Al Huweizah Marsh is considered as the largest marsh at the southern part of Iraq. About one third of the marsh is located within the Iranian territory. Iran began to construct earth dikes along the Iraqi-Iranian international borders to separate the Iranian part of the marsh. The electrical conductivity, EC, value was adopted to be the indicator for the water salinity within the marsh. A steady two-dimensional water quality routing model was implemented by using the RMA2 and RMA4 softwares within the SMS computer package to estimate the distribution of the
EC values within the marsh seasonally during the wet, moderate and dry water years. The EC distribution Patterns were estimated considering the expected two cases of the marsh futu

Worldwide, shipping documents are still primarily created and handled in the traditional paper manner. Processes taking place in shipping ports as a result are time-consuming and heavily dependent on paper. Shipping documents are particularly susceptible to paperwork fraud because they involve numerous parties with competing interests. With the aid of smart contracts, a distributed, shared, and append-only ledger provided by blockchain technology allows for the addition of new records. In order to increase maritime transport and port efficiency and promote economic development, this paper examines current maritime sector developments in Iraq and offers a paradigm to secure the management system based on a hyper-ledger fabric blockchain p

Due to the great losses caused by weeds to the crop, it is necessary to continue testing new herbicides that may be more efficient than the herbicides used that can reduce competition between weeds and crops, Therefore, a field experiment was carried out in the experimental field of the Department of Field Crops, College of Agricultural Engineering Sciences, University of Baghdad (Al-Jadriya) during the winter season of 2021-22 in order to evaluate the efficiency of the herbicide H-199 and compare it with some herbicides used in controlling companion weed to three cultivars of wheat and its effect in the yield and its components. The experiment was applied according to the randomized complete block design with split-plot arrangement

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in

Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducte

Laser shock peening (LSP) is deemed as a deep-rooted technology for stimulating compressive residual stresses below the surface of metallic elements. As a result, fatigue lifespan is improved, and the substance properties become further resistant to wear and corrosion. The LSP provides more unfailing surface treatment and a potential decrease in microstructural damage. Laser shock peening is a well-organized method measured up to the mechanical shoot peening. This kind of surface handling can be fulfilled via an intense laser pulse focused on a substantial surface in extremely shorter intervals. In this work, Hydrofluoric Acid (HF) and pure water as a coating layer were utilized as a new technique to improve the properti

Abstract Physical requirements are an important priority for the development of football gymnastics coaches because the nature of performance is interconnected and interconnected in terms of the player's duties in the match. In the gameplay situations, the player must perform the skill with strength and speed coupled with accuracy and the reactions of the colleague and competitor alike, which represents the normal reality of the football gymnasium Skilled exercises are one of the most suitable technical side exercises as they are built according to the components of the skill requirements of the game and the nature of its performance, which appear on the gro

The reason for applying the project is for the development of some physiological variables and skill performance using a device for the first time applied in Iraq to a sample of badminton players, and this device has a patent on 7/2/2019. An experimental method is used for a sample of (12) players from (Al-Orthodoxy club). The experimental group is applied the proposal technique, while, the control group is instructed by the coach.The results of the research attribute to the role of the proposal exercises of fit light technology, thus, this technology has increased the attention and focusing of the sample and some physiological variables and smash shot skill. It has been concluded that the exercises using fit light technology is helped to i

Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to