Aggregation Algorithm Vs. Average for Time Series Prediction

Learning with expert advice as a scheme of on-line learning has been very successfully applied to various learning problems due to its strong theoretical basis. In this paper, for the purpose of times se- ries prediction, we investigate the application of Aggregation Algorithm, which a generalisation of the famous weighted majority algorithm. The results of the experiments done, show that the Aggregation Algorithm performs very well in comparison to average

Hydrodynamic drag-force measurement and slip length on microstructured surfaces

International audienceIn this paper, a drainage experiment of water between a borosilicate sphere and a microstructured surface constituted by regularly spaced pillars is presented. The microstructured surface has two parts: on one part the liquid forms a Cassie interface and on the second it forms a Wenzel interface. The measured hydrodynamic drag force is larger on the Cassie part compared to the Wenzel part. Furthermore, for the Cassie part, from the hydrodynamic drag force measurements on a pillar and between pillars the corresponding local slip lengths have been extracted. The area average slip length on the surface is in agreement with the value expected by Philip's equation

Detection of Dementia-Related Abnormal Behaviour Using Recursive Auto-Encoders.

Age-related health issues have been increasing with the rise of life expectancy all over the world. One of these problems is cognitive impairment, which causes elderly people to have problems performing their daily activities. Detection of cognitive impairment at an early stage would enable medical doctors to deepen diagnosis and follow-up on patient status. Recent studies show that daily activities can be used to assess the cognitive status of elderly people. Additionally, the intrinsic structure of activities and the relationships between their sub-activities are important clues for capturing the cognitive abilities of seniors. Existing methods perceive each activity as a stand-alone unit while ignoring their inner structural relationships. This study investigates such relationships by modelling activities hierarchically from their sub-activities, with the overall goal of detecting abnormal activities linked to cognitive impairment. For this purpose, recursive auto-encoders (RAE) and their linear vs. greedy and supervised vs. semi-supervised variants are adopted to model the activities. Then, abnormal activities are systematically detected using RAE's reconstruction error. Moreover, to apply RAEs for this problem, we introduce a new sensor representation called raw sensor measurement (RSM) that captures the intrinsic structure of activities, such as the frequency and the order of sensor activations. As real-world data are not accessible, we generated data by simulating abnormal behaviour, which reflects on cognitive impairment. Extensive experiments show that RAEs can be used as a decision-supporting tool, especially when the training set is not labelled to detect early indicators of dementia

Stress hyperglycaemia in critically ill patients and the subsequent risk of diabetes: a systematic review and meta-analysis.

Hyperglycaemia occurs frequently in critically ill patients without diabetes. We conducted a systematic review and meta-analysis to evaluate whether this 'stress hyperglycaemia' identifies survivors of critical illness at increased risk of subsequently developing diabetes.We searched the MEDLINE and Embase databases from their inception to February 2016. We included observational studies evaluating adults admitted to the intensive care unit (ICU) who developed stress hyperglycaemia if the researchers reported incident diabetes or prediabetes diagnosed ≥3 months after hospital discharge. Two reviewers independently screened the titles and abstracts of identified studies and evaluated the full text of relevant studies. Data were extracted using pre-defined data fields, and risk of bias was assessed using the Newcastle-Ottawa Scale. Pooled ORs with 95 % CIs for the occurrence of diabetes were calculated using a random-effects model.Four cohort studies provided 2923 participants, including 698 with stress hyperglycaemia and 131 cases of newly diagnosed diabetes. Stress hyperglycaemia was associated with increased risk of incident diabetes (OR 3.48; 95 % CI 2.02-5.98; I (2)  = 36.5 %). Studies differed with regard to definitions of stress hyperglycaemia, follow-up and cohorts studied.Stress hyperglycaemia during ICU admission is associated with increased risk of incident diabetes. The strength of this association remains uncertain because of statistical and clinical heterogeneity among the included studies.Yasmine Ali Abdelhamid, Palash Kar, Mark E. Finnis, Liza K. Phillips, Mark P. Plummer, Jonathan E. Shaw, Michael Horowitz and Adam M. Dean

Multiplicity of 5' Cap Structures Present on Short RNAs

Most RNA molecules are co- or post-transcriptionally modified to alter their chemical and functional properties to assist in their ultimate biological function. Among these modifications, the addition of 5' cap structure has been found to regulate turnover and localization. Here we report a study of the cap structure of human short (<200 nt) RNAs (sRNAs), using sequencing of cDNA libraries prepared by enzymatic pretreatment of the sRNAs with cap sensitive-specificity, thin layer chromatographic (TLC) analyses of isolated cap structures and mass spectrometric analyses for validation of TLC analyses. Processed versions of snoRNAs and tRNAs sequences of less than 50 nt were observed in capped sRNA libraries, indicating additional processing and recapping of these annotated sRNAs biotypes. We report for the first time 2,7 dimethylguanosine in human sRNAs cap structures and surprisingly we find multiple type 0 cap structures (mGpppC, 7mGpppG, GpppG, GpppA, and 7mGpppA) in RNA length fractions shorter than 50 nt. Finally, we find the presence of additional uncharacterized cap structures that wait determination by the creation of needed reference compounds to be used in TLC analyses. These studies suggest the existence of novel biochemical pathways leading to the processing of primary and sRNAs and the modifications of their RNA 5' ends with a spectrum of chemical modifications

A Highly Sensitive Potentiometric Amphetamine Microsensor Based on All-Solid-State Membrane Using a New Ion-Par Complex, [3,3′-Co(1,2-closo-C2B9H11)2]− C9H13NH+

In the present work a highly sensitive ion-selective microelectrode for the detection of amphetamine is presented. For this purpose, a novel ion-par complex based on the metallocarborane, cobalt bis(dicarbollide) anion ([3,3′-Co(1,2-C2B9H11)2]−) coupled to amphetamonium cation has been prepared as the active site for amphetamine recognition. The prepared ion-par complex was incorporated to a PVC-type sensitive membrane. It was then drop-casted on the top of a gold microelectrode previously modified with a solid contact layer of polypyrrole. This novel amphetamine microsensor has provided excellent and quick response within the range 10−5 M to 10−3 M of amphetamine concentration, a limit of detection of 12 µM and a slope of 60.1 mV/decade. It was also found to be highly selective toward some potential interference compounds when compared to amphetamine.The authors acknowledge the financial support from the European Union’s Horizon 2020 research and innovation programme entitled MicroMole and HEARTEN grant agreement No. 653626 and No. 643694 respectivel

Modelling and simulations of reactor neutron noise induced by mechanical vibrations

Mechanical vibrations of core internals are among the main perturbations that induce oscillations in the neutron flux field, also known as neutron noise. In this work, different simulation models for the study of the influence of the mechanical vibrations of fuel assemblies on the neutron flux in the reactor core have been discussed. These methodologies employ the diffusion approximation, with or without a previous homogenization model, to simulate the neutron noise in the time or the frequency domain. The diffusion-based approach is expected to be less accurate in the vicinity of the vibrating fuel assemblies, but correct when considering distances larger than a few diffusion lengths away from the perturbation. All methodologies provide consistent results and can reproduce typical features of the neutron noise induced by mechanical vibrations of core components. First, FEMFFUSION can perform simulations in both the time and frequency domains. Second, CORE SIM + can be used to study various neutron noise scenarios in realistic three-dimensional reactor configurations. The third methodology is centred on using commercial codes as CASMO-5, SIMULATE-3 and SIMULATE-3K. This methodology allows time domain simulations of the neutron noise induced by different neutron noise sources in a nuclear reactor. Finally, a model for time-dependent geometry is implemented for the code system ATHLET/QUABOX-CUBBOX employing a cross-section-based approach for encoding water gap width variations at the reflector

Feasibility of a Novel Sparse Orthogonal Collimator–Based Preclinical Total Marrow Irradiation for Enhanced Dosimetric Conformality

Total marrow irradiation (TMI) has significantly improved radiation conditioning for hematopoietic cell transplantation in hematologic diseases by reducing conditioning-induced toxicities and improving survival outcomes in relapsed/refractory patients. Recently, preclinical three-dimensional image–guided TMI has been developed to enhance mechanistic understanding of the role of TMI and to support the development of experimental therapeutics. However, a dosimetric comparison between preclinical and clinical TMI reveals that the preclinical TMI treatment lacks the ability to reduce the dose to some of the vital organs that are very close to the skeletal system and thus limits the ability to evaluate radiobiological relevance. To overcome this limit, we introduce a novel Sparse Orthogonal Collimator (SOC)–based TMI and evaluate its ability to enhance dosimetric conformality. The SOC-TMI–based dose modulation technique significantly improves TMI treatment planning by reducing radiation exposures to critical organs that are close to the skeletal system that leads to reducing the gap between clinical and preclinical TMI

Multioccupant Activity Recognition in Pervasive Smart Home Environments

been the center of lot of research for many years now. The aim is to recognize the sequence of actions by a specific person using sensor readings. Most of the research has been devoted to activity recognition of single occupants in the environment. However, living environments are usually inhabited by more than one person and possibly with pets. Hence, human activity recognition in the context of multi-occupancy is more general, but also more challenging. The difficulty comes from mainly two aspects: resident identification, known as data association, and diversity of human activities. The present survey paper provides an overview of existing approaches and current practices for activity recognition in multi-occupant smart homes. It presents the latest developments and highlights the open issues in this field