Optimizing trigger timing in minimal ovarian stimulation for In Vitro fertilization using machine learning models with random search hyperparameter tuning.

Various studies have emphasized the importance of identifying the optimal Trigger Timing (TT) for the trigger shot in In Vitro Fertilization (IVF), which is crucial for the successful maturation and release of oocytes, especially in minimal ovarian stimulation treatments. Despite its significance for the ultimate success of IVF, determining the precise TT remains a complex challenge for physicians due to the involvement of multiple variables. This study aims to enhance TT by developing a machine learning multi-output model that predicts the expected number of retrieved oocytes, mature oocytes (MII), fertilized oocytes (2 PN), and useable blastocysts within a 48-h window after the trigger shot in minimal stimulation cycles. By utilizing this model, physicians can identify patients with possible early, late, or on-time trigger shots. The study found that approximately 27 % of treatments administered the trigger shot on a suboptimal day, but optimizing the TT using the developed Artificial Intelligence (AI) model can potentially increase useable blastocyst production by 46 %. These findings highlight the potential of predictive models as a supplementary tool for optimizing trigger shot timing and improving IVF outcomes, particularly in minimal ovarian stimulation. The experimental results underwent statistical validation, demonstrating the accuracy and performance of the model. Overall, this study emphasizes the value of AI prediction models in enhancing TT and making the IVF process safer and more efficient.

Multilayer Stent for Emergency Treatment of Acute Type B Aortic Dissection: A Case Report.

PURPOSE: We report the case of an acute type B dissection with high-risk features treated with multilayer stent. CASE REPORT: A 50-year-old female patient presented to the emergency department with an acute type B aortic dissection. Conservative medical treatment did control blood pressure but did not alleviate her dissection symptoms. She was treated endovascularly with multilayer stents extensively covering the whole dissected area. HThe aortic arch side branches, visceral arteries and renal arteries remained patent after treatment. The recovery was uneventful, and she was discharged the day after the intervention. At 6- and 12-month follow-up, the patient remained asymptomatic, the true lumen volume increased and all side branches remained patent. CONCLUSION: We present a case of the use of a multilayer stent for acute type B aortic dissection. This technique allows to treat the whole dissection with low risk of paraplegia or side branch occlusion. Long-term results of ongoing clinical studies should confirm the place of the multilayer stent as a treatment option for type B aortic dissection.

The Capabilities of Boltzmann Machines to Detect and Reconstruct Ising System's Configurations from a Given Temperature.

The restricted Boltzmann machine (RBM) is a generative neural network that can learn in an unsupervised way. This machine has been proven to help understand complex systems, using its ability to generate samples of the system with the same observed distribution. In this work, an Ising system is simulated, creating configurations via Monte Carlo sampling and then using them to train RBMs at different temperatures. Then, 1. the ability of the machine to reconstruct system configurations and 2. its ability to be used as a detector of configurations at specific temperatures are evaluated. The results indicate that the RBM reconstructs configurations following a distribution similar to the original one, but only when the system is in a disordered phase. In an ordered phase, the RBM faces levels of irreproducibility of the configurations in the presence of bimodality, even when the physical observables agree with the theoretical ones. On the other hand, independent of the phase of the system, the information embodied in the neural network weights is sufficient to discriminate whether the configurations come from a given temperature well. The learned representations of the RBM can discriminate system configurations at different temperatures, promising interesting applications in real systems that could help recognize crossover phenomena.

Radiographic evaluation of mandibular third molars: an ex vivo comparative study between multilayer and conventional panoramic radiography.

OBJECTIVES: To compare the multilayer panoramic radiography (MPAN) and conventional panoramic radiography (CPAN) in the evaluation of mandibular third molars using cone-beam computed tomography (CBCT) as a reference. METHODS: CPAN, MPAN, and CBCT scans from 33 dry human mandibles were acquired using the OP300 Maxio unit, totalizing 56 mandibular third molars to be evaluated. Three examiners evaluated each third molar according to their position, depth of impaction in the mandibular ramus, proximity between the dental root apexes and the mandibular canal, and the presence of radiographic signs of proximity to the mandibular canal. In addition, when there was a distance between the root apexes and the mandibular canal, it was measured. As a reference, these same parameters were assessed in the CBCT scans by a fourth examiner. For the statistical analysis, the weighted Kappa, Bland Altman, and Wilcoxon tests were performed (α = 0.05). RESULTS: The agreement between the assessments performed in the panoramic modalities with the CBCT ranged from 66.1% to 100.0% for the categorical variables. Overall, the agreement values of CPAN and MPAN with CBCT were similar. The distances between the dental root apex and the mandibular canal for both CPAN and MPAN were significantly underestimated compared to CBCT (p < 0.05). The intra- and interexaminer agreements of the examiners ranged from poor to almost perfect; in general, the agreements were higher in the evaluation performed in the MPAN than in the CPAN. CONCLUSIONS: The MPAN performs similarly to CPAN for evaluating mandibular third molars and their proximity relationship to the mandibular canal. CLINICAL RELEVANCE: Preoperative evaluation of lower mandibular third molars is usually performed using CPAN. Recently, a new tool, MPAN, was developed, which has not yet been tested for the evaluation of mandibular third molars and showed similar performance to CPAN in the present study. Future studies using MPAN are encouraged to evaluate other diagnostic tasks.

A Multi-Layer Feature Fusion Method for Few-Shot Image Classification.

In image classification, few-shot learning deals with recognizing visual categories from a few tagged examples. The degree of expressiveness of the encoded features in this scenario is a crucial question that needs to be addressed in the models being trained. Recent approaches have achieved encouraging results in improving few-shot models in deep learning, but designing a competitive and simple architecture is challenging, especially considering its requirement in many practical applications. This work proposes an improved few-shot model based on a multi-layer feature fusion (FMLF) method. The presented approach includes extended feature extraction and fusion mechanisms in the Convolutional Neural Network (CNN) backbone, as well as an effective metric to compute the divergences in the end. In order to evaluate the proposed method, a challenging visual classification problem, maize crop insect classification with specific pests and beneficial categories, is addressed, serving both as a test of our model and as a means to propose a novel dataset. Experiments were carried out to compare the results with ResNet50, VGG16, and MobileNetv2, used as feature extraction backbones, and the FMLF method demonstrated higher accuracy with fewer parameters. The proposed FMLF method improved accuracy scores by up to 3.62% in one-shot and 2.82% in five-shot classification tasks compared to a traditional backbone, which uses only global image features.

Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments.

At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart factory and Industry 4.0 a reality. Currently, most of the intelligence of smart cyber-physical systems is implemented in software. For this reason, in this work, we focused on the artificial intelligence software design of this technology, one of the most complex and critical. This research aimed to study and compare the performance of a multilayer perceptron artificial neural network designed for solving the problem of character recognition in three implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems. After training and testing the multilayer perceptron, training time and accuracy tests showed each technology has particular characteristics and performance. Nevertheless, the three technologies have a similar performance of 97% accuracy, despite a difference in the training time. The results show that the artificial intelligence embedded in fog technology is a promising alternative for developing smart cyber-physical systems.

Ballistic Behavior of Bioinspired Nacre-like Composites.

In this paper, the ballistic performance of a multilayered composite inspired by the structural characteristics of nacre is numerically investigated using finite element (FE) simulations. Nacre is a natural composite material found in the shells of some marine mollusks, which has remarkable toughness due to its hierarchical layered structure. The bioinspired nacre-like composites investigated here were made of five wavy aluminum alloy 7075-T651 (AA7075) layers composed of ~1.1-mm thick square tablets bonded together with toughened epoxy resin. Two composite configurations with continuous layers (either wavy or flat) were also studied. The ballistic performance of the composite plates was compared to that of a bulk monolithic AA7075 plate. The ballistic impact was simulated in the 300-600 m/s range using two types of spherical projectiles, i.e., rigid and elastoplastic. The results showed that the nacre plate exhibited improved ballistic performance compared to the bulk plate and the plates with continuous layers. The structural design of the nacre plate improved the ballistic performance by producing a more ductile failure and enabling localized energy absorption via the plastic deformation of the tablets and the globalized energy dissipation due to interface debonding and friction. All the plate configurations exhibited a better ballistic performance when impacted by an elastoplastic projectile compared to a rigid one, which is explained by the projectile plastic deformation absorbing some of the impact energy and the enlarged contact area between the projectile and the plates producing more energy absorption by the plates.

Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method.

The estimative of the leaf area using a nondestructive method is paramount for successive evaluations in the same plant with precision and speed, not requiring high-cost equipment. Thus, the objective of this work was to construct models to estimate leaf area using artificial neural network models (ANN) and regression and to compare which model is the most effective model for predicting leaf area in sesame culture. A total of 11,000 leaves of four sesame cultivars were collected. Then, the length (L) and leaf width (W), and the actual leaf area (LA) were quantified. For the ANN model, the parameters of the length and width of the leaf were used as input variables of the network, with hidden layers and leaf area as the desired output parameter. For the linear regression models, leaf dimensions were considered independent variables, and the actual leaf area was the dependent variable. The criteria for choosing the best models were: the lowest root of the mean squared error (RMSE), mean absolute error (MAE), and absolute mean percentage error (MAPE), and higher coefficients of determination (R2). Among the linear regression models, the equation yˆ=0.515+0.584*LW was considered the most indicated to estimate the leaf area of the sesame. In modeling with ANNs, the best results were found for model 2-3-1, with two input variables (L and W), three hidden variables, and an output variable (LA). The ANN model was more accurate than the regression models, recording the lowest errors and higher R2 in the training phase (RMSE: 0.0040; MAE: 0.0027; MAPE: 0.0587; and R2: 0.9834) and in the test phase (RMSE: 0.0106; MAE: 0.0029; MAPE: 0.0611; and R2: 0.9828). Thus, the ANN method is the most indicated and accurate for predicting the leaf area of the sesame.

Trends in Bioactive Multilayer Films: Perspectives in the Use of Polysaccharides, Proteins, and Carbohydrates with Natural Additives for Application in Food Packaging.

The harmful effects on the environment caused by the indiscriminate use of synthetic plastics and the inadequate management of post-consumer waste have given rise to efforts to redirect this consumption to bio-based economic models. In this sense, using biopolymers to produce materials is a reality for food packaging companies searching for technologies that allow these materials to compete with those from synthetic sources. This review paper focused on the recent trends in multilayer films with the perspective of using biopolymers and natural additives for application in food packaging. Firstly, the recent developments in the area were presented concisely. Then, the main biopolymers used (gelatin, chitosan, zein, polylactic acid) and main methods for multilayer film preparation were discussed, including the layer-by-layer, casting, compression, extrusion, and electrospinning methods. Furthermore, we highlighted the bioactive compounds and how they are inserted in the multilayer systems to form active biopolymeric food packaging. Furthermore, the advantages and drawbacks of multilayer packaging development are also discussed. Finally, the main trends and challenges in using multilayer systems are presented. Therefore, this review aims to bring updated information in an innovative approach to current research on food packaging materials, focusing on sustainable resources such as biopolymers and natural additives. In addition, it proposes viable production routes for improving the market competitiveness of biopolymer materials against synthetic materials.

How Does Neural Network Model Capacity Affect Photovoltaic Power Prediction? A Study Case.

The use of models capable of forecasting the production of photovoltaic (PV) energy is essential to guarantee the best possible integration of this energy source into traditional distribution grids. Long Short-Term Memory networks (LSTMs) are commonly used for this purpose, but their use may not be the better option due to their great computational complexity and slower inference and training time. Thus, in this work, we seek to evaluate the use of neural networks MLPs (Multilayer Perceptron), Recurrent Neural Networks (RNNs), and LSTMs, for the forecast of 5 min of photovoltaic energy production. Each iteration of the predictions uses the last 120 min of data collected from the PV system (power, irradiation, and PV cell temperature), measured from 2019 to mid-2022 in Maceió (Brazil). In addition, Bayesian hyperparameters optimization was used to obtain the best of each model and compare them on an equal footing. Results showed that the MLP performs satisfactorily, requiring much less time to train and forecast, indicating that they can be a better option when dealing with a very short-term forecast in specific contexts, for example, in systems with little computational resources.

An implementation of a multilayer network model for the Covid-19 pandemic: A Costa Rica study.

We present a numerical implementation for a multilayer network to model the transmission of Covid-19 or other diseases with a similar transmission mechanism. The model incorporates different contact types between individuals (household, social and sporadic networks) and includes an SEIR type model for the transmission of the virus. The algorithm described in this paper includes the main ideas of the model used to give public health authorities an additional tool for the decision-making process in Costa Rica by simulating extensive possible scenarios and projections. We include two simulations: a study of the effect of restrictions on the transmission of the virus and a Costa Rica case study that was shared with the Costa Rican health authorities.

Fatigue resistance of monolithic and multilayer zirconia crowns using veneer layering or CAD-on technique

Abstract This study aims to evaluate the fatigue resistance of monolithic zirconia (Yz) and multilayer ceramic structures using the CAD-on technique in different thicknesses. Fifty (N=50) standardized single crowns preparations were made in fiberglass-reinforced epoxy resin (NEMA grade G10), digitalized, and restorations were machined in CAD-CAM, composing 5 groups (n= 10): Control: 1.5 mm (milled zirconia framework + manual layered porcelain); Yz monolithic 1.5 mm; Yz monolithic 1.0 mm; CAD-on 1.5 mm; and CAD-on 1.0 mm (milled zirconia framework 0.5 mm thickness bonded by a low fuse ceramic to a milled lithium disilicate layer of 1.0 mm or 0.5 mm, respectively). The G10 bases were conditioned with 10% hydrofluoric acid; the crowns were air abraded with 110 μm alumina particles; and then luted onto each other with self-adhesive resin cement. A cyclic fatigue test was performed (initial load: 400N for 10,000 cycles, frequency of 20 Hz, step size of 200N) until failure, and the data was submitted to a survival statistical analysis. No failures were observed at Yz monolithic 1.5 mm. High and similar performance was observed for Cad-On groups and Yz monolithic 1.0 mm. The control group depicted the worst behavior. The Weibull modulus of CAD-on 1.5 mm was higher than the control while being similar to the other conditions. Both the monolithic systems and the CAD-on technique showed high and similar fatigue fracture behavior and survival rates, which were also higher than the control bilayer system. Both systems reduced the occurrence of delamination failures, making them suitable for clinical use.

Resumo Este estudo teve como objetivo avaliar o comportamento à fadiga de estruturas cerâmicas monolíticas de zircônia (Yz) e multicamadas utilizando a técnica CAD-on em diferentes espessuras. Cinquenta (N=50) preparos unitários padronizados foram confeccionados em resina epóxi reforçada com fibra de vidro (NEMA grau G10), digitalizados e as restaurações usinadas em CAD-CAM, compondo 5 grupos (n= 10): Controle: 1,5 mm (estrutura de zircônia fresada + porcelana estratificada manualmente); Yz monolítica 1,5 mm; Yz monolítica 1,0 mm; CAD-on em 1,5 mm; e CAD-on 1,0 mm (estrutura de zircônia fresada com 0,5 mm de espessura ligada por uma cerâmica de baixa fusão a uma camada de dissilicato de lítio fresado de 1,0 mm ou 0,5 mm, respectivamente). As bases do G10 foram condicionadas com ácido fluorídrico a 10%; as coroas foram jateadas com partículas de alumina de 110 μm; e então cimentadas uma sobre a outra com cimento resinoso autoadesivo. Foi realizado um teste de fadiga cíclica (carga inicial: 400N para 10.000 ciclos, frequência de 20 Hz, step de 200N) até a falha, e os dados foram submetidos a uma análise estatística de sobrevivência. Nenhuma falha foi observada para Yz monolítica de 1,5 mm. Desempenho alto e semelhante foi observado para os grupos Cad-On e Yz monolítica 1,0 mm. O grupo controle apresentou o pior comportamento. O módulo de Weibull do CAD-on 1,5 mm foi maior que o grupo controle, sendo semelhante às outras condições. Tanto os sistemas monolíticos quanto a técnica CAD-on apresentaram alto e semelhante desempenho mecânico e taxas de sobrevivência, que também foram superiores ao sistema bicamada de controle. Ambos os sistemas reduziram a ocorrência de falhas de delaminação, tornando-os adequados para uso clínico.

A novel methodology for optimal location of reactive compensation through deep neural networks.

The present investigation proposes a methodology for the optimal location of reactive compensation in an electrical power system (EPS) through deep neural networks for voltage profile improvement. One of the main parameters to consider regarding EPS reliability is the voltage profile, a parameter that can be affected due to unexpected increases in impedance and loads in the system that translate as overloads in the system and an increase in the number of users. A voltage profile below the minimum or above the maximum accepted in the regulations of each country puts at risk the correct operation of equipment connected to the electrical network and, in turn, can cause economic losses and human lives (e.g by not guaranteeing reliability for hospitals and similar institutions). Economically, one of the most viable alternatives for improving voltage profiles is reactive compensation which in itself is carried out through capacitor banks. Therefore, this work proposes to find the correct location of capacitor banks in an electrical power system (using IEEE 14, 30 and 118 bus-bars systems as cases of study). In each system, the highest reactive load is identified, thus three values for reactive compensation are established as 80%, 50% and 25% of this maximum. Then, with these values, power flows are generated by locating each one of the reactive compensators' possible values in each one of the bars of the system, hence generating a large number of training data so that finally the neural network is capable of providing a quantitative classification highlighting which compensation and in which bus-bar produces the best result. The result is assessed by applying a modified standard deviation which evaluates the separation of the voltage profiles from the ideal desired value of 1pu.

Stability of Non-Concentric, Multilayer, and Fully Aligned Porous MoS2 Nanotubes.

Nanotubes made of non-concentric and multiple small layers of porous MoS2 contain inner pores suitable for membrane applications. In this study, molecular dynamics simulations using reactive potentials were employed to estimate the stability of the nanotubes and how their stability compares to macroscopic single- (1L) and double-layer MoS2 flakes. The observed stability was explained in terms of several analyses that focused on the size of the area of full-covered layers, number of layers, polytype, and size of the holes in the 1L flakes. The reactive potential used in this work reproduced experimental results that have been previously reported, including the small dependency of the stability on the polytype, the formation of S-S bonds between inter- and intra-planes, and the limit of stability for two concentric rings forming a single ring-like flake.

Reconocimiento de rutas biosintéticas para semioquímicos mediante técnicas de aprendizaje de máquina

En este trabajo consideramos 148 semioquímicos reportados para la familia Scarabaeidae, cuya estructura química fue caracterizada empleando un conjunto de 200 descriptores moleculares de cinco clases distintas. La selección de los descriptores más discriminantes se realizó con tres técnicas: análisis de componentes principales, por cada clase de descriptores, bosques aleatorios y Boruta-Shap, aplicados al total de descriptores. A pesar de que las tres técnicas son conceptualmente diferentes, seleccionan un número de descriptores similar de cada clase. Propusimos una combinación de técnicas de aprendizaje de máquina para buscar un patrón estructural en el conjunto de semioquímicos y posteriormente realizar la clasificación de estos. El patrón se estableció a partir de la alta pertenencia de un subconjunto de estos metabolitos a los grupos que fueron obtenidos por un método de agrupamiento basado en lógica difusa, C-means; el patrón descubierto corresponde a las rutas biosintéticas por las cuales se obtienen biológicamente. Esta primera clasificación se corroboró con el empleo de mapas autoorganizados de Kohonen. Para clasificar aquellos semioquímicos cuya pertenencia a una ruta no quedaba claramente definida, construimos dos modelos de perceptrones multicapa, los cuales tuvieron un desempeño aceptable.

In this work we consider 148 semiochemicals reported for the family Scarabaeidae, whose chemical structure was characterized using a set of 200 molecular descriptors from five different classes. The selection of the most discriminating descriptors was carried out with three different techniques: Principal Component Analysis, for each class of descriptors, Random Forests and Boruta-Shap, applied to the total of descriptors. Although the three techniques are conceptually different, they select a similar number of descriptors from each class. We proposed a combination of machine learning techniques to search for a structural pattern in the set of semiochemicals and then perform their classification. The pattern was established from the high belonging of a subset of these metabolites to the groups that were obtained by a grouping method based on fuzzy C-means logic; the discovered pattern corresponds to the biosynthetic pathway by which they are obtained biologically. This first classification was corroborated with Kohonen's self-organizing maps. To classify those semiochemicals whose belonging to a biosynthetic pathway was not clearly defined, we built two models of Multilayer Perceptrons which had an acceptable performance.

Neste trabalho consideramos 148 semioquímicos reportados para a família Scarabaeidae, cuja estrutura química foi caracterizada usando um conjunto de 200 descritores moleculares de 5 classes diferentes. A seleção dos descritores mais discriminantes foi realizada com três técnicas diferentes: Análise de Componentes Principais, para cada classe de descritores, Florestas Aleatórias e Boruta-Shap, aplicadas a todos os descritores. Embora as três técnicas sejam conceitualmente diferentes, elas selecionaram um número semelhante de descritores de cada classe. Nós propusemos uma combinação de técnicas de aprendizado de máquina para buscar um padrão estrutural no conjunto de semioquímicos e então realizar sua classificação. O padrão foi estabelecido a partir da alta pertinência de um subconjunto desses metabólitos aos grupos que foram obtidos por um método de agrupamento baseado em lógica fuzzy, C-means; o padrão descoberto corresponde às rotas biossintéticas pelas quais eles são obtidos biologicamente. Essa primeira classificação foi corroborada com o uso dos mapas auto-organizados de Kohonen. Para classificar os semioquímicos cuja pertença a uma rota não foi claramente definida, construímos dois modelos de Perceptrons Multicamadas que tiveram um desempenho aceitável.

Automated Machine Learning: A Case Study of Genomic "Image-Based" Prediction in Maize Hybrids.

Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as "genomic images." In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP. Also, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.

Wavelet packet transform and multilayer perceptron to identify voices with a mild degree of vocal deviation / Transformada Wavelet packet y Perceptrón Multicapa para identificación de voces con grado leve de desvío vocal

Introduction. Laryngeal disorders are characterized by a change in the vibratory pattern of the vocal folds. This disorder may have an organic origin described by anatomical fold modification, or a functional origin caused by vocal abuse or misuse. The most common diagnostic methods are performed by invasive imaging features that cause patient discomfort. In addition, mild voice deviations do not stop the in-dividual from using their voices, which makes it difficult to identify the problem and increases the possibility of complications. Aim. For those reasons, the goal of the present paper was to develop a noninvasive alternative for the identification of voices with a mild degree of vocal deviation ap-plying the Wavelet Packet Transform (WPT) and Multilayer Perceptron (MLP), an Artificial Neural Network (ANN). Methods. A dataset of 74 audio files were used. Shannon energy and entropy mea-sures were extracted using the Daubechies 2 and Symlet 2 families and then the processing step was performed with the MLP ANN. Results. The Symlet 2 family was more efficient in its generalization, obtaining 99.75% and 99.56% accuracy by using Shannon energy and entropy measures, re-spectively. The Daubechies 2 family, however, obtained lower accuracy rates: 91.17% and 70.01%, respectively. Conclusion. The combination of WPT and MLP presented high accuracy for the identification of voices with a mild degree of vocal deviation

ntroducción. Los trastornos laríngeos se caracterizan por un cambio en el patrón vibratorio de los pliegues vocales. Este trastorno puede tener un origen orgánico, descrito como la modificación anatómica de los pliegues vocales, o de origen fun-cional, provocado por abuso o mal uso de la voz. Los métodos de diagnóstico más comunes se realizan mediante procedimientos invasivos que causan malestar al pa-ciente. Además, los desvíos vocales de grado leve no impiden que el individuo utilice la voz, lo que dificulta la identificación del problema y aumenta la posibilidad de complicaciones futuras.Objetivo. Por esas razones, el objetivo de esta investigación es desarrollar una he-rramienta alternativa, no invasiva para la identificación de voces con grado leve de desvío vocal aplicando Transformada Wavelet Packet (WPT) y la red neuronal artifi-cial del tipo Perceptrón Mutlicapa (PMC). Métodos. Fue utilizado un banco de datos con 78 voces. Fueron extraídas las me-didas de energía y entropía de Shannon usando las familias Daubechies 2 y Symlet 2 para después aplicar la red neuronal PMC. Resultados. La familia Symlet 2 fue más eficiente en su generalización, obteniendo un 99.75% y un 99.56% de precisión mediante el uso de medidas de energía y en-tropía de Shannon, respectivamente. La familia Daubechies 2, sin embargo, obtuvo menores índices de precisión: 91.17% y 70.01%, respectivamente. Conclusión. La combinación de WPT y PMC presentó alta precisión para la iden-tificación de voces con grado leve de desvío vocal

Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold.

The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and ß) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu's version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.

3D-Printed Quasi-Cylindrical Bragg Reflector to Boost the Gain and Directivity of cm- and mm-Wave Antennas.

We demonstrate a concept for a large enhancement of the directivity and gain of readily available cm- and mm-wave antennas, i.e., without altering any property of the antenna design. Our concept exploits the high reflectivity of a Bragg reflector composed of three bilayers made of transparent materials. The cavity has a triangular aperture in order to resemble the idea of a horn-like, highly directive antenna. Importantly, we report gain enhancements of more than 400% in relation to the gain of the antenna without the Bragg structure, accompanied by a highly directive radiation pattern. The proposed structure is cost-effective and easy to fabricate with 3D-printing. Our results are presented for frequencies within the conventional WiFi frequencies, based on IEEE 802.11 standards, thus, enabling easily implementation by non-experts and needing only to be placed around the antenna to improve the directivity and gain of the signal.

Effect of the Graded Silicon Content in SRN/SRO Multilayer Structures on the Si Nanocrystals and Si Nanopyramids Formation and Their Photoluminescence Response.

Two multilayer (ML) structures, composed of five layers of silicon-rich oxide (SRO) with different Si contents and a sixth layer of silicon-rich nitride (SRN), were deposited by low pressure chemical vapor deposition. These SRN/SRO MLs were thermally annealed at 1100 °C for 180 min in ambient N2 to induce the formation of Si nanostructures. For the first ML structure (MLA), the excess Si in each SRO layer was about 10.7 ± 0.6, 9.1 ± 0.4, 8.0 ± 0.2, 9.1 ± 0.3 and 9.7 ± 0.4 at.%, respectively. For the second ML structure (MLB), the excess Si was about 8.3 ± 0.2, 10.8 ± 0.4, 13.6 ± 1.2, 9.8 ± 0.4 and 8.7 ± 0.1 at.%, respectively. Si nanopyramids (Si-NPs) were formed in the SRO/Si substrate interface when the SRO layer with the highest excess silicon (10.7 at.%) was deposited next to the MLA substrate. The height, base and density of the Si-NPs was about 2-8 nm, 8-26 nm and ~6 × 1011 cm-2, respectively. In addition, Si nanocrystals (Si-ncs) with a mean size of between 3.95 ± 0.20 nm and 2.86 ± 0.81 nm were observed for the subsequent SRO layers. Meanwhile, Si-NPs were not observed when the excess Si in the SRO film next to the Si-substrate decreased to 8.3 ± 0.2 at.% (MLB), indicating that there existed a specific amount of excess Si for their formation. Si-ncs with mean size of 2.87 ± 0.73 nm and 3.72 ± 1.03 nm were observed for MLB, depending on the amount of excess Si in the SRO film. An enhanced photoluminescence (PL) emission (eight-fold more) was observed in MLA as compared to MLB due to the presence of the Si-NPs. Therefore, the influence of graded silicon content in SRN/SRO multilayer structures on the formation of Si-NPs and Si-ncs, and their relation to the PL emission, was analyzed.