Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

Software and Mobile Apps as a Strategy for Productivity Improvement in the Construction Industry

The construction industry, known for its low productivity, is increasingly utilising software and mobile apps to enhance efficiency. However, more comprehensive research is needed to understand the effectiveness of these technology applications. The PRISMA principles utilised a scoping review methodology to ascertain pertinent studies and extract significant findings. From 2013 onwards, articles containing data on mobile applications or software designed to enhance productivity in the construction sector were obtained from multiple databases, including Emerald Insight, Science Direct, IEEE Xplore, and Google Scholar. After evaluating 2604 articles, 30 were determined to be pertinent to the study and were subsequently analysed for the review. The review identified five key themes: effectiveness, benefits, successful implementation examples, obstacles and limitations, and a comprehensive list of software and mobile apps. In addition, 71 software and mobile apps have shown potentially how these technologies can improve communication, collaboration, project management, real-time collaboration, document management, and on-the-go project information and estimating processes in the construction industry, increasing efficiency and productivity. The findings highlight the potential of these technologies such as Automation, Radio-Frequency Identification (RFID), Building Information Modeling (BIM), Augmented Reality (AR), Virtual Reality (VR), and Internet of Things (IoT) to improve efficiency and communication in the construction industry. Despite challenges such as cost, lack of awareness, resistance to change, compatibility concerns, human resources, technological and security concerns and licensing issues, the study identifies specific mobile applications and software with the potential to enhance efficiency significantly, improve productivity and streamline workflows. The broader societal impacts of construction software and mobile app development include increased efficiency, job creation, and sustainability.

Behavioral Intention to Continue Using a Library Mobile App

To meet the needs of today’s library users,institutions are developing library mobile apps(LMAs),as their libraries are increasingly intelligent and rely on deep learning.This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA.A research model was constructed based on the technology acceptance model.A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data.The analysis was based on structural equation modeling.The empirical results show that the perceived ease of use,the perceived usefulness,the social influence,and the facilitating conditions affected the users’behavioral intention,but their impacts were different at different times.As the usage time increases,the technology acceptance model is still universal for understanding the user perception of LMA.In addition,two extended variables(social impact and convenience)also affect the user’s behavior intention.User behavior is dynamic and changed over time.This study is important both theoretically and practically,as the results could be used to improve the service quality of LMAs and reduce the loss rate of users.Its findings may help the designers and developers of LMAs to optimize them from the perspective of a user and improve the service experience by providing a deeper understanding of the adoption behavior of information systems by LMA users.

What leads to readers' satisfaction with the mobile news apps service? An investigation into the roles of content originality and user experience

Purpose: This study aims to explore whether content originality and user experience have positive effects on online news readers’ satisfaction with the mobile apps service.Design/methodology/approach: Data was collected via a Web-based survey. Data analysis of this study can be divided into two stages. In the first stage, a descriptive statistical analysis was conducted based on a sample of 612 respondents. In the second stage, the correlation among content originality, user experience and satisfaction was analyzed based on a sample of 377 mobile news apps users. Findings: Results of the first stage showed that social media and mobile news apps were the most important tools for users to access news, and different types of media were playing complementary roles in information transmission. Users held a positive attitude toward their mobile news reading experience and they described news they read on mobile apps with such words as "interesting", "instant", "positive", "profound", and "ironic". Results of the second stage confirmed our hypothesis that content originality and user experience both had positive impacts on user satisfaction.Research limitations: The questionnaires were distributed entirely online, so the sample may not be representative of the general population being studied and thus undermine the reliability and generalization of the findings to some extent. Moreover, this study adopted only one method(survey) and more methods such as interviews can be used to improve the accuracy of the results.Practical implications: The findings of this study can not only provide insights into a better understanding of users’ mobile reading behavior, but also help mobile information service providers attract more users.Originality/value: This is one of the first studies to explore the effects of content originality and user experience on online news readers’ satisfaction with the mobile apps service.

How mobile messaging apps users share information on WeChat: The role of demographic variables

Purpose： We conducted an empirical study to find out the role of demographic variables in affecting information sharing behaviors of college student users of WeChat.Design/methodology/approach： A questionnaire survey was carried out to investigate the relationship between demographic variables （gender, grade level, dating status, and singleparent family background） and information sharing behaviors of WeChat users. The participants were college students and a total of 255 valid questionnaires were collected. Data was analyzed using descriptive statistics and multiple regression analysis.Findings： Grade level and single-parent family background were found to be significantly correlated with information sharing behaviors whereas no effect of gender was found on information sharing behaviors. Dating status had no significant impact on user browsing behavior, but was related to users＇ publishing posts and posting replies.Implications： The study will help understand individual differences in information sharing among WeChat users.Limitations： First, the relatively small sample size is a limitation in exploring the effects of demographic variables on user information sharing behaviors. Second, this study only used questionnaire surveys to collect data and more research methods such as interviews should be adopted to improve the accuracy of the study results.Originality/value： This paper is one of the first studies to explore the relationship between demographic variables and user information sharing behaviors on WeChat.

Characteristics Classification of Mobile Apps on Apple Store Using Clustering

This research is interested in the user ratings of Apps on Apple Stores. The purpose of this research is to have a better understanding of some characteristics of the good Apps on Apple Store so Apps makers can potentially focus on these traits to maximize their profit. The data for this research is collected from kaggle.com, and originally collected from iTunes Search API, according to the abstract of the data. Four different attributes contribute directly toward an App’s user rating: rating_count_tot, rating_count_ver, user_rating and user_rating_ver. The relationship between Apps receiving higher ratings and Apps receiving lower ratings is analyzed using Exploratory Data Analysis and Data Science technique “clustering” on their numerical attributes. Apps, which are represented as a data point, with similar characteristics in rating are classified as belonging to the same cluster, while common characteristics of all Apps in the same clusters are the determining traits of Apps for that cluster. Both techniques are achieved using Google Colab and libraries including pandas, numpy, seaborn, and matplotlib. The data reveals direct correlation from number of devices supported and languages supported to user rating and inverse correlation from size and price of the App to user rating. In conclusion, free small Apps that many different types of users are able to use are generally well rated by most users, according to the data.

Comprehensive English Assessment System Based on Mobile APPs

Comprehensive English is a professional basic course for English majors,with the most class hours and the highest credits.It can be said to be the most important course in English majors.However,the procedural assessment in the previous comprehensive English assessment system,that is,the usual assessment,was largely determined based on the subjective impression of teachers.However,with the emergence of various English learning apps,students’learning methods and content have changed a lot.With major changes,the previous assessment system has not been able to play its due role.Designing a reasonable comprehensive English assessment system based on mobile APP is the unshirkable responsibility of comprehensive English teachers.Carefully and reasonably designed comprehensive English assessment system can promote students to improve learning efficiency,reduce classroom lecture time,improve teaching effect,and play a vital role in cultivating qualified English professionals.

基于HTML5+jQuery Mobile的移动学习平台Web App研究与实现

以传统教学资源网站为基础,利用HTML5与jQueryMobile框架快速开发基于移动浏览器的WebApp,构建移动学习平台的一种方案。

MOOC联合手机APP的间质性肺病网络教学平台的建立与评价

慕课即大规模在线开放课程(massive open online courses,MOOC)和手机应用软件(application,APP)在医学教育领域应用越来越多,其特点是学习方式灵活、学习时空不受限制。间质性肺病(interstitial lung disease,ILD)是一组疾病,具体病种超过200多种,诊断复杂,是当前呼吸系统主要的疑难杂症。传统的医学教育缺乏足够的ILD教学时间,以致多数呼吸内科医师、放射科医师缺乏ILD的相关知识。文章拟建立MOOC联合手机APP的ILD网络教学平台,包括MOOC教学模式、疾病诊断模块、胸部影像图库模块和试题库模块。同时,文章也探讨网络教学平台的应用与评估,以及挑战与解决方案,以期使MOOC联合手机APP在ILD教学中得到充分的应用,提高呼吸医学与放射医学的人才培养质量,提高呼吸内科医师与放射科医师对ILD的认知和诊断水平。

基于用户动态需求的移动医疗APP适老化界面设计研究

目的以老年群体用户需求的动态特征为出发点,对移动医疗APP界面进行适老化设计研究,优化界面设计策略,帮助老年群体适应智慧养老背景下的智慧医疗模式。方法利用网络爬虫软件获取老年群体对移动医疗APP的在线评价数据,运用模糊kano模型对老年群体需求数据进行归纳分类,结合层次分析法确定需求主观权重、熵权法确定需求客观权重、组合赋权法确定需求组合权重,根据权重对老年群体用户需求进行优先级排序,从而进行移动医疗APP适老化界面的优化设计研究。结论将用户动态需求理论应用于移动医疗APP适老化界面设计,重视需求在时间、环境等多维度下的动态变化,从而更好地获取老年群体的多层次需求,拓展移动医疗APP界面适老化设计思路,为移动医疗适老化研究提供借鉴。

基于HTML5和jQuery Mobile的移动学习APP设计与实现

介绍一种快速开发移动学习APP的方法,其中包括使用jQuery Mobile框架实现UI的设计,使用HTML5技术及其Web Storage实现数据存储,以及使用PhoneGap编译成Native App等。

对成年2型糖尿病患者基于移动APP+家属共管血糖的研究

目的评价成年2型糖尿病患者应用移动APP+家属共管血糖改善自我管理行为和血糖控制的有效性及可持续性。方法选取2022年5月—2023年6月广州市某三级甲等医院的成年2型糖尿病患者作为研究对象,将患者随机分为对照组(n=69)和试验组(n=68)。对照组采用移动APP血糖管理模式,试验组采用移动APP+家属共管血糖模式。观察并比较干预前、干预3个月末、干预6个月末2组空腹血糖、糖化血红蛋白、糖尿病自我管理行为量表、家庭成员行为量表、照顾者负担量表的变化和得分差异。由于这5个指标均不服从正态分布,所以在分析之前对其分别做基于秩次的逆正态变换(INT)。结果干预前2组的INT变换后空腹血糖、糖化血红蛋白、糖尿病自我管理行为量表、糖尿病患者家庭成员行为量表和照顾者负担量表得分差异均无统计学意义(P>0.05)。试验组3个月末和6个月末的INT变换后糖尿病自我管理行为量表得分、空腹血糖、糖化血红蛋白均优于对照组(P<0.05)。相较于干预前,试验组干预3个月末的INT变换后糖尿病自我管理行为量表得分升高;干预3个月末和6个月末的INT变换后空腹血糖、糖化血红蛋白、照顾者负担量表得分降低,INT变换后家庭成员行为量表得分升高。结论移动APP+家属共管血糖可以增加家庭支持水平,改善患者自我管理行为和血糖控制,且不增加家属照顾负担。研究者停止干预3个月后,患者仍然具有良好的血糖控制水平,可在2型糖尿病患者居家智能血糖管理中推广。

心力衰竭患者居家康复APP服务质量评价体系的构建

背景心力衰竭患者的心脏康复过程目前大多由医院转移至居家情境进行。移动健康应用程序帮助患者弥合了从医院向居家转移的支持缺口,促进居家康复进程,然而诸多质量问题接踵而至,其服务质量亟待“把关”。目的构建心力衰竭患者居家康复APP服务质量评价体系,为规范、提升我国移动健康APP服务质量提供参考。方法2022年7月—2023年1月基于文献分析、半结构式访谈拟定心力衰竭患者居家康复APP服务质量评价体系,对18名专家开展2轮函询。专家积极程度采用问卷回收率及意见提出率表示,专家权威程度采用权威系数表示,专家意见协调程度采用得分变异系数、肯德尔和谐系数表示。结合专家函询、层次分析法,确立最终的心力衰竭患者居家康复APP服务质量评价体系。结果2轮函询问卷有效回收率均为100%,专家权威系数为0.867,2轮函询肯德尔和谐系数分别为0.128~0.154和0.151~0.197(P<0.001)。最终构建的心力衰竭患者居家康复APP服务质量评价体系由软件属性、医学科学和行为科学特征三个维度组成,包含7项一级指标,24项二级指标和51项三级指标。结论构建的心力衰竭患者居家康复APP服务质量评价体系内容全面,具有科学性,对评价心脏康复APP服务质量及促进类似APP服务的良性发展具有一定借鉴意义。

An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

Recently,various mobile apps have included more features to improve user convenience.Mobile operating systems load as many apps into memory for faster app launching and execution.The least recently used(LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low.However,the LRUbased cached app termination does not distinguish between frequently or infrequently used apps.The app launch performance degrades if LRU terminates frequently used apps.Recent studies have suggested the potential of using users’app usage patterns to predict the next app launch and address the limitations of the current least recently used(LRU)approach.However,existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again.In this paper,we present a new approach for predicting future app launches by utilizing the relaunch distance.We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction(M2ARP).M2ARP utilizes past app usage patterns to predict the relaunch distance.It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.

精神分裂症患者基于移动医疗APP的认知矫正联合干预方案的构建

目的构建精神分裂症患者认知矫正联合干预方案,为开展基于移动端的精神分裂症患者认知干预提供参考依据。方法采用文献分析法,将认知矫正治疗与药物管理技能训练相结合,初步拟定基于移动医疗APP的认知矫正联合干预方案草案,选取14名专家进行2轮德尔菲专家函询,进一步修订形成基于移动医疗APP的认知矫正联合干预方案终稿。结果2轮专家函询问卷有效回收率分别为93%、100%;专家权威系数为0.860;2轮咨询中重要性和可行性的变异系数均<0.25;第2轮专家咨询重要性和可行性的肯德尔协调系数为0.282、0.225(P均<0.05)。最终形成的干预方案包括4项主题、15项认知策略、16项认知练习。结论基于移动医疗APP的认知矫正联合干预方案经过专家函询,具备一定的严谨性、可行性和潜在价值,能够为精神分裂症患者开展基于移动端的认知干预提供参考依据。

基于CNN-GRU的移动APP流行度预测模型

移动APP流行度预测对应用推荐、广告投放等意义重大。但是现有方法大多依赖手工特征工程,工作量大且效率较低。为此,提出一种基于深度神经网络的移动APP流行度预测模型。利用最大信息系数进行特征相关性分析以确保特征选取有效性,结合历史流行度特征,通过门控循环单元(gate recurrent unit,GRU)和注意力机制构建长期演化模型来推演发展趋势,基于多尺度卷积神经网络(convolutional neural networks,CNN)和注意力机制构建短期波动模型以实现预测动态优化,结合其他重要特征利用GRU和注意力机制建立多因素影响模型。通过时间注意力模块将上述模型融合,实现流行度预测。实验结果表明,所提模型在移动APP流行度预测方面相对更为精准有效。

3D打印联合APP终端在骨科教学中的应用

目的探讨3D打印骨骼模型联合移动APP终端在临床专业实习医师骨科带教中的应用效果。方法选择2022年1月—2022年5月滨州医学院烟台附属医院的46名骨科实习医师作为研究对象,按照学号将学生随机分为传统教学组与试验教学组,各23名。传统教学组采用传统教学法,传统教学组采用3D打印骨骼模型联合移动APP终端教学。教学结束后,分别对学生的教学满意度、理论知识考评以及技能操作考评进行评估。结果试验教学组[(88.87±3.68)分]高于传统教学组理论知识得分[(85.20±4.23)分],差异有统计学意义(P<0.05)。试验教学组技能操作考评得分[(91.21±7.14)分]高于传统教学组得分[(77.34±9.85)分],差异有统计学意义(P<0.05)。试验教学组的满意度为91.3%,高于传统教学组的65.22%,,差异有统计学意义(P<0.05)。结论3D打印骨骼模型联合移动APP终端教学模式有效地解决了传统临床教学模式存在的不足之处。该教学模式能够有效地帮助学生更快地理解和掌握骨科相关疾病的发生发展、形态学特征、治疗方法以及实际临床操作等重要内容。

格林模式护理+移动医疗App的人文护理对食管癌患者术后营养状况的影响

目的 探究格林模式护理+移动医疗App的人文护理对食管癌患者术后营养状况的影响。方法 纳入2021年1月至2023年12月福建省立医院接受食管癌手术的60例患者,按照随机数字表法划分对照组(30例)和观察组(30例)。对照组术后开展常规护理支持,观察组术后开展格林模式护理+移动医疗App的人文护理,比较两组护理后的营养状态、自护能力(ESCA量表)及生活质量(WHOQOL-BREF量表)。结果 护理后,观察组血红蛋白、白蛋白、转铁蛋白水平高于对照组(P <0.05),观察组ESCA量表各项评分均高于对照组(P <0.05),观察组WHOQOL-BREF量表各项评分均高于对照组(P <0.05)。结论 食管癌手术患者术后实施格林模式护理+移动医疗App的人文护理,可以提升患者自护能力及生活质量,改善患者营养水平。

问诊类移动医疗APP用户持续使用意愿影响因素研究

[目的/意义]问诊类移动医疗APP是互联网医疗服务的重要实现形式之一,进行问诊、咨询的患者用户是其主要的参与者,了解用户的使用情况及意愿有助于清晰把握APP现状并有针对性地服务于质量提升与可持续发展。[方法/过程]运用扎根理论,通过深度访谈并经过开放式编码、主轴式编码和选择式编码提炼出6个主范畴,探讨APP用户的持续使用意愿影响因素。[结果/结论]用户感知、客观情况、个体状况和隐私关注作为内部和外部客观因素影响用户持续使用意愿;APP质量和医生能力影响用户感知进而影响用户持续使用意愿;在此基础上从用户视角分析提出当前问诊类移动医疗APP存在的问题及改进策略。

Research on Blended Teaching Model in College English Based on Mobile Learning App

Mobile learning is the general trend of the times and the inevitable choice for the reform and development of college English teaching.This article attempts to use blended learning theory,mobile learning theory,and instructional design theory as a guide.In many English mobile learning apps,Superstar Learning Link is used as an example to design an online and offline blended teaching mode.It proposes thinking expansion and interaction in pre-class stage.Consolidation of input and outputoriented process are during class and evaluation and sublimation are made after class.The essay is aimed to break the boundaries between traditional teaching and mobile learning,combining synchronous learning with personalized learning in order to improve comprehensive levels of English.