The human microbiome: Me, myself, us WHAT’S a man? Or, indeed, a woman? Biologically, the answer might seem obvious. A human being is an individual who has grown from a fertilised egg which contained genes from both father and mother. A growing band of biologists, however, think this definition incomplete. They see people not just as individuals, but also as ecosystems. A healthy adult human harbours some 100 trillion bacteria in his gut alone. And it really is a system, for evolution has aligned the interests of host and bugs. That bacteria can cause disease is no revelation. A bug’s life One way to think of the microbiome is as an additional human organ, albeit a rather peculiar one. The microbiome, too, is organised. Specialised; but not monotonous. That detail is significant. This early nutritional role, moreover, is magnified throughout life. The fat of the land This role in nutrition points to one way in which an off-kilter microbiome can affect its host: what feeds a body can also overfeed or underfeed it.
Scaling of cities With urban population increasing dramatically worldwide, cities are playing an increasingly critical role in human societies and the sustainability of the planet. An obstacle to effective policy is the lack of meaningful urban metrics based on a quantitative understanding of cities. Typically, linear per capita indicators are used to characterize and rank cities. 1.15). Figures Citation: Bettencourt LMA, Lobo J, Strumsky D, West GB (2010) Urban Scaling and Its Deviations: Revealing the Structure of Wealth, Innovation and Crime across Cities. Editor: Juan A. Received: May 18, 2010; Accepted: September 16, 2010; Published: November 10, 2010 Copyright: © 2010 Bettencourt et al. Funding: This work was partially supported by a James S. Competing interests: The authors have declared that no competing interests exist. Introduction How rich, creative or safe can we expect a city to be? The use of per capita indicators assumes implicitly that, on average, specific urban characteristics, . Results .
Think Complexity by Allen B. Downey Buy this book from Amazon.com. Download this book in PDF. Read this book online. Description This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science: Data structures and algorithms: A data structure is a collection that contains data elements organized in a way that supports particular operations. This book focuses on discrete models, which include graphs, cellular automata, and agent-based models. Complexity science is an interdisciplinary field---at the intersection of mathematics, computer science and physics---that focuses on these kinds of models. Free books! This book is under the Creative Commons Attribution-NonCommercial 3.0 Unported License, which means that you are free to copy, distribute, and modify it, as long as you attribute the work and don't use it for commercial purposes. Download the LaTeX source code (with figures and a Makefile) in a zip file.
怎样化杂为简 Intelligent Complex Adaptive Systems I don’t believe in the existence of a complex systems theory as such and, so far, I’m still referring to complex systems science (CSS) in order to describe my research endeavours. In my view, the latter is constituted, up until now, by a bundle of loosely connected methods and theories aiming to observe— from contrasted standpoints—these fascinating objects of research called complex adaptive systems. Nearly 40 years after Von Bertalanffy’s General System Theory (1968) and Jacques Monod’s Chance and Necessity (1971), it is fair to look back and to try to assess how much remains to be said about these complex adaptive systems. After all, Prigogine’s Order out of Chaos (1984) already demonstrated that future wasn’t entirely predictable in a history- contingent world. The universe is a massive system of systems -- for example, ecological systems, social systems, commodity and stock markets.
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Complex systems made simple Albert-László Barabási and Yang-Yu Liu, together with their collaborator Jean-Jacques Slotine at M.I.T., have developed a method for observing large, complex systems. In the image above, red dots represent sensor nodes, which are required to reconstruct the entire internal state of one such system. Image by Mauro Martino. Just as the name implies, complex systems are difficult to tease apart. But that may not matter anymore. The approach takes advantage of the interdependent nature of complexity to devise a method for observing systems that are otherwise beyond quantitative scrutiny. “Connectedness is the essence of complex systems,” said Albert-László Barabási, one of the paper’s authors and a Distinguished Professor of Physics with joint appointments in biology and the College of Computer and Information Science. Using their novel approach, the researchers first identify all the mathematical equations that describe the system’s dynamics.
Decision and Information Sciences - CEEESA Decision Analysis Tools The Center for Energy, Environmental, and Economic Systems Analysis (CEEESA) provides decision analysis tools that cover a wide range of issues. The table below summarizes the available models. Click on each model name to get more information. Decision Analysis Model (DAM) DAM was developed to aid decision analysts in solving multiple-criteria decision analysis problems. For more information about the Decision Analysis Model (DAM), contact CEEESA. Interactive Decision Analysis (IDEA) IDEA is a PC-based software package used for a wide variety of decision problems. For more information about Interactive Decision Analysis (IDEA), contact CEEESA. Portfolio Analysis Support System (PASS) PASS helps users effectively allocate limited resources — funds, personnel, and facilities — by better understanding the pros and cons of different proposals. For more information about Portfolio Analysis Support System (PASS), contact CEEESA. Argonne Risk Allocation Model (ARAM)
Observability of complex systems Author Affiliations Edited by Giorgio Parisi, University of Rome, Rome, Italy, and approved December 26, 2012 (received for review September 6, 2012) Abstract A quantitative description of a complex system is inherently limited by our ability to estimate the system’s internal state from experimentally accessible outputs. Footnotes Author contributions: Y. 社會網路分析 出自 MBA智库百科( 社會網路分析(Social Network Analysis) 社會網路分析方法是由社會學家根據數學方法﹑圖論等發展起來的定量分析方法，近年來，該方法在職業流動、城市化對個體幸福的影響、世界政治和經濟體系、國際貿易等領域廣泛應用，併發揮了重要作用。社會網路分析是社會學領域比較成熟的分析方法，社會學家們利用它可以比較得心應手地來解釋一些社會學問題。許多學科的專家如經濟學、管理學等領域的學者們在新經濟時代——知識經濟時代，面臨許多挑戰時，開始考慮借鑒其他學科的研究方法，社會網路分析就是其中的一種。 網路指的是各種關聯，而社會網路(Social Network)即可簡單地稱為社會關係所構成的結構。 從社會網路的角度出發，人在社會環境中的相互作用可以表達為基於關係的一種模式或規則，而基於這種關係的有規律模式反映了社會結構，這種結構的量化分析是社會網路分析的出發點。 網路指的是各種關聯，而社會網路(social network)即可簡單地稱為社會關係所構成的結構。 行動者(actor)：這裡的行動者不但指具體的個人，還可指一個群體、公司或其他集體性的社會單位。 關係紐帶(relational tie)：行動者之間相互的關聯即稱關係紐帶。 二人組(dyad)：由兩個行動者所構成的關係。 三人組(triad)：由三個行動者所構成的關係。 子群(subgroup)：指行動者之間的任何形式關係的子集。 群體(group)：其關係得到測量的所有行動者的集合。 社會網路分析是對社會網路的關係結構及其屬性加以分析的一套規範和方法。 從這個意義上說，社會網路分析不僅是對關係或結構加以分析的一套技術，還是一種理論方法——結構分析思想。 韋爾曼指出，作為一種研究社會結構的基本方法，社會網路分析具有如下基本原理： 1．關係紐帶經常是不對稱地相互作用著的，在內容和強度上都有所不同。 2．關係紐帶間接或直接地把網路成員連接在一起；故必須在更大的網路結構背景中對其加以分析。 3．社會紐帶結構產生了非隨機的網路，因而形成了網路群(network clusters)、網路界限和交叉關聯。 4．交叉關聯把網路群以及個體聯繫在一起。 5．不對稱的紐帶和複雜網路使稀缺資源的分配不平等。 6．網路產生了以獲取稀缺資源為目的的合作和競爭行為。 因此，社會結構有多重含義。 2．1中心性分析