关键词： optical proximity correction   machine learning   artificial neural networks   computational photolithography  

摘要： The paper studies the effectiveness of machine learning methods in computational photolithography. The first task is to determine the direction of displacement of the mask contour fragment. The second task is to determine the amount of displacement of the mask contour fragment. The machine learning models were trained on the data generated with Calibre WORKbench CAD in the form of radiation intensity vectors around the center of the segment. Comparisons were made between linear regression, random forest, gradient boosting, and feedforward convolutional neural network models. The most accurate results were demonstrated by the random forest model. With its help, it is possible to achieve an absolute error of 2 nm and an accuracy of displacement's direction prediction of 97.9%.

关键词： Nanofluid   NuScale SMR nuclear reactor   CFD   Natural circulation   Artificial neural network (ANN)   Genetic algorithm (GA)   Particle swarm optimization (PSO)   Hybrid of PSO-GA (HPSOGA)  

摘要： This investigation studies the optimization of water-based alumina nanofluid to advance heat transfer and safety performance of the NuScale natural circulation reactor. First, comprehensive CFD and neutronic simulation is employed to design a reactor core using nanofluid coolant (0.001-10% volume fractions and 10-90 nm particle sizes). Consequently, the outlined results prove a sufficient enhancement of safety and heat transfer parameters by applying nanofluid coolant. Next, a developed Artificial Neural Network (ANN), utilizing the obtained data, predicts the thermal-hydraulic and neutronic parameters of the NuScale reactor core with Al2O3 /Water nanofluid. Achieving the optimal vol% and size of nanoparticles by implementing Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Hybridized PSO-GA, based on the developed ANN results, are the main goals of this work. These optimization algorithms, which have a significant ability to attain the best solutions, also determine the optimal values of natural circulation parameters (V-max/V-avg, V-out -V-in, and pressure drop), heat transfer coefficient, MDNBR, RPPF, and excess reactivity, for obtained vol% and size. The validation results demonstrate the efficiency of the developed ANN and these three evolutionary computation algorithms for optimization. The differences between the outcomes of implemented algorithms, focusing on how each works and affects optimal solutions in problem space, are also described. Finally, this paper compares the results of optimal design with conventional NuScale, which uses water coolant. This comparison indicates the potential of the proposed nanofluid coolant to increase thermal and safety performance. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Chemical risk assessment   Ecotoxicity   Organic toxicants   Similarity-based methods   In silico methods  

摘要： There has been an increase in the use of non-animal approaches, such as in silico and/or in vitro methods, for assessing the risks of hazardous chemicals. A number of machine learning algorithms link molecular descriptors that interpret chemical structural properties with their biological activity. These computer-aided methods encounter several challenges, the most significant being the heterogeneity of datasets;more efficient and inclusive computational methods that are able to process large and heterogeneous chemical datasets are needed. In this context, this study verifies the utility of similarity-based machine learning methods in predicting the acute aquatic toxicity of diverse organic chemicals on Daphnia magna and Oryzias latipes. Two similarity-based methods were tested that employ a limited training dataset, most similar to a given fitting point, instead of using the entire dataset that encompasses a wide range of chemicals. The kernel-weighted local polynomial approach had a number of advantages over the distance-weighted k-nearest neighbor (k-NN) algorithm. The results highlight the importance of lipophilicity, electrophilic reactivity, molecular polarizability, and size in determining acute toxicity. The rigorous model validation ensures that this approach is an important tool for estimating toxicity in new or untested chemicals.

关键词： Prediction   Machine learning   Reporting  

摘要： Objective: Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology. Study design and setting: We conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods (as defined by primary study authors) in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications. We described overall reporting adherence of included publications and by each section of TRIPOD. Results: Sixty-two publications met the inclusion criteria. 48 were development studies and 14 were development with validation studies. 152 models were developed across all publications. Median adherence to TRIPOD reporting items was 41% [range: 10%-67%] and at least 50% adherence was found in 19% (n = 12/62) of publications. Adherence was lower in development only studies (median: 38% [range: 10%-67%]);and higher in development with validation studies (median: 49% [range: 33%-59%]). Conclusion: Reporting of clinical prediction models using machine learning in oncology is poor and needs urgent improvement, so readers and stakeholders can appraise the study methods, understand study findings, and reduce research waste. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http:// ***/ licenses/ by/ 4.0/ )

摘要： The total organic carbon content (TOC) is a core indicator for shale gas reservoir evaluations. Machine learning-based models can quickly and accurately predict TOC, which is of great significance for the production of shale gas. Based on conventional logs, the measured TOC values, and other data of 9 typical wells in the Jiaoshiba area of the Sichuan Basin, this paper performed a Bayesian linear regression and applied a random forest machine learning model to predict TOC values of the shale from the Wufeng Formation and the lower part of the Longmaxi Formation. The results showed that the TOC value prediction accuracy was improved by more than 50% by using the well-trained machine learning models compared with the traditional Delta LogR method in an overmature and tight shale. Using the halving random search cross-validation method to optimize hyperparameters can greatly improve the speed of building the model. Furthermore, excluding the factors that affect the log value other than the TOC and taking the corrected data as input data for training could improve the prediction accuracy of the random forest model by approximately 5%. Data can be easily updated with machine learning models, which is of primary importance for improving the efficiency of shale gas exploration and development.

关键词： Feedforward neural networks  

摘要： The added value of supervised Machine Learning (ML) methods to determine the Absolute Configuration (AC) of compounds from their Vibrational Circular Dichroism (VCD) spectra was explored. Among all ML methods considered, Random Forest (RF) and Feedforward Neural Network (FNN) yield the best performance for identification of the AC. At its best, FNN allows near-perfect AC determination, with accuracy of prediction up to 0.995, while RF combines good predictive accuracy (up to 0.940) with the ability to identify the spectral areas important for the identification of the AC. No loss in performance of either model is observed as long as the spectral sampling interval used does not exceed the spectral bandwidth. Increasing the sampling interval proves to be the best method to lower the dimensionality of the input data, thereby decreasing the computational cost associated with the training of the models.

关键词： Gold   Molecules   Electrodes   Electrical conductivity   Computational chemistry  

摘要： We propose an efficient method to analyze the influence of thermal fluctuations of a molecular junction on its electronic transport properties, and consequently, reliably predict the time-averaged value of the conductance at a finite temperature in the zero-bias regime. Our multiscale approach combines three complementary techniques, namely, large-scale quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations, active machine learning methods, and nonequilibrium Green's function transport calculations. Results for the exemplary Au (111) -S-C6H4-C6H4-S-Au (111) and Au(111)-N-C6H4-C6H4-NAu(111) junctions indicate the substantial impact of the thermal evolution of the junction on its transport properties, which cannot be forecasted based just on the ground-state geometry. In the experimentally relevant temperature range around the room temperature, the predicted conductance values are 30-40% larger than those calculated for the minimum-energy configuration of the respective junction at 0 K.

关键词： Machine learning   JUNO   Energy reconstruction   Vertex reconstruction  

摘要： The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment designed to study neutrino oscillations. Determination of neutrino mass ordering and precise measurement of neutrino oscillation parameters sin(2) theta(12), Delta m(21)(2) and Delta(2)(31) are the main goals of the experiment. A rich physical program beyond the oscillation analysis is also foreseen. The ability to accurately reconstruct particle interaction events in JUNO is of great importance for the success of the experiment. In this work we present several machine learning approaches applied to the vertex and the energy reconstruction. Multiple models and architectures were compared and studied, including Boosted Decision Trees (BDT), Deep Neural Networks (DNN), a few kinds of Convolution Neural Networks (CNN), based on ResNet and VGG, and a Graph Neural Network based on DeepSphere. The models of BDT and DNN are trained with aggregated information, pre-calculated from PMT signal, while the others are trained with PMT-wise measured information from 17600 PMTs. Based on a study, carried out using the dataset, generated by the official JUNO software, we demonstrate that machine learning approaches achieve the necessary level of accuracy for reaching the physical goals of JUNO: sigma(E) = 3% at E-vis = 1 MeV for the energy and sigma(x,y,z) = 10 cm at E-vis = 1 MeV for the position.

关键词： Graves’ophthalmology   laser-induced breakdown spectroscopy(LIBS)   linear discriminant analysis(LDA)   support vector machine(SVM)   k-nearest neighbor(kNN)   generalized regression neural network(GRNN)  

摘要： Diagnosis of the Graves’ophthalmology remains a significant *** identified between Graves’ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy(LIBS)combined with machine learning *** this work,the paraffin-embedded samples of the Graves’ophthalmology were prepared for LIBS spectra *** metallic elements(Na,K,Al,Ca),non-metallic element(O)and molecular bands((C-N),(C-O))were selected for diagnosing Graves’*** selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis(LDA),support vector machine(SVM),k-nearest neighbor(ANN),and generalized regression neural network(GRNN),*** results showed that the predicted accuracy rates of LDA,SVM,ANN,GRNN were 76.33%,96.28%,96.56%,and 96.33%,*** sensitivity of four models were 75.89%,93.78%,96.78%,and 96.67%,*** specificity of four models were 76.78%,98.78%,96.33%,and 96.00%,*** demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves’ophthalmopathy with a higher rate of *** ANN had the best performance by comparing the three nonlinear ***,LIBS combined with machine learning method can be an effective way to discriminate Graves’ophthalmology.