“社会问题化”视域下大学生就业的困境与出路分析

当前，大学生就业难问题已引起社会各界的广泛关注。文章从“社会问题化’’的视角，指出我国高等教育大众化的过程及随之而来的就业困境，是我国高等教育大众化过程中的教育需求、人口因素、运作状态、就业观念与其建构、认定过程失谐的结果。为此，从“大众化”现象逐步演变的过程以及主观建构和认定的过程中来应对此问题，变得日趋迫切。

基于离散混合入侵杂草优化算法的车辆路径问题研究

容量化车辆路径问题(CVRP)已被证明是NP完全问题,CVRP不仅在学术界受到关注,而且在许多工程领域得到了应用。提出了一种离散混合入侵杂草优化算法(DMIWO)来解决容量化车辆路径问题,引入遗传操作过程中的自适应变异和交叉算子,保证算法的多样性,防止算法陷入局部收敛早熟的局部最优解。使用实矩阵编码,并为父代区域中的子代构建离散化过程。针对容量化车辆路径问题的性质,提出了一种改进的2-Opt和交换操作结构,构建了两阶段混合变量域搜索方法,增强了算法的局部搜索能力和全局搜索能力。将仿真实验和文献中的遗传算法、粒子群算法及量子进化算法进行比较,证明了用DMIWO算法解决离散组合优化问题,简单、高效、适应性强。

弥合全球化时代比较教育研究的问题鸿沟——重审本土意识与国际视野之关系

识别与选择具有全球化时代张力的研究问题是推进比较教育学科现代化与科学化进程的重要环节。在当前的比较教育研究成果中,由于缺乏对本土意识与国际视野关系的多维审思,问题感的匮乏和问题情景的脱嵌已成为全球化时代比较教育研究发展的现实困境。为了建构与完善问题识别的逻辑框架,比较教育研究者须基于本土与国际的对话、超越以及融合不同立场,从理论与实践的互动、问题的双重历史化以及行动空间的建构出发,确立研究的多元问题化路径。在问题识别的基础上,全球化时代的比较教育研究还须转换研究策略,借助问题式的比较教育研究实现全球教育的理解与对话,并通过沟通式的比较教育研究保持对研究问题的长期追索,以此发挥知识生产对社会发展的积极作用。

A genetic algorithm for the pareto optimal solution set of multi-objective shortest path problem

Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the muhi-objective shortest path problem （ MSPP ） has a set of pareto optimal solutions and cannot be solved in polynomial time. The present algorithms focused mainly on how to obtain a precisely pareto optimal solution for MSPP resulting in a long time to obtain multiple pareto optimal solutions with them. In order to obtain a set of satisfied solutions for MSPP in reasonable time to meet the demand of a decision maker, a genetic algo- rithm MSPP-GA is presented to solve the MSPP with typically competing objectives, cost and time, in this pa- per. The encoding of the solution and the operators such as crossover, mutation and selection are developed. The algorithm introduced pareto domination tournament and sharing based selection operator, which can not only directly search the pareto optimal frontier but also maintain the diversity of populations in the process of evolutionary computation. Experimental results show that MSPP-GA can obtain most efficient solutions distributed all along the pareto frontier in less time than an exact algorithm. The algorithm proposed in this paper provides a new and effective method of how to obtain the set of pareto optimal solutions for other multiple objective optimization problems in a short time.

An analytical method to calculate station evacuation capacity

The major objective of this work was to calculate evacuation capacity and solve the optimal routing problem in a given station topology from a network optimization perspective where station facilities were modelled as open finite queueing networks with a multi-objective set of performance measures. The optimal routing problem was determined so that the number of evacuation passengers was maximized while the service level was higher than a certain criterion. An analytical technique for modelling open finite queueing networks, called the iteration generalized expansion method(IGEM), was utilized to calculate the desired outputs. A differential evolution algorithm was presented for determining the optimal routes. As demonstrated, the design methodology which combines the optimization and analytical queueing network models provides a very effective procedure for simultaneously determining the service level and the maximum number of evacuation passengers in the best evacuation routes.

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.

从浅表教学走向深度教学的路径初探——以“自然地理环境的差异性”为例

为贯彻新课程理念、培养学生核心素养，课堂需要走向深度教学。深度教学的一般路径有指向学习内容的结构化路径、指向学习条件的情境化路径、指向学习过程的问题化路径和指向学习策略的自主性路径，四种路径相伴共生，共同促进深度教学的实现。

Robot Positioning and Navigation Based on Hybrid Wireless Sensor Network

Traditional sensor network and robot navigation are based on the map of detecting the fields available in advance. The optimal algorithms are developed to solve the energy saving, the shortest path problems, etc. However, in the practical enviroranent, there are many fields, whose map is difficult to get, and needs to be detected. In this paper a kind of ad-hoc navigation algorithm is explored, which is based on the hybrid sensor network without the prior map in advance. The navigation system is composed of static nodes and dynamic trades. The static nodes monitor the occurrances of the events and broadcast them. In the syston, a kind of algorithm is to locate the rdbot, which is based on duster broadcasting. The dynamic nodes detect the adversary or dangerous fields and broadcast warning messages. The robot gets the message and follows ad-hoc routine to arrive where the events occur. In the whole process, energy saving has been taken into account. The algorithms, which are based on the hybrid sensor network, are given in this paper. The simulation and practical results are also available.