Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emp...Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.展开更多
This paper introduces MultiPHydro,an in-house computational solver developed for simulating hydrodynamic and multiphase fluid—body interaction problems,with a specialized focus on multiphase flow dynamics.The solver ...This paper introduces MultiPHydro,an in-house computational solver developed for simulating hydrodynamic and multiphase fluid—body interaction problems,with a specialized focus on multiphase flow dynamics.The solver employs the boundary data immersion method(BDIM)as its core numerical framework for handling fluid—solid interfaces.We briefly outline the governing equations and physical models integrated within MultiPHydro,including weakly-compressible flows,cavitation modeling,and the volume of fluid(VOF)method with piecewise-linear interface reconstruction.The solver’s accuracy and versatility are demonstrated through several numerical benchmarks:single-phase flow past a cylinder shows less than 10%error in vortex shedding frequency and under 4%error in hydrodynamic resistance;cavitating flows around a hydrofoil yield errors below 7%in maximum cavity length;water-entry cases exhibit under 5%error in displacement and velocity;and water-exit simulations predict cavity length within 7.2%deviation.These results confirm the solver’s capability to reliably model complex fluid-body interactions across various regimes.Future developments will focus on refining mathematical models,improving the modeling of phase-interaction mechanisms,and implementing GPU-accelerated parallel algorithms to enhance compatibility with domestically-developed operating systems and deep computing units(DCUs).展开更多
Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analy...Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analyze the popularity of certain research topics,well-adopted methodologies,influential authors,and the interrelationships among research disciplines.However,the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging.This study combined a Space-Time Cube(STC)and a 3D glyph to represent the complex multivariate bibliographic data.We further implemented a visual design by developing an interactive interface.The effectiveness,understandability,and engagement of ST-Map are evaluated by seven experts in geovisualization.The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.展开更多
Objectives:Somatosensory Interaction Technology(SIT)is used in various aspects of geriatric care.We aimed to conduct a bibliometric analysis to summarize relevant publications and visualize publication characteristics...Objectives:Somatosensory Interaction Technology(SIT)is used in various aspects of geriatric care.We aimed to conduct a bibliometric analysis to summarize relevant publications and visualize publication characteristics,current hotspots,and development trends,thereby inspiring subsequent researches.Methods:We searched theWeb of Science Core Collection database for publications on the application of SIT in geriatric care.Bibliometric visualization and clustering analysis were performed using VOSviewer V1.6.18 Software,while keywords burst detection analysis was conducted with CiteSpace 6.1.R6 Software.Results:After screening,a total of 1,019 publications were included.The number of publications on SIT in geriatric care is gradually increasing,exhibiting a rapid growth rate.The United States,Canada,and Australia led in terms of publication volume.Keyword clustering analysis identified major research hotspots:crisis warning,somatic abilities,rehabilitation training and psychosocial support.Initial studies primarily explored themes such as recovery,movement,systems,and later shifted towards gait analysis,muscle strength,parameters,and home-based care.More recently,research themes have evolved to dementia,machine learning,and gamification.Conclusions:SIT is innovative for promoting active aging,advancing intelligent healthcare,and elevating the daily quality of life for older adults in clinical and domestic settings.Applications of SIT can be categorized into early warning systems for crises,detailed analyses of physical conditions,rehabilitation enhancement,and support for psychosocial health.Research trends have transitioned from whole-body recognition to precise feedback,from a focus on physical health to mental health,and from technical feasibility to user-friendliness.Future research should focus on developing accessible and user-friendly devices,fostering interdisciplinary collaborations for innovation,expanding research to address both the physical and mental health needs of diverse older adults,and integrating emerging technologies to enhance data precision and accelerate the development of intelligent platforms.展开更多
In the interdisciplinary realm of statistics,genetics,and epidemiology,longitudinal sibling pair data offers a unique perspective for investigating complex diseases and traits,allowing the exploration of the dynamic p...In the interdisciplinary realm of statistics,genetics,and epidemiology,longitudinal sibling pair data offers a unique perspective for investigating complex diseases and traits,allowing the exploration of the dynamic processes of gene expression over time by controlling numerous confounding factors.Missing-not-at-random(MNAR)data are commonly used in such types of studies,but no statistical methods specifically tailored have been developed to handle MNAR data in complex longitudinal data in the literature.Here,we propose a new statistical method by jointly modeling longitudinal data from sib-pairs and MNAR data.Extensive simulations demonstrate the excellent finite sample properties of the proposed method.展开更多
Identifying the spatiotemporal interaction pattern of agricultural product circulation(APC)is crucial for agricultural resource adjustment and food security.Current studies are mostly based on static statistical data ...Identifying the spatiotemporal interaction pattern of agricultural product circulation(APC)is crucial for agricultural resource adjustment and food security.Current studies are mostly based on static statistical data over an entire year or a specific period,which cannot describe the spatial pattern of APC and its seasonal variation on a fine spatiotemporal scale.Thus,this study extracts an APC trip chain based on national truck trajectory data and constructs the flow network of the Beijing APC with the city as the spatial unit and the season as the temporal unit.The spatial interaction pattern and seasonal variation in APC are then analyzed from the network spatial form,city node function role,and transportation corridors.The results are as follows:(1)Compared with methods based on static statistical data,the proposed method provides a more complete and refined depiction of the spatiotemporal interaction pattern of APC.(2)The flow network of the Beijing APC involves 316 cities in China,of which 143 cities play a major role with typical seasonal characteristics.These cities can be divided into perennial core cities,perennial major cities,core cities in winter-spring,major cities in winter-spring,core cities in summer-autumn,and major cities in summer-autumn,contributing 2.6%-40.3%to the Beijing APC.(3)There are 6 transportation corridors for the Beijing APC.The Beijing-Tianjin-Hebei corridor and coastal corridor contribute 53.5%and 12.8%of the annual supply,respectively,with a balanced supply in all seasons.The Beijing-Kunming corridor and Beijing-Guangzhou corridor contribute 14.3%and 9.0%,respectively,with much higher supplies in winter and spring.The northeast and northwest corridors contribute 7.3%and 3.3%,respectively,mainly in the summer and autumn.These results help deepen the understanding of agricultural product supply patterns and provide a reference for the design and optimization of agricultural product transportation routes.展开更多
Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during t...Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during the final selection stage, varieties are tested in different environments (locations and years), and breeders need to estimate the phenotypic performance for main traits such as tons of cane yield per hectare (TCH) considering the genotype × environment interaction (GEI) effect. Geneticists and biometricians have used different methods and there is no clear consensus of the best method. In this study, we present a comparison of three methods, viz. Eberhart-Russel (ER), additive main effects and multiplicative interaction (AMMI) and mixed model (REML/BLUP), in a simulation study performed in the R computing environment to verify the effectiveness of each method in detecting GEI, and assess the particularities of each method from a statistical standpoint. In total, 63 cases representing different conditions were simulated, generating more than 34 million data points for analysis by each of the three methods. The results show that each method detects GEI differently in a different way, and each has some limitations. All three methods detected GEI effectively, but the mixed model showed higher sensitivity. When applying the GEI analysis, firstly it is important to verify the assumptions inherent in each method and these limitations should be taken into account when choosing the method to be used.展开更多
Alpha-synuclein plays an important role in Parkinson's disease(PD).The current study of alpha-synuclein mainly concentrates at the gene level.However, it is found that the study at the protein level has special si...Alpha-synuclein plays an important role in Parkinson's disease(PD).The current study of alpha-synuclein mainly concentrates at the gene level.However, it is found that the study at the protein level has special significance.Meanwhile, there is free information on the Internet, such as databases and algorithms of protein-protein interactions(PPIs).In this paper, a novel method which integrates distributed heterogeneous data sources and algorithms to predict PPIs for alpha-synuclein in silico is proposed.The PPIs generated by the method take advantage of various experimental data, and indicate new information about PPIs for alpha-synuclein.In the end of this paper, the result illustrates that the method is practical.It is hoped that the prediction result obtained by this method can provide guidance for biological experiments of PPIs for alpha-synuclein to reveal possible mechanisms of PD.展开更多
Logic regression is an adaptive regression method which searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome, and thus, it reveals interaction effects which ar...Logic regression is an adaptive regression method which searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome, and thus, it reveals interaction effects which are associated with the response. In this study, we extended logic regression to longitudinal data with binary response and proposed “Transition Logic Regression Method” to find interactions related to response. In this method, interaction effects over time were found by Annealing Algorithm with AIC (Akaike Information Criterion) as the score function of the model. Also, first and second orders Markov dependence were allowed to capture the correlation among successive observations of the same individual in longitudinal binary response. Performance of the method was evaluated with simulation study in various conditions. Proposed method was used to find interactions of SNPs and other risk factors related to low HDL over time in data of 329 participants of longitudinal TLGS study.展开更多
In this paper,an interactive method is proposed to describe computer animation data and accelerate the process of animation generation.First,a semantic model and a resource description framework(RDF)are utilized to an...In this paper,an interactive method is proposed to describe computer animation data and accelerate the process of animation generation.First,a semantic model and a resource description framework(RDF)are utilized to analyze and describe the relationships between animation data.Second,a novel context model which is able to keep the context-awareness was proposed to facilitate data organization and storage.In our context model,all the main animation elements in a scene are operated as a whole.Then sketch is utilized as the main interactive method to describe the relationships between animation data,edit the context model and make some other user operations.Finally,a context-aware computer animation data description system based on sketch is generated and it also works well in animation generation process.展开更多
In view of the extensive growth of China's steel production in recent years, this paper analyzed the industrial development background and economic geography theory, and discussed the possible spatial interaction ...In view of the extensive growth of China's steel production in recent years, this paper analyzed the industrial development background and economic geography theory, and discussed the possible spatial interaction mechanism. Based on panel data of China's inter-provincial steel output from 2001 to 2015, using spatial econometric model, this paper also explored whether China's provincial steel production shows material orientation, market orientation and traffic orientation, and isolated spatial interactions of interprovincial steel output. The results showed that the inter-provincial steel production in China did show both material orientation, market orientation and traffic orientation and that there was a significant negative spatial interaction, indicating that there might be strong competition and a crowing-out effect between neighboring provinces, and that the smaller the spatial scope, the more significant the spatial interactions of steel production.展开更多
Detecting genotype-by-environment (GE) interaction effects or yield stability is one of the most important components for crop trial data analysis, especially in historical crop trial data. However, it is statisticall...Detecting genotype-by-environment (GE) interaction effects or yield stability is one of the most important components for crop trial data analysis, especially in historical crop trial data. However, it is statistically challenging to discover the GE interaction effects because many published data were just entry means under each environment rather than repeated field plot data. In this study, we propose a new methodology, which can be used to impute replicated trial data sets to reveal GE interactions from the original data. As a demonstration, we used a data set, which includes 28 potato genotypes and six environments with three replications to numerically evaluate the properties of this new imputation method. We compared the phenotypic means and predicted random effects from the imputed data with the results from the original data. The results from the imputed data were highly consistent with those from the original data set, indicating that imputed data from the method we proposed in this study can be used to reveal information including GE interaction effects harbored in the original data. Therefore, this study could pave a way to detect the GE interactions and other related information from historical crop trial reports when replications were not available.展开更多
As industrial production progresses toward digitalization,massive amounts of data have been collected,transmitted,and stored,with characteristics of large-scale,high-dimensional,heterogeneous,and spatiotemporal dynami...As industrial production progresses toward digitalization,massive amounts of data have been collected,transmitted,and stored,with characteristics of large-scale,high-dimensional,heterogeneous,and spatiotemporal dynamics.The high complexity of industrial big data poses challenges for the practical decision-making of domain experts,leading to ever-increasing needs for integrating computational intelligence with human perception into traditional data analysis.Industrial big data visualization integrates theoretical methods and practical technologies from multiple disciplines,including data mining,information visualization,computer graphics,and human-computer interaction,providing a highly effective manner for understanding and exploring the complex industrial processes.This review summarizes the state-of-the-art approaches,characterizes them with six visualization methods,and categorizes them based on analytical tasks and applications.Furthermore,key research challenges and potential future directions are identified.展开更多
Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interactio...Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interaction patterns underlying human activities.Nevertheless,the inherent heterogeneity in multimodal migration big data has been ignored.This study conducts an in-depth comparison and quantitative analysis through a comprehensive lens of spatial association.Initially,the intercity interactive networks in China were constructed,utilizing migration data from Baidu and AutoNavi collected during the same time period.Subsequently,the characteristics and spatial structure similarities of the two types of intercity interactive networks were quantitatively assessed and analyzed from overall(network)and local(node)perspectives.Furthermore,the precision of these networks at the local scale is corroborated by constructing an intercity network from mobile phone(MP)data.Results indicate that the intercity interactive networks in China,as delineated by Baidu and AutoNavi migration flows,exhibit a high degree of structure equivalence.The correlation coefficient between these two networks is 0.874.Both networks exhibit a pronounced spatial polarization trend and hierarchical structure.This is evident in their distinct core and peripheral structures,as well as in the varying importance and influence of different nodes within the networks.Nevertheless,there are notable differences worthy of attention.Baidu intercity interactive network exhibits pronounced cross-regional effects,and its high-level interactions are characterized by a“rich-club”phenomenon.The AutoNavi intercity interactive network presents a more significant distance attenuation effect,and the high-level interactions display a gradient distribution pattern.Notably,there exists a substantial correlation between the AutoNavi and MP networks at the local scale,evidenced by a high correlation coefficient of 0.954.Furthermore,the“spatial dislocations”phenomenon was observed within the spatial structures at different levels,extracted from the Baidu and AutoNavi intercity networks.However,the measured results of network spatial structure similarity from three dimensions,namely,node location,node size,and local structure,indicate a relatively high similarity and consistency between the two networks.展开更多
Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive atte...Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive attention from researchers.Many centrality methods and machine learning algorithms have been proposed to predict essential proteins.Nevertheless,the topological characteristics learned by the centrality method are not comprehensive enough,resulting in low accuracy.In addition,machine learning algorithms need sufficient prior knowledge to select features,and the ability to solve imbalanced classification problems needs to be further strengthened.These two factors greatly affect the performance of predicting essential proteins.In this paper,we propose a deep learning framework based on temporal convolutional networks to predict essential proteins by integrating gene expression data and protein-protein interaction(PPI)network.We make use of the method of network embedding to automatically learn more abundant features of proteins in the PPI network.For gene expression data,we treat it as sequence data,and use temporal convolutional networks to extract sequence features.Finally,the two types of features are integrated and put into the multi-layer neural network to complete the final classification task.The performance of our method is evaluated by comparing with seven centrality methods,six machine learning algorithms,and two deep learning models.The results of the experiment show that our method is more effective than the comparison methods for predicting essential proteins.展开更多
In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm base...In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.展开更多
In 2007,China surpassed the USA to become the largest carbon emitter in the world.China has promised a 60%–65%reduction in carbon emissions per unit GDP by 2030,compared to the baseline of 2005.Therefore,it is import...In 2007,China surpassed the USA to become the largest carbon emitter in the world.China has promised a 60%–65%reduction in carbon emissions per unit GDP by 2030,compared to the baseline of 2005.Therefore,it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies.This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data.By applying the Exploratory Spatial-Temporal Data Analysis(ESTDA)framework,this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013.The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units.The results show that,firstly,high accuracy was achieved by the model in simulating carbon emissions.Secondly,the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82%and 5.72%,respectively.The overall carbon footprints and carbon deficits were larger in the North than that in the South.There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units.Thirdly,the relative lengths of the Local Indicators of Spatial Association(LISA)time paths were longer in the North than that in the South,and they increased from the coastal to the central and western regions.Lastly,the overall decoupling index was mainly a weak decoupling type,but the number of cities with this weak decoupling continued to decrease.The unsustainable development trend of China’s economic growth and carbon emission load will continue for some time.展开更多
Method for constructing the optimal water supply line and formulas for calculating the targets for single-contaminant regeneration recycling water systems are improved to apply to the situation of variational pararnet...Method for constructing the optimal water supply line and formulas for calculating the targets for single-contaminant regeneration recycling water systems are improved to apply to the situation of variational pararneters in this article. Based on these extending methods, the effect of varying freshwater consumption and regenerated water flow rate on the optimizing results are investigated. The interactions of parameters of regeneration recycling systems are summarized. Finally, all the conclusions are illustrated from the results of mathematical programming through an example.展开更多
This paper aims to explore urban geography with a new perspective. Endowed with the urban geography connotations, an improved data field model is employed to integrate temporal dimension into spatial process of cities...This paper aims to explore urban geography with a new perspective. Endowed with the urban geography connotations, an improved data field model is employed to integrate temporal dimension into spatial process of cities in a typical region in this article. Taking the Beijing-Shanghai Corridor including 18 cities as an example, the authors chose the city centricity index (CCI) and the spatial data field model to analyze the evolution process and features of sub-region and urban spatial interaction in this corridor based on the data of 1991, 1996 and 2002. Through the analy- sis, we found that: 1) with the improvement of the urbanization level and the development of urban economy, the cit- ies’ CCI grew, the urban spatial radiative potential enhanced and the radiative range expanded gradually, which reflects the urban spatial interaction’s intensity has been increasing greatly; 2) although the spatial interaction intensity among the cities and sub-regions in the Beijing-Shanghai Corridor was growing constantly, the gap of the spatial interaction strength among different cities and sub-regions was widening, and the spatial division between the developed areas and the less developed areas was obvious; and 3) the intensity of the spatial interaction of Beijing, Shanghai and their urban agglomerations was far greater than that in small cities of other parts of the corridor, and it may have a strong drive force on the choice of spatial location of the economic activities.展开更多
文摘Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.
文摘This paper introduces MultiPHydro,an in-house computational solver developed for simulating hydrodynamic and multiphase fluid—body interaction problems,with a specialized focus on multiphase flow dynamics.The solver employs the boundary data immersion method(BDIM)as its core numerical framework for handling fluid—solid interfaces.We briefly outline the governing equations and physical models integrated within MultiPHydro,including weakly-compressible flows,cavitation modeling,and the volume of fluid(VOF)method with piecewise-linear interface reconstruction.The solver’s accuracy and versatility are demonstrated through several numerical benchmarks:single-phase flow past a cylinder shows less than 10%error in vortex shedding frequency and under 4%error in hydrodynamic resistance;cavitating flows around a hydrofoil yield errors below 7%in maximum cavity length;water-entry cases exhibit under 5%error in displacement and velocity;and water-exit simulations predict cavity length within 7.2%deviation.These results confirm the solver’s capability to reliably model complex fluid-body interactions across various regimes.Future developments will focus on refining mathematical models,improving the modeling of phase-interaction mechanisms,and implementing GPU-accelerated parallel algorithms to enhance compatibility with domestically-developed operating systems and deep computing units(DCUs).
文摘Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analyze the popularity of certain research topics,well-adopted methodologies,influential authors,and the interrelationships among research disciplines.However,the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging.This study combined a Space-Time Cube(STC)and a 3D glyph to represent the complex multivariate bibliographic data.We further implemented a visual design by developing an interactive interface.The effectiveness,understandability,and engagement of ST-Map are evaluated by seven experts in geovisualization.The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.
基金funded by the Chinese Nursing Association(#ZHKYQ202322)the Shanghai Science and Technology Innovation Action Plan Sailing Project(#21YF1447700)the Shanghai Municipal Health Commission(#2024QN026).
文摘Objectives:Somatosensory Interaction Technology(SIT)is used in various aspects of geriatric care.We aimed to conduct a bibliometric analysis to summarize relevant publications and visualize publication characteristics,current hotspots,and development trends,thereby inspiring subsequent researches.Methods:We searched theWeb of Science Core Collection database for publications on the application of SIT in geriatric care.Bibliometric visualization and clustering analysis were performed using VOSviewer V1.6.18 Software,while keywords burst detection analysis was conducted with CiteSpace 6.1.R6 Software.Results:After screening,a total of 1,019 publications were included.The number of publications on SIT in geriatric care is gradually increasing,exhibiting a rapid growth rate.The United States,Canada,and Australia led in terms of publication volume.Keyword clustering analysis identified major research hotspots:crisis warning,somatic abilities,rehabilitation training and psychosocial support.Initial studies primarily explored themes such as recovery,movement,systems,and later shifted towards gait analysis,muscle strength,parameters,and home-based care.More recently,research themes have evolved to dementia,machine learning,and gamification.Conclusions:SIT is innovative for promoting active aging,advancing intelligent healthcare,and elevating the daily quality of life for older adults in clinical and domestic settings.Applications of SIT can be categorized into early warning systems for crises,detailed analyses of physical conditions,rehabilitation enhancement,and support for psychosocial health.Research trends have transitioned from whole-body recognition to precise feedback,from a focus on physical health to mental health,and from technical feasibility to user-friendliness.Future research should focus on developing accessible and user-friendly devices,fostering interdisciplinary collaborations for innovation,expanding research to address both the physical and mental health needs of diverse older adults,and integrating emerging technologies to enhance data precision and accelerate the development of intelligent platforms.
基金This work was supported by the National Natural Science Foundation of China(12171451).
文摘In the interdisciplinary realm of statistics,genetics,and epidemiology,longitudinal sibling pair data offers a unique perspective for investigating complex diseases and traits,allowing the exploration of the dynamic processes of gene expression over time by controlling numerous confounding factors.Missing-not-at-random(MNAR)data are commonly used in such types of studies,but no statistical methods specifically tailored have been developed to handle MNAR data in complex longitudinal data in the literature.Here,we propose a new statistical method by jointly modeling longitudinal data from sib-pairs and MNAR data.Extensive simulations demonstrate the excellent finite sample properties of the proposed method.
基金Innovation Project of LREIS,No.KPI003National Natural Science Foundation of China,No.42101423Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA23010202。
文摘Identifying the spatiotemporal interaction pattern of agricultural product circulation(APC)is crucial for agricultural resource adjustment and food security.Current studies are mostly based on static statistical data over an entire year or a specific period,which cannot describe the spatial pattern of APC and its seasonal variation on a fine spatiotemporal scale.Thus,this study extracts an APC trip chain based on national truck trajectory data and constructs the flow network of the Beijing APC with the city as the spatial unit and the season as the temporal unit.The spatial interaction pattern and seasonal variation in APC are then analyzed from the network spatial form,city node function role,and transportation corridors.The results are as follows:(1)Compared with methods based on static statistical data,the proposed method provides a more complete and refined depiction of the spatiotemporal interaction pattern of APC.(2)The flow network of the Beijing APC involves 316 cities in China,of which 143 cities play a major role with typical seasonal characteristics.These cities can be divided into perennial core cities,perennial major cities,core cities in winter-spring,major cities in winter-spring,core cities in summer-autumn,and major cities in summer-autumn,contributing 2.6%-40.3%to the Beijing APC.(3)There are 6 transportation corridors for the Beijing APC.The Beijing-Tianjin-Hebei corridor and coastal corridor contribute 53.5%and 12.8%of the annual supply,respectively,with a balanced supply in all seasons.The Beijing-Kunming corridor and Beijing-Guangzhou corridor contribute 14.3%and 9.0%,respectively,with much higher supplies in winter and spring.The northeast and northwest corridors contribute 7.3%and 3.3%,respectively,mainly in the summer and autumn.These results help deepen the understanding of agricultural product supply patterns and provide a reference for the design and optimization of agricultural product transportation routes.
文摘Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during the final selection stage, varieties are tested in different environments (locations and years), and breeders need to estimate the phenotypic performance for main traits such as tons of cane yield per hectare (TCH) considering the genotype × environment interaction (GEI) effect. Geneticists and biometricians have used different methods and there is no clear consensus of the best method. In this study, we present a comparison of three methods, viz. Eberhart-Russel (ER), additive main effects and multiplicative interaction (AMMI) and mixed model (REML/BLUP), in a simulation study performed in the R computing environment to verify the effectiveness of each method in detecting GEI, and assess the particularities of each method from a statistical standpoint. In total, 63 cases representing different conditions were simulated, generating more than 34 million data points for analysis by each of the three methods. The results show that each method detects GEI differently in a different way, and each has some limitations. All three methods detected GEI effectively, but the mixed model showed higher sensitivity. When applying the GEI analysis, firstly it is important to verify the assumptions inherent in each method and these limitations should be taken into account when choosing the method to be used.
基金supported by the National Basic Research Program of China (Grant No.2006CB500702)the Shanghai Lead-ing Academic Discipline Project (Grant No.J50103)Shanghai University Systems Biology Reasearch Funding (GrantNo.SBR08001)
文摘Alpha-synuclein plays an important role in Parkinson's disease(PD).The current study of alpha-synuclein mainly concentrates at the gene level.However, it is found that the study at the protein level has special significance.Meanwhile, there is free information on the Internet, such as databases and algorithms of protein-protein interactions(PPIs).In this paper, a novel method which integrates distributed heterogeneous data sources and algorithms to predict PPIs for alpha-synuclein in silico is proposed.The PPIs generated by the method take advantage of various experimental data, and indicate new information about PPIs for alpha-synuclein.In the end of this paper, the result illustrates that the method is practical.It is hoped that the prediction result obtained by this method can provide guidance for biological experiments of PPIs for alpha-synuclein to reveal possible mechanisms of PD.
文摘Logic regression is an adaptive regression method which searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome, and thus, it reveals interaction effects which are associated with the response. In this study, we extended logic regression to longitudinal data with binary response and proposed “Transition Logic Regression Method” to find interactions related to response. In this method, interaction effects over time were found by Annealing Algorithm with AIC (Akaike Information Criterion) as the score function of the model. Also, first and second orders Markov dependence were allowed to capture the correlation among successive observations of the same individual in longitudinal binary response. Performance of the method was evaluated with simulation study in various conditions. Proposed method was used to find interactions of SNPs and other risk factors related to low HDL over time in data of 329 participants of longitudinal TLGS study.
基金Supported by the National Key Research and Development Plan(2016YFB1001200)the National Natural Science Foundation of China(U1435220,61232013)
文摘In this paper,an interactive method is proposed to describe computer animation data and accelerate the process of animation generation.First,a semantic model and a resource description framework(RDF)are utilized to analyze and describe the relationships between animation data.Second,a novel context model which is able to keep the context-awareness was proposed to facilitate data organization and storage.In our context model,all the main animation elements in a scene are operated as a whole.Then sketch is utilized as the main interactive method to describe the relationships between animation data,edit the context model and make some other user operations.Finally,a context-aware computer animation data description system based on sketch is generated and it also works well in animation generation process.
文摘In view of the extensive growth of China's steel production in recent years, this paper analyzed the industrial development background and economic geography theory, and discussed the possible spatial interaction mechanism. Based on panel data of China's inter-provincial steel output from 2001 to 2015, using spatial econometric model, this paper also explored whether China's provincial steel production shows material orientation, market orientation and traffic orientation, and isolated spatial interactions of interprovincial steel output. The results showed that the inter-provincial steel production in China did show both material orientation, market orientation and traffic orientation and that there was a significant negative spatial interaction, indicating that there might be strong competition and a crowing-out effect between neighboring provinces, and that the smaller the spatial scope, the more significant the spatial interactions of steel production.
文摘Detecting genotype-by-environment (GE) interaction effects or yield stability is one of the most important components for crop trial data analysis, especially in historical crop trial data. However, it is statistically challenging to discover the GE interaction effects because many published data were just entry means under each environment rather than repeated field plot data. In this study, we propose a new methodology, which can be used to impute replicated trial data sets to reveal GE interactions from the original data. As a demonstration, we used a data set, which includes 28 potato genotypes and six environments with three replications to numerically evaluate the properties of this new imputation method. We compared the phenotypic means and predicted random effects from the imputed data with the results from the original data. The results from the imputed data were highly consistent with those from the original data set, indicating that imputed data from the method we proposed in this study can be used to reveal information including GE interaction effects harbored in the original data. Therefore, this study could pave a way to detect the GE interactions and other related information from historical crop trial reports when replications were not available.
基金supported in part by the National Key Research and Development Plan Project(2022YFB3304700)in part by the Xinliao Talent Program of Liaoning Province(XLYC2202002).
文摘As industrial production progresses toward digitalization,massive amounts of data have been collected,transmitted,and stored,with characteristics of large-scale,high-dimensional,heterogeneous,and spatiotemporal dynamics.The high complexity of industrial big data poses challenges for the practical decision-making of domain experts,leading to ever-increasing needs for integrating computational intelligence with human perception into traditional data analysis.Industrial big data visualization integrates theoretical methods and practical technologies from multiple disciplines,including data mining,information visualization,computer graphics,and human-computer interaction,providing a highly effective manner for understanding and exploring the complex industrial processes.This review summarizes the state-of-the-art approaches,characterizes them with six visualization methods,and categorizes them based on analytical tasks and applications.Furthermore,key research challenges and potential future directions are identified.
基金National Natural Science Foundation of China,No.42361040。
文摘Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interaction patterns underlying human activities.Nevertheless,the inherent heterogeneity in multimodal migration big data has been ignored.This study conducts an in-depth comparison and quantitative analysis through a comprehensive lens of spatial association.Initially,the intercity interactive networks in China were constructed,utilizing migration data from Baidu and AutoNavi collected during the same time period.Subsequently,the characteristics and spatial structure similarities of the two types of intercity interactive networks were quantitatively assessed and analyzed from overall(network)and local(node)perspectives.Furthermore,the precision of these networks at the local scale is corroborated by constructing an intercity network from mobile phone(MP)data.Results indicate that the intercity interactive networks in China,as delineated by Baidu and AutoNavi migration flows,exhibit a high degree of structure equivalence.The correlation coefficient between these two networks is 0.874.Both networks exhibit a pronounced spatial polarization trend and hierarchical structure.This is evident in their distinct core and peripheral structures,as well as in the varying importance and influence of different nodes within the networks.Nevertheless,there are notable differences worthy of attention.Baidu intercity interactive network exhibits pronounced cross-regional effects,and its high-level interactions are characterized by a“rich-club”phenomenon.The AutoNavi intercity interactive network presents a more significant distance attenuation effect,and the high-level interactions display a gradient distribution pattern.Notably,there exists a substantial correlation between the AutoNavi and MP networks at the local scale,evidenced by a high correlation coefficient of 0.954.Furthermore,the“spatial dislocations”phenomenon was observed within the spatial structures at different levels,extracted from the Baidu and AutoNavi intercity networks.However,the measured results of network spatial structure similarity from three dimensions,namely,node location,node size,and local structure,indicate a relatively high similarity and consistency between the two networks.
基金the National Natural Science Foundation of China(Nos.11861045 and 62162040)。
文摘Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive attention from researchers.Many centrality methods and machine learning algorithms have been proposed to predict essential proteins.Nevertheless,the topological characteristics learned by the centrality method are not comprehensive enough,resulting in low accuracy.In addition,machine learning algorithms need sufficient prior knowledge to select features,and the ability to solve imbalanced classification problems needs to be further strengthened.These two factors greatly affect the performance of predicting essential proteins.In this paper,we propose a deep learning framework based on temporal convolutional networks to predict essential proteins by integrating gene expression data and protein-protein interaction(PPI)network.We make use of the method of network embedding to automatically learn more abundant features of proteins in the PPI network.For gene expression data,we treat it as sequence data,and use temporal convolutional networks to extract sequence features.Finally,the two types of features are integrated and put into the multi-layer neural network to complete the final classification task.The performance of our method is evaluated by comparing with seven centrality methods,six machine learning algorithms,and two deep learning models.The results of the experiment show that our method is more effective than the comparison methods for predicting essential proteins.
基金The National Natural Science Foundation of China(No.61231002,61273266,61571106)the Foundation of the Department of Science and Technology of Guizhou Province(No.[2015]7637)
文摘In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.
基金National Natural Science Foundation of China Youth Science Foundation ProjectNo.41701170+1 种基金National Natural Science Foundation of China,No.41661025,No.42071216Fundamental Research Funds for the Central Universities,No.18LZUJBWZY068。
文摘In 2007,China surpassed the USA to become the largest carbon emitter in the world.China has promised a 60%–65%reduction in carbon emissions per unit GDP by 2030,compared to the baseline of 2005.Therefore,it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies.This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data.By applying the Exploratory Spatial-Temporal Data Analysis(ESTDA)framework,this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013.The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units.The results show that,firstly,high accuracy was achieved by the model in simulating carbon emissions.Secondly,the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82%and 5.72%,respectively.The overall carbon footprints and carbon deficits were larger in the North than that in the South.There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units.Thirdly,the relative lengths of the Local Indicators of Spatial Association(LISA)time paths were longer in the North than that in the South,and they increased from the coastal to the central and western regions.Lastly,the overall decoupling index was mainly a weak decoupling type,but the number of cities with this weak decoupling continued to decrease.The unsustainable development trend of China’s economic growth and carbon emission load will continue for some time.
基金Supported by the National Natural Science Foundation of China （No.20436040）.
文摘Method for constructing the optimal water supply line and formulas for calculating the targets for single-contaminant regeneration recycling water systems are improved to apply to the situation of variational pararneters in this article. Based on these extending methods, the effect of varying freshwater consumption and regenerated water flow rate on the optimizing results are investigated. The interactions of parameters of regeneration recycling systems are summarized. Finally, all the conclusions are illustrated from the results of mathematical programming through an example.
基金Under the auspices of Key Project of National Natural Science Foundation of China (No. 40635026)National Natural Science Foundation of China (No. 40701045)
文摘This paper aims to explore urban geography with a new perspective. Endowed with the urban geography connotations, an improved data field model is employed to integrate temporal dimension into spatial process of cities in a typical region in this article. Taking the Beijing-Shanghai Corridor including 18 cities as an example, the authors chose the city centricity index (CCI) and the spatial data field model to analyze the evolution process and features of sub-region and urban spatial interaction in this corridor based on the data of 1991, 1996 and 2002. Through the analy- sis, we found that: 1) with the improvement of the urbanization level and the development of urban economy, the cit- ies’ CCI grew, the urban spatial radiative potential enhanced and the radiative range expanded gradually, which reflects the urban spatial interaction’s intensity has been increasing greatly; 2) although the spatial interaction intensity among the cities and sub-regions in the Beijing-Shanghai Corridor was growing constantly, the gap of the spatial interaction strength among different cities and sub-regions was widening, and the spatial division between the developed areas and the less developed areas was obvious; and 3) the intensity of the spatial interaction of Beijing, Shanghai and their urban agglomerations was far greater than that in small cities of other parts of the corridor, and it may have a strong drive force on the choice of spatial location of the economic activities.
