Preparation and Analysis by Ion Exchange Techniques of Sodium Salts of Mandelic Acid Derivatives

Author Institution: Department of Chemistry, Xavier University, Cincinnati, Ohioen sodium salts of mandelic acid derivatives were prepared and analyzed. Results obtained by an ion-exchange-titration method of analysis agreed with those obtained by a gravimetric procedur

Cascading nonlinearities in an organic single crystal core fiber: The Cerenkov regime

The large nonlinear phase shifts imparted to the fundamental beam during Cerenkov second harmonic generation (SHG) in a DAN, 4-(N,N-dimethylamino)-3-acetamidonitrobenzene, single crystal core fiber are explained and modelled numerically. Cascading upconversion and downconversion processes leads to nonlinear phase shifts produced by the second order nonlinear coupling of the guided fundamental mode and the component of the Cerenkov second harmonic field trapped in the fiber cladding

The Effect of Multiple Cooling Channels on the Formation of Dark Compact Objects

A dissipative dark sector can result in the formation of compact objects with masses comparable to stars and planets. In this work, we investigate the formation of such compact objects from a subdominant inelastic dark matter model, and study the resulting distributions of these objects. In particular, we consider cooling from dark Bremsstrahlung and a rapid decay process that occurs after inelastic upscattering. Inelastic transitions introduce an additional radiative processes which can impact the formation of compact objects via multiple cooling channels. We find that having multiple cooling processes changes the mass and abundance of compact objects formed, as compared to a scenario with only one cooling channel. The resulting distribution of these astrophysical compact objects and their properties can be used to further constrain and differentiate between dark sectors.Comment: 20 pages, 4 figure

Freight Demand Model for Southern California Freeways with Owner–Operator Truck Drivers

This study evaluates the demand for truck-only toll lanes on Southern California freeways with owner–operator truck drivers. The study implemented the stated preference survey method to estimate the value placed by drivers on time, reliability, and safety measures using various scenarios geared towards assessing those values. The project team met face-to-face with owner- operator truck drivers near the Ports of Los Angeles and Long Beach to understand the drivers’ perspectives regarding truck-only toll lanes on Southern California freeways. A data set containing 31 survey responses is obtained and used for statistical data analysis using analysis of variable (ANOVA) and two sample t-tests. The analysis results showed that 75.27% of the owner– operator truck drivers responded are willing to pay toll fees when they choose routes. The tolerated average toll fees are $13.77/ hr and$12.82/hr for weekdays and weekends, respectively. The analysis results also showed that owner–operator truck drivers will take truck-only toll lanes when they take the routes used in four comparisons out of six comparisons according to the three measures such as values of time, reliability, and safety, despite sharing a common origin and destination. The highest toll fee per mile on any day that drivers are willing to pay when the main factor being compared is value of time is $0.31/mile or$18.35/hr. The toll fees associated with reliability and safety measures are $0.30/mile or$8.94/hr and $0.22/mile or$11.01/hr, respectively. These results are meaningful for legislators and transportation agencies as the behaviors and route choice characteristics of owner–operator truck drivers help them better understand the utility and demand for truck-only toll lanes

Economic Evaluation of Route Choice Characteristics for Company Truck Drivers and Owner-Operator Truck Drivers in Southern California Freeways

To contribute to the understanding of freeway capacity and financing options, this study evaluates the demand for truck-only toll lanes on Southern California freeways. The study implemented surveys to both company truck drivers and owner-operator truck drivers to estimate the value they place on time, reliability, and safety measures. The research team met face-to-face with both types of truck drivers near the Ports of Los Angeles and Long Beach to understand the drivers’ perspectives regarding truck-only toll lanes on Southern California freeways. A data set containing 45 surveys out of 62 survey responses were used for statistical analysis. The results showed that the tolerated toll fees that both types of truck drivers combined were willing to pay ranged from $3.27 an hour to$41.45 an hour with an average of $20.50 an hour during weekdays, while those fees ranged from$3.04 an hour to $36.12 an hour with an average of$18.12 an hour during weekends. Both types of truck drivers are unwilling to pay toll fees for the routes used in six comparisons out of nine, despite sharing a common origin and destination. Data shows that, regardless of ownership type, both types of truck drivers similarly value a route with truck only lanes. The highest toll fee per mile on any day that drivers are willing to pay when the main factor being compared is value of travel time (VOT) is $0.54 per mile or$32.38 an hour. The figures for the value of reliability (VOR) and safety measures are $0.47 per mile or$15.76 an hour and $0.17 per mile or$9.80 an hour, respectively. The VOR is important because it helps shippers and freight carriers make predictable travel times to remain competitive. These results are meaningful for legislators and transportation agencies because the behaviors and route choice characteristics of both types of drivers help them better reduce scheduling costs, understand the utility and demand for truck-only toll lanes, and resolve traffic congestion in the study area

Global Imbalances and Structural Change in the United States

Since the early 1990s, as the United States borrowed heavily from the rest of the world, employment in the US goods-producing sector has fallen.We construct a dynamic general equilibrium model with several mechanisms that could generate declining goods-sector employment: foreign borrowing, nonhomothetic preferences, and differential productivity growth across sectors. We find that only 15.1 percent of the decline in goods-sector employment from 1992 to 2012 stems from US trade deficits; most of the decline is due to differential productivity growth. As the United States repays its debt, its trade balance will reverse, but goods-sector employment will continue to fall

Do changes in health reveal the possibility of undiagnosed pancreatic cancer? Development of a risk-prediction model based on healthcare claims data.

Background and objectiveEarly detection methods for pancreatic cancer are lacking. We aimed to develop a prediction model for pancreatic cancer based on changes in health captured by healthcare claims data.MethodsWe conducted a case-control study on 29,646 Medicare-enrolled patients aged 68 years and above with pancreatic ductal adenocarcinoma (PDAC) reported to the Surveillance Epidemiology an End Results (SEER) tumor registries program in 2004-2011 and 88,938 age and sex-matched controls. We developed a prediction model using multivariable logistic regression on Medicare claims for 16 risk factors and pre-diagnostic symptoms of PDAC present within 15 months prior to PDAC diagnosis. Claims within 3 months of PDAC diagnosis were excluded in sensitivity analyses. We evaluated the discriminatory power of the model with the area under the receiver operating curve (AUC) and performed cross-validation by bootstrapping.ResultsThe prediction model on all cases and controls reached AUC of 0.68. Excluding the final 3 months of claims lowered the AUC to 0.58. Among new-onset diabetes patients, the prediction model reached AUC of 0.73, which decreased to 0.63 when claims from the final 3 months were excluded. Performance measures of the prediction models was confirmed by internal validation using the bootstrap method.ConclusionModels based on healthcare claims for clinical risk factors, symptoms and signs of pancreatic cancer are limited in classifying those who go on to diagnosis of pancreatic cancer and those who do not, especially when excluding claims that immediately precede the diagnosis of PDAC

Symmetric airfoil geometry effects on leading edge noise

Computational aeroacoustic methods are applied to the modeling of noise due to interactions between gusts and the leading edge of real symmetric airfoils. Single frequency harmonic gusts are interacted with various airfoil geometries at zero angle of attack. The effects of airfoil thickness and leading edge radius on noise are investigated systematically and independently for the first time, at higher frequencies than previously used in computational methods. Increases in both leading edge radius and thickness are found to reduce the predicted noise. This noise reduction effect becomes greater with increasing frequency and Mach number. The dominant noise reduction mechanism for airfoils with real geometry is found to be related to the leading edge stagnation region. It is shown that accurate leading edge noise predictions can be made when assuming an inviscid meanflow, but that it is not valid to assume a uniform meanflow. Analytic flat plate predictions are found to over-predict the noise due to a NACA 0002 airfoil by up to 3 dB at high frequencies. The accuracy of analytic flat plate solutions can be expected to decrease with increasing airfoil thickness, leading edge radius, gust frequency and Mach number

Statistical Workflow for Feature Selection in Human Metabolomics Data.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations