COVID-19 pandemic is plaguing the world and representing the most significant stress test for many national healthcare systems and services, since their foundation. The supply-chain disruption and the unprecedented request for intensive care unit (ICU) beds have created in Europe conditions typical of low-resources settings. This generated a remarkable race to find solutions for the prevention, treatment and management of this disease which is involving a large amount of people. Every day, new Do-It-Yourself (DIY) solutions regarding personal protective equipment and medical devices populate social media feeds. Many companies (e.g., automotive or textile) are converting their traditional production to manufacture the most needed equipment (e.g., respirators, face shields, ventilators etc.). In this chaotic scenario, policy makers, international and national standards bodies, along with the World Health Organization (WHO) and scientific societies are making a joint effort to increase global awareness and knowledge about the importance of respecting the relevant requirements to guarantee appropriate quality and safety for patients and healthcare workers. Nonetheless, ordinary procedures for testing and certification are currently questioned and empowered with fast-track pathways in order to speed-up the deployment of new solutions for COVID-19. This paper shares critical reflections on the current regulatory framework for the certification of personal protective equipment. We hope that these reflections may help readers in navigating the framework of regulations, norms and international standards relevant for key personal protective equipment, sharing a subset of tests that should be deemed essential even in a period of crisis.

COVID-19 pandemic is plaguing the world and representing the most significant stress test for many national healthcare systems and services, since their foundation. The supply-chain disruption and the unprecedented request for intensive care unit (ICU) beds have created in Europe conditions typical of low-resources settings. This generated a remarkable race to find solutions for the prevention, treatment and management of this disease which is involving a large amount of people. Every day, new Do-It-Yourself (DIY) solutions regarding personal protective equipment and medical devices populate social media feeds. Many companies (e.g., automotive or textile) are converting their traditional production to manufacture the most needed equipment (e.g., respirators, face shields, ventilators etc.). In this chaotic scenario, policy makers, international and national standards bodies, along with the World Health Organization (WHO) and scientific societies are making a joint effort to increase global awareness and knowledge about the importance of respecting the relevant requirements to guarantee appropriate quality and safety for patients and healthcare workers. Nonetheless, ordinary procedures for testing and certification are currently questioned and empowered with fast-track pathways in order to speed-up the deployment of new solutions for COVID-19. This paper shares critical reflections on the current regulatory framework for the certification of personal protective equipment. We hope that these reflections may help readers in navigating the framework of regulations, norms and international standards relevant for key personal protective equipment, sharing a subset of tests that should be deemed essential even in a period of crisis.

Active learning is proposed for selection of the next operating points in the design of experiments, for identifying linear parameter-varying systems. We extend existing approaches found in literature to multiple-input multiple-output systems with a multivariate scheduling parameter. Our approach is based on exploiting the probabilistic features of Gaussian process regression to quantify the overall model uncertainty across locally identified models. This results in a flexible framework which accommodates for various techniques to be applied for estimation of local linear models and their corresponding uncertainty. We perform active learning in application to the identification of a diesel engine air-path model, and demonstrate that measures of model uncertainty can be successfully reduced using the proposed framework.

The need for the exact overview of technology, which is the target for some purposes is often very crucial. Patent databases provide nowadays very suitable source of data and information which can be further analyzed. Global overview is thus possible to provide for any kind of technology. Smart Furniture is a very used term recently, which is related to current trends such as digitization, smart city or internet of things. Within these phenomena, Smart Furniture is used in different contexts, and so its concept is not clarified. The aim of the article is to show the technology analysis of Smart Furniture based on the patent data analysis and literature analysis by clustering and visualization. The definition of Smart Furniture was recently provided in literature based on previous research which was undertaken based on searching in scientific and patent databases. Thus, the term is defined by its technical properties and parameters. This definition is put into the context of actual trends of patents content with selected future trends. A patent analysis was undertaken between 20 October 2019 and 09 November 2019, while the Web of Science database was included, which was searched by keywords that included the phrase "Smart Furniture" and variants. Patent searching was performed in the PatentInspiration database. In total 31 articles from scientific database and 491 patent applications were examined against strict criteria containing meaningful definitions of Smart Furniture. Based on the analysis of key technologies and properties, clustering of results and their further analysis, it was found that the concept of smart furniture is specific to the following components: intelligent system, controller operated with user's data and energy sources, sensors and actuators.

The term stealthy has come to encompass a variety of techniques that attackers can employ to avoid being detected. In this manuscript, for a class of perturbed linear time-invariant systems, we propose two security metrics to quantify the potential impact that stealthy attacks could have on the system dynamics by tampering with sensor measurements. We provide analysis tools to quantify these metrics for given system dynamics, control, and system monitor. Then, we provide synthesis tools (in terms of semidefinite programs) to redesign controllers and monitors such that the impact of stealthy attacks is minimized and the required attack-free system performance is guaranteed.

The growth of online video-on-demand consumption continues unabated. Existing heuristic-based adaptive bit-rate (ABR) selection algorithms are typically designed to optimise video quality within a very narrow context. This may lead to video streaming providers implementing different ABR algorithms/players, based on a network connection, device capabilities, video content, etc., in order to serve the multitude of their users' streaming requirements. In this paper, we present SMASH: a Supervised Machine learning approach to Adaptive Streaming over HTTP, which takes a tentative step towards the goal of a one-size-fits-all approach to ABR. We utilise the streaming output from the adaptation logic of nine ABR algorithms across a variety of streaming scenarios (generating nearly one million records) and design a machine learning model, using systematically selected features, to predict the optimal choice of the bitrate of the next video segment to download. Our evaluation results show that SMASH guarantees a high QoE with consistent performance across a variety of streaming contexts.

In this short paper, we present goDASH, an infrastructure for headless streaming of HTTP adaptive streaming (HAS) video content, implemented in the language golang, an open-source programming language supported by Google. goDASH's main functionality is the ability to stream HAS content without decoding actual video (headless player). This results in low memory requirements and the ability to run multiple players in a large-scale-based evaluation setup. goDASH comes complete with numerous state-of-the-art HAS algorithms, and is fully written in the Google golang language, which simplifies the implementation of new adaptation algorithms and functions. goDASH supports two transportation protocols Transmission Control Protocol (TCP) and Quick UDP Internet Connections (QUIC). The QUIC protocol is a relatively new protocol with the promise of performance improvement over the widely used TCP. We believe that goDASH is the first emulation-based HAS player that supports QUIC. The main limitation in using QUIC protocol is the need for a security certificate setup on both ends (client and server) as QUIC demands an encrypted connection. This limitation is eased by providing our own testbed framework, known as goDASHbed. This framework uses a virtual environment to serve video content locally (which allows setting security certificates) through the Mininet virtual emulation tool. As part of Mininet, goDASH can be used in conjunction with other traffic generators.

LS-DYNA is a well-known multiphysics code with both explicit and implicit time stepping capabilities. Implicit simulations rely heavily on sparse matrix computations, in particular direct solvers, and are notoriously much harder to scale than explicit simulations. In this paper, we investigate the scalability challenges of the implicit structural mode of LS- DYNA. In particular, we focus on linear constraint analysis, sparse matrix reordering, symbolic factorization, and numerical factorization. Our problem of choice for this study is a thermomechanical simulation of jet engine models built by Rolls-Royce with up to 200 million degrees of freedom, or equations. The models are used for engine performance analysis and design optimization, in particular optimization of tip clearances in the compressor and turbine sections of the engine. We present results using as many as 131,072 cores on the Blue Waters Cray XE6/XK7 supercomputer at NCSA and the Titan Cray XK7 supercomputer at OLCF. Since the main focus is on general linear algebra problems, this work is of interest for all linear algebra practitioners, not only developers of implicit finite element codes.

In this paper, we show our novel teleoperation system that mediates proximity perception at the slave system as tactile information to the user. We have equipped a robot’s end-effector with a capacitive proximity sensor array. Based on the proximity information, tactile feedback is generated for the user via a tactile display. Thus, the user can feel some of an object’s features through his fingers, without the need for establishing contact between the slave system and the object. In our setting, the proximity sensing-based feedback complements the visual feedback provided by a workspace camera and a robot tool camera. Both the sensor array and the tactile display, have a spatial resolution of 4×4. To evaluate the impact, we conducted a user study covering scenarios with visual occlusion and distortion in pre-touch and pre-manipulation phases. The study revealed an improvement in the accuracy of positioning of the end-effector when the visual and the tactile feedback were both provided to the user. The study also showed high acceptance of the new modality by the users.

Abstract This article reframes the formation of the Ottoman-Habsburg frontier after 1699 in social historical terms. By going beyond diplomatic and military factors, it identifies how the contraction of Ottoman borders affected taxation, landholding, and Muslim-Christian relations in Bosnia. The article argues that peasants in Ottoman Bosnia experienced the mounting pressures of increasing taxation, manipulation over landownership, and religiously inflected hostility, often driven by those Muslim noblemen who tried to capitalize on the destabilizing wake of several wars that the Ottoman Empire fought with the Habsburg, Venetian, and Russian states in the eighteenth century. Through these processes, by the end of the century the meaning of the reaya or raya—an Ottoman term for taxpaying “subjects” that theoretically applied to all denominations, including Muslims—had become synonymous with “Christians,” acquiring a new political significance.

This paper presents an analysis of the depolarization effect in off-body channels, based on a previously developed geometry-based channel model for polarized communications with dynamic users. The model considers Line-of-Sight propagation and components reflected from scatterers distributed on cylinders centered around the user. A mobility model for wearable antennas based on Fourier series is employed to take the effects of user’s motion into account. The focus is on scattered signal components, where the impact of a scatter’s position, its material properties, and the influence of user dynamics on signal depolarization are investigated. It is observed that the wearable antenna motion has a strong impact on the channel’s polarization characteristics, particularly for dynamic on-body placements, such as arms and legs. If the antenna motion is neglected, the error in cross-polarization ratio is greater than 23dB compared to a static approach. The antenna rotation during motion is found to be the dominant factor, while the corresponding displacement can be neglected, with the error not exceeding 1dB. This result justifies the channel model simplification proposed in this paper.