Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of shor...Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of short-term temporal dependencies of lip-shape variations between adjacent frames,which leaves space for further improvement in feature extraction.Methods This article presents a spatiotemporal feature fusion network(STDNet)that compensates for the deficiencies of current lip-reading approaches in short-term temporal dependency modeling.Specifically,to distinguish more similar and intricate content,STDNet adds a temporal feature extraction branch based on a 3D-CNN,which enhances the learning of dynamic lip movements in adjacent frames while not affecting spatial feature extraction.In particular,we designed a local–temporal block,which aggregates interframe differences,strengthening the relationship between various local lip regions through multiscale convolution.We incorporated the squeeze-and-excitation mechanism into the Global-Temporal Block,which processes a single frame as an independent unitto learn temporal variations across the entire lip region more effectively.Furthermore,attention pooling was introduced to highlight meaningful frames containing key semantic information for the target word.Results Experimental results demonstrated STDNet's superior performance on the LRW and LRW-1000,achieving word-level recognition accuracies of 90.2% and 53.56%,respectively.Extensive ablation experiments verified the rationality and effectiveness of its modules.Conclusions The proposed model effectively addresses short-term temporal dependency limitations in lip reading,and improves the temporal robustness of the model against variable-length sequences.These advancements validate the importance of explicit short-term dynamics modeling for practical lip-reading systems.展开更多
Degradation and overstress failures occur in many electronic systems in which the operation load and environmental conditions are complex.The dependency of them called dependent competing failure process(DCFP),has bee...Degradation and overstress failures occur in many electronic systems in which the operation load and environmental conditions are complex.The dependency of them called dependent competing failure process(DCFP),has been widely studied.Electronic system may experience mutual effects of degradation and shocks,they are considered to be interdependent.Both the degradation and the shock processes will decrease the limit of system and cause cumulative effect.Finally,the competition of hard and soft failure will cause the system failure.Based on the failure mechanism accumulation theory,this paper constructs the shock-degradation acceleration and the threshold descent model,and a system reliability model established by using these two models.The mutually DCFP effect of electronic system interaction has been decomposed into physical correlation of failure,including acceleration,accumulation and competition.As a case,a reliability of electronic system in aeronautical system has been analyzed with the proposed method.The method proposed is based on failure physical evaluation,and could provide important reference for quantitative evaluation and design improvement of the newly designed system in case of data deficiency.展开更多
Spatio-temporal models are valuable tools for disease mapping and understanding the geographical distribution of diseases and temporal dynamics. Spatio-temporal models have been proven empirically to be very complex a...Spatio-temporal models are valuable tools for disease mapping and understanding the geographical distribution of diseases and temporal dynamics. Spatio-temporal models have been proven empirically to be very complex and this complexity has led many to oversimply and model the spatial and temporal dependencies independently. Unlike common practice, this study formulated a new spatio-temporal model in a Bayesian hierarchical framework that accounts for spatial and temporal dependencies jointly. The spatial and temporal dependencies were dynamically modelled via the matern exponential covariance function. The temporal aspect was captured by the parameters of the exponential with a first-order autoregressive structure. Inferences about the parameters were obtained via Markov Chain Monte Carlo (MCMC) techniques and the spatio-temporal maps were obtained by mapping stable posterior means from the specific location and time from the best model that includes the significant risk factors. The model formulated was fitted to both simulation data and Kenya meningitis incidence data from 2013 to 2019 along with two covariates;Gross County Product (GCP) and average rainfall. The study found that both average rainfall and GCP had a significant positive association with meningitis occurrence. Also, regarding geographical distribution, the spatio-temporal maps showed that meningitis is not evenly distributed across the country as some counties reported a high number of cases compared with other counties.展开更多
Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are st...Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model （SPAM） has been developed for presenting spa- tio-temporal dynamics of maize cropping system in Northeast China during 1980-2010. The simulated results indicated that （1） maize sown area expanded northwards to 48~N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation （less than 100 m） as well as higher elevation （mainly distributed between 200 m and 350 m）; （2） maize yield has been greatly promoted for most planted area of Northeast China, espe- cially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region; （3） maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.展开更多
As one of the most important indexes to evaluate the quality of software, software reliability experiences an increasing development in recent years. We investigate a software reliability growth model(SRGM). The appli...As one of the most important indexes to evaluate the quality of software, software reliability experiences an increasing development in recent years. We investigate a software reliability growth model(SRGM). The application of this model is to predict the occurrence of the software faults based on the non-homogeneous Poisson process(NHPP). Unlike the independent assumptions in other models, we consider fault dependency. The testing faults are divided into three classes in this model: leading faults, first-step dependent faults and second-step dependent faults. The leading faults occurring independently follow an NHPP, while the first-step dependent faults only become detectable after the related leading faults are detected. The second-step dependent faults can only be detected after the related first-step dependent faults are detected. Then, the combined model is built on the basis of the three sub-processes. Finally, an illustration based on real dataset is presented to verify the proposed model.展开更多
Marine information has been increasing quickly. The traditional database technologies have disadvantages in manipulating large amounts of marine information which relates to the position in 3-D with the time. Recently...Marine information has been increasing quickly. The traditional database technologies have disadvantages in manipulating large amounts of marine information which relates to the position in 3-D with the time. Recently, greater emphasis has been placed on GIS （geographical information system）to deal with the marine information. The GIS has shown great success for terrestrial applications in the last decades, but its use in marine fields has been far more restricted. One of the main reasons is that most of the GIS systems or their data models are designed for land applications. They cannot do well with the nature of the marine environment and for the marine information. And this becomes a fundamental challenge to the traditional GIS and its data structure. This work designed a data model, the raster-based spatio-temporal hierarchical data model （RSHDM）, for the marine information system, or for the knowledge discovery fi＇om spatio-temporal data, which bases itself on the nature of the marine data and overcomes the shortages of the current spatio-temporal models when they are used in the field. As an experiment, the marine fishery data warehouse （FDW） for marine fishery management was set up, which was based on the RSHDM. The experiment proved that the RSHDM can do well with the data and can extract easily the aggregations that the management needs at different levels.展开更多
This paper explores the application of term dependency in information retrieval (IR) and proposes a novel dependency retrieval model. This retrieval model suggests an extension to the existing language modeling (LM) a...This paper explores the application of term dependency in information retrieval (IR) and proposes a novel dependency retrieval model. This retrieval model suggests an extension to the existing language modeling (LM) approach to IR by introducing dependency models for both query and document. Relevance between document and query is then evaluated by reference to the Kullback-Leibler divergence between their dependency models. This paper introduces a novel hybrid dependency structure, which allows integration of various forms of dependency within a single framework. A pseudo relevance feedback based method is also introduced for constructing query dependency model. The basic idea is to use query-relevant top-ranking sentences extracted from the top documents at retrieval time as the augmented representation of query, from which the relationships between query terms are identified. A Markov Random Field (MRF) based approach is presented to ensure the relevance of the extracted sentences, which utilizes the association features between query terms within a sentence to evaluate the relevance of each sentence. This dependency retrieval model was compared with other traditional retrieval models. Experiments indicated that it produces significant improvements in retrieval effectiveness.展开更多
The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation sy...The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.展开更多
Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB ...Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.展开更多
Objective To identify prognostic genes associated with lysosome-dependent cell death(LDCD)in patients with gastric cancer(GC).Methods Differentially expressed genes(DEGs)were identified using The Cancer Genome Atlas-S...Objective To identify prognostic genes associated with lysosome-dependent cell death(LDCD)in patients with gastric cancer(GC).Methods Differentially expressed genes(DEGs)were identified using The Cancer Genome Atlas-Stomach Adenocarcinoma.Weighted gene co-expression network analysis was performed to identify the key module genes associated with LDCD score.Candidate genes were identified by DEGs and key module genes.Univariate Cox regression analysis,and least absolute shrinkage and selection operator regression and multivariate Cox regression analyses were performed for the selection of prognostic genes,and risk module was established.Subsequently,key cells were identified in the single-cell dataset(GSE183904),and prognostic gene expression was analyzed.Cell proliferation and migration were assessed using the Cell Counting Kit-8 assay and the wound healing assay.Results A total of 4,465 DEGs,95 candidate genes,and 4 prognostic genes,including C19orf59,BATF2,TNFAIP2,and TNFSF18,were identified in the analysis.Receiver operating characteristic curves indicated the excellent predictive power of the risk model.Three key cell types(B cells,chief cells,and endothelial/pericyte cells)were identified in the GSE183904 dataset.C19orf59 and TNFAIP2 exhibited predominant expression in macrophage species,whereas TNFAIP2 evolved over time in endothelial/pericyte cells and chief cells.Functional experiments confirmed that interfering with C19orf59 inhibited proliferation and migration in GC cells.Conclusion C19orf59,BATF2,TNFAIP2,and TNFSF18 are prognostic genes associated with LDCD in GC.Furthermore,the risk model established in this study showed robust predictive power.展开更多
Recent discovery of high transition temperature superconductivity in La_(3)Ni_(2)O_(7) has sparked renewed theoretical and experimental interests in unconventional superconductivity. It is crucial to understand the in...Recent discovery of high transition temperature superconductivity in La_(3)Ni_(2)O_(7) has sparked renewed theoretical and experimental interests in unconventional superconductivity. It is crucial to understand the influence of various factors on its superconductivity. By refining the determinant quantum Monte Carlo algorithm, we characterize the parameter dependence of the superconducting transition temperature within a bilayer Hubbard model, which is sign-problem-free at arbitrary filling. A striking feature of this model is its similarity to the bilayer nickelate-based superconductor La_(3)Ni_(2)O_(7), where superconductivity emerges from the bilayer NiO_(2) planes.We find that interlayer spin-exchange J is critical to interlayer pairing, and that on-site interaction U contributes negatively to superconductivity at low doping levels but positively at high doping levels. Our findings can provide a reference for the next step in theoretical research on nickelate-based superconductors.展开更多
Detailed information on the spatio-temporal changes of cropland soil organic carbon(SOC) can significantly contribute to the improvement of soil fertility and mitigate climate change. Nonetheless, information and know...Detailed information on the spatio-temporal changes of cropland soil organic carbon(SOC) can significantly contribute to the improvement of soil fertility and mitigate climate change. Nonetheless, information and knowledge on the national scale spatio-temporal changes and the corresponding uncertainties of SOC in Chinese upland soils remain limited. The CENTURY model was used to estimate the SOC storages and their changes in Chinese uplands from 1980 to 2010. With the Monte Carlo method, the uncertainties of CENTURY-modelled SOC dynamics associated with the spatial heterogeneous model inputs were quantified. Results revealed that the SOC storage in Chinese uplands increased from 3.03(1.59 to 4.78) Pg C in 1980 to 3.40(2.39 to 4.62) Pg C in 2010. Increment of SOC storage during this period was 370 Tg C, with an uncertainty interval of –440 to 1110 Tg C. The regional disparities of SOC changes reached a significant level, with considerable SOC accumulation in the Huang-Huai-Hai Plain of China and SOC loss in the northeastern China. The SOC lost from Meadow soils, Black soils and Chernozems was most severe, whilst SOC accumulation in Fluvo-aquic soils, Cinnamon soils and Purplish soils was most significant. In modelling large-scale SOC dynamics, the initial soil properties were major sources of uncertainty. Hence, more detailed information concerning the soil properties must be collected. The SOC stock of Chinese uplands in 2010 was still relatively low, manifesting that recommended agricultural management practices in conjunction with effectively economic and policy incentives to farmers for soil fertility improvement were indispensable for future carbon sequestration in these regions.展开更多
The objective of this paper is to model the size-dependent thermo-mechanical behaviors of a shape memory polymer (SMP) microbeam.Size-dependent constitutive equations,which can capture the size effect of the SMP,are p...The objective of this paper is to model the size-dependent thermo-mechanical behaviors of a shape memory polymer (SMP) microbeam.Size-dependent constitutive equations,which can capture the size effect of the SMP,are proposed based on the modified couple stress theory (MCST).The deformation energy expression of the SMP microbeam is obtained by employing the proposed size-dependent constitutive equation and Bernoulli-Euler beam theory.An SMP microbeam model,which includes the formulations of deflection,strain,curvature,stress and couple stress,is developed by using the principle of minimum potential energy and the separation of variables together.The sizedependent thermo-mechanical and shape memory behaviors of the SMP microbeam and the influence of the Poisson ratio are numerically investigated according to the developed SMP microbeam model.Results show that the size effects of the SMP microbeam are significant when the dimensionless height is small enough.However,they are too slight to be necessarily considered when the dimensionless height is large enough.The bending flexibility and stress level of the SMP microbeam rise with the increasing dimensionless height,while the couple stress level declines with the increasing dimensionless height.The larger the dimensionless height is,the more obvious the viscous property and shape memory effect of the SMP microbeam are.The Poisson ratio has obvious influence on the size-dependent behaviors of the SMP microbeam.The paper provides a theoretical basis and a quantitatively analyzing tool for the design and analysis of SMP micro-structures in the field of biological medicine,microelectronic devices and micro-electro-mechanical system (MEMS) self-assembling.展开更多
The development of spatio-temporal data model is introduced. According to the soil characteristic of reclamation land, we adopt the base state with amendments model of multi-layer raster to organize the spatio-tempora...The development of spatio-temporal data model is introduced. According to the soil characteristic of reclamation land, we adopt the base state with amendments model of multi-layer raster to organize the spatio-temporal data, using the combined data structure on linear quadtree and linear octree to code. The advantage of this model is that it can easily obtain the information of certain layer and integratedly analyze the data with other methods. Then, the methods of obtain and analyses are introduced. The method can provide a tool for the research of the soil characteristic change and spatial distribution in reclamation land.展开更多
The present work is related to the numerical investigation of the spatio-temporal susceptible-latent-breaking out-recovered(SLBR)epidemic model.It describes the computer virus dynamics with vertical transmission via t...The present work is related to the numerical investigation of the spatio-temporal susceptible-latent-breaking out-recovered(SLBR)epidemic model.It describes the computer virus dynamics with vertical transmission via the internet.In these types of dynamics models,the absolute values of the state variables are the fundamental requirement that must be fulfilled by the numerical design.By taking into account this key property,the positivity preserving algorithm is designed to solve the underlying SLBR system.Since,the state variables associated with the phenomenon,represent the computer nodes,so they must take in absolute.Moreover,the continuous system(SLBR)acquires two steady states i.e.,the virus-free state and the virus existence state.The stability of the numerical design,at the equilibrium points,portrays an exceptional aspect about the propagation of the virus.The designed discretization algorithm sustains the stability of both the steady states.The computer simulations also endorse that the proposed discretization algorithm retains all the traits of the continuous SLBR model with spatial content.The stability and consistency of the proposed algorithm are verified,mathematically.All the facts are also ascertained by numerical simulations.展开更多
By using correlation analysis method,regression analysis method and time sequence method,we combine time and space,to establish grain yield spatio-temporal regression prediction model of Henan Province and all prefect...By using correlation analysis method,regression analysis method and time sequence method,we combine time and space,to establish grain yield spatio-temporal regression prediction model of Henan Province and all prefecture-level cities.At first,we use the grain yield in prefecture-level cities of Henan in the year 2000 and 2005,to establish regression model,and then taking the grain yield in one year as independent variable,we predict the grain yield in the fifth year afterwards.Taking the dependent variable value as independent variable again,we predict the grain yield at an interval of the same years,and based on this,predict year by year forward until the year we need.The research shows that the grain yield of Henan Province in the year 2015 and 2020 is 59.849 6 and 67.929 3 million t respectively,consistent with the research results of other scholars to some extent.展开更多
To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage p...To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.展开更多
Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tan...Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tangshan sequence based on classical empirical laws and a few assumptions. The relative fit of competing models is compared by Akalke Information Criterion. The spatial clustering pattern is well characterized by the model which gives the best fit to the data. A simulated aftershock sequence is generated by thinning algorithm and compared with the real seismicity.展开更多
Groundwater is the water located beneath the earth＇s surface in the soil pore spaces and in the fractures of rock formations. As one of the most important natural resources, groundwater is associated with the environ...Groundwater is the water located beneath the earth＇s surface in the soil pore spaces and in the fractures of rock formations. As one of the most important natural resources, groundwater is associated with the environment, public health, welfare, and long-term economic growth, which affects the daily activities of human beings. In modern urban areas, the primary contaminants of groundwater are artificial products, such as gasoline and diesel. To protect this important water resource, a series of efforts have been exerted, including enforcement and remedial actions. Each year, the TGPC （Texas Groundwater Protection Committee） in US publishes a ＂Joint Groundwater Monitoring and Contamination Report＂ to describe historic and new contamination cases in each county, which is an important data source for the design of prevention strategies. In this paper, a DDM （data dependent modeling） approach is proposed to predict county-level NCC （new contamination cases）. A case study with contamination information from Harris County in Texas was conducted to illustrate the modeling and prediction process with promising results. The one-step prediction error is 1.5%, while the two-step error is 12.1%. The established model can be used at the county-level, state-level, and even at the country-level. Besides, the prediction results could be a reference during decision-making processes.展开更多
The performance of smart structures in trajectory tracking under sub-micron level is hindered by the rate-dependent hysteresis nonlinearity.In this paper,a Hammerstein-like model based on the support vector machines(S...The performance of smart structures in trajectory tracking under sub-micron level is hindered by the rate-dependent hysteresis nonlinearity.In this paper,a Hammerstein-like model based on the support vector machines(SVM)is proposed to capture the rate-dependent hysteresis nonlinearity.We show that it is possible to construct a unique dynamic model in a given frequency range for a rate-dependent hysteresis system using the sinusoidal scanning signals as the training set of signals for the linear dynamic subsystem of the Hammerstein-like model.Subsequently,a two-degree-of-freedom(2DOF)H∞robust control scheme for the ratedependent hysteresis nonlinearity is implemented on a smart structure with a piezoelectric actuator(PEA)for real-time precision trajectory tracking.Simulations and experiments on the structure verify both the efectiveness and the practicality of the proposed modeling and control methods.展开更多
基金Supported by the National Key Research and Development Program of China(2023YFC3306201)the National Natural Science Foundation of China(61772125)the Fundamental Research Funds for the Central Universities(N2317004).
文摘Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of short-term temporal dependencies of lip-shape variations between adjacent frames,which leaves space for further improvement in feature extraction.Methods This article presents a spatiotemporal feature fusion network(STDNet)that compensates for the deficiencies of current lip-reading approaches in short-term temporal dependency modeling.Specifically,to distinguish more similar and intricate content,STDNet adds a temporal feature extraction branch based on a 3D-CNN,which enhances the learning of dynamic lip movements in adjacent frames while not affecting spatial feature extraction.In particular,we designed a local–temporal block,which aggregates interframe differences,strengthening the relationship between various local lip regions through multiscale convolution.We incorporated the squeeze-and-excitation mechanism into the Global-Temporal Block,which processes a single frame as an independent unitto learn temporal variations across the entire lip region more effectively.Furthermore,attention pooling was introduced to highlight meaningful frames containing key semantic information for the target word.Results Experimental results demonstrated STDNet's superior performance on the LRW and LRW-1000,achieving word-level recognition accuracies of 90.2% and 53.56%,respectively.Extensive ablation experiments verified the rationality and effectiveness of its modules.Conclusions The proposed model effectively addresses short-term temporal dependency limitations in lip reading,and improves the temporal robustness of the model against variable-length sequences.These advancements validate the importance of explicit short-term dynamics modeling for practical lip-reading systems.
基金supported by the National Natural Science Foundation of China(61503014,62073009)。
文摘Degradation and overstress failures occur in many electronic systems in which the operation load and environmental conditions are complex.The dependency of them called dependent competing failure process(DCFP),has been widely studied.Electronic system may experience mutual effects of degradation and shocks,they are considered to be interdependent.Both the degradation and the shock processes will decrease the limit of system and cause cumulative effect.Finally,the competition of hard and soft failure will cause the system failure.Based on the failure mechanism accumulation theory,this paper constructs the shock-degradation acceleration and the threshold descent model,and a system reliability model established by using these two models.The mutually DCFP effect of electronic system interaction has been decomposed into physical correlation of failure,including acceleration,accumulation and competition.As a case,a reliability of electronic system in aeronautical system has been analyzed with the proposed method.The method proposed is based on failure physical evaluation,and could provide important reference for quantitative evaluation and design improvement of the newly designed system in case of data deficiency.
文摘Spatio-temporal models are valuable tools for disease mapping and understanding the geographical distribution of diseases and temporal dynamics. Spatio-temporal models have been proven empirically to be very complex and this complexity has led many to oversimply and model the spatial and temporal dependencies independently. Unlike common practice, this study formulated a new spatio-temporal model in a Bayesian hierarchical framework that accounts for spatial and temporal dependencies jointly. The spatial and temporal dependencies were dynamically modelled via the matern exponential covariance function. The temporal aspect was captured by the parameters of the exponential with a first-order autoregressive structure. Inferences about the parameters were obtained via Markov Chain Monte Carlo (MCMC) techniques and the spatio-temporal maps were obtained by mapping stable posterior means from the specific location and time from the best model that includes the significant risk factors. The model formulated was fitted to both simulation data and Kenya meningitis incidence data from 2013 to 2019 along with two covariates;Gross County Product (GCP) and average rainfall. The study found that both average rainfall and GCP had a significant positive association with meningitis occurrence. Also, regarding geographical distribution, the spatio-temporal maps showed that meningitis is not evenly distributed across the country as some counties reported a high number of cases compared with other counties.
基金Foundation： National Natural Science Foundation of China, No.41171328, No.41201184, No.41101537 National Basic Program of China, No.2010CB951502
文摘Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model （SPAM） has been developed for presenting spa- tio-temporal dynamics of maize cropping system in Northeast China during 1980-2010. The simulated results indicated that （1） maize sown area expanded northwards to 48~N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation （less than 100 m） as well as higher elevation （mainly distributed between 200 m and 350 m）; （2） maize yield has been greatly promoted for most planted area of Northeast China, espe- cially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region; （3） maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.
基金the National Natural Science Foundation of China(No.71671016)the School Fund of Beijing Information Science&Technology University(No.1935004)
文摘As one of the most important indexes to evaluate the quality of software, software reliability experiences an increasing development in recent years. We investigate a software reliability growth model(SRGM). The application of this model is to predict the occurrence of the software faults based on the non-homogeneous Poisson process(NHPP). Unlike the independent assumptions in other models, we consider fault dependency. The testing faults are divided into three classes in this model: leading faults, first-step dependent faults and second-step dependent faults. The leading faults occurring independently follow an NHPP, while the first-step dependent faults only become detectable after the related leading faults are detected. The second-step dependent faults can only be detected after the related first-step dependent faults are detected. Then, the combined model is built on the basis of the three sub-processes. Finally, an illustration based on real dataset is presented to verify the proposed model.
基金supported by the National Key Basic Research and Development Program of China under contract No.2006CB701305the National Natural Science Foundation of China under coutract No.40571129the National High-Technology Program of China under contract Nos 2002AA639400,2003AA604040 and 2003AA637030.
文摘Marine information has been increasing quickly. The traditional database technologies have disadvantages in manipulating large amounts of marine information which relates to the position in 3-D with the time. Recently, greater emphasis has been placed on GIS （geographical information system）to deal with the marine information. The GIS has shown great success for terrestrial applications in the last decades, but its use in marine fields has been far more restricted. One of the main reasons is that most of the GIS systems or their data models are designed for land applications. They cannot do well with the nature of the marine environment and for the marine information. And this becomes a fundamental challenge to the traditional GIS and its data structure. This work designed a data model, the raster-based spatio-temporal hierarchical data model （RSHDM）, for the marine information system, or for the knowledge discovery fi＇om spatio-temporal data, which bases itself on the nature of the marine data and overcomes the shortages of the current spatio-temporal models when they are used in the field. As an experiment, the marine fishery data warehouse （FDW） for marine fishery management was set up, which was based on the RSHDM. The experiment proved that the RSHDM can do well with the data and can extract easily the aggregations that the management needs at different levels.
基金Project (No. 2006CB303000) supported in part by the National Basic Research Program (973) of China
文摘This paper explores the application of term dependency in information retrieval (IR) and proposes a novel dependency retrieval model. This retrieval model suggests an extension to the existing language modeling (LM) approach to IR by introducing dependency models for both query and document. Relevance between document and query is then evaluated by reference to the Kullback-Leibler divergence between their dependency models. This paper introduces a novel hybrid dependency structure, which allows integration of various forms of dependency within a single framework. A pseudo relevance feedback based method is also introduced for constructing query dependency model. The basic idea is to use query-relevant top-ranking sentences extracted from the top documents at retrieval time as the augmented representation of query, from which the relationships between query terms are identified. A Markov Random Field (MRF) based approach is presented to ensure the relevance of the extracted sentences, which utilizes the association features between query terms within a sentence to evaluate the relevance of each sentence. This dependency retrieval model was compared with other traditional retrieval models. Experiments indicated that it produces significant improvements in retrieval effectiveness.
基金Under the auspices of National High Technology Research and Development Program of China (No.2007AA12Z242)
文摘The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.
基金supported by the Guangdong Provincial Clinical Research Center for Tuberculosis(No.2020B1111170014)。
文摘Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
基金supported by Hainan Provincial Natural Science Foundation of China(No.820CXTD438)National Natural Science Foundation of China(No.82160634, No.81773495)。
文摘Objective To identify prognostic genes associated with lysosome-dependent cell death(LDCD)in patients with gastric cancer(GC).Methods Differentially expressed genes(DEGs)were identified using The Cancer Genome Atlas-Stomach Adenocarcinoma.Weighted gene co-expression network analysis was performed to identify the key module genes associated with LDCD score.Candidate genes were identified by DEGs and key module genes.Univariate Cox regression analysis,and least absolute shrinkage and selection operator regression and multivariate Cox regression analyses were performed for the selection of prognostic genes,and risk module was established.Subsequently,key cells were identified in the single-cell dataset(GSE183904),and prognostic gene expression was analyzed.Cell proliferation and migration were assessed using the Cell Counting Kit-8 assay and the wound healing assay.Results A total of 4,465 DEGs,95 candidate genes,and 4 prognostic genes,including C19orf59,BATF2,TNFAIP2,and TNFSF18,were identified in the analysis.Receiver operating characteristic curves indicated the excellent predictive power of the risk model.Three key cell types(B cells,chief cells,and endothelial/pericyte cells)were identified in the GSE183904 dataset.C19orf59 and TNFAIP2 exhibited predominant expression in macrophage species,whereas TNFAIP2 evolved over time in endothelial/pericyte cells and chief cells.Functional experiments confirmed that interfering with C19orf59 inhibited proliferation and migration in GC cells.Conclusion C19orf59,BATF2,TNFAIP2,and TNFSF18 are prognostic genes associated with LDCD in GC.Furthermore,the risk model established in this study showed robust predictive power.
基金supported by the National Natural Science Foundation of China (Grant Nos.12234016,12174317 for C.Wu,and 12474218 for R.Ma,Z.Fan,and T.Ma)Beijing Natural Science Foundation (Grant No.1242022 for R.Ma,Z.Fan,and T.Ma)the New Cornerstone Science Foundation。
文摘Recent discovery of high transition temperature superconductivity in La_(3)Ni_(2)O_(7) has sparked renewed theoretical and experimental interests in unconventional superconductivity. It is crucial to understand the influence of various factors on its superconductivity. By refining the determinant quantum Monte Carlo algorithm, we characterize the parameter dependence of the superconducting transition temperature within a bilayer Hubbard model, which is sign-problem-free at arbitrary filling. A striking feature of this model is its similarity to the bilayer nickelate-based superconductor La_(3)Ni_(2)O_(7), where superconductivity emerges from the bilayer NiO_(2) planes.We find that interlayer spin-exchange J is critical to interlayer pairing, and that on-site interaction U contributes negatively to superconductivity at low doping levels but positively at high doping levels. Our findings can provide a reference for the next step in theoretical research on nickelate-based superconductors.
基金Under the auspices of National Key Research and Development Program of China(No.2017YFA0603002)National Natural Science Foundation of China(No.31800358,31700369)+1 种基金Jiangsu Agricultural Science and Technology Innovation Fund(No.CX(19)3099)the Foundation of Jiangsu Vocational College of Agriculture and Forestry(No.2019kj014)。
文摘Detailed information on the spatio-temporal changes of cropland soil organic carbon(SOC) can significantly contribute to the improvement of soil fertility and mitigate climate change. Nonetheless, information and knowledge on the national scale spatio-temporal changes and the corresponding uncertainties of SOC in Chinese upland soils remain limited. The CENTURY model was used to estimate the SOC storages and their changes in Chinese uplands from 1980 to 2010. With the Monte Carlo method, the uncertainties of CENTURY-modelled SOC dynamics associated with the spatial heterogeneous model inputs were quantified. Results revealed that the SOC storage in Chinese uplands increased from 3.03(1.59 to 4.78) Pg C in 1980 to 3.40(2.39 to 4.62) Pg C in 2010. Increment of SOC storage during this period was 370 Tg C, with an uncertainty interval of –440 to 1110 Tg C. The regional disparities of SOC changes reached a significant level, with considerable SOC accumulation in the Huang-Huai-Hai Plain of China and SOC loss in the northeastern China. The SOC lost from Meadow soils, Black soils and Chernozems was most severe, whilst SOC accumulation in Fluvo-aquic soils, Cinnamon soils and Purplish soils was most significant. In modelling large-scale SOC dynamics, the initial soil properties were major sources of uncertainty. Hence, more detailed information concerning the soil properties must be collected. The SOC stock of Chinese uplands in 2010 was still relatively low, manifesting that recommended agricultural management practices in conjunction with effectively economic and policy incentives to farmers for soil fertility improvement were indispensable for future carbon sequestration in these regions.
基金Project supported by the National Key Research and Development Program of China(No.2017YFC0307604)the Talent Foundation of China University of Petroleum(No.Y1215042)the Graduate Innovation Program of China University of Petroleum(East China)(No.YCX2019084)
文摘The objective of this paper is to model the size-dependent thermo-mechanical behaviors of a shape memory polymer (SMP) microbeam.Size-dependent constitutive equations,which can capture the size effect of the SMP,are proposed based on the modified couple stress theory (MCST).The deformation energy expression of the SMP microbeam is obtained by employing the proposed size-dependent constitutive equation and Bernoulli-Euler beam theory.An SMP microbeam model,which includes the formulations of deflection,strain,curvature,stress and couple stress,is developed by using the principle of minimum potential energy and the separation of variables together.The sizedependent thermo-mechanical and shape memory behaviors of the SMP microbeam and the influence of the Poisson ratio are numerically investigated according to the developed SMP microbeam model.Results show that the size effects of the SMP microbeam are significant when the dimensionless height is small enough.However,they are too slight to be necessarily considered when the dimensionless height is large enough.The bending flexibility and stress level of the SMP microbeam rise with the increasing dimensionless height,while the couple stress level declines with the increasing dimensionless height.The larger the dimensionless height is,the more obvious the viscous property and shape memory effect of the SMP microbeam are.The Poisson ratio has obvious influence on the size-dependent behaviors of the SMP microbeam.The paper provides a theoretical basis and a quantitatively analyzing tool for the design and analysis of SMP micro-structures in the field of biological medicine,microelectronic devices and micro-electro-mechanical system (MEMS) self-assembling.
文摘The development of spatio-temporal data model is introduced. According to the soil characteristic of reclamation land, we adopt the base state with amendments model of multi-layer raster to organize the spatio-temporal data, using the combined data structure on linear quadtree and linear octree to code. The advantage of this model is that it can easily obtain the information of certain layer and integratedly analyze the data with other methods. Then, the methods of obtain and analyses are introduced. The method can provide a tool for the research of the soil characteristic change and spatial distribution in reclamation land.
文摘The present work is related to the numerical investigation of the spatio-temporal susceptible-latent-breaking out-recovered(SLBR)epidemic model.It describes the computer virus dynamics with vertical transmission via the internet.In these types of dynamics models,the absolute values of the state variables are the fundamental requirement that must be fulfilled by the numerical design.By taking into account this key property,the positivity preserving algorithm is designed to solve the underlying SLBR system.Since,the state variables associated with the phenomenon,represent the computer nodes,so they must take in absolute.Moreover,the continuous system(SLBR)acquires two steady states i.e.,the virus-free state and the virus existence state.The stability of the numerical design,at the equilibrium points,portrays an exceptional aspect about the propagation of the virus.The designed discretization algorithm sustains the stability of both the steady states.The computer simulations also endorse that the proposed discretization algorithm retains all the traits of the continuous SLBR model with spatial content.The stability and consistency of the proposed algorithm are verified,mathematically.All the facts are also ascertained by numerical simulations.
基金Supported by Philosophical Social Sciences Research Project of Jiangsu Colleges(08SJD7900055)
文摘By using correlation analysis method,regression analysis method and time sequence method,we combine time and space,to establish grain yield spatio-temporal regression prediction model of Henan Province and all prefecture-level cities.At first,we use the grain yield in prefecture-level cities of Henan in the year 2000 and 2005,to establish regression model,and then taking the grain yield in one year as independent variable,we predict the grain yield in the fifth year afterwards.Taking the dependent variable value as independent variable again,we predict the grain yield at an interval of the same years,and based on this,predict year by year forward until the year we need.The research shows that the grain yield of Henan Province in the year 2015 and 2020 is 59.849 6 and 67.929 3 million t respectively,consistent with the research results of other scholars to some extent.
基金supported by National Natural Science Foundation of China(Grant No.62073256)the Shaanxi Provincial Science and Technology Department(Grant No.2023-YBGY-342).
文摘To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.
基金supported by National Natural Science of Foundation of China(No.10871026)
文摘Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tangshan sequence based on classical empirical laws and a few assumptions. The relative fit of competing models is compared by Akalke Information Criterion. The spatial clustering pattern is well characterized by the model which gives the best fit to the data. A simulated aftershock sequence is generated by thinning algorithm and compared with the real seismicity.
文摘Groundwater is the water located beneath the earth＇s surface in the soil pore spaces and in the fractures of rock formations. As one of the most important natural resources, groundwater is associated with the environment, public health, welfare, and long-term economic growth, which affects the daily activities of human beings. In modern urban areas, the primary contaminants of groundwater are artificial products, such as gasoline and diesel. To protect this important water resource, a series of efforts have been exerted, including enforcement and remedial actions. Each year, the TGPC （Texas Groundwater Protection Committee） in US publishes a ＂Joint Groundwater Monitoring and Contamination Report＂ to describe historic and new contamination cases in each county, which is an important data source for the design of prevention strategies. In this paper, a DDM （data dependent modeling） approach is proposed to predict county-level NCC （new contamination cases）. A case study with contamination information from Harris County in Texas was conducted to illustrate the modeling and prediction process with promising results. The one-step prediction error is 1.5%, while the two-step error is 12.1%. The established model can be used at the county-level, state-level, and even at the country-level. Besides, the prediction results could be a reference during decision-making processes.
基金supported by National Natural Science Foundation of China(Nos.91016006 and 91116002)Fundamental Research Funds for the Central Universities(Nos.30420111109,30420120305 and SWJTU11ZT06)in part by a PFund from Louisiana Board of Regents
文摘The performance of smart structures in trajectory tracking under sub-micron level is hindered by the rate-dependent hysteresis nonlinearity.In this paper,a Hammerstein-like model based on the support vector machines(SVM)is proposed to capture the rate-dependent hysteresis nonlinearity.We show that it is possible to construct a unique dynamic model in a given frequency range for a rate-dependent hysteresis system using the sinusoidal scanning signals as the training set of signals for the linear dynamic subsystem of the Hammerstein-like model.Subsequently,a two-degree-of-freedom(2DOF)H∞robust control scheme for the ratedependent hysteresis nonlinearity is implemented on a smart structure with a piezoelectric actuator(PEA)for real-time precision trajectory tracking.Simulations and experiments on the structure verify both the efectiveness and the practicality of the proposed modeling and control methods.
