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Recently, various studies have been devoted to the study of transient receptor potential vanilloid member 1 (TRPV1)-related diseases, potential drugs, and related mechanisms. The objective of this investigation was to examine the significant areas and cutting-edge developments in TRPV1 study within recent decades. Articles or reviews were obtained from the Web of Science Core Collection. VOSviewer 1.6.18 and CiteSpace 6.1 R2 software were utilized to examine publication growth, distribution by country/region, institution, journal, authorship, references, and keywords. The software identified keywords with a high citation burstiness to determine emerging topics. From 1990 to 2023, the annual global publications increased by 62,000%, from 1 to 621. Journal of neuroscience published the most manuscripts and Nature produced the highest citations. The USA, Seoul National University and Di marzo V were the most productive and impactful institution, country, and author, respectively. "TRPV1," "Capsaicin receptor," "Activation," and "Pain" are the most important keywords. The burst keywords "TRPV1 channel," "Oxidative stress," "TRPV1 structure," and "Cancer" are supposed to be the research frontiers. The present study offers valuable insights into the understanding of TRPV1 and pain-related conditions. The research on TRPV1 has demonstrated a steady increase in studies related to pain-related diseases in the past few decades. The significance of TRPV1 in cancer pathogenesis and the resolution of its structure will emerge as a new academic trend in this field, providing direction for more widespread and comprehensive studies in the future. [ABSTRACT FROM AUTHOR]

The human placenta is a multifunctional, disc-shaped temporary fetal organ that develops in the uterus during pregnancy, connecting the mother and the fetus. The availability of large-scale datasets on the gene expression of placental cell types and scholarly articles documenting adverse pregnancy outcomes from maternal infection warrants the use of computational resources to aid in knowledge generation from disparate data sources. Using maternal Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection as a case study in microbial infection, we constructed integrated datasets and implemented visual analytics resources to facilitate robust investigations of placental gene expression data in the dimensions of flow, curation, and analytics. The visual analytics resources and associated datasets can support a greater understanding of SARS-CoV-2-induced changes to the human placental expression levels of 18,882 protein-coding genes and at least 1233 human gene groups/families. We focus this report on the human aquaporin gene family that encodes small integral membrane proteins initially studied for their roles in water transport across cell membranes. Aquaporin-9 (AQP9) was the only aquaporin downregulated in term placental villi from SARS-CoV-2-positive mothers. Previous studies have found that (1) oxygen signaling modulates placental development; (2) oxygen tension could modulate AQP9 expression in the human placenta; and (3) SARS-CoV-2 can disrupt the formation of oxygen-carrying red blood cells in the placenta. Thus, future research could be performed on microbial infection-induced changes to (1) the placental hematopoietic stem and progenitor cells; and (2) placental expression of human aquaporin genes, especially AQP9. [ABSTRACT FROM AUTHOR]

In recent years, digitization in the industrial sector has increased steadily. Digital data not only allows us to monitor the underlying production process using machine learning methods (anomaly detection, behaviour analysis) but also to understand the underlying production process. Insights from Exploratory Data Analysis (EDA) play an important role in building data‐driven processes because data scientists learn essential characteristics of the data in the context of the domain. Due to the complexity of production processes, it is usually difficult for data scientists to acquire this knowledge by themselves. Hence, they have to rely on continuous close collaboration with domain experts and their acquired domain expertise. However, direct communication does not promote documentation of the knowledge transfer from domain experts to data scientists. In this respect, changing team constellations, for example due to a change in personnel, result in a renewed high level of effort despite the same knowledge transfer problem. As a result, EDA is a cost‐intensive iterative process. We, therefore, investigate a system to extract information from the interactions that domain experts perform during EDA. Our approach relies on recording interactions and system states of an exploration tool and generating guided exploration sessions for domain novices. We implement our approach in a software tool and demonstrate its capabilities using two real‐world use cases from the manufacturing industry. We evaluate its feasibility in a user study to investigate whether domain novices can reproduce the most important insights from domain experts about the datasets of the use cases based on generated EDA sessions. From the results of this study, we conclude the feasibility of our system as participants are able to reproduce on average 86.5% of insights from domain experts. [ABSTRACT FROM AUTHOR]

Fuzzy clustering, as a powerful method for pattern recognition and data analysis, often produces complex results that require careful examination of individual clusters. In this paper, advanced visualization techniques are presented that aim to facilitate the analysis of fuzzy clustering results by focusing on the evaluation and interpretation of individual clusters. The presented approach is based on the development of cluster-centric visualization techniques that consider the inherent uncertainty of fuzzy clustering results. The novelty is an assessment of individual clusters with the proposed visualizations. In general, three cluster-centered visualization techniques are presented. These approaches are intended not only to illustrate the overall structure of the fuzzy clustering results but also to enable detailed individual cluster analysis. The performance of the presented visualization techniques is demonstrated by their application to real data sets from different areas. The results show that the techniques provide an effective way to judge individual clusters in fuzzy clustering results for complex data structures. [ABSTRACT FROM AUTHOR]

Readily available software analysis and analytics tools are often operated within external services, where the measured software analysis data are kept internally and no external access to the data is available. We propose an approach to integrate visual software analysis on the GitHub platform by leveraging GitHub Actions and the GitHub API, covering both analysis and visualization. The process is to perform software analysis for each commit, e.g., static source code complexity metrics, and augment the commit using the resulting data, stored as git objects within the same repository. We show that this approach is feasible by integrating it into 64 open source TypeScript projects. Furthermore, we analyze the impact on Continuous Integration (CI) run time and repository storage. The stored software analysis data are externally accessible to allow for visualization tools, such as software maps. The effort to integrate our approach is limited to enabling the analysis component within a project's CI on GitHub and embed an HTML snippet into the project's website for visualization. This enables a large amount of projects to have access to software analysis as well as provide means to communicate the current status of a project. [ABSTRACT FROM AUTHOR]

Dashboards are fundamental to inspect data in smart city and industrial contexts. Due to the complexity of such interfaces, dashboard design and construction can be a difficult and time-consuming activity, usually requiring coding competences, not always available among users. In this paper the Snap4City Dashboard Manager is proposed. Unlike other available solutions, the Dashboard Manager allows users to create dashboards in a graphical way by composing widgets able to display multiple kinds of data. Additionally, each widget can be extended to both retrieve data from any source and enable event-driven communications among different widgets and users.

Visualization provides an important means for understanding software. Many studies focus on software call graphs or software visualization, but there is insufficient attention given to studying them as a whole. This paper presents the extracted call graphs between software packages in Python and within individual packages (modules, file directory, and classes). To avoid potential errors that may arise from directly scanning the source code, we extracts the structure data based on pip and inspect. To facilitate interactive exploration of the data extracted, we provide eight visual layouts based on D3.js and Vue3.js. Additionally, the tool allows for querying module (.py) and class source code and docs. PyVisVue3D3 essentially provides a visual PyDocs that integrates multi-granularity call graphs, aiding developers in quickly extracting knowledge from codebase and improving software comprehension efficiency (https://pyvisvue3d3.yingshinet.com).

The artificial intelligence-based prediction of the mechanical properties derived from the tensile test plays a key role in assessing the application profile of new polymeric materials, especially in the design stage, prior to synthesis. This strategy saves time and resources when creating new polymers with improved properties that are increasingly demanded by the market. A quantitative structure–property relationship (QSPR) model for tensile strength at break is presented in this work. The QSPR methodology applied here is based on machine learning tools, visual analytics methods, and expert-in-the-loop strategies. From the whole study, a QSPR model composed of five molecular descriptors that achieved a correlation coefficient of 0.9226 is proposed. We applied visual analytics tools at two levels of analysis: a more general one in which models are discarded for redundant information metrics and a deeper one in which a chemistry expert can make decisions on the composition of the model in terms of subsets of molecular descriptors, from a physical-chemical point of view. In this way, with the present work, we close a contribution cycle to polymer informatics, providing QSPR models oriented to the prediction of mechanical properties related to the tensile test. [ABSTRACT FROM AUTHOR]

The process of visual analytics is composed of the visual data exploration tasks supporting analytical reasoning. When performing analytical tasks with the interactive visual interfaces displayed by the large screen, physical discomforts such as gorilla-arm effect can be easily caused. To enrich the input space for analysts, there has been some researches concerning the cross-device analysis combining mobile devices with the large display. Although the effectiveness of expert-level designs has been demonstrated, little is known of the ordinary users’ preferences for using a mobile device to issue commands, especially the small one like smartwatch. We implement a three-stage study to investigate and validate these preferences. A total of 181 distinctive gestural inputs and 52 interface designs for 21 tasks were collected from analysts. Expert designers selected the best practices from these user-defined interactions. A performance test was subsequently developed to assess the selected interactions in terms of quantitative statistics and subjective ratings. Our work provides empirical support and proposes a set of design guidelines for optimizing watch-based interactions aimed at remote control of visual data on the large display. Through this research, we hope to advance the development of smartwatches as visual analytics tools and provide visual analysts with a better usage experience.

Visual analytics systems should be able to consolidate data from disparate sources, conduct exploratory analysis, create visualizations that suit different users, and integrate seamlessly with decision-making activities to support data-driven decision-making. However, current mainstream visual analytics solutions often lack support for all these requirements. To address this gap, we propose the use of model-driven engineering to design visual analytics systems. To demonstrate the feasibility of this approach, we developed a Domain-Specific Modeling Language (DSML) named ModelViz to design visual analytics systems for consumer goods supply chain applications. Furthermore, we present the work of our DSML, using data from a manufacturing company as a case study. Finally, we evaluated ModelViz quantitatively by comparing it with other similar works from the literature. Our results demonstrate that this approach meets the requirements and provides a promising direction for designing visual analytics systems by considering domain-specific aspects to help achieve business goals.

Transactional data from point-of-sales systems may not consider customer behavior before purchasing decisions are finalized. A smart shelf system would be able to provide additional data for retail analytics. In previous works, the conventional approach has involved customers standing directly in front of products on a shelf. Data from instances where customers deviated from this convention, referred to as "cross-location", were typically omitted. However, recognizing instances of cross-location is crucial when contextualizing multi-person and multi-product tracking for real-world scenarios. The monitoring of product association with customer keypoints through RANSAC modeling and particle filtering (PACK-RMPF) is a system that addresses cross-location, consisting of twelve load cell pairs for product tracking and a single camera for customer tracking. In this study, the time series vision data underwent further processing with R-CNN and StrongSORT. An NTP server enabled the synchronization of timestamps between the weight and vision subsystems. Multiple particle filtering predicted the trajectory of each customer's centroid and wrist keypoints relative to the location of each product. RANSAC modeling was implemented on the particles to associate a customer with each event. Comparing system-generated customer–product interaction history with the shopping lists given to each participant, the system had a general average recall rate of 76.33% and 79% for cross-location instances over five runs. [ABSTRACT FROM AUTHOR]

In order to reveal the research status, hotspots and trends of smart textile and apparel, the relevant literature in CNKI and WOS database from 2012 to 2022 were used as research objects, and CiteSpace visual analysis software was used to draw the knowledge map, analyse the distribution status and cooperation of the smart textile and apparel field in terms of countries, institutions, authors, and to sort out the research hotspots and cutting-edge trends. The results show that from 2012 to 2022, the number of CNKI database publications increases steadily, the number of WOS database publications grows rapidly, and the number of articles published by Chinese scholars ranks first in the world;Cooperation intensity in CNKI database is low and mainly based on Donghua university and Jiangnan university. While its counterpart in WOS has a close cooperative relationship, high research concentration and cooperation intensity; CNKI database's research focuses more on applications and design patterns, while WOS database focuses more on electronic component manufacturing and the development of smart fibers and fabrics. Health detection, colour-changing texture, bionic design, biomaterials and wearable strain are the current hotspots at the forefront of the field, while electronic component manufacturing, smart fibre and fabric intelligence research are the hotspots and trends for future research. [ABSTRACT FROM AUTHOR]

Point-of-care (POC) detection devices for wound care and monitoring show promise but still have limitations and gaps in effectiveness. Validation through robust clinical trials is necessary to evaluate their accuracy, reliability, and impact on patient outcomes. Improved POC detection devices with enhanced sensitivity, specificity, accessibility, and integration with electronic health records can enhance accuracy and patient care. Challenges to widespread implementation include cost, training requirements, regulatory hurdles, expertise, data management, and interoperability. Technological advancements offer potential solutions, including cost-effective manufacturing, miniaturization, regulatory streamlining, and training solutions. Cloud computing, data storage, analytics, artificial intelligence, and machine learning can address data management challenges and assist analysis and decision support. Healthcare resources are heavily burdened by infections that impede the wound-healing process. A wide range of advanced technologies have been developed for detecting and quantifying infection biomarkers. Finding a timely, accurate, non-invasive diagnostic alternative that does not require a high level of training is a critical step toward arresting common clinical patterns of wound health decline. There is growing interest in the development of innovative diagnostics utilizing a variety of emerging technologies, and new biomarkers have been investigated as potential indicators of wound infection. In this review, we summarize diagnostics available for wound infection, including those used in clinics and still under development. [ABSTRACT FROM AUTHOR]

To clarify the overall development of Diagnosis Related Groups（DRG）research since the new healthcare reform, this paper used the CiteSpace software to visualize and analyze the knowledge mapping of 582 papers about DRG payment research included in CSSCI, CSCD, and Peking University core journals in the database of China Knowledge（CNKI）from April 2009 to October 2023. The study aims to explore the research status, hotspots, and trends of China’s DRG payment research from a quantitative perspective. The results indicated that research authors and institutions have begun to take shape, but there is a lack of collaborative communication among the inter ⁃ regional author groups. The hot topics are mainly centered on four aspects: grouping mechanism and tool development application, cost control effect, performance management and evaluation, and continuous improvement of healthcare quality. The stage of development is divided into three periods: initial exploration, expansion, and leaping up. This study suggested further exploration in broadening the application of DRG in non⁃comprehensive hospitals, innovating targeted responses to DRG implementation, and promoting cross⁃regional and interdisciplinary research. [ABSTRACT FROM AUTHOR]

The study analyzes the current state of research, frontiers, and trends in tolerance studies in multicultural contexts. The Web of Science Core Collection database was used to retrieve relevant literature from 2000 to 2022. CiteSpace and VOSviewer software were employed to analyze keywords through co-occurrence networks, co-polymerization classes, and burst terms, with a corresponding visual atlas created for analysis. A total of 705 valid articles were included. The results revealed that Maykel Verkuyten authored the highest number of articles (n = 12). The United States contributed the highest number of articles (n = 376, 53.33%). The Interpersonal Journal of Intercultural Relations published the most articles (n = 38, 5.39%). The most highly cited author was Shelly P. Harrell (n = 876) in the American Journal of Orthopsychiatry. The keyword with the highest frequency, surpassing 200 mentions, was "prejudice" (n = 205). The top three keywords in terms of centrality were "culture" (n = 0.13), "attitude" (n = 0.12), and "discrimination" (n = 0.11). Major research topics and hotspots in the field of Tolerance in Multicultural Contexts included the Color-blind perspective, Prejudice reduction practices, Tolerance education, and Ethnicity. The study highlights a growing interest in the effectiveness of education as an intervention to reduce prejudice among young people. Further research in this domain should combined the Internet and transcend the confines of prejudice. [ABSTRACT FROM AUTHOR]

In recent times, visual analytics systems (VAS) have been used to solve various complex issues in diverse application domains. Nonetheless, an inherent drawback arises from the insufficient evaluation of VAS, resulting in occasional inaccuracies when it comes to analytical reasoning, information synthesis, and deriving insights from vast, ever-changing, ambiguous, and frequently contradictory data. Hence, the significance of implementing an appropriate evaluation methodology cannot be overstated, as it plays a pivotal role in enhancing the design and development of visualization systems. This paper assesses visualization systems by providing a systematic exploration of various evaluation strategies (ES). While several existing studies have examined some ES, the extent of comprehensive and systematic review for visualization research remains limited. In this work, we introduce seven state-of-the-art and widely recognized ES namely (1) dashboard comparison; (2) insight-based evaluation; (3) log data analysis; (4) Likert scales; (5) qualitative and quantitative analysis; (6) Nielsen's heuristics; and (7) eye trackers. Moreover, it delves into their historical context and explores numerous applications where these ES have been employed, shedding light on the associated evaluation practices. Through our comprehensive review, we overview and analyze the predominant evaluation goals within the visualization community, elucidating their evolution and the inherent contrasts. Additionally, we identify the open challenges that arise with the emergence of new ES, while also highlighting the key themes gleaned from the existing literature that hold potential for further exploration in future studies. [ABSTRACT FROM AUTHOR]

Data visualization is an essential task during the lifecycle of any Data Science (DS) project, particularly during the Exploratory Data Analysis (EDA) for a correct data preparation and understanding. In classification problems, data visualization is useful for revealing the existence of class separability patterns within the dataset. This information is very valuable and can be later used during the process of building a Machine Learning (ML) model. High-Dimensional Data (HDD) arise as one of the biggest challenges in DS. HDD require special treatment since traditional visualization techniques, such as the scatterplot matrix (SPLOM), have limitations when dealing with them due to space restrictions. Other visualization methods involve dimensionality reduction techniques, which can lead to losing important information and reducing the interpretability of the data. In this paper, the Class Separability Visualization (CSViz) method is introduced as a new Visual Analytics (VA) approach to address the challenge of visualizing labeled HDD through subspaces. The proposed method enables an overview of the class separability offering a series of 2-Dimensional subspaces visualizations containing exclusive subsets of points of the original variables that encompass the most valuable and significant separable patterns. The proposed method is tested over 50 datasets with different characteristics providing promising results. In all cases, more than 90% of the data observations are shown with three plots or less. Hence, the presented CSViz significantly eases the EDA by reducing the number of plots to be inspected in a SPLOM and thus, the amount of time invested in it. CSViz graphical abstract [ABSTRACT FROM AUTHOR]

Customer segmentation is considered the cornerstone for personalisation, target advertising, and promotion assisting both researchers and practitioners to enhance customers' buying behaviour understanding. Pertinent literature mainly exploits one distinct segmentation type such as behavioural to segment customers solely under one lens. We develop a two-stage business analytics approach that introduces a combination of geographic and behavioural customer segmentation. Our approach is based on data mining and machine learning techniques. We evaluate the suggested approach using e-commerce home delivery data. First, we segment customers based on the products ordered to identify behavioural customer segments with similar product preferences. Then, we perform geographic segmentation. By applying the approach developed we also identify challenges that affect the segmentation process and results. The suggested approach can serve as a guide to business analysts to understand which are the steps that they should perform when analysing similar datasets. Whereas its results may assist third-party logistics (3PL) companies, retailers, and brands in supporting decision making. [ABSTRACT FROM AUTHOR]

Background: Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology is actively used around the medical world, there is a rising demand for an efficient environment that can effectively display genomic data visualisations on immersive devices such as a Virtual Reality (VR) environment. The VR technology will allow clinicians, biologists, and computer scientists to explore a cohort of individual patients within the 3D environment. However, demonstrating the feasibility of the VR prototype needs domain users' feedback for future user-centred design and a better cognitive model of human–computer interactions. There is limited research work for collecting and integrating domain knowledge into the prototype design. Objective: A usability study for the VR prototype–-Virtual Reality to Observe Oncology data Models (VROOM) was implemented. VROOM was designed based on a preliminary study among medical users. The goals of this usability study included establishing a baseline of user experience, validating user performance measures, and identifying potential design improvements that are to be addressed to improve efficiency, functionality, and end-user satisfaction. Methods: The study was conducted with a group of domain users (10 males, 10 females) with portable VR devices and camera equipment. These domain users included medical users such as clinicians and genetic scientists and computing domain users such as bioinformatics and data analysts. Users were asked to complete routine tasks based on a clinical scenario. Sessions were recorded and analysed to identify potential areas for improvement to the data visual analytics projects in the VR environment. The one-hour usability study included learning VR interaction gestures, running visual analytics tool, and collecting before and after feedback. The feedback was analysed with different methods to measure effectiveness. The statistical method Mann–Whitney U test was used to analyse various task performances among the different participant groups, and multiple data visualisations were created to find insights from questionnaire answers. Results: The usability study investigated the feasibility of using VR for genomic data analysis in domain users' daily work. From the feedback, 65% of the participants, especially clinicians (75% of them), indicated that the VR prototype is potentially helpful for domain users' daily work but needed more flexibility, such as allowing them to define their features for machine learning part, adding new patient data, and importing their datasets in a better way. We calculated the engaged time for each task and compared them among different user groups. Computing domain users spent 50% more time exploring the algorithms and datasets than medical domain users. Additionally, the medical domain users engaged in the data visual analytics parts (approximately 20%) longer than the computing domain users. [ABSTRACT FROM AUTHOR]

One of the challenges in multi-omics data analysis for precision medicine is the efficient exploration of undiscovered molecular interactions in disease processes. We present BioMOBS, a workflow consisting of two data visualization tools integrated with an open-source molecular information database to perform clinically relevant analyses (https://github.com/driesheylen123/BioMOBS). We performed exploratory pathway analysis with BioMOBS and demonstrate its ability to generate relevant molecular hypotheses, by reproducing recent findings in type 2 diabetes UK biobank data. The central visualisation tool, where data-driven and literature-based findings can be integrated, is available within the github link as well. BioMOBS is a workflow that leverages information from multiple data-driven interactive analyses and visually integrates it with established pathway knowledge. The demonstrated use cases place trust in the usage of BioMOBS as a procedure to offer clinically relevant insights in disease pathway analyses on various types of omics data. [ABSTRACT FROM AUTHOR]

Endoscopic ultrasound (EUS) has emerged as a pivotal tool for the screening and diagnosis of submucosal tumors (SMTs). However, the inherently low-quality and highly variable image content presents substantial obstacles to the automation of SMT diagnosis. Deep learning, with its adaptive feature extraction capabilities, offers a potential solution, yet its implementation often requires a vast quantity of high-quality data - a challenging prerequisite in clinical settings. To address this conundrum, this paper proposes a novel data-efficient visual analytics method that integrates human feedback into the model lifecycle, thereby augmenting the practical utility of data. The methodology leverages a two-stage deep learning algorithm, which encompasses self-supervised pre-training and an attention-based network. Comprehensive experimental validation reveals that the proposed approach facilitates the model in deciphering the hierarchical structure information within high-noise EUS images. Moreover, when allied with human-machine interaction, it enhances data utilization, thereby elevating the accuracy and reliability of diagnostic outcomes. The code is available at https://github.com/Zehebi29/LA-RANet.git. [ABSTRACT FROM AUTHOR]

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Motivation Target discovery and drug evaluation for diseases with complex mechanisms call for a streamlined chemical systems analysis platform. Currently available tools lack the emphasis on reaction kinetics, access to relevant databases, and algorithms to visualize perturbations on a chemical scale providing quantitative details as well streamlined visual data analytics functionality. Results CytoCopasi, a Maven-based application for Cytoscape that combines the chemical systems analysis features of COPASI with the visualization and database access tools of Cytoscape and its plugin applications has been developed. The diverse functionality of CytoCopasi through ab initio model construction, model construction via pathway and parameter databases KEGG and BRENDA is presented. The comparative systems biology visualization analysis toolset is illustrated through a drug competence study on the cancerous RAF/MEK/ERK pathway. Availability and implementation The COPASI files, simulation data, native libraries, and the manual are available on https://github.com/scientificomputing/CytoCopasi. [ABSTRACT FROM AUTHOR]

Competitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision‐making. The system searching module uses a retriever and re‐ranker engine that first finds the closest neighbours to the query embedding and then sifts the results through a cross‐encoder model that identifies the most relevant documents. The browsing or visualization module also leverages the embeddings by projecting them onto two dimensions while preserving the multidimensional landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modelling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. We evaluate the system and its components on the 20 newsgroups data set, using the semantic document labels provided, and demonstrate the superiority of Transformer‐based components. Finally, we present a prototype of the system in Python and show how some of its features can be used to acquire intelligence from a news article corpus we collected during a period of 8 months. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Geographies" titled "Perspectives on Advanced Technologies in Spatial Data Collection and Analysis." The editorial discusses the rapid changes that have occurred in the field of geographical information science and systems in recent decades. It highlights various advanced technologies that have contributed to these changes, such as volunteered geographic information (VGI), wearable devices, social media and networking apps, GIS cloud computing, mobile networks and communication techniques, GeoAI, and blockchain. The editorial also mentions the collection of new types of spatial and spatiotemporal data and invites contributions on topics including geospatial open-source software, big data analysis, GeoAI, and geovisual analytics. The authors acknowledge the authors of the papers in the special issue, the anonymous referees, and the editorial staff for their contributions. [Extracted from the article]

Social networking services (SNSs) have become the primary means by which individuals express themselves. Consequently, the thoughts of individuals could be explored by analyzing primary topics on SNSs. In this study, we proposed and developed a novel system for visual analytics to address the following intriguing questions. When do topics change? Do they ever resurface? What do people typically discuss? Using document embedding and dimensionality reduction approaches, we abstracted dynamic topics as several points in a two-dimensional space. In addition, we provided other charts depicting words that appeared at certain moments and their time series dynamics over entire periods. In addition, we created a novel text visualization technique called semantic-preserving word bubbles to visualize words at a specific time. In addition, we demonstrated the efficacy of the proposed system utilizing Twitter data regarding early COVID-19 trends, Fukushima nuclear disaster trends, and user ratings of system usability. In general, we have presented this novel system to aid users in exploring and comprehending the transitions of contents uploaded on SNSs. [ABSTRACT FROM AUTHOR]

In many applications, visual analytics (VA) has developed into a standard tool to ease data access and knowledge generation. VA describes a holistic cycle transforming data into hypothesis and visualization to generate insights that enhance the data. Unfortunately, many data sources used in the VA process are affected by uncertainty. In addition, the VA cycle itself can introduce uncertainty to the knowledge generation process but does not provide a mechanism to handle these sources of uncertainty. In this manuscript, we aim to provide an extended VA cycle that is capable of handling uncertainty by quantification, propagation, and visualization, defined as uncertainty-aware visual analytics (UAVA). Here, a recap of uncertainty definition and description is used as a starting point to insert novel components in the visual analytics cycle. These components assist in capturing uncertainty throughout the VA cycle. Further, different data types, hypothesis generation approaches, and uncertainty-aware visualization approaches are discussed that fit in the defined UAVA cycle. In addition, application scenarios that can be handled by such a cycle, examples, and a list of open challenges in the area of UAVA are provided. [ABSTRACT FROM AUTHOR]

Congestion-level forecasting is essential for optical links to function properly with networking management schemes. Effective dynamical routing and wavelengths assigning (EDRWA) difficulties under the presumptions of perfect and non-ideal structural levels represent some of the foremost challenging optimization challenges faced in constructing 3rd-generation optical links. Such two issues are each NP-complete by design. They are challenging since the search space contains several nearby optima. Heuristics-dependent methods cannot effectively handle such challenges because of the irregular nature of searching space. The route and spectral assigning problems under various demanding scenarios are solved in this study using a genetic method optimization method for creating reliable optical cores. In contexts with stochastic unpredictable needs, this enhances network efficiency. The model outcomes show how successful genetic algorithms are at optimizing dedicated optical spectrum allocation in the face of uncertainty. [ABSTRACT FROM AUTHOR]

One of the main challenges for computational legal research is drawing up innovative heuristics to derive actionable knowledge from legal documents. While a large part of the research has been so far devoted to the extraction of purely legal information, less attention has been paid to seeking out in the texts the clues of more complex entities: legally relevant facts whose detection requires to link and interpret, as a unified whole, legal information and results of empirical analyses. This paper presents an ongoing research that points in this direction, trying to devise new ways to support public prosecutors in assessing the dangerousness of individuals and groups under investigation, an activity that precisely relies on the cross-sectional evaluation of legal and empirical data. A knowledge mining strategy will be outlined that lines up, into a single metaheuristic model, information extraction, network-based inference, machine learning and visual analytics. We will focus, in particular, on the integration of graph-based inference and machine learning methods used both to support classification tasks and to explore new forms of man-machine cooperation. Experiments made involving public prosecutors from the Italian Anti-Mafia Investigation Directorate and using data from real investigations have not only shown the potentialities of our approach but also offered an opportunity to reflect on the role we could assign to AI when thinking about the future of legal science and practice. [ABSTRACT FROM AUTHOR]

Visual analytic for intangible cultural heritage has recently developed in China. Using advanced interactive visualization tools experts can observe data distribution trends and explore the implicit relationships among data within a short time. It can enhance human cognitive and analytical abilities and improve the scientific preservation of intangible cultural heritage. To support this research topic, we have reviewed recent visualization works on intangible cultural heritage in China. We divide these works into three types: text visualization, multi-dimensional visualization, and geographical visualization. Each type is illustrated by several representative works. New development trends in this area are also discussed. [ABSTRACT FROM AUTHOR]

Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes' physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used. [ABSTRACT FROM AUTHOR]

With the availability of big data for food safety, more and more advanced data analysis methods are being applied to risk analysis and prewarning (RAPW). Visual analytics, which has emerged in recent years, integrates human and machine intelligence into the data analysis process in a visually interactive manner, helping researchers gain insights into large-scale data and providing new solutions for RAPW. This review presents the developments in visual analytics for food safety RAPW in the past decade. Firstly, the data sources, data characteristics, and analysis tasks in the food safety field are summarized. Then, data analysis methods for four types of analysis tasks: association analysis, risk assessment, risk prediction, and fraud identification, are reviewed. After that, the visualization and interaction techniques are reviewed for four types of characteristic data: multidimensional, hierarchical, associative, and spatial-temporal data. Finally, opportunities and challenges in this area are proposed, such as the visual analysis of multimodal food safety data, the application of artificial intelligence techniques in the visual analysis pipeline, etc. [ABSTRACT FROM AUTHOR]

However, this is not the case when comparing NOD completers and NOD dropouts (Graph HT ht ). Moreover, a significant main effect of Time (Graph HT ht ), revealed that the overall SUS score was slightly better at the end of the MOOC (Usability mid-term = 76, Usability end-term = 78.2) for both groups. However, this correlation is rather low (Graph HT ht ). [Extracted from the article]

Timely identification of disaster‐prone neighborhoods and examination of disparity in disaster exposure are critical for policymakers to plan efficient disaster management strategies. Many studies have investigated racial, ethnic, and geographic disparities and populations most vulnerable to disasters. However, little attention has been paid to the development of easily accessible and reusable tools to enable: (1) the prompt identification of vulnerable neighborhoods; and (2) the examination of social disparity in disaster impact. In this research, we have developed a visual analytics tool that allows users to: (1) delineate neighborhoods based on their selection of variables; and (2) explore which neighborhoods are susceptible to the impacts of disasters based on specific socioeconomic and demographic characteristics. Through an exploration of COVID‐19 data in the case study, we revealed that the tool can provide new insights into the identification of vulnerable neighborhoods that need immediate attention for disaster control, management, and relief. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Nonferrous Metals (Mining Section) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vehicle-to-grid (V2G) technology has received a lot of attention as a smart interconnection solution between electric vehicles and the grid. This paper analyzes the relevant research progress and hotpots of V2G by using CiteSpace 6.1.R6 software to construct a visualization graph, which includes keyword co-occurrence, clustering, and burstiness, and further systematically summarizes the main trends and key results of V2G research. First, the connection between electric vehicles and the grid is outlined and the potential advantages of V2G technology are emphasized, such as energy management, load balancing, and environmental sustainability. The important topics of V2G, including renewable energy consumption, power dispatch, regulation and optimization of the grid, and the smart grid, are discussed. This paper also emphasizes the positive impacts of V2G technologies on the grid, including reduced carbon emissions, improved grid reliability, and the support for renewable energy integration. Current and future challenges for V2G research, such as standardization, policy support, and business models, are also considered. This review provides a comprehensive perspective for scholars and practitioners in V2G research and contributes to a better understanding of the current status and future trends of V2G technology. [ABSTRACT FROM AUTHOR]

This study analyzes the impact on the sustainability performance of firms during the outbreak of the COVID‐19 pandemic, based on microdata made available by the World Bank through the Enterprise Surveys platform. By employing econometric estimation techniques like multinomial limited dependent variable model alongside data visualization tools, this analysis provides insights into the effects of the pandemic outbreak, as well as the moderating influence of enterprise innovation levels and CSR. The findings highlight that the duration of firm operations and company size play significant roles in mitigating turnover during the initial response to the pandemic. The analysis evaluates the actions taken by companies and governments to navigate the crisis, one and a half years after its appearance: containing the impact has a strong social impact, related to corporate social responsibility. The companies that successfully contained the shock and recovered more quickly are those that relied on innovation through their private investments or government measures. The analysis reveals how evenly distributed government subsidies have failed to produce significant effects. [ABSTRACT FROM AUTHOR]

Current studies on the artificial intelligence (AI) ethics focus either on very broad guidelines or on a very special domain. Therefore, the research outcome can hardly be converted into actionable measures or transferred to other domains. Potential correlations between various cases of AI ethics at different granularity levels are unexplored. To overcome these deficiencies, the authors designed a case-oriented ontological model (COOM) and a hyper-knowledge graph system (HKGS) for the research of collected AI ethics cases. COOM describes criteria for modelling cases by attributes from three perspectives: event attributes, relational attributes, and positional attributes on the value chain. Based on it, HKGS stores the correlation between cases as knowledge and allows advanced visual analysis. The correlations between cases and their dynamic changes on value chain can be observed and explored. In HKGS's implementation part, one of the collected ethics cases is used as an example to demonstrate how to generate a hyper-knowledge graph and to visually analyze it. The authors also anticipated how different practitioners of AI ethics, can achieve the desired outputs from HKGS in their diverse scenarios.

Background: While criteria for the diagnosis of nosocomial pneumonias exist, objective definitions are a challenge and there is no gold standard for diagnosis. We analyzed the impact of the implementation of a logical, consensus-based diagnostic and treatment protocol for managing nosocomial pneumonias in the cardiovascular surgery intensive care unit (CVS-ICU). Methods: We conducted a quasi-experimental, interrupted time series analysis to evaluate the impact of a diagnostic and treatment protocol for nosocomial pneumonias in the CVS-ICU. Impacts were measured relative to patient outcomes, diagnostic processes, and antimicrobial stewardship improvement. Descriptive statistics were used to analyze results. Results: Overall, 35 pre-protocol and 39 post-protocol patients were included. Primary clinical variables suggesting pneumonia in pre- and post-protocol patients were new lung consolidation (50% vs. 71%), new leukocytosis (59% vs. 64%), and positive culture (32% vs. 55%). Appropriate diagnostic testing improved (23% vs. 54%, p = 0.008) after protocol implementation. The proportion of patients meeting the criteria for nosocomial pneumonia (77% vs. 87%) was not statistically significant, though more patients in the post-protocol group met probable diagnostic criteria (51% vs. 77%). Duration of therapy was not significantly different (6 days [IQR = 5.0, 10.0] vs. 7 days [IQR = 6.0, 9.0]). Conclusions: The implementation of a diagnostic and treatment protocol for management of nosocomial pneumonias in the CVS-ICU resulted in improved diagnostic accuracy, advanced antimicrobial and diagnostic stewardship efforts, and laboratory cost savings without an adverse impact on patient-centered outcomes.

Analyzing crowdfunding data has been the focus of many research efforts, where analysts typically explore this data to identify the main factors and characteristics of the lending process as well as to discover unique patterns and anomalies in loan distributions. However, the manual exploration and visualization of such data is clearly an ad hoc, time-consuming, and labor-intensive process. Hence, in this work, we propose LoanVis, which is an automated solution for discovering and recommending those valuable and insightful visualizations. LoanVis is a data-driven system that utilizes objective metrics to quantify the “interestingness” of a visualization and employs such metrics in the recommendation process. We demonstrate the effectiveness of LoanVis in analyzing and exploring different aspects of the Kiva crowdfunding dataset.

Growing global concern over greenhouse gas emissions has led to a demand for understanding and addressing carbon emissions, with China being one of the main contributors to global carbon emissions, committed to reach the carbon peak by 2030. As a result, much previous research has delved into the drivers of carbon emissions in China; however, few studies have included new energy factors in the extended STIRPAT model when analysing the data, employed more advanced visualisation techniques such as force-directed diagrams, and explored factors outside of the industrial and energy sectors in determining China’s ability to reach their environmental goals. In this study, we use the extended STIRPAT model to analyse a more diverse range of drivers for carbon emissions in China and discuss methods to reach peak carbon emissions through the implementation of environmental policies. Using data from China’s 14th Five-Year Plan and Vision 2035 to set up two simulation scenarios, we predict China’s carbon emissions, introducing ridge regression to ensure validity, and employing big data and visualisation techniques to aid in interpreting results. Our findings suggest that China needs to implement more stringent environmental policies to meet its commitment to reach peak carbon emissions by 2030, revealing that factors such as per capita arable land area, per capita GDP, the proportion of people living in extreme poverty, the level of tourism development, the use of fossil fuels, and new energy technologies have a significant impact on China’s carbon emissions. As such, we can recommend more stringent policies relating to the agricultural, energy, and tourism sectors to help China achieve their goal of carbon peak by 2030.

In the field of visualization, understanding users’ analytical reasoning is important for evaluating the effectiveness of visualization applications. Several studies have been conducted to capture and analyze user interactions to comprehend this reasoning process. However, few have successfully linked these interactions to users’ reasoning processes. This paper introduces an approach that addresses the limitation by correlating semantic user interactions with analysis decisions using an interactive wire transaction analysis system and a visual state transition matrix, both designed as visual analytics applications. The system enables interactive analysis for evaluating financial fraud in wire transactions. It also allows mapping captured user interactions and analytical decisions back onto the visualization to reveal their decision differences. The visual state transition matrix further aids in understanding users’ analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to evaluate the effectiveness of our approach in understanding users’ analytical reasoning process by connecting the captured semantic user interactions to their decisions (i.e., suspicious, not suspicious, and inconclusive) on wire transactions. With the algorithms, an average of 72% accuracy is determined to classify the semantic user interactions. For classifying individual decisions, the average accuracy is 70%. Notably, the accuracy for classifying ‘inconclusive’ decisions is 83%. Overall, the proposed approach improves the understanding of users’ analytical decisions and provides a robust method for evaluating user interactions in visualization tools.