Friendly User Research Articles

Business Intelligence for Unemployment Rate Management System

Unemployment rate is one of many problems which being faced by the government for each country, especially in Indonesia. Based on statistics data, Central Bureau of Statistics (Indonesia), unemployment rate in Indonesia is quite high which scattered throughout the province. Those data was obtained with a long proccess, by the time, human resources and cost which are not small. Thus, this paper proposes business intelligence for unemployment rate management system to predict from its several causes which are potentially on increasing unemployment rate, using Business Intelligence Roadmap methods, it used because its adaptive and detail which consists of 18 stages from 6 phases. There is one of the 18 stages, namely data mining, for this data mining using the KNN algorithm. Business intelligence can proccessing data into useful information or knowledge. Generally, business intelligence has main process which are data collection, then this data would be processed by ETL (Extract, Transform, Load) before get into data warehouse as a place data storage, so that those data could functionally used for analysis process with OLAP and data mining to classify the result of unemployment rate prediction from its several potentially causes. This paper would possibly to find patterns in the unemployment rate and its causes then the result of the pattern will visualizations on web application with business intelligence based that would be developed that easy to used / user friendly and attractive user interface

Design of SmartMoving, an application for pedestrians with reduced mobility

The state of sidewalks in Asunción, Paraguay is far from being optimal. There are many problems such as obstructions, necessary repairs, lack of ramps, unevenness of surface, among others. In addition, the Municipality of Asunción does not have automated mechanisms to know the updated status of sidewalks. In this work we propose SmartMoving, a mobile application that collects information on the state of the sidewalks, with the help of citizens, and recommends pedestrian paths with fewer obstacles. The application can be especially useful for people with reduced mobility, as well as for the Municipality of Asunción. This type of application is based on citizen participation, since it receives data from them, and therefore requires a particularly friendly user experience, adapted to users and their daily context. Therefore, the need for a participatory and user-centered design, as a basis for the development of the application. Therefore, in this paper we present the SmartMoving application and the user-centered design process that has been followed for its development, involving users with reduced mobility.

Online Interactive Platform for COVID-19 Literature Visual Analytics: Platform Development Study.

BackgroundPapers on COVID-19 are being published at a high rate and concern many different topics. Innovative tools are needed to aid researchers to find patterns in this vast amount of literature to identify subsets of interest in an automated fashion.ObjectiveWe present a new online software resource with a friendly user interface that allows users to query and interact with visual representations of relationships between publications.MethodsWe publicly released an application called PLATIPUS (Publication Literature Analysis and Text Interaction Platform for User Studies) that allows researchers to interact with literature supplied by COVIDScholar via a visual analytics platform. This tool contains standard filtering capabilities based on authors, journals, high-level categories, and various research-specific details via natural language processing and dozens of customizable visualizations that dynamically update from a researcher’s query.ResultsPLATIPUS is available online and currently links to over 100,000 publications and is still growing. This application has the potential to transform how COVID-19 researchers use public literature to enable their research.ConclusionsThe PLATIPUS application provides the end user with a variety of ways to search, filter, and visualize over 100,00 COVID-19 publications.

Social determinants of addiction in population Middle age in the city of Yazd

Background: There are several factors involved in drug abuse and since some of these effective factors, especially social factors, can be changed and corrected; By recognizing them, more effective prevention programs can be implemented.This case study was performed with the aim of determining the social factors of addiction in people middle age living in the city of Yazd. Methods: In this case-control study during 2019- 2020, 150 Substance user who referred to methadone maintenance treatment centers (MMTC) were involved in a case group and 150 subjects, matched in terms of sex and age, were selected as a control group during 2019. The cases were selected by cluster sampling method from Yazd methadone maintenance treatment centers. The selection of witnesses was done from the neighbors of the cases. Data collection tool was a researcher-made questionnaire that includes three parts: demographic questions, economic and social factors and Substance use-related questions. The content validity of the questionnaire was affirmed by experts and its reliability, obtained by Cronbach's alpha coefficient, was 0.75. The cases were selected by cluster sampling from MMTC. The data were analyzed using SPSS software and logistic regression model was used to find the related characteristics. Results: The results of logistic regression model show that individuals working in non-organizational jobs had the highest share in relation to Substance user , with an odds ratio of 4.65. The use of drugs, Substance user first-degree relatives, and Substance user friends were with the odds ratio of 3.4, 2.97, and 2.6, respectively are significantly related to Substance user and have the highest risk for Substance user (P = 0.0001). Conclusion: The results of this study indicate that Substance user individuals had more social risk factors compared to the general population. Therefore, planned measures to reduce these risk factors among the community, especially young people and their friends, by family and community officials are necessary. And the family plays a decisive role in choosing a friend for their children.

Microrisk Lab: An Online Freeware for Predictive Microbiology.

Microrisk Lab is an R-based online modeling freeware designed to realize parameter estimation and model simulation in predictive microbiology. A total of 36 peer-reviewed models were integrated for parameter estimation (including primary models of bacterial growth/inactivation under static and nonisothermal conditions, secondary models of specific growth rate, and competition models of two-flora growth) and model simulation (including integrated models of deterministic or stochastic bacterial growth/inactivation under static and nonisothermal conditions) in Microrisk Lab. Each modeling section was designed to provide numerical and graphical results with comprehensive statistical indicators depending on the appropriate data set and/or parameter setting. In this study, six case studies were reproduced in Microrisk Lab and compared in parallel with DMFit, GInaFiT, IPMP 2013/GraphPad Prism, Bioinactivation FE, and @Risk, respectively. The estimated and simulated results demonstrated that the performance of Microrisk Lab was statistically equivalent to that of other existing modeling systems. Microrisk Lab allows for a friendly user experience when modeling microbial behaviors owing to its interactive interfaces, high integration, and interconnectivity. Users can freely access this application at https://microrisklab.shinyapps.io/english/ or https://microrisklab.shinyapps.io/chinese/.

Design and implementation of strain testing system based on Qt

Strain testing system is an important research direction in the field of engineering testing, which is widely used in structural health monitoring, motion detection, civil engineering, and other fields. Aiming at the shortcomings of the traditional strain testing system, a strain testing system based on Qt is designed and developed independently, which can collect 8 channels of strain data at the same time and realize the interaction between software and hardware of the system. The system software is developed by Qt, integrated with signal filtering and spectrum analysis algorithms, and designed with a friendly graphical user interface, which is easy to operate. Through the test, the system can reliably measure the strain data and display the waveform of the signal in real-time. It has the advantages of high acquisition efficiency, stable running, good real-time performance and good scalability, which can provide a technical reference for the improvement of the strain test system.

1267-PUB: Sustained Reduction of A1C through Use of AI Digital Coach and Consumer-Friendly Glucose Measurement

Management of diabetes has always depended upon sustained self-management by the person with diabetes. Data continues to show that people do a poor job of diabetes management. Many telehealth solutions achieve short-term, but not sustained results. This investigated platform implements two unique elements to improve the ability to achieve sustained self-management. One element is an Artificial Intelligence (AI) digital coach that exists in the mobile app--keeping the right behaviors in front of people with diabetes and encouraging continual engagement. The second element is an integrated glucose meter with a dramatically more friendly user experience--defined by smaller size, discreet (no visible assembly), and less painful. The platform has been commercialized for more than 12 months. A clinical registry has been established and currently includes 50 commercial users of the platform and more than 25,000 glucose tests are included. Demographics include both type 1 and type 2, 42% male, 58% female, with ages ranging from 24 to 69 years old. With use of the platform, Figure 1 shows a significant decrease in A1C, as calculated from glucose measurements, of 1.4 points (p = 0.0001) in early use to a sustained reduction of 0.9 points (p=0.0001). These results may indicate that easier visibility of results causes immediate reduction of glucose and that this easier visibility combined with the AI coach may sustain results. These results potentially demonstrate that a simpler experience for self-management will improve sustained diabetes management results. Future studies of the registry data will include improved compliance toward ADA guidelines and glucose time in range. Disclosure T. Balder: Advisory Panel; Self; POPS Diabetes Care.

Research and development of visual numerical simulation software for transient process of hydropower station

Transient process is the basis of the safe and stable operation of hydropower station. The calculation of transient process is realized by professional numerical simulation software. This paper presents the research and development of the visual numerical simulation software for transient process of hydropower station. The software is designed and developed based on the Java language and the Eclipse and IntelliJ IDEA development environment. The research and development thought contains five aspects. Firstly, the mathematical model for the transient process of hydropower station is established and the algorithm is adopted. The program for numerical simulation is designed. Secondly, based on the modularization design principle, the object modules and operating condition modules are designed and encapsulated. Thirdly, the user-oriented visual interface of the software is designed and programmed. The visual interface includes menu bar, toolbar and plotting area, which realize the drawing of topology of hydropower station, input of parameters, setting of calculation conditions and post-processing of calculation results. Fourthly, the foreground and background interaction based on graphic interface is achieved to realize the recognition of topology, automatic coding of object modules and transfer of foreground data. Finally, the mathematical model, algorithm, object modules, operating condition modules, visual interface and foreground and background interaction are integrated and the development of the visual numerical simulation software is completed. The visual numerical simulation software for transient process of hydropower station is a general software and has a friendly user interface. Test and application of practical engineering prove the correctness, reliability and precision of the software. The visual numerical simulation software provides a simple, practical and efficient tool for the calculation of transient process of hydropower station.

Correction pen as a hydrophobic/lipophobic barrier plotter integrated with paper-based chips and a mini UV-torch to implement all-in-one device for determination of carbazochrome

The design of a cheap, simple, and handy sensing system for rapid quantitation of pharmaceuticals becomes mandatory to ease drug development procedures, quality control, health care, etc. This work describes a simple, innovative, and easily manufactured paper-based device using a correction pen as a plotter for hydrophobic/lipophobic barriers and graphene quantum dots for recognition and quantification of the hemostatic drug carbazochrome, via fluorescence turn-off mechanism mediated by the inner filter effect. A smartphone-based all-in-one device fitted with an inexpensive 365 nm flashlight as a UV light source and a free image processing software was developed for rapid and reliable interpretation of the fluorescence change from the paper-based device upon introduction of the drug. The simple and convenient steps permit the analysis of many samples in a very short time. The smartphone-based all-in-one device featured excellent sensitivity for carbazochrome with a limit of detection equals to 12 ng/detection zone and good %recovery (100.0 ± 0.4). The reliability of the device was ascertained by favorable statistical comparison with the analogous optimized conventional fluorimetry method and a reference HPLC method. The device has been successfully applied for versatile quantitation of carbazochrome in tablets and on manufacturing equipment surfaces with excellent recoveries. The device offers many green aspects that definitely assist the implementation of the sustainability concept to analytical laboratories. The cost-efficiency, reliability, and ease of fabrication as well as the greenness and user friendship qualify the device for wide application in low-income communities.

GridFree: a python package of imageanalysis for interactive grain counting and measuring.

Grain characteristics, including kernel length, kernel width, and thousand kernel weight, are critical component traits for grain yield. Manual measurements and counting are expensive, forming the bottleneck for dissecting these traits’ genetic architectures toward ultimate yield improvement. High-throughput phenotyping methods have been developed by analyzing images of kernels. However, segmenting kernels from the image background and noise artifacts or from other kernels positioned in close proximity remain as challenges. In this study, we developed a software package, named GridFree, to overcome these challenges. GridFree uses an unsupervised machine learning approach, K-Means, to segment kernels from the background by using principal component analysis on both raw image channels and their color indices. GridFree incorporates users’ experiences as a dynamic criterion to set thresholds for a divide-and-combine strategy that effectively segments adjacent kernels. When adjacent multiple kernels are incorrectly segmented as a single object, they form an outlier on the distribution plot of kernel area, length, and width. GridFree uses the dynamic threshold settings for splitting and merging. In addition to counting, GridFree measures kernel length, width, and area with the option of scaling with a reference object. Evaluations against existing software programs demonstrated that GridFree had the smallest error on counting seeds for multiple crop species. GridFree was implemented in Python with a friendly graphical user interface to allow users to easily visualize the outcomes and make decisions, which ultimately eliminates time-consuming and repetitive manual labor. GridFree is freely available at the GridFree website (https://zzlab.net/GridFree).

Precautionary Procedures in Basrah Oil Fields Drilling Operations Using Smartphones

Basrah oil fields carry multiple drilling problems that are drilled with difficulty, theseproblems are often treated incompetently. This inefficiency in the procedures in handling theproblems often results in a long wait around time. This study aims to offer the bestprecautionary procedures that can be taken in the form of a smartphone’s application(Precautions Enhancer) to reach trustworthy precautions anywhere needed on the oil field.
 The introduction of smartphones to the drilling engineering in particular and the petroleumindustry in general is a critical and a very useful tool that could be developed to gain accuracyand speed on field.The plans of precautions for the expected drilling problems are carried out. The includedformations extend from the upper and lower Faris to Mishrif. The precautions that are set as thebest practice are based on field experience and standardized IWCF methods. The usedprogramming techniques are: the classify algorithm (forward) technique is used to enhancePrecautions Enhancer’s (PE) abilities to avoid the problems.An application called Precautions Enhancer (PE) having a friendly user interface that can be usedin the field is built with a full secure database of the fields’ problems and precautions with adatabase of the drilling problems in Basra oil fields in PE; all of the drilling problems that couldencountered while drilling with their precautionary procedures are all mounted in the database ofPE.

A 2-DOF Needle Insertion Device Using Inertial Piezoelectric Actuator

Insertion into delicate tissue, such as retina vein occlusion, cell manipulation, and vitreoretinal injection, poses challenges to surgeons and biomedical researchers due to the high resolution, large movement range, compact structure, and multi-DOF output requirements. In order to meet these requirements, a 2-DOF arched needle insertion device driven by inertial piezoelectric actuator was proposed in this article. This device could perform theoretical unlimited translational movement and full rotation with high step resolution. A prototype was fabricated with compact structure, the length and diameter were 230 and 32 mm, respectively. The experimental tests were carried out, the insertion device output 2.47 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m and 0.56 mrad step distance with maximum velocity of 382 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m/s and 200.9 mrad/s under 120 V driving signals, the maximum insertion force of 32 mN was measured. Additionally, a handheld controller with friendly user interface was also developed for open-loop control utilization, which could automatically calculate and generate the required control signals according to the motion command. This device explores the biomedical application of inertial piezoelectric actuator within a compact size, providing a new choice for numerous innovative treatment and biological research involving precision operation.

Die Attach Curing Program Automation of N2 Parameter for Process Robustness

In the world of semiconductor industry, automation plays a big role on every manufacturing plants to improve efficiency, prevent product yield losses, and making sure that top quality products will be delivered on end user. On die attach curing process of manufacturing semiconductor integrated circuits (IC) devices, certain problems occur like wrong Nitrogen (N2) parameter setting on the oven curing machine, with human intervention during setup, causing leadframe oxidation related defects. The paper discussed the importance of automation system on processing semiconductor IC products on die attach curing process, eliminating mostly human intervention on setting up machine parameters, storing and managing programs, restrict unauthorize users from accessing, provide friendly user procedures to prevent human errors and prevent oxidation related defects.

Delivering Perinatal Health Information via a Voice Interactive App (SMILE): Mixed Methods Feasibility Study.

BackgroundPerinatal health care is critically important for maternal health outcomes in infants. The United States fares considerably worse than comparable countries for maternal and infant mortality rates. As such, alternative models of care or engagement are warranted. Ubiquitous digital devices and increased use of digital health tools have the potential to extend the reach to women and infants in their everyday lives and make a positive impact on their health outcomes. As voice technology becomes more mainstream, research is prudent to establish evidence-based practice on how to best leverage voice technology to promote maternal-infant health.ObjectiveThe aim of this study is to assess the feasibility of using voice technology to support perinatal health and infant care practices.MethodsPerinatal women were recruited from a large Midwest Children’s Hospital via hospital email announcements and word of mouth. Owing to the technical aspects of the intervention, participants were required to speak English and use an iPhone. Demographics, patterns of technology use, and technology use specific to perinatal health or self-care practices were assessed at baseline. Next, participants were onboarded and asked to use the intervention, Self-Management Intervention–Life Essentials (SMILE), over the course of 2 weeks. SMILE provided users with perinatal health content delivered through mini podcasts (ranging from 3 to 8 minutes in duration). After each podcast, SMILE prompted users to provide immediate verbal feedback to the content. An exit interview was conducted with participants to gather feedback on the intervention and further explore participants’ perceptions of voice technology as a means to support perinatal health in the future.ResultsIn total, 19 pregnant women (17 to 36 weeks pregnant) were consented. Themes identified as important for perinatal health information include establishing routines, expected norms, and realistic expectations and providing key takeaways. Themes identified as important for voice interaction include customization and user preferences, privacy, family and friends, and context and convenience. Qualitative analysis suggested that perinatal health promotion content delivered by voice should be accurate and succinctly delivered and highlight key takeaways. Perinatal health interventions that use voice should provide users with the ability to customize the intervention but also provide opportunities to engage family members, particularly spouses. As a number of women multitasked while the intervention was being deployed, future interventions should leverage the convenience of voice technology while also balancing the influence of user context (eg, timing or ability to listen or talk versus nonvoice interaction with the system).ConclusionsOur findings demonstrate the short-term feasibility of disseminating evidence-based perinatal support via podcasts and curate voice-captured data from perinatal women. However, key areas of improvement have been identified specifically for perinatal interventions leveraging voice technology. Findings contribute to future content, design, and delivery considerations of perinatal digital health interventions.

New Features of P3δ software: Partial Pole Placement via Delay Action

This paper presents the software Partial Pole Placement via Delay Action, or P3$\delta$ for short. P3$\delta$ is a Python software with a friendly user interface for the design of parametric stabilizing feedback laws with time-delays, thanks to two properties of the distribution of quasipolynomials' zeros, called multiplicity-induced-dominancy and coexisting real roots-induced-dominancy. After recalling recent theoretical results on these properties and their use for the feedback stabilization of control systems operating under time delays, the paper presents the main features of the current version of P3$\delta$. We detail, in particular, the assignable admissible region (the set of allowable dominant roots and the corresponding delay), which helps the user in the choice of input information, allowing a reliable stabilizing delayed feedback. We also present the newly set online version of P3$\delta$.

Enhanced Service Framework Based on Microservice Management and Client Support Provider for Efficient User Experiment in Edge Computing Environment

Leveraging the edge computing paradigm, computing resources are deployed in the network edge to provide heterogeneous services. Edge computing delivers sensing and actuating services to the Internet from the constrained Internet of Things (IoT) devices. Meanwhile, management of various elements is provided by offloading sufficient computing and storage to the edge of the networks for the IoT environments such as home, factory, and private spaces without cloud servers. In this paper, we propose an enhanced service framework based on microservice management and client support provider for efficient user experiments in the edge computing environment. For providing the edge computing service and management in the network edge, this paper presents an edge-computing architecture that provides various functions through microservice modules on the edge platform engine. Through the microservices, the interfaces are provided to the client to access the device, data, and additional services. Using Docker, the microservice modules are deployed in the edge platform to provide the services. However, the services and management functions need to be presented to the clients based on the friendly user interfaces. For providing the user interfaces of the services and Docker engine to the clients, the client support service provider is developed and deployed in the network edge based on the edge platform. Therefore, the proposed edge platform provides the services and management to the users for accessing the resources and functions through visualized interfaces in the IoT environment based on edge computing. The performance of our proposed system can be checked through the test result screen and delay time. Compared to controlling edge computing by using a command-line tool for users, we made it easy for general users who are not computer savvy to access edge services through a graphic user interface. And by measuring the delay time and comparing the execution time, it can be seen that the proposed system operates faster.

DAN-SNR

Next (or successive) point-of-interest (POI) recommendation, which aims to predict where users are likely to go next, has recently emerged as a new research focus of POI recommendation. Most of the previous studies on next POI recommendation attempted to incorporate the spatiotemporal information and sequential patterns of user check-ins into recommendation models to predict the target user's next move. However, few of the next POI recommendation approaches utilized the social influence of each user's friends. In this study, we discuss a new topic of next POI recommendation and present a deep attentive network for social-aware next POI recommendation called DAN-SNR. In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner. Moreover, we design and implement two parallel channels to capture short-term user preference and long-term user preference as well as social influence, respectively. By leveraging multi-head self-attention, the DAN-SNR can model long-range dependencies between any two historical check-ins efficiently and weigh their contributions to the next destination adaptively. We also carried out a comprehensive evaluation using large-scale real-world datasets collected from two popular location-based social networks, namely, Gowalla and Brightkite. Experimental results indicate that the DAN-SNR outperforms seven competitive baseline approaches regarding recommendation performance and is highly efficient among six neural-network-based methods, four of which utilize the attention mechanism.

Data Learning of Fluid Dynamics for Physically Informed Digital Twins

We present a novel realtime digital twin based on artificial intelligence to emulate physically sound fluid dynamics, and classify and recognise liquids, with information from video streamings. Results are presented with augmented reality techniques not only for friendly user interaction, but also to provide augmented information in manipulation tasks.

The impact of common neighbor algorithm on individual friend choices and online social networks

Online social platforms have become increasingly more popular for people to make new friends, by relying on the friend recommendation algorithms implemented on social networks. Nevertheless, how these algorithms impact the choice of friends of individual users and the structure of overall social networks remains unknown. In this paper, we introduce a model in which a group of users interact and make friends as recommended by the common neighbor algorithm, which is one of the most commonly used friend recommendation algorithms, to study the impact of the algorithms on the choice of friends of users in social networks. Based on our results, we found that the algorithm is mostly effective in identifying good matches, but users may group themselves into sub-optimal clusters when they over-rely on the algorithms. These results demonstrate the pros and cons of the increasingly more popular common neighbor algorithm applied in social networks. The model is then examined with the attribute similarity matrix obtained from two real datasets, and the results are consistent with our earlier findings. We also investigate the impacts of user reputation on the common neighbor algorithm and found that users with high reputation may become network hubs connected with a majority of users on the platform. Despite the simplicity of our developed model, our results provide interesting insight into the impact of common neighbor algorithm on friend choices of users and the global characteristics of social networks.

BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience

BayeSuites is the first web framework for learning, visualizing, and interpreting Bayesian networks (BNs) that can scale to tens of thousands of nodes while providing fast and friendly user experience. All the necessary features that enable this are reviewed in this paper; these features include scalability, extensibility, interoperability, ease of use, and interpretability. Scalability is the key factor in learning and processing massive networks within reasonable time; for a maintainable software open to new functionalities, extensibility and interoperability are necessary. Ease of use and interpretability are fundamental aspects of model interpretation, fairly similar to the case of the recent explainable artificial intelligence trend. We present the capabilities of our proposed framework by highlighting a real example of a BN learned from genomic data obtained from Allen Institute for Brain Science. The extensibility properties of the software are also demonstrated with the help of our BN-based probabilistic clustering implementation, together with another genomic-data example.