User Interface Research Articles

A Web-Interface Based Decision Support System for Optimizing Home Healthcare Waste Collection Vehicle Routing

Background: The significant increase in home healthcare (HHC) driven by technological advancements, an ageing population, and heightened disease outbreaks—especially evident during the COVID-19 pandemic—has created an urgent need for improved medical waste management. Methods: This paper presents the development of a decision support system with a web-based interface designed for efficient medical waste collection in the HHC sector. Results: The system utilises Flask for backend operations, with HTML and CSS for the user interface, and manages data using JSON files. Its flexible design supports real-time adjustments for various vehicle types and changing waste production locations. It incorporates dynamic routing by employing two sophisticated metaheuristic algorithms: the Strength Pareto Evolutionary Algorithm (SPEA-2) and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). This setup supports different dataset sizes and vehicle fleets, including Internal Combustion Engine (ICE) vehicles and Electric Vehicles (EVs). Conclusions: The automation reduces uncertainties in waste collection by minimising human intervention. The system is built to be easily adaptable for other sectors with minor modifications and can be expanded to test various scenarios with new selectable parameters.

Blood Management System Using Java with GUI and Array List-based Database

This research paper presents the development of a Blood Management System (BMS) using Java with a graphical user interface (GUI), ArrayList as a data structure for managing records, and file handling for persistence. The system provides an efficient and user-friendly platform for blood donation management, focusing on functionalities accessible to an administrative user (admin) only. Key features include donor data management (adding, updating, searching, deleting), stock management of blood groups, and essential application controls like login/logout and exit functionalities. This system aims to streamline the management of blood banks by providing quick access to critical information such as donor location, blood group, and stock availability. The source code for the Blood Management System is accessible at https://github.com/BloodBankManagment.

Patients Monitoring and Data Management System for Hospitals

This work concerns creating a monitoring system for a smart hospital using Raspberry Pi to measure vital signs. The readings are continually sent to central monitoring units outside the room instead of being beside the patients, to ensure less contacting between the medical staff and patients, also the cloud is used for those who leave the hospital, as the design can track on their medical cases. Data presentation and analysis were accomplished by the LabVIEW program. A Graphical User Interface (GUI) has been created by the Virtual-Instrument (VI) of this program that offer real-time access to monitor patients' measurements. If unhealthy states are detected, the design triggers alerts and sends SMS message to the doctor. furthermore, the clinicians can scan a QR code (which is assigned to each patient individually) to access its real-time measurements. The system also utilizes Electrocardiography (ECG) to detect abnormalities and identify specific heart diseases based on its extracted parameters to encourage patients to seek timely medical attention, while aiding doctors in making well-informed decisions. To evaluate the system's performance, it is tested in the hospital on many patients of different ages and diseases as well. According to the results, the accuracy measurement of SpO2 was about 98.39%, 97.7% for (heart rate) and 98.7% for body temperature. This shows that the system can offer many patients receiving health services from various facilities, and it ensures efficient data management, access control, real-time monitoring, and secure patient information aligning with healthcare standards.

Digital Storage Oscilloscope with Frequency Control & Real-time Visualization

Project presents a digital storage oscilloscope with real-time visualization capabilities and frequency control. The system is based on the flexible ESP32 microcontroller architecture, which allows for the creation of a wide range of waveforms at tunable frequencies. Quick waveform adaptation is made possible by users' easy control over the oscillator's frequency in real-time through simple input techniques. Additionally, the system has a graphical user interface that allows users to instantly visualize created signals and easily monitor waveform properties. The oscillator is appropriate for a variety of applications in industries like audio synthesis, instrumentation, and telecommunications because it also provides sophisticated control options and signal processing capabilities. This comprehensive solution meets the needs of both enthusiasts and experts in signal production and visualization by providing a practical and versatile tool for those working in

Based on STM32 SCM Intelligent Greenhouse

With the continuous growth of the global population and the continuous development of agricultural production, there are many problems in the traditional agricultural production mode, such as resource waste, low production efficiency, environmental pollution, and the urgent need for innovative solutions to meet the growing food demand and sustainable development goals. This paper introduces an intelligent greenhouse system based on STM 32 single-chip microcomputer, using advanced technology and automation functions, in improving the efficiency, quality and sustainability of agricultural production. The hardware composition of the intelligent greenhouse system includes STM 32 MCU, various sensor actuators, ESP8266 module and user interface module.

Understanding User Intention to Adopt Augmented Reality Head-Up Display: A Study Based on Design Factors

Augmented Reality Head-Up Display (AR-HUD) is designed to engender immersive experiences and possess vast potential in enhancing vehicular safety. In the context of advancing digital technologies, the intention to use such innovations represents a significant field of study. Drawing on the Perceived Value Theory and the Stimulus-Organism-Response (S-O-R) framework, this study formulates a model to evaluate user intentions regarding the use of automotive Augmented Reality Head-Up Display (AR-HUD). This model integrates design elements, including user interface attractiveness, sense of presence, controllability, and responsiveness, along with perceived utilitarian and hedonic values. Through Structural Equation Modeling (SEM) of 230 valid questionnaire results, the analysis identifies both utilitarian and hedonic values as pivotal in influencing AR-HUD adoption, with utilitarian value being preeminent. Additionally, it is evidenced that design attributes influence the usage intention via perceived utilitarian and hedonic values. Consequently, this study presents design strategies for auto manufacturers and designers aimed at amplifying the advantages of AR-HUD, thus fostering greater user adoption willingness.

TOLGAN: An End-To-End Framework for Producing Traditional Orient Landscape

We present TOLGAN that generates traditional oriental landscape (TOL) image from a map that specifies the locations and shapes of the elements composing TOL. Users can create a TOL map by using a user interface or a segmentation scheme from a photograph. We design the generator of TOLGAN as a series of decoding layers where the map is applied between the layers. The generated TOL image is further enhanced through an AdaIN architecture. The discriminator of TOLGAN processes a generated image and its groundtruth TOL artwork image. TOLGAN is trained through a dataset composed of paired TOL artwork images and their TOL maps. We present a tool through which users can produce a TOL map by specifying and organizing the elements of TOL artworks. TOLGAN successfully generates a series of TOL images from the TOL map. We evaluate our approach using a quantitative way by estimating FID and ArtFID scores and a qualitative way by executing two user studies. Through these studies, we prove the excellence of our approach by comparing our results with those from several important existing works.

Accurate and efficient prediction of atmospheric PM1, PM2.5, PM10, and O3 concentrations using a customized software package based on a machine-learning algorithm.

Particulate matter (PM) and ozone (O3) pollution have been attracting increasing attention recently due to their severe harm to human health. PM and O3 are secondary pollutants, and there remain significant challenges in accurately and efficiently predicting their concentrations in the atmosphere. In this study, one-year monitoring data of PM1, PM2.5, PM10, and O3 concentrations as well as meteorological parameters and concentrations of various precursors (i.e., nitrogen oxides, SO2, CO, alkanes, aldehydes, and ketones) are obtained at a monitoring site in central China's Hunan province. The eXtreme Gradient Boosting model is trained and tested to achieve efficient and accurate predictions of the concentrations of the four pollutants. The effects of different datasets, input features, and model parameters on the prediction accuracy are investigated. Principal component analysis is employed to further reduce the dimensions of features, increasing the prediction efficiency. Finally, all model training and prediction processes are incorporated in an executable application using the PyQt5 framework to build a user interface. The customized software supports user-defined modeling. The software can simultaneously predict PM1, PM2.5, PM10, and O3 concentrations, making the prediction process convenient.

AN APPROACH OF DOM RECORD MANAGEMENT COMPONENT UI CREATION

This paper presents an approach of Document Object Model database record management component user interface (UI) creation. Mainstream of UI development approaches concentrates in few directions, omitting holistic view on the fundamental database operations. The proposed solution uses method lay on of standard clauses adopted by the ECMA Script edition sixth and above. The program logic for the UI component was built with the intention - The existing functionality of such UI component to be extended and complemented.

EasyFlow: An open-source, user-friendly cytometry analyzer with graphic user interface (GUI).

Flow cytometry enables quantitative measurements of fluorescence in single cells. The technique was widely used for immunology to identify populations with different surface protein markers. More recently, the usage of flow cytometry has been extended to additional readouts, including intracellular proteins and fluorescent protein transgenes, and is widely utilized to study developmental biology, systems biology, microbiology, and many other fields. A common file format (FCS format, defined by the International Society for Advancement of Cytometry (ISAC)) has been universally adopted, facilitating data exchange between different machines. A diverse spectrum of software packages has been developed for the analysis of flow cytometry data. However, those are either 1) costly proprietary softwares, 2) open source packages with prerequisite installation of R or Python and sometimes require users to have experience in coding, or 3) online tools that are limiting for analysis of large data sets. Here, we present EasyFlow, an open-source flow cytometry analysis graphic user interface (GUI) based on Matlab or Python, that can be installed and run locally across platforms (Windows, MacOS, and Linux) without requiring previous coding knowledge. The Python version (EasyFlowQ) is also developed on a popular plotting framework (Matplotlib) and modern user interface toolkit (Qt), allowing more advanced users to customize and keep contributing to the software, as well as its tutorials. Overall, EasyFlow serves as a simple-to-use tool for inexperienced users with little coding experience to use locally, as well as a platform for advanced users to further customize for their own needs.

Explainable Machine Learning Models for Real-Time Threat Detection in Cybersecurity

In the rapidly evolving landscape of cybersecurity, traditional machine learning models often operate as "black boxes," providing high accuracy but lacking transparency in decision-making. This lack of explainability poses challenges for trust and accountability, especially in critical areas like threat detection and incident response. Explainable machine learning models aim to address this by making the model's predictions more understandable and interpretable to users. This research integrates explainable machine learning models for real-time threat detection in cybersecurity. Data from multiple sources, including network traffic, system logs, and user behavior, undergo preprocessing such as cleaning, feature extraction, and normalization. The processed data is passed through various machine learning models, including traditional approaches like SVM and decision trees, as well as deep learning models like CNN and RNN. Explainability techniques such as LIME, SHAP, and attention mechanisms provide transparency, ensuring interpretable predictions. The explanations are delivered through a user interface that generates alerts, visualizations, and reports, facilitating effective threat assessment and incident response in decision support systems. This framework enhances model performance, trust, and reliability in complex cybersecurity scenarios.

Challenges and Responsibilities in the UX Design of Text-to-Image AI Models: A Discussion Through a Comparative Heuristics Evaluation

The rapid advancement and adoption of generative AI in creative and professional domains mark a significant evolution in technological interaction, raising pivotal discussions on its integration, usability and broader societal impact. This paper delves into the multifaceted challenges and opportunities presented by generative AI, focusing in particular on text-to-image tools and their integration into daily work practices of artists, designers, architects and researchers. As these creators increasingly incorporate AI's inherent unpredictability and ambiguity into their work, a critical examination of the ethical, legislative and environmental implications becomes imperative. We highlight how the User Experience (UX) and User Interface (UI) aspects of these groundbreaking technologies remain mostly underexplored. UX pioneer Jakob Nielsen has recently warned that professionals in the field must adapt to the fast-paced advancements in AI or become obsolete for an industry that is becoming pivotal in the contemporary creative field. He draws a parallel between the current AI era and the dot-com boom of the 1990s, highlighting how UX professionals were slow to adapt to the Internet, which led to missed opportunities and a lack of user-centred design. Consequently, the usability of generative AI tools is a paramount concern, especially as non-expert users increasingly engage with these systems. This paper explores the usability challenges, specifically the articulation barrier where users struggle to effectively communicate prompts to AI, and the consequent need for more intuitive and accessible interfaces. Through comparative analysis and a heuristic evaluation of four leading models, i.e. Midjourney, Dall.E, Stable Diffusion and Adobe Firefly, which presents a unique case study for comparison due to their divergent UI and UX elements, we aim to provide insights into the current state of AI tools and propose pathways for their responsible and effective use.

The Feasibility of AgileNudge+ Software to Facilitate Positive Behavioral Change: Mixed Methods Design

Background In today’s digital age, web-based apps have become integral to daily life, driving transformative shifts in human behavior. “AgileNudge+” (Indiana University Center for Health Innovation and Implementation Science) is a web-based solution to simplify the process of positive behavior change using nudging as an intervention. By integrating knowledge from behavioral economics with technology, AgileNudge+ organizes multiple steps, simplifies complex tasks, minimizes errors by enhancing user engagement, and provides resources for creating and testing nudge interventions. Objective This paper aimed to outline the design process, methodologies, and usefulness of “AgileNudge+” for the development of evidence-based nudges. It used a mixed methods approach to evaluate the software’s interface usability and usefulness for creating and testing nudge interventions. Methods AgileNudge+ was developed through iterative processes integrating principles from behavioral economics and user-centered design. The content of AgileNudge+ operationalizes an Agile science–based process to efficiently design, embed, and disseminate evidence-based nudges that encourage positive behavior change without limiting choice. Using a mixed methods approach, we tested AgileNudge+ software’s ability to organize and simplify the nudge intervention process, allowing a diverse range of scholars with limited knowledge of Agile science to use nudges. Usability testing assessed the tool’s usefulness and interface with a sample of 18 health care professionals, each asked to interact with the software and create a nudge intervention to solve a problem within their professional project’s sphere. Results The study was funded in August 2022, with data collection occurring from June 2023 to July 2024. As of July 2024, we have enrolled 18 participants. Quantitative results found a mean usefulness rating of AgileNudge+ of 3.83 (95% CI 3.00-4.66). Qualitative results highlighted ways to modify the language used in AgileNudge+ to be more comprehensible to a diverse user base and promoted modifications to the software that facilitate real-time assistance and prioritize time efficiency in user interactions. Feedback further supported the positive impact of gamification on participant motivation when using the software. Conclusions AgileNudge+ is an effective assistive tool for simplifying the positive behavior change process using nudge interventions, with tailored content and interactions to meet users’ needs and demands. Building onto the current design, future iterations of AgileNudge+ will use artificial intelligence to process large volumes of data while reducing the time and mental energy required to scan for existing cognitive biases and nudge prototypes. The software is also being upgraded to build on current gamification efforts, encouraging more sustained motivation by increasing the temporal resolution of the digital interface. These modifications stay true to the agility and user-centered aspects of AgileNudge+, emphasizing the novelty of the constantly evolving software design process.

An Exercise-Based Precision Medicine Tool and Smartphone App for Managing Achilles Tendinopathy (the 'PhysViz' System): User-Centered Development Study.

People with Achilles tendinopathy (AT) experience persistent pain that can limit engagement with daily occupations and negatively impact mental health. Current therapeutic exercise approaches vary in success, with many people experiencing reinjury, leading to a cycle of chronic tendinopathy often lasting years. High-magnitude precision loading may help people exit this feedback cycle, but applying these principles clinically is challenging. This user-centered design case study aims to provide an overview on how the PhysViz (a prototype for a novel remote rehabilitation intervention for AT management) was developed and evaluated following the development phase of the Framework for Accelerated and Systematic Technology-Based Intervention Development and Evaluation Research (FASTER). The development process engaged a multidisciplinary team comprising people with AT experiences, clinicians, and engineers. It followed the 5 stages within the FASTER development phase: empathize, define, ideate, prototype, and test. The PhysViz development and evaluation were informed by needs assessments, surveys, literature reviews, validation studies, case studies, roundtable discussions, and usability testing (some of which have been published previously). The FASTER systematically guided the integration of evidence-based features and behavior change theory. By using the FASTER and ensuring that the PhysViz system was underpinned by diverse stakeholder needs, this work resulted in the development of a working prototype for both the PhysViz physical exercise tool and the accompanying PhysViz software package (mobile app and web application). A variety of study designs informed user-desired features that were integrated into the PhysViz prototype, including real-time biofeedback in the form of precision load monitoring, customizable exercise programs, and pain tracking. In addition, clinicians can visualize client data longitudinally and make changes to client exercise prescriptions remotely based on objective data. The identified areas for improvement, such as upgrading the user interface and user experience and expanding clinical applications, provide valuable insights for future PhysViz iterations. Further research is warranted to assess the long-term efficacy and feasibility of the PhysViz in diverse clinical settings and its potential to improve AT symptoms. Being one of the first technology development initiatives guided by the FASTER, this study exemplifies a systematic and multidisciplinary approach to creating a remote rehabilitation intervention. By incorporating stakeholder feedback and evidence-based features, the PhysViz addresses key challenges in AT rehabilitation, offering a novel solution for precision loading and therapeutic exercise engagement. Positive feedback from users and clinicians underscores the potential impact of the PhysViz in improving AT management outcomes. The PhysViz serves as a model for technology-based intervention development, with potential implications for other tendinopathies and remote rehabilitation strategies.

Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning

The chloride ion permeability resistance of recycled aggregate concrete (RAC) is influenced by multiple factors, and the prediction model for this resistance based on machine learning is still limited. In the paper, six impact factors (IFs), including the carbonation of recycled coarse aggregates (YN), the replacement ratio of recycled coarse aggregates (r), the bending load level (L), the carbonation time (t) and temperature (T) of RAC, and the replacement ratio of carbonated recycled fine aggregates (f), were considered to conduct a chloride penetration test on RAC. Based on the experimental data, four algorithms, including artificial neural network (ANN), support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost), were adopted to establish the machine learning prediction models and study the relationships between the electric flux of RAC and the IFs. The results showed that the predicted values of all four models were in good agreement with the experimental values, and the XGBoost model had the best prediction performance on the testing set. Based on the XGBoost model, the LIME method was adopted to solve the interpretability problem in the prediction process. The importance ranking of IFs on the electric flux was r > t > f > T > L > YN. A graphical user interface (GUI) was developed based on Python 3.8 software to facilitate the use of machine learning models for the chloride ion permeability resistance of RAC. The research results can provide an accurate prediction of the electric flux of RAC.

Saccharomyces Genome Database: Advances in Genome Annotation, Expanded Biochemical Pathways, and Other Key Enhancements.

Budding yeast (Saccharomyces cerevisiae) is the most extensively characterized eukaryotic model organism and has long been used to gain insight into the fundamentals of genetics, cellular biology, and the functions of specific genes and proteins. The Saccharomyces Genome Database (SGD) is a scientific resource that provides information about the genome and biology of S. cerevisiae. For more than 30 years, SGD has maintained the genetic nomenclature, chromosome maps, and functional annotation for budding yeast along with search and analysis tools to explore these data. Here we describe recent updates at SGD, including the two most recent reference genome annotation updates, expanded biochemical pathways representation, changes to SGD search and data files, and other enhancements to the SGD website and user interface. These activities are part of our continuing effort to promote insights gained from yeast to enable the discovery of functional relationships between sequence and gene products in fungi and higher eukaryotes.

The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity.

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7000 publications for >15000 traits, from which more than 625000 lead associations have been curated. Additionally, 85 000 full genome-wide summary statistics datasets-containing association data for all variants in the analysis-are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability.

The Subscription Management System

Subscription-based membership systems have become increasingly popular for digital services like Spotify, Prime Video, and YouTube. This project explores the development of a subscription management system using JavaFX, focusing on creating an intuitive user interface and efficient backend integration. The system supports functionalities such as user registration, subscription management, and payment processing, offering a seamless experience for both administrators and users. This study addresses key aspects such as GUI design, security challenges, performance optimization, and integration of RESTful APIs for backend communication. The implementation aims to provide a scalable, secure, and user-friendly solution for managing various subscription models. Additionally, this research compares different subscription models to optimize user engagement and retention. Advanced features like automated billing, personalized recommendations, and analytical insights are integrated to enhance the overall user experience. The study also highlights best practices for integrating third-party payment gateways and managing user data securely. The results demonstrate the system’s ability to handle a large user base effectively while maintaining high performance and security standards. Future work will explore the integration of machine learning techniques to predict user preferences and improve personalization further

Modernizando a Redação Policial: Protótipo de Aplicativo Android para a Elaboração de Relatos de Ocorrências

This article presents the development of an Android application prototype designed to assist Military Police officers with writing incident reports. The application leverages Google's Gemini AI API to provide real-time grammar correction and intelligent suggestions for writing improvement, acting as an experienced officer reviewing and enhancing the text. The app's architecture, based on the MVVM (Model-View-ViewModel) pattern, and the user interface design, focused on simplicity and usability, are detailed. The implementation of the Gemini AI API, including the system prompt that defines the AI's role and behavior, is explored, along with cost analysis and comparison with other popular natural language processing APIs like OpenAI and Anthropic. Test results using real incident reports demonstrate the app's ability to improve text clarity, conciseness, and professionalism, as well as provide relevant suggestions for police practice, such as missing information, legal procedures, and evidence collection. The article discusses the impacts and benefits of the application for the PMPR, the limitations of the technology, and suggestions for future work, including integration with other platforms and expansion of AI functionalities.

Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete

AbstractConventional ultra-high performance concrete (UHPC) has excellent development potential. However, a significant quantity of CO2 is produced throughout the cement-making process, which is in contrary to the current worldwide trend of lowering emissions and conserving energy, thus restricting the further advancement of UHPC. Considering climate change and sustainability concerns, cementless, eco-friendly, alkali-activated UHPC (AA-UHPC) materials have recently received considerable attention. Following the emergence of advanced prediction techniques aimed at reducing experimental tools and labor costs, this study provides a comparative study of different methods based on machine learning (ML) algorithms to propose an active learning-based ML model (AL-Stacked ML) for predicting the compressive strength of AA-UHPC. A data-rich framework containing 284 experimental datasets and 18 input parameters was collected. A comprehensive evaluation of the significance of input features that may affect compressive strength of AA-UHPC was performed. Results confirm that AL-Stacked ML-3 with accuracy of 98.9% can be used for different general experimental specimens, which have been tested in this research. Active learning can improve the accuracy up to 4.1% and further enhance the Stacked ML models. In addition, graphical user interface (GUI) was introduced and validated by experimental tests to facilitate comparable prospective studies and predictions.