The presence of rich scattering in indoor and urban radio propagation scenarios may cause a high arrival density of multipath components (MPCs). Often the MPCs arrive in clusters at the receiver, where MPCs within one cluster have similar angles and delays. The MPCs arriving within a single cluster are typically unresolvable in the delay domain. In this paper, we analyze the effects of unresolved MPCs on the bias of the delay estimation with a multiband subspace fitting algorithm. We treat the unresolved MPCs as a model error that results in perturbed subspace estimation. Starting from the first-order approximation of the perturbations, we derive the bias of the delay estimate of the line-of-sight (LOS) component. We show that it depends on the power and relative delay of the unresolved MPCs in the first cluster compared to the LOS component. Numerical experiments are included to show that the derived expression for the bias well describes the effects of unresolved MPCs on the delay estimation.

A reliable and precise model of the environment is of the highest importance for autonomous vehicles. Occupancy grids are a well-known approach for environment modeling and are a crucial part of multiple autonomous driving functionalities. The standard method is to use a single 2D occupancy grid to model the environment using nonground points. In this paper, we propose a decentralized occupancy grid filtering chain (pipeline) where a high-density 64-layer LiDAR provided the input to our pipeline. Our approach enables us to obtain detailed 2D and 3D models of the environment simultaneously. The pipeline was validated on different scenarios in both simulation and real world. The performance of the designed occupancy grid pipeline was evaluated by the proposed key performance indicators (KPIs) based on accuracy. The results have shown that the approach was able to extract free space information with a high degree of accuracy, while reducing the size of the unobserved area in the grid compared to the standard methods and achieving real-time performance.

Low power wide area networks support the success of long range Internet of things applications such as agriculture, security, smart cities and homes. This enormous popularity, however, breeds new challenging problems as the wireless spectrum gets saturated which increases the probability of collisions and performance degradation. To this end, smart spectrum decisions are needed and will be supported by wireless technology recognition to allow the networks to dynamically adapt to the ever changing environment where fair co-existence with other wireless technologies becomes essential. In contrast to existing research that assesses technology recognition using machine learning on powerful graphics processing units, this work aims to propose a deep learning solution using convolutional neural networks, cheap software defined radios and efficient embedded platforms such as NVIDIA’s Jetson Nano. More specifically, this paper presents low complexity near-real time multi-band sub-GHz technology recognition and supports a wide variety of technologies using multiple settings. Results show accuracies around 99%, which are comparable with state of the art solutions, while the classification time on a NVIDIA Jetson Nano remains small and offers real-time execution. These results will enable smart spectrum management without the need of expensive and high power consuming hardware.

Introduction Successful rehabilitation of the growing number of older citizens receiving healthcare services can lead to preservation of functional independence and improvement in quality of life. Adequate intake of dietary protein and physical training are key factors in counteracting the age-related decline in strength performance and physical function. However, during rehabilitation, many older people/persons have insufficient protein intake, and difficulties in performing exercise training with sufficient intensity and volume. The primary aim of this trial is to investigate if individualised physical exercise training programmes combined with increased protein intake (IPET+P) can improve measures on all International Classification of Functioning, Disability and Health levels, such as strength, gait speed and health-related quality of life, when compared with care as usual in municipality-based rehabilitation alone (usual care, UC) or care as usual in combination with increased protein intake (UC+P). Further, the trial investigates whether UC+P will potentiate more significant improvements in outcome measures than UC. Methods and analysis The trial is a three-armed multicentre, block-randomised controlled trial consisting of a 12-week intervention period with a 1-year follow-up. Citizens above 65 years referred to rehabilitation in the municipality without restricting comorbidities are eligible. Participants are randomised to either a UC group, a UC group with protein supplementation receiving 27.5 g protein/day (UC+P), or an IPET+P supplementation of 27.5 g protein/day. The Short Musculoskeletal Function Assessment questionnaire is the primary outcome. Ethics and dissemination Approvals from The Ethics Committee in Region Zealand, Denmark (SJ-758), and the General Data Protection Regulation at the University of Southern Denmark, Odense (10.330) have been obtained. Trial registration number NCT04091308

In this paper, we propose a convolutional neural network (CNN) aided automatic modulation recognition (AMR) method for a multiple antenna system. We also present two specific combination strategies, such as the relative majority voting method and arithmetic mean method to improve the classification performance in comparison with the state of the art. Our results are given to verify that the proposed method dominant exploits features and classify the modulation types with higher accuracy in comparison with the AMR employing high order cumulants (HOC) and artificial neural networks (ANN).

High-sensitive cardiac Troponin T (hsTnT) is the most frequently used biomarker for the detection of cardiomyocyte injury. Severe aortic stenosis (AS) leads to an increased left ventricular load, with the potential of myocardial injury reflected by increased TnT levels. However, there is a lack of studies showing the prevalence and prognostic role of elevated hsTnT in patients with asymptomatic AS. To examine the association between the hsTnT levels and AS severity in asymptomatic AS patients. We hypothesized that patients with more severe AS will have elevated hsTnT levels and that hsTnT levels are associated with a higher risk for aortic valve events (AVE) and all-cause mortality (ACM). We performed a post-hoc analysis in 1739 asymptomatic patients with mild to moderate-severe AS, enrolled in the randomized, double-blinded SEAS-study (Simvastatin and Ezetimibe in Aortic Stenosis). All patients had available hsTnT blood samples measured at baseline (Year 0) and Year 1. We defined moderate to severe (mod-severe) AS as a transaortic maximal outflow velocity (Vmax)>3.5 m/s combined with aortic valve area (AVA)<1.0 cm2, otherwise non-severe AS. An hsTnT>14 ng/L was high according to assay (Roche, Elecsys Troponin T hs on cobas e 601). Linear multivariable regression model examined the association of hsTnT levels to clinical and echocardiographic variables. Cox multivariable regression model evaluated competing risks and hazard ratios (HR) of outcomes while adjusting for relevant variables, including a Framingham 10-years risk score of cardiovascular diseases. The competing risks were either ACM or AVE, i.e. the first of AVR, cardiovascular death and heart failure due to AS progression. At baseline, hsTnT was high in 26% (453/1739) patients; 25% (380/1529) in non-severe and 35% (73/210) in mod-severe AS. Relative TnT change over one year was 17% (mean 1.17, SD 1.01); 15% in non-severe vs. 32% in mod-severe AS, and neither associated to AS severity, hsTnT at baseline or lipid-lowering treatment. In multivariable linear regression analysis, there were significant correlations between hsTnT at baseline and age, male gender, creatinine, left ventricular mass index and BMI (all p<0.001, R-square=0.42), but not with AS severity. In multivariable Cox regression analyses, a high hsTnT at baseline was associated with AVE 1.61 [95% CI 1.29–1.99]. In contrast, hsTnT at baseline was not associated to all-cause mortality (see figure). In asymptomatic AS patients without severe AS, high-sensitive Troponin T is not associated with AS severity in cross-sectional analyses, and its levels do not change substantially during one year of follow-up. However, patients with hsTnT >14 ng/l had a sixty percent higher independent risk of subsequent aortic valve events. Multivariable Cox regression Type of funding source: Private company. Main funding source(s): Acknowledgements: Main sponsor (SEAS): MSD Singapore Company, LLC, partnership between Merck & Co. Inc. and Schering-Plough Corporation. Blood analysis sponsor: Roche

Coronary chronic total occlusion (CTO) is characterized by the presence of collateral blood vessels which can provide additional blood supply to CTO-artery dependent myocardium. Successful CTO recanalization is followed by significant decrease in collateral donor artery blood flow and collateral derecruitment. Study aim was to assess time-dependent changes in coronary flow reserve (CFR) in collateral donor artery after CTO recanalization and identify factors that influence these changes. Our study enrolled 31 patients with CTO scheduled for percutaneous coronary intervention (PCI). Non-invasive CFR was measured before PCI in collateral donor artery, and 24h and 6 months post-PCI in CTO and collateral donor artery. Gated SPECT MIBI was performed before PCI, while quality of life was assessed by Seattle angina questionnaire (SAQ) pre-PCI, and 6 months after PCI. Collateral donor artery showed significant increase in CFR 24h after CTO recanalization compared to pre-PCI values (2.30±0.49 vs. 2.71±0.45, p=0.005), which remained unchanged after 6 months (2.68±0.24). Maximum baseline blood flow velocity of the collateral donor artery showed significant decrease measured 24h post-PCI compared to pre-PCI values (0.28±0.06 vs. 0.24±0.04m/s), and remained similar after 6-months. There was no significant difference in maximum hyperemic blood flow velocity pre-PCI, 24h and 6 months post-PCI. CFR change of the collateral donor artery 24h post-PCI compared to pre-PCI values showed inverse correlation with left ventricle ejection fraction (LVEF) measured on SPECT. CFR changes showed no correlation with the changes in quality of life assessed by SAQ post-PCI compared to pre-PCI. Significant increase in CFR of the collateral donor artery was observed within 24h after successful recanalization of CTO artery, which maintained constant after the 6 months follow-up. This increase was largely driven by the significant reduction in the maximum baseline blood flow velocity within 24h after CTO recanalization compared to pre-PCI values. Our results suggest that possible benefit of CTO recanalization could be the improvement in physiology of the collateral donor artery. Type of funding source: None

746 EJD, vol. 30, n◦ 6, November-December 2020 7. Hayami M, Fuzitani T. A case of benigh lymphocytoma found in the stroma of Bowen’s disease. Rinsho hihuka 1998; 52: 1130-3. 8. Vandaele R. Benign lymphocytoma (lymphadenoma) of the skin associated with Bowen’s disease. Arch Belg Dermatol Syphiligr 1957; 13: 494-9. 9. Maubec E, Pinquier L, Viguier M, et al. Vaccination-induced cutaneous pseudolymphoma. J Am Acad Dermatol 2005; 52: 623-9.

The low environmental impact and high long-term performance of products are becoming imperative for the sustainable development of the construction industry. Alkali-activated materials (AAMs) are one of the available low-embodied-carbon alternatives to Portland cement (OPC). For their application in the marine environment or where de-icing salts are used, it is of utmost importance to demonstrate their equal or better performance compared to OPC. The aim of this study was to compare the corrosion behaviour of the steel in AAMs based on different regionally available by-products with the behaviour of the steel in OPC. The by-products used were fly ash, slag, silica fume, and iron-silica fines. The corrosion process of each system was monitored by the corrosion potential and polarisation resistance during exposure to tap water and chloride solution over a period of almost one year. Certain AAMs showed a higher resistance to chloride penetration compared to OPC, which was attributed to the smaller number of capillary pores and higher gel phase precipitation. The same corrosion resistance compared to OPC was achieved with alkali-activated fly ash and alkali-activated slag mortars. The stability of the systems in tap water and chloride solution was confirmed by the visual assessment of the steel surface at the end of the test period.

A new modified ion-selective electrode with membranes of LaF3 single crystals with different internal contacts (solid steel or electrolyte) and with FexOy nanoparticles as loading was developed. The best response characteristic with linear potential change was found in the fluoride concentration range from 10−1 to 3.98 × 10−7 M. The detection limit for the electrolyte contact was determined at 7.41 × 10−8 M with a regression coefficient of 0.9932, while the regression coefficient for the solid contact was 0.9969. The potential change per concentration decade ranged from 50.3 to 62.4 mV, depending on whether the contact was solid or electrolytic. The prepared modified electrode has a long lifetime, as well as the possibility of application in different positions (solid contact), and it can also be used for the determination of iron ions. The electrode characterization was performed with scanning electron microscopy and elemental analysis with the technique of electron-dispersive X-ray spectroscopy.

Exponentially-growing next-generation sequencing data requires high-performance tools and algorithms. Nevertheless, the implementation of high-performance computational genomics software is inaccessible to many scientists because it requires extensive knowledge of low-level software optimization techniques, forcing scientists to resort to high-level software alternatives that are less efficient. Here, we introduce Seq—a Python-based optimization framework that combines the power and usability of high-level languages like Python with the performance of low-level languages like C or C++. Seq allows for shorter, simpler code, is readily usable by a novice programmer, and obtains significant performance improvements over existing languages and frameworks. We showcase and evaluate Seq by implementing seven standard, widely-used applications from all stages of the genomics analysis pipeline, including genome index construction, finding maximal exact matches, long-read alignment and haplotype phasing, and demonstrate its implementations are up to an order of magnitude faster than existing hand-optimized implementations, with just a fraction of the code. By enabling researchers of all backgrounds to easily implement high-performance analysis tools, Seq further opens the door to the democratization and scalability of computational genomics.