Background: The use of osseointegrated fixtures in dentistry has been demonstrated both histologically and clinically to be beneficial in providing long term oral rehabilitation in completely edentulous individual. Most patients suffer from denture instability; particularly with mandibular prosthesis, the use of dental implant will be benefit significantly from even a slight increase in retention. The concept of implanting two to four fixtures in a bony ridge to retain a complete denture prosthesis appealing therefore, as retention, stability and acceptable economic compromise to the expanse incurred with the multiple fixture supported fixed prosthesis. Materials and methods in this study the sample were eight patients selected from a hospital of specialized surgery, these patient were wearing a mandibular implant retained over denture for two years these patients having MIR-OD with Bar-clip, ball-cup and O- ring attachments. Preparative radiography was obtained for this patient from the center .these radiograph was taken to the patient at time of insertion. The second radiograph image was taken to the patient after two years of function with prosthesis. .the scanned images were transfer to special folder in a computer then analysis of bone loss done using Dimax software. After that an accurate calibrations of crestal bone measurement were analyzed for both groups of Radiography. Results it was appeared that the amount of bone loss in ball and bar designs (of mandibular Implant retained overdenture) were within the criteria of successful rate of bone loss during the period of examination, and there was statistically significant difference between both types of anchorage system. Conclusions The amount of bone loss was 0.1 mm after two years follow up, and it was within the acceptable limits of bone lose. A significant difference appeared between both designs of MIR-OD, Ball and bar designs.
Abstract The study aims to clarify the value of auditing economic units and how it can be measured, which is one of the most important challenges to matching the Value Relevance of Accounting Information. The problem of the study was identified with questions that revolve around the extent to which it is possible to measure the value of auditing in Iraqi economic units and the extent to which the value of auditing affects the adequacy of accounting information. Through reviewing the studies discussing this topic, it was found that auditing can provide value through the performance of the auditor and adding value to the economic unit subject to audit. The study recommended the need to study the situational factors of auditing, whether exter
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It is well known that the rate of penetration is a key function for drilling engineers since it is directly related to the final well cost, thus reducing the non-productive time is a target of interest for all oil companies by optimizing the drilling processes or drilling parameters. These drilling parameters include mechanical (RPM, WOB, flow rate, SPP, torque and hook load) and travel transit time. The big challenge prediction is the complex interconnection between the drilling parameters so artificial intelligence techniques have been conducted in this study to predict ROP using operational drilling parameters and formation characteristics. In the current study, three AI techniques have been used which are neural network, fuzzy i
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Corncob is an agricultural biomass waste that was widely investigated as an adsorbent of contaminants after transforming it into activated carbon. In this research carbonization and chemical activation processes were achieved to synthesize corncob-activated carbon (CAC). Many pretreatment steps including crushing, grinding, and drying to obtain corncob powder were performed before the carbonization step. The carbonization of corncob powder has occurred in the absence of air at a temperature of 500 °C. The chemical activation was accomplished by using HCl as an acidic activation agent. Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and Brunauer–Emmett–Teller (BET) facilitate
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Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,
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