In the last few years, the Internet of Things (IoT) is gaining remarkable attention in both academic and industrial worlds. The main goal of the IoT is laying on describing everyday objects with different capabilities in an interconnected fashion to the Internet to share resources and to carry out the assigned tasks. Most of the IoT objects are heterogeneous in terms of the amount of energy, processing ability, memory storage, etc. However, one of the most important challenges facing the IoT networks is the energy-efficient task allocation. An efficient task allocation protocol in the IoT network should ensure the fair and efficient distribution of resources for all objects to collaborate dynamically with limited energy. The canonic

Groundwater is an important source of fresh water especially in countries having a decrease in or no surface water; therefore itis essential to assess the quality of groundwater and find the possibility of its use in different purposes (domestic; agricultural; animal; and other purposes). In this paper samples from 66 wells lying in different places in Baghdad city were used to determine 13 water parameters, to find the quality of groundwater and evaluate the possibility of using it for human, animal and irrigation by calculating WQI, SAR, RSC and Na% and TDS indicators. WQI results showed that the groundwater in all wells are not qualified for human use, while SAR and RSC indicated that most samples are suitable for irrigation use, and

Background: A noninvasive method of determining the severity of coronary artery disease (CAD) is the use of cardiac biomarkers. This study aimed to investigate the use of serum levels of high-sensitivity cardiac troponin I (hs-cTnI) and N-terminal pro–B-type natriuretic peptide (NT-proBNP) in determining CAD severity.Methods: This cross-sectional study included 125 participants categorized by coronary angiographic results into 3 groups: group I (no or < 50% stenosis), group II (1 or 2 vessels with ≥ 50% stenosis), and group III (left main stem or ≥ 3 vessels with ≥ 50% obstruction). Serum hs-cTnI and NT-proBNP levels were measured by enzyme-linked immunosorbent assay. Data were analyzed using SPSS version 25.0 and described as pe

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

     The Non-thermal plasma has a wide range of applications in different areas. In this study, the oxidation of a model fuel containing organo-sulfur compounds (benzothiophene and dibenzothiophene) was investigated using plasma. The process involved ozone generation through dielectric barrier discharge, followed by extraction using acetonitrile. The results demonstrated the efficient oxidation and removal of dibenzothiophene and benzothiophene by non-thermal plasma. The desulfurization efficiency reached 93.78% under the optimum conditions, including a voltage of 11Kv, temperature of 50ºC, duration of 4 hours, and a flow rate of 75 ml/min.