Modeling adhesive wear resistance of Al-Si-Mg-/SiCp PM compacts fabricated by hot pressing process, by means of ANN

In this study, modeling adhesive wear resistance of Al-Si-Mg/SiC_p MMC compacts were performed by ANN, using a back-propagation neural network that uses gradient descent learning algorithm. Powder compacts were fabricated by PM hot pressing process with 5–10–20% SiC_p fractions and contents of specimens (N1, N2, N3 andN4) were given in Table 1. The wear tests were carried out under 10, 20 and 30 N variable loads, while disk rotation speed 90 rpm kept unchanged. Adhesive wear looses were measured and recorded for 250, 500, 1,000 and 1,500 m distances. Microstructure examination at wear surface was investigated by optical microscopy and EDS for metallographic evaluations. In neural networks training module, SiC_p reinforcement fractions (wt), loads and wear distances (m) were used as input, lost mass (g) of specimens were recorded as outputs. Then, the neural network was trained using the prepared training set (also known as learning set). At the end of the training process, the test data were used to check the system accuracy. As a result ANN was found successful in modeling of adhesive wear behavior and lost mass values of Al/SiC_p PM compacts.

Spectral and statistical analysis for ferroresonance phenomenon in electric power systems

This study presents our findings on the ferroresonance phenomenon for the Seyitomer?CIsiklar part of the Electric Power System of 380?kV in Turkey. In this context, the power spectral density approach and Short-Time Fourier Transforms are applied to the voltage variations for phase R of the sample power network. The findings show that the ferroresonance event can be represented by the interharmonics between 50 and 250?Hz, as well as statistical properties. In addition, a histogram of the ferroresonance region shows a different characteristic from the overall data and the non-ferroresonance region. This different characteristic can be defined by an asymmetrical distribution characteristic, and its numerical measure is given by the ??Noise to Signal Ratio?? of 0.624.     

Flexible kanbans to enhance volume flexibility in a JIT environment: a simulation based comparison via ANNs

Kanbans play an important role in the information and material flows in a JIT production system. The traditional kanban system with a fixed number of cards does not work satisfactorily in an unstable environment. In the flexible kanban-type pull control mechanism the number of kanbans is allowed to change with respect to the inventory and backorder level. Based on the need for the flexible kanban, a method was proposed by (Husseini, S.M.M., O’Brien, C., and Hosseini, S.T., 2006. A method to enhance volume flexibility in JIT production control. International Journal of Production Economics, 104 (2), 653–665), using an integer linear programming technique, to flexibly determine the number of kanbans for each stage of a JIT production system, minimising total inventory cost for a given planning horizon. Here, the effectiveness of the method proposed by Husseini et al. is examined by a case study and compared with the results for the conventional method of fixed kanban determination. This is also confirmed by a simulation study using artificial neural networks (ANNs). The main aim of this paper is to show the cost advantage for Husseini et al.'s method over the conventional method in fluctuating demand situations, and especially to prove that simulation via ANNs ensures a simplified representation for this method and is time saving.     

CFD Study of Fluid Flow and Heat Transfer in a Fixed Bed of Cylinders

A comparative study was made of the wall segment and full-bed models of a fixed-bed reactor with cylindrical packing where the wall segment is a 120° segment of the full bed. The flow and heat transfer fields were simulated under industrial steam reformer inlet conditions by the use of computational fluid dynamics (CFD). Good agreement between the two models was found for radial velocity and temperature profiles in the center of the wall segment. The imposition of symmetry walls in the segment caused discrepancies in their vicinity, and in overall averaged quantities such as pressure drop.     

A Back-propagation Neural Network Landmine Detector Using the Delta-technique and S-statistic

Landmines are a major problem facing the world today; there are millions of these deadly weapons still buried in various countries around the world. Humanitarian organizations dedicate an immeasurable amount of time, effort, and money to find and remove as many of these mines as possible. Unfortunately, landmines can be made out of common materials which make the correct detection of them very difficult. This paper analyzes the effectiveness of combining certain statistical techniques with a neural network to improve detection. The detection method must not only detect the majority of landmines in the ground, it must also filter out as many of the false alarms as possible. This is the true challenge to developing landmine detection algorithms. Our approach combines a Back-Propagation Neural Network (BPNN) with statistical techniques and compares the performance of mine detection against the performance of the energy detector and the δ-technique. Our results show that the combination of the δ-technique and the S-statistics with a neural network improves the performance.     

Innate Immune System Response to Burn Damage—Focus on Cytokine Alteration

In the literature, burns are understood as traumatic events accompanied by increased morbidity and mortality among affected patients. Their characteristic feature is the formation of swelling and redness at the site of the burn, which indicates the development of inflammation. This reaction is not only important in the healing process of wounds but is also responsible for stimulating the patient’s innate immune system. As a result of the loss of the protective ability of the epidermis, microbes which include bacteria, fungi, and viruses have easier access to the system, which can result in infections. However, the patient is still able to overcome the infections that occur through a cascade of cytokines and growth factors stimulated by inflammation. Long-term inflammation also has negative consequences for the body, which may result in multi-organ failure or lead to fibrosis and scarring of the skin. The innate immune response to burns is not only immediate, but also severe and prolonged, and some people with burn shock may also experience immunosuppression accompanied by an increased susceptibility to fatal infections. This immunosuppression includes apoptosis-induced lymphopenia, decreased interleukin 2 (IL-2) secretion, neutrophil storm, impaired phagocytosis, and decreased monocyte human leukocyte antigen-DR. This is why it is important to understand how the immune system works in people with burns and during infections of wounds by microorganisms. The aim of this study was to characterize the molecular pathways of cell signaling of the immune system of people affected by burns, taking into account the role of microbial infections.