Visual Analysis Research Articles

Analysis of the current status and hot topics in spinal schwannoma imaging research based on bibliometrics.

This study aims to explore the current hot topics and future research trends in spinal schwannoma imaging research, providing a reference for related studies and promoting the development of spinal schwannoma imaging. We conducted a literature search in the Web of Science database using the search terms (((TS = (Spinal schwannoma)) AND TS = (Imaging)) OR TS = (Spinal schwannoma)) AND TS = (image) to retrieve relevant articles. The collected data, including authors, keywords, journals, countries, institutions, and references, were subjected to visual analysis using the visualization software CiteSpace 6.4.2R and VOSviewer 1.6.19. A total of 310 relevant articles were identified. After further screening based on time limits, inclusion, and exclusion criteria, 179 articles were included in the study, consisting of 132 original articles and 42 reviews. These articles were authored by 1,034 authors from 35 countries and 324 institutions and were published in 82 different journals. The included articles cited a total of 6,583 references from 1,314 journals. Although the field of spinal schwannoma imaging research is not a popular research area in the medical community, there has been an increasing international interest in this field in recent years. While China ranks high in terms of the number of published articles, there is still a gap in terms of the quality and research level compared to developed countries in Europe and America. MRI, as the gold standard for diagnosing spinal schwannomas, is expected to be a research hotspot in terms of feature analysis, enhancement characteristics, and quantitative analysis. It is also hoped that China can increase its investment in research and contribute to the field by publishing high-quality articles in the future.

Current Status, Hotspots, and Trends of "Artificial Intelligence + Education" Research in China

The rapid development of artificial intelligence technology is profoundly leading education towards an intelligent direction. This article takes the literature related to "Artificial Intelligence + Education" published in the core journals of CSSCI from 2013 to 2024 as the research object, and uses the CiteSpace tool for visualization analysis. It combs and discusses the current status of domestic research in this field from aspects such as publication status, authors, institutions, and keyword co-occurrence and burst words, analyzes research hotspots, and predicts research trends. The results show that: in the past decade, the number of publications in this field in China can be divided into three stages: preliminary exploration, rapid development, and steady growth; some high-productivity authors have gradually formed a core group of authors, showing an overall development trend of multi-author cooperation and multi-unit collaboration; the types of publishing institutions are mainly normal colleges and departments of colleges and universities, with relatively low participation of enterprises and government institutions; research hotspots focus on three aspects: educational reform, categories of education, and educational technology, revealing the active exploration of the education field for intelligent technology; with the transformation of education and the development of technology, the combination of artificial intelligence and digital economy, new requirements for citizens' core literacy, and new challenges for teachers' professional literacy and educational models have become hot topics in current artificial intelligence research.

Populist politicians’ representations of the people: A comparative visual content analysis of candidates’ Instagram posts in 2023 Turkish general elections

Social media posts play a key role in understanding how the concept of “the people,” which is a critical concept for populist politicians, is constructed and presented. This study examines how the two leading candidates, as right and left populist figures in Türkiye’s 2023 general elections, portrayed “the people” in their Instagram posts. The Instagram posts of two leaders were coded according to the categories of gender, headscarf, class, and age, and the representations of the people in these posts were analyzed using visual content analysis method. As a result, it was revealed Erdoğan, as a right-wing populist, included more headscarved women in his posts, Kılıçdaroğlu, as a left-wing populist, preferred more heterogeneous visuals in images in terms of gender, and that young individuals were more intensely preferred in the images shared by Kılıçdaroğlu in terms of the category of age.

Research Trend and Impact on Student Learning using Artificial Intelligence-Based Emotion Recognation: Systematic Bibliometric Analysis

Emotions have a significant function in the learning process. Good emotions such as enjoyment and curiosity can increase attention and comprehension, whereas negative emotions such as fear and boredom might impair academic achievement. Therefore, identifying and managing students' emotions is a critical step in developing an effective learning environment. In recent years, there has been a substantial progress in the application of artificial intelligence to detect emotions through facial expression analysis. Systematic literature reviews based on bibliometric analysis of artificial intelligence-based emotion recognition studies are still difficult to locate. This paper aims to undertake bibliometric and visualization analyses with VOSviewer for artificial intelligence-based emotion recognition. The evaluation includes 230 scholarly publications on emotion detection investigations indexed by Scopus quartiles Q1–Q4 from the Scopus database in the recent decade, namely 2014–2024. This bibliometric analysis has major implications for increasing academic, practical, and policy-related elements of artificial intelligence-based emotion recognition in education. By recognizing trends and major contributions in this subject, researchers, practitioners, and policymakers may work together to develop a more flexible and supportive learning environment, thereby boosting the overall quality of education.

Characterizing Flow Dynamics in a Two-dimensional Flow Cell: Developing Methodologies for Enhanced Subsurface Contaminant Remediation Research

This study used a two-dimensional (2-D) flow cell apparatus (25 cm × 25 cm × 1 cm) to characterize water flow and contaminant transport in porous media. The primary objective was to enhance the understanding of flow dynamics and contaminant behavior in controlled 2-D environments, which is crucial for optimizing the application of remediation technologies to treat contaminants in saturated and unsaturated soil conditions. The 2-D cell was designed with transparent walls to simulate subsurface conditions using medium grained sand, allowing for precise control and observation of fluid movement under saturated and quasi-saturated (trapped air) conditions. Visualization techniques and flow measurement tools were employed to capture detailed data on flow patterns, velocities, and dispersion characteristics. Analysis of breakthrough curves for salt tracer tests using electrical conductivity measurements, alongside varying flow rates, flow fractions, and port configurations, provided significant insights into fluid behavior and contaminant transport. The study also explored the effects of varying salt concentrations (and solution densities) on flow dynamics, contributing to a better understanding of the role of contaminant concentration in contaminant plume development and remediation processes. Visual analysis using dyed contaminant complemented the quantitative data, offering real-time observations of flow patterns and contaminant distribution. A custom-built apparatus was used to control the hydraulic head of multiple outlet ports to sample water from different depths. The experiments demonstrated that gravity plays a crucial role in solute (salt) breakthrough, with lower ports showing earlier breakthrough. Flow fields formed vertically, from bottom-to-top, and horizontally, from inlet-to-outlet, with lower portions advancing sooner. However, multi-port configurations, especially involving five ports, disrupted the sequential breakthrough order, possibly due to convection in the clear well that formed the outlet boundary of the flow cell. These findings will support research students in conducting similar experiments with flow and transport that varies with depth, with future insights for professionals in developing and optimizing remediation strategies related to subsurface contaminant flow. Special thanks are extended to Liam Price, Julia-Barnes James, and Brooke Belfall who were helpful during the research and the experimentation process.

Validity and Reliability of OpenPose-Based Motion Analysis in Measuring Knee Valgus during Drop Vertical Jump Test.

OpenPose-based motion analysis (OpenPose-MA), utilizing deep learning methods, has emerged as a compelling technique for estimating human motion. It addresses the drawbacks associated with conventional three-dimensional motion analysis (3D-MA) and human visual detection-based motion analysis (Human-MA), including costly equipment, time-consuming analysis, and restricted experimental settings. This study aims to assess the precision of OpenPose-MA in comparison to Human-MA, using 3D-MA as the reference standard. The study involved a cohort of 21 young and healthy adults. OpenPose-MA employed the OpenPose algorithm, a deep learning-based open-source two-dimensional (2D) pose estimation method. Human-MA was conducted by a skilled physiotherapist. The knee valgus angle during a drop vertical jump task was computed by OpenPose-MA and Human-MA using the same frontal-plane video image, with 3D-MA serving as the reference standard. Various metrics were utilized to assess the reproducibility, accuracy and similarity of the knee valgus angle between the different methods, including the intraclass correlation coefficient (ICC) (1, 3), mean absolute error (MAE), coefficient of multiple correlation (CMC) for waveform pattern similarity, and Pearson's correlation coefficients (OpenPose-MA vs. 3D-MA, Human-MA vs. 3D-MA). Unpaired t-tests were conducted to compare MAEs and CMCs between OpenPose-MA and Human-MA. The ICCs (1,3) for OpenPose-MA, Human-MA, and 3D-MA demonstrated excellent reproducibility in the DVJ trial. No significant difference between OpenPose-MA and Human-MA was observed in terms of the MAEs (OpenPose: 2.4° [95%CI: 1.9-3.0°], Human: 3.2° [95%CI: 2.1-4.4°]) or CMCs (OpenPose: 0.83 [range: 0.99-0.53], Human: 0.87 [range: 0.24-0.98]) of knee valgus angles. The Pearson's correlation coefficients of OpenPose-MA and Human-MA relative to that of 3D-MA were 0.97 and 0.98, respectively. This study demonstrated that OpenPose-MA achieved satisfactory reproducibility, accuracy and exhibited waveform similarity comparable to 3D-MA, similar to Human-MA. Both OpenPose-MA and Human-MA showed a strong correlation with 3D-MA in terms of knee valgus angle excursion.

Typing diagnostic value of 68Ga-pentixafor PET/CT for patients with primary aldosteronism and unilateral nodules.

Our goal was to compare the lateralization of 68Ga-pentixafor PET/CT with adrenal vein sampling (AVS) in primary aldosteronism (PA) patients with unilateral lesions. We retrospectively enrolled 61 patients with PA and all patients showed unilateral nodular lesions on CT and underwent 68Ga-Pentixafor PET/CT. The general clinical data, imaging and AVS results were collected. The diagnostic efficiency of 68Ga-Pentixafor PET/CT imaging in PA patients was calculated by visual and semi-quantitative analysis to compare the consistency with AVS, and the correlation between CXCR4 express and 68Ga-Pentixafor uptake was performed. The study included 42 unilateral PA (UPA) and 19 bilateral PA (BPA). The area under curve (AUC) of 68Ga-Pentixafor PET/CT to diagnosis UPA with 10 min maximum standardized uptake value (SUVmax) > 8.17 was 0.82 ([0.70-0.90], P < 0.001), and the sensitivity and specificity were 0.64 and 0.90, respectively. The maximal AUC of 68Ga-pentixafor PET/CT for the diagnosis UPA in patients with nodules with a diameter ≥1 cm was 0.87 ([0.73-0.95],P both <0.001,[10 min SUVmax=8.17 and 10 min mean standardized uptake value (SUVmean)=5.57]), and the sensitivity and specificity were 0.73 and 0.93, respectively. Unilateral adrenalectomy and significant CXCR4 expression were present in 32 UPA, including 27 aldosterone-producing adenoma and 5 idiopathic adrenal hyperplasia. Additionally, 68Ga-pentixafor uptake in adrenal lesions was significantly correlated with CXCR4 expression, and statistical differences in 68Ga-pentixafor uptake among IRS subgroups. 68Ga-Pentixafor PET/CT can be helpful for subtyping diagnosis of PA patients with unilateral adrenal nodular, showing significant potential in non-invasive PA classification.

Rise of Digital Influencer Marketing

Virtual influencer marketing is a global emerging phenomenon in social media research. Research on traditional influencer marketing and practices has been the locus of attention for years, but fairly scarce research deals with the rise and influence of virtual influencers, also known as artificial intelligence influencers (AIIs) or computer-generated influencers (CGIs). In this article, an explorative, qualitative single case analysis of the Japanese CGI influencer Imma is presented to identify how the CGI is visually constructed on Instagram in a marketing campaign. Two supplementary semiotic lenses are applied, i.e. a visual compositional analysis and a semiotic image analysis. The results indicate that the CGI influencer Imma is visually presented by and large with the same characteristics and attributes as found in similar studies of human influencers. Moreover, the findings indicate subtle yet essential differences in the visual framing of the CGI character to enhance a human-like appearance. Concludingly, this study has preliminary implications for research, practitioners, prospective marketers, and future innovation leaders in the virtual marketing industry, and it is proposed to consider reconceptualizing our understanding of virtual influencers and acknowledging the rise of Virtual Influencer Marketing as a field in its own right.

Knowledge mapping of freezing of gait in Parkinson's disease: a bibliometric analysis.

Among the disturbing motor symptoms in Parkinson's disease (PD), freezing of gait (FOG) stands out as one of the most severe challenges. It typically arises during the initiation of gait or when turning. This phenomenon not only impose a heavy burden on patients, but also on their families. We conduct a bibliometric analysis to summarize current research hotspots and trends concerning freezing of gait in Parkinson's disease (PD-FOG) over past two decades. We retrieved articles and reviews published in English about PD-FOG in the Web of science Core Collection database from 2000 to 2023 on November 30,2023. The tools VOSviewer and CiteSpace facilitated a visual analysis covering various aspects such as publications, countries/regions, organizations, authors, journals, cited references, and keywords. This study includes 1,340 articles from 64 countries/regions. There is a growth in publications related to PD-FOG over the past two decades, maintaining a stable high output since 2018, indicating a promising research landscape in the field of PD-FOG. The United States holds a leading position in this field, with Nieuwboer A and Giladi N being two of the most influential researchers. Over the past two decades, the research hotspots for PD-FOG have primarily encompassed the kinematic characteristics, diagnosis and detection, cognitive deficits and neural connectivity, as well as therapy and rehabilitation of PD-FOG. Topics including functional connectivity, virtual reality, deep learning and machine learning will be focal points of future research. This is the first bibliometric analysis of PD-FOG. We construct this study to summarize the research in this field over past two decades, visually show the current hotspots and trends, and offer scholars in this field concepts and strategies for subsequent studies.

Radiomics analysis of cerebral blood flow suggests a possible link between perfusion homogeneity and poor glioblastoma multiforme prognosis

Objectives. This study investigates the association between cerebral blood flow (CBF) and overall survival (OS) in glioblastoma multiforme (GBM) patients receiving chemoradiation. Identifying CBF biomarkers could help predict patient response to this treatment, facilitating the development of personalized therapeutic strategies. Materials and Methods. This retrospective study analyzed CBF data from dynamic susceptibility contrast (DSC) MRI in 30 newly diagnosed GBM patients (WHO grade IV). Radiomics features were extracted from CBF maps, tested for robustness, and correlated with OS. Kaplan-Meier analysis was used to assess the predictive value of radiomic features significantly associated with OS, aiming to stratify patients into groups with distinct post-treatment survival outcomes. Results. While mean relative CBF and CBV failed to serve as independent prognostic markers for OS, the prognostic potential of radiomic features extracted from CBF maps was explored. Ten out of forty-three radiomic features with highest intraclass correlation coefficients (ICC > 0.9), were selected for characterization. While Correlation and Zone Size Variance (ZSV) features showed significant OS correlations, indicating prognostic potential, Kaplan-Meier analysis did not significantly stratify patients based on these features. Visual analysis of the graphs revealed a predominant association between the identified radiomic features and OS under two years. Focusing on this subgroup, Correlation, ZSV, and Gray-Level Nonuniformity (GLN) emerged as significant, suggesting that a lack of heterogeneity in perfusion patterns may be indicative of a poorer outcome. Kaplan-Meier analysis effectively stratified this cohort based on the features mentioned above. Receiver operating characteristic (ROC) analysis further validated their prognostic value, with ZSV demonstrating the highest sensitivity and specificity (0.75 and 0.85, respectively). Conclusion. Our findings underscored radiomics features sensitive to CBF heterogeneity as pivotal predictors for patient stratification. Our results suggest that these markers may have the potential to identify patients who are unlikely to benefit from standard chemoradiation therapy.

Evolution and governance of online public opinion during COVID-19: a hybrid approach using communication visualization, SIR modeling, and simulation validation

IntroductionThis study investigates the mechanisms of public opinion dissemination and governance strategies during public health events, using a two-stage SIR model informed by the Information Cascade Theory.MethodsThe research employs Gephi visual analysis to identify principal nodes of public opinion and combines model simulations with dynamic propagation analysis to verify the model's precision and applicability.ResultsThe findings reveal that pivotal information nodes significantly accelerate the spread of public opinion, while ordinary nodes contribute to the natural attenuation of public discourse due to their strong spontaneous recovery capabilities. The simulation analysis further identifies the optimal timing for government intervention, particularly during the initial and peak phases of public opinion dissemination.DiscussionBased on the results, the study recommends strategies to strengthen the management of key opinion nodes, enhance public information literacy, optimize policy implementation, and utilize simulation tools to assist in public opinion management. These recommendations offer valuable theoretical and practical insights for managing public opinion during public health events.

Supplemental Material for A Review of Visual Analysis Reporting Procedures in the Functional Communication Training Literature

Supplemental Material for A Review of Visual Analysis Reporting Procedures in the Functional Communication Training Literature

Enhanced Parameter Estimation of DENsity CLUstEring (DENCLUE) Using Differential Evolution

The task of finding natural groupings within a dataset exploiting proximity of samples is known as clustering, an unsupervised learning approach. Density-based clustering algorithms, which identify arbitrarily shaped clusters using spatial dimensions and neighbourhood aspects, are sensitive to the selection of parameters. For instance, DENsity CLUstEring (DENCLUE)—a density-based clustering algorithm—requires a trial-and-error approach to find suitable parameters for optimal clusters. Earlier attempts to automate the parameter estimation of DENCLUE have been highly dependent either on the choice of prior data distribution (which could vary across datasets) or by fixing one parameter (which might not be optimal) and learning other parameters. This article addresses this challenge by learning the parameters of DENCLUE through the differential evolution optimisation technique without prior data distribution assumptions. Experimental evaluation of the proposed approach demonstrated consistent performance across datasets (synthetic and real datasets) containing clusters of arbitrary shapes. The clustering performance was evaluated using clustering validation metrics (e.g., Silhouette Score, Davies–Bouldin Index and Adjusted Rand Index) as well as qualitative visual analysis when compared with other density-based clustering algorithms, such as DPC, which is based on weighted local density sequences and nearest neighbour assignments (DPCSA) and Variable KDE-based DENCLUE (VDENCLUE).

Knowledge Mapping of Lexicography Research: A Visual Analysis With VOSviewer and CiteSpace

Lexicography has seen significant advancements during the past seven decades. This paper aims to provide a thorough review of lexicography research by analysing 7302 related publications from the Web of Science Core Collection database with two visualization tools: VOSviewer and CiteSpace. The lexicography studies are initially evaluated for the heated research topics, followed by identifying the most influential references, authors, journals, institutions, and nations/regions accordingly. Furthermore, through the analysis of co-cited references and keywords co-occurrence, the forthcoming research endeavours in lexicography are believed to encompass the prioritization of the user perspective in dictionary compilation, the emphasis on online and electronic dictionary development, and the exploration of dictionary integration with online technology.

Assessing the Muscle-Bone Unit in Girls Exposed to Different Amounts of Impact-Loading Physical Activity-A Cross-Sectional Association Study.

In children, an association exists between muscle and bone, as well as between physical activity and osteogenesis. Impact loading is a factor in increasing bone accrual during growth. In this work, we explored the muscle-bone association in girls exposed to long-term physical activity at different levels of impact loading. Four groups of girls aged 7-16 were considered. The curricular (C; n = 22) group only had curricular physical activity at school (2 h/w). In addition to curricular physical activity, the girls in the dance (D; n = 21), gymnastics at lower training (GL; n = 14), and gymnastics at higher training (GH; n = 20) groups had 2 h/w, 4 h/w, and 4 h/w < training ≤ 12 h/w additional physical activity, respectively, for at least one year. A visual analysis estimated the respective amounts of impact-loading activity. The bone mineral content (BMC), areal bone mineral density (aBMD), and fat-free soft tissue mass (FFSTM) were assessed with dual-energy X-ray absorptiometry. The results showed that, after adjusting for several confounders, statistically significant correlations were present between muscle mass and several bone mineral variables. A regression analysis confirmed the correlation in the data, and showed the marginal role of other body composition variables and physical activity for predicting BMC and BMD. Skeletal muscle mass is a major determinant of the BMC and BMD of the TBLH, as well as of the Appendicular level, in girls exposed to different amounts of long-term impact-loading physical activity.

INTERPRETING THE CONDITIONS OF BULLYING IN PHOTOBOOK ENTITLED “BULLY”

This study aims to interpret the conditions of bullying as depicted in the photobook "Bully", exploring the symbolic and aesthetic elements used to represent the psychological and emotional dimensions of bullying. Utilizing a qualitative, interpretative approach, the research employs visual analysis and symbolic interpretation to uncover the deeper meanings conveyed through the photobook's imagery. The analysis focuses on how visual elements such as lighting, composition, and perspective reflect the dynamics of power, fear, isolation, and resilience within the context of bullying. The study is grounded in the Social-Ecological Model and Cognitive-Behavioral Theory, which provide frameworks for understanding the multifaceted nature of bullying and the cognitive distortions experienced by both victims and perpetrators. Results reveal that shadows in the images symbolize the psychological distress and cognitive distortions faced by victims and bullies, while light represents hope, resilience, and the potential for cognitive and emotional recovery. The photobook effectively captures the complex interplay between individual experiences and social influences, illustrating how both victims and bullies are affected by and respond to their environments. The findings underscore the importance of addressing both psychological and social factors in bullying interventions and highlight the photobook's role as a powerful medium for visualizing and understanding these dynamics.

Study of obesity research using machine learning methods: A bibliometric and visualization analysis from 2004 to 2023.

Obesity, a multifactorial and complex health condition, has emerged as a significant global public health concern. Integrating machine learning techniques into obesity research offers great promise as an interdisciplinary field, particularly in the screening, diagnosis, and analysis of obesity. Nevertheless, the publications on using machine learning methods in obesity research have not been systematically evaluated. Hence, this study aimed to quantitatively examine, visualize, and analyze the publications concerning the use of machine learning methods in obesity research by means of bibliometrics. The Web of Science core collection was the primary database source for this study, which collected publications on obesity research using machine learning methods over the last 20 years from January 1, 2004, to December 31, 2023. Only articles and reviews that fit the criteria were selected for bibliometric analysis, and in terms of language, only English was accepted. VOSviewer, CiteSpace, and Excel were the primary software utilized. Between 2004 and 2023, the number of publications on obesity research using machine learning methods increased exponentially. Eventually, 3286 publications that met the eligibility criteria were searched. According to the collaborative network analysis, the United States has the greatest volume of publications, indicating a significant influence on this research. coauthor's analysis showed the authoritative one in this field is Leo Breiman. Scientific Reports is the most widely published journal. The most referenced publication is "R: a language and environment for statistical computing." An analysis of keywords shows that deep learning, support vector machines, predictive models, gut microbiota, energy expenditure, and genome are hot topics in this field. Future research directions may include the relationship between obesity and its consequences, such as diabetic retinopathy, as well as the interaction between obesity and epidemiology, such as COVID-19. Utilizing bibliometrics as a research tool and methodology, this study, for the first time, reveals the intrinsic relationship and developmental pattern among obesity research using machine learning methods, which provides academic references for clinicians and researchers in understanding the hotspots and cutting-edge issues as well as the developmental trend in this field to detect patients' obesity problems early and develop personalized treatment plans.

Sharing Stories, Food and Passports: Forced Migration, Museum and Memory Activism in Contemporary Germany

ABSTRACT This article uses the online exhibition ‘Arriving: Life after Forced Migration’ by the Marienfelde Refugee Center Museum in Berlin as a case study to investigate how museum activism can contribute to creating and disseminating memories about contemporary forced migration. Employing a close visual and textual analysis of the online exhibition combined with insights from recent discourses on museum activism, migration and memory studies, the article explores which forms of memory the exhibition creates, how these memories emerge and to which end they are used. The analysis firstly illustrates how the exhibition creates a space for communicative memory to surface within the portrayed refugee families through the sharing of stories, objects and food from ‘home’. Secondly, it goes on to argue that the exhibition engages in memory activism by humanizing the portrayed individuals, exposing discriminatory state practices and challenging the audience through provocative questions. Thirdly, the article submits that this online exhibition contributes to a growing transnational archive of forced migration memories. Comprised of a multitude of online platforms dedicated to collecting, preserving and sharing forced migration stories, this archive stimulates multidirectional memory making and allows counter narratives to surface within an increasingly divided Europe.

How to decipher the environmental resilience performance? A case study of Yangtze River Delta Urban Agglomeration

Improving environmental resilience performance (ERP) is a significant support for achieving sustainable development goals. This study introduces the three-dimensional analysis framework of "object-process-actor", capable of addressing multidimensional and multilevel issues. This framework systematically addresses the "three key questions" of what, how, and who to improve ERP. Results indicate that from 2011 to 2020, the ERP of the Yangtze River Delta Urban Agglomeration (YRDUA) was between 0.42 and 3.30. The violin plot and visualization analysis showed that its probability density and distribution status were stable, with significant regional differences. The ERP of economically developed cities such as Hangzhou and Shanghai is high, while the ERP of cities in the Yancheng-Chuzhou line is low, and the number of high-performing cities increases to 40.7% in 2020, which is a constantly improving development trend. Factors related to market actors have the greatest impact on ERP, with the explanatory power of FDI, green invention patents, and population concentration all exceeding 0.6, and the importance of government and social actors is also supported. The explanatory power of factor interactions was stronger than that of single factors, and land development degree ranked third in terms of its interaction power with other factors and was also a key factor influencing ERP. These findings provide new explanations and knowledge for improving ERP, which can provide references for policymakers and urban planners.

Visual Analysis of Hot Topics and Trends in Nutrition for Decompensated Cirrhosis Between 1994 and 2024

Objective An updated summary of the research profile of nutrition for the last 30 years for decompensated cirrhosis is lacking. This study aimed to explore the literature on nutrition for decompensated cirrhosis, draw a visual network map to investigate the research trends, and provide suggestions for future research. The Web of Science database retrieves the literature on nutrition for decompensated cirrhosis between 1994 and 2024. Methods We used the cooperative, co-occurrence, and co-citation networks in the CiteSpace knowledge graph analysis tool to explore and visualize the relevant countries, institutions, authors, co-cited journals, keywords, and co-cited references. Results We identified 741 articles on nutrition for decompensated cirrhosis. The number of publications and research interests has generally increased. The USA contributed the largest number of publications and had the highest centrality. The University of London ranked first in the number of articles issued, followed by the University of Alberta and Mayo Clinic. TANDON P, a “core strength” researcher, is a central hub in the collaborative network. Of the cited journals, HEPATOLOGY had the highest output (540, 15.3%). Conclusions Over the past three decades, the focus of research on nutrition in decompensated cirrhosis has shifted from “hepatic encephalopathy, intestinal failure, metabolic syndrome, and alcoholic hepatitis” to “sarcopenia and nutritional assessment.” In the future, nutritional interventions for sarcopenia should be based on a multimodal approach to address various causative factors. Its targeted treatment is an emerging area that warrants further in-depth research.