Impact Of The Effective Conducting Path Effect (ECPE) On The Convergence Of The Evanescent And The Polynomial Models: Applied To The Submicronic MOSFET

We present a comparative study of submicronic MOSFET characteristics using analytic models of electrostatic potential in the channel. We are particularly interested in the surface potential, threshold voltage, swing and DIBL using the polynomial model with and without ECPE and the evanescent model to analytically express the electrostatic potential. The results show a good agreement between the polynomial model including ECPE, the evanescent model and measures done by simulation tools.We present a comparative study of submicronic MOSFET characteristics using analytic models of electrostatic potential in the channel. We are particularly interested in the surface potential, threshold voltage, swing and DIBL using the polynomial model with and without ECPE and the evanescent model to analytically express the electrostatic potential. The results show a good agreement between the polynomial model including ECPE, the evanescent model and measures done by simulation tools

Rare event sampling with stochastic growth algorithms

We discuss uniform sampling algorithms that are based on stochastic growth methods, using sampling of extreme configurations of polymers in simple lattice models as a motivation. We shall show how a series of clever enhancements to a fifty-odd year old algorithm, the Rosenbluth method, led to a cutting-edge algorithm capable of uniform sampling of equilibrium statistical mechanical systems of polymers in situations where competing algorithms failed to perform well. Examples range from collapsed homo-polymers near sticky surfaces to models of protein folding.Comment: First International Conference on Numerical Physic

An Analysis and New Methodology for Reverse Engineering of UML Behavioral

The emergence of Unified Modeling Language (UML) as a standard for modeling systems has encouraged the use of automated software tools that facilitate the development process from analysis through coding. Reverse Engineering has become a viable method to measure an existing system and reconstruct the necessary model from its original. The Reverse Engineering of behavioral models consists in extracting high-level models that help understand the behavior of existing software systems. In this paper we present an ongoing work on extracting UML diagrams from object-oriented programming languages. we propose an approach for the reverse engineering of UML behavior from the analysis of execution traces produced dynamically by an object-oriented application using formal and semi-formal techniques for modeling the dynamic behavior of a system. Our methods show that this approach can produce UML behavioral diagrams in reasonable time and suggest that these diagrams are helpful in understanding the behavior of the underlying application

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks

Characterization of intestinal microbiota in celiac children

Celiac disease (CD) is enteropathy autoimmune induced by the ingestion of gluten in genetically predisposed subjects. The ingestion of gluten is responsible for the symptoms of CD, and a disturber of the intestinal microbiota. In this study, 13 Samples of intestinal biopsy, 15 fecal samples from celiac children, and 10 from controls children respectively were collected and analyzed by conventional culture technique to characterize the microbial profile intestinal of celiac children. There was 24 celiac children (8 boys), Mean age at diagnosis was 9.52 years, all have clinical manifestations, positive anti-transglutaminase antibodies and mucosal lesions suggestive of CD (Marsh Classification).We found a difference in intestinal microbiota, between celiac and controls children for example  the  Enterobacteria,  Clostridium sp and Staphylococcus sp were remarkably higher in biopsy and fecal samples of celiac children than in controls. Inversely the Enterococcus sp, Lactobacillus sp and Clostridium sp were slightly lower in celiac children. Our results indicate an imbalance in intestinal microbiota for celiac children as reduction in the numbers of Lactobacillus sp, Enterococcus sp and increases in the numbers of Enterobacteria, Staphylococcus sp and Clostridium sp.Keywords: Celiac disease, Intestinal Microbiota, Anti-transglutaminase, Lacobacillus sp