Three-dimensional phase-field study of crack-seal microstructures - insights from innovative post-processing techniques

Numerical simulations of vein evolution contribute to a better understanding of processes involved in their formation and possess the potential to provide invaluable insights into the rock deformation history and fluid flow pathways. The primary aim of the present article is to investigate the influence of a “realistic” boundary condition, i.e. an algorithmically generated “fractal” surface, on the vein evolution in 3-D using a thermodynamically consistent approach, while explaining the benefits of accounting for an extra dimensionality. The 3-D simulation results are supplemented by innovative numerical post-processing and advanced visualization techniques. The new methodologies to measure the tracking efficiency demonstrate the importance of accounting the temporal evolution; no such information is usually accessible in field studies and notoriously difficult to obtain from laboratory experiments as well. The grain growth statistics obtained by numerically post-processing the 3-D computational microstructures explain the pinning mechanism which leads to arrest of grain boundaries/multi-junctions by crack peaks, thereby, enhancing the tracking behavior

Digestibility and rumen fermentation of a high forage diet pre-treated with a mixture of cellulase and xylanase enzymes

El articulo esta publicado en una revista de Open accessForages play an important role in ruminant animal production worldwide. Unlocking the nutritional potential of poor-quality tropical forages with fibrolytic enzymes would improve forage digestibility and utilization. Using in vitro and in vivo methods this study investigated the effect of pre-treating Smutsfinger hay for 24 hours with a mixture of fibrolytic enzyme (100% cellulase; 75% cellulase: 25% xylanase; 50% cellulase: 50% xylanase; 25% cellulase: 75% xylanase; 100% xylanase and a control with no enzyme) on ruminal fermentation and digestibility of nutrients by sheep. For in vitro fermentation, dry matter, neutral detergent fibre (NDF) degradability and volatile fatty acids (VFA) were determined with standard procedures. The same treatments were used for an in vivo digestibility trial using Merino sheep in a 6 x 6 Latin square design. Feed intake and total tract digestibility were recorded. Rumen fluid samples were collected daily, preserved, and analysed for VFA. The addition of 100% cellulase enzyme to Smutsfinger hay in vitro increased (P <0.05) NDF degradability and gas production compared with the control and inclusion of 100% xylanase enzyme. Both 100% cellulase and xylanase enzymes significantly reduced in vitro end time fermentation pH. A 50:50 mixture of cellulase and xylanase plus enzyme in vivo, increased acetate, total VFA concentration, and higher NDF and ADF digestibility of the test feed compared with the control. Inclusion of a 50-75% mixture of cellulase and 50-25% xylanase enzymes treatment led to higher gas production and butyrate concentration, decreased ruminal pH and improved nutrient digestibility

Application of Random Forests in ToF-SIMS Data

Surface analysis techniques are particularly important in the field of materials science, which help researchers to understand the mechanism behind complex chemical reactions and study the properties of different materials. Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a highly sensitive surface analysis technique, allows the reliable determination of various materials. ToF-SIMS spectra of materials are usually enormously complex since typical raw data may include many peaks over large mass-to-charge ratio (m/z) ranges. Hence, the use of data-mining methods in processing ToF-SIMS data is becoming more popular and important. In this study we show that random forests model can be used to automatically classify several different lithium-containing materials and to extract representative peaks from ToF-SIMS spectra of these materials. Our study shows good performance in analyzing spectra of materials with similar and dissimilar compositions, which can provide researchers with the possibility of quick and automatic analysis of ToF-SIMS data

High-throughput screening with the Eimeria tenella CDC2-related kinase2/cyclin complex EtCRK2/EtCYC3a

The poultry disease coccidiosis, caused by infection with Eimeria spp. apicomplexan parasites, is responsible for enormous economic losses to the global poultry industry. The rapid increase of resistance to therapeutic agents, as well as the expense of vaccination with live attenuated vaccines, requires the development of new effective treatments for coccidiosis. Because of their key regulatory function in the eukaryotic cell cycle, cyclin-dependent kinases (CDKs) are prominent drug targets. The Eimeria tenella CDC2-related kinase 2 (EtCRK2) is a validated drug target that can be activated in vitro by the CDK activator XlRINGO (Xenopus laevis rapid inducer of G2/M progression in oocytes). Bioinformatics analyses revealed four putative E. tenella cyclins (EtCYCs) that are closely related to cyclins found in the human apicomplexan parasite Plasmodium falciparum. EtCYC3a was cloned, expressed in Escherichia coli and purified in a complex with EtCRK2. Using the non-radioactive time-resolved fluorescence energy transfer (TR-FRET) assay, we demonstrated the ability of EtCYC3a to activate EtCRK2 as shown previously for XlRINGO. The EtCRK2/EtCYC3a complex was used for a combined in vitro and in silico high-throughput screening approach, which resulted in three lead structures, a naphthoquinone, an 8-hydroxyquinoline and a 2-pyrimidinyl-aminopiperidine-propane-2-ol. This constitutes a promising starting point for the subsequent lead optimization phase and the development of novel anticoccidial drugs

Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with Machine-Learning-Assisted Analysis of ToF-SIMS Spectra

Detailed knowledge about contamination and passivation compounds on the surface of lithium metal anodes (LMAs) is essential to enable their use in all-solid-state batteries (ASSBs). Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a highly surface-sensitive technique, can be used to reliably characterize the surface status of LMAs. However, as ToF-SIMS data are usually highly complex, manual data analysis can be difficult and time-consuming. In this study, machine learning techniques, especially logistic regression (LR), are used to identify the characteristic secondary ions of 5 different pure lithium compounds. Furthermore, these models are applied to the mixture and LMA samples to enable identification of their compositions based on the measured ToF-SIMS spectra. This machine-learning-based analysis approach shows good performance in identifying characteristic ions of the analyzed compounds that fit well with their chemical nature. Moreover, satisfying accuracy in identifying the compositions of unseen new samples is achieved. In addition, the scope and limitations of such a strategy in practical applications are discussed. This work presents a robust analytical method that can assist researchers in simplifying the analysis of the studied lithium compound samples, offering the potential for broader applications in other material systems

Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate

Lithium-ion batteries with solid electrolytes offer safety, higher energy density and higher long-term performance, which are promising alternatives to conventional liquid electrolyte batteries. Lithium aluminum titanium phosphate (LATP) is one potential solid electrolyte candidate due to its high Li-ion conductivity. To evaluate its performance, influences of the experimental factors on the materials design need to be investigated systematically. In this work, a materials design strategy based on machine learning (ML) is employed to design experimental conditions for the synthesis of LATP. In the variation of parameters, we focus on the tolerance against the possible deviations in the concentration of the precursors, as well as the influence of sintering temperature and holding time. Specifically, models built with different design selection strategies are compared based on the training data assembled from previous laboratory experiments. The best one is then chosen to design new experiment parameters, followed by measuring the corresponding properties of the newly synthesized samples. A previously unknown sample with ionic conductivity of 1.09 × 10$^{-3}$ S cm$^{-1}$ is discovered within several iterations. In order to further understand the mechanisms governing the high ionic conductivity of these samples, the resulting phase compositions and crystal structures are studied with X-ray diffraction, while the microstructures of sintered pellets are investigated by scanning electron microscopy. Our studies demonstrate the advantages of applying machine learning in designing experimental conditions by the synthesis of desired materials, which can effectively help researchers to reduce the number of required experiments

Choreography, controversy and child sex abuse: Theoretical reflections on a cultural criminological analysis of dance in a pop music video

This article was inspired by the controversy over claims of ‘pedophilia!!!!’ undertones and the ‘triggering’ of memories of childhood sexual abuse in some viewers by the dance performance featured in the music video for Sia’s ‘Elastic Heart’ (2015). The case is presented for acknowledging the hidden and/or overlooked presence of dance in social scientific theory and cultural studies and how these can enhance and advance cultural criminological research. Examples of how these insights have been used within other disciplinary frameworks to analyse and address child sex crime and sexual trauma are provided, and the argument is made that popular cultural texts such as dance in pop music videos should be regarded as significant in analysing and tracing public perceptions and epistemologies of crimes such as child sex abuse

Simulation of High-Visual Quality Scenes in Low-Cost Virtual Reality

With the increasing popularity of virtual reality, many video games and virtual experiences with high-visual quality have been developed recently. Virtual reality with a high-quality representation of scenes is still an experience linked to high-cost devices. There are currently low-cost virtual reality solutions by using mobile devices, but in those cases, the visual quality of the presented virtual environments must be simplified for running on mobile devices with limited hardware characteristics. In this work, we present a novel Image-Based Rendering technique for low-cost virtual reality. We have conducted a performance evaluation of three mobile devices with different hardware characteristics. Results show that our technique represents high-visual quality virtual environments with considerably better performance compared to traditional rendering solutions.Workshop: WCGIV – Computación Gráfica, Imágenes y VisualizaciónRed de Universidades con Carreras en Informátic