Application of artificial intelligence for the assessment of the status of power transformers

The Dissolved Gas Analysis (DGA) a non destructive test procedure, has been in vogue for a long time now, for assessing the status of power and related transformers in service. An early indication of likely internal faults that may exist in Transformers has been seen to be revealed, to a reasonable degree of accuracy by the DGA. The data acquisition and subsequent analysis needs an expert in the concerned area to accurately assess the condition of the equipment. Since the presence of the expert is not always guaranteed, it is incumbent on the part of the power utilities to requisition a well planned and reliable artificial expert system to replace, at least in part, an expert. This paper presents the application of Ordered Ant Mner (OAM) classifier for the prediction of involved fault. Secondly, the paper also attempts to estimate the remaining life of the power transformer as an extension to the elapsed life estimation method suggested in the literature.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies-Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

The Edge of Danger: artificial lighting and the dialectics of domestic occupation in Philip Johnson's Glass and Guest Houses

In the first half of the twentieth century the dematerializing of boundaries between enclosure and exposure problematized traditional acts of “occupation” and understandings of the domestic environment. As a space of escalating technological control, the modern domestic interior offered new potential to re-define the meaning and means of habitation. This shift is clearly expressed in the transformation of electric lighting technology and applications for the modern interior in the mid-twentieth century. Addressing these issues, this paper examines the critical role of electric lighting in regulating and framing both the public and private occupation of Philip Johnson’s New Canaan estate. Exploring the dialectically paired transparent Glass House and opaque Guest House (both 1949), this study illustrates how Johnson employed artificial light to control the visual environment of the estate as well as to aestheticize the performance of domestic space. Looking closely at the use of artificial light to create emotive effects as well as to intensify the experience of occupation, this revisiting of the iconic Glass House and lesser-known Guest House provides a more complex understanding of Johnson’s work and the means with which he inhabited his own architecture. Calling attention to the importance of Johnson serving as both architect and client, and his particular interest in exploring the new potential of architectural lighting in this period, this paper investigates Johnson’s use of electric light to support architectural narratives, maintain visual order and control, and to suit the nuanced desires of domestic occupation.

Cow's milk allergy and the development of tolerance

Aims: To gain insight on the immunological processes behind cow’s milk allergy (CMA) and the development of oral tolerance. To furthermore investigate the associations of HLA II and filaggrin genotypes with humoral responses to early oral antigens. Methods: The study population was from a cohort of 6209 healthy, full-term infants who in a double-blind randomized trial received supplementary feeding at maternity hospitals (mean duration 4 days): cow’s milk (CM) formula, extensively hydrolyzed whey formula or donor breast milk. Infants who developed CM associated symptoms that subsided during elimination diet (n=223) underwent an open oral CM challenge (at mean age 7 months). The challenge was negative in 112, and in 111 it confirmed CMA, which was IgE-mediated in 83. Patients with CMA were followed until recovery, and 94 of them participated in a follow-up study at age 8-9 years. We investigated serum samples at diagnosis (mean age 7 months, n=111), one year later (19 months, n=101) and at follow-up (8.6 years, n=85). At follow-up, also 76 children randomly selected from the original cohort and without CM associated symptoms were included. We measured CM specific IgE levels with UniCAP (Phadia, Uppsala, Sweden), and β-lactoglobulin, α-casein and ovalbumin specific IgA, IgG1, IgG4 and IgG levels with enzyme-linked immunosorbent assay in sera. We applied a microarray based immunoassay to measure the binding of IgE, IgG4 and IgA serum antibodies to sequential epitopes derived from five major CM proteins at the three time points in 11 patients with active IgE-mediated CMA at age 8-9 years and in 12 patients who had recovered from IgE-mediated CMA by age 3 years. We used bioinformatic methods to analyze the microarray data. We studied T cell expression profile in peripheral blood mononuclear cell (PBMC) samples from 57 children aged 5-12 years (median 8.3): 16 with active CMA, 20 who had recovered from CMA by age 3 years, 21 non-atopic control subjects. Following in vitro β-lactoglobulin stimulation, we measured the mRNA expression in PBMCs of 12 T-cell markers (T-bet, GATA-3, IFN-γ, CTLA4, IL-10, IL-16, TGF-β, FOXP3, Nfat-C2, TIM3, TIM4, STIM-1) with quantitative real time polymerase chain reaction, and the protein expression of CD4, CD25, CD127, FoxP3 with flow cytometry. To optimally distinguish the three study groups, we performed artificial neural networks with exhaustive search for all marker combinations. For genetic associations with specific humoral responses, we analyzed 14 HLA class II haplotypes, the PTPN22 1858 SNP (R620W allele) and 5 known filaggrin null mutations from blood samples of 87 patients with CMA and 76 control subjects (age 8.0-9.3 years). Results: High IgG and IgG4 levels to β-lactoglobulin and α-casein were associated with the HLA (DR15)-DQB1*0602 haplotype in patients with CMA, but not in control subjects. Conversely, (DR1/10)-DQB1*0501 was associated with lower IgG and IgG4 levels to these CM antigens, and to ovalbumin, most significantly among control subjects. Infants with IgE-mediated CMA had lower β -lactoglobulin and α-casein specific IgG1, IgG4 and IgG levels (p<0.05) at diagnosis than infants with non-IgE-mediated CMA or control subjects. When CMA persisted beyond age 8 years, CM specific IgE levels were higher at all three time points investigated and IgE epitope binding pattern remained stable (p<0.001) compared with recovery from CMA by age 3 years. Patients with persisting CMA at 8-9 years had lower serum IgA levels to β-lactoglobulin at diagnosis (p=0.01), and lower IgG4 levels to β-lactoglobulin (p=0.04) and α-casein (p=0.05) at follow-up compared with patients who recovered by age 3 years. In early recovery, signal of IgG4 epitope binding increased while that of IgE decreased over time, and binding patterns of IgE and IgG4 overlapped. In T cell expression profile in response to β –lactoglobulin, the combination of markers FoxP3, Nfat-C2, IL-16, GATA-3 distinguished patients with persisting CMA most accurately from patients who had become tolerant and from non-atopic subjects. FoxP3 expression at both RNA and protein level was higher in children with CMA compared with non-atopic children. Conclusions: Genetic factors (the HLA II genotype) are associated with humoral responses to early food allergens. High CM specific IgE levels predict persistence of CMA. Development of tolerance is associated with higher specific IgA and IgG4 levels and lower specific IgE levels, with decreased CM epitope binding by IgE and concurrent increase in corresponding epitope binding by IgG4. Both Th2 and Treg pathways are activated upon CM antigen stimulation in patients with CMA. In the clinical management of CMA, HLA II or filaggrin genotyping are not applicable, whereas the measurement of CM specific antibodies may assist in estimating the prognosis.

Diet and physical activity behaviour in nurses: A qualitative study

Issue addressed: Previous research has shown that approximately 60% of nurses in Australia are overweight or obese, insufficiently active and have an unhealthy diet. The aim of this study was to gain an understanding of nurses’ determinants contributing to these behaviours. This will inform a needs assessment for a future workplace health promotion program (WHPP) in this group. Methods: Four focus group discussions (n = 17) were conducted with a convenience sample of nurses aged 25–59 years from three hospitals in the Brisbane metropolitan area. Questions addressed barriers and motivation towards diet and physical activity (PA), and suggestions for future WHPP. Data were analysed with Nvivo10 following a thematic analysis with a realistic approach using Self-determination theory as a framework. Results: Work environment was the main barrier for healthy diet behaviours. Long working hours and lack of breaks challenged nurses’ self-control and self-regulation when making dietary choices. Fatigue was the main barrier for PA. However, relaxation, feeling energised before work and better sleep after working night shifts motivated nurses to do PA. Social environment at work seemed to be an effective external motivation to encourage healthy diet and regular PA. Goal-setting, self-monitoring and social support at work were identified as potential WHHP strategies. Conclusion: The workplace and job demands negatively impacts nurses’ lifestyle behaviours. Future interventions should include social support from colleagues, which could motivate nurses to make healthier food choices at work and be more active outside work.

Investigation of true remnant polarization response in heterostructured artificial biferroics

Epitaxial bilayered thin films composed of ferromagnetic La0.6Sr0.4MnO3 and ferroelectric 0.7Pb (Mg1/3Nb2/3)O3-0.3(PbTiO3) were fabricated on LaAlO3 (100) substrates by pulsed laser ablation. Ferroelectric, ferromagnetic and magneto-dielectric characterizations performed earlier indicated the possible existence of strain-mediated magneto-electric coupling in these biferroic heterostructures. In order to investigate their true remnant polarization characteristics, usable in devices, room-temperature polarization versus electric field, positive-up negative-down (PUND) pulse polarization studies and remnant hysteresis measurements were carried out. The PUND and remnant hysteresis measurements revealed the significant contribution of the non-remnant component in the observed polarization hysteresis response of these heterostructures. (C) 2010 Published by Elsevier Ltd

High cholesterol diet and esterification of cholesterol by the intestinal mucosa of rats

Young male rats maintained on a diet containing 1% cholesterol were sacrificed at the end of 1st, 2nd, 3rd, 5th, and 7th week. Acetone powders prepared from their intestinal mucosa and pancreas were tested for the synthetic and hydrolytic activities for Vitamin A and cholesterol esters. The esterifying activity of the mucosal enzymes for both Vitamin A and cholesterol increased progressively up to the end of the 5th week; the increase in esterification of cholesterol was more marked with respect to saturated fatty acids, as compared to the unsaturated ones. The pancreatic enzymes remained unaffected. It is suggested that one of the reasons for the accumulation of cholesterol esters in animal tissues may be the increased esterification of the sterol in the mucosa induced by dietary cholesterol.

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.

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.

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.

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.