A unified wavelet-based modelling framework for non-linear system identification: the WANARX model structure

A new unified modelling framework based on the superposition of additive submodels, functional components, and wavelet decompositions is proposed for non-linear system identification. A non-linear model, which is often represented using a multivariate non-linear function, is initially decomposed into a number of functional components via the wellknown analysis of variance (ANOVA) expression, which can be viewed as a special form of the NARX (non-linear autoregressive with exogenous inputs) model for representing dynamic input–output systems. By expanding each functional component using wavelet decompositions including the regular lattice frame decomposition, wavelet series and multiresolution wavelet decompositions, the multivariate non-linear model can then be converted into a linear-in-theparameters problem, which can be solved using least-squares type methods. An efficient model structure determination approach based upon a forward orthogonal least squares (OLS) algorithm, which involves a stepwise orthogonalization of the regressors and a forward selection of the relevant model terms based on the error reduction ratio (ERR), is employed to solve the linear-in-the-parameters problem in the present study. The new modelling structure is referred to as a wavelet-based ANOVA decomposition of the NARX model or simply WANARX model, and can be applied to represent high-order and high dimensional non-linear systems

Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information

Background: Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM2.5 ground networks to cover a much larger area. Objectives: In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM2.5 concentrations. Methods: We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM2.5 concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. Results: The AOD model has a higher predicting power judged by adjusted R2 (0.79) than does the non-AOD model (0.48). The predicted PM2.5 concentrations by the AOD model are, on average, 0.8–0.9 μg/m3 higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM2.5, meteorologic parameters are major contributors to the better performance of the AOD model. Conclusions: GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM2.5 concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM2.5 spatial patterns related to AOD availability

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

A comparison of random forests, boosting and support vector machines for genomic selection

Genomic selection (GS) involves estimating breeding values using molecular markers spanning the entire genome. Accurate prediction of genomic breeding values (GEBVs) presents a central challenge to contemporary plant and animal breeders. The existence of a wide array of marker-based approaches for predicting breeding values makes it essential to evaluate and compare their relative predictive performances to identify approaches able to accurately predict breeding values. We evaluated the predictive accuracy of random forests (RF), stochastic gradient boosting (boosting) and support vector machines (SVMs) for predicting genomic breeding values using dense SNP markers and explored the utility of RF for ranking the predictive importance of markers for pre-screening markers or discovering chromosomal locations of QTLs

Are the dead taking over Facebook? A Big Data approach to the future of death online

We project the future accumulation of profiles belonging to deceased Facebook users. Our analysis suggests that a minimum of 1.4 billion users will pass away before 2100 if Facebook ceases to attract new users as of 2018. If the network continues expanding at current rates, however, this number will exceed 4.9 billion. In both cases, a majority of the profiles will belong to non-Western users. In discussing our findings, we draw on the emerging scholarship on digital preservation and stress the challenges arising from curating the profiles of the deceased. We argue that an exclusively commercial approach to data preservation poses important ethical and political risks that demand urgent consideration. We call for a scalable, sustainable, and dignified curation model that incorporates the interests of multiple stakeholders

Microhabitat competition between Iberian fish species and the endangered Júcar nase (Parachondrostoma arrigonis; Steindachner, 1866)

"This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Ecohydraulics on 24-01-2017, available online: https://www.tandfonline.com/doi/full/10.1080/24705357.2016.1276417"[EN] Competition with invasive species is recognized as having a major impact on biodiversity conservation. The upper part of the Cabriel River (Eastern Iberian Peninsula) harbours the most important population of the Júcar nase (Parachondrostoma arrigonis; Steindachner, 1866), a fish species in imminent danger of extinction. Currently, this species cohabits with several non-native species, such as the Iberian nase (Pseudochondrostoma polylepis; Steindachner, 1864) and the bermejuela (Achondrostoma arcasii; Steindachner, 1866). The potential habitat competition with these species was studied by analysing the spatial and temporal overlapping of suitable microhabitats. Generalized Additive Mixed Models (GAMMs) were developed to model microhabitat selection and these GAMMs were used to assess the habitat suitability (i.e. probability of presence) under several flows simulated with River2D. The Júcar nase will compete, spatially and temporally, for the few suitable microhabitats with bermejuela and, to a lesser extent, with small Iberian nase; conversely, large Iberian nase was of minor concern, due to increased differences in habitat preferences. This study represents an important assessment of potential competition and, therefore, these results might assist to better define future management practices in the upper part of the Cabriel River.This study was funded by the Spanish Ministry of Economy and Competitiveness through the SCARCE project (Consolider Ingenio 2010 CSD2009 00065); the Universitat Politècnica de València, through the project UPPTE/2012/294 [PAID 06 12]; it was also partially funded by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economía y Competitividad) and FEDER funds. The authors would like to thank the help of the Conselleria de Territori i Vivenda (Generalitat Valenciana) and the Confederación Hidrográfica del Júcar (Spanish government), which provided environmental data to Alfredo Ollero, and the two anonymous reviewers who first suggested the submission of the paper to a regular journal. 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Comparison of the CDC Backpack aspirator and the Prokopack aspirator for sampling indoor- and outdoor-resting mosquitoes in southern Tanzania.

BACKGROUND\ud \ud Resting mosquitoes can easily be collected using an aspirating device. The most commonly used mechanical aspirator is the CDC Backpack aspirator. Recently, a simple, and low-cost aspirator called the Prokopack has been devised and proved to have comparable performance. The following study evaluates the Prokopack aspirator compared to the CDC backpack aspirator when sampling resting mosquitoes in rural Tanzania.\ud \ud METHODS\ud \ud Mosquitoes were sampled in- and outdoors of 48 typical rural African households using both aspirators. The aspirators were rotated between collectors and households in a randomized, Latin Square design. Outdoor collections were performed using artificial resting places (large barrel and car tyre), underneath the outdoor kitchen (kibanda) roof and from a drop-net. Data were analysed with generalized linear models.\ud \ud RESULTS\ud \ud The number of mosquitoes collected using the CDC Backpack and the Prokopack aspirator were not significantly different both in- and outdoors (indoors p = 0.735; large barrel p = 0.867; car tyre p = 0.418; kibanda p = 0.519). The Prokopack was superior for sampling of drop-nets due to its smaller size. The number mosquitoes collected per technician was more consistent when using the Prokopack aspirator. The Prokopack was more user-friendly: technicians preferred using the it over the CDC backpack aspirator as it weighs considerably less, retains its charge for longer and is easier to manoeuvre.\ud \ud CONCLUSIONS\ud \ud The Prokopack proved in the field to be more advantageous than the CDC Backpack aspirator. It can be self assembled using simple, low-cost and easily attainable materials. This device is a useful tool for researchers or vector-control surveillance programs operating in rural Africa, as it is far simpler and quicker than traditional means of sampling resting mosquitoes. Further longitudinal evaluations of the Prokopack aspirator versus the gold standard pyrethrum spray catch for indoor resting catches are recommended

Childhood Incident Asthma and Traffic-Related Air Pollution at Home and School

Background: Traffic-related air pollution has been associated with adverse cardiorespiratory effects, including increased asthma prevalence. However, there has been little study of effects of traffic exposure at school on new-onset asthma. Objectives: We evaluated the relationship of new-onset asthma with traffic-related pollution near homes and schools. Methods: Parent-reported physician diagnosis of new-onset asthma (n = 120) was identified during 3 years of follow-up of a cohort of 2,497 kindergarten and first-grade children who were asthma- and wheezing-free at study entry into the Southern California Children's Health Study. We assessed traffic-related pollution exposure based on a line source dispersion model of traffic volume, distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide (NO2), and particulate matter were measured continuously at one central site monitor in each of 13 study communities. Hazard ratios (HRs) for new-onset asthma were scaled to the range of ambient central site pollutants and to the residential interquartile range for each traffic exposure metric. Results: Asthma risk increased with modeled traffic-related pollution exposure from roadways near homes [HR 1.51; 95% confidence interval (CI), 1.25-1.82] and near schools (HR 1.45; 95% CI, 1.06-1.98). Ambient NO2 measured at a central site in each community was also associated with increased risk (HR 2.18; 95% CI, 1.18-4.01). In models with both NO2 and modeled traffic exposures, there were independent associations of asthma with traffic-related pollution at school and home, whereas the estimate for NO2 was attenuated (HR 1.37; 95% CI, 0.69-2.71). Conclusions: Traffic-related pollution exposure at school and homes may both contribute to the development of asthma. Editor's SummaryTraffic-related air pollution has been associated with adverse cardiorespiratory effects, including increased asthma prevalence. McConnell et al. (p. 1021) evaluated the relationship of new-onset asthma with traffic-related pollution near homes and schools. Parent-reported physician diagnosis of new-onset asthma was identified during 3 years of follow-up of a cohort of kindergarten and first-grade children who were asthma- and wheezing-free at study entry into the Southern California Children's Health Study. Traffic-related pollution exposure was assessed based on a line source dispersion model of traffic volume, distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide (NO2), and particulate matter were measured continuously at one central site monitor in each of 13 study communities. The authors report an increase in asthma risk with modeled traffic-related pollution exposure from roadways near homes and schools. Ambient NO2 was also associated with increased risk. Models that included both NO2 and modeled traffic exposures suggested independent associations of asthma with traffic-related pollution at school and at home, whereas the estimate for NO2 was attenuated. The authors conclude that traffic-related pollution exposure at school and home may both contribute to the development of asthma

A specific case in the classification of woods by FTIR and chemometric: discrimination of Fagales from Malpighiales

Fourier transform infrared (FTIR) spectroscopic data was used to classify wood samples from nine species within the Fagales and Malpighiales using a range of multivariate statistical methods. Taxonomic classification of the family Fagaceae and Betulaceae from Angiosperm Phylogenetic System Classification (APG II System) was successfully performed using supervised pattern recognition techniques. A methodology for wood sample discrimination was developed using both sapwood and heartwood samples. Ten and eight biomarkers emerged from the dataset to discriminate order and family, respectively. In the species studied FTIR in combination with multivariate analysis highlighted significant chemical differences in hemicelluloses, cellulose and guaiacyl (lignin) and shows promise as a suitable approach for wood sample classification

Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus

BACKGROUND: Diabetic nephropathy is a serious complication of diabetes mellitus and is associated with considerable morbidity and high mortality. There is increasing evidence to suggest that dysregulation of the epigenome is involved in diabetic nephropathy. We assessed whether epigenetic modification of DNA methylation is associated with diabetic nephropathy in a case-control study of 192 Irish patients with type 1 diabetes mellitus (T1D). Cases had T1D and nephropathy whereas controls had T1D but no evidence of renal disease. METHODS: We performed DNA methylation profiling in bisulphite converted DNA from cases and controls using the recently developed Illumina Infinium(R) HumanMethylation27 BeadChip, that enables the direct investigation of 27,578 individual cytosines at CpG loci throughout the genome, which are focused on the promoter regions of 14,495 genes. RESULTS: Singular Value Decomposition (SVD) analysis indicated that significant components of DNA methylation variation correlated with patient age, time to onset of diabetic nephropathy, and sex. Adjusting for confounding factors using multivariate Cox-regression analyses, and with a false discovery rate (FDR) of 0.05, we observed 19 CpG sites that demonstrated correlations with time to development of diabetic nephropathy. Of note, this included one CpG site located 18 bp upstream of the transcription start site of UNC13B, a gene in which the first intronic SNP rs13293564 has recently been reported to be associated with diabetic nephropathy. CONCLUSION: This high throughput platform was able to successfully interrogate the methylation state of individual cytosines and identified 19 prospective CpG sites associated with risk of diabetic nephropathy. These differences in DNA methylation are worthy of further follow-up in replication studies using larger cohorts of diabetic patients with and without nephropathy