Unraveling the complexity of driving while intoxicated: a study into the prevalence of psychiatric and substance abuse comorbidity

Objective Research is beginning to provide an indication of the co-occurring substance abuse and mental health needs for the driving under the influence (DUI) population. This study aimed to examine the extent of such psychiatric problems among a large sample size of DUI offenders entering treatment in Texas. Methods This is a study of 36,373 past year DUI clients and 308,714 non-past year DUI clients admitted to Texas treatment programs between 2005 and 2008. Data were obtained from the State's administrative dataset. Results Analysis indicated that non-past year DUI clients were more likely to present with more severe illicit substance use problems, while past year DUI clients were more likely to have a primary problem with alcohol. Nevertheless, a cannabis use problem was also found to be significantly associated with DUI recidivism in the last year. In regards to mental health status, a major finding was that depression was the most common psychiatric condition reported by DUI clients, including those with more than one DUI offence in the past year. This cohort also reported elevated levels of Bipolar Disorder compared to the general population, and such a diagnosis was also associated with an increased likelihood of not completing treatment. Additionally, female clients were more likely to be diagnosed with mental health problems than males, as well as more likely to be placed on medications at admission and more likely to have problems with methamphetamine, cocaine, and opiates. Conclusions DUI offenders are at an increased risk of experiencing comorbid psychiatric disorders, and thus, corresponding treatment programs need to cater for a range of mental health concerns that are likely to affect recidivism rates.

Bitrate modeling of scalable videos using quantization parameter, frame rate and spatial resolution

The quality and bitrate modeling is essential to effectively adapt the bitrate and quality of videos when delivered to multiplatform devices over resource constraint heterogeneous networks. The recent model proposed by Wang et al. estimates the bitrate and quality of videos in terms of the frame rate and quantization parameter. However, to build an effective video adaptation framework, it is crucial to incorporate the spatial resolution in the analytical model for bitrate and perceptual quality adaptation. Hence, this paper proposes an analytical model to estimate the bitrate of videos in terms of quantization parameter, frame rate, and spatial resolution. The model can fit the measured data accurately which is evident from the high Pearson correlation. The proposed model is based on the observation that the relative reduction in bitrate due to decreasing spatial resolution is independent of the quantization parameter and frame rate. This modeling can be used for rate-constrained bit-stream adaptation scheme which selects the scalability parameters to optimize the perceptual quality for a given bandwidth constraint.

Advertising and Celebrity Endorsement in Burma

Burma (or Myanmar) is not a place that people normally associate with the glamour of film stars, or the fun and frivolity of celebrities, unlike in neighbouring India or Thailand. But each year the very matter-of-factly named ‘Myanmar Economics Import/Export VCD’ company produces a disk of the year’s most memorable television ads, showcasing some of the many Burmese celebrities on television at the moment. As a testament to the catchiness of the ads, disks have become so popular that they can be bought on street corners in Yangon for about 1000 Kyats (US$1). Though advertising in Burma is highly vetted for political content, much like film and print media, the samples featured show a surprising array of entertaining themes and ideas. Much of television advertising, in some way or another, draws upon the profiles of versatile Burmese celebrities to engage and build brand value.

Keeping up with the neighbours : China's soft power ambitions

Over the past century numerous waves of transnational media have washed across East Asia with cycles emanating from various centers of cultural production, such as Tokyo, Hong Kong, and Seoul. Most recently the People’s Republic of China (PRC) has begun to exert growing influence over the production and flow of screen media, a phenomenon tied to the increasing size and power of its overall economy. The country’s rising status achieved truly global recognition during the 2008 Beijing Olympics. In the seven years leading up to the event, the Chinese economy tripled in size, expanding from $1.3 trillion to almost $4 trillion, a figure that made it the world’s third largest economy, slightly behind Japan, but decisively ahead of its European counterparts, Germany, France, and the United Kingdom. The scale and speed of this transformation are stunning. Just as momentous are the changes in its film, television, and digital media markets, which now figure prominently in the calculations of producers throughout East Asia.

Improving the performance of model-order selection criteria by partial-model selection search

The traditional searching method for model-order selection in linear regression is a nested full-parameters-set searching procedure over the desired orders, which we call full-model order selection. On the other hand, a method for model-selection searches for the best sub-model within each order. In this paper, we propose using the model-selection searching method for model-order selection, which we call partial-model order selection. We show by simulations that the proposed searching method gives better accuracies than the traditional one, especially for low signal-to-noise ratios over a wide range of model-order selection criteria (both information theoretic based and bootstrap-based). Also, we show that for some models the performance of the bootstrap-based criterion improves significantly by using the proposed partial-model selection searching method. Index Terms— Model order estimation, model selection, information theoretic criteria, bootstrap 1. INTRODUCTION Several model-order selection criteria can be applied to find the optimal order. Some of the more commonly used information theoretic-based procedures include Akaike’s information criterion (AIC) [1], corrected Akaike (AICc) [2], minimum description length (MDL) [3], normalized maximum likelihood (NML) [4], Hannan-Quinn criterion (HQC) [5], conditional model-order estimation (CME) [6], and the efficient detection criterion (EDC) [7]. From a practical point of view, it is difficult to decide which model order selection criterion to use. Many of them perform reasonably well when the signal-to-noise ratio (SNR) is high. The discrepancies in their performance, however, become more evident when the SNR is low. In those situations, the performance of the given technique is not only determined by the model structure (say a polynomial trend versus a Fourier series) but, more importantly, by the relative values of the parameters within the model. This makes the comparison between the model-order selection algorithms difficult as within the same model with a given order one could find an example for which one of the methods performs favourably well or fails [6, 8]. Our aim is to improve the performance of the model order selection criteria in cases where the SNR is low by considering a model-selection searching procedure that takes into account not only the full-model order search but also a partial model order search within the given model order. Understandably, the improvement in the performance of the model order estimation is at the expense of additional computational complexity.

Impaired drivers : The need to yoke treatment and sanctions

Objective: To understand the levels of substance abuse and dependence among impaired drivers by comparing the differences in patients in substance abuse treatment programs with and without a past-year DUI arrest based on their primary problem substance at admission (alcohol, cocaine, cannabis, or methamphetamine). Method: Records on 345,067 admissions to Texas treatment programs between 2005 and 2008 have been analyzed for differences in demographic characteristics, levels of severity, and mental health problems at admission, treatment completion, and 90-day follow-up. Methods will include t-tests,??, and multivariate logistic regression. Results: The analysis found that DUI arrestees with a primary problem with alcohol were less impaired than non-DUI alcohol patients, had fewer mental health problems, and were more likely to complete treatment. DUI arrestees with a primary problem with cannabis were more impaired than non-DUI cannabis patients and there was no difference in treatment completion. DUI arrestees with a primary problem with cocaine were less impaired and more likely to complete treatment than other cocaine patients, and there was little difference in levels of mental health problems. DUI arrestees with a primary problem with methamphetamine were more similar to methamphetamine non-arrestees, with no difference in mental health problems and treatment completion. Conclusions: This study provides evidence of the extent of abuse and dependence among DUI arrestees and their need for treatment for their alcohol and drug problems in order to decrease recidivism. Treatment patients with past-year DUI arrests had good treatment outcomes but closer supervision during 90 day follow-up after treatment can lead to even better long-term outcomes, including reduced recidivism. Information will be provided on the latest treatment methodologies, including medication assisted therapies and screening and brief interventions, and ways impaired driving programs and substance dependence programs can be integrated to benefit the driver and society.

Beyond sanctions : is there a need to treat first-time offenders?

Aims: Driving Under the Influence (DUI) enforcement can be a broad screening mechanism for alcohol and other drug problems. The current response to DUI is focused on using mechanical means to prevent inebriated persons from driving, with little attention the underlying substance abuse problems. ---------- Methods: This is a secondary analysis of an administrative dataset of over 345,000 individuals who entered Texas substance abuse treatment between 2005 and 2008. Of these, 36,372 were either on DUI probation, referred to treatment by probation, or had a DUI arrest in the past year. The DUI offenders were compared on demographic characteristics, substance use patterns, and levels of impairment with those who were not DUI offenders and first DUI offenders were compared with those with more than one past-year offense. T tests and chi square tests were used to determine significance. ---------- Results: DUI offenders were more likely to be employed, to have a problem with alcohol, to report more past-year arrests for any offense, to be older, and to have used alcohol and drugs longer than the non-DUI clients who reported higher ASI scores and were more likely to use daily. Those with one past-year DUI arrest were more likely to have problems with drugs other than alcohol and were less impaired than those with two or more arrests based on their ASI scores and daily use. Non-DUI clients reported higher levels of mood disorders than DUIs but there was no difference in their diagnosis of anxiety. Similar findings were found between those with one or multiple DUI arrests. ----------Conclusion: Although first-time DUIs were not as impaired as non-DUI clients, their levels of impairment were sufficient to cause treatment. Screening and brief intervention at arrest for all DUI offenders and treatment in combination with abstinence monitoring could decrease future recidivism.

Noise robust voice activity detection using normal probability testing and time-domain histogram analysis

This paper presents a method of voice activity detection (VAD) suitable for high noise scenarios, based on the fusion of two complementary systems. The first system uses a proposed non-Gaussianity score (NGS) feature based on normal probability testing. The second system employs a histogram distance score (HDS) feature that detects changes in the signal through conducting a template-based similarity measure between adjacent frames. The decision outputs by the two systems are then merged using an open-by-reconstruction fusion stage. Accuracy of the proposed method was compared to several baseline VAD methods on a database created using real recordings of a variety of high-noise environments.

Towards defining semantic foundations for purpose-based privacy policies

We define a semantic model for purpose, based on which purpose-based privacy policies can be meaningfully expressed and enforced in a business system. The model is based on the intuition that the purpose of an action is determined by its situation among other inter-related actions. Actions and their relationships can be modeled in the form of an action graph which is based on the business processes in a system. Accordingly, a modal logic and the corresponding model checking algorithm are developed for formal expression of purpose-based policies and verifying whether a particular system complies with them. It is also shown through various examples, how various typical purpose-based policies as well as some new policy types can be expressed and checked using our model.

Assessing the impact of transit and personal characteristics on mode choice of TOD users

Comparison are required to understand transport benefits of Transit Oriented Developments (TODs). Mode shares of TOD users need to be understood. Accurate travel demand models for TODs are needed.

In-depth analysis of the J.A.C.K. model

The Texas Transportation Commission (“the Commission”) is responsible for planning and making policies for the location, construction, and maintenance of a comprehensive system of highways and public roads in Texas. In order for the Commission to carry out its legislative mandate, the Texas Constitution requires that most revenue generated by motor vehicle registration fees and motor fuel taxes be used for constructing and maintaining public roadways and other designated purposes. The Texas Department of Transportation (TxDOT) assists the Commission in executing state transportation policy. It is the responsibility of the legislature to appropriate money for TxDOT’s operation and maintenance expenses. All money authorized to be appropriated for TxDOT’s operations must come from the State Highway Fund (also known as Fund 6, Fund 006, or Fund 0006). The Commission can then use the balance in the fund to fulfill its responsibilities. However, the value of the revenue received in Fund 6 is not keeping pace with growing demand for transportation infrastructure in Texas. Additionally, diversion of revenue to nontransportation uses now exceeds $600 million per year. As shown in Figure 1.1, revenues and expenditures of the State Highway Fund per vehicle mile traveled (VMT) in Texas have remained almost flat since 1993. In the meantime, construction cost inflation has gone up more than 100%, effectively halving the value of expenditure.

Evaluation of TxDOT’s J.A.C.K. model for revenue and expenditure projections

This research report documents work conducted by the Center for Transportation (CTR) at The University of Texas at Austin in analyzing the Joint Analysis using the Combined Knowledge (J.A.C.K.) program. This program was developed by the Texas Department of Transportation (TxDOT) to make projections of revenues and expenditures. This research effort was to span from September 2008 to August 2009, but the bulk of the work was completed and presented by December 2008. J.A.C.K. was subsequently renamed TRENDS, but for consistency with the scope of work, the original name is used throughout this report.

Secure data aggregation in wireless sensor networks

A Wireless Sensor Network (WSN) is a set of sensors that are integrated with a physical environment. These sensors are small in size, and capable of sensing physical phenomena and processing them. They communicate in a multihop manner, due to a short radio range, to form an Ad Hoc network capable of reporting network activities to a data collection sink. Recent advances in WSNs have led to several new promising applications, including habitat monitoring, military target tracking, natural disaster relief, and health monitoring. The current version of sensor node, such as MICA2, uses a 16 bit, 8 MHz Texas Instruments MSP430 micro-controller with only 10 KB RAM, 128 KB program space, 512 KB external ash memory to store measurement data, and is powered by two AA batteries. Due to these unique specifications and a lack of tamper-resistant hardware, devising security protocols for WSNs is complex. Previous studies show that data transmission consumes much more energy than computation. Data aggregation can greatly help to reduce this consumption by eliminating redundant data. However, aggregators are under the threat of various types of attacks. Among them, node compromise is usually considered as one of the most challenging for the security of WSNs. In a node compromise attack, an adversary physically tampers with a node in order to extract the cryptographic secrets. This attack can be very harmful depending on the security architecture of the network. For example, when an aggregator node is compromised, it is easy for the adversary to change the aggregation result and inject false data into the WSN. The contributions of this thesis to the area of secure data aggregation are manifold. We firstly define the security for data aggregation in WSNs. In contrast with existing secure data aggregation definitions, the proposed definition covers the unique characteristics that WSNs have. Secondly, we analyze the relationship between security services and adversarial models considered in existing secure data aggregation in order to provide a general framework of required security services. Thirdly, we analyze existing cryptographic-based and reputationbased secure data aggregation schemes. This analysis covers security services provided by these schemes and their robustness against attacks. Fourthly, we propose a robust reputationbased secure data aggregation scheme for WSNs. This scheme minimizes the use of heavy cryptographic mechanisms. The security advantages provided by this scheme are realized by integrating aggregation functionalities with: (i) a reputation system, (ii) an estimation theory, and (iii) a change detection mechanism. We have shown that this addition helps defend against most of the security attacks discussed in this thesis, including the On-Off attack. Finally, we propose a secure key management scheme in order to distribute essential pairwise and group keys among the sensor nodes. The design idea of the proposed scheme is the combination between Lamport's reverse hash chain as well as the usual hash chain to provide both past and future key secrecy. The proposal avoids the delivery of the whole value of a new group key for group key update; instead only the half of the value is transmitted from the network manager to the sensor nodes. This way, the compromise of a pairwise key alone does not lead to the compromise of the group key. The new pairwise key in our scheme is determined by Diffie-Hellman based key agreement.

Characteristics of Impaired Drivers Who Enter Substance Abuse Treatment

Driving under the influence (DUI) remains a serious concern. Most of the data on characteristics of DUI offenders come from driving records, with little data on the levels of impairment of DUI arrestees. This paper examines data on 103,181DUI offenders admitted to Texas treatment programs between 1988 and 2008. They reported past-year DUI arrests or came to treatment on DUI probation. The changes in the characteristics of DUI offenders over time are examined, along with the factors associated with treatment completion and abstinence 90 days after program discharge. Incorporation of substance abuse treatment with effective DUI education and intervention can improve road safety and reduce the burden of substance-related illness.

Speech recognition in adverse environments using lip information

The performance of automatic speech recognition systems deteriorates in the presence of noise. One known solution is to incorporate video information with an existing acoustic speech recognition system. We investigate the performance of the individual acoustic and visual sub-systems and then examine different ways in which the integration of the two systems may be performed. The system is to be implemented in real time on a Texas Instruments' TMS320C80 DSP.