Preparation and Investigation of Bio-coal Mixture Fuels Used in Large Diesel Engines

Coal slurry was of vital interest during the last century due to its potential as an alternative fuel where liquid fuels were necessary. Recently, environmental impacts of the traditional fuels, similarities of bio-coal to that of coal, and huge bio-coal supply has attracted the attention to prepare bio-coal slurries as a new fuel. Rudolf Diesel who invented the diesel engine on 1895 was of the opinion that diesel engines are capable to use different kinds of fuels due to the special design. He tried some kind of vegetable oil to operate on his IC engine. Recently, due to high energy density and more environmentally friendly fuel, researchers believe that bio-coal slurries could act as a new alternative fuel in large diesel engines. Loads of research on different kinds of bio-coal slurry were done by the other researchers worldwide and a lot of progress to boost slurry’s quality were achieved recently. The present study aims to achieve the ideal condition of different factors affecting on the quality of bio-coal slurry. One charcoal sample and two kinds of torrefied wood were used to investigate and compare the reaction of various factors. The results show a great gap between the quality of slurries made of different samples and more researches are necessary to fully understand the impact of the different parameter and improving the quality.

Factor Analysis of a Large DSGE Model

We study the workings of the factor analysis of high-dimensional data using artificial series generated from a large, multi-sector dynamic stochastic general equilibrium (DSGE) model. The objective is to use the DSGE model as a laboratory that allow us to shed some light on the practical benefits and limitations of using factor analysis techniques on economic data. We explain in what sense the artificial data can be thought of having a factor structure, study the theoretical and finite sample properties of the principal components estimates of the factor space, investigate the substantive reason(s) for the good performance of di¤usion index forecasts, and assess the quality of the factor analysis of highly dissagregated data. In all our exercises, we explain the precise relationship between the factors and the basic macroeconomic shocks postulated by the model.

Modified Large Margin Nearest Neighbor Metric Learning for Regression

The main objective of this letter is to formulate a new approach of learning a Mahalanobis distance metric for nearest neighbor regression from a training sample set. We propose a modified version of the large margin nearest neighbor metric learning method to deal with regression problems. As an application, the prediction of post-operative trunk 3-D shapes in scoliosis surgery using nearest neighbor regression is described. Accuracy of the proposed method is quantitatively evaluated through experiments on real medical data.

Spatial and temporal analysis of laser induced plasma from a polymer sample

Spatial and temporal analyses of the spectra of the laser induced plasma from a polytetrafluroethylene (PTFE) target obtained with the 1.06 mu m radiation from a Q-switched Nd:YAG laser have been carried out. The spatially resolved spectra of the plasma emission show that molecular bands of C2 (Swan bands) and CN are very intense in the outer regions of the plasma, whereas higher ionized states of carbon are predominant in the core region of the plasma emission. The vibrational temperature and population distribution in the different vibrational levels have been studied as a function of laser energy. From the time resolved studies, it has been observed that there exist fairly large time delays for the onset of emission from all the species in the outer region of the plasma. The molecular bands in each region exhibit much larger time delays in comparison to the ionic lines in the plasma.

Phylogenomics of eukaryotes: Impact of missing data on large alignments

Resolving the relationships between Metazoa and other eukaryotic groups as well as between metazoan phyla is central to the understanding of the origin and evolution of animals. The current view is based on limited data sets, either a single gene with many species (e.g., ribosomal RNA) or many genes but with only a few species. Because a reliable phylogenetic inference simultaneously requires numerous genes and numerous species, we assembled a very large data set containing 129 orthologous proteins (similar to30,000 aligned amino acid positions) for 36 eukaryotic species. Included in the alignments are data from the choanoflagellate Monosiga ovata, obtained through the sequencing of about 1,000 cDNAs. We provide conclusive support for choanoflagellates as the closest relative of animals and for fungi as the second closest. The monophyly of Plantae and chromalveolates was recovered but without strong statistical support. Within animals, in contrast to the monophyly of Coelomata observed in several recent large-scale analyses, we recovered a paraphyletic Coelamata, with nematodes and platyhelminths nested within. To include a diverse sample of organisms, data from EST projects were used for several species, resulting in a large amount of missing data in our alignment (about 25%). By using different approaches, we verify that the inferred phylogeny is not sensitive to these missing data. Therefore, this large data set provides a reliable phylogenetic framework for studying eukaryotic and animal evolution and will be easily extendable when large amounts of sequence information become available from a broader taxonomic range.

A combined statistical and dynamical approach for downscaling large-scale footprints of European windstorms

The occurrence of mid-latitude windstorms is related to strong socio-economic effects. For detailed and reliable regional impact studies, large datasets of high-resolution wind fields are required. In this study, a statistical downscaling approach in combination with dynamical downscaling is introduced to derive storm related gust speeds on a high-resolution grid over Europe. Multiple linear regression models are trained using reanalysis data and wind gusts from regional climate model simulations for a sample of 100 top ranking windstorm events. The method is computationally inexpensive and reproduces individual windstorm footprints adequately. Compared to observations, the results for Germany are at least as good as pure dynamical downscaling. This new tool can be easily applied to large ensembles of general circulation model simulations and thus contribute to a better understanding of the regional impact of windstorms based on decadal and climate change projections.

Application of dried blood spots to determine vitamin D status in a large nutritional study with unsupervised sampling: the Food4Me project

An efficient and robust method to measure vitamin D (25-hydroxy vitamin D3 (25(OH)D3) and 25-hydroxy vitamin D2 in dried blood spots (DBS) has been developed and applied in the pan-European multi-centre, internet-based, personalised nutrition intervention study Food4Me. The method includes calibration with blood containing endogenous 25(OH)D3, spotted as DBS and corrected for haematocrit content. The methodology was validated following international standards. The performance characteristics did not reach those of the current gold standard liquid chromatography-MS/MS in plasma for all parameters, but were found to be very suitable for status-level determination under field conditions. DBS sample quality was very high, and 3778 measurements of 25(OH)D3 were obtained from 1465 participants. The study centre and the season within the study centre were very good predictors of 25(OH)D3 levels (P<0·001 for each case). Seasonal effects were modelled by fitting a sine function with a minimum 25(OH)D3 level on 20 January and a maximum on 21 July. The seasonal amplitude varied from centre to centre. The largest difference between winter and summer levels was found in Germany and the smallest in Poland. The model was cross-validated to determine the consistency of the predictions and the performance of the DBS method. The Pearson's correlation between the measured values and the predicted values was r 0·65, and the sd of their differences was 21·2 nmol/l. This includes the analytical variation and the biological variation within subjects. Overall, DBS obtained by unsupervised sampling of the participants at home was a viable methodology for obtaining vitamin D status information in a large nutritional study.

Reverse correlation reveals how observers sample visual information when estimating three-dimensional shape

Human observers exhibit large systematic distance-dependent biases when estimating the three-dimensional (3D) shape of objects defined by binocular image disparities. This has led some to question the utility of disparity as a cue to 3D shape and whether accurate estimation of 3D shape is at all possible. Others have argued that accurate perception is possible, but only with large continuous perspective transformations of an object. Using a stimulus that is known to elicit large distance-dependent perceptual bias (random dot stereograms of elliptical cylinders) we show that contrary to these findings the simple adoption of a more naturalistic viewing angle completely eliminates this bias. Using behavioural psychophysics, coupled with a novel surface-based reverse correlation methodology, we show that it is binocular edge and contour information that allows for accurate and precise perception and that observers actively exploit and sample this information when it is available.

Probing the Large Magellanic Cloud age gap at intermediate cluster masses

The Large Magellanic Cloud (LMC) has a rich star cluster system spanning a wide range of ages and masses. One striking feature of the LMC cluster system is the existence of an age gap between 3 and 10 Gyr. But this feature is not clearly seen among field stars. Three LMC fields containing relatively poor and sparse clusters whose integrated colours are consistent with those of intermediate-age simple stellar populations have been imaged in BVI with the Optical Imager (SOI) at the Southern Telescope for Astrophysical Research (SOAR). A total of six clusters, five of them with estimated initial masses M < 104 M(circle dot), were studied in these fields. Photometry was performed and colour-magnitude diagrams (CMDs) were built using standard point spread function fitting methods. The faintest stars measured reach V similar to 23. The CMD was cleaned from field contamination by making use of the three-dimensional colour and magnitude space available in order to select stars in excess relative to the field. A statistical CMD comparison method was developed for this purpose. The subtraction method has proven to be successful, yielding cleaned CMDs consistent with a simple stellar population. The intermediate-age candidates were found to be the oldest in our sample, with ages between 1 and 2 Gyr. The remaining clusters found in the SOAR/SOI have ages ranging from 100 to 200 Myr. Our analysis has conclusively shown that none of the relatively low-mass clusters studied by us belongs to the LMC age gap.

Metallicity gradients at large galactocentric radii using the near-infrared Calcium triplet

We describe a new spectroscopic technique for measuring radial metallicity gradients out to large galactocentric radii. We use the DEep Imaging Multi-Object Spectrograph (DEIMOS) on the Keck telescope and the galaxy spectrum extraction technique of Proctor et al. We also make use of the metallicity sensitive near-infrared Calcium ii triplet (CaT) features together with single stellar population models to obtain metallicities. Our technique is applied as a pilot study to a sample of three relatively nearby (< 30 Mpc) intermediate-mass to massive early-type galaxies. Results are compared with previous literature inner region values and generally show good agreement. We also include a comparison with profiles from dissipational disc-disc major merger simulations. Based on our new extended metallicity gradients combined with other observational evidence and theoretical predictions, we discuss possible formation scenarios for the galaxies in our sample. The limitations of our new technique are also discussed.

Comparing methods for sampling large- and medium-sized mammals: camera traps and track plots

Activities involving fauna monitoring are usually limited by the lack of resources; therefore, the choice of a proper and efficient methodology is fundamental to maximize the cost-benefit ratio. Both direct and indirect methods can be used to survey mammals, but the latter are preferred due to the difficulty to come in sight of and/or to capture the individuals, besides being cheaper. We compared the performance of two methods to survey medium and large-sized mammal: track plot recording and camera trapping, and their costs were assessed. At Jatai Ecological Station (S21 degrees 31`15 ``- W47 degrees 34`42 ``-Brazil) we installed ten camera traps along a dirt road directly in front of ten track plots, and monitored them for 10 days. We cleaned the plots, adjusted the cameras, and noted down the recorded species daily. Records taken by both methods showed they sample the local richness in different ways (Wilcoxon, T=231; p;;0.01). The track plot method performed better on registering individuals whereas camera trapping provided records which permitted more accurate species identification. The type of infra-red sensor camera used showed a strong bias towards individual body mass (R(2)=0.70; p=0.017), and the variable expenses of this method in a 10-day survey were estimated about 2.04 times higher compared to track plot method; however, in a long run camera trapping becomes cheaper than track plot recording. Concluding, track plot recording is good enough for quick surveys under a limited budget, and camera trapping is best for precise species identification and the investigation of species details, performing better for large animals. When used together, these methods can be complementary.

A finite sample improvement of the fixed effects estimator : applied to technical infficiency

The FE ('fixed effects') estimator of technical inefficiency performs poorly when N ('number of firms') is large and T ('number of time observations') is small. We propose estimators of both the firm effects and the inefficiencies, which have small sample gains compared to the traditional FE estimator. The estimators are based on nonparametric kernel regression of unordered variables, which includes the FE estimator as a special case. In terms of global conditional MSE ('mean square error') criterions, it is proved that there are kernel estimators which are efficient to the FE estimators of firm effects and inefficiencies, in finite samples. Monte Carlo simulations supports our theoretical findings and in an empirical example it is shown how the traditional FE estimator and the proposed kernel FE estimator lead to very different conclusions about inefficiency of Indonesian rice farmers.

Laws of large numbers for non-additive probabilities

We apply the concept of exchangeable random variables to the case of non-additive robability distributions exhibiting ncertainty aversion, and in the lass generated bya convex core convex non-additive probabilities, ith a convex core). We are able to rove two versions of the law of arge numbers (de Finetti's heorems). By making use of two efinitions. of independence we rove two versions of the strong law f large numbers. It turns out that e cannot assure the convergence of he sample averages to a constant. e then modal the case there is a true" probability distribution ehind the successive realizations of the uncertain random variable. In this case convergence occurs. This result is important because it renders true the intuition that it is possible "to learn" the "true" additive distribution behind an uncertain event if one repeatedly observes it (a sufficiently large number of times). We also provide a conjecture regarding the "Iearning" (or updating) process above, and prove a partia I result for the case of Dempster-Shafer updating rule and binomial trials.

The finite-sample size of the BDS test for GARCH standardized residuals

This paper uses a multivariate response surface methodology to analyze the size distortion of the BDS test when applied to standardized residuals of rst-order GARCH processes. The results show that the asymptotic standard normal distribution is an unreliable approximation, even in large samples. On the other hand, a simple log-transformation of the squared standardized residuals seems to correct most of the size problems. Nonethe-less, the estimated response surfaces can provide not only a measure of the size distortion, but also more adequate critical values for the BDS test in small samples.

Impact of worksite health-promotion programs in hospital admission costs and medical leaves in a sample of brazilian companies

Programas de saúde e bem-estar têm sido adotados por empresas como forma de melhorar a saúde de empregados, e muitos estudos descrevem retornos econômicos positivos sobre os investimentos envolvidos. Entretanto, estudos mais recentes com metodologia melhor têm demonstrado retornos menores. O objetivo deste estudo foi investigar se características de programas de saúde e bem-estar agem como preditores de custos de internação hospitalar (em Reais correntes) e da proporção de funcionários que têm licença médica, entre Abril de 2014 e Maio de 2015, em uma amostra não-aleatória de empresas no Brasil, através de parceria com uma empresa gestora de ‘big data’ para saúde. Um questionário sobre características de programas de saúde no ambiente de trabalho foi respondida por seis grandes empresas brasileiras. Dados retirados destes seis questionários (presença e idade de programa de saúde, suas características – inclusão de atividades de screening, educação sobre saúde, ligação com outros programas da empresa, integração do programa à estrutura da empresa, e ambientes de trabalho voltado para a saúde – e a adoção de incentivos financeiros para aderência de funcionários ao programa), bem como dados individuais de idade, gênero e categoria de plano de saúde de cada empregado , foram usados para construir um banco de dados com mais de 76.000 indivíduos. Através de um modelo de regressão múltipla e seleção ‘stepwise’ de variáveis, a idade do empregado foi positivamente associada e a idade do programa de saúde e a categoria ‘premium’ de plano de saúde do funcionário foram negativamente associadas aos custos de internação hospitalar (como esperado). Inesperadamente, a inclusão de programas de screening e iniciativas de educação de saúde nos programas de saúde e bem-estar nas empresas foram identificados como preditores positivos significativos para custos de admissão hospitalar. Para evitar a inclusão errônea de licenças-maternidade, apenas os dados de licença médica de pacientes do sexo masculino foram analisados (dados disponíveis apenas para duas entre as companhias incluídas, com um total de 18.957 pacientes do sexo masculino). Analisando estes dados através de um teste Z para comparação de proporções, a empresa com programa de saúde que inclui atividades voltadas a cessação de hábitos ruins (como tabagismo e etilismo), controle de diabetes e hipertensão, e que adota incentivos financeiros para a aderência de funcionários ao programa tem menor proporção de empregados com licençca médica no período analisado, quando comparada com a outra empresa que não tem estas características (também conforme esperado). Entretanto, a companhia com menor proporção de funcionários com licença médica também foi aquela que adota programa de screening entre as atividades de seu programa de saúde. Potenciais fontes de ameaça à validade interna e externa destes resultados são discutidas, bem como possíveis explicações para a associação entre programas de screening e educação médica a piores indicadores de saúde nesta amostra de companhias são discutidas. Novos estudos com melhor desenho, com amostras maiores e randômicas são necessários para validar estes resultados e possivelmente melhorar a validade interna e externa destes resultados.