Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first c...Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first collected book reviews from these two well-known websites by Python.Through topic analysis of these reviews,we identified recurring topics,including details of her translated works and appreciation of their translation quality.Then,employing sentiment and content analysis methods,the paper explored the emotional attitudes and the specific thoughts of readers toward Can Xue and her translated works.The fingdings revealed that,among the 408 reviews,though the reception of Can Xue’s translated works was relatively positive,the current level of attention and recognition remains insufficient.However,based on the research results,the paper can derive valuable insights into the translation and dissemination processes such as adjusting translation and dissemination strategies,so that the global reach of Chinese literature and culture can be better facilitated.展开更多
App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products.Automating the analysis of these reviews is vital for efficient review management.While t...App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products.Automating the analysis of these reviews is vital for efficient review management.While traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior performance.This research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and satisfaction.We propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification accuracy.Comparative analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,respectively.Thesignificant contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews dataset.These advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.展开更多
This study employs the Harvard analytical framework to evaluate Gree Electric's financial performance across strategy,accounting,finance,and prospects.Results show that Gree leads the industry in profitability and...This study employs the Harvard analytical framework to evaluate Gree Electric's financial performance across strategy,accounting,finance,and prospects.Results show that Gree leads the industry in profitability and long-term debt repayment capacity,supported by strong R&D and cost control from its vertically integrated model.Sustained innovation has generated extensive patents,enabling high-margin premium products.However,Gree faces constraints due to over-reliance on its core air-conditioning business,declining inventory turnover,and slow overseas expansion.Its diversification into new sectors like new energy and smart equipment remains limited,accounting for under 5%of revenue and failing to mitigate core market risks.Operating cash flow volatility further reflects instability in these new ventures.Strategic recommendations include scaling the new energy segment,enhancing overseas localization,and optimizing supply chain finance.The study also suggests that Gree should focus on green,smart technologies and value-added services to strengthen competitive barriers.Future research could incorporate ESG metrics to improve analytical comprehensiveness.展开更多
Studying runoff characteristics and quantifying human activities’impact on northern Shaanxi,a crucial mineral resource area in China,is crucial to alleviate water resource contradictions.In this study,hydrological el...Studying runoff characteristics and quantifying human activities’impact on northern Shaanxi,a crucial mineral resource area in China,is crucial to alleviate water resource contradictions.In this study,hydrological element trends were analyzed using theβ-z-h three-parameter indication method.The Mann-Kendall,Pettitt,moving T,and Yamamoto methods were used to test the mutation point of hydrological elements.The Budyko framework was used to quantitatively assess the impacts of climate change and multiple human activities on runoff reduction.The results showed that(1):Precipitation(PRE),potential evapotranspiration(E0),and temperature(TEM)showed increasing trends;runoff in the Huangfuchuan,Gushanchuan,Kuye River,Tuwei River,Wuding River,Qingjian River,and Yanhe River catchments showed decreasing trends(HFC,GSC,KYR,TWR,WDR,QJR,YR);whereas runoff in the Jialu River(JLR)catchment showed a“V-shaped”trend from 1980 to2020.(2)Runoff was positively correlated with PRE and negatively correlated with E0and the subsurface index(n),with the elasticity coefficients of PRE,E0,and n showing an increasing trend in the change period.(3)Human activities were a key factor in runoff reduction,although the impact of different human activities showed spatial variations.This study provides a scientific foundation for achieving the sustainable development of water resources in mining areas.展开更多
Wide-band oscillations have become a significant issue limiting the development of wind power.Both large-signal and small-signal analyses require extensive model derivation.Moreover,the large number and high order of ...Wide-band oscillations have become a significant issue limiting the development of wind power.Both large-signal and small-signal analyses require extensive model derivation.Moreover,the large number and high order of wind turbines have driven the development of simplified models,whose applicability remains controversial.In this paper,a wide-band oscillation analysis method based on the average-value model(AVM)is proposed for wind farms(WFs).A novel linearization analysis framework is developed,leveraging the continuous-time characteristics of the AVM and MATLAB/Simulink’s built-in linearization tools.This significantly reduces modeling complexity and computational costs while maintaining model fidelity.Additionally,an object-based initial value estimation method of state variables is introduced,which,when combined with steady-state point-solving tools,greatly reduces the computational effort required for equilibrium point solving in batch linearization analysis.The proposed method is validated in both doubly fed induction generator(DFIG)-based and permanent magnet synchronous generator(PMSG)-based WFs.Furthermore,a comprehensive analysis is conducted for the first time to examine the impact of the machine-side system on the system stability of the nonfully controlled PMSG-based WF.展开更多
Background:Three-dimensional(3D)printing has revolutionized craniofacial and craniomaxillofacial applications,leading to substantial advancements in patient-specific treatments.In this study,a bibliometric analysis wa...Background:Three-dimensional(3D)printing has revolutionized craniofacial and craniomaxillofacial applications,leading to substantial advancements in patient-specific treatments.In this study,a bibliometric analysis was performed to identify the key contributors,research trends,thematic developments,and collaboration networks in this evolving field.Methods:Two search strategies were employed to ensure a comprehensive analysis:(1)a broad search,in which selected keywords were searched in the title,abstract,and keyword fields to capture all relevant publications,and(2)a title-specific search,in which keywords were restricted to the title field to identify publications with a strong focus on 3D printing in craniofacial and craniomaxillofacial applications.The retrieved dataset was analyzed using VOSviewer and RStudio(bibliometrix package).Results:The broad search retrieved 3534 publications,whereas the title-specific search yielded 280 publications.The analysis of these 280 papers focused on identifying the top authors,universities,and countries,as well as their research dynamics and collaboration networks.A more detailed approach was adopted by examining the titles of these 280 papers.VOSviewer segmented the titles into approximately 800 words,which were then categorized into 18 distinct thematic groups to represent research trends.The focus areas of the ten most cited papers were also analyzed.Conclusion:This bibliometric study provides valuable insights into the progress in 3D printing for craniofacial and craniomaxillofacial applications.By highlighting the key contributors,thematic developments,and collaborative networks,this study offers a foundation for future research in this rapidly advancing field.展开更多
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
Traditional Chinese medicine(TCM)has played a significant role in the prevention and treatment of chronic heart failure(CHF).To study TCM diagnosis of CHF,a total of 278 Chinese clinical research articles on the study...Traditional Chinese medicine(TCM)has played a significant role in the prevention and treatment of chronic heart failure(CHF).To study TCM diagnosis of CHF,a total of 278 Chinese clinical research articles on the study of CHF syndromes in recent 40 years retrieved from Web of Science,Scopus,Pub Med,Embase,CNKI,Wanfang Data,Cq VIP,and Sino Med.According to cumulative frequency analysis,network analysis,and hierarchical cluster analysis,the study found the distribution of CHF syndromes was syndrome of qi deficiency with blood stasis,syndrome of qi and yin deficiency,syndrome of yang deficiency with water flooding,syndrome of heart blood stasis obstruction,syndrome of turbid phlegm,and syndrome of collapse due to primordial yang deficiency.The syndrome elements on location of illness were heart,kidney,lung,and spleen.The syndrome elements on nature of illness were qi deficiency,blood stasis,yang deficiency,yin deficiency,water retention,and turbid phlegm.These findings can provide reference to the research on diagnosis and treatment of CHF,and contribute to the study on syndrome standardization and objective research of TCM diagnosis.展开更多
The identification of specific galaxy populations in large-scale spectroscopic surveys represents an essential yet challenging task,particularly for rare or anomalous galaxies that deviate from the typical galaxy dist...The identification of specific galaxy populations in large-scale spectroscopic surveys represents an essential yet challenging task,particularly for rare or anomalous galaxies that deviate from the typical galaxy distributions.Traditional methods based on template-fitting or predefining spectral features face challenges in addressing the complexity and scale of modern astronomical data sets.To overcome these limitations,we propose GalSpecEncoder-KB,a modular and flexible framework that combines deep learning with knowledge base retrieval to enable efficient and interpretable analysis of galaxy spectra.The framework integrates a Transformerbased feature encoder,GalSpecEncoder,pre-trained with masked-modeling strategy to capture semantically rich and context-aware spectral representations.By leveraging a Retrieval-Augmented Analysis approach,the knowledge base constructed from catalogs enables metadata retrieval and weighted voting for target galaxy identification.Using the Sloan Digital Sky Survey as a comprehensive case study,we demonstrate the capabilities of the framework for target galaxy search.Experimental results demonstrate the exceptional generalizability and adaptability across diverse galaxy search tasks,including identification of LINERs,Strong Gravitational Lenses,and detection of Outliers,while maintaining robust performance and interpretable spectral analysis capabilities.展开更多
BACKGROUND Working memory serves as a fundamental cognitive function that substantially impacts performance in various cognitive tasks.Extensive neurophysiological research has established that theta oscillations(4-8 ...BACKGROUND Working memory serves as a fundamental cognitive function that substantially impacts performance in various cognitive tasks.Extensive neurophysiological research has established that theta oscillations(4-8 Hz)play an essential role in supporting working memory operations.Theta-band transcranial alternating current stimulation(tACS)offers a potential mechanism for working memory enhancement through direct modulation of these fundamental neural oscillations.Nevertheless,current empirical evidence shows substantial variability in the observed effects of theta-tACS across studies.AIM To conduct a systematic review and meta-analysis evaluating the effects of thetatACS on working memory performance in healthy adults.METHODS A systematic literature search was performed on PubMed,EMBASE,and Web of Science up to March 10,2025.Effect sizes were computed using Hedges’g with 95%confidence intervals(CIs),with separate meta-analyses for all included studies and for distinct working memory paradigms[n-back and delayed matchto-sample(DMTS)tasks]to examine potential task-specific effects.Subgroup analyses and meta-regression were performed to evaluate the influence of key moderating variables.RESULTS The systematic review included 21 studies(67 effect sizes).Initial meta-analysis showed theta-tACS moderately improved working memory(Hedges’g=0.405,95%CI:0.212-0.598).However,this effect became nonsignificant after correcting for publication bias(trim-and-fill adjusted Hedges’g=0.082,95%CI:-0.052 to 0.217).Task-specific analyses revealed significant benefits in n-back tasks(Hedges’g=0.463,95%CI:0.193-0.733)but not in DMTS tasks(Hedges’g=0.257,95%CI:-0.186 to 0.553).Moderator analyses showed that performance in n-back tasks was influenced by stimulation frequency(P=0.001),concurrent status(P=0.014),task modality(P=0.005),and duration(P=0.013),whereas only the region of targeted stimulation(P=0.012)moderated DMTS tasks.CONCLUSION Theta-tACS enhances working memory in healthy adults,with effects modulated by the task type and protocol parameters,offering dual implications for cognitive enhancement and clinical interventions.展开更多
This paper examines the Chinese terminology in 2024 Government Work Report and conducts a comparative analysis of its translation into English and Japanese.The study explores the challenges and strategies involved in ...This paper examines the Chinese terminology in 2024 Government Work Report and conducts a comparative analysis of its translation into English and Japanese.The study explores the challenges and strategies involved in translating these terms,taking into account differences in vocabulary,grammatical structure,and cultural context between the two languages.The findings reveal that the same term may be translated differently into English and Japanese due to the distinct linguistic and cultural characteristics of each language.The research emphasizes that understanding and respecting the unique features of each language and culture are essential for accurately conveying the meaning of Chinese terms.This is crucial for enhancing the international influence of Chinese discourse and promoting cross-cultural communication.展开更多
Objective:To conduct a comprehensive bibliometric and knowledge network analysis of musculoskeletal ultrasound(MSK US)research from 2005 to 2025,with a focus on publication trends,influential authors,institutions,and ...Objective:To conduct a comprehensive bibliometric and knowledge network analysis of musculoskeletal ultrasound(MSK US)research from 2005 to 2025,with a focus on publication trends,influential authors,institutions,and thematic hotspots.Methods:Articles related to MSK US were retrieved from the Web of Science Core Collection using the search strategy TS=(“musculoskeletal ultrasound”OR“MSK ultrasound”OR“musculoskeletal ultrasonography”)AND(tendon OR ligament).Eligible studies included English-language original research and review articles published between 2005 and 2025.Bibliometric analyses were performed using VOSviewer to evaluate publication trends,co-citation networks,author collaborations,institutional and country partnerships,and keyword co-occurrence.Results:A total of 570 articles(476 original research papers and 94 reviews)were included.The annual number of publications increased exponentially,peaking in 2020.Co-citation analysis identified Wakefield(2005),Naredo,and Filippucci as foundational contributors.Author collaboration networks highlighted a strong European core centered on Italy and Spain,while institutional and country analyses revealed a“Europe–North America”dual-center pattern with growing contributions from China.Keyword co-occurrence analysis revealed three main research pillars:inflammatory arthropathies,sports-related injuries,and degenerative conditions,with emerging interest in advanced imaging techniques and artificial intelligence.Conclusion:T MSK US research has demonstrated sustained growth and diversification over the past two decades.Europe remains a traditional leader,but increasing output from North America and Asia reflects a shift toward global,multicenter collaboration.Future research should prioritize methodological standardization,integration of novel imaging technologies,and alignment with clinically meaningful outcomes to optimize diagnostic accuracy and clinical utility.展开更多
Objective:To explore the current status of sepsis-associated acute kidney injury(SA-AKI)research and predict its future research directions.Methods:The bibliometric overview of publications was conducted in the field ...Objective:To explore the current status of sepsis-associated acute kidney injury(SA-AKI)research and predict its future research directions.Methods:The bibliometric overview of publications was conducted in the field of SA-AKI based on Web of Science Core Collection database from January 2013 to August 2023.This study employed software such as CiteSpace and VOSviewer to conduct bibliometric and visualization analysis of the included literature,including publication trends,geographic distribution characteristics,author contributions,citations,funding sources characteristics,and keyword clustering.Results:A total of 6509 articles were analyzed,and the number of publications and citations increased from 2013 to 2022.The United States had the highest number of publications in SA-AKI,while France was the country with the highest number of citations per publication.Keyword clustering analysis showed that the pathophysiology and definition of SA-AKI were the research focus,and the research hotspots were"machine learning","vitamin C","kinase","hemodynamics","renal microcirculation"and"mitochondria".Literature coupling analysis indicated that exploring the management and treatment of SA-AKI was the research frontier.Conclusions:Over the past decade,SA-AKI research has shown a upward trend in terms of the number of publication.Research primarily focuses on exploring mechanisms and improving early warning systems.Mechanisms involve microcirculatory dysfunction,inflammation,and other pathophysiological factors.Future recommendations include continuing basic research,achieving clinical application of novel biomarkers,and prioritizing renal recovery mechanisms in treatment strategies.展开更多
Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured thro...Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured through big data.To address this gap,this study collects and analyzes public attention toward GC using web crawler technology.Based on the data from Sina Weibo,we applied RoBERTa,an advanced NLP model based on transformer architecture,to conduct fine-grained sentiment analysis of the public’s attention,attitudes and hot topics on GC,demonstrating the potential of deep learning methods in capturing dynamic and contextual emotional shifts across time and regions.Among the sample(N=188,509),53.91% expressed a positive attitude,with variation across different times and regions.Temporally,public interest in GC has shown an annual growth rate of 30.23%,gradually shifting fromfulfilling basic needs to prioritizing entertainment consumption.Spatially,GC is most prevalent in the southeast coastal regions of China,with Beijing ranking first across five evaluated domains.Individuals and government-affiliated accounts play a key role in public discussions on social networks,accounting for 45.89% and 30.01% of user reviews,respectively.A significant positive correlation exists between economic development and public attention to GC,as indicated by a Pearson correlation coefficient of 0.55.Companies,in particular,exhibit cautious behavior in the early stages of green product adoption,prioritizing profitability before making substantial investments.These findings provide valuable insights into the evolving public perception of GC,contributing to the development of more effective environmental policies in China.展开更多
Given the importance of sentiment analysis in diverse environments,various methods are used for image sentiment analysis,including contextual sentiment analysis that utilizes character and scene relationships.However,...Given the importance of sentiment analysis in diverse environments,various methods are used for image sentiment analysis,including contextual sentiment analysis that utilizes character and scene relationships.However,most existing works employ character faces in conjunction with context,yet lack the capacity to analyze the emotions of characters in unconstrained environments,such as when their faces are obscured or blurred.Accordingly,this article presents the Adaptive Multi-Channel Sentiment Analysis Network(AMSA),a contextual image sentiment analysis framework,which consists of three channels:body,face,and context.AMSA employs Multi-task Cascaded Convolutional Networks(MTCNN)to detect faces within body frames;if detected,facial features are extracted and fused with body and context information for emotion recognition.If not,the model leverages body and context features alone.Meanwhile,to address class imbalance in the EMOTIC dataset,Focal Loss is introduced to improve classification performance,especially for minority emotion categories.Experimental results have shown that certain sentiment categories with lower representation in the dataset demonstrate leading classification accuracy,the AMSA yields a 2.53%increase compared with state-of-the-art methods.展开更多
Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to p...Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to participate in this online questionnaire survey.The survey tools included a general information questionnaire and a self-made NMOSD symptoms scale,which included the prevalence,severity,and distress of 29 symptoms.Cluster analysis was used to identify symptom clusters,and network analysis was used to analyze the symptom network and node characteristics and central indicators including strength centrality(r_(s)),closeness centrality(r_(c))and betweeness centrality(r_(b))were used to identify core symptoms and symptom clusters.Results The most common symptom was pain(65.7%),followed by paraesthesia(65.0%),fatigue(65.0%),easy awakening(63.6%).Regarding the burden level of symptoms,pain was the most burdensome symptom,followed by paraesthesia,easy awakening,fatigue,and difficulty falling asleep.Six clusters were identified:somatosensory,motor,visual,and memory symptom clusters,bladder and rectum symptom clusters,sleep symptoms clusters,and neuropsychological symptom clusters.Fatigue(r_(s)=12.39,r_(b)=68.00,r_(c)=0.02)was the most central and prominent bridge symptom,and motor symptom cluster(r_(s)=2.68,r_(c)=0.10)was the most central symptom cluster among the six clusters.Conclusions Our study demonstrated the necessity of symptom management targeting fatigue,pain,and motor symptom cluster in patients with NMOSD.展开更多
BACKGROUND Tumor-associated macrophages(TAMs)have demonstrated significant potential as a research and treatment approach for hepatocellular carcinoma(HCC).Nevertheless,a comprehensive quantitative analysis of TAMs in...BACKGROUND Tumor-associated macrophages(TAMs)have demonstrated significant potential as a research and treatment approach for hepatocellular carcinoma(HCC).Nevertheless,a comprehensive quantitative analysis of TAMs in HCC remained insufficient.Therefore,the objective of this study was to employ bibliometric methods to investigate the development trends and research frontiers pertaining to this field.AIM To determine the knowledge structure and current research hotspots by bibliometric analysis of scholarly papers pertaining to TAMs in HCC.METHODS The present study employed the Web of Science Core Collection to identify all papers related to TAMs in HCC research.Utilizing the Analysis Platform of Bibliometrics,CiteSpace 6.2.R4,and Vosviewer 1.6.19,the study conducted a comprehensive analysis encompassing multiple dimensions such as publication quantity,countries of origin,affiliated institutions,publishing journals,contributing authors,co-references,author keywords,and emerging frontiers within this research domain.RESULTS A thorough examination was undertaken on 818 papers within this particular field,published between January 1,1985 to September 1,2023,which has witnessed a substantial surge in scholarly contributions since 2012,with a notable outbreak in 2019.China was serving as the central hub in this field,with Fudan University leading in terms of publications and citations.Chinese scholars have taken the forefront in driving the research expansion within this field.Hepatology emerged as the most influential journal in this field.The study by Qian and Pollard in 2010 received the highest number of co-citations.It was observed that the citation bursts of references coincided with the outbreak of publications.Notably,“tumor microenvironment”,“immunotherapy”,“prognostic”,“inflammation”,and“polarization”,etc.emerged as frequently occurring keywords in this field.Of particular interest,“immune evasion”,“immune infiltration”,and“cancer genome atlas”were identified as emerging frontiers in recent research.CONCLUSION The field of TAMs in HCC exhibited considerable potential,as evidenced by the promising prospects of immunotherapeutic interventions targeting TAMs for the amelioration of HCC.The emerging frontiers in this field primarily revolved around modulating the immunosuppressive characteristics of TAMs within a liver-specific immune environment,with a focus on how to counter immune evasion and reduce immune infiltration.展开更多
GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieve...GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieved by several parameters,such as polynomial terms,periodic terms,offsets,and post-seismic models.The latter contains some stochastic noises,which can be affected by detecting the former parameters.If there are not enough parameters assumed,modeling errors will occur and adversely affect the analysis results.In this study,we propose a processing strategy in which the commonly-used 1-order of the polynomial term can be replaced with different orders for better fitting GNSS time series of the Crustal Movement Network of China(CMONOC)stations.Initially,we use the Bayesian Information Criterion(BIC)to identify the best order within the range of 1-4 during the fitting process using the white noise plus power-law noise(WN+PL)model.Then,we compare the 1-order and the optimal order on the effect of deterministic models in GNSS time series,including the velocity and its uncertainty,amplitudes,and initial phases of the annual signals.The results indicate that the first-order polynomial in the GNSS time series is not the primary factor.The root mean square(RMS)reduction rates of almost all station components are positive,which means the new fitting of optimal-order polynomial helps to reduce the RMS of residual series.Most stations maintain the velocity difference(VD)within ±1 mm/yr,with percentages of 85.6%,81.9%and 63.4%in the North,East,and Up components,respectively.As for annual signals,the numbers of amplitude difference(AD)remained at ±0.2 mm are 242,239,and 200 in three components,accounting for 99.6%,98.4%,and 82.3%,respectively.This finding reminds us that the detection of the optimal-order polynomial is necessary when we aim to acquire an accurate understanding of the crustal movement features.展开更多
The construction of“park cities”requires a systematic thinking to coordinate the networked development of park system and break through the limitation of emphasizing scale and grade and neglecting dynamic correlatio...The construction of“park cities”requires a systematic thinking to coordinate the networked development of park system and break through the limitation of emphasizing scale and grade and neglecting dynamic correlation in traditional planning.Taking Haidian District of Beijing as an example,the social network analysis method is introduced to construct the network model of park green spaces.Through indicators such as clustering coefficient,network density and node centrality,the characteristics of its spatial structure and hierarchical relationship are analyzed.It is found that the network integrity presents the characteristics of“highly local concentration and global fragmentation”,fragmented park green space network and missing spatial connection,isolated clusters and collaborative failure,as well as the spatial mismatch between population and resource supply and demand.Hierarchical issues include“structural imbalance and functional disorder”,disorder between network hierarchy and park level,misalignment of functional hierarchy leading to weakened network risk resistance capacity,and a relatively dense distribution of core nodes,etc.In response to the above problems,a multi-level spatial intervention strategy should be adopted to solve the overall problem of the network.Meanwhile,it is needed to clarify the positioning of a park itself and improve the hierarchical system,so as to construct a multi-level and multi-scale park green space network,contribute to the construction of a park city,and provide residents with more diverse activity venues.展开更多
Cutting off or controlling the enemy’s power supply at critical moments or strategic locations may result in a cascade failure,thus gaining an advantage in a war.However,the exist-ing cascading failure modeling analy...Cutting off or controlling the enemy’s power supply at critical moments or strategic locations may result in a cascade failure,thus gaining an advantage in a war.However,the exist-ing cascading failure modeling analysis of interdependent net-works is insufficient for describing the load characteristics and dependencies of subnetworks,and it is difficult to use for model-ing and failure analysis of power-combat(P-C)coupling net-works.This paper considers the physical characteristics of the two subnetworks and studies the mechanism of fault propaga-tion between subnetworks and across systems.Then the surviv-ability of the coupled network is evaluated.Firstly,an integrated modeling approach for the combat system and power system is predicted based on interdependent network theory.A heteroge-neous one-way interdependent network model based on proba-bility dependence is constructed.Secondly,using the operation loop theory,a load-capacity model based on combat-loop betweenness is proposed,and the cascade failure model of the P-C coupling system is investigated from three perspectives:ini-tial capacity,allocation strategy,and failure mechanism.Thirdly,survivability indexes based on load loss rate and network sur-vival rate are proposed.Finally,the P-C coupling system is con-structed based on the IEEE 118-bus system to demonstrate the proposed method.展开更多
基金supported by the 2023 Youth Fund for Humanities and Social Sciences Research by the Ministry of Education of the People’s Republic of China(Grant No.23YJC740004).
文摘Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first collected book reviews from these two well-known websites by Python.Through topic analysis of these reviews,we identified recurring topics,including details of her translated works and appreciation of their translation quality.Then,employing sentiment and content analysis methods,the paper explored the emotional attitudes and the specific thoughts of readers toward Can Xue and her translated works.The fingdings revealed that,among the 408 reviews,though the reception of Can Xue’s translated works was relatively positive,the current level of attention and recognition remains insufficient.However,based on the research results,the paper can derive valuable insights into the translation and dissemination processes such as adjusting translation and dissemination strategies,so that the global reach of Chinese literature and culture can be better facilitated.
基金supported by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant no.(GPIP:13-612-2024).
文摘App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products.Automating the analysis of these reviews is vital for efficient review management.While traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior performance.This research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and satisfaction.We propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification accuracy.Comparative analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,respectively.Thesignificant contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews dataset.These advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.
文摘This study employs the Harvard analytical framework to evaluate Gree Electric's financial performance across strategy,accounting,finance,and prospects.Results show that Gree leads the industry in profitability and long-term debt repayment capacity,supported by strong R&D and cost control from its vertically integrated model.Sustained innovation has generated extensive patents,enabling high-margin premium products.However,Gree faces constraints due to over-reliance on its core air-conditioning business,declining inventory turnover,and slow overseas expansion.Its diversification into new sectors like new energy and smart equipment remains limited,accounting for under 5%of revenue and failing to mitigate core market risks.Operating cash flow volatility further reflects instability in these new ventures.Strategic recommendations include scaling the new energy segment,enhancing overseas localization,and optimizing supply chain finance.The study also suggests that Gree should focus on green,smart technologies and value-added services to strengthen competitive barriers.Future research could incorporate ESG metrics to improve analytical comprehensiveness.
基金Department of Water Resources of Shaanxi Province of China,No.2023slkj-8National Natural Science Foundation of China,No.51779209。
文摘Studying runoff characteristics and quantifying human activities’impact on northern Shaanxi,a crucial mineral resource area in China,is crucial to alleviate water resource contradictions.In this study,hydrological element trends were analyzed using theβ-z-h three-parameter indication method.The Mann-Kendall,Pettitt,moving T,and Yamamoto methods were used to test the mutation point of hydrological elements.The Budyko framework was used to quantitatively assess the impacts of climate change and multiple human activities on runoff reduction.The results showed that(1):Precipitation(PRE),potential evapotranspiration(E0),and temperature(TEM)showed increasing trends;runoff in the Huangfuchuan,Gushanchuan,Kuye River,Tuwei River,Wuding River,Qingjian River,and Yanhe River catchments showed decreasing trends(HFC,GSC,KYR,TWR,WDR,QJR,YR);whereas runoff in the Jialu River(JLR)catchment showed a“V-shaped”trend from 1980 to2020.(2)Runoff was positively correlated with PRE and negatively correlated with E0and the subsurface index(n),with the elasticity coefficients of PRE,E0,and n showing an increasing trend in the change period.(3)Human activities were a key factor in runoff reduction,although the impact of different human activities showed spatial variations.This study provides a scientific foundation for achieving the sustainable development of water resources in mining areas.
基金supported by the National Natural Science Foundation of China under Grant 52277072.
文摘Wide-band oscillations have become a significant issue limiting the development of wind power.Both large-signal and small-signal analyses require extensive model derivation.Moreover,the large number and high order of wind turbines have driven the development of simplified models,whose applicability remains controversial.In this paper,a wide-band oscillation analysis method based on the average-value model(AVM)is proposed for wind farms(WFs).A novel linearization analysis framework is developed,leveraging the continuous-time characteristics of the AVM and MATLAB/Simulink’s built-in linearization tools.This significantly reduces modeling complexity and computational costs while maintaining model fidelity.Additionally,an object-based initial value estimation method of state variables is introduced,which,when combined with steady-state point-solving tools,greatly reduces the computational effort required for equilibrium point solving in batch linearization analysis.The proposed method is validated in both doubly fed induction generator(DFIG)-based and permanent magnet synchronous generator(PMSG)-based WFs.Furthermore,a comprehensive analysis is conducted for the first time to examine the impact of the machine-side system on the system stability of the nonfully controlled PMSG-based WF.
文摘Background:Three-dimensional(3D)printing has revolutionized craniofacial and craniomaxillofacial applications,leading to substantial advancements in patient-specific treatments.In this study,a bibliometric analysis was performed to identify the key contributors,research trends,thematic developments,and collaboration networks in this evolving field.Methods:Two search strategies were employed to ensure a comprehensive analysis:(1)a broad search,in which selected keywords were searched in the title,abstract,and keyword fields to capture all relevant publications,and(2)a title-specific search,in which keywords were restricted to the title field to identify publications with a strong focus on 3D printing in craniofacial and craniomaxillofacial applications.The retrieved dataset was analyzed using VOSviewer and RStudio(bibliometrix package).Results:The broad search retrieved 3534 publications,whereas the title-specific search yielded 280 publications.The analysis of these 280 papers focused on identifying the top authors,universities,and countries,as well as their research dynamics and collaboration networks.A more detailed approach was adopted by examining the titles of these 280 papers.VOSviewer segmented the titles into approximately 800 words,which were then categorized into 18 distinct thematic groups to represent research trends.The focus areas of the ten most cited papers were also analyzed.Conclusion:This bibliometric study provides valuable insights into the progress in 3D printing for craniofacial and craniomaxillofacial applications.By highlighting the key contributors,thematic developments,and collaborative networks,this study offers a foundation for future research in this rapidly advancing field.
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
基金financed by the grants from the National Natural Science Foundation of China(No.81803996)Shanghai Key Laboratory of Health Identification and Assessment(No.21DZ2271000)。
文摘Traditional Chinese medicine(TCM)has played a significant role in the prevention and treatment of chronic heart failure(CHF).To study TCM diagnosis of CHF,a total of 278 Chinese clinical research articles on the study of CHF syndromes in recent 40 years retrieved from Web of Science,Scopus,Pub Med,Embase,CNKI,Wanfang Data,Cq VIP,and Sino Med.According to cumulative frequency analysis,network analysis,and hierarchical cluster analysis,the study found the distribution of CHF syndromes was syndrome of qi deficiency with blood stasis,syndrome of qi and yin deficiency,syndrome of yang deficiency with water flooding,syndrome of heart blood stasis obstruction,syndrome of turbid phlegm,and syndrome of collapse due to primordial yang deficiency.The syndrome elements on location of illness were heart,kidney,lung,and spleen.The syndrome elements on nature of illness were qi deficiency,blood stasis,yang deficiency,yin deficiency,water retention,and turbid phlegm.These findings can provide reference to the research on diagnosis and treatment of CHF,and contribute to the study on syndrome standardization and objective research of TCM diagnosis.
基金supported by the National Key R&D Program of China(2022YFF0711500)National Natural Science Foundation of China(NSFC,Grant Nos.12273077,12403102,12373110,and 12103070)+4 种基金Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550101)supported by China National Astronomical Data Center(NADC)CAS Astronomical Data CenterChinese Virtual Observatory(China-VO)supported by Astronomical Big Data Joint Research Center,co-founded by National Astronomical Observatories,Chinese Academy of Sciences and Alibaba Cloud.
文摘The identification of specific galaxy populations in large-scale spectroscopic surveys represents an essential yet challenging task,particularly for rare or anomalous galaxies that deviate from the typical galaxy distributions.Traditional methods based on template-fitting or predefining spectral features face challenges in addressing the complexity and scale of modern astronomical data sets.To overcome these limitations,we propose GalSpecEncoder-KB,a modular and flexible framework that combines deep learning with knowledge base retrieval to enable efficient and interpretable analysis of galaxy spectra.The framework integrates a Transformerbased feature encoder,GalSpecEncoder,pre-trained with masked-modeling strategy to capture semantically rich and context-aware spectral representations.By leveraging a Retrieval-Augmented Analysis approach,the knowledge base constructed from catalogs enables metadata retrieval and weighted voting for target galaxy identification.Using the Sloan Digital Sky Survey as a comprehensive case study,we demonstrate the capabilities of the framework for target galaxy search.Experimental results demonstrate the exceptional generalizability and adaptability across diverse galaxy search tasks,including identification of LINERs,Strong Gravitational Lenses,and detection of Outliers,while maintaining robust performance and interpretable spectral analysis capabilities.
基金Supported by Shanghai Municipal Health Commission’s Special Clinical Research Project for the Hygiene Industry,No.20244Y0041Youth Initiation Fund of Naval Medical University,No.2023QN028 and No.2023QN030。
文摘BACKGROUND Working memory serves as a fundamental cognitive function that substantially impacts performance in various cognitive tasks.Extensive neurophysiological research has established that theta oscillations(4-8 Hz)play an essential role in supporting working memory operations.Theta-band transcranial alternating current stimulation(tACS)offers a potential mechanism for working memory enhancement through direct modulation of these fundamental neural oscillations.Nevertheless,current empirical evidence shows substantial variability in the observed effects of theta-tACS across studies.AIM To conduct a systematic review and meta-analysis evaluating the effects of thetatACS on working memory performance in healthy adults.METHODS A systematic literature search was performed on PubMed,EMBASE,and Web of Science up to March 10,2025.Effect sizes were computed using Hedges’g with 95%confidence intervals(CIs),with separate meta-analyses for all included studies and for distinct working memory paradigms[n-back and delayed matchto-sample(DMTS)tasks]to examine potential task-specific effects.Subgroup analyses and meta-regression were performed to evaluate the influence of key moderating variables.RESULTS The systematic review included 21 studies(67 effect sizes).Initial meta-analysis showed theta-tACS moderately improved working memory(Hedges’g=0.405,95%CI:0.212-0.598).However,this effect became nonsignificant after correcting for publication bias(trim-and-fill adjusted Hedges’g=0.082,95%CI:-0.052 to 0.217).Task-specific analyses revealed significant benefits in n-back tasks(Hedges’g=0.463,95%CI:0.193-0.733)but not in DMTS tasks(Hedges’g=0.257,95%CI:-0.186 to 0.553).Moderator analyses showed that performance in n-back tasks was influenced by stimulation frequency(P=0.001),concurrent status(P=0.014),task modality(P=0.005),and duration(P=0.013),whereas only the region of targeted stimulation(P=0.012)moderated DMTS tasks.CONCLUSION Theta-tACS enhances working memory in healthy adults,with effects modulated by the task type and protocol parameters,offering dual implications for cognitive enhancement and clinical interventions.
文摘This paper examines the Chinese terminology in 2024 Government Work Report and conducts a comparative analysis of its translation into English and Japanese.The study explores the challenges and strategies involved in translating these terms,taking into account differences in vocabulary,grammatical structure,and cultural context between the two languages.The findings reveal that the same term may be translated differently into English and Japanese due to the distinct linguistic and cultural characteristics of each language.The research emphasizes that understanding and respecting the unique features of each language and culture are essential for accurately conveying the meaning of Chinese terms.This is crucial for enhancing the international influence of Chinese discourse and promoting cross-cultural communication.
文摘Objective:To conduct a comprehensive bibliometric and knowledge network analysis of musculoskeletal ultrasound(MSK US)research from 2005 to 2025,with a focus on publication trends,influential authors,institutions,and thematic hotspots.Methods:Articles related to MSK US were retrieved from the Web of Science Core Collection using the search strategy TS=(“musculoskeletal ultrasound”OR“MSK ultrasound”OR“musculoskeletal ultrasonography”)AND(tendon OR ligament).Eligible studies included English-language original research and review articles published between 2005 and 2025.Bibliometric analyses were performed using VOSviewer to evaluate publication trends,co-citation networks,author collaborations,institutional and country partnerships,and keyword co-occurrence.Results:A total of 570 articles(476 original research papers and 94 reviews)were included.The annual number of publications increased exponentially,peaking in 2020.Co-citation analysis identified Wakefield(2005),Naredo,and Filippucci as foundational contributors.Author collaboration networks highlighted a strong European core centered on Italy and Spain,while institutional and country analyses revealed a“Europe–North America”dual-center pattern with growing contributions from China.Keyword co-occurrence analysis revealed three main research pillars:inflammatory arthropathies,sports-related injuries,and degenerative conditions,with emerging interest in advanced imaging techniques and artificial intelligence.Conclusion:T MSK US research has demonstrated sustained growth and diversification over the past two decades.Europe remains a traditional leader,but increasing output from North America and Asia reflects a shift toward global,multicenter collaboration.Future research should prioritize methodological standardization,integration of novel imaging technologies,and alignment with clinically meaningful outcomes to optimize diagnostic accuracy and clinical utility.
文摘Objective:To explore the current status of sepsis-associated acute kidney injury(SA-AKI)research and predict its future research directions.Methods:The bibliometric overview of publications was conducted in the field of SA-AKI based on Web of Science Core Collection database from January 2013 to August 2023.This study employed software such as CiteSpace and VOSviewer to conduct bibliometric and visualization analysis of the included literature,including publication trends,geographic distribution characteristics,author contributions,citations,funding sources characteristics,and keyword clustering.Results:A total of 6509 articles were analyzed,and the number of publications and citations increased from 2013 to 2022.The United States had the highest number of publications in SA-AKI,while France was the country with the highest number of citations per publication.Keyword clustering analysis showed that the pathophysiology and definition of SA-AKI were the research focus,and the research hotspots were"machine learning","vitamin C","kinase","hemodynamics","renal microcirculation"and"mitochondria".Literature coupling analysis indicated that exploring the management and treatment of SA-AKI was the research frontier.Conclusions:Over the past decade,SA-AKI research has shown a upward trend in terms of the number of publication.Research primarily focuses on exploring mechanisms and improving early warning systems.Mechanisms involve microcirculatory dysfunction,inflammation,and other pathophysiological factors.Future recommendations include continuing basic research,achieving clinical application of novel biomarkers,and prioritizing renal recovery mechanisms in treatment strategies.
基金supported by the National Nature Foundation of China under Grants(No.72104108)the College Students’Innovation and Entrepreneurship Training Program(No.202410298155Y).
文摘Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured through big data.To address this gap,this study collects and analyzes public attention toward GC using web crawler technology.Based on the data from Sina Weibo,we applied RoBERTa,an advanced NLP model based on transformer architecture,to conduct fine-grained sentiment analysis of the public’s attention,attitudes and hot topics on GC,demonstrating the potential of deep learning methods in capturing dynamic and contextual emotional shifts across time and regions.Among the sample(N=188,509),53.91% expressed a positive attitude,with variation across different times and regions.Temporally,public interest in GC has shown an annual growth rate of 30.23%,gradually shifting fromfulfilling basic needs to prioritizing entertainment consumption.Spatially,GC is most prevalent in the southeast coastal regions of China,with Beijing ranking first across five evaluated domains.Individuals and government-affiliated accounts play a key role in public discussions on social networks,accounting for 45.89% and 30.01% of user reviews,respectively.A significant positive correlation exists between economic development and public attention to GC,as indicated by a Pearson correlation coefficient of 0.55.Companies,in particular,exhibit cautious behavior in the early stages of green product adoption,prioritizing profitability before making substantial investments.These findings provide valuable insights into the evolving public perception of GC,contributing to the development of more effective environmental policies in China.
文摘Given the importance of sentiment analysis in diverse environments,various methods are used for image sentiment analysis,including contextual sentiment analysis that utilizes character and scene relationships.However,most existing works employ character faces in conjunction with context,yet lack the capacity to analyze the emotions of characters in unconstrained environments,such as when their faces are obscured or blurred.Accordingly,this article presents the Adaptive Multi-Channel Sentiment Analysis Network(AMSA),a contextual image sentiment analysis framework,which consists of three channels:body,face,and context.AMSA employs Multi-task Cascaded Convolutional Networks(MTCNN)to detect faces within body frames;if detected,facial features are extracted and fused with body and context information for emotion recognition.If not,the model leverages body and context features alone.Meanwhile,to address class imbalance in the EMOTIC dataset,Focal Loss is introduced to improve classification performance,especially for minority emotion categories.Experimental results have shown that certain sentiment categories with lower representation in the dataset demonstrate leading classification accuracy,the AMSA yields a 2.53%increase compared with state-of-the-art methods.
基金supported by the Specific Research Fund for Top-notch Talents of Guangdong Provincial Hospital of Chinese Medicine(No.2022KT1188).
文摘Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to participate in this online questionnaire survey.The survey tools included a general information questionnaire and a self-made NMOSD symptoms scale,which included the prevalence,severity,and distress of 29 symptoms.Cluster analysis was used to identify symptom clusters,and network analysis was used to analyze the symptom network and node characteristics and central indicators including strength centrality(r_(s)),closeness centrality(r_(c))and betweeness centrality(r_(b))were used to identify core symptoms and symptom clusters.Results The most common symptom was pain(65.7%),followed by paraesthesia(65.0%),fatigue(65.0%),easy awakening(63.6%).Regarding the burden level of symptoms,pain was the most burdensome symptom,followed by paraesthesia,easy awakening,fatigue,and difficulty falling asleep.Six clusters were identified:somatosensory,motor,visual,and memory symptom clusters,bladder and rectum symptom clusters,sleep symptoms clusters,and neuropsychological symptom clusters.Fatigue(r_(s)=12.39,r_(b)=68.00,r_(c)=0.02)was the most central and prominent bridge symptom,and motor symptom cluster(r_(s)=2.68,r_(c)=0.10)was the most central symptom cluster among the six clusters.Conclusions Our study demonstrated the necessity of symptom management targeting fatigue,pain,and motor symptom cluster in patients with NMOSD.
基金Supported by the Sanming Project of Medicine in Shenzhen,No.SZZYSM202111002Shenzhen Medical Research Fund,No.B2302008+1 种基金Shenzhen Science and Technology Program,No.JCYJ20220531091809022,No.JSGG20210802093208023,No.JCYJ20220818103402006,and No.ZDSYS201606081515458Traditional Chinese Medicine Bureau of Guangdong Province,No.20231286.
文摘BACKGROUND Tumor-associated macrophages(TAMs)have demonstrated significant potential as a research and treatment approach for hepatocellular carcinoma(HCC).Nevertheless,a comprehensive quantitative analysis of TAMs in HCC remained insufficient.Therefore,the objective of this study was to employ bibliometric methods to investigate the development trends and research frontiers pertaining to this field.AIM To determine the knowledge structure and current research hotspots by bibliometric analysis of scholarly papers pertaining to TAMs in HCC.METHODS The present study employed the Web of Science Core Collection to identify all papers related to TAMs in HCC research.Utilizing the Analysis Platform of Bibliometrics,CiteSpace 6.2.R4,and Vosviewer 1.6.19,the study conducted a comprehensive analysis encompassing multiple dimensions such as publication quantity,countries of origin,affiliated institutions,publishing journals,contributing authors,co-references,author keywords,and emerging frontiers within this research domain.RESULTS A thorough examination was undertaken on 818 papers within this particular field,published between January 1,1985 to September 1,2023,which has witnessed a substantial surge in scholarly contributions since 2012,with a notable outbreak in 2019.China was serving as the central hub in this field,with Fudan University leading in terms of publications and citations.Chinese scholars have taken the forefront in driving the research expansion within this field.Hepatology emerged as the most influential journal in this field.The study by Qian and Pollard in 2010 received the highest number of co-citations.It was observed that the citation bursts of references coincided with the outbreak of publications.Notably,“tumor microenvironment”,“immunotherapy”,“prognostic”,“inflammation”,and“polarization”,etc.emerged as frequently occurring keywords in this field.Of particular interest,“immune evasion”,“immune infiltration”,and“cancer genome atlas”were identified as emerging frontiers in recent research.CONCLUSION The field of TAMs in HCC exhibited considerable potential,as evidenced by the promising prospects of immunotherapeutic interventions targeting TAMs for the amelioration of HCC.The emerging frontiers in this field primarily revolved around modulating the immunosuppressive characteristics of TAMs within a liver-specific immune environment,with a focus on how to counter immune evasion and reduce immune infiltration.
基金supported by the National Natural Science Foundation of China(Grant Nos.42404017,42122025 and 42174030).
文摘GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieved by several parameters,such as polynomial terms,periodic terms,offsets,and post-seismic models.The latter contains some stochastic noises,which can be affected by detecting the former parameters.If there are not enough parameters assumed,modeling errors will occur and adversely affect the analysis results.In this study,we propose a processing strategy in which the commonly-used 1-order of the polynomial term can be replaced with different orders for better fitting GNSS time series of the Crustal Movement Network of China(CMONOC)stations.Initially,we use the Bayesian Information Criterion(BIC)to identify the best order within the range of 1-4 during the fitting process using the white noise plus power-law noise(WN+PL)model.Then,we compare the 1-order and the optimal order on the effect of deterministic models in GNSS time series,including the velocity and its uncertainty,amplitudes,and initial phases of the annual signals.The results indicate that the first-order polynomial in the GNSS time series is not the primary factor.The root mean square(RMS)reduction rates of almost all station components are positive,which means the new fitting of optimal-order polynomial helps to reduce the RMS of residual series.Most stations maintain the velocity difference(VD)within ±1 mm/yr,with percentages of 85.6%,81.9%and 63.4%in the North,East,and Up components,respectively.As for annual signals,the numbers of amplitude difference(AD)remained at ±0.2 mm are 242,239,and 200 in three components,accounting for 99.6%,98.4%,and 82.3%,respectively.This finding reminds us that the detection of the optimal-order polynomial is necessary when we aim to acquire an accurate understanding of the crustal movement features.
文摘The construction of“park cities”requires a systematic thinking to coordinate the networked development of park system and break through the limitation of emphasizing scale and grade and neglecting dynamic correlation in traditional planning.Taking Haidian District of Beijing as an example,the social network analysis method is introduced to construct the network model of park green spaces.Through indicators such as clustering coefficient,network density and node centrality,the characteristics of its spatial structure and hierarchical relationship are analyzed.It is found that the network integrity presents the characteristics of“highly local concentration and global fragmentation”,fragmented park green space network and missing spatial connection,isolated clusters and collaborative failure,as well as the spatial mismatch between population and resource supply and demand.Hierarchical issues include“structural imbalance and functional disorder”,disorder between network hierarchy and park level,misalignment of functional hierarchy leading to weakened network risk resistance capacity,and a relatively dense distribution of core nodes,etc.In response to the above problems,a multi-level spatial intervention strategy should be adopted to solve the overall problem of the network.Meanwhile,it is needed to clarify the positioning of a park itself and improve the hierarchical system,so as to construct a multi-level and multi-scale park green space network,contribute to the construction of a park city,and provide residents with more diverse activity venues.
基金supported by the National Natural Science Foundation of China(72271242)Hunan Provincial Natural Science Foundation of China for Excellent Young Scholars(2022JJ20046).
文摘Cutting off or controlling the enemy’s power supply at critical moments or strategic locations may result in a cascade failure,thus gaining an advantage in a war.However,the exist-ing cascading failure modeling analysis of interdependent net-works is insufficient for describing the load characteristics and dependencies of subnetworks,and it is difficult to use for model-ing and failure analysis of power-combat(P-C)coupling net-works.This paper considers the physical characteristics of the two subnetworks and studies the mechanism of fault propaga-tion between subnetworks and across systems.Then the surviv-ability of the coupled network is evaluated.Firstly,an integrated modeling approach for the combat system and power system is predicted based on interdependent network theory.A heteroge-neous one-way interdependent network model based on proba-bility dependence is constructed.Secondly,using the operation loop theory,a load-capacity model based on combat-loop betweenness is proposed,and the cascade failure model of the P-C coupling system is investigated from three perspectives:ini-tial capacity,allocation strategy,and failure mechanism.Thirdly,survivability indexes based on load loss rate and network sur-vival rate are proposed.Finally,the P-C coupling system is con-structed based on the IEEE 118-bus system to demonstrate the proposed method.
