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Figures and legends taken from Bicycle Quarterly, Autumn 2010
Interesting graph provided by Shimano regarding the mechanical efficiencies of two of their 8-speed IGHs. This may explain why in some speeds, the hub feels "sluggish" or harder to pedal than in another gear. Note the huge increase in efficiency when switching from 4th to 5th. This is much more pronounced in their lower end hub than in their higher end model.
However, from the perspective of a scientist who generates and scrutinizes such graphs daily, this graph, while interesting, is largely meaningless due to the unit-less y-axis. Are we supposed to be looking at percent efficiency? What is the axis range? If the y-axis in fact represents percent efficiency and the axis range spans 0-100% (for example), then the graph indicates a very pronounced effect from a practical standpoint (i.e., ~35% efficiency in 4th gear, and ~95% efficiency in 5th gear). But if, hypothetically, the axis range represents a narrow efficiency range--say 40-50%--then the peaks and valleys are largely insignificant in the real world (representing about 43% efficiency in 4th compared with 49% efficiency in 5th). Take-home message: be careful how you interpret this data!
Photos taken while I was studying in Toulouse. I left for 10 years, came back 3 years ago, and it's still the same pleasure to stroll around in this light!
from p. 10 of Impresa Consulting report on the Columbia River Crossing, commissioned by Plaid Pantry
the trend line is selected, and analysed from, the red data range... how do you stop it plotting above the blue sinlge point range
Bullet graphs as defined by Steven Few: www.perceptualedge.com/articles/misc/Bullet_Graph_Design_...
Il est rare que j'importe une série. Mais là , j'ai pensé que le "talent de l'artiste" le méritait!
Un vieux poste EDF, sur le parking des "Italiens", en bords de Rhône en Avignon re-décoré par un ou plusieurs grapheurs;
Bravo à eux c'est plus joli comme ça et les GRAPHS n'ont rien à voir avec les TAGS
Quant au © il ne concerne que la prise de vue, le © d'origine a l'artiste inconnu sans doute quelqu'un de "Une autre vision du graffiti" à l'adresse >densoner.fr/
A voir aussi sur: Photoblog: alphagard-photo.blogspot.com/.
Fuji X-E1; Fujinon XF60mm f/2.4; 1/250 sec @ f/4, ISO 6400; Adobe Lightroom 5.3 and Nik Silver Efex Pro 2
A figure used in a lecture from JR James at the Department of Town and Regional Planning at The University of Sheffield between 1967 and 1978.
The Navigator busy at his charts in the Control Room of HMSM GRAPH, ex U-Boat (U 570). She is in the Clyde undergoing trials.
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Zag - bit.ly/1yubJ8G
Photo by Eric Ziegler
More and more companies are building social networks in the enterprise. For those keeping track, the common term used today is Enterprise Social Networks (ESN).In almost all cases, the reasoning for implementing an ESN is to improve collaboration, break down silos, ease the flow information, etc. One that is often missing is how it could influence search results in the enterprise.
In the internet, social networks play a huge role in helping with search results. This help does not come in the straight up indexing of the content and adding to the overwhelming amount of content already being indexed, but rather from the building a a social or knowledge graph from the social networks.
Google, Microsoft, Facebook, etc. have built algorithms to mine the content in social networks and to try to understand the relationships between the interactions happening in the social networks. While this information is used for many purposes, the mining of the data is used build out what is called a social graph and to extend knowledge graphs (think really big taxonomies or ontologies).
There is a big opportunity for companies to leverage their ESN implementations to improve their search results. This concept goes beyond the idea of ESN and could even be applied to any location people interact with each other (online meetings, Chat sessions, even corporate email).
By mining the information from these interactions, a social graph of related to topics and concepts can be built, that will improve how people search and eventually find the information they are most interested in.This note was inspired +David Amerland book, Google Semantic Search - Amazon location 2111.
In weaving we are constantly dealing with a family of four graphs that are readily interconverted: a triangulation, a cubic graph, a medial graph, and a radial graph.