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.

Hierarchical fuzzy logic traffic control at a road junction using genetic algorithms

Traffic control at a road junction by a complex fuzzy logic controller is investigated. The increase in the complexity of junction means more number of input variables must be taken into account, which will increase the number of fuzzy rules in the system. A hierarchical fuzzy logic controller is introduced to reduce the number of rules. Besides, the increase in the complexity of the controller makes formulation of the fuzzy rules difficult. A genetic algorithm based off-line leaning algorithm is employed to generate the fuzzy rules. The learning algorithm uses constant flow-rates as training sets. The system is tested by both constant and time-varying flow-rates. Simulation results show that the proposed controller produces lower average delay than a fixed-time controller does under various traffic conditions.

Connected Communities and Creative Economy; Clash, cluster, complexity, creativity

After a brief personal orientation, this presentation offers an opening section on „clash, cluster, complexity, cities‟ – making the case that innovation (both creative and economic) proceeds not only from incremental improvements within an expert-pipeline process, but also from the clash of different systems, generations, and cultures. The argument is that cultural complexity arises from such clashes, and that clustering is the solution to problems of complexity. The classic, 10,000-year-old, institutional form taken by such clusters is … cities. Hence, a creative city is one where clashing and competitive complexity is clustered… and, latterly, networked.

Genetic Composition and Complexity of Virus Populations at Tungro-Endemic and Outbreak Rice Sites

We have recently demonstrated the geographic isolation of rice tungro bacilliform virus (RTBV) populations in the tungro-endemic provinces of Isabela and North Cotabato, Philippines. In this study, we examined the genetic structure of the virus populations at the tungro-outbreak sites of Lanao del Norte, a province adjacent to North Cotabato. We also analyzed the virus populations at the tungro-endemic sites of Subang, Indonesia, and Dien Khanh, Vietnam. Total DNA extracts from 274 isolates were digested with EcoRV restriction enzyme and hybridized with a full-length probe of RTBV. In the total population, 22 EcoRV-restricted genome profiles (genotypes) were identified. Although overlapping genotypes could be observed, the outbreak sites of Lanao del Norte had a genotype combination distinct from that of Subang or Dien Khanh but a genotype combination similar to that identified earlier from North Cotabato, the adjacent endemic province. Sequence analysis of the intergenic region and part of the ORF1 RTBV genome from randomly selected genotypes confirms the geographic clustering of RTBV genotypes and, combined with restriction analysis, the results suggest a fragmented spatial distribution of RTBV local populations in the three countries. Because RTBV depends on rice tungro spherical virus (RTSV) for transmission, the population dynamics of both tungro viruses were then examined at the endemic and outbreak sites within the Philippines. The RTBV genotypes and the coat protein RTSV genotypes were used as indicators for virus diversity. A shift in population structure of both viruses was observed at the outbreak sites with a reduced RTBV but increased RTSV gene diversity

Managing process model complexity via concrete syntax modifications

While Business Process Management (BPM) is an established discipline, the increased adoption of BPM technology in recent years has introduced new challenges. One challenge concerns dealing with the ever-growing complexity of business process models. Mechanisms for dealing with this complexity can be classified into two categories: 1) those that are solely concerned with the visual representation of the model and 2) those that change its inner structure. While significant attention is paid to the latter category in the BPM literature, this paper focuses on the former category. It presents a collection of patterns that generalize and conceptualize various existing mechanisms to change the visual representation of a process model. Next, it provides a detailed analysis of the degree of support for these patterns in a number of state-of-the-art languages and tools. This paper concludes with the results of a usability evaluation of the patterns conducted with BPM practitioners.

Financial literacy and pension investment decisions

The call for enhanced financial literacy amongst consumers is a global phenomenon, driven by the growing complexity of financial markets and products, and government concerns about the affordability of supporting an ageing population. Worldwide, defined benefit pensions are giving way to the risk and uncertainty of defined contribution superannuation/pension funds where fund members now make choices and decisions that were once made on their behalf. An important prerequisite for informed financial decision-making is adequate financial knowledge and skills to make competent investment decisions. This paper reports the findings of an online survey of the members of a large Australian public sector-based superannuation fund and shows that although respondents generally understand basic financial matters, on average, their understanding of investments concepts, such as the relationship between risk and returns, is inadequate. These results highlight the need for education programs focusing specifically on developing fund members’ investment knowledge and skills to facilitate informed retirement savings decisions.

Controlling chaos? The value and the challenges of applying complexity theory to project management

The literature abounds with descriptions of failures in high-profile projects and a range of initiatives has been generated to enhance project management practice (e.g., Morris, 2006). Estimating from our own research, there are scores of other project failures that are unrecorded. Many of these failures can be explained using existing project management theory; poor risk management, inaccurate estimating, cultures of optimism dominating decision making, stakeholder mismanagement, inadequate timeframes, and so on. Nevertheless, in spite of extensive discussion and analysis of failures and attention to the presumed causes of failure, projects continue to fail in unexpected ways. In the 1990s, three U.S. state departments of motor vehicles (DMV) cancelled major projects due to time and cost overruns and inability to meet project goals (IT-Cortex, 2010). The California DMV failed to revitalize their drivers’ license and registration application process after spending $45 million. The Oregon DMV cancelled their five year, $50 million project to automate their manual, paper-based operation after three years when the estimates grew to $123 million; its duration stretched to eight years or more and the prototype was a complete failure. In 1997, the Washington state DMV cancelled their license application mitigation project because it would have been too big and obsolete by the time it was estimated to be finished. There are countless similar examples of projects that have been abandoned or that have not delivered the requirements.

Path planning, guidance and control for a UAV forced landing

A forced landing is an unscheduled event in flight requiring an emergency landing, and is most commonly attributed to engine failure, failure of avionics or adverse weather. Since the ability to conduct a successful forced landing is the primary indicator for safety in the aviation industry, automating this capability for unmanned aerial vehicles (UAVs) will help facilitate their integration into, and subsequent routine operations over civilian airspace. Currently, there is no commercial system available to perform this task; however, a team at the Australian Research Centre for Aerospace Automation (ARCAA) is working towards developing such an automated forced landing system. This system, codenamed Flight Guardian, will operate onboard the aircraft and use machine vision for site identification, artificial intelligence for data assessment and evaluation, and path planning, guidance and control techniques to actualize the landing. This thesis focuses on research specific to the third category, and presents the design, testing and evaluation of a Trajectory Generation and Guidance System (TGGS) that navigates the aircraft to land at a chosen site, following an engine failure. Firstly, two algorithms are developed that adapts manned aircraft forced landing techniques to suit the UAV planning problem. Algorithm 1 allows the UAV to select a route (from a library) based on a fixed glide range and the ambient wind conditions, while Algorithm 2 uses a series of adjustable waypoints to cater for changing winds. A comparison of both algorithms in over 200 simulated forced landings found that using Algorithm 2, twice as many landings were within the designated area, with an average lateral miss distance of 200 m at the aimpoint. These results present a baseline for further refinements to the planning algorithms. A significant contribution is seen in the design of the 3-D Dubins Curves planning algorithm, which extends the elementary concepts underlying 2-D Dubins paths to account for powerless flight in three dimensions. This has also resulted in the development of new methods in testing for path traversability, in losing excess altitude, and in the actual path formation to ensure aircraft stability. Simulations using this algorithm have demonstrated lateral and vertical miss distances of under 20 m at the approach point, in wind speeds of up to 9 m/s. This is greater than a tenfold improvement on Algorithm 2 and emulates the performance of manned, powered aircraft. The lateral guidance algorithm originally developed by Park, Deyst, and How (2007) is enhanced to include wind information in the guidance logic. A simple assumption is also made that reduces the complexity of the algorithm in following a circular path, yet without sacrificing performance. Finally, a specific method of supplying the correct turning direction is also used. Simulations have shown that this new algorithm, named the Enhanced Nonlinear Guidance (ENG) algorithm, performs much better in changing winds, with cross-track errors at the approach point within 2 m, compared to over 10 m using Park's algorithm. A fourth contribution is made in designing the Flight Path Following Guidance (FPFG) algorithm, which uses path angle calculations and the MacCready theory to determine the optimal speed to fly in winds. This algorithm also uses proportional integral- derivative (PID) gain schedules to finely tune the tracking accuracies, and has demonstrated in simulation vertical miss distances of under 2 m in changing winds. A fifth contribution is made in designing the Modified Proportional Navigation (MPN) algorithm, which uses principles from proportional navigation and the ENG algorithm, as well as methods specifically its own, to calculate the required pitch to fly. This algorithm is robust to wind changes, and is easily adaptable to any aircraft type. Tracking accuracies obtained with this algorithm are also comparable to those obtained using the FPFG algorithm. For all three preceding guidance algorithms, a novel method utilising the geometric and time relationship between aircraft and path is also employed to ensure that the aircraft is still able to track the desired path to completion in strong winds, while remaining stabilised. Finally, a derived contribution is made in modifying the 3-D Dubins Curves algorithm to suit helicopter flight dynamics. This modification allows a helicopter to autonomously track both stationary and moving targets in flight, and is highly advantageous for applications such as traffic surveillance, police pursuit, security or payload delivery. Each of these achievements serves to enhance the on-board autonomy and safety of a UAV, which in turn will help facilitate the integration of UAVs into civilian airspace for a wider appreciation of the good that they can provide. The automated UAV forced landing planning and guidance strategies presented in this thesis will allow the progression of this technology from the design and developmental stages, through to a prototype system that can demonstrate its effectiveness to the UAV research and operations community.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Bounded parameter Markov Decision Processes with average reward criterion

Bounded parameter Markov Decision Processes (BMDPs) address the issue of dealing with uncertainty in the parameters of a Markov Decision Process (MDP). Unlike the case of an MDP, the notion of an optimal policy for a BMDP is not entirely straightforward. We consider two notions of optimality based on optimistic and pessimistic criteria. These have been analyzed for discounted BMDPs. Here we provide results for average reward BMDPs. We establish a fundamental relationship between the discounted and the average reward problems, prove the existence of Blackwell optimal policies and, for both notions of optimality, derive algorithms that converge to the optimal value function.

Overcoming the complexity of diagnostic problems due to sensor network architecture

In fault detection and diagnostics, limitations coming from the sensor network architecture are one of the main challenges in evaluating a system’s health status. Usually the design of the sensor network architecture is not solely based on diagnostic purposes, other factors like controls, financial constraints, and practical limitations are also involved. As a result, it quite common to have one sensor (or one set of sensors) monitoring the behaviour of two or more components. This can significantly extend the complexity of diagnostic problems. In this paper a systematic approach is presented to deal with such complexities. It is shown how the problem can be formulated as a Bayesian network based diagnostic mechanism with latent variables. The developed approach is also applied to the problem of fault diagnosis in HVAC systems, an application area with considerable modeling and measurement constraints.