Numerical Simulation of Thermal Stresses in Prismatic Concrete Beams Reinforced with FRP Bars under Low Temperatures

The thermal properties of fiber reinforced polymer (FRP) bars particularly in the transverse direction are higher than those of hardened concrete and steel bars. The difference in transverse thermal characteristics between FRP bar and concrete generates radial tensile stresses within concrete at the interface of FRP bars/concrete under low temperatures. These thermal stresses may cause circumferential cracks in concrete at the interface and eventually the reduction of the bond that can affect significantly the serviceability of reinforced concrete structures. This paper presents a nonlinear numerical simulation of thermal stresses in prismatic concrete beams reinforced with glass FRP (GFRP) bars submitted to low temperatures when the confining action of concrete is asymmetric. The non linear numerical analysis shows that the first circumferential cracks start to develop within concrete at FRP bar/concrete interface at a temperature decrease DTcr varied between -30°C and -25°C for prismatic concrete beams reinforced with GFRP bars having a ratio of concrete cover thickness to FRP bar diameter (c/db) varied from 1.0 to 3.2. Furthermore, the depths of circumferential cracks propagated from the interface through the concrete cover increase with the decrease of the thermal load DT (from -25 °C to -50 °C). These depths did not reach the outer surface of the concrete cover under low temperatures up to -50 °C. Also, the radial tensile stress at FRP bar/concrete interface increases with the increase in the ratio c/db. The cracking thermal loads and thermal stresses predicted from nonlinear finite element model are compared to those evaluated with analytical models. Comparisons between numerical and analytical results in terms of cracking thermal loads and thermal stresses are presented

Estimation of Properties of Liquid-Vapor Mixture of Some Refrigerants at High Pressure for Solar-Photovoltaic Refrigeration

Abstract. In this work, a hybrid method based on neural network and particle swarm optimization is applied to literature data to develop and validate a model that can predict with precision vapor-liquid equilibrium data for the binary systems (hexafluoroethane (R116(1)), 1,1,1,2-tetrafluoroethane (R134a) and R1234ze) . ANN was used for modelling the non-linear process. The PSO was used for two purposes: replacing the standard back propagation in training the ANN and optimizing the process. The training and validation strategy has been focused on the use of a validation agreement vector, determined from linear regression analysis of the predicted versus experimental outputs, as an indication of the predictive ability of the neural network model. Statistical analysis of the predictability of the optimized neural network model shows excellent agreement with experimental data (coefficient of correlation equal to 0.998). Furthermore, the comparison in terms of average relative deviation (AARD%) between, the predicted results for the whole temperature and pressure range shows that the ANN-PSO model can predict far better the mixture properties than cubic equations of state

A Different Look at Lenin's Legacy: Trust, Risk, Fairness and Cooperativeness in the Two Germanies

What are the long-term effects of Communism on economically relevant notions such as social trust? To answer this question, we use the reunification of Germany as a natural experiment and study the post-reunification trajectory of convergence with regard to individuals' trust and risk, as well as perceived fairness and cooperativeness. Our hypotheses are derived from a model of German reunification that incorporates individual responses both to incentives and to values inherited from earlier generations as recently suggested in the literature. Using data from the German Socio-Economic Panel, we find that despite twenty years of reunification East Germans are still characterized by a persistent level of social distrust. In comparison to West Germans, they are also less inclined to see others as fair or helpful. Implied trajectories can be interpreted as evidence for the passing of cultural traits across generations and for cooperation being sustained by values rather than by reputation. Moreover, East Germans are found to be more risk loving than West Germans. In contrast to trust and fairness, full convergence in risk attitude is reached in recent years

Microwave Correlation Measurement Crossed-pair Antennas Techniques Diagnosis of Breast Cancer

Microwave correlation radiometry is a possible alternative technique to multi-frequency radiometry for obtaining a measurement of the subcutaneous temperature gradient in living tissue. In its simplest form this technique consists of combining the outputs of two antennas feeding a microwave correlation radiometer. Microwave correlation is based on the coherent detection of noise, here presented as the correlated signal from the two antennas. We propose here new processes, an add and square correlation radiometer and the non-resonant perturbation, which thoroughly investigated for different muscle phantom materials to define the optimum penetration depth of the electromagnetic field at fixed distance between the antennas. Keywords: Microwave correlation radiometer, non-resonant perturbation, Medical application Technologies Avancees Vol. 18 2006: pp. 12-1

Screening for genes coding for putative antitumor compounds, antimicrobial and enzymatic activities from haloalkalitolerant and haloalkaliphilic bacteria strains of Algerian Sahara soils

Extreme environments may often contain unusual bacterial groups whose physiology is distinct from those of normal environments. To satisfy the need for new bioactive pharmaceuticals compounds and enzymes, we report here the isolation of novel bacteria from an extreme environment. Thirteen selected haloalkalitolerant and haloalkaliphilic bacteria were isolated from Algerian Sahara Desert soils. These isolates were screened for the presence of genes coding for putative antitumor compounds using PCR based methods. Enzymatic, antibacterial, and antifungal activities were determined by using cultural dependant methods. Several of these isolates are typical of desert and alkaline saline soils, but, in addition, we report for the first time the presence of a potential new member of the genus Nocardiawith particular activity against the yeast Saccharomyces cerevisiae. In addition to their haloalkali character, the presence of genes coding for putative antitumor compounds, combined with the antimicrobial activity against a broad range of indicator strains and their enzymatic potential, makes them suitable for biotechnology applications

Silene aristidis

International audienceThis species is endemic to the "Kabylies-Numidia-Kroumiria" plant diversity hotspot (Véla and Benhouhou 2007) in northern Algeria. The extent of occurrence (EOO) is estimated at 2,623 km2 and the area of occupancy (AOO) is 32 km2. The species is assessed as Vulnerable (A3c; B1ab(iii,iv,v)+2ab(iii,iv,v); C2a(i)) because of its natural restricted population and the future predicted decline of the population due to the presence of some active threats such as quarries close to the localities of Bouzegza and Ifri N'ziri, and the shotcrete concrete on the cliffs of Ammal gorges and Bin El Ghedrin gorges. An improvement in biological and ecological knowledge both in situ and ex situ with awareness-raising could be helpful to protect its natural habitat

Prediction of thermal conductivity of liquid and vapor refrigerants for pure and their binary, ternary mixtures using artificial neural network

International audienceThe determination of thermophysical properties of hydrofluorocarbons (HFCS) is very important, especially the thermal conductivity. The present work investigated the potential of an artificial neural network (ANN) model to correlate the thermal conductivity of (HFCS) at (169.87-533.02) K, (0.047-68.201) MPa, and (0.0089-0.1984) W/(m·K) temperature, pressure, and thermal conductivity ranges, respectively, of 11 systems from 3 different categories including five pure systems (R32, R125, R134a, R152a, R143a), four binary mixtures systems (R32 + R125, R32 + R134a, R125 + R134a, R125 + R143a), and two ternary mixtures systems (R32 + R125 + R134a, R125 + R134a + R143a). Each one received 1817, 794 and 616 data points, respectively. The application of this model for these 3227 data points of liquid and vapor at several temperatures and pressures allowed to train, validate and test the model. This study showed that ANN models represent a good alternative to estimate the thermal conductivity of different refrigerant systems with a good accuracy. The squared correlation coefficients of thermal conductivity predicted by ANN were R2 = 0.998 with an acceptable level of accuracy of RMSE = 0.0035 and AAD = 0.002 %. The results of applying the trained neural network model to the test data indicate that the method has a highly significant prediction capability