既然人類身體百分之六十是『水』
Elements of the body by mass
焉不先想水是什麼乎！
“Quantum Water” Discovered in Carbon Nanotubes
A new quantum state of water found in carbon nanotubes at room temperature could have important implications for life
Many astrobiologists think that water is a key ingredient for life. And not just because life on Earth can’t manage without it.
Water has a weird set of properties that other chemicals simply do not share. One famous example is that water expands when it freezes, ensuring that ice floats rather than sinks. That’s important because if it didn’t, lakes and oceans would freeze from the bottom upwards, making it hard for complex life to survive and evolve.
These and other properties are the result of water molecules’ ability to form hydrogen bonds with each other and this gives these molecules some very special properties.
Today, George Reiter at the University of Houston and a few buddies put forward evidence that water is stranger than anybody thought. In fact, they go as far as to say that when confined on the nanometre scale, it forms into an entirely new type of quantum water.
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或許那靈魂正在觀水也！！
─── 《萬象在說話︰美德可以教導嗎》
假設我們已從
量子力學
1927年第五次索爾維會議，此次會議主題為「電子和光子」，世界上最主要的物理學家聚集在一起討論新近表述的量子理論。
量子力學（英語：quantum mechanics）是物理學的分支學科。它主要描寫微觀的事物，與相對論一起被認為是現代物理學的兩大基本支柱，許多物理學理論和科學，如原子物理學、固態物理學、核物理學和粒子物理學以及其它相關的學科，都是以其為基礎。
19世紀末，人們發現舊有的古典理論無法解釋微觀系統，於是經由物理學家的努力，在20世紀初創立量子力學，解釋了這些現象。量子力學從根本上改變人類對物質結構及其交互作用的理解。除了透過廣義相對論描寫的重力外，迄今所有基本交互作用均可以在量子力學的框架內描述（量子場論）。
愛因斯坦可能是在科學文獻中最先給出術語「量子力學」的物理學者。[1]:86[a]
計算得知 
原子、 
原子之電子結構以及光譜數據；那麼可否借此推斷出『水』 
是什麼呢？比方說︰它的
化學
任何物質的化學性質，均是由其原子或分子的電子結構所決定的。通過解析包括了所有相關的原子核和電子的多粒子薛丁格方程式，可以計算出該原子或分子的電子結構。在實踐中，人們認識到，要計算這樣的方程式實在太複雜，對於許多案例，必需使用簡化的模型，找到可行的數學計算方法，才能夠找到近似的電子結構，從而確定物質的化學性質。[50]:193-195實際上，量子電動力學是化學的基礎原理[51]。
量子力學可以詳細描述原子的電子結構與化學性質。對於只擁有一個束縛電子的氫原子，薛丁格方程式有解析解，可以計算出相關的能級與氫原子軌域，而且能級符合氫原子光譜實驗的數據，從每一種氫原子軌域可以得到對應的電子機率分布。對於其它種原子（多電子原子），薛丁格方程式沒有解析解，只能得到近似解，可以計算出近似氫原子軌域的哈特里原子軌域，形狀相同，但尺寸與能級模式不一樣。使用哈特里原子軌域，可以解釋原子的電子結構與化學性質，週期表的元素排列。[50]:193-195
量子力學能夠解釋，在分子裏的束縛電子怎樣將分子內部的原子綑綁在一起。對於最為簡單，只擁有一個束縛電子的氫分子離子H2+，應用玻恩–歐本海默近似（兩個原子核固定不動），薛丁格方程式有解析解，可以計算出它的分子軌域。但是對於其它更為複雜的分子 ，薛丁格方程式沒有解析解，只能得到近似解，只能計算出近似的分子軌域。理論化學中的分支，量子化學和計算化學，專注於使用近似的薛丁格方程式，來計算複雜的分子的結構及其化學性質 。[50]:235ff
特性哩！就像問著水中『氫離子』 
與『質子』有什麼不同呢？？答之以︰處境曾經不同也！！
因是回顧︰
範疇 (哲學)
在哲學中，範疇（希臘語：κατηγορια）概念被用於對所有存在的最廣義的分類。比如說時間，空間，數量，質量，關係等都是範疇。在分類學中，範疇是最高層次的類的統稱。它既不同於學術界對於學問按照學科的分門別類，又有別於百科全書式的以自然和人類為中心的對知識的分類，範疇論是著眼於存在的本質區別的哲學分類系統，因而範疇論屬於形上學的本體論分支。
亞里士多德的範疇論
亞里士多德是範疇論的開山祖師。他在《範疇篇》這本著作中列舉並且討論了十大基本存在，分別為：
- 實體（ουσία）
- 數量（ποσόν）
- 性質（ποιόν）
- 關係（προς τι）
- 場所（που）
- 時間（πότε）
- 姿勢（κείσθαι）
- 狀態（έχειν）
- 動作（ποιείν）
- 承受（πάσχειν）
並稱它們為範疇。亞里士多德對各種形式的存在作了如下定義：一個存在是任何一個可以用「是」或「有」來描述的對象。要對存在的範疇進行研究，就要首先決定在什麼情況下，我們對事物可以用「是」或「有」來陳述，這種可描述性的本質到底是什麼。一個範疇是指事物的一個最大的分類 — 「事物」在此是指可被稱謂但不能還原成其它類的任何對象。亞里士多德的範疇論提出了第一個哲學的分類系統，並有助於促使哲學家去考慮哲學的研究對象究竟是什麼。
關於「範疇」的補充解釋
直觀上，一個對所有存在的完美的分類系統應該滿足如下3個條件：
- 有限的： 類的數量是有限的。
- 覆蓋的： 任何存在都屬於某一類。也就是說類的集合包含宇宙萬物。
- 無交的： 任何存在都只屬於某一類。也就是說不同的類之間沒有相交。
以上條件在範疇的分界問題上引起困難，比如說性質和關係的區別本來就不明顯，又比如說在場所和時間這樣的連續環境中規定像大小多少這樣的數量的概念本身就沒有界限。
其實現實的分類系統，比如說生物學對物種的分類，圖書館對書籍的分類，皆不能滿足既是覆蓋的，又是無交的。
範疇是一種輔助概念，每一個範疇都是人為創造出來並加以組織化的術語，給科學提供分類樣式，作為思考技術的工具，為進行共同討論限定框架和帶來主題感覺。
「範疇」本身就是一個範疇。範疇被認為是人類意識歸納出的各種對象的心理概念，這種概念是總結性的。人類依靠範疇認識對象，而使自己與其他動物區分開來。
………
和瞻前︰
Data science
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured,[1][2] similar to data mining.
Data science is a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena” with data.[3] It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.
Turing award winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge.[4][5]
In 2012, when Harvard Business Review called it “The Sexiest Job of the 21st Century”,[6] the term “data science” became a buzzword. It is now often used interchangeably with earlier concepts like business analytics,[7]business intelligence, predictive modeling, and statistics. Even the suggestion that data science is sexy was paraphrasing Hans Rosling, featured in a 2011 BBC documentary with the quote, “Statistics is now the sexiest subject around.”[8] Nate Silver referred to data science as a sexed up term for statistics.[9] In many cases, earlier approaches and solutions are now simply rebranded as “data science” to be more attractive, which can cause the term to become “dilute[d] beyond usefulness.”[10] While many university programs now offer a data science degree, there exists no consensus on a definition or suitable curriculum contents.[7] Data science is no guarantee of success, however, and many data-science and big-data projects fail to deliver useful results, often as a result of poor management and utilization of resources.[11][12][13][14]
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※ 參讀︰
THE DATA SCIENCE VENN DIAGRAM
by Drew Conway
How to read the Data Science Venn Diagram
The primary colors of data: hacking skills, math and stats knowledge, and substantive expertise
- On Monday we spent a lot of time talking about “where” a course on data science might exist at a university. The conversation was largely rhetorical, as everyone was well aware of the inherent interdisciplinary nature of the these skills; but then, why have I highlighted these three? First, none is discipline specific, but more importantly, each of these skills are on their own very valuable, but when combined with only one other are at best simply not data science, or at worst downright dangerous.
- For better or worse, data is a commodity traded electronically; therefore, in order to be in this market you need to speak hacker. This, however, does not require a background in computer science—in fact—many of the most impressive hackers I have met never took a single CS course. Being able to manipulate text files at the command-line, understanding vectorized operations, thinking algorithmically; these are the hacking skills that make for a successful data hacker.
- Once you have acquired and cleaned the data, the next step is to actually extract insight from it. In order to do this, you need to apply appropriate math and statistics methods, which requires at least a baseline familiarity with these tools. This is not to say that a PhD in statistics in required to be a competent data scientist, but it does require knowing what an ordinary least squares regression is and how to interpret it.
- In the third critical piece—substance—is where my thoughts on data science diverge from most of what has already been written on the topic. To me, data plus math and statistics only gets you machine learning, which is great if that is what you are interested in, but not if you are doing data science. Science is about discovery and building knowledge, which requires some motivating questions about the world and hypotheses that can be brought to data and tested with statistical methods. On the flip-side, substantive expertise plus math and statistics knowledge is where most traditional researcher falls. Doctoral level researchers spend most of their time acquiring expertise in these areas, but very little time learning about technology. Part of this is the culture of academia, which does not reward researchers for understanding technology. That said, I have met many young academics and graduate students that are eager to bucking that tradition.
- Finally, a word on the hacking skills plus substantive expertise danger zone. This is where I place people who, “know enough to be dangerous,” and is the most problematic area of the diagram. In this area people who are perfectly capable of extracting and structuring data, likely related to a field they know quite a bit about, and probably even know enough R to run a linear regression and report the coefficients; but they lack any understanding of what those coefficients mean. It is from this part of the diagram that the phrase “lies, damned lies, and statistics” emanates, because either through ignorance or malice this overlap of skills gives people the ability to create what appears to be a legitimate analysis without any understanding of how they got there or what they have created. Fortunately, it requires near willful ignorance to acquire hacking skills and substantive expertise without also learning some math and statistics along the way. As such, the danger zone is sparsely populated, however, it does not take many to produce a lot of damage.
I hope this brief illustration has provided some clarity into what data science is and what it takes to get there. By considering these questions at a high level it prevents the discussion from degrading into minutia, such as specific tools or platforms, which I think hurts the conversation.
───
最好齊頭並進與時偕行也◎
當此新春伊始之際，宜建立科學『實驗室』︰
/jupyterlab
JupyterLab computational environment. https://jupyterlab.readthedocs.io/en/…
Installation | Documentation | Contributing | License | Team | Getting help |
JupyterLab
An extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture. Currently ready for users.
JupyterLab is the next-generation user interface for Project Jupyter offering all the familiar building blocks of the classic Jupyter Notebook (notebook, terminal, text editor, file browser, rich outputs, etc.) in a flexible and powerful user interface. JupyterLab will eventually replace the classic Jupyter Notebook.
JupyterLab can be extended using npm packages that use our public APIs. To find JupyterLab extensions, search for the npm keyword jupyterlab-extension or the GitHub topic jupyterlab-extension. To learn more about extensions, see the user documentation.
The current JupyterLab releases are suitable for general usage, and the extension APIs will continue to evolve for JupyterLab extension developers.
Read the latest version’s documentation on ReadTheDocs.
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JupyterLab Documentation
JupyterLab is the next-generation web-based user interface for Project Jupyter. Try it on Binder. JupyterLab follows the Jupyter Community Guides.
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自可鋒利學習『工具』呦☆