Kinetic equilibrium reconstruction with internal safety factor profile constraints on EAST tokamak

Reconstruction of plasma equilibrium plays an important role in the analysis and simulation of plasma experiments. The kinetic equilibrium reconstruction with pressure and edge current constraints has been employed on EAST tokamak. However, the internal safety factor(q) profile is not accurate. This paper proposes a new way of incorporating q profile constraints into kinetic equilibrium reconstruction. The q profile is yielded from the Polarimeter Interferometer(POINT)reconstruction. Virtual probes containing information on q profile constraints are added to inputs of the kinetic equilibrium reconstruction program to obtain the final equilibrium. The new equilibrium produces a more accurate internal q profile. This improved method would help analyze EAST experiments.

User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity

As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.

Reconstructing Sound Speed Profiles with Sea Surface Data

Ocean sound speed profile(SSP) is the key factor affecting acoustic propagation. The acquisition of SSPsin real time with high precision is meaningful for underwater activities. By means of the remote sensing method, thesea surface data could be obtained in near-real time. Typically, the subsurface fields are correlated with the sea surfaceparameters. Thus, the SSPs could be obtained by means of satellite remote sensing. In this paper, the history as wellas the current research over the reconstruction of subsurface fields by means of sea surface data is introduced. Thentwo methods to reconstruct the SSPs with sea surface data, including the linear regression method using the empiricalorthogonal function, and the self-organizing method based on the big data theory, are described in detail in the paper.

Elimination of Wind Wave Effects on Sub-Bottom Profiler Record

With the presence of wind waves, the swaying of survey vessel may effect the quality of sub-bottom profiler records and, therefore, it is necessary to correct the distortions induced by wave action. A major issue is to distinguish wind wave effect and real bedforms such as sand waves. In this paper, a bandstop filter is designed according to the frequency features of wind wave effect to treat the distortion of seabed topography by wind waves. The technique is used to correct the sub-bottom profile in order to eliminate the wave-induced distortions for the sub-bottom profile records from the Yangtze Estuary. This study shows that the undulate seabed record is resulted from wave action, rather than the presence of sand waves, and the filtration technique helps to eliminate the wave effect and recover the real morphology of seabed and the sediment sequence underneath. In addition, a method for data processing is proposed for the case that the record indeed represents a combination of wave effects and real bedforms.

Real data extraction process and procedure of geophysical exploration profile

In this paper,we present an open python procedure with Jupyter notebook,for data extraction and vectorization of geophysical explo ration profile.Constrained by observation routes and traffic conditions,geophysical exploration profiles tend to bend curved roads for easy observation,however,it must be projected onto a straight line when data processing and analyzing.After projection,we don’t know the true position of the obtained crustal structure.Nonetheless,when the results used as an initial constraint condition for other geophysical inversion,such as gravity inversion,we need to know the true position of the data rather than the distance to the starting point.We solved this problem by profile vectorization and reprojection.The method can be used for extraction data of various geophysical exploration profiles,such as seismic reflection profiles,gravity profiles.

Multiple Profiles Sensor-based Engineering Data Processing

Nowadays big data is widely adopted in industry field.In an advanced manufacturing system hundreds of sensors are deployed to collect key variables for system performance and the real-time data would be used for further monitoring and anomaly detection.However,there are many challenges for applying the sensor-based data directly,including the profile data has unsynchronized different length for different samples,the existence of obvious longterm drift,strong correlation of sensor clusters and the particular feature extraction.To solve these problems this invention presents a multiple profiles sensorbased engineering data processing system,including(1)preprocessing the signals to align the data and remove long-term drift,(2)clustering the sensors which have strong correlations,and(3)extracting particular features from different sensor clusters.

基于优化算法竞赛场景的改进data profile技术

数值比较是评估最优化算法特别是全局优化算法必不可少的手段,通常利用已知全局最优值的测试函数集来对优化算法进行性能评估。Data profile技术是用于比较确定性最优化算法的一种数据分析技术,最近被推广到随机优化算法的数值比较中。但data profile技术存在一个不良性质,得到的profile曲线会随着参与比较的优化算法的不同而改变。这一“传递无效性”缺陷不利于普通用户对该技术的认识和使用。分析表明,导致“传递无效性”的根源在于data profile的“收敛条件”定义,笔者提出的新“收敛条件”可以消除该缺陷,条件是需要用到目标函数的全局最优值,因而适用于最优值已知的众多优化算法竞赛场景。大量数值实验表明,改进的data profile技术消除了“传递无效性”缺陷,有利于不同算法竞赛之间结果的相互验证和推广使用。

Co-expression network analysis of virulence genes exoS and exoU of pseudomonas aeruginosa in lower respiratory tract based on histological data expression profiles

Objective:To use the gene chip of pseudomonas aeruginosa as a research sample and to explore it at an omics level,aiming at elucidating the co-expression network characteristics of the virulence genes exoS and exoU of pseudomonas aeruginosa in the lower respiratory tract from the perspective of molecular biology and identifying its key regulatory genes.Methods:From March 2016 to May 2018,312 patients infected with pseudomonas aeruginosa in the lower respiratory tract who were admitted to Department of Respiratory Medicine of Baogang Hospital and given follow-up treatments in the hospital were selected as subjects by use of cluster sampling.Alveolar lavage fluid and sputum collected from those patients were used as biological specimens.The genes of pseudomonas aeruginosa were detected with the help of oligonucleotide probes to make a pre-processing of chip data.A total of 8 common antibiotics(ceftazidime,gentamicin,piperacillin,amikacin,ciprofloxacin,levofloxacin,doripenem and ticarcillin)against Gram-negative bacteria were selected to determine the drug resistance of biological specimens.MCODE algorithm was used to construct a co-expression network model of the drug-resistance genes focused on exoS/exoU.Results:The expression level of exoS/exoU in the drug-resistance group was significantly higher than that in the non-resistance group(p<0.05).The top 5 differentially expressed genes in the alveolar lavage fluid specimens from the drug-resistance group were RAC1,ITGB1,ITGB5,CRK and IGF1R in the order from high to low.In the sputum specimens,the top 5 differentially expressed genes were RAC1,CRK,IGF1R,ITGB1 and ITGB5.In the alveolar lavage fluid specimens,only RAC1 had a positive correlation with the expression of exoS and exoU(p<0.05).In the sputum specimens,RAC1,ITGB1,ITGB5,CRK and IGF1R were positively correlated with the expression of exoS and exoU(p<0.05).The genes included in the co-expression network contained exoS,exoU,RAC1,ITGB1,ITGB5,CRK,CAMK2D,RHOA,FLNA,IGF1R,TGFBR2 and FOS.Among them,RAC1 had a highest score in the aspect of regulatory ability(72.00)and the largest number of regulatory genes(6);followed by ITGB1,ITGB5 and CRK genes.Conclusions:The high expression of exoS and exoU in the sputum specimens suggests that pseudomonas aeruginosa has a higher probability to get resistant to antibiotics;RAC1,ITGB1,ITGB5 and CRK genes may be the key genes that can regulate the expression of exoS and exoU.

基于UML Profile设计OLAP元模型

对UML进行轻量级扩展,建立了一套针对联机分析处理(OLAP)的扩展机制,并建立基于多维建模的OLAP元模型。实现OLAP在概念层上的设计,取代了以往在具体的表结构和数据仓库系统上进行建模的方法。使得OLAP较早地伴随数据仓库系统进入设计阶段,以减少开发的时间和代价。

基于UML Profile的关联规则挖掘元模型研究

针对目前数据挖掘系统缺乏通用性和复用性的问题,对UML进行轻量级扩展,采用UML Profile机制建立了一套关联规则挖掘元模型,实现了关联规则挖掘在概念层上的建模设计,取代了以往在具体的表结构和数据仓库系统上进行建模的方法,并在某大型钢结构企业的决策支持系统中验证了模型的有效性。最后在Analy-sis Services 2008上经过验证,利用UML Profile机制建立的关联规则挖掘元模型可较早地伴随决策系统进入设计阶段,减少开发的时间和代价。

The Impact of AIRS Atmospheric Temperature and Moisture Profiles on Hurricane Forecasts: Ike(2008) and Irene(2011)

Atmospheric InfraRed Sounder （AIRS） measurements are a valuable supplement to current observational data,especially over the oceans where conventional data are sparse.In this study,two types of AIRS-retrieved temperature and moisture profiles,the AIRS Science Team product （SciSup） and the single field-of-view （SFOV） research product,were evaluated with European Centre for Medium-Range Weather Forecasts （ECMWF） analysis data over the Atlantic Ocean during Hurricane Ike （2008） and Hurricane Irene （2011）.The evaluation results showed that both types of AIRS profiles agreed well with the ECMWF analysis,especially between 200 hPa and 700 hPa.The average standard deviation of both temperature profiles was approximately 1 K under 200 hPa,where the mean AIRS temperature profile from the AIRS SciSup retrievals was slightly colder than that from the AIRS SFOV retrievals.The mean SciSup moisture profile was slightly drier than that from the SFOV in the mid troposphere.A series of data assimilation and forecast experiments was then conducted with the Advanced Research version of the Weather Research and Forecasting （WRF） model and its three-dimensional variational （3DVAR） data assimilation system for hurricanes Ike and Irene.The results showed an improvement in the hurricane track due to the assimilation of AIRS clear-sky temperature profiles in the hurricane environment.In terms of total precipitable water and rainfall forecasts,the hurricane moisture environment was found to be affected by the AIRS sounding assimilation.Meanwhile,improving hurricane intensity forecasts through assimilating AIRS profiles remains a challenge for further study.

A new method to retrieve salinity profiles from sea surface salinity observed by SMOS satellite

This paper proposes a new method to retrieve salinity profiles from the sea surface salinity （SSS） observed by the Soil Moisture and Ocean Salinity （SMOS） satellite. The main vertical patterns of the salinity profiles are firstly extracted from the salinity profiles measured by Argo using the empirical orthogonal function. To determine the time coefficients for each vertical pattern, two statistical models are developed. In the linear model, a transfer function is proposed to relate the SSS observed by SMOS （SMOS_SSS） with that measured by Argo, and then a linear relationship between the SMOS_SSS and the time coefficient is established. In the nonlinear model, the neural network is utilized to estimate the time coefficients from SMOS_SSS, months and positions of the salinity profiles. The two models are validated by comparing the salinity profiles retrieved from SMOS with those measured by Argo and the climatological salinities. The root-mean-square error （RMSE） of the linear and nonlinear model are 0.08-0.16 and 0.08-0.14 for the upper 400 m, which are 0.01-0.07 and 0.01-0.09 smaller than the RMSE of climatology. The error sources of the method are also discussed.

One-Dimensional Variational Retrieval of Temperature and Humidity Profiles from the FY4A GIIRS

A physical retrieval approach based on the one-dimensional variational(1 D-Var) algorithm is applied in this paper to simultaneously retrieve atmospheric temperature and humidity profiles under both clear-sky and partly cloudy conditions from FY-4 A GIIRS(geostationary interferometric infrared sounder) observations. Radiosonde observations from upper-air stations in China and level-2 operational products from the Chinese National Satellite Meteorological Center(NSMC)during the periods from December 2019 to January 2020(winter) and from July 2020 to August 2020(summer) are used to validate the accuracies of the retrieved temperature and humidity profiles. Comparing the 1 D-Var-retrieved profiles to radiosonde data, the accuracy of the temperature retrievals at each vertical level of the troposphere is characterized by a root mean square error(RMSE) within 2 K, except for at the bottom level of the atmosphere under clear conditions. The RMSE increases slightly for the higher atmospheric layers, owing to the lack of temperature sounding channels there.Under partly cloudy conditions, the temperature at each vertical level can be obtained, while the level-2 operational products obtain values only at altitudes above the cloud top. In addition, the accuracy of the retrieved temperature profiles is greatly improved compared with the accuracies of the operational products. For the humidity retrievals, the mean RMSEs in the troposphere in winter and summer are both within 2 g kg^(–1). Moreover, the retrievals performed better compared with the ERA5 reanalysis data between 800 h Pa and 300 h Pa both in summer and winter in terms of RMSE.

Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter

The Localized Weighted Ensemble Kalman Filter(LWEnKF)is a new nonlinear/non-Gaussian data assimilation(DA)method that can effectively alleviate the filter degradation problem faced by particle filtering,and it has great prospects for applications in geophysical models.In terms of operational applications,along-track sea surface height(AT-SSH),swath sea surface temperature(S-SST)and in-situ temperature and salinity(T/S)profiles are assimilated using the LWEnKF in the northern South China Sea(SCS).To adapt to the vertical S-coordinates of the Regional Ocean Modelling System(ROMS),a vertical localization radius function is designed for T/S profiles assimilation using the LWEnKF.The results show that the LWEnKF outperforms the local particle filter(LPF)due to the introduction of the Ensemble Kalman Filter(EnKF)as a proposal density;the RMSEs of SSH and SST from the LWEnKF are comparable to the EnKF,but the RMSEs of T/S profiles reduce significantly by approximately 55%for the T profile and 35%for the S profile(relative to the EnKF).As a result,the LWEnKF makes more reasonable predictions of the internal ocean temperature field.In addition,the three-dimensional structures of nonlinear mesoscale eddies are better characterized when using the LWEnKF.

Integrative multi-omics and systems bioinformatics in translational neuroscience:A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information,with its application to neuroscience termed neuroinformatics.Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms,which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases.Importantly,integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile.In this review,we first summarize data mining studies utilizing datasets from the individual type of omics analysis,including epigenetics/epigenomics,transcriptomics,proteomics,metabolomics,lipidomics,and spatial omics,pertaining to Alzheimer's disease,Parkinson's disease,and multiple sclerosis.We then discuss multi-omics integration approaches,including independent biological integration and unsupervised integration methods,for more intuitive and informative interpretation of the biological data obtained across different omics layers.We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks.Finally,we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery,therapeutic development,and elucidation of disease mechanisms.We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

A search for evidence of small-scale inhomogeneities in dense cores from line profile analysis

In order to search for intensity fluctuations on the HCN（1-0） and HCO＋（1-0） line pro- files, which could arise due to possible small-scale inhomogeneous structure, long-term observations of high-mass star-forming cores S140 and S199 were carried out. The data were processed by the Fourier filtering method. Line temperature fluctuations that exceed the noise level were detected. Assuming the cores consist of a large number of randomly moving small thermal fragments, the total number of frag- ments is - 4 × 106 for the region with linear size - 0.1 pc in S140 and - 106 for the region with linear size - 0.3 pc in S 199. Physical parameters of fragments in S 140 were obtained from detailed modeling of the HCN emission in the framework of the clumpy cloud model.

Lunar Subsurface Mineralogy and Density Profile Modelling based on Moon Mineralogy Mapper Observation

The methods of deriving FeO and TiO_(2)contents from the Clementine spacecraft data were discussed,and an approach was developed to derive the content from the measurements using the Moon Mineralogy Mapper(M3)instrument on Chandrayaan-1.The density of lunar bedrock was then modeled on the basis of the derived FeO and TiO_(2)abundances.The FeO and TiO_(2)abundances derived from the M^(3)data were compared with the previous results of the Clementine data and were in good agreement.The FeO abundance data also agreed well with the Lunar Prospector data,which were used as an independent source.The previous Clementine and newly M3 derived abundances were compared with the laboratory measured FeO and TiO2 contents in the Apollo and Luna returned samples.The Clementine derived FeO content was systematically 1%–2%lower than the laboratory measurements in all the returned samples.The M^(3)derived content agreed well with the returned Apollo samples and was within±2.8%of the laboratory measurements.The Clementine derived TiO2 abundance was systematically 0.1%–4%higher than the laboratory measurements of the returned samples.The M3 derived TiO_(2)agreed well(±0.6%)with the laboratory measurements of the returned samples,except for samples with high TiO2 content.However,these results should be carefully interpreted because the error range requires verification.No error analysis was provided with the previous Clementine derived contents.

An integrated strategy for comprehensive characterization of metabolites and metabolic profiles of bufadienolides from Venenum Bufonis in rats

Comprehensive characterization of metabolites and metabolic profiles in plasma has considerable significance in determining the efficacy and safety of traditional Chinese medicine(TCM)in vivo.However,this process is usually hindered by the insufficient characteristic fragments of metabolites,ubiquitous matrix interference,and complicated screening and identification procedures for metabolites.In this study,an effective strategy was established to systematically characterize the metabolites,deduce the metabolic pathways,and describe the metabolic profiles of bufadienolides isolated from Venenum Bufonis in vivo.The strategy was divided into five steps.First,the blank and test plasma samples were injected into an ultra-high performance liquid chromatography/linear trap quadrupole-orbitrap-mass spectrometry(MS)system in the full scan mode continuously five times to screen for valid matrix compounds and metabolites.Second,an extension-mass defect filter model was established to obtain the targeted precursor ions of the list of bufadienolide metabolites,which reduced approximately 39%of the interfering ions.Third,an acquisition model was developed and used to trigger more tandem MS(MS/MS)fragments of precursor ions based on the targeted ion list.The acquisition mode enhanced the acquisition capability by approximately four times than that of the regular data-dependent acquisition mode.Fourth,the acquired data were imported into Compound Discoverer software for identification of metabolites with metabolic network prediction.The main in vivo metabolic pathways of bufadienolides were elucidated.A total of 147 metabolites were characterized,and the main biotransformation reactions of bufadienolides were hydroxylation,dihydroxylation,and isomerization.Finally,the main prototype bufadienolides in plasma at different time points were determined using LC-MS/MS,and the metabolic profiles were clearly identified.This strategy could be widely used to elucidate the metabolic profiles of TCM preparations or Chinese patent medicines in vivo and provide critical data for rational drug use.

A new data acquisition and processing system for profiling sonar

A multi-beam chirp sonar based on IP connections and DSP processing nodes was proposed and designed to provide an expandable system with high-speed processing and mass-storage of real-time signals for multi-beam profiling sonar.The system was designed for seabed petroleum pipeline detection and orientation,and can receive echo signals and process the data in real time,refreshing the display 10 times per second.Every node of the chirp sonar connects with data processing nodes through TCP/IP. Merely by adding nodes,the system’s processing ability can be increased proportionately without changing the software.System debugging and experimental testing proved the system to be practical and stable.This design provides a new method for high speed active sonar.