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This study presents a new methodology for obtaining functional brain networks (FBNs) using multichannel scalp EEG recordings. The developed methodology extracts pair-wise phase synchrony between EEG electrodes to obtain FBNs at δ, θ, and α -bands and investigates their network properties in presence of seizure to detect multiple facets of functional integration and segregation in brain networks. Statistical analysis of the frequency-specific graph measures during seizure and non-seizure intervals reveals their highly discriminative ability between the two EEG states. It is also verified by performing the receiver operating characteristic (ROC) analysis. The results suggest that, for the majority of subjects, the FBNs during seizure intervals exhibit higher modularity and lower global efficiency compared to the FBNs during non-seizure intervals; meaning that during seizure activities the networks become more segregated and less aggregated. Some differences in the results obtained for different subjects can be attributed to the subject-specific nature of seizure networks and the type of epileptic seizure the subject has experienced. The results demonstrate the capacity of the proposed framework for studying different abnormal patterns in multichannel EEG signals.

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This study presents a methodology developed for estimating effective connectivity in brain networks (BNs) using multichannel scalp EEG recordings. The methodology uses transfer entropy as an information transfer measure to detect pair-wise directed information transfer between EEG signals within , , , and -bands. The developed methodology is then used to study the properties of directed BNs in children with attention-deficit hyperactivity disorder (ADHD) and compare them with that of the healthy controls using both statistical and receiver operating characteristic (ROC) analyses. The results indicate that directed information transfer between scalp EEG electrodes in the ADHD subjects differs significantly compared to the healthy ones. The results of the statistical and ROC analyses of frequency-specific graph measures demonstrate their highly discriminative ability between the two groups. Specifically, the graph measures extracted from the estimated directed BNs in the -band show the highest discrimination between the ADHD and control groups. These findings are in line with the fact that -band reflects active concentration, motor activity, and anxious mental states. The reported results show that the developed methodology has the capacity to be used for investigating patterns of directed BNs in neuropsychiatric disorders.

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Optimization of biofuel production from algal oil through utilizing a CaO-based catalyst was carried out in this study. The optimal point for the highest yield of the reactions was determined using machine learning. To implement the optimization task, and to make predictions, we used three different methods, including Quantile regression, Logistic regression, and Gradient Boosted Decision Trees. The regression problem includes the amount of Catalyst, Reaction time, and Methanol/oil as input features, and FAME (fatty acid methyl ester) yield is the single output. We tuned the boosted version of these models with their important hyper-parameters and selected their best combination. Then different standard metrics are employed to assess their performance of them. Considering R2 score, Quantile regression, Logistic regression, and Gradient Boosted Decision Trees have error rates of 0.934, 0.996, and 0.998, and with MAE, they have 1.94, 1.68, and 1.17 errors, respectively. Also, Considering MAPE 2.14×10-2, 1.89×10-2, and 1.29×10-2 values obtained. Gradient Boosting is selected as the most appropriate model finally. Furthermore, the optimal output value with the proposed approach is 97.50, with the input vector being (x1 = 153, x2 = 0.625, x3 = 20).

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Context Hydrogen fluoride (HF) is extensively present in environmental and industrial pollutants. It may harm the health of humans and animals. This work evaluated the adsorption of an (HF)n linear chain (n = 1, 2, 3, and 4) onto an AlP nanocage through ab initio calculations for the evaluation of its performance in sensing and monitoring (HF)n within aqueous and gaseous media. Methods The present work adopted density functional theory (DFT) at the 6–311 G (d, p) basis set to analyze (HF)n linear chain adsorption onto AlP nanocages with the B3LYP functional. This paper examined the adsorption energy, configuration optimization, work function, and charge transfer. In addition, the contributions of the HF linear chain size to electronic properties and adsorption energy were measured. The dimer form of HF on the surface of AlP nanocages was found to have the highest stability based on the adsorption energy values. Once (HF)n was adsorbed onto the nanocage, the HOMO-LUMO energy gap experienced a large reduction from 3.87 to 3.03 eV, enhancing electrical conductivity. In addition, AlP nanocages may serve in the sensing of (HF)n under multiple environmental pollutants.

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Identifying the gender, race, age, and stature of the target during the forensic inquiry is a critical stage in various events such as accidents, bombings, terrorism, wars, and disasters. In this paper, an application has been developed that uses hand X-rays to identify and determine gender for medical applications such as special cases where diagnosing the gender is difficult, like accidents in which the hand is amputated and unknown, severe burns, and in old skeletal structures using deep learning models. For comparative purposes, GoogLeNet and ResNet-18 were employed. Gender determination using hand X-rays yielded positive results. The accuracy of gender detection in the model GoogLeNet (validation, training, test, and total) is (76.67%, 96.68%, 53.33%, and 89.5%) respectively, while the accuracy of gender detection in the model ResNet-18 (validation, training, test, and total) are (80%, 99.29%, 87.5%, 94.63%) respectively. The ResNet-18 model was adopted as the best model for gender detection and determination because high results were obtained. Simulation results showed acceptable results with high accuracy in diagnosis, where the highest gender determination rate was obtained through hand X-ray analysis at 94.63%.