The aim of the present research is to measure the degree of strategic effectiveness (fahs) in the development of grammar skills of high school students in the Kingdom of Saudi Arabia and to achieve the goal of the researcher the researcher used the semi-experimental approach; On ten arbitrators specializing in grammar, morphology, curricula and methods of teaching the Arabic language. After confirming the validity of the guideline, the researcher prepared an achievement test consisting of (22) paragraphs of the type of short answer and multiple choice. The researcher trained a teacher to apply the strategy to the experimental research sample using the (screening) component of (30) students, and the same parameter taught the control sampl

Flow-production systems whose pieces are connected in a row may not have maintenance scheduling procedures fixed because problems occur at different times (electricity plants, cement plants, water desalination plants). Contemporary software and artificial intelligence (AI) technologies are used to fulfill the research objectives by developing a predictive maintenance program. The data of the fifth thermal unit of the power station for the electricity of Al Dora/Baghdad are used in this study. Three stages of research were conducted. First, missing data without temporal sequences were processed. The data were filled using time series hour after hour and the times were filled as system working hours, making the volume of the data relativel

The study aims to evaluate sustainable traffic management strategies for congested intersections in medium-sized Iraqi cities, with a focus on Al-Sa’a Intersection and Al-Jari Street in Hit City. These nodes face severe traffic congestion, delays, and infrastructure limitations that compromise urban mobility and sustainability. A multi-criteria evaluation (MCE) framework was employed to analyze three categories of interventions—engineering, planning, and administrative—based on five weighted criteria: traffic efficiency (40%), delay reduction (25%), cost (20%), environmental impact (10%), and social acceptance (5%). The methodology combined field data collection (traffic counts, travel time, and delays), GIS-based spatial analysis, an

Resumen Introducción Debido a su exigente entorno académico, el dolor lumbar crónico (DLC) es un trastorno musculoesquelético común entre los estudiantes de medicina, lo cual es particularmente preocupante. El tabaquismo es un factor de riesgo conocido por causar diversos problemas de salud y se ha asociado con el DLC, pero la relación específica entre el tabaquismo y el DLC aún no ha sido bien explorada. Objetivo Este estudio tiene como objetivo investigar la asociación entre el tabaquismo y el DLC en estudiantes de medicina en Irak. Diseño Se empleó un diseño descriptivo transversal que involucró entrevistas cara a cara con 200 estudiantes de medicina de 18 años o más. Los datos sobre características demográficas, comport

The internet has been a source of medical information, it has been used for online medical consultation (OMC). OMC is now offered by many providers internationally with diverse models and features. In OMC, consultations and treatments are available 24/7. The covid-19 pandemic across-the-board, many people unable to go to hospital or clinic because the spread of the virus. This paper tried to answer two research questions. The first one on how the OMC can help the patients during covid-19 pandemic. A literature review was conducted to answer the first research question. The second one on how to develop system in OMC related to covid-19 pandemic. The system was developed by Visual Studio 2019 using software object-oriented approach. O

Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s