Thermal conductivity of GeTe crystals based on machine learning potentials

GeTe has attracted extensive research interest for thermoelectric applications.In this paper,we first train a neuroevolution potential(NEP)based on a dataset constructed by ab initio molecular dynamics,with the Gaussian approximation potential(GAP)as a reference.The phonon density of states is then calculated by two machine learning potentials and compared with density functional theory results,with the GAP potential having higher accuracy.Next,the thermal conductivity of a GeTe crystal at 300 K is calculated by the equilibrium molecular dynamics method using both machine learning potentials,and both of them are in good agreement with the experimental results;however,the calculation speed when using the NEP potential is about 500 times faster than when using the GAP potential.Finally,the lattice thermal conductivity in the range of 300 K-600 K is calculated using the NEP potential.The lattice thermal conductivity decreases as the temperature increases due to the phonon anharmonic effect.This study provides a theoretical tool for the study of the thermal conductivity of GeTe.

Prediction of damage potential in mainshock–aftershock sequences using machine learning algorithms

Assessing the potential damage caused by earthquakes is crucial for a community’s emergency response.In this study,four machine learning(ML)methods—random forest,extremely randomized trees,AdaBoost(AB),and gradient boosting(GB)—were employed to develop prediction models for the damage potential of the mainshock(DIMS)and mainshock–aftershock sequences(DIMA).Building structures were modeled using eight single-degree-of-freedom(SDOF)systems with different hysteretic rules.A set of 662 recorded mainshock–aftershock(MS-AS)ground motions was selected from the PEER database.Seven intensity measures(IMs)were chosen to represent the characteristics of the mainshock and aftershock.The results revealed that the selected ML methods can well predict the structural damage potential of the SDOF systems,except for the AB method.The GB model exhibited the best performance,making it the recommended choice for predicting DIMS and DIMA among the four ML models.Additionally,the impact of input variables in the prediction was investigated using the shapley additive explanations(SHAP)method.The high-correlation variables were sensitive to the structural period(T).At T=1.0 s,the mainshock peak ground velocity(PGVM)and aftershock peak ground displacement(PGDA)significantly influenced the prediction of DIMA.When T increased to 5.0 s,the primary high-correlation factor of the mainshock IMs changed from PGVM to the mainshock peak ground displacement(PGDM);however,the highcorrelation variable of the aftershock IMs remained PGDA.The high-correlation factors for DIMS showed trends similar to those of DIMA.Finally,a table summarizing the first and second high-correlation variables for predicting DIMS and DIMA were provided,offering a valuable reference for parameter selection in seismic damage prediction for mainshock–aftershock sequences.

Properties of radiation defects and threshold energy of displacement in zirconium hydride obtained by new deep-learning potential

Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH_(2).Molecular dynamics(MD) and ab initio molecular dynamics(AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform largescale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH_(2) system by using the deep-potential(DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH_(2) system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark(ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε-ZrH_(2).

Geometries and electronic structures of Zr_(n)Cu(n=2–12) clusters: A joint machine-learning potential density functional theory investigation

Zr-based amorphous alloys have attracted extensive attention because of their large glassy formation ability, wide supercooled liquid region, high elasticity, and unique mechanical strength induced by their icosahedral local structures.To determine the microstructures of Zr–Cu clusters, the stable and metastable geometry of Zr_(n)Cu(n=2–12) clusters are screened out via the CALYPSO method using machine-learning potentials, and then the electronic structures are investigated using density functional theory. The results show that the Zr_(n)Cu(n ≥ 3) clusters possess three-dimensional geometries, Zr_(n)Cu(n≥9) possess cage-like geometries, and the Zr_(12)Cu cluster has icosahedral geometry. The binding energy per atom gradually gets enlarged with the increase in the size of the clusters, and Zr_(n)Cu(n=5,7,9,12) have relatively better stability than their neighbors. The magnetic moment of most Zr_(n)Cu clusters is just 1μB, and the main components of the highest occupied molecular orbitals(HOMOs) in the Zr_(12)Cu cluster come from the Zr-d state. There are hardly any localized two-center bonds, and there are about 20 σ-type delocalized three-center bonds.

The Nature and Use of Technology-Based Self-Regulated Learning Strategies Among EFL Students

This study explored the nature and use of technology-based self-regulated learning(SRL)strategies among the Chinese university students.A total of 20 undergraduate students in China's Mainland were invited to participate in a focus group interview.The students reported using four types of technology-based SRL strategies including cognitive,meta-cognitive,social behavioral,and motivational regulation strategies.Among the strategies,technology-based vocabulary learning was reported to be a dominant strategy by the students.This study opens a new window to understanding how English as a foreign language(EFL)students utilize different strategies to learn English in technology-based learning context.

Solution to reinforcement learning problems with artificial potential field

A novel method was designed to solve reinforcement learning problems with artificial potential field.Firstly a reinforcement learning problem was transferred to a path planning problem by using artificial potential field(APF),which was a very appropriate method to model a reinforcement learning problem.Secondly,a new APF algorithm was proposed to overcome the local minimum problem in the potential field methods with a virtual water-flow concept.The performance of this new method was tested by a gridworld problem named as key and door maze.The experimental results show that within 45 trials,good and deterministic policies are found in almost all simulations.In comparison with WIERING's HQ-learning system which needs 20 000 trials for stable solution,the proposed new method can obtain optimal and stable policy far more quickly than HQ-learning.Therefore,the new method is simple and effective to give an optimal solution to the reinforcement learning problem.

Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam

The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like Vietnam.In this article,we proposed new machine learning ensemble techniques namely AdaBoost ensemble(ABLWL),Bagging ensemble(BLWL),Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL)with Locally Weighted Learning(LWL)algorithm as a base classifier to build the groundwater potential map of Gia Lai province in Vietnam.For this study,eleven conditioning factors(aspect,altitude,curvature,slope,Stream Transport Index(STI),Topographic Wetness Index(TWI),soil,geology,river density,rainfall,land-use)and 134 wells yield data was used to create training(70%)and testing(30%)datasets for the development and validation of the models.Several statistical indices were used namely Positive Predictive Value(PPV),Negative Predictive Value(NPV),Sensitivity(SST),Specificity(SPF),Accuracy(ACC),Kappa,and Receiver Operating Characteristics(ROC)curve to validate and compare performance of models.Results show that performance of all the models is good to very good(AUC:0.75 to 0.829)but the ABLWL model with AUC=0.89 is the best.All the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.

Application of deep learning to estimate stratospheric gravity wave potential energy

One of the most important dynamic processes in the middle and upper atmosphere,gravity waves(GWs)play a key role in determining global atmospheric circulation.Gravity wave potential energy(GW Ep)is an important parameter that characterizes GW intensity,so it is critical to understand its global distribution.In this paper,a deep learning algorithm(DeepLab V3+)is used to estimate the stratospheric GW Ep.The deep learning model inputs are ERA5 reanalysis datasets and GMTED2010 terrain data.GW Ep averaged over 20−30 km from 60°S−60°N,calculated by COSMIC radio occultation(RO)data,is used as the measured value corresponding to the model output.The results show that(1)this method can effectively estimate the zonal trend of GW Ep.However,the errors between the estimated and measured value of Ep are larger in low-latitude regions than in mid-latitude regions,possibly due to the large number of convolution operations used in the deep learning model.Additionally,the measured Ep has errors associated with interpolation to the grid;this tends to be amplified in low-latitude regions because the GW Ep is larger and the RO data are relatively sparse,affecting the training accuracy.(2)The estimated Ep shows seasonal variations,which are stronger in the winter hemisphere and weaker in the summer hemisphere.(3)The effect of quasi-biennial oscillation(QBO)can be clearly observed in the monthly variation of estimated GW Ep,and its QBO amplitude may be less than that of the measured Ep.

Multi-step Reinforcement Learning Algorithm of Mobile Robot Path Planning Based on Virtual Potential Field

A algorithm of dynamic multi-step reinforcement learning based on virtual potential field path planning is proposed in this paper. Firstly, it is constructed the virtual potential field according to the known information. And then in view of Q learning algorithm of the QekT algorithm, a multi-step reinforcement learning algorithm is proposed in this paper. It can update current Q value used of future dynamic k steps according to the current environment status. At the same time, the convergence is analyzed. Finally the simulation experiments are done. It shows that the proposed algorithm and convergence and so on are more efficiency than similar algorithms.

Assessing learning potential in people with schizophrenia using the Rey Osterrieth Complex Figure Test

A growing literature indicates that learning potential (LP) measures, which examine performance changes following training on a task, may be important for understanding the role of cognition in functional outcome among people with schizophrenia and other serious mental illnesses. Because much of what is known about LP in this population has been demonstrated using the Wisconsin Card Sorting Test, the present study sought to extend this work by administering the Rey Osterrieth Complex Figure Test (ROCFT) in an LP format. 81 adults with schizophrenia or schizoaffective disorder were tested on the ROCFT using a test-train-test LP protocol. Results indicated significant performance improvements following training on the ROCFT. Further, the LP protocol differentiated subgroups of learners, non-learners, and high scorers, consistent with other LP work. These findings support the feasibility of adapting existing neurocognitive measures to examine learning potential. Further development of the LP literature is needed in order to examine the extent to which LP is test-dependent or is a more generalized construct.

Machine learning potential aided structure search for low-lying candidates of Au clusters

A machine learning(ML)potential for Au clusters is developed through training on a dataset including several different sized clusters.This ML potential accurately covers the whole configuration space of Au clusters in a broad size range,thus expressing a good performance in search of their global minimum energy structures.Based on our potential,the low-lying structures of 17 different sized Au clusters are identified,which shows that small sized Au clusters tend to form planar structures while large ones are more likely to be stereo,revealing the critical size for the two-dimensional(2D)to three-dimensional(3D)structural transition.Our calculations demonstrate that ML is indeed powerful in describing the interaction of Au atoms and provides a new paradigm on accelerating the search of structures.

Atomistic Modeling of Lithium Materials from Deep Learning Potential with Ab Initio Accuracy

Lithium has been paid great attention in recent years thanks to its significant appli-cations for battery and lightweight alloy.Developing a potential model with high ac-curacy and efficiency is impor-tant for theoretical simulation of lithium materials.Here,we build a deep learning potential(DP)for elemental lithium based on a concurrent-learning scheme and DP representation of the density-functional theory(DFT)potential energy surface(PES),the DP model enables material simulations with close-to DFT accuracy but at much lower computational cost.The simulations show that basic parameters,equation of states,elasticity,defects and surface are consistent with the first principles results.More notably,the liquid radial distribution func-tion based on our DP model is found to match well with experiment data.Our results demon-strate that the developed DP model can be used for the simulation of lithium materials.

Application of machine learning to the Vs-based soil liquefaction potential assessment

Earthquakes can cause violent liquefaction of the soil, resulting in unstable foundations that can cause serious damage to facilities such as buildings, roads, and dikes. This is a primary cause of major earthquake disasters. Therefore, the discrimination and prediction of earthquake-induced soil liquefaction has been a hot issue in geohazard research. The soil liquefaction assessment is an integral part of engineering practice. This paper evaluated a dataset of 435 seismic sand liquefaction events using machine learning algorithms. The dataset was analyzed using seven potential assessment parameters. Ten machine learning algorithms are evaluated for their ability to assess seismic sand liquefaction potential, including Linear Discriminant Analysis(LDA), Quadratic Discriminant Analysis(QDA), Naive Bayes(NB), KNearest Neighbor(KNN), Artificial Neural Network(ANN), Classification Tree(CT), Support Vector Machine(SVM), Random Forest(RF), e Xtreme Gradient Boosting(XGBoost), Light Gradient Boosting Machine(Light GBM). A 10-fold cross-validation(CV) method was used in the modeling process to verify the predictive performance of the machine learning models. The final percentages of significant parameters that influenced the prediction results were obtained as Cyclic Stress Ratio(CSR) and Shear-Wave Velocity( VS1) with 56% and 38%, respectively. The final machine learning algorithms identified as suitable for seismic sand liquefaction assessment were the CT, RF, XGBoost algorithms, with the RF algorithm performing best.

The Role of Self-regulation in Blended Learning

Self-regulation is crucial to learners’learning outcomes in a blended education context.This paper first discusses its definitions and importance,then explores factors affecting self-regulation,and finally puts forward several ways to improve learners’self-regulation.

The Effectiveness of Self-regulated Learning Strategies on Chinese College Students' English Learning

The purpose of this paper is to argue the effectiveness of self-regulated learning in English education in Chinese college classroom instruction. A study is given to show whether the introduction of self-regulated learning can help improve Chinese college students' English learning, and help them perform better in the National English test-CET-4 (College English Test Level-4,).

Effects of Chronic Administration of Melatonin on Spatial Learning Ability and Long-term Potentiation in Lead-exposed and Control Rats

Objective To explore the changes in spatial learning performance and long-term potentiation （LTP） which is recognized as a component of the cellular basis of learning and memory in normal and lead-exposed rats after administration of melatonin （MT） for two months. Methods Experiment was performed in adult male Wistar rats （12 controls, 12 exposed to melatonin treatment, 10 exposed to lead and 10 exposed to lead and melatonin treatment）. The lead-exposed rats received 0.2% lead acetate solution from their birth day while the control rats drank tap water. Melatonin （3 mg/kg） or vehicle was administered to the control and lead-exposed rats from the time of their weaning by gastric gavage each day for 60 days, depending on their groups. At the age of 81-90 days, all the animals were subjected to Morris water maze test and then used for extracellular recording of LTP in the dentate gyrus （DG） area of the hippocampus in vivo. Results Low dose of melatonin given from weaning for two months impaired LTP in the DG area of hippocampus and induced learning and memory deficit in the control rats. When melatonin was administered over a prolonged period to the lead-exposed rats, it exacerbated LTP impairment, learning and memory deficit induced by lead. Conclusion Melatonin is not suitable for normal and lead-exposed children.

Rapid detection and risk assessment of soil contamination at lead smelting site based on machine learning

A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R^(2))values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.

基于改进Q-learning算法的移动机器人路径规划

针对传统Q-learning算法应用在路径规划中存在收敛速度慢、运行时间长、学习效率差等问题,提出一种将人工势场法和传统Q-learning算法结合的改进Q-learning算法。该算法引入人工势场法的引力函数与斥力函数,通过对比引力函数动态选择奖励值,以及对比斥力函数计算姿值,动态更新Q值,使移动机器人具有目的性的探索,并且优先选择离障碍物较远的位置移动。通过仿真实验证明,与传统Q-learning算法、引入引力场算法对比,改进Q-learning算法加快了收敛速度,缩短了运行时间,提高了学习效率,降低了与障碍物相撞的概率,使移动机器人能够快速地找到一条无碰撞通路。

Correlating learning and memory improvements to long-term potentiation in patients with brain injury

BACKGROUND： Brain injury patients often exhibit learning and memory functional deficits. Long-term potentiation （LTP） is a representative index for studying learning and memory cellular models; the LTP index correlates to neural plasticity. OBJECTIVE： This study was designed to investigate correlations of learning and memory functions to LTP in brain injury patients, and to summarize the research advancements in mechanisms underlying brain functional improvements after rehabilitation intervention. RETRIEVAL STRATEGY： Using the terms ＂brain injuries, rehabilitation, learning and memory, long-term potentiation＂, manuscripts that were published from 2000-2007 were retrieved from the PubMed database. At the same time, manuscripts published from 2000-2007 were also retrieved from the Database of Chinese Scientific and Technical Periodicals with the same terms in the Chinese language. A total of 64 manuscripts were obtained and primarily screened. Inclusion criteria： studies on learning and memory, as well as LTP in brain injury patients, and studies focused on the effects of rehabilitation intervention on the two indices; studies that were recently published or in high-impact journals. Exclusion criteria： repetitive studies. LITERATURE EVALUATION： The included manuscripts primarily focused on correlations between learning and memory and LTP, the effects of brain injury on learning and memory, as well as LTP, and the effects of rehabilitation intervention on learning and memory after brain injury. The included 39 manuscripts were clinical, basic experimental, or review studies. DATA SYNTHESIS： Learning and memory closely correlates to LTP. The neurobiological basis of learning and memory is central nervous system plasticity, which involves neural networks, neural circuits, and synaptic connections, in particular, synaptic plasticity. LTP is considered to be an ideal model for studying synaptic plasticity, and it is also a classic model for studying neural plasticity of learning and memory. Brain injury patients clinically present with various manifestations, such as paralysis and sensory disability, which closely correlate to injured regions. In addition, learning and memory abilities decrease in brain injury patients and LTP decreases following brain injury. Brain tissue injury will lead to brain functional deficits. Hippocampal LTP is very sensitive. Difficulties in LTP induction are apparent even prior to morphological changes in brain tissue. There are no specific treatments for learning and memory functional deficits following brain injury. At present, behavioral and compensative therapies are the typical forms of rehabilitation. These results indicate that rehabilitation promotes learning and memory functional recovery in brain injury patients by speeding up LTP formation in the hippocampal CA3 region. CONCLUSION： Rehabilitation intervention increases LTP formation in the hippocampal CA3 region and recovers learning and memory functions in brain injury patients.

The Model of Speaking in Teaching Indonesian to Foreign Speakers Based on Self-Regulated Learning and Anxiety Reduction Approaches

Model for spoken is expected to overcome difficulties in teaching and learning Indonesian language for foreign speakers. Language anxiety is the anxiety that arises when a person learns foreign language. Foreign Language Anxiety （anxiety to learn a foreign language） is of concern or negative emotional reactions that arise when studying or using foreign language. Self-regulated learning is an active and constructive process undertaken by learners in setting goals for their learning and trying to monitor, regulate, and control of cognition, motivation, and behavior, then everything is directed and driven by purpose and adapted to the context and environment. The research method used is an R and D （research and development） method with a sample of foreign speakers of Chinese. Variables that receive interference are the ability to speak in Indonesian, while the variables used to interfere with the self-regulated learning and language anxiety as a variable controller. Intrapersonal factors become barriers that cause stuttering speech limited due to the mastering subject content. On the basis of that, this speaking model applies the principle of self-regulated learning in the learning process, using a communicative and contextual approach. This model intended for foreign speakers who learn Indonesian language outside of Indonesia, so to bring the atmosphere mandated in sociolinguistic built through media and relevant teaching methods.