Most vegetation’s are Land cover (LC) for the globe, and there is an increased attention to plants since they represent an element of balance to natural ecology and maintain the natural balance of rapid changes due to systematic and random human uses, including the subject of the current study (Bassia eriophora ) Which represent an essential part of the United Nations system for land cover classification (LCCS), developed by the World Food Organization (FAO) and the world Organization for environmental program (UNEP), to observe basic environmental elements with modern techniques. Although this plant is distributed all over Iraq, we found that this plant exists primarily in the middle

Iraq's water crisis represents one of the most pressing environmental and socioeconomic challenges facing the country today. This study examines the evolution of water resource problems in Iraq through a comprehensive historical comparison between the pre-2003 period under Saddam Hussein's regime and the post-2003 era following the U.S.-led invasion and subsequent political transformation. The research employs a mixed-method approach, analyzing quantitative data on water flow rates, infrastructure development, and qualitative assessments of policy impacts across both periods. Key findings reveal that while the pre-2003 period was characterized by deliberate environmental destruction, particularly the draining of the Mesopotamian Marshes, an

Eight different Dichloro(bis{2-[1-(4-R-phenyl)-1H-1,2,3-triazol-4-yl-κN3]pyridine-κN})iron(II) compounds, 2–9, have been synthesised and characterised, where group R=CH3 (L2), OCH3 (L3), COOH (L4), F (L5), Cl (L6), CN (L7), H (L8) and CF3 (L9). The single crystal X-ray structure was determined for the L3 which was complemented with Density Functional Theory calculations for all complexes. The structure exhibits a distorted octahedral geometry, with the two triazole ligands coordinated to the iron centre positioned in the equatorial plane and the two chloro atoms in the axial positions. The values of the FeII/III redox couple, observed at ca. −0.3 V versus Fc/ Fc+ for complexes 2–9, varied over a very small potential range of 0.05 V.

Background: Hypothyroidism is a decrease in the production of the thyroid hormones and leads to gland dysfunction. Ashwagandha extract was used as an ayurvedic treatment and supposed to be as antihypothyroidism agent.

Objectives: to investigate the impact of ashwagandha (Ash) extract on propylthiouracil (PTU)-induced hypothyroidism in rats.

Subjects and Methods: The rats were divided into three groups, control group, PTU (hypothyroid) group (6mg/kg/day by oral route), PTU (6mg/kg/day by oral route) +Ash (50mg/kg/day by oral route) treated group. All treatment continued for

The purpose of the current work was to evaluate the effect of Radiation of Gamma on the superconducting characteristics of the compound PbBr2Ca1.9Sb0.1Cu3O8+δ utilizing a 137Cs source at doses of 10, 15, and 20MRad. Solid state reaction technology was used to prepare the samples. Before and after irradiation, X-ray diffraction (XRD) and superconductor properties were examined. Results indicated that the tetragonal structure of our chemical corresponds to the Pb-1223 phase with an increase in the ratio c/a as a result of gamma irradiation. (Tc (onset) ) and on set temperature Tc (offset)) were also dropping from 113 to the 85.6 K and 129.5 to 97 K, respectively, for a transition temperatu

Deep learning techniques are applied in many different industries for a variety of purposes. Deep learning-based item detection from aerial or terrestrial photographs has become a significant research area in recent years. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed.  A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles and classification probabilities for an image. In layman's terms, it is a technique for instantly identifying and recognizing

The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. T

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct