Unveiling the Re,Cr,and I diffusion in saturated compacted bentonite using machine-learning methods

The safety assessment of high-level radioactive waste repositories requires a high predictive accuracy for radionuclide diffusion and a comprehensive understanding of the diffusion mechanism.In this study,a through-diffusion method and six machine-learning methods were employed to investigate the diffusion of ReO_(4)^(−),HCrO_(4)^(−),and I−in saturated compacted bentonite under different salinities and compacted dry densities.The machine-learning models were trained using two datasets.One dataset contained six input features and 293 instances obtained from the diffusion database system of the Japan Atomic Energy Agency(JAEA-DDB)and 15 publications.The other dataset,comprising 15,000 pseudo-instances,was produced using a multi-porosity model and contained eight input features.The results indicate that the former dataset yielded a higher predictive accuracy than the latter.Light gradient-boosting exhibited a higher prediction accuracy(R2=0.92)and lower error(MSE=0.01)than the other machine-learning algorithms.In addition,Shapley Additive Explanations,Feature Importance,and Partial Dependence Plot analysis results indicate that the rock capacity factor and compacted dry density had the two most significant effects on predicting the effective diffusion coefficient,thereby offering valuable insights.

Design, Fabrication and Experimentation of a Deep Drawing Machine

This paper presents the work implemented in designing, fabricating and operating a model of a cheap hydraulic DDM （deep drawing machine）, which is currently utilized in the manufacturing processes lab in the IED （Industrial Engineering Department） at An-Najah National University. The machine is used to conduct different experiments related to the deep drawing process. This work was implemented in three stages： the first was the design stage, in which all design calculations of the DDM elements were completed based on the specifications of the product （cup） to be drawn; the second was the construction stage, in which the DDM elements were fabricated and assembled at the engineering workshops of the university; the last was the operating and experimentation stage, in which the DDM was tested by conducting different experiments. The experience gained from designing and constructing such a mechanical lab equipment was found to be successful in terms of obtaining practical results that agree with those available in literature, cost-effective relative to the cost of a similar purchased equipment, as well as enhancing students＇ abilities in understanding the deep drawing process in particular and machine elements design concepts in general.

Improvement of machine learning-based vertex reconstruction for large liquid scintillator detectors with multiple types of PMTs

The precise vertex reconstruction for large liquid scintillator detectors is essential.A novel machine learning-based method was successfully developed to reconstruct an event vertex in JUNO.In this study,the performance of machine learning-based vertex reconstruction was further improved by optimizing the input images of neural networks.By separating the information of different types of PMTs and adding the information of the second hit of PMTs,the vertex resolution was improved by approximately 9.4% at 1 MeV and 9.8% at 11 MeV.

Scratching and Turning Experiments on Machinability of Optical Glass SF6 in Diamond Cutting Process

To improve the machinability of optical glass and achieve optical parts with satisfied surface quality and dimensional accuracy, scratching experiments with increasing cutting depth were conducted on glass SF6 to evaluate the influence of cutting fluid properties on the machinability of glass. The sodium carbonate solution of 10.5% concentration was chosen as cutting fluid. Then the critical depths in scratching experiments with and without cutting fluid were examined. Based on this, turning experiments were carried out, and the surface quality of SF6 was assessed. Compared with the process of dry cutting, the main indexes of surface roughness decrease by over 70% totally. Experimental results indicated that the machinability of glass SF6 can be improved by using the sodium carbonate solution as cutting fluid.

Prediction of Seaward Slope Recession in Berm Breakwaters Using M5＇ Machine Learning Approach

In the design process of berm breakwaters, their front slope recession has an inevitable rule in large number of model tests, and this parameter being studied. This research draws its data from Moghim＇s and Shekari＇s experiment results. These experiments consist of two different 2D model tests in two wave flumes, in which the berm recession to different sea state and structural parameters have been studied. Irregular waves with a JONSWAP spectrum were used in both test series. A total of 412 test results were used to cover the impact of sea state conditions such as wave height, wave period, storm duration and water depth at the toe of the structure, and structural parameters such as berm elevation from still water level, berm width and stone diameter on berm recession parameters. In this paper, a new set of equations for berm recession is derived using the M5＇ model tree as a machine learning approach. A comparison is made between the estimations by the new formula and the formulae recently given by other researchers to show the preference of new M5＇ approach.

Auxiliary guidance manufacture and revealing potential mechanism of perovskite solar cell using machine learning

To promote the development of global carbon neutrality,perovskite solar cells(PSCs)have become a research hotspot in related fields.How to obtain PSCs with expected performance and explore the potential factors affecting device performance are the research priorities in related fields.Although some classical computational methods can facilitate material development,they typically require complex mathematical approximations and manual feature screening processes,which have certain subjectivity and one-sidedness,limiting the performance of the model.In order to alleviate the above challenges,this paper proposes a machine learning(ML)model based on neural networks.The model can assist both PSCs design and analysis of their potential mechanism,demonstrating enhanced and comprehensive auxiliary capabilities.To make the model have higher feasibility and fit the real experimental process more closely,this paper collects the corresponding real experimental data from numerous research papers to develop the model.Compared with other classical ML methods,the proposed model achieved better overall performance.Regarding analysis of underlying mechanism,the relevant laws explored by the model are consistent with the actual experiment results of existing articles.The model exhibits great potential to discover complex laws that are difficult for humans to discover directly.In addition,we also fabricated PSCs to verify the guidance ability of the model in this paper for real experiments.Eventually,the model achieved acceptable results.This work provides new insights into integrating ML methods and PSC design techniques,as well as bridging photovoltaic power generation technology and other fields.

Process Optimization of Ultrasonic Extraction of Puerarin Based on Support Vector Machine

In ultrasonic extraction technology, optimization of technical parameters often considers extraction medium only, without including ultrasonic parameters. This paper focuses on controlling the ultrasonic extraction process of puerarin, investigating the influence of ultrasonic parameters on extraction rate, and empirically analyzing the main components of Pueraria, i.e., isoflavone compounds. A method is presented combining orthogonal experi- mental design with a support vector machine and a predictive model is established for optimization of technical parameters. From the analysis with the predictive model, appropriate process parameters are achieved for higher extraction rate. With these parameters in the ultrasonic extraction of puerarin, the experimental result is satisfactory. This method is of significance to the study of extracfing root-stock plant medicines.

Fault Diagnosis of Cylindrical Grinding Machine

Based on experiment modal analysis(EMA) and operation modal analysis(OMA), the dynamic characteristics of cylindrical grinding machine were measured and provided a basis for further failure analysis.The influences of grinding parameters on dynamic characteristics were studied by analyzing the diagnostic signals extracted from racing and grinding experiments.The significant frequency of 38 Hz related to grinding wheel spindle speed of 2 307 r/min showed that the wheel spindle system was in a state of imbalan...

Prediction of multifaceted asymmetric radiation from the edge movement in density-limit disruptive plasmas on Experimental Advanced Superconducting Tokamak using random forest

Multifaceted asymmetric radiation from the edge(MARFE) movement which can cause density limit disruption is often encountered during high density operation on many tokamaks. Therefore, identifying and predicting MARFE movement is meaningful to mitigate or avoid density limit disruption for the steady-state high-density plasma operation. A machine learning method named random forest(RF) has been used to predict the MARFE movement based on the density ramp-up experiment in the 2022’s first campaign of Experimental Advanced Superconducting Tokamak(EAST). The RF model shows that besides Greenwald fraction which is the ratio of plasma density and Greenwald density limit, dβp/dt,H98and d Wmhd/dt are relatively important parameters for MARFE-movement prediction. Applying the RF model on test discharges, the test results show that the successful alarm rate for MARFE movement causing density limit disruption reaches ~ 85% with a minimum alarm time of ~ 40 ms and mean alarm time of ~ 700 ms. At the same time, the false alarm rate for non-disruptive and non-density-limit disruptive discharges can be kept below 5%. These results provide a reference to the prediction of MARFE movement in high density plasmas, which can help the avoidance or mitigation of density limit disruption in future fusion reactors.

Application Research of Gravel and Machine-Made Sand along the KKH-2 Project in Pakistan on Asphalt Pavement

According to the characteristics of stone along the KKH-2 project in Pakistan, the applicability of gravel and machine-made sand for road engineering was studied. Through investigation, the types of stone along the project were relatively simple, and the stone materials used for road construction were mainly limestone, sandstone and pebbles, and the reserves?were?abundant. The experiment research and analyses comparisons of the parameters and road performance characteristics of natural gravel materials were carried out, and the design parameters and road performance indicators of natural grit in the current code were supplemented and adjusted to make it more suitable for Pakistan to use natural gravel materials for road construction. Thesis combines the project,?proposing that mechanism sand and natural sand mixed concrete?is?not inferior?tonatural sand mixed concrete in terms of technical performance, and the overall cost is lower than that of natural sand mixed concrete. The research results are of great significance for saving engineering construction costs, ensuring road performance and prolonging service life.

Numerical and Experimental Modelling of Gas Flow and Heat Transfer in the Air Gap of an Electric Machine. Part II: Grooved Surfaces

The study deals with the cooling of a high-speed electric machine through an air gap with numerical and experimental methods.The rotation speed of the test machine is between 5000-4000 r/rain and the machine is cooled by a forced gas flow through the air gap.In the previous part of the research the friction coefficient was measured for smooth and grooved stator cases with a smooth rotor.The heat transfer coefficient was recently calculated by a numerical method and measured for a smooth stator-rotor combination.In this report the cases with axial groove slots at the stator and/or rotor surfaces are studied.Numerical flow simulations and measurements have been done for the test machine dimensions at a large velocity range.At constant mass flow rate the heat transfer coefficients by the numerical method attain bigger values with groove slots on the stator or rotor surfaces.The results by the numerical method have been confirmed with measurements.The RdF-sensor was glued to the stator and rotor surfaces to measure the heat flux through the surface,as well as the temperature.

Micro Model of Carbon Fiber/Cyanate Ester Composites and Analysis of Machining Damage Mechanism

Machining damage occurs on the surface of carbon fiber reinforced polymer (CFRP) composites during processing. In the current simulation model of CFRP, the initial defects on the carbon fiber and the periodic random distribution of the reinforcement phase in the matrix are not considered in detail, which makes the characteristics of the cutting model significantly different from the actual processing conditions. In this paper, a novel three-phase model of carbon fiber/cyanate ester composites is proposed to simulate the machining damage of the composites. The periodic random distribution of the carbon fiber reinforced phase in the matrix was realized using a double perturbation algorithm. To achieve the stochastic distribution of the strength of a single carbon fiber, a novel method that combines the Weibull intensity distribution theory with the Monte Carlo method is presented. The mechanical properties of the cyanate matrix were characterized by fitting the stress-strain curves, and the cohesive zone model was employed to simulate the interface. Based on the model, the machining damage mechanism of the composites was revealed using finite element simulations and by conducting a theoretical analysis. Furthermore, the milling surfaces of the composites were observed using a scanning electron microscope, to verify the accuracy of the simulation results. In this study, the simulations and theoretical analysis of the carbon fiber/cyanate ester composite processing were carried out based on a novel three-phase model, which revealed the material failure and machining damage mechanism more accurately.

Machine learning for intrusion detection in industrial control systems:challenges and lessons from experimental evaluation

Gradual increase in the number of successful attacks against Industrial Control Systems(ICS)has led to an urgent need to create defense mechanisms for accurate and timely detection of the resulting process anomalies.Towards this end,a class of anomaly detectors,created using data-centric approaches,are gaining attention.Using machine learning algorithms such approaches can automatically learn the process dynamics and control strategies deployed in an ICS.The use of these approaches leads to relatively easier and faster creation of anomaly detectors compared to the use of design-centric approaches that are based on plant physics and design.Despite the advantages,there exist significant challenges and implementation issues in the creation and deployment of detectors generated using machine learning for city-scale plants.In this work,we enumerate and discuss such challenges.Also presented is a series of lessons learned in our attempt to meet these challenges in an operational plant.

The Validity Analysis of Regression: Combining Uniform Experiment Design with Nonlinear Regression

The data topology structure of uniform experiment design (UD) is too complex to be reasonable regressed. In this paper, the principle and method of distinguish the training data and testing data were described to make a reasonable regression when uniform experiment design combined with support vector regression (SVR). Two equivalent ways which were the smallest enclosing hypersphere perceptron (SEH) and the enclosing simplex perceptron (ES) were provided to discover the topology relationship of the process parameter datum. To give an application, a series of experiments about laser cladding layer quality were conducted by UD to get the relationship of load, velocity and wearing capacity. Results showed that only the testing datum recommended by the two perceptrons got a good forecasting by SVR. Therefore, the two perceptrons could guide experiments with process parameter data of complex topology structure. Further, the application could be extended over a much wider field of experiments.

N6-甲基腺苷相关调节因子与骨关节炎:生物信息学和实验验证分析

背景:越来越多证据表明N6-甲基腺苷(N6-methyladenosine,m6A)调节因子与骨关节炎密切相关,被认为是防治骨关节炎新方向,但具体作用机制不明。目的:通过对骨关节炎基因芯片数据集进行生物信息学分析,探讨m6A对骨关节炎的作用,解析骨关节炎发病机制。方法:首先利用R软件提取GEO数据库中GSE1919数据集中骨关节炎相关m6A调节因子及其表达量,进而对提取结果行基因差异分析及GO、KEGG富集分析;接着对PPI网络拓扑学分析结果和机器学习结果取交集得到m6A关键调节因子,并通过体外细胞实验验证。结果与结论:①提取得到16个骨关节炎相关m6A调节因子表达量,通过差异分析获得ZC3H13、YTHDC1、YTHDF3、HNRNPC等11个m6A差异调节因子;②GO富集分析显示,骨关节炎相关m6A差异调节因子在生物过程中主要于mRNA转运、RNA分解代谢、胰岛素样生长因子受体信号通路调控等发挥作用;③KEGG富集分析显示,差异调节因子主要参与p53、白细胞介素17和AMPK信号通路;④综合PPI网络拓扑学分析和机器学习结果获得m6A关键调节因子——YTHDC1;⑤体外细胞实验结果表明,m6A关键调节因子——YTHDC1在对照组与骨关节炎组中表达存在显著差异(P<0.05);⑥结果显示,YTHDC1与骨关节炎发生发展密切相关,有望成为m6A治疗骨关节炎的分子靶点。

新文科背景下基于Blockly的机器学习实验教学平台设计与实现

在新文科建设背景下,针对文科学生编程经验少、机器学习实验上手难的问题,设计并实现一种基于Blockly编程的机器学习实验教学平台。该实验平台由教师管理端、学生实验端、系统管理端和公共模块四部分构成,采用B/S架构,由计算机编程语言Python和可视化编程工具Blockly实现。并以机器学习中的“自然语言处理”为实验教学案例,全面呈现该实验教学平台与实验教学任务结合的具体应用实践。

基于环境声识别的工业设备状态监测实验系统设计

为了实现低成本的工业设备故障检测,设计了一套基于环境声识别的设备状态监测实验系统。基于Arduino开源硬件开发低成本的智能传感器,并部署限制感受野的深度学习模型RFL-MobileNet。通过在边缘端进行智能数据处理,系统为工厂设备提供了实时且准确的状态监测,以减少传统方法中数据传输到云端所带来的延迟以及数据泄露风险。基于公开数据集和实际数据集的实验结果表明,系统运行良好,电动机故障检测准确率达95.4%。

基于正余弦函数设计位移运动算法的研究与实现

为实现机床加工运动过程中算法规划能输出连续的运动指令,提高位置控制精度,文章基于正余弦函数值周期连续变化的特性,设计出位移运动指令随时间连续变化的算法模型并进行公式推导。结合Matlab对算法的规划细节进行仿真分析,得到算法规划的控制指令平稳且连续,证明了算法模型建立与公式推导过程的正确性。选择机床加工PCB覆铜板运动过程为试验研究对象,使用激光干涉仪对机床加工实际位移距离进行测量,测得采用所设计的位移运动算法进行运动过程规划,位置控制精度能达到1.788μm,验证了位移运动算法的正确性。

糖果包装机虚拟拆装碰撞检测研究

目的为了提高虚拟装配系统中碰撞检测的效率,提出以AABB包围盒粗检测和深度图细检测的碰撞检测方法。方法首先利用SolidWorks软件建立BZ350糖果包装机模型,以STL格式导入到引擎中,通过对零件先建立AABB包围盒做粗检测后,分别对2个零件获取对应的深度图,使用OpenCV对2张深度图相叠加,根据叠加后像素值的大小来判断零件是否发生了碰撞。结果实验表明,当叠加后深度值小于100的数量占所有深度值不为0的数量的比例大于86.2736%时,即可认定两零件发生碰撞。结论对比传统检测算法,此算法在保证了检测速度和检测精度的前提下,对复杂模型的检测具有优势,并且保证了运行的速度在40帧/s以上的流畅运行。

工程领域裂纹检测实验方法的进展

裂纹故障是影响生产生活和设备安全运行的一大因素,裂纹检测实验对于检测技术应用和裂纹故障信息获取具有重要意义,但领域内缺乏对检测实验的系统性归纳与剖析。该文对当前代表性的裂纹检测实验方法进行了分类比较,针对裂纹信息介绍了定位、形貌和深度的检测实验,从先进传感器、数据处理与机器学习应用等3个方面探究了裂纹检测实验方法进展。最后根据国内外裂纹检测实验的研究现状,提出了在裂纹扩展方向检测、误报漏报及实验与实际工况贴近程度方面所面临的挑战,并从实时动态、远程无线和多故障耦合检测等方面进行了展望,为今后裂纹检测实验方法的研究与发展提供了参考。