Diode laser modification of ceramic material surface properties for improved wettability and adhesion

To date, very little work has been published with regard specifically to the use of lasers for modifying the surface properties of materials in order to improve their wettability and adhesion characteristics. Using a 60 W high power diode laser (HPDL) the effects of HPDL radiation on the wettability and adhesion characteristics of certain ceramic materials have been determined. It was found that laser treatment of the materials surfaces’ modified the surface energy and accordingly, wetting experiments, by the sessile drop technique using a variety of test liquids, revealed that laser treatment of the range of ceramic materials surfaces resulted in a decrease in the contact angles. The work shows clearly that laser radiation can be used to alter the wetting and adhesion characteristics of a number of ceramic materials by means of changing the surface energy

Consistently Simulating a Wide Range of Atmospheric Scenarios for K2-18b with a Flexible Radiative Transfer Module

The atmospheres of small, potentially rocky exoplanets are expected to cover a diverse range in composition and mass. Studying such objects therefore requires flexible and wide-ranging modeling capabilities. We present in this work the essential development steps that lead to our flexible radiative transfer module, REDFOX, and validate REDFOX for the Solar system planets Earth, Venus and Mars, as well as for steam atmospheres. REDFOX is a k-distribution model using the correlated-k approach with random overlap method for the calculation of opacities used in the $\delta$-two-stream approximation for radiative transfer. Opacity contributions from Rayleigh scattering, UV / visible cross sections and continua can be added selectively. With the improved capabilities of our new model, we calculate various atmospheric scenarios for K2-18b, a super-Earth / sub-Neptune with $\sim$8 M$_\oplus$ orbiting in the temperate zone around an M-star, with recently observed H$_2$O spectral features in the infrared. We model Earth-like, Venus-like, as well as H$_2$-He primary atmospheres of different Solar metallicity and show resulting climates and spectral characteristics, compared to observed data. Our results suggest that K2-18b has an H$_2$-He atmosphere with limited amounts of H$_2$O and CH$_4$. Results do not support the possibility of K2-18b having a water reservoir directly exposed to the atmosphere, which would reduce atmospheric scale heights, hence too the amplitudes of spectral features inconsistent with the observations. We also performed tests for H$_2$-He atmospheres up to 50 times Solar metallicity, all compatible with the observations.Comment: 28 pages, 13 figures, accepted for publication in Ap

Evaluation of Ankom F58 Filter Bags Compared to Beakers for Analysis of Neutral Detergent Fiber

Feed ingredient, feed refusals, duodenal and fecal samples were analyzed to compare two methods of determining neutral detergent fiber. All samples were weighed into Ankom F58 fiber bags and Analyzed using an Ankom 2000 automated fiber Analyzer. Results were then compared to the Van Soest beaker method. The fiber values determined from both methods were within 3.5% of one another, with the beaker method being consistently greater compared to the Ankom method, except for fecal samples. Variability in fiber estimates for ingredients, feed refusals, and feces translated to substantial inconsistency in estimated neutral detergent fiber digestibilities among treatments. It is important to utilize a technique that results in correct neutral detergent fiber values because these values are used to further calculate digestibility of diets

A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data

Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG is among popular alternatives. However, the performance of sleep staging based on ear-EEG is still inferior to scalp-EEG based sleep staging. In order to address the performance gap between scalp-EEG and ear-EEG based sleep staging, we propose a cross-modal knowledge distillation strategy, which is a domain adaptation approach. Our experiments and analysis validate the effectiveness of the proposed approach with existing architectures, where it enhances the accuracy of the ear-EEG based sleep staging by 3.46% and Cohen's kappa coefficient by a margin of 0.038.Comment: Code available at : https://github.com/Mithunjha/EarEEG_KnowledgeDistillatio

Real-Time Hand Gesture Recognition Using Temporal Muscle Activation Maps of Multi-Channel sEMG Signals

Accurate and real-time hand gesture recognition is essential for controlling advanced hand prostheses. Surface Electromyography (sEMG) signals obtained from the forearm are widely used for this purpose. Here, we introduce a novel hand gesture representation called Temporal Muscle Activation (TMA) maps which captures information about the activation patterns of muscles in the forearm. Based on these maps, we propose an algorithm that can recognize hand gestures in real-time using a Convolution Neural Network. The algorithm was tested on 8 healthy subjects with sEMG signals acquired from 8 electrodes placed along the circumference of the forearm. The average classification accuracy of the proposed method was 94%, which is comparable to state-of-the-art methods. The average computation time of a prediction was 5.5ms, making the algorithm ideal for the real-time gesture recognition applications.Comment: Paper accepted to IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 202

Towards Interpretable Sleep Stage Classification Using Cross-Modal Transformers

Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed, and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite the improved performance, a limitation of most deep-learning based algorithms is their black-box behavior, which has limited their use in clinical settings. Here, we propose a cross-modal transformer, which is a transformer-based method for sleep stage classification. The proposed cross-modal transformer consists of a novel cross-modal transformer encoder architecture along with a multi-scale one-dimensional convolutional neural network for automatic representation learning. Our method outperforms the state-of-the-art methods and eliminates the black-box behavior of deep-learning models by utilizing the interpretability aspect of the attention modules. Furthermore, our method provides considerable reductions in the number of parameters and training time compared to the state-of-the-art methods. Our code is available at https://github.com/Jathurshan0330/Cross-Modal-Transformer.Comment: 11 pages, 7 figures, 6 table

Cross-Disciplinary Genomics Approaches to Studying Emerging Fungal Infections

Emerging fungal pathogens pose a serious, global and growing threat to food supply systems, wild ecosystems, and human health. However, historic chronic underinvestment in their research has resulted in a limited understanding of their epidemiology relative to bacterial and viral pathogens. Therefore, the untargeted nature of genomics and, more widely, -omics approaches is particularly attractive in addressing the threats posed by and illuminating the biology of these pathogens. Typically, research into plant, human and wildlife mycoses have been largely separated, with limited dialogue between disciplines. However, many serious mycoses facing the world today have common traits irrespective of host species, such as plastic genomes; wide host ranges; large population sizes and an ability to persist outside the host. These commonalities mean that -omics approaches that have been productively applied in one sphere and may also provide important insights in others, where these approaches may have historically been underutilised. In this review, we consider the advances made with genomics approaches in the fields of plant pathology, human medicine and wildlife health and the progress made in linking genomes to other -omics datatypes and sets; we identify the current barriers to linking -omics approaches and how these are being underutilised in each field; and we consider how and which -omics methodologies it is most crucial to build capacity for in the near future