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This article evaluates the impact of partial or total covariate inclusion or exclusion on the class enumeration performance of growth mixture models (GMMs). Study 1 examines the effect of including an inactive covariate when the population model is specified without covariates. Study 2 examines the case in which the population model is specified with 2 covariates influencing only the class membership. Study 3 examines a population model including 2 covariates influencing the class membership and the growth factors. In all studies, we contrast the accuracy of various indicators to correctly identify the number of latent classes as a function of different design conditions (sample size, mixing ratio, invariance or noninvariance of the variance-covariance matrix, class separation, and correlations between the covariates in Studies 2 and 3) and covariate specification (exclusion, partial or total inclusion as influencing class membership, partial or total inclusion as influencing class membership, and the growth factors in a class-invariant or class-varying manner). The accuracy of the indicators shows important variation across studies, indicators, design conditions, and specification of the covariates effects. However, the results suggest that the GMM class enumeration process should be conducted without covariates, and should rely mostly on the Bayesian information criterion (BIC) and consistent Akaike information criterion (CAIC) as the most reliable indicators under conditions of high class separation (as indicated by higher entropy), versus the sample size adjusted BIC or CAIC (SBIC, SCAIC) and bootstrapped likelihood ratio test (BLRT) under conditions of low class separation (indicated by lower entropy). (PsycINFO Database Record

With the growing concerns about environmental pollution, climate change, and the global fossil energy crisis, research and development of renewable clean energies has received more attention. The sun as one of the renewable energy sources is the most potent source for human kind. This is because the solar radiation that reaches the Earth surface is about 1.2 × 10 5 TW, which is far greater than the energy consumed by humans [1,2].The most common way to utilize solar energy is to convert it into two easily harnessed forms; electricity and thermal energy. Apart from photovoltaic (PV) which can convert solar radiations to electricity directly, thermal energy also can be converted to electricity, and one promising method is utilizing the thermoelectric generator (TEG). Thermoelectric (TE) devices have many advantages such as gas-free emissions, solid-state operation, maintenance-free operation without any moving parts and chemical reactions, vast scalability, a long life span of reliable operation and no damage to the environment. Therefore, the combination of PV and TE could be considered to produce more electricity.Combining a photovoltaic module and a solar thermoelectric generator would enable photons outside the range of a particular solar cell's narrow absorption wavelength to be directed to the TE modules which generates electricity by the thermoelectric effect. Doing this would allow energy conversion efficiency to be increased while simultaneously reducing the heat dissipated by the PV module. This paper presents a detailed review of the current state of art in solar photovoltaic-thermoelectric hybrid system for electricity generation. It begins with the analysis of the groundwork and feasibility of PV-TE system. An overview of the two main types and Review

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ACTRN12614000184673.