Desktop Apps Research Articles

Improving transparency and quality assurance: Operationalising semi-automated data provenance tracking in a Trusted Research Environment

We present a prototype solution for improving transparency and quality assurance of the data linkage process through a data provenance dashboard designed to assist data analysts, researchers and information governance teams in authenticating and auditing data workflows within a trusted research environment (TRE). Building on prior research (Scheliga, et al., 2022), this work describes our first operationalised prototype tested in a real-world setting. The prototype development involved four stages: (1) Co-design of interfaces with end users for provenance data collection and visualisation, producing a low-fidelity design; (2) Extension and refinement of the Safe Haven Provenance (SHP) ontology (https://tre-provenance.github.io/SHP-ontology/releases/v0.2/index-en.html); (3) Design and implementation of mechanisms for semi-automated collection of data linkage provenance using the SHP ontology; (4) Implementation and user evaluation of the dashboard. A participatory design process with data analysts, researchers and information governance teams resulted in a low-fidelity prototype and was validated via public consultations to ensure it met public trust. The resulting prototype dashboard (https://tre-provenance.github.io/) was built as an offline desktop app to match the deployment TRE requirements. The interactive dashboard displays the data linkage information extracted from a knowledge graph described using the SHP ontology (e.g., modifications of datasets, data release) and results of rule-based validation checks (e.g., checking extracted data against researchers' specification). User evaluations confirmed the dashboard would contribute to better quality of data linkage. This project demonstrates the next stage in advancing transparency and quality assurance within TREs by semi-automating and systematising data tracking from ingress to egress in a single tool.

MetaboReport: from metabolomics data analysis to comprehensive reporting.

Metabolomics, as an essential tool in systems biology, is now widely accessible to researchers of all levels. Yet challenges remain in data analysis and result interpretation. To address these challenges, we introduced MetaboReport, a versatile and interactive web app that simplifies metabolomics experiment design, data preprocessing, exploration, statistical analysis, visualization, and reporting. MetaboReport produces a comprehensive HTML report, including project details, an introduction, interactive plots and tables, statistical results and an in-depth explanations and interpretation of the results. MetaboReport is particularly tailored for research labs and metabolomics core facilities that provide metabolomics services, allowing them to efficiently manage and document different metabolomics projects, and effectively report the metabolomics results to users. MetaboReport is freely accessible on https://metaboreport.com, with source code available on GitHub (https://github.com/YonghuiDong/MetReport). Alternatively, users can install MetaboReport as a standalone desktop app (https://metaboreport.sourceforge.io).

Introducing the trier univalence neutrality ambivalence (TUNA) database: A picture database differentiating complex attitudes.

Using validated stimulus material is crucial for ensuring research comparability and replicability. However, many databases rely solely on bidimensional valence ratings, ranging from negative to positive. While this material might be appropriate for certain studies, it does not reflect the complexity of attitudes and therefore might hamper the unambiguous interpretation of some study results. In fact, most databases cannot differentiate between neutral (i.e., neither positive nor negative) and ambivalent (i.e., simultaneously positive and negative) attitudes. Consequently, even presumably univalent (only positive or negative) stimuli cannot be clearly distinguished from ambivalent ones when selected via bipolar rating scales. In the present research, we introduce the Trier Univalence Neutrality Ambivalence (TUNA) database, a database containing 304,262 validation ratings from heterogeneous samples of 3,232 participants and at least 20 (M = 27.3, SD = 4.84) ratings per self-report scale per picture for a variety of attitude objects on split semantic differential scales. As these scales measure positive and negative evaluations independently, the TUNA database allows to distinguish univalence, neutrality, and ambivalence (i.e., potential ambivalence). TUNA also goes beyond previous databases by validating the stimulus materials on affective outcomes such as experiences of conflict (i.e., felt ambivalence), arousal, anger, disgust, and empathy. The TUNA database consists of 796 pictures and is compatible with other popular databases. It sets a focus on food pictures in various forms (e.g., raw vs. cooked, non-processed vs. highly processed), but includes pictures of other objects that are typically used in research to study univalent (e.g., flowers) and ambivalent (e.g., money, cars) attitudes for comparison. Furthermore, to facilitate the stimulus selection the TUNA database has an accompanying desktop app that allows easy stimulus selection via a multitude of filter options.

Desktop app for detecting sleep spindles from sleep EEG data

Desktop app for detecting sleep spindles from sleep EEG data

Examining the Benefits and Challenges of Using Discord in Online Higher Education Classrooms

Building community and connection in online courses can be challenging. Discord, a mobile and desktop app popular with gamers, is explicitly designed to stimulate discussion, conversations and community. This paper explored student perceptions of the benefits and challenges of using Discord in two upper-year, undergraduate, online social science courses (n = 45). Key benefits of using Discord included connecting to students or the professor, building community, disseminating course information, increasing engagement, and establishing a casual, informal learning environment. Challenges were reported less often than benefits and included wanting a tutorial to use Discord, needing to check for notifications, and occasional technical issues. Students suggested that a more structured use of Discord might further benefit their learning.

Binary Integer Formulations for Task Allocation and Optimal Labor Cost in Small and Medium Apparel Manufacturing

In small apparel manufacturing, unit price determination is often based on production duration given by customers and design complexity rather than information relating to internal labor resources. However, labor expertise and skills are critical factors that outweigh the machinery and technology in small and medium apparel companies. The quality of the product greatly depends on the experience and delicacy of the tailors. Using data on labor skill and wage levels in the planning process will benefit human resource utilization, increasing productivity, and profits effectively. This paper proposes a general mathematical model for task allocation and cost optimization for small and medium apparel companies. The model handles task allocation and cost minimization problems that must ensure processing time requirements and balance workloads for operators. The developed model tests two case studies in a published paper. The results prove that although the proposed model is simple, it has high applicability and efficiency in solving allocation optimization problems. The authors then integrate the formulations into a Standalone desktop app in the MATLAB “App designer” module. With a standalone desktop app, end users can enjoy the application. This app has a user-friendly design. Users unfamiliar with computers or planners with no background in programming can use the app to tackle similar optimization problems. The proposed mathematical model can further expand to include more complex issues in apparel companies and can also be a good reference for other fields.

D-Crop Soil Moisture Monitoring and Controlling System for Crop Production

D-crop Soil Moisture Monitoring and Controlling System for crop production, is a system that based on assisting famers to monitor the moisture (Temperature & Humidity) and uptake of water by the crops for the better growth and development of crops. The system will help to monitor the uptake of water by the crops throughout all seasons to make sure the crops do not run out of water for more crop production. With its capability, the system is able to find out whether or not the crops are running out of water in the soil. When the crops run out of water, the system will detect through soil moisture sensor and sends the notification to the software showing the percentage of water level that is currently in the soil. Etc. The software to control the system is developed in C# language specifically with xamarin cross platform for both desktop and mobile app. The system also engages the firebase and ThingSpeak API for real time Display of data. The system will help both small scale farmers and for those willing to invest much in Farming. Irrigate the correct amount of water at right time which can lead more crop yields, less diseases and serving water as well.

EGMStream, a Desktop App for EGMS Data Downstream

The recent release of European Ground Motion Service (EGMS) products implemented under the responsibility of the Copernicus Land Monitoring Service (CLMS) guarantees free and accessible Europe-wide ground motion data for ground deformation analysis at the local and regional scales. The need for value-adding services and tools for optimal dissemination of radar data from the Copernicus Sentinel-1 satellite mission urges the scientific community to find efficient solutions. A desktop R-based application with a user-friendly interface capable of automatically downloading and transforming EGMS products delivered as large .csv tiles, equivalent to a radar burst into geospatial databases, is presented here. EGMStream is a self-contained desktop app that enables users to systematically store, customize, and convert ground movement data into geospatial databases, burst per burst or for an area of interest directly selectable on the app interface.

Development of an optimal bilayered back propagation neural network (BPNN) to identify thermal behaviors of reactions in isoperibolic semi-batch reactors

It is very important to identify the thermal behaviors of semi-batch reactors (SBRs) in isoperibolic operating mode. First, a default bilayered back propagation neural network (BPNN) is selected from multiple recognition algorithms to achieve this task, which is suitable for three kinds of reactions with arbitrary reaction orders: homogenous, kinetically−controlled, and diffusion−controlled liquid−liquid heterogeneous reactions. Then, it is further optimized by Bayesian regularization and genetic algorithm (GA). The result shows that the optimal bilayered BPNN undoubtedly has better recognition accuracy and generalization performance. It is found that the accuracy of the two test sets are 98.8% and 100%, respectively. Subsequently, a simple and user-friendly standalone desktop application program (app) is designed to easily use the developed optimal bilayered BPNN. Finally, a case of acetic anhydride hydrolysis reaction with acetic acid solvent is studied to prove the good validity of the bilayered BPNN and the great reliability of the standalone desktop app.

Toblerone: detecting exon deletion events in cancer using RNA-seq.

Cancer is driven by mutations of the genome that can result in the activation of oncogenes or repression of tumour suppressor genes. In acute lymphoblastic leukemia (ALL) focal deletions in IKAROS family zinc finger 1 (IKZF1) result in the loss of zinc-finger DNA-binding domains and a dominant negative isoform that is associated with higher rates of relapse and poorer patient outcomes. Clinically, the presence of IKZF1 deletions informs prognosis and treatment options. In this work we developed a method for detecting exon deletions in genes using RNA-seq with application to IKZF1. We developed a pipeline that first uses a custom transcriptome reference consisting of transcripts with exon deletions. Next, RNA-seq reads are mapped using a pseudoalignment algorithm to identify reads that uniquely support deletions. These are then evaluated for evidence of the deletion with respect to gene expression and other samples. We applied the algorithm, named Toblerone, to a cohort of 99 B-ALL paediatric samples including validated IKZF1 deletions. Furthermore, we developed a graphical desktop app for non-bioinformatics users that can quickly and easily identify and report deletions in IKZF1 from RNA-seq data with informative graphical outputs.

Python- An Appetite for the Software Industry

Python is a scripting language that's high- positioned, interpreted, interactive, and object- oriented. Python is intended to be a veritably accessible programming language. It generally uses English terms rather than punctuation, and it has smaller syntactical structures than other languages. Python is a must-have skill for scholars and working professionals who want to become exceptional software masterminds, especially if they work in the web development field. It's a freshman-friendly scripting language. Some of the crucial features of Python programming language are- It supports OOP as well as functional and structured programming methodologies. It can be used as a scripting language or collected into bytecode for large-scale operations. It allows dynamic type verification and provides veritably high-position dynamic data types. It facilitates scrap collection by itself. Numerous different programming languages have been impacted by Python's design and gospel. Some of those languages are Boo, Cobra, CoffeeScript, Go, Swift, Ruby, etc. Some of the advantages of Python programming language are straightforward, free, simple to use, and largely compatible, object- acquainted, has multitudinous libraries, has erected in data structures, has a wide range of uses, boosts productivity and speed, and simple to understand. One of the most extensively used programming languages is Python. It's an open- source language. Python's demand is growing, and its operations are expanding in virtually every assiduity. It's abundant in every way. It has a wide range of capabilities. Python is a popular programming language. It's also developing a strong request in the IT sector. Python is in high demand across the globe. Python helps you negotiate more in lower time. Python has a large community that supports and meets the requirements of inventors. Python is therefore one of the most popular programming languages. It's a veritably reliable and effective programming language. Python programmers are in high demand because Python is being used in a variety of sectors. Python is an extensively used computer language that was created nearly 25 years ago. Python is useful in a variety of fields, including web development, desktop app development, machine literacy, big data, data analysis, and robotics. Clean syntax, extremely clear law, a wide range of uses, packages that help apply features, and a cool community that helps grow this excellent language are just a many of the reasons why people like this language and why it's well suited for different tasks. The Python programming language has a bright future. The advanced technologies like Artificial Intelligence, Machine Learning, Big Data, Cloud Computing, Data Science, etc and world-notorious companies similar as Amazon, Google, Apple, Deloitte, Microsoft, Netflix, and Accenture have the Python programming language as their backbone which states that Python is in demand and AN APPETITE FOR THE SOFTWARE INDUSTRY! A standard and scientific procedure of an Empirical Exploration Methodology (Survey) was conducted to check the statement stated by the author where 900 repliers from colourful corridors of the globe shared their thoughts. From the check, it was concluded that 99.8% of the respondents feel that Python is one of the in- demand programming languages for the digital assiduity in the present time.

Modelling COVID-19 transmission using IDSIM, an epidemiological-modelling desktop app with multi-level immunization capabilities

The COVID-19 pandemic has placed unprecedented demands on local public health units in Ontario, Canada, one of which was the need for in-house epidemiological-modelling capabilities. To address this need, Ontario Tech University and the Durham Region Health Department developed a native Windows desktop app that performs epidemiological modelling of infectious diseases. The app is an implementation of a multi-stratified compartmental epidemiological model that can accommodate multiple virus variants and levels of vaccination, as well as public health measures such as physical distancing, contact tracing followed by quarantine, and testing followed by isolation. This article presents the epidemiological model and epidemiological-simulation results obtained using the developed app. The simulations investigate the effects of different factors on COVID-19 transmission in Durham Region, including vaccination coverage, vaccine effectiveness, waning of vaccine-induced immunity, advent of the Omicron variant and effect of COVID-19 booster vaccines in reducing the number of infections and severe cases. Results indicate that, for the Delta variant, natural immunity, in addition to vaccination-induced immunity, is necessary to achieve herd immunity and that waning of vaccine-induced immunity lengthens the time necessary to reach herd immunity. In the absence of additional public health measures, a wave driven by the Omicron variant is predicted to pose significant public health challenges with infections predicted to peak in approximately two to three months, depending on the rate of administration of booster doses.

Classification and optimization of homogeneous reactions with arbitrary reaction orders performed in isoperibolic semi-batch reactors using θMTSR criterion

Runaway reactions are very dangerous, and studies on them are indispensable. A lot of criteria have been developed to classify different thermal behaviors. A fine criterion (named as MTSRC) for 2-order homogenous reactions performed in isoperibolic SBRs has also been developed in our previous work. In this paper, firstly MTSRC is amended and renamed as θMTSR criterion. Then θMTSR criterion is further extended to reactions with arbitrary reaction orders. Based on θMTSR criterion, the boundary diagrams (BDs) are reconstructed with a wider scope of model parameters and more data points compared to BDs in previous work, and particularly the influence of reaction orders on BDs is studied. Subsequently adiabatic temperature diagrams (ATDs) with arbitrary reaction orders are innovatively plotted. Finally, the practical applications based on θMTSR criterion, constructed BDs and ATDs are discussed. Classification and optimization procedures for this kind of reaction are proposed, and especially a standalone desktop application program (app) is exploited to let users easily execute these two algorithms. A case study is also shown to verify the developed standalone desktop app.

IHVI: AN OPEN-SOURCE TOOLKIT FOR CONSTRUCTING INTEGRATED HEAT VULNERABILITY INDEX IN AUSTRALIA

Abstract. To tackle the increasing issue of heat risk in Australia and pressure of population growth, this project aimed to establish a first nationwide dynamic and interactive heat vulnerability assessment toolkit. The toolkit integrated multiple data sources, cloud computing, and Web GIS technologies to deliver cool intelligence for more heat resilient Australian cities and suburbs. A cloud-based open-source tool, iGEE, was first developed to derive land surface temperature (LST), NDBI (normalized difference built-up index) and NDVI (normalized difference vegetation index) from multiple satellite imagery on Google Earth Engine (GEE), a no-code web application allowing users to retrieve satellite data for a large area at fine scales. Following that, a python-based desktop app was then developed to calculate an integrated Heat Vulnerability Index (iHVI) for any cities and area at a fine scale of Statistical Area 1 (SA1). The iHVI toolkit allows users to construct heat sensitivity, heat adaptive capability indicators, and composite heat vulnerability index, which enables modelling of the relationships between heat, environmental and socioeconomic factors.

CortexVR: Immersive analysis and training of cognitive executive functions of soccer players using virtual reality and machine learning.

GoalThis paper presents an immersive Virtual Reality (VR) system to analyze and train Executive Functions (EFs) of soccer players. EFs are important cognitive functions for athletes. They are a relevant quality that distinguishes amateurs from professionals.MethodThe system is based on immersive technology, hence, the user interacts naturally and experiences a training session in a virtual world. The proposed system has a modular design supporting the extension of various so-called game modes. Game modes combine selected game mechanics with specific simulation content to target particular training aspects. The system architecture decouples selection/parameterization and analysis of training sessions via a coaching app from an Unity3D-based VR simulation core. Monitoring of user performance and progress is recorded by a database that sends the necessary feedback to the coaching app for analysis.ResultsThe system is tested for VR-critical performance criteria to reveal the usefulness of a new interaction paradigm in the cognitive training and analysis of EFs. Subjective ratings for overall usability show that the design as VR application enhances the user experience compared to a traditional desktop app; whereas the new, unfamiliar interaction paradigm does not negatively impact the effort for using the application.ConclusionThe system can provide immersive training of EF in a fully virtual environment, eliminating potential distraction. It further provides an easy-to-use analyzes tool to compare user but also an automatic, adaptive training mode.

Automatic computer science domain multiple-choice questions generation based on informative sentences.

Students require continuous feedback for effective learning. Multiple choice questions (MCQs) are extensively used among various assessment methods to provide such feedback. However, manual MCQ generation is a tedious task that requires significant effort, time, and domain knowledge. Therefore, a system must be present that can automatically generate MCQs from the given text. The automatic generation of MCQs can be carried out by following three sequential steps: extracting informative sentences from the textual data, identifying the key, and determining distractors. The dataset comprising of various topics from the 9th and 11th-grade computer science course books are used in this work. Moreover, TF-IDF, Jaccard similarity, quality phrase mining, K-means, and bidirectional encoder representation from transformers techniques are utilized for automatic MCQs generation. Domain experts validated the generated MCQs with 83%, 77%, and 80% accuracy, key generation, and distractor generation, respectively. The overall MCQ generation achieved 80% accuracy through this system by the experts. Finally, a desktop app was developed that takes the contents in textual form as input, processes it at the backend, and visualizes the generated MCQs on the interface. The presented solution may help teachers, students, and other stakeholders with automatic MCQ generation.

Abstract 6400: JBrowse 2: An extensible open-source platform for modern genome analysis

Abstract Introduction: Genome browsers are useful tools for research in human genetics, able to display and integrate biological information such as long and short read sequencing data, variant calls, and annotations. While traditional linear genome browsers have demonstrated broad utility for many applications, there is an increasing need for visualizing complex structural variants (SVs) beyond the linear view. To meet these challenges, we created JBrowse 2: an extensible open-source platform for visualizing and integrating genomic data. Results: JBrowse 2 is a flexible platform that provides the foundation for the development of applications and web dashboards that combine novel views and representations extending beyond linear displays in genomic reference coordinates. For example, a flagship JBrowse 2 application is the SV Inspector, which lets users open a list of structural variants in a data table and see the results in a whole-genome circular view. Clicking on a given variant in the table or circular view opens up a linear view that displays the read evidence supporting the SV, even across complex breakpoints like translocations. Dotplot and synteny views are built-in, enabling “long read vs reference” dotplot visualizations, or alignments of de-novo assembled contigs to the genome. These views are integrated with each other, so that (for example) a long read is just a few clicks away from the corresponding dotplot. In addition to including these new views, the JBrowse 2 platform is designed from the ground up to enable third-party plugins to add new views, data adapters, and track types, which has facilitated the creation of new plugins that address some very specific use cases. These include our MSA view plugin for viewing multiple sequence alignments, data adapters that download data from UCSC and CIVIC APIs, and the Quantseq plugin for viewing quantitative motif scores as a genome browser track. JBrowse 2 is available as both a web app or a local desktop app. We also offer re-usable components on NPM, and users of R can programmatically create an instance of JBrowse 2 with the JBrowseR package on CRAN. JBrowse 2 can also generate high quality SVG snapshots from inside the app, and we also created a CLI tool called jb2export to perform automated or bulk exports of JBrowse 2 visualizations. We anticipate that JBrowse 2 will better serve genome scientists with its structural variant visualization capabilities, and will provide the flexibility to adapt to new visualization and analysis challenges as they emerge in the coming years. Citation Format: Scott Cain, Robin Haw, Caroline Bridge, Junjun Zhang, Robert Buels, Colin Diesh, Garrett Stevens, Teresa Martinez, Peter K. Xie, Elliot Hershberg, Shihab Dider, Lincoln Stein, Ian Holmes. JBrowse 2: An extensible open-source platform for modern genome analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6400.

Increasing On-Task Behavior Using Technology-Based Self-Monitoring: A Meta-Analysis of I-Connect

Self-monitoring is a promising evidence-based intervention for students who benefit from supplemental supports to stay on-task during academic periods. I-Connect, a technology-based self-monitoring intervention with a substantial body of research, allows students to discretely recognize and record their behavior on a mobile or desktop app at scheduled intervals, to improve positive behavior and increase inclusion opportunities. This meta-analytic review examined the effect of I-Connect on the on-task behavior of students with or at risk for disabilities to determine the omnibus effect of using I-Connect across students and intervention packages. Students received 20–45 minutes of training before using I-Connect and most students monitored their on-task behavior every 30-seconds during 10-minute monitoring sessions. Under these conditions, I-Connect was found to demonstrate strong functional relations, an abrupt increase in on-task behavior and consistently positive parametric effects across all 14 elementary and secondary students receiving special education.

Digital‐first assessments: A security framework

AbstractBackgroundDigital‐first assessments leverage the affordances of technology in all elements of the assessment process: from design and development to score reporting and evaluation to create test taker‐centric assessments.ObjectivesThe goal of this paper is to describe the engineering, machine learning, and psychometric processes and technologies of a test security framework (part of a larger ecosystem; Burstein et al., 2021) that can be used to create systems that protect the integrity of test scores.MethodsWe use the Duolingo English Test to exemplify the processes and technologies that are presented. This includes methods for actively detecting and deterring malicious behaviour (e.g., a custom desktop app). It also includes methods for passively detecting and deterring malicious behaviour (e.g., a large item bank created through automatic generation methods). We describe the certification process that each test administration undergoes, which includes both automated and human review. Additionally, we describe our quality assurance dashboard which leverages psychometric data mining techniques to monitor test quality and inform decisions about item pool maintenance.Results and ConclusionsAs assessment developers transition to online delivery and to a design approach that places the test taker at the centre, it becomes increasingly important to take advantage of the tools and methodological advances in different fields (e.g., engineering, machine learning, psychometrics). These tools and methods are essential to maintaining the security of assessments so that the score reliability is sustained and the interpretations and uses of test scores remain valid.

Improving Material Property Understanding with Virtual Experiments: A New Approach to Teach About Mechanical Testing of Materials Using an Interactive Desktop App

The knowledge of the mechanical properties of polymers and other materials is essential for several tasks in the field of materials science, like component design and material development. The tensile test is one method of determining many of the material’s most defining mechanical characteristics. However, the teaching of laboratory experiments can be difficult when it needs to be incorporated into online-based education. This study presents a teaching concept that leads students on an online learning platform through the execution of the tensile test with educational videos, handouts, and an interactive desktop app. The app allows to simulate tensile tests of currently 79 materials at various testing temperatures and to compare their properties. Students can learn from exploring the different materials and are also able to extend the app’s database for their personal use. Learning progress was evaluated with tests before and after the teaching unit, which significantly increased the achieved score afterward. Further results from a survey of the participating students indicate that they appreciate the online-based teaching concept and that the prepared learning tools motivated them to deal with the subject matter.