User Applications Research Articles

Assessing the Impact of Human and Technological Factors on Hospital Management Information System Utilization: A Case Study at Hospital X In Padang City Indonesia

This study examines the application of the HOT-Fit method, which evaluates the relationship between Human, Technology, and Net Benefit components within the Hospital Management Information System (HMIS) at Hospital X Padang. The Human component is assessed based on system usage and user satisfaction, while the Technology component is analyzed through information quality, service quality, and system quality. This study employs a quantitative crosssectional design, with the research population comprising all active users of the HMIS application at Hospital X Padang, including employees from various departments interacting with the system. The research aims to determine the extent to which the Human and Technology components influence the Net Benefit of the HMIS and to explore these relationships in greater depth. The findings reveal a significant relationship between the Human component and the Net Benefit, as well as between the Technology component and the Net Benefit of the HMIS. Among the factors examined, Technology emerges as the most dominant factor affecting the Net Benefit of the system. These results provide valuable insights for optimizing the implementation and impact of HMIS in healthcare settings.

PhantomAR: gamified mixed reality system for alleviating phantom limb pain in upper limb amputees—design, implementation, and clinical usability evaluation

BackgroundPhantom limb pain (PLP) is a restrictive condition in which patients perceive pain in a limb that is no longer present, greatly reducing their quality of life. Mirror Therapy, wherein patients observe a mirror reflection of their intact limb, has demonstrated efficacy in alleviating PLP. However, its unilateral and seated nature presents limitations. To address these constraints while still reducing PLP, and evaluating the impact of different virtual limb representations (anthropomorphic vs. non-anthropomorphic) on the user’s sense of ownership, agency, and embodiment, PhantomAR was developed. Leveraging wearable first-person augmented reality (AR) technology, PhantomAR extends traditional Mirror Therapy by enabling users to move freely and engage in bimanual tasks.MethodsThe assistive mixed reality game application PhantomAR was deployed on the Microsoft HoloLens 2 and augmented the user’s residual limb by superimposing a virtual arm or tentacle that was controlled via residual muscles on their stump using an EMG electrode array. This setup allowed patients to engage in a first-person perspective and manipulate virtual objects with both the healthy and augmented limbs, free from the confines of a seated position. The study enrolled 10 able-bodied individuals and 8 individuals with unilateral, transradial amputation. All amputees experienced PLP. The usability of the PhantomAR application was evaluated using the System Usability Scale (SUS) and a user-centric survey. Additionally, the Game Experience was assessed on a 5-point Likert questionnaire (GEQ). Participants rated their phantom sensations using the Numerical Rating Scale and McGill Pain Questionnaire before, during, and after interaction with PhantomAR. The embodiment and agency of the virtual superimposed arm were evaluated with an altered Prosthesis Embodiment Scale. The study protocol included two sessions of 30 min each, during which participants experienced PhantomAR.ResultsParticipants (n = 18) rated PhantomAR highly usable (SUS m = 90.8%, SD = 6.88). Feedback on the Game Experience Questionnaire was overwhelmingly positive, showing high immersion (m = 4.46, SD = 0.08) and positive affect (m = 4.97, SD = 0.05). PLP (n = 8) significantly decreased post-intervention (NRS and McGill Pain Questionnaire, p < .001). Skin temperature in the residual limb increased significantly post-intervention (p < .01) but did not correlate with PLP (r = − 0.08, p = 0.83). Tentacle overlay yielded mixed ownership but high agency ratings.ConclusionPhantomAR leverages mixed reality to significantly reduce Phantom Limb Pain, enhance user engagement, and alter perceptions of ownership and agency of their augmented limb through bi-manual, dynamic, full-body interactions.Trial registration DRKS00033208 (Jan. 5th 2024)

Analisis Efektivitas Pelaporan Pajak Pertambahan Nilai Dengan Menggunakan E-Faktur

This study aims to analyze the effectiveness of value added tax (VAT) reporting using the e-invoice application at PT YYY during the period from 2022 to 2024. This research method uses a descriptive qualitative approach that describes the level of effectiveness of reporting Periodic VAT Returns. Data collection techniques are interviews and documentation. The results of the study show that although there were 3 delays out of a total of 36 reports during the observation period, the effectiveness of reporting Periodic VAT Returns of PT YYY can be concluded as effective. The obstacle in the form of the expiration of the electronic certificate is the main cause of the delay in reporting Periodic VAT Returns. In addition, employee knowledge of the users of the E-invoice application is also a problem. Therefore, it is necessary to improve the Company's reporting performance and knowledge regarding the expiration date of electronic certificates in order to avoid errors that hinder the VAT reporting process.

Mathematical optimisation model for searching duplicate string objects in the memory snapshot

The purpose of this paper is to identify the increased memory usage of software appli-cations. The modern software development cycle focuses on functionality and often ignores aspects of optimal resource usage. Limited physical scaling sets an upper limit on the system's capacity to process requests. The presence of unchanged objects with the same information is a sign of increased memory consumption. Avoiding duplicate objects in memory allows for a more rational use of the existing resource and an increase in the amount of information proc-essed. Existing scientific publications focus on the study of memory leakage problems, and limit their attention to excessive memory usage due to the lack of a unified model for finding excessive memory usage. It is worth noting that existing programming templates contain the ‘object pool’ template, but leave the conclusion about the feasibility of its implementation to engineers without providing a mathematical basis. The paper presents the development of a mathematical model for the process of detecting duplicate objects with the immutability prop-erty of the String type in a memory snapshot. Industrial systems that require hundreds of GB of RAM to operate and contain millions of objects in RAM are analysed. Given this scale of data, there is a need to optimise the duplicate detection process. The research method is to analyse memory snapshots of highly loaded systems using the software code developed on .NET technology and the ClrMD library. The memory snapshot reflects the state of the proc-ess under study at a given time, contains all objects, threads and operations performed. The ClrMD library allows you to programmatically examine objects, their types, get field values, and build graphs of relationships between objects. Based on the results of the study, an opti-misation was proposed that allows to speed up the process of finding duplicates several times. The scientific contribution of the study is the creation of a mathematically sound approach that significantly reduces the use of memory resources and optimises computing processes. The practical usefulness of the model is confirmed by the optimisation results achieved through the recommendations, reduced hosting costs (which provides greater cost-effectiveness in the deployment and use of software systems in industrial environments), and increased data processing.

Cinta di Era Digital: Mengungkap Mencari Pasangan di Waplog melalui Perspektif Fenomenologi Alfred Schutz

This research aims to understand the phenomenon of looking for a partner on the online dating application Waplog, through Alfred Schurtz's phenomenological approach. This research focuses on the motives, meanings and communication experiences experienced by users of the online dating application Waplog in their efforts to find a partner. Qualitative methods with a phenomenological approach were used to explore the subjective experiences of informants selected purposively. Data collected through in-depth interviews and analyzed using phenomenological interpretation. The research results show that the motives of Waplog application users vary from the need for social interaction to efforts to forget painful past experiences. The meanings formed in this application also differ depending on the user's background and expectations, be it for a friend to talk to, starting a new relationship, having the courage to express feelings, or starting a more serious relationship. The communication experience that occurs also reflects a unique social experience, where online dating users on the Waplog application have meaning and management in virtual interactions. This research provides new insights into how individuals interpret their experiences in the digital world in the context of finding a partner. These findings also highlight that technology shapes social interaction patterns and the dynamics of interpersonal relationships in the digital era.

A Comprehensive Review on Machine Learning Approaches for Smartphone Addiction Prediction

Smartphone addiction has become a pressing public health concern, with far-reaching implications for mental health, social interactions, and overall well-being. This study proposes an unsupervised machine learning framework to predict smartphone addiction by analyzing user behavior data, such as screen time, application usage patterns, and psychological factors. The research leverages unsupervised learning techniques to identify patterns and commonalities in unlabelled data, aiming to classify individuals at risk of addiction without predefined categories. Using advanced machine learning libraries like TensorFlow and OpenCV, the system incorporates a robust pipeline involving data collection, preprocessing, and training/testing with high-performance models. The findings highlight excessive social media use and notification frequency as significant predictors, with applications ranging from personalized intervention programs and mental health monitoring to enhanced parental control systems. With a focus on scalable and adaptive methodologies, this framework not only addresses the challenges of smartphone addiction but also offers valuable insights for researchers, policymakers, and app developers to design effective solutions for healthier technology use in an increasingly digital society Key Words: Smartphone addiction, machine learning, user behavior analysis, mental health, unsupervised learning.

A Systematic Analysis on the Effectiveness of Mobile Applications for Suicide Prevention

Objective: This descriptive systematic review was evaluated the effectiveness of mobile applications designed to aimed to prevent suicide. Material and Method: Within the scope of the study, open access Turkish and English articles published in online databases (Web of Science, PubMed, WİLEY, Taylor &amp; 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. All that, it has been determined that mobile applications are usable but that there is strictly 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 alone effectiveness. It is important to increase randomized controlled studies with a large sample, long follow-up period, and high level of evidence.

Balancing privacy protecting and auditability: A signal protocol based E2EE communication scheme for group chatting

With the development of the Internet, instant messaging applications have ascended to a crucial position in our daily lives. As users increasingly attach importance to privacy issues, end-to-end encryption(E2EE) has become an indispensable requirement for messaging services. However, E2EE communication software face two major challenges. One is that completely being unregulated is rather perilous, as criminals may take advantage of it to cover up crimes and endanger society. Secondly, ordinary E2EE software only works between two parties, and there has been relatively little research on group chatting scenarios. Against these backdrops, this paper devises practical solutions to the problem of allowing audit access by authorized parties while safeguarding privacy in the context of group chatting. By leveraging the Signal protocol based on the open Noise protocol framework [1], we ingeniously transform the problem into multiple two-way chatting channels, thereby ensuring E2EE security and facilitating efficient group communication. Additionally, to address the auditing challenge, we delicately design the model with multiple cryptological techniques and safeguard the security of ordinary users while expediting the auditing process. This solution strikes a balance between privacy concerns and regulatory demands, and offers practical and innovative model design to secure group chatting, enhancing the usability and security of instant messaging applications.

Modeling the Efficacy of Phosphine on Eggs of Major Stored-Product Insect Species: Investigating Strains with Varying Susceptibility Levels

Phosphine is a widely utilized fumigant insecticide in stored-product facilities; however, its excessive application and improper usage have contributed to the emergence of resistance in insect populations. Although phosphine resistance is globally recognized, limited studies address its effects across different developmental stages. This research investigates the efficacy of phosphine on the eggs of major stored-product pests, including Oryzaephilus surinamensis (L.) (Coleoptera: Silvanidae), Rhyzopertha dominica (F.) (Coleoptera: Bostrichidae), Tribolium castaneum, and T. confusum (Coleoptera: Tenebrionidae), focusing on strains with varying levels of phosphine susceptibility. Egg hatching rates were analyzed following exposure to phosphine concentrations ranging from 50 to 1000 ppm for durations of 1 to 7 days. Predictive models were constructed to correlate phosphine concentration and exposure time, facilitating the determination of optimal pest control strategies. The findings reveal significant differences in efficacy among species and strains, with eggs from susceptible strains experiencing complete hatch failure at 50 ppm, while resistant strains showed higher tolerance. Lethal time (LT50 and LT99) values were determined, with LT99 ranging from 6.91 to 12.95 days at 50 ppm, highlighting species-specific and age-related differences in phosphine susceptibility. Lethal concentration (LC50 and LC99) values could only be estimated for T. castaneum, with LC99 for 1-day-old eggs ranging from 773.67 ppm to 923.03 ppm after 2.5 days of exposure. Furthermore, egg age influenced susceptibility, with 2-day-old eggs exhibiting greater mortality compared to 1-day-old eggs. This study underscores the critical role of phosphine concentration, exposure duration, and developmental stage in resistance management, providing valuable insights for enhancing fumigation protocols and improving the control of stored-product pests.

Exploring Mobile Application User Experience Through Topic Modeling

Exploring user satisfaction and experience is the first step of software product improvement and business sustainability. The primary goal of this research is to investigate how companies can use topic modeling to understand mobile application user experience and offer methodological steps for identifying factors shaping it. SalesForce was selected for this case study as it was the most widely used CRM application in 2023. This study aims to uncover factors influencing positive and negative user experience, to compare and systematize them, and to indicate the business implications of topic modeling findings. For this study, authors collected 9081 online reviews of the SalesForce application from Google Play Store. The corpus is divided into three subsets based on the associated star ratings, where four and five stars indicate positive sentiment, three mixed sentiment, and two and one negative. Each subset is analyzed using the Latent Dirichlet Allocation algorithm, hyper-parameters were fine-tuned, and the experimental models were evaluated with coherence measures to determine the model with the optimal number of topics. The results indicate that the driving factors of positive experience are seamless functionality and reliability, design flaws and performance issues shape negative experiences, and mixed experiences arise from inconsistencies in usability and authentication challenges.

Continuance Intention Analysis Using Unified Theory Of Acceptance And Use Of Technology And Unfavorable Attitude Toward Cash Payment Model In The Case Study Of Allo Bank M-Banking Application

The aim of this study is to examine how to maintain users of the Allo Bank mobile banking application through continuance intention, the UTAUT model, and the UATC variables. This research is classified as a quantitative study, with data collection conducted via a survey involving 310 respondents and 51 questions. However, 8 respondent data points were eliminated due to identified outliers, in order to meet the multivariate normality data requirements. The data analysis technique used is the Covariance-based Structural Equation Model (CB-SEM), with the Maximum Likelihood (ML) approach for parameter estimation, relying on multivariate normal distribution through AMOS software. When the multivariate normality test using skewness-kurtosis did not meet the standard of &lt; 2.58, Mahalanobis D-Squared was employed to identify outliers and remove data points with Mahalanobis values &gt; 87.97, resulting in the elimination of 8 data points. Reliability testing was performed using Cronbach’s Alpha, validity was assessed through standardized regression weight, and model fit was evaluated using RMSEA, CFI, and Chi-Square tests. The statistical test results indicate that the reliability test exceeded 0.9, meaning its reliability was good. The multivariate normality test produced a value of 2.245, indicating acceptable conditions. The model fit tests showed a goodness of fit based on RMSEA (0.047), CFI (0.928), and Chi-Square (1.673). Additionally, the path coefficient test results were consistent with the theoretical expectations for each variable, including Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Unfavorable Attitude toward Cash Payment, and Satisfaction, all of which had significant effects on Continuance Intention. From a technical perspective, the Allo Bank mobile banking application can provide a reliable system for users, as Performance Expectancy and Effort Expectancy significantly influence Continuance Intention. The study also suggests that the application can enhance the social influence of users to encourage application usage, as indicated by the social influence variable. Additionally, users displayed an unfavorable attitude towards cash transactions based on UATC respondent feedback.

User Satisfaction Analysis of SMOPI Application: The Role of User Interface, Perceived Ease of Use, and Behavioral Intention at the Irrigation Engineering Center - Ministry of Public Works and Public Housing

This research analyzed how the user interface, perceived ease of use, and behavioral intention impact user satisfaction with the SMOPI application based on the technology acceptance model. A quantitative survey using a cross-sectional design collected data from application users through sampling. Structural equation modeling with SMARTPLS analyzed the results. The findings demonstrated that the user interface significantly influenced satisfaction, with a path coefficient of 0.645 and P-value of 0.021, indicating a comfortable and relevant interface enhances satisfaction. However, perceived ease of use and behavioral intention did not significantly impact satisfaction, recording path coefficients of -0.015 (P-value 0.947) and 0.189 (P-value 0.591), respectively. In closing, the user interface emerged as the most consequential factor in determining satisfaction with the SMOPI application. Optimizing features to align with consumer needs while refining the design were recommended to boost satisfaction overall. Adopting the technology acceptance model in this research provided useful insights into user acceptance and the satisfaction-influencing elements.

Oncology Clinicians' Perspectives of a Remote Patient Monitoring Program: A Multi-Modal Case Study Approach.

Remote patient monitoring (RPM) aims to improve patient access to care and communication with clinical providers. Overall, understanding the usability of RPM applications and their influence on clinical care workflows is limited from the perspectives of clinician end users at a cancer center in the Northeast, United States. Explore the usability and functionality of RPM and elicit the perceptions and experiences of oncology clinicians using RPM for oncology patients after hospital discharge. The sample included 30 of 98 clinicians (31% response rate) managing at least five patients in the RPM program and responding to the m-Health Usability between March 2021- October 2021. Overall, clinicians responded positively to the survey. Item responses with the highest proportion of disagreement were explored further. A nested sample of five clinicians who responded to the study survey (30% response rate) participated in interview sessions conducted from November 2021 to February 2022, and averaged 60 minutes each. Survey responses highlighted that RPM was easy to use and learn and verified symptom alerts during follow-up phone calls. Areas to improve identified practice changes from reporting RPM alerts through digital portals and its influence on clinicians' workload burden. Interview sessions revealed three main themes: clinician understanding and usability constraints, patient constraints, and suggestions for improving the program. Subthemes for each theme were explored, characterizing technical and functional limitations that could be addressed to enhance efficiency, workflow, and user experience. Clinicians support the value of RPM for improving symptom management and engaging with providers. Functional changes to enhance the program's utility, such as input from patients about temporal changes in their symptoms and technical resources for home monitoring devices.

Application of The UTAUT Model In Determining Behavioral Intentions To Use Shopee Paylater

In the era of digitalization, it is increasingly facilitated by the existence of several e-commerce services that provide electronic money services as a tool for conducting transactions, in addition to cellular payments there are also those that result in paylater, both of which aim to make it easier for the public to use the new features provided by each e-commerce platform trade. Shopeepay Later is present as a form of non-cash electronic money transactions that can help the public make transactions easier. This study aims to find out what factors make someone have the intention of using Shopeepay Later. This study uses a survey method conducted by distributing questionnaires through Google Forms to collect data. The method used is purposive sampling with certain criteria. The number of samples in this study were 214 respondents with the criteria of respondents being shopee application users aged 18 years and over and not using shopeepay later. The study used the UTAUT model with multiple linear regression analysis. The results of this study indicate that performance expectancy and the social influence on the intention to use shopeepay later. Meanwhile, effort expectancy and facilitating condition have no effect on behavioral intentions to use shopeepay later.

Intergenerational Methods to Promote Digital Application Usage among Older Adults: A Scoping Review

The increase in the aging population highlights the importance of digital literacy for improving the quality of life of older adults. Despite their interest in incorporating digital technology into their daily lives, numerous studies have shown that older adults often struggle with its use due to a lack of learning interest, poor memory retention, and improper instruction. To address these challenges, a scoping review was conducted, drawing from 14 eligible articles sourced from Web of Science and Scopus. The review aimed to identify recent intergenerational methods that effectively facilitate the adoption of digital applications among older adults. The findings were categorized into five intergenerational methods: collaborative, virtual meeting, co-activity, family intergenerational learning, and reverse mentoring. Within these categories, the study identified key elements crucial for the success of technology-based intergenerational programs, including creating clear objectives and goals, developing comprehensive planning and structure, demonstrating effective recruitment, providing training and support, offering facilitation, ensuring flexibility and adaptability in content, and conducting thorough evaluation and feedback. By exploring these elements, this paper provides valuable insights for researchers seeking to develop and understand how technology-based intergenerational programs are conducted.

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.

Blockchain‐Powered Framework for Trust Enhancement in FinTech: A Comprehensive Trust Evaluation Approach

ABSTRACTThe rapid advancement of financial technology (FinTech) has led to the integration of advanced technologies like data science, blockchain, cloud computing, and artificial intelligence. However, trust evaluation remains a critical challenge in dynamic landscape. Existing trust evaluation methods often neglect key aspects of timeliness, reliability, and non‐invasiveness, leading to imprecise trust assessments and insufficient detection of malicious user behavior. This paper introduces a robust four‐layer architectural framework with the blockchain layer, edge computing service layer, cloud computing service layer, and terminal user application layer leveraging blockchain technology for authentication and trust evaluation. Blockchain technology transforms FinTech data into linked data, ensuring data security and decentralization during information transfers. A novel hybrid consensus protocol combining Proof of Elapsed Time (PoET) and Proof of Stake (PoS) is introduced to enhance the efficiency and security of the blockchain. Extensive simulation experiments have demonstrated significant improvements in data security, reliability, and accuracy of trust assessments compared to existing methods. This paper presents a comprehensive solution for enhancing trust evaluation in FinTech, emphasizing timeliness, reliability, and non‐invasiveness of assessments.

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].