Navigation Interface Research Articles

Evaluating Usability and Accessibility of Visual Programming Tools for Novice Programmers—The Case of App Inventor, Scratch, and StarLogo

The current state of the labor market requires modern engineers to acquire programming skills at different levels of advancement and to apply them in multidisciplinary environments. Not all modern engineers will become fully fledged programmers, but sometimes the possibility to use low-code programming environments like LabView or MIT App Inventor will be sufficient. In order to give good foundations for adulthood, schools use elements of visual programming, e.g., Scratch or StarLogo, to enhance the critical and algorithmic thinking of future engineers. This article attempts to answer the question of whether anyone cares about following general accessibility and usability guidelines in the case of solutions like Scratch, App Inventor, and StarLogo. Another goal is to show a set of tools that is successful in such an assessment. The authors used Nielsen’s heuristics, followed by analyzing WAVE output and Web Content Accessibility Guidelines compliance. Especially, the last one provides insights usually omitted when evaluating low-code environments. It was found that Scratch and App Inventor are leading solutions in terms of look and feel, functionality, documentation, interface navigation, and memorization. The StarLogo interface, on the other hand, is less friendly in terms of aesthetics and functionality.

Clinicians' use of Health Information Exchange technologies for medication reconciliation in the U.S. Department of Veterans Affairs: a qualitative analysis.

Medication reconciliation is essential for optimizing medication use. In part to promote effective medication reconciliation, the Department of Veterans Affairs (VA) invested substantial resources in health information exchange (HIE) technologies. The objectives of this qualitative study were to characterize VA clinicians' use of HIE tools for medication reconciliation in their clinical practice and to identify facilitators and barriers. We recruited inpatient and outpatient prescribers (physicians, nurse practitioners, physician assistants) and pharmacists at four geographically distinct VA medical centers for observations and interviews. Participants were observed as they interacted with HIE or medication reconciliation tools during routine work. Participants were interviewed about clinical decision-making pertaining to medication reconciliation and use of HIE tools, and about barriers and facilitators to use of the tools. Qualitative data were analyzed via inductive and deductive approaches using a priori codes. A total of 63 clinicians participated. Over half (58%) were female, and the mean duration of VA clinical experience was 7 (range 0-32) years. Underlying motivators for clinicians seeking data external to their VA medical center were having new patients, current patients receiving care from an external institution, and clinicians' concerns about possible medication discrepancies among institutions. Facilitators for using HIE software were clinicians' familiarity with the HIE software, clinicians' belief that medication information would be available within HIE, and their confidence in the ability to find HIE medication-related data of interest quickly. Six overarching barriers to HIE software use for medication coordination included visual clutter and information overload within the HIE display; challenges with HIE interface navigation; lack of integration between HIE and other electronic health record interfaces, necessitating multiple logins and application switching; concerns with the dependability of HIE medication information; unfamiliarity with HIE tools; and a lack of HIE data from non-VA facilities. This study is believed to be the first to qualitatively characterize clinicians' HIE use with respect to medication reconciliation. Results inform recommendations to optimize HIE use for medication management activities. We expect that healthcare organizations and software vendors will be able to apply the findings to develop more effective and usable HIE information displays.

Research on Universal Design of Smart Home Interface Navigation Based on Visual Interaction

The study focuses on analyzing the design characteristics of display and control interface navigation in smart home terminals for users of different ages. By examining the design features of the current typical smart home terminal display and control interface navigation, interface navigation design is the research object. Experimental research is conducted with young adult group (n = 15) and middle-aged to elderly group (n = 15) participants to identify the optimal interface navigation design through specific behavioral indicators, eye movement indicators, and subjective indicators. The results show that different interface layouts impact task completion performance. Middle-aged and older individuals perform better with multi-level highlighting operations. Both age groups prefer interface layouts featuring less-level dual navigation, specifically the left-side type, and multi-level highlighting. These results can improve the versatility of smart home interface design, expand the user base, and provide information for developing smart products.

Towards a Deep Learning-Powered Herbarium Image Analysis Platform

Global digitization efforts have archived millions of specimen scans worldwide in herbarium collections, which are essential for studying plant evolution and biodiversity. ReColNat hosts, at present, over 10 million images. However, analyzing these datasets poses crucial challenges for botanical research. The application of deep learning in biodiversity analyses, particularly in analyzing herbarium scans, has shown promising results across numerous tasks (Ariouat et al. 2023, Ariouat et al. 2024, Groom et al. 2023, Sahraoui et al. 2023). Within the e-Col+project (ANR-21-ESRE-0053), we are developing multiple deep learning models aimed at identifying plant morphological traits. We have developed pipelines and models for cleaning, analyzing, and transforming herbarium images, including models for: i) detecting non-vegetal elements, such as barcodes, envelopes, labels, etc.; ii) detecting plant organs, including leaves, flowers, fruits, etc.; and iii) segmenting to recognize plant parts for image cleaning. We are also developing models for classification tasks related to various morphological traits. To validate these models, improve their generalization, and make them easily usable by end-users, deploying them within a generic platform is crucial. The generic platform called PlantAI, currently under development by the e-Col+ project, should enable easy deployment during development for testing and allow users to load annotations for new traits in order to train a model and add it to the existing catalog. The platform is based on a microservice architecture, allowing users to upload images, create custom datasets, and access various AI models for image analysis. The platform is composed of four main modules, as illustrated in Fig. 1. The first module is the collaborative workspace manager, which allows users to create projects and image datasets and invite other users to collaborate on a project. The second module is the navigation interface and dashboards. This module integrates a search engine using metadata and AI annotations, a navigation interface between projects, datasets, and specimens, as well as dashboards for analysis across datasets, specimens, and AI models. The third module is the dataset manager, which handles metadata and annotations associated with the specimens. These annotations can be produced either by expert users or by AI models. The fourth module is the AI models management module, so that models can be used to generate AI annotations of specimen. During the development lifecycle of an AI model, users can create datasets and annotate them with AI models. These annotations can be in two possible states: validated by experts and non-validated. Users collaborating on a project can indicate errors in the model predictions and leave comments to explain their evaluations. These corrections made by experts can be used to retrain the models and thus improve their performance. This platform, will be highly beneficial for botanists, enhancing the efficiency and effectiveness of biodiversity analyses from herbarium scans. We aim to provide users with a catalog of AI models through this platform and allow them to import their own datasets with their own annotations regarding traits of their choice. Users will be able to select a model from the AI model catalog and train it using their dataset. Ultimately, the model obtained from this training will be automatically deployed to be available for AI annotation. The annotations produced by this model will be automatically available in the filtering and navigation interface, thus allowing for dynamic and automatic integration of the AI annotations into the navigation interface.

Unlocking Interactive Learning: Building a Robust Quiz Portal with MERN Stack Technology"

Online learning has become increasingly popular in the digital era, necessitating innovative platforms to effectively engage students. This research outlines the development and implementation of an interactive quiz portal utilizing the MERN (MongoDB, Express.js, React.js, and Node.js) stack to enhance learning outcomes. The portal features a user-friendly interface for easy navigation and access to a diverse range of quizzes on various subjects. To ensure scalability, performance, and real-time interactions, Node.js handles backend operations, React.js manages dynamic interfaces, Express.js is used for server-side development, and MongoDB stores data. The portal includes instant feedback mechanisms, gamification elements, and personalized user profiles to enhance user motivation and involvement. Furthermore, adaptive algorithms are employed to adjust quiz content based on user proficiency levels. This paper presents the design, development, and implementation of an online test platform using the MERN (MongoDB, Express, React, and Node.js) stack. The portal functions as an all-inclusive platform for administering tests, assessments, and learning evaluations in an online setting. The portal provides smooth integration of frontend and backend activities, guaranteeing strong performance and user interaction, by utilizing the adaptability and scalability of the MERN stack. The portal enables quick development and deployment cycles with the help of MongoDB's document-based data storage, Express.js's effective middleware and routing, React.js's dynamic UI components, and Node.js's event-driven architecture. To improve user experience and learning results, features including interactive feedback mechanisms, real-time performance monitoring, quiz design and administration, and user authentication are easily integrated.

A Collaborative Code Platform with Advanced AI Features and Real-Time Collaboration Tools

Abstract: Good collaboration and code management are crucial in today's software development environment. Our research has launched a new collaborative process that combines AI resources, including AI and global research, to improve products and enhance and facilitate collaboration. The platform features real-time collaboration tools such as dynamic text with multilanguage support, dialog functionality, and notepad functionality. With OAuth authentication, users can seamlessly perform CRUD operations on files and directories, share code repositories, and participate in live coding sessions. The platform supports compilation and execution of a code file and follows good design principles such as caching, microservices architecture, and containerization to ensure scalability and performance. The platform focuses on user experience by providing simple user interface design and easy interface navigation. Users can create workspaces, share them collaboratively, and seamlessly import repositories from Git. The platform's intelligent data manager helps manage files and directories, while the collaborative platform allows developers to collaborate on code libraries efficiently. The platform is modern web-based, using React on the frontend and Node.js on the backend; Supported with syntax highlighting and smart initialization along with Socket.io for runtime with SQL and NoSQL databases. Provides comprehensive language support. All progress is saved, providing users with peace of mind. While the basic design is for desktops, future iterations are expected to support mobile devices.

Criminal Investigation Tracker with Suspect Prediction

This initiative seeks to craft an inclusive and intuitive web-based platform tailored for criminal identification via image and video surveillance, leveraging cutting-edge facial recognition technology. The system integrates a registration portal for inputting data and images of known criminals, utilizing OpenCV and advanced facial recognition algorithms to securely analyze and store their facial attributes. In addition to allowing users to upload images for analysis, the system offers immediate feedback on potential matches with registered criminals. Moreover, the video surveillance module extends this capability to short videos, employing video analytics to identify faces within the footage. The platform ensures real-time feedback for successful identifications and provides an advanced feature enabling users to download details of identified criminals in an Excel format. By amalgamating state-of-the-art technology with an intuitive interface, this project endeavors to bolster law enforcement endeavors by furnishing an efficient and precise tool for criminal identification and tracking. Its objectives encompass developing a robust system for identifying and tracking criminals through advanced facial recognition algorithms and OpenCV technology, designing a user-friendly web interface for seamless navigation across various modules, and establishing a secure and efficient registration section for compiling comprehensive databases of facial features.

Unlocking Athletic Potential The Athle-E-Team Software Solution

The "Athle-E-Team" project is an innovative collaborative sports platform designed to revolutionize the way athletes connect and engage in sports within their local communities. With a focus on enhancing the overall sports experience, the platform utilizes cutting-edge technologies to foster real-time connections and facilitate seamless play among nearby athletes. Key features include a user-friendly interface for easy navigation, integrated chat functionality for communication, and robust search capabilities for both players and nearby sports facilities. This dynamic platform not only addresses the challenges of traditional sports community engagement but also empowers athletes to organize impromptu games, locate available grounds, and build a vibrant and connected local sports community. "Athle-E-Team" represents a significant leap forward in leveraging technology to create a more accessible, interactive, and inclusive environment for sports enthusiasts.

HaLo‐NeRF: Learning Geometry‐Guided Semantics for Exploring Unconstrained Photo Collections

AbstractInternet image collections containing photos captured by crowds of photographers show promise for enabling digital exploration of large‐scale tourist landmarks. However, prior works focus primarily on geometric reconstruction and visualization, neglecting the key role of language in providing a semantic interface for navigation and fine‐grained understanding. In more constrained 3D domains, recent methods have leveraged modern vision‐and‐language models as a strong prior of 2D visual semantics. While these models display an excellent understanding of broad visual semantics, they struggle with unconstrained photo collections depicting such tourist landmarks, as they lack expert knowledge of the architectural domain and fail to exploit the geometric consistency of images capturing multiple views of such scenes. In this work, we present a localization system that connects neural representations of scenes depicting large‐scale landmarks with text describing a semantic region within the scene, by harnessing the power of SOTA vision‐and‐language models with adaptations for understanding landmark scene semantics. To bolster such models with fine‐grained knowledge, we leverage large‐scale Internet data containing images of similar landmarks along with weakly‐related textual information. Our approach is built upon the premise that images physically grounded in space can provide a powerful supervision signal for localizing new concepts, whose semantics may be unlocked from Internet textual metadata with large language models. We use correspondences between views of scenes to bootstrap spatial understanding of these semantics, providing guidance for 3D‐compatible segmentation that ultimately lifts to a volumetric scene representation. To evaluate our method, we present a new benchmark dataset containing large‐scale scenes with ground‐truth segmentations for multiple semantic concepts. Our results show that HaLo‐NeRF can accurately localize a variety of semantic concepts related to architectural landmarks, surpassing the results of other 3D models as well as strong 2D segmentation baselines. Our code and data are publicly available at https://tau‐vailab.github.io/HaLo‐NeRF/.

Multi-institutional evaluation of a Pareto navigation guided automated radiotherapy planning solution for prostate cancer

BackgroundCurrent automated planning solutions are calibrated using trial and error or machine learning on historical datasets. Neither method allows for the intuitive exploration of differing trade-off options during calibration, which may aid in ensuring automated solutions align with clinical preference. Pareto navigation provides this functionality and offers a potential calibration alternative. The purpose of this study was to validate an automated radiotherapy planning solution with a novel multi-dimensional Pareto navigation calibration interface across two external institutions for prostate cancer.MethodsThe implemented ‘Pareto Guided Automated Planning’ (PGAP) methodology was developed in RayStation using scripting and consisted of a Pareto navigation calibration interface built upon a ‘Protocol Based Automatic Iterative Optimisation’ planning framework. 30 previous patients were randomly selected by each institution (IA and IB), 10 for calibration and 20 for validation. Utilising the Pareto navigation interface automated protocols were calibrated to the institutions’ clinical preferences. A single automated plan (VMATAuto) was generated for each validation patient with plan quality compared against the previously treated clinical plan (VMATClinical) both quantitatively, using a range of DVH metrics, and qualitatively through blind review at the external institution.ResultsPGAP led to marked improvements across the majority of rectal dose metrics, with Dmean reduced by 3.7 Gy and 1.8 Gy for IA and IB respectively (p < 0.001). For bladder, results were mixed with low and intermediate dose metrics reduced for IB but increased for IA. Differences, whilst statistically significant (p < 0.05) were small and not considered clinically relevant. The reduction in rectum dose was not at the expense of PTV coverage (D98% was generally improved with VMATAuto), but was somewhat detrimental to PTV conformality. The prioritisation of rectum over conformality was however aligned with preferences expressed during calibration and was a key driver in both institutions demonstrating a clear preference towards VMATAuto, with 31/40 considered superior to VMATClinical upon blind review.ConclusionsPGAP enabled intuitive adaptation of automated protocols to an institution’s planning aims and yielded plans more congruent with the institution’s clinical preference than the locally produced manual clinical plans.

EEG and EMG-based human-machine interface for navigation of mobility-related assistive wheelchair (MRA-W)

The control of human-machine interfaces (HMIs), such as motorized wheelchairs, has been widely investigated using biopotentials produced by electrochemical processes in the human body. However, many studies in this field sometimes overlook crucial factors like special users' needs, who often have inadequate muscle mass and strength, and paresis needed to operate a wheelchair. This study proposes a novel solution: an economical, universally compatible, and user-centric manual-to-powered wheelchair conversion kit. The powered wheelchair is operated using a hybrid control system integrating electroencephalogram (EEG) and electromyography (EMG), utilizing an LSTM network. It uses a low-cost electroencephalogram (EEG) headset and a wearable electromyography (EMG) electrode armband to solve these constraints. The proposed system comprised three crucial objectives: the development of an EEG-based user attentive detection system, an EMG-based navigation system, and a transform conventional wheelchair into a powered wheelchair. Human test subjects were utilized to evaluate the proposed system, and the study complied with accepted ethical guidelines. We selected four EEG features (p < 0.023) for the attentive detection system and six EMG features (p < 0.037) to detect navigation intentions. User attentive detection was achieved at 83.33 (±0.34) %, while the navigation intention system produced 86.67 (±0.52) % accuracy. The overall system was successful in reaching an accuracy rate of 85.0 (±0.19) % and a weighted average precision of 0.89. After the dataset was trained using an LSTM network, the overall accuracy produced was 97.3 (±0.5) %, higher than the accuracy produced by the Quadratic SVM classifier. By giving older and disabled people a more convenient way to use powered wheelchairs, this research helps to build ergonomic and cost-effective biopotential-based HMIs, enhancing their quality of life.

Evaluating the Usability, Perceived Performance, and Perceived Effects of KBGAN iHealth© and KBGAN iFeed© Mobile Apps for Buffalo Management in Selected Municipalities in the Philippines

This study evaluates the KBGAN iHealth© and KBGAN iFeed© mobile apps designed for buffalo health and feeding management, particularly for agricultural extension professionals (AEPs) in selected Philippine municipalities. These apps aim to address challenges in buffalo management, such as limited access to veterinary expertise, personalized recommendations, organized data, communication channels, and difficulties in calculating ideal feed compositions and meeting the distinct needs of smallholder farmers and AEPs. Despite System Usability Scale (SUS) scores indicating marginal acceptability for both apps, weighted mean scores by AEPs for statements assessed on a 5-point Likert scale (1 as strongly disagree and 5 as strongly agree), demonstrate that AEPs reported high confidence in the accuracy of buffalo health diagnostics (Mean of 4.20) and health management recommendations (Mean of 4.17) provided by KBGAN iHealth©. Similarly, KBGAN iFeed© received favorable ratings, with AEPs expressing agreement on the accuracy of feeding recommendations (Mean of 3.89) and the facilitation of feeding ration computations (Mean of 4.00). These positive perceived performance outcomes, coupled with increased confidence (Mean of 4.03) and motivation (Mean of 4.00) among AEPs, suggest the potential for frequent and consistent app usage despite usability concerns. Chi-square tests examining the relationship between AEP characteristics and SUS scores for the apps reveal significant associations between AEPs' education and experience levels and SUS scores for KBGAN iFeed©. The choice of IT device also influences KBGAN iHealth© SUS scores. Proposed enhancements by AEPs include refining algorithms, improving the user interface for navigation, speed, and efficiency, and incorporating features such as photo uploads and geotagging.

A Last-100-Meters User Acceptance Issue: Navigating Fully Autonomous Vehicles through on-Board Navigation Systems

As the navigation interface of fully autonomous vehicles (FAV) will be wholly dependent on On-board Navigation Systems (ONS), it is foreseeable that ONS will replace steering wheels as the primary control mode of future FAVs. Thus, the ease of setting precise destinations using ONS may become a key factor affecting FAV user acceptance. A survey (N = 200) comprising Navigation Anxiety Questionnaire and User Preference Questionnaire reveals the user’s acceptable measurements of distance error and execution time when navigating FAVs in six common situations. Subsequently, an in-vehicle simulation test (N = 24) found most participants in the simulation test performed worse than the user’s acceptable measurements obtained through the questionnaire survey, which indicated that the existing ONS failed to meet users’ expectations for the FAV navigation experience. Findings of the questionnaire survey and the simulation test reveal a severe “last-100 meters” issue when controlling FAVs by ONS, and that destination familiarity, gender difference, text input behavior, map scale, and map display mode should be seriously considered when designing ONS for FAV control.

Safety Production Detection Algorithm of Smart Factory Based on PC Control Technology

Abstract In order to obtain the best scheme of safety production and operation decision-making of a smart factory and improve the rapid response ability of product information of a smart factory, the safety production detection algorithm of a smart factory is designed by using programmable computer (PC) control technology. The main interface, login interface, function interface, and navigation interface are designed, respectively, to realize the front-end design of the smart factory safety production detection platform. Based on PC control technology, the hardware equipment of perception layer, network layer, and application layer are installed, and on this basis, the database of the smart factory safety production detection platform is established. Once the operation data information is collected and detected, it is stored to the equipment fault experience knowledge base, and the fault diagnosis method of the minimum cut set of the fault tree is used to implement the on-site equipment diagnosis of the underlying Internet of Things of the smart factory, and finally the information sharing function of safety production detection through the information transmission of the safety production links of each smart factory is realized. Experiments show that the throughput data of the designed algorithm run stably, improve the control accuracy of safety production, and effectively improve the work efficiency.

A collection of read depth profiles at structural variant breakpoints

SWaveform, a newly created open genome-wide resource for read depth signal in the vicinity of structural variant (SV) breakpoints, aims to boost development of computational tools and algorithms for discovery of genomic rearrangement events from sequencing data. SVs are a dominant force shaping genomes and substantially contributing to genetic diversity. Still, there are challenges in reliable and efficient genotyping of SVs from whole genome sequencing data, thus delaying translation into clinical applications and wasting valuable resources. SWaveform includes a database containing ~7 M of read depth profiles at SV breakpoints extracted from 911 sequencing samples generated by the Human Genome Diversity Project, generalised patterns of the signal at breakpoints, an interface for navigation and download, as well as a toolbox for local deployment with user’s data. The dataset can be of immense value to bioinformatics and engineering communities as it empowers smooth application of intelligent signal processing and machine learning techniques for discovery of genomic rearrangement events and thus opens the floodgates for development of innovative algorithms and software.

Programmable tactile feedback system for blindness assistance based on triboelectric nanogenerator and self-excited electrostatic actuator

Vibro-tactile devices are highly resistant to interference and perform excellently in noisy environments, rendering them the preferred method for blindness assistance. To construct an effective haptic feedback system, two key elements are required: a highly sensitive vibration actuator and a safe high-voltage power source. In this work, a self-powered tactile feedback system (STFS) is proposed on the basis of triboelectric nanogenerator (TENG) and self-excited electrostatic actuator (SEEA). The performances of the TENG and SEEA have been systematically investigated and optimized to ensure that the former can power the latter to produce sufficient vibration stimuli on the user’s skin. With the help of a programmed switch matrix, a series of practical applications have been demonstrated, including a refreshable Braille system, a haptic navigation interface, and tactile-assisted encrypted communication. The proposed STFS holds the merits of being portable, cost-effective, multifunctional, and high-accurate, demonstrating the great potential to substantially improve the quality of life for visually impaired individuals.

How information within the perceptual span guides visual search and aids perception

We explore the guiding effect of task-related information appearing within the perceptual span on visual search based on elements such as the range of effective information attention within the perceptual span and the orientation of task-related objects. In the first experiment, we recorded reaction time and eye movement data of 48 subjects performing a visual search task for target information in the navigation interface and divided them into four experimental groups according to the perceptual span (within or outside the range) and orientation (left or right) of the task-related information. The experimental results showed that: (a) task-related information appearing within the perceptual span had a guiding effect on target search; and (b) the right side closer to the target in task search have better performance in guiding. In the second experiment, eye-movement experiments of the navigation interface based on the eye-movement processing theoretical model of linguistics showed that: (a) within the perceptual span, the shifting of attention causing eye movements (saccade) and brings gaze into the area of interest, and (b) the perceptual span moves with attention shifts. This study provides evidence for cognitive processing of information responses within the perceptual span and dynamic adjustment of perceptual span location and provides a reliable method for effective information presentation and visual layout in navigation interfaces, with a view to providing reference and learning for related studies.

Affective Design of Smart TV Navigation Interface Considering the Diversity of User Needs

Identifying users’ diversified needs and designing products that match those needs is important in the era of mass personalization. Smart TV navigation interfaces lack good affective design, and difficulties remain in responding to diversified affective needs. Therefore, an affective design approach that considers the diversity of user needs is presented to optimize the design of smart TV navigation interfaces. First, the laddering interview is conducted to capture the diversified affective needs and multi-layer interface design elements perceived by users. Kansei distance is introduced to characterize diversified needs. Then, single-user relationship models between user perceptions and interface attributes are constructed. Finally, the two-stage interface optimization is performed to obtain common and individual optimization attributes, which are further embodied as parameter-layer optimization solutions through user experiments. This method is capable of capturing the diversified affective needs for smart TV navigation interfaces and achieving differentiated product design.

Improving pedestrians’ navigation safety at night by enhancing legibility of foreground and background information on the display

Traffic intersections are dangerous areas for pedestrians at night, primarily when people use their mobile phones while walking; pedestrians’ line of sight switches between their phones and the environment, which causes inattentional blindness during emergencies. The salience of the foreground and background information is different by the lightness conditions; therefore, identifying the lightness level of the foreground and background is critical. In this study, the brightness of urban traffic intersection scenes in the nighttime was analyzed, and improved color tones of the navigation interface for enhancing information recognition were identified. Moreover, identify the balance of visual attention by adjusting lightness and color treatment between foreground and background information. A glance legibility experiment indicated that a warm color tone with contrast and gamma correction under a lighting value of 65 in the LAB color mode is the optimal recognition combination. The results of this study indicated that low gamma under high lighting effectively reduced glare-related distractions and resulted in the lowest error rate in background information recognition. This paper proposes increasing the visual attention of urban pedestrians during nighttime navigation through the balancing of the lighting of the foreground interface and the color treatment of the background. Navigating at nighttime can be safer when their attention toward the environment increases and decreases attention distraction.

Research on navigation interface design of children's health application based on AHP-entropy weight method

Research on navigation interface design of children's health application based on AHP-entropy weight method