A Trajectory Evaluator by Sub-tracks for Detecting VOT-based Anomalous Trajectory

With the popularization of visual object tracking (VOT), more and more trajectory data are obtained and have begun to gain widespread attention in the fields of mobile robots, intelligent video surveillance, and the like. How to clean the anomalous trajectories hidden in the massive data has become one of the research hotspots. Anomalous trajectories should be detected and cleaned before the trajectory data can be effectively used. In this article, a Trajectory Evaluator by Sub-tracks (TES) for detecting VOT-based anomalous trajectory is proposed. Feature of Anomalousness is defined and described as the Eigenvector of classifier to filter Track Lets anomalous trajectory and IDentity Switch anomalous trajectory, which includes Feature of Anomalous Pose and Feature of Anomalous Sub-tracks (FAS). In the comparative experiments, TES achieves better results on different scenes than state-of-the-art methods. Moreover, FAS makes better performance than point flow, least square method fitting and Chebyshev Polynomial Fitting. It is verified that TES is more accurate and effective and is conducive to the sub-tracks trajectory data analysis.

International Research Progress and Evolution Trend of Interpersonal Trust—Prospects under COVID-19 Pandemic

Retrospecting articles on interpersonal trust is of great importance for understanding its current status and future development in the context of the COVID-19 pandemic, especially, with the widespread use of Big Data and Blockchain. In total, 1532 articles related to interpersonal trust were collected as research database to draw keyword co-occurrence mapping and timeline mapping by VOSviewer and CiteSpace. On this basis, the research content and evolution trend of interpersonal trust were systematically analyzed. The results show that: (1) Data cleaning by code was first integrated with Knowledge Mapping and then used to review the research of interpersonal trust; (2) Developed countries have contributed the most to the research of interpersonal trust; (3) Social capital, knowledge sharing, job and organizational performance, Chinese Guanxi are the research hotspots of interpersonal trust; (4) The research hotspots on interpersonal trust evolve from the level of individual psychology and behavior to the level of social stability and development and then to the level of organization operation and management; (5) At present, the research on interpersonal trust is in the outbreak period; fMRI technology and Big Data and Blockchain technology gradually become vital research tools of interpersonal trust, which provides significant prospects for the following research of interpersonal trust under the COVID-19 pandemic.

Graph Convolutional Networks for Cross-Modal Information Retrieval

In recent years, due to the wide application of deep learning and more modal research, the corresponding image retrieval system has gradually extended from traditional text retrieval to visual retrieval combined with images and has become the field of computer vision and natural language understanding and one of the important cross-research hotspots. This paper focuses on the research of graph convolutional networks for cross-modal information retrieval and has a general understanding of cross-modal information retrieval and the related theories of convolutional networks on the basis of literature data. Modal information retrieval is designed to combine high-level semantics with low-level visual capabilities in cross-modal information retrieval to improve the accuracy of information retrieval and then use experiments to verify the designed network model, and the result is that the model designed in this paper is more accurate than the traditional retrieval model, which is up to 90%.

Bibliometric Analysis of Highly Cited Papers on Resources and the Environment in China

To review the current state of resources and environmental sciences in China, this study assessed highly cited papers of five leading CSSCI journals sourced from the Chinese National Knowledge Infrastructure database. The fields of resources and environmental sciences were the research focus, and the bibliometric analysis software CiteSpace was used to perform co-occurrence analysis on keywords, authors, and research institutions based on bibliometrics and social network analysis. Furthermore, the research hotspots, scientist groups, and main cooperation models in the field of resources and environmental sciences in China were also explored. The results show that: (1) For 30 years, the interdisciplinarity of resources and environmental sciences has become more and more intense, and research themes have become increasingly extensive. The research hotspots of highly cited papers focused on energy, ecology, land, water resources, and sustainable development. In recent years, problems associated with energy and carbon emissions have aroused great interest. The ecological and sustainable development of resources and environmental elements has emerged as a future research trend. (2) An analysis of scientist-oriented networks shows that highly cited papers are mostly published by group authors. Scientists work closely within their respective academic groups, while intergroup academic cooperation is rare. Furthermore, connectedness between cooperation networks is poor, and scientists are largely connected through their research institutions. Cooperation among scientists is greatly affected by their geographical locations. Research institutions in the same region are more likely to cooperate. Beijing and Nanjing are high-producing areas of highly cited papers. The Institute of Geographic Sciences and Resources, CAS, is the most influential research institution. This paper introduces the state-of-the-art research hotspots of Chinese resources and environmental sciences to international academic circles and provides a basis for the research practice of resources and environmental sciences worldwide.

Mapping Thematic Trends and Analysing Hotspots Concerning the Use of Stem Cells for Cartilage Regeneration: A Bibliometric Analysis From 2010 to 2020

Background: Defects of articular cartilage represent a common condition that usually progresses to osteoarthritis with pain and dysfunction of the joint. Current treatment strategies have yielded limited success in these patients. Stem cells are emerging as a promising option for cartilage regeneration. We aim to summarize the developmental history of stem cells for cartilage regeneration and to analyse the relevant trends and hotspots.Methods: We screened all relevant literature on stem cells for cartilage regeneration from Web of Science during 2010–2020 and analysed the research trends in this field by VOSviewer and CiteSpace. We also summarized previous clinical trials.Results: We screened 1,011 publications. China contributed the largest number of publications (317, 31.36%) and citations (81,376, 48.61%). The United States achieved the highest H-index (39). Shanghai Jiao Tong University had the largest number of publications (34) among all full-time institutions. The Journal of Biomaterials and Stem Cell Research and Therapy published the largest number of studies on stem cells for cartilage regeneration (35). SEKIYA I and YANG F published the majority of articles in this field (14), while TOH WS was cited most frequently (740). Regarding clinical research on stem cells for cartilage regeneration, the keyword “double-blind” emerged in recent years, with an average year of 2018.75. In tissue engineering, the keyword “3D printing” appeared latest, with an average year of 2019.625. In biological studies, the key word “extracellular vesicles” appeared latest, with an average year of 2018.9091. The current research trend indicates that basic research is gradually transforming to tissue engineering. Clinical trials have confirmed the safety and feasibility of stem cells for cartilage regeneration.Conclusion: Multiple scientific methods were employed to reveal productivity, collaborations, and research hotspots related to the use of stem cells for cartilage regeneration. 3D printing, extracellular vesicles, and double-blind clinical trials are research hotspots and are likely to be promising in the near future. Further studies are needed for to improve our understanding of this field, and clinical trials with larger sample sizes and longer follow-up periods are needed for clinical transformation.

Mapping Intellectual Structures and Research Hotspots of Triple Negative Breast Cancer: A Bibliometric Analysis

BackgroundTriple negative breast cancer (TNBC) is a highly heterogeneous breast cancer subtype with a poor prognosis due to its extremely aggressive nature and lack of effective treatment options. This study aims to summarize the current hotspots of TNBC research and evaluate the TNBC research trends, both qualitatively and quantitatively.MethodsScientific publications of TNBC-related studies from January 1, 2010 to October 17, 2020 were obtained from the Web of Science database. The BICOMB software was used to obtain the high-frequency keywords layout. The gCLUTO was used to produce a biclustering analysis on the binary matrix of word-paper. The co-occurrence and collaboration analysis between authors, countries, institutions, and keywords were performed by VOSviewer software. Keyword burst detection was performed by CiteSpace.ResultsA total of 12,429 articles related to TNBC were identified. During 2010-2020, the most productive country/region and institution in TNBC field was the USA and The University of Texas MD Anderson Cancer Center, respectively. Cancer Research, Journal of Clinical Oncology, and Annals of Oncology were the first three periodicals with maximum publications in TNBC research. Eight research hotspots of TNBC were identified by co-word analysis. In the core hotspots, research on neoadjuvant chemotherapy, paclitaxel therapy, and molecular typing of TNBC is relatively mature. Research on immunotherapy and PARP inhibitor for TNBC is not yet mature but is the current focus of this field. Burst detection of keywords showed that studies on TNBC proteins and receptors, immunotherapy, target, and tumor cell migration showed bursts in recent three years.ConclusionThe current study revealed that TNBC studies are growing. Attention should be paid to the latest hotspots, such as immunotherapy, PARP inhibitors, target, and TNBC proteins and receptors.

Research Trends, Hot Spots, and Prospects for Traditional Chinese Medicine in the Field of Ischemia-Reperfusion Injury

Ischemia-reperfusion (I/R) injury is one of the most common phenomena in ischemic disease or processes that causes progressive disability or even death. It has a major impact on global public health. Traditional Chinese medicine (TCM) has a long history of application in ischemic diseases and has significant clinical effect. Numerous studies have shown that the formulas or single herbs in TCM have specific roles in regulating oxidative stress, anti-inflammatory, inhibiting cell apoptosis, etc., in I/R injury. We used bibliometrics to quantitatively analyze the global output of publications on TCM in the field of I/R injury published in the period 2001–2021 to identify research hotspots and prospects. We included 446 related documents published in the Web of Science during 2001–2021. Visualization analysis revealed that the number of publications related to TCM in the field of I/R injury has increased year by year, reaching a peak in 2020. China is the country with the largest number of publications. Keywords and literature analyses demonstrated that neuroregeneration is likely one of the research hotspots and future directions of research in the field. Taken together, our findings suggest that although the inherent limitations of bibliometrics may affect the accuracy of the literature-based prediction of research hotspots, the results obtained from the included publications can provide a reference for the study of TCM in the field of I/R injury.

Pesticide Research on Environmental and Human Exposure and Risks in Sub-Saharan Africa: A Systematic Literature Review

On the African continent, ongoing agriculture intensification is accompanied by the increasing use of pesticides, associated with environmental and public health concerns. Using a systematic literature review, we aimed to map current geographical research hotspots and gaps around environmental and public health risks research of agriculture pesticides in Sub-Saharan Africa (SSA). Studies were included that collected primary data on past and current-used agricultural pesticides and assessed their environmental occurrence, related knowledge, attitude and practice, human exposure, and environmental or public health risks between 2006 and 2021. We identified 391 articles covering 469 study sites in 37 countries in SSA. Five geographical research hotspots were identified: two in South Africa, two in East Africa, and one in West Africa. Despite its ban for agricultural use, organochlorine was the most studied pesticide group (60%; 86% of studies included DDT). Current-used pesticides in agriculture were studied in 54% of the study sites (including insecticides (92%), herbicides (44%), and fungicides (35%)). Environmental samples were collected in 67% of the studies (e.g., water, aquatic species, sediment, agricultural produce, and air). In 38% of the studies, human subjects were investigated. Only few studies had a longitudinal design or assessed pesticide’s environmental risks; human biomarkers; dose-response in human subjects, including children and women; and interventions to reduce pesticide exposure. We established a research database that can help stakeholders to address research gaps, foster research collaboration between environmental and health dimensions, and work towards sustainable and safe agriculture systems in SSA.

Developments and Emerging Trends of Blended Learning: A Document Co-citation Analysis (2003–2020)

This study aims to analyze and visualize the research hotspots, evolution, and emerging trends of blended learning in a holistic way. In this study, 1657 biblio-metric records together with 48310 citations are collected from SCIE, SSCI and A&HCI databases. CiteSpace is adopted in the analysis and visualization. Results show: enhancing collaborative learning, pattern, and teacher training are the re-search hotspots in Period I, instructor perception, possible future direction, and research trend are the research hotspots in Period II, general science classroom, blended learning environment, and measuring student engagement are the re-search hotspots in Period III; the themes of covid-19 remain similar along the de-velopment, while the themes of digital health education change a lot; blended learning environment, online component, covid-19 pandemic, procrastinating be-havior, active blended learning, and observed learning orientation are the emerg-ing trends. These findings could provide research directions for future studies in blended learning.