Research Hotspot Research Articles

Research trends and perspective of low-intensity pulsed ultrasound in orthopedic rehabilitation treatment based on Web of Science: A bibliometric analysis.

Ultrasound has a long history as a diagnostic and therapeutic tool. Low-intensity pulsed ultrasound (LIPUS), whose intensity is below 300 mW/cm2, has been widely used in orthopedic rehabilitation treatment. However, the detailed bioeffects and underlying mechanisms of LIPUS treatment need to be explored. To make a comprehensive view of the field, bibliometric and visualization analysis was used to reveal the global research trends of LIPUS in orthopedics and rehabilitation treatment between 1994 and 2023. All literature data on LIPUS were retrieved from the Web of Science Core Collection database. VOSviewer and CiteSpace were applied for the bibliometric and visualization analysis. A total of 760 publications were included. The distribution of publications generally showed an unstable rising trend. China had the highest number of publications (28.0%), and Chong Qing Medical University was the organization with the highest number of publications (5.8%). Ultrasound in Medicine and Biology had the highest number of publications (8.8%), while BMJ-British Medical Journal had the highest impact factor among the retrieved journals. Ling Qin from the Chinese University of Hong Kong was the most active researcher. Our overlay visualization map showed that the keywords such as pain, knee osteoarthritis, apoptosis, chondrocytes, cartilage, and autophagy, which link to osteoarthritis, have becoming the new research trends and hotspots. LIPUS is a popular and increasingly important area of orthopedic rehabilitation, and collaboration of authors from different countries should be further strengthened. Predictably, clinical application of LIPUS on chronic inflammation-related diseases and regenerative medicine, and in-depth biological mechanisms are the orientations of LIPUS in orthopedic rehabilitation treatment.

Amendments affect the community assembly and co-occurrence network of microorganisms in Cd and Pb tailings of the Eucalyptus camaldulensis rhizosphere

Mining tailings containing large amounts of Pb and Cd cause severe regional ecosystem pollution. Soil microorganisms play a regulatory role in the restoration of degraded ecosystems. The remediation of heavy metal-contaminated tailings with amendments and economically valuable Eucalyptus camaldulensis is a research hotspot due to its cost-effectiveness and sustainability. However, the succession and co-occurrence patterns of these microbial communities in this context remain unclear. Tailing samples of five kinds of Cd and Pb were collected in E. camaldulensis restoration models. Physicochemical properties, the proportions of different Cd and Pb forms, microbial community structure, and the co-occurrence network of rhizosphere tailings during different restoration process (organic bacterial manure, organic manure, inorganic fertilizer, bacterial agent) were considered. Organic and organic bacterial manures significantly increased pH, cation exchange capacity, and the proportion of residual Pb. Still, there was a significant decrease in the proportion of reducible Pb. The changes in microbial communities were related to physicochemical properties and the types of amendments. Organic and organic bacterium manures decreased the relative abundance of oligotrophic groups and increased the relative abundance of syntrophic groups. Inorganic fertilizers and bacterial agents decreased the relative abundance of saprophytic fungi. B. subtilis would play a better role in the environment improved by organic manure, increasing the relative abundance of beneficial microorganism and reducing the relative abundance of pathogenic microorganism. pH, cation exchange capacity, and the proportion of different forms of Pb were the main factors affecting the bacterial and fungi variation. All four amendments transformed the main critical groups of the microbial network structure from acidophilus and pathogenic microorganisms to beneficial microorganisms. Heavy metal-resistant microorganisms, stress-resistant microorganisms, beneficial microorganisms that promote nutrient cycling, and copiotrophic groups have become critical to building stable rhizosphere microbial communities. The topological properties and stability of the rhizosphere co-occurrence network were also enhanced. Adding organic and organic bacterium manures combined with E. camaldulensis to repair Cd and Pb tailings improved (1) pH and cation exchange capacity, (2) reduced the biological toxicity of Pb, (3) enhanced the stability of microbial networks, and (4) improved ecological network relationships. These positive changes are conducive to the restoration of the ecological functions of tailings.

Research trends and hotspots of circular RNA in cardiovascular disease: A bibliometric analysis

From a global perspective, cardiovascular diseases (CVDs), the leading factor accounting for population mortality, and circRNAs, RNA molecules with stable closed-loop structures, have been proven to be closely related. The latent clinical value and the potential role of circRNAs in CVDs have been attracting increasing, active research interest, but bibliometric studies in this field are still lacking. Thus, in this study, we conducted a bibliometric analysis by using software such as VOSviewer, CiteSpace, Microsoft Excel, and the R package to determine the current research progress and hotspots and ultimately provide an overview of the development trends and future frontiers in this field. In our study, based on our search strategy, a total of 1206 publications published before July 31, 2023 were accessed from the WOSCC database. According to our findings, there is a notable increasing trend in global publications in the field of circRNA in CVDs. China was found to be the dominant country in terms of publication number, but a lack of high-quality articles was a significant fault. A cluster analysis on the co-cited references indicated that dilated cardiomyopathy, AMI, and cardiac hypertrophy are the greatest objects of concern. In contrast, a keywords analysis indicated that high importance has been ascribed to MI, abdominal aortic aneurysm, cell proliferation, and coronary artery diseases.

Discovering the conceptual building blocks of blockchain technology applications in the agri-food supply chain: a review and research agenda

PurposeThis paper aims to provide an overview of the application of blockchain to agri-food supply chains, including key issues and trends. It examines the state of the art and conceptual structure of the field and proposes an agenda to guide future research.Design/methodology/approachThis article performs a bibliometric analysis using VOSviewer software on a sample of 205 articles from the WoS database to identify research trend topics.FindingsThe number of publications in this area has increased since 2020, which shows a growing research interest. The research hotspots are related to the integration of blockchain technology in the agri-food supply chain for traceability, coordination between all actors involved, transparency of operations and improvement of food safety. Furthermore, this is linked to sustainability and the achievement of the sustainable development gtoals (SDGs), while addressing key challenges in the implementation of blockchain-based technologies in the agri-food supply chain.Practical implicationsThe application of blockchain in the agri-food supply chain may consider four key aspects. Firstly, the implementation of blockchain can improve the traceability of food products. Secondly, this technology supports sustainability issues and could avoid disruptions in the agri-food supply chain. Third, blockchain improves food quality and safety control throughout the supply chain. Fourthly, the findings show that regulation is needed to improve trust between stakeholders.Originality/valueThe paper provides a comprehensive overview of the blockchain phenomenon in the agri-food supply chain by optimising the search criteria. Moreover, it serves to bridge to future research by identifying gaps in the field.

Advances in Adjoint Functions of Connection Number in Water Resources Complex Systems: A Systematic Review.

The adjoint function of connection number has unique advantages in solving uncertainty problems of water resource complex systems, and has become an important frontier and research hotspot in the uncertainty research of water resource complex problems. However, in the rapid evolution of the adjoint function, some problems greatly limit the application of the adjoint function in the research of water resources. Therefore, based on bibliometric analysis, development, practical application issues, and prospects of the hot directions are analyzed. It is found that the development of the connection number of water resource set pair analysis can be divided into three stages: (1) relatively sluggish development before 2005, (2) a period of rapid advancement in adjoint function research spanning from 2005 to 2017, and (3) a subsequent surge post-2018. The introduction of the adjoint function of connection number promotes the continuous development of set pair analysis of water resources. Set pair potential and partial connection number are the crucial research directions of the adjoint function. Subtractive set pair potential has rapidly developed into a relatively independent and important trajectory. The research on connection entropy is comparatively less, which needs to be further strengthened, while that on adjacent connection number is even less. The adjoint function of set pair potential can be divided into three major categories: division set pair potential, exponential set pair potential, and subtraction set pair potential. The subtraction set pair potential, which retains the original dimension and quantity variation range of the connection number, is widely used in water resources and other fields. Coupled with the partial connection number, a series of new connection number adjoint functions have been developed. The partial connection number can be mainly divided into two categories: total partial connection number, and semi-partial connection number. Among these, the calculation expression and connotation of total partial connection numbers have not yet reached a consensus, accompanied by the slow development of high-order partial connection numbers. Semi-partial connection number can describe the mutual migration movement between different components of the connection number, which develops rapidly. With the limitations and current situation described above, promoting the exploration and application of the adjoint function of connection number in the field of water resources and other fields of complex systems has become the focus of future research.

Systematic Review and Prospects on Social Risks of Artificial Intelligence— Visual Analysis Based on CiteSpace Knowledge Graph

As artificial intelligence becomes more and more embedded in human society, it also creates social risks. With the help of CiteSpace software, 200 documents related to social risks of artificial intelligence searched from SSCI and SCI journals from 2000 to 2024 were sorted out to sort out the research progress and hot spots. Research shows that there are currently some highly influential scholars and institutions in the field of artificial intelligence social risks, and the number of relevant studies is increasing year by year. Risk resolution and sustainable development issues of artificial intelligence have become important research directions for the continuous development of artificial intelligence.

Review and bibliometric analysis of AI-driven advancements in healthcare

Purpose: This research intends to use literature review and bibliometric analysis methods to visually review the development status and important historical milestones of Artificial Intelligence, as well as the basic research, key topics, and future potential research hot spots of AI in the healthcare field. Methodology: Conduct in-depth analysis of AI in healthcare through bibliometrics methods such as publication activity analysis, co-occurrence analysis, and co-authorship analysis. Findings: This study outlines the development time trajectory of AI technology and its application in healthcare. Research shows that "algorithm", "machine learning", "deep learning", "controlled study", "major clinical study" and "healthcare delivery" as well as "decision support systems" are key topics for research. Gender-related research and ethical issues are areas of future focus. Research implications: The practical significance is that it can clarify and optimize the key directions of AI to improve the quality of medical decision-making, improve diagnostic accuracy and guide market investment. The originality is reflected in the comprehensive analysis of the development trajectory of AI in the medical and health field. Through a unique perspective and systematic approach, it provides an important reference for research trends and future directions in the field.

Regenerated Fiber's Ideal Target: Comparable to Natural Fiber.

The toughness of silk naturally obtained from spiders and silkworms exceeds that of all other natural and man-made fibers. These insects transform aqueous protein feedstocks into mechanically specialized materials, which represents an engineering phenomenon that has developed over millions of years of natural evolution. Silkworms have become a new research hotspot due to the difficulties in collecting spider silk and other challenges. According to continuous research on the natural spinning process of the silkworm, it is possible to divide the main aspects of bionic spinning into two main segments: the solvent and behavior. This work focuses on the various methods currently used for the spinning of artificial silk fibers to replicate natural silk fibers, providing new insights based on changes in the fiber properties and production processes over time.

Research hot spots and trends in endocrine-related adverse events caused by immune checkpoint inhibitors: a bibliometric analysis and visualization research.

In recent years, with the widespread use of immune checkpoint inhibitors (ICIs) in cancer treatment, the toxicity associated with immunotherapy of ICIs has attracted more attention from scholars. Endocrine toxicity is the most likely immune-related adverse events (irAEs) and is often irreversible, posing a significant clinical treatment challenge. In this study, bibliometric methods were used to analyze relevant literature in screening endocrine-related adverse events caused by ICIs in the Web of Science core collection database (WoSCC) and to summarize the status, research hot spots, and future trends in this field. 321 countries, 297 institutions, 365 authors, and 305 journals had published 671 English documents on endocrine adverse reactions of ICIs as of 1 December, 2022. The United States, Japan, and China were the top three countries with the most publications. The University of Texas MD Anderson Cancer Center, Harvard Medical School, and Memorial Sloan Kettering Cancer Center were the top three research institutions in terms of publication output. F Stephen Hodi, from the Dana-Farber Cancer Institute in the United States, contributed the largest number of publications. Frontiers in Oncology, which was the most widely distributed publication in the field. The main keywords or clusters identified that current research hotspots include the management of endocrine-related adverse events, hypophysitis, thyroid dysfunction, type I diabetes mellitus, and the impact of endocrine adverse events on survival of patients in this field. The basic knowledge structure of the field of endocrine-related adverse events of ICIs, including publication trends, authors, institutions, countries, keywords, journals and publications, and cited documents, was visually analyzed in this bibliometric analysis. The research results comprehensively demonstrated the hot spots and future trends in the research field, as well as its broad prospects, thus providing a reference for the researchers.

Research on Key Technologies for Optimization of Forming Parameters of Aluminum Alloy Hood Outer Panel

At present, computer technology and finite element software analysis technology have been widely used in the stamping and forming process of aluminum alloy hood outer plate to predict the possible quality defects in the stamping process, and effectively improve the quality and production efficiency of parts. The stamping and forming of the outer plate of the automobile aluminum alloy hood is a highly nonlinear process, and its forming quality involves many forming factors, if the variable design is not reasonable, it may lead to quality defects such as wrinkles or cracks of the molded parts. Due to the relatively complex shape and structure of the aluminum alloy hood outer plate and its high requirements for the quality of forming, it is difficult to obtain the optimal process parameters in a short time if the finite element software simulation optimization is carried out manually. The machine learning algorithm is applied to the research on the stamping and forming of the outer plate of the aluminum alloy hood, and the multi-objective genetic algorithm is used to optimize the stamping process parameters of the covers, which can meet the maximum thinning rate of the formed parts and control the rebound of the formed parts to a certain extent. How to combine machine learning algorithms with finite element simulations to reasonably adjust the process parameters in the stamping and forming process by using machine learning algorithms to meet the forming quality requirements of aluminum alloy hood outer plate has always been a research hotspot and difficulty in the field of aluminum alloy hood outer plate stamping.

Characteristics Analysis of Influence of Multiple Parameters of Mixed Sea Waves on Delay–Doppler Map in Global Navigation Satellite System Reflectometry

Feature capture and recognition of sea wave components in radar systems especially in global navigation satellite system reflectometry (GNSS-R) using signal processing approaches or computer simulative methods has become a research hotspot in recent years. At the same time, parameter inversion of marine phenomena from the discovered characteristics plays a significant role in monitoring and forewarning the different components of sea waves. This paper aims to investigate the impact of multiple parameters, such as the wind speed, directionality variable, wave amplitude, wave length, and directions of sea wave components, on the delay waveform of the delay–Doppler map (DDM). Two types of wind waves and the 2-D sinusoidal sea surface are chosen to be analyzed. By comparing and analyzing the discrepancy of delay waveforms under different conditions, it can be concluded that the increased MSS which arises from the increase in the roughness of the sea surface can lead to the difference in the peak value or trial edges exhibited in delay waveforms. The values of delay waveforms at zero chip along the increasing direction of long-crest wind waves exhibit the periodic spikes shape, which is the opposite of the short-crest wind waves, and the fluctuation of the periodic profiles decreases with the increase in the amplitude of waves. The results and conclusions can provide a foundation for the parameter inversion, tracking, and early warning of anomalous formations of waves in bistatic radar configuration.

Visualising Knowledge, Research Hotspots and Trends of Literacy Studies in the Context of Library, 1969-2021

Visualising Knowledge, Research Hotspots and Trends of Literacy Studies in the Context of Library, 1969-2021

Research Progress of eco-friendly Sugar-based Surfactants

Surfactant is a common industrial product widely used in chemical industry, food production, biomedicine and other fields. A variety of glucose-based bisubstituted surfactants have been developed, which not only contain the advantages of other classes of sugar-based surfactants, but also have the properties of mild conditions of use, good bactericidal ability, and non-irritation of the skin, etc. In addition, sugars are widely sourced and renewable, and the production of sugar-based surfactants with degradability has become a hot spot of the current research. This paper summarises the types of common sugar-based surfactants, the test methods of related properties and the application prospects.

Analysis of Hotspots and Trends in Domestic and International Data Asset Research: A Knowledge Graph Analysis Based on CiteSpace

The article summarized 363 literature on data assets in the CNKI and WOS databases and conducted a knowledge map analysis using CiteSpace. The research found that the study of data assets at home and abroad is continuously growing, but there are differences in origins, content, and hotspots. Foreign research focuses on areas such as big data, data governance, machine learning, and the Internet, while domestic research focuses on data assets, the digital economy, data transactions, and accounting. Domestic research is gradually expanding to include data middle platforms and asset confirmation, while foreign research focuses on the Internet and artificial intelligence. This analysis helps grasp the research trends and hotspots in the field of data assets, promoting cooperation and knowledge sharing.

Point-Sim: A Lightweight Network for 3D Point Cloud Classification

Analyzing point clouds with neural networks is a current research hotspot. In order to analyze the 3D geometric features of point clouds, most neural networks improve the network performance by adding local geometric operators and trainable parameters. However, deep learning usually requires a large amount of computational resources for training and inference, which poses challenges to hardware devices and energy consumption. Therefore, some researches have started to try to use a nonparametric approach to extract features. Point-NN combines nonparametric modules to build a nonparametric network for 3D point cloud analysis, and the nonparametric components include operations such as trigonometric embedding, farthest point sampling (FPS), k-nearest neighbor (k-NN), and pooling. However, Point-NN has some blindness in feature embedding using the trigonometric function during feature extraction. To eliminate this blindness as much as possible, we utilize a nonparametric energy function-based attention mechanism (ResSimAM). The embedded features are enhanced by calculating the energy of the features by the energy function, and then the ResSimAM is used to enhance the weights of the embedded features by the energy to enhance the features without adding any parameters to the original network; Point-NN needs to compute the similarity between each feature at the naive feature similarity matching stage; however, the magnitude difference of the features in vector space during the feature extraction stage may affect the final matching result. We use the Squash operation to squeeze the features. This nonlinear operation can make the features squeeze to a certain range without changing the original direction in the vector space, thus eliminating the effect of feature magnitude, and we can ultimately better complete the naive feature matching in the vector space. We inserted these modules into the network and build a nonparametric network, Point-Sim, which performs well in 3D classification tasks. Based on this, we extend the lightweight neural network Point-SimP by adding some trainable parameters for the point cloud classification task, which requires only 0.8 M parameters for high performance analysis. Experimental results demonstrate the effectiveness of our proposed algorithm in the point cloud shape classification task. The corresponding results on ModelNet40 and ScanObjectNN are 83.9% and 66.3% for 0 M parameters—without any training—and 93.3% and 86.6% for 0.8 M parameters. The Point-SimP reaches a test speed of 962 samples per second on the ModelNet40 dataset. The experimental results show that our proposed method effectively improves the performance on point cloud classification networks.

Preparation of SiO2/chitosan composite coating on stainless steel mesh for efficient and stable oil/water separation

In recent years, the application of underwater superoleophobic coatings in the field of oil/water separation has received increasing attention, and chitosan (CS) coatings have become a research hotspot due to their advantages of hydrophilicity, non-toxicity, biodegradability, and antibacterial properties. In this work, a SiO2/CS underwater superoleophobic composite coating was prepared on 304 stainless steel mesh by a simple dip-coating method. The underwater oil droplet contact angle (OCA) of the SiO2/CS coating was 151.2°, and the oil droplet showed a stable underwater Cassie state on the coating. The stainless steel mesh with SiO2/CS coating could effectively separate different kinds of oil/water mixtures, and the separation efficiency for the kerosene/water mixture was 99.2 %. After 20 cycles of separation, the separation efficiency was more than 98 %, indicating that the SiO2/CS coating had excellent recycling performance. In addition, the prepared underwater superoleophobic coating exhibited excellent long-term stability, acid and alkali resistant stability and UV resistant stability. This SiO2/CS coating has promising practical application, which could expand the use of underwater superoleophobic coatings.

Rack force fault tolerance estimation of steer-by-wire system under different resolver faults based on sensor flows

In the context of vehicle intelligence, steer-by-wire (SbW) has great potential for development and has become a current research hotspot. The key challenge for SbW systems is to obtain the nonlinear rack force generated by tire-road interaction, which is the source of the driver's real steering feel and the main disturbance of steering angle tracking control. Existing rack force estimators either are only suitable for non-aggressive steering or assume that the measured signals are reliable. This paper proposes a high-precision rack force estimation algorithm that is robust to primary sensor faults of a SbW system. Further, a rack force estimator based on the established high-order SbW mechanism model is designed, and a detailed stability proof and feedback gain selection guidance are given. The resolver sensor model is provided and analyzed, and a fault detection method based on discrete Fourier transform (DFT) and data fusion is developed, which has good real-time performance and high accuracy. The performance of rack force estimator was verified and compared by simulations and hardware-in-loop (HIL) experiments with various steering maneuvers and different resolver faults. The experiment results show that the proposed rack force estimation method can not only effectively improve the estimation accuracy even in aggressive steering condition and uneven road profiles, but also achieve accurate estimation of the rack force in the case of resolver faults. The proposed method obtains high-precision and high-reliability rack force estimation results while being easily embedded applied in engineering practice.

A Bibliometric Study of English Textbook Analyses (1979‒2022)

In recent years, the field of English textbook analysis has garnered increasing attention from scholars worldwide, emerging as a burgeoning research hotspot. However, this field currently lacks a comprehensive and systematic analysis. This study adopts a bibliometric approach, using raw data from Scopus to review research on English textbooks from 1979 to 2022. Overall, research in this field has shown a steady upward trend, with a significant acceleration in growth over the past five years. The results indicate that scholars from Asia, especially East Asia, have made the most significant contributions to this field. Most studies typically utilize methods such as corpus approach and critical discourse analysis to explore themes such as ideology, gender, culture, and language teaching in English textbooks, thereby making this research field a pivotal lens for comprehending global language education and societal dynamics, fostering mutual understanding and communication across diverse regions worldwide. Through these studies, the field of English textbook analysis not only advances language education but also provides significant insights and support for educational reforms and societal development on a global magnitude.

Construction of an Intelligent Identification Model for Drugs in Near Infrared Spectroscopy and Research on Drog Classification based on Improved Deep Algorithm

Near-infrared spectroscopy has important applications in drug and food identification. Combining machine learning with near-infrared spectroscopy to achieve intelligent identification of drugs has become a research hotspot in recent years. To solve the problem of machine learning’s inefficiency in classifying small-scale data, a drug identification model based on near-infrared spectroscopy combined with a random fading depth belief network is proposed. Aiming at the problem that the training time of the machine learning algorithm is too long, the extreme learning machine is used to replace the back propagation algorithm to optimize the stack sparse auto-encoder network. Additionally, the stack sparse auto-encoder algorithm based on extreme learning machine algorithm is constructed. The study found that the precision of the Dropout Deep Belief Network model was 99.12%, which was higher than the other three models. Additionally, the area under the curve value of the Dropout Deep Belief Network model was 0.87, which was 0.04 higher than the binary whale optimization algorithm model, 0.26 higher than the factor decomposition machine and depth neural network model, and 0.05 higher than the random forest network model. The sparse auto-encoder algorithm based on the extreme learning machine algorithm achieved a precision of 99.72%. The study proposes two algorithm models that can effectively identify drugs using near-infrared spectroscopy. This has a positive impact on the medical industry and the safety of patients' lives and health.

A Hermeneutic Perspective on Translator’s Subjectivity

Traditional translation studies emphasize that the translated text needs to remain "equivalent" to the source text and that the translator needs to be "faithful" to the author, thus the subjectivity and creativity of the translator have long been neglected. With the rise of the "Cultural Turn" in translation, this situation has been changed. The translator's subjectivity and creativity in the translation process has become a hot spot in translation research. Based on George Steiner's Hermeneutic Motion, this paper analyzes the translator's subjectivity in the Chinese translation of Walden by Zhong Ze, trying to explore how the translator's subjectivity affects the translation, in order to provide new thoughts on the practical application of Hermeneutic Motion.