Traditional Online Research Articles

Online Order Management System

Abstract: Our proposed online order management system is a platform that provides sellers with a comprehensive overview of their stock and orders. The platform streamlines the order management process, allowing the seller to view and manage their products and stocks with ease. Unlike traditional online order management systems, our platform goes beyond basic stock management and incorporates two machine learning algorithms to improve the seller's efficiency and profitability. The first machine learning algorithm provides the seller with insights into the popularity of a particular product category during a specific month. By notifying the seller of trends, they can better plan their stock levels to maximize profits. For example, if a seller notices that a particular product category is in high demand during the holiday season, they can increase their stock levels to meet the increased demand and maximize their profits. The second machine learning algorithm dynamically sets prices for the seller's products based on factors such as demand, date, and product category. This algorithm takes into account market trends and consumer behavior to set a price that is both competitive and profitable for the seller. For example, if a particular product is in high demand during a particular day, the algorithm will adjust the price to reflect the increased demand and allow the seller to sell the product at a higher price. In addition to these two machine learning algorithms, our online order management system includes a range of other features to help sellers manage their business effectively and efficiently. This includes order tracking, stock management, and customer management, all of which can be accessed from a single dashboard. The implementation of these machine learning algorithms is a crucial aspect of our online order management system, as they allow sellers to make informed decisions about their business. By using data and insights to optimize stock levels and pricing strategies, sellers can maximize their profits and minimize their losses. It is a comprehensive platform that provides sellers with the tools they need to manage their business effectively and efficiently.

Devil's Advocate: Evidence-Based Recommendation for "One Breast-One Insertion Funnel" Policy.

Rates of capsular contracture have reduced significantly since the use of insertion funnels to place breast implants became routine. However, due to financial constraints, the same funnel is usually used for implantation of both sides. The aim of this study was to determine whether the risk of capsular contracture is higher for the second breast when the same insertion funnel is used for both breasts. The authors collected a sample of the insertion funnel tip immediately after removing the funnel from its sterile packaging and another tip sample after the funnel had been used to insert the first implant. These samples were sent for microbiological culture evaluations. Capsular contracture rates in the first implanted breast vs the second implanted breast were then retrospectively analyzed. All samples taken from the funnel before the first implantation showed no bacterial growth. All 10 samples taken from the funnel after the first implantation showed organism growth (8 were positive for Staphylococcus epidermidis and 2 for Cutibacterium acnes). Retrospective analysis of the results revealed that the overall capsular contracture rate had reduced after the authors began to use insertion funnels. However, this complication was still more common on the second implanted breast. Surgeons should consider the use of separate insertion funnels for each breast. This might help to slightly reduce the incidence of capsular contracture.See the abstract translated into Hindi, Portuguese, Korean, German, Italian, Arabic, and Chinese (Simplified and Traditional) online here: https://doi.org/10.1093/asj/sjad288.

A model of online opinion dissemination based on vector autoregressive model of emotional information entropy

Abstract In the context of the digitalization of big data information, to solve the problems of timeliness and accuracy of traditional online opinion dissemination models. This paper firstly proposes a vector autoregressive model in the research problem of online opinion dissemination model and constructs a vector autoregressive model based on multivariate time series and unit root test. Then the emotional information entropy is determined based on the statistical characteristics of the emotional index, and the evaluation index is mainly composed of the current mainstream online public opinion dissemination modes. Finally, data analysis of online opinion dissemination modes based on the vector autoregressive model is conducted, and the dissemination efficiency of different modes is compared based on the analysis results. The results show that in the microblog dissemination mode, online public opinion’s dissemination efficiency remains in the range of 38.02% to 44.81%, and its average dissemination efficiency is 41.51%. In the online short video dissemination mode: the online public opinion dissemination efficiency remains between 32.15% to 35.74%, and its average dissemination efficiency is 33.97%. In the consultation mode, online public opinion’s dissemination efficiency remains within the range of 22.06% to 28.99%, and its average dissemination efficiency is 25.61%. In terms of the average dissemination efficiency, the microblog dissemination mode performs better than the other two. Through a comprehensive and objective study of online public opinion dissemination modes, this study has a guiding reference value for improving online public opinion dissemination to promote the development of online public opinion dissemination in China.

Understanding the Continuance Intention for Artificial Intelligence News Anchor: Based on the Expectation Confirmation Theory

The Metaverse accelerates the development of the meta-human industry and human-AI interactions in both traditional media outlets and online platforms. As a typical application of meta-human, artificial intelligence (AI) news anchors have been gradually utilized for program reports instead of newscasters in China. In this paper, through the lens of expectation confirmation theory, we establish a conceptual model consisting of perceived anthropomorphism (ANT), perceived intelligence (PI), perceived attractiveness (PA), perceived novelty (PN), information quality (IQ), confirmation of expectation (CE), trust (TRU), and satisfaction (SAT) to explore continuous intention (CI) of watching news reported by AI anchors among online users. By leveraging on a sample of 598 eligible questionnaires, the partial least square structural equation model is employed and the results show that the holistic continuing intention for AI news anchor is positive but not robust. Further analysis indicates that SAT, PI, and TRU can predict CI directly, meanwhile CE, ANT, and PA associate with CI through the mediation of satisfaction. In addition, trust and satisfaction serve as serial mediators between IQ and CI. There is no direct relationship between CE & CI, ANT & CI, and PN & SAT. Nevertheless, user gender and previous experience can moderate the relationships of ANT & CI and PN & SAT, respectively. It can be seen that the proposed model can explain 80.1% of the variance in CI. The implications are intended to provide references for further commercialization of AI news anchors.

EEGAlzheimer’sNet: Development of transformer-based attention long short term memory network for detecting Alzheimer disease using EEG signal

A previous diagnosis of Alzheimer's disease (AD) in its initial stages is needed for patient care because it helps patients adopt preventative measures before irreversible brain damage occurs. Several studies have used computers to detect AD, although hereditary results limit most computer detection methods. There is no straightforward method to screen for AD, partly because the condition is difficult to diagnose and sometimes requires costly and occasionally intrusive testing that is uncommon outside of highly specialized clinical settings. Therefore, this study implements a deep learning strategy for detecting AD with the help of the “Electroencephalogram (EEG)” signal. Initially, the required EEG signal is obtained from traditional online databases and then applied to the 3-level “Lifting Wavelet Transform (LWT)” decomposition to decompose the signal into many wavelets. From the decomposed signal, the temporal features are retrieved by a “Recurrent Neural Network (RNN)”, and the spatial features are extracted from a “Multi-scale dilated Convolutional Neural Network (CNN)”. Further, the Enhanced Wild Geese Lemurs Optimizer (EWGLO) algorithm is implemented to find the optimal weight value for acquiring the weighted stacked features. These resultant weighted stacked features are applied to the semi-detection stage, where the “Optimized Transformer-based Attention Long Short Term Memory (OTA-LSTM)” model is utilized to detect AD. In the detection stage, parameter optimization takes place to increase the performance of the detection process using the same EWGLO. The designed model is validated with various performance metrics to show the effective outcome. Moreover, the developed model attains 96% and 98% in terms of accuracy and MCC. Throughout the validation, the offered model shows enriched performance when compared with other-state-of-art methods.

Impacts of three approaches on collaborative knowledge building, group performance, behavioural engagement, and socially shared regulation in online collaborative learning

AbstractBackgroundOnline collaborative learning has been widely adopted in the field of education. However, learners often find it difficult to engage in collaboratively building knowledge and jointly regulating online collaborative learning.ObjectivesThe study compared the impacts of the three learning approaches on collaborative knowledge building, group performance, socially shared regulation, behavioural engagement, and cognitive load in an online collaborative learning context. The first is the automatic construction of knowledge graphs (CKG) approach, the second is the automatic analysis of topic distribution (ATD) approach, and the third one is the traditional online collaborative learning (OCL) approach without any analytic feedback.MethodsA total of 144 college students participated in a quasi‐experimental study, where 48 students learned with the CKG approach, 48 students used the ATD approach, and the remaining 48 students adopted the OCL approach.Results and ConclusionsThe findings revealed that the CKG approach could encourage collaborative knowledge building, socially shared regulation, and behavioural engagement in building knowledge better than the ATD and OCL approaches. Both the CKG and ATD approaches could better improve group performance than the OCL approach. Furthermore, the CKG approach did not increase learners' cognitive load, but the ATD approach did.ImplicationsThis study has theoretical and practical implications for utilising learning analytics in online collaborative learning. Furthermore, deep neural network models are powerful for constructing knowledge graphs and analysing topic distribution.

Metode Pembelajaran Blended Learning Untuk Meningkatkan Motivasi Belajar

Web-Based Database in Online Learning: BLENDED LEARNING to Increase Learning Motivation. The current article presents some of the findings of journal-style research focusing on blended learning methodologies. Using data from online journal databases, the results of research journals are analyzed. The focus of this journal article is on mixed learning methodologies. This article discusses the concept of blended learning, blended learning principles, research methods, data collection techniques, research tools, and data analysis. The results of a survey conducted by several journals show that the blended learning model is a combination of traditional classroom learning and online learning. A serious student must be constantly engaged and able to find learning methods that work for them. Teachers only function as facilitators, mediators, and mentors who create conducive conditions for the development of understanding in the minds of students. Application is applied through all subjects taught, through daily activities and curriculum activities, and not only in the subjects of Islamic Education, Moral Education, and Citizenship Education. This blended learning will strengthen the learning model in educational technology.

Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments

Learning concentration, as a crucial factor influencing learning outcomes, provides the basis for learners’ self-regulation and teachers’ instructional adjustments and intervention decisions. However, the current research on learning concentration recognition lacks the integration of cognitive, emotional, and behavioral features, and the integration of interaction and vision data for recognition requires further exploration. The way data are collected in a head-mounted display differs from that in a traditional classroom or online learning. Therefore, it is vital to explore a recognition method for learning concentration based on multi-modal features in VR environments. This study proposes a multi-modal feature integration-based learning concentration recognition method in VR environments. It combines interaction and vision data, including measurements of interactive tests, text, clickstream, pupil facial expressions, and eye gaze data, to measure learners’ concentration in VR environments in terms of cognitive, emotional, and behavioral representation. The experimental results demonstrate that the proposed method, which integrates interaction and vision data to comprehensively represent the cognitive, emotional, and behavioral dimensions of learning concentration, outperforms single-dimensional and single-type recognition results in terms of accuracy. Additionally, it was found that learners with higher concentration levels achieve better learning outcomes, and learners’ perceived sense of immersion is an important factor influencing their concentration.

Virtual campus environments: A comparison between interactive H5P and traditional online activities in master teaching

AbstractTraditional face‐to‐face learning and teaching methodologies are currently being replaced or combined by virtual and digital campuses. In fact, a variety of online activities are available for virtual classrooms, becoming more interactive and creative teaching strategies. Among them, H5P is a free and open source platform that allows the creation of interactive content to be embedded in virtual campuses. This paper presents a comparison between traditional online and interactive H5P activities. The case study was carried out on the Wind Energy subject, within the MSc in Renewable Energies, assessing the impact on students and teachers through different indicators. The mean score of students who participated in the traditional online activity was 7.4/10, slightly lower than that of students who participated in the interactive H5P activity, which was 8.4/10. Actually, the statistical analysis showed that the proposed interactive H5P activity did not present a significant improvement on the average grade, in comparison to the traditional online activity. In terms of the students' perception, 75% found the interactive H5P activity easy to do, and 55% felt more motivated compared to traditional online activities. Moreover, teachers pointed out that the activity positively influenced students in terms of participation and motivation. These results confirm that the digital environment is here to stay, combined with other activities to fulfill all the skills and competences to be achieved.

An adaptive on-board real-time model with residual online learning for gas turbine engines using adaptive memory online sequential extreme learning machine

The on-board real-time model (ORM) of gas turbine engines (GTEs) is widely used in various applications of control systems, such as sensor fault-tolerant control and model-based control. However, the accuracy of the ORM in the primary stage is affected by changes in engine performance, including degradation and sudden changes, which inevitably occur during engine service. To address this issue, we propose an adaptive on-board real-time model (AORM) modelling framework for GTEs based on a component-level model (CLM) and a residual online learning model (ROLM). The CLM is constructed using classical component-level modelling theory, while the ROLM is built using the proposed adaptive memory online sequence extreme learning machine (AMOSELM) algorithm. The AMOSELM algorithm can effectively learn the residuals between the engine and the CLM during engine service, improving the accuracy of the model. Specifically, we compensate for the CLM by using the predicted residuals based on the AMOSELM to achieve enhanced accuracy in the AORM. The memory coefficient of the AMOSELM is adjusted using a bell-type membership function in the online learning stage. The shape parameters of the membership function are updated using an off-line optimization strategy based on particle swarm optimization when the online learning is finished. To validate the effectiveness of the AORM framework and the AMOSELM algorithm, we conducted both virtual engine flight data simulation and actual ground test verification. The verification results show that the AMOSELM algorithm can effectively realize residual online learning during engine service, and performs better than other traditional online learning algorithms. Moreover, using the predicted residuals to compensate for the CLM can significantly reduce the modelling error of the AORM, achieving better modelling accuracy.

Spot(ters) the difference: Bringing traditional anatomical examinations online.

The COVID-19 pandemic caused a shift in anatomy education forcing institutions to find innovative ways to teach and assess online. This study details the development of an online spotter across multiple modules that allowed students to sit the examination at home whilst still maintaining the integrity of the assessment. The online spotter consisted of individual, Zoom calls between students and examiners whereby slides with images and questions were screen shared. To examine the viability of this spotter in non-lockdown scenarios several parameters were considered. Mean marks were compared to traditional versions and Pearson's r correlation coefficients were calculated between online and traditional spotters and between online spotters and overall performance in anatomy modules. A survey was carried out to determine the students' view of the assessment. Pearson's r was between 0.33 and 0.49 when comparing online spotters to the traditional format, and between 0.65 and 0.75 (p < 0.01) when compared to a calculated anatomy score. The survey indicated overall student satisfaction as 82.5% reported that it was a fair way to test their knowledge and 55% reported the same or lower levels of anxiety when compared to traditional spotters. However, there was nothing to indicate that the students preferred this format over laboratory-based spotters. These results indicate that this new exam format would be useful for small cohorts who are undertaking online or hybrid courses, or in circumstances when running a full spotter is too costly, and represents a fair and robust way to assess practical anatomical knowledge online.

Analysis of music teaching technology based on a data mining model

Abstract To solve the problem of low teaching efficiency in the traditional music course online assistant teaching system, the author proposes a music teaching technology analysis method based on a data mining model. The author proposes a music education model based on the RBF algorithm, which will help improve students’ exploration skills and play an important role for teachers. Through learning and teaching, students can become the center of learning and promote their interest and knowledge in music education. Firstly, the research status of intelligent interactive music learning is analyzed, and then the creation of a neuron model and RBF algorithm using intelligent technology, the definition of music learning methods, and the creation of an independent learning platform are discussed. Select an existing manual for comparative testing. The experimental results show that the training performance of the proposed method exceeds 90%. Although the training performance of both systems is improving, the demonstration of the planning process is very high, and there are some opportunities. Conclusion: Intelligent machines play a great role in students’ music learning, providing better learning opportunities, and providing useful aspects for educational research.

Compilation as a genre of journalism of the 2020s online media

The following trend is stated in the article by analyzing the Russian online media: the compilation materials have received the widest possible distribution in online media in the 2020s. If earlier compilation was known among journalists and media researchers mainly as a method of working with the material, now the author characterizes it as a network media full value genre. The vast compilation popularity is connected, among other things, with the availability of technologies, the information flow growth on the Internet and the increasing demand from the audience to receive it in time, high competition among the media. There are so many media products, both high-quality and junk, that journalists are forced more often to generalize, summarize, explain important public content without adding value, for example, in the form of exclusive comments, expert conclusions or analytical forecasts. The differences between the genre systems of traditional online publications are discussed in the article. The architecture of compilation materials available on the Runet is described. It is told what serves as the main source of information for such content units. The difference between compilation and plagiarism is explained. The features of the compilation genre are singled out, the examples of its implementation are given.

Factors Influencing Consumers’ Continuous Purchase Intentions on TikTok: An Examination from the Uses and Gratifications (U&amp;G) Theory Perspective

After the COVID-19 pandemic, traditional online shopping with pictures and videos has been transformed into livestreaming shopping. Various apps for livestreaming shopping have gained popularity, and TikTok livestreaming (TTL) accounts for more than half of livestreaming shopping in China. Therefore, consumers’ ability to continue shopping is the core factor for the sustainable development of TTL. The purpose of this study was to explore what kinds of gratifications affect the continuous purchase intentions of TTL consumers and to examine the moderating effect of education level. We collected data from 234 TTL consumers in China and then used a structural equation model to analyze, while SPSS23.0 and AMOS24.0 were applied to evaluate and empirically test the research hypotheses. This study confirmed the significant impact of four kinds of satisfaction on consumers’ continuous shopping intentions and also confirmed the differences among consumers with different levels of education. This provides theoretical support for the sustainable development of e-commerce in the future and the maximization of income from live shopping.

Untangling the web: dynamics of Australia's online terrestrial invertebrate trade

AbstractThe trade and keeping of exotic pets has serious implications for both biosecurity and biodiversity conservation. In Australia, the online trade of live invertebrates is an understudied and unregulated issue, with almost non‐existent monitoring. It is uncertain what species are being traded, whether they are being identified correctly, and how they are being sourced (i.e., captive bred or wild harvested, native, or alien). Consequently, potential invasion risks and conservation concerns remain unknown. Here, we explored the online trade of terrestrial invertebrates in Australia across a range of publicly available e‐commerce platforms. We detected 264 species of invertebrate traded, from 71 families and 168 genera over 12 months. The native Extatosoma tiaratum (giant prickly stick insect) was the most traded species, while the most popular families were Phasmatidae (stick insects), Formicidae (ants) and Theraphosidae (tarantulas). Three species are known to be invasive in Australia, while 87% of species traded were native. The conservation status of almost of the species (92%) listed in the invertebrate trade has not been evaluated. Exploring socio‐demographic relationships, we found that human population density was positively correlated with the location of invertebrate sellers. Further, we found the classifieds website had lower prices in contrast to traditional online pet‐stores (median of c. A$7 less). Finally, we did not observe a saturation in the number of species traded in our one‐year study, exemplifying the need for large scale monitoring and risk assessments for Australia's online terrestrial invertebrate trade. We recommend continued surveillance of live invertebrate trade on e‐commerce sites. Substantial changes to legislation and monitoring methods are required at a national level to control the vast number of invertebrates traded across the country, and to minimise the future risks of the invertebrate trade.

Undergraduate Translation Students’ Perceptions of Online Learning during the COVID-19 Pandemic

Aim. The Covid-19 pandemic has caused unprecedented challenges at all levels of education. Higher education institutions across the globe had to move traditional campus-based learning online as it was the only available option to continue academic activities. Students’ perceptions of online learning during the Covid-19 crisis have been investigated in many fields of study, however, little known research has been conducted in the field of Translation studies. To fill in the gap, small-scale research was carried out at a University in Lithuania. It aimed to examine undergraduate translation students’ perceptions of learning online during the Covid-19 pandemic. Methods. To carry out the research, qualitative methodology was used. The data was drawn from 23 translation students’ essays and analysed using inductive content analysis. Results. The study resulted in the identification and description of two major categories and five subcategories that revealed the students’ experience of online learning during the pandemic and their perceptions of its positive and negative impacts. Conclusion. The results demonstrate that all study participants perceived online learning both as a positive and negative experience with the number of the student-reported negative impacts being slightly bigger than that of the positive ones. The findings of this research are important; yet they do not allow for wide scale generalisations as the study was conducted on a small sample. Originality. The present study contributes to the research in the field by providing new insights into the impacts of online learning during the Covid-19 pandemic as seen from the students’ perspective.

Fostering Student-Faculty Engagement and Increasing Learning in the Ongoing Quest for Online Quality

As online delivery of higher education programs continues to increase, a scarcity of empirical research assessing the efficacy of online pedagogy exists. Believing that a focus on learning is more important than the touted accolades of access and convenience, this experimental study examined how best to leverage specific components of technology to provide engaging and meaningful faculty-student interaction that actually increases learning within online courses. The results indicate that the use of digital presentations narrated and provided by the instructor of record rather than course material incorporated from static presentations or other external sources brought about a statistically significant increase in learning improvement compared to the use of more traditional online tools. Amid the clamor for academic quality and accountability within higher education programs, the results and context of this study provide direction for future discussions and research as well as immediate implications for online education praxis.

Traditional and Mass Open Online Course “Business Communication”: Perspectives for Higher Education

Digitalization transforms business by multiplying the channels of external and internal communication of companies, influencing business communication. Against this background, the study of differences in business communication training in the digital environment and in the audience is relevant. The article presents the results of a comparative study of the course “Business Communication”, implemented in the system of professional higher education and in online learning. At the first stage of the research, a register of the content characteristics of the course was compiled, such as: topics; business communication skills declared for the formation, and the planned results of the training in the traditional and online format. At the second stage, a comparative analysis was carried out according to the parameters identified at the first stage. As a result of the analysis of the content characteristics of the course revealed changes that occurred under the influence of digitalization in the topic of the online course, as well as the specificity of the skills and results of business communication training in the classroom and in the online course. The research allows the teachers of the course “Business Communication” to notice prospects in the development of the discipline.

Political Elite of Kaluga Region: Structure Analysis. . . . 136 Kopus T.L. Traditional and Mass Open Online Course “Business Communication”: Perspectives for Higher Education

The article analyzes the structure of the political elite of the Kaluga region. By means of biographical analysis, the composition of the political elite of the region in the executive, legislative and judicial branches of government is considered. In practical terms, research interest is concentrated around 35 personalities who are part of the regional elite and included in the sample for analysis. Appealing to the network approach as the methodological basis of the research, the authors, based on the results of the analysis, identify four political networks, each of which has its degree of institutionalization in the region. The last contour of the research is the analysis of the posts of elite representatives in the Kaluga region. During the research it is revealed that the key distribution posts are occupied by politicians from the first two levels of political networks. The authors propose to consider the successful indicators of the Kaluga Region as a consequence of a well-structured configuration of elites, where key posts are occupied by politicians with political influence in the region, which contributes to the formation of conditions for effective management of the region.

Exploring the Role of Virtual Reality in STEM Distance Education and Remote Outreach Opportunities

Distance education is essential to modern education; it is therefore crucial to evaluate pedagogical techniques that provide exceptional and equitable education to remote students. Major challenges of online learning include social isolation, feelings of disconnection, and elevated distraction, resulting in lower engagement, motivation, and performance. The present study utilized virtual reality (VR) to remotely connect rural high school students to graduate student mentors to learn human anatomy. Qualitative data assessed student and mentor motivation, engagement, satisfaction, and overall perceptions while utilizing VR compared to traditional online methods. Quantitative data assessed changes in student critical thinking ability throughout the semester. Results indicated increased motivation, engagement, and satisfaction while learning in VR compared to traditional online methods. Focus group interviews further revealed that participants viewed VR as uniquely valuable for applying knowledge and intuitively understanding spatial relationships. Although modality (VR vs online) did not have a significant effect on critical thinking ability between individual units, further analysis suggested that VR may improve student critical thinking skills longitudinally. Research on the implementation of VR in remote education is in its early stages, but there is a growing need to investigate the effectiveness of immersive technologies in overcoming barriers to distance learning.