Arab Academy for Science, Technology & Maritime Transport

About: Arab Academy for Science, Technology & Maritime Transport is a based out in . It is known for research contribution in the topics: Computer science & Support vector machine. The organization has 1341 authors who have published 2126 publications receiving 15919 citations.

Causes of Delay in Building Construction Projects in Egypt

Abstract: Delay in construction projects is considered one of the most common problems causing a multitude of negative effects on the project and its participating parties. This paper aims to identify the main causes of delay in construction projects in Egypt from the point of view of contractors, consultants, and owners. A literature review was conducted to compile a list of delay causes that was purged based on appropriateness to Egypt in seven semistructured interviews. The resulting list of delay causes was subjected to a questionnaire survey for quantitative confirmation and identification of the most important causes of delay. The overall results indicated that the most important causes are: financing by contractor during construction, delays in contractor's payment by owner, design changes by owner or his agent during construction, partial payments during construction, and nonutilization of professional construction/contractual management. The contractor and owner were found to have opposing views, mostly blaming one another for delays, while the consultant was seen as having a more intermediate view. Results' analyses suggest that in order to significantly reduce delay a joint effort based on teamwork is required. Furthermore, causes of project delay were discussed based on the type and size of the project.

Operative repair or endovascular stent graft in blunt traumatic thoracic aortic injuries: Results of an American Association for the Surgery of Trauma multicenter study

Abstract: Introduction: The purpose of this American Association for the Surgery of Trauma multicenter study is to assess the early efficacy and safety of endovascular stent grafts (SGs) in traumatic thoracic aortic injuries and compare outcomes with the standard operative repair (OR). Patients: Prospective, multicenter study. Data for the following were collected: age, blood pressure, and Glasgow Coma Scale (GCS) at admission, type of aortic injury, injury severity score, abbreviate injury scale (AIS), transfusions, survival, ventilator days, complications, and intensive care unit and hospital days. The outcomes between the two groups (open repair or SG) were compared, adjusting for presence of critical extrathoracic trauma (head, abdomen, or extremity AIS >3), GCS score ≤8, systolic blood pressure 55 years. Separate multivariable analysis was performed, one for patients without and one for patients with associated critical extrathoradc injuries (head, abdomen, or extremity AIS >3), to compare the outcomes of the two therapeutic modalities adjusting for hypotension, GCS score ≤8, and age >55 years. Results: One hundred ninety-three patients met the criteria for inclusion. Overall, 125 patients (64.9%) were selected for SG and 68 (352%) for OR. SG was selected in 71.6% of the 74 patients with major extrathoracic injuries and in 60.0% of the 115 patients with no major extrathoradc injuries. SG patients were significantly older than OR patients. Overall, 25 patients in the SG group (20.0%) developed 32 device-related complications. There were 18 endoleaks (14.4%), 6 of which needed open repair. Procedure-related paraplegia developed in 2.9% in the OR and 0.8% in the SG groups (p = 0.28). Multivariable analysis adjusting for severe extrathoradc injuries, hypotension, GCS, and age, showed that the SG group had a significantly lower mortality (adjusted odds ratio: 8.42; 95% CI: [2.76-25.69]; adjusted p value <0.001), and fewer blood transfusions (adjusted mean difference: 4.98; 95% CI: [0.14-9.82]; adjusted p value = 0.046) than the OR group. Among the 115 patients without major extrathoradc injuries, higher mortality and higher transfusion requirements were also found in the OR group (adjusted odds ratio for mortality: 13.08; 95% CI [2.53-67.53], adjusted p value = 0.002 and adjusted mean difference in transfusion units: 4.45; 95% CI [139-7.51]; adjusted p value = 0.004). Among the 74 patients with major extrathoracic injuries, significantly higher mortality and pneumonia rate were found in the OR group (adjusted p values 0.04 and 0.03, respectively). Multivariate analysis showed that centers with high volume of endovascular procedures had significantly fewer systemic complications (adjusted p value 0.001), fewer local complications (adjusted? value p = 0.033), and shorter hospital lengths of stay (adjusted p value 0.005) than low-volume centers. Conclusions: Most surgeons select SG for traumatic thoracic aortic ruptures, irrespective of associated injuries, injury severity, and age. SG is associated with significantly lower mortality and fewer blood transfusions, but there is a considerable risk of serious device-related complications. There is a major and urgent need for improvement of the available endovascular devices.

Breast cancer detection using deep convolutional neural networks and support vector machines

Abstract: It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.