Adsorption of Cu, Ag, and Au atoms on graphene including van der Waals interactions

We performed a systematic density functional study of the adsorption of copper, silver, and gold adatoms on graphene, especially accounting for van der Waals interactions by the vdW-DF and the PBE+D2 methods. In particular, we analyze the preferred adsorption site (among top, bridge, and hollow positions) together with the corresponding distortion of the graphene sheet and identify diffusion paths. Both vdW schemes show that the coinage metal atoms do bind to the graphene sheet and that in some cases the buckling of the graphene can be significant. The results for silver are at variance with those obtained with GGA, which gives no binding in this case. However, we observe some quantitative differences between the vdW-DF and the PBE+D2 methods. For instance the adsorption energies calculated with the PBE+D2 method are systematically higher than the ones obtained with vdW-DF. Moreover, the equilibrium distances computed with PBE+D2 are shorter than those calculated with the vdW-DF method

Psychophysiological body activation characteristics in daily routines

We present a novel approach to analyse and model psychophysiological body activation patterns that emerge from physical and mental activity during daily routines. We analyse our approach on a 62h dataset of daily routine recordings using acceleration and heart rate sensors. We present a descriptive analysis of psychophysiological activations during the routines using a novel visualisation technique. Our results show that daily routines exhibit different psychophysiological body activation characteristics. While physically-related routines are correlated with heart activity, mentally-related routines show activation patterns without physical activity. © 2009 IEEE

Bite weight prediction from acoustic recognition of chewing

Automatic dietary monitoring (ADM) offers new perspectives to reduce the self-reporting burden for participants in diet coaching programs. This paper presents an approach to predict weight of individual bites taken. We utilize a pattern recognition procedure to spot chewing cycles and food type in continuous data from an ear-pad chewing sound sensor. The recognized information is used to predict bite weight. We present our recognition procedure and demonstrate its operation on a set of three selected foods of different bite weights. Our evaluation is based on chewing sensor data of eight healthy study participants performing 504 habitual bites in total. The sound-based chewing recognition achieved recalls of 80% at 60%-70% precision. Food classification of chewing sequences resulted in an average accuracy of 94%. In total, 50 variables were derived from the chewing microstructure, and were analyzed for correlations between chewing behavior and bite weight. A subset of four variables was selected to predict bite weight using linear food-specific models. Mean weight prediction error was lowest for apples (19.4%) and largest for lettuce (31%) using the sound-based recognition. We conclude that bite weight prediction using acoustic chewing recordings is a feasible approach for solid foods, and should be further investigated

Early indication of decompensated heart failure in patients on home-telemonitoring: a comparison of prediction algorithms based on daily weight and noninvasive transthoracic bio-impedance

Background: Heart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation. Objective: In this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations. Methods: We monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM). Results: Algorithms using NITTI in the 2 weeks preceding decompensation predicted events (P<.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines. Conclusions: NITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation

Cardiorespiratory fitness estimation using wearable sensors: laboratory and free-living analysis of context-specific submaximal heart rates

In this work, we propose to use pattern recognition methods to determine submaximal heart rate (HR) during specific contexts, such as walking at a certain speed, using wearable sensors in free-living, and use context-specific HR to estimate cardiorespiratory fitness (CRF). CRF of 51 participants was assessed by a maximal exertion test (VO2max). Participants wore a combined accelerometer and HR monitor during a laboratory based simulation of activities of daily living and for two weeks in free-living. Anthropometrics, HR while lying down and walking at predefined speeds in laboratory settings were used to estimate CRF. Explained variance (R2) was 0.64 for anthropometrics, and increased up to 0.74 for context-specific HR (0.73 to 0.78 when including fat-free mass). Then, we developed activity recognition and walking speed estimation algorithms to determine the same contexts (i.e. lying down and walking) in free-living. Context-specific HR in free-living was highly correlated with laboratory measurements (Pearson's r = 0.71-0.75). R2 for CRF estimation was 0.65 when anthropometrics were used as predictors, and increased up to 0.77 when including free-living context-specific HR (i.e. HR while walking at 5.5 km/h). R2 varied between 0.73 and 0.80 when including fat-free mass among the predictors. RMSE was reduced from 354.7 ml/min to 281.0 ml/min by the inclusion of context-specific HR parameters (21% error reduction). We conclude that pattern recognition techniques can be used to contextualize HR in free-living and estimated CRF with accuracy comparable to what can be obtained with laboratory measurements of HR response to walking

A molecular mechanism for the water-hydroxyl balance during wetting of TiO2

We show that the formation of the wetting layer and the experimentally observed continuous shift of the H2O-OH balance towards molecular water at increasing coverage on a TiO2(110) surface can be rationalized on a molecular level. The mechanism is based on the initial formation of stable hydroxyl pairs, a repulsive interaction between these pairs and an attractive interaction with respect to water molecules. The experimental data are obtained by synchrotron radiation photoelectron spectroscopy and interpreted with the aid of density functional theory calculations and Monte Carlo simulations

Microcities: A Platform Based on Microclouds for Neighborhood Services

International audienceThe current datacenter-centralized architecture limits the cloud to the location of the datacenters, generally far from the user. This architecture collides with the latest trend of ubiquity of Cloud computing. Distance leads to increased utilization of the broadband Wide Area Network and poor user experience, especially for interactive applications. A semi-decentralized approach can provide a better Quality of Experience (QoE) in large urban populations in mobile cloud networks, by confining local traffic near the user while maintaining centralized characteristics, running on the users and network devices. In this paper, we propose a novel semi-decentralized cloud architecture based on microclouds. Microclouds are dynamically created and allow users to contribute resources from their computers, mobile and network devices to the cloud. Microclouds provide a dynamic and scalable system without an extra investment in infrastructure. We also provide a description of a realistic mobile cloud use case, and its adaptation to microclouds

Cerebral Small Vessel Disease burden is increased in Systemic Lupus Erythematosus

BACKGROUND AND PURPOSE—: Systemic lupus erythematosus (SLE) increases stroke risk, but the mechanism is uncertain. This study aimed to determine the association between SLE and features on neuroimaging of cerebral small vessel disease (SVD), a risk factor for stroke. METHODS—: Consecutive patients attending a clinic for SLE were recruited. All patients underwent brain magnetic resonance imaging; had blood samples taken for markers of inflammation, endothelial dysfunction, cholesterol, and autoantibodies; and underwent cognitive and psychiatric testing. The data were compared with sex- and age-matched healthy controls and patients with minor stroke. Features of SVD were measured, a total SVD score calculated, and associations sought with vascular risk factors, cognition, SLE activity, and disease duration. RESULTS—: Fifty-one SLE patients (age: 48.8 years; SD: 14.3 years) had a greater total SVD score compared with healthy controls (1 versus 0; P<0.0001) and stroke patients (1 versus 0; P=0.02). There were higher perivascular spaces and deep white matter hyperintensity scores and more superficial brain atrophy in SLE patients versus healthy controls. Despite fewer vascular risk factors than similarly aged stroke patients, SLE patients had similar or more of some SVD features. The total SVD score was not associated with SLE activity, cognition, disease duration, or any blood measure. CONCLUSIONS—: In this data set, SLE patients had a high burden of SVD features on magnetic resonance imaging, particularly perivascular spaces. A larger longitudinal study is warranted to determine the causes of SVD features in SLE and clinical implications