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Learning Automata (LAs) are adaptive decision-making models designed to find an appropriate action in unknown environments. LAs can be classified into two classes: variable structure and fixed structure. To the best of our knowledge, there is no hybrid model based on both of these classes. In this paper, we propose a model that brings together the benefits of both classes of LAs. In the proposed model, called an HLA, the action switching phase of a fixed structure learning automaton is fused with a variable structure learning automaton. Several computer simulations are conducted to study the performance of the proposed model with respect to the total number of rewards and action switching in addition to the convergence rate. The proposed model is compared to both variable structure and fixed structure learning automata, and in most cases, the numerical results demonstrate its superiority. In order to show the applicability of the HLA, a novel adaptive dropout mechanism in deep neural networks was suggested. The results of the simulations show that the proposed mechanism performs better than the simple dropout mechanism with respect to network accuracy. [ABSTRACT FROM AUTHOR]

Summary: Cognitive peer‐to‐peer networks are obtained from a combination of cognitive networking concepts and peer‐to‐peer networks. These networks are able to improve their performance while operating under dynamic and unknown environments. A cognitive peer‐to‐peer network tries to learn an appropriate configuration for itself considering the unknown physical properties of peers. Cognitive mobile peer‐to‐peer networks refer to cognitive peer‐to‐peer networks which are built over mobile ad hoc networks. In these networks, heterogeneity of the mobility of peers and resource limitation in wireless networks create challenges for network management algorithms. Because of the dynamicity of these networks, the management algorithms should be designated in self‐adaptive manner. In one type of these networks, some peers, called super‐peers, undertake to perform network managerial tasks. The mobility of peers leads to connection failure among peers and reselection of new super‐peers. Therefore, the selection of super‐peers, due to their influential role, requires an algorithm that considers the peers' mobility. Up to now, no self‐adaptive algorithm has been designated for super‐peer selection considering the mobility of peers in a self‐adaptive manner. This paper proposes M‐SSBLA, a self‐adaptive algorithm for super‐peer selection considering the mobility of peers based on learning automata. The proposed algorithm is obtained from cooperation between a learning automata‐based cognitive engine and MIS. MIS is a well‐known super‐peer selection algorithm in mobile peer‐to‐peer networks. We compared the proposed algorithm with recently reported algorithms, especially for a network with high mobility. Simulation results show that the proposed algorithm can cover maximum ordinary‐peer with a few super‐peer and improve robustness against super‐peer failures while decreasing maintenance overhead. [ABSTRACT FROM AUTHOR]

Quantum Machine Learning is an interdisciplinary field that combines the principles of quantum physics, quantum computers, and machine learning to enhance computational performance. In recent years, there have been significant advancements in the field of quantum machine learning. Researchers have developed quantum algorithms for various tasks. Furthermore, quantum machine learning has been applied to various domains, such as chemistry, cryptography, and finance. Previous bibliometric surveys in this area covered the period from 2014 to 2020. However, as demonstrated in this paper, the pace of publication has increased significantly from 2018 to the present. Therefore, it has become essential to provide a new analysis of the current state of research in this field. In this paper, we deployed visualization tools to analyze co-authorships, co-occurrences, and keyword density in this research area. Our study covers and analyzes a total of 918 publications from the Web of Science database and 1171 publications from the Scopus database from 2006 to 2022. Following the analysis, we identify research questions, opportunities, and research gaps in the field of Quantum Machine Learning. [ABSTRACT FROM AUTHOR]

Artificial immune systems (AIS), as nature-inspired algorithms, have been developed to solve various types of problems, ranging from machine learning to optimization. This paper proposes a novel hybrid model of AIS that incorporates cellular automata (CA), known as the cellular automata-based artificial immune system (CaAIS), specifically designed for dynamic optimization problems where the environment changes over time. In the proposed model, antibodies, representing nominal solutions, are distributed across a cellular grid that corresponds to the search space. These antibodies generate hyper-mutation clones at different times by interacting with neighboring cells in parallel, thereby producing different solutions. Through local interactions between neighboring cells, near-best parameters and near-optimal solutions are propagated throughout the search space. Iteratively, in each cell and in parallel, the most effective antibodies are retained as memory. In contrast, weak antibodies are removed and replaced with new antibodies until stopping criteria are met. The CaAIS combines cellular automata computational power with AIS optimization capability. To evaluate the CaAIS performance, several experiments have been conducted on the Moving Peaks Benchmark. These experiments consider different configurations such as neighborhood size and re-randomization of antibodies. The simulation results statistically demonstrate the superiority of the CaAIS over other artificial immune system algorithms in most cases, particularly in dynamic environments. [ABSTRACT FROM AUTHOR]

Cellular learning automata (CLAs) are learning models that bring together the computational power of cellular automata and also the learning capability of learning automata in unknown environments. CLAs can be open or closed. In a closed CLA , the action of each learning automaton depends on the neighboring cells, whereas in an open CLA , the action of each learning automaton depends on the neighboring cells, and a global environment. These models can be synchronous or asynchronous . In a synchronous CLA , all cells are activated at the same time, but in an asynchronous CLA , at a given time only some cells are activated. These models can be also static or dynamic . In a dynamic CLA, one of its aspects such as structure, local rule or neighborhood may vary with time . All existing dynamic models of the CLAs are closed. In this paper, an open asynchronous dynamic CLA has been introduced. In order to show the potential of this model, an algorithm based on this model for solving allocation hub location problem with imprecise distances among nodes has been designed. To evaluate the proposed algorithm computer simulations have been conducted. The results of simulations show that the proposed algorithm is more robust to imprecise distances as compared to existing algorithms. [ABSTRACT FROM AUTHOR]

Peer-to-peer networks are overlay networks that are built on top of communication networks that are called underlay networks. In these networks, peers are unaware of the underlying networks, so the peers choose their neighbors without considering the underlay positions, and therefore, the resultant overlay network may have mismatches with its underlying network, causing redundant end-to-end delay. Landmark clustering algorithms, such as mOverlay, are used to solve topology mismatch problem. In the mOverlay algorithm, the overlay network is formed by clusters in which each cluster has a landmark peer. One of the drawbacks of mOverlay is that the selected landmark peer for each cluster is fixed during the operation of the network. Because of the dynamic nature of peer-to-peer networks, using a non-adaptive landmark selection algorithm may not be appropriate. In this paper, an adaptive landmark clustering algorithm obtained from the combination of mOverlay and learning automata is proposed. Learning automata are used to adaptively select appropriate landmark peers for the clusters in such a way that the total communication delay will be minimized. Simulation results have shown that the proposed algorithm outperforms the existing algorithms with respect to communication delay and average round-trip time between peers within clusters. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An obituary for a doctor of pediatrics, Hajar Afsar Lajevardi, is presented.

The Internet of Things (IoT) is an increasingly important part of social and economic developments around the world which supports homes and industrial automatons. The most important change brought about by IoT in the home is the automation of tasks (adjusting heating, cooling, and security systems). There is no longer a need for manual controls in homes as these can be operated everywhere at every time. In recent years, traditional industries in Iran have been transforming into modern and smart industries. IoT-based home industries in Iran are keeping up with developments in modern countries. In the literature, there has been no analysis or investigation to establish a strategic plan for the improvement and development of IoT-based home industries in Iran. In response, we decided to propose a set of strategies for improving these industries. The goal of this article is therefore to extract various strategies for the IoT home industries in Iran. In reviewing the literature, we used some techniques from the field of strategic management to achieve our goal. To propose an optimal direction for IoT-based home industries in Iran, we used external factors (opportunities and threats) and internal factors (strengths and weaknesses) to determine the best strategies which will improve the deployment of these industries in Iran. [ABSTRACT FROM AUTHOR]

Background: This paper outlines the current status and mechanism for identifying dental implants, with emphasis on future direction and updated technology, and covers the existing factors influencing the identification of implant systems. Main body: A search was performed on the current methods of identifying dental implants between January 2000 through Feb 2020 using online databases for articles published in English. The search was performed using the Google, Rutgers library, PubMed, MEDLINE databases via OVID using the following keywords: implant types identification by x-ray imaging, forensic identification of dental implant, surface types, threaded, non-threaded, software identification, recent technologies, which evaluated different methods in the identification of dental implants and its clinical importance for the dentist and the patient. Of the 387 articles found in initial search results, 10 met the inclusion criteria set for this review. These 10 studies were directly related to the identification of different implant systems. Many studies have indicated identifying dental implants as problematic due to many confounding factors, and the difficulty in finding the specific parts for the dental implant itself. The contribution of digital dentistry is critical. Factors like increasing number of implant manufacturers, dental tourism, and cost, make it difficult to detect and match dental implants by dentists during the chairside time. Conclusion: These factors give rise to the need for a new system to help clinicians in decision making. Artificial intelligence seems to have shown potential to help in this case. However, detailed regulatory mechanisms are still needed for diagnosis and analysis. [ABSTRACT FROM AUTHOR]

Objective. A scanning electron microscope (SEM) is a popular tool for investigating the root canal surface to visualize dentinal tubules, the smear layer and various root canal filling materials in endodontics. Most of the SEM micrographs taken in endodontic research are in secondary electrons (SE) mode, in which the topographic view of a subject can be demonstrated without giving any information about the real structure. Back-scattered electron (BSE) images are also used, which reveal some information about the internal structure while providing no topographic details. The aim of this study was to investigate the possibility of using back-scattered (BSE) and secondary electron (SE) mode of scanning electron microscopy (SEM) together for obtaining detailed information about biomaterials in relation to dental structures. Materials and methods. Mesiobuccal roots of four permanent maxillary molars were cleaned and shaped with rotary instruments. Two samples were obturated with gutta-percha and sealer. After 2 weeks, gutta-perch was removed using rotary instruments and chloroform. In the other phase of the study, white mineral trioxide aggregate was mixed and packed into five glass tubes and exposed to blood, deionized water, synthetic tissue fluid and egg white. All the samples were prepared for visualization under SE and BSE modes of SEM to observe the characteristics of material remnants and surface structures. Results. BSE mode illustrated different grey scale views which made it possible to differentiate dentin chips from filling material remnants on the surface of root canal dentin. In addition, SE mode focused on image topography, while a BSE detector showed new texture formation on the surface of white mineral trioxide aggregate exposed to proteinaceous fluids such as blood or egg white. Conclusions. Mapping BSE and SE micrographs helped us to better understand the structure of materials on the surface of root canal dentin and MTA. Moreover, analysis of structure of materials on the surface of root canal dentine and MTA can be performed better by mapping of BSE and SE micrographs. [ABSTRACT FROM AUTHOR]

Background: This paper outlines the current status and mechanism for identifying dental implants, with emphasis on future direction and updated technology, and covers the existing factors influencing the identification of implant systems. Main body: A search was performed on the current methods of identifying dental implants between January 2000 through Feb 2020 using online databases for articles published in English. The search was performed using the Google, Rutgers library, PubMed, MEDLINE databases via OVID using the following keywords: implant types identification by x-ray imaging, forensic identification of dental implant, surface types, threaded, non-threaded, software identification, recent technologies, which evaluated different methods in the identification of dental implants and its clinical importance for the dentist and the patient. Of the 387 articles found in initial search results, 10 met the inclusion criteria set for this review. These 10 studies were directly related to the identification of different implant systems. Many studies have indicated identifying dental implants as problematic due to many confounding factors, and the difficulty in finding the specific parts for the dental implant itself. The contribution of digital dentistry is critical. Factors like increasing number of implant manufacturers, dental tourism, and cost, make it difficult to detect and match dental implants by dentists during the chairside time. Conclusion: These factors give rise to the need for a new system to help clinicians in decision making. Artificial intelligence seems to have shown potential to help in this case. However, detailed regulatory mechanisms are still needed for diagnosis and analysis. [ABSTRACT FROM AUTHOR]

Diabetes associated complications, including diabetic retinopathy and loss of vision, are major health concerns. Detecting early retinal vascular changes during diabetes is not well documented, and only few studies have addressed this domain. The purpose of this study was to noninvasively evaluate temporal changes in retinal vasculature at very early stages of diabetes using fundus images from preclinical models of diabetes. Non-diabetic and Akita/+ male mice with different duration of diabetes were subjected to fundus imaging using a Micron III imaging system. The images were obtained from 4 weeks- (onset of diabetes), 8 weeks-, 16 weeks-, and 24 weeks-old male Akita/+ and non-diabetic mice. In total 104 fundus images were subjected to analysis for various feature extractions. A combination of Canny Edge Detector and Angiogenesis Analyzer plug-ins in ImageJ were utilized to quantify various retinal vascular changes in fundus images. Statistical analyses were conducted to determine significant differences in the various extracted features from fundus images of diabetic and non-diabetic animals. Our novel image analysis method led to extraction of over 20 features. These results indicated that some of these features were significantly changed with a short duration of diabetes, and others remained the same but changed after longer duration of diabetes. These patterns likely distinguish acute (protective) and chronic (damaging) associated changes with diabetes. We show that with a combination of various plugging one can extract over 20 features from retinal vasculature fundus images. These features change during diabetes, thus allowing the quantification of quality of retinal vascular architecture as biomarkers for disease progression. In addition, our method was able to identify unique differences among diabetic mice with different duration of diabetes. The ability to noninvasively detect temporal retinal vascular changes during diabetes could lead to identification of specific markers important in the development and progression of diabetes mediated-microvascular changes, evaluation of therapeutic interventions, and eventual reversal of these changes in order to stop or delay disease progression. [ABSTRACT FROM AUTHOR]