Purpose: The present study aims to identify and explain the futures of Iranian provinces in the competition for the development of NBIC technological Convergence (synergy between nanotechnology, biotechnology, information technology and cognitive sciences) with a focus on Yazd province. Method: This research, which has been conducted with a qualitative and descriptive approach, is classified as an exploratory futures-study. In this way, the relevant research records were first reviewed to compile a list of drivers in the development of convergent technologies. The list was then refined and prioritized to identify two key drivers, with the Delphi process attended by fourteen experts. By intersecting the uncertainties of these two drivers, four scenarios of the province's future in technological Convergence competition emerged. Findings: The two key drivers affecting the success of the province in the competition for convergent technologies are: the rate of development of the innovation ecosystem and the amount of financial resources allocated to this process. Based on this, four scenarios of the future situation of the province in this field were identified, which have been named as "Vanguard", "Buyer", "Remnant" and "Hired". Conclusion: In order for the provinces to be able to succeed in the forthcoming competitions in the production and operation of convergent technologies, it is necessary to plan for strengthening the ecosystem infrastructure from now; at the same time, it is necessary to make efficient efforts to provide sufficient financial resources for the research and commercialization of these technologies.

Face recognition from digital images is used for surveillance and authentication in cities, organizations, and personal devices. Internet of Things (IoT)-powered face recognition systems use multiple sensors and one or more servers to process data. All sensor data from initial methods was sent to the central server for processing, raising concerns about sensitive data disclosure. The main concern was that all data from all sectors that could contain confidential information was placed in a central server. Federated learning can solve this problem by using several local model training servers for each region and a central aggregation server to form a global model in IoT networks. This article presents a novel approach to optimize data transfer and Convergence time in federated learning for a face recognition task using Non-dominated Sorting Genetic Algorithm II (NSGA II). The aim of the study is to balance the trade-off between training time and model accuracy in a federated learning environment. The results demonstrate the effectiveness of the proposed approach in reducing data transfer and Convergence time, leading to improved performance in face recognition accuracy. This research provides insights for researchers and practitioners to enhance the efficiency of federated learning in real-world applications.

In this paper, we consider an inverse problem for the time-fractional diffusion equation on the sphere where the final data on the sphere are given. The problem is ill-posed in the sense of Hadamard. Hence, the regularization method has to be used for the stable approximate solution. Then the well-posedness of the proposed regularizing problem and Convergence property of the regularizing solution to the exact one is proved. Error estimates for this method are provided together with a selection rule for the regularization parameter.

We discuss two new concepts of Convergence in Lp-spaces, the socalled weak S-Convergence and strong S-Convergence, which are intermediate between classical weak Convergence and strong Convergence. We also introduce the concept of S-Convergence for Radon measures. Our basic tool is the classical Gelfand representation theory. Apart from being a natural generalization of well-known two-scale Convergence theory, the present study lays the foundation of the mathematical framework that is needed to undertake a systematic study of deterministic homogenization problems beyond the usual periodic setting. A few homogenization problems are worked out by way of illustration.

media Convergence has resulted in globalization of the media industry while changing the basis of the economy of the industry and management of the media agencies, so that this change has relentlessly developed the workforce of the media companies. The issue has also changed the regime of industrial economy to a service economy. These changes have therefore created new agencies and caused their merger in various sectors of the media market and ultimately created developments in the principles of the global trade. The media Convergence has changed the media organizations and created particular developments in the principles of the global trade. This has caused expansion of the media companies in foreign dimensions. The technological Convergence has facilitated and expanded global exports and direct investment of the telecommunication companies in the media thus increasing the profits, merging transnational ownerships and creating a new wave of the merger of the media institutes. In this article we will try to answer these question: 1-what is the media Convergence.2-what is the impact of media Convergence on mass media legislation.