a-SiH p-i-n structures with extreme i-layer thickness

We present measurements and numerical simulation of a-Si:H p-i-n detectors with a wide range of intrinsic layer thickness between 2 and 10 pm. Such a large active layer thickness is required in applications like elementary particle detectors or X-ray detectors. For large thickness and depending on the applied bias, we observe a sharp peak in the spectral response in the red region near 700 nm. Simulation results obtained with the program ASCA are in agreement with the measurement and permit the explanation of the experimental data. In thick samples holes recombine or are trapped before reaching the contacts, and the conduction mechanism is fully electron dominated. As a consequence, the peak position in the spectral response is located near the optical band gap of the a-Si:H i-layer. (C) 2009 Elsevier B.V. All rights reserved.http://ac.els-cdn.com/S0040609009003393/1-s2.0-S0040609009003393-main.pdf?_tid=1048124c-577b-11e3-bf9f-00000aab0f27&acdnat=1385567361_28bd7b0c0165dd29d894b963fd4cbd1

Complex quantum transport in a modulation doped strained Ge quantum well heterostructure with a high mobility 2D hole gas

The complex quantum transport of a strained Ge quantum well (QW) modulation doped heterostructure with two types of mobile carriers has been observed. The two dimensional hole gas (2DHG) in the Ge QW exhibits an exceptionally high mobility of 780 000 cm2/Vs at temperatures below 10 K. Through analysis of Shubnikov de-Haas oscillations in the magnetoresistance of this 2DHG below 2 K, the hole effective mass is found to be 0.065 m0. Anomalous conductance peaks are observed at higher fields which deviate from standard Shubnikov de-Haas and quantum Hall effect behaviour due to conduction via multiple carrier types. Despite this complex behaviour, analysis using a transport model with two conductive channels explains this behaviour and allows key physical parameters such as the carrier effective mass, transport, and quantum lifetimes and conductivity of the electrically active layers to be extracted. This finding is important for electronic device applications, since inclusion of highly doped interlayers which are electrically active, for enhancement of, for example, room temperature carrier mobility, does not prevent analysis of quantum transport in a QW

Estudio de la anteversión femoral

Se estudian los valores angulares de la anteversión femoral en 30 fémures humanos secos normales, del lado izquierdo y pertenecientes a individuos adultos. Los valores angulares de la muestra son sometidos a estudio estadístico dando una fiabilidad de la media para p = 0,01 . Se revisa la bibliografía de otras mediciones realizadas en hueso seco y se comparan con los valores obtenidos en este trabajo. Los valores obtenidos contribuirán a un mejor conocimiento del ángulo de anteversión femoral y consecuentemente a la aplicación clínica del mismo

Study of trap states in zinc oxide (ZnO) thin films for electronic applications

The electrical properties of ZnO thin films grown by pulsed laser deposition were studied. Field-effect devices with a mobility reaching 1 cm2/V s show non-linearities both in the current–voltage and in the transfer characteristics which are explained as due to the presence of trap states. These traps cause a reversible threshold voltage shift as revealed by low-frequency capacitance–voltage measurements in metal insulator semiconductor (MIS) capacitors. Thermal detrapping experiments in heterojunctions confirm the presence of a trap state located at 0.32 eV

Relativistic K shell decay rates and fluorescence yields for Zn, Cd and Hg

In this work we use the multiconfiguration Dirac-Fock method to calculate the transition probabilities for all possible decay channels, radiative and radiationless, of a K shell vacancy in Zn, Cd and Hg atoms. The obtained transition probabilities are then used to calculate the corresponding fluorescence yields which are compared to existing theoretical, semi-empirical and experimental results

MetaMap versus BERT models with explainable active learning: ontology-based experiments with prior knowledge for COVID-19

Emergence of the Coronavirus 2019 Disease has highlighted further the need for timely support for clinicians as they manage severely ill patients. We combine Semantic Web technologies with Deep Learning for Natural Language Processing with the aim of converting human-readable best evi-dence/practice for COVID-19 into that which is computer-interpretable. We present the results of experiments with 1212 clinical ideas (medical terms and expressions) from two UK national healthcare services specialty guides for COVID-19 and three versions of two BMJ Best Practice documents for COVID-19. The paper seeks to recognise and categorise clinical ideas, performing a Named Entity Recognition (NER) task, with an ontology providing extra terms as context and describing the intended meaning of categories understandable by clinicians. The paper investigates: 1) the performance of classical NER using MetaMap versus NER with fine-tuned BERT models; 2) the integration of both NER approaches using a lightweight ontology developed in close collaboration with senior doctors; and 3) the easy interpretation by junior doctors of the main classes from the ontology once populated with NER results. We report the NER performance and the observed agreement for human audits

MetaMap versus BERT models with explainable active learning: ontology-based experiments with prior knowledge for COVID-19

Emergence of the Coronavirus 2019 Disease has highlighted further the need for timely support for clinicians as they manage severely ill patients. We combine Semantic Web technologies with Deep Learning for Natural Language Processing with the aim of converting human-readable best evi-dence/practice for COVID-19 into that which is computer-interpretable. We present the results of experiments with 1212 clinical ideas (medical terms and expressions) from two UK national healthcare services specialty guides for COVID-19 and three versions of two BMJ Best Practice documents for COVID-19. The paper seeks to recognise and categorise clinical ideas, performing a Named Entity Recognition (NER) task, with an ontology providing extra terms as context and describing the intended meaning of categories understandable by clinicians. The paper investigates: 1) the performance of classical NER using MetaMap versus NER with fine-tuned BERT models; 2) the integration of both NER approaches using a lightweight ontology developed in close collaboration with senior doctors; and 3) the easy interpretation by junior doctors of the main classes from the ontology once populated with NER results. We report the NER performance and the observed agreement for human audits