The bond of near-surface mounted reinforcement to low-strength concrete

This paper focuses on the bond between near-surface mounted (NSM) reinforcement and low-strength concrete. In order to investigate this, eight beams made of low-strength concrete were made. The compressive strength of this concrete varied from 14.22?MPa to 16.83?MPa. These beams were then tensioned under monotonic loading until failure. The test setups differed in terms of their groove size and the type of reinforcement (a rod and plate of carbon fiber reinforced polymer, prestressing steel). Based on the achieved results and analysis, it was found that the NSM method can be applied to low-strength concrete. Furthermore, the application of a NSM reinforcement rod and plate, made of the carbon fiber reinforced polymer, and prestressing steel showed a satisfactory bond strength when compared to low-strength concrete. However, the carbon plates performed better in terms of failure load and rate use than the rods made of carbon and the prestressing steel. Moreover, the results showed that the increase of groove size for the near-surface mounted reinforcement made of prestressing steel did not have an effect on the failure mode. In addition, a significant increase of the failure load was observed for the prestressing steel. Finally, the effect of concrete strength was analyzed and compared with the results found in literature.     

How fuzzy logic can improve PEM fuel cell MPPT performances?

This paper addresses the development of new variable step size fuzzy based MPPT controller. In this study, the fuzzy logic approach is firstly used to auto-scale the variable step size of the Incremental Conductance (IC) MPPT controller. Secondly, the proposed variable step size fuzzy based MPPT controller is used to track the output power of the PEM fuel cell system composed of 7 kW fuel cell supplying a 50Ω resistive load via a DC-DC boost converter controlled using the proposed MPPT. The proposed variable step size fuzzy-based MPPT controller is compared to the conventional fixed step size IC, the variable step size IC and the fuzzy scaled variable step size IC MPPTs using the implemented Matlab/Simulink PEM Fuel Cell power system model. Comparative simulation results between the four studied MPPTs show better performances for the proposed fuzzy based variable step size MPPT in term of: response time reduction between 3.6% and 82.35%; overshoot reduction between 34.55% and 100%; and ripple reduction between 70.93% and 100%, improving as consequence the fuel cell lifetime affected by high current ripple.     

Hydrogen production over the hetero-junction MnO2/SiO2

The motivation of this work is the hydrogen production as a non-polluting renewable energy under visible light. For this purpose, the hetero-junction 2%MnO₂/SiO₂ was synthetized by the impregnation method and characterized by X-ray diffraction, scanning electron microcopy (MEB), FTIR spectroscopy, diffuse reflectance and electrical conductivity. The X-ray analysis shows the amorphous silica substrate and the rutile phase MnO₂. The optical curve (αhν)² as a function of hν shows a direct transition of 1.83 eV for MnO₂. The capacitance measurement of the rutile indicates a flat band potential of ?0.027 V_SCE, more negative than the hydrogen evolution in SO₃^2? aqueous solution. As application, hydrogen was successfully evolved over the hetero-junction MnO₂/SiO₂. A good H₂ production was obtained after 30 min of irradiation corresponding to 5.1 mmol h^?1 g^?1 under visible light (29 mWcm^?2). The improved photocatalytic performance is due to the sensitizer MnO₂ homogeneously supported on porous SiO₂ substrate acting as templating agent.     

Relation between mechanical microhardness and impedance variations in eddy current testing

According to the state of the art, eddy current testing (EC) has a strong application in defect detection. The sensitivity to characterize defects and other parameters can be improved by an optimal choice of probes and operation frequency.The work discussed here is a study to materials microstructure characterization and especially the heat affected zones (HAZ) near welds using eddy current techniques.These zones are sometimes brittle and characterized by a microstructure gradient. The aim of this work is to study by eddy current the behavior of such cases. For each damage mechanism, the authors have prepared series of samples. These specimens were tested using NDT methods based on magnetic and electric properties.Electric conductive material samples were made of Aluminum and steel and heated at different temperatures with variable exposure times.The aluminum samples were then exposed to corrosion using an electrochemical process.The applied NDT methods can characterize grain size changes, microstructure types, micro structural changes, hardness changes after thermal treatment.The objective of our work is to detect some metallurgical characteristics by non-destructive methods. The characterization of the microstructure modifications by eddy currents allows detecting mechanical and metallurgical parameters of materials.     

An evidential fusion approach for activity recognition in ambient intelligence environments

With the growing emergence of ambient intelligence, ubiquitous computing, sensor networks and wireless networking technologies, “ubiquitous networked robotics” is becoming an active research domain of intelligent autonomous systems. It targets new innovative applications in which robotic systems will become part of these networks of artifacts to provide novel capabilities and various assistive services anywhere and anytime, such as healthcare and monitoring services for elderly in Ambient Assisted Living (AAL) environments. Situation recognition, in general, and activity recognition, in particular, provide an added value on the contextual information that can help the ubiquitous networked robot to autonomously provide the best service that meet the needs of the elderly. Dempster–Shafer theory of evidence and its derivatives are an efficient tool to handle uncertainty and incompleteness in smart homes and ubiquitous computing environments. However, their combination rules yield counter-intuitive results in high conflicting activities. In this paper, we propose a new approach to support conflict resolution in activity recognition in AAL environments. This approach is based on a new mapping for conflict evidential fusion to increase the efficiency and accuracy of activity recognition. It gives intuitive interpretation for combining multiple sources in all conflicting situations. The proposed approach, evaluated on a real world smart home dataset, achieves 78% of accuracy in activity recognition. The obtained results outperform those obtained with the existing combination rules.     

Adaptive GA-based reconfiguration of photovoltaic array combating partial shading conditions

This paper concerns the study and simulation of a PV array self-organizing configuration. It introduces a new method to reconfigure the PV array using a genetic algorithm in order to maximize the output power as well as reducing the number of switching. The proposed method involves the simulation of a PV array composed of 16 panels 4 strings with 4 panels in series and associated parallel, as well as an algorithm that controls the improvement of the overall performance under different shading conditions. The obtained results using MATLAB/Simulink simulation show improvement rating varying between 106.49 and 171.03%, which is huge compared to a static configuration operating below the total available power. Another important point is the number of iterations needed to find the optimal configuration (between 6 and 132 for a population of 50 configurations tested at each generation); this means that in the worst case (132 iterations), the proposed algorithm performed 132 × 50 = 6600 configurations instead of 16¹⁶ = 1.84 × 10¹⁹ necessary in case of exhaustive search to test all possible configurations. This last point is very important in the implementation of the proposed system in auto-tuning of the system in real-time condition. Besides using genetic algorithm to track the optimal configuration, our main contribution consists of improving the output power while reducing the number of switching by keeping PV modules, if possible, in same position (0 switching) or on the same line/column (1 switching) in few iteration needing only two sensors one for the voltage and another for the current of the PV array.

     

An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem

Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partial FJSP and Total FJSP, are tested. Experimental results proved that the developed PSO is enough effective and efficient to solve the FJSP. Our other objective in this paper, is to study the distribution of the PSO-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events. For this aim, two multi-agent based approaches are proposed and compared using different benchmark instances.     

Crystallization kinetics study of dynamically vulcanized PA6/NBR/HNTs nanocomposites by nonisothermal differential scanning calorimetry

Investigation of crystallization behavior and kinetics of thermoplastic elastomer nanocomposites was the subject of limited works because of complexities associated with semiexperimental modeling of such phenomenon in a system containing components having completely different behavior in the molten state. Nonisothermal crystallization kinetics of dynamically vulcanized PA6/NBR/HNTs thermoplastic elastomer nanocomposites was mathematically modeled applying well‐known Avrami, Ozawa, and Mo theoretical models to the differential scanning calorimetry data gathered at various cooling rates. It was found that HNTs contribute as nucleating agents to the crystallization kinetics and cause acceleration of crystallization. Activation energy of the crystallization was calculated by correlating the crystallization peak temperature with the cooling rate using Kissinger model. It was found that Mo equation could properly describe nonisothermal crystallization kinetics of the PA6/NBR/HNTs thermoplastic elastomer nanocomposites. This was recognized from the obtained parameters of Mo equation in terms of HNTs loading level, which suggested a higher rate for dynamic crystallization. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46488.     

Characterization of tin dioxide film for chemical vapors sensor

Recently, oxide semiconductor material used as transducer has been the central topic of many studies for gas sensor. In this paper we investigated the characteristic of a thick film of tin dioxide (SnO₂) film for chemical vapor sensor. It has been prepared by screen-printing technology and deposited on alumina substrate provided with two gold electrodes. The morphology, the molecular composition and the electrical properties of this material have been characterized respectively by Atomic Force Spectroscopy (AFM), Fourier Transformed Infrared Spectroscopy (FTIR) and Impedance Spectroscopy (IS). The electrical properties showed a resistive behaviour of this material less than 300 °C which is the operating temperature of the sensor. The developed sensor can identify the nature of the detected gas, oxidizing or reducing.