Investigation on mechanical properties regulation of rock-like specimens based on 3D printing and similarity quantification

3D printing is widely adopted to quickly produce rock mass models with complex structures in batches,improving the consistency and repeatability of physical modeling.It is necessary to regulate the mechanical properties of 3D-printed specimens to make them proportionally similar to natural rocks.This study investigates mechanical properties of 3D-printed rock analogues prepared by furan resin-bonded silica sand particles.The mechanical property regulation of 3D-printed specimens is realized through quantifying its similarity to sandstone,so that analogous deformation characteristics and failure mode are acquired.Considering similarity conversion,uniaxial compressive strength,cohesion and stress–strain relationship curve of 3D-printed specimen are similar to those of sandstone.In the study ranges,the strength of 3D-printed specimen is positively correlated with the additive content,negatively correlated with the sand particle size,and first increases then decreases with the increase of curing temperature.The regulation scheme with optimal similarity quantification index,that is the sand type of 70/140,additive content of 2.5‰and curing temperature of 81.6℃,is determined for preparing 3D-printed sandstone analogues and models.The effectiveness of mechanical property regulation is proved through uniaxial compression contrast tests.This study provides a reference for preparing rock-like specimens and engineering models using 3D printing technology.

Quantification of the concrete freeze–thaw environment across the Qinghai–Tibet Plateau based on machine learning algorithms

The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze–thaw cycles(NFTCs) on the QTP. The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest(RF) model, generalized boosting method(GBM), generalized linear model(GLM), and generalized additive model(GAM), are used to fit the NFTCs. The root mean square error(RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R^(2) values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R^(2) values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP. Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.

Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming

Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.

High-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer based on probability density evolution method

This paper proposed an efficient research method for high-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer.Firstly,the dynamic model of projectile motion is established considering the flexible deformation of the barrel and the interaction between the projectile and the barrel.Subsequently,the accuracy of the dynamic model is verified based on the external ballistic projectile attitude test platform.Furthermore,the probability density evolution method(PDEM)is developed to high-dimensional uncertainty quantification of projectile motion.The engineering example highlights the results of the proposed method are consistent with the results obtained by the Monte Carlo Simulation(MCS).Finally,the influence of parameter uncertainty on the projectile disturbance at muzzle under different working conditions is analyzed.The results show that the disturbance of the pitch angular,pitch angular velocity and pitch angular of velocity decreases with the increase of launching angle,and the random parameter ranges of both the projectile and coupling model have similar influence on the disturbance of projectile angular motion at muzzle.

Uncertainty quantification of mechanism motion based on coupled mechanism—motor dynamic model for ammunition delivery system

In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to propose a novel mechanism-motor coupling dynamic modeling method,in which the relationship between mechanism motion and motor rotation is established according to the geometric coordination of the system.The advantages of this include establishing intuitive coupling between the mechanism and motor,facilitating the discussion for the influence of both mechanical and electrical parameters on the mechanism,and enabling dynamic simulation with controller to take the randomness of the electric load into account.Dynamic simulation considering feedback control of ammunition delivery system is carried out,and the feasibility of the model is verified experimentally.Based on probability density evolution theory,we comprehensively discuss the effects of system parameters on mechanism motion from the perspective of uncertainty quantization.Our work can not only provide guidance for engineering design of ammunition delivery mechanism,but also provide theoretical support for modeling and uncertainty quantification research of mechatronics system.

Revolutionizing disease diagnosis and management:Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification

Artificial intelligence(AI),particularly machine learning(ML)and deep learning(DL)techniques,such as convolutional neural networks(CNNs),have emerged as transformative technologies with vast potential in healthcare.Body iron load is usually assessed using slightly invasive blood tests(serum ferritin,serum iron,and serum transferrin).Serum ferritin is widely used to assess body iron and drive medical management;however,it is an acute phase reactant protein offering wrong interpretation in the setting of inflammation and distressed patients.Magnetic resonance imaging is a non-invasive technique that can be used to assess liver iron.The ML and DL algorithms can be used to enhance the detection of minor changes.However,a lack of open-access datasets may delay the advancement of medical research in this field.In this letter,we highlight the importance of standardized datasets for advancing AI and CNNs in medical imaging.Despite the current limitations,embracing AI and CNNs holds promise in revolutionizing disease diagnosis and treatment.

Multi-Compartment SCFA Quantification in Human

Short-chain fatty acids (SCFA) play an important role in human biochemistry. They originate primarily from the digestive system through carbohydrates microbial fermentation. Most SCFA produced in the colon are absorbed by the intestinal wall and enter the bloodstream to be distributed throughout the body for multiple purposes. At the intestinal level, SCFA play a role in controlling fat storage and fatty acid metabolism. The effects of these beneficial compounds therefore concern overall health. They facilitate energy expenditure and are valuable allies in the fight against obesity and diabetes. SCFA are also involved in the regulation of the levels of several neurotransmitters such as GABA (γ-aminobutyric acid), glutamate, serotonin, dopamine, and norepinephrine. Their role is also highlighted in many inflammatory and neurodegenerative diseases such as Alzheimer’s disease (AD) or Parkinson’s disease (PD). To have a realistic picture of the distribution of SCFA in different biological compartments of the human body, we propose to study SCFA simultaneously in five human biological samples: feces, saliva, serum, cerebrospinal fluid (CSF), and urine, as well as in Dried Blood Spot (DBS). To evaluate their concentration and repeatability, we used 10 aliquots from pooled samples, analyzed by 3-nitrophenylhydrazine (3-NPH) derivation and liquid chromatography coupled with high sensitivity mass spectrometry (LC-QqQ-MS). We also evaluated the SCFA assay on Dried Blood Spot (DBS). In this work, we adapted the pre-analytical parts for each sample to be able to use a common calibration curve, thus facilitating multi-assay quantification studies and so being less time-consuming. Moreover, we proposed new daughter ions from the same neutral loss (43 Da) to quantify SCFAs, thus improving the sensitivity. In conclusion, our methodology, based on a unique calibration curve for all samples for each SCFA, is well-suited to quantified them in a clinical context.

Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods

Complex systems exist widely,including medicines from natural products,functional foods,and biological samples.The biological activity of complex systems is often the result of the synergistic effect of multiple components.In the quality evaluation of complex samples,multicomponent quantitative analysis(MCQA)is usually needed.To overcome the difficulty in obtaining standard products,scholars have proposed achieving MCQA through the“single standard to determine multiple components(SSDMC)”approach.This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia.Depending on a convenient(ultra)high-performance liquid chromatography method,how can the repeatability and robustness of the MCQA method be improved?How can the chromatography conditions be optimized to improve the number of quantitative components?How can computer software technology be introduced to improve the efficiency of multicomponent analysis(MCA)?These are the key problems that remain to be solved in practical MCQA.First,this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years,as well as the method robustness and accuracy evaluation.Second,it also summarizes methods to improve peak capacity and quantitative accuracy in MCA,including column selection and twodimensional chromatographic analysis technology.Finally,computer software technologies for predicting chromatographic conditions and analytical parameters are introduced,which provides an idea for intelligent method development in MCA.This paper aims to provide methodological ideas for the improvement of complex system analysis,especially MCQA.

Molecular diagnosis and direct quantification of cereal cyst nematode(Heterodera filipjevi) from field soil using TaqMan real-time PCR

Heterodera filipjevi continues to be a major threat to wheat production worldwide.Rapid detection and quantification of cyst nematodes are essential for more effective control against this nematode disease.In the present study,a TaqManminor groove binder(TaqMan-MGB)probe-based fluorescence quantitative real-time PCR(qPCR)was successfully developed and used for quantifying H.filipjevi from DNA extracts of soil.The primers and probe designed from the obtained RAPD-SCAR marker fragments of H.filipjevi showed high specificity to H.filipjevi using DNA from isolatesconfirmed species of 23 Heterodera spp.,1 Globodera spp.and 3 Pratylenchus spp.The qPCR assay is highly sensitive and provides improved H.filipjevi detection sensitivity of as low as 4^(-3) single second-stage juvenile(J2)DNAs,10^(-3) female DNAs,and 0.01μgμL^(-1) genomic DNAs.A standard curve relating to the threshold cycle and log values of nematode numbers was generated and validated from artificially infested soils and was used to quantify H.filipjevi in naturally infested field soils.There was a high correlation between the H.filipjevi numbers estimated from 32 naturally infested field soils by both conventional methods and the numbers quantified using the qPCR assay.qPCR potentially provides a useful platform for the efficient detection and quantification of H.filipjevi directly from field soils and to quantify this species directly from DNA extracts of field soils.

Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks

High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.

Detection and quantification of Pb and Cr in oysters using laser-induced breakdown spectroscopy

The quantitative determination of heavy metals in aquatic products is of great importance for food security issues.Laser-induced breakdown spectroscopy(LIBS)has been used in a variety of foodstuff analysis,but is still limited by its low sensitivity when targeting trace heavy metals.In this work,we compare three sample enrichment methods,namely drying,carbonization,and ashing,for increasing detection sensitivity by LIBS analysis for Pb and Cr in oyster samples.The results demonstrate that carbonization can remove a significant amount of the contributions of organic elements C,H,N and O;meanwhile,the signals of the metallic elements such as Cu,Pb,Sr,Ca,Cr and Mg are enhanced by3–6 times after carbonization,and further enhanced by 5–9 times after ashing.Such enhancement is not only due to the more concentrated metallic elements in the sample compared to the dried ones,but also the unifying of the matter in carbonized and ashed samples from which higher plasma temperature and electron density are observed.This condition favors the detection of trace elements.According to the calibration curves with univariate and multivariate analysis,the ashing method is considered to be the best choice.The limits of detection of the ashing method are 0.52 mg kg-1 for Pb and0.08 mg kg-1 for Cr,which can detect the presence of heavy metals in the oysters exceeding the maximum limits of Pb and Cr required by the Chinese national standard.This method provides a promising application for the heavy metal contamination monitoring in the aquatic product industry.

Host cell protein quantification workflow using optimized standards combined with data-independent acquisition mass spectrometry

Monitoring of host cell proteins(HCPs)during the manufacturing of monoclonal antibodies(mAb)has become a critical requirement to provide effective and safe drug products.Enzyme-linked immunosorbent assays are still the gold standard methods for the quantification of protein impurities.However,this technique has several limitations and does,among others,not enable the precise identification of proteins.In this context,mass spectrometry(MS)became an alternative and orthogonal method that delivers qualitative and quantitative information on all identified HCPs.However,in order to be routinely implemented in biopharmaceutical companies,liquid chromatography-MS based methods still need to be standardized to provide highest sensitivity and robust and accurate quantification.Here,we present a promising MS-based analytical workflow coupling the use of an innovative quantification standard,the HCP Profiler solution,with a spectral library-based data-independent acquisition(DIA)method and strict data validation criteria.The performances of the HCP Profiler solution were compared to more conventional standard protein spikes and the DIA approach was benchmarked against a classical datadependent acquisition on a series of samples produced at various stages of the manufacturing process.While we also explored spectral library-free DIA interpretation,the spectral library-based approach still showed highest accuracy and reproducibility(coefficients of variation<10%)with a sensitivity down to the sub-ng/mg mAb level.Thus,this workflow is today mature to be used as a robust and straightforward method to support mAb manufacturing process developments and drug products quality control.

Hepatic MR imaging using IDEAL-IQ sequence:Will Gd-EOB-DTPA interfere with reproductivity of fat fraction quantification?

BACKGROUND Iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification sequence(IDEAL-IQ)is based on chemical shift-based water and fat separation technique to get proton density fat fraction.Multiple studies have shown that using IDEAL-IQ to test the stability and repeatability of liver fat is acceptable and has high accuracy.AIM To explore whether Gadoxetate Disodium(Gd-EOB-DTPA)interferes with the measurement of the hepatic fat content quantified with the IDEAL-IQ and to evaluate the robustness of this technique.METHODS IDEAL-IQ was used to quantify the liver fat content at 3.0T in 65 patients injected with Gd-EOB-DTPA contrast.After injection,IDEAL-IQ was estimated four times,and the fat fraction(FF)and R2* were measured at the following time points:Precontrast,between the portal phase(70 s)and the late phase(180 s),the delayed phase(5 min)and the hepatobiliary phase(20 min).One-way repeated-measures analysis was conducted to evaluate the difference in the FFs between the four time points.Bland-Altman plots were adopted to assess the FF changes before and after injection of the contrast agent.P<0.05 was considered statistically significant.RESULTS The assessment of the FF at the four time points in the liver,spleen and spine showed no significant differences,and the measurements of hepatic FF yielded good consistency between T1 and T2[95%confidence interval:-0.6768%,0.6658%],T1 and T3(-0.3900%,0.3178%),and T1 and T4(-0.3750%,0.2825%).R2* of the liver,spleen and spine increased significantly after injection(P<0.0001).CONCLUSION Using the IDEAL-IQ sequence to measure the FF,we can obtain results that will not be affected by Gd-EOB-DTPA.The high reproducibility of the IDEAL-IQ sequence makes it available in the scanning interval to save time during multiphase examinations.

Two-point Dixon and six-point Dixon magnetic resonance techniques in the detection,quantification and grading of hepatic steatosis

BACKGROUND Hepatic steatosis is a very common problem worldwide.AIM To assess the performance of two-and six-point Dixon magnetic resonance(MR)techniques in the detection,quantification and grading of hepatic steatosis.METHODS A single-center retrospective study was performed in 62 patients with suspected parenchymal liver disease.MR sequences included two-point Dixon,six-point Dixon,MR spectroscopy(MRS)and MR elastography.Fat fraction(FF)estimates on the Dixon techniques were compared to the MRS-proton density FF(PDFF).Statistical tests used included Pearson’s correlation and receiver operating characteristic.RESULTS FF estimates on the Dixon techniques showed excellent correlation(≥0.95)with MRS-PDFF,and excellent accuracy[area under the receiver operating characteristic(AUROC)≥0.95]in:(1)Detecting steatosis;and(2)Grading severe steatosis,(P<0.001).In iron overload,two-point Dixon was not evaluable due to confounding T2*effects.FF estimates on six-point Dixon vs MRS-PDFF showed a moderate correlation(0.82)in iron overload vs an excellent correlation(0.97)without iron overload,(P<0.03).The accuracy of six-point Dixon in grading mild steatosis improved(AUROC:0.59 to 0.99)when iron overload cases were excluded.The excellent correlation(>0.9)between the Dixon techniques vs MRSPDFF did not change in the presence of liver fibrosis(P<0.01).CONCLUSION Dixon techniques performed satisfactorily for the evaluation of hepatic steatosis but with exceptions.

Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art performance.However,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models fail.In this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called SAEU.In SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks.Based on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault diagnosis.Moreover,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability analy-sis.Finally,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

中国数字经济政策的焦点变迁与演进规律

中国为发展数字经济出台了一系列政策,目前已形成较完备的数字经济政策体系,规范与引导数字经济有序健康发展。为了揭示不同时期数字经济政策体系变迁的演进路径、内在逻辑与基本规律,本文在构建中国数字经济政策体系框架的基础上,对1977—2023年国家层面颁布的数字经济政策展开文本挖掘与政策梳理分析。研究发现:①中国数字经济政策历经萌芽、信息化、“互联网+”、数字经济四个演进阶段;②政策发布数量阶段性差异明显,总体呈现波动性增长,政策发布主体呈现多元化特征;③政策焦点具有阶段演化性,随着国内外信息技术及相关产业的创新发展,以及国家对于数字经济发展规律的深入认识,在关键历史事件推动下,数字经济政策重点从基础生产制造逐渐转向研发与应用。

基础研究对生物医药企业科学产出的贡献研究——单篇论文属性划分视角

基础研究是核心技术的根基,尤其对于高科技企业的发展具有重要影响。如何准确地识别并测量研究开发中的基础研究对于探究基础研究的影响与作用机制具有重要意义。本文采用一种可定量衡量单篇论文基础程度的指标“LevelScore”,以全球生物医药企业作为研究对象,量化分析了其在2015—2017年引用和发表科学论文的研究性质与规律。研究结果表明,①基础研究为大部分生物医药企业的发展提供了重要参考与支撑,然而,同时进行基础研究和应用研究需要企业具备一定的规模和实力。中小型生物医药企业更倾向于从事极端基础或者极端应用的研究,而大型企业发表论文的基础程度居中。②生物医药企业科研产出的主要知识来源是基础研究而非应用研究,其科研活动对基础研究具有极强的依赖性,同时需要一定的应用研究支持。③生物医药企业的基础研究产出依赖于基础研究型的知识投入。④应用研究型的产出同样受益于基础研究型投入的支持;反之,则较难发生。这验证了基础研究在科技发展中的先导性和奠基性作用。

普通话特殊型语言障碍儿童辖域指派的研究

有关正常儿童辖域指派的研究已取得不少成果,但到目前为止尚无研究关注特殊型语言障碍(SLI)儿童的辖域知识。本研究采用真值判断任务考察学龄前普通话SLI儿童处理全称量词和否定词的辖域关系的能力。结果表明,与正常发展儿童不同,SLI儿童偏好全部否定解读,全称量词和否定词在句子表层结构的位置关系并不会对SLI儿童的辖域指派产生明显影响。本研究验证了“语义子集原则”同样适用于SLI儿童,并提出SLI儿童在句子层面也存在“部分量化缺失”的问题。研究发现可为普通话SLI儿童的鉴定和干预提供重要参考。

基于关系图的Linux内核兼容性量化分析研究

内核升级导致设备驱动模块、应用系统库的移植工作缺少有效的理论指导,给操作系统开发和部署带来诸多不便。针对上述情况,提出基于内核模块差异性检测和依赖关系分析的内核兼容性量化分析方法,结合开源Linux内核,构造内核模块依赖关系图,统计内核模块出入度、依赖深度、中心性等影响内核兼容性的图特征,分析系统调用和导出函数2类兼容性强相关函数的变化情况及其对内核兼容性的影响,给出测算内核模块兼容率和影响域的基本方法,并在Linux内核5.x系列版本和麒麟典型版本中进行实验验证。

我国农业现代化政策量化评价基于PMC指数模型的分析

我国农业农村现代化建设的推进关系着当前我国全面建设社会主义现代化国家的发展进程。农业农村的现代化建设是解决我国发展目前存在的不平衡不充分现象的重要措施,更是推动农业农村高质量发展的必由之路,对当前现行的有关政策进行梳理与评价有着十分重要的意义。通过数据挖掘以及PMC指数模型构建,对中国在2012—2022年发布的100项农业农村现代化相关政策做出分析,运用文本挖掘、内容分析等方法从政策文本中提取出高频词,并参照已有学者相关政策指标遴选设计出农业农村现代化建设评价指标,利用PMC指数模型对其中4项农业现代化政策进行量化评价。结果表明:P1、P2为优秀水平,P3、P4为良好水平,我国农业农村现代化政策整体质量表现良好,但仍有一定的提升空间;政策的专指性越强,PMC指数的得分越低;政策的发文单位级别越高,PMC指数的得分越高。