Study on Water Resources Regulation and Reservoir Operating Prediction for Suppression of Salt Water Intrusion in the Pearl River Estuary

Salt water intrusion occurred frequently during dry season in Modaomen waterway of the Pearl River Estuary. With the development of region's economy and urbanization, the salt tides affect the region's water supply more and more seriously in recent years. Regulation and allocation of freshwater resources of the upper rivers of the estuary to suppress the salt tides is becoming important measures for ensuring the water supply security of the region in dry season. The observation data analysis showed that the flow value at the Wuzhou hydrometric station on the upper Xijiang river had a good correlation with the salinity in Modaomen estuary. Thus the flow rate of Wuzhou has been used as a control variable for suppression of salt tides in Modaomen estuary. However, the runoff at Wuzhou mainly comes from the discharge of Longtan reservoir on the upper reaches of Xijiang river and the runoff in the interval open valley between Longtan and Wuzhou sections. As the long distance and many tributaries as well as the large non-controlled watershed between this two sections, the reservoir water scheduling has a need for reasonable considering of interaction between the reservoir regulating discharge and the runoff process of the interval open watershed while the deployment of suppression flow at Wuzhou requires longer lasting time and high precision for the salt tide cycles. For this purpose, this study established a runoff model for Longtan - Wuzhou interval drainage area and by model calculations and observation data analysis, helped to understand the response patterns of the flow rate at Wuzhou to the water discharge of Longtan under the interval water basin runoff participating conditions. On this basis, further discussions were taken on prediction methods of Longtan reservoir discharge scheduling scheme for saline intrusion suppression and provided scientific and typical implementation programs for effective suppression flow process at the Wuzhou section.

Prediction of soil organic carbon partition coefficients by soil column liquid chromatography

To avoid the limitation of the widely used prediction methods of soil organic carbon partition coefficients (K-OC) from hydrophobic parameters, e.g., the n-octanol/water partition coefficients (K-OW) and the reversed phase high performance liquid chromatographic (RP-HPLC) retention factors, the soil column liquid chromatographic (SCLC) method was developed for K-OC prediction. The real soils were used as the packing materials of RP-HPLC columns, and the correlations between the retention factors of organic compounds on soil columns (k(soil)) and K-OC measured by batch equilibrium method were studied. Good correlations were achieved between k(soil) and K-OC for three types of soils with different properties. All the square of the correlation coefficients (R-2) of the linear regression between log k(soi) and log K-OC were higher than 0.89 with standard deviations of less than 0.21. In addition, the prediction of K-OC from K-OW and the RP-HPLC retention factors on cyanopropyl (CN) stationary phase (k(CN)) was comparatively evaluated for the three types of soils. The results show that the prediction of K-OC from k(CN) and K-OW is only applicable to some specific types of soils. The results obtained in the present study proved that the SCLC method is appropriate for the K-OC prediction for different types of soils, however the applicability of using hydrophobic parameters to predict K-OC largely depends on the properties of soil concerned. (C) 2004 Elsevier B.V. All rights reserved.

New uniform algorithm to predict reversed phase retention values under different gradient conditions

A new numerical emulation algorithm was established to calculate retention parameters in RP-HPLC with several retention times under different linear or nonlinear binary gradient elution conditions and further predict the retention time under any other binary gradient conditions. A program was written according to this algorithm and nine solutes were used to test the program. The prediction results were excellent. The maximum relative error of predicted retention time was less than 0.45%. (C) 2002 Elsevier Science B.V. All rights reserved.

Abnormal phenomenon of the dependence of the retention factors for uncharged species on applied voltage in capillary electrochromatography

Polymethacrylate-based monolithic columns were prepared for capillary electrochromatography (CEC) by in situ copolymerization of butyl methacrylate (BMA), 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPS), and ethylene dimethacrylate (EDMA) in the presence of a porogen in fused-silica capillaries of 100 mum I.D. The abnormal phenomenon that retention factors for neutral species decreases with applied voltage in CEC was observed. Capillary electrophoresis (CE) instruments usually require a period of time to increase voltage from 0 kV to desired value, which is called as ramp time. Such ramp time and any error in the determination of dead time should be taken into account during the accurate calculation of retention factors. After the correction of the retention factors, the plots of the corrected factors for alkylbenzene versus applied voltage were made, the absolute value of the plot slopes are less than 1.8 X 10(-4), Which indicates that the corrected retention times for neutral species do not show any dependence on applied voltage. Further, the plots of the corrected retention times for acidic and basic compounds versus the reciprocal of applied voltage were drawn, where the target compounds were eluted in neutral form. The very nice linearity of the plots was obtained. The linear correlation coefficients are over 0.999. Here, the slopes of the plots represent

Feeding level-scaled retention efficiency, growth and energy partitioning of a marine detritivorous fish, redlip mullet (Liza haematocheila T. & S.)

The effects of feeding level on growth, retention efficiency, faeces production and energy partitioning of redlip mullet were studied. A practical diet was used and fed at six levels from starvation, 1%, 2%, 3%, 4% of body weight (BW) to satiation for 3 weeks. The temperature was kept at 24 +/- 1 degrees C. Reducing the feeding amount resulted in significantly lower weight gain, and retention efficiency was significantly affected by feeding levels and attained the maximum at maximum feeding intake. Feeding 2% BW was the minimum required for fish to maintain growth. Fish carcass composition under different feeding levels could be divided into three groups: (1) starvation and FL1; (2) FL2 and FL3 and (3) FL4 and satiation, with significant differences among the groups but no differences in the groups except that ash content remained at constant value. Body composition of fish of group 2 was close to initial fish. The thermal-unit coefficient was 0.0381 at satiation, and significantly increased with increasing feeding levels. In order to accurately estimate basal metabolism (HeE), another trial on the relationship between HeE (kJ) and BW (g) was carried out. An exponential curve as HeE=0.1255BW(0.8386) explained this relationship. Intake energy (IE) increased from 11.30 to 63.08 kJ per fish, matching with different feeding levels. Energy allocated to growth of IE decreased with reducing feeding amount. There was a linear relationship between metabolism energy and retention energy in percentage.

An empirical equation for the calculation of retention time with extrapolation of temperature in gas chromatography

An empirical equation is proposed to accurately correlate isothermal data over a wide range of temperature With the equation ln k = A* + B*/T-lambda the retention times of different solutes tested on OV-101, SE-54 and PEG 20M capillary columns have been achieved even when lambda is assigned a constant value of 1.7 Comparison with ln k = A + B/T and in k = c + d/T+ h/T-2, shows that the proposed equation is of higher accuracy and is applicable to extrapolation calculation, especially from data at high temperature to those at low temperature. Parameters A* and B* as well as A and B are also discussed. The linear correlation of A* and B* is weaker than that of A and B.

Prediction of soil organic partition coefficients by a soil leaching column chromatographic method

The soil organic partition coefficient (K-oc) is one of the most important parameters to depict the transfer and fate of a chemical in the soil-water system. Predicting K-oc by using a chromatographic technique has been developing into a convenient and low-cost method. In this paper, a soil leaching column chromatograpy (SLCC) method employing the soil column packed with reference soil GSE 17201 (obtained from Bayer Landwirtschaftszentrum, Monheim, Germany) and methanol-water eluents was developed to predict the K-oc of hydrophobic organic chemicals (HOCs), over a log K-oc range of 4.8 orders of magnitude, from their capacity factors. The capacity factor with water as an eluent (k(w)') could be obtained by linearly extrapolating capacity factors in methanol-water eluents (k') with various volume fractions of methanol (phi). The important effects of solute activity coefficients in water on k(w)' and K-oc were illustrated. Hence, the correlation between log K-oc and log k(w)' (and log k') exists in the soil. The correlation coefficient (r) of the log K-oc vs. log k(w)' correlation for 58 apolar and polar compounds could reach 0.987, while the correlation coefficients of the log K-oc-log k' correlations were no less than 0.968, with phi ranging from 0 to 0.50. The smaller the phi, the higher the r. Therefore, it is recommended that the eluent of smaller phi, such as water, be used for accurately estimating K-oc. Correspondingly, the r value of the log K-oc-log k(w)' correlation on a reversed-phase Hypersil ODS (Thermo Hypersil, Kleinostheim, Germany) column was less than 0.940 for the same solutes. The SLCC method could provide a more reliable route to predict K-oc indirectly from a correlation with k(w)' than the reversed-phase liquid chromatographic (RPLC) one.

Resolution prediction and optimization of temperature programme in comprehensive two-dimensional gas chromatography

A model is developed for predicting the resolution of interested component pair and calculating the optimum temperature programming condition in the comprehensive two-dimensional gas chromatography (GC x GC). Based on at least three isothermal runs, retention times and the peak widths at half-height on both dimensions are predicted for any kind of linear temperature-programmed run on the first dimension and isothermal runs on the second dimension. The calculation of the optimum temperature programming condition is based on the prediction of the resolution of "difficult-to-separate components" in a given mixture. The resolution of all the neighboring peaks on the first dimension is obtained by the predicted retention time and peak width on the first dimension, the resolution on the second dimension is calculated only for the adjacent components with un-enough resolution on the first dimension and eluted within a same modulation period on the second dimension. The optimum temperature programming condition is acquired when the resolutions of all components of interest by GC x GC separation meet the analytical requirement and the analysis time is the shortest. The validity of the model has been proven by using it to predict and optimize GC x GC temperature programming condition of an alkylpyridine mixture. (c) 2005 Elsevier B.V. All rights reserved.

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Prediction of adipose tissue composition using Raman spectroscopy: average properties and individual fatty acids.

Abstract: Raman spectroscopy has been used for the first time to predict the FA composition of unextracted adipose tissue of pork, beef, lamb, and chicken. It was found that the bulk unsaturation parameters could be predicted successfully [R-2 = 0.97, root mean square error of prediction (RMSEP) = 4.6% of 4 sigma], with cis unsaturation, which accounted for the majority of the unsaturation, giving similar correlations. The combined abundance of all measured PUFA (>= 2 double bonds per chain) was also well predicted with R-2 = 0.97 and RMSEP = 4.0% of 4 sigma. Trans unsaturation was not as well modeled (R-2 = 0.52, RMSEP = 18% of 4 sigma); this reduced prediction ability can be attributed to the low levels of trans FA found in adipose tissue (0.035 times the cis unsaturation level). For the individual FA, the average partial least squares (PLS) regression coefficient of the 18 most abundant FA (relative abundances ranging from 0.1 to 38.6% of the total FA content) was R-2 = 0.73; the average RMSEP = 11.9% of 4 sigma. Regression coefficients and prediction errors for the five most abundant FA were all better than the average value (in some cases as low as RMSEP = 4.7% of 4 sigma). Cross-correlation between the abundances of the minor FA and more abundant acids could be determined by principal component analysis methods, and the resulting groups of correlated compounds were also well-predicted using PLS. The accuracy of the prediction of individual FA was at least as good as other spectroscopic methods, and the extremely straightforward sampling method meant that very rapid analysis of samples at ambient temperature was easily achieved. This work shows that Raman profiling of hundreds of samples per day is easily achievable with an automated sampling system.

Multivariate prediction of clarified butter composition using Raman spectroscopy

Raman spectroscopy has been used to predict the abundance of the FA in clarified butterfat that was obtained from dairy cows fed a range of levels of rapeseed oil in their diet. Partial least squares regression of the Raman spectra against FA compositions obtained by GC showed good prediction for the five major (abundance >5%) FA with R-2=0.74-0.92 and a root mean SE of prediction (RMSEP) that was 5-7% of the mean. In general, the prediction accuracy fell with decreasing abundance in the sample, but the RMSEP was 1.25%. The Raman method has the best prediction ability for unsaturated FA (R-2=0.85-0.92), and in particular trans unsaturated FA (best-predicted FA was 18:1 tDelta9). This enhancement was attributed to the isolation of the unsaturated modes from the saturated modes and the significantly higher spectral response of unsaturated bonds compared with saturated bonds. Raman spectra of the melted butter samples could also be used to predict bulk parameters calculated from standard analyzes, such as iodine value (R-2=0.80) and solid fat content at low temperature (R-2=0.87). For solid fat contents determined at higher temperatures, the prediction ability was significantly reduced (R-2=0.42), and this decrease in performance was attributed to the smaller range of values in solid fat content at the higher temperatures. Finally, although the prediction errors for the abundances of each of the FA in a given sample are much larger with Raman than with full GC analysis, the accuracy is acceptably high for quality control applications. This, combined with the fact that Raman spectra can be obtained with no sample preparation and with 60-s data collection times, means that high-throughput, on-line Raman analysis of butter samples should be possible.

Prediction of the formation and stabilities of energetic salts and ionic liquids based on ab initio electronic structure calculations

A computational approach to predict the thermodynamics for forming a variety of imidazolium-based salts and ionic liquids from typical starting materials is described. The gas-phase proton and methyl cation acidities of several protonating and methylating agents, as well as the proton and methyl cation affinities of many important methyl-, nitro-, and cyano- substituted imidazoles, have been calculated reliably by using the computationally feasible DFT (B3LYP) and MP2 (extrapolated to the complete basis set limit) methods. These accurately calculated proton and methyl cation affinities of neutrals and anions are used in conjunction with an empirical approach based on molecular volumes to estimate the lattice enthalpies and entropies of ionic liquids, organic solids, and organic liquids. These quantities were used to construct a thermodynamic cycle for salt formation to reliably predict the ability to synthesize a variety of salts including ones with potentially high energetic densities. An adjustment of the gas phase thermodynamic cycle to account for solid- and liquid-phase chemistries provides the best overall assessment of salt formation and stability. This has been applied to imidazoles (the cation to be formed) with alkyl, nitro, and cyano substituents. The proton and methyl cation donors studied were as follows: HCl, HBr, HI, (HO)(2)SO2, HSO3CF3 (TfOH), and HSO3(C6H4)CH3 (TsOH); CH3Cl, CH3Br, CH3I, (CH3O)(2)SO2, CH3SO3CF3 (TfOCH3) and CH3SO3(C6H4)CH3 (TsOCH3). As substitution of the cation with electron-withdrawing groups increases, the triflate reagents appear to be the best overall choice as protonating and methylating agents. Even stronger alkylating agents should be considered to enhance the chances of synthetic success. When using the enthalpies of reaction for the gas-phase reactants (eq 6) to form a salt, a cutoff value of - 13 kcal mol(-1) or lower (more negative) should be used as the minimum value for predicting whether a salt can be synthesized.

Using time-to-contact information to assess potential collision modulates both visual and temporal prediction networks

Accurate estimates of the time-to-contact (TTC) of approaching objects are crucial for survival. We used an ecologically valid driving simulation to compare and contrast the neural substrates of egocentric (head-on approach) and allocentric (lateral approach) TTC tasks in a fully factorial, event-related fMRI design. Compared to colour control tasks, both egocentric and allocentric TTC tasks activated left ventral premotor cortex/frontal operculum and inferior parietal cortex, the same areas that have previously been implicated in temporal attentional orienting. Despite differences in visual and cognitive demands, both TTC and temporal orienting paradigms encourage the use of temporally predictive information to guide behaviour, suggesting these areas may form a core network for temporal prediction. We also demonstrated that the temporal derivative of the perceptual index tau (tau-dot) held predictive value for making collision judgements and varied inversely with activity in primary visual cortex (V1). Specifically, V1 activity increased with the increasing likelihood of reporting a collision, suggesting top-down attentional modulation of early visual processing areas as a function of subjective collision. Finally, egocentric viewpoints provoked a response bias for reporting collisions, rather than no-collisions, reflecting increased caution for head-on approaches. Associated increases in SMA activity suggest motor preparation mechanisms were engaged, despite the perceptual nature of the task.

Stiffness prediction for bolted moment-connections between cold-formed steel members

The authors have recently described a cold-formed steel portal framing system in which simple bolted moment-connections, formed through brackets, were used for the eaves and apex joints. Such connections, however, cannot be considered as rigid because of localised in-plane elongation of the bolt-holes caused by bearing against the bolt-shanks. To therefore predict the initial stiffness of such connections, it is necessary to know the initial bolt-hole elongation stiffness k(b). In this paper, a finite element-solid idealisation of a bolted lap joint in shear will be described that can be used to determine k(b); the results obtained are validated against experimental data. A beam idealisation of a cold-formed steel bolted moment-connection is then described, in which spring elements are used to idealise the rotational flexibility of the bolt-groups resulting from bolt-hole elongation: Using the value of k(b) in the beam idealisation, the deflections predicted are shown to be similar to those measured experimentally in laboratory tests conducted on the apex joint of a cold-formed steel portal frame. (C) 2003 Elsevier Ltd. All rights reserved.

A multi-fibre multi-layer representative volume element (M2RVE) for prediction of matrix and interfacial damage in composite laminates

Multiscale micro-mechanics theory is extensively used for the prediction of the material response and damage analysis of unidirectional lamina using a representative volume element (RVE). Th is paper presents a RVE-based approach to characterize the materi al response of a multi-fibre cross-ply laminate considering the effect of matrix damage and fibre-matrix interfacial strength. The framework of the homogenization theory for periodic media has been used for the analysis of a 'multi-fibre multi-layer representative volume element' (M2 RVE) representing cross-ply laminate. The non-homogeneous stress-strain fields within the M2RVE are related to the average stresses and strains by using Gauss theorem and the Hill-Mandal strain energy equivalence principle. The interfacial bonding strength affects the in-plane shear stress-strain response significantl y. The material response predicted by M2 RVE is in good agreement with the experimental results available in the literature. The maximum difference between the shear stress predicted using M2 RVE and the experimental results is ~15% for the bonding strength of 30MPa at the strain value of 1.1%