To comprehensively understand the Arctic and Antarctic upper atmosphere, it is often crucial to analyze various data that are obtained from many regions. Infrastructure that promotes such interdisciplinary studies on ...To comprehensively understand the Arctic and Antarctic upper atmosphere, it is often crucial to analyze various data that are obtained from many regions. Infrastructure that promotes such interdisciplinary studies on the upper atmosphere has been developed by a Japanese inter-university project called the Inter-university Upper atmosphere Global Observation Network （1UGONET）. The objective of this paper is to describe the infrastructure and tools developed by IUGONET. We focus on the data analysis software. It is written in Interactive Data Language （IDL） and is a plug-in for the THEMIS Data Analysis Software suite （TDAS）, which is a set of IDL libraries used to visualize and analyze satellite- and ground-based data. We present plots of upper atmospheric data provided by IUGONET as examples of applications, and verify the usefulness of the software in the study of polar science. We discuss IUGONET＇s new and unique developments, i.e., an executable file of TDAS that can run on the IDL Virtual Machine, IDL routines to retrieve metadata from the IUGONET database, and an archive of 3-D simulation data that uses the Common Data Format so that it can easily be used with TDAS.展开更多
Background: Weibo is a Twitter-like micro-blog platform in China where people post their real-life events as well as express their feelings in short texts. Since the outbreak of the Covid-19 pandemic, thousands of peo...Background: Weibo is a Twitter-like micro-blog platform in China where people post their real-life events as well as express their feelings in short texts. Since the outbreak of the Covid-19 pandemic, thousands of people have expressed their concerns and worries about the outbreak via Weibo, showing the existence of public panic. Methods: This paper comes up with a sentiment analysis approach to discover public panic. First, we used Octoparse to obtain Weibo posts about the hot topic Covid-19 Pandemic. Second, we break down those sentences into independent words and clean the data by removing stop words. Then, we use the sentiment score function that deals with negative words, adverbs, and sentiment words to get the sentiment score of each Weibo post. Results: We observe the distribution of sentiment scores and get the benchmark to evaluate public panic. Also, we apply the same process to test the mass sentiment under other topics to test the efficiency of the sentiment function, which shows that our function works well.展开更多
Lunar wrinkle ridges are an important stress geological structure on the Moon, which reflect the stress state and geological activity on the Moon. They provide important insights into the evolution of the Moon and are...Lunar wrinkle ridges are an important stress geological structure on the Moon, which reflect the stress state and geological activity on the Moon. They provide important insights into the evolution of the Moon and are key factors influencing future lunar activity, such as the choice of landing sites. However, automatic extraction of lunar wrinkle ridges is a challenging task due to their complex morphology and ambiguous features. Traditional manual extraction methods are time-consuming and labor-intensive. To achieve automated and detailed detection of lunar wrinkle ridges, we have constructed a lunar wrinkle ridge data set, incorporating previously unused aspect data to provide edge information, and proposed a Dual-Branch Ridge Detection Network(DBR-Net) based on deep learning technology. This method employs a dual-branch architecture and an Attention Complementary Feature Fusion module to address the issue of insufficient lunar wrinkle ridge features. Through comparisons with the results of various deep learning approaches, it is demonstrated that the proposed method exhibits superior detection performance. Furthermore, the trained model was applied to lunar mare regions, generating a distribution map of lunar mare wrinkle ridges;a significant linear relationship between the length and area of the lunar wrinkle ridges was obtained through statistical analysis, and six previously unrecorded potential lunar wrinkle ridges were detected. The proposed method upgrades the automated extraction of lunar wrinkle ridges to a pixel-level precision and verifies the effectiveness of DBR-Net in lunar wrinkle ridge detection.展开更多
This paper aims to develop a unified Bayesian approach for clustered data analysis when observations are subject to missingness at random.The authors consider a general framework in which the parameters of interest ar...This paper aims to develop a unified Bayesian approach for clustered data analysis when observations are subject to missingness at random.The authors consider a general framework in which the parameters of interest are defined through estimating equations,and the probability of missingness follows a general parametric form.The generalized method of moments framework is employed to derive an optimal combination of inverse-probability-weighted estimating equations for the parameters of interest and score equations for propensity score.Using this framework,the authors develop a quasi-Bayesian analysis for clustered samples with missing values.A unified model selection approach is also proposed to compare models characterized by different moment conditions.The authors systematically evaluate the large-sample properties of the proposed quasi-posterior density with both fixed and shrinking priors and establish the selection consistency of the proposed model selection criterion.The proposed results are valid under very mild conditions and offer significant advantages for parameters defined through non-smooth estimating functions.Extensive numerical studies demonstrate that the proposed method performs exceptionally well in finite samples.展开更多
Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular...Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular devices,affect the distribution and uploading processes of model parameters.In FL-assisted Internet of Vehicles(IoV)scenarios,challenges such as data heterogeneity,limited device resources,and unstable communication environments become increasingly prominent.These issues necessitate intelligent vehicle selection schemes to enhance training efficiency.Given this context,we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions,and develop a dynamic interval multi-objective optimization algorithm to jointly optimize various factors including training experiments,system energy consumption,and bandwidth utilization to meet multi-criteria resource optimization requirements.For the problem at hand,we design a dynamic interval multi-objective optimization algorithm based on interval overlap detection.Simulation results demonstrate that our method outperforms other solutions in terms of accuracy,training cost,and server utilization.It effectively enhances training efficiency under wireless channel environments while rationally utilizing bandwidth resources,thus possessing significant scientific value and application potential in the field of IoV.展开更多
Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recov...Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery. We first derive the projection formulas for a vector onto the feasible sets. The centralized circumcentered-reflection method is designed to solve the convex feasibility problem. Some numerical experiments demonstrate the feasibility and effectiveness of the proposed algorithm, showing superior performance compared to conventional alternating projection methods.展开更多
Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is q...Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.展开更多
Real-time multi-person pose estimation(MPE)built upon neural network architectures aims to simultaneously detect multiple human instances and regress joint coordinates in dynamic scenes.However,due to factors such as ...Real-time multi-person pose estimation(MPE)built upon neural network architectures aims to simultaneously detect multiple human instances and regress joint coordinates in dynamic scenes.However,due to factors such as high model complexity and limited expression of keypoint information,both the efficiency and accuracy of real-time MPE remain to be improved.To mitigate the adverse impacts caused by the aforementioned issues,this work develops FSEM-Pose,a real-time MPE model rooted in the YOLOv10 framework.In detail,first,FSEM-Pose upgrades the backbone module of the baseline network by introducing the Feature Shuffling-Convolution(FS-Conv),which effectively reduces the backbone size while maximizing the retention of spatial information from the input image.Second,FSEM-Pose incorporates a Feature Saliency Enhancement Module(FSEM)to strengthen the feature encoding of human keypoints,thereby improving the accuracy of pose estimation.Finally,FSEM-Pose further enhances inference efficiency via a lightweight optimization of the head using shared convolutional layers.Our method achieves competitive results across multiple accuracy and efficiency metrics on the MS COCO 2017 and CrowdPose datasets.While being lightweight in design,it improves average precision(AP)by 2.1%and 2.5%,respectively.展开更多
Conventional Tb^(3+)-doped phosphors typically suffer from concentration quenching once the doping level exceeds a critical threshold.Consequently,the development of Tb^(3+)phosphors with intrinsic resistance to conce...Conventional Tb^(3+)-doped phosphors typically suffer from concentration quenching once the doping level exceeds a critical threshold.Consequently,the development of Tb^(3+)phosphors with intrinsic resistance to concentration quenching has become a key research focus.In this work,we successfully synthesized KBi(MoO_(4))_(2):x Tb^(3+)(x=0-100 at%)(denoted as KBM:x Tb^(3+))phosphors via a high-temperature solid-state reaction.Remarkably,no concentration quenching was observed across the entire doping range.This anti-quenching behavior originates from the large Tb^(3+)-Tb^(3+)interionic distance(>5Å)inherent to the quasi-layered crystal structure,which effectively suppresses multipole-interaction-mediated energy migration.At full Tb^(3+)substitution(x=100 at%),the material undergoes a structural phase transition from the monoclinic KBM phase to the triclinicα-KTb(MoO_(4))_(2)(α-KTM)phase.Theα-KTM phosphor exhibits excellent thermal stability(activation energy=0.6129 eV)and a single-exponential decay profile,whereas KBM:x Tb^(3+)(x<100%)display double-exponential decay behaviors,attributed to dual energy transfer pathways.These findings provide new insights into the luminescence mechanisms of high-concentration rare-earth-doped systems and offer guidance for designing nextgeneration anti-quenching phosphors.展开更多
In this paper,we first obtain the density of compactly supported bounded functions in anisotropic infinite dimensional Banach space-valued Musielak-Orlicz spaces.Then,we present the sufficient condition for the space ...In this paper,we first obtain the density of compactly supported bounded functions in anisotropic infinite dimensional Banach space-valued Musielak-Orlicz spaces.Then,we present the sufficient condition for the space of compactly supported smooth functions to be dense in anisotropic infinite dimensional Banach space-valued Musielak-Orlicz spaces.Moreover,the modular density is also given.展开更多
Background:Immune-mediated inflammatory disease(IMID)and cancer share underlying mechanisms.We aimed to comprehensively evaluate the associations between IMIDs and cancers from global,population and genetic perspectiv...Background:Immune-mediated inflammatory disease(IMID)and cancer share underlying mechanisms.We aimed to comprehensively evaluate the associations between IMIDs and cancers from global,population and genetic perspectives.Methods:A triangulation framework was employed to assess the association between IMIDs and cancers,using the Global Burden of Disease Study(2012-2021)to analyse six IMIDs and 33 cancers.The UK Biobank(UKBB)prospective cohort was subsequently used to validate these associations,with hazard ratios(HRs)and 95%confidence intervals(CIs)estimated by Cox proportional hazards models.Causal inference based on genetic instruments was performed in the FinnGen and UKBB to assess the potential causal effects between IMIDs and cancers.Results:IMIDs were positively associated with the occurrence of cancers from a global perspective.Moreover,170 specific IMID-cancer pairs revealed statistically significant associations.A total of 20 pairs of specific IMID-cancer associations were further confirmed in the UKBB cohort.Among these,the five most pronounced associations included atopic dermatitis with Hodgkin lymphoma(HR=12.56,95%CI:1.76-89.59),with ovarian cancer(HR=5.65,95%CI:1.41-22.65)and with non-Hodgkin lymphoma(HR=5.11,95%CI:1.91-13.63);rheumatoid arthritis with Hodgkin lymphoma(HR=3.85,95%CI:1.11-13.32);and psoriasis with Hodgkin lymphoma(HR=3.43,95%CI:1.69-6.96).Additionally,a positive causal association between rheumatoid arthritis and Hodgkin lymphoma(inverse variance weighted OR=1.31,95%CI:1.10-1.57)was observed.Conclusions:This study provides comprehensive evidence of the relationships between IMIDs and cancers from global,population and genetic perspectives and identifies 20 pairs of specific IMID-cancer associations,thereby contributing to advancements in cancer prevention and control.展开更多
The recent effort by The Cancer Genome Atlas(TCGA)Network has revealed that gastric cancer,which is a leading cause of cancerrelated deaths worldwide with a 5-year survival rate less than 25%,is a much more heterogene...The recent effort by The Cancer Genome Atlas(TCGA)Network has revealed that gastric cancer,which is a leading cause of cancerrelated deaths worldwide with a 5-year survival rate less than 25%,is a much more heterogeneous disease than previously thought.And yet,conventional treatment approaches and clinical trials have assumed it is a single disease.展开更多
Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as vi...Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximiza- tion and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffu- sion analysis are listed for further study.展开更多
OBJECTIVE:To systematically investigate the clinical effectiveness and safety of traditional Chinese herbs(TCHs)as an alternative to conventional medicine(CM)in children with cough variant asthma(CVA).METHODS:Randomiz...OBJECTIVE:To systematically investigate the clinical effectiveness and safety of traditional Chinese herbs(TCHs)as an alternative to conventional medicine(CM)in children with cough variant asthma(CVA).METHODS:Randomized controlled trial(RCT)studies that were published from their inceptions to March 31,2020,were identified from the electronic databases of China National Knowledge Infrastructure,Wangfang,Pub Med,and Cochrane Central Library.The primary outcome of the review was the total effective rate(TER),and the secondary outcomes were immunoglobulin E(Ig E),peak expiratory flow(PEF),adverse drug reactions,and relapse rates of interventions.RESULTS:For the Meta-analysis,13 studies involving 992 children with CVA were included.In terms of TER and Ig E,the experimental interventions of TCH,when compared with the control interventions of CM,on pediatric CVA were found to be significantly effective(P<0.0001),whereas for spirometry,PEF was not significantly improved in the TCH group(P=0.48).The incident rates of adverse drug reaction and relapse were found to be significantly lower in the TCH group than those in the CM group(P=0.02 and P<0.0001,respectively).CONCLUSION:Compared with CM therapy,the effects of TCH therapy on pediatric CVA were significantly beneficial in terms of TER and Ig E,but not for PEF,and the methodological quality of included studies was poor.Therefore,the results should be interpreted with caution.More randomized controlled trials with rigorous experimental methodologies are required for objectivity in the future.展开更多
Coal is a crucial fossil energy in today’s society,and the detection of sulfir(S) and nitrogen(N)in coal is essential for the evaluation of coal quality.Therefore,an efficient method is needed to quantitatively analy...Coal is a crucial fossil energy in today’s society,and the detection of sulfir(S) and nitrogen(N)in coal is essential for the evaluation of coal quality.Therefore,an efficient method is needed to quantitatively analyze N and S content in coal,to achieve the purpose of clean utilization of coal.This study applied laser-induced breakdown spectroscopy(LIBS) to test coal quality,and combined two variable selection algorithms,competitive adaptive reweighted sampling(CARS) and the successive projections algorithm(SPA),to establish the corresponding partial least square(PLS) model.The results of the experiment were as follows.The PLS modeled with the full spectrum of 27,620 variables has poor accuracy,the coefficient of determination of the test set(R^2 P) and root mean square error of the test set(RMSEP) of nitrogen were 0.5172 and 0.2263,respectively,and those of sulfur were0.5784 and 0.5811,respectively.The CARS-PLS screened 37 and 25 variables respectively in the detection of N and S elements,but the prediction ability of the model did not improve significantly.SPA-PLS finally screened 14 and 11 variables respectively through successive projections,and obtained the best prediction effect among the three methods.The R^2 P and RMSEP of nitrogen were0.9873 and 0.0208,respectively,and those of sulfur were 0.9451 and 0.2082,respectively.In general,the predictive results of the two elements increased by about 90% for RMSEP and 60% for R2 P compared with PLS.The results show that LIBS combined with SPA-PLS has good potential for detecting N and S content in coal,and is a very promising technology for industrial application.展开更多
Wireless channel characteristics have significant impacts on channel modeling,estimation,and communication performance.While the channel sparsity is an important characteristic of wireless channels.Utilizing the spars...Wireless channel characteristics have significant impacts on channel modeling,estimation,and communication performance.While the channel sparsity is an important characteristic of wireless channels.Utilizing the sparse nature of wireless channels can reduce the complexity of channel modeling and estimation,and improve system design and performance analysis.Compared with the traditional sub6 GHz channel,millimeter wave(mmWave)channel has been considered to be more sparse in existing researches.However,most research only assume that the mmWave channel is sparse,without providing quantitative analysis and evaluation.Therefore,this paper evaluates the sparsity of mmWave channels based on mmWave channel measurements.A vector network analyzer(VNA)-based mmWave channel sounder is developed to measure the channel at 28 GHz,and multi-scenario channel measurements are conducted.The Gini index,Rician𝐾factor and rootmean-square(RMS)delay spread are used to measure channel sparsity.Then,the key factors affecting mmWave channel sparsity are explored.It is found that antenna steering direction and scattering environment will affect the sparsity of mmWave channel.In addition,the impact of channel sparsity on channel eigenvalue and capacity is evaluated and analyzed.展开更多
Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir development.Traditional physics-based numerical simulations suffer from insuffic...Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir development.Traditional physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production optimization.We establish an ensemble proxy-model-assisted optimization framework combining the Bayesian random forest(BRF)with the particle swarm optimization algorithm(PSO).The BRF method is implemented to construct a proxy model of the injectioneproduction system that can accurately predict the dynamic parameters of producers based on injection data and production measures.With the help of proxy model,PSO is applied to search the optimal injection pattern integrating Pareto front analysis.After experimental testing,the proxy model not only boasts higher prediction accuracy compared to deep learning,but it also requires 8 times less time for training.In addition,the injection mode adjusted by the PSO algorithm can effectively reduce the gaseoil ratio and increase the oil production by more than 10% for carbonate reservoirs.The proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry,which can provide more options for the project decision-makers to balance the oil production and the gaseoil ratio considering physical and operational constraints.展开更多
Foot-and-mouth disease(FMD)is an acute,highly infectious and pathogenic animal disease.In recent years,with the rapid development of the swine breeding industry in China,pig farms have shown a trend of larger-scale de...Foot-and-mouth disease(FMD)is an acute,highly infectious and pathogenic animal disease.In recent years,with the rapid development of the swine breeding industry in China,pig farms have shown a trend of larger-scale development.Large-scale pig farms employ standardized management,a high level of automation,and a strict_system.However,these farms have a large trading volume,and increased transmission intensity of FMD is noted inside the farm.At present,the main control measure against FMD is pig vaccination.However,a standard for immunization procedures is not available,and currently adopted immunization procedures have not been effectively and systematically evaluated.Taking a typical large-scale pig farm in China as the research subject and considering the breeding pattern,piggery structure,age structure and immunization procedures,an individual-based state probability model is established to evaluate the effectiveness of the immune procedure.Based on numerical simulation,it is concluded that the optimal immunization program involves primary immunization at 40 days of age and secondary immunization at 80 days of age for commercial pigs.Breeding boars and breeding sows are immunized 4 times a year,and reserve pigs are immunized at 169 and 259 days of age.According to the theoretical analysis,the average control reproduction number of individuals under the optimal immunization procedure in the farm is 0.4927.In the absence of immunization,the average is 1.7498,indicating that the epidemic cannot be controlled without immunization procedures.展开更多
Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to pro...Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations.This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC problem.The new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential areas.First,it identifies tree canopy regions using a deep learning network from high-resolution imagery.It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing.Finally,it calculates and describes the number of individual trees and tree canopies.The proposed approach is experimented with the data from Shanghai,China.Our results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland scene.Meanwhile,the R^(2) value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,respectively.These results confirm that the proposed method is robust enough for urban tree planning and management.展开更多
An intersection of two or more roads poses a risk for potential conflicts among vehicles.Often the reasons triggering such conflicts are not clear,as they might be too subtle for the human eye.The environment also pla...An intersection of two or more roads poses a risk for potential conflicts among vehicles.Often the reasons triggering such conflicts are not clear,as they might be too subtle for the human eye.The environment also plays a part in understanding where,when,and why a particular vehicle interaction has occurred in a certain way.Therefore,it is of paramount importance to dive deeper into the vehicle interaction at a micro-scale within the embedded geographical environment,particularly at the intersections.This would in turn assist in evaluating the association of vehicle interactions with conflict risks and near-miss accidents.Moreover,detection of such micro traffic interactions could also be used to improvise the complexity of the already established transport infrastructure.Conversely,traffic at intersections has been explored mainly for flow estimation,capacity and width measurements,and traffic congestion,etc.,whereas the detection of micro-scale traffic interactions at intersections remains relatively under-explored.In this paper,we present a novel approach to retrieve and represent micro-scale traffic movement interactions at a non-signalized T-junction by extending a recently introduced qualitative spatiotemporal Point-Descriptor-Precedence(PDP)representation.We study how the PDP representation offers a fine solution to study the interaction of traffic flows at intersections.This permits tracking the micro-movement of vehicles in much finer detail,which is used later to retrieve movement patterns from a motion dataset.Unlike conventional approaches,we start our approach with the actual movements before modeling the static intersection environment.Additionally,with the aid of illustrative examples,we discuss how the length,width,and speed of the vehicles can be exploited in our approach to detect specific patterns more accurately.Additionally,we address the potential benefits of our approach for traffic safety assessment and how it can be extended to a network of intersections using different transport modes.展开更多
基金supported by the Special Edu-cational Research Budget(Research Promotion)[FY2009]the Special Budget(Project)[FY2010 and later years]from the Ministry of Education,Culture,Sports,Science and Technology(MEXT),Japansupported by the GRENE Arctic Climate Change Research Project,Japan
文摘To comprehensively understand the Arctic and Antarctic upper atmosphere, it is often crucial to analyze various data that are obtained from many regions. Infrastructure that promotes such interdisciplinary studies on the upper atmosphere has been developed by a Japanese inter-university project called the Inter-university Upper atmosphere Global Observation Network （1UGONET）. The objective of this paper is to describe the infrastructure and tools developed by IUGONET. We focus on the data analysis software. It is written in Interactive Data Language （IDL） and is a plug-in for the THEMIS Data Analysis Software suite （TDAS）, which is a set of IDL libraries used to visualize and analyze satellite- and ground-based data. We present plots of upper atmospheric data provided by IUGONET as examples of applications, and verify the usefulness of the software in the study of polar science. We discuss IUGONET＇s new and unique developments, i.e., an executable file of TDAS that can run on the IDL Virtual Machine, IDL routines to retrieve metadata from the IUGONET database, and an archive of 3-D simulation data that uses the Common Data Format so that it can easily be used with TDAS.
文摘Background: Weibo is a Twitter-like micro-blog platform in China where people post their real-life events as well as express their feelings in short texts. Since the outbreak of the Covid-19 pandemic, thousands of people have expressed their concerns and worries about the outbreak via Weibo, showing the existence of public panic. Methods: This paper comes up with a sentiment analysis approach to discover public panic. First, we used Octoparse to obtain Weibo posts about the hot topic Covid-19 Pandemic. Second, we break down those sentences into independent words and clean the data by removing stop words. Then, we use the sentiment score function that deals with negative words, adverbs, and sentiment words to get the sentiment score of each Weibo post. Results: We observe the distribution of sentiment scores and get the benchmark to evaluate public panic. Also, we apply the same process to test the mass sentiment under other topics to test the efficiency of the sentiment function, which shows that our function works well.
文摘Lunar wrinkle ridges are an important stress geological structure on the Moon, which reflect the stress state and geological activity on the Moon. They provide important insights into the evolution of the Moon and are key factors influencing future lunar activity, such as the choice of landing sites. However, automatic extraction of lunar wrinkle ridges is a challenging task due to their complex morphology and ambiguous features. Traditional manual extraction methods are time-consuming and labor-intensive. To achieve automated and detailed detection of lunar wrinkle ridges, we have constructed a lunar wrinkle ridge data set, incorporating previously unused aspect data to provide edge information, and proposed a Dual-Branch Ridge Detection Network(DBR-Net) based on deep learning technology. This method employs a dual-branch architecture and an Attention Complementary Feature Fusion module to address the issue of insufficient lunar wrinkle ridge features. Through comparisons with the results of various deep learning approaches, it is demonstrated that the proposed method exhibits superior detection performance. Furthermore, the trained model was applied to lunar mare regions, generating a distribution map of lunar mare wrinkle ridges;a significant linear relationship between the length and area of the lunar wrinkle ridges was obtained through statistical analysis, and six previously unrecorded potential lunar wrinkle ridges were detected. The proposed method upgrades the automated extraction of lunar wrinkle ridges to a pixel-level precision and verifies the effectiveness of DBR-Net in lunar wrinkle ridge detection.
基金supported by the National Key R&D Program of China under Grant No.2022YFA1003701the National Natural Science Foundation of China under Grant Nos.12331009 and 12071416the Yunnan Fundamental Research Projects under Grant No.202201AV070006。
文摘This paper aims to develop a unified Bayesian approach for clustered data analysis when observations are subject to missingness at random.The authors consider a general framework in which the parameters of interest are defined through estimating equations,and the probability of missingness follows a general parametric form.The generalized method of moments framework is employed to derive an optimal combination of inverse-probability-weighted estimating equations for the parameters of interest and score equations for propensity score.Using this framework,the authors develop a quasi-Bayesian analysis for clustered samples with missing values.A unified model selection approach is also proposed to compare models characterized by different moment conditions.The authors systematically evaluate the large-sample properties of the proposed quasi-posterior density with both fixed and shrinking priors and establish the selection consistency of the proposed model selection criterion.The proposed results are valid under very mild conditions and offer significant advantages for parameters defined through non-smooth estimating functions.Extensive numerical studies demonstrate that the proposed method performs exceptionally well in finite samples.
基金supported in part by the Central Guidance for Local Science and Technology Development Funds under Grant No.YDZJSX2025D049Shanxi Provincial Graduate Innovation Research Program under Grant No.2024KY652.
文摘Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular devices,affect the distribution and uploading processes of model parameters.In FL-assisted Internet of Vehicles(IoV)scenarios,challenges such as data heterogeneity,limited device resources,and unstable communication environments become increasingly prominent.These issues necessitate intelligent vehicle selection schemes to enhance training efficiency.Given this context,we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions,and develop a dynamic interval multi-objective optimization algorithm to jointly optimize various factors including training experiments,system energy consumption,and bandwidth utilization to meet multi-criteria resource optimization requirements.For the problem at hand,we design a dynamic interval multi-objective optimization algorithm based on interval overlap detection.Simulation results demonstrate that our method outperforms other solutions in terms of accuracy,training cost,and server utilization.It effectively enhances training efficiency under wireless channel environments while rationally utilizing bandwidth resources,thus possessing significant scientific value and application potential in the field of IoV.
基金Supported by the Natural Science Foundation of Guangxi Province(Grant Nos.2023GXNSFAA026067,2024GXN SFAA010521)the National Natural Science Foundation of China(Nos.12361079,12201149,12261026).
文摘Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery. We first derive the projection formulas for a vector onto the feasible sets. The centralized circumcentered-reflection method is designed to solve the convex feasibility problem. Some numerical experiments demonstrate the feasibility and effectiveness of the proposed algorithm, showing superior performance compared to conventional alternating projection methods.
基金supported by the National Key R&D Program of China under Grant No.2022YFA1003701the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University under Grant No.SMDAYB2023004。
文摘Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.
基金supported by the Talent Startup Program of Huangshan University under Grant No.2025xkjq003Additional partial funding was gratefully received from the Scientific Research Project of the Anhui Provincial Department of Education under Grant No.2025AHGXZK40303.
文摘Real-time multi-person pose estimation(MPE)built upon neural network architectures aims to simultaneously detect multiple human instances and regress joint coordinates in dynamic scenes.However,due to factors such as high model complexity and limited expression of keypoint information,both the efficiency and accuracy of real-time MPE remain to be improved.To mitigate the adverse impacts caused by the aforementioned issues,this work develops FSEM-Pose,a real-time MPE model rooted in the YOLOv10 framework.In detail,first,FSEM-Pose upgrades the backbone module of the baseline network by introducing the Feature Shuffling-Convolution(FS-Conv),which effectively reduces the backbone size while maximizing the retention of spatial information from the input image.Second,FSEM-Pose incorporates a Feature Saliency Enhancement Module(FSEM)to strengthen the feature encoding of human keypoints,thereby improving the accuracy of pose estimation.Finally,FSEM-Pose further enhances inference efficiency via a lightweight optimization of the head using shared convolutional layers.Our method achieves competitive results across multiple accuracy and efficiency metrics on the MS COCO 2017 and CrowdPose datasets.While being lightweight in design,it improves average precision(AP)by 2.1%and 2.5%,respectively.
基金supported by the Natural Science Research Project of Anhui Province Education Department for Excellent Young Scholars(Grant No.2024AH030007)the National Natural Science Foundation of China(Grant No.52202001)。
文摘Conventional Tb^(3+)-doped phosphors typically suffer from concentration quenching once the doping level exceeds a critical threshold.Consequently,the development of Tb^(3+)phosphors with intrinsic resistance to concentration quenching has become a key research focus.In this work,we successfully synthesized KBi(MoO_(4))_(2):x Tb^(3+)(x=0-100 at%)(denoted as KBM:x Tb^(3+))phosphors via a high-temperature solid-state reaction.Remarkably,no concentration quenching was observed across the entire doping range.This anti-quenching behavior originates from the large Tb^(3+)-Tb^(3+)interionic distance(>5Å)inherent to the quasi-layered crystal structure,which effectively suppresses multipole-interaction-mediated energy migration.At full Tb^(3+)substitution(x=100 at%),the material undergoes a structural phase transition from the monoclinic KBM phase to the triclinicα-KTb(MoO_(4))_(2)(α-KTM)phase.Theα-KTM phosphor exhibits excellent thermal stability(activation energy=0.6129 eV)and a single-exponential decay profile,whereas KBM:x Tb^(3+)(x<100%)display double-exponential decay behaviors,attributed to dual energy transfer pathways.These findings provide new insights into the luminescence mechanisms of high-concentration rare-earth-doped systems and offer guidance for designing nextgeneration anti-quenching phosphors.
基金Supported by the National Natural Science Foundation of China(Grant No.12161022)the Science and Technology Project of Guangxi(Grant No.Guike AD23023002).
文摘In this paper,we first obtain the density of compactly supported bounded functions in anisotropic infinite dimensional Banach space-valued Musielak-Orlicz spaces.Then,we present the sufficient condition for the space of compactly supported smooth functions to be dense in anisotropic infinite dimensional Banach space-valued Musielak-Orlicz spaces.Moreover,the modular density is also given.
基金National Natural Science Foundation of China,Grant/Award Numbers:82404340,82273722,82373685,82204143CAMS Innovation Fund for Medical Science,Grant/Award Number:2021-I2M-1-067Open Research Fund Programme of Changzhou Institute for Advanced Study of Public Health,Nanjing Medical University,Grant/Award Number:CPHM202301。
文摘Background:Immune-mediated inflammatory disease(IMID)and cancer share underlying mechanisms.We aimed to comprehensively evaluate the associations between IMIDs and cancers from global,population and genetic perspectives.Methods:A triangulation framework was employed to assess the association between IMIDs and cancers,using the Global Burden of Disease Study(2012-2021)to analyse six IMIDs and 33 cancers.The UK Biobank(UKBB)prospective cohort was subsequently used to validate these associations,with hazard ratios(HRs)and 95%confidence intervals(CIs)estimated by Cox proportional hazards models.Causal inference based on genetic instruments was performed in the FinnGen and UKBB to assess the potential causal effects between IMIDs and cancers.Results:IMIDs were positively associated with the occurrence of cancers from a global perspective.Moreover,170 specific IMID-cancer pairs revealed statistically significant associations.A total of 20 pairs of specific IMID-cancer associations were further confirmed in the UKBB cohort.Among these,the five most pronounced associations included atopic dermatitis with Hodgkin lymphoma(HR=12.56,95%CI:1.76-89.59),with ovarian cancer(HR=5.65,95%CI:1.41-22.65)and with non-Hodgkin lymphoma(HR=5.11,95%CI:1.91-13.63);rheumatoid arthritis with Hodgkin lymphoma(HR=3.85,95%CI:1.11-13.32);and psoriasis with Hodgkin lymphoma(HR=3.43,95%CI:1.69-6.96).Additionally,a positive causal association between rheumatoid arthritis and Hodgkin lymphoma(inverse variance weighted OR=1.31,95%CI:1.10-1.57)was observed.Conclusions:This study provides comprehensive evidence of the relationships between IMIDs and cancers from global,population and genetic perspectives and identifies 20 pairs of specific IMID-cancer associations,thereby contributing to advancements in cancer prevention and control.
文摘The recent effort by The Cancer Genome Atlas(TCGA)Network has revealed that gastric cancer,which is a leading cause of cancerrelated deaths worldwide with a 5-year survival rate less than 25%,is a much more heterogeneous disease than previously thought.And yet,conventional treatment approaches and clinical trials have assumed it is a single disease.
基金supported by National Natural Science Foundation of China(Nos.61703386,U1605251 and91546103)the Anhui Provincial Natural Science Foundation(No.1708085QF140)+1 种基金the Fundamental Research Funds for the Central Universities(No.WK2150110006)the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2014299)
文摘Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximiza- tion and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffu- sion analysis are listed for further study.
文摘OBJECTIVE:To systematically investigate the clinical effectiveness and safety of traditional Chinese herbs(TCHs)as an alternative to conventional medicine(CM)in children with cough variant asthma(CVA).METHODS:Randomized controlled trial(RCT)studies that were published from their inceptions to March 31,2020,were identified from the electronic databases of China National Knowledge Infrastructure,Wangfang,Pub Med,and Cochrane Central Library.The primary outcome of the review was the total effective rate(TER),and the secondary outcomes were immunoglobulin E(Ig E),peak expiratory flow(PEF),adverse drug reactions,and relapse rates of interventions.RESULTS:For the Meta-analysis,13 studies involving 992 children with CVA were included.In terms of TER and Ig E,the experimental interventions of TCH,when compared with the control interventions of CM,on pediatric CVA were found to be significantly effective(P<0.0001),whereas for spirometry,PEF was not significantly improved in the TCH group(P=0.48).The incident rates of adverse drug reaction and relapse were found to be significantly lower in the TCH group than those in the CM group(P=0.02 and P<0.0001,respectively).CONCLUSION:Compared with CM therapy,the effects of TCH therapy on pediatric CVA were significantly beneficial in terms of TER and Ig E,but not for PEF,and the methodological quality of included studies was poor.Therefore,the results should be interpreted with caution.More randomized controlled trials with rigorous experimental methodologies are required for objectivity in the future.
基金the Jiangsu Government Scholarship for Overseas Studies (JS-2019-031)the Startup Foundation for Introducing Talent of NUIST (2243141701023)。
文摘Coal is a crucial fossil energy in today’s society,and the detection of sulfir(S) and nitrogen(N)in coal is essential for the evaluation of coal quality.Therefore,an efficient method is needed to quantitatively analyze N and S content in coal,to achieve the purpose of clean utilization of coal.This study applied laser-induced breakdown spectroscopy(LIBS) to test coal quality,and combined two variable selection algorithms,competitive adaptive reweighted sampling(CARS) and the successive projections algorithm(SPA),to establish the corresponding partial least square(PLS) model.The results of the experiment were as follows.The PLS modeled with the full spectrum of 27,620 variables has poor accuracy,the coefficient of determination of the test set(R^2 P) and root mean square error of the test set(RMSEP) of nitrogen were 0.5172 and 0.2263,respectively,and those of sulfur were0.5784 and 0.5811,respectively.The CARS-PLS screened 37 and 25 variables respectively in the detection of N and S elements,but the prediction ability of the model did not improve significantly.SPA-PLS finally screened 14 and 11 variables respectively through successive projections,and obtained the best prediction effect among the three methods.The R^2 P and RMSEP of nitrogen were0.9873 and 0.0208,respectively,and those of sulfur were 0.9451 and 0.2082,respectively.In general,the predictive results of the two elements increased by about 90% for RMSEP and 60% for R2 P compared with PLS.The results show that LIBS combined with SPA-PLS has good potential for detecting N and S content in coal,and is a very promising technology for industrial application.
基金supported by National Key R&D Program of China under Grant 2022YFF0608103the National Natural Science Foundation of China under Grant 61922012+1 种基金the Science and Technology Program of State Administration for Market Regulation under Grant 2021MK155the Fundamental Funds of National Institute of Metrology under Grant AKYZD2116-2.
文摘Wireless channel characteristics have significant impacts on channel modeling,estimation,and communication performance.While the channel sparsity is an important characteristic of wireless channels.Utilizing the sparse nature of wireless channels can reduce the complexity of channel modeling and estimation,and improve system design and performance analysis.Compared with the traditional sub6 GHz channel,millimeter wave(mmWave)channel has been considered to be more sparse in existing researches.However,most research only assume that the mmWave channel is sparse,without providing quantitative analysis and evaluation.Therefore,this paper evaluates the sparsity of mmWave channels based on mmWave channel measurements.A vector network analyzer(VNA)-based mmWave channel sounder is developed to measure the channel at 28 GHz,and multi-scenario channel measurements are conducted.The Gini index,Rician𝐾factor and rootmean-square(RMS)delay spread are used to measure channel sparsity.Then,the key factors affecting mmWave channel sparsity are explored.It is found that antenna steering direction and scattering environment will affect the sparsity of mmWave channel.In addition,the impact of channel sparsity on channel eigenvalue and capacity is evaluated and analyzed.
基金the financial support of this work from the National Natural Science Foundation of China(Grant No.11972073,Grant No.51974357,and Grant No.52274027)supported by China Postdoctoral Science Foundation(Grant No.2022M713204)Scientific Research and Technology Development Project of China National Petroleum Corporation(Grant No.2121DJ2301).
文摘Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir development.Traditional physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production optimization.We establish an ensemble proxy-model-assisted optimization framework combining the Bayesian random forest(BRF)with the particle swarm optimization algorithm(PSO).The BRF method is implemented to construct a proxy model of the injectioneproduction system that can accurately predict the dynamic parameters of producers based on injection data and production measures.With the help of proxy model,PSO is applied to search the optimal injection pattern integrating Pareto front analysis.After experimental testing,the proxy model not only boasts higher prediction accuracy compared to deep learning,but it also requires 8 times less time for training.In addition,the injection mode adjusted by the PSO algorithm can effectively reduce the gaseoil ratio and increase the oil production by more than 10% for carbonate reservoirs.The proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry,which can provide more options for the project decision-makers to balance the oil production and the gaseoil ratio considering physical and operational constraints.
基金supported by the National Key Research and Development Program of China(2016YFD0501501)the National Natural Science Foundation of China under Grant(11601292,61873154,11801398)+4 种基金Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province(20210009)General Youth Fund project in Shanxi Province(201901D211158)the 1331 Engineering Project of Shanxi Province,Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi Province(2019L0114)Key Projects of Health Commission of Shanxi Province(No.2020XM18)the Key Research and Development Project in Shanxi Province(202003D31011/GZ).
文摘Foot-and-mouth disease(FMD)is an acute,highly infectious and pathogenic animal disease.In recent years,with the rapid development of the swine breeding industry in China,pig farms have shown a trend of larger-scale development.Large-scale pig farms employ standardized management,a high level of automation,and a strict_system.However,these farms have a large trading volume,and increased transmission intensity of FMD is noted inside the farm.At present,the main control measure against FMD is pig vaccination.However,a standard for immunization procedures is not available,and currently adopted immunization procedures have not been effectively and systematically evaluated.Taking a typical large-scale pig farm in China as the research subject and considering the breeding pattern,piggery structure,age structure and immunization procedures,an individual-based state probability model is established to evaluate the effectiveness of the immune procedure.Based on numerical simulation,it is concluded that the optimal immunization program involves primary immunization at 40 days of age and secondary immunization at 80 days of age for commercial pigs.Breeding boars and breeding sows are immunized 4 times a year,and reserve pigs are immunized at 169 and 259 days of age.According to the theoretical analysis,the average control reproduction number of individuals under the optimal immunization procedure in the farm is 0.4927.In the absence of immunization,the average is 1.7498,indicating that the epidemic cannot be controlled without immunization procedures.
基金supported by the project funded by International Research Center of Big Data for Sustainable 740 Development Goals[Grant Number CBAS2022GSP07]Fundamental Research Funds for the Central Universities,Chongqing Natural Science Foundation[Grant Number CSTB2022NSCQMSX 2069]Ministry of Education of China[Grant Number 19JZD023].
文摘Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations.This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC problem.The new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential areas.First,it identifies tree canopy regions using a deep learning network from high-resolution imagery.It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing.Finally,it calculates and describes the number of individual trees and tree canopies.The proposed approach is experimented with the data from Shanghai,China.Our results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland scene.Meanwhile,the R^(2) value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,respectively.These results confirm that the proposed method is robust enough for urban tree planning and management.
基金supported by the Higher Education Commission(HEC),Pakistan[grant number 50040696]Bernard De Baets and Guy De Tréreceived funding from the Flemish Government under the“Onderzoeksprogramma Artificiële Intelligentie(AI)Vlaanderen”program.
文摘An intersection of two or more roads poses a risk for potential conflicts among vehicles.Often the reasons triggering such conflicts are not clear,as they might be too subtle for the human eye.The environment also plays a part in understanding where,when,and why a particular vehicle interaction has occurred in a certain way.Therefore,it is of paramount importance to dive deeper into the vehicle interaction at a micro-scale within the embedded geographical environment,particularly at the intersections.This would in turn assist in evaluating the association of vehicle interactions with conflict risks and near-miss accidents.Moreover,detection of such micro traffic interactions could also be used to improvise the complexity of the already established transport infrastructure.Conversely,traffic at intersections has been explored mainly for flow estimation,capacity and width measurements,and traffic congestion,etc.,whereas the detection of micro-scale traffic interactions at intersections remains relatively under-explored.In this paper,we present a novel approach to retrieve and represent micro-scale traffic movement interactions at a non-signalized T-junction by extending a recently introduced qualitative spatiotemporal Point-Descriptor-Precedence(PDP)representation.We study how the PDP representation offers a fine solution to study the interaction of traffic flows at intersections.This permits tracking the micro-movement of vehicles in much finer detail,which is used later to retrieve movement patterns from a motion dataset.Unlike conventional approaches,we start our approach with the actual movements before modeling the static intersection environment.Additionally,with the aid of illustrative examples,we discuss how the length,width,and speed of the vehicles can be exploited in our approach to detect specific patterns more accurately.Additionally,we address the potential benefits of our approach for traffic safety assessment and how it can be extended to a network of intersections using different transport modes.
