Characterization of physical, mechanical and chemical properties of quiscal fibres : the influence of atmospheric DBD plasma treatment

This paper reports the first attempt of characterizing various physical, mechanical and chemical properties of Quiscal fibres, used by the native communities in Chile and investigating the influence of atmospheric dielectric barrier discharge plasma treatment on various properties such as diameter and linear density, fat, wax and impurity%, moisture regain, chemical elements and groups, thermal degradation, surface morphology, etc. According to the experimental observations, Quiscal fibre has lower tenacity than most of the technical grade natural fibres such as sisal, hemp, flax, etc., and plasma treatment at optimum dose improved its tenacity to the level of sisal fibres. Plasma treatment also reduced the amount of fat, wax and other foreign impurities present in Quiscal fibres as well as removed lignin and hemicellulose partially from the fibre structure. Plasma treatment led to functionalization of Quiscal fibre surface with chemical groups, as revealed from attenuated total reflection spectroscopy and also confirmed from the elemental analysis using energy dispersive Xray technique and pH and conductivity measurements of fibre aqueous extract. The wetting behavior of Quiscal fibre also improved considerably through plasma treatment. However, untreated and plasma treated Quiscal fibres showed similar thermal degradation behavior, except the final degradation stage, in which plasma treated fibres showed higher stability and incomplete degradation unlike the untreated fibres. The experimental results suggested that the plasma treated Quiscal fibres, like other technical grade natural fibres, can find potential application as reinforcement of composite materials for various industrial applications.

A soft computing approach to kidney diseases evaluation

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis.The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

Thrombophilia screening: An artificial neural network approach

Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.

A demonstration case on the derivation of process-level logical architectures for ambient assisted living ecosystems

When representing the requirements for an intended software solution during the development process, a logical architecture is a model that provides an organized vision of how functionalities behave regardless of the technologies to be implemented. If the logical architecture represents an ambient assisted living (AAL) ecosystem, such representation is a complex task due to the existence of interrelated multidomains, which, most of the time, results in incomplete and incoherent user requirements. In this chap- ter, we present the results obtained when applying process-level modeling techniques to the derivation of the logical architecture for a real industrial AAL project. We adopt a V-Model–based approach that expresses the AAL requirements in a process-level perspec- tive, instead of the traditional product-level view. Additionally, we ensure compliance of the derived logical architecture with the National Institute of Standards and Technology (NIST) reference architecture as nonfunctional requirements to support the implementa- tion of the AAL architecture in cloud contexts.