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这是适用于Windows NT 4 和 Windows 95 的 Windows Media Player 7，适和做体验包大佬用。

The Monte Carlo code MCML (Monte Carlo modeling of light transport in multi-layered tissue) has been the gold standard for simulations of light transport in multi-layer tissue, but it is ineffective in the presence of three-dimensional (3D) heterogeneity. New techniques have been attempted to resolve this problem, such as MCLS, which is derived from MCML, and tMCimg, which draws upon image datasets. Nevertheless, these approaches are insufficient because of their low precision or simplistic mode

用关键字捕捉推文 通过该项目，您可以使用Twitter API使用输入的单词和日期从API中提取数据。 输出示例 入门 这些说明将为您提供在本地计算机上运行并运行的项目的副本，以进行开发和测试。 先决条件 Python 2.7和Pip 正在安装 git clone https://github.com/dogukanayd/Catch-Tweet-with-Keyword.git cd Catch-Tweet-with-Keyword pip install -r requirements.txt 在settings.py中输入您自己的密钥 YOUR_CONSUMER_KEY = 'Y

部分博友在看过我的博文http://blog.csdn.net/y601500359/article/details/72877966#reply例子后，说编译不过工程，所以我把编译好的工程传了上来，含debug和release版本

PeriDEM-基于Peridynamics的颗粒系统离散元素模型 目录 构建代码 有关快速构建的建议 运行模拟 两粒墙 耐压测试 开发者 介绍 结合了周动力学和离散元方法（DEM）优点的粒状介质高保真模型的实现。 与现有的粒状介质机械模型相比，该模型具有以下优点： 处理颗粒内部变形和破裂/损坏 处理任意形状的粒子。 粒子间接触并非特定于任何形状的粒子 可调粒子间接触参数 易于为单个粒子变形在周动力学内添加不同的机械本构定律 有关模型和结果的更多详细信息，请参考本文： Prashant K.Jha，Prathamesh S.Desai，Debdeep Bhattacharya，Robert P Lipton（2020年）。 基于Peridynamics的离散系统离散元方法（PeriDEM）模型，涉及任意形状的颗粒破裂。 固体力学与物理学杂志，第151卷，2021年。Doi https

特点： - 播放列表 - 在不同的文件夹中保存歌曲 - 基于 GStreamer 的 API - 寻找秒而不是基于滚动的寻找 - 内置 IRC 客户端

jquery实现网页在线预览pdf文件的js包，使用简单只需引入该js文件，并在页面添加 即可

Python Social Media Analytics by Siddhartha Chatterjee English | 28 July 2017 | ISBN: 1787121488 | ASIN: B01MXL4UYG | 312 Pages | AZW3 | 8.63 MB Leverage the power of Python to collect, process, and mine deep insights from social media data About This Book Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more Analyze and extract actionable insights from your social data using various Python tools A highly practical guide to conducting efficient social media analytics at scale Who This Book Is For If you are a programmer or a data analyst familiar with the Python programming language and want to perform analyses of your social data to acquire valuable business insights, this book is for you. The book does not assume any prior knowledge of any data analysis tool or process. What You Will Learn Understand the basics of social media mining Use PyMongo to clean, store, and access data in MongoDB Understand user reactions and emotion detection on Facebook Perform Twitter sentiment analysis and entity recognition using Python Analyze video and campaign performance on YouTube Mine popular trends on GitHub and predict the next big technology Extract conversational topics on public internet forums Analyze user interests on Pinterest Perform large-scale social media analytics on the cloud In Detail Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using var

MEDIASTREAMER2分析研究， 介绍的很详细， 有助于大家理解， 我会在后续把我总结的mediastreamer， 发出来， 哈哈