Study of The Technical Index of Online Learning Behavior Analysis of Nursing Majors on The Superstar Platform Based on The Kirkpatrick Evaluation Model

Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.

The Application of ARCS Motivation Model in Online College English Class

Since the epidemic,online classes have been a common practice,and learning motivation plays a key role in the efficiency of the online English,which should not be underestimated in improving online foreign language teaching.This paper discusses how to effectively apply ARCS motivation model in online English teaching design with examples of college English textbooks,so as to motivate students and improve students’online classroom efficiency by improving their learning motivation.

Influence of Online Learning Attitude in the Blended Teaching Model on the Final Grades

The combination of online teaching and traditional offline teaching can maximize the advantages of both.Based on the blended teaching of English Reading course,39 students were selected as the research subjects to study the relationship between their online learning attitudes and their grades in the final examination.Judged from the number of times for each student to download teaching resources,the number of assignments submitted online,and the quality of the submitted assignments,each student’s attitude toward online learning was examined comprehensively,and a correlation analysis was conducted through SPSS Statistics 21.0 to explore the influence of online learning attitude on English reading performance.Through data collection and analysis of the online learning attitudes over a 16-week period,a significant positive correlation was found between the online learning attitudes and the English reading grades,indicating that the online learning attitude in the blended learning model plays a crucial role in improving the English reading skill,and students should maintain a positive attitude toward online teaching in blended learning.

Modeling online social networks based on preferential linking

We study the phenomena of preferential linking in a large-scale evolving online social network and find that the linear preference holds for preferential creation, preferential acceptance, and preferential attachment. Based on the linear preference, we propose an analyzable model, which illustrates the mechanism of network growth and reproduces the process of network evolution. Our simulations demonstrate that the degree distribution of the network produced by the model is in good agreement with that of the real network. This work provides a possible bridge between the micro=mechanisms of network growth and the macrostructures of online social networks.

Online Capacitance Modeling Tool for Conductors Represented as Simply-Connected Polygonal Geometries in 2D

We present an online tool for calculating the capacitance between two conductors represented as simply-connected polygonal geometries in 2D with Dirichlet boundaries and homogeneous dielectric. Our tool can be used to model the so-called 2.5D geometries, where the 3rd dimension can be extruded out of plane. Micro-electro-mechanical systems (MEMS) with significant facing surfaces may be approximated with 2.5D geometry. Our tool compares favorably in accuracy and speed to the finite element method (FEM). We achieve modeling accuracy by treating the corners exactly with a Schwarz-Christoffel mapping. And we achieve fast results by not needing to discretize boundaries and subdomains. As a test case, we model a MEMS torsional actuator. Our tool computes capacitance about 1000 times faster than FEM with 4.7% relative error.

Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

一新为周期的生物过程的联机预兆的监视和预言模型被建议用多方法 non-Gaussian 建模。这条途径的基本想法是多使用方法 non-Gaussian 当模特儿在一个周期的过程从每日的正常操作数据提取某主导的关键组分，并且随后把这些部件与预兆的统计过程监视相结合技术。建议预兆的监视方法被使用了在生物废水处理过程指责察觉和诊断，它基于强壮日报特征。结果显示出力量和优点连续法的建议预兆的监视用多方法预兆监视概念，它因此能为一个每日的监视过程并且也给很有用的概念的结果比另外的传统的监视方法启用过程差错的更快速的察觉。

Modeling and Adaptive Self-Tuning MVC Control of PAM Manipulator Using Online Observer Optimized with Modified Genetic Algorithm

In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Model-based observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is proposed from the genetic algorithm with important additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, MGA-based identification method is used to identify the parameters of the nonlinear PAM manipulator described by an ARX model in the presence of white noise and this result will be validated by MGA and compared with the simple genetic algorithm (GA) and LMS (Least mean-squares) method. Secondly, the intrinsic features of the hysteresis as well as other nonlinear disturbances existing intuitively in the PAM system are estimated online by a Modified Recursive Least Square (MRLS) method in identification experiment. Finally, a highly efficient self-tuning control algorithm Minimum Variance Control (MVC) is taken for tracking the joint angle position trajectory of this PAM manipulator. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the NARX model-based MVC control system of the PAM system. These results can be applied to model, identify and control other highly nonlinear systems as well.

An Intelligent Model for Online Recruitment Fraud Detection

This study research attempts to prohibit privacy and loss of money for individuals and organization by creating a reliable model which can detect the fraud exposure in the online recruitment environments. This research presents a major contribution represented in a reliable detection model using ensemble approach based on Random forest classifier to detect Online Recruitment Fraud (ORF). The detection of Online Recruitment Fraud is characterized by other types of electronic fraud detection by its modern and the scarcity of studies on this concept. The researcher proposed the detection model to achieve the objectives of this study. For feature selection, support vector machine method is used and for classification and detection, ensemble classifier using Random Forest is employed. A freely available dataset called Employment Scam Aegean Dataset (EMSCAD) is used to apply the model. Pre-processing step had been applied before the selection and classification adoptions. The results showed an obtained accuracy of 97.41%. Further, the findings presented the main features and important factors in detection purpose include having a company profile feature, having a company logo feature and an industry feature.

Clicking through the Clickstream: A Novel Statistical Modeling Approach to Improve Information Usage of Clickstream Data by E-Commerce Entities

Success or failure of an E-commerce platform is often reduced to its ability to maximize the conversion rate of its visitors. This is commonly regarded as the capacity to induce a purchase from a visitor. Visitors possess individual characteristics, histories, and objectives which complicate the choice of what platform features that maximize the conversion rate. Modern web technology has made clickstream data accessible allowing a complete record of a visitor’s actions on a website to be analyzed. What remains poorly constrained is what parts of the clickstream data are meaningful information and what parts are accidental for the problem of platform design. In this research, clickstream data from an online retailer was examined to demonstrate how statistical modeling can improve clickstream information usage. A conceptual model was developed that conjectured relationships between visitor and platform variables, visitors’ platform exit rate, boune rate, and decision to purchase. Several hypotheses on the nature of the clickstream relationships were posited and tested with the models. A discrete choice logit model showed that the content of a website, the history of website use, and the exit rate of pages visited had marginal effects on derived utility for the visitor. Exit rate and bounce rate were modeled as beta distributed random variables. It was found that exit rate and its variability for pages visited were associated with site content, site quality, prior visitor history on the site, and technological preferences of the visitor. Bounce rate was also found to be influenced by the same factors but was in a direction opposite to the registered hypotheses. Most findings supported that clickstream data is amenable to statistical modeling with interpretable and comprehensible models.

Design of Online Vision Detection System for Stator Winding Coil

The quality of the stator winding coil directly affects the performance of the motor.A dual-camera online machine vision detection method to detect whether the coil leads and winding regions were qualified was designed.A vision detection platform was designed to capture individual winding images,and an image processing algorithm was used for image pre-processing,template matching and positioning of the coil lead area to set up a coordinate system.After eliminating image noise by Blob analysis,the improved Canny algorithm was used to detect the location of the coil lead paint stripped region,and the time was reduced by about half compared to the Canny algorithm.The coil winding region was trained with the ShuffleNet V2-YOLOv5s model for the dataset,and the detect file was converted to the Open Neural Network Exchange(ONNX)model for the detection of winding cross features with an average accuracy of 99.0%.The software interface of the detection system was designed to perform qualified discrimination tests on the workpieces,and the detection data were recorded and statistically analyzed.The results showed that the stator winding coil qualified discrimination accuracy reached 96.2%,and the average detection time of a single workpiece was about 300 ms,while YOLOv5s took less than 30 ms.

Direct immune-SCIR public-opinion propagation model based on real-time online users

Current public-opinion propagation research usually focused on closed network topologies without considering the fluctuation of the number of network users or the impact of social factors on propagation. Thus, it remains difficult to accurately describe the public-opinion propagation rules of social networks. In order to study the rules of public opinion spread on dynamic social networks, by analyzing the activity of social-network users and the regulatory role of relevant departments in the spread of public opinion, concepts of additional user and offline rates are introduced, and the direct immune-susceptible, contacted, infected, and refractory (DI-SCIR) public-opinion propagation model based on real-time online users is established. The interventional force of relevant departments, credibility of real information, and time of intervention are considered, and a public-opinion propagation control strategy based on direct immunity is proposed. The equilibrium point and the basic reproduction number of the model are theoretically analyzed to obtain boundary conditions for public-opinion propagation. Simulation results show that the new model can accurately reflect the propagation rules of public opinion. When the basic reproduction number is less than 1, public opinion will eventually disappear in the network. Social factors can significantly influence the time and scope of public opinion spread on social networks. By controlling social factors, relevant departments can analyze the rules of public opinion spread on social networks to suppress the propagate of negative public opinion and provide a powerful tool to ensure security and stability of society.

Teaching Model Integrating Online and Offline in Real Time ——An Exploration of New Teaching Mode for Regular Epidemic Prevention and Control

During epidemic,students in medium-risk or high-risk areas are unable to return to school on time.In response to this new challenge,there is an urgent need to create a new teaching mode to offer on-line courses to those absent from the offline classes,and we propose a model integrating online and offline teaching.It is based on“dual-camera”method,which allows off-campus students to virtually build up a physical classroom scenario on campus through computers and mobile phones.Using this model,students can participate in class remotely.In order to enhance the engagement of off-campus online students,emphasis is placed on interactive teaching.Teachers are required to design their teaching in advance and to work in collaboration with multiple departments,then using information technology and suitable teaching methods to enable students to participate in physical classroom teaching.This model has been tested in practice and has been successful in meeting the challenges.Finally,4 areas for improvement and refinement are identified.

Quality Models for Open, Flexible, and Online Learning

This article is based on research conducted for the European CommissionEducation & Training 2020 working group on digital and online learning(ET2020 WG-DOL) specifically regarding policy challenges, such as thefollowing: 1) Targeted policy guidance on innovative and open learningenvironments under outcome;2) Proposal for a quality assurance modelfor open and innovative learning environments, its impact on specificassessment frameworks and its implication for EU recognition and transparencyinstruments. The article aims to define quality in open, flexible,and online learning, particularly in open education, open educationalresources (OER), and massive open online courses (MOOC). Hence,quality domains, characteristics, and criteria are outlined and discussed,as well as how they contribute to quality and personal learning so thatlearners can orchestrate and take responsibility for their own learningpathways. An additional goal is to identify the major stakeholders directlyinvolved in open online education and to describe their visions, communalities,and conflicts regarding quality in open, flexible, and online learning.The article also focuses on quality in periods of crisis, such as duringthe pandemic in 2020. Finally, the article discusses the rationale and needfor a model of quality in open, flexible, and online learning based on threemajor criteria for quality: excellence, impact, and implementation fromthe learner’s perspective.

基于Model Online给水管网水力模型的分析与应用

为了更好地模拟和优化西南H镇城乡供水管网的设计,应用Model Online给水管网平差软件,综合考虑供水安全性与经济性对管网进行优化和模拟,得出最佳供水效果,以指导设计施工,为供水技术进入互联网时代探寻新的道路。

An Instructional Model of Online Synchronous Instruction--A Case Study of College English Course for College Students

Online synchronous educational experience via videoconferencing has become ubiquitous domestically and internationally with the spread of Covid-19 Virus.Online teaching theories and educational experiences through computer-mediated communication have been integrating and evolving during the current situation.The objective of this paper is to demonstrate a practical teaching model for Chinese college students’English as a foreign language learning course.The key finding of the research is that the 5-step-teaching Model developed under the context of online synchronous learning and teaching is both functional and efficient on the theoretical basis of constructivist approach.This teaching model is proved to be practical,interesting,useful and functional to both second-years and graduates during the implementation of educational transaction according to the survey conducted at the end of the semester.The ubiquity of online synchronous learning and teaching would lead to a meaningful and worthwhile educational experience and have a far-reaching implication for both educators and student’s personal development,especially students’critical thinking capability.The findings of the paper are pedagogically valuable for e-teachers in conducting online courses.It also holds true that the management of education and society could benefit and adopt this teaching model to various courses in different institutions.This study is hopefully to provide research-and-experience-based findings to enrich course design and instructional approaches and educational experiences.Thus,it is of interest to online teachers,instructional designers and programme developers in the field of education.

Vapor Online Monitor Model of Vapor Power Station Based on UML

We presents a vapor online monitor system model of vapor power station developed by visual tool rational rose 2000. Use cases such as on line instrument (onlineinstr), control, query, report, real database (realdb) and alarm are generated according to the system requirements. Use case view and class view of the system are formed at the same time. As for all the UML models of the system, this paper focuses the discussion on the class view, the component diagram of the control class and the sequence diagram of the query class. Corresponding C++ codes are produced and finally transferred into the spot running software.

A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing

To overcome the large time-delay in measuring the hardness of mixed rubber, rheological parameters were used to predict the hardness. A novel Q-based model updating strategy was proposed as a universal platform to track time-varying properties. Using a few selected support samples to update the model, the strategy could dramatically save the storage cost and overcome the adverse influence of low signal-to-noise ratio samples. Moreover, it could be applied to any statistical process monitoring system without drastic changes to them, which is practical for industrial practices. As examples, the Q-based strategy was integrated with three popular algorithms(partial least squares(PLS), recursive PLS(RPLS), and kernel PLS(KPLS)) to form novel regression ones, QPLS, QRPLS and QKPLS, respectively. The applications for predicting mixed rubber hardness on a large-scale tire plant in east China prove the theoretical considerations.

Some Features of Neural Networks as Nonlinearly Parameterized Models of Unknown Systems Using an Online Learning Algorithm

This paper deals with deriving the properties of updated neural network model that is exploited to identify an unknown nonlinear system via the standard gradient learning algorithm. The convergence of this algorithm for online training the three-layer neural networks in stochastic environment is studied. A special case where an unknown nonlinearity can exactly be approximated by some neural network with a nonlinear activation function for its output layer is considered. To analyze the asymptotic behavior of the learning processes, the so-called Lyapunov-like approach is utilized. As the Lyapunov function, the expected value of the square of approximation error depending on network parameters is chosen. Within this approach, sufficient conditions guaranteeing the convergence of learning algorithm with probability 1 are derived. Simulation results are presented to support the theoretical analysis.

UTM:A trajectory privacy evaluating model for online health monitoring

A huge amount of sensitive personal data is being collected by various online health monitoring applications.Although the data is anonymous,the personal trajectories(e.g.,the chronological access records of small cells)could become the anchor of linkage attacks to re-identify the users.Focusing on trajectory privacy in online health monitoring,we propose the User Trajectory Model(UTM),a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data(e.g.,number of individuals covered by each small cell),and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness(i.e.,the expectation and the variance).Eventually,exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation,confirm our theoretical deductions and present counter-intuitive insights.