Lebensqualität bei deutschsprachigen Patienten mit Rückenmarkverletzungen und Blasenfunktionsstörungen: Validierung der deutschen Adaption des Qualiveen®-Fragebogens

Zusammenfassung: Hintergrund: Blasenfunktionsstörungen bei Rückenmarkverletzten können zu erheblichen Einschränkungen der Lebensqualität führen. Zur Erfassung existiert ein validierter Fragebogen in französischer Sprache. Ziel war es, den Fragebogen in deutscher Sprache zu validieren. Material und Methoden: Übersetzung, sprachliche und interkulturelle Adaption erfolgten in Kooperation mit einer Forschungsstelle für Gesundheitssystemforschung. Die so entstandene Version wurde von 439Patienten an 18 Zentren in Deutschland, Österreich und der Schweiz ausgefüllt. Die Daten wurden deskriptiv hinsichtlich klinischer und soziodemographischer Charakteristika ausgewertet. Die Gütekriterien der Items und Skalen wurden mit einer detaillierten Skalenanalyse geprüft. Ergebnisse: Die Stichprobe bestand aus 65,8% Paraplegikern und 32,8% Tetraplegikern. Interne Konsistenz, Reliabilität und Validität des Fragebogens waren sehr gut. Differenzielle Effekte in den erhobenen klinischen Variablen wurden sichtbar. Schlussfolgerungen: Der Qualiveen®-Fragebogen steht als erstes Instrument in deutscher Sprache zur Untersuchung des Einflusses von Blasenfunktionsstörungen auf die Lebensqualität bei Rückenmarkverletzten zur Verfügun

Adaptive Horizon Model Predictive Control and Al'brekht's Method

A standard way of finding a feedback law that stabilizes a control system to an operating point is to recast the problem as an infinite horizon optimal control problem. If the optimal cost and the optmal feedback can be found on a large domain around the operating point then a Lyapunov argument can be used to verify the asymptotic stability of the closed loop dynamics. The problem with this approach is that is usually very difficult to find the optimal cost and the optmal feedback on a large domain for nonlinear problems with or without constraints. Hence the increasing interest in Model Predictive Control (MPC). In standard MPC a finite horizon optimal control problem is solved in real time but just at the current state, the first control action is implimented, the system evolves one time step and the process is repeated. A terminal cost and terminal feedback found by Al'brekht's methoddefined in a neighborhood of the operating point is used to shorten the horizon and thereby make the nonlinear programs easier to solve because they have less decision variables. Adaptive Horizon Model Predictive Control (AHMPC) is a scheme for varying the horizon length of Model Predictive Control (MPC) as needed. Its goal is to achieve stabilization with horizons as small as possible so that MPC methods can be used on faster and/or more complicated dynamic processes.Comment: arXiv admin note: text overlap with arXiv:1602.0861

Security framework for industrial collaborative robotic cyber-physical systems

The paper introduces a security framework for the application of human-robot collaboration in a futuristic industrial cyber-physical system (CPS) context of industry 4.0. The basic elements and functional requirements of a secure collaborative robotic cyber-physical system are explained and then the cyber-attack modes are discussed in the context of collaborative CPS whereas a defense mechanism strategy is proposed for such a complex system. The cyber-attacks are categorized according to the extent on controllability and the possible effects on the performance and efficiency of such CPS. The paper also describes the severity and categorization of such cyber-attacks and the causal effect on the human worker safety during human-robot collaboration. Attacks in three dimensions of availability, authentication and confidentiality are proposed as the basis of a consolidated mitigation plan. We propose a security framework based on a two-pronged strategy where the impact of this methodology is demonstrated on a teleoperation benchmark (NeCS-Car). The mitigation strategy includes enhanced data security at important interconnected adaptor nodes and development of an intelligent module that employs a concept similar to system health monitoring and reconfiguration

Colorimetric sensor for bad odor detection using automated color correction

Colorimetric sensors based on color-changing dyes offer a convenient approach for the quantitative measurement of gases. An integrated, mobile colorimetric sensor can be particularly helpful for occasional gas measurements, such as informal air quality checks for bad odors. In these situations, the main requirement is high availability, easy usage, and high specificity towards one single chemical compound, combined with cost-efficient production. In this contribution, we show how a well stablished colorimetric method can be adapted for easy operation and readout, making it suitable for the untrained end user. As an example, we present the use of pH indicators for the selective and reversible detection of NH3 in air (one relevant gas contributing to bad odors) using gas-sensitive layers dip coated on glass substrates. Our results show that the method can be adapted to detect NH3 concentrations lower than 1 ppm, with measure-to-result times in the range of a few minutes. We demonstrate that the color measurements can be carried out with the optical signals of RGB sensors, without losing quantitative performance

The FLASHForward Facility at DESY

The FLASHForward project at DESY is a pioneering plasma-wakefield acceleration experiment that aims to produce, in a few centimetres of ionised hydrogen, beams with energy of order GeV that are of quality sufficient to be used in a free-electron laser. The plasma wave will be driven by high-current density electron beams from the FLASH linear accelerator and will explore both external and internal witness-beam injection techniques. The plasma is created by ionising a gas in a gas cell with a multi-TW laser system, which can also be used to provide optical diagnostics of the plasma and electron beams due to the <30 fs synchronisation between the laser and the driving electron beam. The operation parameters of the experiment are discussed, as well as the scientific program.Comment: 19 pages, 9 figure

Biomechanical modeling of human-robot accident scenarios: a computational assessment for heavy-payload-capacity robots

Exponentially growing technologies such as intelligent robots in the context of Industry 4.0 are radically changing traditional manufacturing to intelligent manufacturing with increased productivity and flexibility. Workspaces are being transformed into fully shared spaces for performing tasks during human-robot collaboration (HRC), increasing the possibility of accidents as compared to the fully restricted and partially shared workspaces. The next technological epoch of Industry 5.0 has a heavy focus on human well-being, with humans and robots operating in synergy. However, the reluctance to adopt heavy-payload-capacity robots due to safety concerns is a major hurdle. Therefore, the importance of analyzing the level of injury after impact can never be neglected for the safety of workers and for designing a collaborative environment. In this study, quasi-static and dynamic analyses of accidental scenarios during HRC are performed for medium-and low-payload-capacity robots according to the conditions given in ISO TS 15066 to assess the threshold level of injury and pain, and is subsequently extended for high speeds and heavy payloads for collaborative robots. For this purpose, accidental scenarios are simulated in ANSYS using a 3D finite element model of an adult human index finger and hand, composed of cortical bone and soft tissue. Stresses and strains in the bone and tissue, and contact forces and energy transfer during impact are studied, and contact speed limit values are estimated. It is observed that heavy-payload-capacity robots must be restricted to 80% of the speed limit of low-payload-capacity robots. Biomechanical modeling of accident scenarios offers insights and, therefore, gives confidence in the adoption of heavy-payload robots in factories of the future. The analysis allows for prediction and assessment of different hypothetical accidental scenarios in HRC involving high speeds and heavy-payload-capacity robots

Understanding vulnerabilities in cyber physical production systems

Development of future manufacturing systems is featured with flexibility, mass customization, intelligence and context based learning to produce smart products. These production systems are characterized through networked, cooperating objects called cyber physical systems (CPSs). From the manufacturing perspective, the ability to communicate data and develop interaction between devices, manufacturing machinery, raw materials, working robots, humans and the plant environment develops the concept of cyber physical production systems (CPPS). Human-robot collaboration is a technology area that will be an integrated part of the future factory floor and the CPPS. With the involvement of human part in the automated system industrial scenarios, practical safety issues are expected to arise in the connected environment due to the use of a large number of devices, sensors, and cloud services causing complex network, IP conflicts, compromised nodes and communication issues. This all may lead to occupational safety issues on the factory floor in different ways and combinations. Overall, the system's physical vulnerability will be increased in the context of compromised connected working space and cyber-security. In this paper, the authors developed a risk assessment based on system vulnerability of a CPPS developed for a use case requirement and performed a simulated approach by launching a cyber-attack and measuring the causal effect to identify implications on human worker safety