Unrelated Parallel-Machine Scheduling Problems with General Truncated Job-Dependent Learning Effect

In this paper, we consider scheduling problems with general truncated job-dependent learning effect on unrelated parallel-machine. The objective functions are to minimize total machine load, total completion (waiting) time, total absolute differences in completion (waiting) times respectively. If the number of machines is fixed, these problems can be solved in time respectively, where m is the number of machines and n is the number of jobs.

Mitigating Straggler Effect in Federated Learning Based on Reconfigurable Intelligent Surface over Internet of Vehicles

To protect vehicular privacy and speed up the execution of tasks,federated learning is introduced in the Internet of Vehicles(IoV)where users execute model training locally and upload local models to the base station without massive raw data exchange.However,heterogeneous computing and communication resources of vehicles cause straggler effect which weakens the reliability of federated learning.Dropping out vehicles with limited resources confines the training data.As a result,the accuracy and applicability of federated learning models will be reduced.To mitigate the straggler effect and improve performance of federated learning,we propose a reconfigurable intelligent surface(RIS)-assisted federated learning framework to enhance the communication reliability for parameter transmission in the IoV.Furthermore,we optimize the phase shift of RIS to achieve a more reliable communication environment.In addition,we define vehicular competence to measure both vehicular trustworthiness and resources.Based on the vehicular competence,the straggler effect is mitigated where training tasks of computing stragglers are offloaded to surrounding vehicles with high competence.The experiment results verify that our proposed framework can improve the reliability of federated learning in terms of computing and communication in the IoV.

A Study on the Relationship between English Learning Motivation and Learning Effect

In order to explore the relationship between college students’English learning motivation and learning effect,this paper uses a questionnaire survey to investigate and analyze the English classroom learning motivation of first-year non-English majors.Based on the final English score,the paper analyzes the correlation between English learning motivation and learning effect.

Statistical Analysis of Students’Learning Effectiveness in Online Courses Offered by British Partners in China-UK Joint Education Program

In the context of internationalization,China-UK Joint Education Programs are receiving increasing attention from universities.Based on the difficulties faced in China-UK Joint Education Program,this paper adopts a questionnaire survey method to study the learning effectiveness of students majoring in digital media technology in the China-UK Joint Education Program at Guangxi University of Finance and Economics,focusing on four aspects:learning materials,learning content,teacher conditions,and student learning outcomes.The research analysis in this paper not only provides strong support for the construction of China-UK Joint Education Program but also offers references for other China-UK Joint Education Programs.

The Effectiveness of Group Cooperative Learning Method in Badminton Teaching in Colleges and Universities

In college badminton teaching,teachers utilize the group cooperative learning method,which not only helps to improve students’badminton skill level but also cultivates their teamwork spirit,communication skills,and self-management ability unconsciously.In view of this,this paper mainly describes the significance of applying the group cooperative learning method in college badminton teaching,analyzes the current problems in college badminton teaching,and aims to discover effective development strategies for group cooperative learning method in college badminton teaching in order to improve the effectiveness of college badminton teaching.

Creating Effective Learning Contests in Children's Classroom English Teaching in China

During the past decades, the opening China saw an unprecedented desire for foreign language learning. This English fever also involves children in primary school and even in kindergarten: For children, to create a positive and vivid contest during the class is an effective way of learning, since such a contest will arouse their interest and help them understand how to use what they have learned. This paper aims at discussing methods to create effective contests for children during English classroom.

A Study on Washback Effect of CET-6 Translation Test Towards College English Teaching and Learning

CET-6 is a nationwide and standardized test to evaluate college students' English levels. Now more than 10 million college students take part in the exam every year. On August 14, 2013, the CET-4 and CET-6 examination committee reformed the structure of the CET-6 papers. After that the sentence translation was changed into paragraph translation,the scores of translation section also increased to 15 points which is more difficult and more important than before. There is no doubt that CET-6will exert a great impact on the teaching and learning of College English in China. This thesis focuses on the washback effect of CET-6 translation test towards college English teaching and learning.First of all, this paper introduces the CET-6 translation test requirements, and the score requirements of translation part. The author also illustrates some significant language testing research of both domestic and foreign scholars, and explains the concept of reliability and validity. Secondly, through the survey method of interviews and questionnaire the author study the characteristics of teaching in college classroom, the effect of translation teaching, and how do students to study translation. The author makes a further research on the interaction between the translation test and the teaching and learning based on the relevant theories. At last, the author finds that the negative washback effect of the CET-6 translation test is greater than the positive one. The author reflects on the current translation teaching and learning in China, and hopes to minimize the negative effects of CET-6 translation test.

Perceptual learning for treating amblyopia in children based on activation of visual signal pathway Relationship of curative effects and time

BACKGROUND： Conventional methods （such as occlusion therapy, fine manipulation, complementary, and alternative medicine） take effects slowly, are time and labor consuming, and have uncertain curative effects in the treatment of amblyopia. Perceptual learning, a new method for treating amblyopia, improves the ability to process signals from the cerebral optic nerve system by specific visual stimulation and visual learning, as well as activation of the visual signal pathway utilizing brain nervous system plasticity. OBJECTIVE： This study investigated and evaluated the curative effects of perceptual learning, which can directionally increase brain plasticity, on the treatment of amblyopia in children. The relationship between curative effect and time was also analyzed. DESIGN： A self-control experiment. SETTING： Visual Science and Optometry Center, People＇s Hospital of Guangxi Zhuang Autonomous Region. PARTICIPANTS： A total of 125 amblyopic children （250 amblyopic eyes）, 73 males, 52 females, averaging （6±2） years of age, received treatment at the Visual Science and Optometry Center, People＇s Hospital of Guangxi Zhuang Autonomous Region between September 2006 and February 2007 and were recruited for this study. All children presented with no structural disease of the eyeballs. Written informed consent for therapeutic regiments was obtained from each child＇s parent. The protocol received approval from the Hospital＇s Ethics Committee. METHODS： Visual function was tested with a perceptual learning system （Research Center for Human Health and Development of Sun Yat-sen University, National Engineering Technique Research Center for Medical Care Implement） for visual noise, position noise, contour discrimination, contrast sensitivity, grating stereogram, and random-dot fusion. These tests helped to evaluate the efficiency of visual information processing of these children, and to determine the degree of defects of the optic nerve cells and the connections of visual cortical neurons. According to results of visual function tests, individualized treatment was adopted for each amblyopia patient using perceptual learning system. One course of treatment lasted one month, and treatment was performed twice every day with two training procedures （each training procedure lasted for ten minutes）. There was a ten-minute time interval between the two training procedures. The training treatment was performed in a quiet and dark environment. Visual acuity and recovery of visual function were tested every month. Original training procedure was continued or adjusted according to the results of visual function. MAIN OUTCOME MEASURES： Visual function change; relationship of curative effects and curative time. RESULTS： A total of 125 amblyopia children were included in the final analysis. The total efficiency of perceptual learning for treating amblyopia in children was 75.2%. Visual acuity began to greatly increase 3 months after treatment （P 〈 0.05）. Visual acuity was best corrected from 0.60 ± 0.23 before treatment to 0.86 ± 0.26 after treatment （P 〈 0.05）. The mean time to reach improved levels with curative effects was （2.82 ± 1.30） months, and to reach a basically cured level was （2.87 ±1.40） months. Percentage of improved visual acuity was the highest [98% （39/40）] in children that received 3 months of treatment and the lowest [55% （31/56）] in children that received 1 month of treatment （P 〈 0.05）. The percentage of basically cured levels with curative effects increased with length of learning time and was the greatest in children that received 4 months of treatment [67% （31/46）, P 〈 0.05]. CONCLUSION： Perceptual learning rapidly and remarkably improves visual function of amblyopia children; however, the curative effects are first apparent two and three months after intervention.

The single-machine scheduling problems with deteriorating jobs and learning effect

In this paper we consider a single-machine scheduling model with deteriorating jobs and simultaneous learning, and we introduce polynomial solutions for single machine makespan minimization, total flow times minimization and maximum lateness minimization corresponding to the first and second special cases of our model under some agreeable conditions. However, corresponding to the third special case of our model, we show that the optimal schedules may be different from those of the classical version for the above objective functions.

Kongsheng Zhenzhong pill's effect on the learning and memory ability and its neuroprotective effects in vascular dementia rats

Clinical reports have demonstrated that the Kongsheng Zhenzhong pill （KSZZP）, a classical prescription deriving from Valuable Prescription for Emergencies, has good therapeutic effects on vascular dementia. However, the mechanisms that mediate its effects remain unclear. In this study, the expression of N-methyI-D-aspartate receptor 1 mRNA, the content of nitric oxide, and the concentration of calcium in neurons was determined with in situ hybridization, spectrophotometry and flow cytometry, respectively. In addition, the expressions of N-methyI-D-aspartate receptor 1, nerve growth factor protein, and glial cell line-derived neurotrophic factor protein were detected with immunohistochemistry. We found that KSZZP could significantly decrease the expression of N-methyI-D-aspartate receptor 1 mRNA and protein, the content of nitric oxide, and the concentration of calcium in neurons. KSZZP also increased the expression of nerve growth factor and glial cell line-derived neurotrophic factor protein in the hippocampus CA1 region and in the cerebral cortex. Morris water maze and passive avoidance tests verified that KSZZP ameliorated the cognitive impairments of vascular dementia rats. Moreover, the KSZZP-induced improvements in the cognitive functions of vascular dementia rats were correlated with both inhibition of N-methyl-D-aspartate-induced excitable neurotoxicity and elevation of neurotrophic factor expression.

Machine learning-based analyses for total ionizing dose effects in bipolar junction transistors

Machine learning methods have proven to be powerful in various research fields.In this paper,we show that research on radiation effects could benefit from such methods and present a machine learning-based scientific discovery approach.The total ionizing dose(TID)effects usually cause gain degradation of bipolar junction transistors(BJTs),leading to functional failures of bipolar integrated circuits.Currently,many experiments of TID effects on BJTs have been conducted at different laboratories worldwide,producing a large amount of experimental data which provides a wealth of information.However,it is difficult to utilize these data effectively.In this study,we proposed a new artificial neural network(ANN)approach to analyze the experimental data of TID effects on BJTs An ANN model was built and trained using data collected from different experiments.The results indicate that the proposed ANN model has advantages in capturing nonlinear correlations and predicting the data.The trained ANN model suggests that the TID hardness of a BJT tends to increase with base current I.A possible cause for this finding was analyzed and confirmed through irradiation experiments.

Micropillar compression using discrete dislocation dynamics and machine learning

Discrete dislocation dynamics(DDD)simulations reveal the evolution of dislocation structures and the interaction of dislocations.This study investigated the compression behavior of single-crystal copper micropillars using fewshot machine learning with data provided by DDD simulations.Two types of features are considered:external features comprising specimen size and loading orientation and internal features involving dislocation source length,Schmid factor,the orientation of the most easily activated dislocations and their distance from the free boundary.The yielding stress and stress-strain curves of single-crystal copper micropillar are predicted well by incorporating both external and internal features of the sample as separate or combined inputs.It is found that the machine learning accuracy predictions for single-crystal micropillar compression can be improved by incorporating easily activated dislocation features with external features.However,the effect of easily activated dislocation on yielding is less important compared to the effects of specimen size and Schmid factor which includes information of orientation but becomes more evident in small-sized micropillars.Overall,incorporating internal features,especially the information of most easily activated dislocations,improves predictive capabilities across diverse sample sizes and orientations.

A hybrid machine learning optimization algorithm for multivariable pore pressure prediction

Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when forming particular structures and lithology.In this paper,a machine learning algorithm and effective stress theorem are used to establish the transformation model between rock physical parameters and pore pressure.This study collects data from three wells.Well 1 had 881 data sets for model training,and Wells 2 and 3 had 538 and 464 data sets for model testing.In this paper,support vector machine(SVM),random forest(RF),extreme gradient boosting(XGB),and multilayer perceptron(MLP)are selected as the machine learning algorithms for pore pressure modeling.In addition,this paper uses the grey wolf optimization(GWO)algorithm,particle swarm optimization(PSO)algorithm,sparrow search algorithm(SSA),and bat algorithm(BA)to establish a hybrid machine learning optimization algorithm,and proposes an improved grey wolf optimization(IGWO)algorithm.The IGWO-MLP model obtained the minimum root mean square error(RMSE)by using the 5-fold cross-validation method for the training data.For the pore pressure data in Well 2 and Well 3,the coefficients of determination(R^(2))of SVM,RF,XGB,and MLP are 0.9930 and 0.9446,0.9943 and 0.9472,0.9945 and 0.9488,0.9949 and 0.9574.MLP achieves optimal performance on both training and test data,and the MLP model shows a high degree of generalization.It indicates that the IGWO-MLP is an excellent predictor of pore pressure and can be used to predict pore pressure.

Unveiling the Re,Cr,and I diffusion in saturated compacted bentonite using machine-learning methods

The safety assessment of high-level radioactive waste repositories requires a high predictive accuracy for radionuclide diffusion and a comprehensive understanding of the diffusion mechanism.In this study,a through-diffusion method and six machine-learning methods were employed to investigate the diffusion of ReO_(4)^(−),HCrO_(4)^(−),and I−in saturated compacted bentonite under different salinities and compacted dry densities.The machine-learning models were trained using two datasets.One dataset contained six input features and 293 instances obtained from the diffusion database system of the Japan Atomic Energy Agency(JAEA-DDB)and 15 publications.The other dataset,comprising 15,000 pseudo-instances,was produced using a multi-porosity model and contained eight input features.The results indicate that the former dataset yielded a higher predictive accuracy than the latter.Light gradient-boosting exhibited a higher prediction accuracy(R2=0.92)and lower error(MSE=0.01)than the other machine-learning algorithms.In addition,Shapley Additive Explanations,Feature Importance,and Partial Dependence Plot analysis results indicate that the rock capacity factor and compacted dry density had the two most significant effects on predicting the effective diffusion coefficient,thereby offering valuable insights.

Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

Protective Effect of Mulberry Extract against Pb-induced Learning and Memory Deficits in Mice

Lead （Pb） is ubiquitous in the environment, and low-level Pb exposure can cause neurotoxicity and irreversible damage to children＇s cognition, learning and memory ability. Nutritional intervention is an effective method to prevent Pb poisoning. Mul- berry is rich in anthocyanins, possessing protective effects for nerves. This study investigated the neuroprotective effects of mulberry extract （ME） against Pb-induced learning and memory deficits in mice. The results showed that the learning and memory abilities of mice, assessed using the Morris test, improved significantly after treatment with ME at a dose of 100 mg/kg body weight. The level of Pb in the brains of mice in the three ME intervention groups decreased significantly, while NO production and anti-oxidant enzymes were significantly restored. It is suggested that ME inhibits Pb-induced neurotoxicity by reversing Pb-induced alterations in the aspect of neurotoxic effects and improving learning and memory.

Flow Shop Scheduling Problem with Convex Resource Allocation and Learning Effect

In this paper, we consider the no-wait two-machine scheduling problem with convex resource allocation and learning effect under the condition of common due date assignment. We take the total earliness, tardiness and common due date cost as the objective function, and find the optimal common due date, the resource allocation and the schedule of jobs to make the objective function minimum under the constraint condition that the total resource is limited. The corresponding algorithm is given and proved that the problem can be solved in polynomial time.

Effect of tetramethylpyrazine on the spatial learning and memory function of rats after focal cerebral ischemia

BACKGROUND： Tetramethylpyrazine （TMP） presents the effect of anti-platelet aggregation, reduces arteria resistance, increases cerebral blood flow, and improves microcirculation. OBJECTIVE： To observe the effects of TMP on the learning and memory abilities and the number of neurons in cortex and hippocampus after focal cerebral ischemia in rats DESIGN： A randomized controlled tria SETTING： Department of Human Anatomy and Histological Embryology, School of Medicine, Xi＇an Jiaotong University. MATERIALS： Fifty adult male Sprague-Dawley rats, weighing 250-300 g were supplied by the Experimental Animal Center, School of Medicine, Xi＇an Jiaotong University. TMP was purchased from Wuxi Seventh Pharmaceutical Co.Ltd （Lot Number： 2004051106, Specification： 2 mL/piece）. METHODS ： The experiments were carried out in School of Medicine of Xi＇an Jiaotong University from June 2004 to May 2005. The 50 rats were randomly divided into five groups according to the random number table method： sham-operated group, cerebral ischemia control group, low-dose TMP group, middle-dose TMP group and high-dose TMP group, 10 rats in each group. Rats in the TMP groups were immediately treated with intraperitoneal injection of TMP of 40, 80 and 120 mg/kg respectively, and those in the sham-operated group and cerebral ischemia control group were injected intraperitoneally by isovolume saline, once a day for 14 days successively. On the 15^th day, the spatial learning and memory abilities of the rats were assessed with the Morris water maze test, and then the changes of neuron numbers in cortex and hippocampus were observed by Nissl staining of brain sections. MAIN OUTCOME MEASURES ： The results of Morris water maze test and the changes of neuron numbers in cortex and hippocampus by Nissl staining of brain sections were observed. RESULTS： Finally 39 rats were involved in the analysis of results, and the other 11 died of excessive anesthesia or failure in model establishment. ① The rats in the cerebral ischemia control group manifested obvious spatial cognitive deficits in the place navigation trial and spatial probe trial. The mean values of escape latency in the sham-operated group, low, middle and high-dose TMP groups were obviously shorter than that in the cerebral ischemia control group [（23.92±2.21）, （41.84±3.74）, （39.50 ±3.80）, （31.38_±3.72）, （61.60±3.61） s, P 〈 0.05-0.01]. In the spatial probe trial, significant differences in the percentage of time spending in the former platform quadrant and frequency of crossing the former platform site in the sham-operated group, lose, middle and high-dose TMP groups were obviously higher or more than those in the cerebral ischemia control group [（36.27±3.42） %, （35.84±2.54）%, （38.43±3.08）%, （36.51±1.96）%, （22.24±3.46）%; （11 ±1 ）, （10±1）, （8_±1）, （8±1）, （4±1） times, P 〈 0.01]. ② In the morphological observation, the numbers of neurons in ipsilateral （left） parietal cortex in the sham-operated group, low, middle and high-dose TMP groups were obviously more than that in the cerebral ischemia control group [（98±8）, （65±5）, （53±6）, （57±6）, （37±6）/0.625 mm^2, P 〈 0.01], but the number of neurons in left hippocampus had no obvious differences among the groups （P 〉 0.05）. CONCLUSION ： TMP can improve obviously the spatial learning and memory function after permanent focal cerebral ischemia in rats, and the neuroprotective role of the drug in cortex may be involved in its mechanism.

Learning Effectiveness Oriented Hybrid Teaching Mode

In the course of program comprehension and analysis,we have carried out an effectiveness oriented hybrid teaching mode,and carried out the reform of student-centered teaching methods.The teaching content puts forward specific solution cases from the perspective of industrial practice.Latest literatures from top conferences are analyzed and disused in class,so as to track the state art of research and practice.Practical project is assigned to improve students’practice and innovation ability.Teaching,learning and practice are closely integrated.In each link,students are guided to carry out autonomous and inquiry learning to improve their learning effectiveness.Good learning results have been achieved.

Sampled-data control through model-free reinforcement learning with effective experience replay

Reinforcement Learning(RL)based control algorithms can learn the control strategies for nonlinear and uncertain environment during interacting with it.Guided by the rewards generated by environment,a RL agent can learn the control strategy directly in a model-free way instead of investigating the dynamic model of the environment.In the paper,we propose the sampled-data RL control strategy to reduce the computational demand.In the sampled-data control strategy,the whole control system is of a hybrid structure,in which the plant is of continuous structure while the controller(RL agent)adopts a discrete structure.Given that the continuous states of the plant will be the input of the agent,the state–action value function is approximated by the fully connected feed-forward neural networks(FCFFNN).Instead of learning the controller at every step during the interaction with the environment,the learning and acting stages are decoupled to learn the control strategy more effectively through experience replay.In the acting stage,the most effective experience obtained during the interaction with the environment will be stored and during the learning stage,the stored experience will be replayed to customized times,which helps enhance the experience replay process.The effectiveness of proposed approach will be verified by simulation examples.