Lipid free radical generation and brain cell membrane alteration following nitric oxide synthase inhibition during cerebral hypoxia in the newborn piglet

Nitric oxide (NO) is reported to cause neuronal damage through various mechanisms. The present study tests the hypothesis that NO synthase inhibition by N(omega)-nitro-L-arginine (NNLA) will result in decreased oxygen-derived free radical production leading to the preservation of cell membrane structure and function during cerebral hypoxia. Ten newborn piglets were pretreated with NNLA (40 mg/kg); five were subjected to hypoxia, whereas the other five were maintained with normoxia. An additional 10 piglets without NNLA treatment underwent the same conditions. Hypoxia was induced with a lowered FiO2 and documented biochemically by decreased cerebral ATP and phosphocreatine levels. Free radicals were detected by using electron spin resonance spectroscopy with a spin trapping technique. Results demonstrated that free radicals, corresponding to alkoxyl radicals, were induced by hypoxia but were inhibited by pretreatment with NNLA before inducing hypoxia. NNLA also inhibited hypoxia-induced generation of conjugated dienes, products of lipid peroxidation. Na+,K+-ATPase activity, an index of cellular membrane function, decreased following hypoxia but was preserved by pretreatment with NNLA. These data demonstrate that during hypoxia NO generates free radicals via peroxynitrite production, presumably causing lipid peroxidation and membrane dysfunction. These results suggest that NO is a potentially limiting factor in the peroxynitrite-mediated lipid peroxidation resulting in membrane injury.     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

An integrated modeling approach to predict flooding on urban basin.

Correct prediction of flood extents in urban catchments has become a challenging issue. The traditional urban drainage models that consider only the sewerage-network are able to simulate the drainage system correctly until there is no overflow from the network inlet or manhole. When such overflows exist due to insufficient drainage capacity of downstream pipes or channels, it becomes difficult to reproduce the actual flood extents using these traditional one-phase simulation techniques. On the other hand, the traditional 2D models that simulate the surface flooding resulting from rainfall and/or levee break do not consider the sewerage network. As a result, the correct flooding situation is rarely addressed from those available traditional 1D and 2D models. This paper presents an integrated model that simultaneously simulates the sewerage network, river network and 2D mesh network to get correct flood extents. The model has been successfully applied into the Tenpaku basin (Nagoya, Japan), which experienced severe flooding with a maximum flood depth more than 1.5 m on September 11, 2000 when heavy rainfall, 580 mm in 28 hrs (return period > 100 yr), occurred over the catchments. Close agreements between the simulated flood depths and observed data ensure that the present integrated modeling approach is able to reproduce the urban flooding situation accurately, which rarely can be obtained through the traditional 1D and 2D modeling approaches.     

A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

The sliding-window packet switch: a new class of packet switch architecture with plural memory modules and decentralized control

Shared-memory based packet switches are known to provide the best possible throughput performance for bursty data traffic in high-speed packet networks and internets compared with other buffering strategies under conditions of identical memory resources deployed in the switch. However, scaling of shared-memory packet switches to a larger size has been restricted mainly due to the physical limitations imposed by the memory-access speed and the centralized control for switching functions in shared-memory switches. A new scalable architecture for a shared-memory packet switch, called the sliding-window (SW) switch, is proposed to overcome these limitations. The SW switch introduces a new class of switching architecture, where physically separate multiple memory modules are logically shared among all the ports of the switch, and the control is decentralized. The SW switch alleviates the bottleneck caused by the centralized control of switching functions in large shared-memory switches. Decentralized switching functions enable the SW switch to operate in a pipeline fashion to enhance scalability and switching capacity compared with that of previously known classes of shared-memory switch architecture.     

Theoretical investigation of penetration characteristics in gas metal-arc welding using finite element method

In the modeling of the gas metal-arc (GMA) welding process, heat inputs to the workpiece by the arc and the metal transfers have been considered separately. The heat energy delivered due to the metal transfer has been approximated in the form of a cylindrical volumetric heat source, whose dimensions of the radius and the height are dependent on the molten metal droplet characteristics. The pinch instability theory (PIT) and the static force balance theory (SFBT) of drop detachment have independently been used to obtain the expressions for various characteristics of the drop,i.e., the drop radius, the drop velocity, and the drop frequency at various welding parameters. The occurrence or the nonoccurrence of finger penetration, routinely found in the GMA welding at high welding currents, has been satisfactorily explained by the cylindrical heat source model. The effect of various welding parameters,e.g., the welding current, the wire radiusetc., on the weld bead penetration characteristics has been investigated. In this modeling effort, the heat conduction equation has been solved in three dimensions.