“问题群”在初中历史教学中的实践应用

初中历史教学重视培养学生的核心素养,引导学生坚定政治方向,形成历史思维。新课改要求初中历史教师创新教学模式,满足学生个性化的学习需求,实现高效教学目标。“问题群”是以问题教学理论为基础的新型教学模式,强调学生在问题导向下灵活运用所学知识,理解知识内涵,培养学科素养以及问题分析和解决能力等。概述“问题群”教学模式,分析“问题群”在初中历史教学中的应用策略,以促进教学实践的优化。

建构“问题群”教学模式,打造初中历史高效课堂

打造初中历史高效课堂,需要建构高效的教学模式。在课改不断深入推进的形势下,文章作者经过长期探索、实践,发现"问题群"教学模式能够在初中历史教学中发挥重要作用,能培养学生发现问题、分析问题、解决问题的能力,也能使历史课堂变得生动、活泼、高效。文章探讨"问题群"教学模式在初中历史教学中的作用,并以人教版七年级上册第18课"东晋南朝时期江南地区的开发"为例展示"问题群"教学模式的操作流程,提出建构"问题群"教学模式的建议,以供各位专家、同行了解、实践这种教学模式,打造初中历史高效课堂。

设置“问题群”培养政治理性思维

本文论述在对学生进行思维培养的过程中,通过采取＂问题群＂的教学方法,从链接热点,多维解析;铺设梯度,拾级而上;纵横比较,形成体系;学科整合,延伸视野四个方面入手,全面提升高中生的政治理性思维。

“问题群”教学模式在高中思想政治课教学中运用研究

在新课标背景下,高中思想政治课程改革已成为一种必然趋势,更加注重有效教学。随之,＂问题群＂教学模式逐渐被广泛应用到高中思想政治课教学中,有利于改变课堂教学现状,构建高效课堂,为学生营造一种＂轻松、愉快＂的课堂氛围,促使学生更好地学习政治知识与技能,促进他们德、智、体等全面发展。

“问题群”教学模式在初中历史教学中的运用

初中历史学科是一门培养学生核心素养,帮助他们树立正确历史观、坚定正确的政治方向、提高综合素质的重要学科。随着新课程改革的推进,传统的历史课堂教学模式已经无法满足学生学习的需求,"问题群"教学模式是新课程改革后推行的一种新型的课堂教学模式,是根据教学内容把问题组织成群,以所设置的"问题"为中心,让学生在分析问题和解决问题的过程中掌握知识并提升能力的教学模式。在初中历史教学中运用"问题群"教学模式,能培养学生分析、解决问题的能力和学科素养,促进他们的全面发展,对提升其综合素质有着重要的意义,有助于使历史教学变得更加生动高效。

“问题群”教学模式在高中思想政治课教学中运用研究

对于高中思想政治课教学内容来说,它是培养高中生思想道德、政治素养的重要教学内容,在高中教学中起到非常主要的作用。在现今教育领域,随着新课改的不断深入,高中思想政治课教学形式也发生了改变。"问题群"教学形式是新课改后一新型的教学形式,它是以问题为教学中心,以问题为教学主体的教学形式,将问题组织成群,让学生在解决问题当中,提升学习能力,对促进学生综合能力的提升有效极大的帮助。

以问题任务群助推整本书阅读教学--以《骆驼祥子》整本书阅读教学为例

问题任务群的整本书阅读教学策略,不仅能够有效督促学生完成整本书阅读,而且能够引发学生关注整本书文字背后的人性美、人情美,关怀个体生命,更是为学生搭建从浅显思考走向深度思维的台阶,从而真正提升学生整本书阅读的能力和品质。

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.

C-means-based ant colony algorithm for TSP

To solve the traveling salesman problem with the characteristics of clustering,a novel hybrid algorithm,the ant colony algorithm combined with the C-means algorithm,is presented.In order to improve the speed of convergence,the traveling salesman problem(TSP)data is specially clustered by the C-means algorithm,then,the result is processed by the ant colony algorithm to solve the problem.The proposed algorithm treats the C-means algorithm as a new search operator and adopts a kind of local searching strategy—2-opt,so as to improve the searching performance.Given the cluster number,the algorithm can obtain the preferable solving result.Compared with the three other algorithms—the ant colony algorithm,the genetic algorithm and the simulated annealing algorithm,the proposed algorithm can make the results converge to the global optimum faster and it has higher accuracy.The algorithm can also be extended to solve other correlative clustering combination optimization problems.Experimental results indicate the validity of the proposed algorithm.

Improved ant colony optimization for multi-depot heterogeneous vehicle routing problem with soft time windows

Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.

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.

TOPOLOGY OF CONFIGURATION SPACE FOR A THREE-BODY PROBLEM

A mechanics system consisting of three mass points on sphere S 2 is considered. The configuration space of the system is a fibre bundle over S 2 . It is proved that first Chern class of the bundle is -2 c 1(γ) where γ is the canonical line bundle over the complex projective space CP 1=S 2 , which shows the bundle is non trivial. The information about the first Chern class makes the cohomology groups and homotopy groups of the configuration space worked out. In addition the effects of these topolo gical properties of the configuration space on the behavior in large scale of the system, as the number of equilibrium positions, periodic orbits and reduced phase space, are discussed.

Hopfield neural network based on ant system

Hopfield neural network is a single layer feedforward neural network. Hopfield network requires some control parameters to be carefully selected, else the network is apt to converge to local minimum. An ant system is a nature inspired meta heuristic algorithm. It has been applied to several combinatorial optimization problems such as Traveling Salesman Problem, Scheduling Problems, etc. This paper will show an ant system may be used in tuning the network control parameters by a group of cooperated ants. The major advantage of this network is to adjust the network parameters automatically, avoiding a blind search for the set of control parameters. This network was tested on two TSP problems, 5 cities and 10 cities. The results have shown an obvious improvement.

Swarm intelligence optimization and its application in geophysical data inversion

The inversions of complex geophysical data always solve multi-parameter, nonlinear, and multimodal optimization problems. Searching for the optimal inversion solutions is similar to the social behavior observed in swarms such as birds and ants when searching for food. In this article, first the particle swarm optimization algorithm was described in detail, and ant colony algorithm improved. Then the methods were applied to three different kinds of geophysical inversion problems： （1） a linear problem which is sensitive to noise, （2） a synchronous inversion of linear and nonlinear problems, and （3） a nonlinear problem. The results validate their feasibility and efficiency. Compared with the conventional genetic algorithm and simulated annealing, they have the advantages of higher convergence speed and accuracy. Compared with the quasi-Newton method and Levenberg-Marquardt method, they work better with the ability to overcome the locally optimal solutions.

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.

Dynamic services selection algorithm in Web services composition supporting cross-enterprises collaboration

Based on the deficiency of time convergence and variability of Web services selection for services composition supporting cross-enterprises collaboration,an algorithm QCDSS(QoS constraints of dynamic Web services selection)to resolve dynamic Web services selection with QoS global optimal path,was proposed.The essence of the algorithm was that the problem of dynamic Web services selection with QoS global optimal path was transformed into a multi-objective services composition optimization problem with QoS constraints.The operations of the cross and mutation in genetic algorithm were brought into PSOA(particle swarm optimization algorithm),forming an improved algorithm(IPSOA)to solve the QoS global optimal problem.Theoretical analysis and experimental results indicate that the algorithm can better satisfy the time convergence requirement for Web services composition supporting cross-enterprises collaboration than the traditional algorithms.

A Dynamic Job Shop Scheduling Method Based on Ant Colony Coordination System

Due to the stubborn nature of dynamic job shop scheduling problem,a novel ant colony coordination mechanism is proposed in this paper to search for an optimal schedule in dynamic environment.In ant colony coordination mechanism,the dynamic job shop is composed of several autonomous ants.These ants coordinate with each other by simulating the ant foraging behavior of spreading pheromone on the trails,by which they can make information available globally,and further more guide ants make optimal decisions.The proposed mechanism is tested by several instances and the results confirm the validity of it.

Self-adaptive inversion method of electromagnetic-propagation resistivity logging while drilling data

Because of the constraint mode of the inversion objective function in the traditional resistivity-inversion method of electromagnetic-propagation resistivity logging while drilling(EPR-LWD),obvious differences appear in the radial and vertical investigation characteristics between the amplitude-ratio and phase difference,which affect the practical application of EPR-LWD data.In this paper,according to the EPR-LWD data,a self-adaptive constraint resistivity-inversion method,which adopts a self-adaptive constraint weighted expression in the objective function to balance the contributions of the phase difference and amplitude attenuation,is proposed.A particle swarm optimization algorithm is also introduced to eliminate the dependence of the accuracy and convergence on the initial value of the inversion.According to the inversion results of multiple classical formation models for EPR-LWD,the differences between the adaptive constraint inversion-resistivity logs with the traditional amplitude-ratio and the phase difference of the resistivity logs are discussed in detail.The results demonstrate that the adaptive resistivity logs take into account the advantages of the amplitude-ratio logs in the radial investigation and phase difference logs in the vertical resolution.Further,it is superior in thin-layer identification and invasion-effect appraisal compared with the single-amplitude-ratio and phase difference logs.The inversion results can provide a theoretical reference for research on the resistivity-inversion method of electromagnetic wave LWD.

Attractor for a Fourth-order Equation in the Whole Line

This paper studies the long time behavior of the following Cauchy problem δu/δt＋σδ^4u/δx^4＋ru＋uδu/δx=f,x∈R,t〉0,The authors prove the existence of global attractor by showing the corresponding operator semigroup is asymptotically compact.

A discrete multi-swarm optimizer for radio frequency identification network scheduling

Due to the effectiveness, simple deployment and low cost, radio frequency identification （RFID） systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another＇s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called pseo to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of pS20 is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS20 algorithm is proposed. With seven discrete benchmark functions, PS20 is proved to have significantly better performance than the original PSO and a binary genetic algorithm, pS20 is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.