Search Results (47)

Headache represents one of the most prevalent and disabling conditions in the pediatric population, with significant repercussions on mental and psychological well-being, as well as on academic achievement and social functioning, ultimately leading to a marked reduction in quality of life. Currently, the diagnosis of headache is based on the clinical criteria of the third edition of the International Classification of Headache Disorders (ICHD-3). However, the characteristics of headache may differ between adults and children, as well as the ability of children to provide a complete description of the pain and associated symptoms. The immature narrative skills of children can represent a limitation in defining the clinical phenotype of headache, making the diagnosis more complex. This is even more challenging when extracting information about the characteristics of the headache in children whose verbal expression is poorly developed or completely absent. Given these limitations, clinical psychology has long used drawing as an effective diagnostic instrument to bypass verbal communication barriers. This tool provides unique access to children’s psychological and emotional states, as a direct window into their inner world and as an expressive medium that often generates more detailed, accurate, and clinically actionable information, compared to verbal reports alone. For these reasons, drawing has been recognized as a valuable diagnostic tool for decades, with multiple studies demonstrating specificity and accuracy rates comparable to standard clinical assessments. Particularly for young children, drawings may give access to fundamental information that might otherwise remain inaccessible, thereby allowing both accurate diagnosis and individualized treatment planning. Multiple studies have highlighted and confirmed the graphic differences between representations of various types of headaches and the undeniable utility of an “artistic diagnosis” alongside the clinical one. Furthermore, the literature suggests and encourages the use of drawing in clinical practice, both in the diagnostic process and during subsequent follow-up, as an effective, enjoyable, easy-to-use, and low-cost resource. Accordingly, we propose a narrative review accompanied by a curated collection of drawings that may help identify and categorize specific correlations between graphic representations and clinical phenotypes, such as pain location, quality, intensity, association with nausea and vomiting, photophobia and phonophobia, and types of migraine aura. Our goal is to create a visual reference that can aid clinicians in the accurate interpretation of children’s drawings. Additionally, we aim to promote the integration of this method into routine clinical practice to improve diagnostic precision and support a more child-centered model of care. We also hope to propose new iconographic models to further enrich the diagnostic framework. Full article

Urban documents like city planning reports and environmental data often feature complex charts and texts that require effective summarization tools, particularly in smart city management systems. These documents increasingly use graphical abstracts alongside textual summaries to enhance readability, making automated abstract generation crucial. This study explores the application of summarization technology using scientific paper abstract generation as a case. The challenge lies in processing the longer multimodal content typical in research papers. To address this, a deep multimodal-interactive network is proposed for accurate document summarization. This model enhances structural information from both images and text, using a combination module to learn the correlation between them. The integrated model aids both summary generation and significant image selection. For the evaluation, a dataset is created that encompasses both textual and visual components along with structural information, such as the coordinates of the text and the layout of the images. While primarily focused on abstract generation and image selection, the model also supports text–image cross-modal retrieval. Experimental results on the proprietary dataset demonstrate that the proposed method substantially outperforms both extractive and abstractive baselines. In particular, it achieves a Rouge-1 score of 46.55, a Rouge-2 score of 16.13, and a Rouge-L score of 24.95, improving over the best comparison abstractive model (Pegasus: Rouge-1 43.63, Rouge-2 14.62, Rouge-L 24.46) by approximately 2.9, 1.5, and 0.5 points, respectively. Even against strong extractive methods like TextRank (Rouge-1 30.93) and LexRank (Rouge-1 29.63), our approach shows gains of over 15 points in Rouge-1, underlining its effectiveness in capturing both textual and visual semantics. These results suggest significant potential for smart city applications—such as accident scene documentation and automated environmental monitoring summaries—where rapid, accurate processing of urban multimodal data is essential. Full article

This manuscript associates with a study of Frobenius–Euler–Simsek-type Polynomials. In this research work, we construct a new sequence of Szász–Beta type operators via Frobenius–Euler–Simsek-type Polynomials to discuss approximation properties for the Lebesgue integrable functions, i.e., $L^p[0,\infty )$, $1\le p<\infty$. Furthermore, estimates in view of test functions and central moments are studied. Next, rate of convergence is discussed with the aid of the Korovkin theorem and the Voronovskaja type theorem. Moreover, direct approximation results in terms of modulus of continuity of first- and second-order, Peetre’s K-functional, Lipschitz type space, and the $r^{th}$-order Lipschitz type maximal functions are investigated. In the subsequent section, we present weighted approximation results, and statistical approximation theorems are discussed. To demonstrate the effectiveness and applicability of the proposed operators, we present several illustrative examples and visualize the results graphically. Full article

A common problem for interdisciplinary sustainability research is that scientists trained in different disciplines are often not rowing their boat effectively in the same direction. Sustainability tools can aid the implementation of this team-melding process. Here, our purpose is to illustrate our Multi-step Integrated graphical and structured discussion eXercise (MIX) tool that transforms diverse disciplinary experts into an interdisciplinary team. We use a visual puzzle-solving approach based on the blind men and the elephant metaphor (BMEM) because this story illustrates the shortcomings of siloed viewpoints and the need to integrate multiple perspectives. Our six-step MIX tool provides step-specific objectives, group activities, discussion questions, and learning outcomes. Activities promote experiential learning for team problem solving. The step-specific structured discussions are designed to get each individual to change their focus from their own discipline (i.e., an elephant trunk, tail, leg, or other isolated pieces of the whole animal) to the team’s interdisciplinary goal (i.e., the whole elephant or the entire multi-faceted problem). In our example proof of concept, we show that a narrow focus on only economic yield (trunk), ecological conservation (legs), or human values (tail) misrepresents the biologically involved sustainability problem (elephant) and blocks innovative solutions. Full article

Visual development in infancy is crucial for establishing neural connections and enhancing the growth of the visual center. Adequate visual stimulation supports cognitive learning, helping children integrate images, colors, and shapes. This research examines the design and use of a visual image teaching aid to guide children in exploring image cognition and developing fine motor skills. This study involved 70 kindergarten teachers who participated in a questionnaire survey about 60 children aged from 5 to 6 years old. The results show that visual graphic aids effectively promoted coordination, control, and the integration of visual and fine motor skills in children. Furthermore, these aids supported the development of spatial and environmental relationships through hands-on activities. Full article

Every texture in the real world provides us with the essential information to identify the physical characteristics of real objects. In addition to sight, humans use the sense of touch to explore their environment. Through haptic interaction we obtain unique and distinct information about the texture and the shape of objects. In this paper, we enhance X3D 3D graphics files with haptic features to create 3D objects with haptic feedback. We propose haptic attributes such as static and dynamic friction, stiffness, and maximum altitude that provide the optimal user experience in a virtual haptic environment. After numerous optimization attempts on the haptic textures, we propose various haptic geometrical textures for creating a virtual 3D haptic environment for the tactile Internet. These tangible geometrical textures can be attached to any geometric shape, enhancing the haptic sense. We conducted a study of user interaction with a virtual environment consisting of 3D objects enhanced with haptic textures to evaluate performance and user experience. The goal is to evaluate the realism and recognition accuracy of each generated texture. The findings of the study aid visually impaired individuals to better understand their physical environment, using haptic devices in conjunction with the enhanced haptic textures. Full article

As global urbanization accelerates, the integration, readability, and connectivity of urban spaces are becoming focal points of international concern, particularly in rapidly developing regions like Asia. The inadequacies of urban wayfinding systems directly affect pedestrians’ wayfinding experiences within city spaces. Pedestrian wayfinding signage, as a critical element supporting pedestrian navigation and urban readability, is often neglected in vehicle-centric urban planning. This study explores the visual preferences of pedestrians regarding wayfinding signage and how these perceptions can be incorporated into the design process. A photo-based survey consisting of 385 pedestrians in Nanning, China, reveals that while many wayfinding signs provide cognitive information, they score lower in visual preference due to poor sensory perception and difficulty in conveying meaning. This study also highlights that the material, color, graphics, and text on signage are key physical attributes influencing the visual preferences of pedestrians. Demographic factors such as age, gender, and professional background also impact these preferences. These findings underscore a broader urban design issue: wayfinding signs must not only clearly communicate navigational information but also enhance the aesthetic and cultural expressions of urban spaces. Current signage systems often neglect these aesthetic and cultural needs, potentially leading to visual fatigue or cultural disconnection, thereby affecting navigation efficiency and urban experience. The results provide empirical foundations for optimizing wayfinding signage designs in urban areas, aiding urban planners and designers in integrating pedestrian preferences to create clearer, more attractive navigation systems, thus improving walking experiences and significantly enhancing the daily lives of city residents. Full article

Cystic fibrosis is caused by biallelic pathogenic variants in the CFTR gene, which contains a polymorphic (TG)_mT_n sequence (the “poly-T/TG tract”) in intron 9. While T₉ and T₇ alleles are benign, T₅ alleles with longer TG repeats, e.g., (TG)₁₂T₅ and (TG)₁₃T₅, are clinically significant. Thus, professional medical societies currently recommend reporting the TG repeat size when T₅ is detected. Sanger sequencing is a cost-effective method of genotyping the (TG)_mT_n tract; however, its polymorphic length substantially complicates data analysis. We developed CFTR-TIPS, a freely available web-based software tool that infers the (TG)_mT_n genotype from Sanger sequencing data. This tool detects the (TG)_mT_n tract in the chromatograms, quantifies goodness of fit with expected patterns, and visualizes the results in a graphical user interface. It is broadly compatible with any Sanger chromatogram that contains the (TG)_mT_n tract ± 15 bp. We evaluated CFTR-TIPS using 835 clinical samples previously analyzed in a CLIA-certified, CAP-accredited laboratory. When operated fully automatically, CFTR-TIPS achieved 99.8% concordance with our clinically validated manual workflow, while generally taking less than 10 s per sample. There were two discordant samples: one due to a co-occurring heterozygous duplication that confounded the tool and the other due to incomplete (TG)_mT_n tract detection in the reverse chromatogram. No clinically significant misclassifications were observed. CFTR-TIPS is a free, accurate, and rapid tool for CFTR (TG)_mT_n tract genotyping using cost-effective Sanger sequencing. This tool is suitable both for automated use and as an aid to manual review to enhance accuracy and reduce analysis time. Full article

Minor bodies exhibit considerable variability in shape and surface morphology, posing challenges for spacecraft operations, which are further compounded by highly non-linear dynamics and limited communication windows with Earth. Additionally, uncertainties persist in the shape and surface morphology of minor bodies due to errors in ground-based estimation techniques. The growing need for autonomy underscores the importance of robust image processing and visual-based navigation methods. To address this demand, it is essential to conduct tests on a variety of body shapes and with different surface morphological features. This work introduces the procedural Minor bOdy geNErator Tool (MONET), implemented using an open-source 3D computer graphics software. The starting point of MONET is the three-dimensional mesh of a generic minor body, which is procedurally modified by introducing craters, boulders, and surface roughness, resulting in a photorealistic model. MONET offers the flexibility to generate a diverse range of shapes and surface morphological features, aiding in the recreation of various minor bodies. Users can fine-tune relevant parameters to create the desired conditions based on the specific application requirements. The tool offers the capability to generate two default families of models: rubble-pile, characterized by numerous different-sized boulders, and comet-like, reflecting the typical morphology of comets. MONET serves as a valuable resource for researchers and engineers involved in minor body exploration missions and related projects, providing insights into the adaptability and effectiveness of guidance and navigation techniques across a wide range of morphological scenarios. Full article

Given its detrimental effect on the brain, alcoholism is a severe disorder that can produce a variety of cognitive, emotional, and behavioral issues. Alcoholism is typically diagnosed using the CAGE assessment approach, which has drawbacks such as being lengthy, prone to mistakes, and biased. To overcome these issues, this paper introduces a novel paradigm for identifying alcoholism by employing electroencephalogram (EEG) signals. The proposed framework is divided into various steps. To begin, interference and artifacts in the EEG data are removed using a multiscale principal component analysis procedure. This cleaning procedure contributes to information quality improvement. Second, an innovative graphical technique based on fast fractional Fourier transform coefficients is devised to visualize the chaotic character and complexities of the EEG signals. This elucidates the properties of regular and alcoholic EEG signals. Third, thirty-four graphical features are extracted to interpret the EEG signals’ haphazard behavior and differentiate between regular and alcoholic trends. Fourth, we propose an ensembled feature selection method for obtaining an effective and reliable feature group. Following that, we study many neural network classifiers to choose the optimal classifier for building an efficient framework. The experimental findings show that the suggested method obtains the best classification performance by employing a recurrent neural network (RNN), with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the sixteen selected features. The proposed framework can aid physicians, businesses, and product designers to develop a real-time system. Full article

Weed management impacts crop yield and quality. Machine vision technology is crucial to the realization of site-specific precision weeding for sustainable crop production. Progress has been made in developing computer vision algorithms, machine learning models, and datasets for weed recognition, but there has been a lack of open-source, publicly available software tools that link imaging hardware and offline trained models for system prototyping and evaluation, hindering community-wise development efforts. Graphical user interfaces (GUIs) are among such tools that can integrate hardware, data, and models to accelerate the deployment and adoption of machine vision-based weeding technology. This study introduces a novel GUI called OpenWeedGUI, designed for the ease of acquiring images and deploying YOLO (You Only Look Once) models for real-time weed detection, bridging the gap between machine vision and artificial intelligence (AI) technologies and users. The GUI was created in the framework of PyQt with the aid of open-source libraries for image collection, transformation, weed detection, and visualization. It consists of various functional modules for flexible user controls and a live display window for visualizing weed imagery and detection. Notably, it supports the deployment of a large suite of 31 different YOLO weed detection models, providing flexibility in model selection. Extensive indoor and field tests demonstrated the competencies of the developed software program. The OpenWeedGUI is expected to be a useful tool for promoting community efforts to advance precision weeding technology. Full article

Mathematical modeling and use of technology have been recognized as powerful vehicles for advancing mathematical thinking. The use of technology to aid students to gain understanding about real-world mathematical problems has long been a subject of inquiry. Using the extended Blum’s modeling cycle as the theoretical framework, this study explores how computer simulations influence fifth-grade students’ mathematical modeling processes. We investigated interpretations and perceptions of students regarding a situation model, and how these interpretations and perceptions changed as a result of exposure to the simulation. Observing students as they worked with three interactive computer simulations during task-based interviews, we found that animated graphics of the simulations helped the participants to visualize the problem and learn about the effect of different variables and their interactions on the outcomes of the simulations. Students explored simulation environments, ran simulations to observe outcomes of specific settings, and formed conjectures to describe the effect of simulation variables on the outcomes. Analyzing participants’ activities and movements along the extended Blum modeling cycle, we propose a three-stage learning model composed of “exploration and discovery”, “structured inquiry”, and “synthesizing”. Full article

In the field of software engineering, large and complex code bases may lead to some burden of understanding their structure and meaning for developers. To reduce the burden on developers, we consider a code base visualization method to visually express the meaning of code bases. Inspired by remote sensing imagery, we employ graphical representations to illustrate the semantic connections within Java code bases, aiming to help developers understand its meaning and logic. This approach is segmented into three distinct levels of analysis. First, at the project-level, we visualize Java projects by portraying each file as an element within a code forest, offering a broad overview of the project’s structure. This macro-view perspective aids in swiftly grasping the project’s layout and hierarchy. Second, at the file-level, we concentrate on individual files, using visualization techniques to highlight their unique attributes and complexities. This perspective enables a deeper understanding of each file’s structure and its role within the larger project. Finally, at the component-level, our focus shifts to the detailed analysis of Java methods and classes. We examine these components for complexity and other specific characteristics, providing insights that are crucial for the optimization of code and the enhancement of software quality. By integrating remote sensing technology, our method offers software engineers deeper insights into code quality, significantly enhancing the software development lifecycle and its outcomes. Full article

Background: The primary objective of this study was to examine the advancements in sustainable supplier selection through multi-criteria decision making (MCDM) from the years 2013 to 2022. In the recent past, researchers have carried out a significant amount of research in this field over the course of several years; Methods: a total of 121 scientific publications sourced from the Scopus database were chosen for analysis, employing the bibliometric method and graphical visualization of the VOS viewer application to visually analyze and map research networks and collaboration patterns, aiding in the evaluation of scientific impact and knowledge dissemination; Results: the findings of this study indicate that the research trend in sustainable supplier selection through MCDM witnessed its most significant growth in the year 2019. Researchers predominantly disseminated their scientific findings through articles, accounting for 81% of the publications, followed by conference papers at 14%, and book chapters at 2.5%; Conclusions: the primary area of focus in these studies pertains to decision-making processes involved in sustainable supplier selection. The implications and theoretical contributions derived from this research, coupled with the latest advancements, serve as a foundation for further exploration and development of sustainable supplier selection research through MCDM. Full article

The need for reliable, state-of-the-art environmental investigations and pioneering approaches to address pressing ecological dilemmas and to nurture the sustainable development goals (SDGs) cannot be overstated. With the power to revolutionize desalination processes, artificial intelligence (AI) models hold the potential to address global water scarcity challenges and contribute to a more sustainable and resilient future. The realm of desalination has exhibited a mounting inclination toward modeling the efficacy of the hybrid nanofiltration/reverse osmosis (NF–RO) process. In this research, the performance of NF–RO based on permeate conductivity was developed using deep learning long short-term memory (LSTM) integrated with an optimized metaheuristic crow search algorithm (CSA) (LSTM-CSA). Before model development, an uncertainty Monte Carlo simulation was adopted to evaluate the uncertainty attributed to the prediction. The results based on several performance statistical criteria (root mean square error (RMSE) and mean absolute error (MAE)) demonstrated the reliability of both LSTM (RMSE = 0.1971, MAE = 0.2022) and the LSTM-CSA (RMSE = 0.1890, MAE = 0.1420), with the latter achieving the highest accuracy. The accuracy was also evaluated using new 2D graphical visualization, including a cumulative distribution function (CDF) and fan plot to justify the other evaluation indicators such as standard deviation and determination coefficients. The outcomes proved that AI could optimize energy usage, identify energy-saving opportunities, and suggest more sustainable operating strategies. Additionally, AI can aid in developing advanced brine treatment techniques, facilitating the extraction of valuable resources from the brine, thus minimizing waste and maximizing resource utilization. Full article