Search

Objective: This study aimed to investigate differences in brain networks between healthy children and children with attention deficit hyperactivity disorder (ADHD) during an attention test. Method: To fulfill this, we constructed weighted directed graphs based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children with the same age. Nodes of graphs were 19 EEG electrodes, and the edges were phase transfer entropy (PTE) between each pair of electrodes. PTE is a measure for directed connectivity that determines the effective relationship between signals in linear and nonlinear coupling. Connectivity graphs of each sample were constructed using PTE in the five frequency bands as follows: delta, theta, alpha, beta, and gamma. To investigate the differences in connectivity strength of each node after the sparsification process with two values (0.5 and 0.25), the permutation statistical test was used with the statistical significance level of p

Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods. Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA). Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy. Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

Wastewater stabilization ponds (WSPs) are commonly used to reduce wastewater metal(loid) concentrations from drinking water treatment plants (DWTPs) through sedimentation. However, this results in increased sediment concentrations that can be released back into the overlying water. Thus, our goal was to evaluate the WSP metal(loid)s occurrence and leaching potential. Currently, a Saskatchewan based DWTP's WSP system was investigated given historically elevated effluent As and Fe concentrations. The WSP consists of five ponds that were sampled on six occasions in 2019 and 2020. In addition, sediments were used in laboratory-based experiments to determine their leaching potential. Overall, the sediments were found to contain elevated concentrations of As and Fe of 25 to 400 mg/kg and 10 000 to 45 000 mg/kg, respectively. Leaching experiments indicated that the pond sediments could potentially release As and Fe with log [K.sub.d] values ranging from 2.21 to 4.31 L/kg, whereas Fe ranged from 3.32 to 5.53 L/kg. Key words: arsenic, iron, wastewater stabilization ponds, drinking water treatment, sediment leaching. Les bassins de stabilisation des eaux usees (BSEU) sont couramment utilises pour reduire les concentrations de metaux (metalloides) provenant des usines de production d'eau potable (UEP) par sedimentation. Toutefois, cela entraine une augmentation des concentrations de sediments qui peuvent etre rejetes dans l'eau sus-jacente. Ainsi, l'objectif des auteurs etait d'evaluer la presence de metaux (metalloides) dans les BSEU et leur potentiel de lixiviation. A l'heure actuelle, un systeme de BSEU d'une UEP en Saskatchewan a fait l'objet d'une etude, compte tenu des concentrations historiquement elevees d'As et de Fe dans les effluents. Le BSEU comprend cinq bassins qui ont ete echantillonnes a six reprises en 2019 et 2020. De plus, les sediments ont ete utilises dans des experiences en laboratoire pour determiner leur potentiel de lixiviation. Dans l'ensemble, les sediments contenaient des concentrations elevees d'As et de Fe de 25 a 400 et de 10 000 a 45 000 mg/kg, respectivement. Les experiences de lixiviation ont indique que les sediments du bassin pourraient rejeter de l'As et du Fe avec des valeurs de log [K.sub.d] ([K.sub.d], rapport de distribution) allant de 2,21 a 4,31 L/kg pour l'As, tandis que Fe variait de 3,32 a 5,53 L/kg. Mots-cles : arsenic (As), fer (Fe), bassins de stabilisation des eaux usees (BSEU), production de l'eau potable, lixiviation des sediments., Introduction Raw water sources for drinking water treatment plants (DWTPs) include surface waters and groundwaters that may contain elevated concentrations of heavy metals and metalloids. Of most interest in the [...]

Drinking water treatment plants (DWTPs) produce wastewaters with elevated concentrations of heavy metals, metalloids, ammonia, and other contaminants. These wastewaters require treatment via processes including wastewater stabilization ponds (WSPs). This study assessed the arsenic (As), iron (Fe), and ammonia (N[H.sub.3]) concentrations in a Saskatchewan DWTP WSP system of five sequential ponds. Drone imaging combined with flow and depth data was used to estimate retention times which increased from 7-9 to 8-10 days after the DWTP upgrade. Concentration trends showed Fe decreased from Pond 1 to 3 and increased in Ponds 3 and 5, while As decreased from Pond 1 to 5. Average effluent As concentrations of 10.6 [micro]g/L were above the 5.0 [micro]g/L guideline, while concentration guidelines for both Fe and N[H.sub.3] were easily met postupgrade in 2020. Several actions are recommended to ensure adequate WSP operation including dredging, aeration, and installing macrophytes capable of As uptake. Key words: arsenic, iron, wastewater stabilization ponds, drinking water treatment, residual wastewaters. Les usines de production d'eau potable (UPEP) rejettent des eaux usees contenant des concentrations elevees de metaux lourds, de metalloides, d'ammoniac et d'autres contaminants. Ces eaux usees doivent etre traitees au moyen de procedes, y compris les bassins de stabilisation des eaux usees (BSEU). Cette etude a evalue les concentrations d'arsenic (As), de fer (Fe) et d'ammoniac (N[H.sub.3]) dans un systeme UPEP BSEU de cinq etangs sequentiels en Saskatchewan. L'imagerie de drones, jumelee aux donnees de debit et de profondeur, a ete utilisee pour estimer les temps de retention qui sont passes de 7-9 a 8-10 jours apres la mise a niveau de l'UPEP. Les tendances en matiere de concentration ont montre que Fe diminuait du bassin 1 a 3 et augmentait dans les bassins 3 et 5, tandis que l'As diminuait du bassin 1 a 5. Les concentrations moyennes d'As dans l'effluent de 10,6 [micro]g/L etaient superieures a la recommandation de 5,0 [micro]g/L , les concentrations de Fe et de N[H.sub.3] selon les recommandations ont ete facilement atteintes apres la mise a niveau en 2020. Plusieurs mesures sont recommandees pour assurer un fonctionnement adequat des BSEU, y compris le dragage, l'aeration et l'installation de macrophytes capables d'absorber l'As. [Traduit par la Redaction] Mots-cles : arsenic, fer, bassins de stabilisation des eaux usees (BSEU), production d'eau potable, eaux usees residuelles., 1. Introduction Drinking water treatment plants (DWTPs) use a suite of treatment processes to improve the quality of source surface waters or groundwaters to meet relevant drinking water standards. As [...]