Data mining analysis of an urban tunnel pressure drop based on CFD data

An accurate estimation of pressure drop due to vehicles inside an urban tunnel plays a pivotal role in tunnel ventilation issue. The main aim of the present study is to utilize computational intelligence technique for predicting pressure drop due to cars in traffic congestion in urban tunnels. A supervised feed forward back propagation neural network is utilized to estimate this pressure drop. The performance of the proposed network structure is examined on the dataset achieved from Computational Fluid Dynamic (CFD) simulation. The input data includes 2 variables, tunnel velocity and tunnel length, which are to be imported to the corresponding algorithm in order to predict presure drop. 10-fold Cross validation technique is utilized for three data mining methods, namely: multi-layer perceptron algorithm, support vector machine regression, and linear regression. A comparison is to be made to show the most accurate results. Simulation results illustrate that the Multi-layer perceptron algorithm is able to accurately estimate the pressure drop.

A preliminary, qualitative exploration of the influences associated with drop-out from cognitive-behavioural therapy for problem gambling : an Australian perspective

It has been estimated that 80% of Australians engage in some form of gambling, with approximately 115,000 Australians experiencing severe problems (Productivity Commission 2010). Very few people with problem gambling seek help and, of those who do, large numbers drop-out of therapy before completing their program. To gain insights into these problems, participants who had either completed or withdrawn prematurely from an individual CBT-based problem gambling treatment program were interviewed to examine factors predictive of premature withdrawal from therapy as well as people's 'readiness' for change. The results indicated that there might be some early indicators of risk for early withdrawal. These included: gambling for pleasure or social interaction; non-compliance with homework tasks; gambling as a strategy to avoid personal issues or dysphoric mood; high levels of guilt and shame; and a lack of readiness for change. The study further showed that application of the term 'drop-out' to some clients may be an unnecessarily negative label in that a number appear to have been able to reduce their gambling urges even after a short exposure to therapy.

Treatment outcomes and predictors of drop out for problem gamblers in South Australia : a cohort study

OBJECTIVES: Recent prevalence studies in Australia, the USA and Canada have estimated 1-2% of the adult population meet the diagnostic criteria for problem or pathological gambling. The Statewide Gambling Therapy Service (SGTS) provides treatment for problem gamblers in key metropolitan and rural regions in South Australia. The aims of this study were two-fold: to analyse the short and mid-term outcomes following treatment provided by SGTS and to identify factors associated with treatment drop-out. METHOD: A cohort of treatment seeking problem gamblers was recruited through SGTS in 2008. Repeated outcome measures included problem gambling screening, gambling related cognitions and urge. Treatment drop-out was defined as participants attending three or less treatment sessions, whilst potential predictors of drop-out included perceived social support , anxiety and sensation-seeking traits. RESULTS: Of 127 problem gamblers who participated in the study, 69 (54%) were males with a mean age of 43.09 years (SD = 12.65 years) and with 65 (52%) reporting a duration of problem gambling greater than 5 years. Follow up time for 50% of participants was greater than 8.9 months and, overall, 41 (32%) participants were classified as treatment drop-outs. Results indicated significant improvement over time on all outcome measures except alcohol use for both treatment completers and drop-outs, although to a lesser extent for the treatment drop-out group. A significant predictor of treatment drop-out was sensation-seeking traits. CONCLUSION: These results will inform future treatment planning and service delivery, and guide research into problem gambling including aspects of treatment drop-out.

An ensemble of intelligent water drop algorithms and its application to optimization problems

Crown Copyright © 2015 Published by Elsevier Inc. All rights reserved. The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems.