Measures of Predictive Success for Rating Functions

Aim of our paper is to develop an adequate measure of predictive success and accuracy of rating functions. At first, we show that the common measures of rating accuracy, i.e. area under curve and accuracy ratio, respectively, lack of informative value of single rating classes. Selten (1991) builds up an axiomatic framework for measures of predictive success. Therefore, we introduce a measure for rating functions that fulfills the axioms proposed by Selten (1991). Furthermore, an empirical investigation analyzes predictive power and accuracy of Standard & Poor's and Moody's ratings, and compares the rankings according to area under curve and our measure.Accuracy Measure, Rating Functions, Predictive Success, Discriminative Power

ZERO ORDER AND AREA UNDER CURVE SPECTROPHOTOMETRIC METHODS FOR DETERMINATION OF AMOXICILLIN TRIHYDRATE IN PHARMACEUTICAL FORMULATION.

Objective: - A simple, accurate, precise and specific zero order and area under curve spectrophotometric methods has been developed for determination of Amoxicillin Trihydrate in its tablet dosage form by using methanol as a solvent.  Methods: - (1) Derivative Spectrophotometric Methods: - The amplitudes in the zero order derivative of the resultant spectra at 229 nm was selected to find out Amoxicillin Trihydrate in its tablet dosage form by using methanol as a solvent.(2) Area under curve (Area calculation): -The proposed area under curve method involves measurement of area at selected wavelength ranges. Two wavelength ranges were selected 224-232 nm for estimation of Amoxicillin Trihydrate.Result & Discussion: -The linearity was found to be 5-25 μg/ml for Amoxicillin Trihydrate. The mean % recoveries were found to be 100.33% and 99.42% of zero order derivative and area under curve method of Amoxicillin Trihydrate. For Repeatability, Intraday precision, Interday precision, % RSD were found to be 0.0036, 0.0028 and 0.5987, 0.4188 for zero order and 0.0019, 0.0031 and 1.6357, 1.7159 for area under curve method respectively. Limit of Detection and Limit of Quantitation were found to be 0.9142μg/ml and 2.8128μg/ml for zero order and 0.5798μg/ml and 1.3153μg/ml for area under curve method respectively. Assay results of market formulation were found to be 100.84% for zero order and 99.65% area under curve method respectively. The proposed method has been validated as per ICH guidelines and successfully applied to the estimation of Amoxicillin Trihydrate in its Tablet dosage form.Conclusion: - The developed methods can be concluded as accurate, sensitive and precise and can be easily applied to the pharmaceutical formulation

Estimation of Valsartan in Pharmaceutical Formulation by Area under Curve Spectrophotometric Method

For estimation of Valsartan in pharmaceutical dosage form, a simple, accurate and precise area under curve spectrophotometric method was developed. The area under two points on the mixture spectra is directly proportional to the concentration of the component of interest is the AUC curve. The area selected for estimation of Valsartan was between 238.20 to 254.40 nm. The method represented regression coefficient (r2 = 0.996) at concentration rang 2-10 μg/ml. Estimation of the drugs was found up to 100 % representing the accuracy of the method. The recovery of the Valsartan was found up to 100 %. Validation of the proposed method was carried out for its accuracy, precision and specificity according to ICH Q2 (R1) guidelines. The developed methods can be successfully applied in routine work for the estimation of Valsartan in its pharmaceutical dosage form

Measures of Predictive Success for Rating Functions

Aim of our paper is to develop an adequate measure of predictive success and accuracy of rating functions. At first, we show that the common measures of rating accuracy, i.e. area under curve and accuracy ratio, respectively, lack of informative value of single rating classes. Selten (1991) builds up an axiomatic framework for measures of predictive success. Therefore, we introduce a measure for rating functions that fulfills the axioms proposed by Selten (1991). Furthermore, an empirical investigation analyzes predictive power and accuracy of Standard & Poor\u27s and Moody\u27s ratings, and compares the rankings according to area under curve and our measure.Aim of our paper is to develop an adequate measure of predictive success and accuracy of rating functions. At first, we show that the common measures of rating accuracy, i.e. area under curve and accuracy ratio, respectively, lack of informative value of single rating classes. Selten (1991) builds up an axiomatic framework for measures of predictive success. Therefore, we introduce a measure for rating functions that fulfills the axioms proposed by Selten (1991). Furthermore, an empirical investigation analyzes predictive power and accuracy of Standard & Poor\u27s and Moody\u27s ratings, and compares the rankings according to area under curve and our measure

Area Under Curve by UV Spectrophotometric Method for Determination Albendazole in Bulk

The aim of present investigation is to establish simple, precise, and rapid Spectrophotometric method for the quantification of Albendazole in Active Pharmaceutical Ingredient. In this, work is carried out to for estimation of Albendazole bulk by utilizing an Area under Curve (AUC) method using UV – Visible Spectrophotometry. The study is designed to validate the developed methods as per ICH guidelines. For this purpose the wavelength range between 200-400 nm was selected. Methanolic distilled water (50 ml methanol used for stock solution and serial dilution in 25 ml distilled water) was used as a solvent throughout the work. Linearity was obtained in concentration range 2 to 10 ɥg/ml (r2 = 0.992) for the method. The developed method was found to be simple, linear, accurate, precise and highly sensitive and which can be used for routine quality control analysis for Spectrophotometric estimation of Active Pharmaceutical Ingredient. KeywordS: Albendazole, linearity, AUC, spectrophotometer, methanol, distilled water

Comparison of gluten-free dough ability to produce leavening gas during baking and its impact on crumb characteristics

Dough ability to produce leavening gas and its impact on bread crumb characteristics was evaluated on wheat, buckwheat and rice dough. The ability to produce leavening gas was recorded as thermally-dependent changes of dough volume; curve gradient, area under curve and the temperature range of leavening gas production were evaluated. Wheat dough reached the highest curve gradient a (28 center dot 10(-3) mm center dot s(-1)), contributing to the large area under curve (7,169 mm center dot s). Significantly lower curve increase (10 center dot 10-3 mm center dot s(-1); 5 center dot 10(-3) mm center dot s(-1)) as well as area under curve (6,291 mm center dot s; 53 mm center dot s) were obtained in buckwheat and rice doughs. The rising values of curve characteristics increased crumb quality. Even if gas retention ability was not evaluated, gas production ability signifi cantly impacted crumb quality

Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning

Antihydrogen is at the forefront of antimatter research at the CERN Antiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry and antigravity effects require the efficient detection of antihydrogen annihilation events, which is performed using highly granular tracking detectors installed around an antimatter trap. Improving the efficiency of the antihydrogen annihilation detection plays a central role in the final sensitivity of the experiments. We propose deep learning as a novel technique to analyze antihydrogen annihilation data, and compare its performance with a traditional track and vertex reconstruction method. We report that the deep learning approach yields significant improvement, tripling event coverage while simultaneously improving performance by over 5% in terms of Area Under Curve (AUC)