Heterogeneous photocatalytic degradation of reactive dyes in aqueous TiO(2) suspensions: Decolorization kinetics

This work assesses the photocatalytic (TiO(2)/UV) degradation of a simulated reactive dye bath (Black 5, Red 239, Yellow 17, and auxiliary chemicals). Color removal was monitored by spectrophotometry. Mineralization was determined by DOC analyses. Photocatalytic, photolytic, and adsorption experiments were performed, showing that adsorption was negligible. After 30 min of irradiation, it was achieved 97% and 40% of color removal with photocatalysis and photolysis, respectively. No mineralization occurred within 30 min. A kinetic model composed of two, first-order in-series reactions was used. The first photocatalytic decolorization rate constant was k(1) = 2.6 min(-1) and the second k(2) = 0.011 min(-1). The fast decolorization of Reactive Black 5 dye is an indication that the number of azo and vinylsulfone groups in the dye molecule maybe a determining factor for the increased photolytic and photocatalytic color removal and degradation rates. (C) 2008 Elsevier B.V. All rights reserved.

Simultaneous Differential Pulse Voltammetric Determination of Ascorbic Acid and Caffeine in Pharmaceutical Formulations Using a Boron-Doped Diamond Electrode

A cathodically pretreated boron-doped diamond electrode was used for the simultaneous anodic determination of ascorbic acid (AA) and caffeine (CAF) by differential pulse voltammetry Linear calibration curves (r = 0 999) were obtained from 1 9 x 10(-5) to 2 I x 10(-4) mol L(-1) for AA and from 9 7 x 10(-6) to 1 1 x 10-4 mol L(-1) for CAF. with detection limits of 19 wool L(-1) and 7 0 mu nol L(-1). respectively This method was successfully applied for the determination of AA and CAF in pharmaceutical formulations. with results equal to those obtained using a HPLC reference method

Simultaneous voltammetric determination of paracetamol and caffeine in pharmaceutical formulations using a boron-doped diamond electrode

A simple and highly selective electrochemical method was developed for the single or simultaneous determination of paracetamol (N-acetyl-p-aminophenol, acetaminophen) and caffeine (3,7-dihydro-1,3,7-trimethyl-1H-purine-2,6-dione) in aqueous media (acetate buffer, pH 4.5) on a boron-doped diamond (BDD) electrode using square wave voltammetry (SWV) or differential Pulse voltammetry (DPV). Using DPV with the cathodically pre-treated BDD electrode, a separation of about 550 mV between the peak oxidation potentials Of paracetamol and caffeine present in binary mixtures was obtained. The calibration curves for the simultaneous determination of paracetamol and caffeine showed an excellent linear response, ranging from 5.0 x 10(-7) mol L(-1) to 8.3 x 10(-7) mol L(-1) for both compounds. The detection limits for the simultaneous determination of paracetamol and caffeine were 4.9 x 10(-7) mol L-1 and 3.5 x 10(-8) mol L(-1), respectively. The proposed method Was Successfully applied in the simultaneous determination of paracetamol and caffeine in several pharmaceutical formulations (tablets), with results similar to those obtained using a high-performance liquid chromatography method (at 95% confidence level). (C) 2008 Elsevier BY. All rights reserved.

Simultaneous prediction of symptom severity and cause in data from a test battery for Parkinson patients, using machine learning methods

The main purpose of this thesis project is to prediction of symptom severity and cause in data from test battery of the Parkinson’s disease patient, which is based on data mining. The collection of the data is from test battery on a hand in computer. We use the Chi-Square method and check which variables are important and which are not important. Then we apply different data mining techniques on our normalize data and check which technique or method gives good results.The implementation of this thesis is in WEKA. We normalize our data and then apply different methods on this data. The methods which we used are Naïve Bayes, CART and KNN. We draw the Bland Altman and Spearman’s Correlation for checking the final results and prediction of data. The Bland Altman tells how the percentage of our confident level in this data is correct and Spearman’s Correlation tells us our relationship is strong. On the basis of results and analysis we see all three methods give nearly same results. But if we see our CART (J48 Decision Tree) it gives good result of under predicted and over predicted values that’s lies between -2 to +2. The correlation between the Actual and Predicted values is 0,794in CART. Cause gives the better percentage classification result then disability because it can use two classes.

Optical Characterization and Optimization of Display Components : Some Applications to Liquid-Crystal-Based and Electrochromics-Based Devices

This dissertation is focused on theoretical and experimental studies of optical properties of materials and multilayer structures composing liquid crystal displays (LCDs) and electrochromic (EC) devices. By applying spectroscopic ellipsometry, we have determined the optical constants of thin films of electrochromic tungsten oxide (WOx) and nickel oxide (NiOy), the films’ thickness and roughness. These films, which were obtained at spattering conditions possess high transmittance that is important for achieving good visibility and high contrast in an EC device. Another application of the general spectroscopic ellipsometry relates to the study of a photo-alignment layer of a mixture of azo-dyes SD-1 and SDA-2. We have found the optical constants of this mixture before and after illuminating it by polarized UV light. The results obtained confirm the diffusion model to explain the formation of the photo-induced order in azo-dye films. We have developed new techniques for fast characterization of twisted nematic LC cells in transmissive and reflective modes. Our techniques are based on the characteristics functions that we have introduced for determination of parameters of non-uniform birefringent media. These characteristic functions are found by simple procedures and can be utilised for simultaneous determination of retardation, its wavelength dispersion, and twist angle, as well as for solving associated optimization problems. Cholesteric LCD that possesses some unique properties, such as bistability and good selective scattering, however, has a disadvantage – relatively high driving voltage (tens of volts). The way we propose to reduce the driving voltage consists of applying a stack of thin (~1µm) LC layers. We have studied the ability of a layer of a surface stabilized ferroelectric liquid crystal coupled with several retardation plates for birefringent color generation. We have demonstrated that in order to accomplish good color characteristics and high brightness of the display, one or two retardation plates are sufficient.

Fitting conditional and simultaneous autoregressive spatial models in hglm

We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for conditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SAR random effects into an independent, but eteroscedastic, Gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR and SAR models. This gives a computationally efficient algorithm for moderately sized problems.

Perspective: LaGuardia Community College Newsletter Vol. 1, No. 2

"A newsletter published periodically to keep the faculty, students, staff, and community informed about the activities taking place on the campus of LaGuardia Community College." No date [1973]; Vol. 1, No. 2; 4 pages; Color (green, black & white); black & white photographs. Cover article: “BLACK HISTORY WEEK AT LAGUARDIA”. Other entries include: “LAGUARDIA’S FIRST ANNUAL COMMENCEMENT SCHEDULED FOR SUNDAY, SEPT. 16;” “LAGUARDIA FIVE SEEKS FIRST VICTORY.”

Uncertainties In Future Projections Of Extreme Rainfall: The Role Of Climate Model, Emission Scenario And Randomness

Climate change has resulted in substantial variations in annual extreme rainfall quantiles in different durations and return periods. Predicting the future changes in extreme rainfall quantiles is essential for various water resources design, assessment, and decision making purposes. Current Predictions of future rainfall extremes, however, exhibit large uncertainties. According to extreme value theory, rainfall extremes are rather random variables, with changing distributions around different return periods; therefore there are uncertainties even under current climate conditions. Regarding future condition, our large-scale knowledge is obtained using global climate models, forced with certain emission scenarios. There are widely known deficiencies with climate models, particularly with respect to precipitation projections. There is also recognition of the limitations of emission scenarios in representing the future global change. Apart from these large-scale uncertainties, the downscaling methods also add uncertainty into estimates of future extreme rainfall when they convert the larger-scale projections into local scale. The aim of this research is to address these uncertainties in future projections of extreme rainfall of different durations and return periods. We plugged 3 emission scenarios with 2 global climate models and used LARS-WG, a well-known weather generator, to stochastically downscale daily climate models’ projections for the city of Saskatoon, Canada, by 2100. The downscaled projections were further disaggregated into hourly resolution using our new stochastic and non-parametric rainfall disaggregator. The extreme rainfall quantiles can be consequently identified for different durations (1-hour, 2-hour, 4-hour, 6-hour, 12-hour, 18-hour and 24-hour) and return periods (2-year, 10-year, 25-year, 50-year, 100-year) using Generalized Extreme Value (GEV) distribution. By providing multiple realizations of future rainfall, we attempt to measure the extent of total predictive uncertainty, which is contributed by climate models, emission scenarios, and downscaling/disaggregation procedures. The results show different proportions of these contributors in different durations and return periods.

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