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
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
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
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 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
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
Population growth leads to increased utilization of water resources.One of these resources is groundwater,which has steadily declined each year.The depletion of these resources brings about various environmental chall...Population growth leads to increased utilization of water resources.One of these resources is groundwater,which has steadily declined each year.The depletion of these resources brings about various environmental challenges.The present study aimed to explore the relationship between groundwater fluctuations and land subsidence in the Malayer Plain,Iran,focusing on quantifying subsidence resulting from groundwater extraction.Using Sentinel-1 satellite data(2014–2019)and monthly piezometric measurements(1996–2018),the analysis revealed an average deformation velocity of–6.3 cm yr–1,with accumulated subsidence of–32 cm over the 2014–2019 period.The maximum subsidence rate reached 10.3 cm yr–1 in areas of intensive agricultural activity.A wavelet-PCA spatiotemporal analysis of groundwater fluctuations identified critical multi-scale patterns strongly correlated with subsidence trends.Regression analysis between subsidence rates and groundwater fluctuations at various wavelet decomposition levels explained 75%of the variance(R2=0.75),indicating that intermediate-scale groundwater declines were the primary drivers of subsidence.Furthermore,land use analysis using Landsat data(1999–2021)revealed a 6230-ha increase in irrigated farmland,contributing to heightened groundwater extraction and subsidence rates.These findings highlight the critical need for sustainable groundwater management to mitigate the risks of continued subsidence in the region.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
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
Reducing raw materials consumption(RMC)in electric arc furnace(EAF)steelmaking process is beneficial to the reduction in resource and energy consumption.The conventional indicator of evaluating RMC only focuses on EAF...Reducing raw materials consumption(RMC)in electric arc furnace(EAF)steelmaking process is beneficial to the reduction in resource and energy consumption.The conventional indicator of evaluating RMC only focuses on EAF inputs and outputs,neglecting the associations between smelting operations and RMC.Traditional methods of reducing RMC rely on manual experience and lack a standard operation guidance.A method based on association rules mining and metallurgical mechanism(ARM-MM)was proposed.ARM-MM proposed an improved evaluation indicator of RMC and the indicator independently showed the associations between smelting operations and RMC.On the basis,1265 heats of real EAF data were used to obtain the operation guidance for RMC reduction.According to the ratio of hot metal(HM)in charge metals,data were divided into all dataset,low HM ratio dataset,medium HM ratio dataset,and high HM ratio dataset.ARM algorithm was used in each dataset to obtain specific operation guidance.The real average RMC under all dataset,medium HM ratio dataset,and high HM ratio dataset was reduced by 279,486,and 252 kg/heat,respectively,when obtained operation guidance was applied.展开更多
39 soil samples surrounding a lead-zinc mining area in Guangxi were collected,and the contents of Pb,Hg,Cd,Cr,As,Cu,Zn,and Ni were determined to investigate the pollution characteristics and sources of heavy metals.Ar...39 soil samples surrounding a lead-zinc mining area in Guangxi were collected,and the contents of Pb,Hg,Cd,Cr,As,Cu,Zn,and Ni were determined to investigate the pollution characteristics and sources of heavy metals.ArcGIS inverse distance weight difference method was used to analyze the characteristics of pollution distribution,and single-factor pollution index,Nemerow comprehensive pollution index,ground accumulation index,and potential ecological risk index were selected to evaluate the characteristics of heavy metal pollution.Based on correlation analysis,the absolute principal component-multiple linear regression(APCS-MLR)and positive definite matrix factorization(PMF)models were used to analyze the sources of soil heavy metals.The results showed that the average concentrations of all eight heavy metals exceeded both national and Guangxi soil background values.Hg,Cd,and Zn exhibited high variation(greater than 0.5),indicating significant external disturbances,and their spatial distribution was closely related to mining activity locations.The single-factor pollution index evaluation indicated varying degrees of pollution risk for Cd,Zn,and As,with Cd and Zn being the most severe pollutants,as 69.23%and 30.77%of the samples fell into the moderate pollution or higher category.The geoaccumulation index analysis ranked the mean pollution levels of the eight elements as follows:Zn>Cd>Ni>Pb>Cu>Cr>Hg>As,with Cd and Zn showing the most severe contamination,and 51.28%of the samples exhibiting moderate or higher pollution levels.The Nemerow comprehensive pollution index evaluation showed that 74.35%of soil samples were classified as moderate to heavy pollution.The potential ecological risk index assessment indicated significant ecological risks posed by Cd and Zn,with 82.05%and 5.12%of the samples classified as causing strong to extreme ecological risks,respectively.The source apportionment analysis revealed minor differences between the two models.The APCS-MLR model identified three pollution sources and their contribution rates:anthropogenic mining sources(31.13%),parent material sources(40.38%),and unidentified sources(28.49%).The PMF model identified three pollution sources with contribution rates of anthropogenic mining sources(26.10%),parent material sources(46.96%),and a combined traffic and agricultural source(26.61%).Pb,Hg,Cd,and Zn mainly originated from mining activities;Cr,As,and Ni were primarily derived from the parent material,while Cu was predominantly attributed to traffic and agricultural sources.These findings provide a scientific basis for the prevention and control of heavy metal pollution in mining areas.展开更多
Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and...Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.展开更多
Stability of base-exposed backfill roof in underhand drift-and-fill mining is crucial for the safety of those working beneath.Given the commonly used primary-and-secondary mining sequence,interfaces are formed between...Stability of base-exposed backfill roof in underhand drift-and-fill mining is crucial for the safety of those working beneath.Given the commonly used primary-and-secondary mining sequence,interfaces are formed between adjacent filled drifts,which can weaken the integrity of the backfill roof.These interfaces also lead to two common drift layouts:aligned drifts and staggered drifts.However,less attention has been paid to the interfaces and the two drift layouts were not adequately distinguished in previous studies.In this paper,the interfaces between filled drifts were firstly considered to investigate the stability of backfill roof.Failure modes and strength requirements of backfill roof in aligned and staggered drifts are comprehensively investigated by FLAC3D,with a focus on considerations of varied shear parameters of the interfaces.Results show that failure modes in aligned drifts transition from block sliding to top caving,bottom caving or sloughing as the interface cohesion increases from zero to at least half of the backfill cohesion.Further increases in interface cohesion allow aligned drifts to behave as if there are no interfaces between them.The critical stability conditions of backfill roof in aligned drifts were mostly determined by the interface strength instead of the backfill strength.However,the stability of backfill roof in staggered drifts is barely affected by the interface strength.The outcomes are expected to provide references for mining engineers to optimize drift layouts and perform cost-effective backfill roof strength design at mines using underhand drift-and-fill mining method.展开更多
Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.Howev...Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.展开更多
Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a...Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.展开更多
With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service...With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.展开更多
Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and ...As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.展开更多
Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
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.
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).
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.
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Population growth leads to increased utilization of water resources.One of these resources is groundwater,which has steadily declined each year.The depletion of these resources brings about various environmental challenges.The present study aimed to explore the relationship between groundwater fluctuations and land subsidence in the Malayer Plain,Iran,focusing on quantifying subsidence resulting from groundwater extraction.Using Sentinel-1 satellite data(2014–2019)and monthly piezometric measurements(1996–2018),the analysis revealed an average deformation velocity of–6.3 cm yr–1,with accumulated subsidence of–32 cm over the 2014–2019 period.The maximum subsidence rate reached 10.3 cm yr–1 in areas of intensive agricultural activity.A wavelet-PCA spatiotemporal analysis of groundwater fluctuations identified critical multi-scale patterns strongly correlated with subsidence trends.Regression analysis between subsidence rates and groundwater fluctuations at various wavelet decomposition levels explained 75%of the variance(R2=0.75),indicating that intermediate-scale groundwater declines were the primary drivers of subsidence.Furthermore,land use analysis using Landsat data(1999–2021)revealed a 6230-ha increase in irrigated farmland,contributing to heightened groundwater extraction and subsidence rates.These findings highlight the critical need for sustainable groundwater management to mitigate the risks of continued subsidence in the region.
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.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
基金supported by National Natural Science Foundation of China(Nos.52174328 and 52474368)Fundamental Research Funds for Central Universities of Central South University(Nos.2022ZZTS0084 and 2024ZZTS0062).
文摘Reducing raw materials consumption(RMC)in electric arc furnace(EAF)steelmaking process is beneficial to the reduction in resource and energy consumption.The conventional indicator of evaluating RMC only focuses on EAF inputs and outputs,neglecting the associations between smelting operations and RMC.Traditional methods of reducing RMC rely on manual experience and lack a standard operation guidance.A method based on association rules mining and metallurgical mechanism(ARM-MM)was proposed.ARM-MM proposed an improved evaluation indicator of RMC and the indicator independently showed the associations between smelting operations and RMC.On the basis,1265 heats of real EAF data were used to obtain the operation guidance for RMC reduction.According to the ratio of hot metal(HM)in charge metals,data were divided into all dataset,low HM ratio dataset,medium HM ratio dataset,and high HM ratio dataset.ARM algorithm was used in each dataset to obtain specific operation guidance.The real average RMC under all dataset,medium HM ratio dataset,and high HM ratio dataset was reduced by 279,486,and 252 kg/heat,respectively,when obtained operation guidance was applied.
文摘39 soil samples surrounding a lead-zinc mining area in Guangxi were collected,and the contents of Pb,Hg,Cd,Cr,As,Cu,Zn,and Ni were determined to investigate the pollution characteristics and sources of heavy metals.ArcGIS inverse distance weight difference method was used to analyze the characteristics of pollution distribution,and single-factor pollution index,Nemerow comprehensive pollution index,ground accumulation index,and potential ecological risk index were selected to evaluate the characteristics of heavy metal pollution.Based on correlation analysis,the absolute principal component-multiple linear regression(APCS-MLR)and positive definite matrix factorization(PMF)models were used to analyze the sources of soil heavy metals.The results showed that the average concentrations of all eight heavy metals exceeded both national and Guangxi soil background values.Hg,Cd,and Zn exhibited high variation(greater than 0.5),indicating significant external disturbances,and their spatial distribution was closely related to mining activity locations.The single-factor pollution index evaluation indicated varying degrees of pollution risk for Cd,Zn,and As,with Cd and Zn being the most severe pollutants,as 69.23%and 30.77%of the samples fell into the moderate pollution or higher category.The geoaccumulation index analysis ranked the mean pollution levels of the eight elements as follows:Zn>Cd>Ni>Pb>Cu>Cr>Hg>As,with Cd and Zn showing the most severe contamination,and 51.28%of the samples exhibiting moderate or higher pollution levels.The Nemerow comprehensive pollution index evaluation showed that 74.35%of soil samples were classified as moderate to heavy pollution.The potential ecological risk index assessment indicated significant ecological risks posed by Cd and Zn,with 82.05%and 5.12%of the samples classified as causing strong to extreme ecological risks,respectively.The source apportionment analysis revealed minor differences between the two models.The APCS-MLR model identified three pollution sources and their contribution rates:anthropogenic mining sources(31.13%),parent material sources(40.38%),and unidentified sources(28.49%).The PMF model identified three pollution sources with contribution rates of anthropogenic mining sources(26.10%),parent material sources(46.96%),and a combined traffic and agricultural source(26.61%).Pb,Hg,Cd,and Zn mainly originated from mining activities;Cr,As,and Ni were primarily derived from the parent material,while Cu was predominantly attributed to traffic and agricultural sources.These findings provide a scientific basis for the prevention and control of heavy metal pollution in mining areas.
基金supported by the International Partnership program of the Chinese Academy of Sciences(170GJHZ2023074GC)National Natural Science Foundation of China(42425706 and 42488201)+1 种基金National Key Research and Development Program of China(2024YFF0807902)Beijing Natural Science Foundation(8242041),and China Postdoctoral Science Foundation(2025M770353).
文摘Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.
基金supported by Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(Grant No.2024ZD1003705)the Beijing Nova Program(Grant No.20220484057)support from China Scholarship Council under Grant CSC No.202110300001.
文摘Stability of base-exposed backfill roof in underhand drift-and-fill mining is crucial for the safety of those working beneath.Given the commonly used primary-and-secondary mining sequence,interfaces are formed between adjacent filled drifts,which can weaken the integrity of the backfill roof.These interfaces also lead to two common drift layouts:aligned drifts and staggered drifts.However,less attention has been paid to the interfaces and the two drift layouts were not adequately distinguished in previous studies.In this paper,the interfaces between filled drifts were firstly considered to investigate the stability of backfill roof.Failure modes and strength requirements of backfill roof in aligned and staggered drifts are comprehensively investigated by FLAC3D,with a focus on considerations of varied shear parameters of the interfaces.Results show that failure modes in aligned drifts transition from block sliding to top caving,bottom caving or sloughing as the interface cohesion increases from zero to at least half of the backfill cohesion.Further increases in interface cohesion allow aligned drifts to behave as if there are no interfaces between them.The critical stability conditions of backfill roof in aligned drifts were mostly determined by the interface strength instead of the backfill strength.However,the stability of backfill roof in staggered drifts is barely affected by the interface strength.The outcomes are expected to provide references for mining engineers to optimize drift layouts and perform cost-effective backfill roof strength design at mines using underhand drift-and-fill mining method.
基金supported by the National Key R&D Program of China[Grant No.2023YFF0713600]the National Natural Science Foundation of China[Grant No.62275062]+3 种基金Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments[Grant No.2023-SGTTXM-002 and 2024-SGTTXM-005]the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)[Grant No.YDZX2023115]the Taishan Scholar Special Funding Project of Shandong Provincethe Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai[Grant No.ZL202402].
文摘Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.
文摘Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.
文摘With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金supported by the National Science Foundation of China(No.62171387)the Science and Technology Program of Sichuan Province(No.2024NSFSC0468)the China Postdoctoral Science Foundation(No.2019M663475).
文摘As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
