Image and video completion are essential tasks in the field of image and video processing, often used for restoring damaged regions in images and video frames. The primary challenge in these tasks is to complete them in such a way that they do not introduce noticeable artifacts or inconsistencies to the viewer. While image completion focuses on filling in missing parts in a static context, video completion requires additional considerations due to the temporal dimension. The motion of objects and the preservation of temporal consistency are critical factors in video completion. This research proposes a novel method for image and video completion based on Neutrosophic theory, which handles uncertainty in both spatial and intensity domains. Neutrosophy is utilized to interpret the indeterminacy present in images, allowing for more accurate Segmentation and better handling of incomplete data. The proposed method first segments the image using Neutrosophic-based Segmentation and then uses the segmented information to guide the completion of missing regions. For video completion, a two-step approach is introduced that separates static backgrounds from moving objects. The background is reconstructed using image completion based on Neutrosophic-based Segmentation, and the foreground is completed by identifying appropriate data that best match the missing parts; this data is chosen using a contour-based method, which this method applies neutrosophic sets to get to the most suitable data. The novelty of the approach lies in several key contributions: 1) the use of Neutrosophic theory to handle spatial and intensity uncertainties, 2) a Neutrosophic-based similarity measure for image Segmentation, 3) a new metric for finding the most suitable patch for hole-filling, and 4) a novel method for preserving boundaries and uniformity in video completion, particularly in the presence of moving objects. Experimental results demonstrate the effectiveness of the proposed methods, with improved visual quality and reduced inconsistencies compared to previous state-of-the-art methods. However, challenges remain in applying the method to highly detailed images with many classes and handling dynamic backgrounds.

In this paper, the effect of surface curvature on calibration coefficients of the hole-Drilling method has been studied. Firstly, an FE simulation of the drilling process has been modeled for a flat surface. Then, the simulation has been implemented on a cylinder with different diameters and the calibration coefficients have been extracted. In all analyses, the stain-gauge rosette and hole diameter remain constant and mesh removal in the drilling simulation was achieved in eight increments. The comparison of simulation results and standard published calibration coefficients data for a flat surface show an acceptable error equal to 2.8 variation of errors between 0.6 to 6.2.

Foreground-background image Segmentation has been an important research problem. It is one of the main tasks in the field of computer vision whose purpose is detecting variations in image sequences. It provides candidate objects for further attentional selection, e. g., in video surveillance. In this paper, we introduce an automatic and efficient Foreground-background Segmentation. The proposed method starts with the detection of visually salient image regions with a saliency map that uses Fourier transform and a Gaussian filter. Then, each point in the maps classifies as salient or non-salient using a binary threshold. Next, a hole filling operator is applied for filling holes in the achieved image, and the area-opening method is used for removing small objects from the image. For better separation of the foreground and background, dilation and erosion operators are also used. Erosion and dilation operators are applied for shrinking and expanding the achieved region. Afterward, the foreground and background samples are achieved. Because the number of these data is large, K-means clustering is used as a sampling technique to restrict computational efforts in the region of interest. K cluster centers for each region are set for training of Support Vector Machine (SVM). SVM, as a powerful binary classifier, is used to segment the interest area from the background. The proposed method is applied on a benchmark dataset consisting of 1000 images and experimental results demonstrate the supremacy of the proposed method to some other foreground-background Segmentation methods in terms of ER, VI, GCE, and PRI.

There are issues in organizations that require new theoretical formulation. Hence, metaphorical theorizing is used in the study of organizations to interpret them and understand their complexities. In this method, the organization is likened to an entity and one of the key features of that entity is generalized to the organization. It should be borne in mind that most organizational theories are based on a positivist paradigm that focuses on knowledge and data in an attempt to discover the nomothetic of social systems. This article is an attempt to build a theory based on interpretive paradigm that puts the organizational black hole theory forward in a metaphorical way. The black hole theory of the organization has been adopted from the cosmic black hole metaphor. In the first step, the key features of the organizational black hole were summarized in accordance with the researcher's interpretation, taking into account the one-on-one correspondence and adopting the cosmic black hole characteristics. In the second step, the definition of the organizational black hole was presented based on key features, and in the third step the overall framework of the theory was formulated. In addition, by analyzing the characteristics of the organizational black hole theory, it was found that this theory falls under the interpretative paradigm. Finally, how one can test the theory in terms of falsifiability or utility is discussed.

Introduction: After cleaning and shaping of the root canal, to prevent recontamination and to ensure the success of root canal therapy, it is necessary to fill root canal space. Studies, so far, have not been able to identify a specific method as the best method of obturation. The present study was an attempt to introduce and compare different root canal obturation techniques.Materials and Methods: In this review, using specific key words such as root canal obturation, root canal filling materials and gutta-percha, search was run in Pubmed, Cochrane and ISI Web of Science databases for high-quality articles published from 1960 to 2013.Results: There are several methods to obturate the root canal system, designed based on the use of plastic materials, injectable materials, single cones, or compaction of gutta-percha. A brief review of each technique and its advantages and disadvantages were discussed. The reasons for choosing the cold lateral compaction technique as a frequently used and the selected technique by many dentists as well as academic centers were explained.Conclusion: Considering the advantages and disadvantages and long-term outcomes of root canal obturation techniques, it seems cold lateral compaction is the selected root canal filling technique in the majority of cases.