Evolutionary authoring tool for adaptive hypermedia with multimodal navigation

This paper discusses the importance of user adaptation and software evolution in hypermedia applications, and reviews some of the most relevant approaches to both fields. The paper describes a model that has been conceived for the development, maintenance and navigation of adaptive hypermedia systems. This model, called SEMantic, systemic and evolutionary model to develop HyPermedia systems (SEM-HP), includes an incremental design process, a layered architecture and an authoring tool integrated with a navigation tool. SEM-HP architecture is composed of four subsystems, which allow the separation of aspects related to knowledge representation, presentation, navigation and user adaptation. In addition, SEM-HP has a higher layer, which acts as a meta-system and allows a consistent evolution of the elements defined in each of the four subsystems, as well as their automatic co-evolution. Regarding user interaction, four alternative ways of navigating hypermedia information are supported. Finally, the paper shows the main results of two case studies carried out with the authoring and navigation tool, JSEM-HP, at the University of Granada, Spain.     

Microbiological shelf life and sensory evaluation of fruit juices treated by high-intensity pulsed electric fields and antimicrobials

The effect of combining high-intensity pulsed electric fields (HIPEF) with citric acid or cinnamon bark oil, as antimicrobial substances, on the microbiological shelf life of strawberry, orange, apple, pear and tomato juices was evaluated in addition to the sensory properties of these products. An extension of the microbiological shelf life of fruit juices treated by HIPEF with or without antimicrobial substances was observed in comparison with those juices without processing. Naturally occurring microorganisms in the juices were inactivated by HIPEF treatment. Among the HIPEF treated juices, those from strawberry and orange did not show microbial growth along the 91 days of storage at 5 °C. However, resident microbial populations in apple, pear and tomato juices only were controlled during that time when HIPEF was combined with antimicrobials. Therefore, combinations of those treatments may be a feasible alternative to thermal pasteurization to ensure the microbiological quality and safety in juices, and to avoid the risk of foodborne illness caused by the consumption of these commodities. No significant changes on the sensory attributes in all studied fruit juices processed by HIPEF were found; but, when citric acid or cinnamon bark oil were added, noticeable changes on some sensory attributes such as aroma, taste and sourness of these fruit juices were perceived.     

Geographical Authentication of Tequila According to its Mineral Content by Means of Support Vector Machines

The elemental profile of tequila samples from the three main production areas of the State of Jalisco, namely Amatitlan, Guadalajara, and Tequila, was used to carry out their geographical characterization. With this aim, the concentration of Al, Ba, Ca, Cu, Fe, K, Mg, Mn, Na, S, Sr, and Zn was determined by inductively coupled plasma atomic emission spectroscopy. Principal component analysis was addressed to visualize data trends. The number of input variables was reduced by means of backward stepwise linear discriminant analysis and support vector machines were used to construct an adequate classification model. The best classification performance was obtained by a linear support vector machine model with 100% of prediction ability.     

Effect of extra virgin olive oil on glycaemia in healthy young subjects

In this approach we studied the glycaemia levels in 20 healthy young volunteers (26 ± 2 years), before and after a 30‐day intake of 50 mL of extra virgin olive oil (EVOO). We selected an oil rich in phenolic compounds (523 mg/L) with a high content of secoiridoidic derivatives (over 94.5%). The findings from our study reveal a significant decrease of glycaemia from 89.6 ± 6.8 to 82.7 ± 10.3 mg/dL (p<0.05), related to a long term daily intake of the study EVOO, as the only added fat. A significant increment of the HDL cholesterol, from 68.7 ± 11.5 to 75.2 ± 4.9 mg/dL, was also highlighted. Total cholesterol, LDL, VLDL, triglycerides, and blood pressure did not show significant variation after the 30‐day consumption of this EVOO. So far, few articles have described the influence of EVOO consumption, on plasma glucose levels in humans. This effect is observed in a group of healthy young humans. Moreover, we confirm that the level of free hydroxytyrosol (OH‐Tyr) in plasma increased up to fourfold (p<0.05) after the 30‐day intake of this EVOO. In addition, the excretion in urine of the main metabolite of OH‐Tyr, homovanillic acid (HVA), significantly increased.     

Comparative study between large-scale and small-scale electrochemical potentiokinetic reactivation performed on AISI 316L austenitic stainless steel

This work optimizes the application of electrochemical potentiokinetic reactivation (EPR) to assess the degree of sensitization (DOS) of AISI 316L and compares the large-scale and small-scale EPR with the aim of improving the study of the different zones of AISI 316L welded joints by using an electrochemical minicell. The optimized EPR allows to discriminate better than the standardized EPR among different DOS. Small-scale EPR shows greater sensitivity to assess the DOS than large-scale EPR: (i) at lower deformation levels; (ii) for shorter sensitization times; (iii) when localized microstructural regeneration is caused by the combined effect of deformation and subsequent sensitization.     

Immediate water quality assessment in shrimp culture using fuzzy inference systems

The continuous monitoring of physical, chemical and biological parameters in shrimp culture is an important activity for detecting potential crisis that can be harmful for the organisms. Water quality can be assessed through toxicological tests evaluated directly from water quality parameters involved in the ecosystem; these tests provide an indicator about the water quality. The aim of this study is to develop a fuzzy inference system based on a reasoning process, which involves aquaculture criteria established by official organizations and researchers for assessing water quality by analyzing the main factors that affect a shrimp ecosystem. We propose to organize the water quality parameters in groups according to their importance; these groups are defined as daily, weekly and by request monitoring. Additionally, we introduce an analytic hierarchy process to define priorities for more critical water quality parameters and groups. The proposed system analyzes the most important parameters in shrimp culture, detects potential negative situations and provides a new water quality index (WQI), which describes the general status of the water quality as excellent, good, regular and poor. The Canadian water quality and other well-known hydrological indices are used to compare the water quality parameters of the shrimp water farm. Results show that WQI index has a better performance than other indices giving a more accurate assessment because the proposed fuzzy inference system integrates all environmental behaviors giving as result a complete score. This fuzzy inference system emerges as an appropriated tool for assessing site performance, providing assistance to improve production through contingency actions in polluted ponds.     

Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques

Penicillium fungi are among the main defects that may affect the commercialization of citrus fruits. Economic losses in fruit production may become enormous if an early detection of that kind of fungi is not carried out. That early detection is usually based either on UltraViolet light carried out manually. This work presents a new approach based on hyperspectral imagery for defect segmentation. Both the physical device and the data processing (geometric corrections and band selection) are presented. Achieved results using classifiers based on Artificial Neural Networks and Decision Trees show an accuracy around 98%; it shows up the suitability of the proposed approach.     

Optimizing location and size of rural schools in Chile

The Chilean Ministry of Education oversees preschool, primary, and secondary education in both urban and rural areas. Many parts of Chile are sparsely populated and there are currently over 4,000 rural schools (almost 38% of all schools in Chile) educating 9.5% of the students in the country. Many of the rural schools are small with only one teacher responsible for instruction of all local students (multigrade schools). The geographical distribution of the rural schools has not been coordinated and this has resulted in unequal utilization of existing schools and some unreasonably long travel distances by students. Good management of the rural schools is fundamental to meeting Chile's goal of providing quality education to its citizens. Seeking to improve the situation, the Ministry of Education ordered a study of the optimal location and size of rural schools with the general goals of reducing the number of lesser quality multigrade schools and reducing student travel distances while maintaining reasonable costs. This paper presents results of this study obtained using an integer linear program that has been embedded in a geographical information system. We present computational results for the entire country. Recommendations include where to open new rural schools as well as where to expand, reduce, close, or leave unchanged existing schools. We show how recommendations are sensitive to key parameters such as the cost of transportation.     

FPGA-based architecture for the real-time computation of 2-D convolution with large kernel size

Bidimensional convolution is a low-level processing algorithm of interest in many areas, but its high computational cost constrains the size of the kernels, especially in real-time embedded systems. This paper presents a hardware architecture for the FPGA-based implementation of 2-D convolution with medium–large kernels. It is a multiplierless solution based on Distributed Arithmetic implemented using general purpose resources in FPGAs. Our proposal is modular and coefficient independent, so it remains fully flexible and customizable for any application. The architecture design includes a control unit to manage efficiently the operations at the borders of the input array. Results in terms of occupied resources and timing are reported for different configurations. We compare these results with other approaches in the state of the art to validate our approach.