The technique of data mining was provided to predict gas disaster in view of the characteristics of coal mine gas disaster and feature knowledge based on gas disaster. The rough set theory was used to establish data m...The technique of data mining was provided to predict gas disaster in view of the characteristics of coal mine gas disaster and feature knowledge based on gas disaster. The rough set theory was used to establish data mining model of gas disaster prediction, and rough set attributes relations was discussed in prediction model of gas disaster to supplement the shortages of rough intensive reduction method by using information en- tropy criteria.The effectiveness and practicality of data mining technology in the prediction of gas disaster is confirmed through practical application.展开更多
Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted freque...Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted frequent pattern over data streams. SWFP-Miner is based on sliding window and can discover important frequent pattern from the recent data. A new refined weight definition is proposed to keep the downward closure property, and two pruning strategies are presented to prune the weighted infrequent pattern. Experimental studies are performed to evaluate the effectiveness and efficiency of SWFP-Miner.展开更多
The safe production of coalmine can be further improved by forecasting the quantity of gas emission based on the real-time data and historical data which the gas monitoring system has saved. By making use of the advan...The safe production of coalmine can be further improved by forecasting the quantity of gas emission based on the real-time data and historical data which the gas monitoring system has saved. By making use of the advantages of data warehouse and data mining technology for processing large quantity of redundancy data, the method and its application of forecasting mine gas emission quantity based on FDM were studied. The constructing fuzzy resembling relation and clustering analysis were proposed, which the potential relationship inside the gas emission data may be found. The mode finds model and forecast model were presented, and the detailed approach to realize this forecast was also proposed, which have been applied to forecast the gas emission quantity efficiently.展开更多
Efficient preparation and assembly guidance for complex products relies heavily on semantic information in assembly process documents.This information encompasses various levels of elements and complex semantic relati...Efficient preparation and assembly guidance for complex products relies heavily on semantic information in assembly process documents.This information encompasses various levels of elements and complex semantic relationships.However,there is currently a scarcity of effective modeling techniques to express these documents'inherent assembly process knowledge.This study introduces a method for constructing an Assembly Process Knowledge Graph of Complex Products(APKG-CP)utilizing text mining techniques to tackle the challenges of high costs,low efficiency,and difficulty reusing process knowledge.Developing the assembly process knowledge graph involves categorizing entity and relationship classes from multiple levels.The Bert-BiLSTM-CRF model integrates BERT(bidirectional encoder representations from transformers),BiLSTM(bidirectional long short-term memory),and CRF(conditional random field)to extract knowledge entities and relationships in assembly process documents automatically.Furthermore,the knowledge fusion method automatically instantiates the assembly process knowledge graph.The proposed construction method is validated by constructing and visualizing an assembly process knowledge graph using data from an aerospace enterprise as an example.Integrating the knowledge graph with the assembly process preparation system demonstrates its effectiveness for process design.展开更多
In the present study,data mining and network pharmacology were utilized to explore the principles and mechanisms of traditional Chinese medicine(TCM)in treating acute appendicitis.The goal was to provide a scientific ...In the present study,data mining and network pharmacology were utilized to explore the principles and mechanisms of traditional Chinese medicine(TCM)in treating acute appendicitis.The goal was to provide a scientific basis for clinical treatment and further research on this disease.First,we searched the National Patent Database for Chinese herbal compound prescriptions used to treat acute appendicitis.We then applied frequency analysis,character and taste meridian analysis,association rule analysis,and hierarchical cluster analysis to identify the patterns of TCM treatment for acute appendicitis,selecting key combinations of Chinese medicines.Next,we screened the main active components of these key TCM based on quality markers.Using databases such as SwissTargetPrediction,SymMap,ETCM,and STRING,we analyzed the pharmacological mechanisms of these key TCM in treating acute appendicitis.Key active components and targets were further verified through molecular docking.We identified a total of 129 patents involving 316 Chinese medicines,with 24 being frequently used.The results indicated that most Chinese herbs used for acute appendicitis were heat-clearing drugs,blood-activating and stasis-removing drugs,and purging drugs.The primary active ingredients of the Rhubarb-cortex moutan-flos lonicerae combination for treating acute appendicitis included Emodin,Paeonol,Physcion,Chlorogenic acid,Chrysophanol,Rhein acid,and Aloe-emodin.These ingredients targeted key proteins such as ALB,TP53,BCL2,STAT3,IL-6,and TNF,and were involved in cellular responses to lipopolysaccharides,cell composition,and various cytokine-mediated biological processes.They also interacted with signaling pathways like AGE-RAGE,TNF,IL-17,and FoxO.Based on patent data,this study analyzed medication patterns in the treatment of acute appendicitis,discussed the possible mechanisms of key TCM combinations,and provided a scientific basis and new perspectives for the diagnosis and treatment of the disease.展开更多
With the gradual acceleration of information construction in colleges and universities,digital campus and smart campus have gradually become important means for colleges and universities to scientifically manage the c...With the gradual acceleration of information construction in colleges and universities,digital campus and smart campus have gradually become important means for colleges and universities to scientifically manage the campus.They have been applied to teaching,scientific research,student management,and other fields,improving the quality and efficiency of management.This paper mainly studies the intelligent educational administration management system based on data mining technology.Firstly,this paper introduces the application process of data mining technology,and builds an intelligent educational administration management system based on data mining technology.Then,this paper optimizes the application of the Apriori algorithm in educational administration management through transaction compression and frequent sampling.Compared with the traditional Apriori algorithm,the optimized Apriori algorithm in this paper has a shorter execution time under the same minimum support.展开更多
Objective To identify core acupoint patterns and elucidate the molecular mechanisms of acupuncture for primary depressive disorder(PDD)through data mining and network analysis.Methods A comprehensive literature search...Objective To identify core acupoint patterns and elucidate the molecular mechanisms of acupuncture for primary depressive disorder(PDD)through data mining and network analysis.Methods A comprehensive literature search was conducted across PubMed,Embase,Ovid Technologies(OVID),Web of Science,Cochrane Library,China National Knowledge Infrastructure(CNKI),China National Knowledge Infrastructure Database(VIP),Wanfang Data,and SinoMed Database from database foundation to January 31,2025,for clinical studies on acupuncture treatment of PDD.Descriptive statistics,high-frequency acupoint analysis,degree and betweenness centrality evaluation,and core acupoint prescription mining identified predominant therapeutic combinations for PDD.Network acupuncture was used to predict therapeutic target for the core acupoint prescription.Subsequent protein-protein interaction(PPI)network and molecular complex detection(MCODE)analyses were conducted to identify the key targets and functional modules.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)analyses explored the underlying biological mechanisms of the core acupoint prescription in treating PDD.Results A total of 57 acupoint prescriptions underwent systematic analysis.The core therapeutic combinations comprised Baihui(GV20),Yintang(GV29),Neiguan(PC6),Hegu(LI4),and Shenmen(HT7).Network acupuncture analysis identified 88 potential therapeutic targets(79 overlapping with PDD),while PPI network analysis revealed central regulatory nodes,including interleukin(IL)-6,IL-1β,tumor necrosis factor(TNF)-α,toll-like receptor 4(TLR4),IL-10,brain-derived neurotrophic factor(BDNF),transforming growth factor(TGF)-β1,C-XC motif chemokine ligand 10(CXCL10),mitogen-activated protein kinase 3(MAPK3),and nitric oxide synthase 1(NOS1).MCODE-based modular analysis further elucidated three functionally coherent clusters:inflammation-homeostasis(score=6.571),plasticity-neurotransmission(score=3.143),and oxidative stress(score=3.000).GO and KEGG analyses demonstrated significant enrichment of the MAPK,phosphoinositide 3-kinase/protein kinase B(PI3K/Akt),and hypoxia-inducible factor(HIF)-1 signaling pathways.These mechanistic insights suggested that the antidepressant effects mediated through mechanisms of neuroinflammatory regulation,neuroplasticity restoration,and immune-oxidative stress homeostasis.Conclusion This study reveals that acupuncture alleviates depression through a multi-level mechanism,primarily involving the neuroinflammation suppression,neuroplasticity enhancement,and oxidative stress regulation.These findings systematically clarify the underlying mechanisms of acupuncture’s antidepressant effects and identify novel therapeutic targets for further mechanistic research.展开更多
Objective To explore the optimization and principles of acupoint selection and coordination in the treatment of adult abdominal obesity using acupuncture and moxibustion over the past decade using data mining.Methods ...Objective To explore the optimization and principles of acupoint selection and coordination in the treatment of adult abdominal obesity using acupuncture and moxibustion over the past decade using data mining.Methods Clinical studies of abdominal obesity treated with acupuncture and moxibustion,collected in the past 10 years,were searched from China Biology Medicine disc(CBMdisc),China National knowledge infrastructure(CNKI),Wanfang,China Science and Technology Journal Database(VIP),Pubmed,Embase,Google Scholar,Web of Science,(The Cumulative Index to Nursing and Allied Health Literature)CINAHL,Psyclnfo and Scopus,dated from March 1,2013 to March 31,2023.Using IBM SPSS Modeler 18.0 and other software,the frequency analysis,association-rules analysis and cluster analysis were conducted on interventions,traditional Chinese medicine(TCM)patterns,use frequency of acupoint,meridian attribution of acupoint,acupoint location,etc.Results A total of 55 articles were included,with 102 prescriptions and 71 acupoints involved.The top 3 interventions were acupoint embedding method,simple electroacupuncture and simple filiform needling.Seventeen patterns/syndromes of TCM differentiation were collected,dominated by spleen deficiency and damp blockage,spleen and kidney yang deficiency and heat accumulation in stomach and intestines.The acupoints in clinical practice were mostly at the foot-yangming stomach meridian,the conception vessel and the foot-taiyin spleen meridian,and located at the abdominal region.The top 5 acupoints of high frequency were Tianshu(ST25),Zhongwan(CV12),Daheng(SP15),Zusanli(ST36),Huaroumen(ST24)and Daimai(GB26).The specific points of the high frequency were the crossing points and front-mu points,of which,ST25 and CV12 were the most prominent.After association-rules analysis on the high-frequency acupoints,20 groups of associated acupoints were obtained,in which,the core acupoints included ST25,CV12,SP15 and ST36.Conclusion In recent 10 years,abdominal obesity is treated by the acupoints of foot-yangming stomach meridian,the conception vessel and the foot-taiyin spleen meridian.Compared with the regimen for simple obesity,the acupoints at the abdominal region are specially selected in treatment of abdominal obesity,such as ST25,CV12,SP15 and ST36.Supplementary acupoints are selected based on syndrome differentiation to simultaneously address both the disease manifestations and root causes.展开更多
A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safe...A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.展开更多
Objective:To explore the core acupuncture acupoints and pattern-adapted acupoint combination rules for autism spectrum disorder(ASD)complicated with sleep disorder using clinical data mining technology.Methods:A retro...Objective:To explore the core acupuncture acupoints and pattern-adapted acupoint combination rules for autism spectrum disorder(ASD)complicated with sleep disorder using clinical data mining technology.Methods:A retrospective analysis was conducted on the diagnosis and treatment data of 104 children with ASD complicated with sleep disorder admitted to Xi’an Traditional Chinese Medicine(TCM)Encephalopathy Hospital from January 2022 to December 2024.Cross-pattern main acupoints were screened via frequency statistics,chi-square test,and factor analysis;pattern-specific auxiliary acupoints were extracted by combining multiple correspondence analysis,cluster analysis,and association rule mining.Results:Ten cross-pattern main acupoints(Baihui,Sishenzhen,Language Area 1,Language Area 2,Neiguan,Shenmen,Yongquan,Xuanzhong)were identified,and acupoint combination schemes for four major TCM patterns(Hyperactivity of Liver and Heart Fire,Deficiency of Kidney Essence,Deficiency of Both Heart and Spleen,Hyperactivity of Liver with Spleen Deficiency)were established.Conclusion:Acupuncture treatment should follow the principle of“regulating spirit and calming the brain as the root,and dredging collaterals based on pattern differentiation as the branch”.The synergy between main and auxiliary acupoints can accurately regulate the disease,providing a basis for precise clinical treatment.展开更多
Introduction Neurosurgical emergencies such as spontaneous intracerebral hemorrhage(ICH),traumatic brain injury(TBI),and acute brain herniation are among the most time-sensitive and high-stakes conditions in modern me...Introduction Neurosurgical emergencies such as spontaneous intracerebral hemorrhage(ICH),traumatic brain injury(TBI),and acute brain herniation are among the most time-sensitive and high-stakes conditions in modern medicine.Clinical decisions often must be made within minutes,yet these decisions are traditionally guided by limited information,heuristic reasoning,and past experience.In this context,the rise of medical data mining and real-time analytics offers a transformative opportunity:to extract actionable intelligence from the flood of clinical,imaging,and physiological data already being collected,and to use this intelligence to guide care in real time[1–3](Figure 1).展开更多
Magnesium(Mg)based materials hold immense potential for various applications due to their lightweight and high strength-to-weight ratio.However,to fully harness the potential of Mg alloys,structured analytics are esse...Magnesium(Mg)based materials hold immense potential for various applications due to their lightweight and high strength-to-weight ratio.However,to fully harness the potential of Mg alloys,structured analytics are essential to gain valuable insights from centuries of accumulated knowledge.Efficient information extraction from the vast corpus of scientific literature is crucial for this purpose.In this work,we introduce MagBERT,a BERT-based language model specifically trained for Mg-based materials.Utilizing a dataset of approximately 370,000 abstracts focused on Mg and its alloys,MagBERT is designed to understand the intricate details and specialized terminology of this domain.Through rigorous evaluation,we demonstrate the effectiveness of MagBERT for information extraction using a fine-tuned named entity recognition(NER)model,named MagNER.This NER model can extract mechanical,microstructural,and processing properties related to Mg alloys.For instance,we have created an Mg alloy dataset that includes properties such as ductility,yield strength,and ultimate tensile strength(UTS),along with standard alloy names.The introduction of MagBERT is a novel advancement in the development of Mg-specific language models,marking a significant milestone in the discovery of Mg alloys and textual information extraction.By making the pre-trained weights of MagBERT publicly accessible,we aim to accelerate research and innovation in the field of Mg-based materials through efficient information extraction and knowledge discovery.展开更多
For various reasons,many of the security programming rules applicable to specific software have not been recorded in official documents,and hence can hardly be employed by static analysis tools for detection.In this p...For various reasons,many of the security programming rules applicable to specific software have not been recorded in official documents,and hence can hardly be employed by static analysis tools for detection.In this paper,we propose a new approach,named SVR-Miner(Security Validation Rules Miner),which uses frequent sequence mining technique [1-4] to automatically infer implicit security validation rules from large software code written in C programming language.Different from the past works in this area,SVR-Miner introduces three techniques which are sensitive thread,program slicing [5-7],and equivalent statements computing to improve the accuracy of rules.Experiments with the Linux Kernel demonstrate the effectiveness of our approach.With the ten given sensitive threads,SVR-Miner automatically generated 17 security validation rules and detected 8 violations,5 of which were published by Linux Kernel Organization before we detected them.We have reported the other three to the Linux Kernel Organization recently.展开更多
COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans.Different countries have tried different solutions to control the spread of the disease,including lockdowns of coun...COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans.Different countries have tried different solutions to control the spread of the disease,including lockdowns of countries or cities,quarantines,isolation,sanitization,and masks.Patients with symptoms of COVID-19 are tested using medical testing kits;these tests must be conducted by healthcare professionals.However,the testing process is expensive and time-consuming.There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken.This paper introduces a novel technique based on deep learning(DL)that can be used as a surveillance system to identify infected individuals by analyzing tweets related to COVID-19.The system is used only for surveillance purposes to identify regions where the spread of COVID-19 is high;clinical tests should then be used to test and identify infected individuals.The system proposed here uses recurrent neural networks(RNN)and word-embedding techniques to analyze tweets and determine whether a tweet provides information about COVID-19 or refers to individuals who have been infected with the virus.The results demonstrate that RNN can conduct this analysis more accurately than other machine learning(ML)algorithms.展开更多
A data mining method for quality prediction using association rule (DMAR) is presented in this paper. Association rule is used to mine the valuable relations of items among amounts of textile process data for ANN pred...A data mining method for quality prediction using association rule (DMAR) is presented in this paper. Association rule is used to mine the valuable relations of items among amounts of textile process data for ANN prediction model. DMAR consists of three main steps: setup knowledge data set; data cleaning and converting; find the item set with large supports and generate the expected rules. DMAR effectively improves the precision of prediction in yarn breaking. It rapidly gets rid of the negative influence of training parameters on prediction model. Then more satisfactory quality prediction result can be reached.展开更多
Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play...Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.展开更多
Different acupuncture-moxibustion therapies can produce different clinical effects, that is, the effect has specificity, which is significantly important in obtaining acupuncture-moxibustion efficacy. In this study, t...Different acupuncture-moxibustion therapies can produce different clinical effects, that is, the effect has specificity, which is significantly important in obtaining acupuncture-moxibustion efficacy. In this study, the clinical application laws of fire needle, acupoint injection, catgut embedment, acupoint application, moxibustion therapy and filiform needle acupuncture were summarized in the aspects of category of disease, efficacy and related prescriptions （such as medication and acupoint selection） based on the result of data mining, and the general applicable categories of disease of acupuncture-moxibustion treatment methods were further screened, so as to guide the clinical application and give play to the best efficacy.展开更多
基金the National Natural Science Foundation of China(70572070)the Liaoning Province Talents Fund Projects(2005219005)the Technology Key Project of Liaoning Province(2006220019)
文摘The technique of data mining was provided to predict gas disaster in view of the characteristics of coal mine gas disaster and feature knowledge based on gas disaster. The rough set theory was used to establish data mining model of gas disaster prediction, and rough set attributes relations was discussed in prediction model of gas disaster to supplement the shortages of rough intensive reduction method by using information en- tropy criteria.The effectiveness and practicality of data mining technology in the prediction of gas disaster is confirmed through practical application.
文摘Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted frequent pattern over data streams. SWFP-Miner is based on sliding window and can discover important frequent pattern from the recent data. A new refined weight definition is proposed to keep the downward closure property, and two pruning strategies are presented to prune the weighted infrequent pattern. Experimental studies are performed to evaluate the effectiveness and efficiency of SWFP-Miner.
文摘The safe production of coalmine can be further improved by forecasting the quantity of gas emission based on the real-time data and historical data which the gas monitoring system has saved. By making use of the advantages of data warehouse and data mining technology for processing large quantity of redundancy data, the method and its application of forecasting mine gas emission quantity based on FDM were studied. The constructing fuzzy resembling relation and clustering analysis were proposed, which the potential relationship inside the gas emission data may be found. The mode finds model and forecast model were presented, and the detailed approach to realize this forecast was also proposed, which have been applied to forecast the gas emission quantity efficiently.
基金Supported by National Natural Science Foundation of China(Grant No.52375479)。
文摘Efficient preparation and assembly guidance for complex products relies heavily on semantic information in assembly process documents.This information encompasses various levels of elements and complex semantic relationships.However,there is currently a scarcity of effective modeling techniques to express these documents'inherent assembly process knowledge.This study introduces a method for constructing an Assembly Process Knowledge Graph of Complex Products(APKG-CP)utilizing text mining techniques to tackle the challenges of high costs,low efficiency,and difficulty reusing process knowledge.Developing the assembly process knowledge graph involves categorizing entity and relationship classes from multiple levels.The Bert-BiLSTM-CRF model integrates BERT(bidirectional encoder representations from transformers),BiLSTM(bidirectional long short-term memory),and CRF(conditional random field)to extract knowledge entities and relationships in assembly process documents automatically.Furthermore,the knowledge fusion method automatically instantiates the assembly process knowledge graph.The proposed construction method is validated by constructing and visualizing an assembly process knowledge graph using data from an aerospace enterprise as an example.Integrating the knowledge graph with the assembly process preparation system demonstrates its effectiveness for process design.
基金Henan Province Special Research Project of Tra ditional Chinese Medicine(Grant No.2022ZY1090).
文摘In the present study,data mining and network pharmacology were utilized to explore the principles and mechanisms of traditional Chinese medicine(TCM)in treating acute appendicitis.The goal was to provide a scientific basis for clinical treatment and further research on this disease.First,we searched the National Patent Database for Chinese herbal compound prescriptions used to treat acute appendicitis.We then applied frequency analysis,character and taste meridian analysis,association rule analysis,and hierarchical cluster analysis to identify the patterns of TCM treatment for acute appendicitis,selecting key combinations of Chinese medicines.Next,we screened the main active components of these key TCM based on quality markers.Using databases such as SwissTargetPrediction,SymMap,ETCM,and STRING,we analyzed the pharmacological mechanisms of these key TCM in treating acute appendicitis.Key active components and targets were further verified through molecular docking.We identified a total of 129 patents involving 316 Chinese medicines,with 24 being frequently used.The results indicated that most Chinese herbs used for acute appendicitis were heat-clearing drugs,blood-activating and stasis-removing drugs,and purging drugs.The primary active ingredients of the Rhubarb-cortex moutan-flos lonicerae combination for treating acute appendicitis included Emodin,Paeonol,Physcion,Chlorogenic acid,Chrysophanol,Rhein acid,and Aloe-emodin.These ingredients targeted key proteins such as ALB,TP53,BCL2,STAT3,IL-6,and TNF,and were involved in cellular responses to lipopolysaccharides,cell composition,and various cytokine-mediated biological processes.They also interacted with signaling pathways like AGE-RAGE,TNF,IL-17,and FoxO.Based on patent data,this study analyzed medication patterns in the treatment of acute appendicitis,discussed the possible mechanisms of key TCM combinations,and provided a scientific basis and new perspectives for the diagnosis and treatment of the disease.
文摘With the gradual acceleration of information construction in colleges and universities,digital campus and smart campus have gradually become important means for colleges and universities to scientifically manage the campus.They have been applied to teaching,scientific research,student management,and other fields,improving the quality and efficiency of management.This paper mainly studies the intelligent educational administration management system based on data mining technology.Firstly,this paper introduces the application process of data mining technology,and builds an intelligent educational administration management system based on data mining technology.Then,this paper optimizes the application of the Apriori algorithm in educational administration management through transaction compression and frequent sampling.Compared with the traditional Apriori algorithm,the optimized Apriori algorithm in this paper has a shorter execution time under the same minimum support.
文摘Objective To identify core acupoint patterns and elucidate the molecular mechanisms of acupuncture for primary depressive disorder(PDD)through data mining and network analysis.Methods A comprehensive literature search was conducted across PubMed,Embase,Ovid Technologies(OVID),Web of Science,Cochrane Library,China National Knowledge Infrastructure(CNKI),China National Knowledge Infrastructure Database(VIP),Wanfang Data,and SinoMed Database from database foundation to January 31,2025,for clinical studies on acupuncture treatment of PDD.Descriptive statistics,high-frequency acupoint analysis,degree and betweenness centrality evaluation,and core acupoint prescription mining identified predominant therapeutic combinations for PDD.Network acupuncture was used to predict therapeutic target for the core acupoint prescription.Subsequent protein-protein interaction(PPI)network and molecular complex detection(MCODE)analyses were conducted to identify the key targets and functional modules.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)analyses explored the underlying biological mechanisms of the core acupoint prescription in treating PDD.Results A total of 57 acupoint prescriptions underwent systematic analysis.The core therapeutic combinations comprised Baihui(GV20),Yintang(GV29),Neiguan(PC6),Hegu(LI4),and Shenmen(HT7).Network acupuncture analysis identified 88 potential therapeutic targets(79 overlapping with PDD),while PPI network analysis revealed central regulatory nodes,including interleukin(IL)-6,IL-1β,tumor necrosis factor(TNF)-α,toll-like receptor 4(TLR4),IL-10,brain-derived neurotrophic factor(BDNF),transforming growth factor(TGF)-β1,C-XC motif chemokine ligand 10(CXCL10),mitogen-activated protein kinase 3(MAPK3),and nitric oxide synthase 1(NOS1).MCODE-based modular analysis further elucidated three functionally coherent clusters:inflammation-homeostasis(score=6.571),plasticity-neurotransmission(score=3.143),and oxidative stress(score=3.000).GO and KEGG analyses demonstrated significant enrichment of the MAPK,phosphoinositide 3-kinase/protein kinase B(PI3K/Akt),and hypoxia-inducible factor(HIF)-1 signaling pathways.These mechanistic insights suggested that the antidepressant effects mediated through mechanisms of neuroinflammatory regulation,neuroplasticity restoration,and immune-oxidative stress homeostasis.Conclusion This study reveals that acupuncture alleviates depression through a multi-level mechanism,primarily involving the neuroinflammation suppression,neuroplasticity enhancement,and oxidative stress regulation.These findings systematically clarify the underlying mechanisms of acupuncture’s antidepressant effects and identify novel therapeutic targets for further mechanistic research.
基金Supported by Shanghai College Students Innovation and Entrepreneurship Training Program Project:202310268066The 16th Batch of Science And Technology Innovation Projects of Shanghai University of Traditional Chinese Medicine:SHUTCM2023010+1 种基金2024 Shanghai Oriental Talent Program Youth Project2021 High-level Local University Innovation Team Project of Shanghai University of Traditional Chinese Medicine:No.3 Shanghai Education Commission Personnel [2022]。
文摘Objective To explore the optimization and principles of acupoint selection and coordination in the treatment of adult abdominal obesity using acupuncture and moxibustion over the past decade using data mining.Methods Clinical studies of abdominal obesity treated with acupuncture and moxibustion,collected in the past 10 years,were searched from China Biology Medicine disc(CBMdisc),China National knowledge infrastructure(CNKI),Wanfang,China Science and Technology Journal Database(VIP),Pubmed,Embase,Google Scholar,Web of Science,(The Cumulative Index to Nursing and Allied Health Literature)CINAHL,Psyclnfo and Scopus,dated from March 1,2013 to March 31,2023.Using IBM SPSS Modeler 18.0 and other software,the frequency analysis,association-rules analysis and cluster analysis were conducted on interventions,traditional Chinese medicine(TCM)patterns,use frequency of acupoint,meridian attribution of acupoint,acupoint location,etc.Results A total of 55 articles were included,with 102 prescriptions and 71 acupoints involved.The top 3 interventions were acupoint embedding method,simple electroacupuncture and simple filiform needling.Seventeen patterns/syndromes of TCM differentiation were collected,dominated by spleen deficiency and damp blockage,spleen and kidney yang deficiency and heat accumulation in stomach and intestines.The acupoints in clinical practice were mostly at the foot-yangming stomach meridian,the conception vessel and the foot-taiyin spleen meridian,and located at the abdominal region.The top 5 acupoints of high frequency were Tianshu(ST25),Zhongwan(CV12),Daheng(SP15),Zusanli(ST36),Huaroumen(ST24)and Daimai(GB26).The specific points of the high frequency were the crossing points and front-mu points,of which,ST25 and CV12 were the most prominent.After association-rules analysis on the high-frequency acupoints,20 groups of associated acupoints were obtained,in which,the core acupoints included ST25,CV12,SP15 and ST36.Conclusion In recent 10 years,abdominal obesity is treated by the acupoints of foot-yangming stomach meridian,the conception vessel and the foot-taiyin spleen meridian.Compared with the regimen for simple obesity,the acupoints at the abdominal region are specially selected in treatment of abdominal obesity,such as ST25,CV12,SP15 and ST36.Supplementary acupoints are selected based on syndrome differentiation to simultaneously address both the disease manifestations and root causes.
基金the National Key Research and Development Program of China(No.2023 YFC2811600)the National Natural Science Foundation of China(Nos.52301349,52088102)+1 种基金the Major Science and Technology Innovation Program of Qingdao(No.223-3-hygg-10-hy)the Qingdao Science Foundation for Post-doctoral Scientists(Nos.QDBSH20220202070,QDBSH20220201015)。
文摘A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.
基金Song Hujie’s Inheritance Studio of National Renowned Traditional Chinese Medicine Experts.
文摘Objective:To explore the core acupuncture acupoints and pattern-adapted acupoint combination rules for autism spectrum disorder(ASD)complicated with sleep disorder using clinical data mining technology.Methods:A retrospective analysis was conducted on the diagnosis and treatment data of 104 children with ASD complicated with sleep disorder admitted to Xi’an Traditional Chinese Medicine(TCM)Encephalopathy Hospital from January 2022 to December 2024.Cross-pattern main acupoints were screened via frequency statistics,chi-square test,and factor analysis;pattern-specific auxiliary acupoints were extracted by combining multiple correspondence analysis,cluster analysis,and association rule mining.Results:Ten cross-pattern main acupoints(Baihui,Sishenzhen,Language Area 1,Language Area 2,Neiguan,Shenmen,Yongquan,Xuanzhong)were identified,and acupoint combination schemes for four major TCM patterns(Hyperactivity of Liver and Heart Fire,Deficiency of Kidney Essence,Deficiency of Both Heart and Spleen,Hyperactivity of Liver with Spleen Deficiency)were established.Conclusion:Acupuncture treatment should follow the principle of“regulating spirit and calming the brain as the root,and dredging collaterals based on pattern differentiation as the branch”.The synergy between main and auxiliary acupoints can accurately regulate the disease,providing a basis for precise clinical treatment.
文摘Introduction Neurosurgical emergencies such as spontaneous intracerebral hemorrhage(ICH),traumatic brain injury(TBI),and acute brain herniation are among the most time-sensitive and high-stakes conditions in modern medicine.Clinical decisions often must be made within minutes,yet these decisions are traditionally guided by limited information,heuristic reasoning,and past experience.In this context,the rise of medical data mining and real-time analytics offers a transformative opportunity:to extract actionable intelligence from the flood of clinical,imaging,and physiological data already being collected,and to use this intelligence to guide care in real time[1–3](Figure 1).
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2023-00221186).
文摘Magnesium(Mg)based materials hold immense potential for various applications due to their lightweight and high strength-to-weight ratio.However,to fully harness the potential of Mg alloys,structured analytics are essential to gain valuable insights from centuries of accumulated knowledge.Efficient information extraction from the vast corpus of scientific literature is crucial for this purpose.In this work,we introduce MagBERT,a BERT-based language model specifically trained for Mg-based materials.Utilizing a dataset of approximately 370,000 abstracts focused on Mg and its alloys,MagBERT is designed to understand the intricate details and specialized terminology of this domain.Through rigorous evaluation,we demonstrate the effectiveness of MagBERT for information extraction using a fine-tuned named entity recognition(NER)model,named MagNER.This NER model can extract mechanical,microstructural,and processing properties related to Mg alloys.For instance,we have created an Mg alloy dataset that includes properties such as ductility,yield strength,and ultimate tensile strength(UTS),along with standard alloy names.The introduction of MagBERT is a novel advancement in the development of Mg-specific language models,marking a significant milestone in the discovery of Mg alloys and textual information extraction.By making the pre-trained weights of MagBERT publicly accessible,we aim to accelerate research and innovation in the field of Mg-based materials through efficient information extraction and knowledge discovery.
基金National Natural Science Foundation of China under Grant No.60873213,91018008 and 61070192Beijing Science Foundation under Grant No. 4082018Shanghai Key Laboratory of Intelligent Information Processing of China under Grant No. IIPL-09-006
文摘For various reasons,many of the security programming rules applicable to specific software have not been recorded in official documents,and hence can hardly be employed by static analysis tools for detection.In this paper,we propose a new approach,named SVR-Miner(Security Validation Rules Miner),which uses frequent sequence mining technique [1-4] to automatically infer implicit security validation rules from large software code written in C programming language.Different from the past works in this area,SVR-Miner introduces three techniques which are sensitive thread,program slicing [5-7],and equivalent statements computing to improve the accuracy of rules.Experiments with the Linux Kernel demonstrate the effectiveness of our approach.With the ten given sensitive threads,SVR-Miner automatically generated 17 security validation rules and detected 8 violations,5 of which were published by Linux Kernel Organization before we detected them.We have reported the other three to the Linux Kernel Organization recently.
基金support from Taif university through Researchers Supporting Project number(TURSP-2020/231),Taif University,Taif,Saudi Arabia.
文摘COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans.Different countries have tried different solutions to control the spread of the disease,including lockdowns of countries or cities,quarantines,isolation,sanitization,and masks.Patients with symptoms of COVID-19 are tested using medical testing kits;these tests must be conducted by healthcare professionals.However,the testing process is expensive and time-consuming.There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken.This paper introduces a novel technique based on deep learning(DL)that can be used as a surveillance system to identify infected individuals by analyzing tweets related to COVID-19.The system is used only for surveillance purposes to identify regions where the spread of COVID-19 is high;clinical tests should then be used to test and identify infected individuals.The system proposed here uses recurrent neural networks(RNN)and word-embedding techniques to analyze tweets and determine whether a tweet provides information about COVID-19 or refers to individuals who have been infected with the virus.The results demonstrate that RNN can conduct this analysis more accurately than other machine learning(ML)algorithms.
文摘A data mining method for quality prediction using association rule (DMAR) is presented in this paper. Association rule is used to mine the valuable relations of items among amounts of textile process data for ANN prediction model. DMAR consists of three main steps: setup knowledge data set; data cleaning and converting; find the item set with large supports and generate the expected rules. DMAR effectively improves the precision of prediction in yarn breaking. It rapidly gets rid of the negative influence of training parameters on prediction model. Then more satisfactory quality prediction result can be reached.
基金sponsored by the National Science and Technology Major Project(No.2011ZX05023-005-006)
文摘Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.
基金National Natural Science Foundation of China:81072883,81173342,81473773Scientific Research Project of Hebei Education Department:Z 2014145Planned Project of Young Talents in Colleges and Universities in Hebei Province:BJ 2014047
文摘Different acupuncture-moxibustion therapies can produce different clinical effects, that is, the effect has specificity, which is significantly important in obtaining acupuncture-moxibustion efficacy. In this study, the clinical application laws of fire needle, acupoint injection, catgut embedment, acupoint application, moxibustion therapy and filiform needle acupuncture were summarized in the aspects of category of disease, efficacy and related prescriptions （such as medication and acupoint selection） based on the result of data mining, and the general applicable categories of disease of acupuncture-moxibustion treatment methods were further screened, so as to guide the clinical application and give play to the best efficacy.
