Electroweak Phase Transition, Gravitational Waves and Dark Matter in Two Scalar Singlet Extension of The Standard Model

In this paper, the electroweak phase transition, the gravitational waves and the dark matter issues are investigated in two scalar singlet extension of the standard model. The detectability of the gravitational wave signals are discussed by comparing the results with the sensitivity curves of $\mathbf{eLISA}$, $\mathbf{ALIA}$, $\mathbf{DECIGO}$ and $\mathbf{BBO}$ detectors. It is shown that the results support the recent reports on the dark matter relic density by $\mathbf{Planck}$ $\mathbf{2018}$ collaboration and the direct detection experiment by $\mathbf{XENON1T}$ $\mathbf{2018}$ collaboration.Comment: 18 pages, 2 figures (4 subfigures), 4 tables, final version to match the published versio

Mass of the Stabilized Radion in the Limit of Finite Quartic Coupling

We present an exact treatment of the modulus stabilization condition with the general boundary conditions of the bulk scalar field in the Randall-Sundrum model. We find analytical expressions for the value of the modulus and the mass of the radion.Comment: 11 pages, 3 figures, Accepted for publication in AHE

Strong Electroweak Phase Transition in a Model with Extended Scalar Sector

We consider an extension of the Standard Model (SM) with additional gauge singlets which exhibits a strong first-order phase transition. Due to this first-order phase transition in the early universe gravitational waves are produced. We estimate the contributions such as the sound wave, the bubble wall collision, and the plasma turbulence to the stochastic gravitational wave background, and we find that the strength at the peak frequency is large enough to be detected at future gravitational interferometers such as eLISA. Deviations in the various Higgs boson self-couplings are also evaluated

Resilient and Sustainable Modular System for Temporary Sheltering in Emergency Condition

[EN] During the hazard impact, it is very important to manage the emergency condition. Temporary sheltering is one of the preliminary and main requirements of disaster management. COVID 19 poses the necessity of using fast and modular temporary sheltering in the crowded cities to improve treating and curing services for the hospitals. However, successful emergency management for current societies is achievable if the resilience approach has been implemented in all procedures of emergency management. The concept of resilience could make a new sense of motivation in disaster management while recent research shows that resilience makes a significant improvement in the traditional approach of safety and security during disasters. Temporary shelters play an important role in the temporary settlement and also commanding the emergency condition during a disaster period. This study aims to develop a resilient modular design of shelters based on a sustainable industrialized Building system (IBS) under the main critical success factors with the approach of resilience and sustainability. Critical success factors (CSFs), resilience and sustainability criteria are extracted from literature and the CSFs are evaluated based on the questionnaire survey and using VIKOR as a multi-criteria decision-making method. The reduction of mortar usage, IBS, and Interconnected structure are the most impressive factors. Based on these factors, the symmetric orthogonal modular system was selected. The robustness of the selected system was calculated under the explosive load test. Interconnectivity, modularity, mortar-less erecting, disassembling and reassembling abilities are some of the advantages. They improve rapidity, transformability of this structure following capacities of resilience.Nekooie, MA.; Tofighi, M. (2020). Resilient and Sustainable Modular System for Temporary Sheltering in Emergency Condition. VITRUVIO - International Journal of Architectural Technology and Sustainability. 5(2):1-15. https://doi.org/10.4995/vitruvio-ijats.2020.11946OJS1155

Hand Pointing Detection Using Live Histogram Template of Forehead Skin

Hand pointing detection has multiple applications in many fields such as virtual reality and control devices in smart homes. In this paper, we proposed a novel approach to detect pointing vector in 2D space of a room. After background subtraction, face and forehead is detected. In the second step, forehead skin H-S plane histograms in HSV space is calculated. By using these histogram templates of users skin, and back projection method, skin areas are detected. The contours of hand are extracted using Freeman chain code algorithm. Next step is finding fingertips. Points in hand contour which are candidates for the fingertip can be found in convex defects of convex hull and contour. We introduced a novel method for finding the fingertip based on the special points on the contour and their relationships. Our approach detects hand-pointing vectors in live video from a common webcam with 94%TP and 85%TN.Comment: Accepted for oral presentation in DSP201

Comparison of the function of ELM and RBF models for estimating the porosity of the Asmari Formation, in one of the offshore fields of the northwest Persian Gulf

Abstract Nowadays, the use of artificial intelligence is common to increase the accuracy of the study and, close to reality, is used in the oil industry to increase the accuracy of studying and understanding the relationship between various parameters. The main purpose of this study is to compare the performance of the two methods of Extreme Learning Machine (ELM) and Radial Basis Function (RBF) in porosity estimation, which is static oil modeling. The data from seven wells in the offshore field (Hendijan Oilfield) of the northwestern Persian Gulf were examined. In this regard, post-stack seismic attributes which have a significant relationship with porosity and porosity log for each well were used to compare the performance of the ELM and RBF networks under the same conditions. Eventually, it reveals that ELM is quite sensitive to the data set and needs more data points to prepare a map (quantitatively), but is better than RBF in terms of classification (qualitative). On the other hand, RBF is one of the most powerful algorithms in mapping, especially in low numbers of data points, which can be challenging for others