Romanian River Basins Lag Time Analysis. The SCS-CN Versus RNS Comparative Approach Developed for Small Watersheds

Romanian policy makers have to perceive that human intervention on river basins land cover is influencing rainfall-runoff relation and the used methodology cannot accurately estimate watershed surface flow transformations. Global water cycles and energy fluxes understanding is leading to better predictions of land atmosphere interaction and local hydro-climates evolution. The water transfer time determination from rainfall to runoff needs accurate measurements of river basins hydrological parameters. Here, we analyzed and compared the lag time value results of two different methodologies (curve number and rational methodology) used for 54 Romanian small catchment areas study. The focus of this paper is the lag time evaluation and interpretation for an effective implementation of the best methodology approach in the Romanian geographical space. Our research in small river basins was developed using remote sensing technology maps, GIS and environmental datasets in combination with field work on every drainage basin in order to assess the specific morphological features and validate the land cover typology. We found that Soil Conservation Service - Curve Number (SCS-CN) method is widely used according to USA landscape features classification, but not necessarily applicable to Romanian river basins characteristics. Our results show how the official Romanian rational methodology national standard (RNS) can be improved and the limits of SCS-CN method.

     

Group sequential crossover trial designs with strong control of the familywise error rate

Crossover designs are an extremely useful tool to investigators, and group sequential methods have proven highly proficient at improving the efficiency of parallel group trials. Yet, group sequential methods and crossover designs have rarely been paired together. One possible explanation for this could be the absence of a formal proof of how to strongly control the familywise error rate in the case when multiple comparisons will be made. Here, we provide this proof, valid for any number of initial experimental treatments and any number of stages, when results are analyzed using a linear mixed model. We then establish formulae for the expected sample size and expected number of observations of such a trial, given any choice of stopping boundaries. Finally, utilizing the four-treatment, four-period TOMADO trial as an example, we demonstrate that group sequential methods in this setting could have reduced the trials expected number of observations under the global null hypothesis by over 33%.     

Oxidative degradation of polyamide reverse osmosis membranes: Studies of molecular model compounds and selected membranes

Selected aromatic amides were used to model the chemical reactivity of aromatic polyamides found in thin‐film composite reverse osmosis (RO) membranes. Chlorination and possible amide bond cleavage of aromatic amides upon exposure to aqueous chlorine, which can lead to membrane failure, were investigated. Correlations are made of the available chlorine concentration, pH, and exposure time with chemical changes in the model compounds. From the observed reactivity trends, insights are obtained into the mechanism of RO membrane performance loss upon chlorine exposure. Two chemical pathways for degradation are shown, one at constant pH and another that is pH‐history dependent. An alternative strategy is presented for the design of chlorine‐resistant RO membranes, and an initial performance study of RO membranes incorporating this strategy is reported. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 90: 1173–1184, 2003     

Experimental Study of Ice Jam Thickening under Dynamic Flow Conditions

River ice jams are a common occurrence on northern rivers, and their formation can present a severe flood risk to nearby communities. As more and more river regulation projects are developed to provide an alternative to fossil fuels for electrical power-generating capacity, our need to understand the mechanisms associated with ice jam formation under variable flow conditions becomes more vital. This is because, at present, hydropeaking operations are often severely curtailed during the ice-affected seasons due to concerns that sudden flow fluctuations might instigate ice jams and associated flooding. Here, an experimental investigation explores the effects of rapid increases in discharge on ice jam formation and evolution. It is found that the thickness of ice jams formed under highly dynamic flow conditions tend to be slightly thinner than those formed during steady carrier flows for comparable discharges. Also, despite the highly dynamic nature of these consolidation events, the resulting ice thicknesses appear reasonably well approximated by steady flow theory.     

Acquiring rule sets as a product of learning in a logical neuralarchitecture

Envisioning neural networks as systems that learn rules calls forth the verification issues already being studied in knowledge-based systems engineering, and complicates these with neural-network concepts such as nonlinear dynamics and distributed memories. We show that the issues can be clarified and the learned rules visualized symbolically by formalizing the semantics of rule-learning in the mathematical language of two-valued predicate logic. We further show that this can, at least in some cases, be done with a fairly simple logical model. We illustrate this with a combination of two example neural-network architectures, LAPART, designed to learn rules as logical inferences from binary data patterns, and the stack interval network, which converts real-valued data into binary patterns that preserve the semantics of the ordering of real values. We discuss the significance of the formal model in facilitating the analysis of the underlying logic of rule-learning and numerical data representation. We provide examples to illustrate the formal model, with the combined stack interval/LAPART networks extracting rules from numerical data.     

The time course of plausibility effects on eye movements in reading: Evidence from noun-noun compounds.

Readers' eye movements were monitored as they read sentences containing noun-noun compounds that varied in frequency (e.g., elevator mechanic, mountain lion). The left constituent of the compound was either plausible or implausible as a head noun at the point at which it appeared, whereas the compound as a whole was always plausible. When the head noun analysis of the left constituent was implausible, reading times on this word were inflated, beginning with the first fixation. This finding is consistent with previous demonstrations of very rapid effects of plausibility on eye movements. Compound frequency did not modulate the plausibility effect, and all disruption was resolved by the time readers' eyes moved to the next word. These findings suggest (contra Kennison, 2005) that the parser initially analyzes a singular noun as a head instead of a modifier. In addition, the findings confirm that the very rapid effect of plausibility on eye movements is not due to strategic factors, because in the present experiment, unlike in previous demonstrations, this effect appeared in sentences that were globally plausible. (PsycINFO Database Record (c) 2010 APA, all rights reserved)