Memory and the Reporting of Remittance in a Sample of Elderly Persons in Thailand

Accurate reporting of sources of income is needed in studies of intergenerational transfers and welfare of the elderly. The objective of this paper is to investigate how memory in the elderly is related to the reporting of remittances received from their children in Thailand. Using data from a longitudinal study of 1036 persons age 60 to 93 in a province in Thailand, the influence of a word recall (verbal learning and recall), and nickname recall (verbal learning and recall) on reporting of remittance was assessed. Initially, both measures were positively and significantly related to the reporting of remittance. The results remained significant only for the nickname task after controlling for education, wealth, living arrangements and disability. The results highlight the importance of cognitive issues in the quality of data and the need for attention to memory in questionnaire design.     

Tracking sand dune migration in the Rub Al-Khali with ICESat laser altimetry

Sand dune migration poses serious environmental and social problems for both the infrastructure and communities in the Saudi Arabian Peninsula. In order to monitor and better understand dune migration, a new method is proposed to estimate sand dune displacement/migration by using satellite laser altimetry data from the ICESat mission in the Rub Al-Khali. This method will exploit not only the differences in the observed ICESat elevation profiles, but also the phase differences between two co-located tracks when observed at different times. By using the phase differences, the proposed method will be able to estimate dune displacement vectors with an accuracy of 5 m root mean square. This accuracy has been validated using synthetic data with known displacements. The major causes for dune migration are the wind regime and the type of dunes (e.g. grain size and vegetation cover). A comparison of estimated dune displacement correlated well with the prevailing wind regime during the observation time period. A comparison with satellite imagery revealed that the dune shape and direction (i.e. the representative parameters of dune migration) are in agreement with the estimated displacement vectors. This method represents one of the first attempts to monitor dune migration using space observations only.     

Optimized state estimation by application of machine learning

The requirements concerning the technical availability as part of the overall equipment effectiveness increase constantly in production nowadays. Unplanned downtimes have to be prevented via efficient methods. Predictive, condition-based maintenance represents a valuable approach for fulfilling these demands, but precise models for state estimation are missing. From the manufacturers’ point of view the challenge consists in wear models with the capability of specifying the correct component’s state as well as providing reliable failure forecasts. Unfortunately, nowadays creation of wear models is based on specific stress tests or design of experiments from the manufacturer. The integration of the production phase or even data feedback and user knowledge does not take place. New potential is promised by cross-cutting technologies from ICT like cloud technologies—in general virtual platform concepts—or approaches of machine learning as enabling technologies. The objective of this paper is to adopt existing algorithms to the new application of condition monitoring in order to evaluate the applicability for automated training of robust wear models. In that context the most commonly used algorithms are described and the reader gets an impression what challenges have to be met when dealing with machine learning. A selection of about ten algorithms with 45 variants is evaluated for four different features within a packaging machine. In the outlook the embedding of the trained model in a cloud architecture is presented.     

Failure of Titanium Condenser Tubes After 24 Years Power Plant Service

The titanium condenser has been in operation for 24 years at Amager unit 3 power plant. In February 2012, the plant was contaminated by seawater due to a failed condenser tube and some tubes were plugged. A month later, the plant tripped again. Small leaks were found again and finally approx. 200 tubes were plugged before the condenser was in service again. A series of inspections, NDT, and destructive examinations were conducted to try and understand the cause of failure in the tubes within the condenser. After such investigations, degradation mechanisms such as inner fouling, steam impingement, and fretting/erosion around the supports could be discounted. Ductile cracks were found in the tube within the tubesheet. From circumstantial evidence, it was concluded that failure was caused by a semi-filled condenser which led to a mismatch in expansion coefficients of filled tubes and unfilled tubes during a plant trip. In addition, small amounts of titanium hydride were revealed to be present in the tubes within the tubesheet indicating that the carbon steel tubesheet was corroding due to ingress of salt water. Although this was not the reason for the failure, it indicated the need for repair of the epoxy coating in the waterbox.     

Design of an Ultrafast G Protein Switch Based on a Mouse Melanopsin Variant

The primary goal of optogenetics is the light-controlled noninvasive and specific manipulation of various cellular processes. Herein, we present a hybrid strategy for targeted protein engineering combining computational techniques with electrophysiological and UV/visible spectroscopic experiments. We validated our concept for channelrhodopsin-2 and applied it to modify the less-well-studied vertebrate opsin melanopsin. Melanopsin is a promising optogenetic tool that functions as a selective molecular light switch for G protein-coupled receptor pathways. Thus, we constructed a model of the melanopsin G_q protein complex and predicted an absorption maximum shift of the Y211F variant. This variant displays a narrow blue-shifted action spectrum and twofold faster deactivation kinetics compared to wild-type melanopsin on G protein-coupled inward rectifying K⁺ (GIRK) channels in HEK293 cells. Furthermore, we verified the in vivo activity and optogenetic potential for the variant in mice. Thus, we propose that our developed concept will be generally applicable to designing optogenetic tools.     

Modeling,simulation, and optimization of a microalgae biomass drying process

The aim of the present work was to develop a transient mathematical model focused on microalgae biomass drying, considering two phases: solid (wet biomass) and gas (drying air). Mass and thermal energy balances were written for each phase producing a system of ordinary differential equations (ODE). The solution of the ODE set delivers the temperature and air humidity ratio and biomass profiles with respect to time. The numerical results were directly compared with temperature experimental measurements—for both phases—and with the biomass humidity content. Data from experiment 1 were used to carry out the mathematical model adjustment, whereas data from experiment 2 were used for the experimental validation of the model. The model was adjusted by proposing a new correlation for the mass transfer coefficient and by calibrating the heat transfer coefficient. The transient numerical results were in good quantitative and qualitative agreement with the experimental results, ie, within the experimental error bars. Then the experimentally validated mathematical model was utilized to optimize the following parameters: (i) the electric heater power ( ) and the dry air mass flow rate ( ) and (ii) the convection oven length to width ratio (L/W). The goal was to minimize system energy consumption (objective function). The optimization procedure was subject to the following physical constraints: (i) fixed convection oven total volume and (ii) fixed biomass and drying air contact surface area. For the oven original geometry,  = 3.0 kW and  = 9 g s^?1 were numerically found for minimum energy consumption, so that 36.9% and 43.5% energy consumption decreases were obtained, respectively, in comparison with the measurements of experiment 1. Next, the numerical geometric optimization found (L/W)_opt = 9, with and , which was capable to reach a 51.6% energy consumption reduction in comparison with the original system tested in experiment 1. The novelty of this work consists of the development and experimental validation of a physically based microalgae biomass drying mathematical model, ie, instead of using empirical correlations to predict the drying time and temperature profiles and then minimize system energy consumption. Therefore, the results show that it is reasonable to state that the model could be used to design, control, and optimize drying systems with configurations similar to the one analyzed in this study.     

Solvent Influence on the Hydrodeoxygenation of Guaiacol over Pt/SiO2 and Pt/H‐MFI 90 Catalysts

Second generation biofuels are produced in the bioliq® process at the Karlsruhe Institute of Technology via gasification of pyrolysis oil and synthesis of gasoline from the emerging synthesis gas. An alternative strategy is the direct upgrading of the pyrolysis oil by hydrodeoxygenation (HDO). The present study reports on the HDO of guaiacol as one of the phenolic compounds strongly abundant in such mixtures. Special focus was laid on the solvent influence using Pt‐based catalysts. Higher HDO ability was seen using nonpolar solvents and acidic supports. Characterization of the catalysts before and after the test showed that the solvent did not only influence the reactivity, but also the catalyst stability.     

Development and validation of a new LC–MS/MS method for the simultaneous determination of six major ergot alkaloids and their corresponding epimers. Application to some food and feed commodities

A liquid chromatography–tandem mass spectrometry (LC–MS/MS) method was developed and validated for the simultaneous determination of six major ergot alkaloids, ergometrine, ergosine, ergotamine, ergocornine, ergokryptine and ergocristine, as well as their corresponding epimers in food and feed samples. The method involves extraction under alkaline conditions and subsequent clean-up by applying a simple and rapid liquid–liquid partitioning procedure prior to LC–MS/MS analysis. Evaluation of the method revealed good linearity, accuracy and precision. The limits of quantification varied from 0.1 to 1 μg/kg depending on the analyte and matrix. The average extraction and clean-up recoveries in different matrices were between 45 (only for ergometrine in biscuit) and 90%. The uncertainty associated with the analytical method was not higher than 51% and 30%, at concentration levels of 2.5 and 150 μg/kg respectively. Analyte epimerization proved to be minimal during the analytical procedure. The method has been successfully applied to the determination of ergot alkaloids in some Belgian food and feed commodities. Ergot alkaloids were found in 104 out of 122 samples investigated. Ergosine was the most frequently occurring alkaloid, while the highest levels were observed for ergotamine, ergocristine or ergosine, depending on the product type. The total alkaloid content in positive samples varied from 1 to 1145 μg/kg.