Hibernate 3.0 released!

Hibernate 3.0 是世界上最复杂的 ORXM 解决方案,让 Java 应用与持久数据交互更便捷。它提供全面的对象/关系查询功能,相比 Hibernate 2.1 有诸多改进,如更灵活的 O/R 映射、新增过滤器等,还介绍了其新特性、文档增强等内容。
Hibernate 3.0 is the world's most sophisticated ORXM (Object/Relational/XML
Mapping) solution. Hibernate3 makes it easier than ever before for Java
applications to interact with persistent data, allowing a single definition of
the transformation between various in-memory representations of the entity
data and the relational schema, even in the case of very complex legacy
schemas and schemas for historical data or data with visibility rules.
Hibernate3 also provides the most comprehensive object/relational query
functionality, with three full-featured query facilities: Hibernate Query
Language, the newly enhanced Hibernate Criteria Query API, and enhanced
support for queries expressed in the native SQL dialect of the database.

Compared to Hibernate 2.1 - the most popular object/relational mapping
solution in any language - Hibernate 3.0 offers:

    * Much more flexible O/R mapping: support for exotic association and
      inheritance mappings, and greater flexibility when working with legacy
      data.

    * Hibernate3 filters: a unique feature for working with temporal
      (historical), regional or permissioned data.

    * Unprecendented flexibility for mixing handwritten and generated SQL
      within a single application or even a single entity: full support for
      "derived" entities and attributes defined in the mapping document, full
      support for overriding any generated SQL statement with handwritten SQL,
      support for stored procedures.

    * Object/Relational/XML mapping: query XML directly from the database for
      reporting, replicate data between databases via intermediate XML,
      externalize entity data as XML when interacting with remote systems.

    * Enhanced ease of use: better defaulting, an unchecked exceptions model,
      simplified natural (and composite) key support, simplified CMT
      integration.

    * Enhanced Criteria query API: with full support for
      projection/aggregation and subselects.

    * Runtime performance monitoring: via JMX or local Java API, including a
      second-level cache browser.

    * Brand new AST-based HQL parser: bulk update/delete enhancement, better
      syntax validation.

    * JBoss EJB 3.0 preview: support for annotation-based O/R mappings, full
      support for EJB-QL 3.0, support for EJB 3.0 persist()/merge() lifecycle,
      JACC-based security model.

    * Hibernate Tools preview: a full suite of Eclipse plugins for working
      with Hibernate 3.0, including mapping editor, interactive query
      prototyping, schema reverse engineering tool.

    * Many new extension points: including a new, extensible, event-driven
      architecture

    * Documentation enhancements.

    * Brand new test suite, including many useful examples of exotic Hibernate
      mappings.

A full list of new features may be found at:

http://www.hibernate.org/About/RoadMap

Download link:

http://www.hibernate.org/Download

The Hibernate 3.0 core is 68,549 lines of Java code together with 27,948 lines
of unit tests, all freely available under the LGPL, and has been in
development for well over a year. We would like to thank the many people who
contributed to this release!

The Hibernate Team,

     Max Rydahl Andersen, JBoss Inc
     Christian Bauer, JBoss Inc
     Emmanuel Bernard
     David Channon
     Joshua Davis
     Steve Ebersole, JBoss Inc
     Michael Gloegl
     Gavin King, JBoss Inc
     Anthony Patricio

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内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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