The spectrum of uncertainty in flood damage assessment

The future of the world is becoming more uncertain owing to climate change. The unfolding impacts of climate change are affecting human societies and natural ecosystems. Projections of climate change impacts are associated with a cascade of uncertainties including greenhouse gas emissions scenarios, climate models, and associated processes. Climate models are essential for predicting flow and flood peaks, neces-sitating proper means of quantification and re-use to help refine the predictions made. This study presents an innovative new framework to quantify flood damage assessment as the climate changes. To integrate uncertainty in modelling catchment behaviour, the Quantile Flow Deviation (QFD) metric was used to attribute different sources of uncertainty, including those from variations in climate from point measurements and from extrapolation to flood peaks from the limited observed flows that are available. The square root of error variance (SREV) calculated from global climate model (GCM) precipitation outputs was used to quantify climate change uncertainty, thereby enabling the estimation of the uncertainty in modelled streamflow to allow the extent of change in flood damage to be assessed. Using data from the Leaf River catchment in the USA, this study presents the increase in flood damage uncertainty resulting from explicit consideration of uncertainty as well as the change in the climate as a function of global temperature rise

New nanomaterial and process for the production of biofuel from metal hyper accumulator water hyacinth

Three different studies were performed for the conversion of water hyacinth (Eichhornia crassipes) plant into biofuel. In the first study, water hyacinth was saccharified with diluted sulfuric acid (1% v/v at 110°C for one hour), fermented by yeast (Saccharomyces cerevisiae). The results showed the formation of 55.20% ethanol and 41.66% acetic acid. In another experiment, water hyacinth was gasified by using Ni and Co nano catalysts at 50 - 400°C and atmospheric pressure. In catalytic gasification, CH4 (2.41 - 6.67%), C2H4 (19.74 - 45.52%), C3H4 (21.04 - 45.52%), CH3OH (1.43 - 24.67%), and C3H8 / CH3CHO (0.33 -26.09%) products were obtained. In this study, anatase form of titanium dioxide photocatalyst was used. The reaction was performed at room temperature which gives methane, methanol and ethanol.This study also reports an interesting finding that metal contaminated water hyacinth could be used for not only the production of biofuel but also hydrocarbons

Phytoremediation technologies for Ni++ by water hyacinth

Phytoremediation of metal contaminated soil and water by Eichhornia crassipes (water hyacinth) is promising. The study was conducted to compare the phytoremoval of Ni++ from soil and wastewater. For the measurement of phytoremoval from soil, E. crassipes was used in a pot experiment. Results showed the removal of (Ni) 24.23 μg/g dry weight of plant and large level calculations show removal of 3449.76 kg/ha of soil, corresponding to 25 μg/g of the added Ni++. In the second experiment, Ni++ contaminated Hoagland’s solution was used for the hydroponic growth of water hyacinth. The result of hydroponic experiment showed the phytoremoval of Ni++ from Ni++ contaminated wastewater; maximum removal was 1.954 μg/g of dry weight. In third experiment, ash of water hyacinth was used for the adsorption and desorption of Ni++. The adsorption capacity was 1.978 μg/g of ash. For the extraction(desorption) of Ni++, 3 M HNO3 was used. Desorption capacity was 3.71 μg/g of ash. The results of comparative study show order of nickel phytoremediation from soil to be greater than that from water by adsorption which was greater than that from water by hydroponic study. For phytoremoval of Ni++ from soil and water, water hyacinth plant and its ash showed excellence. The desorbed Ni++ can be used in the industries e.g. in Ni plating.Keywords: Nickel, phytoremediation, soil, waste water, water hyacinth, biosorptio

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles.

The rapid proliferation of the emerging yet promising notion of the Internet-of-Vehicles (IoV) has led to the development of a variety of conventional trust assessment schemes to tackle insider attackers. The primary reliance of these frameworks is on the accumulation of individual trust attributes. While aggregating these influential parameters, weights are often associated with each individual attribute to reflect its impact on the final trust score. It is of paramount importance that such weights be precise to lead to an accurate trust assessment. Moreover, the value of the minimum acceptable trust threshold employed for the identification of dishonest vehicles needs to be carefully defined to avoid delayed or erroneous detection. This paper employs an IoT data set from CRAWDAD by suitably transforming it into an IoV format. This data set encompasses information regarding 18,226 interactions among 76 nodes, both honest and dishonest. First, the influencing parameters (i.e., packet delivery ratio, familiarity, timeliness and interaction frequency) were computed, and two feature matrices were formed. The first matrix (FM1) takes into account all the pairwise individual parameters as individual features, whereas the second matrix (FM2) considers the average of all pairwise computations performed for each individual parameter as one feature. Subsequently, unsupervised learning is employed to achieve the ground truth prior to applying supervised machine learning algorithms for classification purposes. It is worth noting that Subspace KNN yielded a perfect precision, recall, and the F1-score equal to 1 for individual parametric scores, whereas Subspace Discriminant returned an ideal precision, recall, and the F1-score equal to 1 for mean parametric scores. It is also evident from extensive simulations that FM2 yielded more accurate classification results compared to FM1. Furthermore, decision boundaries among honest and dishonest vehicles have also been computed for respective feature matrices

Measurement of Exclusive B Decays to Final States Containing a Charmed Baryon

Using data collected by the CLEO detector in the Upsilon(4S) region, we report new measurements of the exclusive decays of B mesons into final states of the type Lambda_c^+ p-bar n(pi), where n=0,1,2,3. We find signals in modes with one, two and three pions and an upper limit for the two body decay Lambda_c^+ pbar. We also make the first measurements of exclusive decays of B mesons to Sigma_c p-bar n(pi), where n=0,1,2. We find signals in modes with one and two pions and an upper limit for the two body decay Sigma_c p-bar. Measurements of these modes shed light on the mechanisms involved in B decays to baryons.Comment: 11 pages postscript, also available through http://w4.lns.cornell.edu/public/CLNS, submitted to PR

Effect of tannery effluents on seed germination and growth of two sunflower cultivars

The wastewater of a tannery in Multan, Pakistan, was alkaline with high biochemical oxygen demand (BOD) and chemical oxygen demand (COD) values along with much higher concentrations of total settle able salts and suspended solids, sodium adsorption ratio and high amount of sodium having the water quality class C3S1. Effluent was examined for its chemical constituents and the effect of its various dilutions was examined in greenhouse on two newly recommended sunflower cultivars (FH-330 and FH- 245) during their whole growth period. Percentage of germination, chlorophyll, carbohydrates and protein contents of both the sunflower cultivars showed significant (p = 0.05) decreasing trend with increasing effluent concentrations. Vegetative growth parameters like plant height and number of leaves per plant were significantly (p = 0.05) reduced with the increasing levels of effluents. Rate of leaf senescence of both cultivars was higher under higher effluent concentrations. Yield of sunflower crop in both cultivars was significantly (p = 0.05) reduced due to effluent concentrations. Seeds per capitulum were decreased to 49%, seeds weight per plant to 61 - 66% and 100-seed weight to 49 - 59%. The appearance of pale yellowish color of the affected plants was due to reduction in photosynthetic material under higher effluent concentration. Full strength effluent concentration caused the reduction in biomass accumulation and reproductive growth of sunflower cultivars. The results revealed that cultivar FH-330 was relatively resistant to varying effluent concentrations as compared to the cultivar FH- 245. However, the tannery effluents due to the presence of chemicals are not suitable for inclusion in irrigation system.Key words: Tannery effluents, sunflower, seed germination, growth, adverse effect, reduced yield

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

The Formation of the First Massive Black Holes

Supermassive black holes (SMBHs) are common in local galactic nuclei, and SMBHs as massive as several billion solar masses already exist at redshift z=6. These earliest SMBHs may grow by the combination of radiation-pressure-limited accretion and mergers of stellar-mass seed BHs, left behind by the first generation of metal-free stars, or may be formed by more rapid direct collapse of gas in rare special environments where dense gas can accumulate without first fragmenting into stars. This chapter offers a review of these two competing scenarios, as well as some more exotic alternative ideas. It also briefly discusses how the different models may be distinguished in the future by observations with JWST, (e)LISA and other instruments.Comment: 47 pages with 306 references; this review is a chapter in "The First Galaxies - Theoretical Predictions and Observational Clues", Springer Astrophysics and Space Science Library, Eds. T. Wiklind, V. Bromm & B. Mobasher, in pres

Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease

Biomarkers are becoming increasingly important in the clinical management of complex diseases, yet our ability to discover new biomarkers remains limited by our dependence on endogenous molecules. Here we describe the development of exogenously administered 'synthetic biomarkers' composed of mass-encoded peptides conjugated to nanoparticles that leverage intrinsic features of human disease and physiology for noninvasive urinary monitoring. These protease-sensitive agents perform three functions in vivo: they target sites of disease, sample dysregulated protease activities and emit mass-encoded reporters into host urine for multiplexed detection by mass spectrometry. Using mouse models of liver fibrosis and cancer, we show that these agents can noninvasively monitor liver fibrosis and resolution without the need for invasive core biopsies and substantially improve early detection of cancer compared with current clinically used blood biomarkers. This approach of engineering synthetic biomarkers for multiplexed urinary monitoring should be broadly amenable to additional pathophysiological processes and point-of-care diagnostics.National Institutes of Health (U.S.) (Bioengineering Research Partnership R01 CA124427)Kathy and Curt Marble Cancer Research FundNational Institutes of Health (U.S.). Ruth L. Kirschstein National Research Service Award (F32CA159496-01

In-situ local phase-transitioned MoSe2 in La0.5Sr0.5CoO3-?? heterostructure and stable overall water electrolysis over 1000 hours

Developing efficient bifunctional catalysts for overall water splitting that are earth-abundant, cost-effective, and durable is of considerable importance from the practical perspective to mitigate the issues associated with precious metal-based catalysts. Herein, we introduce a heterostructure comprising perovskite oxides (La0.5Sr0.5CoO3?????) and molybdenum diselenide (MoSe2) as an electrochemical catalyst for overall water electrolysis. Interestingly, formation of the heterostructure of La0.5Sr0.5CoO3????? and MoSe2 induces a local phase transition in MoSe2, 2???H to 1???T phase, and more electrophilic La0.5Sr0.5CoO3????? with partial oxidation of the Co cation owing to electron transfer from Co to Mo. Together with these synergistic effects, the electrochemical activities are significantly improved for both hydrogen and oxygen evolution reactions. In the overall water splitting operation, the heterostructure showed excellent stability at the high current density of 100???mA???cm???2 over 1,000???h, which is exceptionally better than the stability of the state-of-the-art platinum and iridium oxide couple