Biochemical processes present complex mechanisms and can be described by various computational models. Complex systems present a variety of problems, especially the loss of intuitive understanding. The present work uses fractional-order calculus to obtain mathematical models for erythritol and mannitol synthesis. The obtained models are useful for both prediction and process optimization. The models present the complex behavior of the process due to the fractional order, without losing the physical meaning of gain and time constants. To validate each obtained model, the simulation results were compared with experimental data. In order to highlight the advantages of fractional-order models, comparisons with the corresponding integer-order models are presented.

Cancer is one of the deadliest diseases in the present days. Its survivability is mostly corelated to early detection and treatment, which means that it is of utmost importance to successfully diagnose the patients. Unfortunately, even with years of experience human errors can happen which leads to the death of many individuals being misdiagnosed. Throughout the years there have been several applications created which could possibly aid doctors in the diagnosis. Neural Networks have always been a powerful tool which can be used in different applications that require an accurate model and the complexity of these models exceeds a human's computational capabilities. In image processing for example, a convolutional neural network can analyze each particular pixel and determine through the convolution function the common properties of different pictures. The objective of this study is to analyze different types of cancer diagnosing methods that have been developed and tested using image processing methods. The analyzed factors are training parameters, image processing technique and the obtained performances. This survey/review can be of significant value to researchers and professionals in medicine and computer science, highlighting areas where there are opportunities to make significant new contributions. INDEX TERMSCancer diagnosis, Image processing, Neural networks I.

To improve food production via fermentation with co-cultures of microorganisms (e.g., multiple lactic acid bacteria-LAB strains), one must fully understand their metabolism and interaction patterns in various conditions. For example, LAB can bring added quality to bread by releasing several bioactive compounds when adding soy flour to wheat flour, thus revealing the great potential for functional food development. In the present work, the fermentation of three soy and wheat flour mixtures is studied using single cultures and co-cultures of Lactobacillus plantarum and Lactobacillus casei. Bio-chemical processes often require a significant amount of time to obtain the optimal amount of final product; creating a mathematical model can gain important information and aids in the optimization of the process. Consequently, mathematical modeling is used to optimize the fermentation process by following these LAB’s growth kinetics and viability. The present work uses both multiple regression and artificial neural networks (ANN) to obtain the necessary mathematical model, useful in both prediction and process optimization. The main objective is to find a model with optimal performances, evaluated using an ANOVA test. To validate each obtained model, the simulation results are compared with the experimental data.

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