User Applications Research Articles

MOBILE APPLICATIONS AND SUICIDE: A SYSTEMATIC ANALYSIS ON THE EFFECTIVENESS OF MOBILE APPLICATIONS FOR SUICIDE PREVENTION

MOBILE APPLICATIONS AND SUICIDE: A SYSTEMATIC ANALYSIS ON THE EFFECTIVENESS OF MOBILE APPLICATIONS FOR SUICIDE PREVENTION ABSTRACT Objective: This study aimed to evaluate the effectiveness of mobile applications designed to prevent suicide. Design: Within the scope of the study, open access Turkish and English articles published in online databases (Web of Science, PubMed (MEDLINE), WİLEY, Taylor & Francis Online, Science Direct) between 2019 and August 2024 were reviewed. Randomized controlled trials on mobile applications whose role in preventing suicide was clearly stated in the articles were included in the study. Results: As a result of the screening, seven studies that met the inclusion criteria were included in this study. In four of the included studies, a routine intervention (awareness training, psychotherapy or medical treatment) was compared with an additional mobile application; in three studies, a routine intervention was compared with a mobile application. As a result of the evaluation, different results were obtained in terms of the effectiveness, acceptability and usability of mobile applications. It was concluded that mobile applications with psychotherapy content, especially applied together with face-to-face interventions, provided more positive results in reducing the risk of suicide. However, the level of effectiveness may vary depending on the content of the application, the characteristics of the sample, and whether it is a treatment support. At the same time, it has been determined that mobile applications are usable but that there is limited data on their acceptability. Conclusion: Mobile applications aimed at preventing suicide are potential tools that can be used in preventing suicide. However, there is no definitive result regarding their effectiveness. It is important to increase randomized controlled studies with a large sample, long follow-up period, and high level of evidence. Keywords: Mental health, mobile application, suicide

Coarse Grained Task Parallelization by Dynamic Profiling for Heterogeneous SoC Based Embedded System

In this study, we introduce a methodology for automatically transforming user applications written in C/C++ to a parallel representation consisting of coarse-grained tasks based on dynamic profiling. Such a parallel representation is suitable for mapping applications onto heterogeneous SoCs. We present our approach for instrumenting the user application binary during the compilation process with parallel primitives that enable the runtime system to schedule and execute independent computation-intensive coarse-grained tasks concurrently. We use the proposed compilation and code transformation methodology to retarget each application for execution on a heterogeneous SoC composed of processor cores and accelerators. We demonstrate the capabilities of our integrated compile time and runtime flow through task-level parallelization and functionally correct execution of real-world applications in the communication systems and radar processing domains. We demonstrate the functionality of our integrated system by executing six distinct applications with different degrees of parallelism on four different platforms: an eight-core general-purpose processor, a heterogeneous SoC simulator, and two heterogeneous SoCs utilizing the Xilinx Zynq UltraScale+ FPGA and the Nvidia Jetson AGX board. Our integrated approach offers a path forward for application developers to take full advantage of the target SoC without requiring users to become hardware or parallel programming experts.

AncestryPainter 2.0: Visualizing Ancestry Composition and Admixture History Graph.

The earlier vertion of AncestryPainter is a Perl program to display the ancestry composition of numerous individuals using a rounded graph. Motivated by the requests of users in practical applications, we updated AncestryPainter to version 2.0 by coding in an R package and improving the layout, providing more options and compatible statistical functions for graphing. Apart from improving visualization functions per se in this update, we added an extra graphing module to visualize genetic distance through radial bars of varying lengths surrounding a core. Notably, AncestryPainter 2.0 implements a method admixture history graph (AHG) to infer the admixture sequence of multiple ancestry populations, and allows for multiple pie charts at the center of the graph to display the ancestry composition of more than one target population. We validated the six AHG metrics using both simulated and real data and implemented a Pearson coefficient-based metric with the best performance in AncestryPainter 2.0. Furthermore, a statistical module to merge ancestry proportion matrices. AncestryPainter 2.0 is freely available at https://github.com/Shuhua-Group/AncestryPainterV2 and https://pog.fudan.edu.cn/#/Software.

Novel Multicomponent Digital Care Assistant and Support Program for People After Stroke or Transient Ischaemic Attack: A Pilot Feasibility Study

Evidence is increasing for digital health programs targeting the secondary prevention of stroke. We aimed to determine the feasibility of the novel Care Assistant and support Program for people after Stroke (CAPS) or transient ischaemic attack (TIA) by combining person-centred goal setting and risk-factor monitoring through a web-based clinician portal, SMS messages, a mobile application (app), and a wearable device. We conducted a 12-week mixed-methods, open-label feasibility study. Participants (6 months–3 years after stroke or TIA, access to the internet via a smartphone/tablet) were recruited via the Australian Stroke Clinical Registry. Participants set one or two secondary prevention goals with a researcher and provided access and training in technology use. Feasibility outcomes included recruitment, retention, usability, acceptability, and satisfaction. Secondary outcomes included goal attainment, health outcomes, and program costs. Following 600 invitations, 58 responded, 34/36 (94%) eligible participants commenced the program (one withdrawal; 97% retention), and 10 were interviewed. Participants (27% female, 33% TIA) generally rated the usability of the mobile application as ‘Good’ to ‘Excellent’ (System Usability Scale). Most (94%) agreed the program helped with engagement in health self-monitoring. Overall, 52 goals were set, predominantly regarding exercise (21/52), which were the most frequently achieved (9/21). At 12 weeks, participants reported significant improvements (p < 0.05) in self-efficacy (Cohen’s d = 0.40), cardiovascular health (d = 0.71), and the mental health domain of the PROMIS GH (d = 0.63). CAPS was acceptable, with good retention and engagement of participants. Evaluation of this program in a randomised controlled trial is warranted.

Hierarchical multi-label classification model for science and technology news based on heterogeneous graph semantic enhancement

In the context of high-quality economic development, technological innovation has emerged as a fundamental driver of socio-economic progress. The consequent proliferation of science and technology news, which acts as a vital medium for disseminating technological advancements and policy changes, has attracted considerable attention from technology management agencies and innovation organizations. Nevertheless, online science and technology news has historically exhibited characteristics such as limited scale, disorderliness, and multi-dimensionality, which is extremely inconvenient for users of deep application. While single-label classification techniques can effectively categorize textual information, they face challenges in leading science and technology news classification due to a lack of a hierarchical knowledge framework and insufficient capacity to reveal knowledge integration features. This study proposes a hierarchical multi-label classification model for science and technology news, enhanced by heterogeneous graph semantics. The model captures multi-dimensional themes and hierarchical structural features within science and technology news through a hierarchical transmission module. It integrates graph convolutional networks to extract node information and hierarchical relationships from heterogeneous graphs, while also incorporating prior knowledge from domain knowledge graphs to address data scarcity. This approach enhances the understanding and classification capabilities of the semantics of science and technology news. Experimental results demonstrate that the model achieves precision, recall, and F1 scores of 84.21%, 88.89%, and 86.49%, respectively, significantly surpassing baseline models. This research presents an innovative solution for hierarchical multi-label classification tasks, demonstrating significant application potential in addressing data scarcity and complex thematic classification challenges.

Modeling and understanding the impact of COVID-19 containment policies on mobile service consumption in French cities

Amid recent studies that have been exploring the wide impact that COVID-19 containment policies have had across sectors and industries, we investigate how mobility restrictions enacted in French cities during the later stages of the pandemic have affected the usage of smartphones and mobile applications. Leveraging a large-scale dataset of over 14 billion records, we unveil and quantify the substantial incidence of the different measures enforced in urban France to combat the COVID-19 epidemic on mobile service consumption. We present a simple but effective spatial linear model that can relate changes occurring at fine-grained spatial zoning in both global and per-service traffic to a limited set of socioeconomic indicators. Our model unveils some of the mechanisms that drove the significant evolution of mobile data traffic demands during the pandemic. It allows observing how the demand for mobile services has been affected by COVID-19 in very different ways across urban areas characterized by diverse population density, income levels and leisure area presence. It also discloses that usages of individual smartphone applications have been impacted in highly heterogeneous ways by the pandemic, even more so when considering the composite impacts of different transitions between periods characterized by diverse restrictions. Our results can aid governments in understanding how their measures were received across the space and different portions of population, and network operators to comprehend changes in usage due to extraordinary events, which can be used to optimize service provisioning.

Young athletes’ mental well-being is associated with smartphone social networking application usage and moderated by performance level and app type

Smartphones and social media have become an integral part of human daily life and they influence mental well-being. These accounts have been researched in the general population, but not in athletes. This is critical given enhanced physical and emotional stressors in athletes versus non-athletes. Therefore, we here studied intra- and interindividual relationships of four social media sites (WhatsApp, TikTok, Instagram, and Snapchat) with mood, stress, recovery, and sleep using log-based media usage tracking as an objective measure in 53 competitive athletes aged 12–27 years. Multilevel modeling revealed that intraindividual TikTok usage negatively predicted sleep (β = −0.10, p = 0.040) and recovery (β = −0.15, p < 0.002), and positively predicted stress (β = 0.12, p = 0.018). Interindividual Instagram usage predicted feelings of calmness (β = 0.27, p = 0.029) and valence (β = 0.20, p = 0.043). Intriguingly, competition level moderated the usage outcomes of all four apps: Athletes on low performance levels showed lower mental well-being when using social media longer, yet in national and international athletes the strength of these relationships was weaker or even reversed. Our study unravels social media usage associations with mental well-being to vary as a function of performance level and provides findings on intra- and interindividual effects of critical importance to inform future population-tailored and expedient interventions.

Effectiveness of smartphone-based music intervention on perinatal depression: protocol for a randomized controlled trial

BackgroundPerinatal depression has become a global public health issue with a high prevalence rate. Multiple studies have confirmed the effectiveness of music intervention. Still, the current form of offline intervention ignores issues such as convenient transportation and the stigma of diseases that pregnant women are concerned about, resulting in poor adherence. This randomized controlled study will evaluate the impact of music intervention on depression, anxiety, and stress in perinatal women through the WeChat application. In addition, the usability of the WeChat application and the adherence of PND women will also be evaluated.MethodsThis randomized, open, single-center parallel controlled trial randomly divided 110 women with perinatal depression who met the inclusion criteria into a smartphone-based music intervention group and usual care group in a 1:1 ratio. The main outcome measures will consist of EPDS and WeChat application usability score. The secondary outcome measures will consist of GAD-7, CPSS, and Knowledge-Attitude-Practice questionnaire scores.DiscussionThis study will test the effectiveness of music intervention based on the WeChat application for perinatal depression and the usability of the WeChat application, to provide practical guidance for perinatal mental health care.Trial RegistrationThis protocol was registered at the Chinese Clinical Trials.gov (Identifier: ChiCTR2400088924) on August 29, 2024.

Development of Mathematical Algorithm for Detecting XSS Attacks on Web Applications

The widespread usage of web applications has led to an increase in security threats, with Cross-Site Scripting (XSS) attacks being one of the most prevalent and damaging. Detecting and mitigating XSS attacks is crucial to ensure the integrity and confidentiality of sensitive user data. This article presents the mathematical algorithm and a way to identify XSS attacks using a bounded function from below, which depends on the input string, and highlights its potential impact in bolstering web application security. To construct this function, we used special characters and keywords that are frequently found in the construction of XSS attacks.

Implementation and evaluation of a mobile application to support occupational therapy education

Purpose This study aims to evaluate the usability and effectiveness of a mobile application, ValTO, designed to enhance communication and professional reasoning skills in occupational therapy students through a case-based learning approach. Design/methodology/approach A descriptive exploratory study was conducted with 32 second-year occupational therapy students. The usability of the app was assessed using the Mobile Application Rating Scale and the System Usability Scale, complemented by the University of Oviedo’s Learning Satisfaction Survey. Findings The majority of students (77.8%) rated the app above average on the System Usability Scale, with 50% scoring it as excellent. Mobile Application Rating Scale scores revealed high ratings across functionality, aesthetics and information quality, with a significant correlation between app usability and user satisfaction. Increased student satisfaction was also observed in the Learning Satisfaction Survey compared to previous years. Originality/value ValTO integrates modern mobile health tools into occupational therapy education, enhancing students’ decision-making skills in an innovative, real-world context. This study contributes to the growing body of research on mobile health applications in educational settings, demonstrating their potential to improve both student engagement and learning outcomes.

Penggunaan Aplikasi Pegadaian Digital Service (PDS) Terhadap Transaksi Nasabah Pada PT. Pegadaian (PERSERO) UPC Ngrambe

The Pegadaian Digital Service (PDS) application, launched by PT. Pegadaian in 2018, aims to facilitate customer transactions. Despite its potential, most customers at PT. Pegadaian UPC Ngrambe, who are predominantly elderly, do not understand or are uninterested in using the application. This study explores the impact of the PDS application on customer transactions and the level of assistance it provides. Using a quantitative research method and a questionnaire-based survey, 100 respondents were randomly selected. Data analysis conducted with SPSS version 29 revealed significant findings: the significance value (Sig.) of 0.006 is below 0.05, indicating a meaningful impact of application use on customer transactions. The t-test result (t count = 2.975 &gt; 2.228) confirmed that application usage significantly influences customer transactions (X on Y). However, the effect size is relatively modest, contributing only 24% to the improvement of customer transactions, suggesting that other factors also play a role. This limited influence could be attributed to well-functioning traditional transactions and customers' limited digital literacy. The study recommends enhancing socialization efforts and providing targeted training, particularly for elderly users, to increase their understanding and usage of the PDS application, thereby maximizing its benefits for customer transactions.

Umami-gcForest: Construction of a predictive model for umami peptides based on deep forest

Umami peptides have recently gained attention for their ability to enhance umami flavor, reduce salt content, and provide nutritional benefits. However, traditional wet laboratory methods to identify them are time-consuming, laborious, and costly. Therefore, we developed the Umami-gcForest model using the deep forest algorithm. It constructs amino acid feature matrices using ProtBERT, amino acid composition, composition-transition-distribution, and pseudo amino acid composition, applying mutual information for feature selection to optimize dimensions. Compared to other machine learning baseline, umami peptide prediction, and composite models, the validation results of Umami-gcForest on different test sets demonstrated outstanding predictive accuracy. Using SHapley Additive exPlanations to calculate feature contributions, we found that the key features of Umami-gcForest were hydrophobicity, charge, and polarity. Based on this, an online platform was developed to facilitate its user application. In conclusion, Umami-gcForest serves as a powerful tool, providing a solid foundation for the efficient and accurate screening of umami peptides.

Understanding the Usage of Artificial Intelligence ‎Applications by Kuwaiti Teachers with Disabled ‎Students

Understanding the Usage of Artificial Intelligence ‎Applications by Kuwaiti Teachers with Disabled ‎Students

Comparison of Approaches to the Systematization and Presentation of Punctuation Rules in Compound Sentences in Two Sources (“Rules of Russian Orthography and Punctuation” 1956 and “Rules of Russian Orthography and Punctuation: Complete Academic Handbook”)

The subject of this study is the punctuation rules for compound sentences as presented in two sources: the 1956 “Rules of Russian Orthography and Punctuation” and the “Complete Academic Handbook” edited by V. V. Lopatin. The objective is to examine the different approaches to presenting punctuation rules for compound sentences in these two sources, as well as their impact on user comprehension and practical application. The author carefully explores aspects such as the grouping of all punctuation rules, the terminology used in formulating rules, the structure and presentation of rules for compound sentences, (non)correspondence between examples and rules, consistency within each source, and completeness in accounting for exceptions and complex cases. Special attention is given to the differences in how each source represents rules for complex cases, the explanatory methods for examples, and their influence on the precision of rule application for users. The methodology of this study includes a comparative analysis of the punctuation rules for compound sentences found in these two sources, focusing on rule structure and formulation, terminology, and example correspondence. The scientific novelty lies in the comparative analysis of the formulations and systematization of punctuation rules for compound sentences across two key reference sources, the 1956 Rules and the Complete Academic Handbook (PAS), each representing different stages in the development of Russian written language norms. This study reveals shifts in rule presentation and codification principles and identifies the challenges users face when interpreting norms from each source. The study’s findings indicate that PAS offers a more flexible, detailed system with scientific commentary, simplifying understanding and application of rules in modern contexts.

Employee well-being in the digital age: Assessing the impacts of a smartphone application in the workplace

Recently, providing smartphone-based health-improving applications to employees has emerged as a promising strategy for sustaining their well-being. This study estimates the impact of the routine use of an application, introduced in 2020 by a Japanese manufacturing company, on various health-related behaviours and outcomes among employees by exploiting a distinctive large-scale longitudinal dataset and personnel records. The analysis addresses potential selection biases arising from the non-random nature of application usage by employing the instrumental variable approach. Regular application use generates significant positive impacts on health-related habits, including moderate alcohol consumption, regular breakfast intake and refraining from eating two hours before bedtime. Furthermore, regarding physical and psychological stress, noteworthy reductions in physical burden and less frequent experiences of annoyance are observed. Employees also report a lower frequency of dizziness, headaches and palpitations, albeit an increase in the frequency of strained eyes is noted. Additionally, application use is associated with lower systolic and diastolic blood pressure, as well as decreased levels of triglycerides and gamma-GTP.

Using AI for Dynamic Resource Allocation and Performance Optimization in Software Systems

This research work aims at evaluating the possibilities of using artificial intelligence in dynamic resource management and optimization of software system performance. In today’s complex world of application usage, normal methods of resource management are unable to cater to these dynamic needs and fulfill its usage potential. In this work, an assessment of three mainstream AI techniques – reinforcement learning, neural network, and genetic algorithm – is performed based on performance indicators such as resource utilization and consumption, average response time, throughput, costs, prediction capability, stability, and time taken to converge. The results show that the neural networks have the best resource acquisition performance as well as response rates, while the reinforcement learning has the best cost management and flexibility rates. As it has been pointed out, genetic algorithms are quite useful in finding optimization solutions, however real-time responsiveness is lack. Thus, the results provide significant understating of how to choose the proper AI technique depending on the specific application needs which in turn will be useful for organizations willing to improve their resource management using AI-based solutions.

Wireless Sensing Network for Implementation of Air Quality Monitoring System and Indoor Air Quality Index Application

Indoor air quality significantly impacts respiratory health and mental activity. This study utilizes a wireless sensing network (WSN) based on the Internet of Things (IoT) to monitor indoor air quality, referred to as an indoor air quality monitoring system. The system was installed and applied on campus at the University of Baghdad. The present study aims to monitor air quality parameters continuously within laboratories. Carbon monoxide, sulfur dioxide, nitrogen dioxide, ammonia, and particulate objects are the pollutants chosen to be monitored by the installed system in this study. These pollutants were selected because they affect indoor facilities' comfort, health, and working conditions. Colored coded data was employed in the monitoring system; defined ranges for each pollutant were also integrated. Sensor nodes, wireless modules that connect to the IoT server, and user applications are the main components of the IAQMS system. LCDs, mobile applications, the ThingSpeak web server, and the LabView platform are examples of techniques used to present data collected by the system. Additionally, the system includes a notification function that alerts students and lab personnel when indoor air quality index IAQI values signal unhealthy indoor air quality. The proactive approach ensures a regulated standard for indoor air quality and pollutants.

ANALISIS USABILITY PADA APLIKASI SISTEM PELAYANAN RAKYAT SID`OARJO (SIPRAJA) MENGGUNAKAN METODE HEURISTIC EVALUATION DAN SYSTEM USABILITY SCALE

This study aims to analyze the usability of the Sidoarjo Public Service System (SIPRAJA) application using Heuristic Evaluation and the System Usability Scale (SUS) methods. The SUS results provide an average score of 49.63. The Heuristic Evaluation identifies problems and strengths based on design principles, while SUS quantitatively assesses the application's usability. The Heuristic Evaluation results show that most usability aspects fall into the "poorly functioning features" category, with some aspects such as "Error Prevention" receiving better ratings. indicating that the SIPRAJA application requires significant improvements to enhance usability and user satisfaction. This research provides a basis for improvement recommendations to increase the efficiency and effectiveness of public services through this application.

Usability and engagement of web application on parental behaviors for child obesity intervention

Background: Childhood obesity is a growing global health issue, with increasing prevalence in Asian countries, including Malaysia. Web applications such as JEJAK BMI offer an alternative intervention to address this concern by targeting parental behaviors that could influence their children’s weight. The usability and engagement of the app used are important elements that could influence the intervention outcome. Objective: To assess the engagement and usability of JEJAK BMI apps that could affect parental use of apps in child obesity interventions. Methods: To evaluate the app’s usability, three criteria were evaluated which were ease of use, visual appeal and functionality, post-intervention questionnaires were completed by the parents. Results: The study found that most parents considered the app practical, easy to use, visually engaging and functional. However, the impact on children's BMI over the short term was inconclusive. Conclusion: The JEJAK BMI web application demonstrated strong usability and positive effects on parental behaviors. Nonetheless, significant changes in children's BMI may require longer-term interventions and additional strategies.

The analysis of partial consent implementation effectiveness for terms &amp; conditions on data awareness and data security of social media application users

Terms and Conditions are always encountered when using social media applications to determine which data can be accessed and what cannot. However, there are shortcomings in their implementation and communication, often causing users to be unwilling to read them. Therefore, this study aimed to analyze the effectiveness of implementing partial consent in Terms and Conditions concerning user Data Awareness and Data Security in social media. This Paper administered a questionnaire, distributed with a form, to students who use social media to understand their opinions regarding the partial consent concept. This paper analyzed the data using descriptive statistical methods. The results show a positive response from respondents towards implementing the partial consent concept, the users feel the terms and conditions are more effective in increasing user data awareness and security.