The leaf miners Pegoinya terbrans (Rondani) and P. bicolor (Wiedemann) (Diptera; Anthomyiidae) were newly recorded in Iraq. Host plants of these leaf miners and P. cunicularia (Rondani) were identified: P. bicolor was found to be monophagous, whereas P. terbrans and P. cunicularia were oligophagous . It was found that Cirsixim syriaca and Silybum marianum were more susceptible to P. terbrans than the other ones. Infectivity and severity of infestation were estimated for most susceptible weeds against P. terbrans and P.bicolor. These leaf miners. Have two generations a year.

The quality of groundwater should be improved by keeping safe water sources from contaminants in protective way by doing regular measuring and checkup before it supplied for usage. Private Wells do not receive the same services that wells supplying the public do. Well owners are responsible for protecting their drinking water. This work was carried out in Badra city, Iraq from December 2017 to May 2018, six wells water were investigated to determine the general characteristics of wells as well as studying the effect of environmental factors on the quality of water. The average of six wells were eleven parameters that is out of permissible limits were EC, Sal., Alk., TH, TDS, Na, Ca, Cl, SO₄, Fe, Zn (4402-5183 /cm, 2.76-3.9 ppt

Background: Dysfunction of sinoatrial node is a set of abnormal rhythms which are resulted from the sinoatrial node malfunction of the sinus node, the chief natural cardiac pacemaker. The common, and occasionally, the single method for treatment of heart arrhythmias was implantation of pacemaker, which reduce symptoms exactly occurs after implantation. Aim: To detect the role of smoking on cardiac electrophysiology parameters in sinus node dysfunction in Iraqi patients such as SNRT and AH. Methods: A cross sectional study, was conducted on 59 patient ranging between 20-50 years old and involving 35 female and 24 male patients, suffering from an unexplained symptoms of sinoatrial node dysfunction (SND). The enrolled participants were gathere

Abstract. In this research, the uranium concentration in (16) water samples collected from some agricultural areas surrounded with AlTuwitha nuclear site in Baghdad-Iraq was measured by using a CR-39 detector. The concentration of uranium in this study was from (0.6 ± 0.33mg/l) to (2.51 ± 0.49 mg/l), and the weighted average for the concentrations (1.262 ± 0.402 mg/l). The results showed it is a concentration of uranium level in water samples studied is higher than the allowed limit recommended by WHO and ICRP.

The density functional B3LYP is used to investigate the effect of decorating the silver (Ag) atom on the sensing capability of an AlN nanotube (AlN-NT) in detecting thiophosgene (TP). There is a weak interaction between the pristine AlN-NT and TP with the sensing response (SR) of approximately 9.4. Decoration of the Ag atom into the structure of AlN-NT causes the adsorption energy of TP to decrease from − 6.2 to − 22.5 kcal/mol. Also, the corresponding SR increases significantly to 100.5. Moreover, the recovery time when TP is desorbed from the surface of the Ag-decorated AlN-NT (Ag@AlN-NT) is short, i.e., 24.9 s. The results show that Ag@AlN-NT can selectively detect TP among other gases, such as N2, O2, CO2, CO, and H2O.

The use of credit cards for online purchases has significantly increased in recent years, but it has also led to an increase in fraudulent activities that cost businesses and consumers billions of dollars annually. Detecting fraudulent transactions is crucial for protecting customers and maintaining the financial system's integrity. However, the number of fraudulent transactions is less than legitimate transactions, which can result in a data imbalance that affects classification performance and bias in the model evaluation results. This paper focuses on processing imbalanced data by proposing a new weighted oversampling method, wADASMO, to generate minor-class data (i.e., fraudulent transactions). The proposed method is based on th

The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med

Geotechnical engineering like any other engineering field has to develop and cope with new technologies. This article intends to investigate the spatial relationships between soil’s liquid limit (LL), plasticity index (PI) and Liquidity index (LI) for particular zones of Sulaymaniyah City. The main objective is to study the ability to produce digital soil maps for the study area and determine regions of high expansive soil. Inverse Distance Weighting (IDW) interpolation tool within the GIS (Geographic Information System) program was used to produce the maps. Data from 592 boreholes for LL and PI and 245 boreholes for LI were used for this study. Layers were allocated into three depth ranges (1 to 2, 2 to 4 and 4 to 6)

An intrusion detection system (IDS) is key to having a comprehensive cybersecurity solution against any attack, and artificial intelligence techniques have been combined with all the features of the IoT to improve security. In response to this, in this research, an IDS technique driven by a modified random forest algorithm has been formulated to improve the system for IoT. To this end, the target is made as one-hot encoding, bootstrapping with less redundancy, adding a hybrid features selection method into the random forest algorithm, and modifying the ranking stage in the random forest algorithm. Furthermore, three datasets have been used in this research, IoTID20, UNSW-NB15, and IoT-23. The results are compared with the three datasets men

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural