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Research shows that Attention Deficit Hyperactivity Disorder (ADHD) is related to a disorder in brain networks. The purpose of this study is to use an effective connectivity measure and graph theory to examine the impairments of brain connectivity in ADHD. Weighted directed graphs based on electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children were constructed. The edges between two nodes (electrodes) were calculated by Phase Transfer Entropy (PTE). PTE is calculated for five frequency bands: delta, theta, alpha, beta, and gamma. The graph theory measures were divided into two categories: global and local. Statistical analysis with global measures indicates that in children with ADHD, the segregation of brain connectivity increases while the integration of the brain connectivity decreases compared to healthy children. These brain network differences were identified in the delta and theta frequency bands. The classification accuracy of 89.4% is obtained for both in-degree and strength measures in the theta band. Our result indicated local graph measures classified ADHD and healthy subjects with accuracy of 91.2 and 90% in theta and delta bands, respectively. Our analysis may provide a new understanding of the differences in the EEG brain network of children with ADHD and healthy children.

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 Kd values ranging from 2.21 to 4.31 L/kg, whereas Fe ranged from 3.32 to 5.53 L/kg.

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 (NH3) 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 µg/L were above the 5.0 µg/L guideline, while concentration guidelines for both Fe and NH3 were easily met post-upgrade in 2020. Several actions are recommended to ensure adequate WSP operation including dredging, aeration, and installing macrophytes capable of As uptake.

Breast cancer continues to be a widespread health concern all over the world. Mammography is an important method in the early detection of breast abnormalities. In recent years, using an automatic Computer-Aided Detection (CAD) system based on image processing techniques has been a more reliable interpretation in the illustration of breast distortion. In this study, a fully process-integrated approach with developing a CAD system is presented for the detection of breast masses based on texture description, spectral clustering, and Support Vector Machine (SVM). To this end, breast Regions of Interest (ROIs) are automatically detected from digital mammograms via gray-scale enhancement and data cleansing. The ROIs are segmented as labeled multi-sectional patterns using spectral clustering by the means of intensity descriptors relying on the region's histogram and texture descriptors based on the Gray Level Co-occurrence Matrix (GLCM). In the next step, shape and probabilistic features are derived from the segmented sections and given to the Genetic Algorithm (GA) to do the feature selection. The optimal feature vector comprising a fusion of selected shape and probabilistic features is submitted to linear kernel SVM for robust and reliable classification of mass tissues from the non-mass. Linear discrimination analysis (LDA) is also performed to ascertain the significance of the nominated feature space. The classification results of the proposed approach are presented by sensitivity, specificity, and accuracy measures, which are 89.5%, 91.2%, and 90%, respectively.

Directed information flow between brain regions might be disrupted in children with Attention Deficit Hyperactivity Disorder (ADHD) which is related to the behavioral characteristics of ADHD. This paper aims to investigate the different information pathways of brain networks in children with ADHD in comparison with healthy subjects. EEG recordings were obtained from 61 children with ADHD and 60 healthy children without neurological disorders during attentional visual task. Effective connectivity among all scalp channels was calculated using directed phase transfer entropy (dPTE) for delta, theta, alpha, beta, and lower-gamma frequency bands. Group differences were evaluated using permutation tests in connectivity between regions. Significant posterior to anterior patterns of information flow in theta frequency bands were found in healthy subjects (p-value

measuring human body temperature has always been important in monitoring people’s health; High body temperature is especially dangerous in infants and also indicates an infection or disease. Over time, different thermometers have been made, each with different accuracy according to the manufacturing technology. Also, the advancement of technology in data transmission has caused the transfer of data such as vital signs, and the possibility of updating this data to be considered. In this research, thermometry has been designed and built with online monitoring and SMS alert. This device has two separate circuits that the two circuits communicate with each other by the radio receiver and transmitter module. The thermometer sensor used in this research is MAX30205, which measures body temperature with high accuracy and it’s in the first circuit. The sensor is controlled by a microcontroller on the UNO Arduino board and the measured temperature is sent to the second circuit by a radio transceiver. In the second circuit, the temperature sent by the receiver is received and displayed on the display. Also, in the construction of this device, an SMS module was used so that if the temperature exceeds the normal threshold of human body temperature, the second circuit will send a warning message to the mobile number defined for the device. The performance of the device was tested on 7 samples and compared with the results of a reference digital thermometer. The average difference between the recorded temperatures and the temperature calculated by the reference thermometer was less than 0.1 ° C.

Electrocardiogram (ECG) is one of the most important tools to diagnose the heart health. Patients with arrhythmias diagnosed by ECG are commonly seen in clinical practice. Incorrectly interpreted ECGs might result in inappropriate clinical decisions. It is necessary to reduce physicians' pressure and increase accuracy in diagnostic and therapeutic processes with automatic diagnosis of various cardiac disorders and arrhythmias. The following article suggests classifying the different types of cardiac arrhythmias from ECG signals. The arrhythmia classification is based on the morphological features of the signal and features extracted by a Discrete Wavelet Transform (DWT). The Artificial Neural Network (ANN) has been used as a classifier. MIT-BIH ECG arrhythmia database acquired from pysionet.org is used for analysis purposes. ANN is used to classify four groups, including three types of arrhythmias, Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), and Normal group (N). The K-fold cross-validation method results show that using morphological features and wavelet coefficients together, the average classification accuracy is 93%. Average classification accuracy is 83% when only the signal's morphological features are used, and if only the wavelet coefficients are used, it is 88%. In this way, the highest classification accuracy is obtained using both categories of features.

The study of brain connectivity is one of the recent and growing concepts which has been very important as exploring brain function discrepancy between healthy and patient ones. In this study, a non-invasive Electroencephalography (EEG) method is used to record the electrical activity of the brain. EEG signals are recorded of individuals at resting-state and performing mathematical calculations. According to the quality of performing mathematical calculations, 36 study participants were classified into two groups of bad and good counters. To analyze brain connectivity, two approaches are considered. The first analysis is comparing the performance of good and bad counters, and the next step involved comparing rest and doing task patterns among all cases. In this regard, graph theory has been used. These mentioned graphs are constructed using Pearson correlation analysis and by using different thresholds, the graphs are sparsed. At each threshold, graph features are extracted. In the continuation of the research, after extracting features such as global efficiency, local efficiency, clustering coefficient, transitivity, and modularity, a non-parametric statistical test is used for evaluation. The results show a significant difference in the features of global efficiency and modularity at the 0.65 threshold in both resting and performing mental tasks in all individuals. Moreover, there is no statistically significant difference in thresholds when comparing good and bad counters features at resting-state.

considering the prevalence of hypertension in communities, the measurement of blood pressure is one of the most important parameters in the field of health. The first biomedical parameter taken from clients in health centers is blood pressure changes. With the advancement of technology in the field of medical engineering, analog blood pressure monitors have gradually been replaced by digital devices, such that the digital blood pressure monitors are used today. In this study, a system was designed and built that measures changes in blood pressure non-invasively and by an oscillometric method with high sampling frequency. In this research, first, the blood pressure hardware was designed and manufactured by instrumentation amplifier circuits to obtain the highest quality pressure signal. Then, in order to receive the signal and send digital data, the sampling frequency of one kHz was set in the microcontroller software to digitize the analog signal with high accuracy. Finally, the blood pressure signals were recorded with very low noise, and also systolic and diastolic pressures were calculated with appropriate quality in LabVIEW software. The performance of the device was tested on 20 subjects and the results were compared with valid digital sphygmomanometer. The number of heartbeats was also displayed according to the processing performed on the signal of changes in blood pressure. The system designed in this study provides the possibility of research on cardiovascular diseases by considering the record of the signal of high sampling frequency blood pressure changes and storing its data on the computer.

Pharmaceutical waste has attracted significant attention in the past two decades due to the current high consumption of pharmaceuticals together with the development of reliable detection technologies. In order to acquire better understanding on pharmaceuticals removal in biological processes, the treatment of synthetic wastewater containing diclofenac (DFN) and ibuprofen (IBU), two of the most commonly prescribed medicines worldwide, was studied using a moving bed biofilm reactor (MBBR). An 8.5-L aerobic MBBR with Kaldnes packing filling ratio of 40% was designed. The controlled parameters were pH within neutral range, temperature of 37 °C, mixed liquor suspended solids (MLSS) of 2100 mg/L, and attached growth equal to 1300 mg/L. Tests were conducted for four different initial pharmaceuticals concentrations: 2, 4, 7 and 10 mg/L, two hydraulic retention times (HRT): 5 and 10 h and two chemical oxygen demands (COD): 500 and 1000 mg/L. Results demonstrated that generally, DFN had higher removal percentage than IBU in the MBBR. At HRT = 10 h, DFN removal was between 30.83 and 66.01%, while it was 11.33 and 37.33% for IBU. At HRT = 5 h, DFN and IBU removal were respectively between 31.10–65.33 and 0–35.10%. It can be concluded that HRT = 5 h is the optimal time for DFN, while 10 h HRT promises noticeably better IBU removal. Furthermore, results revealed that DFN is better removed in the lower COD, while IBU showed better removal in the higher COD. Finally, the study on their toxicity reveals that pharmaceuticals exert slightly negative effect on COD removal.

Color Vision Deficiency (CVD) is one of the most common types of vision deficiency. People with CVD have difficulty seeing color spectra depending on what types of retina photoreceptors are impaired. In this paper, the Ishihara test with 38 plates was used to examine the Electroencephalogram (EEG) of ten subjects with CVD plus ten healthy individuals. The recording was performed according to the 10–20 international system. The C-based software was programmed so that subjects could select the number or path in each test plate in the software options while recording EEG. Frequency features in different frequency bands were extracted from the EEG signals of the two groups during the Ishihara test. Statistically significant differences (P

Background:: Results of various studies suggest that the hypertrophic and keloid scars are highly prevalent in the general population and are irritating both physically and mentally. Objective:: Considering the variety of existing therapies, intense pulsed light (IPL) method along with corticosteroid injection was evaluated in treating these scars. Materials and Methods:: 86 subjects were included in this clinical trial. Eight sessions of therapeutic intervention were done with IPL along with corticosteroid intralesional injection using 450 to 1200 NM filter, Fluence 30-40 J/cm2, pulse duration of 2.1-10 ms and palsed delay 10-40 ms with an interval of three weeks. To specify the recovery consequences and complication rate and to determine features of the lesion, the criteria specified in the study of Eroll and Vancouver scar scale were used. Results:: The level of clinical improvement, color improvement and scar height was 89.1%, 88.8% and 89.1% respectively. The incidence of complications (1 telangiectasia case, 7 hyperpigmentation cases and 2 atrophy cases) following treatment with IPL was 11.6%. Moreover, the participants’ satisfaction with IPL method was 88.8%. Conclusions:: This study revealed that a combined therapy (intralesional corticosteroid injection + IPL) increases the recovery level of hypertrophic and keloid scars. It was also demonstrated that this method had no significant side effect and patients were highly satisfied with this method.

Recently, narrow-band ultraviolet B (NB-UVB) and topical immunomodulators have been successfully used in the treatment of vitiligo.To determine whether the combination of pimecrolimus with NB-UVB accelerates the response time and/or improves the degree of response in patients with vitiligo.Sixty-eight patients with vitiligo enrolled in this randomized, double-blind, placebo-controlled study. The patients were randomized into two groups and treated with NB-UVB plus either pimecrolimus or placebo for 3 months. Tri-weekly radiation was started at 280 mJ/cm(2), with 15% increments for each subsequent treatment until erythema was reported or a maximum of 800 mJ/cm was achieved. At baseline and 6 and 12 weeks after commencement of therapy, vitiliginous patches were measured.Fifty patients completed the 3-month study. No significant side effects except self-limited erythema and pruritus were observed. After 12 weeks of treatment, repigmentation of the facial lesions was higher in patients treated with combined pimecrolimus and NB-UVB compared with the placebo plus NB-UVB group (64.3 vs 25.1%) (p0.05%). There was no statistically significant difference in the repigmentation rate between the two groups on other body areas.On the face, NB-UVB works better if combined with pimecrolimus 1% cream rather than used alone.