A new method for the experimental assessment of finger haemodynamic effects induced by a hydraulic breaker in operative conditions

A New Method for the Experimental Assessment of Finger Haemodynamic Effects Induced by a Hydraulic Breaker in Operative Conditions: Matteo Valentino, et al. Clinic of Occupational Medicine, University of Ancona, Italy-The haemodynamic effects, in terms of grip force and hand-transmitted accelerations, produced on the fingers of 10 healthy subjects by operation of a hydraulic breaker held with a low or a high grip were investigated with a new experimental set-up. A novel apparatus consisting of a capacitive sensor matrix and a laser Doppler vibrometer was used to measure the two parameters during breaker operation. Finger blood flow in basal conditions and after each test was evaluated in the four long fingers of both hands with a photoplethysmograph and superficial skin thermometry. The amplitude of the accelerations transmitted to the hand surface was greater during the test with the low grip. Photoplethysmographic and thermometric values were significantly lower than basal values after either test. The amplitude of photoplethysmographic values during the tests was significantly lower with the low than with the high grip in 6 subjects, in both index fingers, and in the right middle finger. The authors show that the level of grip force used for holding the breaker causes transmission of vibrations of wider amplitude, resulting in greater reduction in finger blood flow. In line with the literature, stimulus magnitude, frequency and duration affected finger blood flow, especially in the index finger

Thermodynamic Formalism Of Neural Computing

Neural networks are systems of interconnected processors mimicking some of the brain functions. After a rapid overview of neural computing, the thermodynamic formalism of the learning procedure is introduced. Besides its use in introducing efficient stochastic learning algorithms, it gives an insight in terms of information theory. Main emphasis is given in the information restitution process; stochastic evolution is used as the starting point for introducing statistical mechanics of associative memory. Instead of formulating problems in their most general setting, it is preferred stating precise results on specific models. In this report are mainly presented those features that are relevant when the neural net becomes very large. A survey of the most recent results is given and the main open problems are pointed out