagadougou. Annual electricity consumption was measured at 124387.34 kWh, closely aligned with the average annual electricity bill (125224.31 kWh), with a maximum relative deviation of 1%, followed by a carbon emission balance of 58337.66 kg eq CO2 per year. This validation confirmed the effectiveness of our tool. In addition, following the analysis of electricity consumption using our tool, the university campus was classified in energy class B and environmental class C. These results will be based on the emission factors of the energy mix of the West African Economic and Monetary Union (WAEMU) territory, with particular emphasis on Burkina Faso.

Research and Practice of Flipped Classroom Teaching Mode of Integrated Business English Course Under MOOC Environment

As an online course-teaching mode,Massive Open Online Course(MOOC)has drawn great attention from both teachers and students.It is necessary to give full play to the advantages of MOOC resources and to enhance teachers’consciousness of“identity remodeling”in the process of Integrated Business English flipping classroom teaching.Combining the advantages of MOOC and flipped classroom teaching during before-,during-,and after-class will greatly help promote the overall quality of business English teaching in universities.

幼儿园自然探究活动中师幼互动的提升策略——基于CLASS评估调研分析

自然探究活动在培养幼儿实践能力与创造力方面具有显著优势,而活动中的师幼互动是保障活动质量的关键。使用CLASS评估系统,对广州市南沙区两所公办幼儿园教师在自然探究活动中的师幼互动情况展开实证调研的结果显示,教师在情感支持方面表现出积极态度,但在教育支持,尤其是促进幼儿概念发展和语言示范方面仍有待加强。其原因在于,教师科学素养较低,科学领域核心经验匮乏;教师组织管理能力存在短板,亟需深化有效教学策略;情感支持得分虚高,教师敏感性差异明显。基于此,需要加强专业培训,优化探究体验;提升师幼互动知识技能,补齐活动组织短板;优化情感支持策略,提升教师敏感性。

The Integration Strategy of Class Teacher Management and Mathematics Teaching

The professional and moral education of high school mathematics teachers will make classroom management work better,but their work pressure will also lead to classroom management problems.To do a good job in high school class teacher management and organically integrate it with mathematics teaching,we need to start from two aspects:mathematics teaching class teachers and class teacher work teaching,and penetrate mathematical thinking into daily classroom management,moral education,and classroom culture construction.Based on the attributes of the subject,we guide high school students to reflect after class to stimulate their self-management initiative through the cultivation of qualified class representatives.In addition,it is necessary to skillfully resolve classroom generative problems,change the roles of teachers and students,and integrate classroom management with mathematics teaching.