Accurate Quantitative Relationship Research Articles

Stochastic reconstruction and performance prediction of cathode microstructures based on deep learning

The effective properties of lithium-ion battery (LIB) cathode are determined by both the volume fractions of constituents and the morphological features of microstructure. However, it is difficult to establish an accurate quantitative relationship between the macroscopic effective properties and microstructural features. Deep learning techniques, due to their exceptional nonlinear fitting capabilities, have been widely applied in various complex fields. Our study presents a generation scheme of numerous three-dimensional (3D) digital microstructures of cathode, using a deep convolutional neural network (CNN)-based stochastic reconstruction algorithm combining with the scanning electron microscope (SEM) images. The reconstructed samples are substituted with the corresponding finite element (FE) models, and the effective mechanical and electrochemical properties are assessed through the FE-based homogenization theory. Finally, the generated cathode samples and their effective properties are used to train the 3D CNN for performance prediction. This study demonstrates that the deep learning approaches can accurately and rapidly reconstruct the microstructure of cathode and predict their effective properties. Furthermore, the established framework can be extended to other heterogeneous materials.

Machine learning-based quantitative analysis of metal ductile fracture surface

Quantitative fracture analysis (QFA) can infer mechanical properties and fracture mechanism of materials based on quantitative statistics of various topographical features on the fracture surface. However, it is still a challenge to screen the key fracture surface descriptors for a given property through experimental insight and theory. Obtaining an accurate quantitative relationship is difficult as different properties involve a variety of fracture surface descriptors. Herein, a QFA based on machine learning is proposed. The fracture surface images are first divided into three classes: intergranular, quasi-cleavage and dimple, by an unsupervised classification model based on a convolutional neural network with an accuracy of 0.867. The fracture surface descriptors of the identified dimple images are extracted by image segmentation and used to train separate regression models to predict the values of fracture toughness (KIC), tensile strength (Rm), yield strength (Re), elongation after fracture (A) and fracture shrinkage (Z). These models demonstrate good performance with an R2 in the test set of 0.934, 0.944, 0.840, 0.890, 0.900, respectively. In addition, with KIC and Rm as dependent variables, the explicit relationships between them and key morphological features are established based on symbolic regression. The R2 between the predicted and experimental values of the two formulae reach 0.925 and 0.926, respectively. Such a machine learning-based QFA can assist in the rapid prediction and evaluation of mechanical properties of materials in engineering applications.

Застосування нечіткої нейронної мережі для визначення інформативності факторів впливу на реалізацію зчіпної здатності дороги і шини

The task of assessing the traction of the road and tires is one of the most relevant in the technical examination of road accidents, as the results of its solution directly affect the assessment of the effectiveness of braking of wheeled vehicles, as the main method of preventing road accidents on motor transport. In the presence of such damage to the vehicle, which makes it impossible to conduct road tests, the expert has to use outdated calculation methods. This contributes to errors and increases the uncertainty of the data on which expert opinions are formed.The paper proposes ways to improve existing approaches to estimating the adhesion factor and indicators of braking efficiency of vehicles in the automotive examination of accidents in the presence of compositional (stochastic and fuzzy) uncertainty. Analysis of the use of mathematical methods in the practice of accident investigation showed that in the absence of the possibility of using traditional mathematical methods based on the detection of accurate quantitative relationships, to study accidents in uncertainty, it is advisable to use approximate modeling methods based on fuzzy (continuous) logics. The results of the study of the braking efficiency of vehicles of category M1 in operating conditions and the results of the evaluation of the informativeness of the factors influencing the adhesion factor, using the Fuzzy Logic Toolbox of the Matlab computing environment were used to the choice and substantiation of the method of estimating the coupling qualities of car tires in the study of road accidents in conditions of uncertainty. As a result, it was found that the improvement of existing approaches to estimating the adhesion factor and efficiency of vehicle braking in autotechnical examination of road accidents in the presence of compositional uncertainty can be achieved through the use of simple ANFIS models that provide better generalizing properties

Data-driven approach to learning salience models of indoor landmarks by using genetic programming

ABSTRACT In landmark-based way-finding, determining the most salient landmark from several candidates at decision points is challenging. To overcome this problem, current approaches usually rely on a linear model to measure the salience of landmarks. However, linear models are not always able to establish an accurate quantitative relationship between the attributes of a landmark and its perceived salience. Furthermore, the numbers of evaluated scenes and of volunteers participating in the testing of these models are often limited. With the aim of overcoming these gaps, we propose learning a non-linear salience model by means of genetic programming. We compared our proposed approach with conventional algorithms by using photographs of two hundred test scenes collected from two shopping malls. Two hundred volunteers who were not in these environments were asked to answer questionnaires about the collected photographs. The results from this experiment showed that in 76% of the cases, the most salient landmark (according to the volunteers' perception) was correctly predicted by our proposed approach. This accuracy rate is considerably higher than the ones achieved by conventional linear models.

Stochastic dynamics and stable equilibrium of evolutionary optional public goods game in finite populations

Continuous noise caused by mutation is widely present in evolutionary systems. Considering the noise effects and under the optional participation mechanism, a stochastic model for evolutionary public goods game in a finite size population is established. The evolutionary process of strategies in the population is described as a multidimensional ergodic and continuous time Markov process. The stochastic stable state of the system is analyzed by the limit distribution of the stochastic process. By numerical experiments, the influences of the fixed income coefficient for non-participants and the investment income coefficient of the public goods on the stochastic stable equilibrium of the system are analyzed. Through the numerical calculation results, we found that the optional participation mechanism can change the evolutionary dynamics and the equilibrium of the public goods game, and there is a range of parameters which can effectively promote the evolution of cooperation. Further, we obtain the accurate quantitative relationship between the parameters and the probabilities for the system to choose different stable equilibriums, which can be used to realize the control of cooperation.

Linear relationship between peak and season-long abundances in insects.

An accurate quantitative relationship between key characteristics of an insect population, such as season-long and peak abundances, can be very useful in pest management programs. To the best of our knowledge, no such relationship has yet been established. Here we establish a predictive linear relationship between insect catch Mpw during the week of peak abundance, the length of seasonal flight period, F (number of weeks) and season-long cumulative catch (abundance) A = 0.41MpwF. The derivation of the equation is based on several general assumptions and does not involve fitting to experimental data, which implies generality of the result. A quantitative criterion for the validity of the model is presented. The equation was tested using extensive data collected on captures of male gypsy moths Lymantria dispar (L.) (Lepidoptera: Erebidae) in pheromone-baited traps during 15 years. The model was also tested using trap catch data for two species of mosquitoes, Culex pipiens (L.) (Diptera: Culicidae) and Aedes albopictus (Skuse) (Diptera: Culicidae), in Gravid and BG-sentinel mosquito traps, respectively. The simple, parameter-free equation approximates experimental data points with relative error of 13% and R2 = 0.997, across all of the species tested. For gypsy moth, we also related season-long and weekly trap catches to the daily trap catches during peak flight. We describe several usage scenarios, in which the derived relationships are employed to help link results of small-scale field studies to the operational pest management programs.

Surface effect investigation on multipactor in microwave components using the EM-PIC method

Multipactor poses a great risk to microwave components in space and its accurate controllable suppression is still lacking. To evaluate the secondary electron emission (SEE) of arbitrary surface states on multipactor, metal samples fabricated with ideal smoothness, random roughness, and micro-structures on the surface are investigated through SEE experiments and multipactor simulations. An accurate quantitative relationship between the SEE parameters and the multipactor discharge threshold in practical components has been established through Electromagnetic Particle-In-Cell (EM-PIC) simulation. Simulation results of microwave components, including the impedance transformer and the coaxial filter, exhibit an intuitive correlation between the critical SEE parameters, varied due to different surface states, and multipactor thresholds. It is demonstrated that it is the surface micro-structures with certain depth and morphology that determine the average yield of secondaries, other than the random surface relieves. Both the random surface relieves and micro-structures have a scattering effect on SEE, and the yield is prone to be identical upon different elevation angles of incident electrons. It possesses a great potential in the optimization and improvement of suppression technology without the exhaustion of the technological parameter.

On an analytical method to solve singular integrals both in space and time for 2-D elastodynamics by TD-BEM

A joint analytical–numerical integration method is proposed to solve the time domain boundary integral equations for 2-D elastodynamics. The analytical integration method is adopted to solve the discrete boundary integral equations for elastodynamics with singular integrals, while the numerical integration method is conventionally adopted to solve the discrete boundary integral equations for elastodynamics without singular integrals. Accurate quantitative relationships for G and H matrices between elastostatics and elastodynamics are established, under certain assumptions. All the singularities in G and H matrices for 2-D elastodynamics are analytically solved in the process of the discretization in space and time. The analytical method to solve singular integrals in space and time is verified by the examples of 1-D rod and 2-D cantilever beam under sudden loads.

Development of validated QSPR models for impact sensitivity of nitroaliphatic compounds

The European regulation of chemicals named REACH implies the assessment of a large number of substances based on their hazardous properties. However, the complete characterization of physico-chemical, toxicological and eco-toxicological properties by experimental means is incompatible with the imposed calendar of REACH. Hence, there is a real need in evaluating the capabilities of alternative methods such as quantitative structure–property relationship (QSPR) models, notably for physico-chemical properties.In the present work, the molecular structures of 50 itroaliphatic compounds were correlated with their impact sensitivities (h50%) using such predictive models. More than 400 olecular descriptors (constitutional, topological, geometrical, quantum chemical) were calculated and linear and multi-linear regressions were performed to find accurate quantitative relationships with experimental impact sensitivities. Considering different sets of descriptors, four predictive models were obtained and two of them were selected for their predictive reliability. To our knowledge, these QSPR models for the impact sensitivity of nitroaliphatic compounds are the first ones being rigorously validated (both internally and externally) with defined applicability domains. They hence follow all OECD principles for regulatory acceptability of QSPRs, allowing possible application in REACH.

Thermodynamic study and cellular automaton simulation of thermal stability of nanocrystalline SmCo7 alloy

The accurate quantitative relationship between the excess volume at the grain boundary and the nanograin size in nanocrystalline alloy is deduced. The fundamental thermodynamic function of nanocrystalline alloy is derived as a function of nanograin size and temperature. By taking the SmCo7 alloy for example, the thermal stability of the nanocrystalline alloy, as well as its evolution characteristics, is studied based on the calculated excess Gibbs free energy of nanograin boundary. The results show that the nanostructure with grain size below a critical value that corresponds to the maximum excess Gibbs free energy can have higher thermal stability than a coarser nanograin structure. Once the grain size is larger than the critical value, the nanostructure may lose its stability and undergo discontinuous grain growth. By combining the nanothermodynamic model with the cellular automaton algorithm, the quantitative and visual simulations of nanograin growth in nanocrystalline SmCo7 alloy are performed. The nanograin growth behavior described by the two approaches are consistent with each other, which validates the conclusion of the thermal stability of nanocrystalline alloy, drawn from the present thermodynamic study.

Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanisms

The molecular structures of 77 nitroaromatic compounds have been correlated to their thermal stabilities by combining the quantitative structure-property relationship (QSPR) method with density functional theory (DFT). More than 300 descriptors (constitutional, topological, geometrical and quantum chemical) have been calculated, and multilinear regressions have been performed to find accurate quantitative relationships with experimental heats of decomposition (-ΔH). In particular, this work demonstrates the importance of accounting for chemical mechanisms during the selection of an adequate experimental data set. A reliable QSPR model that presents a strong correlation with experimental data for both the training and the validation molecular sets (R (2) = 0.90 and 0.84, respectively) was developed for non-ortho-substituted nitroaromatic compounds. Moreover, its applicability domain was determined, and the model's predictivity reached 0.86 within this applicability domain. To our knowledge, this work has produced the first QSPR model, developed according to the OECD principles of regulatory acceptability, for predicting the thermal stabilities of energetic compounds.

Data mining for seeking accurate quantitative relationship between molecular structure and GC retention indices of alkanes by projection pursuit

Primary data mining on alkanes for seeking accurate quantitative relationship between molecular structure and retention indices of gas chromatography is developed in this paper. Based on the results obtained from projection pursuit (PP), a new variable named class distance variable, which essentially describes the branching structure of the alkanes, is proposed. With the help of the new variable, both fitting and prediction accuracy of the regression model can be dramatically improved. The results obtained in this work show that the technique of PP developed in statistics is a quite promising tool for seeking accurate quantitative structure–activity relationship (QSAR) and/or quantitative structure–property relationship (QSPR) researches.

Data Mining for Seeking an Accurate Quantitative Relationship Between Molecular Structure and GC Retention Indices of Alkenes by Projection Pursuit.

ChemInformVolume 34, Issue 4 Other Subjects Data Mining for Seeking an Accurate Quantitative Relationship Between Molecular Structure and GC Retention Indices of Alkenes by Projection Pursuit. Yiping Du, Yiping Du Coll. Chem. Chem. Eng., Cent. South Univ. Technol., Changsha, Hunan 410083, Peop. Rep. ChinaSearch for more papers by this authorYizeng Liang, Yizeng Liang Coll. Chem. Chem. Eng., Cent. South Univ. Technol., Changsha, Hunan 410083, Peop. Rep. ChinaSearch for more papers by this authorDong Yun, Dong Yun Coll. Chem. Chem. Eng., Cent. South Univ. Technol., Changsha, Hunan 410083, Peop. Rep. ChinaSearch for more papers by this author Yiping Du, Yiping Du Coll. Chem. Chem. Eng., Cent. South Univ. Technol., Changsha, Hunan 410083, Peop. Rep. ChinaSearch for more papers by this authorYizeng Liang, Yizeng Liang Coll. Chem. Chem. Eng., Cent. South Univ. Technol., Changsha, Hunan 410083, Peop. Rep. ChinaSearch for more papers by this authorDong Yun, Dong Yun Coll. Chem. Chem. Eng., Cent. South Univ. Technol., Changsha, Hunan 410083, Peop. Rep. ChinaSearch for more papers by this author First published: 05 March 2003 https://doi.org/10.1002/chin.200304208AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume34, Issue4January 28, 2003 RelatedInformation

Data mining for seeking an accurate quantitative relationship between molecular structure and GC retention indices of alkenes by projection pursuit.

Primary data mining on alkenes for seeking an accurate quantitative relationship between the molecular structure and retention indices of gas chromatography is developed in this paper. Based on the results obtained from projection pursuit, all alkenes investigated show an interesting classification. Thus, a new variable named class distance variable of alkenes, which essentially describes information about the branch, position of the double bonds, the number of double bonds, and so on for alkenes, is proposed. With the help of the new variable, both fitting and prediction accuracy of the regression model can be dramatically improved. The results obtained in this work show that the technique of projection pursuit developed in statistics is a quite promising tool for seeking an accurate quantitative structure-retention relationship (QSRR).

Enzyme aspects of acute intermittent porphyria.

Patients with acute intermittent porphyria are now known to have a decrease of the third enzyme and, in liver, an increase of the first enzyme of the heme biosynthetic pathway. It is possible that the induction of the first enzyme (ALA synthetase) in the liver of these patients results from the partial block in heme synthesis, since heme is known to be involved in the repression of hepatic ALA synthetase via a closed negative feedback loop. Presumably, an increase of hepatic ALA synthetase allows the delivery of a higher substrate concentration to the enzyme at the level of the block, thus raising the rate of the synthesis of end product toward “normal”. Using a simplified Michaelis-Menten model of an irreversible pathway in a homogeneous system, quantitative relationships between the degree of block and the magnitude of induction of the first enzyme necessary to return the steady state rate of the pathway to normal have been developed. This is intended as a point of departure for refinements which may ultimately lead to more accurate quantitative relationships.

Effect of Arrival Time on Stereophonic Localization

Exploratory tests under a variety of conditions were made during Bell Telephone Laboratories' studies on stereophonic reproduction to determine the effect of arrival time as a factor in localization. The recent rapid rise of interest in stereophony appears to warrant a summary of these old results (1934), for which the Laboratories has granted permission. It is shown that arrival time differences have essentially the same effect whether they are generated in the pickup room, listening room, or in the electrical system. They can therefore be made to add or compensate. Small differences (0-3 milliseconds) cause large shifts of localization angle, but the effect becomes relatively constant for larger differences. Time differences can be compensated by level differences. The results of two other experimenters are given for comparison. It is concluded that reasonable agreement is attained, considering the small total amount of data, but that more experiment is required to establish accurate quantitative relationships for practical stereophonic reproduction.

Effect of Arrival Time on Stereophonic Localization

Exploratory tests under a variety of conditions were made during Bell Telephone Laboratories' studies on stereophonic reproduction to determine the effect of arrival time as a factor in localization. The recent rapid rise of interest in stereophony appears to warrant a summary of these old results (1934), for which the Laboratories has granted permission. It is shown that arrival time differences have essentially the same effect whether they are generated in the pickup room, listening room, or in the electrical system. They can therefore be made to add or compensate. Small differences (0–3 milliseconds) cause large shifts of localization angle, but the effect becomes relatively constant for larger differences. Time differences can be compensated by level differences. The results of two other experimenters are given for comparison. It is concluded that reasonable agreement is attained, considering the small total amount of data, but that more experiment is required to establish accurate quantitative relationships for practical stereophonic reproduction.