In this paper, some basic notions and facts in the b-modular space similar to those in the modular spaces as a type of generalization are given.  For example, concepts of convergence, best approximate, uniformly convexity etc. And then, two results about relation between semi compactness and approximation are proved which are used to prove a theorem on the existence of best approximation for a semi-compact subset of b-modular space.

KE Sharquie, SM Al-Tammimy, S Al-Mashhadani, RK Hayani, AA Al-Nuaimy, Dermatology online journal, 2006 - Cited by 34

The Asymmetrical Castellated concavely – curved soffit Steel Beams with RPC and Lacing Reinforcement improves compactness and local buckling (web and flange local buckling), vertical shear strength at gross section (web crippling and web yielding at the fillet), and net section ( net vertical shear strength proportioned between the top and bottom tees relative to their areas (Yielding)), horizontal shear strength in web post (Yielding), web post-buckling strength, overall beam flexure strength, tee Vierendeel bending moment and lateral-torsional buckling, as a result of steel section encasement. This study presents two concentrated loads test results for seven specimens Asymmetrical Castellated concavely – curved soffit Steel Be

It is often noted that disordered materials have different chemical properties to their more “ordered” cousins. Quantifying these effects in terms of thermodynamics is challenging in part because disordered materials can be difficult to characterize and are frequently relatively unstable. During the course of our experiments to understand the effects of disorder in catalysts for water oxidation we observed that many disordered manganese and cobalt oxide water oxidation catalysts directly oxidized peroxide in contrast to their more ordered analogues which catalyzed its disproportionation, that is, MnO2+2H+ +H2O2! Mn2+ +2H2O+O2(oxidation) versus H2O2!H2O+1=2 O2(disproportionation). By measuring the efficiency for one reaction over the oth

     The control charts are one of the scientific technical statistics tools that will be used to control of production and always contained from three lines  central line and upper, lower lines to control quality of production and represents set of numbers so finally the operating productivity under control or nor than depending on the actual observations. Some times to calculating the control charts are not accurate and not confirming, therefore the Fuzzy Control Charts are using instead of Process Control Charts so this method is more sensitive, accurate and economically for assisting decision maker to control the operation system as early time. In this project will be used set data fr

L1 adaptive controller has proven to provide fast adaptation with guaranteed transients in a large variety of systems. It is commonly used for controlling systems with uncertain time-varying unknown parameters. The effectiveness of  L1 adaptive controller for position control of single axis has been examined and compared with Model Reference Adaptive Controller (MRAC). The Linear servo motor is one of the main constituting elements of the x-y table which is mostly used in automation application. It is characterized by time-varying friction and disturbance.

    The tracking and steady state performances of both controllers have been assessed fo

In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual output of the system & to find the jacobain of the system. Which appears to be of critical importance parameter as it is used for the feedback controller and the second stage is learned on-line to modify the weights of the model in order to control the variable parameters that will occur to the system. A back propagation neural network is appl