Output feedback controller for polytopic systems exploiting the direct searching of the design space

This paper deals with the problem of designing a robust static output feedback controller for polytopic systems. The current research that tackled this problem is mainly based on LMI method, which is conservative by nature. In this paper, a novel approach is proposed, which considers the design space of the controller parameters and iteratively partitions the space to small simplexes. Then, by assessing the stability in each simplex, the solution space for design parameters is directly determined. It has been theoretically proved that, if there exists a feasible solution in the design space, the algorithm can find it. To validate the result of the proposed approach, comparative simulation examples are given to illustrate the performance of the design methodology as compared to those of previous approaches.     

Flexible poly(styrene-ethylene-butadiene-styrene) hybrid nanofibers for bioengineering and water filtration applications

Among the thermoplastic elastomers that play important roles in the polymer industry due to their superior properties, styrene-based species and polyurethane block copolymers are of great interest. Poly(styrene-ethylene-butadiene-styrene) (SEBS) as a triblock copolymer seems to have the potential to meet many demands in different applications due to various industrial requirements where durability, biocompatibility, breaking elongation, and interfacial adhesion are important. In this study, the SEBS triblock copolymer was functionalized with natural (Satureja hortensis, SH) and synthetic (nanopowder, TiO₂) agents to obtain composite nanofibers by electrospinning and electrospraying methods for use in biomedical and water filtration applications. The results were compared with thermoplastic polyurethane (TPU) composite nanofibers, which are commonly used in these fields. Here, functionalized SEBS nanofibers exhibited antibacterial effect while at the same time improving cell viability. In addition, because of successful water filtration by using the SEBS composite nanofibers, the material may have a good potential to be used comparably to TPU for the application.     

Effect of fortification with asparagus powder on the qualitative properties of processed cheese

The qualitative properties of processed cheese (PC) fortified with different levels of asparagus powder (AP) (0.5%, 1% and 1.5% wt/wt) were evaluated during storage. AP decreased the pH and lipolysis indexes and increased the phenolic content, antioxidant activity and proteolysis of the processed cheeses. AP made the structure of the cheese more elastic, increased the rigidity and decreased the spreadability compared with the control sample, which corresponded to the results obtained using dynamic oscillatory rheometry. The results showed that AP as a rich source of bioactive components could be used for the fortification of processed cheeses.     

A Novel Approach to Analyzing the Occupancy of the Geo/Geo/∞ Queueing System

Discrete time queueing systems have been successfully used to model packet queues in computer and digital communications systems. In this article we introduce a novel approach to analyzing the Geo^X/Geo^Y/∞ queueing system, which is suitable for modelling high speed computer and communications systems. The approach is based on characterizing the system by a functional equation which is then solved recursively to give identical results to those obtained using other approaches.     

A quick neural network for computer vision of gray images

This paper presents a comparison between the traditional image processing method and the area vector concept as well as the new technique of artificial neural networks. Freeman chain coding is considered in the study, and the principle of segmentation may be based and implemented for further investigations resulting from the proposed work. The pattern recognition concept is analyzed and defined through the sigmoid function and the determination of the threshold of a gray image for an object. The block schemes for the given protocols are summarized in a single scheme for illustration and comparison purposes. The synthetic pictures are generated and investigated regarding the dependence of computer vision on the contents of the artificial neural network. The normalization technique is included to eliminate noise and zooming problems. The minimum computational time for image processing with the generated pictures is also determined. The rate of deflection in the computational time is recommended for sensing the minimum computational time according to the variation of the number of hidden units in the hidden layer. A three-layer neural network has been used. The study of gray binary imaging for color pictures is illustrated to save computational time and effort.     

Simulation of Natural Depletion and Miscible Gas Injection Effects on Asphaltene Stability in Petroleum Reservoir Fluids

Natural depletion of petroleum reservoirs as well as gas injection for enhance oil recovery, are unavoidable processes in the oil industry. Foremost, prediction of the problems due to these two processes is very necessary and important. So many field and experimental experiences have shown that heavy organic depositions, especially asphaltene deposition, are principal results during these processes. Results of laboratory simulation of asphaltene deposition during the natural depletion of petroleum reservoirs and also during gas injection and enhanced oil recovery (EOR) processes are reported here. This is achieved through the design of a new experimental setup for the investigation of pressure and composition effects on asphaltene deposition in petroleum fluids at high pressure and high temperature conditions. In this work, asphaltene deposition during decreasing pressure, from pressures greater than reservoir pressure to pressures below the bubble point pressure (natural depletion) and also asphaltene deposition during natural gas injection in reservoir conditions, are studied for three samples—one recombined sample and two bottomhole samples. All of the obtained results from this work conform to theoretical and other experimental works.     

Treating smokers before the quit date: can nicotine patches and denicotinized cigarettes reduce cravings?

The present study investigated whether treatment with the combination of denicotinized cigarettes and 21-mg nicotine patch for 2 weeks before a designated quit date could lessen cravings for smoking, thereby helping smokers abstain from smoking. The study was a randomized controlled clinical trial conducted at Roswell Park Cancer Institute, Buffalo, New York, in 2004 and 2005. Patients included 98 adult heavy smokers (using 20 or more cigarettes/day). Half of the subjects received 2 weeks of combination of denicotinized cigarettes (Quest 3) and 21-mg nicotine patch for 2 weeks before the quit date. The remaining smokers were switched to light cigarettes (Quest 1) during the 2 weeks before the quit date. After the quit date, all subjects received counseling for smoking cessation and were provided nicotine patches for up to 8 weeks after the quit date. Self-reported cravings for smoking, withdrawal symptoms, and smoking abstinence were measured at predetermined intervals using phone-based surveys and in clinical visits. The group that used denicotinized cigarettes and nicotine patch before quitting reported less frequent and less intense cravings for cigarettes in the 2 weeks before and after the designated quit date. Self-reported withdrawal symptoms and quit rates did not differ significantly between the groups. The use of a denicotinized cigarette combined with the nicotine patch appears to lessen cravings to smoke in the immediate postcessation period. A larger, better-powered study is needed to test if this treatment combination has merit for increasing quit rates.     

Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.