浅谈如何培养学生的提问能力

培养学生提问能力,要营造环境,让学生敢问;教师鼓励学生主动质疑,让学生想问;给学生提供质疑的时间和空间,让学生能问;指导学生发现问题,让学生会问。

从无到有 从有到优——数学问题意识培养的几点思考

＂核心素养＂处于课程改革的基础地位,其体系结构中＂问题意识＂越来越被教师所关注。对此,教师可以从以下三方面着手：营造自主学习场,注意方法指导,让学生能问;巧抓教学时机,搭建思考脚手架,让学生善问;立足数学本质点,关注教学及生活实践,引领学生辨问。化心动为行动,努力为学生的可持续发展夯实基础。

如何培养学生的问题意识

“学成于思，思源于疑。”问题是一切思维活动的起点，也是探求真理、创造发明的起点。有了问题才能引起思维，才能进行探索，科学研究是如此，学生学习、掌握知识也同样如此。

例谈如何有效培养学生的问题意识

问题是小学数学课堂教学的心脏,问题意识是小学生学习数学必须具备的一个核心意识。培养学生的问题意识,教师要大胆地问,有目的地问。

浅谈《历史与社会》课学生问题意识的培养

本文主要在探讨在历史与社会课中学生问题意识的对学生发展的重要意义,分析在现实的教育中问题意识培养存在的误区,在教学中该如何培养学生的问题意识,从而发展学生思维能力和创新能力。

让课堂充满问题

新课程理念下的思想政治课教学应该是一个不断发现问题、分析问题、解决问题的动态过程。从某种意义上说,一堂好课就是由一串问题构建而成的。教师应善问、能问;学生应敢问、乐问,让问题激发学生学习的兴趣,增强学习的动机,让学生充分领略到做学习主人的充实感、成功感,快乐感。

夯实科学探究 培养问题意识

探究源于问题，有问题才会引发探究活动。问题意识的缺失，会影响学生的自主学习能力和探究能力。小学科学课的教师要引领学生的问题思维，培养学生提问意识，提高学生提问的高度和创新度。

Energy-efficient mechanism based on ACO for the coverage problem in sensor networks

An energy-efficient heuristic mechanism is presented to obtain the optimal solution for the coverage problem in sensor networks. The mechanism can ensure that all targets are fully covered corresponding to their levels of importance at minimum cost, and the ant colony optimization algorithm （ACO） is adopted to achieve the above metrics. Based on the novel design of heuristic factors, artificial ants can adaptively detect the energy status and coverage ability of sensor networks via local information. By introducing the evaluation function to global pheromone updating rule, the pheromone trail on the best solution is greatly enhanced, so that the convergence process of the algorithm is speed up. Finally, the optimal solution with a higher coverage- efficiency and a longer lifetime is obtained.

数学教学中学生质疑能力培养初探

本文从营造质疑氛围、创设质疑情境、留给质疑空间、教给质疑方法四个方面，阐述了小学数学教学中如何培养学生进行质疑，通过培养质疑进而调动学生学习主动性和积极性，让学生的创新能力、思维能力等得到充分发展。

Problems and Countermeasures of the Bio-energy Development in China

The current development and utilization status of bio-energy at home and abroad was summarized, and a multi-angle in-depth analysis and reflection was given to the problems encountered in the development of bio-energy. Furthermore, some suggestions were put forward to accelerate the further development of bio-en-ergy industry in China.

让语文课堂充满活力

在教学中，教师要引导学生大胆提出自己的见解，展开不同意见的争论，打破盲目顺从、迷信书本、被动地听讲、被动地回答问题的局面。

ENERGY-EFFICIENT HEURISTIC METRIC FOR SCP IN SENSOR NETWORKS

A heuristic metric is presented to achieve the optimal connected set covering problem （SCP） in sensor networks. The coverage solution with the energy efficiency can guarantee that all targets are fully covered. Among targets, the crucial ones are redundantly covered to ensure more reliable monitors. And the information collected by the above coverage solution can be transmitted to Sink by the connected data-gathering structure. A novel ant colony optimization （ACO） algorithm--improved-MMAS-ACS-hybrid algorithm （IMAH） is adopted to achieve the above metric. Based on the design of the heuristic factor, artificial ants can adaptively detect the coverage and energy status of sensor networks and find the low-energy-cost paths to keep the communication connectivity to Sink. By introducing the pheromone-judgment-factor and the evaluation function to the pheromone updating rule, the pheromone trail on the global-best solution is enhanced, while avoiding the premature stagnation. Finally, the energy efficiency set can be obtained with high coverage-efficiency to all targets and reliable connectivity to Sink and the lifetime of the connected coverage set is prolonged.

Six—Parameter Exponential—Type Potential and the Identity for the Exponential—Type Potentials

We propose a six-parameter exponential-type potential (SPEP), which has been shown to be a shape-invariant potential with a translation of parameters. For this reducible potential, the exact energy levels are obtained byusing the supersymmetric shape invariance technique. Choosing appropriate parameters, four classes of exponential-typepotentials and their exact energy spectra are reduced from the SPEP and a general energy level formula, respectively.Each class shows the identity except for the different definitions of parameters.

Virtual Machine Scheduling for Improving Energy Efficiency in laaS Cloud

In IaaS Cloud,different mapping relationships between virtual machines(VMs) and physical machines(PMs) cause different resource utilization,so how to place VMs on PMs to reduce energy consumption is becoming one of the major concerns for cloud providers.The existing VM scheduling schemes propose optimize PMs or network resources utilization,but few of them attempt to improve the energy efficiency of these two kinds of resources simultaneously.This paper proposes a VM scheduling scheme meeting multiple resource constraints,such as the physical server size(CPU,memory,storage,bandwidth,etc.) and network link capacity to reduce both the numbers of active PMs and network elements so as to finally reduce energy consumption.Since VM scheduling problem is abstracted as a combination of bin packing problem and quadratic assignment problem,which is also known as a classic combinatorial optimization and NP-hard problem.Accordingly,we design a twostage heuristic algorithm to solve the issue,and the simulations show that our solution outperforms the existing PM- or network-only optimization solutions.

Balanced energy-efficient relay selection scheme in wireless mobile relay networks

In order to resolve the relay selection problem in wireless mobile relay networks （WMRNs）, a novel balanced energy-efficient mobile relay selection scheme is proposed in this paper. Compared with traditional counter-based algorithm, distance and energy consumption are considered from network respect to provide a better network lifetime performance in the proposed scheme. Also, it performs well when nodes move freely at high speed. A random assessment delay （RAD） mechanism is added to avoid collisions and improve transmission efficiency. Simulation results reveal that, the proposed scheme has advantages in prolonging network lifetime, balancing energy compared with existing counter-based scheme. consumption and reducing the total energy consumption

An immune-swarm intelligence based algorithm for deterministic coverage problems of wireless sensor networks

A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters.

Method for improving MapReduce performance by prefetching before scheduling

In this paper, a prefetching technique is proposed to solve the performance problem caused by remote data access delay. In the technique, the map tasks which will cause the delay are predicted first and then the input data of these tasks will be preloaded before the tasks are scheduled. During the execution, the input data can be read from local nodes. Therefore, the delay can be hidden. The technique has been implemented in Hadoop-0. 20.1. The experiment results have shown that the technique reduces map tasks causing delay, and improves the performance of Hadoop MapRe- duce by 20%.

Improvement of Lagrangian relaxation performance for open pit mines constrained long-term production scheduling problem

Constrained long-term production scheduling problem(CLTPSP) of open pit mines has been extensively studied in the past few decades due to its wide application in mining projects and the computational challenges it poses become an NP-hard problem.This problem has major practical significance because the effectiveness of the schedules obtained has strong economical impact for any mining project.Despite of the rapid theoretical and technical advances in this field,heuristics is still the only viable approach for large scale industrial applications.This work presents an approach combining genetic algorithms(GAs) and Lagrangian relaxation(LR) to optimally determine the CLTPSP of open pit mines.GAs are stochastic,parallel search algorithms based on the natural selection and the process of evolution.LR method is known for handling large-scale separable problems; however,the convergence to the optimal solution can be slow.The proposed Lagrangian relaxation and genetic algorithms(LR-GAs) combines genetic algorithms into Lagrangian relaxation method to update the Lagrangian multipliers.This approach leads to improve the performance of Lagrangian relaxation method in solving CLTPSP.Numerical results demonstrate that the LR method using GAs to improve its performance speeding up the convergence.Subsequently,highly near-optimal solution to the CLTPSP can be achieved by the LR-GAs.

Membrane-inspired quantum bee colony optimization and its applications for decision engine

In order to effectively solve combinatorial optimization problems,a membrane-inspired quantum bee colony optimization(MQBCO)is proposed for scientific computing and engineering applications.The proposed MQBCO algorithm applies the membrane computing theory to quantum bee colony optimization(QBCO),which is an effective discrete optimization algorithm.The global convergence performance of MQBCO is proved by Markov theory,and the validity of MQBCO is verified by testing the classical benchmark functions.Then the proposed MQBCO algorithm is used to solve decision engine problems of cognitive radio system.By hybridizing the QBCO and membrane computing theory,the quantum state and observation state of the quantum bees can be well evolved within the membrane structure.Simulation results for cognitive radio system show that the proposed decision engine method is superior to the traditional intelligent decision engine algorithms in terms of convergence,precision and stability.Simulation experiments under different communication scenarios illustrate that the balance between three objective functions and the adapted parameter configuration is consistent with the weights of three normalized objective functions.

Supply Problems of Sales Points Performance in City Centres： Results of the DORED Program Survey Research

The paper regards to problems of sales point performance being situated in the centres of big cities in Poland. Problems are investigated with the use of surveys being carried out in 900 sales points according to author＇s DORED （DORED-dobre rozwiazania dla dostaw in Polish, good solutions for delivery in English） program performance. The DORED program has been mentioned for the first time in Logistics （a Polish newspaper） in February, 2005. The developed topic has been the pilot project of DORED program. That was in Wroclaw City （Poland）, where the regions of Przedmiescie swidnickie were the first test of method of the DORED program. The mentioned topic has encompassed the city logistic problems, land transportation and movement organization performance in the city centres.