High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys

Magnesium(Mg)alloys have shown great prospects as both structural and biomedical materials,while poor corrosion resistance limits their further application.In this work,to avoid the time-consuming and laborious experiment trial,a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics,from both the thermodynamic and kinetic perspectives.The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified.Then,the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated,and the corrosion exchange current density is further calculated by a hydrogen evolution reaction(HER)kinetic model.Several intermetallics,e.g.Y_(3)Mg,Y_(2)Mg and La_(5)Mg,are identified to be promising intermetallics which might effectively hinder the cathodic HER.Furthermore,machine learning(ML)models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function(W_(f))and weighted first ionization energy(WFIE).The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error(RMSE)of 0.11 eV.This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion,but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy,which can be extended to ternary Mg alloys or other alloy systems.

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

A survey of multi-modal learning theory

Deep multi-modal learning,a rapidly growing field with a wide range of practical applications,aims to effectively utilize and integrate information from multiple sources,known as modalities.Despite its impressive empirical performance,the theoretical foundations of deep multi-modal learning have yet to be fully explored.In this paper,we will undertake a comprehensive survey of recent developments in multi-modal learning theories,focusing on the fundamental properties that govern this field.Our goal is to provide a thorough collection of current theoretical tools for analyzing multi-modal learning,to clarify their implications for practitioners,and to suggest future directions for the establishment of a solid theoretical foundation for deep multi-modal learning.

Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software devel-opment.However,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity measures.This paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of SPL.In the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and business.Specifically,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient ecosystems.Our experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in SPLs.As digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

Machine learning with active pharmaceutical ingredient/polymer interaction mechanism:Prediction for complex phase behaviors of pharmaceuticals and formulations

The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients(APIs)with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical formulations.In this work,a developed machine-learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular descriptors.Under the few-shot learning framework,thermodynamic theory(perturbed-chain statistical associating fluid theory)was used for data augmentation,and computational chemistry was applied for molecular descriptors'screening.The results showed that the developed machine-learning model can predict the API-polymer phase diagram accurately,broaden the solubility data of APIs in polymers,and reproduce the relationship between API solubility and the interaction mechanisms between API and polymer successfully,which provided efficient guidance for the development of pharmaceutical formulations.

Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning

Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience.Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.

Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

SOME RESULTS REGARDING PARTIAL DIFFERENTIAL POLYNOMIALS AND THE UNIQUENESS OF MEROMORPHIC FUNCTIONS IN SEVERAL VARIABLES

In this paper,we mainly investigate the value distribution of meromorphic functions in Cmwith its partial differential and uniqueness problem on meromorphic functions in Cmand with its k-th total derivative sharing small functions.As an application of the value distribution result,we study the defect relation of a nonconstant solution to the partial differential equation.In particular,we give a connection between the Picard type theorem of Milliox-Hayman and the characterization of entire solutions of a partial differential equation.

The Quantum Chromodynamics Gas Density Drop and the General Theory of Relativity Ether

β decay is one of the most fundamental and thoroughly studied nuclear decay. Surprisingly, the β decay rates were found to have a periodic time variability [1]. However, others argued that there is no evidence for such cyclic deviation from the exponential first order kinetics decay law [2]. Here we propose that the β decay is a pseudo-first order exchange reaction triggered by uddũexotic mesons and propose a QCD gas theory. In analogy to the atmospheric gas density, the proposed QCD gas density drops with elevation from the sun. Accordingly, we propose that the β decay rate periodic variability is due to the pseudo-first order exchange reaction kinetics and the QCD gas atmospheric density drop. The proposed QCD gas may be a possible candidate for Einstein’s general theory of relativity ether [3]. Our main results are the derived formulas for calculating the effective mass of the QCD gas and the cosmology perfect fluid equation of state dimensionless parameter, based on the measured ratio of the β decay rates at the earth trajectory aphelion and perihelion dates. .

The Influences of Affective Variables on and Implications for English Teaching and Learning

Admittedly cognitive variables such as intelligence and aptitude exert great impact on English learning. Affective variables, however, are of intense importance in determining English learning as well, because affect is a starting machine that sets the learning mechanism in motion and learning will run into difficulty if affect does not work properly. Besides, there is mounting interest in exploring the affective domain. Therefore, this paper focuses upon the analyses of four affective variables(attitude, motivation, self-esteem and anxiety) that have bearings on English learning and sets forth Implications for English teaching.

Solutions to Rural College Students' Psychological Plight in Career Choice Based on Social Learning Theory:A Case Study of Northwest A&F University

The rural college students are facing psychological plight in their career choice.The social learning theory can use the triadic theory of learning to set reasonable career choice goals,the observational learning theory can be employed to establish a correct outlook on career choice,and the self-efficacy theory can be adopted to make up for the deficiencies in career choice.

On the Correlations among Affective Variables,Learning Strategies and Language Proficiency

Affective variables and learning strategies are two important factors that influence language proficiency. In the investigation conducted by the author,there is no sure causal connection being found among the three factors. Further,affective variables seems prevail over learning strategies of the influence on language proficiency and it is the primary element in language study process.

Efficiency of Managers as Role Models:A Social Learning Theory Perspective

This systematic literature review aimed to analyze and synthesize studies that indicated the importance of behavioral observation in the organizational context.Based on Social Learning Theory and by considering relevant recent findings and theories,the impact of managers as role models for employees is researched and analyzed.The importance of this topic is to determine ways that learning and enhancing performance in the workplace can be applied for people management development.The literature for theory was numerous,however studies on the particular topic were limited and not expanded in the organizational context.The key message of this review is that the impact of managers and leaders can be positive and progressive both for the employees and for the organization.

An Experimental Study of Flipped Classroom English Teaching Based on Autonomous Learning Theory

In order to improve students' English autonomous learning abilities, the study applied the flipped classroom teaching model based on autonomous learning theory. According to the experimental data analysis and the survey, it is found that flipped classroom teaching plays a more positive role in cultivating students' autonomous learning ability, improving their comprehensive English proficiency, and building their confidence and interest in college English learning.

The Application of Sociolinguistics Theory in Language Learning and Teaching

It is universally acknowledged that language is used for the purposes of communication. However, in traditional class language learning is treated as a process of acquiring knowledge, ignoring the most significant goal of language acquisition. Considering this situation, this paper mainly focuses on the theory which has shed some new light on language teaching and learning.In the beginning, the significance of the socio-cultural theory in language teaching will be introduced and then comes the main characteristics, finally, the implication of it on language learning and teaching.

Call for papers Journal of Control Theory and Applications Special issue on Approximate dynamic programming and reinforcement learning

Approximate dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain and nonconvex environments over time.

Fe基MOFs及其复合材料自组装与性能研究进展

金属-有机骨架(metal-organic frameworks, MOFs)是一种通过有机配体连接的新兴多孔材料,在气体储存、药物运输、催化和化学传感等方面存在广泛的应用前景。概述了基于密度泛函理论(density functional theory, DFT)与机器学习(machine learning, ML)相结合预测与设计Fe基MOFs的最新研究进展,详细描述了当前主要的Fe基MOFs材料的合成方法,指出了该类材料的晶体结构及配位环境特点。通过将纳米粉体与Fe基MOFs材料相结合的方式对Fe基复合材料的合成方法进行概括。总结了Fe-MOFs及其复合材料在电催化固氮、吸附、导电、催化等性能的应用,并指出了当前Fe-MOFs及其复合材料在发展中存在的不足。最后,对Fe基MOFs及其复合材料进行总结与展望。

On the Context Vein of“Competence”and Its Generating Mecha­nism-based on the Perspective of Situational Learning Theory

“Key competence”has become a hot vocabulary in educational reform in recent years.However,the essential connotation and specific generation mechanism of“competence”as its basic concept are still far from clear,and often controversial due to vague expressions.The rise of the concept of“key competence”undoubtedly originated in the West,but in the context of Chinese,its meaning has changed significantly.By analyzing the origin and evolution of the concept of“competence”in the Western social context and the concept from“quality”to“competence”in the Chinese social context,we can deeply understand the essence of the concept of“competence”and further clarify the specific generation mechanism of“competence”and its relationship with education.Analyzing the formation of competence based on situational learning theory emphasizes the occurrence of competence through participation in situational activities,the development of learning courses and identity consultation,which can provide some inspiration for the formation of competence.

A Complexity Theory Perspective on the Dynamics of Second Language Learning Strategies and the Theoretical Implications

This paper presents a qualitative study to investigate the dynamics in second language(L2)learning strategies under the guidance of the complexity theory.A group of Chinese undergraduate students studying at an international university in Thailand were selected as the research participants.Research instruments include interviews,observations,records of participants’on-line chat and posts,and a research journal.The research findings indicate that the changes in the participants’strategies for learning English exhibit typical features of the complex system.The study will provide implications for probing into the nature of L2 strategy and for applying complexity theory to future researches on L2 strategies.