Environmentally Friendly Pervious Concrete for Treating Deicer-Laden Stormwater: Phase II

In Phase I of this project, graphene oxide (GO)-modified pervious concrete was developed using coal fly ash as the sole binder. The primary objectives of Phase II of this project were (1) to evaluate the stormwater infiltration capacity of GO-modified fly ash pervious concrete; (2) to evaluate the durability performance of GO-modified fly ash pervious concrete using freeze/thaw and salt resistance testing methods; and (3) to use advanced analytical tools to fully characterize the GO-modified fly ash binder. Test results indicate different degrees of reduction in concentrations of possible pollutants in stormwater—copper, zinc, sulphate, chloride, ammonia, nitrate, and total phosphate. The incorporation of GO significantly improved the resistance of pervious concrete to freeze/thaw cycles and ambient-temperature salt attack. The specimens were examined using X-ray diffraction, which revealed that the mineralogy and the chemical composition of fly ash pastes differ considerably from those of cement pastes. Nuclear magnetic resonance was used to study the chemical structure and ordering of different hydrates, and provided enhanced understanding of the freeze/thaw and salt scaling resistance of fly ash pervious concrete and the role of GO

Data mining: a tool for detecting cyclical disturbances in supply networks.

Disturbances in supply chains may be either exogenous or endogenous. The ability automatically to detect, diagnose, and distinguish between the causes of disturbances is of prime importance to decision makers in order to avoid uncertainty. The spectral principal component analysis (SPCA) technique has been utilized to distinguish between real and rogue disturbances in a steel supply network. The data set used was collected from four different business units in the network and consists of 43 variables; each is described by 72 data points. The present paper will utilize the same data set to test an alternative approach to SPCA in detecting the disturbances. The new approach employs statistical data pre-processing, clustering, and classification learning techniques to analyse the supply network data. In particular, the incremental k-means clustering and the RULES-6 classification rule-learning algorithms, developed by the present authors’ team, have been applied to identify important patterns in the data set. Results show that the proposed approach has the capability automatically to detect and characterize network-wide cyclical disturbances and generate hypotheses about their root cause

Using the Sun to estimate Earth-like planets detection capabilities. V. Parameterizing the impact of solar activity components on radial velocities

Stellar activity induced by active structures (eg, spots, faculae) is known to strongly impact the radial velocity time series. It then limits the detection of small planetary RV signals (eg, an Earth-mass planet in the habitable zone of a solar-like star). In previous papers, we studied the detectability of such planets around the Sun seen as an edge-on star. For that purpose, we computed the RV and photometric variations induced by solar magnetic activity, using all active structures observed over one entire cycle. Our goal is to perform similar studies on stars with different physical and geometrical properties. As a first step, we focus on Sun-like stars seen with various inclinations, and on estimating detection capabilities with forthcoming instruments. To do so, we first parameterize the solar active structures with the most realistic pattern so as to obtain results consistent with the observed ones. We simulate the growth, evolution and decay of solar spots, faculae and network, using parameters and empiric laws derived from solar observations and literature. We generate the corresponding structure lists over a full solar cycle. We then build the resulting spectra and deduce the RV and photometric variations for a `Sun' seen with various inclinations. The produced RV signal takes into account the photometric contribution of structures as well as the attenuation of the convective blueshift. The comparison between our simulated activity pattern and the observed one validates our model. We show that the inclination of the stellar rotation axis has a significant impact on the time series. RV long-term amplitudes as well as short-term jitters are significantly reduced when going from edge-on to pole-on configurations. Assuming spin-orbit alignment, the optimal configuration for planet detection is an inclined star (i~45{\deg}).Comment: Accepted to Astronomy and Astrophysics on May, 27th 2015. The manuscript includes 22 pages, 20 figure

Light and circadian regulation of clock components aids flexible responses to environmental signals

The circadian clock measures time across a 24h period, increasing fitness by phasing biological processes to the most appropriate time of day. The interlocking feedback loop mechanism of the clock is conserved across species; however, the number of loops varies. Mathematical and computational analyses have suggested that loop complexity affects the overall flexibility of the oscillator, including its responses to entrainment signals. We used a discriminating experimental assay, at the transition between different photoperiods, in order to test this proposal in a minimal circadian network (in Ostreococcus tauri) and a more complex network (in Arabidopsis thaliana). Transcriptional and translational reporters in O.tauri primarily tracked dawn or dusk, whereas in A.thaliana, a wider range of responses were observed, consistent with its more flexible clock. Model analysis supported the requirement for this diversity of responses among the components of the more complex network. However, these and earlier data showed that the O.tauri network retains surprising flexibility, despite its simple circuit. We found that models constructed from experimental data can show flexibility either from multiple loops and/or from multiple light inputs. Our results suggest that O.tauri has adopted the latter strategy, possibly as a consequence of genomic reduction

Impact of environmental inputs on reverse-engineering approach to network structures

Background: Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. Results: With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. Conclusion: We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations