The thermal energy of the sun can be used for electricity generation. Solar thermal energy can be applied with fossil fuels or independently in order to reduce the cost of generated electricity and CO2 emission. Several cycles are introduced to extract solar thermal energy to be used in power plants. Brayton cycles, Rankine‐Brayton cycles, and supercritical Brayton cycles are among the most conventional ones. Based on the reviewed researches, using solar energy in addition to fossil fuels results in lower carbon dioxide emission and lower levelized cost of the generated electricity. Moreover, thermodynamic and economic analyses of the cycles revealed that heat recovery leads to higher efficiency while increase capital cost. The efficiency of solar‐assisted gas turbines depend on various parameters including pressure ratio, turbine inlet temperature, heat absorber geometry and the performance of the components. The enhancement in the efficiency of the cycles by applying each method depends on the configuration, operating condition. For instance, results have shown that 10% increase in turbine efficiency can led to 6%‐12% improvement in the efficiency of a closed‐Brayton cycle.

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Thermal conductivity of nanofluids depends on several parameters including temperature, concentration, and size of nanoparticles. Most of the proposed models utilized concentration and temperature as influential factors in their modeling. In this study, group method of data handling (GMDH) artificial neural networks is applied in order to model the dependency of thermal conductivity on the mentioned factors. Firstly, temperature and concentration considered as inputs and a model is represented. Afterwards, the size of nanoparticles is added to the input variables and the results are compared. Based on obtained results, GMDH is an appropriate method to predict thermal conductivity of the nanofluids. In addition, it is necessary to consider size of nanoparticles in order to have a more precise model.

In the present study, efforts have been made to theoretically study the diffraction of plane harmonic compressional waves by a spherical nano-inclusion based on the Gurtin-Murdoch surface/interface elasticity theory in which the interface between the nano-inclusion and the matrix is considered as the material surface which has their own mechanical properties. Furthermore, a nano-composite has been considered in order to assess the size effect on the wave propagation characteristics of a plane compressional elastic wave containing the randomly distributed spherical nano-inclusions. Also, the phase velocities and attenuations of P and SV elastic waves along with the related dynamic effective elastic properties have been investigated for a wide variety of frequencies and volume fractions.

According to Bain & Company, CRM is at the top of management tools in recent years. This article aims to answer the productivity paradox of CRM and investigates the impact of both CRM and innovation on firm performance and also investigating mediating role of innovation to explain the effect of CRM on performance. To obtain research objective, an empirical study was conducted. For evaluating conceptual model, survey instrument was developed. The relationship between dimensions of CRM and innovation, as well as the relationship between innovation and business performance, were approved, but direct relationship between dimensions of CRM and business performance, according to the data collected, was not approved. Innovation and CRM both are valuable capabilities, which are viewed necessary to achieve a competitive advantage. However, there are little researches about how the interaction of these two concepts improve performance, and despite massive investments in the field of CRM, its impact on business is ambiguous.

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This paper aims to conduct a comparative study on four different models of effective field and effective medium for modeling propagation of plane elastic waves through the composites containing spherical particles with random distribution. Effective elastic properties along with the normalized phase velocity and attenuation of the average wave was numerically evaluated by the models. The plane incident wave was considered longitudinal to get the results. The numerical analyses were performed on four types of composites in the range of low to intermediate frequency and different volume fractions. Judgment about this comparative study is done based on physical and theoretical concepts in the wave propagation phenomenon. The obtained results provide a good viewpoint in using different models for studying propagation of the plane elastic waves in various particulate composites.