Early detection of corrosion activity in reinforced concrete slab by AE technique

Chloride induced corrosion of steel reinforcement in concrete is the main cause of damage to concrete structures in marine, or structures exposed to de-icing salt environments. The need for early detection of corrosion in reinforced concrete structures is widely acknowledged in corrosion health monitoring for strategic civil engineering projects. This paper summarizes results from a laboratory experiment, using Acoustic Emission (AE) technique, regarding early detection of corrosion in reinforced concrete slabs that are exposed to chloride. The Results showed that while AE was able to determine the initiation of corrosion in reinforced concrete slab, Half-cell potential (HCP) was not able to detect it at the early stage of corrosion

Lack of association between TRAF1/C5 rs10818488 polymorphism and rheumatoid arthritis in Iranian population

Rheumatoid arthritis (RA) is a multifactorial disorder related to the inflammatory response system with debilitating and painful conditions. Both genetic and environmental factors, with unknown etiology, play important roles in this disease pathogenesis. Recently, TRAF1/C5 (Tumor Necrosis Factor Receptor-Associated Factor 1/. Complement Component 5) polymorphism associated with increased risk for RA has been studied in different populations worldwide, and inconsistent results have been obtained. rs10818488 allele is located on TRAF1/C5 intergenic region, and has been predicted to be functional. A total of 100 sex- and age-matched people including RA patients (n= 50) and healthy individuals (n= 50) from Iran have been entered in this study and genotyped for rs10818488 (A/G) polymorphism, using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP). In our study, rs10818488 allele was not associated with risk for RA in Iranian population (p>. 0.05, OR = 1.27, 95% CI = 0.72-2.23). Results revealed that this allele might be population-specific and not to be associated with their corresponding gene pool. However, further analyses are required to clarify other RA-associated markers in our community. © 2012

A Real-time Global Optimal Path Planning for mobile robot in Dynamic Environment Based on Artificial Immune Approach

This paper illustrates a method to finding a globaloptimal path in a dynamic environment of known obstacles foran Mobile Robot (MR) to following a moving target. Firstly, theenvironment is defined by using a practical and standard graphtheory. Then, a suboptimal path is obtained by using DijkstraAlgorithm (DA) that is a standard graph searching method. Theadvantages of using DA are; elimination the uncertainness ofheuristic algorithms and increasing the speed, precision andperformance of them. Finally, Continuous Clonal SelectionAlgorithm (CCSA) that is combined with Negative SelectionAlgorithm (NSA) is used to improve the suboptimal path andderive global optimal path. To show the effectiveness of themethod it is compared with some other methods in this area

A comparative survey of abundance and biomass of Caspian Sea macrobenthos in coastal waters of Mazandaran Province

Caspian Sea macrobenthos was surveyed every two months from December 2007 to October 2008, in the west, east and central parts of Mazandaran province waters. Each area was sampled with 3 replicates at 2 depths of 5 and 10m by Van Veen grab. Five different classes were recognized, including Polychaeta (52.7%), Oligochaeta (27.8%), Bivalvia (12%), Cnistacea (7.5%) and Insects (0.07%). Total mean (LSD) abundance and biomass were 2727± 1303 individual/m2 and 88.9±22.93, respectively. The Polychaeta demonstrated the highest abundance and Bivalvia had the highest biomass. The highest abundance of macrobenthos was found in eastern and the highest biomass in western coasts of Mazandaran. In August 2008, macrobenthos abundance showed higher values. In October, remarkable difference was observed between the abundance of Polychaeta and other macrobenthos organisms. According to Kniskal-Wallis test, abundance and biomass of the entire macrobenthos classes except Insects, showed a significant difference between sampling months (P<0.05). Macrobenthos biomass had no significant difference among the three areas whereas abundance demonstrated a significant difference within these areas (P< 0.05)

Rate splitting in MIMO RIS-assisted systems with hardware impairments and improper signaling

In this paper, we propose an optimization framework for rate splitting (RS) techniques in multiple-input multiple-output (MIMO) reconfigurable intelligent surface (RIS)-assisted systems, possibly with I/Q imbalance (IQI). This framework can be applied to any optimization problem in which the objective and/or constraints are linear functions of the rates and/or transmit covariance matrices. Such problems include minimum-weighted and weighted-sum rate maximization, total power minimization for a target rate, minimum-weighted energy efficiency (EE) and global EE maximization. The framework may be applied to any interference-limited system with hardware impairments. For the sake of illustration, we consider a multicell MIMO RIS-assisted broadcast channel (BC) in which the base stations (BSs) and/or the users may suffer from IQI. Since IQI generates improper noise, we consider improper Gaussian signaling (IGS) as an interference-management technique that can additionally compensate for IQI. We show that RS when combined with IGS can substantially improve the spectral and energy efficiency of overloaded networks (i.e., when the number of users per cell is larger than the number of transmit/receive antennas).The work of Ignacio Santamaria has been partly supported by the project ADELE PID2019-104958RB-C43, funded by MCIN/AEI/10.13039/501100011033. The work of Eduard Jorswieck was supported in part by the Federal Ministry of Education and Research (BMBF, Germany) in the program of “Souver¨an. Digital. Vernetzt.” joint project 6G-RIC, project identification number: 16KISK020K and 16KISK031

GASL: Guided Attention for Sparsity Learning in Deep Neural Networks

The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the computational speedup. In most of the previous research works, sparsity is imposed stochastically without considering any prior knowledge of the weights distribution or other internal network characteristics. Enforcing too much sparsity may induce accuracy drop due to the fact that a lot of important elements might have been eliminated. In this paper, we propose Guided Attention for Sparsity Learning (GASL) to achieve (1) model compression by having less number of elements and speed-up; (2) prevent the accuracy drop by supervising the sparsity operation via a guided attention mechanism and (3) introduce a generic mechanism that can be adapted for any type of architecture; Our work is aimed at providing a framework based on interpretable attention mechanisms for imposing structured and non-structured sparsity in deep neural networks. For Cifar-100 experiments, we achieved the state-of-the-art sparsity level and 2.91x speedup with competitive accuracy compared to the best method. For MNIST and LeNet architecture we also achieved the highest sparsity and speedup level