The Necessary And Sufficient Condition for Generalized Demixing

Demixing is the problem of identifying multiple structured signals from a superimposed observation. This work analyzes a general framework, based on convex optimization, for solving demixing problems. We present a new solution to determine whether or not a specific convex optimization problem built for generalized demixing is successful. This solution will also bring about the possibility to estimate the probability of success by the approximate kinematic formula

"Can the neuro fuzzy model predict stock indexes better than its rivals?"

This paper develops a model of a trading system by using neuro fuzzy framework in order to better predict the stock index. Thirty well-known stock indexes are analyzed with the help of the model developed here. The empirical results show strong evidence of nonlinearity in the stock index by using KD technical indexes. The trading point analysis and the sensitivity analysis of trading costs show the robustness and opportunity for making further profits through using the proposed nonlinear neuro fuzzy system. The scenario analysis also shows that the proposed neuro fuzzy system performs consistently over time.

Asthma Exacerbation in Children: A Practical Review

Asthma is the most common chronic lower respiratory tract disease in childhood throughout the world. Despite advances in asthma management, acute exacerbations continue to be a major problem in patients and they result in a considerable burden on direct/indirect health care providers. A severe exacerbation occurring within 1 year is an independent risk factor. Respiratory tract viruses have emerged as the most frequent triggers of exacerbations in children. It is becoming increasingly clear that interactions may exist between viruses and other triggers, increasing the likelihood of an exacerbation. In this study, we provide an overview of current knowledge about asthma exacerbations, including its definition, impact on health care providers, and associated factors. Prevention management in intermittent asthma as well as intermittent wheeze in pre-school children and those with persistent asthma are discussed. Our review findings support the importance of controlling persistent asthma, as indicated in current guidelines. In addition, we found that early episodic intervention appeared to be crucial in preventing severe attacks and future exacerbations. Besides the use of medication, timely education after an exacerbation along with a comprehensive plan in follow up is also vitally important

Validating a Data Quality Framework in Engineering Asset Management

Data Quality (DQ) has been an acknowledged issue for a long time. Several researchers have indicated that maintaining the quality of data is often acknowledged as problematic, but is also seen as critical to effective decision-making in engineering asset management (AM). The study presents an AM specific DQ framework, which aims to provide a comprehensive structure for understanding, identifying AM DQ problems in an organised way. The framework was examined in a preliminary case study of two large Australian engineering organisations. The empirical findings from the research were used to validate the proposed AM DQ framework. As AM data and informational needs are very different to a typical business environment, a gap exists in the availability of DQ solutions for engineering asset management. Thus there is a need for the development of DQ solutions for engineering asset management

A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.

"A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?"

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.