Search

Antimicrobial activity of the Zn1-xErxO (0, 1, 5, 10 at.%) nanoparticles were tested against Staphylococcus aureus and Escherichia coli. The nanoparticles were successfully synthesized by wet chemical route, where polyvinyl alcohol and sucrose were used. The influence of erbium content in structural, optical, and antimicrobial activity was analyzed. The average crystallite size is under 15 nm for all the samples according to X-ray diffraction results, and no secondary phases were observed even at high erbium content. Optical results exhibit a blue shift in the ultra-violet region. The X-ray photoelectron spectroscopy analysis confirmed the variations of Zn/O ratio, together with particles size and band gap are key factor in antimicrobial properties. The microbiological essays exhibit to these nanoparticles as a high potential agent with antibacterial activity versus S. aureus, with lower impact in E. coli. The absorbance results of these assays were used in two theoretical approaches. At first, Gompertz model used in the regression analysis showed the best fit for bacterial growth. Additionally, an artificial neural network was trained to forecast the result of new experiments, showing a good performance.

Brain–computer interfaces (BCI) rely on classification algorithms to detect the patterns of the brain signals that encode the mental task performed by the user. Therefore, robust and reliable classification techniques should be developed and evaluated to recognize the user's mental task with high accuracy. This paper proposes the use of the novel dendrite morphological neural networks (DMNN) for the recognition of voluntary movements from electroencephalographic (EEG) signals. This technique was evaluated with two studies. The first aimed to evaluate the performance of DMNN in the recognition of motor execution and motor imagery tasks and to carry out a systematic comparison with support vector machine (SVM) and linear discriminant analysis (LDA) which are the two classifiers mostly used in BCI systems. EEG signals from twelve healthy students were recorded during a cue-based hand motor execution and imagery experiment. The results showed that DMNN provided decoding accuracies of 80% for motor execution and 77% for motor imagery, which were significantly different than the chance level (p

Retinal images can be used to detect and follow up several important chronic diseases. The classification of retinal images requires an experienced ophthalmologist. This has been a bottleneck to implement routine screenings performed by general physicians. It has been proposed to create automated systems that can perform such task with little intervention from humans, with partial success. In this work, we report advances in such endeavor, by using a Lattice Neural Network with Dendritic Processing (LNNDP). We report results using several metrics, and compare against well known methods such as Support Vector Machines (SVM) and Multilayer Perceptrons (MLP). Our proposal shows better performance than other approaches reported in the literature. An additional advantage is that unlike those other tools, LNNDP requires no parameters, and it automatically constructs its structure to solve a particular problem. The proposed methodology requires four steps: (1) Pre-processing, (2) Feature computation, (3) Classification and (4) Post-processing. The Hotelling T2 control chart was used to reduce the dimensionality of the feature vector, from 7 that were used before to 5 in this work. The experiments were run on images of DRIVE and STARE databases. The results show that on average, F1-Score is better in LNNDP, compared with SVM and MLP implementations. Same improvement is observed for MCC and the accuracy. HighlightsFirst implementation of a Lattice Neural Network with Dendritic Processing to solve classify retinal images.The performance is competitive compared with common approaches like Support Vector Machines and Multilayer Perceptrons.The Lattice Neural Network with Dendritic Processing does not require the adjustment of parameters by the user.