DVCC-Based Non-linear Feedback Neural Circuit for?Solving System of Linear Equations

A neural circuit to solve a system of simultaneous linear equations is presented. The circuit employs non-linear feedback to achieve a transcendental energy function that ensures fast convergence to the exact solution while enjoying reduction in hardware complexity over existing schemes. A new building block for analog signal processing, the digitally controlled differential voltage current conveyor (DC-DVCC) is introduced and is utilized for the non-linear synaptic interconnections between neurons. The proof of the energy function has been given and it is shown that the gradient network converges exactly to the solution of the system of equations. PSPICE simulation results are presented for linear systems of equations of various sizes and are found to be in close agreement with the algebraic solution. The use of CMOS DC-DVCCs and operational amplifiers facilitates monolithic integration.     

New Method for Testing Fiber-Reinforced Polymer Rods under Fatigue

This paper presents a newly developed specimen for testing fiber-reinforced polymer (FRP) composite reinforcements under cyclic loading. In this configuration the FRP rod is completely encased in concrete. The specimen is formed of three discrete blocks. The end blocks serve as anchors for the rod, whereas the central block provides the concrete environment to the part of the rod where failure is expected to occur. The specimen can be easily handled and uses a very simple test setup that can be adapted to all universal testing machines. An extensive research program has been initiated by the writers to study the influence of varying parameters on the fatigue performance of FRP products. Results of demonstration tests performed on a proprietary carbon FRP rod using the specimen developed are presented in this paper. The results show consistent findings and great potential for using the specimen in different situations such as tensile and cyclic testing of FRP reinforcements.     

Efficient prediction of drug–drug interaction using deep learning models

A drug–drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug–drug interaction is defined as an ill‐posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug–drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug–drug interaction score efficiently. However, these models suffer from the over‐fitting issue. Therefore, these models are not so‐effective for predicting the drug–drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.Inspec keywords: cancer, learning (artificial intelligence), drugs, recurrent neural nets, convolutional neural nets, drug delivery systemsOther keywords: drug synergy, drug–drug interaction score, drug–drug interaction prediction, deep learning, cancer treatment, machine learning, convolutional mixture density recurrent neural network     

Effects of some plating variables on the electrodeposition of catalytically active nickel-thallium alloy powders

The electrodeposition of nickel-thallium alloy powders was investigated from a selected bath of the composition 0.0125 NiSO₄·6H₂O, 0.01 TICI, 0.10 (NH₄)₂SO₄, 0.10 H₃BO₃ and 0.07 Na₂SO₄·10 H₂O (mol dm^?3). The effects of the pH of the bath, deposition current density and deposition time on the cathodic polarisation, current efficiency and composition of the electrodeposited alloy were determined. The properties of the electrodeposits examined, as a function of the above-mentioned variables, were surface morphology and catalytic activity towards the decomposition of 0.4% H₂O₂ solution. It was found from this investigation that the operating variables influence to a great extent the current efficiency, composition and surface morphology of the electrodeposited alloys; and the catalytic activity is primarily controlled by the surface morphology.     

Reliability-based design of blast-resistant composite laminates incorporating carbon nanotubes

Research efforts have reported on the ability of carbon nanotubes (CNTs) to enhance the stiffness and strength of carbon fiber composites. Systematic design of blast-resistant composites requires considering CNTs–carbon composites characteristics and the uncertainty of blast events. This article suggests a systematic reliability-based design approach where system reliability is used to compute the probability of failure of a composite laminate. A case study for the design of a five-layer composite laminate incorporating CNTs and subjected to uncertain blast event is demonstrated. 0.5–1% CNTs contents by weight seem capable of significantly reducing the composite probability of failure during blast.     

Cytotoxic T Cell Expression of Leukocyte-Associated Immunoglobulin-Like Receptor-1 (LAIR-1) in Viral Hepatitis C-Mediated Hepatocellular Carcinoma

Virus-related hepatocellular carcinoma (HCC) pathogenesis involves liver inflammation, therefore, despite successful treatment, hepatitis C virus (HCV) may progress to HCC from initiated liver cirrhosis. Cytotoxic T cells (Tcs) are known to be involved in HCV-related cirrhotic complications and HCC pathogenesis. The inhibitory checkpoint leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1) is expressed on Tcs. Therefore, we aimed to determine whether the Tc expression level of LAIR-1 is associated with HCC progression and to evaluate LAIR-1 expression as a noninvasive biomarker for HCC progression in the context of liver cirrhosis related to HCV genotype 4 (G4) in Egyptian patients’ peripheral venous blood liquid biopsy. A total of 64 patients with HCC and 37 patients with liver cirrhosis were enrolled in this case-controlled study, and their LAIR-1 expression on Tc related to the progression of liver cirrhosis was examined and compared to that of the apparently healthy control group (n = 20). LAIR-1 expression was analyzed using flow cytometry. Results: The HCC group had significantly higher LAIR-1 expression on Tc and percentage of Tc positive for LAIR-1 (LAIR-1+Tc%) than the HCV G4-related liver cirrhosis group. LAIR-1+Tc% was correlated with the HCC surrogate tumor marker AFP (r = 0.367, p = 0.001) and insulin resistance and inflammation prognostic ratios/indices. A receiver operating characteristic (ROC) curve revealed that adding LAIR-1+Tc% to AFP can distinguish HCC transformation in the Egyptian patients’ cohort. Upregulated LAIR-1 expression on Tc could be a potential screening noninvasive molecular marker for chronic inflammatory HCV G4 related liver cirrhosis. Moreover, LAIR-1 expression on Tc may be one of the players involved in the progression of liver cirrhosis to HCC.     

Removal of cyanide in aqueous solution by oxidation with hydrogen peroxide in presence of copper-impregnated activated carbon

This work is devoted to the removal of free cyanide from aqueous solution by oxidation with hydrogen peroxide H₂O₂ catalyzed by copper-impregnated activated carbon. Effects of initial molar ratio [H₂O₂]₀/[CN⁻]₀, copper-impregnated activated carbon amount, pH and the temperature on cyanide removal have been investigated.The presence of copper-impregnated activated carbon has increased the reaction rate showing thus a catalytic activity. The rate of cyanides removal increases with the raise of the initial molar ratio [H₂O₂]₀/[CN⁻]0 and decreases with the increase in the pH from 8 to 12. The increase in the copper-impregnated activated carbon amount from 1.5 to 10 g/L in reaction solution has a beneficial effect. Beyond this value, the impact of activated carbon amount is not anymore significant. The temperature does not have a significant effect between 20 and 35 °C. The four successive times re-use of catalyst shows a good stability. The kinetics of cyanide removal has been found to be of pseudo-second-order with respect to cyanide and the rate constants have been determined. This process seems very interesting because the rate of cyanides removal is very fast, the reaction does not use soluble metal catalyst and it consumes only hydrogen peroxide as chemical product.     

Queuing analysis of shared-buffer switches with control scheme under bursty traffic

Shared-buffer switches have many advantages such as relatively low cell loss rate and good buffer utilization, and they are increasingly favoured in recent VLSI switch designs for ATM. However, their performance degrades dramatically under nonuniform traffic due to the monopolization of the buffer by some favoured cells. To overcome this, restricted types of sharing and hot-spot pushout (HSPO) have been proposed, and the latter has been shown by simulation to perform better in all situations. In this paper we develop an analytical model for performance evaluation of a shared-buffer asynchronous transfer mode (ATM) switch with HSPO under bursty traffic. This analytical model is an improved version of the first model ever developed for this purpose. We balance the relative queues to approximate the effects of pushout, while keeping only four state-variables, and our model gives a good agreement with simulation, for calculating throughput and cell loss.     

A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based on stochastic modeling or artificial intelligence (AI) techniques. In this article, an adaptive neuro-fuzzy inference system (ANFIS) model is proposed to forecast the inflow for the Nile River at Aswan High Dam (AHD) on monthly basis. A major advantage of the fuzzy system is its ability to deal with imprecision and vagueness in inflow database. The ANFIS model divides the input space into fuzzy sub-spaces and maps the output using a set of linear functions. A historical database of monthly inflows at AHD recorded over the past 130 years is used to train the ANFIS model and test its performance. The performance of the ANFIS model is compared to a recently developed artificial neural networks (ANN) model. The results show that the ANFIS model was capable of providing higher inflow forecasting accuracy specially at extreme inflow events compared with that of the ANN model. It is concluded that the ANFIS model can be quite beneficial in water management of Lake Nasser reservoir at AHD.     

The corrosion behaviour of Cr/Co/Mo-alloyed low residual carbon steels in seawater solutions

The general corrosion behaviour of low residual steels alloyed with chromium or cobalt or molybdenum in seawater solutions was studied. The carbon content of low residual steels ranged from 0.1 to 0.4%, chromium content between 1.0 and 4.0%, cobalt content between 1.0 and 3.0%, and molybdenum content between 0.3 and 1.0%. Repeated laboratory tests demonstrated that low levels of chromium reduce the dissolution rate of low residual steels in seawater solutions. The results of this study suggest that cobalt alloying plays a neutral role, whereas molybdenum plays a major role in reducing the corrosion rates, particularly in higher carbon steels.