Optimization of the grapes reception process

Neural Computing and Applications - Grapes reception is a key process in wine production. The harvest days are extremely challenging days in managing the reception of the grapes, as the winery...     

Missing data imputation for fuzzy rule-based classification systems

Fuzzy rule-based classification systems (FRBCSs) are known due to their ability to treat with low quality data and obtain good results in these scenarios. However, their application in problems with missing data are uncommon while in real-life data, information is frequently incomplete in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formerly known as imputation. In this work, we focus on FRBCSs considering 14 different approaches to missing attribute values treatment that are presented and analyzed. The analysis involves three different methods, in which we distinguish between Mamdani and TSK models. From the obtained results, the convenience of using imputation methods for FRBCSs with missing values is stated. The analysis suggests that each type behaves differently while the use of determined missing values imputation methods could improve the accuracy obtained for these methods. Thus, the use of particular imputation methods conditioned to the type of FRBCSs is required.     

Fuzzy ontologies-based user profiles applied to enhance e-learning activities

Nowadays, the impact of technological developments on improving human activities is becoming more evident. In e-learning, this situation is no different. There are common to use systems that assist the daily activities of students and teachers. Typically, e-learning recommender systems are focused on students; however, teachers can also benefit from these type of tools. A recommender system can propose actions and resources that facilitate teaching activities like structuring learning strategies. In any case, a complete user’s representation is required. This paper shows how a fuzzy ontology can be used to represent user profiles into a recommender engine and enhances the user’s activities into e-learning environments. A fuzzy ontology is an extension of domain ontologies for solving the problems of uncertainty in sharing and reusing knowledge on the Semantic Web. The user profile is built from learning objects published by the user himself into a learning object repository. The initial experiment confirms that the automatically obtained fuzzy ontology is a good representation of the user’s preferences. The experiment results also indicate that the presented approach is useful and warrants further research in recommending and retrieval information.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.     

On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization

Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.     

Flavonoids of “La Alcarria” honey A study of their botanical origin

Summary The flavonoids present in ten selected samples of La Alcarria honey with different pollen compositions have been HPLC analysed in order to establish if correlations between botanical origin and flavonoid profiles are possible. A common flavonoid pattern is observed in the different samples showing that pollen is not the main source of honey flavonoids. A close correlation between the flavonoid patterns of honey flavonoids and propolis flavonoids has been found suggesting that flavonoid analysis could be more useful in geographical origin determinations than in botanical origin studies.

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Flavonoide des La-Alcarria-Honigs Eine Studie ihres botanischen Ursprungs

Zusammenfassung Die Flavonoide in Proben in Alcarria-Honig mit unterschiedlicher Pollenzusammensetzung wurden untersucht, um Kortrelationen zwischen dem botanischen Ursprung und den möglichen Flavonoiden zu finden. In den verschiedenen Proben wurde ein Flavonoid-Muster gefunden, wobei der Pollen nicht die Hauptquelle der Honigflavonoide ist. Es wurde jedoch eine enge Korrelation zwischen den Flavonoid-Mustern des Honigs und des Bienenkittharzes gefunden, was für die geographische Herkunft wichtiger ist als die botanische.

     

Vapor pressure measurements of para-hydrogen adsorbed on exfoliated graphite

Monolayer isotherms were measured for 15, 18, and 20 K for para-hydrogen adsorbed on exfoliated graphite foam, and for the second, third, and fourth layers in the temperature interval of 8 to 20 K, using the standard volumetric method. From the data, the isosteric heat of adsorption, molar entropies and internal energies were obtained as a function of coverage. The values for coverages above the third layer were compared to those at the p-H₂ bulk triple point, showing that, within our experimental uncertainty, the results are closer to the values of bulk solid para-hydrogen. Critical temperatures for the second and third layers and the triple point temperature for the second layer, were determined, yielding values of T_c2 = (10.0±0.1) K, T_c3 = (11.0±0.5) K and T_t2 = (6.5±0.1) K respectively. Features occurring along the monolayer coverages are compared to transitions which occur in the para-hydrogen phase diagrams adsorbed on graphite, obtained from heat capacity measurements by other authors. From the isotherms, compressibilities were calculated as a function of coverage for several temperatures. Whenever possible the obtained results were compared to existing data in the literature.     

Influence of preparation conditions on characteristics of the YBa2Cu3O7−δ compound

Synthesis of ceramic YBa₂Cu₃O_7– by solid-state reaction was performed under different conditions. Different values of cooling rate and oxygen flow were used, and no significant influence on superconducting characteristics of the samples was observed. A major influence on their mechanical properties was found.     

MMP1 and MMP11 Expression in Peripheral Blood Mononuclear Cells upon Their Interaction with Breast Cancer Cells and Fibroblasts

Tumor-infiltrating immune cells phenotype is associated with tumor progression. However, little is known about the phenotype of the peripheral blood mononuclear cells (PBMC) from breast cancer patients. We investigated MMP1 and MMP11 expression in PBMC from breast cancer patients and we analyzed gene expression changes upon their interaction with cancer cells and cancer-associated fibroblasts (CAF). We measured the impact of PBMC on proinflammatory gene expression in breast cancer cells, normal fibroblast (NF), and CAF and the impact on proliferation and invasiveness capacity of breast cancer cells. Gene expression of MMP1 and MMP11 in PBMC from breast cancer patients (n = 54) and control (n = 28); expression of IL1A, IL6, IL17, IFNβ, and NFĸB in breast cancer cell lines (MCF-7 and MDA-MB-231); and, additionally, IL10 and MMP11 in CAF and NF were analyzed by qRT-PCR before and after co-culture. Our results show the existence of a subpopulation of breast cancer patients (25.9%) with very high levels of MMP11 gene expression in PBMC. Also, gene expression of MMP1 and MMP11 increases in PBMC after co-culture with breast cancer cell lines, NF or CAF. PBMC from healthy or breast cancer patients induce an increased proliferation rate on MCF-7 and an increased invasiveness capacity of MDA-MB-231. Finally, we show a differential expression profile of inflammatory genes in NF and CAF when co-cultured with control or breast cancer PBMC. We have observed that MMPs’ expression in PBMC is regulated by the microenvironment, while the expression of inflammatory genes in NF or CAF is differentially regulated by PBMC. These findings confirm the importance of the crosstalk between stromal cells and suggest that PBMC would play a role in promoting aggressive tumor behavior.