Measurement of fatty acids in toenail clippings using GC-MS

Title from PDF of title page (University of Missouri--Columbia, viewed on March 11, 2013).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. J. David RobertsonIncludes bibliographical references.M.S. University of Missouri--Columbia 2012."December 2012"Omega-3 fatty acids play an important role in human health and nutrition. Here, we investigated human toenail clippings as a potential, non-invasive and cumulative biomonitor for fatty acids, and successfully developed a method to extract fatty acids and quantified them by gas chromatography-mass spectrometry. Although omega-3 fatty acids were not detected in the nail samples, the nail may still prove to be a biomonitor for them if the percent recoveries and detection limits could be improved

Robust passivity of coupled Cohen-Grossberg neural networks with reaction-diffusion terms

In this paper, we deal with the robust passivity problem for coupled reaction-diffusion Cohen-Grossberg neural networks (CRDCGNNs) with spatial diffusion coupling and state coupling. First, we present the network model for CRDCGNNs with state coupling and establish some robust passivity conditions for this kind of CRDCGNNs. Then, the investigation on robust passivity for CRDCGNNs with spatial diffusion coupling is carried out similarly. At last, the feasibility of the obtained theoretical results is demonstrated by one example with simulation results

General decay lag anti-synchronization of multi-weighted delayed coupled neural networks with reaction–diffusion terms

We propose a new anti-synchronization concept, called general decay lag anti-synchronization, by combining the definitions of decay synchronization and lag synchronization. Novel criteria for the decay lag anti-synchronization of multi-weighted delayed coupled reaction–diffusion neural networks (MWDCRDNNs) with and without bounded distributed delays are derived by constructing an appropriate nonlinear controller and using the Lyapunov functional method. Moreover, the robust decay lag anti-synchronization of MWDCRDNNs with and without bounded distributed delays is considered. Finally, two numerical simulations are performed to validate the obtained results

ψ-type stability of reaction–diffusion neural networks with time-varying discrete delays and bounded distributed delays

In this paper, the ψ-type stability and robust ψ-type stability for reaction–diffusion neural networks (RDNNs) with Dirichlet boundary conditions, time-varying discrete delays and bounded distributed delays are investigated, respectively. Firstly, we analyze the ψ-type stability and robust ψ-type stability of RDNNs with time-varying discrete delays by means of ψ-type functions combined with some inequality techniques, and put forward several ψ-type stability criteria for the considered networks. Additionally, the models of RDNNs with bounded distributed delays are established and some sufficient conditions to guarantee the ψ-type stability and robust ψ-type stability are given. Lastly, two examples are provided to confirm the effectiveness of the derived results

Passivity and synchronization of coupled different dimensional delayed reaction-diffusion neural networks with dirichlet boundary conditions

Two types of coupled different dimensional delayed reaction-diffusion neural network (CDDDRDNN) models without and with parametric uncertainties are analyzed in this paper. On the one hand, passivity and synchronization of the raised network model with certain parameters are studied through exploiting some inequality techniques and Lyapunov stability theory, and some adequate conditions are established. On the other hand, the problems of robust passivity and robust synchronization of CDDDRDNNs with parameter uncertainties are solved. Finally, two numerical examples are given to testify the effectiveness of the derived passivity and synchronization conditions

Event-triggered communication for passivity and synchronisation of multi-weighted coupled neural networks with and without parameter uncertainties

A multi-weighted coupled neural networks (MWCNNs) model with event-triggered communication is studied here. On the one hand, the passivity of the presented network model is studied by utilising Lyapunov stability theory and some inequality techniques, and a synchronisation criterion based on the obtained output-strict passivity condition of MWCNNs with eventtriggered communication is derived. On the other hand, some robust passivity and robust synchronisation criteria based on output-strict passivity of the proposed network with uncertain parameters are presented. At last, two numerical examples are provided to testify the effectiveness of the output-strict passivity and robust synchronisation results

Integration on acceleration signals by adjusting with envelopes

Direct integration of acceleration often causes unrealistic drifts in velocity and displacement. A method of integration on acceleration data to acquire realistic velocity and displacement is proposed. In this approach, drifts are estimated by using the mean of the upper and lower envelopes of signals after integration from acceleration into velocity and displacement. The experimental results obtained by using simulated data and real world signals are presented to demonstrate the effectiveness of the method

Identifying outliers in astronomical images with unsupervised machine learning

Astronomical outliers, such as unusual, rare or unknown types of astronomical objects or phenomena, constantly lead to the discovery of genuinely unforeseen knowledge in astronomy. More unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey data. However, it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant workload. Supervised learning is also unsuitable for this purpose since designing proper training sets for unanticipated signals is unworkable. Motivated by these challenges, we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical outliers. For comparison, we construct three methods, which are built upon the k-nearest neighbors (KNN), Convolutional Auto-Encoder (CAE)+ KNN, and CAE + KNN + Attention Mechanism (attCAE KNN) separately. Testing sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above methods. Results show that attCAE KNN achieves the best recall (78%), which is 53% higher than the classical KNN method and 22% higher than CAE+KNN. The efficiency of attCAE KNN (10 minutes) is also superior to KNN (4 hours) and equal to CAE+KNN(10 minutes) for accomplishing the same task. Thus, we believe it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised manner. Next, we will apply attCAE KNN to available survey datasets to assess its applicability and reliability

Passivity and synchronization of coupled complex-valued memristive neural networks

The coupled complex-valued memristive neural networks (CCVMNNs) are investigated in this study. First, we analyze the passivity of the proposed network model by designing an appropriate controller and using certain inequalities as well as Lyapunov functional method, and provide a passivity condition for the considered CCVMNNs. In addition, a criterion for guaranteeing synchronization of this kind of network is established. Finally, the effectiveness and correctness of the acquired theoretical results are verified by a numerical example