Machine learning enabled fast optical identification and characterization of 2D materials.

Two-dimensional materials are a class of atomically thin materials with assorted electronic and quantum properties. Accurate identification of layer thickness, especially for a single monolayer, is crucial for their characterization. This characterization process, however, is often time-consuming, requiring highly skilled researchers and expensive equipment like atomic force microscopy.

Interface Analysis between Inconel 625 and Cobalt-Chromium Alloy Fabricated by Powder Bed Fusion Using Pulsed Wave Laser.

A few components used in the aerospace and petrochemical industries serve in corrosive environments at high temperatures. Corrosion-resistant metals or unique processes, such as coating and fusion welding, are required to improve the performance of the parts. We have used laser powder bed fusion (LPBF) technology to deposit a 5 mm thick corrosion-resistant CoCrMo layer on a high-strength IN625 substrate to improve the corrosion resistance of the core parts of a valve.

Multimetal Research in Powder Bed Fusion: A Review.

This article discusses the different forms of powder bed fusion (PBF) techniques, namely laser powder bed fusion (LPBF), electron beam powder bed fusion (EB-PBF) and large-area pulsed laser powder bed fusion (L-APBF). The challenges faced in multimetal additive manufacturing, including material compatibility, porosity, cracks, loss of alloying elements and oxide inclusions, have been extensively discussed. Solutions proposed to overcome these challenges include the optimization of printing parameters, the use of support structures, and post-processing techniques.

Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits.

An agent in a nonstationary contextual bandit problem should balance between exploration and the exploitation of (periodic or structured) patterns present in its previous experiences. Handcrafting an appropriate historical context is an attractive alternative to transform a nonstationary problem into a stationary problem that can be solved efficiently. However, even a carefully designed historical context may introduce spurious relationships or lack a convenient representation of crucial information.

Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools.

The current gold standard of gait diagnostics is dependent on large, expensive motion-capture laboratories and highly trained clinical and technical staff. Wearable sensor systems combined with machine learning may help to improve the accessibility of objective gait assessments in a broad clinical context. However, current algorithms lack flexibility and require large training datasets with tedious manual labelling of data.

Systematic identification of DNA variants associated with ultraviolet radiation using a novel Geographic-Wide Association Study (GeoWAS).

Background: Long-term environmental variables are widely understood to play important roles in DNA variation. Previously, clinical studies examining the impacts of these variables on the human genome were localized to a single country, and used preselected DNA variants. Furthermore, clinical studies or surveys are either not available or difficult to carry out for developing countries.

Analysis of the genetic basis of disease in the context of worldwide human relationships and migration.

Genetic diversity across different human populations can enhance understanding of the genetic basis of disease. We calculated the genetic risk of 102 diseases in 1,043 unrelated individuals across 51 populations of the Human Genome Diversity Panel. We found that genetic risk for type 2 diabetes and pancreatic cancer decreased as humans migrated toward East Asia.