Nutritional and antioxidant status of an institutionalized elderly group in Murcia (Spain)

The aging of the population is giving rise to a large number of studies aimed at improving the quality of life, considering diet as a major contributing factor in this life stage. The objectives of the present study were to evaluate the nutritional status of a group of institutionalized elderly in Murcia, Spain, and to relate the diet to anthropometric and biochemical indices, paying special attention to total antioxidant status. The dietary evaluation showed excessive intakes of proteins and lipids, and deficiencies in those of carbohydrates, dietary fiber, zinc, iodine and vitamins A, E and D. The high proportion of overweight observed were not significantly correlated to the energy intakes. Although total plasma antioxidant status was low (0.62 mM eq Trolox and 0.98 mM eq Fe(II)) compared with reference values, estimated oxidative risk in this group of elderly was low according to plasma levels of vitamins C, A & E.     

Using <Superscript>1</Superscript>H and <Superscript>13</Superscript>C NMR techniques and artificial neural networks to detect the adulteration of olive oil with hazelnut oil

The lack of any official analytical method to detect the adulteration of olive oil with a low percentage of hazelnut oil is explained by the similarities in the chemical compositions of both kinds of oils. To counter this problem, an artificial neural network based on ¹H-NMR and ¹³C-NMR data has been developed to detect olive oil adulteration, and the results from this ANN are presented here. A training set consisting of hazelnut oils, pure olive oils, and olive oils blended with 2–20% hazelnut oils was used to design and train a multilayer perceptron with 100% correct classifications. This mathematical model was also validated using an external validation set of blend samples (3–15%) and genuine samples. The detection limit of the model was around 8%.     

Self-reported injuries among seafarers. Questionnaire validity and results from an international study

International surveys of occupational injuries among seafarers have so far been missing. It was the aim to test the method of self-report of injuries and length of time at risk during the latest duty period and second to study the injury incidence rate among seafarers by use of the method. A pilot study was conducted (n = 1068) in Finland, Denmark, the Philippines, Croatia and Spain using self-completed questionnaires with questions about the person, the ship, the duration of latest duty period and injuries. The duration of the self-reporting duty period was in the Danish part compared with information from the crew register of the Maritime Authority. For seafarers from merchant ships in the Danish sub-study there was acceptable correspondence between the information from the seafarers and the Maritime Authority, but not when referring to ferries and non-specified types of ship. Unadjusted and adjusted injury incidence rates-ratios (IRRs) based on number of injuries per number of work hours were calculated. Adjusted IRRs for ordinary seamen/officers: IRR = 2.43 (95% CI: 1.25-4.72); for age < 35/35+ years: IRR = 1.97 (1.02-3.81); length of tour: 117 days or longer compared with < 117 days: IRR = 0.46 (95% CI: 0.22-0.95); 57-70 working hours per week compared with < 57 h: IRR = 1.26 (0.48-3.29), 71+h compared with < 57 h: IRR = 2.12 (0.84-5.36). Non-significant IRRs >1.00 were found for ships under 10,000 GT compared with larger ships and for own flagged ships compared with ships under flag of convenience. In conclusion, more than 70 h of work per week was related to a higher rate of injuries for seafarers on merchant ships, but the result was not statistically significant. Self-report of the duration of the latest tour of duty is useful for seafarers from merchant ships with short-term employments, but not for ferries and other, non-specified types of ship with other or permanent employment.     

Decoding two-dimensional complex multicomponent separations by autocovariance function

A new method for decoding two-dimensional (2D) multicomponent separations based on the use of the 2D Autocovariance function (2D-ACVF) has been developed. Theoretical models of single component (SC) spot distributions in 2D separations, both random and structured, are developed as the basis for a nonlinear estimation of both sample and separation system parameters from experimental 2D separations. The number of SCs, the average spot size, the spot capacity, and the saturation factor can be evaluated in the case of random SC spot patterns. The procedure was validated by extensive numerical simulation under conditions close to those usually found in GC x GC or 2D-polyacrylamid gel electrophoresis of proteins. The worse precision degree was no greater than 10% in the case of maximum spot density. This imprecision was fully accounted for, and it seems acceptable owing to the intrinsic statistical character of the estimation method. Structured multicomponent 2D separations, where SCs are linked by linear relationships, give rise to specific structured patterns in 2D-ACVF plots from which the parameters (phase and frequency) of the structured SC sequences can be evaluated: the study of 2D-ACVF makes it possible to decode multicomponent 2D separation, that is, to determine the number, relative abundance, and structural similarities of the single components. Pertinent expressions of the theoretical 2D-ACVF were derived for simple cases, and a procedure for decoding cases of structured 2D separations was developed and applied. It was shown that 2D separations containing both random and structured patterns of SC spots give rise to 2D-EACVF, which is the superimposition of the two component parts. This feature allows one, in principle, to decode the two components. The relevance of these results for Giddings sample dimensionality and separation dimensionality and their effective experimental evaluation is discussed.     

Industrial capacity planning in oligopolistic markets

Available productive capacity is determinant of a company’s success once it allows meeting the current and future demand. This article proposes a quantitative model for determining long-term productive capacity in competitive oligopolistic markets, based on the Nash Equilibrium formulated as a Variational Inequality problem. Numerical examples enable an analytical evaluation of the economic equilibrium’s sensitivity to marginal costs, investment costs, hurdle rate, and market saturation. Results show that, in order to achieve a greater market share, it is important to adopt strategies that reduce marginal costs. On the other hand, variations in the hurdle rate may or may not reinforce the position of a competitor in the market and his interest in investing in capacity expansion. Additionally, market saturation may be achieved, beyond which investment becomes unattractive. Each of these is a positive outcome for society, triggering diversified investments and competition in economic sectors where competition is low and profits are high.     

The application of uncertainty measures in the training and evaluation of supervised classifiers

The production of thematic maps from remotely sensed images requires the application of classification methods. A great variety of classifiers are available, producing frequently considerably different results. Therefore, the automatic extraction of thematic information requires the choice of the most appropriate classifier for each application. One of the main objectives of the research described in this article is to evaluate the performance of supervised classifiers using the information provided by the application of uncertainty measures to the testing sets, instead of statistical accuracy indices. The second main objective is to show that the information provided by the uncertainty measures for the training set may be used to assess and redefine the sample sites included in this set, in order to improve the classification results. To achieve the proposed objectives, two supervised classifiers, one probabilistic and another fuzzy, were applied to a very high spatial resolution (VHSR) image. The results show that similar conclusions on the classifiers’ performance are obtained with the uncertainty measures and the traditional accuracy indices obtained from error matrices. It is also shown that the redefinition of the training set based on the information provided by the uncertainty measures may generate more accurate outputs.     

Crystallization and melting properties of extra virgin olive oil studied by synchrotron XRD and DSC

Crystallization and melting properties of triacylglycerols in extra virgin olive oil were studied by using synchrotron X‐ray diffraction (XRD) and differential scanning calorimetry (DSC). The phase transitions were monitored by cooling and heating the samples at 2°C/min from 60 to ?60°C and vice versa. Upon cooling, a first DSC endothermic peak was recorded at ?9.6°C followed by one at ?33.5°C. These thermal events were associated to the formation of two different structures: a triple‐chain length (3L) having a c parameter of about 58.38 Å and a quadruple chain length structure (4L) with a c parameter of about 89.99 Å, respectively. Both structures evidenced a cell packing arrangement ascribable to a β′ form. During heating, part of the metastable β′ crystals rearranged into the more thermodynamically stable β form. Then, upon further heating, the sequential melting of the two crystal structures was observed. The melting was completed at 10.7°C. Beside this interpretation of XRD data, a model considering a cell with a c parameter of about 170 Å and a hexagonal crystal system was proposed. Even if more research is needed to validate this approach, it allowed all XRD events recorded during the experiments to be described. See commentary by Chiavaro [p. 267–269], http://dx.doi.org/10.1002/ejlt.201200415