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Statistical energy consumption analysis and optimization for relaying transmission with wireless power transfer 认领 引用
1
作者 Fang Xu Yuanchen Wang +2 位作者 Xinyu Zhang Yiyuan Xie Ramy Samy 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期677-685,共9页
Improving the energy efficiency of information transmission is very critical to the development of future Internet of Things(IoT).Considering the sporadic characteristics for IoT transmissions,the energy consumption o... Improving the energy efficiency of information transmission is very critical to the development of future Internet of Things(IoT).Considering the sporadic characteristics for IoT transmissions,the energy consumption of a specific transmission session significantly varies with channel condition and Quality of Service(QoS)requirements.In this study,we focus on the analysis and optimization for wireless relaying communications'statistical energy consumption.Particularly,we investigate a wirelessly-powered DF relaying communication system.Under Time Switching(TS)and Power Splitting(PS)modes,we analyze and minimize the statistical energy consumption of transmitting a fixed amount of data using mathematical analysis.Through showing some selected numerical examples,we discuss various design tradeoffs.These results will provide some important guidelines for the design of green IoT communication systems. 展开更多
关键词 Internet of Things Wireless power transfer Relay communication Statistical energy consumption Mathematical analysis
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A New Knowledge Mining and Root Cause Analysis Methodology for Multivariate Time Series 认领 引用
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作者 Xiaoliang Wang Faming Lu +1 位作者 MengChu Zhou Qingtian Zeng 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第5期1054-1067,共14页
Root cause analysis(RCA)aims to discover the root causes of abnormal events.Causal relations reveal the evolution process of abnormal events,which plays a crucial role in RCA.However,existing methods neither explicitl... Root cause analysis(RCA)aims to discover the root causes of abnormal events.Causal relations reveal the evolution process of abnormal events,which plays a crucial role in RCA.However,existing methods neither explicitly emphasize the“AND/OR”relations among causes,nor consider the synergy effects owned by non-causal variables on causal rules,thereby affecting the credibility of RCA.To address the issues,by fusing Petri nets and Bayesian networks,this study proposes a new knowledge mining and RCA methodology for multivariate time series,called synergy-incorporated Bayesian time Petri net.It integrates the advantages of Petri nets in modeling and analyzing complex temporal dependencies and Bayesian networks in evidence reasoning.It takes into account“AND/OR”relations and synergy effects in temporal knowledge mining and RCA.Two cases are employed to verify its performance in knowledge mining and RCA,including a case study of quality anomaly detection of solar panels and the Tennessee-Eastman process.The experimental results from both cases indicate that it can effectively consider“AND/OR”relations and synergy effects.When applied to the former,it outperforms the state-of-the-art RCA methods in accuracy by over 11%. 展开更多
关键词 Bayesian network(BN) knowledge mining multivariate time series Petri net(PN) root cause analysis(RCA) synergy effect
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Current Situation of Application and Development Prospects of the Statistical Analysis of Big Data 认领 引用
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作者 Zhuoran LI 《Meteorological and Environmental Research》 2026年第1期45-47,共3页
With the advent of the big data era,modern statistics has enjoyed unprecedented development opportunities and also faced numerous new challenges.Traditional statistical computing methods are often limited by issues su... With the advent of the big data era,modern statistics has enjoyed unprecedented development opportunities and also faced numerous new challenges.Traditional statistical computing methods are often limited by issues such as computer memory capacity and distributed storage of data across different locations,and are unable to directly apply to large-scale data sets.Therefore,in the context of big data,designing efficient and theoretically guaranteed statistical learning and inference algorithms has become a key issue that the current field of statistics urgently needs to address.In this paper,the application status of statistical analysis methods in the big data environment was systematically reviewed,and its future development directions were analyzed to provide reference and support for the further development of theory and methods of the statistical analysis of big data. 展开更多
关键词 Big data Statistical analysis Current status Development prospects
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Research on the Innovation of Statistical Analysis Methods at the Grassroots Level with the Aid of AI Technology 认领 引用
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作者 Meixia Zhang 《Proceedings of Business and Economic Studies》 2026年第2期7-11,共5页
Against the backdrop of the booming digital economy,the working environment of grassroots statistics has undergone significant changes.Faced with challenges such as the explosive growth of massive statistical data and... Against the backdrop of the booming digital economy,the working environment of grassroots statistics has undergone significant changes.Faced with challenges such as the explosive growth of massive statistical data and diversified analytical demands,improving the efficiency of grassroots statistical data processing and the depth of analysis has become the primary goal of current reforms.The innovative development of artificial intelligence(AI)technology has brought more possibilities for the innovation of grassroots statistical analysis methods in the new era.This paper focuses on the application of AI technology in the innovation of grassroots statistical analysis methods.Based on an analysis of the main pain points in current grassroots statistical analysis methods,it proposes effective paths to promote the innovation of grassroots statistical analysis methods,aiming to provide a reference for relevant work. 展开更多
关键词 AI technology Grassroots statistics Analysis methods Innovation
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Combined Use of Multivariate Statistical Analysis and Hydrochemical Analysis for Groundwater Quality Evolution: A Case Study in North Chain Plain 认领 引用 被引量：9
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作者 Rong Ma Jiansheng Shi +1 位作者 Jichao Liu Chunlei Gui 《Journal of Earth Science》 SCIE CAS CSCD 2014年第3期587-597,共11页
Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were ... Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis（DA） method, which can afford 100% correct assignation according to the 3 different clusters（good water（GW）, poor water（PW）, and very poor water（VPW）） obtained from cluster analysis（CA）. According to factor analysis（FA）, 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development. 展开更多
关键词 factor groundwater quality hydrochemical variable industrial activity multivariate statistical analysis.
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Multivariate Statistical Process Monitoring of an Industrial Polypropylene Catalyzer Reactor with Component Analysis and Kernel Density Estimation 认领 引用 被引量：16
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作者 熊丽 梁军 钱积新 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2007年第4期524-532,共9页
Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the t... Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator. 展开更多
关键词 multivariate statistical process monitoring principal comPonent analysis kermel density estimation polypropylene catalyzer reactor fault detection data-driven tools
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Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study:Fasa Plain,Iran) 认领 引用 被引量：3
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作者 Mehdi Bahrami Elmira Khaksar Elahe Khaksar 《Journal of Groundwater Science and Engineering》 2020年第3期230-243,共14页
Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A la... Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A large population also uses local groundwater for drinking purposes.Therefore,in this study,this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis(CA),Discriminant Analysis(DA),and Principal Component Analysis(PCA).Water quality data was monitored at 22 different wells,for five years(2009-2014)with 10 water quality parameters.By using cluster analysis,the sampling wells were grouped into two clusters with distinct water qualities at different locations.The Lasso Discriminant Analysis(LDA)technique was used to assess the spatial variability of water quality.Based on the results,all of the variables except sodium absorption ratio(SAR)are effective in the LDA model with all variables affording 92.80%correct assignation to discriminate between the clusters from the primary 10 variables.Principal component(PC)analysis and factor analysis reduced the complex data matrix into two main components,accounting for more than 95.93%of the total variance.The first PC contained the parameters of TH,Ca2+,and Mg2+.Therefore,the first dominant factor was hardness.In the second PC,Cl-,SAR,and Na+were the dominant parameters,which may indicate salinity.The originally acquired factors illustrate natural(existence of geological formations)and anthropogenic(improper disposal of domestic and agricultural wastes)factors which affect the groundwater quality. 展开更多
关键词 Groundwater Iran Multivariate statistical methods Pollution
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Statistical characteristics mining of measured machining error of multi-stage compressor blisks 认领 引用
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作者 Yue DAN Limin GAO +3 位作者 Yuyang HAO Qiusheng LUO Ruiyu LI Guang YANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期173-185,共13页
Blisks have been widely adopted in various aero-engines due to the advantages such as simple structure and low loss.However,influenced by machining errors,the geometric inconsistency of blisk blades is significant,lea... Blisks have been widely adopted in various aero-engines due to the advantages such as simple structure and low loss.However,influenced by machining errors,the geometric inconsistency of blisk blades is significant,leading to deviations in the compressor performance from the design and scatter increase.To accurately assess performance uncertainty effects of machining errors using uncertainty quantification methods,‘statistical characteristics of machining errors’as uncertainty quantification inputs are particularly critical.This study is the first to highlight measured machining errors'uncertainty analysis for blisks.Measured machining errors from the front,middle,and rear stages of multi-stage compressor blisks are analyzed regarding their systematic deviations and scatters along the radial direction,and probability distribution characteristics.The results show that due to differences in clamping and fixing methods,the statistical characteristics of machining errors for‘blisk'differ from those of‘single blade’.Additionally,variations in material properties and sizes of blades at different compressor stages lead to differences in the statistical characteristics of machining errors.For different sections,systematic deviations and scatters in machining errors are notably significant near the blade tip,making it challenging to ensure machining consistency.For different stages,machining errors of the rear stage blades are the most scattering.Compared with the design geometry,several phenomena observed in most blades,such as‘under deflection’,‘thicker pressure/suction surfaces’and‘larger leading-edge radius’,should be improved,owing to their adverse effects in compressors.Furthermore,probability distributions of machining errors exhibit characteristics such as‘skewness’,‘bimodality’,and‘data missing’,indicating that traditional normal distributions are insufficient for accurately characterizing the above distributions.The research results provide a clear demonstration of the machining capabilities of compressor blisks and offer data support for correctly constructing probability models of machining errors,thereby enabling accurate prediction of their performance uncertainty effects. 展开更多
关键词 Blade Compressor Machining error Measured data Statistical analysis
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Multivariate Adjustment in the IAU-Based Tropical Cyclone Initialization Scheme in the TRAMS Model 认领 引用
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作者 Shaojing ZHANG Jeremy Cheuk-Hin LEUNG +6 位作者 Daosheng XU Liwen WANG Yuxiao CHEN Yanyan HUANG Suhong MA Wenshou TIAN Banglin ZHANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第2期436-450,I0027-I0031,共15页
The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initializatio... The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initialization scheme,developed based on the incremental analysis updates(IAU)technique,effectively reduces initial bias.However,the original IAU-based TC initialization scheme only adjusts the wind field at the analysis moment,with other variables adjusted implicitly under the model's constraints according to a gradually inserted wind increment(named“univariate adjustment scheme”hereafter).The univariate adjustment scheme requires approximately 3 h to reach a dynamic equilibrium state,which constrains the assimilation of hourly TC observations and causes excessive dissipation of meaningful short-wave information in adjustment increments.To address this limitation,this study develops a multivariate adjustment IAU-based TC initialization scheme that incorporates gradient wind balance and hydrostatic balance as its largescale constraints.Numerical experiments with TC Hato(2017)demonstrate that the multivariate adjustment scheme reduces the IAU relaxation time to 1 h while marginally improving forecast skill.These findings are consistently replicated across 12 additional TC cases.The development of the IAU-based multivariate adjustment initialization scheme establishes a foundation for 4-D initialization using hourly TC observations. 展开更多
关键词 tropical cyclone initialization multivariate adjustment incremental analysis updates numerical prediction
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Outlier management in data analysis:a checklist for authors and reviewers 认领 引用
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作者 Evgenios Agathokleous Tao Xu Lei Yu 《Journal of Forestry Research》 SCIE EI CAS CSCD 2026年第3期1-6,共6页
The improper handling of outliers in the analysis of variance(ANOVA)presents a persistent challenge in forestry research,which may lead to biased results,inflated Type I error rates,and obscured scientific signals.The... The improper handling of outliers in the analysis of variance(ANOVA)presents a persistent challenge in forestry research,which may lead to biased results,inflated Type I error rates,and obscured scientific signals.The current practice is often an ad hoc method,potentially driven by a need to achieve statistical significance rather than principled scientific reasoning.This Editorial paper addresses this systemic issue by proposing a structured,step-by-step framework for the diagnosis and management of outliers.The framework guides researchers to first investigate the cause of an outlier(data error,measurement error,or genuine extreme value),then statistically assess its impact on ANOVA results and assumptions,and finally,make a transparent decision on its treatment.We strongly advise against the statistically problematic practice of replacing outliers with the mean of other replicates,as it violates data integrity and obscures true variability.Instead,we recommend robust alternatives,including data transformation,non-parametric tests,or the use of trimmed means.This approach aims to uphold statistical robustness and scientific integrity,thereby improving the rigor of forestry research and its publications. 展开更多
关键词 Journal editor Peer review Statistical analysis Science communication Scientific writing
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Construction of Inorganic Elemental Fingerprint and Multivariate Statistical Analysis of Marine Traditional Chinese Medicine Meretricis concha from Rushan Bay 认领 引用 被引量：6
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作者 WU Xia ZHENG Kang +2 位作者 ZHAO Fengjia ZHENG Yongjun LI Yantuan 《Journal of Ocean University of China》 SCIE CAS 2014年第4期712-716,共5页
Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental... Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, the elemental contents of M. concha from five sampling points in Rushan Bay have been determined by means of inductively coupled plasma optical emission spectrometry(ICP-OES). Based on the contents of 14 inorganic elements(Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, and Zn), the inorganic elemental fingerprint which well reflects the elemental characteristics was constructed. All the data from the five sampling points were discriminated with accuracy through hierarchical cluster analysis(HCA) and principle component analysis(PCA), indicating that a four-factor model which could explain approximately 80% of the detection data was established, and the elements Al, As, Cd, Cu, Ni and Pb could be viewed as the characteristic elements. This investigation suggests that the inorganic elemental fingerprint combined with multivariate statistical analysis is a promising method for verifying the geographical origin of M. concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM. 展开更多
关键词 Meretricis concha traditional Chinese medicine inorganic elemental fingerprint multivariate statistical analysis Rushan Bay
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Groundwater quality assessment using multivariate analysis,geostatistical modeling, and water quality index(WQI): a case of study in the Boumerzoug-El Khroub valley of Northeast Algeria 认领 引用 被引量：5
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作者 Oualid Bouteraa Azeddine Mebarki +2 位作者 Foued Bouaicha Zeineddine Nouaceur Benoit Laignel 《Acta Geochimica》 EI CAS CSCD 2019年第6期796-814,共19页
In this study,the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-E1 khroub valley has been processed simultaneously with Multivariate analysis,geostatistical modeling,WQI,and geo... In this study,the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-E1 khroub valley has been processed simultaneously with Multivariate analysis,geostatistical modeling,WQI,and geochemical modeling.Cluster analysis identified three main water types based on the major ion contents,where mineralization increased from group 1 to group 3.These groups were confirmed by FA/PCA,which demonstrated that groundwater quality is influenced by geochemical processes(water-rock interaction)and human practice(irrigation).The exponential semivariogram model WQI.Groundwater chemistry has a strong spatial structure for Mg,Na,Cl,and NO3,and a moderate spatial structure for EC,Ca,K,HCO3,and SO4.Water quality maps generated using ordinary Kriging are consistent with the HCA and PCA results.All water groups are supersaturated with respect to carbonate minerals,and dissolution of kaolinite and Ca-smectite is one of the processes responsible for hydrochemical evolution in the area. 展开更多
关键词 Groundwater Multivariate analysis Geostatistical modeling Geochemical modeling Mineralization Ordinary Kriging
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Multivariate Statistical Analysis of Dominating Groundwater Mineralization and Hydrochemical Evolution in Gao,Northern Mali 认领 引用 被引量：1
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作者 Adiaratou Traore Xumei Mao +2 位作者 Alhousseyni Traore Yahaya Yakubu Aboubacar Modibo Sidibe 《Journal of Earth Science》 SCIE CAS CSCD 2024年第5期1692-1703,共12页
Population growth and expanding urbanization have caused persistent shortages and contamination of groundwater resources in Mali,Africa.The increase in groundwater salinity makes it more difficult for residents to obt... Population growth and expanding urbanization have caused persistent shortages and contamination of groundwater resources in Mali,Africa.The increase in groundwater salinity makes it more difficult for residents to obtain drinking water,it is necessary to clarify the causes and control factors of groundwater mineralization in Gao region,northern Mali.Based on the analysis of the hydrochemical composition of groundwater in 24 boreholes,Piper and Sch?eller diagrams,principal component analysis(PCA)and hierarchical cluster analysis(HCA)are used to carry out multivariate statistical analysis on the main ions.The results show that the groundwater samples are weakly alkaline,with pH values ranging from 5.83 to 8.40,and the average values of boreholes are 7.50,respectively.The average electrical conductivity(EC)value is 354.4(μS/cm),and the extreme value is between 124.0 and 1247(μS/cm).Water is usually mineralized and presents nine types of water phase.The three principal components explain 84.42%of the total variance for 13 parameters.The factor F1(58.85%),the factor F2(16.88%)and the factor F3(8.69%)present for the majority of the total data set.In addition,multivariate statistical analysis confirmed the genetic relationship among aquifers and identified three main clusters.Clustering related to groundwater mineralization(F1),clustering related to oxide reduction and iron enrichment(F2),and clustering of groundwater pollution caused by nitrate and magnesium(F3).We found that agriculture,weathering activities and dissolution of geological materials promote the mineralization of groundwater.Groundwater quality in the Gao region is becoming less and less potable because of increasing salinity. 展开更多
关键词 hydrochemical composition multivariate statistical analysis mineralization hydro-chemical evolution Gao northern Mali hydrogeology
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Study on the Relationship between Soil and Environment Based on Multivariate Statistical Analysis 认领 引用
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作者 DONG Li-li 《Meteorological and Environmental Research》 2012年第5期1-3,8,共3页
[Objective] The study aimed to study the relationship between soil and environment on the basis of multivariate statistical analysis. [ Method] Through field investigation, sampling and laboratory analysis, we discuss... [Objective] The study aimed to study the relationship between soil and environment on the basis of multivariate statistical analysis. [ Method] Through field investigation, sampling and laboratory analysis, we discussed the relationship between soil properties and environmental factors in Mizhi County, North Shaanxi by using Canoco multivariate statistical analysis. [ Result]According to the effects of various environmental factors on soil properties, the influencing order of environmental factors was land use way 〉 vegetation type 〉 vegetation restoration years 〉 vegeta- tion coverage 〉 slope aspect 〉 gradient 〉 elevation. In a word, soil properties were significantly affected by land use way and vegetation type which were the most important environmental factors of soil properties in spatial variation, while vegetation restoration years were closely related to the ac- cumulation of soil nutrients. [ Condusion]The research could provide theoretical references for the construction of ecological environment in Loess Plateau of China. 展开更多
关键词 Soil properties Multivariate statistical analysis Land use Vegetation types China
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Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran 认领 引用 被引量：5
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作者 Mojtaba ZERAATPISHEH Shamsollah AYOUBI +1 位作者 Magboul SULIEMAN JesusRODRIGO-COMINO 《Journal of Arid Land》 SCIE CSCD 2019年第4期551-566,共16页
Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most re... Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most relevant to predicting soil properties at the catchment scale in semi-arid areas. Thus, this research aims to investigate the ability of multivariate statistical analyses to distinguish which soil properties follow a clear spatial pattern conditioned by specific environmental characteristics in a semi-arid region of Iran. To achieve this goal, we digitized parent materials and landforms by recent orthophotography. Also, we extracted ten topographical attributes and five remote sensing variables from a digital elevation model(DEM) and the Landsat Enhanced Thematic Mapper(ETM), respectively. These factors were contrasted for 334 soil samples(depth of 0–30 cm). Cluster analysis and soil maps reveal that Cluster 1 comprises of limestones, massive limestones and mixed deposits of conglomerates with low soil organic carbon(SOC) and clay contents, and Cluster 2 is composed of soils that originated from quaternary and early quaternary parent materials such as terraces, alluvial fans, lake deposits, and marls or conglomerates that register the highest SOC content and the lowest sand and silt contents. Further, it is confirmed that soils with the highest SOC and clay contents are located in wetlands, lagoons, alluvial fans and piedmonts, while soils with the lowest SOC and clay contents are located in dissected alluvial fans, eroded hills, rock outcrops and steep hills. The results of principal component analysis using the remote sensing data and topographical attributes identify five main components, which explain 73.3% of the total variability of soil properties. Environmental factors such as hillslope morphology and all of the remote sensing variables can largely explain SOC variability, but no significant correlation is found for soil texture and calcium carbonate equivalent contents. Therefore, we conclude that SOC can be considered as the best-predicted soil property in semi-arid regions. 展开更多
关键词 soil properties remote sensing data topographical attributes multivariate statistical analyses geographic information systems land management
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Advancing statistical methodologies in traditional Chinese medicine and acupuncture research:Enhancing transparency and analytical sophistication 认领 引用
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作者 Remy MACDONALD Dong-han BAI +6 位作者 Zi-hao ZHANG Nan-xi HUANG Jing-yue GAO Xu ZHANG Lei FAN Shu-min CHEN Lu LUO 《World Journal of Acupuncture-Moxibustion》 CAS CSCD 2026年第2期141-148,共8页
Background Traditional Chinese Medicine(TCM),with over 5000 years of empirical practice,increasingly employs modern scientific frameworks such as randomized controlled trials(RCTs)to validate therapeutic claims,yet it... Background Traditional Chinese Medicine(TCM),with over 5000 years of empirical practice,increasingly employs modern scientific frameworks such as randomized controlled trials(RCTs)to validate therapeutic claims,yet its research reliability hinges critically on robust statistical rigor.Methods By systematically analyzing articles from Phytomedicine and Journal of Ethnopharmacology,this study evaluates statistical methodologies in TCM research,focusing on the adoption of advanced analytical techniques(e.g.,multivariate modeling)versus reliance on basic methods(e.g.,ANOVA)and identifies reporting gaps in trial design(e.g.,sample size estimation).Results Key findings indicate that foundational statistical methods,such as one-way ANOVA,were predominantly used(83.4%of articles),whereas more advanced approaches appeared in only 34.6%of studies.However,methodological rigor should not be equated with statistical complexity.The selection of analytical techniques must be driven by the research objectives,data structure,study design,and the complexity of the scientific questions under investigation.Advanced methods are not inherently superior;rather,the most appropriate approach is the one that is methodologically justified and aligned with underlying assumptions.Notably,substantial deficiencies in trial design and reporting were observed.A striking 81.5%of studies lacked pre-specified power calculations or sample size justifications,raising concerns about statistical validity.Reporting transparency was similarly limited:48.3%of articles did not adequately describe statistical procedures,and 69.8%failed to provide confidence intervals for primary effect estimates.Collectively,these limitations increase the risk of biased interpretation and undermine the robustness,reproducibility,and credibility of the findings.Conclusion Strengthening statistical rigor—through improved trial design transparency and adoption of advanced methods—is essential to enhance the credibility of TCM research,mitigate biases,and foster its integration into evidence-based medicine,ultimately ensuring clinically meaningful and actionable therapeutic insights. 展开更多
关键词 Traditional chinese medicine(TCM) Statistical analysis Multivariate analysis Evidence-based medicine Clinical trials Meta-analysis
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Impact of blast design parameters on rock fragmentation in sub-level caving:A multivariate regression approach 认领 引用
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作者 Ahmadreza Khodayari Chaoshui Xu +3 位作者 Peter Dare-Bryan Peter Dowd Veljko Lapcevic Andrew Metcalfe 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第5期3348-3364,共17页
Sub-level caving(SLC)is a mass mining method suitable for large,steeply dipping orebodies.The particle size distribution(PSD)of blasted material affects material flow through the stope.Improving blast-induced fragment... Sub-level caving(SLC)is a mass mining method suitable for large,steeply dipping orebodies.The particle size distribution(PSD)of blasted material affects material flow through the stope.Improving blast-induced fragmentation can enhance draw point extraction,increasing ore recovery,reducing dilution,and lowering costs in loading and crushing.Numerical simulations using the Mechanistic Blasting Model(MBM)explored these improvements.MBM simulates the explosive loading,rock fracturing,and dynamic explosive gas effects.It addresses uneven explosive distribution from fan-shaped blast holes and complex broken ground conditions.The simulations used Ernest Henry Mine(EHM)data to define the baseline blast design and rock mass and compared field and modelled fragmentation sizes for varying explosive densities and burden sizes.Then,MBM simulations incorporated different rock mass fracture densities,tensile strengths and in-situ stresses,and further blast design changes in the blasthole diameter and charge spacings.A total of 34 scenarios were modelled.Multivariate regression analysis identified key parameters,and new regression models for P20,P50,and P80 passing sizes were developed and validated against the EHM and MBM simulation data.Additional simulations confirmed that while regression predictive models were slightly less accurate,they provided efficient predictions with acceptable accuracy. 展开更多
关键词 Sublevel caving Mechanistic blast model Ernest Henry mine Multivariate regression analysis Particle size distribution
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Lifestyle behaviors,serum metabolites and high myopia:Mendelian randomization and mediation analysis 认领 引用
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作者 Nian-En Liu Xiao-Tong Xu Xiao-Bing Yu 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第1期140-148,共9页
AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analy... AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analysis.METHODS:The causal effects of several behavioral factors,including screen time,education time,time spent outdoors,and physical activity,on the risk of HM using univariable Mendelian randomization(MR)and MVMR analyses were first assessed.Genome-wide association study summary statistics of serum metabolites were also used in mediation analysis to determine the extent to which serum metabolites mediate the effects of behavioral factors on HM.RESULTS:MR analyses indicated that both increased time spent outdoors and a higher frequency of moderate physical activity significantly reduced the risk of HM.Further MVMR analysis confirmed that moderate physical activity independently contributed to a lower risk of HM.Additionally,MR analyses identified 13 serum metabolites significantly associated with HM,of which 12 were lipids and one was an amino acid derivative.Mediation analysis revealed that six lipid metabolites mediated the protective effects of moderate physical activity on HM,with the highest mediation proportion observed for 1-(1-enyl-palmitoyl)-GPC(p-16:0;30.83%).CONCLUSION:This study suggests that in addition to outdoor time,moderate physical activity habits may have an independent protective effect against HM and pointed to lipid metabolites as priority targets for the prevention due to low physical activity.These results emphasize the importance of physical activity and metabolic health in HM and underscore the need for further study of these complex associations. 展开更多
关键词 high myopia physical activity serum metabolites multivariable Mendelian randomization mediation analysis
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Quality assessment of Jinhongtang Granule using UFLC-MS/MS and multivariate statistical analysis 认领 引用
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作者 Fan Wu Yu Zhang +5 位作者 Yanling Qiao Ting Zhao Baojing Zhang Bangjiang Fang Xiaokui Huo Xiaochi Ma 《Asian Journal of Traditional Medicines》 2021年第4期191-202,共12页
Jinhongtang is a traditional Chinese medicine formula composed of Rheum palmatum L.stem,Sargentodoxa cuneata stem,and Taraxacum mongolicum and is used for the treatment of sepsis.However,quality assessment method for ... Jinhongtang is a traditional Chinese medicine formula composed of Rheum palmatum L.stem,Sargentodoxa cuneata stem,and Taraxacum mongolicum and is used for the treatment of sepsis.However,quality assessment method for Jinhongtang is not available.In present study,we developed a UFLC-MS/MS method to determine 16 analytes in 20 batches of home-made and commercial Jinhongtang.Multivariate statistical analysis revealed the significant differences in the quality of home-made and commercial Jinhongtang and the difference in the quality of home-made samples was more significant.The integrated strategy based on UFLC-MS/MS and multivariate statistical analysis provided a new basis for the overall quality assessment of Jinhongtang. 展开更多
关键词 Jinhongtang quality assessment UFLC-MS/MS multivariate statistical analysis
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Structure Sorting of Multiple Macromolecular States in Heterogeneous Cryo-EM Samples by 3D Multivariate Statistical Analysis 认领 引用 被引量：1
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作者 Bruno P. Klaholz 《Open Journal of Statistics》 2015年第7期820-836,共17页
Heterogeneity of biological samples is usually considered a major obstacle for three-dimensional (3D) structure determination of macromolecular complexes. Heterogeneity may occur at the level of composition or conform... Heterogeneity of biological samples is usually considered a major obstacle for three-dimensional (3D) structure determination of macromolecular complexes. Heterogeneity may occur at the level of composition or conformational variability of complexes and affects most 3D structure determination methods that rely on signal averaging. Here, an approach is described that allows sorting structural states based on a 3D statistical approach, the 3D sampling and classification (3D-SC) of 3D structures derived from single particles imaged by cryo electron microscopy (cryo-EM). The method is based on jackknifing & bootstrapping of 3D sub-ensembles and 3D multivariate statistical analysis followed by 3D classification. The robustness of the statistical sorting procedure is corroborated using model data from an RNA polymerase structure and experimental data from a ribosome complex. It allows resolving multiple states within heterogeneous complexes that thus become amendable for a structural analysis despite of their highly flexible nature. The method has important implications for high-resolution structural studies and allows describing structure ensembles to provide insights into the dynamics of multi-component macromolecular assemblies. 展开更多
关键词 Heterogeneity Structural Biology Cryo Electron Microscopy Particle Sorting Multiple States Macromolecular Complexes Resampling Jackknifing Bootstrapping Multivariate Statistical Analysis 3D MSA 3D-SC Ribosome RNA Polymerase
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