A Multiagent Platform for Ubiquitous Service Provision

The Virtual Home Environment is very important in contemporary mobile telecommunications infrastructure as it caters for the ubiquitous provision of services irrespective of network, location and user device. The universality of systems like Universal Mobile Telecommunications System and wi-fi increases the need for the rapid introduction of efficient VHE schemes. In this paper, we study the adoption of Mobile Agents for handling the VHE functionality. Mobile agents are nicely harmonized with the broader idea of VHE as they allow the autonomous execution of tasks by components that roam from node to node and network to network. We present the detailed modeling of a VHE provisioning architecture and investigate its suitability for different use cases and technical options (e.g., end user devices). The adoption of mobile agents for the ubiquitous provision of telecommunication services is quite promising in terms of efficiency. Through a series of experiments we quantify the performance benefits stemming from the adoption of mobile agents in contrast to conventional service provisioning schemes.     

Parsing human skeletons in an operating room

Multiple human pose estimation is an important yet challenging problem. In an operating room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures and 2D body part localization across all camera views, using convolutional neural networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.     

Estimating spine shape in lateral sleep positions using silhouette-derived body shape models

This study aims at evaluating spinal alignment during sleep by combining personalized human models with mattress indentation measurements. A generic surface model has been developed that can be personalized based on anthropometric parameters derived from silhouette extraction. Shape assessment of the personalized surface models, performed by comparison with 3-D surface scans of the trunk, showed a mean unsigned distance of 9.77 mm between modeled and scanned surface meshes. The surface model is combined with an inner skeleton model, allowing the model to simulate distinct sleep postures. An automatic fitting algorithm sets the appropriate degrees of freedom to position the model on the measured indentation according to the adopted sleep posture. Validation on lateral sleep positions showed good intraclass correlations (0.73–0.88) between estimated and measured angular spinal deformations, indicating that silhouette-derived body shape models provide a valuable tool for the unobtrusive assessment of spinal alignment during sleep.Relevance to industry: A common drawback of the available techniques to assess spinal deformation on bedding systems is that they interfere with the actual sleep process. The current study presents a novel method based on silhouette-derived body shape models in order to estimate spine shape during sleep unobtrusively.     

Susceptible Workload Evaluation and Protection using Selective Fault Tolerance

Low power fault tolerance design techniques trade reliability to reduce the area cost and the power overhead of integrated circuits by protecting only a subset of their workload or their most vulnerable parts. However, in the presence of faults not all workloads are equally susceptible to errors. In this paper, we present a low power fault tolerance design technique that selects and protects the most susceptible workload. We propose to rank the workload susceptibility as the likelihood of any error to bypass the logic masking of the circuit and propagate to its outputs. The susceptible workload is protected by a partial Triple Modular Redundancy (TMR) scheme. We evaluate the proposed technique on timing-independent and timing-dependent errors induced by permanent and transient faults. In comparison with unranked selective fault tolerance approach, we demonstrate a) a similar error coverage with a 39.7% average reduction of the area overhead or b) a 86.9% average error coverage improvement for a similar area overhead. For the same area overhead case, we observe an error coverage improvement of 53.1% and 53.5% against permanent stuck-at and transition faults, respectively, and an average error coverage improvement of 151.8% and 89.0% against timing-dependent and timing-independent transient faults, respectively. Compared to TMR, the proposed technique achieves an area and power overhead reduction of 145.8% to 182.0%.     

Antidiabetic Drugs in the Treatment of Alzheimer’s Disease

The public health burden of type 2 diabetes mellitus and Alzheimer’s disease is steadily increasing worldwide, especially in the population of older adults. Epidemiological and clinical studies suggest a possible shared pathophysiology between the two diseases and an increased risk of AD in patients with type 2 diabetes mellitus. Therefore, in recent years, there has been a substantial interest in identifying the mechanisms of action of antidiabetic drugs and their potential use in Alzheimer’s disease. Human studies in patients with mild cognitive impairment and Alzheimer’s disease have shown that administration of some antidiabetic medications, such as intranasal insulin, metformin, incretins, and thiazolidinediones, can improve cognition and memory. This review aims to examine the latest evidence on antidiabetic medications as a potential candidate for the treatment of Alzheimer’s disease.     

Investigating Biomarkers for USH2A Retinopathy Using Multimodal Retinal Imaging

Pathogenic mutations in USH2A are a leading cause of visual loss secondary to non-syndromic or Usher syndrome-associated retinitis pigmentosa (RP). With an increasing number of RP-targeted clinical trials in progress, we sought to evaluate the photoreceptor topography underlying patterns of loss observed on clinical retinal imaging to guide surrogate endpoint selection in USH2A retinopathy. In this prospective cross-sectional study, twenty-five patients with molecularly confirmed USH2A-RP underwent fundus autofluorescence (FAF), spectral-domain optical coherence tomography (SD-OCT) and adaptive optics scanning laser ophthalmoscopy (AOSLO) retinal imaging. Analysis comprised measurement of FAF horizontal inner (IR) and outer (OR) hyperautofluorescent ring diameter; SD-OCT ellipsoid zone (EZ) and external limiting membrane (ELM) width, normalised EZ reflectance; AOSLO foveal cone density and intact macular photoreceptor mosaic (IMPM) diameter. Thirty-two eyes from 16 patients (mean age ± SD, 36.0 ± 14.2 years) with USH2A-associated Usher syndrome type 2 (n = 14) or non-syndromic RP (n = 2) met the inclusion criteria. Spatial alignment was observed between IR-EZ and OR-ELM diameters/widths (p < 0.001). The IMPM border occurred just lateral to EZ loss (p < 0.001), although sparser intact photoreceptor inner segments were detected until ELM disruption. EZ width and IR diameter displayed a biphasic relationship with cone density whereby slow cone loss occurred until retinal degeneration reached ~1350 μm from the fovea, beyond which greater reduction in cone density followed. Normalised EZ reflectance and cone density were significantly associated (p < 0.001). As the strongest correlate of cone density (p < 0.001) and best-corrected visual acuity (p < 0.001), EZ width is the most sensitive biomarker of structural and functional decline in USH2A retinopathy, rendering it a promising trial endpoint.     

Prediction of Coating Properties of Thermally Sprayed WC–Co on Complex Geometries

This work outlines the development of an analytical software tool that enables the prediction of various coating properties on any given sprayable geometry. The prediction is achieved by analyzing the input computer-aided design geometry and by correlating the resulting kinematic conditions with experimental measurements. The results of the developed tool have been validated experimentally using HVOF-sprayed WC-17Co coatings. Specifically, coating thickness, microhardness, WC vol.% and specific sliding wear rate are examined and their values are predicted for the case of the external spray of a rotor-like model.     

Leading edge erosion of wind turbines: Effect of solid airborne particles and rain on operational wind farms

Leading edge erosion (LEE) affects almost all wind turbines, reducing their annual energy production and lifetime profitability. This study presents results of an investigation into 18 operational wind farms to assess the validity of the current literature consensus surrounding LEE. Much of the historical research focuses on rain erosion, implying that this is the predominant causal factor. However, this study showed that the impact of excessive airborne particles from seawater aerosols or from adverse local environments such as nearby quarries greatly increases the levels of LEE. Current testing of leading edge protection coatings or tapes is based on a rain erosion resistivity test, which does little to prove its ability to withstand solid particle erosion and may drive coating design in the wrong direction. Furthermore, it was shown that there is little correlation between test results and actual field performance. A method of monitoring the expected level of erosion on an operational wind turbine due to rain erosion is also presented. Finally, the energy losses associated with LEE on an operational wind farm are examined, with the average annual energy production dropping by 1.8% due to medium levels of erosion, with the worst affected turbine experiencing losses of 4.9%.     

Multiscale quality assessment of Global Human Settlement Layer scenes against reference data using statistical learning

A method for quality assessment of the Global Human Settlement Layer scenes against reference data is presented. It relies on two settlement metrics; the local average and gradient functions that quantify the notions of settlement density and flexible settlement limits respectively. They are both utilized as generalization functions for increasing the level of abstraction of the sets under comparison. Generalization compensates for inaccuracies of the automatic target extraction method and can be computed at multiple scales. The comparison between the target built-up layers and the reference data employs an ordered multi-scale, linear regression computing the goodness of fit measure R²$R^2$. An optimized assessment procedure is investigated in a pilot study and is further employed in a big data exercise. A newly introduced quality metric returns the agreement between automatically extracted built-up from a set of 13605 scenes and the MODIS 500 urban layer, that was found too be as high as 91% for selected sensors. A final experiment attempts a performance increase at lower scales by correlating the target layer with automatically selected training subsets. At 50 m the adjusted R²$R^2$ increases by 3% with a mean squared error improvement of 2% compared to the performance achieved without statistical learning. The experiment suggests that the GHSL assessment at a global scale can be carried out based on limited high resolution reference data of minimal spatial coverage.