Rails和Merb合并为一个项目,这不是愚人节新闻

Merb团队宣布将与Rails核心团队合作,计划将Merb的独特特性整合进Rails 3中,使得Rails 3能够覆盖Merb用户的使用场景。
刚刚准备睡觉的,突然看到这条消息,刚刚发布的,而且,它是真的。千言万语归到一句话:Merb2就是Rails3! 估计现在JE的各位名记现在正在梦乡吧? 不好意思,被小生抢先了。

段落大意:今天是个好日子,Merb team会跟Rails team一起合作,将Merb中的与众不同的东西结合到rails中。

一个直接的影响是:现在正在写的几本merb相关的书像merb in action、beginning merb估计要大改特改了。

OK,俺也睡觉去了,想想其实俺也挺八卦的,唉

[quote]Today is a fairly momentous day in the history of Ruby web frameworks. You will probably find the news I’m about to share with you fairly shocking, but I will attempt to explain the situation.

Before talking tech, and even going into the details of the announcement, I want to assure everyone that the incredible members of the thriving Merb community are top priority, and that this could not have been possible without every one of them.

Merb is an open, ever-changing project, and some its best ideas have come from not-core regular-Joe community members. It’s gotten where it has because of the community, and the community will get us even further in the future. Your ideas, feedback and even complaints will be 100% welcome in the future, just as they have been in the past. I believe in the tremendous value an open community and just generally open attitude bring to the table, and am counting on those things to continue ushering in the future of Ruby.

On to the news: beginning today, the Merb team will be working with the Rails core team on a joint project. The plan is to merge in the things that made Merb different. This will make it possible to use Rails 3 for the same sorts of use-cases that were compelling for Merb users. [b]Effectively, Merb 2 is Rails 3.[/b][/quote]

详细消息请点击[url]http://yehudakatz.com/2008/12/23/rails-and-merb-merge/[/url]

还有[url]http://developers.slashdot.org/article.pl?sid=08/12/23/2135246[/url]

rails官方blog[url]http://weblog.rubyonrails.org/2008/12/23/merb-gets-merged-into-rails-3[/url]
【无人机】基于改进粒子群算法的无人机路径规划研究[遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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