Energy evolution and structural health monitoring of coal under different failure modes:An experimental study

Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.The focus of this work is on understanding energy evolution patterns in coal-rock bodies under complex conditions by using shear,splitting,and uniaxial compression tests.We examine the changes in energy parameters during various loading stages and the effects of various failure modes,resulting in an innovative energy dissipation-based health evaluation technique for coal.Key results show that coal bodies go through transitions between strain hardening and softening mechanisms during loading,indicated by fluctuations in elastic energy and dissipation energy density.For tensile failure,the energy profile of coal shows a pattern of “high dissipation and low accumulation” before peak stress.On the other hand,shear failure is described by “high accumulation and low dissipation” in energy trends.Different failure modes correlate with an accelerated increase in the dissipation energy before destabilization,and a significant positive correlation is present between the energy dissipation rate and the stress state of the coal samples.A novel mathematical and statistical approach is developed,establishing a dissipation energy anomaly index,W,which categorizes the structural health of coal into different danger levels.This method provides a quantitative standard for early warning systems and is adaptable for monitoring structural health in complex underground engineering environments,contributing to the development of structural health monitoring technology.

Stability analysis of intermittently jointed rock slopes based on the stepped failure mode

In practical engineering,due to the noncontinuity characteristics of joints in rock slopes,in addition to plane failure,stepped sliding failure may occur for intermittently jointed rock slopes.Especially for intermittently bedding jointed rock slopes,the correlation and difference in strength parameters between joints and rock bridges,along with the various failure modes and intermittency of rock bridges,contribute to the complexity of stepped failure modes and the unpredictability of failure regions.Based on the upper-bound limit analysis method and multi-sliders step-path failure mode,considering the shear and tensile failure of rock bridges and the weakened relationship between the strength parameters of rock bridges and jointed surfaces,by introducing the modified M-C failure criterion and the formula for calculating the energy consumption of tensile failure of rock bridges,two failure mechanisms are constructed to obtain the safety factor(F_(s))of intermittently jointed rock slopes.The sequential quadratic programming method is used to obtain the optimal upper-bound solution for F_(s).The influence of multiple key parameters(slope height H,horizontal distance L,Slope angleβ,shear strength parameters of the rock bridgeφr and cr,Dimensionless parameter u,weakening coefficients of the internal friction angle and cohesion between the rock bridges and joint surfaces Kφand Kc)on the stability analysis of intermittently jointed rock slopes under the shear failure mode of rock bridges as well as under the tensile failure mode is also explored.The reliability of the failure mechanisms is verified by comparative analysis with theoretical results,numerical results,and landslide cases,and the variation rules of F_(s)with each key parameter are obtained.The results show that F_(s) varies linearly withφr and cr of the rock bridge and with K_(φ)and K_(c),whereas F_(s)changes nonlinearly with H and L.In particular,with the increase in Kφand Kc,Fs increases by approximately 52.78%and 171.02%on average,respectively.For rock bridge tensile failure,F_(s) shows a nonlinearly positive correlation withφr,cr,Kφand Kc.In particular,with the increase in Kφand Kc,Fs increases by approximately 13%and 61.69%on average,respectively.Fs decreases rapidly with increasing slope gradientβand decreasing dimensionless parameterμ.When Kφand Kc are both less than 1.0,the stepped sliding surface occurs more easily than the plane failure surface,especially in the case of tensile failure of the rock bridge.In addition,rock slopes with higher strength parameters,taller heights,and greater weakening coefficients are prone to rock bridge tension failure with lower Fs,and more attention should be given to the occurrence of such accidents in actual engineering.

Evolution model and failure mechanisms of rainfall-induced cracked red clay slopes:insights from Xinshao County,China

Red clay landslides are widely distributed worldwide,resulting in severe loss of life and property.Although rainfall-induced red clay slopes have received extensive attention,the role of cracks in the evolutionary process of red clay slopes and their connection to failure mechanisms is still poorly understood.A comprehensive approach integrating field investigation,laboratory tests,and numerical simulations was conducted to study the 168 red clay landslides in Xinshao County,China.The results show that red clay is prone to forming cracks at high moisture content due to its low swelling and high shrinkage properties.The failure mode of red clay slopes can be summarized in three stages:crack generation,slope excavation,and slope failure.Furthermore,the retrospective analysis and numerical simulations of the typical landslide in Guanchong indicated that intense rainfall primarily impacts the shallow layer of soil within approximately 0.5 m on the intact slope.However,cracks change the pattern of rainfall infiltration in the slope.Rainwater infiltrates rapidly through the preferential channels induced by the cracks rather than uniformly and slowly from the slope surface.This results in a significant increase in both the depth of infiltration and the saturated zone area of the cracked slope,reaching 3.8 m and 36.2 m^(2),respectively.Consequently,the factor of safety of the slope decreases by 13.4%compared to the intact slope,ultimately triggering landslides.This study can provide valuable insights into understanding the failure mechanisms of red clay slopes in China and other regions with similar geological settings.

Finite Element Simulations on Failure Behaviors of Granular Materials with Microstructures Using a Micromechanics-Based Cosserat Elastoplastic Model

This paper presents a micromechanics-based Cosserat continuum model for microstructured granular materials.By utilizing this model,the macroscopic constitutive parameters of granular materials with different microstructures are expressed as sums of microstructural information.The microstructures under consideration can be classified into three categories:a medium-dense microstructure,a dense microstructure consisting of one-sized particles,and a dense microstructure consisting of two-sized particles.Subsequently,the Cosserat elastoplastic model,along with its finite element formulation,is derived using the extended Drucker-Prager yield criteria.To investigate failure behaviors,numerical simulations of granular materials with different microstructures are conducted using the ABAQUS User Element(UEL)interface.It demonstrates the capacity of the proposed model to simulate the phenomena of strain-softening and strain localization.The study investigates the influence of microscopic parameters,including contact stiffness parameters and characteristic length,on the failure behaviors of granularmaterials withmicrostructures.Additionally,the study examines themesh independence of the presented model and establishes its relationship with the characteristic length.A comparison is made between finite element simulations and discrete element simulations for a medium-dense microstructure,revealing a good agreement in results during the elastic stage.Somemacroscopic parameters describing plasticity are shown to be partially related to microscopic factors such as confining pressure and size of the representative volume element.

Development and validation of a new prognostic model for patients with acute-on-chronic liver failure in intensive care unit

BACKGROUND Cirrhotic patients with acute-on-chronic liver failure(ACLF)in the intensive care unit(ICU)have a poor but variable prognoses.Accurate prognosis evaluation can guide the rational management of patients with ACLF.However,existing prognostic scores for ACLF in the ICU environment lack sufficient accuracy.AIM To develop a new prognostic model for patients with ACLF in ICU.METHODS Data from 938 ACLF patients in the Medical Information Mart for Intensive Care(MIMIC)database were used to develop a new prognostic model(MIMIC ACLF)for ACLF.Discrimination,calibration and clinical utility of MIMIC ACLF were assessed by area under receiver operating characteristic curve(AUROC),calibration curve and decision curve analysis(DCA),respectively.MIMIC ACLF was then externally validated in a multiple-center cohort,the Electronic Intensive Care Collaborative Research Database and a single-center cohort from the Second Hospital of Hebei Medical University in China.RESULTS The MIMIC ACLF score was determined using nine variables:ln(age)×2.2+ln(white blood cell count)×0.22-ln(mean arterial pressure)×2.7+respiratory failure×0.6+renal failure×0.51+cerebral failure×0.31+ln(total bilirubin)×0.44+ln(internationalized normal ratio)×0.59+ln(serum potassium)×0.59.In MIMIC cohort,the AUROC(0.81/0.79)for MIMIC ACLF for 28/90-day ACLF mortality were significantly greater than those of Chronic Liver Failure Consortium ACLF(0.76/0.74),Model for End-stage Liver Disease(MELD;0.73/0.71)and MELD-Na(0.72/0.70)(all P<0.001).The consistency between actual and predicted 28/90-day survival rates of patients according to MIMIC ACLF score was excellent and superior to that of existing scores.The net benefit of MIMIC ACLF was greater than that achieved using existing scores within the 50%threshold probability.The superior predictive accuracy and clinical utility of MIMIC ACLF were validated in the external cohorts.CONCLUSION We developed and validated a new prognostic model with satisfactory accuracy for cirrhotic patients with ACLF hospitalized in the ICU.The model-based risk stratification and online calculator might facilitate the rational management of patients with ACLF.

Mitochondrial dysfunction affects hepatic immune and metabolic remodeling in patients with hepatitis B virus-related acute-onchronic liver failure

BACKGROUND Immune dysregulation and metabolic derangement have been recognized as key factors that contribute to the progression of hepatitis B virus(HBV)-related acute-on-chronic liver failure(ACLF).However,the mechanisms underlying immune and metabolic derangement in patients with advanced HBV-ACLF are unclear.AIM To identify the bioenergetic alterations in the liver of patients with HBV-ACLF causing hepatic immune dysregulation and metabolic disorders.METHODS Liver samples were collected from 16 healthy donors(HDs)and 17 advanced HBV-ACLF patients who were eligible for liver transplantation.The mitochondrial ultrastructure,metabolic characteristics,and immune microenvironment of the liver were assessed.More focus was given to organic acid metabolism as well as the function and subpopulations of macrophages in patients with HBV-ACLF.RESULTS Compared with HDs,there was extensive hepatocyte necrosis,immune cell infiltration,and ductular reaction in patients with ACLF.In patients,the liver suffered severe hypoxia,as evidenced by increased expression of hypoxia-inducible factor-1α.Swollen mitochondria and cristae were observed in the liver of patients.The number,length,width,and area of mitochondria were adaptively increased in hepatocytes.Targeted metabolomics analysis revealed that mitochondrial oxidative phosphorylation decreased,while anaerobic glycolysis was enhanced in patients with HBV-ACLF.These findings suggested that,to a greater extent,hepa-tocytes used the extra-mitochondrial glycolytic pathway as an energy source.Patients with HBV-ACLF had elevated levels of chemokine C-C motif ligand 2 in the liver homogenate,which stimulates peripheral monocyte infiltration into the liver.Characterization and functional analysis of macrophage subsets revealed that patients with ACLF had a high abundance of CD68^(+)HLA-DR^(+)macrophages and elevated levels of both interleukin-1βand transforming growth factor-β1 in their livers.The abundance of CD206^(+)CD163^(+)macrophages and expression of interleukin-10 decreased.The correlation analysis revealed that hepatic organic acid metabolites were closely associated with macrophage-derived cytokines/chemokines.CONCLUSION The results indicated that bioenergetic alteration driven by hypoxia and mitochondrial dysfunction affects hepatic immune and metabolic remodeling,leading to advanced HBV-ACLF.These findings highlight a new therapeutic target for improving the treatment of HBV-ACLF.

Modeling Expected Failure Considering Repair Time and Degradation: A Rail System Case Study

The repeated failures of any equipment or systems are modeled as a renewal process. The management needs an assessment of the number of future failures to allocate the resources needed for fast repairs. Based on the idea of expectation by conditioning, special Volterra-type integral equations are derived for general types of repairs, considering the length of repair and reduced degradation of the idle object. In addition to minimal repair and failure replacement, partial repairs are also discussed when the repair results in reduction of the number of future failures or decreases the effective age of the object. Numerical integration-based algorithm and simulation study are performed to solve the resulting integral equation. Since the geometry degradation in different dimensions of a rail track and controlling and maintaining defects are of importance, a numerical example using the rail industry data is conducted. Expected number of failures of different failure type modes in rail track is calculated within a two-year interval. Results show that within a two-year period, anticipated occurrences of cross level failures, surface failures, and DPI failures are 2.4, 3.8, and 5.8, respectively.

Construction of a predictive model for acute liver failure after hepatectomy based on neutrophil-to-lymphocyte ratio and albuminbilirubin score

BACKGROUND Acute liver failure(ALF)is a common cause of postoperative death in patients with hepatocellular carcinoma(HCC)and is a serious threat to patient safety.The neutrophil-to-lymphocyte ratio(NLR)is a common inflammatory indicator that is associated with the prognosis of various diseases,and the albumin-bilirubin score(ALBI)is used to evaluate liver function in liver cancer patients.Therefore,this study aimed to construct a predictive model for postoperative ALF in HCC tumor integrity resection(R0)based on the NLR and ALBI,providing a basis for clinicians to choose appropriate treatment plans.AIM To construct an ALF prediction model after R0 surgery for HCC based on NLR and ALBI.METHODS In total,194 patients with HCC who visited The First People’s Hospital of Lianyungang to receive R0 between May 2018 and May 2023 were enrolled and divided into the ALF and non-ALF groups.We compared differences in the NLR and ALBI between the two groups.The risk factors of ALF after R0 surgery for HCC were screened in the univariate analysis.Independent risk factors were analyzed by multifactorial logistic regression.We then constructed a prediction model of ALF after R0 surgery for HCC.A receiver operating characteristic curve,calibration curve,and decision curve analysis(DCA)were used to evaluate the value of the prediction model.RESULTS Among 194 patients with HCC who met the standard inclusion criteria,46 cases of ALF occurred after R0(23.71%).There were significant differences in the NLR and ALBI between the two groups(P<0.05).The univariate analysis showed that alpha-fetoprotein(AFP)and blood loss volume(BLV)were significantly higher in the ALF group compared with the non-ALF group(P<0.05).The multifactorial analysis showed that NLR,ALBI,AFP,and BLV were independent risk factors for ALF after R0 surgery in HCC.The predictive efficacy of NLR,ALBI,AFP,and BLV in predicting the occurrence of ALT after R0 surgery for HCC was average[area under the curve(AUC)NLR=0.767,AUCALBI=0.755,AUCAFP=0.599,AUCBLV=0.718].The prediction model for ALF after R0 surgery for HCC based on NLR and ALBI had a better predictive efficacy(AUC=0.916).The calibration curve and actual curve were in good agreement.DCA showed a high net gain and that the model was safer compared to the curve in the extreme case over a wide range of thresholds.CONCLUSION The prediction model based on NLR and ALBI can effectively predict the risk of developing ALF after HCC R0 surgery,providing a basis for clinical prevention of developing ALF after HCC R0 surgery.

Predicting depression in patients with heart failure based on a stacking model

BACKGROUND There is a lack of literature discussing the utilization of the stacking ensemble algorithm for predicting depression in patients with heart failure(HF).AIM To create a stacking model for predicting depression in patients with HF.METHODS This study analyzed data on 1084 HF patients from the National Health and Nutrition Examination Survey database spanning from 2005 to 2018.Through univariate analysis and the use of an artificial neural network algorithm,predictors significantly linked to depression were identified.These predictors were utilized to create a stacking model employing tree-based learners.The performances of both the individual models and the stacking model were assessed by using the test dataset.Furthermore,the SHapley additive exPlanations(SHAP)model was applied to interpret the stacking model.RESULTS The models included five predictors.Among these models,the stacking model demonstrated the highest performance,achieving an area under the curve of 0.77(95%CI:0.71-0.84),a sensitivity of 0.71,and a specificity of 0.68.The calibration curve supported the reliability of the models,and decision curve analysis confirmed their clinical value.The SHAP plot demonstrated that age had the most significant impact on the stacking model's output.CONCLUSION The stacking model demonstrated strong predictive performance.Clinicians can utilize this model to identify highrisk depression patients with HF,thus enabling early provision of psychological interventions.

Development and validation of a predictive model for acute-onchronic liver failure after transjugular intrahepatic portosystemic shunt

BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is a cause of acute-onchronic liver failure(ACLF).AIM To investigate the risk factors of ACLF within 1 year after TIPS in patients with cirrhosis and construct a prediction model.METHODS In total,379 patients with decompensated cirrhosis treated with TIPS at Nanjing Drum Tower Hospital from 2017 to 2020 were selected as the training cohort,and 123 patients from Nanfang Hospital were included in the external validation cohort.Univariate and multivariate logistic regression analyses were performed to identify independent predictors.The prediction model was established based on the Akaike information criterion.Internal and external validation were conducted to assess the performance of the model.RESULTS Age and total bilirubin(TBil)were independent risk factors for the incidence of ACLF within 1 year after TIPS.We developed a prediction model comprising age,TBil,and serum sodium,which demonstrated good discrimination and calibration in both the training cohort and the external validation cohort.CONCLUSION Age and TBil are independent risk factors for the incidence of ACLF within 1 year after TIPS in patients with decompensated cirrhosis.Our model showed satisfying predictive value.

Application and Promotion of Whole-Process Capacity Management Model for CHF Patients Led by Specialist Nurses in “Heart Failure Center Alliance Unit”

Objective: To implement the whole-process capacity management model led by specialist nurses, improve the capacity management behavior of medical staff, and build a standardized, standardized and operable CHF capacity management system. Methods: According to the evidence pyramid principle and search strategy, 2 evidence-based nursing backbone completed literature search in both Chinese and English, and finally included 7 literatures. Results: Around the three key links of capacity assessment, monitoring and management, stakeholders were invited to evaluate each evidence according to the FAME principle, that is, the feasibility, suitability, effectiveness and clinical significance of evidence. Finally, 11 best evidences were obtained and 5 clinical review indicators of the cost project were transformed. This study formulated the competence management plan for CHF patients based on the current situation, established competence load evaluation criteria for CHF patients, and determined the target “dry weight” value for CHF patients. Conclusion: The whole-course volume management model of CHF patients guided by specialist nurses should be established and applied and promoted in the “heart failure Center Alliance unit”, so as to improve the capacity management ability of medical staff for CHF patients, enhance the self-management ability of CHF patients, improve the capacity management behavior and health outcomes, and effectively reduce the hospitalization rate and mortality rate of CHF patients in the region.

Numerical three-dimensional modeling of earthen dam piping failure

A physically-based numerical three-dimensional earthen dam piping failure model is developed for homogeneous and zoned soil dams.This model is an erosion model,coupled with force/moment equilibrium analyses.Orifice flow and two-dimensional(2D)shallow water equations(SWE)are solved to simulate dam break flows at different breaching stages.Erosion rates of different soils with different construction compaction efforts are calculated using corresponding erosion formulae.The dam's real shape,soil properties,and surrounding area are programmed.Large outer 2D-SWE grids are used to control upstream and downstream hydraulic conditions and control the boundary conditions of orifice flow,and inner 2D-SWE flow is used to scour soil and perform force/moment equilibrium analyses.This model is validated using the European Commission IMPACT(Investigation of Extreme Flood Processes and Uncertainty)Test#5 in Norway,Teton Dam failure in Idaho,USA,and Quail Creek Dike failure in Utah,USA.All calculated peak outflows are within 10%errors of observed values.Simulation results show that,for a V-shaped dam like Teton Dam,a piping breach location at the abutment tends to result in a smaller peak breach outflow than the piping breach location at the dam's center;and if Teton Dam had broken from its center for internal erosion,a peak outflow of 117851 m'/s,which is 81%larger than the peak outflow of 65120 m3/s released from its right abutment,would have been released from Teton Dam.A lower piping inlet elevation tends to cause a faster/earlier piping breach than a higher piping inlet elevation.

Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNNModel

Oil and gas pipelines are affected by many factors,such as pipe wall thinning and pipeline rupture.Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management.Aiming at the shortcomings of the BP Neural Network(BPNN)model,such as low learning efficiency,sensitivity to initial weights,and easy falling into a local optimal state,an Improved Sparrow Search Algorithm(ISSA)is adopted to optimize the initial weights and thresholds of BPNN,and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established.Taking 61 sets of pipelines blasting test data as an example,the prediction model was built and predicted by MATLAB software,and compared with the BPNN model,GA-BPNN model,and SSA-BPNN model.The results show that the MAPE of the ISSA-BPNN model is 3.4177%,and the R2 is 0.9880,both of which are superior to its comparison model.Using the ISSA-BPNN model has high prediction accuracy and stability,and can provide support for pipeline inspection and maintenance.

Construction of A Prediction Model for Atrial Fibrillation in Patients with Dilated Cardiomyopathy and Heart Failure

Dilated cardiomyopathy(DCM)is a common myocardial disease characterized by enlargement of the heart cavity and decreased systolic function,often leading to heart failure(HF)and arrhythmia.The occurrence of atrial fibrillation(AF)is closely related to the progression and prognosis of the disease.In recent years,with the advancement of medical imaging and biomarkers,models for predicting the occurrence of AF in DCM patients have gradually become a research hotspot.This article aims to review the current situation of AF in DCM patients and explore the importance and possible methods of constructing predictive models to provide reference for clinical prevention and treatment.We comprehensively analyzed the risk factors for AF in DCM patients from epidemiological data,pathophysiological mechanisms,clinical and laboratory indicators,electrocardiogram and imaging parameters,and biomarkers,and evaluated the effectiveness of existing predictive models.Through analysis of existing literature and research,this article proposes a predictive model that integrates multiple parameters to improve the accuracy of predicting AF in DCM patients and provide a scientific basis for personalized treatment.

Prediction of column failure modes based on artificial neural network

To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center(PEER).The main factors affecting failure modes of columns include the hoop ratios,longitudinal reinforcement ratios,ratios of transverse reinforcement spacing to section depth,aspect ratios,axial compression ratios,and flexure-shear ratios.This study proposes a data-driven prediction model based on an artificial neural network(ANN)to identify the column failure modes.In this study,111 groups of data are used,out of which 89 are used as training data and 22 are used as test data,and the ANN prediction model of failure modes is developed.The results show that the proposed method based on ANN is superior to traditional methods in identifying the column failure modes.

A failure criterion for shale considering the anisotropy and hydration based on the shear slide failure model

A failure criterion fully considering the anisotropy and hydration of shale is essential for shale formation stability evaluation.Thus,a novel failure criterion for hydration shale is developed by using Jaeger’s shear failure criterion to describe the anisotropy and using the shear strength reduction caused by clay minerals hydration to evaluate the hydration.This failure criterion is defined with four parameters in Jaeger’s shear failure criterion(S_(1),S_(2),a andφ),three hydration parameters(k,ω_(sh)andσ_(s))and two material size parameters(d and l0).The physical meanings and determining procedures of these parameters are described.The accuracy and applicability of this failure criterion are examined using the published experimental data,showing a cohesive agreement between the predicted values and the testing results,R^(2)=0.916 and AAREP(average absolute relative error percentage)of 9.260%.The error(|D_(p)|)is then discussed considering the effects ofβ(angle between bedding plane versus axial loading),moisture content and confining pressure,presenting that|Dp|increases whenβis closer to 30°,and|D_(p)|decreases with decreasing moisture content and with increasing confining pressure.Moreover,|D_(p)|is demonstrated as being sensitive to S1and being steady with decrease in the data set whenβis 0°,30°,45°and 90°.

Development characteristics and failure modes of reactivated ancient landslides in the Sichuan–Tibet transportation corridor,China

The risk of reactivated ancient landslides in the Sichuan–Tibet transportation corridor in China is significantly increasing,primarily driven by the intensification of engineering activities and the increased frequency of extreme weather events.This escalation has resulted in a considerable number of fatalities and extensive damage to critical engineering infrastructure.However,the factors contributing to the reactivation and modes of destruction of ancient landslides remain unknown.Therefore,it is imperative to systematically analyze the developmental characteristics and failure modes of reactivated ancient landslides to effectively mitigate disaster risks.Based on a combination of data collection,remote sensing interpretation,and field investigations,we delineated the developmental attributes of typical ancient landslides within the study area.These attributes encompass morphological and topographic aspects,material composition,and spatial structure of ancient landslides.Subsequently,we identified the key triggers for the reactivation of ancient landslides,including water infiltration,reservoir hydrodynamics,slope erosion,and excavation,by analyzing representative cases in the study area.Reactivation of ancient landslides is sometimes the result of the cumulative effects of multiple predisposing factors.Furthermore,our investigations revealed that the reactivation of these ancient landslides primarily led to local failures.However,over extended periods of dynamic action,the entire zone may experience gradual creep.We categorized the reactivation modes of ancient landslides into three distinct types based on the reactivation sequences:progressive retreat,backward thrusting,and forward pulling–backward thrusting.This study is of great significance for us to identify ancient landslides,deepen our understanding of the failure modes and risks of reactivated ancient landslides on the eastern margin of the Tibetan Plateau,and formulate effective disaster prevention and mitigation measures.

Reliability modeling of mutual DCFP considering failure physical dependency

Degradation and overstress failures occur in many electronic systems in which the operation load and environmental conditions are complex.The dependency of them called dependent competing failure process(DCFP),has been widely studied.Electronic system may experience mutual effects of degradation and shocks,they are considered to be interdependent.Both the degradation and the shock processes will decrease the limit of system and cause cumulative effect.Finally,the competition of hard and soft failure will cause the system failure.Based on the failure mechanism accumulation theory,this paper constructs the shock-degradation acceleration and the threshold descent model,and a system reliability model established by using these two models.The mutually DCFP effect of electronic system interaction has been decomposed into physical correlation of failure,including acceleration,accumulation and competition.As a case,a reliability of electronic system in aeronautical system has been analyzed with the proposed method.The method proposed is based on failure physical evaluation,and could provide important reference for quantitative evaluation and design improvement of the newly designed system in case of data deficiency.

基于主客观环流分型的强降水数值预报MODE检验方法及其在2019年暖季东北地区的应用

本文构建了基于主客观环流分型的强降水数值预报空间检验(MODE)方法框架,并利用该框架对欧洲中期天气预报中心全球模式(ECMWF)和中国气象局区域中尺度数值天气预报模式(CMA_MESO)的2019年暖季东北地区强降水预报进行检验。结果表明,2019年暖季东北地区54个强降水日的环流型可分为:西风槽型(15个)、副热带高压影响型(13个)、急流型(5个)、西部(12个)和东部冷涡型(9个)。其中,西风槽型和急流型以区域性强降水为主,模式对其强降水发生与否的预报能力强,TS评分较高;西部、东部冷涡型强降水的局地性强,模式对其强降水发生与否的预报能力差,TS评分低;副热带高压影响型也以区域性强降水为主,模式对其强降水发生与否的预报能力也比较强,但是对其强降水质心位置、强度、面积等属性预报偏差较大,TS评分也相对较低。另外,从两种模式预报性能对比看,CMA_MESO强降水强度和面积预报较实况普遍偏强,虽然其预报的TS评分一般高于ECMWF,但其对强降水预报的空报率也都比ECMWF大,对强降水的属性预报偏差一致性一般也低于ECMWF,其预报的可订正性整体上不及ECMWF。

Acute liver failure:A systematic review and network meta-analysis of optimal type of stem cells in animal models

BACKGROUND The therapeutic effects of various stem cells in acute liver failure(ALF)have been demonstrated in preclinical studies.However,the specific type of stem cells with the highest therapeutic potential has not been determined.AIM To validate the efficacy of stem cells in ALF model and to identify the most promising stem cells.METHODS A search was conducted on the PubMed,Web of Science,Embase,Scopus,and Cochrane databases from inception to May 3,2022,and updated on November 16,2022 to identify relevant studies.Two independent reviewers performed the literature search,identification,screening,quality assessment,and data extraction.RESULTS A total of 89 animal studies were included in the analysis.The results of traditional meta-analysis showed that stem cell therapy could significantly reduce the serum levels of alanine aminotransferase[weighted mean difference(WMD)=-181.05(-191.71,-170.39)],aspartate aminotransferase[WMD=-309.04(-328.45,-289.63)],tumor necrosis factor-alpha[WMD=-8.75(-9.93,-7.56)],and interleukin-6[WMD=-10.43(-12.11,-8.76)]in animal models of ALF.Further subgroup analysis and network meta-analysis showed that although mesenchymal stem cells are the current research hotspot,the effect of liver stem cells(LSCs)on improving liver function is significantly better than that of the other five types of stem cells.In addition,the ranking results showed that the possibility of LSCs improving liver function ranked first.This fully proves the great therapeutic potential of LSCs,which needs to be paid more attention in the future.CONCLUSION LSCs may have a higher therapeutic potential.Further high-quality animal experiments are needed to explore the most effective stem cells for ALF.