Evaluating the effectiveness of an autism-specific public transport app for individuals on the autism spectrum: a pilot study.

PURPOSE: Autism is characterised by differences in social skills, limited communication abilities and repetitive behaviour, which often result in increased reliance on other people. Transportation is but one task that is commonly burdened on family members. Public transport is an inexpensive and widely available form of travel which facilitates independence. However, it presents unique challenges for individuals on the spectrum, as it requires complex skills including, but not limited to, understanding abstract information (e.g., maps, service schedules, etc.), problem-solving unexpected situations and timely management of transfers. As such, most individuals on the autism spectrum do not use public transport and have never considered using it. Here we evaluate the effectiveness of an autism-specific public transport app, OrienTrip, with autistic individuals and allied health professionals. METHODS: A total of 16 individuals on the autism spectrum (eight male and eight female participants) and 22 allied health professionals (19 females and three male participants) were recruited for the pilot study. RESULTS: We found that OrienTrip is effective in facilitating public transport use for autistic individuals. Individuals on the autism spectrum expressed their satisfaction with the app and agreed that it makes public transport easy to use. Similarly, allied health professionals also indicated that OrienTrip is helpful in assisting autistic individuals use public transport safely. CONCLUSION: Our findings demonstrate that OrienTrip can be used to facilitate independent travel for individuals on the autism spectrum using public transport. This can improve community participation opportunities for autistic individuals, including enhanced education, employment and social outcomes.Implications for rehabilitationIndividuals on the autism spectrum heavily rely on other people, namely family members, for their transportation needs.Public transport is an inexpensive and widely available form of travel which facilitates independence; however, it presents unique challenges for autistic individuals, as such, most individuals do not use it or consider using it.In this research, we have developed and evaluated one of the first autism-specific public transport mobile apps that facilitates independent public transport use.This tool can improve community participation opportunities for autistic individuals, including enhanced education, employment and social outcomes.

Public transport planning tool for users on the autism spectrum: from concept to prototype.

PURPOSE: This research explored the challenges of public transport use for individuals on the autism spectrum. It, subsequently, proposed a mobile application solution, coproduced by individuals on the autism spectrum, to facilitate public transport use. METHODS: We, first, conducted a review of the literature to highlight the challenges people on the autism spectrum face when utilizing public transport. We, then, designed a list of mobile application functionalities that address the identified problems. To validate these functionalities, 27 young autistic adults and 19 families of autistic individuals were employed. Finally, based on the findings, we designed a mobile application that helps facilitate public transport use for those on the autism spectrum. RESULTS: We found that the most prevalent concerns, in public transport use, amongst autistic individuals and their families are safety and spatial awareness. Specific problems include finding one's way to the bus stop, boarding the correct service and disembarking at the correct stop. Interestingly, anxiety about unexpected events was also a barrier. Sensory sensitivity, similarly, was found to be an obstacle. CONCLUSIONS: This study defined the challenges of public transport use for autistic individuals and proposed a technological solution. The findings can also inform innovators, public transport providers and policymakers to improve public transport accessibility.Implications for rehabilitationPeople on the autism spectrum heavily rely on other individuals, namely family and friends, for their transportation needs. This dependence results in immobility for the autistic individuals and significant time and economical sacrifice for the person responsible for the transportation.Public transport, a cheap and widely available form of transportation, has not yet been clearly studied with individuals on the autism spectrum.We clearly define the challenges of using public transport and put forward a trip planner mobile application, coproduced by autistic individuals, that facilitate it.In the long term, this enhanced travel independence can lead to greater education and employment opportunities and an overall improved quality of life.

Service delivery innovation for hospital emergency management using rich organizational modelling.

The purpose of this article is to identify and assess service delivery issues within a hospital emergency department and propose an improved model to address them. Possible solutions and options to these issues are explored to determine the one that best fits the context. In this article, we have analysed the emergency department's organizational models through i* strategic dependency and rational modelling technique before proposing updated models that could potentially drive business process efficiencies. The results produced by the models, framework and improved patient journey in the emergency department were evaluated against the statistical data revealed from a reputed government organization related to health, to ensure that the key elements of the issues such as wait time, stay time/throughput, workload and human resource are resolved. The result of the evaluation was taken as a basis to determine the success of the project. Based on these results, the article recommends implementing the concept on actual scenario, where a positive result is achievable.

A deep hybrid model to detect multi-locus interacting SNPs in the presence of noise.

Identifying genetic variants associated with complex diseases is a central focus of genome-wide association studies. These studies extensively adopt univariate analysis by ignoring interaction effects. It is widely accepted that the etiology of most complex diseases depends on interactions between genetic variants and / or environmental factors. Several machine learning and data mining methods have been consistently successful in exposing these interaction effects. However, there has been no major breakthrough due to various biological complexities, and statistical computational challenges facing in the field of genetic epidemiology, despite of many efforts. Deep learning is emerging machine learning approach that promises to reveal the hidden patterns of big data for accurate predictions. In this study, a deep neural network is unified with a random forest by forming hybrid architecture, for achieving reliable detection of multi-locus interactions between single nucleotide polymorphisms. The proposed hybrid method is evaluated on various simulated scenarios in the absence of main effect for six epistasis models. The best model with optimal hyper-parameters (grid and random grid search) is chosen to enhance the power of the method by maximising the model's prediction accuracy. The performance metrics of each model is analysed for both training and validation. Further, the performance of the method in the presence of noise due to missing data, genotyping errors, genetic heterogeneity, and phenocopy, and their combined effects are evaluated. The power of the method in detecting two-locus interactions is compared with the previous methods in the presence and absence of noise. On an average, the power of the proposed method is much higher than the previous methods for all simulated scenarios. Finally, findings are confirmed on a chronical dialysis patient's data, obtained from the published study performed at the Kaohsiung Chang Gung Memorial Hospital. It is observed that the interaction between SNP 21 (2) and SNP 28 (2) in the mitochondrial D-loop has the highest risk for the disease manifestation.

A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data.

In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing technologies enables genetic epidemiological analysis of large scale data. These advances have led to the identification of a number of single nucleotide polymorphisms (SNPs) responsible for disease susceptibility. The interactions between SNPs associated with complex diseases are increasingly being explored in the current literature. These interaction studies are mathematically challenging and computationally complex. These challenges have been addressed by a number of data mining and machine learning approaches. This paper reviews the current methods and the related software packages to detect the SNP interactions that contribute to diseases. The issues that need to be considered when developing these models are addressed in this review. The paper also reviews the achievements in data simulation to evaluate the performance of these models. Further, it discusses the future of SNP interaction analysis.

A neural network approach for the prediction of in vitro culture parameters for maximum biomass yields in hairy root cultures.

The present study deals with ANN based prediction of culture parameters in terms of inoculum density, pH and volume of growth medium per culture vessel and sucrose content of the growth medium for Glycyrrhiza hairy root cultures. This kind of study could be a model system in exploitation of hairy root cultures for commercial production of pharmaceutical compounds using large bioreactors. The study is aimed to evaluate the efficiency of regression neural network and back propagation neural network for the prediction of optimal culture conditions for maximum hairy root biomass yield. The training data for regression and back propagation networks were primed on the basis of function approximation, where final biomass fresh weight (f(wt)) was considered as a function of culture parameters. On this basis the variables in culture conditions were described in the form of equations which are for inoculum density: y=0.02x+0.04, for pH of growth medium: y=x+2.8, for sucrose content in medium: y=9.9464x+(-9.7143) and for culture medium per culture vessel: y=10x. The fresh weight values obtained from training data were considered as target values and further compared with predicted fresh weight values. The empirical data were used as testing data and further compared with values predicted from trained networks. Standard MATLAB inbuilt generalized regression network with radial basis function radbas as transfer function in layer one and purelin in layer two and back propagation having purelin as transfer function in output layer and logsig in hidden layer were used. Although in comparative assessment both the networks were found efficient for prediction of optimal culture conditions for high biomass production, more accuracy in results was seen with regression network.