Tetrathiafulvalene-based architectures : from recognition properties to self-assembly

Date du colloque&nbsp;: 06/2010</p

Deep convolutional neural networks for Bearings failure predictionand temperature correlation

Rolling elements bearings (REBs) is one of the most sensitive components and the common failure unit in mechanical equipment. Bearings failure prognostics, which aims to achieve an effective way to handle the increasing requirements for higher reliability and in the same time reduce unnecessary costs, has been an area of extensive research. The accurate prediction of bearings Remaining Useful Life (RUL) is indispensable for safe and lifetime-optimized operations. To monitor this vital component and planning repair work, a new intelligent method based on Wavelet Packet Decomposition (WPD) and deep learning networks is proposed in this paper. Firstly, features extraction from WPD used as input data. Secondly, these selected features are fed into deep Convolutional Neural Networks (CNNs) to construct the Health Indicator (HI). This study focuses on analysing the relationships such as correlations between the HI and temperature. We develop a solution for the Connectiomics contest dataset of bearings under different operating conditions and severity of defects. The performance of the proposed method is verified by four bearing data sets collected from experimental setup called “PRONOSTIA”. The results show that the health indicator obtains fairly high monotonicity and correlation values and it is beneficial to bearing life prediction. In addition, it is experimentally demonstrated that the proposed method is able to achieve better performance than a traditional neural network based method

C60 Recognition from Extended Tetrathiafulvalene Bis-acetylide Platinum(II) Complexes

The favorable spatial organization imposed by the square planar 4,4′-di(tert-butyl)-2,2′-bipyridine (dbbpy) platinum(II) complex associated with the electronic and shape complementarity of π-extended tetrathiafulvalene derivatives (exTTF) toward fullerenes is usefully exploited to construct molecular tweezers, which display good affinities for C60

Facilitating new forms of discourse for learning and teaching: harnessing the power of Web 2.0 practices

When asked what they would find most helpful to enable them to use technologies more in their teaching, most teachers say "give me examples, in my subject area" and "point me to relevant people I can discuss these issues with". Web 2.0 technologies - with their emphasis on sharing, networking and user production - seem to offer a potential solution. However uptake and use of web 2.0 sites such as blogs, social networking and wikis by teachers for sharing and discussing practice has being marginal so far. This paper focuses on work we are undertaking as part of the OU Learning Design Initiative (http://ouldi.open.ac.uk) and the Hewlett-funded Olnet initiative (http://olnet.org). A key focus of our work is the development of tools, methods and approaches to support the design of innovative learning activities and Open Educational Resources (OER). In this paper I want to focus on one strand of our work; namely how to leverage technologies to promote better sharing and discussing of learning and teaching ideas and designs

Behavioural cloning of teachers for automatic homework selection

© Springer Nature Switzerland AG 2019. We describe a machine-learning system for supporting teachers through the selection of homework assignments. Our system uses behavioural cloning of teacher activity to generate personalised homework assignments for students. Classroom use is then supported through additional mechanisms to combine these predictions into group assignments. We train and evaluate our system against 50,065 homework assignments collected over two years by the Isaac Physics platform. We use baseline policies incorporating expert curriculum knowledge for evaluation and find that our technique improves on the strongest baseline policy by 18.5% in Year 1 and by 13.3% in Year 2.Cambridge Assessmen

Interferon-α resistance in renal carcinoma cells is associated with defective induction of signal transducer and activator of transcription 1 which can be restored by a supernatant of phorbol 12-myristate 13-acetate stimulated peripheral blood mononuclear cells

Therapy of selected human malignancies with interferon-α is widely accepted but often complicated by the emergence of interferon-α resistance. Interferon is a pleiotropic cytokine with antiproliferative, antitumour, antiviral and immunmodulatory effect; it signals through the Jak-STAT signal transduction pathway where signal transducer and activator of transcription 1 plays an important role. Here we report both, a lack of signal transducer and activator of transcription induction in interferon-α resistant renal cell carcinoma cells and signal transducer and activator of transcription 1 reinduction of phorbol 12-myristate 13-acetate-stimulated peripheral blood mononuclear cells supernatant. Preliminary experiments on the identification of the molecules that reinducing signal transducers and activators of transcription 1 indicate that interferon-γ may be the responsible candidate cytokine, but several others may be involved as well. This work provides the basis for therapeutic strategies directed at the molecular modulation of interferon-α resistance in human neoplasms

Draft genome sequence of marine alphaproteobacterial strain HIMB11, the first cultivated representative of a unique lineage within the Roseobacter clade possessing an unusually small genome

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Standards in Genomic Sciences 9 (2014): 632-645, doi:10.4056/sigs.4998989.Strain HIMB11 is a planktonic marine bacterium isolated from coastal seawater in Kaneohe Bay, Oahu, Hawaii belonging to the ubiquitous and versatile Roseobacter clade of the alphaproteobacterial family Rhodobacteraceae. Here we describe the preliminary characteristics of strain HIMB11, including annotation of the draft genome sequence and comparative genomic analysis with other members of the Roseobacter lineage. The 3,098,747 bp draft genome is arranged in 34 contigs and contains 3,183 protein-coding genes and 54 RNA genes. Phylogenomic and 16S rRNA gene analyses indicate that HIMB11 represents a unique sublineage within the Roseobacter clade. Comparison with other publicly available genome sequences from members of the Roseobacter lineage reveals that strain HIMB11 has the genomic potential to utilize a wide variety of energy sources (e.g. organic matter, reduced inorganic sulfur, light, carbon monoxide), while possessing a reduced number of substrate transporters.We gratefully acknowledge the support of the Gordon and Betty Moore Foundation, which funded the sequencing of this genome. Annotation was performed as part of the 2011 C-MORE Summer Course in Microbial Oceanography (http://cmore.soest.hawaii.edu/summercourse/2011/index.htm), with support by the Agouron Institute, the Gordon and Betty Moore Foundation, the University of Hawaii and Manoa School of Ocean and Earth Science and Technology (SOEST), and the Center for Microbial Oceanography: Research and Education (C-MORE), a National Science Foundation-funded Science and Technology Center (award No. EF0424599)