This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for ...This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.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
Objectives:SocialNetworkAddiction(SNA)is becoming increasingly prevalent among college students;however,there remains a lack of consensus regarding the measurement tools and their optimal cutoff score.This study aims ...Objectives:SocialNetworkAddiction(SNA)is becoming increasingly prevalent among college students;however,there remains a lack of consensus regarding the measurement tools and their optimal cutoff score.This study aims to validate the 21-item SocialNetwork Addiction Scale-Chinese(SNAS-C)in its Chinese version and to determine its optimal cutoff score for identifying potential SNA cases within the college student population.Methods:A crosssectional survey was conducted,recruiting 3387 college students.Latent profile analysis(LPA)and receiver operating characteristic(ROC)curve analysis were employed to establish the optimal cutoff score for the validated 21-item SNAS-C.Results:Three profile models were selected based on multiple statistical criteria,classifying participants into low-risk,moderate-risk,and high-risk groups.The highest-risk group was defined as“positive”for SNA,while the remaining groups were considered“negative”,serving as the reference standard for ROC analysis.The optimal cutoff score was determined to be 72(sensitivity:98.2%,specificity:96.86%),with an overall classification accuracy of 97.0%.The“positive”group reported significantly higher frequency of social network usage,greater digitalmedia dependence scores,and a higher incidence of network addiction.Conclusion:This study identified the optimal cutoff score for the SNAS-C as≥72,demonstrating high sensitivity,specificity,and diagnostic accuracy.This threshold effectively distinguishes between high-risk and low-risk SNA.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
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
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
Cryptocurrency has gained popularity as a potential new global payment method.It has the potential to be faster,cheaper,and more secure than existing payment networks,making it a game-changer in the global economy.How...Cryptocurrency has gained popularity as a potential new global payment method.It has the potential to be faster,cheaper,and more secure than existing payment networks,making it a game-changer in the global economy.However,more research is needed to identify the factors driving cryptocurrency adoption and understand its impact.We use social network analysis(SNA)to identify the influencing factors and reveal the impact of each on cryptocurrency adoption.Our analysis initially revealed 44 influential factors,which were later reduced to 25 factors,each exerting a different influence.Based on the SNA,we classify these factors into highly,moderately,and least influential categories.Discomfort and optimism are the most influential determinants of adoption.Moderately influential factors include trust,risk,relative advantage,social influence,and perceived behavioral control.Price/value,facilitating conditions,compatibility,and usefulness are the least influential.The factors affecting cryptocurrency adoption are interdependent.Our findings can help policymakers understand the factors influencing cryptocurrency adoption and aid in developing appropriate legal frameworks for cryptocurrency use.展开更多
Objectives:This study aimed to assess the reliability and validity of the abbreviated Committed Action Questionnaire(CAQ-8)in a cohort of 1635 Chinese university students.Methods:Participants completed the Chinese ver...Objectives:This study aimed to assess the reliability and validity of the abbreviated Committed Action Questionnaire(CAQ-8)in a cohort of 1635 Chinese university students.Methods:Participants completed the Chinese version of the CAQ-8 along with other standardized measures,including the Acceptance and Action Questionnaire-II(AAQ-II),the Valuing Questionnaire(VQ),the Satisfaction with Life Scale(SWLS),the Depression Anxiety Stress Scales(DASS-21),and the World Health Organization Fiveitem Well-Being Index(WHO-5).A retest was conducted one month later with 300 valid responses.Results:Exploratory factor analysis(n=818)identified a 2-factor structure,confirmed through validated factor analysis(n=817),showing good fit indices(CFI=0.990,RMSEA=0.040).Measurement equivalence across genders was established.The CAQ-8 showed significant positive correlations with life satisfaction,mental health,and values,and negative correlations with depression,anxiety,stress,and experiential avoidance.The scale demonstrated good internal consistency(Cronbach’sα=0.76)and retest reliability(ICC=0.70).Network analysis confirmed the robustness of the 2-factor model,with item 4 in CAQ-8 identified as a core item.Conclusion:The CAQ-8 is a reliable and valid tool for measuring committed action within the psychological flexibility model in Chinese populations.展开更多
Frequent extreme heat events around the world not only pose a major threat to human health but also cause significant economic losses to the livestock industry.The existing management practices are insufficient to ful...Frequent extreme heat events around the world not only pose a major threat to human health but also cause significant economic losses to the livestock industry.The existing management practices are insufficient to fully prevent heat stress with an urgent need to develop preventive medicines.The aim of this study was to develop an antiheat stress Chinese herbal formula(CHF)via big data analysis techniques and to evaluate its anti-heat stress effect and mechanism of action via pharmacodynamic evaluation and network pharmacology analysis.Many anti-heat stress CHFs were collected from the Chinese National Knowledge Infrastructure(CNKI)database.Three alternative CHFs were obtained via unsupervised entropy hierarchical clustering analysis,and the most effective CHF against heat stress,Shidi Jieshu decoction(SJD),was obtained by screening in a mouse heat stress model.In dry and hot environments,SJD significantly improved the heat tolerance of AA broilers by 4-6℃.In a humid and hot environment,pretreatment with 2%SJD resulted in 100%survival of Wenchang chickens at high temperatures.The main active ingredients of SJD were identified as muntjacoside E,timosaponin C,macrostemonoside H and mangiferin via ultraperformance liquid chromatography/mass spectrometry(UPLC/MS)and database comparison.The active ingredients of SJD were found to target tumor necrosis factor-α(TNF-α),signal transducer activator of transcription 3(STAT3)and epidermal growth factor receptor(EGFR).Finally,the safety of the new formulation was assessed in an acute oral toxicity study in rats.The SJDs developed in this study provide a new option for the prevention of heat stress in animal husbandry and offer new insights for further research on anti-heat stress.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
BACKGROUND Depression and anxiety are prevalent psychological challenges among patients with adolescent idiopathic scoliosis(AIS),affecting individuals across both sex and age groups.AIM To explore the network structu...BACKGROUND Depression and anxiety are prevalent psychological challenges among patients with adolescent idiopathic scoliosis(AIS),affecting individuals across both sex and age groups.AIM To explore the network structure of depression and anxiety symptoms,with a focus on identifying differences at the symptom level between sex and age subgroups.METHODS A total of 1955 participants diagnosed with AIS aged 10-18 years were assessed using the Patient Health Questionnaire Depression Scale(PHO-9)and the Generalized Anxiety Disorder Scale(GAD-7),and 765 patients exhibiting PHQ-9 or GAD-7 scores ≥ 5 were enrolled in our study. Network analysis and network comparison tests were utilized toconstruct and compare the depression-anxiety symptoms networks among sex and age subgroups.RESULTSThe results revealed GAD3 “Excessive worry” and PHQ2 “Sad mood” were the most significant central symptomsin all subgroups, while “Sad mood” had higher strength than “Excessive worry” in the lower age group. In thenetwork comparisons, the female network exhibited tighter connectivity, especially on GAD6 “Irritability” andGAD2 “Uncontrollable worry”, while only PHQ3 “Sleep” and PHQ9 “Suicidal ideation” had differences at thelocal level in the lower age group.CONCLUSIONSeveral interventions targeting excessive worry and sad mood could reduce the risk of depression and anxietysymptoms in the AIS population. Furthermore, specific anxiety symptoms in females, along with sleep disturbancesand suicidal ideation in the lower age group, should be addressed at an early stage to prevent significantdisruptions in mental health trajectories.展开更多
With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extract...With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis.To address these challenges,this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception(TGICP).Specifically,we utilize a Inter-sample Commonality Perception(ICP)module to extract common features from similar samples within the same modality,and use these common features to enhance the original features of each modality,thereby obtaining a richer and more complete multimodal sentiment representation.Subsequently,in the cross-modal interaction stage,we design a Text-Gated Interaction(TGI)module,which is text-driven.By calculating the mutual information difference between the text modality and nonverbal modalities,the TGI module dynamically adjusts the influence of emotional information from the text modality on nonverbal modalities.This helps to reduce modality information asymmetry while enabling full cross-modal interaction.Experimental results show that the proposed model achieves outstanding performance on both the CMU-MOSI and CMU-MOSEI baseline multimodal sentiment analysis datasets,validating its effectiveness in emotion recognition tasks.展开更多
In modern wireless communication and electromagnetic control,automatic modulationclassification(AMC)of orthogonal frequency division multiplexing(OFDM)signals plays animportant role.However,under Doppler frequency shi...In modern wireless communication and electromagnetic control,automatic modulationclassification(AMC)of orthogonal frequency division multiplexing(OFDM)signals plays animportant role.However,under Doppler frequency shift and complex multipath channel conditions,extracting discriminative features from high-order modulation signals and ensuring model inter-pretability remain challenging.To address these issues,this paper proposes a Fourier attention net-work(FAttNet),which combines an attention mechanism with a Fourier analysis network(FAN).Specifically,the method directly converts the input signal to the frequency domain using the FAN,thereby obtaining frequency features that reflect the periodic variations in amplitude and phase.Abuilt-in attention mechanism then automatically calculates the weights for each frequency band,focusing on the most discriminative components.This approach improves both classification accu-racy and model interpretability.Experimental validation was conducted via high-order modulationsimulation using an RF testbed.The results show that under three different Doppler frequencyshifts and complex multipath channel conditions,with a signal-to-noise ratio of 10 dB,the classifi-cation accuracy can reach 89.1%,90.4%and 90%,all of which are superior to the current main-stream methods.The proposed approach offers practical value for dynamic spectrum access and sig-nal security detection,and it makes important theoretical contributions to the application of deeplearning in complex electromagnetic signal recognition.展开更多
To improve design accuracy and reliability of structures,this study solves the uncertain natural frequencies with consideration for geometric nonlinearity and structural uncertainty.Frequencies of the laminated plate ...To improve design accuracy and reliability of structures,this study solves the uncertain natural frequencies with consideration for geometric nonlinearity and structural uncertainty.Frequencies of the laminated plate with all four edges clamped(CCCC)are derived based on Navier's method and Galerkin's method.The novelty of the current work is that the number of unknowns in the displacement field model of a CCCC plate with free midsurface(CCCC-2 plate)is only three compared with four or five in cases of other exposed methods.The present analytical method is proved to be accurate and reliable by comparing linear natural frequencies and nonlinear natural frequencies with other models available in the open literature.Furthermore,a novel method for analyzing effects of mean values and tolerance zones of uncertain structural parameters on random frequencies is proposed based on a self-developed Multiscale Feature Extraction and Fusion Network(MFEFN)system.Compared with a direct Monte Carlo Simulation(MCS),the MFEFNbased procedure significantly reduces the calculation burden with a guarantee of accuracy.Our research provides a method to calculate nonlinear natural frequencies under two boundary conditions and presentes a surrogate model to predict frequencies for accuracy analysis and optimization design.展开更多
β-ray-induced X-ray spectroscopy(BIXS)is a promising technique for tritium analysis that offers several unique advantages,including substantial detection depth,nondestructive testing capabilities,and ease of operatio...β-ray-induced X-ray spectroscopy(BIXS)is a promising technique for tritium analysis that offers several unique advantages,including substantial detection depth,nondestructive testing capabilities,and ease of operation.For thin solid tritium-containing samples with substrates,the currently used BIXS analysis method can measure the tritium depth profile and content when the sample thickness is known.In this study,a backpropagation(BP)neural network algorithm was used to predict the tritium content and thickness of a thin solid tritium-containing sample with substrates and a uniformly distributed tritium profile.A semi-analytical method was used to generate datasets for training and testing the BP neural network.A dataset ofβ-decay X-ray spectra from 420 tritium-containing zirconium models with different known thicknesses and tritium-tozirconium ratios was used as the input data.The corresponding zirconium thicknesses and tritium-to-zirconium ratios served as the output for training and testing the BP neural network.The mean relative errors(MREs)of the zirconium thickness in the training and test datasets were 0.56%and 0.42%,respectively,whereas the MREs of the tritium-to-zirconium ratio were 0.59%and 0.38%,respectively.Furthermore,the trained BP neural network demonstrates excellent predictive capability across various levels of statistical uncertainty.For the experimentalβ-decay X-ray spectra of two tritium-containing samples,the predicted zirconium thicknesses and tritium-to-zirconium ratios showed good agreement with the results obtained through the elastic backscattering spectrometry(EBS).展开更多
Co-word networks are constructed with author-provided keywords in academic publications and their relations of co-occurrence.As special form of scientific knowledge networks,they represent the cognitive structure of s...Co-word networks are constructed with author-provided keywords in academic publications and their relations of co-occurrence.As special form of scientific knowledge networks,they represent the cognitive structure of scientific literature.This paper analyzes the complex structure of a co-word network based on 8,190 author-provided keywords extracted from 3,651 papers in five Chinese core journals in the field of management science.Small-world and scale-free phenomena are found in this network.A large-scale co-word network graph,which consists of one major giant component and many small isolated components,has been generated with the GUESS software.The dynamic growth of keywords and keyword co-occurrence relationships are described with four new informetrics measures.The results indicate that existing concepts always serve as the intellectual base of new ideas as represented by keywords.展开更多
To resolve the ontology understanding problem, the structural features and the potential important terms of a large-scale ontology are investigated from the perspective of complex networks analysis. Through the empiri...To resolve the ontology understanding problem, the structural features and the potential important terms of a large-scale ontology are investigated from the perspective of complex networks analysis. Through the empirical studies of the gene ontology with various perspectives, this paper shows that the whole gene ontology displays the same topological features as complex networks including ＂small world＂ and ＂scale-free＂,while some sub-ontologies have the ＂scale-free＂ property but no ＂small world＂ effect.The potential important terms in an ontology are discovered by some famous complex network centralization methods.An evaluation method based on information retrieval in MEDLINE is designed to measure the effectiveness of the discovered important terms.According to the relevant literature of the gene ontology terms,the suitability of these centralization methods for ontology important concepts discovering is quantitatively evaluated.The experimental results indicate that the betweenness centrality is the most appropriate method among all the evaluated centralization measures.展开更多
In order to improve the prediction accuracy and test the generalization ability of the dam deformation analysis model, the back-propagation（BP） neural network model for dam deformation analysis is studied, and the m...In order to improve the prediction accuracy and test the generalization ability of the dam deformation analysis model, the back-propagation（BP） neural network model for dam deformation analysis is studied, and the merging model is built based on the neural network BP algorithm and the traditional statistical model. The three models mentioned above are calculated and analyzed according to the long-term deformation observation data in Chencun Dam. The analytical results show that the average prediction accuracies of the statistical model and the BP neural network model are ~ 0.477 and ＋- 0.390 mm, respectively, while the prediction accuracy of the merging model is ~0. 318 mm, which is improved by 33% and 18% compared to the other two models, respectively. And the merging model has a better generalization ability and broad applicability.展开更多
The network on chip（NoC）is used as a solution for the communication problems in a complex system on chip（SoC）design.To further enhance performances,the NoC architectures,a high level modeling and an evaluation met...The network on chip（NoC）is used as a solution for the communication problems in a complex system on chip（SoC）design.To further enhance performances,the NoC architectures,a high level modeling and an evaluation method based on OPNET are proposed to analyze their performances on different injection rates and traffic patterns.Simulation results for general NoC in terms of the average latency and the throughput are analyzed and used as a guideline to make appropriate choices for a given application.Finally,a MPEG4 decoder is mapped on different NoC architectures.Results prove the effectiveness of the evaluation method.展开更多
An analytical approach to evaluate the performance of the 3G/ad hoc integrated network is presented. A channel model capturing both path loss and shadowing is applied to the analysis so as to characterize power fallof...An analytical approach to evaluate the performance of the 3G/ad hoc integrated network is presented. A channel model capturing both path loss and shadowing is applied to the analysis so as to characterize power falloff vs. distance. The 3G/ad hoc integrated network scenario model is introduced briefly. Based on this model, several performances of the 3G/ ad hoc integrated network in terms of outage probability, call dropping probability and new call blocking probability are evaluated. The corresponding performance formulae are deduced in accordance with the analytical models. Meanwhile, the formula of the 3G/ad hoc integrated network capacity is deduced on the basis of the formula of the outage probability. It is observed from extensive simulation and numerical analysis that the 3G/ad hoc integrated network remarkably outperforms the 3G network with regards to the network performance. This derived evaluation approach can be applied into planning and optimization of the 3G/ad hoc network.展开更多
基金supported by Istanbul Technical University(Project No.45698)supported through the“Young Researchers’Career Development Project-training of doctoral students”of the Croatian Science Foundation.
文摘This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.
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 National Natural Science Foundation of China(Grant No.72364006).
文摘Objectives:SocialNetworkAddiction(SNA)is becoming increasingly prevalent among college students;however,there remains a lack of consensus regarding the measurement tools and their optimal cutoff score.This study aims to validate the 21-item SocialNetwork Addiction Scale-Chinese(SNAS-C)in its Chinese version and to determine its optimal cutoff score for identifying potential SNA cases within the college student population.Methods:A crosssectional survey was conducted,recruiting 3387 college students.Latent profile analysis(LPA)and receiver operating characteristic(ROC)curve analysis were employed to establish the optimal cutoff score for the validated 21-item SNAS-C.Results:Three profile models were selected based on multiple statistical criteria,classifying participants into low-risk,moderate-risk,and high-risk groups.The highest-risk group was defined as“positive”for SNA,while the remaining groups were considered“negative”,serving as the reference standard for ROC analysis.The optimal cutoff score was determined to be 72(sensitivity:98.2%,specificity:96.86%),with an overall classification accuracy of 97.0%.The“positive”group reported significantly higher frequency of social network usage,greater digitalmedia dependence scores,and a higher incidence of network addiction.Conclusion:This study identified the optimal cutoff score for the SNAS-C as≥72,demonstrating high sensitivity,specificity,and diagnostic accuracy.This threshold effectively distinguishes between high-risk and low-risk SNA.
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.
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.
文摘Cryptocurrency has gained popularity as a potential new global payment method.It has the potential to be faster,cheaper,and more secure than existing payment networks,making it a game-changer in the global economy.However,more research is needed to identify the factors driving cryptocurrency adoption and understand its impact.We use social network analysis(SNA)to identify the influencing factors and reveal the impact of each on cryptocurrency adoption.Our analysis initially revealed 44 influential factors,which were later reduced to 25 factors,each exerting a different influence.Based on the SNA,we classify these factors into highly,moderately,and least influential categories.Discomfort and optimism are the most influential determinants of adoption.Moderately influential factors include trust,risk,relative advantage,social influence,and perceived behavioral control.Price/value,facilitating conditions,compatibility,and usefulness are the least influential.The factors affecting cryptocurrency adoption are interdependent.Our findings can help policymakers understand the factors influencing cryptocurrency adoption and aid in developing appropriate legal frameworks for cryptocurrency use.
文摘Objectives:This study aimed to assess the reliability and validity of the abbreviated Committed Action Questionnaire(CAQ-8)in a cohort of 1635 Chinese university students.Methods:Participants completed the Chinese version of the CAQ-8 along with other standardized measures,including the Acceptance and Action Questionnaire-II(AAQ-II),the Valuing Questionnaire(VQ),the Satisfaction with Life Scale(SWLS),the Depression Anxiety Stress Scales(DASS-21),and the World Health Organization Fiveitem Well-Being Index(WHO-5).A retest was conducted one month later with 300 valid responses.Results:Exploratory factor analysis(n=818)identified a 2-factor structure,confirmed through validated factor analysis(n=817),showing good fit indices(CFI=0.990,RMSEA=0.040).Measurement equivalence across genders was established.The CAQ-8 showed significant positive correlations with life satisfaction,mental health,and values,and negative correlations with depression,anxiety,stress,and experiential avoidance.The scale demonstrated good internal consistency(Cronbach’sα=0.76)and retest reliability(ICC=0.70).Network analysis confirmed the robustness of the 2-factor model,with item 4 in CAQ-8 identified as a core item.Conclusion:The CAQ-8 is a reliable and valid tool for measuring committed action within the psychological flexibility model in Chinese populations.
基金supported by the Major Special Science and Technology Plan(202302AA310020)the National Natural Science Foundation of China(NSFC)(32072925,32473087)the National Student Innovation and Entrepreneurship Training Program of Huazhong Agricultural University(202310504018)。
文摘Frequent extreme heat events around the world not only pose a major threat to human health but also cause significant economic losses to the livestock industry.The existing management practices are insufficient to fully prevent heat stress with an urgent need to develop preventive medicines.The aim of this study was to develop an antiheat stress Chinese herbal formula(CHF)via big data analysis techniques and to evaluate its anti-heat stress effect and mechanism of action via pharmacodynamic evaluation and network pharmacology analysis.Many anti-heat stress CHFs were collected from the Chinese National Knowledge Infrastructure(CNKI)database.Three alternative CHFs were obtained via unsupervised entropy hierarchical clustering analysis,and the most effective CHF against heat stress,Shidi Jieshu decoction(SJD),was obtained by screening in a mouse heat stress model.In dry and hot environments,SJD significantly improved the heat tolerance of AA broilers by 4-6℃.In a humid and hot environment,pretreatment with 2%SJD resulted in 100%survival of Wenchang chickens at high temperatures.The main active ingredients of SJD were identified as muntjacoside E,timosaponin C,macrostemonoside H and mangiferin via ultraperformance liquid chromatography/mass spectrometry(UPLC/MS)and database comparison.The active ingredients of SJD were found to target tumor necrosis factor-α(TNF-α),signal transducer activator of transcription 3(STAT3)and epidermal growth factor receptor(EGFR).Finally,the safety of the new formulation was assessed in an acute oral toxicity study in rats.The SJDs developed in this study provide a new option for the prevention of heat stress in animal husbandry and offer new insights for further research on anti-heat stress.
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.
基金Supported by The Sanming Project of Medicine in Shenzhen,No.SZSM202211003Shenzhen-Hong Kong Jointly Funded Project,Shenzhen Science and Technology Program,No.SGDX20230116093645007+1 种基金Shenzhen Second People's Hospital Clinical Project,No.20243357003Shenzhen Medical Research Fund,No.B2303005.
文摘BACKGROUND Depression and anxiety are prevalent psychological challenges among patients with adolescent idiopathic scoliosis(AIS),affecting individuals across both sex and age groups.AIM To explore the network structure of depression and anxiety symptoms,with a focus on identifying differences at the symptom level between sex and age subgroups.METHODS A total of 1955 participants diagnosed with AIS aged 10-18 years were assessed using the Patient Health Questionnaire Depression Scale(PHO-9)and the Generalized Anxiety Disorder Scale(GAD-7),and 765 patients exhibiting PHQ-9 or GAD-7 scores ≥ 5 were enrolled in our study. Network analysis and network comparison tests were utilized toconstruct and compare the depression-anxiety symptoms networks among sex and age subgroups.RESULTSThe results revealed GAD3 “Excessive worry” and PHQ2 “Sad mood” were the most significant central symptomsin all subgroups, while “Sad mood” had higher strength than “Excessive worry” in the lower age group. In thenetwork comparisons, the female network exhibited tighter connectivity, especially on GAD6 “Irritability” andGAD2 “Uncontrollable worry”, while only PHQ3 “Sleep” and PHQ9 “Suicidal ideation” had differences at thelocal level in the lower age group.CONCLUSIONSeveral interventions targeting excessive worry and sad mood could reduce the risk of depression and anxietysymptoms in the AIS population. Furthermore, specific anxiety symptoms in females, along with sleep disturbancesand suicidal ideation in the lower age group, should be addressed at an early stage to prevent significantdisruptions in mental health trajectories.
基金supported by the Natural Science Foundation of Henan under Grant 242300421220the Henan Provincial Science and Technology Research Project under Grants 252102211047 and 252102211062+3 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126.
文摘With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis.To address these challenges,this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception(TGICP).Specifically,we utilize a Inter-sample Commonality Perception(ICP)module to extract common features from similar samples within the same modality,and use these common features to enhance the original features of each modality,thereby obtaining a richer and more complete multimodal sentiment representation.Subsequently,in the cross-modal interaction stage,we design a Text-Gated Interaction(TGI)module,which is text-driven.By calculating the mutual information difference between the text modality and nonverbal modalities,the TGI module dynamically adjusts the influence of emotional information from the text modality on nonverbal modalities.This helps to reduce modality information asymmetry while enabling full cross-modal interaction.Experimental results show that the proposed model achieves outstanding performance on both the CMU-MOSI and CMU-MOSEI baseline multimodal sentiment analysis datasets,validating its effectiveness in emotion recognition tasks.
基金supported by the National Natural Science Foundation of China(No.62027801).
文摘In modern wireless communication and electromagnetic control,automatic modulationclassification(AMC)of orthogonal frequency division multiplexing(OFDM)signals plays animportant role.However,under Doppler frequency shift and complex multipath channel conditions,extracting discriminative features from high-order modulation signals and ensuring model inter-pretability remain challenging.To address these issues,this paper proposes a Fourier attention net-work(FAttNet),which combines an attention mechanism with a Fourier analysis network(FAN).Specifically,the method directly converts the input signal to the frequency domain using the FAN,thereby obtaining frequency features that reflect the periodic variations in amplitude and phase.Abuilt-in attention mechanism then automatically calculates the weights for each frequency band,focusing on the most discriminative components.This approach improves both classification accu-racy and model interpretability.Experimental validation was conducted via high-order modulationsimulation using an RF testbed.The results show that under three different Doppler frequencyshifts and complex multipath channel conditions,with a signal-to-noise ratio of 10 dB,the classifi-cation accuracy can reach 89.1%,90.4%and 90%,all of which are superior to the current main-stream methods.The proposed approach offers practical value for dynamic spectrum access and sig-nal security detection,and it makes important theoretical contributions to the application of deeplearning in complex electromagnetic signal recognition.
基金the research project funded by the Fundamental Research Funds for the Central Universities(No.HIT.OCEP.2024038)the National Natural Science Foundation of China(No.52372351)the State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster,China(No.MS02240107)。
文摘To improve design accuracy and reliability of structures,this study solves the uncertain natural frequencies with consideration for geometric nonlinearity and structural uncertainty.Frequencies of the laminated plate with all four edges clamped(CCCC)are derived based on Navier's method and Galerkin's method.The novelty of the current work is that the number of unknowns in the displacement field model of a CCCC plate with free midsurface(CCCC-2 plate)is only three compared with four or five in cases of other exposed methods.The present analytical method is proved to be accurate and reliable by comparing linear natural frequencies and nonlinear natural frequencies with other models available in the open literature.Furthermore,a novel method for analyzing effects of mean values and tolerance zones of uncertain structural parameters on random frequencies is proposed based on a self-developed Multiscale Feature Extraction and Fusion Network(MFEFN)system.Compared with a direct Monte Carlo Simulation(MCS),the MFEFNbased procedure significantly reduces the calculation burden with a guarantee of accuracy.Our research provides a method to calculate nonlinear natural frequencies under two boundary conditions and presentes a surrogate model to predict frequencies for accuracy analysis and optimization design.
基金supported by the National Natural Science Foundation of China(No.12175158)the Institute of Nuclear Physics and Chemistry,China Academy of Engineering Physics(No.HG2022022)。
文摘β-ray-induced X-ray spectroscopy(BIXS)is a promising technique for tritium analysis that offers several unique advantages,including substantial detection depth,nondestructive testing capabilities,and ease of operation.For thin solid tritium-containing samples with substrates,the currently used BIXS analysis method can measure the tritium depth profile and content when the sample thickness is known.In this study,a backpropagation(BP)neural network algorithm was used to predict the tritium content and thickness of a thin solid tritium-containing sample with substrates and a uniformly distributed tritium profile.A semi-analytical method was used to generate datasets for training and testing the BP neural network.A dataset ofβ-decay X-ray spectra from 420 tritium-containing zirconium models with different known thicknesses and tritium-tozirconium ratios was used as the input data.The corresponding zirconium thicknesses and tritium-to-zirconium ratios served as the output for training and testing the BP neural network.The mean relative errors(MREs)of the zirconium thickness in the training and test datasets were 0.56%and 0.42%,respectively,whereas the MREs of the tritium-to-zirconium ratio were 0.59%and 0.38%,respectively.Furthermore,the trained BP neural network demonstrates excellent predictive capability across various levels of statistical uncertainty.For the experimentalβ-decay X-ray spectra of two tritium-containing samples,the predicted zirconium thicknesses and tritium-to-zirconium ratios showed good agreement with the results obtained through the elastic backscattering spectrometry(EBS).
基金supported by the National Natural Science Foundation of China(Grant Nos.71003078and 70833005)sponsored by SRF for ROCS and SEM
文摘Co-word networks are constructed with author-provided keywords in academic publications and their relations of co-occurrence.As special form of scientific knowledge networks,they represent the cognitive structure of scientific literature.This paper analyzes the complex structure of a co-word network based on 8,190 author-provided keywords extracted from 3,651 papers in five Chinese core journals in the field of management science.Small-world and scale-free phenomena are found in this network.A large-scale co-word network graph,which consists of one major giant component and many small isolated components,has been generated with the GUESS software.The dynamic growth of keywords and keyword co-occurrence relationships are described with four new informetrics measures.The results indicate that existing concepts always serve as the intellectual base of new ideas as represented by keywords.
基金The National Basic Research Program of China (973Program) (No.2005CB321802)Program for New Century Excellent Talents in University (No.NCET-06-0926)the National Natural Science Foundation of China (No.60873097,90612009)
文摘To resolve the ontology understanding problem, the structural features and the potential important terms of a large-scale ontology are investigated from the perspective of complex networks analysis. Through the empirical studies of the gene ontology with various perspectives, this paper shows that the whole gene ontology displays the same topological features as complex networks including ＂small world＂ and ＂scale-free＂,while some sub-ontologies have the ＂scale-free＂ property but no ＂small world＂ effect.The potential important terms in an ontology are discovered by some famous complex network centralization methods.An evaluation method based on information retrieval in MEDLINE is designed to measure the effectiveness of the discovered important terms.According to the relevant literature of the gene ontology terms,the suitability of these centralization methods for ontology important concepts discovering is quantitatively evaluated.The experimental results indicate that the betweenness centrality is the most appropriate method among all the evaluated centralization measures.
基金The Scientific Innovation Research of College Graduates in Jiangsu Province(No.CXLX11_0143)
文摘In order to improve the prediction accuracy and test the generalization ability of the dam deformation analysis model, the back-propagation（BP） neural network model for dam deformation analysis is studied, and the merging model is built based on the neural network BP algorithm and the traditional statistical model. The three models mentioned above are calculated and analyzed according to the long-term deformation observation data in Chencun Dam. The analytical results show that the average prediction accuracies of the statistical model and the BP neural network model are ~ 0.477 and ＋- 0.390 mm, respectively, while the prediction accuracy of the merging model is ~0. 318 mm, which is improved by 33% and 18% compared to the other two models, respectively. And the merging model has a better generalization ability and broad applicability.
基金Supported by the Natural Science Foundation of China(61076019)the China Postdoctoral Science Foundation(20100481134)+1 种基金the Natural Science Foundation of Jiangsu Province(BK2008387)the Graduate Student Innovation Foundation of Jiangsu Province(CX07B-105z)~~
文摘The network on chip（NoC）is used as a solution for the communication problems in a complex system on chip（SoC）design.To further enhance performances,the NoC architectures,a high level modeling and an evaluation method based on OPNET are proposed to analyze their performances on different injection rates and traffic patterns.Simulation results for general NoC in terms of the average latency and the throughput are analyzed and used as a guideline to make appropriate choices for a given application.Finally,a MPEG4 decoder is mapped on different NoC architectures.Results prove the effectiveness of the evaluation method.
基金The National Natural Science Foundation of China(No.60872004)the Research Fund of National Mobile Communications Research Laboratory of Southeast University(No.2010A08)the Fundamental Research Funds for the Central Universities(No.2009B21814)
文摘An analytical approach to evaluate the performance of the 3G/ad hoc integrated network is presented. A channel model capturing both path loss and shadowing is applied to the analysis so as to characterize power falloff vs. distance. The 3G/ad hoc integrated network scenario model is introduced briefly. Based on this model, several performances of the 3G/ ad hoc integrated network in terms of outage probability, call dropping probability and new call blocking probability are evaluated. The corresponding performance formulae are deduced in accordance with the analytical models. Meanwhile, the formula of the 3G/ad hoc integrated network capacity is deduced on the basis of the formula of the outage probability. It is observed from extensive simulation and numerical analysis that the 3G/ad hoc integrated network remarkably outperforms the 3G network with regards to the network performance. This derived evaluation approach can be applied into planning and optimization of the 3G/ad hoc network.
