“一体双翼”嵌入式人才培养模式创新——浙江农林大学应用化学专业建设实践

浙江农林大学应用化学专业是首批浙江省一流专业建设点,也是第三批国家级一流本科专业建设点。本文分析了传统专业人才培养中存在的问题,提出了共建人才培养方案、教师队伍资源库、实践教学基地、课程教材建设、教学方法创新等优质教学资源,构建“一体双翼”嵌入式人才培养新模式,以范例介绍模式内容及保障措施,分析了模式成效。

面向新农科有机化学实验课程育人实践

“新农科”有机化学实验课程思政的实施是以课程作为载体,通过深入挖掘有机化学实验课程丰富的育人元素,围绕农林特色精心选择实验内容,创新教学模式,将思想政治教育寓于、融入有机化学实验课的教育实践活动中,构建农林院校有机化学实验课程“三全育人”的新体系,助力新时代知农爱农和强农兴农的“新农人”培养。

A Hybrid Immigrants Scheme for Genetic Algorithms in Dynamic Environments

动态优化问题是随着时间的过去包含变化的一种优化问题。自从球门不再是寻找一个固定问题的最佳的答案，但是随着时间的过去追踪动人的最佳，他们提出严肃的挑战到传统的优化方法以及常规基因算法。动态优化问题在最近的年里从基因算法社区吸引了成长兴趣。几条途径被开发了在动态环境提高基因算法的表演。一条途径是经由随机的移民维持人口的差异。这篇论文建议联合精英统治，两重性和随机的移民的概念让基因算法处理动态优化问题的一个混合移民计划。在这个混合计划，最好的个人，即，精英，作为底从以前的产生和它的双个人被检索经由传统的变化计划创造移民。和一些随机的移民的这些基于精英统治、基于两重性的移民被代替进当前的人口，代替在人口的最糟的个人。这些三种移民试图分别地探讨细微、中等、重要的度的环境变化并且高效地因此使基因算法适应服从于变化的不同严厉的动态环境。基于一系列系统地构造的动态测试问题，实验被执行与混合移民计划和传统的随机的移民计划调查基因算法的表演。试验性的结果为在动态环境改进基因算法的表演验证建议混合移民计划的效率。关键词基因算法 - 随机的移民 - 基于精英统治的移民 - 两重性 - 动态优化问题这个工作被英国 EPSRC 支持(没有。EP/E060722/01 ) 并且巴西 FAPESP (Proc。04/04289-6 ) 。杨胜祥收到了 B.Sc。并且 M.Sc。在自动控制的学位和在从东北大学设计的系统的博士学位， P. R。在 1993， 1996 和 1999 的中国分别地。他是在计算机科学系的一个博士后的研究伙伴，从 1999 年 10 月的国王鈥檚 学院伦敦到 2000 年 10 月。他当前是在在累斯特的大学的计算机科学系的一个讲师，英国。他在书，杂志和会议出版了超过 50 篇论文。他有 co-guest-edited 为基因编程和可发展的机器的杂志的一期特刊并且共同编辑一本书在动态、不明确的环境的进化计算，在 2007 年 3 月出版了。他的当前的研究兴趣包括进化、基因的算法，为组合优化问题的人工的神经网络，安排问题，动态优化问题，和网络流动问题和算法。杨博士担任区域编辑，伙伴编辑和三本国际杂志的编委的成员。他是在动态、不明确的环境的进化计算上的工作组的一个成员，进化计算技术委员会， IEEE 计算智力社会(CIS ) 。他是 IEEE 和 ACM SIGEVO 的一个成员。Renato 听贸 s 收到了 B.Sc。在从 S 的州立大学的电力工程的度 ? o Paulo (UNESP ) ，在 1994 的巴西，和 M.Sc。并且在来自 S 的大学的电力工程的博士学位 ? 在 S 的 o Paulo (USP )? o 卡洛斯，在 1999 和 2003 的巴西分别地。他然后在 S 加入了 USP 的计算机科学系 ? o 卡洛斯作为一个研究科学家。他当前是在物理的系和在 Ribeir 的 USP 的数学的一个助教 ? o Preto。他的研究兴趣包括进化算法，动态优化，机器人学，容错，和神经网络。

Energy-aware Integrated Scheduling for Container Terminals with Conflict-free AGVs

For automated container terminals,the effective integrated scheduling of different kinds of equipment such as quay cranes(QCs),automated guided vehicles(AGVs),and yard cranes(YCs)is of great significance in reducing energy consumption and achieving sustainable development.Aiming at the joint scheduling of AGVs and YCs with consideration of conflict-free path planning for AGVs as well as capacity constraints on AGV-mate which is also called buffer bracket in blocks,a mixed integer programming model is established to minimize the energy consumption of AGVs and YCs for the given loading/unloading task.A solution method based on a novel bi-level genetic algorithm(BGA),in which the outer and the inner layer search the optimal dispatching strategy for QCs and YCs,respectively,is designed.The validity of the model and the algorithm is verified by simulation experiments,which take the Port of Qingdao as an example and the performance under different conflicting resolution strategies is compared.The results show that,for the given task,the proposed solution to conflict-free path and the schedule provided by the algorithm can complete the task with minimum energy consumption without loss of AGVs utilization,and the number of AGV-mates should be adjusted according to the task rather than keeping unchanged.Comparison results indicate that our proposed approach could efficiently find solutions within 6%optimality gaps.Energy consumption is dropped by an average of 15%.

Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines,and the rationality of their routes plays the direct impact on operation safety and energy consumption.Rich studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil,however,less works are for electric trackless rubber-tyred vehicles.Furthermore,energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving,especially the limited cruising ability of electric trackless rubber-tyred vehichles(TRVs).To address this issue,an energy consumption model of an electric trackless rubber-tyred vehicle is formulated,in which the effects from total mass,speed profiles,slope of roadways,and energy management mode are all considered.Following that,a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance,allowable load,and endurance power.As a problem-solver,an improved artificial bee colony algorithm is put forward.More especially,an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific space.In order to assign onlookers to some promising food sources reasonably,their selection probability is adaptively adjusted.For a stagnant food source,a knowledge-driven initialization is developed to generate a feasible substitute.The experimental results on four real-world instances indicate that improved artificial bee colony algorithm(IABC)outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.