Learning disability prediction tool using ANN and ANFIS

Learning Disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 15 % of children enrolled in schools. The prediction of LD is a vital and intricate job. The aim of this paper is to design an effective and powerful tool, using the two intelligent methods viz., Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System, for measuring the percentage of LD that affected in school-age children. In this study, we are proposing some soft computing methods in data preprocessing for improving the accuracy of the tool as well as the classifier. The data preprocessing is performed through Principal Component Analysis for attribute reduction and closest fit algorithm is used for imputing missing values. The main idea in developing the LD prediction tool is not only to predict the LD present in children but also to measure its percentage along with its class like low or minor or major. The system is implemented in Mathworks Software MatLab 7.10. The results obtained from this study have illustrated that the designed prediction system or tool is capable of measuring the LD effectively

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions

Toxic Metal Distribution in the Core Sediment of Cochin Estuarine System (CES)

This article present the result from a study of two sediment cores collected from the environmentally distinct zones of CES. Accumulation status of five toxic metals: Cadmium (Cd), Chromium (Cr), Cobalt (Co), Copper (Cu) and Lead (Pb) were analyzed. Besides texture and CHNS were determined to understand the composition of the sediment. Enrichment Factor (EF) and Anthropogenic Factor (AF) were used to differentiate the typical metal sources. Metal enrichment in the cores revealed heavy load at the northern (NS1 ) region compared with the southern zone (SS1). Elevation of metal content in core NS1 showed the industrial input. Statistical analyses were employed to understand the origin of metals in the sediment samples. Principal Component Analysis (PCA) distinguishes the two zones with different metal accumulation capacity: highest at NS1 and lowest at SS1. Correlation analysis revealed positive significant relation only in core NS1, adhering to the exposition of the intensified industrial pollution

Studies on collective excitations of quasi-two-dimensional Bose-Einstein condensate

Theory Division Department of Physics

Status and Potential of Smallholder Livestock Production Systems in Xishuangbanna, southern P.R. China

With Chinas rapid economic development during the last decades, the national demand for livestock products has quadrupled within the last 20 years. Most of that increase in demand has been answered by subsidized industrialized production systems, while million of smallholders, which still provide the larger share of livestock products in the country, have been neglected. Fostering those systems would help China to lower its strong urban migration streams, enhance the livelihood of poorer rural population and provide environmentally save livestock products which have a good chance to satisfy customers demand for ecological food. Despite their importance, China’s smallholder livestock keepers have not yet gained appropriate attention from governmental authorities and researchers. However, profound analysis of those systems is required so that adequate support can lead to a better resource utilization and productivity in the sector. To this aim, this pilot study analyzes smallholder livestock production systems in Xishuangbanna, located in southern China. The area is bordered by Lao and Myanmar and geographically counts as tropical region. Its climate is characterized by dry and temperate winters and hot summers with monsoon rains from May to October. While the regionis plain, at about 500 m asl above sea level in the south, outliers of the Himalaya mountains reach out into the north of Xishuangbanna, where the highest peak reaches 2400 m asl. Except of one larger city, Jinghong, Xishuangbanna mainly is covered by tropical rainforest, areas under agricultural cultivation and villages. The major income is generated through inner-Chinese tourism and agricultural production. Intensive rubber plantations are distinctive for the lowland plains while small-scaled traditional farms are scattered in the mountane regions. In order to determine the current state and possible future chances of smallholder livestock production in that region, this study analyzed the current status of the smallholder livestock sector in the Naban River National Nature Reserve (NRNNR), an area which is largely representative for the whole prefecture. It covers an area of about 50square kilometer and reaches from 470 up to 2400 m asl. About 5500 habitants of different ethnic origin are situated in 24 villages. All data have been collected between October 2007 and May 2010. Three major objectives have been addressed in the study: 1. Classifying existing pig production systems and exploring respective pathways for development 2. Quantifying the performance of pig breeding systemsto identify bottlenecks for production 3. Analyzing past and current buffalo utilization to determine the chances and opportunities of buffalo keeping in the future In order to classify the different pig production s ystems, a baseline survey (n=204, stratified cluster sampling) was carried out to gain data about livestock species, numbers, management practices, cultivated plant species and field sizes as well associo-economic characteristics. Sampling included two clusters at village level (altitude, ethnic affiliation), resulting in 13 clusters of which 13-17 farms were interviewed respectively. Categorical Principal Component Analysis (CatPCA) and a two-step clustering algorithm have been applied to identify determining farm characteristics and assort recorded households into classes of livestock production types. The variables keep_sow_yes/no, TLU_pig, TLU_buffalo, size_of_corn_fields, altitude_class, size_of_tea_plantationand size_of_rubber_fieldhave been found to be major determinants for the characterization of the recorded farms. All farms have extensive or semi-intensive livestock production, pigs and buffaloes are predominant livestock species while chicken and aquaculture are available but play subordinate roles for livelihoods. All pig raisers rely on a single local breed, which is known as Small Ear Pig (SMEP) in the region. Three major production systemshave been identified: Livestock-corn based LB; 41%), rubber based (RB; 39%) and pig based (PB;20%) systems. RB farms earn high income from rubber and fatten 1.9 ±1.80 pigs per household (HH), often using purchased pig feed at markets. PB farms own similar sized rubber plantations and raise 4.7 ±2.77 pigs per HH, with fodder mainly being cultivated and collected in theforest. LB farms grow corn, rice and tea and keep 4.6 ±3.32 pigs per HH, also fed with collected and cultivated fodder. Only 29% of all pigs were marketed (LB: 20%; RB: 42%; PB: 25%), average annual mortality was 4.0 ±4.52 pigs per farm (LB: 4.6 ±3.68; RB: 1.9 ±2.14; PB: 7.1 ±10.82). Pig feed mainly consists of banana pseudo stem, corn and rice hives and is prepared in batches about two to three times per week. Such fodder might be sufficient in energy content but lacks appropriate content of protein. Pigs therefore suffer from malnutrition, which becomes most critical in the time before harvest season around October. Farmers reported high occurrences of gastrointestinal parasites in carcasses and often pig stables were wet and filled with manure. Deficits in nutritional and hygienic management are major limits for development and should be the first issues addressed to improve productivity. SME pork was found to be known and referred by local customers in town and by richer lowland farmers. However, high prices and lacking availability of SME pork at local wet-markets were the reasons which limited purchase. If major management constraints are overcome, pig breeders (PB and LB farms) could increase the share of marketed pigs for town markets and provide fatteners to richer RB farmers. RB farmers are interested in fattening pigs for home consumption but do not show any motivation for commercial pig raising. To determine the productivity of input factors in pig production, eproductive performance, feed quality and quantity as well as weight development of pigs under current management were recorded. The data collection included a progeny history survey covering 184 sows and 437 farrows, bi-weekly weighing of 114 pigs during a 16-months time-span on 21 farms (10 LB and 11 PB) as well as the daily recording of feed quality and quantity given to a defined number of pigs on the same 21 farms. Feed samples of all recorded ingredients were analyzed for their respective nutrient content. Since no literature values on thedigestibility of banana pseudo stem – which is a major ingredient of traditional pig feed in NRNNR – were found, a cross-sectional digestibility trial with 2x4 pigs has been conducted on a station in the research area. With the aid of PRY Herd Life Model, all data have been utilized to determine thesystems’ current (Status Quo = SQ) output and the productivity of the input factor “feed” in terms of saleable life weight per kg DM feed intake and monetary value of output per kg DM feed intake.Two improvement scenarios were simulated, assuming 1) that farmers adopt a culling managementthat generates the highest output per unit input (Scenario 1; SC I) and 2) that through improved feeding, selected parameters of reproduction are improved by 30% (SC II). Daily weight gain averaged 55 ± 56 g per day between day 200 and 600. The average feed energy content of traditional feed mix was 14.92 MJ ME. Age at first farrowing averaged 14.5 ± 4.34 months, subsequent inter-farrowing interval was 11.4 ± 2.73 months. Littersize was 5.8 piglets and weaning age was 4.3 ± 0.99 months. 18% of piglets died before weaning. Simulating pig production at actualstatus, it has been show that monetary returns on inputs (ROI) is negative (1:0.67), but improved (1:1.2) when culling management was optimized so that highest output is gained per unit feed input. If in addition better feeding, controlled mating and better resale prices at fixed dates were simulated, ROI further increased to 1:2.45, 1:2.69, 1:2.7 and 1:3.15 for four respective grower groups. Those findings show the potential of pork production, if basic measures of improvement are applied. Futureexploration of the environment, including climate, market-season and culture is required before implementing the recommended measures to ensure a sustainable development of a more effective and resource conserving pork production in the future. The two studies have shown that the production of local SME pigs plays an important role in traditional farms in NRNNR but basic constraints are limiting their productivity. However, relatively easy approaches are sufficient for reaching a notable improvement. Also there is a demand for more SME pork on local markets and, if basic constraints have been overcome, pig farmers could turn into more commercial producers and provide pork to local markets. By that, environmentally safe meat can be offered to sensitive consumers while farmers increase their income and lower the risk of external shocks through a more diverse income generating strategy. Buffaloes have been found to be the second important livestock species on NRNNR farms. While they have been a core resource of mixed smallholderfarms in the past, the expansion of rubber tree plantations and agricultural mechanization are reasons for decreased swamp buffalo numbers today. The third study seeks to predict future utilization of buffaloes on different farm types in NRNNR by analyzing the dynamics of its buffalo population and land use changes over time and calculating labor which is required for keeping buffaloes in view of the traction power which can be utilized for field preparation. The use of buffaloes for field work and the recent development of the egional buffalo population were analyzed through interviews with 184 farmers in 2007/2008 and discussions with 62 buffalo keepers in 2009. While pig based farms (PB; n=37) have abandoned buffalo keeping, 11% of the rubber based farms (RB; n=71) and 100% of the livestock-corn based farms (LB; n=76) kept buffaloes in 2008. Herd size was 2.5 ±1.80 (n=84) buffaloes in early 2008 and 2.2 ±1.69 (n=62) in 2009. Field work on own land was the main reason forkeeping buffaloes (87.3%), but lending work buffaloes to neighbors (79.0%) was also important. Other purposes were transport of goods (16.1%), buffalo trade (11.3%) and meat consumption(6.4%). Buffalo care required 6.2 ±3.00 working hours daily, while annual working time of abuffalo was 294 ±216.6 hours. The area ploughed with buffaloes remained constant during the past 10 years despite an expansion of land cropped per farm. Further rapid replacement of buffaloes by tractors is expected in the near future. While the work economy is drastically improved by the use of tractors, buffaloes still can provide cheap work force and serve as buffer for economic shocks on poorer farms. Especially poor farms, which lack alternative assets that could quickly be liquidizedin times of urgent need for cash, should not abandon buffalo keeping. Livestock has been found to be a major part of small mixed farms in NRNNR. The general productivity was low in both analyzed species, buffaloes and pigs. Productivity of pigs can be improved through basic adjustments in feeding, reproductive and hygienic management, and with external support pig production could further be commercialized to provide pork and weaners to local markets and fattening farms. Buffalo production is relatively time intensive, and only will be of importance in the future to very poor farms and such farms that cultivate very small terraces on steep slopes. These should be encouraged to further keep buffaloes. With such measures, livestock production in NRNNR has good chances to stay competitive in the future.

Sparse Correlation Kernel Analysis and Reconstruction

This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel Analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class- specific basis functions. The basis functions that we use are the correlation functions of the class of signals we are analyzing. To choose the appropriate features from this large dictionary, we use Support Vector Machine (SVM) regression and compare this to traditional Principal Component Analysis (PCA) for the tasks of signal reconstruction, superresolution, and compression. The testbed we use in this paper is a set of images of pedestrians. This paper also presents results of experiments in which we use a dictionary of multiscale basis functions and then use Basis Pursuit De-Noising to obtain a sparse, multiscale approximation of a signal. The results are analyzed and we conclude that 1) when used with a sparse representation technique, the correlation function is an effective kernel for image reconstruction and superresolution, 2) for image compression, PCA and SVM have different tradeoffs, depending on the particular metric that is used to evaluate the results, 3) in sparse representation techniques, L_1 is not a good proxy for the true measure of sparsity, L_0, and 4) the L_epsilon norm may be a better error metric for image reconstruction and compression than the L_2 norm, though the exact psychophysical metric should take into account high order structure in images.

Two-dimensional Photonic Crystals Fabricated by Nanoimprint Lithography

We report on the process parameters of nanoimprint lithography (NIL) for the fabrication of two-dimensional (2-D) photonic crystals. The nickel mould with 2-D photonic crystal patterns covering the area up to 20mm² is produced by electron-beam lithography (EBL) and electroplating. Periodic pillars as high as 200nm to 250nm are produced on the mould with the diameters ranging from 180nm to 400nm. The mould is employed for nanoimprinting on the poly-methyl-methacrylate (PMMA) layer spin-coated on the silicon substrate. Periodic air holes are formed in PMMA above its glass-transition temperature and the patterns on the mould are well transferred. This nanometer-size structure provided by NIL is subjective to further pattern transfer.

A tale of two logits, compositional data analysis and zero observations

The application of compositional data analysis through log ratio trans- formations corresponds to a multinomial logit model for the shares themselves. This model is characterized by the property of Independence of Irrelevant Alter- natives (IIA). IIA states that the odds ratio in this case the ratio of shares is invariant to the addition or deletion of outcomes to the problem. It is exactly this invariance of the ratio that underlies the commonly used zero replacement procedure in compositional data analysis. In this paper we investigate using the nested logit model that does not embody IIA and an associated zero replacement procedure and compare its performance with that of the more usual approach of using the multinomial logit model. Our comparisons exploit a data set that com- bines voting data by electoral division with corresponding census data for each division for the 2001 Federal election in Australia

Monitoring procedures in environmental geochemistry and compositional data analysis theory

First discussion on compositional data analysis is attributable to Karl Pearson, in 1897. However, notwithstanding the recent developments on algebraic structure of the simplex, more than twenty years after Aitchison’s idea of log-transformations of closed data, scientific literature is again full of statistical treatments of this type of data by using traditional methodologies. This is particularly true in environmental geochemistry where besides the problem of the closure, the spatial structure (dependence) of the data have to be considered. In this work we propose the use of log-contrast values, obtained by a simplicial principal component analysis, as LQGLFDWRUV of given environmental conditions. The investigation of the log-constrast frequency distributions allows pointing out the statistical laws able to generate the values and to govern their variability. The changes, if compared, for example, with the mean values of the random variables assumed as models, or other reference parameters, allow defining monitors to be used to assess the extent of possible environmental contamination. Case study on running and ground waters from Chiavenna Valley (Northern Italy) by using Na+, K+, Ca2+, Mg2+, HCO3-, SO4 2- and Cl- concentrations will be illustrated

Analysis of Pleistocene paleodrainage evolution in the Po Basin (Italy) by multivariate statistical techniques

In order to obtain a high-resolution Pleistocene stratigraphy, eleven continuously cored boreholes, 100 to 220m deep were drilled in the northern part of the Po Plain by Regione Lombardia in the last five years. Quantitative provenance analysis (QPA, Weltje and von Eynatten, 2004) of Pleistocene sands was carried out by using multivariate statistical analysis (principal component analysis, PCA, and similarity analysis) on an integrated data set, including high-resolution bulk petrography and heavy-mineral analyses on Pleistocene sands and of 250 major and minor modern rivers draining the southern flank of the Alps from West to East (Garzanti et al, 2004; 2006). Prior to the onset of major Alpine glaciations, metamorphic and quartzofeldspathic detritus from the Western and Central Alps was carried from the axial belt to the Po basin longitudinally parallel to the SouthAlpine belt by a trunk river (Vezzoli and Garzanti, 2008). This scenario rapidly changed during the marine isotope stage 22 (0.87 Ma), with the onset of the first major Pleistocene glaciation in the Alps (Muttoni et al, 2003). PCA and similarity analysis from core samples show that the longitudinal trunk river at this time was shifted southward by the rapid southward and westward progradation of transverse alluvial river systems fed from the Central and Southern Alps. Sediments were transported southward by braided river systems as well as glacial sediments transported by Alpine valley glaciers invaded the alluvial plain. Kew words: Detrital modes; Modern sands; Provenance; Principal Components Analysis; Similarity, Canberra Distance; palaeodrainage

Dynamic graphics of parametrically linked multivariate methods used in compositional data analysis

Many multivariate methods that are apparently distinct can be linked by introducing one or more parameters in their definition. Methods that can be linked in this way are correspondence analysis, unweighted or weighted logratio analysis (the latter also known as "spectral mapping"), nonsymmetric correspondence analysis, principal component analysis (with and without logarithmic transformation of the data) and multidimensional scaling. In this presentation I will show how several of these methods, which are frequently used in compositional data analysis, may be linked through parametrizations such as power transformations, linear transformations and convex linear combinations. Since the methods of interest here all lead to visual maps of data, a "movie" can be made where where the linking parameter is allowed to vary in small steps: the results are recalculated "frame by frame" and one can see the smooth change from one method to another. Several of these "movies" will be shown, giving a deeper insight into the similarities and differences between these methods

Diagnosing Patients Combining Principal Components Analysis and Case Based Reasoning

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage

Factores asociados a mortalidad en pacientes colombianos con enfermedades autoinmunes en la unidad de cuidado intensivo, de un hospital de tercer nivel en Bogotá Colombia

Antecedentes: El interés en las enfermedades autoinmunes (EA) y su desenlace en la unidad de cuidado intensivo (UCI) han incrementado debido al reto clínico que suponen para el diagnóstico y manejo, debido a que la mortalidad en UCI fluctúa entre el 17 – 55 %. El siguiente trabajo representa la experiencia de un año de nuestro grupo en un hospital de tercer nivel. Objetivo: Identificar factores asociados a mortalidad particulares de los pacientes con enfermedades autoinmunes que ingresan a una UCI, de un hospital de tercer nivel en Bogotá, Colombia. Métodos: El uso de análisis de componentes principales basado en el método descriptivo multivariado y análisis de múltiple correspondencia fue realizado para agrupar varias variables relacionadas con asociación significativa y contexto clínico común. Resultados: Cincuenta pacientes adultos con EA con una edad promedio de 46,7 ± 17,55 años fueron evaluados. Los dos diagnósticos más comunes fueron lupus eritematoso sistémico y esclerosis sistémica, con una frecuencia de 45% y 20% de los pacientes respectivamente. La principal causa de admisión en la UCI fue la infección seguido de actividad aguda de la EA, 36% y 24% respectivamente. La mortalidad durante la estancia en UCI fue del 24%. El tiempo de hospitalización antes de la admisión a la UCI, el choque, soporte vasopresor, ventilación mecánica, sepsis abdominal, Glasgow bajo y plasmaféresis fueron factores asociados con mortalidad. Dos fenotipos de variables fueron definidos relacionadas con tiempo en la UCI y medidas de soporte en UCI, las cuales fueron asociadas supervivencia y mortalidad. Conclusiones: La identificación de factores individuales y grupos de factores por medio del análisis de componentes principales permitirá la implementación de medidas terapéutica de manera temprana y agresiva en pacientes con EA en la UCI para evitar desenlaces fatales.

Fronts from complex two-dimensional dispersal kernels: theory and application to Reid's paradox

Bimodal dispersal probability distributions with characteristic distances differing by several orders of magnitude have been derived and favorably compared to observations by Nathan [Nature (London) 418, 409 (2002)]. For such bimodal kernels, we show that two-dimensional molecular dynamics computer simulations are unable to yield accurate front speeds. Analytically, the usual continuous-space random walks (CSRWs) are applied to two dimensions. We also introduce discrete-space random walks and use them to check the CSRW results (because of the inefficiency of the numerical simulations). The physical results reported are shown to predict front speeds high enough to possibly explain Reid's paradox of rapid tree migration. We also show that, for a time-ordered evolution equation, fronts are always slower in two dimensions than in one dimension and that this difference is important both for unimodal and for bimodal kernels