Preparation and characterization of novel bioactive α-Tricalcium Phosphate doped with Dicalcium Silicate ceramics

This work describes the evaluation of pure α-Tricalcium Phosphate (TCP) and silica containing TCP ceramics in the system Dicalcium Silicate–Tricalcium Phosphate (C₂S–TCP) as a potential substrate for bone tissue engineering. The ceramics were soaked in dynamic simulated body fluid (SBF) for 2 weeks and characterized by SEM-WDS, XRD, and TEM analysis, and the results indicated that a carbonated hydroxyapatite (CHA) was formed on the surface of the ceramics. In addition, cell attachment assay showed that the ceramics supported the adult mesenchymal stem cells of human origin (hMSCs-A) adhesion and spreading, and the cells established close contacts with the ceramics after 24 h of culture. Also, cellular assays have shown a greater ability of hMSCs-A to express markers of the osteoblast phenotype (ALP, Col I, OC) in the C₂S doped α-TCP ceramics, indicating osteoblastic differentiation as a result of the increased concentration of silicon in solid solution in α-TCP. These findings indicate that the C₂S doped α-TCP ceramics possess good bioactivity, and biocompatibility, and might be promising for bone implant material.     

A fuzzy regression model based on distances and random variables with crisp input and fuzzy output data: a case study in biomass production

Least-squares technique is well-known and widely used to determine the coefficients of a explanatory model from observations based on a concept of distance. Traditionally, the observations consist of pairs of numeric values. However, in many real-life problems, the independent or explanatory variable can be observed precisely (for instance, the time) and the dependent or response variable is usually described by approximate values, such as “about £300\pounds300” or “approximately $500”, instead of exact values, due to sources of uncertainty that may affect the response. In this paper, we present a new technique to obtain fuzzy regression models that consider triangular fuzzy numbers in the response variable. The procedure solves linear and non-linear problems and is easy to compute in practice and may be applied in different contexts. The usefulness of the proposed method is illustrated using simulated and real-life examples.     

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.     

An energy efficient middleware for an ad-hoc AAL wireless sensor network

DIA (Dispositivo Inteligente de Alarma, in Spanish) is an AAL (Ambient Assisted Living) system that allows to infer a potential dangerous action of an elderly person living alone at home. This inference is obtained by a specific sensorisation with sensor nodes (portables and fixes) and a reasoning layer embedded in a PC that learns of the users behaviour patterns and advices when actual one differs significantly of the normal patterns. In AAL systems, energy is a limited resource therefore sensor devices need to be properly managed to conserve energy. In this paper, we introduce the design and implementation of innovative and specific mechanisms at the sensory layer middleware which is capable of, first to discriminate spurious motion detections assuming that these signals do not resemble the patterns of real motion detections and, second to reduce the dynamics of messages by a sensor signal processing in order to compress the whole information in one single event. The middleware achieves power saving by modifying the raw information from sensors and adapting it to the predefined semantic of the reasoning layer. It manages the important task of data processing from sensors (raw information), and transfers the pre-processed information into the top layer of reasoning in a more energy efficient way. We also address the trade-off between reducing power consumption and reducing delay for incoming data. We present results from experiments using our implementation of these mechanisms at the middleware that comprises from node firmware to the PC driver. The number of messages of the proposed method with respect to the raw data is reduced by approximately 98.5%. The resources used in the PIR signal processing is reduced by approximately 85%. The resulting delay introduced is small (10–19 s) but system dynamics is slow enough to avoid contextualisation errors or reduction of system performance. We consider these results as very satisfactory.     

Experimental validation of a type-2 fuzzy logic controller for energy management in hybrid electrical vehicles

The aim of this paper is to present experimental validation results of an energy management system for hybrid electrical vehicles based on type-2 fuzzy logic. The energy management system (EMS) is designed by extracting knowledge from several experts using surveys. The consideration of interval type-2 fuzzy sets enables modeling the uncertainty in the answers of the experts. The validation of the EMS is performed on a real-scale heavy duty vehicle equipped with different energy sources such as batteries, fuel cell system and ultracapacitors. Experimental results are strong evidence that type-2 fuzzy logic is wide adapted for performing the energy management in hybrid electrical vehicles.     

Modeling of the geometry built-up by coaxial laser material deposition process

Coaxial laser material deposition (also known as laser-cladding process) is being used for selective material deposition on complex parts since the 1990s. Its main applications include coatings, rapid manufacturing, or high added value part repairing. The main advantages of the coaxial laser-cladding process are the high-quality bonding interface between added and substrate material, excellent mechanical behavior, and high precision of the resulting geometry in comparison with conventional welding processes. Moreover, the laser-cladding process involves low-heat input, which leads on minimum thermal distortions and reduced heat-affected zone. Thus, high responsibility components like aeronautic turbine parts, tooling for die and mold industry or medical implants, among others, can be processed with laser cladding. This paper presents a laser material deposition process model for the estimation of the geometric characteristics of added material layers, including the prediction of the 3D shape of single and multiple clads and considering overlapping effect, which is the common situation on industrial applications. The model only needs the typical process parameters as the laser power, substrate material, powder material and size, etc. The model has been applied to different laser material deposition strategies and validated for two different materials (AISI D2 and Inconel 718) by experimental testing, obtaining good agreement between estimated and measured values. Finally, a case study for a turbine engine fixture is presented as a possible application. Therefore, the proposed algorithm can be applied for estimating the resulting geometry of the laser-cladding process and predict the optimum laser-cladding strategy, number of layers, and distance between layers before programming the operation.     

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.     

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.     

Effect of a cold‐active pectinolytic system on colour development of Malbec red wines elaborated at low temperature

The effect of a new cold‐active pectinolytic system on colour of Malbec wines was studied under the following winemaking conditions: (i) fermentation at low temperature (20 °C) and (ii) prefermentative cold maceration (PCM) (5 °C–7 days) followed by traditional fermentation (28 °C). The pectinolytic system was mainly composed of polymethylgalacturonase and pectin lyase activities, detected under similar conditions to those in winemaking (pH 3.6–20 °C). The results show that the enzyme system significantly accelerated colour extraction by reducing the maceration time necessary for vinification at low temperature and shortening the PCM stage. Enzyme‐treated wines exhibited better chromatic parameters than their controls at devatting and after 6 months of storage. The cold‐active enzyme compensated the decrease in colour extraction due to the low maceration temperature, achieving high‐quality wines with chromatic characteristics similar to those of traditional wines.     

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.