Acute carpal tunnel syndrome of the hand following a cat bite

Cat bites at the hand are common situation in emergency departments. Neglected or poorly supported, these lesions sometimes lead to serious injuries that may compromise the function of the hand. Pasteurellamultocida is the most offending germ in these lesions, despite their sensitivity to antibiotics; it can sometimes lead to deep infections involving the skin, bones and joints. Acute carpal tunnel syndrome is exceptional after cat bite. We report a case of a 56 Year old female presenting with an acute carpal tunnel syndrome associated with compartment syndrome of the right hand 6 days after a cat bite of her right thumb. The patient was treated by surgery to relieve the median nerve. Microbiology identified PasteurellaMultocida

The Motor Branch of the Median Nerve, a Rare Localization of Schwannoma

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A wavelet neural network based non-linear model predictive controller for a multi-variable coupled tank system

In this paper, a novel real time non-linear model predictive controller (NMPC) for a multi-variable coupled tank system (CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output (MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings, interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation (RTO) of the manipulated variable at every sampling time. A novel wavelet neural network (WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions