The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also e...The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.展开更多
As-forged WSTi6421 titanium alloy billet afterβannealing was investigated.Abnormally coarse grains larger than adjacent grains could be observed in the microstructures,forming abnormal grain structures with uneven si...As-forged WSTi6421 titanium alloy billet afterβannealing was investigated.Abnormally coarse grains larger than adjacent grains could be observed in the microstructures,forming abnormal grain structures with uneven size distribution.Through electron backscattered diffraction(EBSD),the forged microstructure at various locations of as-forged WSTi6421 titanium alloy billet was analyzed,revealing that the strength of theβphase cubic texture generated by forging significantly influences the grain size afterβannealing.Heat treatment experiments were conducted within the temperature range from T_(β)−50°C to T_(β)+10°C to observe the macro-and micro-morphologies.Results show that the cubic texture ofβphase caused by forging impacts the texture of the secondaryαphase,which subsequently influences theβphase formed during the post-βannealing process.Moreover,the pinning effect of the residual primaryαphase plays a crucial role in the growth ofβgrains during theβannealing process.EBSD analysis results suggest that the strength ofβphase with cubic texture formed during forging process impacts the orientation distribution differences ofβgrains afterβannealing.Additionally,the development of grains with large orientations within the cubic texture shows a certain degree of selectivity duringβannealing,which is affected by various factors,including the pinning effect of the primaryαphase,the strength of the matrix cubic texture,and the orientation relationship betweenβgrain and matrix.Comprehensively,the stronger the texture in a certain region,the less likely the large misoriented grains suffering secondary growth,thereby aggregating the difference in microstructure and grain orientation distribution across different regions afterβannealing.展开更多
Male infertility can result from impaired sperm motility caused by multiple morphological abnormalities of the flagella(MMAF).Distinct projections encircling the central microtubules of the spermatozoal axoneme play p...Male infertility can result from impaired sperm motility caused by multiple morphological abnormalities of the flagella(MMAF).Distinct projections encircling the central microtubules of the spermatozoal axoneme play pivotal roles in flagellar bending and spermatozoal movement.Mammalian sperm-associated antigen 17(SPAG17)encodes a conserved axonemal protein of cilia and flagella,forming part of the C1a projection of the central apparatus,with functions related to ciliary/flagellar motility,skeletal growth,and male fertility.This study investigated two novel homozygous SPAG17 mutations(M1:NM_206996.2,c.829+1G>T,p.Asp212_Glu276del;and M2:c.2120del,p.Leu707*)identified in four infertile patients from two consanguineous Pakistani families.These patients displayed the MMAF phenotype confirmed by Papanicolaou staining and scanning electron microscopy assays of spermatozoa.Quantitative real-time polymerase chain reaction(PCR)of patients’spermatozoa also revealed a significant decrease in SPAG17 mRNA expression,and immunofluorescence staining showed the absence of SPAG17 protein signals along the flagella.However,no apparent ciliary-related symptoms or skeletal malformations were observed in the chest X-rays of any of the patients.Transmission electron microscopy of axoneme cross-sections from the patients showed incomplete C1a projection and a higher frequency of missing microtubule doublets 1 and 9 compared with those from fertile controls.Immunofluorescence staining and Western blot analyses of spermatogenesis-associated protein 17(SPATA17),a component of the C1a projection,and sperm-associated antigen 6(SPAG6),a marker of the spring layer,revealed disrupted expression of both proteins in the patients’spermatozoa.Altogether,these findings demonstrated that SPAG17 maintains the integrity of spermatozoal flagellar axoneme,expanding the phenotypic spectrum of SPAG17 mutations in humans.展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine...Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine preparation used to treat RA.ZF may cause liver injury.In this study,we aimed to develop a prediction model for abnormal liver function caused by ZF.Methods This retrospective study collected data from multiple centers from January 2018 to April 2023.Abnormal liver function was set as the target variable according to the alanine transaminase(ALT)level.Features were screened through univariate analysis and sequential forward selection for modeling.Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.Results This study included 1,913 eligible patients.The LightGBM model exhibited the best performance(accuracy=0.96)out of the 10 learning models.The predictive metrics of the LightGBM model were as follows:precision=0.99,recall rate=0.97,F1_score=0.98,area under the curve(AUC)=0.98,sensitivity=0.97 and specificity=0.85 for predicting ALT<40 U/L;precision=0.60,recall rate=0.83,F1_score=0.70,AUC=0.98,sensitivity=0.83 and specificity=0.97 for predicting 40≤ALT<80 U/L;and precision=0.83,recall rate=0.63,F1_score=0.71,AUC=0.97,sensitivity=0.63 and specificity=1.00 for predicting ALT≥80 U/L.ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels,the combination of TNF-αinhibitors,JAK inhibitors,methotrexate+nonsteroidal anti-inflammatory drugs,leflunomide,smoking,older age,and females in middle-age(45-65 years old).Conclusion This study developed a model for predicting ZF-induced abnormal liver function,which may help improve the safety of integrated administration of ZF and Western medicine.展开更多
Abnormal grain growth(AGG),a prevalent phenomenon in dilute magnesium(Mg)alloys during elevated-temperature processing,significantly compromises mechanical performance through microstructural degradation.This study in...Abnormal grain growth(AGG),a prevalent phenomenon in dilute magnesium(Mg)alloys during elevated-temperature processing,significantly compromises mechanical performance through microstructural degradation.This study investigates AGG evolution in a heat-treatable Mg-1Al-0.3Ca-0.5Mn(wt.%)alloy,revealing its fundamental mechanism through phase interaction analysis.The AGG initiation is predominantly driven by Zener pinning force attenuation around abnormally coarsened Al_(8)Mn_(5) precipitates.Mechanistically,this heterogeneous coarsening stems from preferential Al_(8)Mn_(5) phase growth kinetics adjacent to Al_(2)Ca phase during homogenization treatment,creating localized pinning force discontinuities.However,addition of 0.2 wt.%Gd facilitates phase transformation from Al_(8)Mn_(5) to thermally stable Al_(8)Mn_(4)Gd with lower Gibbs free energy,thereby promoting a more uniform and refined precipitate distribution.Consequently,the Gd-containing alloy exhibits enhanced grain thermal stability,maintaining a refined microstructure with average grain size of∼7.7μm even after T4 treatment at 500℃ for 1 h,which simultaneously improves strength and ductility compared to the Gd-free alloy.展开更多
The deformation behavior of hot-rolled AZ31 magnesium(Mg)alloy sheet was analyzed when subjected to uniaxial tension along its normal direction at temperatures ranging from 100 to 400℃and strain rates ranging from 0....The deformation behavior of hot-rolled AZ31 magnesium(Mg)alloy sheet was analyzed when subjected to uniaxial tension along its normal direction at temperatures ranging from 100 to 400℃and strain rates ranging from 0.5 to 100 mm/min.Based on the stress−strain curves and the dynamic material model,the hot processing map was established,which demonstrates that the power dissipation factor(η)is the most sensitive to strain rate at 400℃via absorption of dislocations.At 400℃,sample at 0.5 mm/min possessesηof 0.89 because of its lower kernel average misorientation(KAM)value of 0.51,while sample at 100 mm/min possessesηof 0.46 with a higher KAM value of 1.147.In addition,the flow stress presents a slight decrease of 25.94 MPa at 10 mm/min compared to that at 100 mm/min and 100℃.The reasons are twofold:a special~34°texture component during 100℃-100 mm/min favoring the activation of basal slip,and dynamic recrystallization(DRX)also providing softening effect to some extent by absorbing dislocations.Difference in activation of basal slip among twin laminas during 100℃-100 mm/min results in deformation inhomogeneity within the grains,which generates stress that helps matrix grains tilt to a direction favorable to basal slip,forming the special~34°texture component.展开更多
The widespread adoption of tunnel boring machines(TBMs)has led to an increased focus on disc cutter wear,including both normal and abnormal types,for efficient and safe TBM excavation.However,abnormal wear has yet to ...The widespread adoption of tunnel boring machines(TBMs)has led to an increased focus on disc cutter wear,including both normal and abnormal types,for efficient and safe TBM excavation.However,abnormal wear has yet to be thoroughly investigated,primarily due to the complexity of considering mixed ground conditions and the imbalance in the number of instances between the two types of wear.This study developed a prediction model for abnormal TBM disc cutter wear,considering mixed ground conditions,by employing interpretable machine learning with data augmentation.An equivalent elastic modulus was used to consider the characteristics of mixed ground conditions,and wear data was obtained from 65 cutterhead intervention(CHI)reports covering both mixed ground and hard rock sections.With a balanced training dataset obtained by data augmentation,an extreme gradient boosting(XGB)model delivered acceptable results with an accuracy of 0.94,an F1-score of 0.808,and a recall of 0.8.In addition,the accuracy for each individual disc cutter exhibited low variability.When employing data augmentation,a significant improvement in recall was observed compared to when it was not used,although the difference in accuracy and F1-score was marginal.The subsequent model interpretation revealed the chamber pressure,cutter installation radius,and torque as significant contributors.Specifically,a threshold in chamber pressure was observed,which could induce abnormal wear.The study also explored how elevated values of these influential contributors correlate with abnormal wear.The proposed model offers a valuable tool for planning the replacement of abnormally worn disc cutters,enhancing the safety and efficiency of TBM operations.展开更多
BACKGROUND Liver function of chronic hepatitis B(CHB)patients is essentially normal after treatment with antiviral drugs.In rare cases,persistently abnormally elevatedα-fetoprotein(AFP)is seen in CHB patients followi...BACKGROUND Liver function of chronic hepatitis B(CHB)patients is essentially normal after treatment with antiviral drugs.In rare cases,persistently abnormally elevatedα-fetoprotein(AFP)is seen in CHB patients following long-term antiviral treatment.However,in the absence of imaging evidence of liver cancer,a reasonable expla-nation for this phenomenon is still lacking.AIM To explore the causes of abnormal AFP in patients with CHB who were not diag-nosed with liver cancer.METHODS From November 2019 to May 2023,15 patients with CHB after antiviral treatment and elevated AFP were selected.Clinical data and quality indicators related to laboratory testing,imaging data,and pathological data were obtained through inpatient medical records.RESULTS All patients had increased AFP and significantly elevated IgG.Cancer was excluded by imaging examination.Only four patients had elevated alanine ami-notransferase,10 had elevated aspartate aminotransferase,nine had elevated total bilirubin,and two had antinuclear antibodies.The liver biopsy and histopatho-logical examination indicated that 14 patients had rosette,moderate,or higher interfacial inflammation,lymphocyte infiltration,and severe hepatic fibers(11 cases),which was consistent with the pathological features of autoimmune hepa-titis(AIH).After 8-12 week of hormone therapy,the levels of AFP and IgG,and liver function returned to normal(P<0.05).CONCLUSION For patients with CHB and elevated AFP after antiviral treatment,autoimmune hepatitis should be considered.CHB with AIH is clinically insidious and difficult to detect,and prone to progression to cirrhosis.Liver puncture pathological examination should be performed when necessary to confirm diagnosis.展开更多
BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited rese...BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited research on contributing factors in other types of surgeries.AIM To identify the risk factors for early postoperative abnormal liver function in multiple surgery types and construct a risk prediction model.METHODS This retrospective cohort study involved 3720 surgical patients from 5 surgical departments at Guangdong Provincial Hospital of Traditional Chinese Medicine.Patients were divided into abnormal(n=108)and normal(n=3612)groups based on liver function post-surgery.Univariate analysis and LASSO regression screened variables,followed by logistic regression to identify risk factors.A prediction model was constructed based on the variables selected via logistic re-gression.The goodness-of-fit of the model was evaluated using the Hosm-er–Lemeshow test,while discriminatory ability was measured by the area under the receiver operating characteristic curve.Calibration curves were plotted to visualize the consistency between predicted probabilities and observed outcomes.RESULTS The key factors contributing to abnormal liver function after surgery include elevated aspartate aminotransferase and alanine aminotransferase levels and reduced platelet counts pre-surgery,as well as the sevoflurane use during the procedure,among others.CONCLUSION The above factors collectively represent notable risk factors for postoperative liver function injury,and the prediction model developed based on these factors demonstrates strong predictive efficacy.展开更多
Nowadays,abnormal traffic detection for Software-Defined Networking(SDN)faces the challenges of large data volume and high dimensionality.Since traditional machine learning-based detection methods have the problem of ...Nowadays,abnormal traffic detection for Software-Defined Networking(SDN)faces the challenges of large data volume and high dimensionality.Since traditional machine learning-based detection methods have the problem of data redundancy,the Metaheuristic Algorithm(MA)is introduced to select features beforemachine learning to reduce the dimensionality of data.Since a Tyrannosaurus Optimization Algorithm(TROA)has the advantages of few parameters,simple implementation,and fast convergence,and it shows better results in feature selection,TROA can be applied to abnormal traffic detection for SDN.However,TROA suffers frominsufficient global search capability,is easily trapped in local optimums,and has poor search accuracy.Then,this paper tries to improve TROA,namely the Improved Tyrannosaurus Optimization Algorithm(ITROA).It proposes a metaheuristic-driven abnormal traffic detection model for SDN based on ITROA.Finally,the validity of the ITROA is verified by the benchmark function and the UCI dataset,and the feature selection optimization operation is performed on the InSDN dataset by ITROA and other MAs to obtain the optimized feature subset for SDN abnormal traffic detection.The experiment shows that the performance of the proposed ITROA outperforms compared MAs in terms of the metaheuristic-driven model for SDN,achieving an accuracy of 99.37%on binary classification and 96.73%on multiclassification.展开更多
High-order asymmetric flatness defects resulting from the abnormal state of roll system are the main issue of precision rolling mill in the manufacturing process of high-strength thin strip.Due to the difficulty of mo...High-order asymmetric flatness defects resulting from the abnormal state of roll system are the main issue of precision rolling mill in the manufacturing process of high-strength thin strip.Due to the difficulty of monitoring and adjusting the abnormal state,the spatial state of roll system cannot be controlled by traditional methods.It is difficult to fundamentally improve these high-order asymmetric flatness defects.Therefore,a digital twin model of flatness control process for S6-high rolling mill was established,which could be used to analyze the influence of the abnormal state on the flatness control characteristic and propose improvement strategies.The internal relationship between the force state of side support roll system and the abnormal state of roll system was proposed.The XGBoost algorithm model was established to analyze the contribution degree of the side support roll system force to the flatness characteristic quantity.The abnormal state of roll system in the S6-high rolling mill can be diagnosed by analyzing the flatness characteristic difference between flatness value of the rolled strip and calculated characteristic value of finite element simulation.The flatness optimization model of the gray wolf optimization–long short-term memory non-dominated sorting whale optimization algorithm(GWO-LSTM-NSWOA)was established,and the decision-making selection was made from the Pareto frontier based on the flatness requirements of cold rolling to regulate the abnormal state of the roll system.The results indicate that the contribution degree of the force of the side support roll system to the flatness characteristics is more than 25%,which is the main influence of high-order asymmetric flatness defect.The performance of the GWO-LSTM flatness feature prediction model has clear advantages over back propagation and LSTM.The practical applications show that optimizing the force of side support roll system can reduce the high point of high-strength strip flatness from 13.2 to 6 IU and decrease the percentage of low-strength strip flatness defects from 1.6%to 1.2%.This optimization greatly reduced the proportion of flatness defects,improved the accuracy level of flatness control of precision rolling mill,and provided a guarantee for the stable production of thin strip.展开更多
Objective:To analyze the characteristics of ambulatory blood pressure in elderly patients with hypertension and find out the risk factors of abnormal circadian rhythm.Methods:According to the circadian rhythm of patie...Objective:To analyze the characteristics of ambulatory blood pressure in elderly patients with hypertension and find out the risk factors of abnormal circadian rhythm.Methods:According to the circadian rhythm of patients’blood pressure,they were divided into group A,group B,and group C,and all the data of hypertension patients in this study were collected,including age,gender,BMI,smoking,drinking,basic diseases(diabetes,cerebrovascular disease,hyperlipidemia,etc.),fasting blood glucose,ambulatory blood pressure(24-hour mean systolic pressure,24-hour mean diastolic pressure,daytime mean systolic pressure and daytime mean diastolic pressure).Results:There were significant differences in mean systolic blood pressure and mean diastolic blood pressure at night among group A,group B and group C(P<0.05).Age,hyperlipidemia and fasting blood glucose were risk factors for circadian rhythm abnormality(P<0.05),and 24-hour urinary sodium was a protective factor for circadian rhythm abnormality(P<0.05).Conclusion:Age,hyperlipidemia and fasting blood glucose are risk factors for circadian rhythm abnormality(P<0.05),and 24-hour urinary sodium is a protective factor for circadian rhythm abnormality(P<0.05).展开更多
Objective:To analyze the characteristics of ambulatory blood pressure in elderly patients with hypertension and find out the risk factors of abnormal circadian rhythm.Methods:According to the circadian rhythm of patie...Objective:To analyze the characteristics of ambulatory blood pressure in elderly patients with hypertension and find out the risk factors of abnormal circadian rhythm.Methods:According to the circadian rhythm of patients'blood pressure,they were divided into Group A,Group B and Group C,and all the data of hypertension patients in this study were collected,including age,gender,BMI,smoking,drinking,basic diseases(diabetes,cerebrovascular disease,hyperlipidemia,etc.),fasting blood glucose,ambulatory blood pressure(24-hour mean systolic pressure,24-hour mean diastolic pressure,daytime mean systolic pressure and daytime mean diastolic pressure).Results:There were significant differences in mean systolic blood pressure and mean diastolic blood pressure at night among Group A,Group B and Group C(P<0.05).Age,hyperlipidemia and fasting blood glucose were risk factors for circadian rhythm abnormality(P<0.05),and 24-hour urinary sodium was a protective factor for circadian rhythm abnormality(P<0.05).Conclusion:Age,hyperlipidemia and fasting blood glucose are risk factors for circadian rhythm abnormality(P<0.05),and 24-hour urinary sodium is a protective factor for circadian rhythm abnormality(P<0.05).展开更多
Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells an...Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.展开更多
Blockchain platform swith the unique characteristics of anonymity,decentralization,and transparency of their transactions,which are faced with abnormal activities such as money laundering,phishing scams,and fraudulent...Blockchain platform swith the unique characteristics of anonymity,decentralization,and transparency of their transactions,which are faced with abnormal activities such as money laundering,phishing scams,and fraudulent behavior,posing a serious threat to account asset security.For these potential security risks,this paper proposes a hybrid neural network detection method(HNND)that learns multiple types of account features and enhances fusion information among them to effectively detect abnormal transaction behaviors in the blockchain.In HNND,the Temporal Transaction Graph Attention Network(T2GAT)is first designed to learn biased aggregation representation of multi-attribute transactions among nodes,which can capture key temporal information from node neighborhood transactions.Then,the Graph Convolutional Network(GCN)is adopted which captures abstract structural features of the transaction network.Further,the Stacked Denoising Autoencode(SDA)is developed to achieve adaptive fusion of thses features from different modules.Moreover,the SDA enhances robustness and generalization ability of node representation,leading to higher binary classification accuracy in detecting abnormal behaviors of blockchain accounts.Evaluations on a real-world abnormal transaction dataset demonstrate great advantages of the proposed HNND method over other compared methods.展开更多
To address the limitations of existing abnormal traffic detection methods,such as insufficient temporal and spatial feature extraction,high false positive rate(FPR),poor generalization,and class imbalance,this study p...To address the limitations of existing abnormal traffic detection methods,such as insufficient temporal and spatial feature extraction,high false positive rate(FPR),poor generalization,and class imbalance,this study proposed an intelligent detection method that combines a Stacked Convolutional Network(SCN),Bidirectional Long Short-Term Memory(BiLSTM)network,and Equalization Loss v2(EQL v2).This method was divided into two components:a feature extraction model and a classification and detection model.First,SCN was constructed by combining a Convolutional Neural Network(CNN)with a Depthwise Separable Convolution(DSC)network to capture the abstract spatial features of traffic data.These features were then input into the BiLSTM to capture temporal dependencies.An attention mechanism was incorporated after SCN and BiLSTM to enhance the extraction of key spatiotemporal features.To address class imbalance,the classification detection model applied EQL v2 to adjust the weights of the minority classes,ensuring that they received equal focus during training.The experimental results indicated that the proposed method outperformed the existing methods in terms of accuracy,FPR,and F1-score and significantly improved the identification rate of minority classes.展开更多
Nuclearβ-decay,a typical decay process for unstable nuclei,is a key mechanism for producing heavy elements in the Universe.In this study,neural networks were employed to predictβ-decay half-lives and,for the first t...Nuclearβ-decay,a typical decay process for unstable nuclei,is a key mechanism for producing heavy elements in the Universe.In this study,neural networks were employed to predictβ-decay half-lives and,for the first time,to identify abnormal trends in nuclearβ-decay half-lives based on deviations between experimental values and the predictions of neural networks.Nuclei exhibiting anomalous increases,abrupt peaks,sharp decreases,abnormal odd-even oscillations,and excessively large experimental errors in theirβ-decay half-lives,which deviate from systematic patterns,were identified through deviations.These anomalous phenomena may be associated with shell effects,shape coexistence,or discrepancies in the experimental data.The discovery and analysis of these abnormal nuclei provide a valuable reference for further investigations using sophisticated microscopic theories,potentially offering insights into new physics through studies of nuclearβ-decay half-lives.展开更多
Thermal power generation systems have stringent requirements for water and steam quality,i.e.,condensate water quality is one of the critical issues.In this paper,we designed a two-layer model based on an autoencoder ...Thermal power generation systems have stringent requirements for water and steam quality,i.e.,condensate water quality is one of the critical issues.In this paper,we designed a two-layer model based on an autoencoder and expert knowledge to achieve the early warning and causal analysis of condensate water quality abnormalities.An early warning model using an autoencoder model is built based on the historical data affecting the condensate water quality.Next,an analytical model of condensate water quality abnormalities was then developed by combining expert knowledge and trend test algorithms.Two different datasets were used to test the proposed model,respectively.The accuracy of the autoencoder model in the short-period test set is 88.83%,which shows that the early warning model can accurately analyze the condensate water quality data and achieve the purpose of early warning.For the long-time period test set,the model can correctly identify each abnormality and simultaneously indicates the cause of the abnormal condensate water quality.The proposed model can correctly identify abnormal working conditions and it is applicable to other thermal power plants.展开更多
To investigate the correlation between propacetamol and postoperative liver enzyme abnormalities among patients,a retrospective analysis was conducted on inpatients in the thoracic surgery department spanning from Jan...To investigate the correlation between propacetamol and postoperative liver enzyme abnormalities among patients,a retrospective analysis was conducted on inpatients in the thoracic surgery department spanning from January 1 to June 30,2023.Causality assessment regarding propacetamol and postoperative liver enzyme abnormalities was performed using the updated Roussel Uclaf Causality Assessment Method(RUCAM).Furthermore,independent risk factors for liver enzyme abnormalities were identified through both univariate and multivariate analyses,followed by the construction and validation of a clinical nomogram.A total of 247 patients who received propacetamol were ultimately included in the study.Liver enzyme abnormalities post-surgery were more accurately predicted by considering the daily dose of propacetamol and the number of medications(OR(95%CI),4.831(2.797,8.344),P<0.001;10.007(3.878,25.823),P<0.001).A clinical predictive nomogram model was developed,incorporating these two independent risk factors,which exhibited favorable discrimination(AUC(95%CI),0.811(0.750,0.872)),calibration,and decision curve analysis(DCA)demonstrating the highest net benefits across a broad spectrum of threshold probabilities(10%to 90%).The daily dose of propacetamol and the number of medications were found to be independently associated with postoperative liver enzyme abnormalities.This user-friendly nomogram,comprising these two factors,might assist clinicians in assessing the risks of propacetamol-related liver dysfunction following surgery.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62371187the Hunan Provincial Natural Science Foundation of China under Grant Nos.2024JJ8309 and 2023JJ50495.
文摘The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.
基金Key Research and Development Plan of Shaanxi Province(2023-YBGY-493)。
文摘As-forged WSTi6421 titanium alloy billet afterβannealing was investigated.Abnormally coarse grains larger than adjacent grains could be observed in the microstructures,forming abnormal grain structures with uneven size distribution.Through electron backscattered diffraction(EBSD),the forged microstructure at various locations of as-forged WSTi6421 titanium alloy billet was analyzed,revealing that the strength of theβphase cubic texture generated by forging significantly influences the grain size afterβannealing.Heat treatment experiments were conducted within the temperature range from T_(β)−50°C to T_(β)+10°C to observe the macro-and micro-morphologies.Results show that the cubic texture ofβphase caused by forging impacts the texture of the secondaryαphase,which subsequently influences theβphase formed during the post-βannealing process.Moreover,the pinning effect of the residual primaryαphase plays a crucial role in the growth ofβgrains during theβannealing process.EBSD analysis results suggest that the strength ofβphase with cubic texture formed during forging process impacts the orientation distribution differences ofβgrains afterβannealing.Additionally,the development of grains with large orientations within the cubic texture shows a certain degree of selectivity duringβannealing,which is affected by various factors,including the pinning effect of the primaryαphase,the strength of the matrix cubic texture,and the orientation relationship betweenβgrain and matrix.Comprehensively,the stronger the texture in a certain region,the less likely the large misoriented grains suffering secondary growth,thereby aggregating the difference in microstructure and grain orientation distribution across different regions afterβannealing.
基金supported by the National Natural Science Foundation of China(No.82171599 and No.32270901)the National Key Research and Developmental Program of China(2022YFC2702601 and 2022YFA0806303)the Global Select Project(DJKLX-2022010)of the Institute of Health and Medicine,Hefei Comprehensive National Science Center.
文摘Male infertility can result from impaired sperm motility caused by multiple morphological abnormalities of the flagella(MMAF).Distinct projections encircling the central microtubules of the spermatozoal axoneme play pivotal roles in flagellar bending and spermatozoal movement.Mammalian sperm-associated antigen 17(SPAG17)encodes a conserved axonemal protein of cilia and flagella,forming part of the C1a projection of the central apparatus,with functions related to ciliary/flagellar motility,skeletal growth,and male fertility.This study investigated two novel homozygous SPAG17 mutations(M1:NM_206996.2,c.829+1G>T,p.Asp212_Glu276del;and M2:c.2120del,p.Leu707*)identified in four infertile patients from two consanguineous Pakistani families.These patients displayed the MMAF phenotype confirmed by Papanicolaou staining and scanning electron microscopy assays of spermatozoa.Quantitative real-time polymerase chain reaction(PCR)of patients’spermatozoa also revealed a significant decrease in SPAG17 mRNA expression,and immunofluorescence staining showed the absence of SPAG17 protein signals along the flagella.However,no apparent ciliary-related symptoms or skeletal malformations were observed in the chest X-rays of any of the patients.Transmission electron microscopy of axoneme cross-sections from the patients showed incomplete C1a projection and a higher frequency of missing microtubule doublets 1 and 9 compared with those from fertile controls.Immunofluorescence staining and Western blot analyses of spermatogenesis-associated protein 17(SPATA17),a component of the C1a projection,and sperm-associated antigen 6(SPAG6),a marker of the spring layer,revealed disrupted expression of both proteins in the patients’spermatozoa.Altogether,these findings demonstrated that SPAG17 maintains the integrity of spermatozoal flagellar axoneme,expanding the phenotypic spectrum of SPAG17 mutations in humans.
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
基金supported by the Budgeted Fund of Shanghai University of Traditional Chinese Medicine(Natural Science)(No.2021LK037)the Open Project of Qinghai Province Key Laboratory of Tibetan Medicine Pharmacology and Safety Evaluation(No.2021-ZY-03).
文摘Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine preparation used to treat RA.ZF may cause liver injury.In this study,we aimed to develop a prediction model for abnormal liver function caused by ZF.Methods This retrospective study collected data from multiple centers from January 2018 to April 2023.Abnormal liver function was set as the target variable according to the alanine transaminase(ALT)level.Features were screened through univariate analysis and sequential forward selection for modeling.Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.Results This study included 1,913 eligible patients.The LightGBM model exhibited the best performance(accuracy=0.96)out of the 10 learning models.The predictive metrics of the LightGBM model were as follows:precision=0.99,recall rate=0.97,F1_score=0.98,area under the curve(AUC)=0.98,sensitivity=0.97 and specificity=0.85 for predicting ALT<40 U/L;precision=0.60,recall rate=0.83,F1_score=0.70,AUC=0.98,sensitivity=0.83 and specificity=0.97 for predicting 40≤ALT<80 U/L;and precision=0.83,recall rate=0.63,F1_score=0.71,AUC=0.97,sensitivity=0.63 and specificity=1.00 for predicting ALT≥80 U/L.ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels,the combination of TNF-αinhibitors,JAK inhibitors,methotrexate+nonsteroidal anti-inflammatory drugs,leflunomide,smoking,older age,and females in middle-age(45-65 years old).Conclusion This study developed a model for predicting ZF-induced abnormal liver function,which may help improve the safety of integrated administration of ZF and Western medicine.
基金supports from The National Natural Science Foundation of China (Nos. 52222409, U24A20104 and 52401049)The National Key Research and Development Program (No. 2024YFB3408900) are greatly acknowledgedPartial financial support came from the Fundamental Research Funds for the Central Universities, JLU
文摘Abnormal grain growth(AGG),a prevalent phenomenon in dilute magnesium(Mg)alloys during elevated-temperature processing,significantly compromises mechanical performance through microstructural degradation.This study investigates AGG evolution in a heat-treatable Mg-1Al-0.3Ca-0.5Mn(wt.%)alloy,revealing its fundamental mechanism through phase interaction analysis.The AGG initiation is predominantly driven by Zener pinning force attenuation around abnormally coarsened Al_(8)Mn_(5) precipitates.Mechanistically,this heterogeneous coarsening stems from preferential Al_(8)Mn_(5) phase growth kinetics adjacent to Al_(2)Ca phase during homogenization treatment,creating localized pinning force discontinuities.However,addition of 0.2 wt.%Gd facilitates phase transformation from Al_(8)Mn_(5) to thermally stable Al_(8)Mn_(4)Gd with lower Gibbs free energy,thereby promoting a more uniform and refined precipitate distribution.Consequently,the Gd-containing alloy exhibits enhanced grain thermal stability,maintaining a refined microstructure with average grain size of∼7.7μm even after T4 treatment at 500℃ for 1 h,which simultaneously improves strength and ductility compared to the Gd-free alloy.
基金Project(52005362) supported by the National Natural Science Foundation of ChinaProjects(202303021221005,202303021211045) supported by the Natural Science Foundation of Shanxi Province,China+1 种基金Project(202402003) supported by the Patent Commercialization Program of Shanxi Province,ChinaProject supported by the Key Research and Development Plan of Xinzhou City,China。
文摘The deformation behavior of hot-rolled AZ31 magnesium(Mg)alloy sheet was analyzed when subjected to uniaxial tension along its normal direction at temperatures ranging from 100 to 400℃and strain rates ranging from 0.5 to 100 mm/min.Based on the stress−strain curves and the dynamic material model,the hot processing map was established,which demonstrates that the power dissipation factor(η)is the most sensitive to strain rate at 400℃via absorption of dislocations.At 400℃,sample at 0.5 mm/min possessesηof 0.89 because of its lower kernel average misorientation(KAM)value of 0.51,while sample at 100 mm/min possessesηof 0.46 with a higher KAM value of 1.147.In addition,the flow stress presents a slight decrease of 25.94 MPa at 10 mm/min compared to that at 100 mm/min and 100℃.The reasons are twofold:a special~34°texture component during 100℃-100 mm/min favoring the activation of basal slip,and dynamic recrystallization(DRX)also providing softening effect to some extent by absorbing dislocations.Difference in activation of basal slip among twin laminas during 100℃-100 mm/min results in deformation inhomogeneity within the grains,which generates stress that helps matrix grains tilt to a direction favorable to basal slip,forming the special~34°texture component.
基金support of the“National R&D Project for Smart Construction Technology (Grant No.RS-2020-KA157074)”funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land,Infrastructure and Transport,and managed by the Korea Expressway Corporation.
文摘The widespread adoption of tunnel boring machines(TBMs)has led to an increased focus on disc cutter wear,including both normal and abnormal types,for efficient and safe TBM excavation.However,abnormal wear has yet to be thoroughly investigated,primarily due to the complexity of considering mixed ground conditions and the imbalance in the number of instances between the two types of wear.This study developed a prediction model for abnormal TBM disc cutter wear,considering mixed ground conditions,by employing interpretable machine learning with data augmentation.An equivalent elastic modulus was used to consider the characteristics of mixed ground conditions,and wear data was obtained from 65 cutterhead intervention(CHI)reports covering both mixed ground and hard rock sections.With a balanced training dataset obtained by data augmentation,an extreme gradient boosting(XGB)model delivered acceptable results with an accuracy of 0.94,an F1-score of 0.808,and a recall of 0.8.In addition,the accuracy for each individual disc cutter exhibited low variability.When employing data augmentation,a significant improvement in recall was observed compared to when it was not used,although the difference in accuracy and F1-score was marginal.The subsequent model interpretation revealed the chamber pressure,cutter installation radius,and torque as significant contributors.Specifically,a threshold in chamber pressure was observed,which could induce abnormal wear.The study also explored how elevated values of these influential contributors correlate with abnormal wear.The proposed model offers a valuable tool for planning the replacement of abnormally worn disc cutters,enhancing the safety and efficiency of TBM operations.
文摘BACKGROUND Liver function of chronic hepatitis B(CHB)patients is essentially normal after treatment with antiviral drugs.In rare cases,persistently abnormally elevatedα-fetoprotein(AFP)is seen in CHB patients following long-term antiviral treatment.However,in the absence of imaging evidence of liver cancer,a reasonable expla-nation for this phenomenon is still lacking.AIM To explore the causes of abnormal AFP in patients with CHB who were not diag-nosed with liver cancer.METHODS From November 2019 to May 2023,15 patients with CHB after antiviral treatment and elevated AFP were selected.Clinical data and quality indicators related to laboratory testing,imaging data,and pathological data were obtained through inpatient medical records.RESULTS All patients had increased AFP and significantly elevated IgG.Cancer was excluded by imaging examination.Only four patients had elevated alanine ami-notransferase,10 had elevated aspartate aminotransferase,nine had elevated total bilirubin,and two had antinuclear antibodies.The liver biopsy and histopatho-logical examination indicated that 14 patients had rosette,moderate,or higher interfacial inflammation,lymphocyte infiltration,and severe hepatic fibers(11 cases),which was consistent with the pathological features of autoimmune hepa-titis(AIH).After 8-12 week of hormone therapy,the levels of AFP and IgG,and liver function returned to normal(P<0.05).CONCLUSION For patients with CHB and elevated AFP after antiviral treatment,autoimmune hepatitis should be considered.CHB with AIH is clinically insidious and difficult to detect,and prone to progression to cirrhosis.Liver puncture pathological examination should be performed when necessary to confirm diagnosis.
基金Supported by Guangdong Provincial Hospital of Chinese Medicine Science and Technology Research Special Project,No.YN2023WSSQ01State Key Laboratory of Traditional Chinese Medicine Syndrome.
文摘BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited research on contributing factors in other types of surgeries.AIM To identify the risk factors for early postoperative abnormal liver function in multiple surgery types and construct a risk prediction model.METHODS This retrospective cohort study involved 3720 surgical patients from 5 surgical departments at Guangdong Provincial Hospital of Traditional Chinese Medicine.Patients were divided into abnormal(n=108)and normal(n=3612)groups based on liver function post-surgery.Univariate analysis and LASSO regression screened variables,followed by logistic regression to identify risk factors.A prediction model was constructed based on the variables selected via logistic re-gression.The goodness-of-fit of the model was evaluated using the Hosm-er–Lemeshow test,while discriminatory ability was measured by the area under the receiver operating characteristic curve.Calibration curves were plotted to visualize the consistency between predicted probabilities and observed outcomes.RESULTS The key factors contributing to abnormal liver function after surgery include elevated aspartate aminotransferase and alanine aminotransferase levels and reduced platelet counts pre-surgery,as well as the sevoflurane use during the procedure,among others.CONCLUSION The above factors collectively represent notable risk factors for postoperative liver function injury,and the prediction model developed based on these factors demonstrates strong predictive efficacy.
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘Nowadays,abnormal traffic detection for Software-Defined Networking(SDN)faces the challenges of large data volume and high dimensionality.Since traditional machine learning-based detection methods have the problem of data redundancy,the Metaheuristic Algorithm(MA)is introduced to select features beforemachine learning to reduce the dimensionality of data.Since a Tyrannosaurus Optimization Algorithm(TROA)has the advantages of few parameters,simple implementation,and fast convergence,and it shows better results in feature selection,TROA can be applied to abnormal traffic detection for SDN.However,TROA suffers frominsufficient global search capability,is easily trapped in local optimums,and has poor search accuracy.Then,this paper tries to improve TROA,namely the Improved Tyrannosaurus Optimization Algorithm(ITROA).It proposes a metaheuristic-driven abnormal traffic detection model for SDN based on ITROA.Finally,the validity of the ITROA is verified by the benchmark function and the UCI dataset,and the feature selection optimization operation is performed on the InSDN dataset by ITROA and other MAs to obtain the optimized feature subset for SDN abnormal traffic detection.The experiment shows that the performance of the proposed ITROA outperforms compared MAs in terms of the metaheuristic-driven model for SDN,achieving an accuracy of 99.37%on binary classification and 96.73%on multiclassification.
基金financially supported by the National Key Research and Development Program of China(Grant No.2023YFB3812602).
文摘High-order asymmetric flatness defects resulting from the abnormal state of roll system are the main issue of precision rolling mill in the manufacturing process of high-strength thin strip.Due to the difficulty of monitoring and adjusting the abnormal state,the spatial state of roll system cannot be controlled by traditional methods.It is difficult to fundamentally improve these high-order asymmetric flatness defects.Therefore,a digital twin model of flatness control process for S6-high rolling mill was established,which could be used to analyze the influence of the abnormal state on the flatness control characteristic and propose improvement strategies.The internal relationship between the force state of side support roll system and the abnormal state of roll system was proposed.The XGBoost algorithm model was established to analyze the contribution degree of the side support roll system force to the flatness characteristic quantity.The abnormal state of roll system in the S6-high rolling mill can be diagnosed by analyzing the flatness characteristic difference between flatness value of the rolled strip and calculated characteristic value of finite element simulation.The flatness optimization model of the gray wolf optimization–long short-term memory non-dominated sorting whale optimization algorithm(GWO-LSTM-NSWOA)was established,and the decision-making selection was made from the Pareto frontier based on the flatness requirements of cold rolling to regulate the abnormal state of the roll system.The results indicate that the contribution degree of the force of the side support roll system to the flatness characteristics is more than 25%,which is the main influence of high-order asymmetric flatness defect.The performance of the GWO-LSTM flatness feature prediction model has clear advantages over back propagation and LSTM.The practical applications show that optimizing the force of side support roll system can reduce the high point of high-strength strip flatness from 13.2 to 6 IU and decrease the percentage of low-strength strip flatness defects from 1.6%to 1.2%.This optimization greatly reduced the proportion of flatness defects,improved the accuracy level of flatness control of precision rolling mill,and provided a guarantee for the stable production of thin strip.
文摘Objective:To analyze the characteristics of ambulatory blood pressure in elderly patients with hypertension and find out the risk factors of abnormal circadian rhythm.Methods:According to the circadian rhythm of patients’blood pressure,they were divided into group A,group B,and group C,and all the data of hypertension patients in this study were collected,including age,gender,BMI,smoking,drinking,basic diseases(diabetes,cerebrovascular disease,hyperlipidemia,etc.),fasting blood glucose,ambulatory blood pressure(24-hour mean systolic pressure,24-hour mean diastolic pressure,daytime mean systolic pressure and daytime mean diastolic pressure).Results:There were significant differences in mean systolic blood pressure and mean diastolic blood pressure at night among group A,group B and group C(P<0.05).Age,hyperlipidemia and fasting blood glucose were risk factors for circadian rhythm abnormality(P<0.05),and 24-hour urinary sodium was a protective factor for circadian rhythm abnormality(P<0.05).Conclusion:Age,hyperlipidemia and fasting blood glucose are risk factors for circadian rhythm abnormality(P<0.05),and 24-hour urinary sodium is a protective factor for circadian rhythm abnormality(P<0.05).
文摘Objective:To analyze the characteristics of ambulatory blood pressure in elderly patients with hypertension and find out the risk factors of abnormal circadian rhythm.Methods:According to the circadian rhythm of patients'blood pressure,they were divided into Group A,Group B and Group C,and all the data of hypertension patients in this study were collected,including age,gender,BMI,smoking,drinking,basic diseases(diabetes,cerebrovascular disease,hyperlipidemia,etc.),fasting blood glucose,ambulatory blood pressure(24-hour mean systolic pressure,24-hour mean diastolic pressure,daytime mean systolic pressure and daytime mean diastolic pressure).Results:There were significant differences in mean systolic blood pressure and mean diastolic blood pressure at night among Group A,Group B and Group C(P<0.05).Age,hyperlipidemia and fasting blood glucose were risk factors for circadian rhythm abnormality(P<0.05),and 24-hour urinary sodium was a protective factor for circadian rhythm abnormality(P<0.05).Conclusion:Age,hyperlipidemia and fasting blood glucose are risk factors for circadian rhythm abnormality(P<0.05),and 24-hour urinary sodium is a protective factor for circadian rhythm abnormality(P<0.05).
基金funded by the China Chongqing Municipal Science and Technology Bureau,grant numbers 2024TIAD-CYKJCXX0121,2024NSCQ-LZX0135Chongqing Municipal Commission of Housing and Urban-Rural Development,grant number CKZ2024-87+3 种基金the Chongqing University of Technology graduate education high-quality development project,grant number gzlsz202401the Chongqing University of Technology-Chongqing LINGLUE Technology Co.,Ltd.,Electronic Information(Artificial Intelligence)graduate joint training basethe Postgraduate Education and Teaching Reform Research Project in Chongqing,grant number yjg213116the Chongqing University of Technology-CISDI Chongqing Information Technology Co.,Ltd.,Computer Technology graduate joint training base.
文摘Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.
文摘Blockchain platform swith the unique characteristics of anonymity,decentralization,and transparency of their transactions,which are faced with abnormal activities such as money laundering,phishing scams,and fraudulent behavior,posing a serious threat to account asset security.For these potential security risks,this paper proposes a hybrid neural network detection method(HNND)that learns multiple types of account features and enhances fusion information among them to effectively detect abnormal transaction behaviors in the blockchain.In HNND,the Temporal Transaction Graph Attention Network(T2GAT)is first designed to learn biased aggregation representation of multi-attribute transactions among nodes,which can capture key temporal information from node neighborhood transactions.Then,the Graph Convolutional Network(GCN)is adopted which captures abstract structural features of the transaction network.Further,the Stacked Denoising Autoencode(SDA)is developed to achieve adaptive fusion of thses features from different modules.Moreover,the SDA enhances robustness and generalization ability of node representation,leading to higher binary classification accuracy in detecting abnormal behaviors of blockchain accounts.Evaluations on a real-world abnormal transaction dataset demonstrate great advantages of the proposed HNND method over other compared methods.
基金supported by the National Natural Science Foundation of China(Grant No.62102449).
文摘To address the limitations of existing abnormal traffic detection methods,such as insufficient temporal and spatial feature extraction,high false positive rate(FPR),poor generalization,and class imbalance,this study proposed an intelligent detection method that combines a Stacked Convolutional Network(SCN),Bidirectional Long Short-Term Memory(BiLSTM)network,and Equalization Loss v2(EQL v2).This method was divided into two components:a feature extraction model and a classification and detection model.First,SCN was constructed by combining a Convolutional Neural Network(CNN)with a Depthwise Separable Convolution(DSC)network to capture the abstract spatial features of traffic data.These features were then input into the BiLSTM to capture temporal dependencies.An attention mechanism was incorporated after SCN and BiLSTM to enhance the extraction of key spatiotemporal features.To address class imbalance,the classification detection model applied EQL v2 to adjust the weights of the minority classes,ensuring that they received equal focus during training.The experimental results indicated that the proposed method outperformed the existing methods in terms of accuracy,FPR,and F1-score and significantly improved the identification rate of minority classes.
基金supported by the‘Young Scientist Scheme’of the National Key R&D Program of China(No.2021YFA1601500)National Natural Science Foundation of China(Nos.12075104,12375109,11875070,and 11935001)+1 种基金Anhui Project(Z010118169)Key Research Foundation of the Education Ministry of Anhui Province(No.2023AH050095)。
文摘Nuclearβ-decay,a typical decay process for unstable nuclei,is a key mechanism for producing heavy elements in the Universe.In this study,neural networks were employed to predictβ-decay half-lives and,for the first time,to identify abnormal trends in nuclearβ-decay half-lives based on deviations between experimental values and the predictions of neural networks.Nuclei exhibiting anomalous increases,abrupt peaks,sharp decreases,abnormal odd-even oscillations,and excessively large experimental errors in theirβ-decay half-lives,which deviate from systematic patterns,were identified through deviations.These anomalous phenomena may be associated with shell effects,shape coexistence,or discrepancies in the experimental data.The discovery and analysis of these abnormal nuclei provide a valuable reference for further investigations using sophisticated microscopic theories,potentially offering insights into new physics through studies of nuclearβ-decay half-lives.
基金supported by the Jingneng Shiyan Thermal Power Co.,Ltd.(TPRI/TR-CA-006-2023)Huaihe Energy Power Group Co.,Ltd.(TPRI/TR-CA-040-2023)Xi'an Thermal Power Research Institute Co.,Ltd.(TPRI/TR-CA-110-2021A/H1).
文摘Thermal power generation systems have stringent requirements for water and steam quality,i.e.,condensate water quality is one of the critical issues.In this paper,we designed a two-layer model based on an autoencoder and expert knowledge to achieve the early warning and causal analysis of condensate water quality abnormalities.An early warning model using an autoencoder model is built based on the historical data affecting the condensate water quality.Next,an analytical model of condensate water quality abnormalities was then developed by combining expert knowledge and trend test algorithms.Two different datasets were used to test the proposed model,respectively.The accuracy of the autoencoder model in the short-period test set is 88.83%,which shows that the early warning model can accurately analyze the condensate water quality data and achieve the purpose of early warning.For the long-time period test set,the model can correctly identify each abnormality and simultaneously indicates the cause of the abnormal condensate water quality.The proposed model can correctly identify abnormal working conditions and it is applicable to other thermal power plants.
基金The Medical Education Research Program of Henan Province,China(Grant No.WJLX2023015)and the Chinese International Medical Foundation for Clinical Pharmacy,China(Grant No.Z-2021-46-2101).
文摘To investigate the correlation between propacetamol and postoperative liver enzyme abnormalities among patients,a retrospective analysis was conducted on inpatients in the thoracic surgery department spanning from January 1 to June 30,2023.Causality assessment regarding propacetamol and postoperative liver enzyme abnormalities was performed using the updated Roussel Uclaf Causality Assessment Method(RUCAM).Furthermore,independent risk factors for liver enzyme abnormalities were identified through both univariate and multivariate analyses,followed by the construction and validation of a clinical nomogram.A total of 247 patients who received propacetamol were ultimately included in the study.Liver enzyme abnormalities post-surgery were more accurately predicted by considering the daily dose of propacetamol and the number of medications(OR(95%CI),4.831(2.797,8.344),P<0.001;10.007(3.878,25.823),P<0.001).A clinical predictive nomogram model was developed,incorporating these two independent risk factors,which exhibited favorable discrimination(AUC(95%CI),0.811(0.750,0.872)),calibration,and decision curve analysis(DCA)demonstrating the highest net benefits across a broad spectrum of threshold probabilities(10%to 90%).The daily dose of propacetamol and the number of medications were found to be independently associated with postoperative liver enzyme abnormalities.This user-friendly nomogram,comprising these two factors,might assist clinicians in assessing the risks of propacetamol-related liver dysfunction following surgery.
