Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Information Sharing in Banking: A Collusive Device?

We show that information sharing among banks may serve as a collusive device. An informational sharing agreement is an a-priori commitment to reduce informational asymmetries between banks in future lending. Hence, information sharing tends to increase the intensity of competition in future periods and, thus, reduces the value of informational rents in current competition. We contribute to the existing literature by emphasizing that a reduction in informational rents will also reduce the intensity of competition in the current period, thereby reducing competitive pressure in current credit markets. We provide a large class of economic environments, where a ban on information sharing would be strictly welfare-enhancing.

Decision support requirements for intelligent operation of southern grid in India

Control centers (CC) play a very important role in power system operation. An overall view of the system with information about all existing resources and needs is implemented through SCADA (Supervisory control and data acquisition system) and an EMS (energy management system). As advanced technologies have made their way into the utility environment, the operators are flooded with huge amount of data. The last decade has seen extensive applications of AI techniques, knowledge-based systems, Artificial Neural Networks in this area. This paper focuses on the need for development of an intelligent decision support system to assist the operator in making proper decisions. The requirements for realization of such a system are recognized for the effective operation and energy management of the southern grid in India The application of Petri nets leading to decision support system has been illustrated considering 24 bus system that is a part of southern grid.

Tool wear monitoring using ant behaviour

In this paper we show the applicability of Ant Colony Optimisation (ACO) techniques for pattern classification problem that arises in tool wear monitoring. In an earlier study, artificial neural networks and genetic programming have been successfully applied to tool wear monitoring problem. ACO is a recent addition to evolutionary computation technique that has gained attention for its ability to extract the underlying data relationships and express them in form of simple rules. Rules are extracted for data classification using training set of data points. These rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in ACO based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The classification accuracy obtained in ACO based approach is as good as obtained in other biologically inspired techniques.

Multiple contour extraction from graylevel images using an artificial neural network

For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of ail edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

Geometric quantum computation using fictitious spin-1/2 subspaces of strongly dipolar coupled nuclear spins

Geometric phases have been used in NMR to implement controlled phase shift gates for quantum-information processing, only in weakly coupled systems in which the individual spins can be identified as qubits. In this work, we implement controlled phase shift gates in strongly coupled systems by using nonadiabatic geometric phases, obtained by evolving the magnetization of fictitious spin-1/2 subspaces, over a closed loop on the Bloch sphere. The dynamical phase accumulated during the evolution of the subspaces is refocused by a spin echo pulse sequence and by setting the delay of transition selective pulses such that the evolution under the homonuclear coupling makes a complete 2 pi rotation. A detailed theoretical explanation of nonadiabatic geometric phases in NMR is given by using single transition operators. Controlled phase shift gates, two qubit Deutsch-Jozsa algorithm, and parity algorithm in a qubit-qutrit system have been implemented in various strongly dipolar coupled systems obtained by orienting the molecules in liquid crystal media.

Electron-beam melting and centrifugal splat-quenching technique for rapid solidification of titanium alloys

An electron-beam melting and centrifugal splat-quenching technique for the production of microflakes of Ti-6A1-4V (wt%) alloy quenched at an average cooling rate of about 105 K sec–1 is described. The effect of substrate angle on the shape, size, microstructure and average cooling rate of the flakes of major sieve fractions is discussed. Morphologies of particles of minor sieve fractions are dealt with briefly.

Computation-Based Reliability Analysis in Real Time Applications

Reliability analysis for computing systems in aerospace applications must account for actual computations the system performs in the use environment. This paper introduces a theoretical nonhomogeneous Markov model for such applications.

Electrocardiographic parameters of ventricular repolarization : modifiers and the prognostic value

Electric activity of the heart consists of repeated cardiomyocyte depolarizations and repolarizations. Abnormalities in repolarization predispose to ventricular arrhythmias. In body surface electrocardiogram, ventricular repolarization generates the T wave. Several electrocardiographic measures have been developed both for clinical and research purposes to detect repolarization abnormalities. The study aim was to investigate modifiers of ventricular repolarization with the focus on the relationship of the left ventricular mass, antihypertensive drugs, and common gene variants, to electrocardiographic repolarization parameters. The prognostic value of repolarization parameters was also assessed. The study subjects originated from a population of more than 200 middle-aged hypertensive men attending the GENRES hypertension study, and from an epidemiological survey, the Health 2000 Study, including more than 6000 participants. Ventricular repolarization was analysed from digital standard 12-lead resting electrocardiograms with two QT-interval based repolarization parameters (QT interval, T-wave peak to T-wave end interval) and with a set of four T-wave morphology parameters. The results showed that in hypertensive men, a linear change in repolarization parameters is present even in the normal range of left ventricular mass, and that even mild left ventricular hypertrophy is associated with potentially adverse electrocardiographic repolarization changes. In addition, treatments with losartan, bisoprolol, amlodipine, and hydrochlorothiazide have divergent short-term effects on repolarization parameters in hypertensive men. Analyses of the general population sample showed that single nucleotide polymorphisms in KCNH2, KCNE1, and NOS1AP genes are associated with changes in QT-interval based repolarization parameters but not consistently with T-wave morphology parameters. T-wave morphology parameters, but not QT interval or T-wave peak to T-wave end interval, provided independent prognostic information on mortality. The prognostic value was specifically related to cardiovascular mortality. The results indicate that, in hypertension, altered ventricular repolarization is already present in mild left ventricular mass increase, and that commonly used antihypertensive drugs may relatively rapidly and treatment-specifically modify electrocardiographic repolarization parameters. Common variants in cardiac ion channel genes and NOS1AP gene may also modify repolarization-related arrhythmia vulnerability. In the general population, T-wave morphology parameters may be useful in the risk assessment of cardiovascular mortality.

Diabetic cardiomyopathy and post-infarct ventricular remodelling : Effects of levosimendan in a rodent model of type II diabetes

Type 2 diabetes is a risk factor for the development of cardiovascular disease. Recently, the term diabetic cardiomyopathy has been proposed to describe the changes in the heart that occur in response to chronic hyperglycemia and insulin resistance. Ventricular remodelling in diabetic cardiomyopathy includes left ventricular hypertrophy, increased interstitial fibrosis, apoptosis and diastolic dysfunction. Mechanisms behind these changes are increased oxidative stress and renin-angiotensin system activation. The diabetic Goto-Kakizaki rat is a non-obese model of type 2 diabetes that exhibits defective insulin signalling. Recently two interconnected stress response pathways have been discovered that link insulin signalling, longevity, apoptosis and cardiomyocyte hypertrophy. The insulin-receptor PI3K/Ak pathway inhibits proapoptotic FOXO3a in response to insulin signalling and the nuclear Sirt1 deacetylase inhibits proapoptotic p53 and modulates FOXO3a in favour of survival and growth. --- Levosimendan is a calcium sensitizing agent used for the management of acute decompensated heart failure. Levosimendan acts as a positive inotrope by sensitizing cardiac troponin C to calcium and exerts vasodilation by opening mitochondrial and sarcolemmal ATP-sensitive potassium channels. Levosimendan has been described to have beneficial effects in ventricular remodelling after myocardial infarction. The aims of the study were to characterize whether diabetic cardiomyopathy associates with cardiac dysfunction, cardiomyocyte apoptosis, hypertrophy and fibrosis in spontaneously diabetic Goto-Kakizaki (GK) rats, which were used to model type 2 diabetes. Protein expression and activation of the Akt FOXO3a and Sirt1 p53 pathways were examined in the development of ventricular remodelling in GK rats with and without myocardial infarction (MI). The third and fourth studies examined the effects of levosimendan on ventricular remodelling and gene expression in post-MI GK rats. The results demonstrated that diabetic GK rats develop both modest hypertension and features similar to diabetic cardiomyopathy including cardiac dysfunction, LV hypertrophy and fibrosis and increased apoptotic signalling. MI induced a sustained increase in cardiomyocyte apoptosis in GK rats together with aggravated LV hypertrophy and fibrosis. The GK rat myocardium exhibited decreased Akt- FOXO3a phosphorylation and increased nuclear translocation of FOXO3a and overproduction of the Sirt1 protein. Treatment with levosimendan decreased cardiomyocyte apoptosis, senescence and LV hypertrophy and altered the gene expression profile in GK rat myocardium. The findings indicate that impaired cardioprotection via Akt FOXO3a and p38 MAPK is associated with increased apoptosis, whereas Sirt1 functions in counteracting apoptosis and the development of LV hypertrophy in the GK rat myocardium. Overall, levosimendan treatment protects against post-MI ventricular remodelling and alters the gene expression profile in the GK rat myocardium.

Fabrication of device quality films of high loaded PPy/MWCNT nanocomposites using pulsed laser deposition

Polypyrrole (PPy) - multiwalled carbonnanotubes (MWCNT) nanocomposites with various MWCNT loading were prepared by in situ inversion emulsion polymerization technique. High loading of the nano filler were evaluated because of available inherent high interface area for charge separation in the nanocomposites. Solution processing of these conducting polymer nanocomposites is difficult because, most of them are insoluble in organic solvents. Device quality films of these composites were prepared by using pulsed laser deposition techniques (PLD). Comparative study of X-ray photoelectron spectroscopy (XPS) of bulk and film show that there is no chemical modification of polymer on ablation with laser. TEM images indicate PPy layer on MWCNT surface. SEM micrographs indicate that the MWCNT's are distributed throughout the film. It was observed that MWCNT in the composite held together by polymer matrix. Further more MWCNT diameter does not change from bulk to film indicating that the polymer layer remains intact during ablation. Even for very high loadings (80 wt.% of MWCNT's) of nanocomposites device quality films were fabricated, indicating laser ablation is a suitable technique for fabrication of device quality films. Conductivity of both bulk and films were measured using collinear four point probe setup. It was found that overall conductivity increases with increase in MWCNT loading. Comparative study of thickness with conductivity indicates that maximum conductivity was observed around 0.2 mu m. (C) 2010 Elsevier B.V. All rights reserved.

Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures

In this paper, we present a generic method/model for multi-objective design optimization of laminated composite components, based on Vector Evaluated Artificial Bee Colony (VEABC) algorithm. VEABC is a parallel vector evaluated type, swarm intelligence multi-objective variant of the Artificial Bee Colony algorithm (ABC). In the current work a modified version of VEABC algorithm for discrete variables has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria: failure mechanism based failure criteria, maximum stress failure criteria and the tsai-wu failure criteria. The optimization method is validated for a number of different loading configurations-uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA). The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations. (C) 2009 Elsevier B.V. All rights reserved.

Artificial neural networks and multicriterion analysis for sustainable irrigation planning

The objective of the present paper is to select the best compromise irrigation planning strategy for the case study of Jayakwadi irrigation project, Maharashtra, India. Four-phase methodology is employed. In phase 1, separate linear programming (LP) models are formulated for the three objectives, namely. net economic benefits, agricultural production and labour employment. In phase 2, nondominated (compromise) irrigation planning strategies are generated using the constraint method of multiobjective optimisation. In phase 3, Kohonen neural networks (KNN) based classification algorithm is employed to sort nondominated irrigation planning strategies into smaller groups. In phase 4, multicriterion analysis (MCA) technique, namely, Compromise Programming is applied to rank strategies obtained from phase 3. It is concluded that the above integrated methodology is effective for modeling multiobjective irrigation planning problems and the present approach can be extended to situations where number of irrigation planning strategies are even large in number. (c) 2004 Elsevier Ltd. All rights reserved.