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This literature research, although not exhaustive, gives perspective to solar-driven photocatalysis, such as solar photo-Fenton and TiO 2 solar photocatalysis, reported in the literature for the degradation of aqueous organic pollutants. Parameters that influence the degradation and mineralization of organics like catalyst preparation, type and load of catalyst, catalyst phase, pH, applied potential, and type of organic pollutant are addressed. Such parameters may also affect the photoactivity of the catalysts used in the studied solar processes. Solar irradiation is a renewable, abundant, and pollution-free energy source for low-cost commercial applications. Therefore, these solar processes represent an environmentally friendly alternative mainly because the use of electricity can be decreased/avoided.

In this work, a bibliographic analysis on artificial neural networks (ANNs) using fractional calculus (FC) theory has been developed to summarize the main features and applications of the ANNs. ANN is a mathematical modeling tool used in several sciences and engineering fields. FC has been mainly applied on ANNs with three different objectives, such as systems stabilization, systems synchronization, and parameters training, using optimization algorithms. FC and some control strategies have been satisfactorily employed to attain the synchronization and stabilization of ANNs. To show this fact, in this manuscript are summarized, the architecture of the systems, the control strategies, and the fractional derivatives used in each research work, also, the achieved goals are presented. Regarding the parameters training using optimization algorithms issue, in this manuscript, the systems types, the fractional derivatives involved, and the optimization algorithm employed to train the ANN parameters are also presented. In most of the works found in the literature where ANNs and FC are involved, the authors focused on controlling the systems using synchronization and stabilization. Furthermore, recent applications of ANNs with FC in several fields such as medicine, cryptographic, image processing, robotic are reviewed in detail in this manuscript. Works with applications, such as chaos analysis, functions approximation, heat transfer process, periodicity, and dissipativity, also were included. Almost to the end of the paper, several future research topics arising on ANNs involved with FC are recommended to the researchers community. From the bibliographic review, we concluded that the Caputo derivative is the most utilized derivative for solving problems with ANNs because its initial values take the same form as the differential equations of integer-order.

Heat and mass transfer in individual coffee beans during roasting were simulated using computational fluid dynamics (CFD). Numerical equations for heat and mass transfer inside the coffee bean were solved using the finite volume technique in the commercial CFD code Fluent; the software was complemented with specific user-defined functions (UDFs). To experimentally validate the numerical model, a single coffee bean was placed in a cylindrical glass tube and roasted by a hot air flow, using the identical geometrical 3D configuration and hot air flow conditions as the ones used for numerical simulations. Temperature and humidity calculations obtained with the model were compared with experimental data. The model predicts the actual process quite accurately and represents a useful approach to monitor the coffee roasting process in real time. It provides valuable information on time-resolved process variables that are otherwise difficult to obtain experimentally, but critical to a better understanding of the coffee roasting process at the individual bean level. This includes variables such as time-resolved 3D profiles of bean temperature and moisture content, and temperature profiles of the roasting air in the vicinity of the coffee bean.

Maternal obesity has been related to adverse neonatal outcomes and fetal programming. Oxidative stress and adipokines are potential biomarkers in such pregnancies; thus, the measurement of these molecules has been considered critical. Therefore, we developed artificial neural network (ANN) models based on maternal weight status and clinical data to predict reliable maternal blood concentrations of these biomarkers at the end of pregnancy. Adipokines (adiponectin, leptin, and resistin), and DNA, lipid and protein oxidative markers (8-oxo-2′-deoxyguanosine, malondialdehyde and carbonylated proteins, respectively) were assessed in blood of normal weight, overweight and obese women in the third trimester of pregnancy. A Back-propagation algorithm was used to train ANN models with four input variables (age, pre-gestational body mass index (p-BMI), weight status and gestational age). ANN models were able to accurately predict all biomarkers with regression coefficients greater than R2 = 0.945. P-BMI was the most significant variable for estimating adiponectin and carbonylated proteins concentrations (37%), while gestational age was the most relevant variable to predict resistin and malondialdehyde (34%). Age, gestational age and p-BMI had the same significance for leptin values. Finally, for 8-oxo-2′-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care.