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.

Gravitational search algorithm (GSA) is one of the newest swarm based optimization algorithms, which has been inspired by the Newtonian laws of gravity and motion. GSA has empirically shown to be an efficient and robust stochastic search algorithm. Since introducing GSA a Convergence analysis of this algorithm has not yet been developed. This paper introduces the first attempt to a formal Convergence analysis of the standard gravitational search algorithm which involves with randomness and time varying parameters. In this analysis the behavior of GSA on the facet of mass interaction is considered. The paper provides a formal proof that each object converges to a stable point.

Governments resort to instruments of power towards continuing and flourishing their political life seeking to guarantee their national security and interests through increasing their national power. One of the ways of acquiring power and enhancing it is Convergence and creation of beyond-national institutions in one region. Taking a descriptive analytical method, the present article seeks to study and analyze Convergence experiences in the Muslim world. It is an attempt to show that European Union has managed to reach most of its objectives in the light of the theories of Convergence. However, Muslim countries have not been able to take an effective step towards Convergence up to now. In fact, various initiatives have been made towards Convergence since the establishment of the nation-state system in Muslim lands. However in practice, some of them have failed and some others have continued their activities hoping to succeed in future. This failure is due to different conditions governing Muslim countries. In fact there are great obstacles to Convergence in the Muslim world many of which are not found in the free societies of the west. These obstacles are internal despotism, political differences, chauvinism, political instability, regional rivalries, colonialism and Zionism. Out of these, internal despotism and colonizers domination are among the most significant factors intervening the process of Convergence of the Muslim world. As a result, the conventional theories of Convergence are not enough for the realization of this goal in the Islamic countries. Muslim countries are in need of an example capable of removing these obstacles decisively. This example does not come about but in the light of the theory of Islam. Beyond the conventional theories of Convergence and as the greatest unifying factor among Muslims, Islam is able to put the Muslim world in the path of unity and integrity.

We study the space of all continuous fuzzy-valued functions from a space X into the space of fuzzy numbers (e1, d¥) endowed with the pointwise Convergence topology. Our results generalize the classical ones for continuous real-valued functions. The field of applications of this approach seems to be large, since the classical case allows many known devices to be fitted to general topology, functional analysis, coding theory, Boolean rings, etc.

In this paper, the Convergence and stability conditions of extended DMC in the control of nonlinear SISO and MIMO systems are investigated. The formulations are based on the ordinary DMC in which, with successive linearization of the nonlinear model and new interpretation of disturbance, the nonlinear extension is deduced. In addition, new Convergence and stability criteria are derived for SISO and MIMO systems. These criteria include Convergence and stability in the case of longer control (M>1) and prediction (P>1) horizons, as well as the finite and infinite sampling time. Finally, the simulation results for a MIMO (3\times 3) model, based on a power unit nonlinear plant, are presented.