Burst Detection Research Articles

Acoustic Emission-Based Method for IFSS Characterization in Single-Fiber Fragmentation Tests

Carbon fiber-reinforced polymers (CFRP) are widely used composite materials in structural applications, where their mechanical performance is significantly influenced by interfacial shear strength (IFSS). The single fiber fragmentation test (SFFT) is a common technique for characterizing IFSS, but its reliance on optical microscopy makes it time-consuming and impractical for opaque matrices. This study presents an alternative methodology based on acoustic emission (AE) analysis, enabling the estimation of fragment lengths through statistical modeling. The AE technique captures the energy released during fiber fragmentation, represented as AE bursts, whose accurate detection is crucial. A signal-processing approach based on progressive simplification enhances burst detection. To refine the estimation of fragment lengths, a gamma distribution is fitted to experimental data, accounting for observed asymmetry in optical measurements. Results indicate that this approach achieves an IFSS determination error of 14.16% at a 95% confidence level. This study demonstrates the feasibility of using AE for IFSS characterization in SFFT and contributes to future research on AE applications in composite materials.

B vitamins and colorectal cancer: exploring research hotspots and frontiers from a bibliometric and visual analysis (1994-2024).

Most studies suggest that B vitamins can reduce the risk of colorectal cancer (CRC), and research in this field has been growing. Focusing on 2617 articles in the field, this study used CiteSpace and VOSviewer software to evaluate the contributions of various countries/regions, institutions, authors and journals. The United States and Harvard University were identified as the most productive nation and institution, respectively, with Edward L. Giovannucci (Harvard) being the top contributor. Cancer Epidemiology Biomarkers & Prevention was recognized as the leading journal. Through the analysis of keywords and citations, we found that the potential of B vitamins (B1, B2, B6, B9, and B12) in the prevention and treatment of CRC and their mechanisms including regulation of gene expression, anti-inflammatory and antioxidant, and modulation of gut microenvironment are hot topics of research in this field. Burst detection analysis further revealed that the application of nanoparticle-based targeted drug delivery systems (such as folate-conjugated nanocarriers) in the treatment of CRC represents both a current hotspot and a future trend. This study offers a comprehensive overview of the field, highlights research hotspots and trends, and offers valuable information for researchers to further grasp the research direction.

Bibliometric analysis and knowledge mapping of diabetes mellitus combined with tuberculosis research: trends from 1995 to 2023.

The synergistic epidemic of diabetes mellitus and tuberculosis (DM-TB) has created a dual disease burden, challenging global health systems with complex pathophysiological interactions and suboptimal treatment outcomes. To decode the evolving research landscape, this study presents the latest comprehensive bibliometric analysis mapping the intellectual architecture of DM-TB research over three decades. We systematically analyzed 791 peer-reviewed articles from the Web of Science Core Collection (1995-2023) using CiteSpace, VOSviewer, and Bibliometrix. Advanced metrics including co-citation networks, keyword burst detection, and institutional collaboration patterns were employed to identify paradigm-shifting trends. Three distinct growth phases were observed: initial stagnation (1995-2007, <10 annual publications), exponential growth (2008-2019), and research diversification (2020-2023). The United States dominated scientific output (27.3% of total publications), while the London School of Hygiene & Tropical Medicine emerged as the central hub for international collaborations (TLS=176). Keyword evolution revealed three transformative phases: (1) Pathomechanistic exploration (1995-2016): Focused on hyperglycemia-immunity interplay and epidemiological surveillance; (2) Translational innovation (2017-2020): Shifted to preclinical models, pharmacokinetic optimization, and multidrug resistance; (3) Precision medicine era (2021-2023): Emerging hotspots in latent TB screening (burst strength=6.82), metformin-mediated immunomodulation, and AI-driven diagnostic biomarkers. Beyond delineating historical trajectories, this study identifies critical knowledge gaps in inflammation-resolution mechanisms and insulin resistance pathways, proposing a roadmap for targeted biomarker discovery and global health policy formulation. The constructed knowledge framework empowers strategic resource allocation for combating the DM-TB syndemic.

Mapping Data-Driven Research Impact Science: The Role of Machine Learning and Artificial Intelligence

In an era of evolving scholarly ecosystems, machine learning (ML) and artificial intelligence (AI) have become pivotal in advancing research impact analysis. Despite their transformative potential, the fragmented body of literature in this domain necessitates consolidation to provide a comprehensive understanding of their applications in multidimensional impact assessment. This study bridges this gap by employing bibliometric methodologies, including co-authorship analysis, citation burst detection, and advanced topic modelling using BERTopic, to analyse a curated corpus of 1608 scholarly articles. Guided by three core research questions, this study investigates how ML and AI enhance research impact evaluation, identifies dominant methodologies, and outlines future research directions. The findings underscore the transformative potential of ML and AI to augment traditional bibliometric indicators by uncovering latent patterns in collaboration networks, institutional influence, and knowledge dissemination. In particular, the scalability and semantic depth of BERTopic in thematic extraction, combined with the visualisation capabilities of tools such as CiteSpace and VOSviewer, provide novel insights into the dynamic interplay of scholarly contributions across dimensions. Theoretically, this research extends the scientometric discourse by integrating advanced computational techniques and reconfiguring established paradigms for assessing research contributions. Practically, it provides actionable insights for researchers, institutions, and policymakers, enabling enhanced strategic decision-making and visibility of impactful research. By proposing a robust, data-driven framework, this study lays the groundwork for holistic and equitable research impact evaluation, addressing its academic, societal, and economic dimensions.

Bibliometric Analysis of Antipsychotic-induced Metabolic Disorder from 2006 to 2021 Based on WoSCC Database.

With the frequent use of antipsychotics, the metabolic disorder (MetD) caused by drugs has received increasing attention. However, the mechanism of drug-induced MetD is still unclear and is being explored. Keeping abreast of the progress and trending knowledge in this area is conducive to further work. The aim of this study is to analyze the latest status and trends of research on antipsychoticinduced metabolic disorder (AIMetD) by bibliometric and visual analysis. 3478 publications of AIMetD from 2006 to 2021 were retrieved from the Web of Science Core Collection database. R-biblioshiny was used for descriptive analysis, CiteSpace for cooperative network, co-citation analysis and burst detection, and VOSviewer for co-occurrence keywords was used. Since 2006, the publications have been growing fluctuantly. These studies have extensive cooperation among countries/regions. The most influential country/region, institution and author are the USA, King's College London and Christoph U Correll. Analysis of references shows the largest cluster of "antipsychotic-induced metabolic dysfunction", which is an important basis for MetD. The recent contents of the burst citation are related to "glucose homeostasis" and "cardiovascular metabolism". Several bursting keywords were discerned at the forefront, including "LC-MS/MS", "major depressive disorder", "expression", and "homeostasis". The AIMetD study is in a state of sustained development. Close cooperation between countries/ regions has promoted progress. For grasping the foundation, development, and latest trends of AIMetD, it is recommended to focus on active institutions and authors. Based on AIMetD, subdivision areas such as "LC-MS/MS", "expression", and "homeostasis" are forefronts that deserve constant attention.

Chemotherapy-Induced Bone Marrow Suppression: A Bibliometrics Analysis.

This study seeks to investigate the key research hotspots and evolving trends in chemotherapy-induced bone marrow suppression over the past two decades by employing bibliometric visualization analysis, thereby providing a robust theoretical and empirical foundation for advancements in both scientific understanding and clinical practice. Utilizing the Web of Science Core Database, a total of 6,110 documents were retrieved and analyzed using CiteSpace and VOSviewer software. This analysis traces and evaluates the current research status, key advancements, and development trajectories of chemotherapy-induced bone marrow suppression research from 2004 to 2023. The analysis included 6,110 documents, revealing a significant upward trend in research volume. The United States (1,688 papers) and China (1,528 papers) are the leading contributors, with Cancer Chemotherapy and Pharmacology being the most prolific journal. Keyword burst detection identified key research focuses, including nasopharyngeal carcinoma, immunotherapy, and agents such as nivolumab and pembrolizumab. These findings indicate promising directions for further exploration, such as immunotherapy for nasopharyngeal carcinoma and innovative strategies for managing bone marrow suppression with specific drugs. Future research may concentrate on immune checkpoint inhibitors and nasopharyngeal carcinoma. Additionally, new treatment such as the natural compound curcumin has demonstrated protective effects during chemotherapy, effectively alleviating side effects. Nutritional support strategies significantly enhance patients' quality of life during treatment, while integrative approaches that combine traditional Chinese medicine with modern medical practices reveal the potential for synergistic effects. Together, these strategies could improve clinical outcomes and effectively address the challenges posed by chemotherapy-induced bone marrow suppression.

Performance analysis of different BERT implementation for event burst detection from social media text

&lt;p&gt;The language models play very important role in natural language processing (NLP) tasks. To understand natural languages, the learning models are required to be trained on large corpus. This requires a lot of time and computing resources. The detection of information like events, and locations from text is an important NLP task. As events detection is to be done in real-time so that immediate actions can be taken, hence we need efficient decision-making models. The pertained models like bi-directional encoders representation from transformers (BERT) gaining popularity to solve NLP problems. As BERT based models are pre-trained on large language corpus it requires very less time to adapt for domain specific NLP task. Different implementations of BERT have been proposed to enhance efficiency and applicability of the base model. The selection of right implementation is essential for overall performance of NLP based system. This work presents the comparative insights of five widely used BERT implementations named as BERT-base, BERT-large, Distill BERT, Robust BERT approach (RoBERTa-base) and RoBERT-large for event detection from the text extracted from social media streams. The results show that Distill-BERT model outperforms on basis of performance metric like precision, recall, and F1-score while the fastest to train also.&lt;/p&gt;

What Else Is Happening to the Mirror Neurons?-A Bibliometric Analysis of Mirror Neuron Research Trends and Future Directions (1996-2024).

Since its discovery in the late 20th century, research on mirror neurons has become a pivotal area in neuroscience, linked to various cognitive and social functions. This bibliometric analysis explores the research trajectory, key research topics, and future trends in the field of mirror neuron research. We searched the Web of Science Core Collection (WoSCC) database for publications from 1996 to 2024 on mirror neuron research. Statistical and visualization analyses were performed using CiteSpace and VOSviewer. Publication output on mirror neurons peaked in 2013 and remained active. High-impact journals such as Science, Brain, Neuron, PNAS, and NeuroImage frequently feature findings on the mirror neuron system, including its distribution, neural coding, and roles in intention understanding, affective empathy, motor learning, autism, and neurological disorders. Keyword clustering reveals major directions in cognitive neuroscience, motor neuroscience, and neurostimulation, whereas burst detection underscores the emerging significance of brain-computer interfaces (BCIs). Research methodologies have been evolving from traditional electrophysiological recordings to advanced techniques such as functional magnetic resonance imaging, transcranial magnetic stimulation, and BCIs, highlighting a dynamic, multidisciplinary progression. This study identifies key areas associated with mirror neurons and anticipates that future work will integrate findings with artificial intelligence, clinical interventions, and novel neuroimaging techniques, providing new perspectives on complex socio-cognitive issues and their applications in both basic science and clinical practice.

Graphene Oxide Research: Current Developments and Future Directions.

Graphene oxide (GO), a pivotal derivative of graphene, has revolutionized nanotechnology with its tunable physicochemical properties and interdisciplinary applications in energy storage, environmental remediation, and biomedicine. Despite its exponential research growth, existing reviews remain fragmented, lacking holistic insights into evolving synthesis-application linkages, global collaboration patterns, and emerging convergence trends. This study bridges these gaps through a scientometric analysis of 14,124 peer-reviewed articles (2022-2025) from the Web of Science Core Collection, utilizing CiteSpace for co-occurrence network mapping, burst detection, and cluster analysis. Key findings reveal (1) a thematic shift from traditional synthesis optimization (e.g., Hummers' method) toward driven material design and sustainable applications like GO membranes for water purification; (2) China's dominance in publication output (38.5%) contrasts with the U.S. and Europe's leadership in global collaborations; (3) interdisciplinary journals such as Chemical Engineering Journal (centrality: 0.25) and emerging clusters like "circular economy" signal transformative priorities; and (4) critical gaps in scalability, ecological safety, and cost-effective production hinder industrial translation. This work provides a roadmap for aligning research with sustainability goals, fostering global partnerships, and accelerating innovations in scalable nanotechnology.

Knowledge Mapping of Tourism Emergency Research: A Scientometric Review

All kinds of crises have brought great challenges to the survival and development of tourism. In view of the variety of crisis and the limitation of ex ante control effect, tourism emergency have substantially affected the decision-making behaviors of tourists. The academia worldwide has investigated tourism emergency from divergent perspectives to aid the tourism industry in improving its efficiency in tourism crisis management. This study utilizes the increasingly popular approach in scientometrics to analyze emergency research in tourism, thereby systematically and comprehensively presenting these studies as a whole. A total of 824 articles between 2001 and 2022 were collected. Subsequently, comprehensive knowledge maps of tourism emergency research, were identified using co-occurrence network and burst detection with research among institutions, authors, country, hotspots, based on institution-author networks, and identified tourism emergency discipline as well as contemporary research topics and most influential researchers.

Mapping nine decades of research integrity studies (1935–2024): A scientometric analysis

ABSTRACT Background: Research integrity is fundamental to responsible research practice. Despite attention, the intellectual structure and evolution of this field remains underexplored. This study maps the knowledge landscape of research integrity, identifying key themes, contributions, and trends. Methods: A scientometric analysis was conducted on 6,895 records from Web of Science and Scopus (1935–2024). CiteSpace facilitated network analysis, including co-authorship, keyword co-occurrence, and co-citation patterns, while burst detection identified topics. Results: Research integrity studies have grown significantly since the 1980s, with interdisciplinary collaboration. Keyword and co-citation analyses reveal a shift from early discussions on scientific misconduct to concerns such as open science, AI ethics, and research governance. A collaboration network has emerged, with leading contributions from North America, Europe, and Asia. Conclusions: Research integrity has matured into an interdisciplinary field, reaching academic consensus with growing integration of policies, regulations, and technology. Future research is expected to focus on AI’s role in research integrity. Key areas of concern include algorithmic bias, automation ethics, and implications for scholarly publishing. Open science and transparency will remain central, particularly in addressing data fabrication, paper mills, and predatory publishing. Institutional policies will continue evolving, embedding integrity principles into governance and public engagement initiatives.

An activity transition in FRB 20201124A: Methodological rigor, detection of frequency-dependent cessation, and a geometric magnetar model.

We report detections of fast radio bursts (FRBs) from the repeating source with Apertif/WSRT and GMRT, and measurements of basic burst properties, especially the dispersion measure (DM) and fluence. Based on comparisons of these properties with previously published larger samples, we argue that the excess DM reported earlier for pulses with integrated signal-to-noise ratios lesssim 1000 is due to incompletely accounting for what is known as the sad trombone effect, even when using structure-maximizing DM algorithms. Our investigations of fluence distributions next lead us to advise against formal power-law fitting; we especially caution against the use of the least-squares method, and we demonstrate the large biases involved. A maximum likelihood estimator (MLE) provides a much more accurate estimate of the power law, and we provide accessible code for direct inclusion in future research. Our GMRT observations were fortuitously scheduled around the end of the Spring 2021 activity window as recorded by FAST. We detected several bursts (one of them very strong) at 400/600,MHz, a few hours after sensitive FAST non-detections already showed the 1.3,GHz FRB emission to have ceased. After FRB,20180916B, this is a second example of a frequency-dependent activity window identified in a repeating FRB source. Since numerous efforts have so far failed to determine a spin period for FRB,20201124A, we conjecture that it is an ultra-long-period magnetar, with a period on the scale of months, and with a very wide, highly irregular duty cycle. Assuming the emission comes from closed field lines, we used radius-to-frequency mapping and polarization information from other studies to constrain the magnetospheric geometry and location of the emission region. Our initial findings are consistent with a possible connection between FRBs and crustal motion events.

Sensor Cooperation Gain System for Burst Monitoring in Water Distribution Network: Concept, Design, and Evaluation

AbstractPipe bursts in water distribution networks (WDNs) not only cause significant water wastage but also pose risks of secondary disasters, such as road collapses. In most cases, these failures often result in a simultaneous decrease in values across multiple pressure sensors, indicating strong correlations in pressure data from the same event. This correlation suggests that leveraging the cooperative interaction between multiple sensors can significantly enhance burst detection sensitivity and expand coverage—an advantage largely overlooked in current methods. To address this gap, this study introduces a sensor cooperation gain system that constructs virtual sensors based on pressure data correlations and applies value fusion principles to form a multi‐sensor, cooperation‐based burst detection model, thus enabling more accurate system‐wide decision‐making. Case studies demonstrate that, compared to the existing method, the proposed gain fusion approach improves monitoring coverage by 25.34% and reduces the minimum coverage burst flow rate by 29.69 m3/hr.

Multi-category solar radio burst detection based on task-aligned one-stage object detection model

Multi-category solar radio burst detection based on task-aligned one-stage object detection model

Transforming Narratives and Engagement of AI and its Related Technology in Different Domains of Journalism - A Bibliometric Analysis

Introduction: This research paper explores the profound impact of Artificial Intelligence (AI) on the print media landscape, highlighting its transformative effects on narrative construction and audience engagement. As AI technologies advance, Indian media organizations are actively integrating these innovations to enhance content creation and audience interaction. Objectives: Citation analysis employs a full counting method, while timeline and burst detection analyses uncover significant topic trends and recent citations. Methods: The paper conducts an extensive bibliometric analysis of AI utilization in media research, drawing from publications in the Scopus database over the years. The analysis encompasses yearly publications, types of publications, and trends across various domains, including content creation and audience engagement. Results: The research highlights bibliometric findings related to authors, organizations, publication types, and documents with the strongest collaborative linkages within the context of AI in Indian media. Conclusions: The paper provides valuable insights into how AI is reshaping the print media sector in India, emphasizing the key trends and collaborative efforts driving this transformation.

A Comparative Bibliometric Analysis of Chinese and Foreign Articles in Fisheries Cooperation Research

As a highly globalized sector, fisheries play a pivotal role in ensuring global food security, providing high-quality protein, and promoting socio-economic development. Due to its dependence on marine resources, the fisheries industry chain values close international resource sharing and cooperation that help facilitate sustainable utilization of fisheries resources, promote regional and international economic growth, maintain eco-balance, and address global challenges. This paper leverages 1253 articles from Web of Science (WOS) core collection data and 828 articles from China Knowledge Network (CNKI) journal literature as data sources to analyze the publication volume, author and research institution cooperation network, co-occurrence of Chinese and English keywords, citation clustering, and burst detection in this field from 1991 to 2022. This study finds that although a relatively stable publication growth trend has been observed in fisheries cooperation research, the fragmentation of collaboration networks indicates a lack of strong research partnerships. By conducting an in-depth analysis of the underlying factors and proposing policy recommendations, we aim to promote future international cooperation and research innovation in this field.

Mapping the evolution and hotspots of future orientation research: a visualized analysis from 1991 to 2023 using Citespace

Although future orientation has garnered considerable scholarly attention, comprehensive bibliometric analyses of the field are still relatively scarce. Employing a bibliometric methodology, this study analyzed the growth and thematic trends of future orientation research using a bibliometric approach on Web of Science publications from 1991 to 2023, focusing on citation counts, keyword clusters, and influential authors. Results showed a consistent and significant increase in publication volume, with the United States being the foremost contributor. Major journals and prolific authors were identified based on citation metrics and publication frequency. Employing the Citespace tool, keyword cluster analysis revealed four main research hotspots: education, occupational outcomes, psychological well-being, and risk behaviors. Furthermore, the detection of keyword bursts over the past 5 years highlighted a burgeoning interest in education and school-related topics. The analysis informs future research directions, with specific recommendations provided to address gaps in the current understanding of future orientation.

Transient response and analytical solution of burst phenomenon in long distance water pipeline

The rapid detection and localisation of pipe burst incidents in water pipe systems are crucial for timely repair, minimisation of leakage, and assurance of water supply security. However, previous researches have insufficiently emphasised the identification of pressure characteristics during pipe burst, failing to accurate detection and localisation of pipe burst. To address this problem, an experimental system was designed to elucidate the complete dynamic process of pipe burst pressure fluctuations. Specifically, the continuous transition from steady state to transient state and back to steady state in the hydraulic state of the system after a pipe burst event, was observed both in time and space dimensions. The effects of initial pressure and flow rate at steady state, location and size of pipe burst on pressure characteristics were carefully investigated. Subsequently, a pipe burst model considering unsteady friction effect was developed and validated through experiments. Based on the above study, analytical solution regarding pressure drop during burst events and steady-state pressure at the burst point after burst events were derived to quantify the influence laws of different parameters and validated by the pipe burst model. The obtained results are characterised by the dominance analysis so as to explore the importance ranking of different factors to the pressure drop and steady-state burst pressure in the pipeline system. The analysis solution of pressure drop indicates the following ranking of factors from high to low is: pipe/burst area ratio, initial steady-state pressure at the burst point, wave speed; while the ranking of influencing factors for steady-state burst pressure from high to low is: the boundary pressures, the location of the pipe burst, the initial steady state flow rate, and the burst outflow coefficient. The research findings can provide theoretical and practical guidance for the rapid diagnosis and quantitative analysis of burst pipes.

Data governance and open sharing in the fields of life sciences and medicine: A bibliometric analysis.

This study aims to conduct a bibliometric analysis of literature related to data governance and open sharing in the fields of life sciences and medicine, so as to clarify the characteristics of publications and explore research hotspots and trends. A total of 2529 valid documents published in the Web of Science Core Collection database from 2000 to 2024 were included in this study. VOSviewer was used for co-occurrence analysis, while CiteSpace was employed for clustering, burst detection, and thematic evolution analysis. Between 2000 and 2024, the number of studies on data governance and open sharing in the fields of life sciences and medicine has increased annually, indicating the growing importance of research in this area. The USA led as the country with the most research output in this field. The University of Oxford was the institution with the highest publication volume, Amy L. McGuire was the most active author, and the Journal of Medical Internet Research and the Journal of the American Medical Informatics Association were the most frequent publication outlets. The most cited reference was 'Comment: The FAIR Guiding Principles for Scientific Data Management and Stewardship'. Topics such as the FAIR principles, ethical issues, public attitudes toward data sharing, data quality, databases, and big data analysis techniques are hotspots in this field. Potential research frontiers include the FAIR principles, data quality, public trust and attitudes toward data sharing, the application of artificial intelligence technology in data governance and sharing, governance and sharing of epidemiological and public health data, governance and sharing of data on chronic diseases such as diabetes, and the construction of data governance models.

Line detections in photospheric radius expansion bursts from 4U 1820-303

Context. The Neutron star Interior Composition ExploreR (NICER) is the instrument of choice for the spectral analysis of type I X-ray bursts, as it provides high throughput at the X-ray CCD resolution down to 0.3 keV. Aims. Triggered by the detection of absorption and emission lines in the first four photospheric radius expansion (PRE) bursts detected by NICER, we wish to test the dependence of the absorption line energies on the inferred blackbody radius because it was reported that the absorption line energies were positively correlated with the inferred blackbody radius. This was tentatively explained by a combination of a weaker gravitational redshift and higher blueshifts in a burst with a larger blackbody radius. Methods. We thus reanalysed these four bursts and analysed another eight bursts from 4U 1820-303, for which we report evidence for PRE. We first followed the spectral evolution of the burst on the shortest possible timescales (tenth of a second). We adopted two parallel continuum descriptions to characterise the photospheric expansion and line evolution. Using the accretion-enhanced model, in which the burst emission is modelled as the sum of a blackbody and a component describing the persistent emission recorded prior to the burst and multiplied by a constant (fa), we inferred maximum equivalent blackbody radii up to ∼900 km. The peak bolometric (0.1–20 keV) luminosity reached between 4 and 7 × 1038 erg s−1 (and even higher when absorption from a putative photoionised absorber is accounted for) in our sample of bursts. This exceeds the Eddington luminosity of a helium accretor. In individual bursts, we detected absorption lines and assessed their significance through extensive Monte Carlo simulations. To characterise the spectral lines, we used dedicated plasma codes available within SPEX with a phenomenological continuum. A deep search throughout the temperature–velocity parameter space was run to explore Doppler shifts and minimise the chance of becoming stuck in local minima. Results. We detected several significant (&gt; 99.9 % significance) absorption lines, including the 2.97 keV line that was previously reported. We do not confirm the correlation between the line energies and the inferred blackbody radius, but for some bursts with larger radii, up to four lines are reported, and the line strength is higher. From the modelling of the feature lines, a photoionised or collisionally ionised slightly redshifted (almost rest-frame) gas in emission is suggested in most cases. In particular for the burst presenting the greatest PRE, a combination of photoionisation plasma in emission and absorption is preferred, however.