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.     

Determination of flonicamid and its metabolites in bell pepper using ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (Orbitrap)

The development and validation of a method to determine flonicamid and its metabolites as TFNA (4-trifluoromethylnicotinic acid), TFNG (N-(4-trifluoromethylnicotinoyl) glycine) and TFNA-AM (4-trifluoromethylnicotinamide) in bell pepper samples by ultra-high-performance liquid chromatography coupled to Orbitrap mass spectrometry (UHPLC-Orbitrap-MS) was performed. A fast and simple extraction procedure with acidified acetonitrile and salts (magnesium sulfate and sodium chloride) was used. The methodology was validated, checking for specificity, recovery, precision, limits of detection (LODs) and limits of quantification (LOQs). The recoveries ranged from 84% to 98%, and precisions were lower than 17%. Finally, LODs ranged from 1 µg kg^–1 (flonicamid) to 6 µg kg^–1 (TFNA-AM), while LOQs ranged from 10 µg kg^–1 (flonicamid) to 30 µg kg^–1 (TFNA-AM). Bell pepper samples were analysed and concentrations up to 98 µg kg^–1 (flonicamid) were detected, although the sum of flonicamid and metabolites did not exceed the maximum residue limit (MRL) set by the European Union.     

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.     

Simultaneous determination of neonicotinoid insecticides in sunflower-treated seeds (hull and kernel) by LC-MS/MS

A validated analytical method to determine seven neonicotinoids (dinotefuran, nitenpyram, thiamethoxam, clothianidin, imidacloprid, acetamiprid and thiacloprid) in sunflower seeds (hull and kernel) using HPLC coupled to electrospray ionisation mass spectrometry (ESI-MS) is presented. Sample clean-up based on a solid–liquid extraction, and the removal of lipid fraction, in the case of kernels, is proposed and optimised. Low limits of detection and quantification were obtained, ranging from 0.3 × 10^–3 to 1.2 × 10^–3 µg g^–1 and from 1.0 × 10^–3 to 4.0 × 10^–3 µg g^–1, with good precision, and recovery values ranged from 90% to 104% for hulls and kernels. The method was applied for the analysis of five thiamethoxam-dressed sunflower seeds and four non-treated seeds, where, besides thiamethoxam, residues of the other neonicotinoid, clothianidin, were also detected and confirmed via tandem mass spectrometry (LC-ESI-MS/MS). Finally, the presence of residues of thiamethoxam and clothianidin in collected sunflower seeds (hulls) coming from coated seeds confirmed the translocation of these neonicotinoids through the plant up to these seeds.     

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.     

Air and HST Multimodal Products. A Segmentation Analysis for Policy Makers

Different solutions for the integration of high-speed rail (HSR) and air transport could be implemented, ranging from very basic integration to more sophisticated systems that include ticket and handling integration. This paper uses two statistical techniques, name cluster analysis and discrete choice models in order to investigate how different market segments have a determining influence in being more proactive to change to HSR for the second leg in multimodal trips. A discrete choice experiment is conducted to better understand passengers’ preferences. We obtain a number of clusters and estimate flexible choice models, taking into account the panel nature of stated preference data. We obtain a range of willingness-to-pay values for service quality attributes, finding results that can be used to infer policy conclusions about the real attractiveness of the Air-HSR integrated alternative. In this respect, clusters and schedule coordination, which reduces connecting time, are crucial to explain HSR attractiveness.     

Bulk and Surface Properties of Liquid Al-Li and Li-Zn Alloys

Physicochemical properties like density, surface tension, and viscosity of liquid binary Al-Li and Li-Zn alloys have been measured using draining crucible method. The experimentally measured surface-tension values have been compared to theoretical results based either on the Butler model or the compound formation model assuming the existence of the most favored A ₁ B ₂ and A ₂ B ₃ clusters. Several models for viscosity calculation have been also applied and discussed in confrontation with measured data. Finally, the clustering effects in the liquid Al-Li and Li-Zn alloys have been examined using two microscopic functions, i.e., the concentration fluctuation function in the long-wavelength limit and the Warren-Cowley short-range order parameter.     

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.