Determinants of Early Marriage from Married Girls' Perspectives in Iranian Setting: A Qualitative Study

Early marriage is a worldwide problem associated with a range of health and social consequences for teenage girls. Designing effective health interventions for managing early marriage needs to apply the community-based approaches. However, it has received less attention from policymakers and health researchers in Iran. Therefore, the current study aimed to explore determinants of early marriage from married girls' perspectives. The study was conducted from May 2013 to January 2015 in Ahvaz, Iran. A purposeful sampling method was used to select fifteen eligible participants. Data were collected through face-to-face, semistructured interviews and were analyzed using the conventional content analysis approach. Three categories emerged from the qualitative data including "family structure," "Low autonomy in decision-making," and "response to needs." According to the results, although the participants were not ready to get married and intended to postpone their marriage, multiple factors such as individual and contextual factors propelled them to early marriage. Given that early marriage is a multifactorial problem, health care providers should consider a multidimensional approach to support and empower these vulnerable girls. � 2016 Simin Montazeri et al

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach

Data Availability Statement: Tabriz University’s ethics committee in Tabriz, Iran. Data access is private and not publicly available.Copyright © 2023 by the authors. A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A multi-level driver tiredness diagnostic database based on physiological signals including ECG, EEG, EMG, and respiratory effort was developed for this aim. The EEG signal was used for processing and other recorded signals were used to confirm the driver’s fatigue so that fatigue was not confirmed based on self-report questionnaires. A customized architecture based on adversarial generative networks and convolutional neural networks (end-to-end) was utilized to select/extract features and classify different levels of fatigue. In the customized architecture, with the objective of eliminating uncertainty, type 2 fuzzy sets were used instead of activation functions such as Relu and Leaky Relu, and the performance of each was investigated. The final accuracy obtained in the three scenarios considered, two-level, three-level, and five-level, were 96.8%, 95.1%, and 89.1%, respectively. Given the suggested model’s optimal performance, which can identify five various levels of driver fatigue with high accuracy, it can be employed in practical applications of driver fatigue to warn drivers.This research received no external funding

Dean flow-coupled inertial focusing in curved channels

Passive particle focusing based on inertial microfluidics was recently introduced as a high-throughput alternative to active focusing methods that require an external force field to manipulate particles. In inertial microfluidics, dominant inertial forces cause particles to move across streamlines and occupy equilibrium positions along the faces of walls in flows through straight micro channels. In this study, we systematically analyzed the addition of secondary Dean forces by introducing curvature and show how randomly distributed particles entering a simple u-shaped curved channel are focused to a fixed lateral position exiting the curvature. We found the lateral particle focusing position to be fixed and largely independent of radius of curvature and whether particles entering the curvature are pre-focused (at equilibrium) or randomly distributed. Unlike focusing in straight channels, where focusing typically is limited to channel cross-sections in the range of particle size to create single focusing point, we report here particle focusing in a large cross-section area (channel aspect ratio 1: 10). Furthermore, we describe a simple u-shaped curved channel, with single inlet and four outlets, for filtration applications. We demonstrate continuous focusing and filtration of 10 mu m particles (with > 90% filtration efficiency) from a suspension mixture at throughputs several orders of magnitude higher than flow through straight channels (volume flow rate of 4.25ml/min). Finally, as an example of high throughput cell processing application, white blood cells were continuously processed with a filtration efficiency of 78% with maintained high viability. We expect the study will aid in the fundamental understanding of flow through curved channels and open the door for the development of a whole set of bio-analytical applications

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network

Data Availability Statement: The EEG dataset is available online at https://mindbigdata.com/opendb/ (Accessed on 12 February 2020).Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of perception occurring by vision on brain activity. In this paper, the impact of arithmetic concepts on vision-related brain records has been considered and an efficient convolutional neural network-based generative adversarial network (CNN-GAN) is proposed to map the electroencephalogram (EEG) to salient parts of the image stimuli. The first part of the proposed network consists of depth-wise one-dimensional convolution layers to classify the brain signals into 10 different categories according to Modified National Institute of Standards and Technology (MNIST) image digits. The output of the CNN part is fed forward to a fine-tuned GAN in the proposed model. The performance of the proposed CNN part is evaluated via the visually provoked 14-channel MindBigData recorded by David Vivancos, corresponding to images of 10 digits. An average accuracy of 95.4% is obtained for the CNN part for classification. The performance of the proposed CNN-GAN is evaluated based on saliency metrics of SSIM and CC equal to 92.9% and 97.28%, respectively. Furthermore, the EEG-based reconstruction of MNIST digits is accomplished by transferring and tuning the improved CNN-GAN’s trained weights.This research received no external funding

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks

Data Availability Statement: In this research, experimental data were not recorded.Copyright © 2024 by the authors. Nowadays, the automatic detection of driver fatigue has become one of the important measures to prevent traffic accidents. For this purpose, a lot of research has been conducted in this field in recent years. However, the diagnosis of fatigue in recent research is binary and has no operational capability. This research presents a multi-class driver fatigue detection system based on electroencephalography (EEG) signals using deep learning networks. In the proposed system, a standard driving simulator has been designed, and a database has been collected based on the recording of EEG signals from 20 participants in five different classes of fatigue. In addition to self-report questionnaires, changes in physiological patterns are used to confirm the various stages of weariness in the suggested model. To pre-process and process the signal, a combination of generative adversarial networks (GAN) and graph convolutional networks (GCN) has been used. The proposed deep model includes five convolutional graph layers, one dense layer, and one fully connected layer. The accuracy obtained for the proposed model is 99%, 97%, 96%, and 91%, respectively, for the four different considered practical cases. The proposed model is compared to one developed through recent methods and research and has a promising performance.This research received no external funding

Size and shape evolution of embedded single-crystal αα-Fe nanowires

The size and shape evolution of embedded ferromagnetic αα-Fe nanowires is discussed. The αα-Fe nanowires are formed by pulsed-laser deposition of La0.5Sr0.5FeO3−xLa0.5Sr0.5FeO3−x on single-crystal SrTiO3SrTiO3 (001) substrate in reducing atmosphere. The average diameter of the nanowires increases from d ≈ 4d≈4 to 50 nm as the growth temperature increases from T = 560T=560 to 840 °C. Their in-plane shape evolves from circular to octahedral and square shape with [110] facets dominating as the growth temperature increases. A fitting to a theoretical calculation shows that the circular shape is stable when the diameter of the nanowires is smaller than 8 nm.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87835/2/203110_1.pd

Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system

EEG-based functional connectivity analysis of brain abnormalities: A systematic review study

Several imaging modalities and many signal recording techniques have been used to study the brain activities. Significant advancements in medical device technologies like electroencephalographs have provided conditions for recording neural information with high temporal resolution. These recordings can be used to calculate the connections between different brain areas. It has been proved that brain abnormalities affect the brain activity in different brain regions and the connectivity patterns between them are changed as a result. This paper studies the electroencephalogram (EEG) functional connectivity methods and investigates the impacts of brain abnormalities on brain functional connectivities. The effects of different brain abnormalities including stroke, depression, emotional disorders, epilepsy, attention deficit hyperactivity disorder (ADHD), autism, and Alzheimer's disease on functional connectivity of the EEG recordings have been explored in this study. The EEG-based metrics and network properties of different brain abnormalities have been discussed to present a comparison of the connectivities affected by each abnormality. Also, the effects of therapy and medical intake on the EEG functional connectivity network of each abnormality have been reviewed.This research received no external funding

Optimization of performance and emission of compression ignition engine fueled with propylene glycol and biodiesel–diesel blends using artificialintelligence method of ANN-GA-RSM

The present study proposes the hybrid machine learning algorithm of artificial neural network-genetic algorithm-response surface methodology (ANN-GA-RSM) to modelthe performance and the emissionsof a single cylinder diesel engine fueled by diesel and propylene glycol additive. The evaluations areperformed using the correlation coefficient (CC), and the root mean square error (RMSE) values. The best model for prediction of the dependent variables is reported ANN-GA with the RMSE values of 0.0398, 0.0368, 0.0529, 0.0354, 0.0509 and 0.0409 and CC 0.988, 0.987, 0.977, 0.994, 0.984, 0.990, respectively for brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), CO, CO2, NOx and SO2. The proposed hybrid model reduces BSFC, NOx, and CO by −30.82%, 21.32%, and 11.32%, respectively. The model also increases the engine efficiency and CO2 emission by 17.29% and 31.05%, respectively, compared to a single RSM in the optimized level of independent variables (69% of biodiesel's oxygen content and 32% of the oxygen content of propylene glycol)