Game Analysis of Regulation for Online Prescription Drugs under Customer Feedback Mechanism

Objective To study the way to better regulate the online sales of prescription drugs,and to provide reference for the adjustment of relevant policies since the online sales of prescription drugs has become an inevitable trend.Methods A game model was constructed for the strategy choice of pharmaceutical e-commerce platform,customers and government departments based on differential game theory and Nash equilibrium game model to analyze the pure strategy Nash equilibrium,Nash equilibrium dominant strategy of each subject and the mixed strategy Nash equilibrium under different conditions.Besides,Matlab was used to carry out simulation analysis.Results and Conclusion The study shows that:(1)Improving the credibility of the government and reducing the cost of government regulation can not only make the pharmaceutical e-commerce platform operate with high quality,but also give greater play to government functions;(2)The greater the influence of social evaluation on pharmaceutical e-commerce platforms,the lower the cost of high-quality operation of pharmaceutical e-commerce platform,and the greater the probability of customer choosing real evaluation strategy;(3)The greater the customers’perception of potential risk,the greater the compensation,and the lower the cost of reporting.Then,the greater the probability that government departments will choose strict regulation.Finally,the model solution and simulation analysis are combined to provide countermeasures and suggestions for the safety regulation of online sales of prescription drugs.

Strategies for Fostering Interaction in Online Classrooms:A Conversation Analysis of Teacher-Student Verbal Interaction in Random Questioning in Pandemic-Initiated Online Teaching

This paper aims to explore how a veteran teacher organizes online teaching initiated by the pandemic and how she deals with the problems in online teacher-student verbal interaction.By analyzing a corpus of 20 audio-recorded online lessons between a math teacher and her students during the COVID-19 pandemic from April 11 to May 10,2022,four interactional segments are selected as the focus of the study.The results of the conversation analysis of the segments showed that students’modesty,lack of confidence,lack of ability,and network delay are the main factors affecting online teacher-student interaction.By encouraging students to answer questions,enlightening students to give answers,enriching students’answers,and entertaining the teaching atmosphere(“4Es”strategies),the teacher solved the problems successfully.The findings from this study can provide pedagogical experience and implications for practical teaching.

Nonlinear Analysis of Structures by Total Potential Optimization Using Metaheuristic Algorithms (TPO/MA)

Structural analysis problems can be formulized as either root finding problems,or optimization problems.The general practice is to choose the first option directly or to convert the second option again to a root finding problem by taking relevant derivatives and equating them to zero.The second alternative is used very randomly as it is and only for some simple demonstrative problems,most probably due to difficulty in solving optimization problems by classical methods.The method called TPO/MA(Total Potential Optimization using Metaheuristic Algorithms)described in this study successfully enables to handle structural problems with optimization formulation.Using metaheuristic algorithms provides additional advantages in dealing with all kinds of constraints.

Geometric and material nonlinear analysis of tensegrity structures

A numerical method is presented for the large deflection in elastic analysis of tensegrity structures including both geometric and material nonlinearities.The geometric nonlinearity is considered based on both total Lagrangian and updated Lagrangian formulations,while the material nonlinearity is treated through elastoplastic stress-strain relationship.The nonlinear equilibrium equations are solved using an incremental-iterative scheme in conjunction with the modified Newton-Raphson method.A computer program is developed to predict the mechanical responses of tensegrity systems under tensile,compressive and flexural loadings.Numerical results obtained are compared with those reported in the literature to demonstrate the accuracy and efficiency of the proposed program.The flexural behavior of the double layer quadruplex tensegrity grid is sufficiently good for lightweight large-span structural applications.On the other hand,its bending strength capacity is not sensitive to the self-stress level.

Online pharmaceutical process analysis of Chinese medicine using a miniature mass spectrometer: Extraction of active ingredients as an example

The automation of traditional Chinese medicine(TCM)pharmaceuticals has driven the development of process analysis from offline to online.Most of common online process analytical technologies are based on spectroscopy,making the identification and quantification of specific ingredients still a challenge.Herein,we developed a quality control(QC)system for monitoring TCM pharmaceuticals based on paper spray ionization miniature mass spectrometry(mini-MS).It enabled real-time online qualitative and quantitative detection of target ingredients in herbal extracts using mini-MS without chromatographic separation for the first time.Dynamic changes of alkaloids in Aconiti Lateralis Radix Praeparata(Fuzi)during decoction were used as examples,and the scientific principle of Fuzi compatibility was also investigated.Finally,the system was verified to work stably at the hourly level for pilot-scale extraction.This mini-MS based online analytical system is expected to be further developed for QC applications in a wider range of pharmaceutical processes.

Orthogonal expansion of ground motion and PDEM-based seismic response analysis of nonlinear structures

This paper introduces an orthogonal expansion method for general stochastic processes. In the method, a normalized orthogonal function of time variable t is first introduced to carry out the decomposition of a stochastic process and then a correlated matrix decomposition technique, which transforms a correlated random vector into a vector of standard uncorrelated random variables, is used to complete a double orthogonal decomposition of the stochastic processes. Considering the relationship between the Hartley transform and Fourier transform of a real-valued function, it is suggested that the first orthogonal expansion in the above process is carried out using the Hartley basis function instead of the trigonometric basis function in practical applications. The seismic ground motion is investigated using the above method. In order to capture the main probabilistic characteristics of the seismic ground motion, it is proposed to directly carry out the orthogonal expansion of the seismic displacements. The case study shows that the proposed method is feasible to represent the seismic ground motion with only a few random variables. In the second part of the paper, the probability density evolution method （PDEM） is employed to study the stochastic response of nonlinear structures subjected to earthquake excitations. In the PDEM, a completely uncoupled one-dimensional partial differential equation, the generalized density evolution equation, plays a central role in governing the stochastic seismic responses of the nonlinear structure. The solution to this equation will yield the instantaneous probability density function of the responses. Computational algorithms to solve the probability density evolution equation are described. An example, which deals with a nonlinear frame structure subjected to stochastic ground motions, is illustrated to validate the above approach.

Behavior analysis and formative assessments in online oral medicine education during the COVID-19 pandemic

BACKGROUND During the coronavirus disease 2019(COVID-19)pandemic,traditional teaching methods were disrupted and online teaching became a new topic in education reform and informatization.In this context,it is important to investigate the necessity and effectiveness of online teaching methods for medical students.This study explored stomatology education in China to evaluate the development and challenges facing the field using massive open online courses(MOOCs)for oral medicine education during the pandemic.AIM To investigate the current situation and challenges facing stomatology education in China,and to assess the necessity and effectiveness of online teaching methods among medical students.METHODS Online courses were developed and offered on personal computers and mobile terminals.Behavioral analysis and formative assessments were conducted to evaluate the learning status of students.RESULTS The results showed that most learners had already completed MOOCs and achieved better results.Course behavior analysis and student surveys indicated that students enjoyed the learning experience.However,the development of oral MOOCs during the COVID-19 pandemic faced significant challenges.CONCLUSION This study provides insights into the potential of using MOOCs to support online professional learning and future teaching innovation,but emphasizes the need for careful design and positive feedback to ensure their success.

Full－range nonlinear analysis of fatigue behaviors of reinforced concrete structures by finite element method

The offshore reinforced concrete structures are always subject to cyclic load, such as wave load.In this paper a new finite element analysis model is developed to analyze the stress and strain state of reinforced concrete structures including offshore concrete structures, subject to any number of the cyclic load. On the basis of the anal ysis of the experimental data,this model simplifies the number of cycles-total cyclic strain curve of concrete as three straight line segments,and it is assumed that the stress-strain curves of different cycles in each segment are the same, thus the elastoplastic analysis is only needed for the first cycle of each segment, and the stress or strain corresponding to any number of cycles can be obtained by superposition of stress or strain obtained by the above e lastoplastic analysis based on the cyclic numbers in each segment.This model spends less computer time,and can obtain the stress and strain states of the structures after any number of cycles.The endochronic-damage and ideal offshore concrete platform subject to cyclic loading are experimented and analyzed by the finite element method based on the model proposed in this paper. The results between the experiment and the finite element analysis are in good agreement,which demonstrates the validity and accuracy of the proposed model.

Nonlinear seismic response analysis of reinforced concrete tube in tube structure

Super-highly reinforced concrete tube in tube structure is a developing structure system of high-rise building. The more reasonable derivation process of the multi-vertical-line-element model stiffness matrix is given.On the premise of pointing out the problems of present multi-spring element model, combined with present multivertical-line-element model for analyzing on shear wall, the model is expanded to spatial one, and the stiffness matrix of which is derived. Combined with hysteretic axial model and hysteretic shear model, it is suitable for columns,wall limbs and beams with all kinds of section form. Some examples are calculated and compared with test results,which shows that the models have relatively good accuracy. On the base of the experimental phenomenon and failure mechanism for tube in tube structure specimen, nonlinear seismic responses analysis program on the basis of the advantaged element model for tube in tube structure is developed. Calculation results are in good agreement with those of the pseudo-dynamic tests and the failure mechanism can be well reflected.

The Driving Strategy of Online Ideological and Political Class in Universities Based on SWOT Analysis

The epidemic provided practical opportunities for online classes and also tested its drawbacks. To analyze the internal strengths (S) and weaknesses (W) as well as the external opportunities (O) and threats (T) of the online ideological and political class with SWOT theory, based on which to explore targeted solutions, is a “symptomatic” approach to improving the quality of the online ideological and political class. The analysis results are as follows: S includes “not limited by time and space”, “comprehensive teaching monitoring data”, “diverse and innovative interactive forms”, and “abundant high-quality teaching resources”. W refers to “increased difficulty in classroom control”, “teachers’ psychological pressure caused by online teaching”, and “limited by hardware conditions”. O means the opportunity provided by the modernization of ideological and political education and epidemics. T indicates external threats, such as limitations from the network and the severe impact of wrong values on the internet. The driving strategy for above case could be “upgrading of online teaching platforms”, “building a teaching team skilled in information technology teaching”, “improving hardware facilities” and “joining forces to confront value crisis”.

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

Online Capacitor Voltage Transformer Measurement Error State Evaluation Method Based on In-Phase Relationship and Abnormal Point Detection

The assessment of the measurement error status of online Capacitor Voltage Transformers (CVT) within the power grid is of profound significance to the equitable trade of electric energy and the secure operation of the power grid. This paper advances an online CVT error state evaluation method, anchored in the in-phase relationship and outlier detection. Initially, this method leverages the in-phase relationship to obviate the influence of primary side fluctuations in the grid on assessment accuracy. Subsequently, Principal Component Analysis (PCA) is employed to meticulously disentangle the error change information inherent in the CVT from the measured values and to compute statistics that delineate the error state. Finally, the Local Outlier Factor (LOF) is deployed to discern outliers in the statistics, with thresholds serving to appraise the CVT error state. Experimental results incontrovertibly demonstrate the efficacy of this method, showcasing its prowess in effecting online tracking of CVT error changes and conducting error state assessments. The discernible enhancements in reliability, accuracy, and sensitivity are manifest, with the assessment accuracy reaching an exemplary 0.01%.

Genetic Diversity and Population Structure Analysis of Barley Landraces from Shanghai Region Using Genotyping-by-Sequencing

Barley(Hordeum vulgare L.)is an important economic crop for food,feed and industrial raw materials.In the present research,112 barley landraces from the Shanghai region were genotyped using genotyping-by-sequencing(GBS),and the genetic diversity and population structure were analyzed.The results showed that 210,268 Single Nucleotide Polymorphisms(SNPs)were present in total,and the average poly-morphism information content(PIC)was 0.1642.Genetic diversity and population structure analyses suggested that these barley landraces were differentiated and could be divided into three sub-groups,with morphological traits of row-type and adherence of the hulls the main distinguishing factors between groups.Genotypes with similar or duplicated names were also investigated according to their genetic backgrounds and seed appearances.This study provided valuable information on barley landraces from the Shanghai region,and showed that all these barley landraces should be protected and used for future breeding programs.

Dynamic analysis and nonlinear identification of space deployable structure

The dynamic equivalent continuum modeling method of the mast which is based on energy equivalency principle was investigated. And three kinds of mast dynamic model were established, which were equivalent continuum model, finite element model and simulation model, respectively. The mast frequencies and mode shapes were calculated by these models and compared with each other. The error between the equivalent continuum model and the finite element model is less than 5% when the mast length is longer. Dynamic responses of the mast with different lengths are tested, the mode frequencies and mode shapes are compared with finite element model. The mode shapes match well with each other, while the frequencies tested by experiments are lower than the results of the finite element model, which reflects the joints lower the mast stiffness. The nonlinear dynamic characteristics are presented in the dynamic responses of the mast under different excitation force levels. The joint nonlinearities in the deployable mast are identified as nonlinear hysteresis contributed by the coulomb friction which soften the mast stiffness and lower the mast frequencies.

Analysis on the status and influencing factors of undergraduate nursing students’ online learning engagement in the context of the pandemic

Objective:This project has mainly studied the online learning engagement of undergraduate nursing students and analyzes influencing factors of online learning and teaching mode during the Novel Coronavirus(COVID-19).This research has significant references for improving the efficiency and quality of the online learning mode of students.Methods:In this study,212 undergraduate nursing students were selected from a comprehensive university in Jilin Province by combining convenience sampling and cluster sampling methods.And these students were conducted with a general information questionnaire,Online Academic Emotion Scale,and Online Learning Engagement Scale.The influencing factors of this teaching mode were analyzed by multiple linear stepwise regression.Results:The total score of online learning engagement of undergraduate students was 53.85±7.38,which positively correlated with positive high arousal emotion and negative high arousal emotion,but weakly negatively correlated with negative low arousal emotion(r=0.661,0.246,-0.187,P<0.001).Grade,type of online class,online learning time,and positively high arousal emotion were mainly affected the online learning engagement of undergraduate nursing students,which explained 78.5%of the total variation(P<0.001).Conclusion:The online learning engagement of undergraduate nursing students was above the middle level under the background of the COVID-19 pandemic.Lectures and professors who teach undergraduate nursing students,should integrate the individuation characters of nursing students,and motivate their positively high arousal emotion to improve online learning engagement of students to ensure the quality of online teaching mode.

Rapid online analysis of trace elements in steel using a mobile fiber-optic laser-induced breakdown spectroscopy system

A mobile fiber-optic laser-induced breakdown spectrometer(FO-LIBS) prototype was developed to rapidly detect a large quantity of steel material online and quantitatively analyze the trace elements in a large-diameter steel tube.Twenty-four standard samples and a polynomial fitting method were used to establish calibration curve models.The R^2 factors of the calibration curves were all above 0.99,except for Cu,indicating the elements’ strong self-absorption effect.Five special steel materials were rapidly detected in the steel mill.The average absolute errors of Mn,Cr,Ni,V,Cu,and Mo in the special steel materials were 0.039,0.440,0.033,0.057,0.003,and0.07 wt%,respectively,and their average relative errors fluctuated from 2.9% to 15.7%.The results demonstrated that the performance of this mobile FO-LIBS prototype can be compared with that of most conventional LIBS systems,but the more robust and flexible characteristics of the FO-LIBS prototype provide a feasible approach for promoting LIBS from the laboratory to the industry.

Genetic diversity and population structure analysis of 161 broccoli cultivars based on SNP markers

To better understand the genetic diversity and population structure of broccoli cultivars planted in China,a total of 161 representative broccoli cultivars in the past 25 years were collected and analysed based on single nucleotide polymorphism(SNP)markers.Ten pairs of primers with good polymorphism and high resolution were screened from 315 pairs of SNP primers by 3 broccoli accessions(inbred lines)with different phenotypes and maturity.The 10 pairs of SNP primers were selected,producing 78 alleles.The diversity analysis indicated that the polymorphism information content(PIC)of SNP primer ranged from 0.64 to 0.90.The observed number of alleles(Na)was 2.00,the effective number of alleles(Ne)was 1.11–2.00,the Nei’s gene diversity(H)was 0.10–0.50,and Shannon information index(I)was 0.20–0.70 using PopGene32 software.The clustering results showed that the 161 broccoli cultivars could be divided into 4 major subgroups(A,B,C and D),foreign cultivars were all assigned to subgroup A,and domestic cultivars were assigned to 3 subgroups of B,C,and D.This study indicated that some domestic cultivars and foreign cultivars were similar in genetic background,but most domestic cultivars were still different from the Japanese cultivars.When K=2,the population structure result presented that 161 broccoli cultivars could be divided into 1 simple group(2 groups)and 1 mixed group.When Q≥0.6,143(88.82%)broccoli cultivars belonged to the simple groups.In simple groups 68(42.24%)broccoli cultivars of group 1 were derived from Japan,the United States,Switzerland,the Netherlands,China-Taiwan,and China-Mainland;75(46.58%)broccoli cultivars belonged to group 2;when Q<0.6,18(11.18%)broccoli cultivars belonged to the mixed groups.This study is helpful to understand the diversity and resolution of broccoli cultivars from worldwide,which is beneficial to plant breeding and materials innovation.And meanwhile,this result is also used for construction of broccoli fingerprint serving for cultivar identification.

Topic Sentiment Analysis in Online Learning Community from College Students

Purpose:Opinion mining and sentiment analysis in Online Learning Community can truly reflect the students’learning situation,which provides the necessary theoretical basis for following revision of teaching plans.To improve the accuracy of topic-sentiment analysis,a novel model for topic sentiment analysis is proposed that outperforms other state-of-art models.Methodology/approach:We aim at highlighting the identification and visualization of topic sentiment based on learning topic mining and sentiment clustering at various granularitylevels.The proposed method comprised data preprocessing,topic detection,sentiment analysis,and visualization.Findings:The proposed model can effectively perceive students’sentiment tendencies on different topics,which provides powerful practical reference for improving the quality of information services in teaching practice.Research limitations:The model obtains the topic-terminology hybrid matrix and the document-topic hybrid matrix by selecting the real user’s comment information on the basis of LDA topic detection approach,without considering the intensity of students’sentiments and their evolutionary trends.Practical implications:The implication and association rules to visualize the negative sentiment in comments or reviews enable teachers and administrators to access a certain plaint,which can be utilized as a reference for enhancing the accuracy of learning content recommendation,and evaluating the quality of their services.Originality/value:The topic-sentiment analysis model can clarify the hierarchical dependencies between different topics,which lay the foundation for improving the accuracy of teaching content recommendation and optimizing the knowledge coherence of related courses.

Automatic Persian Text Summarization Using Linguistic Features from Text Structure Analysis

With the remarkable growth of textual data sources in recent years,easy,fast,and accurate text processing has become a challenge with significant payoffs.Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents,which must be done without losing important features and information.This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure.The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the input text,which improves the sentence feature selection process and leads to the generation of unambiguous,concise,consistent,and coherent summaries.The paper also presents the results of the evaluation of the proposed method based on precision and recall criteria.It is shown that the method produces summaries consisting of chains of sentences with the aforementioned characteristics from the original text.

Development and Analysis of a Machine Learning Based Software for Assisting Online Classes during COVID-19

Amid the Covid-19 widespread, it has been challenging for educational institutions to conduct online classes, facing multiples challenges. This paper provides an insight into different approaches in facing those challenges which includes conducting a fair online class for students. It is tough for an instructor to keep track of their students at the same time because it is difficult to screen if any of the understudies within the class are not present, mindful, or drowsing. This paper discusses a possible solution, something new that can offer support to instructors seeing things from a more significant point of view. The solution is a facial analysis computer program that can let instructors know which students are attentive and who is not. There’s a green and red square box for face detection, for which Instructors can watch by seeing a green box on those mindful students conjointly, a red box on those who are not mindful at all. This paper finds that the program can automatically give attendance by analyzing data from face detection. It has other features for which the teacher can also know if any student leaves the class early. In this paper, model design, performance analysis, and online class assistant aspects of the program have been discussed.