MARKET SEGMENTATION AND WILLINGNESS TO PAY FOR ORGANIC PRODUCTS IN SPAIN

In recent years, consumer concerns on environmental and health issues related to food products have increased and, as a result, the demand for organically grown production has grown. Higher costs of production and retailer margins generate a gap between real prices and those that consumers are willing to pay for organic food. In this article, consumer willingness to pay for organic food in two Spanish regions is analyzed. Markets in both regions are segmented considering consumers lifestyles. Results indicate that consumers concerned about healthy diet and environmental degradation are the most likely to buy organic food, and are willing to pay a high premium. Organic attributes are easily identified in perishable products as the premium consumers would pay for organic meat, fruits, and vegetables is higher.Demand and Price Analysis,

Confidence in the Beef Production System as a Key Factor to Mitigate the Impact of BSE on Beef Consumption

Recent food scares in the food market has caused a reduction in consumer's confidence in the food system that it has induced a significant reduction in consumption in a sector, the beef sector that was already characterized by a saturated trend in quantity terms. In this context, all participants in the beef production system are facing to a great challenge, to retrieve consumer's confidence in the food chain and to mitigate the reduction in beef consumption. The aim of the paper is to analyse the impact of consumer's confidence in the food system as well as other factors on the explanation of food consumption reduction. A structural modelling approach has been used to analyse factors affecting the reduction in beef consumption in two different regions characterised by different production systems and different marketing strategies (PGI beef label). Results indicate that main factor explaining the reduction in beef consumption is the confidence in the beef and a positive relation has been found. Moreover, confidence in a product is directly related to the perceived quality offered by farmers and other decision makers on the beef chain, and to the consumer involvement with the product. Therefore, the main implication is that participants in the food chain has to develop adequate communication strategies such as quality labelling in order to increase consumers perceive quality because, higher quality perception will recover consumers' confidence in beef, and therefore, it will mitigate beef consumption reduction.food confidence, consumer behaviour, structural equation modelling, beef sector, quality label, Food Consumption/Nutrition/Food Safety,

Attribute selection via multi-objective evolutionary computation applied to multi-skill contact center data classification

Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We propose the application of the multi-objective evolutionary algorithm ENORA to the task of feature selection for multi-class classification of data extracted from an integrated multi-channel multi-skill contact center, which include technical, service and central data for each session. Additionally, we propose a methodology to integrate feature selection for classification, model evaluation, and decision making to choose the most satisfactory model according to a "a posteriori" process in a multi-objective context. We check out our results by comparing the performance and the classification rate against the well-known multi-objective evolutionary algorithm NSGA-II. Finally, the best obtained solution is validated by a data expert’s semantic interpretation of the classifier

Modelo de expectativas en torno a la seguridad pública en microempresarios del centro de México

El objetivo del presente trabajo ha sido establecer la consistencia interna de un modelo a fin de poder validar su estructura y especificarla para contrastarla en otros escenarios similares al que prevalece en el centro de México. La pregunta de investigación se circunscribió a la pertinencia de la explicación universal sobre la problemática de seguridad local, pero la hipótesis aceptada sugirió que los marcos teóricos no sólo explican al fenómeno y más bien lo predicen. En tal sentido es que el estudio de la seguridad organizacional deberá transitar por el contraste de modelo que por su grado de porcentaje de varianza explicada incluirá otros factores relativos a la seguridad informática, digital o electrónica que inhibe los procesos innovadores en las organizaciones dedicadas a la creación del conocimiento.La seguridad organizacional ha sido propuesta como un constructo para dar cuenta de la importancia de la prevención del delito en las empresas cuyo valor radica en la creación del conocimiento. En tal sentido es que el objetivo del presente trabajo fue explicar las dimensiones de la seguridad organizacional. Se realizó un estudio no experimental con una selección no probabilística de 258 microempresarios del centro de México. A partir de un modelo estructural ⌠X2 = 324,35 (21gl) p = 0,007; GFI = 0,990; CFI = 0,995; RMSEA = 0,009⌡ se encontró que el factor relativo a la seguridad tecnológica esperada reflejó al constructo de seguridad organizacional (0,731)

Representation of Robots in Matlab

Electronic versíon of an article published as International Journal of Software Engineering and Knowledge Engineering, Volume 29, issue 1, 2019, pp. 23-42. 10.1142/S0218194019500025 © World Scientific Publishing Company[EN] This paper presents a new software tool, namely RoboClass, to include and manage realistic robots and elements of the environment in Matlab simulations. These elements are loaded from CAD models using an STL-file and can be as detailed as desired. All the steps involved in the process are explained in detail. Furthermore, two illustrative examples are considered to show the effectiveness and versatility of the proposed approach: the ABB-IRB120 industrial robot and the CSA research robot. The developed tool is especially useful both for robotics research and teaching.Sanchez, A.; Gracia Calandin, LI.; Morales, R.; Perez-Vidal, C. (2019). Representation of Robots in Matlab. International Journal of Software Engineering and Knowledge Engineering. 29(1):23-42. https://doi.org/10.1142/S0218194019500025S234229

Exploring the Concept of the Digital Educator During COVID-19

T In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. In this paper, we propose two new multivariate feature ranking methods based on pairwise correlation and pairwise consistency, which have been applied for cancer gene expression and genotype-tissue expression classification tasks using public datasets. We statistically proved that the proposed methods outperform the state-of-the-art feature ranking methods Clustering Variation, Chi Squared, Correlation, Information Gain, ReliefF and Significance, as well as other feature selection methods for attribute subset evaluation based on correlation and consistency with the multi-objective evolutionary search strategy, and with the embedded feature selection methods C4.5 and LASSO. The proposed methods have been implemented on the WEKA platform for public use, making all the results reported in this paper repeatable and replicabl

Chance and predictability in evolution : The genomic basis of convergent dietary specializations in an adaptive radiation

The coexistence of multiple eco-phenotypes in independently assembled communities makes island adaptive radiations the ideal framework to test convergence and parallelism in evolution. In the radiation of the spider genus Dysdera in the Canary Islands, species diversification occurs concomitant with repeated events of trophic specialization. These dietary shifts, to feed primarily on woodlice, are accompanied by modifications in morphology (mostly in the mouthparts), behaviour and nutritional physiology. To gain insight into the molecular basis of this adaptive radiation, we performed a comprehensive comparative transcriptome analysis of five Canary Island Dysdera endemics representing two evolutionary and geographically independent events of dietary specialization. After controlling for the potential confounding effects of hemiplasy, our differential gene expression and selective constraint analyses identified a number of genetic changes that could be associated with the repeated adaptations to specialized diet of woodlice, including some related to heavy metal detoxification and homeostasis, the metabolism of some important nutrients and venom toxins. Our results shed light on the genomic basis of an extraordinary case of dietary shift convergence associated with species diversification. We uncovered putative molecular substrates of convergent evolutionary changes at different hierarchical levels, including specific genes, genes with equivalent functions and even particular amino acid positions. This study improves our knowledge of rapid adaptive radiations and provides new insights into the predictability of evolution.Peer reviewe

Inflammatory markers and bone mass in children with overweight/obesity: the role of muscular fitness

Objectives To examine which inflammatory markers are associated with bone mass and whether this association varies according to muscular fitness in children with overweight/obesity. Methods Plasma interleukin-1β (IL-1β), IL-6, tumor necrosis factor-α (TNF-α), epidermal growth factor, vascular endothelial growth factor A (VEGF), and C-reactive protein were analyzed in 55 children aged 8–11 years. A muscular fitness score was computed. Bone mineral content (BMC) of the total body-less head (TBLH) and lumbar spine (LS) were assessed using dual-energy x-ray absorptiometry. Results IL-6 (β = −0.136) and VEGF (β = −0.099) were associated with TBLH BMC, while TNF-α (β = −0.345) and IL-1β (β = 0.212) were associated with LS BMC (P < 0.05). The interaction effect of muscular fitness showed a trend in the association of VEGF with TBLH BMC (P = 0.122) and TNF-α with LS BMC (P = 0.057). Stratified analyses by muscular fitness levels showed an inverse association of VEGF with TBLH BMC (β = −0.152) and TNF-α with LS BMC (β = −0.491) in the low-fitness group, while no association was found in the high-fitness group. Conclusion IL-6, VEGF, TNF-α, and IL-1β are significantly associated with bone mass. Higher muscular fitness may attenuate the adverse effect of high VEGF and TNF-α on bone mass