درخت حوزه‌های تخصصی

The reconstruction of 3D images is always a difficult task for the researchers. The 3D reconstruction of the image is a core technique of various fields such as Computer graphics, computer vision, CAD systems, medical science, computer application, etc. Reconstruction of the 3D image allows us to gather the quantitative features of the objects such as the shape, size, and volume of the objects. The existing computer algorithms need spatial dimension information to make the distinguished inference from the given 3D image which is not always possible. This paper simplifies the 3D reconstruction of the image. This research paper introduced a novel algorithm for the representation of the Three Dimensional images into a textual form.  The syntactic approach is used for the extraction of the features of the image and these are called knowledge vectors. The knowledge vector consists of the direction information and length information. This a new approach in the field of image processing where images can be represented as a knowledge vector and it could be a great contribution in the field where security is a major concern. Further, the knowledge vector is used for the reconstruction of the 3D image. The performance of the algorithm is evaluated on the PASCAL 3D + and example-based Synthesis of the 3D Object Arrangements dataset. According to the obtained results, the proposed methodology is having better accuracy, and the processing time of reconstruction of the original 3D image is 1.02 Seconds. Single-pass is sufficient for reconstructing the original image

Allocating fixed costs with undesirable data has recently been one of the most important issues for managers to discuss. Lack of attention to undesirable data may lead to incorrect cost allocation. Considering and determining undesirable inputs and outputs, data envelopment analysis (DEA) technique can be significantly helpful in determining the cost allocation strategy. In-puts and outputs are divided into two desirable and undesirable groups. Obviously, desirable inputs and undesirable outputs must be reduced and undesirable inputs and desirable outputs must be increased to improve performance. This manuscript presents two strategies for allocating fixed costs with undesirable data. In the first strategy, each decision making unit (DMU) first determines the minimum and maximum shares that it can receive from the fixed resources while the efficiency of that DMU and other DMUs re-mains the same after receiving the fixed resources. Finally, the decision maker chooses the fixed cost for each DMU between the minimum and maxi-mum cost values proposed. In the second strategy, the allocation of fixed costs is done using the CCR multiplicative model with undesirable data. The effectiveness of both methods is examined by an applied study on the commercial banks.

Our main aim was to identify and prioritize the obstacles to the implementation of decent work in the industry. The current research was applied in terms of purpose. It was also descriptive survey in terms of collecting data. Due to the large extent of industry sector actors, only one side of the main actors' triangle in the industrial sector in Yazd province was selected as the statistical sample.This side of triangle included the heads of unions and workers’ representatives in the industry sector. Thus the data was collected purposefully from this sample. To collect data, in the first step, obstacles to the decent work and its components were identified based on library studies and in-depth initial interviews. In the second step, though, using the Delphi-Fuzzy method and the opinion of some of the experts in the industry sector, the identified obstacles in the first step were analyzed and summarized. In the third step, a questionnaire was designed for ranking the identified obstacles based on the Fuzzy Best-Worst Method. The questionnaire was distributed among a sample of 130 members, of which 80 questionnaires were returned. The final findings of the current study indicated that from the workers’ point of view, the shortcoming in upstream laws, lack of a trust in dialogues, allocating welfare budget to unrelated areas, government-owned industries and lack of trust in other members (workers, employers, and government), were the most difficult obstacles in the implementation of decent work.

The aim of this study is to develop a conceptual model for purchase intention of foreign food products in the Iranian context. Based on an in-depth review of past literature, the sub-factors related to customer’s purchase intention were extracted. Then, exploratory factor analysis and confirmatory factor analysis were applied to identify and confirm the factors affecting purchase intention of foreign food products. The obtained results illustrate the thirteen factors as follows: “corporate social responsibility”, “customer knowledge and awareness”, “perceived risk”, “retailer’s commercial image”, “customer’s personality characteristics”, “social identity”, “product features”, “attitude”, “country-of origin”, “perceived value”, “subjective norm”, “loyalty”, and “perceived behavioral control”. The proposed model could contribute to international food companies and retailers to understand customers’ expectations and to gain more market share in Iran. Designing a conceptual model for purchase intention of foreign food products in Iran based on a comprehensive set of the effective factors, developing the theoretical literature in the field of the factors affecting customer’s purchase intention, and presenting valuable results for academicians, marketing managers, and retailers.