The aim of this research work is to study the effect of stabilizing gypseous soil, which covers vast areas in the middle, west and south parts of Iraq, using liquid asphalt on its strength properties to be used as a base course layer replacing the traditional materials of coarse aggregate and broken stones which are scarce at economical prices and hauling distances. Gypseous soil brought from Al-Ramadi City, west of Iraq, with gypsum content of 66.65%, medium curing cutback asphalt (MC-30), and hydrated lime are used in this study. The conducted tests on untreated and treated gypseous soil with different percentages of medium curing cutback asphalt (MC-30), water, and lime were: unconfined compression strength, and one dimensional confine

In this investigation, water-soluble N-Acetyl Cysteine Capped-Cadmium Telluride QDs (NAC/CdTe nanocrystals), utilizing N-acetyl cysteine as a stabilizer, were prepared to assess their potential in differentiating between DNA extracted from pathogenic bacteria (e.g. Escherichia coli isolated from urine specimen) and intact DNA (extracted from blood of healthy individuals) for biomedical sensing prospective. Following the optical characterization of the synthesized QDs, the XRD analysis illustrated the construction of NAC-CdTe-QDs with a grain size of 7.1 nm. The prepared NAC-CdTe-QDs exhibited higher PL emission features at of 550 nm and UV-Vis absorption peak at 300 nm. Additionally, the energy gap quantified via PL and UV–Vis were 2.2 eV

We manufactured the nanoparticles light emitting diode (NPs-LED) for organic and inorganic semiconductors to achieve electroluminescence (EL). The nanoparticles of Europium oxide(Eu₂O₃) were incorporated into the thin film layers of the organic compounds, poly(3,4,- ethylene dioxythiophene)/polystyrene sulfonic acid (PEDOT:PSS), N,N’–diphenyl-N,N’ –bis(3-methylphenyl)-1,1’-biphenyl 4,4’- diamine (poly TPD) and polymethyl methacrylate (PMMA), by the spin coating and with the help of the phase segregation method. The EL of NPs-LED, was study for the different bias voltages (20, 25, 30) V at the room temperature, from depending on the CIE 1931 color spaces and it was generated the white light at 20V, t

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an

Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and