User Interface Research Articles

A Support Tool for Emergency Management in Smart Campuses: Reference Architecture and Enhanced Web User Interfaces

A Support Tool for Emergency Management in Smart Campuses: Reference Architecture and Enhanced Web User Interfaces

VISION - an open-source software for automated multi-dimensional image analysis of cellular biophysics.

Environment-sensitive probes are frequently used in spectral/multi-channel microscopy to study alterations in cell homeostasis. However, the few open-source packages available for processing of spectral images are limited in scope. Here, we present VISION, a stand-alone software based on Python for spectral analysis with improved applicability. In addition to classical intensity-based analysis, our software can batch-process multidimensional images with an advanced single-cell segmentation capability and apply user-defined mathematical operations on spectra to calculate biophysical and metabolic parameters of single cells. VISION allows for 3D and temporal mapping of properties such as membrane fluidity and mitochondrial potential. We demonstrate the broad applicability of VISION by applying it to study the effect of various drugs on cellular biophysical properties; the correlation between membrane fluidity and mitochondrial potential; protein distribution in cell-cell contacts; and properties of nanodomains in cell-derived vesicles. Together with the code, we provide a graphical user interface for facile adoption.

Application of Machine Learning in a Rodent Malaria Model for Rapid, Accurate, and Consistent Parasite Counts.

Rodent malaria models serve as important preclinical antimalarial and vaccine testing tools. Evaluating treatment outcomes in these models often requires manually counting parasite-infected red blood cells (iRBCs), a time-consuming process, which can be inconsistent between individuals and laboratories. We have developed an easy-to-use machine learning (ML)-based software, Malaria Screener R, to expedite and standardize such studies by automating the counting of Plasmodium iRBCs in rodents. This software can process Giemsa-stained blood smear images captured by any camera-equipped microscope. It features an intuitive graphical user interface that facilitates image processing and visualization of the results. The software has been developed as a desktop application that processes images on standard Windows and MacOS computers. A previous ML model created by the authors designed to count Plasmodium falciparum-infected human RBCs did not perform well counting Plasmodium-infected mouse RBCs. We leveraged that model by loading the pretrained weights and training the algorithm with newly collected data to target Plasmodium yoelii- and Plasmodium berghei-infected mouse RBCs. This new model reliably measured both P. yoelii and P. berghei parasitemia (R2 = 0.9916). Additional rounds of training data to incorporate variances due to length of Giemsa staining and type of microscopes, etc., have produced a generalizable model, meeting WHO competency level 1 for the subcategory of parasite counting using independent microscopes. Reliable, automated analyses of blood-stage parasitemia will facilitate rapid and consistent evaluation of novel vaccines and antimalarials across laboratories in an easily accessible invivo malaria model.

Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II

Reinforced concrete (RC) flanged shear wall has good lateral strength and stiffness, which has been widely used in building structures. Due to the coupling effect of many factors such as wall section shape, shear span ratio, so the shear performance evaluation of flanged wall is still very limited. This paper proposed a prediction framework for the shear capacity of RC flanged shear walls. A database containing 14 input variables, 1 output variable and 153 samples was constructed to evaluate the prediction accuracy of 11 existing design methods. The Pearson coefficient was used to preliminarily analyze the correlation between variables. The grid search was used to optimize the hyperparameters of 4 machine learning models, and six statistical indicators ( R2, R, RMSE, SD, MAE, and MAPE) were used to comprehensively compare the prediction results of the ML models to determine the best model. On this basis, SHapley Additive exPlanations (SHAP) was used to enhance the interpretability of the prediction models, and the mechanism of the input variables on the shear capacity was quantified. A graphical user interface (GUI) was proposed to guide the engineering design. A multi-objective model (MOO) was established to analyze the trade-off between shear performance and cost, thereby determining the best optimal scheme. The results show that the prediction accuracy of the ML models is better than the existing design methods. The XGB model has the best prediction performance, with R2, R, RMSE are 0.99, 0.99, 118.96, respectively. The SHAP method can effectively enhance the interpretability of the ML models, and tw, lw and f ′c are the key parameters affecting the shear capacity of the flanged shear wall.

Developing MATLAB graphical user interface for acquiring single star SCIDAR data

To enhance operational efficiency and meet experimental demands, we have developed a graphical user interface (GUI) using MATLAB for Acquiring Single Star SCIDAR Data, leveraging the software’s integrated GUI Development Environment (GUIDE) tool. This interface streamlines the preprocessing and numerical computation of the power spectrum of atmospheric speckles while providing real-time graphical representations of atmospheric parameters, including the vertical profile of the refractive index structure function Cn2(h). It also incorporates parameters related to adaptive optics and high angular resolution, such as seeing, enabling immediate and instantaneous visual assessment of observational conditions. Furthermore, the novelty of this GUI lies in the ease of acquiring and processing data from various atmospheric parameters. The Single Star SCIDAR (Scintillation Detection and Ranging) method relies on analyzing the scintillation of light from single stars to assess the turbulent characteristics of the atmosphere. This assessment is based on the description provided by Cn2(h) derived from minimizing an objective function determined using the power spectrum of atmospheric speckles from single stars. For this purpose, a minimization algorithm called active-set is used.

Comparative study of statistical computational approaches to investigate the degraded compressive strength of concrete under the freeze-thaw effect

The primary cause of frost damage and early failure in concrete structures is the repeated freezing and thaw cycles (FTC). The evolution of internal fractures and scaling at the surface of concrete can be used to assess the impact of frost damage. Surface scaling mechanisms and interior frost damage are contingent upon numerous environmental factors, including the rate of freezing, low temperature, and duration of the freezing point. However, evaluating the amount of strength loss of concrete material is a challenging factor to consider. This work explores the application of predictive modelling tools, including random forest (RF), multilayer perceptron (MLP), decision tree (DT), and bagging, to evaluate the degraded compressive strength (D-CS) of concrete. The models use five input variables: initial compressive strength (I-CS), water-to-cement ratio (W/C), FTC, minimum temperature (Tmin), and maximum temperature (Tmax), with D-CS as the output. Model performance was compared using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) metrics, with the bagging model achieving the highest predictive performance at 92 %. A 10-fold cross-validation approach validated the model's accuracy. The influence of each variable was assessed using SHapley Additive exPlanations (SHAP) analysis. Importantly, a user-friendly Graphical User Interface (GUI) was developed based on the models, making it easy for researchers and professionals to make predictions and thereby increasing the practicality and accessibility of this research. This study aids the research community in selecting appropriate models to forecast the strength of various concrete types, thereby enhancing the practical application of this research.

Investigating Human-Robot Interactions with a Quadruped Robot

Robots are becoming more prevalent in our daily lives. As robots become more common in society, we must understand how people react and interact with them. This study leverages Boston Dynamics' Spot robot to examine human responses to quadruped robots. Previous studies had Spot walking alongside participants at fixed speeds, independent of their gait. Improving upon this method, I developed a program that enables Spot to detect and follow fiducial markers (AprilTags). Using the Robot Operating System (ROS), the depth and position of the tag within the camera’s frame were extracted and translated into motion commands for the robot. The tag is attached to an apparatus on the hip that extends the tag near the subject’s knee, allowing Spot to track the participant’s trunk speed and maintain a consistent distance, offering a more adaptive walking experience. The program allows for variations in the acceleration, height, and camera selection (front/back, left/right) through a custom Graphical User Interface (GUI), allowing for diverse trial conditions. Extensive testing identified considerations to optimize the walking experience. Factors included gradual acceleration to allow Spot to react and maintain detections, and the set requirement to run only a single camera at a time for optimized processing. Additionally, keeping the tag unobstructed and square to the camera is critical to avoid failures in detection. Overall, Spot's new capabilities will assist in further studies of human and robot interactions.

AARS Online: A collaborative database on the structure, function, and evolution of the aminoacyl-tRNA synthetases.

The aminoacyl-tRNA synthetases (aaRS) are a large group of enzymes that implement the genetic code in all known biological systems. They attach amino acids to their cognate tRNAs, moonlight in various translational and non-translational activities beyond aminoacylation, and are linked to many genetic disorders. The aaRS have a subtle ontology characterized by structural and functional idiosyncrasies that vary from organism to organism, and protein to protein. Across the tree of life, the 22 coded amino acids are handled by 16 evolutionary families of Class I aaRS and 21 families of Class II aaRS. We introduce AARS Online, an interactive Wikipedia-like tool curated by an international consortium of field experts. This platform systematizes existing knowledge about the aaRS by showcasing a taxonomically diverse selection of aaRS sequences and structures. Through its graphical user interface, AARS Online facilitates a seamless exploration between protein sequence and structure, providing a friendly introduction to the material for non-experts and a useful resource for experts. Curated multiple sequence alignments can be extracted for downstream analyses. Accessible at www.aars.online, AARS Online is a free resource to delve into the world of the aaRS.

Fully coupled finite strain consolidation analysis for soft clay treated by prefabricated horizontal drain with vacuum and heat preloading

This paper established a one-dimensional fully coupled finite strain consolidation analysis for soft clay treated by thermal prefabricated horizontal drain under vacuum and heat preloading with the consideration of thermal elastic viscoplastic (TEVP) behaviors of soft clay. Firstly, the governing equations for finite strain consolidation, heat conduction, and heat convention process have been carefully derived based on the well-established Gibson’s large strain consolidation theory and TEVP constitutive model. Secondly, the accuracy and reliability of the numerical method have been verified with benchmark cases and three physical model tests. Then, improvement of consolidation efficiency rate (IER) was defined and used to study the effect of three variables on consolidation efficiency. Last but not least, a graphical user interface (GUI) was provided to support researchers and engineers in utilizing the proposed numerical method for conducting consolidation analysis of soft soil treated in the same or similar fashion.

A Study of Key Factors Influencing the Usability of Smartphone Graphical User Interfaces for Older Adults

This study investigates the key factors influencing the usability of smartphone graphical user interfaces (GUIs) among elderly users in Shanxi Province, China. A total of 1,111 healthy individuals aged 60-75 years participated in the study. Quantitative analysis methods, including descriptive statistics, Pearson correlation, regression analysis, and mediation and moderation analysis, were employed to examine the interactions between user characteristics (age, education, experience, health status) and GUI design factors (interface layout, text and icon design, feedback mechanisms, information presentation) about usability metrics (cognitive load, interaction barriers, user experience). The analysis revealed that both user characteristics and GUI design factors significantly impact usability outcomes. Specifically, GUI design factors were found to mediate the relationship between user characteristics and usability, while user characteristics moderated the effects of GUI design on usability. These findings underscore the necessity of tailored interface design to enhance user experience and promote digital inclusion among elderly populations. Practical recommendations include the use of larger and more readable text, simplified iconography, and clear, immediate feedback to users. Such adjustments can significantly improve usability and foster greater digital inclusion. This research contributes valuable insights to both theoretical understanding and practical applications in the design of age-appropriate mobile interfaces.

MPPD: A User-Friendly Posture Deformation Program for Mesh-Type Computational Phantoms.

Recently, the International Commission on Radiological Protection (ICRP) released adult Mesh-type Reference Computational Phantoms (MRCPs), which have great advantage in high deformability. Previous studies have exploited their high deformability to investigate the dosimetric influence of varying statures and postures, demonstrating significant variations in radiation doses. However, the previous studies are constrained by their inability to consider both stature and posture concurrently and by the limited range of postures analyzed. In the present study, a computer program named MPPD (Mesh-type Phantom Posture Deformer) was developed, a user-friendly graphical user interface that enables users to adjust the posture of adult MRCPs and corresponding library phantoms. The MPPD program was applied to deform five adult male phantoms of different statures into sitting and kneeling postures, showcasing its rapid computational speed and minimal RAM usage. The effectiveness of the MPPD program for dose calculation was also investigated by computing the detriment-weighted doses for MPPD-deformed adult male MRCPs, which showed good agreement with dose values for existing posture-deformed phantoms of the previous study. Furthermore, as an application of the MPPD program, the combined dosimetric impact of stature and posture was investigated, which is the inaugural effort to estimate doses by considering these factors concurrently. The result showed that the impact of stature and posture on radiation doses could largely vary depending on the radiation source, highlighting the importance of simultaneous consideration of stature and posture for accurate dose estimation.

Эргономические проблемы создания информационных систем, включающих элементы искусственного интеллекта

The paper analyses the main problems of creating information systems containing elements of artificial intelligence. The work considers separately the problems of designing the user interface and blocks that ensure the operation of artificial intelligence elements. A methodological approach to designing systems with elements of artificial intelligence is proposed. The authors examine and analyze bottlenecks of the proposed approach. The paper formulates the proposals for ensuring effective ergonomic design of information systems for various purposes.

Design: UI/UX Penjualan Telur Ayam di Cilacap dengan Metode R&D

Nowadays, more and more e-commerce platforms are emerging, creating a highly competitive environment for both individuals and groups. User experience and interface are key factors in attracting users' attention and fulfilling various needs to survive in a crowded market. In this context, the author proposes the prototyping of a chicken egg sales application named EiTech Supply in Cilacap Regency. This application is designed to overcome the problems that chicken egg farmers often face, such as competition between regions and the entry of new farmers with very low prices to attract customers. With the EiTech Supply application, it is expected to reduce these problems and increase the competitiveness of chicken egg farmers both inside and outside the region.

Optimizing Real-Time Advanced Ultrasound Supervision in a University Setting

Introduction: Ultrasound-based diagnosis requires great expertise for reliability and accuracy, thus practical training is vital. Digital training can offer students the possibility of practical training in their local setups. However, there are critical issues that remote educators must address, particularly in advanced ultrasound training. The present study was therefore aiming at developing a suitable technical setup for supervision and education in tele-ultrasound that can be applied to a variety of advanced diagnostic and therapeutic areas. Methods: Using an action research approach including four cycles of action, we tested four technical setups with different software and hardware components across four ultrasound courses. Based on evaluation forms, written reflections, and two focus group interviews from the four cycles of action, respectively, we modified the setups to improve the technical solution for the next cycle. Results: The initial set-up was using commercial video call apps via mobile phones. Although no additional equipment or expertise was required, it was precluded by the poor image quality and motion blur resulting from capturing the ultrasound image on the smartphone. Nevertheless, this solution is viable when quick clarifications are needed. Additional stabilization support in the second set-up did not provide satisfactory image quality. The best technical solution was found in the third set-up, using a frame grabber and Reacts, which allows simultaneous live streaming of the ultrasound screen image and two web cameras. However, the cumbersome software interface, the need for an expensive user license, and problems with the internet connection reduced the acceptability of this setup. The optimal setup in cycle four, was a reasonable frame grabber, and Zoom, which is free, has a simple user interface and can stream live audio-visual ultrasound data from the probe and user simultaneously. Its annotation function is also beneficial in supervision. Conclusion: Through an action research approach including four cycles of action and thorough, subsequent evaluations, we found that a videoconferencing platform, such as Zoom, a computer, and a frame grabber connected to the ultrasound scanner was the optimal solution as this set-up is simple and affordable and is facilitating a good video-transmitted learning situation for students and supervisors. This technical set-up could therefore be suitable for a wide range of remote ultrasound courses.

Delta GUI change detection using inferred models

Recent software development methodologies emphasize iterative and incremental evolution to align with stakeholders’ needs. This perpetual and rapid software evolution demands ongoing research into verification practices and technologies that ensure swift responsiveness and effective management of software delta increments. Strategies such as code review have been widely adopted for development and verification, ensuring readability and consistency in the delta increments of software projects. However, the integration of techniques to detect and visually report delta changes within the Graphical User Interface (GUI) software applications remains an underutilized process. In this paper, we set out to achieve two objectives. First, we aim to conduct a comprehensive review of existing studies concerning GUI change detection in desktop, web, and mobile applications to recognize common practices. Second, we introduce a novel change detection tool capable of highlighting delta GUI changes for this diverse range of applications. To accomplish our first objective, we performed a systematic mapping of the literature using the Scopus database. To address the second objective, we designed and developed a GUI change detection tool. This tool simultaneously transits and compares state models inferred by a scriptless testing tool, enabling the detection and highlighting of GUI changes to detect the widgets or functionalities that have been added, removed, or modified. Our study reveals the existence of a multitude of techniques for change detection in specific GUI systems with different objectives. However, there is no widely adopted technique suitable for the diverse range of existing desktop, web, and mobile applications. Our tool and findings demonstrate the effectiveness of using inferred state models to highlight between 8 and 20 GUI changes in software delta increments containing a large number of changes over months and between 4 and 6 GUI changes in delta increments of small iterations performed over multiple weeks. Moreover, some of these changes were recognized by the software developers as GUI failures that required a fix. Finally, we expose the motivation for using this technique to help developers and testers analyze GUI changes to validate delta increments and detect potential GUI failures, thereby fostering knowledge dissemination and paving the way to standard practices.

Promoting Collaborative Scholarship During the COVID-19 Pandemic Through an Innovative COVID-19 Data Explorer and Repository at Yale School of Medicine: Development and Usability Study.

The COVID-19 pandemic sparked a surge of research publications spanning epidemiology, basic science, and clinical science. Thanks to the digital revolution, large data sets are now accessible, which also enables real-time epidemic tracking. However, despite this, academic faculty and their trainees have been struggling to access comprehensive clinical data. To tackle this issue, we have devised a clinical data repository that streamlines research processes and promotes interdisciplinary collaboration. This study aimed to present an easily accessible up-to-date database that promotes access to local COVID-19 clinical data, thereby increasing efficiency, streamlining, and democratizing the research enterprise. By providing a robust database, a broad range of researchers (faculty and trainees) and clinicians from different areas of medicine are encouraged to explore and collaborate on novel clinically relevant research questions. A research platform, called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), was constructed to house cleaned, highly granular, deidentified, and continually updated data from over 18,000 patients hospitalized with COVID-19 from January 2020 to January 2023, across the Yale New Haven Health System. Data across several key domains were extracted including demographics, past medical history, laboratory values during hospitalization, vital signs, medications, imaging, procedures, and outcomes. Given the time-varying nature of several data domains, summary statistics were constructed to limit the computational size of the database and provide a reasonable data file that the broader research community could use for basic statistical analyses. The initiative also included a front-end user interface, the DOM-CovX Explorer, for simple data visualization of aggregate data. The detailed clinical data sets were made available for researchers after a review board process. As of January 2023, the DOM-CovX Explorer has received 38 requests from different groups of scientists at Yale and the repository has expanded research capability to a diverse group of stakeholders including clinical and research-based faculty and trainees within 15 different surgical and nonsurgical specialties. A dedicated DOM-CovX team guides access and use of the database, which has enhanced interdepartmental collaborations, resulting in the publication of 16 peer-reviewed papers, 2 projects available in preprint servers, and 8 presentations in scientific conferences. Currently, the DOM-CovX Explorer continues to expand and improve its interface. The repository includes up to 3997 variables across 7 different clinical domains, with continued growth in response to researchers' requests and data availability. The DOM-CovX Data Explorer and Repository is a user-friendly tool for analyzing data and accessing a consistently updated, standardized, and large-scale database. Its innovative approach fosters collaboration, diversity of scholarly pursuits, and expands medical education. In addition, it can be applied to other diseases beyond COVID-19.

Machine learning models for estimating the compressive strength of rubberized concrete subjected to elevated temperature: Optimization and hyper-tuning

The incorporation of rubber fibers (RFs) brings about significant divergence in the characteristics of rubberized concrete when contrasted with traditional varieties. Thus, raising concerns about performance under elevated temperature and prolonged exposure. This study effectively addresses the challenges of incorporating rubber fibers in concrete by using artificial neural network (ANN), gene expression programming (GEP), and bagging to examine the impact of input factors such as water-to-cement ratio (W/C), rubber fiber content (RF), elevated temperature (T), and exposure duration (t) on air-cooled compressive strength (CSA). The comprehensive literature review and advanced modeling techniques reveal that ANN excels in capturing complex relationships. In addition, GEP provides clear and accurate models through its unique approach, and Bagging enhances model stability and accuracy. These methods together offer a robust framework for estimating the CSA of rubberized concrete. Thus, contributing valuable insights for optimizing its use in construction. All three models exhibited strong performance, with the ANN emerging as the most effective choice among the evaluated models. Notably, ANN displayed the highest coefficient of determination (R2) value of 0.984, indicating its superior predictive accuracy compared to both GEP (0.982), and bagging (0.970). Moreover, ANN demonstrated the lowest mean absolute error (MAE) score of 0.621 and root mean square error (RMSE) of 0.867, underscoring its precision in forecasting the CSA of rubberized concrete with minimal deviation from experimental values. In addition, the SHapley Additive exPlaination (SHAP) method is employed to comprehend the model estimations. The ICE and PDP plots demonstrate an initial increase in CSA up to 150°C, followed by a significant decrease as temperature rises. Furthermore, CSA decreases with higher RF contents, and linearly declines with increasing W/C ratio. The SHAP analysis provides clear evidence of the strong negative correlation between T and CSA, along with a negative association with RF. A graphical user interface has been developed to estimate the CSA of rubberized concrete, enabling efficient and user-friendly model interaction without the need for physical experimentation.

Atten‐SEVNETR for volumetric segmentation of glioblastoma and interactive refinement to limit over‐segmentation

AbstractPrecise localization and volumetric segmentation of glioblastoma before and after surgery are crucial for various clinical purposes, including post‐surgery treatment planning, monitoring tumour recurrence, and creating radiotherapy maps. Manual delineation is time‐consuming and prone to errors, hence the adoption of automated 3D quantification methods using deep learning algorithms from MRI scans in recent times. However, automated segmentation often leads to over‐segmentation or under‐segmentation of tumour regions. Introducing an interactive deep‐learning tool would empower radiologists to rectify these inaccuracies by adjusting the over‐segmented and under‐segmented voxels as needed. This paper proposes a network named Atten‐SEVNETR, that has a combined architecture of vision transformers and convolutional neural networks (CNN). This hybrid architecture helps to learn the input volume representation in sequences and focuses on the global multi‐scale information. An interactive graphical user interface is also developed where the initial 3D segmentation of glioblastoma can be interactively corrected to remove falsely detected spurious tumour regions. Atten‐SEVNETR is trained on BraTS training dataset and tested on BraTS validation dataset and on Uppsala University post‐operative glioblastoma dataset. The methodology outperformed state‐of‐the‐art networks like nnFormer, SwinUNet, and SwinUNETR. The mean dice score achieved is 0.7302, and the mean Hausdorff distance‐95 got is 7.78 mm for the Uppsala University dataset.

Accelerating joint species distribution modelling with Hmsc-HPC by GPU porting.

Joint species distribution modelling (JSDM) is a widely used statistical method that analyzes combined patterns of all species in a community, linking empirical data to ecological theory and enhancing community-wide prediction tasks. However, fitting JSDMs to large datasets is often computationally demanding and time-consuming. Recent studies have introduced new statistical and machine learning techniques to provide more scalable fitting algorithms, but extending these to complex JSDM structures that account for spatial dependencies or multi-level sampling designs remains challenging. In this study, we aim to enhance JSDM scalability by leveraging high-performance computing (HPC) resources for an existing fitting method. Our work focuses on the Hmsc R-package, a widely used JSDM framework that supports the integration of various dataset types into a single comprehensive model. We developed a GPU-compatible implementation of its model-fitting algorithm using Python and the TensorFlow library. Despite these changes, our enhanced framework retains the original user interface of the Hmsc R-package. We evaluated the performance of the proposed implementation across various model configurations and dataset sizes. Our results show a significant increase in model fitting speed for most models compared to the baseline Hmsc R-package. For the largest datasets, we achieved speed-ups of over 1000 times, demonstrating the substantial potential of GPU porting for previously CPU-bound JSDM software. This advancement opens promising opportunities for better utilizing the rapidly accumulating new biodiversity data resources for inference and prediction.

Mini-map design as a cartographic element of the user interface - based on video games

Mini-map design as a cartographic element of the user interface - based on video games