Mosaicism of 50,XX/51,XX in a Murrah buffalo Bubalus bubalis

International audienc

Applications of Machine Learning in Chemical and Biological Oceanography

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.Comment: 58 Pages, 5 Figure

Evidence-based robust optimization of pulsed laser orbital debris removal under epistemic uncertainty

An evidence-based robust optimization method for pulsed laser orbital debris removal (LODR) is presented. Epistemic type uncertainties due to limited knowledge are considered. The objective of the design optimization is set to minimize the debris lifetime while at the same time maximizing the corresponding belief value. The Dempster–Shafer theory of evidence (DST), which merges interval-based and probabilistic uncertainty modeling, is used to model and compute the uncertainty impacts. A Kriging based surrogate is used to reduce the cost due to the expensive numerical life prediction model. Effectiveness of the proposed method is illustrated by a set of benchmark problems. Based on the method, a numerical simulation of the removal of Iridium 33 with pulsed lasers is presented, and the most robust solutions with minimum lifetime under uncertainty are identified using the proposed method

Quantum confinement and photoresponsivity ofβ-In2Se3 nanosheets grown by physical vapour transport

We demonstrate that β-In2Se3 layers with thickness ranging from 2.8 to 100 nm can be grown on SiO2/Si, mica and graphite using a physical vapour transport method. The β-In2Se3 layers are chemically stable at room temperature and exhibit a blue-shift of the photoluminescence emission when the layer thickness is reduced, due to strong quantum confinement of carriers by the physical boundaries of the material. The layers are characterised using Raman spectroscopy and x-ray diffraction from which we confirm lattice constants c = 28.31 ± 0.05 Å and a = 3.99 ± 0.02 Å. In addition, these layers show high photoresponsivity of up to ~2 × 103 A W−1 at λ = 633 nm, with rise and decay times of τ r = 0.6 ms and τ d = 2.5 ms, respectively, confirming the potential of the as-grown layers for high sensitivity photodetectors

Emergent Phenomena Induced by Spin-Orbit Coupling at Surfaces and Interfaces

Spin-orbit coupling (SOC) describes the relativistic interaction between the spin and momentum degrees of freedom of electrons, and is central to the rich phenomena observed in condensed matter systems. In recent years, new phases of matter have emerged from the interplay between SOC and low dimensionality, such as chiral spin textures and spin-polarized surface and interface states. These low-dimensional SOC-based realizations are typically robust and can be exploited at room temperature. Here we discuss SOC as a means of producing such fundamentally new physical phenomena in thin films and heterostructures. We put into context the technological promise of these material classes for developing spin-based device applications at room temperature

Range dependent processing of visual numerosity: similarities across vision and haptics

‘Subitizing’ refers to fast and accurate judgement of small numerosities, whereas for larger numerosities either counting or estimation are used. Counting is slow and precise, whereas estimation is fast but imprecise. In this study consisting of five experiments we investigated if and how the numerosity judgement process is affected by the relative spacing between the presented numerosities. To this end we let subjects judge the number of dots presented on a screen and recorded their response times. Our results show that subjects switch from counting to estimation if the relative differences between subsequent numerosities are large (a factor of 2), but that numerosity judgement in the subitizing range was still faster. We also show this fast performance for small numerosities only occurred when numerosity information is present. This indicates this is typical for number processing and not magnitude estimation in general. Furthermore, comparison with a previous haptic study suggests similar processing in numerosity judgement through haptics and vision

Giant spin Hall effect in graphene grown by chemical vapour deposition

Advances in large-area graphene synthesis via chemical vapour deposition on metals like copper were instrumental in the demonstration of graphene-based novel, wafer-scale electronic circuits and proof-of-concept applications such as flexible touch panels. Here, we show that graphene grown by chemical vapour deposition on copper is equally promising for spintronics applications. In contrast to natural graphene, our experiments demonstrate that chemically synthesized graphene has a strong spin-orbit coupling as high as 20 meV giving rise to a giant spin Hall effect. The exceptionally large spin Hall angle ∼0.2 provides an important step towards graphene-based spintronics devices within existing complementary metal-oxide-semiconductor technology. Our microscopic model shows that unavoidable residual copper adatom clusters act as local spin-orbit scatterers and, in the resonant scattering limit, induce transverse spin currents with enhanced skew-scattering contribution. Our findings are confirmed independently by introducing metallic adatoms-copper, silver and gold on exfoliated graphene samples