a summary a week(1305)

最新推荐文章于 2025-11-17 09:51:43 发布

原创最新推荐文章于 2025-11-17 09:51:43 发布 · 694 阅读

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本文提供了两个VBScript示例，展示了如何使用Response对象来输出HTML内容和提示信息。通过这些示例，读者可以了解如何在VBScript中生成动态网页。

1.about quote in vbscript

compare two examples below

	  Response.Write "input ’index' please"

<%
  Response.Write "<html>"
    Response.write "<head align='center'>welcome to my homepage"
    Response.write "</head>"
  Response.write "</html>"
%>

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专栏目录

a summary a week（1315）

LightChan2442的专栏

06-05

456

1.[Microsoft][ODBC Microsoft Access Driver] Microsoft Jet 数据库引擎打不开文件'(未知的)'。它已经被别的用户以独占方式打开，或没有查看数据的权限。 http://www.haogongju.net/art/318333

a summary a week(1311)

LightChan2442的专栏

05-06

694

1.linux shell startup files From http://mmahmoodict.wikidot.com/blog:linux-startup-files When an interactive login shell starts, it executes series of pre-defined startup files. Thelogin shell e

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a summary a week(1304)

LightChan2442的专栏

03-19

596

1.What's the fuck difference between java Test and java Test.class?

Machine Learning Study Summary – Week 1

m0_74270467的博客

07-21

1361

where:

a summary a week(1302~1303)

LightChan2442的专栏

03-11

760

Summary and Review of 2019, A

程序萌部落

12-27

375

This article is a set of memories, belonging to the list of RMBATS. Say goodbye to 2019, the year that changed my life and destiny. The first half of 2019From the second half of...

Summary for week1

07-24

399

July 13th: Attend the induction day and listening to some lectures. July 14th: 10:00AM trade types & lifecycle, P&L system overview learn how to manage different libraries. weekly report

Summary for week4

08-08

362

3rd August Morning: have an overview about the project now. Afternoon: use train to build and release our project 4th August Made fake examples Afternoon: have an overview about trades and

Summary of this week-Sep. 1,2013

秋风大叔的专栏

09-01

552

[ENG] 1. Do what you can, with what you have, where you are! 无论身处何种境地，都要倾你所有，尽你所能! 2. Every day is a new day. It is better to be lucky. But I would rather be exact. Then when luck comes you are

SELECT a.FAB_ID, a.DEPT, a.WEEK, A.MOVE, A.ALMGROUP, sum(a.TOTAL) AS TOTAL2, CASEWHEN sum(a.MOVE)!= 0 THEN round(sum(a.TOTAL)/sum(a.MOVE)*1000, 2) ELSE 0 END ALARM_KMOVE FROM mfgdev3.EQP_ALARM_SUMMARY_GMP a WHERE 1=1 AND a.FAB_ID = 'F16' AND a.RECTIME >= sysdate-155 AND a.DATA_MODE = 'Original' AND a.ALARM_PRTY_CHK = '非0' AND A.DEPT LIKE '%LIT2%' GROUP BY a.FAB_ID, A.DEPT, a.WEEK, a.MOVE, A.ALMGROUP 以上SQLquery 出的data 在同一WEEK和同一ALMGROUP裏面對應了多個MOVE，導致數據庫臃腫，修改SQL，讓其顯示在同一week 和ALMGROUP是只保留最大的move

10-15

SELECT a.FAB_ID, a.DEPT, a.WEEK, A.MOVE, A.ALMGROUP, sum(a.TOTAL) AS TOTAL2, CASE WHEN sum(a.MOVE)!=0 THEN round(sum(a.TOTAL)/sum(a.MOVE)*1000, 2) ELSE 0 END AS ALARM_KMOVE FROM mfgdev3.EQP_...

原始SQL如下SELECT a.FAB_ID, a.DEPT, a.WEEK, A.MOVE, A.RECTIME,A.ALMGROUP, sum(a.TOTAL) AS TOTAL2, CASE WHEN sum(a.MOVE)!= 0 THEN round(sum(a.TOTAL)/sum(a.MOVE)*1000, 2) ELSE 0 END ALARM_KMOVE FROM mfgdev3.EQP_ALARM_SUMMARY_GMP a WHERE 1=1 AND a.FAB_ID = 'F16' AND a.RECTIME >= sysdate-155 AND a.DATA_MODE = 'Original' AND a.ALARM_PRTY_CHK = '非0' AND (A.DEPT LIKE '%LIT2%' or A.DEPT LIKE '%LIT1%') GROUP BY a.FAB_ID, A.DEPT, a.WEEK, a.MOVE, A.ALMGROUP, A.RECTIME,請根據上述要求進行修改

10-14

然而，原始SQL中使用了GROUP BY a.FAB_ID, A.DEPT, a.WEEK, a.MOVE, A.ALMGROUP, A.RECTIME，这意味着每个不同的RECTIME都会分到不同的组。这样，同一个WEEK和ALMGROUP下如果有多个RECTIME，就会有多条记录。 ...

27、A/B测试助力更优营销策略

a2b3c4d5e的博客

11-17

本文详细介绍了如何利用A/B测试优化营销策略，涵盖统计假设检验原理、Python和R语言的实际分析步骤，以及数据可视化与结果解读。通过真实数据集的案例分析，展示了从数据获取到策略选择的完整流程，并提供了注意事项与最佳实践，帮助读者科学决策，提升营销效果。

翻译下面这段文本：Good morning Thank for the review last week, it was great to get a better understanding. I have a few questions that I would appreciate your responses to: Architecture landscape On the Ali site there is an architecture datasheet: Salesforce on Alibaba Cloud China architecture datasheet - Salesforce on Alibaba Cloud - Alibaba Cloud Documentation Center This appears to be a summary and there is reference to a “full 30-page ‘China Architecture Guide’, please reach out to your sales representative”. Could you please provide? If only available in Chinese I am fine to translate. Middleware (MW) options Last week during the call it was discussed that MuleSoft is available for deployment as an on-premise option or deployed on AliCloud. There was also mention that there may be Aliyun options to use as a MW. Currently KPMG China do not utilise a third party MW, but as our application platform grows with a mix of on-premise, SAAS and other Cloud hosted solutions the require

04-09

好的，用户需要将一段关于Salesforce架构文档、中间件选项以及阿里云解决方案的英文文本翻译成中文。首先，我要确认用户的具体需求。他们提到的是架构和中间件相关内容，可能涉及到技术术语，需要准确翻译。...

$sql="SELECT COUNT(a.id) AS cnt,(SUM(CASE WHEN YEARWEEK(b.upload_time, 1) = YEARWEEK(CURDATE(), 1) THEN 1 ELSE 0 END) - SUM(CASE WHEN DATE(b.upload_time) = CURDATE() - INTERVAL 1 DAY THEN 1 ELSE 0 END)- SUM(CASE WHEN DATE(b.upload_time) = CURDATE() THEN 1 ELSE 0 END)) AS this_week_cnt FROM summary_info a LEFT JOIN (SELECT pallet_number, upload_time FROM inbound_info GROUP BY pallet_number) b ON a.pallet_number = b.pallet_number WHERE a.status = '$status'"; 这段代码也优化一下，索引不起作用

12-05

FROM summary_info a LEFT JOIN ( SELECT pallet_number, upload_time FROM inbound_info GROUP BY pallet_number ) b ON a.pallet_number = b.pallet_number WHERE a.status = '$status' ``` ### ✅ 业务...

【医学图像处理】基于openslide的SVS切片多尺度分析：病理AI研究中的高分辨率图像处理与批量裁剪技术应用

01-03

内容概要：本文是一份关于使用openslide处理显微镜病理SVS切片的全流程技术教程，涵盖从环境搭建、基础读取与可视化，到进阶裁剪、批量处理以及科研级应用拓展。文章详细介绍了SVS切片的特点和openslide库的功能，提供了Python代码示例，实现切片多层级图像提取、感兴趣区域裁剪、大批量SVS文件自动化处理，并进一步引导读者结合深度学习模型进行病变分割、细胞计数、多尺度分析及交互式可视化报告开发。同时附带常见问题避坑指南，帮助用户应对内存不足、坐标错乱和格式兼容等问题。; 适合人群：计算机或生物医学工程相关专业，正在进行病理图像分析方向毕业设计的学生，具备一定Python编程和图像处理基础的研发人员。; 使用场景及目标：① 掌握openslide对超大SVS切片的高效读取与多层级可视化方法；② 实现病理图像的精准裁剪与批量预处理，为AI模型训练准备数据；③ 构建从全切片到局部细节的多尺度分析流程，提升毕设的技术深度与科研价值。; 阅读建议：建议边实践边学习，配合提供的代码链接动手操作，优先在小样本上验证流程，并结合ImageScope软件进行坐标定位与结果验证，注意处理大文件时的内存优化策略。

update9-20260103.5.211.slice.img.7z.003

01-03

小雉系统分卷源码

QT学习实例 QLineEdit QLCDNumber

01-03

下载方式：https://pan.quark.cn/s/f4ef8119fda2 通过QT学习实例，可以展示QLineEdit的多样化应用方式，并运用QLCDNumber来以类似液晶显示屏的数字形式呈现数值

半监督和无监督极限学习机（SS-US-ELM）（Matlab代码实现）

01-03

半监督和无监督极限学习机（SS-US-ELM）（Matlab代码实现）内容概要：本文档主要介绍了一种半监督和无监督极限学习机（SS-US-ELM）的Matlab代码实现方法，属于机器学习与深度学习领域的前沿探索内容。文中强调该技术在时序预测、分类识别、回归分析等方面的应用，并指出其适用于缺乏充足标签数据的实际科研场景。资源还涵盖了极限学习机（ELM）系列算法与其他智能优化算法、神经网络模型的结合应用，展示了其在电力系统、负荷预测、电池健康状态评估等多个工程领域的实践价值。此外，文档附带了多个Matlab仿真实例及相关网盘资源链接，便于读者复现和扩展研究。; 适合人群：具备一定Matlab编程基础，从事科研工作或研究生阶段的学习者，尤其适合研究机器学习、电力系统优化、智能算法应用等相关方向的人员。; 使用场景及目标：①用于解决标签数据稀缺情况下的分类与预测问题；②结合智能优化算法提升极限学习机性能；③应用于电力负荷预测、新能源系统调度、电池状态估计等实际工程问题的研究与模型构建。; 阅读建议：建议读者结合提供的Matlab代码实例进行实践操作，优先掌握ELM基本原理后再深入SS-US-ELM的实现逻辑，同时利用网盘资源完成代码复现与参数调优，以达到理论与实践相结合的学习效果。

电机控制基于Simulink的永磁同步电机模型预测控制仿真：FCS-MPCC算法实现与动态性能分析