A STAMP-Game model for accident analysis in oil and gas industry

Accidents in engineered systems are usually generated by complex socio-technical factors.It is beneficial to investigate the increasing complexity and coupling of these factors from the perspective of system safety.Based on system and control theories,System-Theoretic Accident Model and Processes(STAMP)is a widely recognized approach for accident analysis.In this paper,we propose a STAMP-Game model to analyze accidents in oil and gas storage and transportation systems.Stakeholders in accident analysis by STAMP can be regarded as players of a game.Game theory can,thus,be adopted in accident analysis to depict the competition and cooperation between stakeholders.Subsequently,we established a game model to study the strategies of both supervisory and supervised entities.The obtained results demonstrate that the proposed game model allows for identifying the effectiveness deficiency of the supervisory entity,and the safety and protection altitudes of the supervised entity.The STAMP-Game model can generate quantitative parameters for supporting the behavior and strategy selections of the supervisory and supervised entities.The quantitative data obtained can be used to guide the safety improvement,to reduce the costs of safety regulation violation and accident risk.

Sub-Homogeneous Peridynamic Model for Fracture and Failure Analysis of Roadway Surrounding Rock

The surrounding rock of roadways exhibits intricate characteristics of discontinuity and heterogeneity.To address these complexities,this study employs non-local Peridynamics(PD)theory and reconstructs the kernel function to represent accurately the spatial decline of long-range force.Additionally,modifications to the traditional bondbased PD model are made.By considering the micro-structure of coal-rock materials within a uniform discrete model,heterogeneity characterized by bond random pre-breaking is introduced.This approach facilitates the proposal of a novel model capable of handling the random distribution characteristics of material heterogeneity,rendering the PD model suitable for analyzing the deformation and failure of heterogeneous layered coal-rock mass structures.The established numerical model and simulation method,termed the sub-homogeneous PD model,not only incorporates the support effect but also captures accurately the random heterogeneous micro-structure of roadway surrounding rock.The simulation results obtained using this model show good agreement with field measurements from the Fucun coal mine,effectively validating the model’s capability in accurately reproducing the deformation and failure mode of surrounding rock under bolt-supported(anchor cable).The proposed subhomogeneous PD model presents a valuable and effective simulation tool for studying the deformation and failure of roadway surrounding rock in coal mines,offering new insights and potential advancements.

Mortality outcomes of low-dose computed tomography screening for lung cancer in urban China:a decision analysis and implications for practice

Background: Mortality outcomes in trials of low-dose computed tomography(CT) screening for lung cancer are inconsistent. This study aimed to evaluate whether CT screening in urban areas of China could reduce lung cancer mortality and to investigate the factors that associate with the screening effect.Methods: A decision tree model with three scenarios(low-dose CT screening, chest X-ray screening, and no screening) was developed to compare screening results in a simulated Chinese urban cohort(100,000 smokers aged45-80 years). Data of participant characteristics were obtained from national registries and epidemiological surveys for estimating lung cancer prevalence. The selection of other tree variables such as sensitivities and specificities of low-dose CT and chest X-ray screening were based on literature research. Differences in lung cancer mortality(primary outcome), false diagnoses, and deaths due to false diagnosis were calculated. Sensitivity analyses were performed to identify the factors that associate with the screening results and to ascertain worst and optimal screening effects considering possible ranges of the variables.Results: Among the 100,000 subjects, there were 448,541, and 591 lung cancer deaths in the low-dose CT, chest X-ray, and no screening scenarios, respectively(17.2% reduction in low-dose CT screening over chest X-ray screening and 24.2% over no screening). The costs of the two screening scenarios were 9387 and 2497 false diagnoses and 7and 2 deaths due to false diagnosis among the 100,000 persons, respectively. The factors that most influenced death reduction with low-dose CT screening over no screening were lung cancer prevalence in the screened cohort, lowdose CT sensitivity, and proportion of early-stage cancers among low-dose CT detected lung cancers. Considering all possibilities, reduction in deaths(relative numbers) with low-dose CT screening in the worst and optimal cases were16(5.4%) and 288(40.2%) over no screening, respectively.Conclusions: In terms of mortality outcomes, our findings favor conducting low-dose CT screening in urban China.However, approaches to reducing false diagnoses and optimizing important screening conditions such as enrollment criteria for screening are highly needed.

中国城市人群低剂量计算机断层扫描筛查肺癌的死亡率结果:决策分析和实践意义

背景与目的不同低剂量计算机断层扫描(computed tomography,CT)筛查肺癌试验得出的死亡率结果不一致。本研究旨在评价中国城市CT筛查是否能够降低肺癌死亡率,并探讨影响筛查效果的因素。方法建立了3种场景(低剂量CT筛查、胸部X线筛查和无筛查)的决策树模型来比较模拟的中国城市人群(100,000例年龄45–80岁的吸烟者)的筛查结果。从国家登记和流行病学调查中获得了参与者特征数据并估算了肺癌患病比例。根据文献调研选择了低剂量CT和胸部X线筛查的灵敏度和特异度等其他树变量。计算了3种筛查方法间肺癌死亡率(主要结局)、误诊和误诊导致死亡的差异。通过敏感性分析找出了与筛查结果相关的因素,并确定了在变量可能范围内的最差和最优筛查效果。结果 100,000例研究对象中,低剂量CT、胸部X线和无筛查场景分别有448例、541例和591例肺癌死亡(低剂量CT筛查比胸部X线筛查死亡人数减少17.2%,比无筛查减少24.2%)。100,000例研究对象中,2种筛查场景的代价分别为9387例和2497例误诊及7例和2例误诊导致的死亡。低剂量CT筛查比无筛查的死亡数降低,影响最大的因素是筛查人群的肺癌患病比例、低剂量CT的灵敏度和低剂量CT检出肺癌中早期癌的比例。综合了所有可能性,低剂量CT筛查肺癌的死亡人数(相对数)在最坏和最好情况下分别比无筛查降低了16例(5.4%)和288例(40.2%)。结论就死亡率结局而言,我们的研究结果支持在中国城市人群中进行低剂量CT筛查。然而,急需策略来减少误诊和优化纳入标准等重要筛查条件。

Prediction of visibility in the Arctic based on dynamic Bayesian network analysis

With the accelerated warming of the world,the safety and use of Arctic passages is receiving more attention.Predicting visibility in the Arctic has been a hot topic in recent years because of navigation risks and opening of ice-free northern passages.Numerical weather prediction and statistical prediction are two methods for predicting visibility.As microphysical parameterization schemes for visibility are so sophisticated,visibility prediction using numerical weather prediction models includes large uncertainties.With the development of artificial intelligence,statistical prediction methods have received increasing attention.In this study,we constructed a statistical model with a physical basis,to predict visibility in the Arctic based on a dynamic Bayesian network,and tested visibility prediction over a 1°×1°grid area averaged daily.The results show that the mean relative error of the predicted visibility from the dynamic Bayesian network is approximately 14.6%compared with the inferred visibility from the artificial neural network.However,dynamic Bayesian network can predict visibility for only 3 days.Moreover,with an increase in predicted area and period,the uncertainty of the predicted visibility becomes larger.At the same time,the accuracy of the predicted visibility is positively correlated with the time period of the input evidence data.It is concluded that using a dynamic Bayesian network to predict visibility can be useful over Arctic regions for projected climatic changes.

Influence of temperature and alloying elements on the threshold displacement energies in concentrated Ni-Fe-Cr alloys

Concentrated solid-solution alloys(CSAs)have demonstrated promising irradiation resistance depending on their compositions.Under irradiation,various defects can be produced.One of the most important parameters characterizing the defect production and the resulting defect number is the threshold displacement energies(Ed).In this work,we report the results of Ed values in a series of Ni-Fe-Cr concentrated solid solution alloys through molecular dynamics(MD)simulations.Based on several different empirical potentials,we show that the differences in the Ed values and its angular dependence are mainly due to the stiffness of the potential in the intermediate regime.The influences of different alloying elements and temperatures on Ed values in different CSAs are further evaluated by calculating the defect production probabilities.Our results suggest a limited influence of alloying elements and temperature on Ed values in concentrated alloys.Finally,we discuss the relationship between the primary damage and Ed values in different alloys.Overall,this work presents a thorough study on the Ed values in concentrated alloys,including the influence of empirical potentials,their angular dependence,temperature dependence,and effects on primary defect production.

Development of an electronic stopping power model based on deep learning and its application in ion range prediction

Deep learning algorithm emerges as a new method to take the raw features from large dataset and mine their deep implicit relations,which is promising for solving traditional physical challenges.A particularly intricate and difficult challenge is the energy loss mechanism of energetic ions in solid,where accurate prediction of stopping power is a longtime problem.In this work,we develop a deep-learning-based stopping power model with high overall accuracy,and overcome the long-standing deficiency of the existing classical models by improving the predictive accuracy of stopping power for ultra-heavy ion with low energy,and the corresponding projected range.This electronic stopping power model,based on deep learning algorithm,could be hopefully applied for the study of ion-solid interaction mechanism and enormous relevant applications.

Texture Feature Extraction Method for Ground Nephogram Based on Contourlet and the Power Spectrum Analysis Algorithm

It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection.In this paper,a new method is presented to capture the contour edge,texture and geometric structure of cloud images by using Contourlet and the power spectrum analysis algorithm.More abundant texture information is extracted.Cloud images can be obtained a multiscale and multidirection decomposition.The coefficient matrix from Contourlet transform of ground nephogram is calculated.The energy,mean and variance characteristics calculated from coefficient matrix are composed of the feature information.The frequency information of the data series from the feature vector values is obtained by the power spectrum analysis.Then Support Vector Machines(SVM)classifier is used to classify according to the frequency information of the trend graph of data series.It is shown that altocumulus and stratus with different texture frequencies can be effectively recognized and further subdivided the types of clouds.

Analysis and Test on Influence Factors of Dew Drop Condensation in Dew Point Hygrometer

The condensation process of dew droplets is influenced by many factors. Adew point condensation image observation system was built to improve the responsespeed of dew point detector under different measuring conditions. The basic mechanismof dew drop condensation growth was studied and the influence of various factors on thedew drop growth rate were analyzed. And the accuracy of the influence results wasverified based on the improved Hough transform circle detection. The results show thatthe growth rate of dew droplets is affected by ambient temperature, dew pointtemperature, mirror temperature and air velocity. The observed variation of the averageradius of dew droplets is consistent with the theoretical calculations. The maximumradius error is less than 4 μm, the initial error is larger, and the error oscillates in themiddle and late stages of condensation. The establishment of condensation mechanism ishelpful to solve the problem in fast determination of dew point temperature under thecold start of dew point meter, and to improve the response speed.

基于近场动力学的窄煤柱宽度优化研究

煤岩体具有极为复杂的非连续变形、非均质性特征,以有限元法为代表的传统数值方法难以准确描述其损伤累积、渐进破坏全过程.基于非局部近场动力学方法(Peridynamics,PD),通过重构本构力函数核函数,推导了相应的微模量函数与临界伸长率.在均匀离散模型中引入用随机预断“键”表征的非均质性,使近场动力学方法能够适用于天然非均质材料与结构的非连续变形破坏分析.以付村煤矿为例,建立了近场动力学数值计算模型,分析了不同煤柱宽度条件下巷道围岩变形、破坏规律以及煤柱破损特征.研究发现:当煤柱宽度为5 m时,巷道煤壁处于挤压变形区边缘,由于支承压力剧烈变化,巷帮变形破损严重;煤柱宽度增大至6 m和7 m时,巷道煤壁逐渐远离挤压变形区,煤柱受到采空区基本顶回转运动的影响相对减弱,巷帮变形和破损量较小;当煤柱宽度继续增大,巷道围岩将进入到应力增高区域,由于外应力场支承压力较大,巷帮变形及破损量会随之增大.综合考虑巷道围岩变形破坏、煤柱破损特征,最终确定煤柱留设宽度为7 m.本文工作表明,提出的煤岩体破损分析的近场动力学模型能够有效描述煤岩体的损伤累积和渐进破坏过程,为沿空掘巷护巷煤柱尺寸优化提供了新的计算工具.

Formal analysis of TPM2.0 key management APIs

The trusted platform module(TPM),a system component implemented on physical resources,is designed to enable computers to achieve a higher level of security than the security level that it is possible to achieve by software alone.For this reason,the TPM provides a way to store cryptographic keys and other sensitive data in its memory,which is shielded from access by any entity other than the TPM.Users who want to use those keys and data to achieve some security goals are restricted to interact with the TPM through its APIs defined in the TPM specification.Therefore,whether the TPM can provide Protected Capabilities it claimed depends to a large extent on the security of its APIs.In this paper,we devise a formal model,which is accessible to a fully mechanized analysis,for the key management APIs in the TPM2.0 specification.We identify and formalize security properties of these APIs in our model and then successfully use the automated prover Tamarin to obtain the first mechanized analysis of them.The analysis shows that the key management subset of TPM APIs preserves the secrecy of non-duplicable keys for unbounded numbers of fresh keys and handles.The analysis also reports that the key duplication mechanism,used to duplicate a key between two hierarchies,is vulnerable to impersonation attacks,which enable an adversary to recover the duplicated key of the originating hierarchy or import his own key into the destination hierarchy.Aiming at avoiding these vulnerabilities,we proposean approach,which restricts the originating and destination TPMs to authenticate each other’s identity during duplication.Then we formally demonstrate that our approach maintains the secrecy of duplicable keys when they are duplicated.

Defect properties in a VTaCrW equiatomic high entropy alloy(HEA)with the body centered cubic(bcc)structure

We report first-principles results of the point defect properties in a V-Ta-Cr-W high-entropy alloy(HEA)with the body-centered cubic(bcc)structure.Different from the widely-investigated face-centered cubic(fcc)HEAs,the local lattice distortion is more pronounced in bcc ones,which has a strong influence on the defect properties and defect evolution under irradiation.Due to the large size of Ta,the exchange between vacancies and Ta exhibits lower energy barriers.On the other hand,interstitial dumbbells containing V and Cr possess lower formation energies.These defect energetics predicts an enrichment of V and Cr and a depletion of Ta andWin the vicinity of defect sinks.Besides,we find that interstitial dumbbells favor the[110]orientation in the HEA,instead of[111]direction in most nonmagnetic bcc metals,which helps to slow down interstitial diffusion significantly.Consequently,the distribution of migration energies for vacancies and interstitials exhibit much larger overlap regions in the bcc HEA compared to fcc HEAs,leading to the good irradiation resistance by enhancing defect recombination.Our results suggest that HEAs with the bcc structure may bear excellent irradiation tolerance due to the particular defect properties.

Strengthening in Al-,Mo-or Ti-doped CoCrFeNi high entropy alloys:A parallel comparison

In the current work,a parallel comparison of the influence of Al,Mo and Ti,on the microstructure and strengthening of the CoCrFeNi alloy was conducted.To achieve this,inconsistencies on variables including the extent of alloying,thermomechanical processing and property-evaluation method were avoided.Microstructurally,following cold-rolling,annealing of the 4 at.%Al-doped alloys at 800-1000℃ did not result in phase separation;nevertheless,that of the 4 at.%Mo-and Ti-doped alloys led to the respective formation ofσandηphase and,consequently,caused extra strengthening through the Orowan dislocation bypassing mechanism.Our systematic qualitative analysis and DFT calculations showed that Al and Ti are more effective than Mo in reducing the stacking fault energy(SFE)of the CoCrFeNi alloy,because they can induce more considerable deformation of electronic density,making the gliding of atomic layers easier.Following identical thermomechnical processing,Al-,Mo-,and Ti-doping causes different extent of solid solution strengthening and grain boundary strengthening.Mo causes the most pronounced solid solution strengthening but does not benefit the grain boundary strengthening;in contrast,the effectiveness of grain boundary strengthening is boosted by the doping Al and Ti.Current analyses support that Labusch instead of Fleischer mechanism is applicable to explain the differences in solid solution strengthening,and the observed differences in grain boundary strengthening arise from the different tendency of Al,Mo and Ti to reduce the SFE of CoCrFeNi.In addition,we determined the value of the dimensionless parameter f in the Labusch model for CoCrFeNi-based alloys and observed a close relation between Hall-Petch slope and SFE.Although more in-depth studies are needed to provide full and mechanistic understandings,both these findings in fact presents significant values toward designing novel singlephase high-strength CoCrFeNi-based alloys through manipulating the solid solution and grain boundary strengthening by compositional tuning.

Design high-entropy carbide ceramics from machine learning

High-entropy ceramics(HECs)have shown great application potential under demanding conditions,such as high stresses and temperatures.However,the immense phase space poses great challenges for the rational design of new high-performance HECs.In this work,we develop machine-learning(ML)models to discover high-entropy ceramic carbides(HECCs).Built upon attributes of HECCs and their constituent precursors,our ML models demonstrate a high prediction accuracy(0.982).Using the well-trained ML models,we evaluate the single-phase probability of 90 HECCs that are not experimentally reported so far.Several of these predictions are validated by our experiments.We further establish the phase diagrams for non-equiatomic HECCs spanning the whole composition space by which the single-phase regime can be easily identified.Our ML models can predict both equiatomic and non-equiatomic HECs based solely on the chemical descriptors of constituent transition-metal-carbide precursors,which paves the way for the high-throughput design of HECCs with superior properties.

Local-environment dependence of stacking fault energies in concentrated solid-solution alloys

Concentrated solid-solution alloys(CSAs)based on 3d transition metals have demonstrated extraordinary mechanical properties and radiation resistance associated with their low stacking fault energies(SFEs).Owing to the intrinsic disorder,SFEs in CSAs exhibit distributions depending on local atomic configurations.In this work,the distribution of SFEs in equiatomic CSAs of NiCo,NiFe,and NiCoCr are investigated based on empirical potential and first-principles calculations.We show that the calculated distribution of SFEs in chemically disordered CSAs depends on the stacking fault area using empirical potential calculations.Based on electronic structure calculations,we find that local variations of SFEs in CSAs correlate with the charge density redistribution in the stacking fault region.We further propose a bond breaking and forming model to understand and predict the SFEs in CSAs based on the local structure alone.It is shown that the perturbation induced by a stacking fault is localized in the first-nearest planes for NiCo,but extends up to the third nearest planes for NiFe and NiCoCr because of partially filled d electrons in Fe and Cr.

The effect of Laves phase on heavy-ion radiation response of Nb-containing FeCrAl alloy for accident-tolerant fuel cladding

As a promising candidate material for the accident tolerant fuel cladding in light water reactors,the Nb-containing FeCrAl alloy has shown outstanding out-of-pile service performance due to the Laves phase precipitation.In this work,the radiation response in FeCrAl alloys with gradient Nb content under heavy ion radiation has been investigated.The focus is on the effect of the Laves phase on irradiation-induced defects and hardening.We found that the phase boundary between the matrix and Laves phase can play a critical role in capturing radiation defects,as verified by in-situ heavy-ion radiation experiments and molecular dynamic simulations.Additionally,the evolution of Laves phase under radiation is analyzed.Radiation-induced amorphization and segregations observed at high radiation doses will deepen the fundamental understanding of the stability of Laves phases in the radiation environment.

Studying bona fide SARS-CoV-2 biology in a BSL-2 biosafety environment using a split-virus-genome system

Dear Editor,Severe acute respiratory syndrome coronavirus 2(SARSCoV-2)was identified as the pathogen causing the coronavirus disease(COVID-19),which sometimes resulted in fatal pneumonia(Hu et al.,2021).SARS-CoV-2 is a biosafety level 3(BSL-3)pathogen,and the requirement for high containment conditions is a bottleneck for basic research on viral biology.To help general researchers who wish to study SARS-CoV-2 but do not have access to a BSL-3 facility,a system that(1)can mimic the real life cycle of the virus;(2)allows easy genetic manipulation;and(3)shows high biosafety in BSL-2 laboratory is required.

Visibility Characteristics over the South China Sea during 1980-2018 Based on Gridded Data Generated by Artificial Neural Network

This paper generated gridded visibility(Vis)data from 1980 to 2018 over the South China Sea(SCS)based on artificial neural network(ANN),and the accuracy of the generated data was tested.Then,temporal and spatial characteristics of Vis in the area were analyzed based on the generated Vis data.The results showed that Vis in the southern SCS was generally better than that in the northern SCS.In the past 39 years,Vis in both spring and winter has improved,especially in winter at a significant increased speed of 0.37 km decade^(-1).However,Vis in both summer and autumn has decreased,especially in summer with an obvious reduction of 0.84 km decade^(-1).Overall,Vis is best in summer and worst in winter,averaging 31.89 km in summer and 20.96 km in winter,which may be related to the difference of climatic conditions and wind speed in different seasons.At the same time,probability of low Vis in spring is significantly higher than that in other seasons,especially in the northwest of Hainan Island and the northwest of Malaysia.

Updated overall survival and circulating tumor DNA analysis of ensartinib for crizotinib-refractory ALK-positive NSCLC from a phase II study

Background:The initial phase II stuty(NCT03215693)demonstrated that ensartinib has shown clinical activity in patients with advanced crizotinib-refractory,anaplastic lymphoma kinase(ALK)-positive non-small cell lung cancer(NSCLC).Herein,we reported the updated data on overall survival(OS)and molecular profiling from the initial phase Ⅱ study.Methods:In this study,180 patients received 225 mg of ensartinib orally once daily until disease progression,death or withdrawal.OS was estimated by Kaplan‒Meier methods with two-sided 95%confidence intervals(CIs).Next-generation sequencing was employed to explore prognostic biomarkers based on plasma samples collected at baseline and after initiating ensartinib.Circulating tumor DNA(ctDNA)was detected to dynamically monitor the genomic alterna-tions during treatment and indicate the existence of molecular residual disease,facilitating improvement of clinical management.Results:At the data cut-off date(August 31,2022),with a median follow-up time of 53.2 months,97 of 180(53.9%)patients had died.The median OS was 42.8 months(95%CI:29.3-53.2 months).A total of 333 plasma samples from 168 patients were included for ctDNA analysis.An inferior OS correlated sig-nificantly with baseline ALK or tumor protein 53(TP53)mutation.In addition,patients with concurrent TP53 mutations had shorter OS than those without con-current TP53 mutations.High ctDNA levels evaluated by variant allele frequency(VAF)and haploid genome equivalents per milliliter of plasma(hGE/mL)at baseline were associated with poor OS.Additionally,patients with ctDNA clear-ance at 6 weeks and slow ascent growth had dramatically longer OS than those with ctDNA residual and fast ascent growth,respectively.Furthermore,patients who had a lower tumor burden,as evaluated by the diameter of target lesions,had a longer OS.Multivariate Cox regression analysis further uncovered the independent prognostic values of bone metastases,higher hGE,and elevated ALK mutation abundance at 6 weeks.Conclusion:Ensartinib led to a favorable OS in patients with advanced,crizotinib-resistant,and ALK-positive NSCLC.Quantification of ctDNA levels also provided valuable prognostic information for risk stratification.