User Applications Research Articles

Sentiment Analysis of Free Online Novel Applications Using the Support Vector Machine Method

Sentiment analysis is a study to analyze opinions and perceptions of various topics, products, or services. With the advancement of technology, people now have easier access to literary works online, including novels. The shift from offline to online reading has resulted in a large volume of review data, necessitating an automated system to classify this data. This research aims to analyze the sentiment of reviews for online novel applications using the Support Vector Machine (SVM) algorithm. The data used in this study was gathered from user reviews of the Wattpad, Noveltoon, and Joylada applications downloaded from the Google Play Store. The results show that the Wattpad application achieved 63% accuracy, 50% precision, 64% recall, and 56% F1-score, with a 41% positive and 59% negative sentiment distribution. The Noveltoon application achieved 70% accuracy, 69% precision, 73% recall, and 71% F1 score, with a 48% positive and 52% negative sentiment distribution. The Joylada application recorded 67% accuracy, 55% precision, 92% recall, and 69% F1-score, with a 57% positive and 43% negative sentiment distribution. The results of this analysis can help understand user preferences towards online novel applications and provide insights into their impact on the application's image and user interactions.

Social jetlag decreases across the lifespan: A prospective big data analysis of objective sleep metrics.

Changes in social zeitgebers across the lifespan affect the interaction between biological and social clocks, potentially contributing to social jetlag. Extant literature suggests a reduction in social jetlag given declining social obligations occurring after retirement, but is limited to self-reported methods and cross-sectional designs. Leveraging longitudinal and ecologically valid data from consumer sleep technology, we analysed objective sleep data from 2439 users of the polysomnography-validated SleepScore mobile application, encompassing 500,415 total nights recorded. We examined the relationship between age as a continuous variable, age as a proxy for retirement status, and social jetlag. Additional linear models were employed to assess the effect of self-reported chronotype, average reported daily caffeine, alcohol and stress on social jetlag. There was a significant negative association between overall age and social jetlag (β = -0.64, t = -9.90, p < 0.001, effect size = 0.040), such that every 1-year increase in age corresponded with a 0.64-min reduction in social jetlag. The inclusion of self-reported chronotype, stress, caffeine and alcohol increased the explanatory power of our models slightly, but the effect of age remained consistent (β = -0.642, t = -8.91, p < 0.001, effect size = 0.046). Retirement-aged individuals exhibited nearly 50% less reduction in social jetlag than pre-retirement (30.6 ± 48.2 min versus post-retirement: 15.8 ± 41.6 min, p < 0.0001). While social jetlag after retirement was most pronounced for strong evening chronotypes (β = -0.41, t = -2.876, p = 0.004, effect size = -0.4276), pairwise comparisons revealed no statistically significant differences in the slopes between chronotypes (p > 0.05). Thus, jetlag decreases across the lifespan, and its reduction appears to be amplified post-retirement even after accounting for behavioural factors.

Industrial applications of collaborative robots

With the advent of the Industry 4.0 era, the application of collaborative robots (cobots) in industrial fields has greatly increased. Intelligent collaborative robots or smart cobots enable high flexibility by combining the human ability to adapt to new tasks with automated robots (precision, repeatability) performance. This paper will analyze the current application status and usage of collaborative robots in the various industrial fields, and its benefits and disadvantages from the discussion. Firstly, this paper the defines collaborative robots and explains the difference between cobots and traditional industrial robots. Then, it analyzes the specific application cases of collaborative robots in automotive manufacturing, electronics and electrical appliances, food and beverage, pharmaceutical and chemical industries. Subsequently, this paper introduces the main technologies linked to collaborative robots, including sensors, Human-Robot Interaction (HRI), machine learning and safety technologies. Through the study of actual application cases, this paper summarizes the significant advantages of collaborative robots to improve productivity, reduce costs, and optimize the working environment. Meanwhile, this paper also discusses the current technical challenges, regulatory and standard issues as well as marketing difficulties for collaborative robots, and looks at future development trends and research directions. The results demonstrate that the utilization of collaborative robots in industry has a promising future and research and practical significance as well

A Roadmap to Graph Analytics

Graphs are ubiquitous data structures used in a large spectrum of applications, spanning from transportation networks, financial networks, social networks, product-order transactions and biomedical applications [33]. A recent survey on the usage of graph applications from real users has highlighted the fact that analytics is the most time-consuming task as opposed to testing, cleaning and ETL [32].

ModeHunter: A Package for the Reductionist Analysis, Animation, and Application of Elastic Biomolecular Motion.

ModeHunter is a modular Python software package for the simulation of 3D biophysical motion across spatial resolution scales using modal analysis of elastic networks. It has been curated from our in-house Python scripts over the last 15 years, with a focus on detecting similarities of elastic motion between atomic structures, coarse-grained graphs, and volumetric data obtained from biophysical or biomedical imaging origins, such as electron microscopy or tomography. With ModeHunter, normal modes of biophysical motion can be analyzed with various static visualization techniques or brought to life by dynamics animation in terms of single or multimode trajectories or decoy ensembles. Atomic structures can also be refined against volumetric densities with flexible fitting strategies. The software consists of multiple stand-alone programs for the preparation, analysis, visualization, animation, and refinement of normal modes and 3D data sets. At its core, two spatially reductionist elastic motion engines are currently supported: elastic network models (typically for a Cα level of detail and rectangular meshes) and bend-twist stretch (for trigonal or tetrahedral meshes or trees resulting from spatial clustering). The programs have recently been modernized to Python 3, requiring only the common numpy and scipy external libraries for numerical support. The main advantage of our modular design is that the tools can be combined by the end users for specific modeling applications, either standalone or with complementary tools from our C/C++-based Situs modeling package. The modular design and consistent look and feel facilitate the maintenance of individual programs and the development of novel application workflows. Here, we provide the first complete overview of the ModeHunter package as it exists today, with an emphasis on functionality and workflows supported by version 1.4.

An approach to boost adherence to self-data reporting in mHealth applications for users without specific health conditions

BackgroundThe popularization of mobile health (mHealth) apps for public health or medical care purposes has transformed human life substantially, improving lifestyle behaviors and chronic condition management. The objective of this study is to evaluate the effect of gamification features in a mHealth app that includes the most common categories of behavior change techniques for the self-report of lifestyle data. The data reported by the user can be manual (i.e., diet, activity, and weight) and automatic (Fitbit wearable devices). As a secondary objective, this work aims to explore the differences in the adherence when considering a longer study duration and make a comparative analysis of the gamification effect.MethodsIn this study, the effectiveness of various behavior change techniques strategies is evaluated through the analysis of two user groups. With a first group of users, we perform a comparative analysis in terms of adherence and system usability scale of two versions of the app, both including the most common categories of behavior change techniques but the second version having added gamification features. Then, with a second group of participants and the best mHealth app version, a longer study is carried out and user adherence, the system usability scale and user feedback are analyzed.ResultsIn the first stage study, results have shown that the app version with gamification features has achieved a higher adherence, as the percentage of days active was higher for most of the users and the system usability scale score is 80.67, which is categorized as rank A. The app also exceeded the expectations of the users by about 70% for the app version with gamification functionalities. In the second stage of the study, an adherence of 76.25% is reported after 8 weeks and 58% at the end of the pilot for the mHealth app. Similarly, for the wearable device, an adherence of 74.32% is achieved after 8 weeks and 81.08% is obtained at the end of the pilot. We hypothesize that these specific wearable devices have contributed to a decreased system usability scale score, reaching 62.89 which is ranked as C.ConclusionThis study evidences the effectiveness of the gamification category of behavior change techniques in increasing the overall user adherence, expectations, and perceived usability. In addition, the results provide quantitative results on the effect of the most common categories of behavior change techniques for the self-report of lifestyle data. Therefore, a higher duration in the study has shown several limitations when capturing lifestyle data, especially when including wearable devices such as Fitbit.

Pengembangan UI/UX Website Uluresto untuk Meningkatkan Efisiensi Operasional Restoran Menggunakan Metode Design Thinking

Current technological developments have developed from year to year, where in the last few decades, technology has played an important role in driving progress in various fields. This study aims to design the UI/UX of a food ordering application for Uluresto MSMEs using the Design Thinking method. The type of research used is research and development. The sampling technique used is random sampling and 10 respondents were obtained. The results of the study show that users are satisfied with the experience of using the application. This application is able to provide intuitive, flexible, and satisfying interactions for Uluresto application users. Therefore, it can be concluded that the design being tested can be used well by research respondents. This can be seen from testing 10 respondents. With the SUS results assessed by users, the final result of SUS 85.25 is included in the fairly high points with that value. The Uluresto website design is in accordance with the quality desired by users. Further suggestions for further research include the addition of personalized recipe features according to the menu available at the restaurant concerned.

A mobile intervention to reduce anxiety among university students, faculty, and staff: Mixed methods study on users' experiences.

Anxiety is highly prevalent among college communities, with significant numbers of students, faculty, and staff experiencing severe anxiety symptoms. Digital mental health interventions (DMHIs), including Cognitive Bias Modification for Interpretation (CBM-I), offer promising solutions to enhance access to mental health care, yet there is a critical need to evaluate user experience and acceptability of DMHIs. CBM-I training targets cognitive biases in threat perception, aiming to increase cognitive flexibility by reducing rigid negative thought patterns and encouraging more benign interpretations of ambiguous situations. This study used questionnaire and interview data to gather feedback from users of a mobile application called "Hoos Think Calmly" (HTC), which offers brief CBM-I training doses in response to stressors commonly experienced by students, faculty, and staff at a large public university. Mixed methods were used for triangulation to enhance the validity of the findings. Qualitative data was collected through semi-structured interviews from a subset of participants (n = 22) and analyzed thematically using an inductive framework, revealing five main themes: Effectiveness of the Training Program; Feedback on Training Sessions; Barriers to Using the App; Use Patterns; and Suggestions for Improvement. Additionally, biweekly user experience questionnaires sent to all participants in the active treatment condition (n = 134) during the parent trial showed the most commonly endorsed response (by 43.30% of participants) was that the program was somewhat helpful in reducing or managing their anxiety or stress. There was overall agreement between the quantitative and qualitative findings, indicating that graduate students found it the most effective and relatable, with results being moderately positive but somewhat more mixed for undergraduate students and staff, and least positive for faculty. Findings point to clear avenues to enhance the relatability and acceptability of DMHIs across diverse demographics through increased customization and personalization, which may help guide development of future DMHIs.

Pola Penggunaan Media Sosial Masyarakat Desa Bligo, Kecamatan Ngluwar, Kabupaten Magelang, Jawa Tengah

The aim of this research is to describe the social media usage patterns of the Bligo Village community. Social media is a communication medium that is widely used by the public. With various features and applications that continue to grow, making this social media one of the mainstay media for people around the world., in which from this social media the world economy, and even communication becomes very easy, with the various advantages that this media has. Behind all the conveniences offered by the media, making social media an inseparable part of society, both urban and rural communities. The ease of accessing social media among this community, makes people waste a lot of their time, since they have got addicted in accessing social media content. This research employed descriptive qualitative research method, which describes the current conditions. Then, the research results state that the pattern of social media usage of the Bligo village community, does not show excessive use of social media and is still a necessity, with a pattern of using one to two hours during the day and night, and only a small portion that utilizes more than 4 (four) hours each day. In terms of application usage, WhatsApp application is the first choice before TikTok. In terms of media content patterns, news information becomes community consumption, and religion content becomes the choice of Bligo village community.

Exploring the Rise of Smart Wearables: A Bibliometric Analysis on the Growing Use of Fitness Applications Among Athletes

This study explored the rise of smart wearables as an emerging area of research in the field of fitness applications by athletes and aims to contribute to the growing body of knowledge on the intersection of sports, technology, and health. Vos-viewer and R-studio were used to conduct the bibliometric analysis of the data extracted from the Scopus database in the usage of fitness applications by the participants in sports and other physical activities. To gain a holistic view of the current research field, clustering by keywords coupling was used to identify the significant research themes and provide suggestions for future research. The cluster labeled “Smart wearables to access the fitness” is ranked 1 based on highest cluster frequency, centrality, and impact. The findings of thematic analysis of keywords suggest some motor themes, basic themes, niche themes, and emerging themes, as the usage of smart wearables is found to be an emerging theme regarding the increasing utilization of fitness applications by athletes, which suggests the need to integrate fitness applications in physical education to promote fitness in children. The findings also provide valuable insights into trends and advancements in the field of fitness applications, highlighting the potential benefits for athletes, trainers, and researchers alike. As smart wearables continue to evolve, they are expected to play an increasingly integral role in optimizing sport performance and fostering a new era of data-driven training methodologies.

DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer.

Digital twin models are of high interest to Head and Neck Cancer (HNC) oncologists, who have to navigate a series of complex treatment decisions that weigh the efficacy of tumor control against toxicity and mortality risks. Evaluating individual risk profiles necessitates a deeper understanding of the interplay between different factors such as patient health, spatial tumor location and spread, and risk of subsequent toxicities that can not be adequately captured through simple heuristics. To support clinicians in better understanding tradeoffs when deciding on treatment courses, we developed DITTO, a digital-twin and visual computing system that allows clinicians to analyze detailed risk profiles for each patient, and decide on a treatment plan. DITTO relies on a sequential Deep Reinforcement Learning digital twin (DT) to deliver personalized risk of both long-term and short-term disease outcome and toxicity risk for HNC patients. Based on a participatory collaborative design alongside oncologists, we also implement several visual explainability methods to promote clinical trust and encourage healthy skepticism when using our system. We evaluate the efficacy of DITTO through quantitative evaluation of performance and case studies with qualitative feedback. Finally, we discuss design lessons for developing clinical visual XAI applications for clinical end users.

Status of macromolecular crystallography beamlines at SSRF.

Shanghai Synchrotron Radiation Facility (SSRF) is a third-generation 3.5 GeV synchrotron facility located on the Chinese mainland, operational for user applications since 2009. With the completion of its Phase II project this year, SSRF now supports over 40 experimental stations across various research fields. For the structural biology community, there are three macromolecular crystallography (MX) beamlines (BL02U1, BL17UM and BL10U2) and one endstation at the white X-ray beamline (BL03HB) managed by SSRF to meet the needs of both academic and industrial users seeking to determine macromolecular crystal structures. The MX group at SSRF is dedicated to continuously upgrading these beamlines in terms of technology and scientific capabilities. This paper reports on the current status of all the MX beamlines at SSRF and discusses emerging trends.

Implementation of a mobile application for the management of maintenance tasks and documentation in automotive workshops

This article presents a detailed report of a study, which evaluates the implementation of a mobile application, designed for the management of maintenance tasks and documentation in an automotive workshop. Developed to drive operational efficiency and employee acceptance of new technologies, the quantitative descriptive methodology included pre- and post-implementation surveys, as well as automatic application usage records. The increase in operational efficiency with a corresponding increase in the accuracy of documentation in the survey results confirms the main assumption. This article provides a clear picture of the impact of using mobile technologies in the workplace.

Quality Evaluation of Ticketing Management System Using ISO/IEC25010:2023 Standards and AHP Method

Purpose: Information Technology plays a crucial role in supporting education service systems. When system-related issues arise, a Ticket Management System (TMS) becomes essential to address various software and hardware problems. Evaluating the performance and quality of TMS applications is necessary to ensure their effectiveness. This study assesses the quality of a TMS application developed by University of Amikom Yogyakarta, using ISO/IEC 25010:2023. Methods/Study design/approach: The Analytic Hierarchy Process method is employed to prioritize three key ISO/IEC 25010 characteristics by engaging TMS application users. Following the ranking, the study conducts quality measurements using questionnaires and black box testing. The questionnaire results are assessed using a Likert scale to determine scores for the TMS application based on the sub-characteristics of the three selected ISO/IEC 25010:2023 characteristics and the AHP-derived rankings. Result/Findings: The findings indicate that the TMS application achieved a quality score of 4.354. This shows that the TMS application is in the good category. Novelty/Originality/Value: The study highlights the need for performance efficiency improvements, specifically in the Time Behavior sub-characteristic, to enhance the overall quality of the TMS application.

Analysis of graphical user interfaces of music services taking into account universal design principles

While creating services, great emphasis is placed on usability and accessibility of the user interface. These elements allow applications to reach the widest possible audience. The purpose of this study was to examine the impact of contrast, font size and Universal Design principles on the usability of music services. The evaluations were carried out with the help of two applications, first that meets and second that does not meet WCAG 2.1 and Universal Design principles. Using eye tracking technology, LUT and SUS surveys and the WAVE tool. Twenty-one participants took part in the eye tracker study. Comparative factors were average times to first fixation, number of fixations and survey results. The results showed that the use of Universal Design principles and WCAG 2.1 has a significant impact on the accessibility and usability of applications.

AI IN ENGLISH LANGUAGE LEARNING: SAUDI LEARNERS’ PERSPECTIVES AND USAGE

Artificial intelligence (AI) is gaining wide attention in second language learning as a beneficial tool. The current research investigates EFL learners’ perceptions and usage of AI applications among 68 undergraduate English language major students. The aim is to enhance students’ awareness of valuable AI applications and involve them with AI applications to help them correct their English language errors and develop their English usage. The intervention involved guiding participants’ engagement with AI applications through specific prompts and analysing their errors into surface and linguistic categories. The objective of this approach is to raise participants’ awareness of recurrent errors in their English essays. Also, the study gathers participants’ perceptions before and after using the AI applications through pre- and post-questionnaires and focus group interviews. The participants’ responses are mainly positive, indicating AI holds the potential to significantly enhance their English language across a variety of skills. The results revealed positive engagement with AI applications, although participants expressed cautious usage due to concerns about overreliance and reliability of the information provided. The study recommends that AI can facilitate language learning through immediate feedback and productive interactions. Further research is needed to guide teachers and L2 learners to use AI while learning English ethically.

FinTech privacy security and customer engagement in Nigerian financial sector

One of the key achievements of digitization is the emergence of finance resources that could manage massive customer needs in the financial sector and tagged ‘Fintech’ the proliferation of this technology has become commonplace; creating plausible value addition in service delivery; as well as privacy concern on the technology effect on customer personal security/privacy information. The objective of this study is to examine fin-tech privacy security and customer engagement in Nigerian Financial Sector. Key issues to be considered include: customer privacy data protection, user application policy reforms, and customer trust. The Technology Acceptance Model (TAM) was adopted as the theoretical basis for articulating the model used for the study. Descriptive research design was adopted, in which 357questionnairs was administered to respondents in Lagos, Nigeria. The respondents were bank customers selected from the 2023 top five most profitable banks in Nigeria. systematic random sampling technique was adopted in selecting the respondents, which were administered based on the ranked percentages, additionally, personal interview was conducted to obtain information on where the close ended questionnaire could not cover. Data obtained was collated and analyzed. The study findings revealed that consistent breaches in customer privacy data, affect customers trust in Fintech, and the need to augment existing efforts in finding solutions to customer understanding of fintech application. The study recommends massive application of digital solutions by customers and service providers; as a means of countering the ‘gloomy side’ of fin-tech

Differences in Emotional Expressiveness Reviewed by Genderin TikTok Application Users

Students in early adulthood often face various emotional and social challenges. TikTok, a platform that allows users to share short video content, has become a popular medium for students to express their emotions. This study aims to examine differences in emotional expression between male and female students. Based on Ekman’s theory (2003), it is understood that several emotions are considered universal and are similarly recognized by individuals across different cultural backgrounds. These universal emotions include anger, disgust, fear, happiness, sadness, and surprise. This study hypothesizes that (1) there is a difference in emotional expression between males and females; (2) there is no difference in emotional expression between males and females. A quantitative approach with a survey design was used, employing a comparative research method. The sampling technique was purposive sampling, and data were collected through questionnaires distributed to students aged 17–24 years in Padang City. The participants included 15 male and 39 female students. Statistical analysis was performed using SPSS software. The results showed no significant difference in emotional expression between male and female TikTok users (p-value = 0.244 &gt; 0.05). These findings are consistent with the research hypothesis.

Pemanfaatan Perplexity.ai sebagai Instrumen Asesmen Diagnostik Produk Puisi Kelas VIII SMP

Diagnostic assessment is essential for teachers to determine the initial steps before learning begins. One useful instrument in this process is performance-based assessment, which evaluates products such as writing or other creative works. This study aims to describe the process and outcomes of diagnosing poetry products using the Perplexity.ai tool. Employing a qualitative descriptive method, the research analyzed 30 student poems collected as part of a diagnostic assessment of Indonesian language performance for class VIII H students at SMP Negeri 1 Salatiga. Data were gathered through initial observation and documentation of the Perplexity.ai application usage. Data analysis followed Milles and Huberman's interactive approach, encompassing data reduction, categorization, presentation, and conclusion drawing.The study found that (1) the Perplexity.ai application can be effectively utilized in learning, particularly for assessing students' poetry products. Teachers input students' poetry results into the application, specify the necessary assessment indicators, and allow the tool to evaluate the poems systematically. (2) The tool significantly aids teachers in analyzing student work, offering clear insights into key aspects. The diagnostic assessment results of students' poetry products highlighted three main evaluative aspects: rhyme, diction, and figures of speech. Overall, the Perplexity.ai application streamlines the diagnostic process and enhances the efficiency of evaluating student performance in poetry writing.

Usability Analysis of Mobile Applications Used for Foreign Language Learning

The aim of this study is to evaluate the usability of mobile applications for learning foreign languages. The study examined the effectiveness, efficiency and user satisfaction of the selected applications. The research was conducted using a user-based approach and observed the behavior of real users while using the applications and collected their opinions. The study participants consisted of 10 university students. The participants were selected using the convenience sampling method. This method allowed researchers to quickly reach existing participants. Participants performed five different tasks using applications they had never used before. Applications included Duolingo, Cake and Memrise. In the efficiency dimension, Cake had the highest success rate (54%), while Duolingo had the lowest success rate (34%). In the efficiency dimension, Duolingo had the fastest task completion time, while Cake took the longest. In terms of user satisfaction, Cake received the highest satisfaction rating. Participants reported that Cake was easier to use due to its simple tasks and user-friendly interface. On the other hand, Duolingo's complex interface and repetitive structure made it difficult for users to use the application long-term. Overall, all applications were rated as moderately user-friendly. The study highlights the importance of user-centered design to increase the effectiveness of mobile language learning applications and provides insights into future research.