1,818 research outputs found
Noninteractive fuzzy rule-based systems
In this paper, we have introduced a noninteractive model for fuzzy rule-based systems. A critical aspect of this noninteractive model is the introduction of a new set of rules with fewer parameters and without considering the interaction between the functionality of inputs. The new noninteractive model of the fuzzy rule-based system represents the output as a linear combination of the nonlinear function of individual inputs
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Industrial application of fuzzy systems; adaptive fuzzy control of solder paste stencil printing
This paper presents an adaptive fuzzy control algorithm for the control of the solder paste stencil printing stage of surface mount printed circuit board assembly. The proposed method of automatic solder paste stencil printing consist of four blocks; fuzzy feature extraction, defect classifcation of paste deposits, adaptive fuzzy rule-based model identifcation and subsequently an optimal control action for the stencil printing process. Experimental results are presented to illustrate the capability of the algorithm
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Intelligent techniques in condition monitoring based on forecasting of vibrational signals
Semantic-based decision support for remote care of dementia patients
This paper investigates the challenges in developing a semantic-based Dementia Care Decision Support System based on the non-intrusive monitoring of the patient's behaviour. Semantic-based approaches are well suited for modelling context-aware scenarios similar to Dementia care systems, where the patient's dynamic behaviour observations (occupants movement, equipment use) need to be analysed against the semantic knowledge about the patient's condition (illness history, medical advice, known symptoms) in an integrated knowledgebase. However, our research findings establish that the ability of semantic technologies to reason upon the complex interrelated events emanating from the behaviour monitoring sensors to infer knowledge assisting medical advice represents a major challenge. We attempt to address this problem by introducing a new approach that relies on propositional calculus modelling to segregate complex events that are amenable for semantic reasoning from events that require pre-processing outside the semantic engine before they can be reasoned upon. The event pre-processing activity also controls the timing of triggering the reasoning process in order to further improve the efficiency of the inference process. Using regression analysis, we evaluate the response-time as the number of monitored patients increases and conclude that the incurred overhead on the response time of the prototype decision support systems remains tolerable
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The Investigation of Rhodiola Crenulata Root Extract Effects on Obesity Associated Inflammation and the Antineoplastic Mechanism in Breast Cancer Cells
Obesity and breast cancer are two disease models that directly affect the United States population, as more than 35% of the adult population is obese [8], and more than 200,000 new cases of breast cancer are diagnosed in the United States per year [34]. Several diseases are associated with obesity including, cardiovascular disease, insulin resistance, increased inflammation and increased cancer risk [9,10]. Therefore it essential to understand the risks associated with obesity as well as to investigate possible preventive and/or therapeutic treatment strategies.
Rhodiola crenulata is a Tibetan plant that has been used in Eastern traditional medicine to relieve depression, anxiety, fatigue and to aid in high altitude biological adjustment [1]. Studies have also suggested that treatment with Rhodiola sp. and their components can improve glucose homeostasis in rodent models of insulin resistance[2-4] and inhibit tumor growth in various rodent models for cancer [5-7]. However, these studies have been plagued by the lack of strong mechanistic data.
The overall goal of this dissertation is to determine the mechanism by which R. crenulata affects glucose homeostasis in female mice subjected to Diet Induced Obesity (DIO) and to evaluate the effect of R. crenulata on 2 important cancer signaling pathways (canonical Wnt signaling and Estrogen receptor signaling) in breast cancer cells in vitro. In the work presented in this dissertation, we tested two main hypotheses; 1) 12 weeks of treatment with R. crenulata extract will decrease adiposity, improves glucose metabolism and obesity associated inflammation in female mice subjected to a high fat diet and 2) R. crenulata treatment will decrease Wnt/b-catenin signaling and Estrogen Receptor (ER) signaling in cancer cell lines in vitro. We used a wide variety of in vitro and in vivo techniques to test our hypotheses. Our results suggest that that R. crenulata can be beneficial for controlling insulin resistance and liver inflammation in a model for diet induced obesity. We also demonstrate two critical pathways in breast cancer cells that are controlled by R. crenulata. We show that treatment with a hydroalcoholic extract inhibits the canonical Wnt signaling pathway, which could explain some of the anti-neoplastic observations previously described by the Schneider lab. We also confirm that this R. crenulata extract contains estrogenic compounds; however despite this estrogenic activity, R.crenulata controlled proliferation and decreased tumorsphere growth and survival when cultures were treated for a longer period of time. Optimistically, the results from these studies will encourage further research on R. crenulata and possible usage as a preventive agent
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