Predictive models for deterioration of concrete structures

Permeation characteristics and fracture strength are the fundamental properties of concrete that influence the initiation and extent of damage and can form the basis by which deterioration can be predicted. The relationship between these properties and deterioration mechanisms is discussed along with the different models representing their interaction with the environment. Mehta presented a holistic model of the deterioration of concrete based on the environmental action on the microstructure of concrete. Using a similar approach, a detailed investigation on the causes of concrete deterioration is used to develop a macro-model for each mechanism relating to the physical properties of concrete. A single interaction model is then presented for all types of deterioration, emphasizing the permeation properties of concrete. Data from an in situ investigation of concrete bridges in Northern Ireland is used to validate this model. This is followed by a micro-predictive model which includes an ionic transport sub-model, a deterioration sub-model and a structural sub-model and affords quantitative prediction of the deterioration of concrete structures. The quantitative predictive capabilities of the micro-model are demonstrated with the use of reported experimental data.

Detecting the effects of diversity on measures of ecosystem function: experimental design, null models and empirical observations

Here we present a novel experimental approach to examine the relationship between diversity and ecosystem Function. We develop four null predictive models, with which to differentiate between the 'sampling effect' - the chance inclusion of a highly productive species, and 'species complementarity' - the complementary use of resources by species that differ in their niche or resource use. We investigate the effects of manipulating species and functional richness on ecosystem function in marine benthic system and using empirical data from our own experiments we illustrate the application of these models.

Strategies for Energy-Efficient Resource Management of Hybrid Programming Models

Many scientific applications are programmed using hybrid programming models that use both message passing and shared memory, due to the increasing prevalence of large-scale systems with multicore, multisocket nodes. Previous work has shown that energy efficiency can be improved using software-controlled execution schemes that consider both the programming model and the power-aware execution capabilities of the system. However, such approaches have focused on identifying optimal resource utilization for one programming model, either shared memory or message passing, in isolation. The potential solution space, thus the challenge, increases substantially when optimizing hybrid models since the possible resource configurations increase exponentially. Nonetheless, with the accelerating adoption of hybrid programming models, we increasingly need improved energy efficiency in hybrid parallel applications on large-scale systems. In this work, we present new software-controlled execution schemes that consider the effects of dynamic concurrency throttling (DCT) and dynamic voltage and frequency scaling (DVFS) in the context of hybrid programming models. Specifically, we present predictive models and novel algorithms based on statistical analysis that anticipate application power and time requirements under different concurrency and frequency configurations. We apply our models and methods to the NPB MZ benchmarks and selected applications from the ASC Sequoia codes. Overall, we achieve substantial energy savings (8.74 percent on average and up to 13.8 percent) with some performance gain (up to 7.5 percent) or negligible performance loss.

Efficient Architectural Design Space Exploration via Predictive Modeling

Efficiently exploring exponential-size architectural design spaces with many interacting parameters remains an open problem: the sheer number of experiments required renders detailed simulation intractable.We attack this via an automated approach that builds accurate predictive models. We simulate sampled points, using results to teach our models the function describing relationships among design parameters. The models can be queried and are very fast, enabling efficient design tradeoff discovery. We validate our approach via two uniprocessor sensitivity studies, predicting IPC with only 1–2% error. In an experimental study using the approach, training on 1% of a 250-K-point CMP design space allows our models to predict performance with only 4–5% error. Our predictive modeling combines well with techniques that reduce the time taken by each simulation experiment, achieving net time savings of three-four orders of magnitude.

Dinoflagellate cyst-based reconstructions of mid to late Holocene winter sea-surface temperature and productivity from an anoxic fjord in the NE Pacific Ocean

Published contemporary dinoflagellate distributional data from the NE Pacific margin and estuarine environments (n = 136) were re-analyzed using Canonical Correspondence Analysis (CCA) and partial Canonical Correspondence Analysis (pCCA). These analyses illustrated the dominant controls of winter temperature and productivity on the distribution of dinoflagellate cysts in this region. Dinoflagellate cyst-based predictive models for winter temperature and productivity were developed from the contemporary distributional data using the modern analogue technique and applied to subfossil data from two mid to late Holocene (~5500 calendar years before present–present) cores; TUL99B03 and TUL99B11, collected from Effingham Inlet, a 15 km long anoxic fjord located on the southwest coast of Vancouver Island that directly opens to the Pacific Ocean through Barkley Sound. Sedimentation within these basins largely comprises annually deposited laminated couplets, each made up of a winter deposited terrigenous layer and spring to fall deposited diatomaceous layer. The Effingham Inlet dinoflagellate cyst record provides evidence of a mid-Holocene gradual decline in winter SST, ending with the initiation of neoglacial advances in the region by ~3500 cal BP. A reconstructed Late Holocene increase in winter SST was initiated by a weakening of the California Current, which would have resulted in a warmer central gyre and more El Niño-like conditions.

Hydrobiogeochemical Interactions along “Flow Tubes” Controls: Watershed Scale Contaminant Flow and Transformation at the Oak Ridge IFRC

Geochemical,spectrographic, microbiological and hydrogeologic studies at the ORIFRC site indicate that groundwater transport in structured media may behave as a system of parallel flow tubes. These tubes are preferred flowpaths that enable contaminant transport parallel to bedding planes (strike) over distances of 1000s of meters. A significant flux of groundwater is focused within an interval defined by the interface between the competent bedrock and overlying highly-weathered saprolite, commonly referred to as the"transition zone." Characteristics of this transition zone are dense fractures and the relative absence of weathering products (e.g. clays)results in a significantly higher permeability compared to both the overlying clay-saprolite and underlying bedrock. Several stratabound low seismic velocity zones located below the transition zone were identified during geophysics studies and were also determined to be fractured high permeability preferred contaminant transport pathways during subsequent drilling activities. XANES analysis of precipitates collected from these deeper flow zones indicate 95% or more of the U deposited is U(VI). Linear combination fitting of the EXAFS data shows that precipitates are ~51±5% U(VI)-carbonate-like phase (e.g., liebigite) and ~49±5% U(VI) associated with an iron oxide phase; inclusion of a third component in the fit suggests that up to 15% of the U(VI) may be associated with a phosphate phase or OH- phase (e.g.,schoepite). Although precipitates with similar U(VI)-carbonate and/or phosphate associations were identified in the transition zone pathways,there were also U(VI) complexes adsorbed to mineral surfaces that would tend to be more readily mobilized. Groundwater in the different flow tubes has been determined to consist of different water quality types that vary with the solid phase encountered (e.g., clays, carbonates, clastics) as contaminants migrate along the flow paths. This lateral and vertical variability in geochemistry, particularly pH, has a significant impact on microbiological community composition and activity. Ribosomal RNA gene analyses coupled with physiological and genomic analyses suggest that bacteria from the genus Rhodanobacter(a diverse population of denitrifiers that are moderately acid tolerant) have a high relative abundance in the acidic source zone at the ORIFRC site.Watershed-scale analysis across different flow paths/tubes revealed strong negative correlation between pH and the absolute and relative abundance of Rhodanobacter. Recent studies also confirmed that the ORIFRC site hosts a diverse fungal community, with significant differences observed between acidic (pH <5) and circumneutral (>5) wells. The lack of nitrous oxide reduction capability in fungi, and the detection of denitrification potential in slurry microcosms suggest that fungi may have aheretofore under appreciated role in biogeochemical transformations, with implications forsite remediation and greenhouse gas emissions. Further research is needed to determine if these organisms can influence U(VI) mobility either directly through immobilization or indirectly through the depletion of nitrate.In conclusion, additional studies are required to quantify the processes (e.g., solid phase reactions, recharge, diffusion, microbial interactions) that are occurring along the groundwater flow tubes identified at the ORIFRC so predictive models can be parameterized and used to assess long-term contaminant fate and transport and remedial options.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.

MicroRNA signatures differentiate preserved from reduced ejection fraction heart failure

AIMS: Differentiation of heart failure with reduced (HFrEF) or preserved (HFpEF) ejection fraction independent of echocardiography is challenging in the community. Diagnostic strategies based on monitoring circulating microRNA (miRNA) levels may prove to be of clinical value in the near future. The aim of this study was to identify a novel miRNA signature that could be a useful HF diagnostic tool and provide valuable clinical information on whether a patient has HFrEF or HFpEF.

METHODS AND RESULTS: MiRNA biomarker discovery was carried out on three patient cohorts, no heart failure (no-HF), HFrEF, and HFpEF, using Taqman miRNA arrays. The top five miRNA candidates were selected based on differential expression in HFpEF and HFrEF (miR-30c, -146a, -221, -328, and -375), and their expression levels were also different between HF and no-HF. These selected miRNAs were further verified and validated in an independent cohort consisting of 225 patients. The discriminative value of BNP as a HF diagnostic could be improved by use in combination with any of the miRNA candidates alone or in a panel. Combinations of two or more miRNA candidates with BNP had the ability to improve significantly predictive models to distinguish HFpEF from HFrEF compared with using BNP alone (area under the receiver operating characteristic curve >0.82).

CONCLUSION: This study has shown for the first time that various miRNA combinations are useful biomarkers for HF, and also in the differentiation of HFpEF from HFrEF. The utility of these biomarker combinations can be altered by inclusion of natriuretic peptide. MiRNA biomarkers may support diagnostic strategies in subpopulations of patients with HF.

Discovery of Novel Allosteric Modulators of Metabotropic Glutamate Receptor Subtype 5 Reveals Chemical and Functional Diversity and In Vivo Activity in Rat Behavioral Models of Anxiolytic and Antipsychotic Activity

Modulators of metabotropic glutamate receptor subtype 5 (mGluR5) may provide novel treatments for multiple central nervous system (CNS) disorders, including anxiety and schizophrenia. Although compounds have been developed to better understand the physiological roles of mGluR5 and potential usefulness for the treatment of these disorders, there are limitations in the tools available, including poor selectivity, low potency, and limited solubility. To address these issues, we developed an innovative assay that allows simultaneous screening for mGluR5 agonists, antagonists, and potentiators. We identified multiple scaffolds that possess diverse modes of activity at mGluR5, including both positive and negative allosteric modulators (PAMs and NAMs, respectively). 3-Fluoro-5-(3-(pyridine-2-yl)-1,2,4-oxadiazol-5-yl) benzonitrile (VU0285683) was developed as a novel selective mGluR5 NAM with high affinity for the 2-methyl-6-(phenyl-ethynyl)-pyridine (MPEP) binding site. VU0285683 had anxiolytic-like activity in two rodent models for anxiety but did not potentiate phen-cyclidine-induced hyperlocomotor activity. (4-Hydroxypiperidin-1-yl)(4-phenylethynyl) phenyl) methanone (VU0092273) was identified as a novel mGluR5 PAM that also binds to the MPEP site. VU0092273 was chemically optimized to an orally active analog, N-cyclobutyl-6-((3-fluorophenyl) ethynyl) nicotinamide hydrochloride (VU0360172), which is selective for mGluR5. This novel mGluR5 PAM produced a dose-dependent reversal of amphetamine-induced hyperlocomotion, a rodent model predictive of antipsychotic activity. Discovery of structurally and functionally diverse allosteric modulators of mGluR5 that demonstrate in vivo efficacy in rodent models of anxiety and antipsychotic activity provide further support for the tremendous diversity of chemical scaffolds and modes of efficacy of mGluR5 ligands. In addition, these studies provide strong support for the hypothesis that multiple structurally distinct mGluR5 modulators have robust activity in animal models that predict efficacy in the treatment of CNS disorders.