建构“材料超市”场景,激活学生“学习能量”

文章将英国教育界"学会学习"的新理念"学习能量"进行了本土化的实践,从在课堂中设立"材料超市"的场景建构出发,组织课堂教学,并具体梳理在这个过程中师生角色定位中的行为及提问方式,从而肯定学生的"探究意识",提升学生的"战略意识",培育学生"改变与学习"的自觉性,激发学生的"创造力",促进学生的"意义构建",增进学生的"学习关系",增强学生的"心理弹性",激发学生的"学习能量",从而实现学生七个维度的全面发展,提升科学素养。

“学习能量”理论下的初中心育教学策略优化探究

“学会学习”是中国学生的核心发展素养之一。“学习能量”理论是有关“学会学习”的一种新的理念体系,它包括改变与学习、意义建构、探究意识、创造力、学习关系、战略意识和心理弹性七个维度。用“学习能量”理论指导初中心理健康教育教学,有利于心理课堂更好地发挥实际效用,为初中生的学习赋能。

Impact energy analysis of turbulent water sprays for continuous centrifugal concentration

A SLon full-scale continuous centrifugal concentrator was used to reconcentrate hematite from a high gradient magnetic separation concentrate to study the effect of impact angle, concentrate mass and drum rotation speed on the impact energy of turbulent water sprays for continuous centrifugal concentration, under conditions of feed volume flow rate around 9 m3/h, feed solid concentration of 25%-35% and reciprocating velocity of water sprays at 0.05 m/s. The results indicate that a minimal critical impact energy is required in the water sprays for achieving continuous concentration of the concentrator; an unfitted impact angle reduces the impact efficiency, and the highest impact efficiency of 0.6416 is found at the mpact angle of 60°; the increase in concentrate mass leads to an increase in impact energy, and the highest impact efficiency is maintained when the concentrate mass varies in the range of 0.44-0.59 kg/s; when the concentrate mass and the pressure of water sprays are kept at around 0.45 kg/s and in the range of 0.4-0.6 MPa respectively, the impact energy increases proportionally with the increase of drum rotation speed.

Theoretical and Experimental Study of a Cylindro-Parabolic Solar Collector

This article outlines the theoretical and experimental performance studies of a cylindro-parabolic solar collector. The theoretical study consists on the establishment, through mass and energy balances, of a mathematical model to control the exiting temperature of the heating fluid as well as the temperatures of the absorber and the glass. The experimental level investigates the influence of the solar absorber tube diameter on the performances of the driving device. Several experiments were made in order to know the possibility to reach temperatures being able to ensure for example the ammonia vaporization in the generator of a solar absorption refrigeration system. These experiments were carried out under various operating and climatic conditions. The results are presented and discussed.

A Quantized Kernel Least Mean Square Scheme with Entropy-Guided Learning for Intelligent Data Analysis

Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.

Lexical Relations： Measuring the Proficiency of the Saudi EFL Learners and Analysing the Obstacles

As teachers of EFL （English as a Foreign Language） in the Kingdom of Saudi Arabia, we are sometimes flabbergasted by the errors that the/earners make in their English scripts. Apparently, there was no pattern to these errors. However, when the researcher observed the frequency of similar error over a few years of her experience as an EFL faculty for Saudi learners, the theory of Lexical Relations appeared to offer an answer. Therein also lies the genesis of the current study. In a positive development, the study did bring to light certain factors that directly played a role in the Saudi EFL learners＇ English errors. These have been presented in this paper with the hope that with the diagnosis in hand, it will be easier for the EFL teachers to find a solution to the dearth of English proficiency among the Saudi EFL learners.

Negative Lexical Transfer of Native Language in Chinese College Students＇ English Writing

This paper focuses on manifestations of negative transfer of native language at lexical level reflected in Chinese college students＇ English writing. A classification as well as its exemplification is offered concerning all these lexical transfer errors. And the cause of formation is analyzed for the corresponding class of lexical transfer.

Supervisory control of the hybrid off-highway vehicle for fuel economy improvement using predictive double Q-learning with backup models

This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimize the engine fuel in real-world driving and improve energy efficiency with a faster and more robust learning process.Unlike the existing“model-free”methods,which solely follow on-policy and off-policy to update knowledge bases(Q-tables),the PDQL is developed with the capability to merge both on-policy and off-policy learning by introducing a backup model(Q-table).Experimental evaluations are conducted based on software-in-the-loop(SiL)and hardware-in-the-loop(HiL)test platforms based on real-time modelling of the studied vehicle.Compared to the standard double Q-learning(SDQL),the PDQL only needs half of the learning iterations to achieve better energy efficiency than the SDQL at the end learning process.In the SiL under 35 rounds of learning,the results show that the PDQL can improve the vehicle energy efficiency by 1.75%higher than SDQL.By implementing the PDQL in HiL under four predefined real-world conditions,the PDQL can robustly save more than 5.03%energy than the SDQL scheme.

Error Analysis on Hérmite Learning with Gradient Data

This paper deals with Hermite learning which aims at obtaining the target function from the samples of function values and the gradient values. Error analysis is conducted for these algorithms by means of approaches from convex analysis in the frame- work of multi-task vector learning and the improved learning rates are derived.

A learning method for energy optimization of the plug-in hybrid electric bus

The optimal energy management for a plug-in hybrid electric bus(PHEB)running along the fixed city bus route is an important technique to improve the vehicles’fuel economy and reduce the bus emission.Considering the inherently high regularities of the fixed bus routes,the continuous state Markov decision process(MDP)is adopted to describe a cost function as total gas and electric consumption fee.Then a learning algorithm is proposed to construct such a MDP model without knowing the all parameters of the MDP.Next,fitted value iteration algorithm is given to approximate the cost function,and linear regression is used in this fitted value iteration.Simulation results show that this approach is feasible in searching for the control strategy of PHEB.Simultaneously this method has its own advantage comparing with the CDCS mode.Furthermore,a test based on a real PHEB was carried out to verify the applicable of the proposed method.