New High-Speed a-Si/c-Si- and a-SiC/c-Si-Based Switches

The electrical and optical characteristics of the new high-speed Al/a-Si/c-Si(p)/c-Si(n+)/Al and Al/a- SiC/c-Si(p)/c-Si(n+)/Al optically controlled switches are presented in this paper. These switches exhibit the lowest ever reported values of rise and fall times, for this kind of switches, of about 3ns. They also exhibit a temperature and light reversibly controlled forward breakover voltage (VBF), together with high values of light triggering sensitivity

High Specific Energy Lithium Cells for Space Exploration

The paper discusses development under an ESA TRP activity (Contract No. 4000109879/13/NL/LvH) with a target of high specific energy Lithium-ion cells, capable of operating under low temperature conditions, i.e. −40 °C. Such cells may be encountered in future exploration missions, which do not consider the use of Radioisotope Heater Units. During the activity, ≥1 Ah silicon-based high energy density prototype cells, following components characterization and optimization, were designed, developed, manufactured and tested under room and subzero temperature conditions down to −40 °C. The developed and tested prototype cells exhibited energy density of around 208 Wh/Kg at room temperature under C/10 charge-discharge rate within voltage range of 2.8 V and 4.1 V. Moreover, the prototype cells could retain and deliver more than 75% of their capacity at room temperature upon cycling at −40 °C, demonstrating an energy density of 140 Wh/kg

Permanent water swelling effect in low temperature thermally reduced graphene oxide

We demonstrate permanent water trapping in reduced graphene oxide after high relative humidity exposure. For this purpose, we grew graphene oxide films via spin-coating on glass substrates followed by thermal reduction. The electrical resistance of the planar device was then measured. We observed that resistance is significantly increased after water vapor exposure and remains stable even after 250 days in ambient conditions. Various techniques were applied to desorb the water and decrease (recover) the material's resistance, but it was achieved only with low temperature thermal annealing (180 °C) under forming gas (H2/N2 mixture). The permanent effect of water absorption was also detected by x-ray photoelectron spectroscopy.</p

Classification of Foetal Distress and Hypoxia Using Machine Learning Approaches

© 2018, Springer International Publishing AG, part of Springer Nature. Foetal distress and hypoxia (oxygen deprivation) is considered as a serious condition and one of the main factors for caesarean section in the obstetrics and Gynecology department. It is the third most common cause of death in new-born babies. Many foetuses that experienced some sort of hypoxic effects can develop series risks including damage to the cells of the central nervous system that may lead to life-long disability (cerebral palsy) or even death. Continuous labour monitoring is essential to observe the foetal well being. Foetal surveillance by monitoring the foetal heart rate with a cardiotocography is widely used. Despite the indication of normal results, these results are not reassuring, and a small proportion of these foetuses are actually hypoxic. In this paper, machine-learning algorithms are utilized to classify foetuses which are experiencing oxygen deprivation using PH value (a measure of hydrogen ion concentration of blood used to specify the acidity or alkalinity) and Base Deficit of extra cellular fluid level (a measure of the total concentration of blood buffer base that indicates the metabolic acidosis or compensated respiratory alkalosis) as indicators of respiratory and metabolic acidosis, respectively, using open source partum clinical data obtained from Physionet. Six well know machine learning classifier models are utilised in our experiments for the evaluation; each model was presented with a set of selected features derived from the clinical data. Classifier’s evaluation is performed using the receiver operating characteristic curve analysis, area under the curve plots, as well as the confusion matrix. Our simulation results indicate that machine-learning algorithms provide viable methods that could delivery improvements over conventional analysis

Fluid flow and heat transfer in microchannel devices for cooling applications: experimental and numerical approaches

Microchannel heat sinks are pointed to have a great potential in cooling systems. This paper presents a systematic study to develop a microchannel heat sink to be used in PV panels cooling. A systematic experimental approach is used to optimize the heat sink geometry. Then the potential advantage of using flow boiling conditions is explored in both numerical and experimental approaches. The results show that a heat exchanger with thin walls and wide channels can dissipate a greater amount of heat. Comparing the results obtained for one and two-phase flow conditions, one must conclude that although in the boiling tests the heat transfer coefficient was higher, the cooling method with single-phase flow using water dissipated a greater amount of heat, which was mainly due to flow instabilities. In this context, the numerical work clearly evidences that boiling can be an advantage in microchannel heat sinks, as long as the flow is controlled. The work also shows that the considered numerical simulation tool is sensitive enough to quantify the heat transfer enhancement due to boiling within the examined microchannel paths

Unbiased Bayesian inference for population Markov jump processes via random truncations

We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as these are continuous time, discrete state systems with potentially infinite state-space. Here we propose a novel efficient algorithm for joint state / parameter posterior sampling in population Markov Jump processes. We introduce a class of pseudo-marginal sampling algorithms based on a random truncation method which enables a principled treatment of infinite state spaces. Extensive evaluation on a number of benchmark models shows that this approach achieves considerable savings compared to state of the art methods, retaining accuracy and fast convergence. We also present results on a synthetic biology data set showing the potential for practical usefulness of our work