Techniques for Estimating Flood-Frequency Discharges for Streams in Iowa, HR-395A, 2001

A statewide study was conducted to develop regression equations for estimating flood-frequency discharges for ungaged stream sites in Iowa. Thirty-eight selected basin characteristics were quantified and flood-frequency analyses were computed for 291 streamflow-gaging stations in Iowa and adjacent States. A generalized-skew-coefficient analysis was conducted to determine whether generalized skew coefficients could be improved for Iowa. Station skew coefficients were computed for 239 gaging stations in Iowa and adjacent States, and an isoline map of generalized-skew-coefficient values was developed for Iowa using variogram modeling and kriging methods. The skew map provided the lowest mean square error for the generalized-skew- coefficient analysis and was used to revise generalized skew coefficients for flood-frequency analyses for gaging stations in Iowa. Regional regression analysis, using generalized least-squares regression and data from 241 gaging stations, was used to develop equations for three hydrologic regions defined for the State. The regression equations can be used to estimate flood discharges that have recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years for ungaged stream sites in Iowa. One-variable equations were developed for each of the three regions and multi-variable equations were developed for two of the regions. Two sets of equations are presented for two of the regions because one-variable equations are considered easy for users to apply and the predictive accuracies of multi-variable equations are greater. Standard error of prediction for the one-variable equations ranges from about 34 to 45 percent and for the multi-variable equations range from about 31 to 42 percent. A region-of-influence regression method was also investigated for estimating flood-frequency discharges for ungaged stream sites in Iowa. A comparison of regional and region-of-influence regression methods, based on ease of application and root mean square errors, determined the regional regression method to be the better estimation method for Iowa. Techniques for estimating flood-frequency discharges for streams in Iowa are presented for determining ( 1) regional regression estimates for ungaged sites on ungaged streams; (2) weighted estimates for gaged sites; and (3) weighted estimates for ungaged sites on gaged streams. The technique for determining regional regression estimates for ungaged sites on ungaged streams requires determining which of four possible examples applies to the location of the stream site and its basin. Illustrations for determining which example applies to an ungaged stream site and for applying both the one-variable and multi-variable regression equations are provided for the estimation techniques.

The effect of workplace smoking bans on heart rate variability and pulse wave velocity of non-smoking hospitality workers

OBJECTIVES: To investigate the effect of a change in second-hand smoke (SHS) exposure on heart rate variability (HRV) and pulse wave velocity (PWV), this study utilized a quasi-experimental setting when a smoking ban was introduced. METHODS: HRV, a quantitative marker of autonomic activity of the nervous system, and PWV, a marker of arterial stiffness, were measured in 55 non-smoking hospitality workers before and 3-12 months after a smoking ban and compared to a control group that did not experience an exposure change. SHS exposure was determined with a nicotine-specific badge and expressed as inhaled cigarette equivalents per day (CE/d). RESULTS: PWV and HRV parameters significantly changed in a dose-dependent manner in the intervention group as compared to the control group. A one CE/d decrease was associated with a 2.3 % (95 % CI 0.2-4.4; p = 0.031) higher root mean square of successive differences (RMSSD), a 5.7 % (95 % CI 0.9-10.2; p = 0.02) higher high-frequency component and a 0.72 % (95 % CI 0.40-1.05; p < 0.001) lower PWV. CONCLUSIONS: PWV and HRV significantly improved after introducing smoke-free workplaces indicating a decreased cardiovascular risk.

Effects of prior short multiple-sprint exercises with different intersprint recoveries on the slow component of oxygen uptake during high-intensity exercise.

This study compares the effects of two short multiple-sprint exercise (MSE) (6 × 6 s) sessions with two different recovery durations (30 s or 180 s) on the slow component of oxygen uptake ([Formula: see text]O(2)) during subsequent high-intensity exercise. Ten male subjects performed a 6-min cycling test at 50% of the difference between the gas exchange threshold and [Formula: see text]O(2peak) (Δ50). Then, the subjects performed two MSEs of 6 × 6 s separated by two intersprint recoveries of 30 s (MSE(30)) and 180 s (MSE(180)), followed 10 min later by the Δ50 (Δ50(30) and Δ50(180), respectively). Electromyography (EMG) activities of the vastus medialis and lateralis were measured throughout each exercise bout. During MSE(30), muscle activity (root mean square) increased significantly (p ≤ 0.04), with a significant leftward-shifted median frequency of the power density spectrum (MDF; p ≤ 0.01), whereas MDF was significantly rightward-shifted during MSE(180) (p = 0.02). The mean [Formula: see text]O(2) value was significantly higher in MSE(30) than in MSE(180) (p < 0.001). During Δ50(30), [Formula: see text]O(2) and the deoxygenated hemoglobin ([HHb]) slow components were significantly reduced (-27%, p = 0.02, and -34%, p = 0.003, respectively) compared with Δ50. There were no significant modifications of the [Formula: see text]O(2) slow component in Δ50(180) compared with Δ50 (p = 0.32). The neuromuscular and metabolic adaptations during MSE(30) (preferential activation of type I muscle fibers evidenced by decreased MDF and a greater aerobic metabolism contribution to the required energy demands), but not during MSE(180), may lead to reduced [Formula: see text]O(2) and [HHb] slow components, suggesting an alteration in motor units recruitment profile (i.e., change in the type of muscle fibers recruited) and (or) an improved muscle O(2) delivery during subsequent exercise.

Simultaneous measurement of regional cerebral blood flow by perfusion CT and stable xenon CT: a validation study.

BACKGROUND AND PURPOSE: Knowledge of cerebral blood flow (CBF) alterations in cases of acute stroke could be valuable in the early management of these cases. Among imaging techniques affording evaluation of cerebral perfusion, perfusion CT studies involve sequential acquisition of cerebral CT sections obtained in an axial mode during the IV administration of iodinated contrast material. They are thus very easy to perform in emergency settings. Perfusion CT values of CBF have proved to be accurate in animals, and perfusion CT affords plausible values in humans. The purpose of this study was to validate perfusion CT studies of CBF by comparison with the results provided by stable xenon CT, which have been reported to be accurate, and to evaluate acquisition and processing modalities of CT data, notably the possible deconvolution methods and the selection of the reference artery. METHODS: Twelve stable xenon CT and perfusion CT cerebral examinations were performed within an interval of a few minutes in patients with various cerebrovascular diseases. CBF maps were obtained from perfusion CT data by deconvolution using singular value decomposition and least mean square methods. The CBF were compared with the stable xenon CT results in multiple regions of interest through linear regression analysis and bilateral t tests for matched variables. RESULTS: Linear regression analysis showed good correlation between perfusion CT and stable xenon CT CBF values (singular value decomposition method: R(2) = 0.79, slope = 0.87; least mean square method: R(2) = 0.67, slope = 0.83). Bilateral t tests for matched variables did not identify a significant difference between the two imaging methods (P >.1). Both deconvolution methods were equivalent (P >.1). The choice of the reference artery is a major concern and has a strong influence on the final perfusion CT CBF map. CONCLUSION: Perfusion CT studies of CBF achieved with adequate acquisition parameters and processing lead to accurate and reliable results.

Testing a new multigroup inference approach to reconstructing past environmental conditions

A new, quantitative, inference model for environmental reconstruction (transfer function), based for the first time on the simultaneous analysis of multigroup species, has been developed. Quantitative reconstructions based on palaeoecological transfer functions provide a powerful tool for addressing questions of environmental change in a wide range of environments, from oceans to mountain lakes, and over a range of timescales, from decades to millions of years. Much progress has been made in the development of inferences based on multiple proxies but usually these have been considered separately, and the different numeric reconstructions compared and reconciled post-hoc. This paper presents a new method to combine information from multiple biological groups at the reconstruction stage. The aim of the multigroup work was to test the potential of the new approach to making improved inferences of past environmental change by improving upon current reconstruction methodologies. The taxonomic groups analysed include diatoms, chironomids and chrysophyte cysts. We test the new methodology using two cold-environment training-sets, namely mountain lakes from the Pyrenees and the Alps. The use of multiple groups, as opposed to single groupings, was only found to increase the reconstruction skill slightly, as measured by the root mean square error of prediction (leave-one-out cross-validation), in the case of alkalinity, dissolved inorganic carbon and altitude (a surrogate for air-temperature), but not for pH or dissolved CO2. Reasons why the improvement was less than might have been anticipated are discussed. These can include the different life-forms, environmental responses and reaction times of the groups under study.

Forecasting tourism demand using consumer expectations

Abstract Purpose- There is a lack of studies on tourism demand forecasting that use non-linear models. The aim of this paper is to introduce consumer expectations in time-series models in order to analyse their usefulness to forecast tourism demand. Design/methodology/approach- The paper focuses on forecasting tourism demand in Catalonia for the four main visitor markets (France, the UK, Germany and Italy) combining qualitative information with quantitative models: autoregressive (AR), autoregressive integrated moving average (ARIMA), self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. The forecasting performance of the different models is evaluated for different time horizons (one, two, three, six and 12 months). Findings- Although some differences are found between the results obtained for the different countries, when comparing the forecasting accuracy of the different techniques, ARIMA and Markov switching regime models outperform the rest of the models. In all cases, forecasts of arrivals show lower root mean square errors (RMSE) than forecasts of overnight stays. It is found that models with consumer expectations do not outperform benchmark models. These results are extensive to all time horizons analysed. Research limitations/implications- This study encourages the use of qualitative information and more advanced econometric techniques in order to improve tourism demand forecasting. Originality/value- This is the first study on tourism demand focusing specifically on Catalonia. To date, there have been no studies on tourism demand forecasting that use non-linear models such as self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. This paper fills this gap and analyses forecasting performance at a regional level. Keywords Tourism, Forecasting, Consumers, Spain, Demand management Paper type Research paper

Improving the Quality of EEG Data in Patients With Alzheimers Disease Using ICA

Does Independent Component Analysis (ICA) denature EEG signals? We applied ICA to two groups of subjects (mild Alzheimer patients and control subjects). The aim of this study was to examine whether or not the ICA method can reduce both group di®erences and within-subject variability. We found that ICA diminished Leave-One- Out root mean square error (RMSE) of validation (from 0.32 to 0.28), indicative of the reduction of group di®erence. More interestingly, ICA reduced the inter-subject variability within each group (¾ = 2:54 in the ± range before ICA, ¾ = 1:56 after, Bartlett p = 0.046 after Bonfer- roni correction). Additionally, we present a method to limit the impact of human error (' 13:8%, with 75.6% inter-cleaner agreement) during ICA cleaning, and reduce human bias. These ¯ndings suggests the novel usefulness of ICA in clinical EEG in Alzheimer's disease for reduction of subject variability.

Estudo da estrutura e da solvatação do HNP-3, um antibiótico natural, por dinâmica molecular

The structure and hydration of the HNP-3 have been derived from molecular dynamics data using root mean square deviation, radial and energy distributions. Three antiparallel beta sheets were found to be preserved. 15 intramolecular hydrogen bonds were identified together with 36 hydrogen bonds on the backbone and 35 on the side chain atoms. From the point of view of the hydration dynamics, the analysis shows a high solvent accessibility of the monomer and attractive interactions with water molecules.

Comparação entre métodos compostos no cálculo de afinidades por próton e elétron em sistemas moleculares

The CBS-4M, CBS-QB3, G2, G2(MP2), G3 and G3(MP2) model chemistry methods have been used to calculate proton and electron affinities for a set of molecular and atomic systems. Agreement with the experimental value for these electronic properties is quite good considering the uncertainty in the experimental data. A comparison among the six theories using statistical analysis (average value, standard deviation and root-mean-square) showed a better performance of CBS-QB3 to obtain these properties.

Espectroscopia no infravermelho médio e análise sensorial aplicada à detecção de adulteração de café torrado por adição de cascas de café

Mid-infrared spectroscopy and chemometrics were used to identify adulteration in roasted and ground coffee by addition of coffee husks. Consumers' sensory perception of the adulteration was evaluated by a triangular test of the coffee beverages. Samples containing above 0.5% of coffee husks from pure coffees were discriminated by principal component analysis of the infrared spectra. A partial least-squares regression estimated the husk content in samples and presented a root-mean-square error for prediction of 2.0%. The triangular test indicated that were than 10% of coffee husks are required to cause alterations in consumer perception about adulterated beverages.

Implementação de um método robusto para o controle fiscal de umidade em queijo minas artesanal. Abordagem metrológica multivariada

This study developed and validated a method for moisture determination in artisanal Minas cheese, using near-infrared spectroscopy and partial-least-squares. The model robustness was assured by broad sample diversity, real conditions of routine analysis, variable selection, outlier detection and analytical validation. The model was built from 28.5-55.5% w/w, with a root-mean-square-error-of-prediction of 1.6%. After its adoption, the method stability was confirmed over a period of two years through the development of a control chart. Besides this specific method, the present study sought to provide an example multivariate metrological methodology with potential for application in several areas, including new aspects, such as more stringent evaluation of the linearity of multivariate methods.

REDUÇÃO DE VARIÁVEIS DE ENTRADA DE REDES NEURAIS ARTIFICIAIS A PARTIR DE DADOS DE ANÁLISE DE COMPONENTES PRINCIPAIS NA MODELAGEM DE OXIGÊNIO DISSOLVIDO

The objective of this work is to demonstrate the efficient utilization of the Principal Components Analysis (PCA) as a method to pre-process the original multivariate data, that is rewrite in a new matrix with principal components sorted by it's accumulated variance. The Artificial Neural Network (ANN) with backpropagation algorithm is trained, using this pre-processed data set derived from the PCA method, representing 90.02% of accumulated variance of the original data, as input. The training goal is modeling Dissolved Oxygen using information of other physical and chemical parameters. The water samples used in the experiments are gathered from the Paraíba do Sul River in São Paulo State, Brazil. The smallest Mean Square Errors (MSE) is used to compare the results of the different architectures and choose the best. The utilization of this method allowed the reduction of more than 20% of the input data, which contributed directly for the shorting time and computational effort in the ANN training.

Description of epidemics of coffee rust with neural networks

The objective of this paper was to evaluate the potential of neural networks (NN) as an alternative method to the basic epidemiological approach to describe epidemics of coffee rust. The NN was developed from the intensities of coffee (Coffea arabica) rust along with the climatic variables collected in Lavras-MG between 13 February 1998 and 20 April 2001. The NN was built with climatic variables that were either selected in a stepwise regression analysis or by the Braincel® system, software for NN building. Fifty-nine networks and 26 regression models were tested. The best models were selected based on small values of the mean square deviation (MSD) and of the mean prediction error (MPE). For the regression models, the highest coefficients of determination (R²) were used. The best model developed with neural networks had an MSD of 4.36 and an MPE of 2.43%. This model used the variables of minimum temperature, production, relative humidity of the air, and irradiance 30 days before the evaluation of disease. The best regression model was developed from 29 selected climatic variables in the network. The summary statistics for this model were: MPE=6.58%, MSE=4.36, and R²=0.80. The elaborated neural networks from a time series also were evaluated to describe the epidemic. The incidence of coffee rust at four previous fortnights resulted in a model with MPE=4.72% and an MSD=3.95.

Production of baruzeiro seedling in different protected environments and substrates

The high seedlings quality is essential for deployment of homogeneous orchards. This study evaluated the baruzeiro (Dipteryx alata Vog) seedlings formation on different substrates within protected environments. It was used substrates with100% of cattle manure; 100% of cassava stems; 100% of vermiculite; 50% of cattle manure + 50% of cassava stems; 50% of cattle manure + 50% of vermiculite; 50% of cassava stems + 50% of vermiculite; and + ⅓ of cattle manure + ⅓ of cassava stems + ⅓ of vermiculite. These substrates were tested in protected areas: greenhouse; black shade net of 50% shading; and aluminized thermo-reflective screen of 50% shading. A completely randomized experimental design with five replicates of four plants was adopted. Initially, data were submitted to analysis of individual variance of the substrates, in each environment of cultivation, then performing the evaluation of the residual mean square and the analysis of these environments together for comparison. The best substrate for baruzeiro seedlings was pure vermiculite. The substrates with 100% of manure and the substrate with 33.33% of the mixed studied materials can be used for seedlings formation. The environment with screen can be indicated for the production of baruzeiro seedlings, since it gave vigor to the seedlings.

Seedlings of Acrocomia aculeata in diferent substrates and protected environments

The seedling production stage is the key to achieve uniformity in tree breeding stage. This study evaluated "bocaiúva" (Acrocomia aculeata) seedling formation, with pre-germinated seeds in different substrates and protected environments, in the University of Mato Grosso do Sul State, Aquidauana, MS. As substrates, we used 100% cattle manure (M), 100% cassava branches (CB), 100% vermiculite (V), 50% cattle manure + 50% cassava branches, 50 % cattle manure + 50% vermiculite, 50% cassava branches + 50% vermiculite and ⅓ cattle manure + ⅓ cassava branches + ⅓ vermiculite. These substrates were tested in a greenhouse covered with 150 µm low density polyethylene (LDPE) film under thermo-reflective screen with 50% shading under film; black screen with 50% shading on the sides; black monofilament screen with 50% shading set on roof and sides; and aluminized thermo- reflective screen with 50% shading set on roof and sides. The completely randomized experimental design with 5 replications of 5 plants each was adopted. Initially, data were submitted to analysis of substrate individual variance in each growing environment, then performing the waste mean square evaluation and their environment joint analysis for comparison. The best growing environment is the thermo-reflective screen compared to LDPE greenhouse and black screen set. All substrates containing manure are recommended for bocaiúva seedlings formation. The pure cassava branch is not indicated for seedling, even using chemical fertilizer.