Research on the Relationship Between Learning Motivation and Neural Activity in the Learning Process of Instructional Video:A NIRS Study

As the intrinsic driving force to promote learner’s learning,learning motivation is one of the key factors that affect learning engagement and efficiency.In terms of optimizing instructional videos and strengthening learning effects,it is particularly important to understand the cognitive neural mechanism and influencing factors of the changes of learning motivation.By using the near-infrared spectrometer technology,the paper has collected the state of neural activity while learners were learning different instructional videos,and has analyzed the relationship between the learning motivation of instructional videos and the state of neural activity in the learning process from the angle of cognitive neuroscience.It is found that both the intrinsic and extrinsic learning motivation of instructional videos will affect the state of neural activity in the learning process;the learning process will also affect the intensity of learning motivation,while the preparation of fine instructional videos will also cause the transfer of learning motivation.

Impacts of Online Remote Education on the Learning Process among Nursing Students

The period of existence and spread of Corona virus has led to the use of all means of remote education as an urgent necessity for all educational facilities, especially universities. Aim: Therefore, it was necessary to study the impacts of online remote education on the learning process among nursing students through studying of two courses;health information management at 6th semester and gerontology nursing course at 4th semester. Tool of Data Collection: A modified questionnaire comprised of forty statements was used through paper-based survey and online survey. Sample: A total samples (224) of nursing students were participated in the survey who enrolled in 2019 and 2020 spring semesters. Setting: The field of study was the nursing department of Applied Medical Science at Misr University for Science and Technology. Results: Induced positive impacts of online education on the learning process for nursing students experience were proven as more than half of the students (53.9%) had prior experience on online system use, and more than two thirds (62.5%) were competent in mobile/computer applications. Almost, two thirds (59.3%) agreed about online assessment experience, except that the online exam was anxious, and the time was insufficient to answer all questions. Also, more than two thirds (64.7%) agreed about the learning process of the two nursing courses. Conclusion: The study concluded that there were positive impacts of online education system on the learning process for nursing students except that the students were not able to decide that the remote online education system can replace traditional face-to-face learning as the clinical experience was not evaluated through this study. Recommendation: This study is recommended to be repeated on a large scale of participants to assess the possibility of achieving clinical experience through online remote education if Corona virus still coexists.

Mathematical Debates as an Integral Part of the Learning Process

In this report, we want to touch upon two aspects of teaching mathematics in middle and high school. The first of them is the eternal question of how to teach, to motivate students and make them involved in the educational process, particularly in mathematics, where the most important factor is the natural gifts. The second aspect concerns the question which became very urgent in the modern world： what, in fact, we want to teach the students in a world over-saturated with information of any kind. As a result of the information blowup, two aspects emerge. On the one hand, straight passing over the skills and knowledge to the students becomes irrelevant today （just like a teacher or lecturer, merely speaking to an audience, who is not that relevant for young people, accustomed from childhood to perceive information through dynamic color visuals）. On the other hand, there is a change in emphasis in the objective function of the educational process from gaining knowledge to acquisition of skills of working with information, consideration and estimation, and choosing of the optimal strategy of a number of possibilities. This trend can be seen in the selection of problems in the international examination PISA （Program for international Student Assessment）, in the new curriculum in mathematics and in the selection of problems in the matriculation exams. These considerations （along with others） make teachers took for rtew forms of teaming, more appropriate to the demands of modernity. In this report we suggest the idea of using a mathematical competition called ＂Mathematical debate＂ （mathematical fight） as an integral part of the educational process at different levels of learning mathematics, as an appropriate tool.

The Learning Process： A Tourist Visitor to the National Museum

This paper aims to develop an understanding of the undergraduate students＇ learning by applying Kolb＇s （1984） learning concepts and theories. This research aims： （1） to study the visitors＇ learning experience; （2） to compare the learning process of visitors; and （3） to design learning process development for the visitors of the Phra Pathom Chedi National Museum. The quantitative methodology was used for data collection. The population was focused on group samplings of 300 participants and the selection method was a non-probability and purposive sampling. The research instrument was the structured questionnaire. Descriptive statistics, T-test, F-test （one-way analysis of variance （ANOVA））, and regression analysis were used for data analysis. According to the first objective, the study revealed that most of visitors were female, at the age of 19 years old, had a bachelor degree, and had income less than 5,000 baht. Their learning levels at the Phra Pathom Chedi National Museum were high. According to the second objective, the study found that there was no correlation between gender and income to the visitors＇ learning process related to the theoretical four learning processes which are： （1） before learning; （2） learning behavior; （3） while learning; and （4） the best ways of learning that create the most understanding. However, age and education varied the level of visitors＇ leaming process. According to the third objective regarding the four models of learning process development design, the study presented that： （1） For the accommodators, the visitors should be male, at a young age, and have a bachelor degree; （2） For the divergers, the visitors should be at a young age and have a bachelor degree; （3） For the convergers, the visitors should be at a young age, have a bachelor degree, and not with high income; and （4） For the assimilators, the visitors should be at a young age, have a bachelor degree, and with high income.

A Survey on College Students' English Learning Motivation and the Learning Results

This survey has been processed on 78 graduate students in grade 2010, majoring in science and engineer, from different classes. By analyzing the results of College English Test 4 and questionnaire, it shows that the students' English learning motivation roots in Certificate. Differences between the winners and losers are not reflected in the time they have used to study, but the learning motivation they possess.

Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.

Reliable calculations of nuclear binding energies by the Gaussian process of machine learning

Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process. First, the binding energies for 2238 nuclei with Z > 20 and N > 20 are calculated using the Gaussian process in a physically motivated feature space, yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV. The results show the good learning ability of the Gaussian process in the studies of binding energies. Then, the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020. The theoretical results are in good agreement with the experimental data, reflecting the good predictive power of the Gaussian process. Moreover, the α-decay energies for 1169 nuclei with 50 ≤ Z ≤ 110 are derived from the theoretical binding energies calculated using the Gaussian process. The average deviation and the standard deviation are, respectively, 0.047 MeV and 0.070 MeV. Noticeably, the calculated α-decay energies for the two new isotopes ^ (204 )Ac(Huang et al. Phys Lett B 834, 137484(2022)) and ^ (207) Th(Yang et al. Phys Rev C 105, L051302(2022)) agree well with the latest experimental data. These results demonstrate that the Gaussian process is reliable for the calculations of nuclear binding energies. Finally, the α-decay properties of some unknown actinide nuclei are predicted using the Gaussian process. The predicted results can be useful guides for future research on binding energies and α-decay properties.

Reinforcement Learning in Process Industries:Review and Perspective

This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.

Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods

The aim of this work is to predict,for the first time,the high temperature flow stress dependency with the grain size and the underlaid deformation mechanism using two machine learning models,random forest(RF)and artificial neural network(ANN).With that purpose,a ZK30 magnesium alloy was friction stir processed(FSP)using three different severe conditions to obtain fine grain microstructures(with average grain sizes between 2 and 3μm)prone to extensive superplastic response.The three friction stir processed samples clearly deformed by grain boundary sliding(GBS)deformation mechanism at high temperatures.The maximum elongations to failure,well over 400% at high strain rate of 10^(-2)s^(-1),were reached at 400℃ in the material with coarsest grain size of 2.8μm,and at 300℃ for the finest grain size of 2μm.Nevertheless,the superplastic response decreased at 350℃ and 400℃ due to thermal instabilities and grain coarsening,which makes it difficult to assess the operative deformation mechanism at such temperatures.This work highlights that the machine learning models considered,especially the ANN model with higher accuracy in predicting flow stress values,allow determining adequately the superplastic creep behavior including other possible grain size scenarios.

Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.

State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

Transfer learning framework for multi-scale crack type classification with sparse microseismic networks

Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.

Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in women.Early detection and effective treatment of BC can help save women’s lives.Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques.This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data set.The novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated dataset.The analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.

Combining reinforcement learning with mathematical programming:An approach for optimal design of heat exchanger networks

Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.

Machine learning-driven optimization of plasma-catalytic dry reforming of methane

This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data.To address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML model.The ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM process.The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance.We found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/min.Furthermore,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM process.The results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction performance.This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

Recorded recurrent deep reinforcement learning guidance laws for intercepting endoatmospheric maneuvering missiles

This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation noise.The attack-defense engagement scenario is modeled as a partially observable Markov decision process(POMDP).Given the benefits of recurrent neural networks(RNNs)in processing sequence information,an RNN layer is incorporated into the agent’s policy network to alleviate the bottleneck of traditional deep reinforcement learning methods while dealing with POMDPs.The measurements from the interceptor’s seeker during each guidance cycle are combined into one sequence as the input to the policy network since the detection frequency of an interceptor is usually higher than its guidance frequency.During training,the hidden states of the RNN layer in the policy network are recorded to overcome the partially observable problem that this RNN layer causes inside the agent.The training curves show that the proposed RRTD3 successfully enhances data efficiency,training speed,and training stability.The test results confirm the advantages of the RRTD3-based guidance laws over some conventional guidance laws.

Deep Reinforcement Learning for Energy-Efficient Edge Caching in Mobile Edge Networks

Edge caching has emerged as a promising application paradigm in 5G networks,and by building edge networks to cache content,it can alleviate the traffic load brought about by the rapid growth of Internet of Things(IoT)services and applications.Due to the limitations of Edge Servers(ESs)and a large number of user demands,how to make the decision and utilize the resources of ESs are significant.In this paper,we aim to minimize the total system energy consumption in a heterogeneous network and formulate the content caching optimization problem as a Mixed Integer Non-Linear Programming(MINLP).To address the optimization problem,a Deep Q-Network(DQN)-based method is proposed to improve the overall performance of the system and reduce the backhaul traffic load.In addition,the DQN-based method can effectively solve the limitation of traditional reinforcement learning(RL)in complex scenarios.Simulation results show that the proposed DQN-based method can greatly outperform other benchmark methods,and significantly improve the cache hit rate and reduce the total system energy consumption in different scenarios.