Objective: To identify the effect of the cube model on visual-spatial intelligence and learning the skill of spikinging in volleyball for female students, The researchers used the experimental method by designing two equivalent groups with pre- and post-measurements. Research methodology: The main research sample of (30) female students was selected from the research community represented by second-stage students in the College of Physical Education and Sports Sciences - University of Baghdad for the academic year (2024-2025). The sample was divided equally into two control and experimental groups. The researchers conducted the sample homogenization process and the equivalence process between the two groups in the variables of visua

Background Parkinson’s disease (PD) is currently the fastest-growing neurological disorder in the world. Patients with PD face numerous challenges in managing their chronic condition, particularly in countries with scarce healthcare infrastructure. Objective This qualitative study aimed to delve into neurologists’ perspectives on challenges and gaps in the Iraqi healthcare system that influence the management of PD, as well as strategies to mitigate these obstacles. Method Semi-structured interviews were conducted with neurologists from five different Iraqi provinces, working in both hospitals and private neurology clinics, between November 2024 and January 2025. A thematic analysis approach was employed to identify the main challenge

Objective: Synthesized a series of new thiourea (TU) derivatives, tested their antioxidant activity, and investigated their expected biological activity by theoretical study (computational methods). Methods: The derivatives were made using a one-pot reaction with two steps. Initially, succinyl chloride was mixed with KSCN to make succinyl isothiocyanate. Then, primary and secondary amines were used to make TU derivatives. The theoretical studies were done by Swiss ADME and molecular docking via Genetic Optimization of Linkage Docking (GOLD). Then evaluate antioxidant activity using the DPPH scavenging method. Results: FT-IR, 1H NMR, and 13C NMR spectroscopy show the verification of all the prepared derivatives. Compounds (II), (VIII),

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental

In this research the Inter-Particle Expectation Values have been studied for atomics Helium (He) and Beryllium (Be) also for He-like ions, Be-like ions (Li-1, B+1? Li+1, Be+2, B+3) by using Hartree-Fock wave functions, We compared the results to some ions which have the same atomic number from each group with atomic number, We compared the results with published calculations to the last studied .

L-arabinose isomerase from Escherichia coli O157:H7 Was immobilized with activated Bentonite from local markets of Baghdad, Iraq by 10% 3-APTES and treated with 10% aqueous glutaraldehyde, the results refer that the yield of immobilization was 89%, and pH profile of free and immobilized L-arabinose isomerase was 7 and 7.5 and it is stable at 6-8 for 60 min respectively, while, the optimum temperature was 30 and 35°C and it was stable at 35 and 40°C for 60 min but it loses more than 60 and 30% from its original activity at 50°C for free and immobilized L-arabinose isomerase respectively. Immobilized enzyme retained its full activity for 32 day, but it retained 73.58% of its original activity after storage for 60 d

Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co

A group of acceptance sampling to testing the products was designed when the life time of an item follows a log-logistics distribution. The minimum number of groups (k) required for a given group size and acceptance number is determined when various values of Consumer’s Risk and test termination time are specified. All the results about these sampling plan and probability of acceptance were explained with tables.