Aging of polyethylene/polypropylene (PE/PP) dual layer pressure pipe by outdoor exposure

This project investigated the effect of PP skin on the PE core pipe in a PP/PE dual layer pipe during production and outdoor exposure under various radiation dosages by comparing the results with the corresponding uncoated pipe using different characterization techniques. It was found for unaged samples that after extrusion the adhesion reducer present in the PP skin migrated to the PE core pipe outer surface but had little effect on the electrofusion (EF) joint quality. The PP skin prevents the PE core pipe from quenching therefore more perfect PE crystal is formed as shown by a higher crystallinity and the residual stress is reduced as shown by a slit ring method. Due to the reduced residual stress, the skinned pipe had higher long term hydrostatic strength (LTHS) than the uncoated pipe. After outdoor weathering, photo-oxidation products were evident at the solar irradiated PP outer surface after 3 GJ/m2 weathering and the whole PP outer surface was oxidized after 10 GJ/m2 weathering. By deconvoluting the IR peaks, ketones, carboxylic acid and esters were found the main products. Although only slight photo-oxidation was identified after 10 GJ/m2 aging on the uncoated PE pipe outer surface, oxidation induction time (OIT) results indicated that the solar irradiated side of the surface lost most of its antioxidants after only 1 GJ/m2 weathering which led to production of weak layer in the EF joint. In the middle pipe wall and at the inner surface, a more gradual decrease of antioxidant was found. The skinned pipe showed better resistance to antioxidant loss than the uncoated pipe and still had adequate antioxidant for EF. The thermal effect of solar irradiation was thought to cause secondary crystallization of the uncoated pipe at the irradiated side and release of residual stress of both uncoated and skinned pipes after aging. The residual stress release rate was found to decline with weathering. As the pipe with thicker skin always had a lower residual stress, it can be inferred that the skinned pipe still had a higher LTHS value than the uncoated pipe even after aging

Robust stability conditions for remote SISO DMC controller in networked control systems

A two level hierarchy is employed in the design of Networked Control Systems (NCSs) with bounded random transmission delay. At the lower level a local controller is designed to stabilize the plant. At the higher level a remote controller with the Dynamic Matrix Control (DMC) algorithm is implemented to regulate the desirable set-point for the local controller. The conventional DMC algorithm is not applicable due to the unknown transmission delay in NCSs. To meet the requirements of a networked environment, a new remote DMC controller is proposed in this study. Two methods, maximum delayed output feedback and multi-rate sampling, are used to cope with the delayed feedback sensory data. Under the assumption that the closed-loop local system is described by one FIR model of an FIR model family, the robust stability problem of the remote DMC controller is investigated. Applying Jury's dominant coefficient lemma and some stability results of switching discrete-time systems with multiple delays; several stability criteria are obtained in the form of simple inequalities. Finally, some numerical simulations are given to demonstrate the theoretical results

Table_2_The causal effect of two occupational factors on osteoarthritis and rheumatoid arthritis: a Mendelian randomization study.DOCX

BackgroundOsteoarthritis (OA) and rheumatoid arthritis (RA) are two common types of arthritis. We conducted a two-sample Mendelian randomization (MR) study to estimate the causal effects of two common occupational factors—job involves heavy manual or physical work and job involves mainly walking or standing—on OA and RA in individuals of European ancestry.MethodsInstruments were chosen from genome-wide association studies (GWASs) that identified independent single nucleotide polymorphisms (SNPs) robustly linked to job involves heavy manual or physical work (N = 263,615) as well as job involves mainly walking or standing (N = 263,556). Summary statistics for OA and RA were taken from the Integrative Epidemiology Unit (IEU) GWAS database; both discovery and replication GWAS datasets were considered. The primary analysis utilized the inverse variance weighted (IVW) MR method supplemented by various sensitivity MR analyses.ResultsIn the IVW model, we found that genetically predicted job involves heavy manual or physical work was significantly associated with OA in both the discovery [odds ratio (OR) = 1.034, 95% confidence interval (CI): 1.016–1.053, P = 2.257 × 10−4] and replication (OR = 1.857, 95% CI: 1.223–2.822, P = 0.004) analyses. The causal associations were supported in diverse sensitivity analyses. MR analyses suggested no causal effect of genetically predicted job involves heavy manual or physical work on RA. Similarly, our data provided no evidence that genetically predicted job involves mainly walking or standing was related to OA and RA.ConclusionsOur MR study suggests that job involves heavy manual or physical work is a risk factor for OA. It is of utmost importance to create preventive strategies aimed at reducing its impact on OA at such work sites.</p

Table_1_The causal effect of two occupational factors on osteoarthritis and rheumatoid arthritis: a Mendelian randomization study.docx

BackgroundOsteoarthritis (OA) and rheumatoid arthritis (RA) are two common types of arthritis. We conducted a two-sample Mendelian randomization (MR) study to estimate the causal effects of two common occupational factors—job involves heavy manual or physical work and job involves mainly walking or standing—on OA and RA in individuals of European ancestry.MethodsInstruments were chosen from genome-wide association studies (GWASs) that identified independent single nucleotide polymorphisms (SNPs) robustly linked to job involves heavy manual or physical work (N = 263,615) as well as job involves mainly walking or standing (N = 263,556). Summary statistics for OA and RA were taken from the Integrative Epidemiology Unit (IEU) GWAS database; both discovery and replication GWAS datasets were considered. The primary analysis utilized the inverse variance weighted (IVW) MR method supplemented by various sensitivity MR analyses.ResultsIn the IVW model, we found that genetically predicted job involves heavy manual or physical work was significantly associated with OA in both the discovery [odds ratio (OR) = 1.034, 95% confidence interval (CI): 1.016–1.053, P = 2.257 × 10−4] and replication (OR = 1.857, 95% CI: 1.223–2.822, P = 0.004) analyses. The causal associations were supported in diverse sensitivity analyses. MR analyses suggested no causal effect of genetically predicted job involves heavy manual or physical work on RA. Similarly, our data provided no evidence that genetically predicted job involves mainly walking or standing was related to OA and RA.ConclusionsOur MR study suggests that job involves heavy manual or physical work is a risk factor for OA. It is of utmost importance to create preventive strategies aimed at reducing its impact on OA at such work sites.</p

Using Historical Atlas Data to Develop High-Resolution Distribution Models of Freshwater Fishes

<div><p>Understanding the spatial pattern of species distributions is fundamental in biogeography, and conservation and resource management applications. Most species distribution models (SDMs) require or prefer species presence and absence data for adequate estimation of model parameters. However, observations with unreliable or unreported species absences dominate and limit the implementation of SDMs. Presence-only models generally yield less accurate predictions of species distribution, and make it difficult to incorporate spatial autocorrelation. The availability of large amounts of historical presence records for freshwater fishes of the United States provides an opportunity for deriving reliable absences from data reported as presence-only, when sampling was predominantly community-based. In this study, we used boosted regression trees (BRT), logistic regression, and MaxEnt models to assess the performance of a historical metacommunity database with inferred absences, for modeling fish distributions, investigating the effect of model choice and data properties thereby. With models of the distribution of 76 native, non-game fish species of varied traits and rarity attributes in four river basins across the United States, we show that model accuracy depends on data quality (e.g., sample size, location precision), species’ rarity, statistical modeling technique, and consideration of spatial autocorrelation. The cross-validation area under the receiver-operating-characteristic curve (AUC) tended to be high in the spatial presence-absence models at the highest level of resolution for species with large geographic ranges and small local populations. Prevalence affected training but not validation AUC. The key habitat predictors identified and the fish-habitat relationships evaluated through partial dependence plots corroborated most previous studies. The community-based SDM framework broadens our capability to model species distributions by innovatively removing the constraint of lack of species absence data, thus providing a robust prediction of distribution for stream fishes in other regions where historical data exist, and for other taxa (e.g., benthic macroinvertebrates, birds) usually observed by community-based sampling designs.</p></div

Gaussian and non-Gaussian Double Subspace Statistical Process Monitoring Based on Principal Component Analysis and Independent Component Analysis

This study proposes a new statistical process monitoring method based on variable distribution characteristic (VDSPM) with consideration that variables submit to different distributions in chemical processes and that principal component analysis (PCA) and independent component analysis (ICA) are, respectively, suitable for processing data with Gaussian and non-Gaussian distribution. In VDSPM, D-test is first employed to identify the normality of process variables. The process variables under Gaussian distribution are classified into Gaussian subspace and the others belong to non-Gaussian subspace. PCA and ICA models are respectively built for fault detection in Gaussian and non-Gaussian subspaces. Bayesian inference is used to combine the monitoring results of the two subspaces to create a final statistic. The proposed method is applied to a numerical system and to the Tennessee Eastman benchmark process. Results proved that the proposed system outperformed the PCA and ICA methods

Angle-Based Multiblock Independent Component Analysis Method with a New Block Dissimilarity Statistic for Non-Gaussian Process Monitoring

In recent years, the multiblock method has attracted substantial attention. Conventional multiblock methods divide an entire data set into several blocks, and the monitoring in each block is conducted separately. The multiblock method highlights local information but ignores the information among different blocks. In this paper, we propose an angle-based multiblock independent component analysis (MBICA) method and create a new block dissimilarity (BD) statistic to measure the changes between blocks. Hierarchical clustering is adopted to cluster variables with small angles into a block. ICA models are then built into each block. Support vector data description (SVDD) is introduced to yield a final monitoring decision. The changes of blocks are determined by the differences between the angles of the monitored data and the benchmark data, leading to BD statistics. The proposed MBICA-BD method is applied to the Tennessee Eastman process. The simulation results demonstrate the superiority of the MBICA-BD method

Comparing the performance of Lasso logistic regression model and boosted regression tree (BRT) models in terms of the area under the receiver-operating-characteristic (ROC) curve in the 5-fold cross validation for 76 species in the four selected river basins (i.e., New River, Illinois River, Brazos River and Snake River).

<p>The results from the two set of models were generally in agreement, with Pearson’s <i>r</i> over 0.9. For fish species Mountain whitefish, <i>Prosopium williamsoni</i> and Torrent sculpin, <i>Cottus rhotheus</i> (marked as circles) in the Snake River where occurrence data was relatively sparse, the Lasso logistic models outperformed the BRT models.</p

The sources and descriptions of environmental variables used to develop species distribution models for the 76 native stream fish species in the United States.

<p>Data are from NHDplusV1 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref050" target="_blank">50</a>] and NHDplusV2 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref051" target="_blank">51</a>], NFHAP [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref053" target="_blank">53</a>], USGS-LCI [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref049" target="_blank">49</a>], and PRISM [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref033" target="_blank">33</a>]. The environmental variables, if not specified, were measured per inter-confluence river segment.</p><p>^a This index is calculated based on 15 disturbance variables [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.ref006" target="_blank">6</a>]. The influence of each distribution variable was weighted by the results of multiple linear regression of all variables against a commonly used biological indicator of habitat condition (i.e., percent intolerant fishes at a site).</p><p>The sources and descriptions of environmental variables used to develop species distribution models for the 76 native stream fish species in the United States.</p

A map showing the distribution of four river basins (i.e., New River, Illinois River, Brazos River, and Snake River) selected for this study in the contiguous United States.

<p>We can see that all these four rivers pass through multiple states. Fish presence data are sufficient in these four basins in the <i>IchthyMap</i> database for developing and validating species distribution models (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129995#pone.0129995.s002" target="_blank">S2 File</a>). Specifically, the number of presence records of non-game species used to develop species distribution models was 2,716 for Brazos River Basin, 5,635 for Illinois River Basin, 5,192 for New River Basin and, 412 for the Snake river Basin.</p