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

Kirchhoff Time migration was applied in Pre and Post-Stack for 2D seismic survey for line AJ-99N, that is located in Ajeel oilfield in Salah Al-Din Governorate, Central Iraq. The process follows several accurate steps to reach the final time migration stage. The results of applied time migration give an accurate image for the Ajeel anticline reservoir and to improve the signal to noise ratio. Pre-Stack shows a clearer image for the structure in the study area, and the time-frequency analysis insure the result.

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

The study has used Geographic Information Systems (GIS) data and Arc GIS software, to produce a new map. This map contains a spatial database of the newly constructed buildings, in the area of water resources engineering college, and using the above tool for updating contains the college in any time, also the map and browse all buildings individually or in clustered, for example, browsing geometric data,  statistical data and the plans of drawings, and make any necessary changes to these sectors immediately, or in the future, like the expansion of the study halls by constructed a new floor, or add supplements, prepare any electrical to any building, and  anything else.  The paper also, updates the old Google Earth image, f

     An interpretative study of the two-dimensional seismic data of the Afaq area was conducted using the Petrel 2017 software. 2D seismic reflection sections are used to give a structural interpretation of Afaq structure based on synthetic seismogram and well log data. Three reflectors, Zubair, Yamama, and Gotina Formations, were selected. These reflectors are defined from well west kifl (wk-1), Where located adjacent to the study area. Structural maps of the Zubair, Yamama, and Gotnia formations are prepared and interpreted, including TWT maps, Average velocity maps, and depth maps. The studies concluded that the Afaq structure area does not contain main faults, but secondary faults with short and limited extensions

In this study, SnO2 nanoparticles were prepared from cost-low tin chloride (SnCl2.2H2O) and ethanol by adding ammonia solution by the sol-gel method, which is one of the lowest-cost and simplest techniques. The SnO2 nanoparticles were dried in a drying oven at a temperature of 70°C for 7 hours. After that, it burned in an oven at a temperature of 200°C for 24 hours. The structure, material, morphological, and optical properties of the synthesized SnO2 in nanoparticle sizes are studied utilizing X-ray diffraction. The Scherrer expression was used to compute nanoparticle sizes according to X-ray diffraction, and the results needed to be scrutinized more closely. The micro-strain indicates the broadening of diffraction peaks for nano

     Well log rock physics and seismic facies analysis was carried out with a view to enhancing reservoir sand characterization of Mafe Field of Niger Delta. Lithofacies were identified using suites of well logs and correlated across the block. Rock properties were estimated from wireline logs using empirical methods. Vp-porosity crossplot was used to characterize the delineated sandstone reservoirs by comparing observed clusters and trends with various rock physics models. Seismic attribute analysis was employed to detect lateral changes in lithology across the field. Reservoir A is a relatively clean sand, with low average volume of shale of 0.4, average thickness of 55m, good average porosity of 0.

Discriminant analysis is a technique used to distinguish and classification an individual to a group among a number of  groups based on a linear combination of a set of relevant variables know discriminant function. In this research  discriminant analysis used to analysis data from repeated measurements design. We  will  deal  with the problem of  discrimination  and  classification in the case of  two  groups by assuming the Compound Symmetry covariance structure  under  the  assumption  of  normality for  univariate  repeated measures data.

Tau-P linear noise attenuation filter (TPLNA) was applied on the 3D seismic data of Al-Samawah area south west of Iraq with the aim of attenuating linear noise. TPLNA transforms the data from time domain to tau-p domain in order to increase signal to noise ratio. Applying TPLNA produced very good results considering the 3D data that usually have a large amount of linear noise from different sources and in different azimuths and directions. This processing is very important in later interpretation due to the fact that the signal was covered by different kinds of noise in which the linear noise take a large part.