An Advance Computing Numerical Heuristic of Nonlinear SIR Dengue Fever System Using the Morlet Wavelet Kernel.

This study is associated to solve the nonlinear SIR dengue fever system using a computational methodology by operating the neural networks based on the designed Morlet wavelet (MWNNs), global scheme as genetic algorithm (GA), and rapid local search scheme as interior-point algorithm (IPA), i.e., GA-IPA. The optimization of fitness function based on MWNNs is performed for solving the nonlinear SIR dengue fever system. This MWNNs-based fitness function is accessible using the differential system and initial conditions of the nonlinear SIR dengue fever system. The designed procedures based on the MWNN-GA-IPA are applied to solve the nonlinear SIR dengue fever system to check the exactness, precision, constancy, and efficiency. The achieved numerical form of the nonlinear SIR dengue fever system via MWNN-GA-IPA was compared with the Runge-Kutta numerical results that verify the significance of MWNN-GA-IPA. Moreover, statistical reflections through different measures for the nonlinear SIR dengue fever system endorse the precision and convergence of the computational MWNN-GA-IPA.

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model.

The aim of this work is to introduce a stochastic solver based on the Levenberg-Marquardt backpropagation neural networks (LMBNNs) for the nonlinear host-vector-predator model. The nonlinear host-vector-predator model is dependent upon five classes, susceptible/infected populations of host plant, susceptible/infected vectors population, and population of predator. The numerical performances through the LMBNN solver are observed for three different types of the nonlinear host-vector-predator model using the authentication, testing, sample data, and training. The proportions of these data are chosen as a larger part, i.e., 80% for training and 10% for validation and testing, respectively. The nonlinear host-vector-predator model is numerically treated through the LMBNNs, and comparative investigations have been performed using the reference solutions. The obtained results of the model are presented using the LMBNNs to reduce the mean square error (MSE). For the competence, exactness, consistency, and efficacy of the LMBNNs, the numerical results using the proportional measures through the MSE, error histograms (EHs), and regression/correlation are performed.

Clinicopathological features of 214 maxillary sinus pathologies. A ten-year single-centre retrospective clinical study.

BACKGROUND: Diagnosis of maxillary sinus pathology must include the clinical radiological study (CRS) and histopathological analysis. The aim of this study is 1) to describe the clinicopathological features of maxillary sinus lesions, obtained successively in a single medical centre over the last 10 years and 2) to determine the sensitivity and specificity for the diagnosis of malignant lesions based exclusively on the CRS. METHODS: It is a single-centre observational retrospective clinical study on patients who attended the University Hospital Complex of Santiago de Compostela (CHUS) with sinus pathologies during the period of 2009-2019. RESULTS: The sample consisted of 133 men (62.1%) and 81 women (37.9%), with an average age of 46.9 years (SD = 18.8). In terms of frequency, the most frequent pathology was the unspecified sinusitis (44.4%), followed by polyps (18.2%), malignant tumours (9.8%), inverting papilloma (7.5%), fungal sinusitis (4.7%), cysts (3.7%), benign tumours (2.3%), mucocele (2.3%) and other lesions (1.9%). Cysts and benign tumours were diagnosed earliest Vs malignant tumours (65.2 years (SD = 16.1)) were diagnosed the latest (p < 0.001). Based only on the CRS for malignancies, diagnostic indexes were 71.4% sensitivity and 97.9% specificity, with a Kappa value of 0.68 with (p < 0.001). CONCLUSION: Maxillary sinus pathology is very varied with therapeutic and prognostic repercussions. CRS is sometimes insufficient and histopathological confirmation is essential.