Global warming alters sound transmission: differential impact on the prey detection ability of echolocating bats

Climate change impacts the biogeography and phenology of plants and animals, yet the underlying mechanisms are little known. Here, we present a functional link between rising temperature and the prey detection ability of echolocating bats. The maximum distance for echo-based prey detection is physically determined by sound attenuation. Attenuation is more pronounced for high-frequency sound, such as echolocation, and is a nonlinear function of both call frequency and ambient temperature. Hence, the prey detection ability, and thus possibly the foraging efficiency, of echolocating bats and susceptible to rising temperatures through climate change. Using present-day climate data and projected temperature rises, we modelled this effect for the entire range of bat call frequencies and climate zones around the globe. We show that depending on call frequency, the prey detection volume of bats will either decrease or increase: species calling above a crossover frequency will lose and species emitting lower frequencies will gain prey detection volume, with crossover frequency and magnitude depending on the local climatic conditions. Within local species assemblages, this may cause a change in community composition. Global warming can thus directly affect the prey detection ability of individual bats and indirectly their interspecific interactions with competitors and prey.     

Simplified detection of broken rotor bars in induction motors controlled in field reference frame

In this paper, a simple method for additional on-line detection of broken rotor bars in a squirrel cage induction motor controlled in rotor field co-ordinates using existing hardware is presented. Based on a previously presented approach, an algorithm for on-line calculation of the variance of stator voltage reference, which depends on the number of broken bars, has been developed. Due to its simplicity, it could run in parallel with a standard control algorithm in field reference frame using contemporary fixed- and floating-point processors, thus requiring minimum processing time. The algorithm uses internal reference values of the stator voltage; therefore no additional dedicated measurements are needed. Results were obtained at different operating points on an induction motor with gradually damaged rotor. Comparison with commonly used diagnostic method confirms the validity of the approach.     

Evaluating probability of default: Intelligent agents in managing a multi-model system

For the successful probability of default (PD) evaluation with the application of multiple prediction models two issues should be addressed: the accuracy of the analytic models which decreases over time and the evaluation of results which must be presented in a uniform shape. To deal with these two issues, a multi-agent system (MAS) and knowledge management systems (KMS) based process management system is proposed. The proposed system has two goals: to prevent the PD information quality deterioration by active management of analytical processes and to provide a universal access point allowing the simultaneous use of multiple prediction models.     

Hydrogenation of CO2 and CO in a high temperature gradient field between catalyst surface and opposite inert cool plate

Methanol synthesis was carried out at 25 bar in slit formed by two parallel plates 5 mm apart. Upper plate was covered by catalyst layer and heated up to 250°C, whereas lower one was kept at about 30°C. Reaction stream in laminar flow consisted of H_2, CO₂, and CO in concentration range usually encountered in industrial processes. Catalyst layer was prepared by spraying CuO/ZnO/Al₂O₃/V₂O₃ slurry on SS‐plate. Continuous removal of methanol and water by condensation on the cool surface shifted equilibrium toward products formation. At isothermal conditions with no temperature gradient in slit, total carbon conversion approached the thermodynamic equilibrium when residence time was long enough. Experiments with high temperature difference showed total carbon conversion much larger compared to the thermodynamic one calculated at plate‐catalyst temperature. Three‐dimensional model predicted total carbon conversion for both isothermal and high temperature gradient operation reasonably well. © 2013 American Institute of Chemical Engineers AIChE J 60: 613–622, 2014     

Recombinant lamb chymosin as an alternative coagulating enzyme in cheese production

Recombinant lamb chymosin (RLC) was prepared and tested for its potential use in cheese production. The milk clotting activity and proteolytic activity of RLC were evaluated in comparison with commercial recombinant calf chymosin (RCC), cow rennet (CR), and microbial coagulant (MC). RLC, RCC, and MC showed similar responses to pH, with a sharp increase of the coagulation time at pH 6.6 to 6.8 and decrease of curd firmness at the pH 6.5 to 6.6. In the case of CR, we observed two clear increases in the coagulation time and decreases in the curd firmness, at pH 6.4 to 6.5 and 6.6 to 6.8. Optimal clotting activity was obtained for RLC at 40 degrees C, for both CR and RCC at 45 degrees C, and for MC at 60 degrees C. The temperature instability of RLC at temperatures above 45 degrees C could constitute a benefit in making hard cheese varieties. The additon of CaCl2 to milk resulted in enhanced clotting activity of all coagulants, most prominently for CR. The proteolytic activity of RLC was significantly lower from that of CR but not significantly different from the activity of RCC. The lower proteolytic activity in the cheese made with RLC did not have negative effect on organoleptic properties. The overall quality of the cheese made with RLC was at least comparable to that of the cheese made with RCC, and both cheeses were better scored than the cheese made with CR.     

Influence of elongational flow generating nozzles on material properties of polypropylene nanocomposites

In this study, polymer nanocomposites (PNCs) based on polypropylene (PP), organoclay (used as nanoclay), and compatibilizer were prepared by a masterbatch (MB or two‐step) as well as by a one‐step process. These compounds were then injection molded into tensile test specimens and rectangular plates using conically and hyperbolically shaped nozzles. The use of these nozzles in the injection unit was to enhance intercalation and exfoliation in the PNCs by means of elongational flow. The pressure loss and temperature rise in the compound induced by this flow were characterized in temperature and pressure measurements. An additional calculation using a new analytic model showed good correlation with the measured values for the conically shaped nozzles whereas the pressure drops in the hyperbolically shaped nozzles were underpredicted. Mechanical as well as rheological characterizations were performed on the produced test specimens. The compounds from the MB process showed good intercalation and exfoliation and improved mechanical properties without any special elongational flow treatment. Similar properties were achieved with the one‐step process and elongational treatment using a short hyperbolically or conically shaped nozzle. These results indicated that it was possible to eliminate one complete compounding step and achieve the same mechanical properties in polypropylene nanocomposites when elongational elements were used. POLYM. ENG. SCI., 58:3–12, 2018. © 2016 Society of Plastics Engineers     

The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines

Machine learning models in metabolomics, despite their great prediction accuracy, are still not widely adopted owing to the lack of an efficient explanation for their predictions. In this study, we propose the use of the general explanation method to explain the predictions of a machine learning model to gain detailed insight into metabolic differences between biological systems. The method was tested on a dataset of ¹H NMR spectra acquired on normal lung and mesothelial cell lines and their tumor counterparts. Initially, the random forests and artificial neural network models were applied to the dataset, and excellent prediction accuracy was achieved. The predictions of the models were explained with the general explanation method, which enabled identification of discriminating metabolic concentration differences between individual cell lines and enabled the construction of their specific metabolic concentration profiles. This intuitive and robust method holds great promise for in‐depth understanding of the mechanisms that underline phenotypes as well as for biomarker discovery in complex diseases.