• http://www.public.coe.edu/~theller/soj/now/betsey.htm
• I like it because:
• It includes a trip to a worlds fair by train, including hitching a ride
• I know it's 1876
• The principals are like some wonderful women I know
• Reference to Maine's marine/trade heritage
• Spoiler alert:
• Betsy gets a bit of cash from a vistor. She sallies forth, walking to the station but before she get there she hitches a ride on a frieght. At the Philadelphia Centennial she befriends everyone, buys gifts for here friends and comes home.
• This looks like a great site for all things Sarah Orne Jewett

• via Marine Technology Reporter September 2009
• http://www.renewableenergyworld.com/rea/news/article/2009/10/large-wind
• pdafriendly

From virtually nothing in 2000, the industry today can boast 1.5 GW of installed offshore wind capacity, of which 334 MW – more than one fifth – was installed in 2008 alone, see Figure 1, (below). An additional 1.5 GW is currently under construction, and Douglas-Westwood forecasts more than 5 GW will be in the water by 2012.

The theoretical setting of hierarchical Bayesian inference is gaining acceptance as a framework for understanding cortical computation. In this paper, we describe how Bayesian belief propagation in a spatio-temporal hierarchical model, called Hierarchical Temporal Memory (HTM), can lead to a mathematical model for cortical circuits. An HTM node is abstracted using a coincidence detector and a mixture of Markov chains. Bayesian belief propagation equations for such an HTM node define a set of functional constraints for a neuronal implementation. Anatomical data provide a contrasting set of organizational constraints. The combination of these two constraints suggests a theoretically derived interpretation for many anatomical and physiological features and predicts several others. ...

Citation: George D, Hawkins J (2009) Towards a Mathematical Theory of Cortical Micro-circuits. PLoS Comput Biol 5(10): e1000532. doi:10.1371/journal.pcbi.1000532

(defprocess root (fuzziness)
 (defvariable blobs #’union)
 (always
  (when (eq (read-sensor ’image) ’black)
   (subprocess (measure-blob) :gradient fuzziness)
   (setf blobs
    (list (get-from-sub (measure-blob) blob)))))
 (avoid
  (read-sensor ’query)
  (let ((q (first (read-sensor ’query))))
   (cond
    ((eq q ’blobs)
     (actuate ’response (regional blobs)))
    ((eq q ’area)
     (actuate ’response
      (fold #’+ (mapcar #’second
       (regional blobs)))))))))
(defprocess measure-blob ()
 (defvariable uid #’max :atomic :base 0 :init (random 1))
 (defvariable area #’sum :reduction :base 0 :init 1)
 (defvariable blob :local)
 (always
  (setf blob (list uid area))))

Code to find a set of fuzzy blobs and their areas in a binary image. Each contiguous black area of the image runs a connected measure-blob pro- cess that names it and calculates its area. The set of blobs is collected by the root process and made accessible to the user on the response actuator...

Are there yet ZigBee products on the market?

Yes, there are. Some implement an actual ZigBee stack while others feature a ZigBee ready platform like EmberNet.

Examples in the residential space include Control4 (lighting), Eaton (home automation), Golden Power Manufacturing (sprinklers and thermostats), Hawking Technology (home gateways), Kalirel (heating), Mija (fire extinguishers), Nice (shutters), and TSC Systems (home automation).

Examples in the commercial space include Mija (fire extinguishers), Philips (lighting), Siemens (building automation), and TAC (building automation).

Networks of coupled dynamical systems have been used to model ... and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly 6 degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks.

Strategic Management Database in Brief
To help the busy executive keep a half-step ahead of the game