Determinants of Online Buying Behaviour of Social Media Users in Cameroon

Online shopping in Cameroon is growing rapidly and gaining considerable ground.The phenomenon is relatively new as compared to the traditional brick-and-mortar store(serving customers face-to-face in a building rather than online).As the world faces digital transformations,businesses in Cameroon are seeking new ways to reach their customers and create favourable environments to effectively carryout online purchase.This study examines the factors that affect consumer online buying behaviour in Cameroon.Three theories guided this investigation:the Technology Acceptance Model(Davis,1989),Diffusion of Innovations Theory(Rogers,1995),and Uses and Gratifications Theory(Katz&Blumler,1974).The study focuses on students of the University of Buea who carry out online shopping.Purposive sampling is used,and data are collected from 365 respondents through a questionnaire with open and closed ended questions.Analysis of data is done with the use of the Statistical Package for Social Scientists(SPSS)Version 21 to determine the types of products consumers buy online;the degree to which consumer trust and satisfaction affect consumer loyalty;and the specific social,economic,and market factors that affect online buying behaviour.Findings indicate that:Consumers mostly buy fashion items(74.3%),electronics(44.7%),cosmetics(37.3%),and house equipment(34%)online.Consumers will repeat purchase from a marketer if they trust and are satisfied with the product and service quality(81.1%);hence,they will also encourage others to buy.If they are dissatisfied,they will not repeat purchase from the online store.Advertisement(76.1%),attractive pricing/discount(71.7%),product quality(71%),service quality(64.1%),convenience(72.3%),available income/money(49.6%),word of mouth recommendation(41.9%),and personal motivation(62.7%)constitute the major factors that affect consumer online buying behaviour.Results of the hypotheses testing show that:H1=Consumers buy more of fashion items online(X^(2)=9.950;df=16;p=0.869);H2=There is a significant relationship between trust and satisfaction and consumers online buying behaviour(X^(2)=270.765;df=16;p=0.000);and H3=Social,economic,and market factors significantly affect consumer online buying behaviour(X^(2)=106.328;df=16;p=0.000).The study recommends online marketers to develop their marketing strategies towards customer orientation and focus on the ease of use of their online shopping services.

TPACK框架下GeoScene Online与地理教学融合的实践

技术革新影响学科教学方式选择,TPACK模式为解决当下学科教学应用新技术“两张皮”问题提供了新思路。本文从科勒和米什拉的理论出发,尝试将GeoScene Online平台与地理课堂教学融合,提出循序渐进、跨学科、基于真实情境和交互式的融合原则,进而在TPACK模式下将GeoScene Online功能特点与高中地理必修内容进行融合分析,构建以PCK、TCK、TPK三条子路径为导向的GeoScene Online与地理教学融合模式,并以“耕地”为主题进行案例实践探索。

Optimized operation scheme of flash-memory-based neural network online training with ultra-high endurance

With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

Early warning method for thermal runaway of lithium-ion batteries under thermal abuse condition based on online electrochemical impedance monitoring

Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application scenarios.Timely and effective TR early warning technology is urgently required considering the current fire safety situation of LIBs.In this work,we report an early warning method of TR with online electrochemical impedance spectroscopy(EIS)monitoring,which overcomes the shortcomings of warning methods based on traditional signals such as temperature,gas,and pressure with obvious delay and high cost.With in-situ data acquisition through accelerating rate calorimeter(ARC)-EIS test,the crucial features of TR were extracted using the RReliefF algorithm.TR mechanisms corresponding to the features at specific frequencies were analyzed.Finally,a three-level warning strategy for single battery,series module,and parallel module was formulated,which can successfully send out an early warning signal ahead of the self-heating temperature of battery under thermal abuse condition.The technology can provide a reliable basis for the timely intervention of battery thermal management and fire protection systems and is expected to be applied to electric vehicles and energy storage devices to realize early warning and improve battery safety.

Efficient unequal error protection for online fountain codes

In this paper,an efficient unequal error protection(UEP)scheme for online fountain codes is proposed.In the buildup phase,the traversing-selection strategy is proposed to select the most important symbols(MIS).Then,in the completion phase,the weighted-selection strategy is applied to provide low overhead.The performance of the proposed scheme is analyzed and compared with the existing UEP online fountain scheme.Simulation results show that in terms of MIS and the least important symbols(LIS),when the bit error ratio is 10-4,the proposed scheme can achieve 85%and 31.58%overhead reduction,respectively.

Online Consensus Control of Nonlinear Affine Systems From Disturbed Data

Dear Editor,In this letter, we introduce a novel online distributed data-driven robust control approach for learning controllers of unknown nonlinear multi-agent systems(MASs) using state-dependent representations.

Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

Will Online Car-Hailing Affect Consumers’ Decisions about Automobile Purchase?—An Empirical Study Based on Questionnaire Investigation

The online car-hailing industry, which provides the right of use, has a certain impact on the traditional automobile market, but there is no unified theory on whether it has a positive impact or a negative impact. Based on 362 consumer questionnaire data, this study builds a structural equation model to discuss the driving factors of residents’ choice of online car-hailing and whether the development of online car-hailing will have a certain substitution impact on the sales of private cars. From the perspective of consumers’ purchase intention, the research results show that consumers’ price consciousness, convenience consciousness, environmental protection consciousness and possession tendency will affect their choice of travel mode, and the use of online car-hailing is positively correlated with consumers’ willingness to replace private car ownership with online car-hailing.

English Speaking Online in the Turkish Teaching Context:A Theoretical Approach

The world initiated a new era with the emergence of the Covid-19 pandemic.Education all around the globe,including Turkey,was impacted to a great extent.Both teachers and learners were required to adapt to online education within a short period of time.Although previous studies had focused on the impacts of teaching English in general for teachers and learners,studies focusing on teaching of the speaking skills online particularly in the Turkish context was absent in the literature.Therefore,the gap which was to be considered appropriate for this research is related to comprehending the perceptions and challenges EFL teachers confronted while attempting to teach the speaking skills online within the Turkish context.This article aims to have a theoretical contribution to the current research,in order to see the significance of the Turkish context,as online teaching of English speaking was to be considered beneficial for learners,as it increased confidence for the interaction.

Research on the Collaborative Governance of Social Responsibility in Online Audiovisual Enterprises

This paper aims to analyze the present conditions of the social responsibility ecosystem in online audiovisual enterprises in the digital age. It focuses on the governance of social responsibility in these enterprises and conducts an in-depth analysis of the problems and influencing factors related to the social responsibility aberrations of online audiovisual enterprises. Drawing upon social responsibility theory and collaborative governance theory, this research constructs a social responsibility guidance and governance system guided by the public, supported by the voluntary fulfillment of responsibilities by online audiovisual enterprises, and based on the collaborative participation of diverse stakeholders. It explores and optimizes the implementation pathways of this system, providing theoretical support and practical guidance for promoting the sustainable development of online audiovisual enterprises. Furthermore, it aims to contribute to the creation of a harmonious Internet ecosystem.

A Study on the Effects of Different Interaction Combinations and Language Levels on Continuous Writing in Online Environments

This study was conducted with non-English sophomore students,aiming to explore the effects of different interaction combinations and language levels on continuous writing in an online environment,and compare the differences in lexical alignments and composition quality of learners with different interaction combinations and language levels in the same continuous writing task through experiments.The results show that the mean values of the word-phrase alignment of the paired group were higher than those of the individual group in different interaction combinations,and the two groups showed significant differences;in terms of composition quality,the individual group was better than the paired group,but there was no significant difference between the two groups in terms of task continuation.Secondly,the word-phrase alignment and composition scores of the different language-level groups were higher than those of the same language-level groups,and there was a significant difference between the two groups in terms of word-phrase alignments,but not in terms of composition scores.The results of this study can be useful and informative for second language teachers in future continuous teaching in online environments.

Efficacy of Online Learning and Influence on Skin and Eyes Among College Students

To evaluate the efficacy of online learning and explore the impact of long-term use of electronic products on facial skin as well as eyes.A cross-sectional survey was conducted to 180 sophomores in Xi′an Jiaotong University by cluster random sampling from September to October 2021.The questionnaire covering study condition,skin lesion and Ocular Surface Disease Index.χ_(2) test was used to compare the facial skin condition among different groups,and spearman correlation test was used to test the correlation of rank data.During online education,students′learning pressure is reduced,their autonomy is improved,and the learning efficiency is reduced.There were differences in the incidence of facial itching and papules among different groups.Duration of use of electronic products was positively correlated with the facial itching,with an r value of 0.231(P<0.05);the proportion of pigmentation in non-blue light protection groups(12.8%)was higher than that in blue light protection groups(1.7%),the difference was statistically significant(χ_(2)=8.384,P<0.05).The prevalence of dry eye among college students is 66.7%,and the proportion of moderate to severe dry eye is 34.5%.The study autonomy has been improved during online teaching.Long-term use of electronic products and no blue light protection have an impact on facial skin.Students should enhance the knowledge of skin-care and eye-care and develop better habits.

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%.

Online Fault Monitoring of On-Load Tap-Changer Based on Voiceprint Detection

The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing major failures and ensuring the reliability of the electrical grid. This research paper proposes an innovative approach that combines voiceprint detection using MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced signal processing techniques and machine learning algorithms in MATLAB, the proposed method accurately detects faults in OLTC, providing real-time monitoring and proactive maintenance strategies.

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.

Research on the Blended Online and Offline Teaching Mode of Ideological and Political Courses in Universities

With the development of information technology,the blended online and offline teaching mode has gradually become a new trend in the teaching of ideological and political theory courses in universities.This article analyzes the current situation and existing problems of blended online and offline teaching of ideological and political courses in universities,and explores how to effectively combine online and offline teaching resources to improve the teaching effectiveness of ideological and political courses in universities.

Online asymmetry estimation for whole angle mode coriolis vibratory gyroscopes by high frequency injection

The whole angle mode gyroscope(WAMG)is considered to be the next generation architecture,but it is suffered from the asymmetry errors to conduct real products.This paper proposes a novel high frequency injection based approach for the error parameters online identification for the WAMG.The significance is that it can separate physical and error fingerprints to enable online calibration.The nonlinear WAMG dynamics are discretized to meet the requirement of numerical precision and computation efficiency.The optimized estimation methods are then constructed and compared to track asymmetry error parameters continuously.In the validation part,its results firstly prove that the proposed scheme can accurately identify constant asymmetry parameters with an overall tracking error of less than 1 ppm and the extreme numerical convergence can reach 10^(-12)ppm.Under the dynamic asymmetry variation condition,the root mean square errors(RMSE)indicate that the tracking accuracy can reach the level of10^(-3),which shows the robustness of the proposed scheme.In summary,the proposed method can effectively estimate the WAMG asymmetry errors online with satisfied performance and practical values.