Variables affecting the precision and accuracy of the Intoxilyzer 5000

The Intoxilyzer 5000 was tested for calibration curve linearity for ethanol vapor concentration between 0.020 and 0.400g/210L with excellent linearity. Calibration error using reference solutions outside of the allowed concentration range, response to the same ethanol reference solution at different temperatures between 34 and 38$\sp\circ$C, and its response to eleven chemicals, 10 mixtures of two at the time, and one mixture of four chemicals potentially found in human breath have been evaluated. Potential interferents were chosen on the basis of their infrared signatures and the concentration range of solutions corresponding to the non-lethal blood concentration range of various volatile organic compounds reported in the literature. The result of this study indicates that the instrument calibrates with solutions outside the allowed range up to $\pm$10% of target value. Headspace FID dual column GC analysis was used to confirm the concentrations of the solutions. Increasing the temperature of the reference solution from 34 to 38$\sp\circ$C resulted in linear increases in instrument recorded ethanol readings with an average increase of 6.25%/$\sp\circ$C. Of the eleven chemicals studied during this experiment, six, isopropanol, toluene, methyl ethyl ketone, trichloroethylene, acetaldehyde, and methanol could reasonably interfere with the test at non-lethal reported blood concentration ranges, the mixtures of those six chemicals showed linear additive results with a combined effect of as much as a 0.080g/210L reading (Florida's legal limit) without any ethanol present. ^

High performance shift invariant motion estimation and compensation in wavelet domain video compression

The contributions of this dissertation are in the development of two new interrelated approaches to video data compression: (1) A level-refined motion estimation and subband compensation method for the effective motion estimation and motion compensation. (2) A shift-invariant sub-decimation decomposition method in order to overcome the deficiency of the decimation process in estimating motion due to its shift-invariant property of wavelet transform. ^ The enormous data generated by digital videos call for an intense need of efficient video compression techniques to conserve storage space and minimize bandwidth utilization. The main idea of video compression is to reduce the interpixel redundancies inside and between the video frames by applying motion estimation and motion compensation (MEMO) in combination with spatial transform coding. To locate the global minimum of the matching criterion function reasonably, hierarchical motion estimation by coarse to fine resolution refinements using discrete wavelet transform is applied due to its intrinsic multiresolution and scalability natures. ^ Due to the fact that most of the energies are concentrated in the low resolution subbands while decreased in the high resolution subbands, a new approach called level-refined motion estimation and subband compensation (LRSC) method is proposed. It realizes the possible intrablocks in the subbands for lower entropy coding while keeping the low computational loads of motion estimation as the level-refined method, thus to achieve both temporal compression quality and computational simplicity. ^ Since circular convolution is applied in wavelet transform to obtain the decomposed subframes without coefficient expansion, symmetric-extended wavelet transform is designed on the finite length frame signals for more accurate motion estimation without discontinuous boundary distortions. ^ Although wavelet transformed coefficients still contain spatial domain information, motion estimation in wavelet domain is not as straightforward as in spatial domain due to the shift variance property of the decimation process of the wavelet transform. A new approach called sub-decimation decomposition method is proposed, which maintains the motion consistency between the original frame and the decomposed subframes, improving as a consequence the wavelet domain video compressions by shift invariant motion estimation and compensation. ^

A study of moderating influences of adolescent alcohol drinking conditioned by perception of peer alcohol drinking

Despite a long history of prevention efforts and federal laws prohibiting the consumption of alcohol for those below the age of 21 years, underage drinking continues at both a high prevalence rate and high incidence rate. The purpose of this research study is to explain underage drinking of alcohol conditioned by perception of peer drinking. An acquisition model is conjectured and then a relationship within the model is explained with a national sample of students. From a developmental perspective, drinking alcohol is acquired in a reasonably ordered fashion that reflects the influences over time of the culture, family, and peers. The study measures perceptions of alcohol drinking during early adolescence when alcohol use begins the maintenance phase of the behavior. The correlation between drinking alcohol and perception of classmate drinking can be described via social learning theory. Simultaneously the moderating effects of grade level, gender, and race/ethnicity are used to explain differences between groups. Multilevel logistic regression was used to analyze the relations. The researcher found support for an association between adolescent drinking and perceptions of classmate drinking. Gender and grade level moderated the relation. African-Americans consistently demonstrated less drinking and less perception of classmate drinking than either whites or other students not white nor African-American. The importance of a better understanding of the process of acquiring drinking behaviors is discussed in relation to future research models with longitudinal data. ^

Application of intervention analysis to evaluate the impacts of special events on freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Stereotype threat and the standardized testing experiences of African American children at an urban elementary school

Stereotype threat (Steele & Aronson, 1995) refers to the risk of confirming a negative stereotype about one’s group in a particular performance domain. The theory assumes that performance in the stereotyped domain is most negatively affected when individuals are more highly identified with the domain in question. As federal law has increased the importance of standardized testing at the elementary level, it can be reasonably hypothesized that the standardized test performance of African American children will be depressed when they are aware of negative societal stereotypes about the academic competence of African Americans. This sequential mixed-methods study investigated whether the standardized testing experiences of African American children in an urban elementary school are related to their level of stereotype awareness. The quantitative phase utilized data from 198 African American children at an urban elementary school. Both ex-post facto and experimental designs were employed. Experimental conditions were diagnostic and non-diagnostic testing experiences. The qualitative phase utilized data from a series of six focus group interviews conducted with a purposefully selected group of 4 African American children. The interview data were supplemented with data from 30 hours of classroom observations. Quantitative findings indicated that the stereotype threat condition evoked by diagnostic testing depresses the reading test performance of stereotype-aware African American children (F[1, 194] = 2.21, p < .01). This was particularly true of students who are most highly domain-identified with reading (F[1, 91] = 19.18, p < .01). Moreover, findings indicated that only stereotype-aware African American children who were highly domain-identified were more likely to experience anxiety in the diagnostic condition (F[1, 91] = 5.97, p < .025). Qualitative findings revealed 4 themes regarding how African American children perceive and experience the factors related to stereotype threat: (1) a narrow perception of education as strictly test preparation, (2) feelings of stress and anxiety related to the state test, (3) concern with what “others” think (racial salience), and (4) stereotypes. A new conceptual model for stereotype threat is presented, and future directions including implications for practice and policy are discussed.

Design of an automated book reader as an assistive technology tool for blind persons

This dissertation introduces a novel automated book reader as an assistive technology tool for persons with blindness. The literature shows extensive work in the area of optical character recognition, but the current methodologies available for the automated reading of books or bound volumes remain inadequate and are severely constrained during document scanning or image acquisition processes. The goal of the book reader design is to automate and simplify the task of reading a book while providing a user-friendly environment with a realistic but affordable system design. This design responds to the main concerns of (a) providing a method of image acquisition that maintains the integrity of the source (b) overcoming optical character recognition errors created by inherent imaging issues such as curvature effects and barrel distortion, and (c) determining a suitable method for accurate recognition of characters that yields an interface with the ability to read from any open book with a high reading accuracy nearing 98%. This research endeavor focuses in its initial aim on the development of an assistive technology tool to help persons with blindness in the reading of books and other bound volumes. But its secondary and broader aim is to also find in this design the perfect platform for the digitization process of bound documentation in line with the mission of the Open Content Alliance (OCA), a nonprofit Alliance at making reading materials available in digital form. The theoretical perspective of this research relates to the mathematical developments that are made in order to resolve both the inherent distortions due to the properties of the camera lens and the anticipated distortions of the changing page curvature as one leafs through the book. This is evidenced by the significant increase of the recognition rate of characters and a high accuracy read-out through text to speech processing. This reasonably priced interface with its high performance results and its compatibility to any computer or laptop through universal serial bus connectors extends greatly the prospects for universal accessibility to documentation.

The Food Service Industry: Beliefs Held by Academics

In his study - The Food Service Industry: Beliefs Held by Academics - by Jack Ninemeier, Associate Professor, School of Hotel, Restaurant and Institutional Management at Michigan State University, Associate Professor Ninemeier initially describes his study this way: “Those in the academic sector exert a great deal of influence on those they are training to enter the food service industry. One author surveyed educational institutions across the country to ascertain attitudes of teachers toward various segments of the industry.” Those essential segments of the industry serve as the underpinnings of this discussion and are four-fold. They are lodging, institutional, multi-unit, and single-unit properties. For each segment the analysis addressed factors relating to Marketing, management and operating concerns: Marketing, operations, fiscal management, innovation, future of the segment Employee-related concerns: quality of work life, training/education opportunities, career opportunities The study uses a survey of academicians as a guide; they point to segments of the food service industry students might be inclined to enter, or even ignore. The survey was done via a questionnaire sent from the campus of the School of Hotel, Restaurant and Institutional Management at Michigan State University to 1850 full-time faculty members in two and four-year hospitality programs in the United States. Through the survey, Ninemeier wishes to reasonably address specific problems now confronting the food service industry. Those problems include but are not limited to: reducing employee turnover, retaining staff, increasing productivity and revenue, and attracting new staff. “Teachers in these programs are, therefore, an important plank in industry's platform designed to recruit students with appropriate background knowledge and interest in their operations,” Ninemeier says. Your author actually illustrates the survey results, in table form. The importance to an employee, of tangibles and intangibles such as morale, ego/esteem, wages, and benefits are each explored through the survey. According to the study, an interesting dichotomy exists in the institutional property element. Although, beliefs the academics hold about the institutional element suggest that it offers low job stress, attractive working conditions, and non-demanding competitive pressures, the survey and Ninemeier also observe: “Academics do not believe that many of their graduates will enter the institutional segment.” “If academic beliefs are incorrect, an educational program to educate academics about management and employee opportunities in the segment may be in order,” Ninemeier waxes philosophically.

Advanced methodologies in dynamic traffic assignment modeling of managed lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. ^ Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. ^ Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. ^ With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.^

Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Stereotype Threat and the Standardized Testing Experiences of African American Children at an Urban Elementary School

Stereotype threat (Steele & Aronson, 1995) refers to the risk of confirming a negative stereotype about one’s group in a particular performance domain. The theory assumes that performance in the stereotyped domain is most negatively affected when individuals are more highly identified with the domain in question. As federal law has increased the importance of standardized testing at the elementary level, it can be reasonably hypothesized that the standardized test performance of African American children will be depressed when they are aware of negative societal stereotypes about the academic competence of African Americans. This sequential mixed-methods study investigated whether the standardized testing experiences of African American children in an urban elementary school are related to their level of stereotype awareness. The quantitative phase utilized data from 198 African American children at an urban elementary school. Both ex-post facto and experimental designs were employed. Experimental conditions were diagnostic and non-diagnostic testing experiences. The qualitative phase utilized data from a series of six focus group interviews conducted with a purposefully selected group of 4 African American children. The interview data were supplemented with data from 30 hours of classroom observations. Quantitative findings indicated that the stereotype threat condition evoked by diagnostic testing depresses the reading test performance of stereotype-aware African American children (F[1, 194] = 2.21, p < .01). This was particularly true of students who are most highly domain-identified with reading (F[1, 91] = 19.18, p < .01). Moreover, findings indicated that only stereotype-aware African American children who were highly domain-identified were more likely to experience anxiety in the diagnostic condition (F[1, 91] = 5.97, p < .025). Qualitative findings revealed 4 themes regarding how African American children perceive and experience the factors related to stereotype threat: (1) a narrow perception of education as strictly test preparation, (2) feelings of stress and anxiety related to the state test, (3) concern with what “others” think (racial salience), and (4) stereotypes. A new conceptual model for stereotype threat is presented, and future directions including implications for practice and policy are discussed.