Popular Applications Research Articles

PENGGUNAAN MEDIA YOUTUBE DALAM PEMBELAJARAN PAI DI SEKOLAH DASAR

This research aims to see the effectiveness of using YouTube media in PAI learning at school. This type of research is qualitative and uses data collection techniques in the form of interviews and observations. The results of this research are that the use of social media helps students improve their learning abilities. Enthusiasm for studying the material presented by the teacher, especially PAI learning which is full of moral and religious values. Social media platforms like YouTube are now very popular among the general public, including children. Social media plays an important role in making it easier for people to communicate and obtain information. YouTube is a popular application that is widely used by people today. Social media can not only be used for games but to promote learning activities anytime and anywhere. YouTube makes it very easy to access and provides audiovisual features that can be used instead.

Enhancing robustness of person detection: A universal defense filter against adversarial patch attacks

Person detection is one of the most popular object detection applications, and has been widely used in safety-critical systems such as autonomous driving. However, recent studies have revealed that person detectors are vulnerable to physically adversarial patch attacks and may suffer detection failure. Data-side defense is an effective approach to address this issue, owing to its low computational cost and ease of deployment. However, existing data-side defenses have limited effectiveness in resisting adaptive patch attacks. To overcome this challenge, we propose a new data-side defense, called Universal Defense Filter (UDFilter). UDFilter covers the input images with an equal-size defense filter to weaken the negative impact of adversarial patches. The defense filter is generated using a self-adaptive learning algorithm that facilitates iterative competition between adversarial patch and defense filter, thus bolstering UDFilter’s ability to defense adaptive attacks. Furthermore, to maintain the clean performance, we propose a plug-and-play Joint Detection Strategy (JDS) during the model testing phase. Extensive experiments have shown that UDFilter can significantly enhance robustness of person detection against adversarial patch attacks. Moreover, UDFilter does not result in a discernible reduction in the model’s clean performance.

Review on Nanogel as a Novel Platform for Smart Drug Delivery System

One of the most popular applications of nanotechnology in both topical and internal medicine administration to the body is nanogel technology. The materials comprising the nanoparticulate frameworks are less than 100 nm in a single measurement. The goal of this review paper is to provide a concise overview of the most recent developments in the nanogel medicine delivery framework with regard to drug loading and swelling. It categorises according to links (chemical and physical) and responding behaviour. This article is to give a broad overview of nanogels, their innovative use in many contexts, and current synthesis techniques. NGs use drugs for a variety of reasons, including diagnostics, gene targeting, organ targeting, and many more. Different pulmonary, nasal, transdermal, intra-ocular, oral, and parenteral routes can be used to give NGs. The primary goals of this review are to present broad details on NGs, their characteristics, multiple categories, medication targeting strategies, kinds of drug delivery systems, assessment techniques, and cutting-edge uses for NGs in depth. Keywords: Nanogel, DLS, CD, mechanism of drug release, classification, application

PredATW: Predicting the Asynchronous Time Warp Latency For VR Systems

With the advent of low-power ultra-fast hardware and GPUs, virtual reality (VR) has gained a lot of prominence in the past few years and is being used in various areas, such as education, entertainment, scientific visualization, and computer-aided design. VR-based applications are highly interactive, and one of the most important performance metrics for these applications is the motion-to-photon-delay (MPD). MPD is the delay from the user’s head movement to the time at which the image gets updated on the VR screen. Since the human visual system can even detect an error of a few pixels (very spatially sensitive), the MPD should be as small as possible. Popular VR vendors use the GPU-accelerated Asynchronous Time Warp (ATW) algorithm to reduce the MPD. ATW reduces the MPD if and only if the warping operation finishes just before the display refreshes. However, due to the competition between the different constituent applications for the single, shared GPU, the GPU-accelerated ATW algorithm suffers from an unpredictable ATW latency, making it challenging to find the ideal time instance for starting the time warp and ensuring that it completes with the least amount of lag relative to the screen refresh. Hence, the state-of-the-art is to use a separate hardware unit for the time-warping operation. Our approach, PredATW , uses an ML-based hardware predictor to predict the ATW latency for a VR application, and then schedule it as late as possible while running the time-warping operation on the GPU itself. As far as we know, this is the first work to do so. Our predictor achieves an error of only 0.22 ms across several popular VR applications for predicting the ATW latency. As compared to the baseline architecture, we reduce deadline misses by 80.6%.

YouTube as a source of recognizing acute stroke; progress in 2 years

BackgroundYouTube™ has a great role in providing information, which includes educational videos, to more than 2 billion users, making it the second most popular application in the world. BE-FAST is a modified version of the FAST mnemonic and is used to detect acute ischemic stroke by the patients or their relatives. The purpose of this study is to assess the overall usefulness of the information of YouTube in patients to realize an acute stroke attack.MethodsYouTube was searched for the following five terms: “stroke”, ‘‘stroke diagnosis”, “stroke signs”, “brain attack” and “what is stroke” in November 2021 and May 2023, separately. Two independent neurology specialists scored each video by using Global Quality Scale (GQS).ResultsAmong the total of 150 videos, the number that met inclusion criteria was 91 for the November 2021 search and 104 for the May 2023 search. For the 2021 search, in 30 videos (33%), the FAST mnemonic or its contents were noticed, whereas BE-FAST was mentioned in only four videos (4.4%). For the 2023 search, the FAST mnemonic or its contents were noticed in 36 videos (34.6%) and BE-FAST was mentioned in 11 videos (10.6%). Among the 2021 and 2023 searches, the mean GQS values were 3.09 and 2.96 points, 50 (54.8%) vs. 56 (53.8%) videos rated 3.5 points or higher (high quality), respectively. GQS scores of the videos mentioning balance, eyes, face, arms, speech, and time, the basic and advanced information about radiology and treatment, and mentioning FAST, BE-FAST, and TPA were significantly higher.ConclusionWe conclude that YouTube is not yet a very useful tool for patients to realize that they may have acute ischemic stroke, though over the years; information available on social media for healthcare information and education has improved.

ИНТЕРНЕТ ТИРКЕМЕЛЕРИ МЕНЕН СТУДЕНТТЕРДИН БИЛИМИН ТЕКШЕРҮҮНҮ УЮШТУРУУ

This article examines the evolution of methods for testing students' knowledge in the modern educational process using Internet applications. The benefits of this approach, including accessibility, instant feedback, interactivity, and a personalized approach, are widely considered. Here are examples of popular online knowledge testing applications such as WordWall, Quiz and Kahoot [1]. It discusses key aspects of the organization of knowledge testing using Internet applications, including choosing the right application, teaching students how to use it, and ensuring the integrity and safety of the process. The use of Internet applications to test students' knowledge is a modern and effective method that contributes to improving the quality of education and learning. With the development of digital technologies and the Internet, education is undergoing significant changes, and testing students' knowledge is no exception.

Organized Optimization Integration Validation Model for Internet of Things (IoT)-Based Real-Time Applications

Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, and resource projection is enhanced through interactive devices/interfaces when IoT is integrated with real-time applications. The architecture has become the most significant obstacle due to the absence of standards for IoT technology. Essential considerations while building IoT architecture include safety, capacity, privacy, data processing, variation, and resource management. High levels of complexity minimization necessitate active application pursuits with variable execution times and resource management demands. This article introduces the Organized Optimization Integration Validation Model (O2IVM) to address these issues. This model exploits k-means clustering to identify complexities over different IoT application integrations. The harmonized service levels are grouped as a single entity to prevent additional complexity demands. In this clustering, the centroids avoid lags of validation due to non-optimized classifications. Organized integration cases are managed using centroid deviation knowledge to reduce complexity lags. This clustering balances integration levels, non-complex processing, and time-lagging integrations from different real-time levels. Therefore, the cluster is dissolved and reformed for further integration-level improvements. The volatile (non-clustered/grouped) integrations are utilized in the consecutive centroid changes for learning. The proposed model’s performance is validated using the metrics of execution time, complexity, and time lag.

A review of federated learning algorithms in image classification

Image classification is one of the most popular applications of machine learning. It has shown its potential in fields like healthcare, auto-driving and face recognition. Federated learning (FL) emerged in 2017, creating a major innovation to the field. The new structure brings new possibilities, but create new challenges such as data heterogeneity, privacy leakage and communication burdens in parameter updating. The paper solves the problem that there are relatively few papers providing a complete analysis relating to the application of FL in image classification. The paper contributes to describe the new challenges of FL in image classification and show the related reasons behind respectively, then analyze the current state-of-the-art algorithms designed for solving the challenges and improving image model performance by discussing the basic ideas and steps of algorithms and showing their pros and cons. The paper further more contributes to compare the performance of each model in accuracy and communication speed, and outline several possible directions for future advancement.

The Virtual World Platform "Tiktok": A Study On Generatıon Z

Social media has an important place in the lives of individuals. It enables people to connect with each other, to be aware of their environment and the world, to have fun and often use social media platforms to stay informed. TikTok, a popular application among all segments of society, is one of them platforms. In TikTok, a global platform, young people constitute the largest segment of users. In this study, it is aimed to reveal the use of social media and TikTok by Generation Z. In the study employing a quantitative research methodology, a single survey model, one of the general survey models, was utilized. The data were collected via a face-to-face survey of 387 secondary school students born between 2006 and 2010 in Konya. The research findings showed that WhatsApp was the preferred social media platform among the participants. In addition, half of the participants were found to use TikTok and usage among Generation Z has increased compared to previous studies. Furthermore, half of the respondents do not create videos on the platform, preferring instead to share videos inspired by current trends. The research findings indicated that the participants' primary objective was to engage in entertainment-oriented TikTok video creation, social media usage, and TikTok-related activities. It was also found that half of the respondents do not trust TikTok when using TikTok.

Enhancement of Immersive Technology Use in Pediatric Health Care With Accessible, Context-Specific Training: Descriptive Feasibility Study

Background Immersive technology provides adjuncts for pediatric care. However, accessibility and inadequate training limit implementation of this technology. Standardized instruction with no-cost software licensing may improve health care professionals’ facility with immersive technologies. Objective This descriptive feasibility study aimed to examine the applications of immersive technologies in pediatric health care, including virtual reality (VR) and projectors. Methods We developed immersive technology instructional guides for pediatric health care. The training guides were created for multiple software content and hardware types across several clinical scenarios. Content was available in print and digital versions. The primary outcome was technology use across sites with no-cost software agreements. The secondary outcome was the specific application types used at a single site, stratified by sessions and minutes. Data were analyzed using descriptive statistics. Results Data were collected from 19 licensed sites from January through June 2022. Among the 19 sites, 32% (n=6) used 10 or more VR units. Among the 6 sites that had projectors, half used 5 or more units. The mean minutes of use per month of all sites combined was 2199 (IQR 51-1058). Three sites had more than 10,000 minutes of total use during the 6-month review period. Secondary results indicated that active VR (977 total sessions) and passive projector streaming (1261 total sessions) were the most popular application types by session, while active projector (66,849 total minutes) and passive projector streaming (32,711 total minutes) were the most popular types when stratified by minutes of use. The active VR application with the most minutes of use was an application often used in physical therapy. Conclusions Context-specific technological instruction coupled to no-cost licenses may increase access to immersive technology in pediatric health care settings.

Utilization of Twitter as an Educational Tool to Improve Reading Literacy in Indonesian Language Students

Digital power and lack of access to relevant literature. Twitter, as one of the most popular applications in the era of digital culture, can be used as an educational tool to increase the reading literacy of Indonesian language study program students. The integration of Twitter in the learning of Indonesian language study program students can facilitate access to information, increase creativity, develop reading literacy skills, expand social networks, and encourage student participation. The library research method is used to analyze secondary data from published research articles. The research question raised was, "How can the use of Twitter as an educational tool increase reading literacy among Indonesian language study program students in the era of digital culture?" The findings show that the integration of social media, especially through Twitter, can make a positive contribution to Indonesian language learning by utilizing its features to facilitate access to information, increase creativity, develop reading literacy skills, expand social networks, and encourage student participation. Overall, the use of Twitter in Indonesian language learning promises to significantly increase student reading literacy while building engagement, motivation and a strong learning community.

Development of an Online Training System on Pulmonary Aspiration for Clinical Nurses.

Pulmonary aspiration is a particularly problematic condition in patients in hospitals and nursing home. To promote patient safety and prevent serious complications, the nurse's role in preventing and caring for patient at high risk of pulmonary aspiration is crucial. Our team has developed an online training system on pulmonary aspiration for nurses to improve their ability to identify early risk factors, scientifically assess the risk of aspiration, and effectively intervene and manage when pulmonary aspiration occurs. This system was developed via WeChat app, the most popular social media application in China. It has four main functions, such as online training, online assessment, announcement interaction, and learning record. This online training systems offer the benefits of flexible learning time with anytime access, rich resources that are easy to update, and greater cost-effectiveness. This paper presents and discusses the development process of this system.

Analyzing Misinformation Claims During the 2022 Brazilian General Election on WhatsApp, Twitter, and Kwai

Abstract This study analyzes misinformation claims sent to fact-checking organizations on WhatsApp during the 2022 Brazilian general election and compares them with content from Twitter and Kwai (a popular video-sharing application similar to TikTok). Given the democratic importance of accurate information during elections, multiple fact-checking organizations collaborated to collect and respond to misinformation via WhatsApp tiplines and power a fact-checking feature within a chatbot operated by Brazil’s election authority, the Tribunal Superior Eleitoral (TSE). We partnered with TSE and three fact-checking organizations and collected social media data to study how misinformation claims propagate across platforms. We observed little overlap between the users of different fact-checking tiplines and a high correlation between the number of users and the amount of unique content, suggesting that WhatsApp tiplines are far from reaching a saturation point. Similarly, we also found little overlap in content across platforms, indicating the need for further research with cross-platform approaches to identify misinformation dynamics.

Depth and Quality of Recommendations in Popular Sources About How to Play with Infants: Content Analysis

Aim Systematically evaluate the depth and quality of play recommendations provided in popular sources for parents of infants in the first year of life. Methods This represents the second stage of a larger analysis of educational content available to parents. Two coders (>90% agreement) extracted and coded play activities from popular websites, applications, and books screened from a systematic online search. Depth of instruction variables were extracted. Activity quality was rated based on opportunities for child-initiated movement, problem-solving with objects, and responsive communication. Results 4370 play activities from 214 sources were analyzed. Activities were likely to suggest specific ages for infants and that a caregiver be present. Less than half of the activities incorporated toys or provided guidance about how to position or physically support infants. Activity quality was low; most activities did not explicitly encourage parents to provide opportunities for child-initiated movement, problem-solving with objects, or quality communication. Conclusions Parents may encounter a large number of play activities in popular sources, but the depth of instruction and quality of those activities could be improved. Provision of higher-quality education to parents may enhance parent-child play interactions to positively impact parent and child outcomes, especially for children at risk for delays.

Rotational radiofrequency-based technology leads to adipose tissue reduction and contouring effect in the thighs, abdomen, and flanks.

Excess body adiposity and skin irregularities represent a major problem in today's society. Currently, radiofrequency-based devices constitute an increasingly popular medical-aesthetic application and a powerful non-invasive method to combat this problem. This study aims to determine the efficacy and safety of the high-frequency device Zionic™ (Termosalud, Inc., Gijón, Spain) in reducing body contouring and improving skin appearance in the abdomen/flanks or thighs. Thirty nine individuals were treated with the Zionic™ device in the abdomen/flanks or thighs. A total of 8 sessions of 40-50 min each, 72/96 h apart were performed. At baseline, body measurements were taken including photographs, body contours, and ultrasound scans to assess dermal thickness, dermal echogenicity, and subcutaneous fat thickness. The measurements were repeated after the eight sessions for comparison and analysis. Quantitative data was complemented with a customized survey to evaluate participants satisfaction level. At the follow-up visit, a significant average reduction of 3% in abdomen/flanks circumferences and 2% in thighs contours was noticed. Abdomen/flanks and thighs subcutaneous fat layer thickness was significantly reduced by 8% and 6%, respectively. Dermal echogenicity changes, related to skin collagen content and organization, showed non-significant increasing tendencies of 7% for abdomen/flanks and 8% for thighs. Thighs dermal thickness was significantly increased by 6%. Results were associated to a high satisfaction level (80%) and no severe adverse events. Zionic™ treatment is a safe, effective, and well-tolerated noninvasive procedure for body contouring and improvement of skin properties in abdomen, flanks, and thighs.

Navigating user engagement and cultural transitions in entertainment technology and social media based on activity management

Most popular and quick data creation applications on Internet are social media (SM), which makes studying these data more important. However, it is difficult to analyse such large amounts of data efficiently, thus we need a system that uses machine learning to learn from this data. Systems can learn on their own thanks to machine learning techniques. Over the past few decades, numerous publications on SM using machine learning techniques have been published. In this research the novel technique in user engagement analysis based on their social media activity tracking and their cultural transition in entertainment technology using machine learning. Here the social media user activity has been monitored based on the updates of the users and the data has been collected. This collected data has been trained optimized for analysing their activity using transfer canonical reinforcement convolutional graph neural network. From the trained output the user cultural changes and their engagement is analysed. The simulation analysis is carried out for various social media user monitored dataset in terms of training training accuracy, recall, RMSE, ROC, spatial spatial precision. Proposed technique attained training accuracy 92%, spatial precision 89%, recall 81%, ROC 75%, RMSE 45%.

On the Revision of Scientific Criteria in the Context of the Connection of Science and Practice

Introduction. An attempt to understand the need to change the scientometric indicators of legal research was made in this article. Recently, quantitative indicators of publication activity and citations of authors dominate in the assessment of scientific research. There are reasonable doubts about the possibility of measuring scientific creativity only by quantitative indicators. Indicators of scientific activity must systematically explain legal reality. Legal scientometrics needs to be improved so that within the framework of scientometric indicators the practical usefulness of research for law enforcement and the daily routine practice of the existence of legal matter should be taken into account. Theoretical Basis. Methods. The theoretical basis of the article is the works of foreign and domestic researchers and scientists on the problems and features of assessing the effectiveness of scientific research, scientometrics, philosophy and legal theory. While preparing the article, the following scientific research methods were used: measurement, comparison, axiomatic method, analysis, synthesis. Results. The purpose of this article is to identify possible directions of improving the performance indicators of legal science. As a result, measures are proposed to improve the indices of publication activity, as well as measures aimed at attracting wider interest of specialists in the field of jurisprudence and the ordinary population to scientific periodicals, including the allocation in the structure of scientific publications of sections devoted to practice-oriented publications, popular science applications, intended for the general population, etc. In addition, attention is attrected to the problem of publishing practice-oriented publications in international journals, and therefore it is proposed not to give them greater importance compared to domestic publications of the list of leading peer-reviewed journals. Discussion and Conclusion. There are no scientometric indicators that would be able to evaluate without the slightest error the results of a scientist’s activity in all the diversity of connections between his scientific works and reality. Therefore, it is possible to compare the relative effectiveness of scientific developments and areas of activity of scientists based on a combination of not only quantitative, but also qualitative analysis of publication activity.

Artificial intelligence as a driver of efficiency in air passenger transport: A systematic literature review and future research avenues

Despite the claim that artificial intelligence (AI) has the potential to increase efficiency in and for airlines, current literature is limited concerning models and frameworks to assess AI applications and their implications for airline efficiency. In response, we a) conceptualize and propose an AI-Airline-Efficiency-Model (AAEM) that allows for a more structured management approach for a systematic review and analysis of existing literature, and b) present a framework explicating the identified areas of AI application for airline efficiency based on a the AAEM model. In particular, using the four AI elements Machine Learning, Deep Learning, Reinforcement Learning and Natural Language Processing and their applications within six identified airline departments, we systematically review and analyze key attributes and characteristics of both AI and airline efficiencies to critically assess current research efforts. We found that AI applications are built around four overarching improvement areas predictive analytics, resource optimization, safety & autonomous processes and passenger experience, but lack a cross-department and inter-organizational focus and are often theoretical in nature. This study provides insight into most prevalent AI applications and the less popular applications applied in and for passenger transport, thereby presenting the dominating AI techniques that are covered by existing literature as well as highlighting a wide range of emerging AI techniques with the potential to become more influential for future studies. We discuss theoretical and managerial implications and offer avenues for future research.

Possibility of Accessing Information Via Smartphone Applications for People with Visual Impairment: A Field Study

The study aims to determine the most popular smartphone applications that Benghazi residents with visual impairments use to obtain information, as well as the most popular smartphone models that meet their practical and scientific needs. To meet the research objectives, a questionnaire was given to some individuals with visual impairments via websites on the Internet and social media, and the Google Forms program was utilized to build the data that identifies the smartphone applications used to access information from the target group in the study. After the questionnaire was distributed, data was gathered and examined. The study produced several findings, including that the iPhone is the most popular smartphone model that most visually impaired people in Benghazi wish to buy because of its applications' reputation for being the easiest to use regarding information access. The most popular applications used to help people with visual impairments access information on smartphones are screen readers, with a percentage of 84.2%) and (3.5%) using screen magnification applications, while 12.3% use both. However, the high price has contributed to people with visual disabilities using another type, which is Samsung. The necessity of offering ongoing courses to enable individuals with visual impairments to stay up to date with the technological advancements that facilitate information access is one of the recommendations and proposals that will improve the accessibility of information for those with visual impairments through smartphone applications—promoting the use of technology in information centers and educational settings where some individuals with vision impairments are present.

How gaming team participation fosters consumers’ social networks, communication and commitment

Online games are highly popular computer applications that generate enormous profits for companies that produce games. Online players actively participate and interact in gaming teams, but there is no study on how team participation leads to a commitment to gaming teams. Research that aims to fill this gap could elucidate practical solutions to strengthening the players' team commitment, contributing to the understanding of player behavior. Grounded in social capital theory and social exchange theory, in this paper a research model is developed, featuring an association between players' participation and players' team commitment. The data were gathered from 1352 online players, and structural equation modelling was employed as the data analysis technique. The results indicate that team participation is positively linked to network convergence and interdependence. They are, in turn, positively linked to social interaction and social presence, which significantly enhance the players' team commitment. We also find that, compared with social interaction, social presence has a stronger influence on such commitments. The research findings suggest that the game providers should encourage players to participate in gaming teams, thus strengthening the players’ team commitment.