Structural health monitoring is becoming more reliable as technology advances. Many structures are in use throughout cities and environments that require constant attention due to fatigue of corrosion and environmental effects such as floods, earthquakes, or strong winds. This means that with the advent of new damage detection techniques, authorities can make sure that these vital structural elements, such as road signs or traffic lights and so on, are safe. The important key to these techniques is that they must be simple and relatively inexpensive. Therefore, an attempt was made to use techniques in medical practice and adapt those to structural members. In this article, the effectiveness of X-ray computed tomography in the inspection of urban structures and building structures is discussed. X-ray tomography is a non-destructive method based on X-ray absorption, widely used in the medical field. By using this method, it is possible to study and examine the internal structure of old structures to fix defects and maintain the safety of residents, as well as to maintain them as best as possible. For example, urban structures such as electric poles and traffic signs cause financial losses or even deaths due to natural hazards or wear and tear every year. One of the features of this method is its high accuracy and speed in checking the member. Considering the capabilities of this method, in this research, a baggage inspection device at the MehrAbad airport was used to examine a number of concrete and wooden samples to identify defects such as cracks, holes, or knots in wooden samples. The investigation results are directly related to the device's power and the duration of radiation used on the sample. According to the results, defects such as cracks in concrete or knots in wood can be observed.

Introduction: Long-term clinical evaluation of dental implants and their surrounding structures is of utmost importance to acquire knowledge about reasons for implant success and failure.However, accurate and reproducible results are difficult to obtain. The aim of the present study was to examine bone height around endosseous implants on digital conventional radiographs (DCR) and direct digital subtraction images (DSI) prior to loading.Materials and methods: In this experimental study, bone height around 10 implants in 6 patients was assessed by 2 observers. Standardized digital radiographs were obtained just a week and 3 months postoperatively and subtracted by means of EMAGO software. Then the observers evaluated bone height on DCRs and DSIs. MINITAB software and paired t-test were used for statistical analysis (a=0.05).Results: Comparative evaluation of bone height indicated significantly higher values on DCR than on DSI (p value=0.002). The observers also had statistically significant variability in this assessment (p value=0.00003).Conclusion: DSI demonstrated lower values of linear measurement of bone height around endosseous implants compared with DCR. Interobserver variability should be considered when comparing values from follow-up studies.

Tumor detection and isolation in magnetic resonance imaging (MRI) is a significant consideration, but when done manually by people, it is very time consuming and may not be accurate. Also, the appearance of the tumor tissue varies from patient to patient, and there are similarities between the tumor and the natural tissue of the brain. In this paper, we have tried to provide an automated method for diagnosing and displaying brain tumors in MRI images. Images of patients with glioblastoma were used after applying pre-processing and removing areas that have no useful information (such as eyes, scalp, etc.). We used a bounding box algorithm, to create a projection for to determining the initial range of the tumor in the next step, an artificial bee colony algorithm, to determine an initial point of the tumor area and then the Grow cut algorithm for, the exact boundary of the tumor area. Our method is automatic and extensively independent of the operator. comparison between results of 12 patients in our method with other similar methods indicate a high accuracy of the proposed method (about 98%) in comparison s.

Aim and Background Pancreatic cancer leaves little hope for survival among patients. This is due to its cancerous cell resistance to radiation and chemicals. Materials & Methods Synergistic effect between three modalities of pancreatic cancer treatment is investigated. These are radiation therapy, Hyperthermia and graphene oxide nanosheets. The aim is to overcome resistance of pancreatic cancer cell against radiation therapy. Cancerous cell lines were treated by each one of three modalities separately. Other samples were treated with various combinations of these modalities. Hyperthermia accomplished with placing cell lines for 15 min in 42°C. In the course of radiation therapy, the cancerous cells were irradiated by 6 MV Linac for two cases of 2 Gy and 3 Gy. The cell line viability was readout by MTT assay in 24 hours and 48 hours after treatment. Results In single modality treatment it was shown that 24h after the treatment, the group treated by RT 3 Gy had the highest cell killing result. Following up the result in 48h readout, hyperthermia and 3 Gy radiotherapy had similar result. In double modality treatment, for both 24h and 48h viability readout, the group graphene-oxide plus 2 Gy radiotherapy showed cell survival amounting to 67. 07% and 43. 23% respectively being the lowest cell survival among all double combinations. Conclusion In triple modality treatment, the cell viability for 24h showed no significant improvement but in 48h the hyperthermia plus Graphene-oxide and 3 Gy radiotherapy had very low cell viability. Significant sensitizing effect for GO when combined by radiation therapy was observed.

Background and Objectives: medical image Segmentation is a challenging task due to low contrast between Region of Interest and other textures, hair artifacts in dermoscopic medical images, illumination variations in images like Chest-Xray and various imaging acquisition conditions. Methods: In this paper, we have utilized a novel method based on Convolutional Neural Networks (CNN) for medical image Segmentation and finally, compared our results with two famous architectures, include U-net and FCN neural networks. For loss functions, we have utilized both Jaccard distance and Binary-crossentropy and the optimization algorithm that has used in this method is SGD+Nestrov algorithm. In this method, we have used two preprocessing include resizing image’ s dimensions for increasing the speed of our process and Image augmentation for improving the results of our network. Finally, we have implemented threshold technique as postprocessing on the outputs of neural network to improve the contrast of images. We have implemented our model on the famous publicly, PH2 Database, toward Melanoma lesion segmentation and chest Xray images because as we have mentioned, these two types of medical images contain hair artifacts and illumination variations and we are going to show the robustness of our method for segmenting these images and compare it with the other methods. Results: Experimental results showed that this method could outperformed two other famous architectures, include Unet and FCN convolutional neural networks. Additionally, we could improve the performance metrics that have used in dermoscopic and Chest-Xray segmentation which used before. Conclusion: In this work, we have proposed an encoder-decoder framework based on deep convolutional neural networks for medical image segmentation on dermoscopic and Chest-Xray medical images. Two techniques of image augmentation, image rotation and horizontal flipping on the training dataset are performed before feeding it to the network for training. The predictions produced from the model on test images were postprocessed using the threshold technique to remove the blurry boundaries around the predicted lesions.

Objectives: Quantitatively define the radiographic locations of the major soft-tissue attachments about the elbow. Methods: In 10 cadaveric elbows, the attachments of the medial ulnar collateral ligament, lateral ulnar collateral ligament, annular ligament, triceps, and biceps were marked with radiopaque spheres. Measurements were made on calibrated AP and lateral fluoroscopic images from known osseous landmarks. Results: On AP radiographs,the anterior bundle of the MUCL (aMUCL) measured 28. 6mm (95% CI, 27. 5-29. 8mm) from the humeral attachment to the midpoint of the MUCL ridge on the ulna and 14. 3mm, (95% CI 13. 0-15. 5) to the olecranon. The LUCL was 39. 9mm (95% CI, 38. 6 –,41. 1mm) from the humeral attachment to the supinator crest attachment and 8. 9mm (95% CI, 8. 1-9. 8mm) to the lateral epicondyle. On the lateral radiographs, the humeral attachment of the aMUCL to the medial coronoid was 27. 1mm (95% CI, 25. 9-28. 2mm) and 9. 3mm (95%CI, 17. 5-21. 2mm) to the tip. The LUCL humeral attachment to the supinator crest was 45. 4mm (95%CI, 44. 1-46. 8mm). The LUCL humeral attachment was located 8. 9mm (95%CI, 8. 0-9. 7mm) posterior from the anterior humeral line. Conclusion: The soft-tissue attachments about the elbow were reproducibly demonstrated on radiographs in relation to osseous landmarks and radiographic lines. The radiographic relationships will allow for improved identification of the ligament and tendon attachment sites of the elbow for intraoperative assessment and postoperative evaluation following reconstruction.

Purpose: Something that happens in the future of a scientific field depends on today's view of young people about the future. The perception of young people from the future and their conception of the future is very important. The purpose of this study was to imaging the future of sports science from the perspective of educated youth. Method: The method of the present research is qualitative and exploratory research and in terms of purpose is applied research. The statistical population of the study included educated youth who are familiar with sports science and the snowball sampling method was used for interviews and theoretical saturation were obtained with 23 interviews. To analyze the data, the content analysis method has been used to obtain the information of this research. Findings: The results show the future of sports science among educated youth consists of seven main concepts that include: hope for a green education system, excessive interest in working in the sports industry, basic reengineering, relative disappointment from the ability of authorities, social position through academic education, desire for empowerment and lack of skills, injustice, and utilitarianism. Conclusion: Finally, by observing the mental images of the youth, it can be stated that sports science will move towards the dystopia if the current situation continues, and it can move towards the utopia with fundamental changes. In this direction, the stakeholders of sports sciences need to be together and disagreements need to be managed as well as arbitrariness of sports science professionals should be reduced.