Emerging Factors Affecting Blended Learning in Virtual Learning Environment Framework （VLEF）

The classical definition of learning is： change and modification of behavior as a result of experience, training, and practice. In modem technological world; this definition has been changed and modified to include the changes and modification in behavior as a result of experience, training, and practice by using modem toolkits and metacognition for self-regulated, lifelong, open, flexible, and continuous learning in Virtual Learning Environment Framework （VLEF）. The classical factors of learning included： motivation, attitude, interest, attention, readiness, participation, and reinforcement. But, due to the modem toolkits, gadgets, and ICT technologies, there are new varieties in modes of presentations and instructions. The emerging learning factors to be integrated and blended include： multimedia, hypermedia, and multimodality. The researcher has intended to identify the gap in the existing literature for the inclusion of new emerging factors such as multimedia, hypermedia, and multimodal presentations. The purpose of the study was to identify the gap in the factors of learning for learning blend in this era of science and technology, especially, in the ICT-integrated form of blended learning. The methodology of the study was analysis and synthesis of literature in the form of reviewing literature for developing a model of blended leaning. The study is significant because it has filled the gap in factors of learning for making it blended in VLEF. The factors were analyzed and synthesized in the form of a model. The findings were summarized for drawing conclusion, and at the end there were made some suggestions and recommendations for blended learning environments.

Sociocultural Factors and Foreign Language Learning and Teaching

Issues caused by different social cultures A clulture has a close connection with a particular language and plays an improtant role in the learn-ing and teaching of it.As we know,every nation has its own culture and the cultures of nations of theworld,generally,are different from each other.Owing to the disparities in culture,to people whospeak different languages,a word or phrase used to express good intention may surprise or irritate peo-ple;a common sentence can sometimes amuse one person greatly and cause laughter,but‘strange e-nough’,it can be too,dull enough in the eyes of another person with different culture background.

Study of a New Improved PSO-BP Neural Network Algorithm

In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based on adaptive particle swarm optimization( PSO) algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP( IPSO-BP) model,it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals,and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP,PSO-BP and linear PSO-BP( LPSO-BP) algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed,lower errors,higher diagnostic accuracy and learning ability.

Application of artificial neural network to calculation of solitary wave run-up

The prediction of solitary wave run-up has important practical significance in coastal and ocean engineering, but the calculation precision is limited in the existing models. For improving the calculation precision, a solitary wave run-up calculation model was established based on artificial neural networks in this study. A back-propagation （BP） network with one hidden layer was adopted and modified with the additional momentum method and the auto-adjusting learning factor. The model was applied to calculation of solitary wave run-up. The correlation coefficients between the neural network model results and the experimental values was 0.996 5. By comparison with the correlation coefficient of 0.963 5, between the Synolakis formula calculation results and the experimental values, it is concluded that the neural network model is an effective method for calculation and analysis of solitary wave ran-up.

Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization

The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.

Effect of calcitonin gene-related peptide and nerve growth factor on spatial learning and memory abilities of rats following focal cerebral ischemia/reperfusion

BACKGROUND: Calcitonin gene-related peptide (CGRP) and nerve growth actor (NGF) cam improve spatial learning and memory abilities of rats with cerebral ischemia/reperfusion; however, the effect of combination of them on relieving learning and memory injury following cerebral ischemia/reperfusion should be further studied. OBJECTIVE: To study the effects of exogenous CGRP and NGF on learning and memory abilities of rats with focal cerebral ischemia/reperfusion. DESIGN: Randomized controlled animal study. SETTING: Department of Neurosurgery, the Second Hospital of Xiamen; Department of Neurosurgery, the Second Affiliated Hospital of China Medical University; Department of Neurobiology, Basic Medical College of China Medical University. MATERIALS: A total of 30 healthy male SD rats, aged 8 weeks, of clean grade, weighing 250-300 g, were provided by Experimental Animal Department of China Medical University. All rats were randomly divided into sham-operation group, ischemia/reperfusion group and treatment group with 10 in each group. The main reagents were detailed as the follows: 100 g/L chloral hydrate, 0.5 mL CGRP (2 mg/L, Sigma Company, USA), and NGF (1× 106 U/L, 0.5 mL, Siweite Company, Dalian). METHODS: The experiment was carried out in the Department of Neurobiology, Basic Medical College of China Medical University from February to July 2005. Rat models of middle cerebral artery occlusion were established by method of occlusion, 2 hours after that rats were anesthetized and the thread was slightly drawn out for 10 mm under direct staring to perform reperfusion. Rats in the ischemia/reperfusion group received intraperitoneal injection of 1 mL saline via the abdomen at two hours later, while rats in the treatment group at 2 hours later received intraperitoneal injection of 2 mg/L CGRP (0.5 mL) and 1×106 U/L NGF (0.5 mL) once a day for 10 successive days. First administration was accomplished within 15 minutes after ischemia/reperfusion. Rats in the sham-operation group were separated of the vessels without occlusion or administration. The neural function was evaluated with Zea Longa 5-grade scale. Animals with the score of one, two and three points received Morris water-maze test to measure searching time on platform (omitting platform-escaping latency). Meanwhile, leaning and memory abilities of animals were reflected through testing times of passing through platform per minute. MAIN OUTCOME MEASURES: Experimental results of omitting platform-escaping latency and spatial probe. RESULTS: Three and two rats in the ischemia/reperfusion group and treatment group respectively were not in accordance with the criteria in the process, and the rest were involved in the final analysis. ① Comparisons of platform-escaping latency during Morris water-maze test in all the three groups: Ten days after modeling, the platform-escaping latency in the ischemia/reperfusion group was obviously longer than that in sham-operation group (P < 0.01), and was significantly shorter than that in the treatment group (P < 0.01). ② Comparisons of times of passing through platform in all the three groups: Times of passing through platform were remarkably less in the ischemia/reperfusion group than those in the sham-operation group [(1.79±0.39), (4.30±0.73) times/minute, P < 0.01], and those were markedly more in the treatment group than the ischemia/reperfusion group [(3.16±1.03), (1.79±0.39) times/minute, P < 0.01]. CONCLUSION: CGRP and NGF are capable of ameliorating the abilities of spatial learning and memory in MCAO rats, which indicates that CGRP and NGF can protect ischemic neurons.

ON MAJOR FACTORS OF SUCCESSFUL LANGUAGE LEARNING

As is well known, some people are more successful thanothers in learning. This different levels of achievement may beattributed to variables associated with the learner. In recentyears there has been extensive research into aspects of differencesin learning a second language. This paper briefly reviews anddiscusses the major parameters of the differences among individu-als which research studies indicate may influence the success ofsecond language learning, citing six areas of interest: age, intel-ligence, cognitive styles, personality, motivation and attitude.

Recommender systems based on ranking performance optimization

The rapid development of online services and information overload has inspired the fast development of recommender systems, among which collaborative filtering algorithms and model-based recommendation approaches are wildly exploited. For instance, matrix factorization （MF） demonstrated successful achievements and advantages in assisting internet users in finding interested information. These existing models focus on the prediction of the users＇ ratings on unknown items. The performance is usually evaluated by the metric root mean square error （RMSE）. However, achieving good performance in terms of RMSE does not always guarantee a good ranking performance. Therefore, in this paper, we advocate to treat the recommendation as a ranking problem. Normalized discounted cumulative gain （NDCG） is chosen as the optimization target when evaluating the ranking accuracy. Specifically, we present three ranking-oriented recommender algorithms, NSME AdaMF and AdaNSME NSMF builds a NDCG approximated loss function for Matrix Factorization. AdaMF is based on an algorithm by adaptively combining component MF recommenders with boosting method. To combine the advantages of both algorithms, we propose AdaNSME which is a hybird of NSMF and AdaME and show the superiority in both ranking accuracy and model generalization. In addition, we compare our proposed approaches with the state-of-the-art recommendation algorithms. The comparison studies confirm the advantage of our proposed approaches.

Methods for Population-Based eQTL Analysis in Human Genetics

Gene expression is a critical process in biological system that is influenced and modulated by many factors including genetic variation. Expression Quantitative Trait Loci（e QTL） analysis provides a powerful way to understand how genetic variants affect gene expression. For genome wide e QTL analysis, the number of genetic variants and that of genes are large and thus the search space is tremendous. Therefore, e QTL analysis brings about computational and statistical challenges. In this paper, we provide a comprehensive review of recent advances in methods for e QTL analysis in population-based studies. We first present traditional pairwise association methods, which are widely used in human genetics. To account for expression heterogeneity, we investigate the methods for correcting confounding factors. Next, we discuss newly developed statistical learning methods including Lasso-based models. In the conclusion, we provide an overview of future method development in analyzing e QTL associations. Although we focus on human genetics in this review, the methods are applicable to many other organisms.