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Penalized regression splines and distributed lag models were used to evaluate the effects of species mixing on productivity and climate-related resistance via tree-ring width measurements from sample cores. Data were collected in Lower Austria from sample plots arranged in a triplet design. Triplets were established for sessile oak [Quercus petraea (Matt.) Liebl.] and Scots pine (Pinus sylvestris L.), European beech (Fagus sylvatica L.) and Norway spruce [Picea abies (L.) H. Karst.], and European beech and European larch (Larix decidua Mill.). Mixing shortened the temporal range of time-lagged climate effects for beech, spruce, and larch, but only slightly changed the effects for oak and pine. Beech and spruce as well as beech and larch exhibited contrasting climate responses, which were consequently reversed by mixing. Single-tree productivity was reduced by between − 15% and − 28% in both the mixed oak–pine and beech–spruce stands but only slightly reduced in the mixed beech–larch stands. Measures of climate sensitivity and resistance were derived by model predictions of conditional expectations for simulated climate sequences. The relative climate sensitivity was, respectively, reduced by between − 16 and − 39 percentage points in both the beech–spruce and beech–larch mixed stands. The relative climate sensitivity of pine increased through mixing, but remained unaffected for oak. Mixing increased the resistance in both the beech–larch and the beech–spruce mixed stand. In the mixed oak–pine stand, resistance of pine was decreased and remained unchanged for oak.

Given the availability of lightweight radio and processing technology, it becomes feasible to imagine spectrum sensing systems using weather balloons. Such balloons navigate the airspace up to 40 km, and can provide a bird's eye and clear view of terrestrial, as well as aerial spectrum use. In this paper, we present SkySense, which is an extension of the Electrosense sensing framework with mobile GPS-located sensors and local data logging. In addition, we present 6 different sensing campaigns, targeting multiple terrestrial or aerial technologies such as ADS-B, AIS or LTE. For instance, for ADS-B, we can clearly conclude that the number of airplanes that are detected is the same for each balloon altitude, but the message reception rate decreases strongly with altitude because of collisions. For each sensing campaign, the dataset is described, and some example spectrum analysis results are presented. In addition, we analyse and quantify important trends visible when sensing from the sky, such as temperature and hardware variations, increased ambient interference levels, as well as hardware limitations of the lightweight system. A key challenge is the automatic gain control and dynamic range of the system, as a radio navigating over 30km, sees a very wide range of possible signal levels. All data is publicly available through the Electrosense framework, to encourage the spectrum sensing community to further analyse the data or motivate further measurement campaigns using weather balloons. TRUE pub

A universal, mobile protection system for work close to patients suffering from acute infectious diseases with aerosol formation has been developed. It is considered useful by the main medical disciplines involved in the treatment of CoVid patients. In those times, disposable protection gear is scarce, and the robust, reusable protection system might be helpful for medical personnel to work more safely in vulnerable situations.

Old-growth beech (Fagus sylvatica L.) forests in Europe show a structural heterogeneity, which distinguish from managed ones. Recent investigations revealed that facilitative belowground interactions might counteract aboveground competition for light. However, the exact interaction of these mechanisms is unclear. We developed the BEEchPlasticity (BEEP) model to investigate whether the mere focus on the aboveground competition for canopy space with subsequent modeling of the light transmittance through the forest canopy suffices to reproduce the observed structural attributes. The BEEP model is individual-based, and explicitly describes the plasticity of tree crowns in a three-dimensional space through a geometric approach. This conceptual design allows tracing neighborhood effects to the forest stand level. We ran 10 simulation experiments with 1000 time steps on a simulation area of 0.5oha. We analyzed the emerging spatial point patterns, gap dynamics as well as distributions of tree age, tree height, tree crown projection area, and tree diameter. By applying various indices that revealed the emergent horizontal and vertical forest structure, we were able to show a strong mutual link between beech crown plasticity and forest heterogeneity. The crown plasticity enabled the trees to close small gaps, which resulted in a highly dynamic tree regeneration, which in turn led to a small-scale heterogeneous forest structure. We found that, over long periods, crown centroids of canopy trees taller than 20om were more regularly distributed than stem foot points. Absolute crown displacements were greater than reported from beech stands in North-Germany and the Sudetes in the Czech Republic. This indicates a need to restrain the model crown growth in order to recognize tree architecture and stability. We could simulate an increasing forest structural heterogeneity. Starting with a structural homogeneous layer of tree saplings, we showed that forest structures similar to those in unmanaged beech forests took several tree generations or approximately 700 time steps to emerge. We recommend enhancing this model approach with spatial explicit modeling of the leaf-area distribution, which has already been done for single beech trees.

The Dark Web, a conglomerate of services hidden from search engines and regular users, is used by cyber criminals to offer all kinds of illegal services and goods. Multiple Dark Web offerings are highly relevant for the cyber security domain in anticipating and preventing attacks, such as information about zero-day exploits, stolen datasets with login information, or botnets available for hire. In this work, we analyze and discuss the challenges related to information gathering in the Dark Web for cyber security intelligence purposes. To facilitate information collection and the analysis of large amounts of unstructured data, we present BlackWidow, a highly automated modular system that monitors Dark Web services and fuses the collected data in a single analytics framework. BlackWidow relies on a Docker-based micro service architecture which permits the combination of both preexisting and customized machine learning tools. BlackWidow represents all extracted data and the corresponding relationships extracted from posts in a large knowledge graph, which is made available to its security analyst users for search and interactive visual exploration. Using BlackWidow, we conduct a study of seven popular services on the Deep and Dark Web across three different languages with almost 100,000 users. Within less than two days of monitoring time, BlackWidow managed to collect years of relevant information in the areas of cyber security and fraud monitoring. We show that BlackWidow can infer relationships between authors and forums and detect trends for cybersecurity-related topics. Finally, we discuss exemplary case studies surrounding leaked data and preparation for malicious activity.

In wireless networked control systems (WNCS), sensors, controllers, and actuators exchange data to solve control tasks. Operation of WNCS usually occurs under real-time constraints, in particular regarding synchronicity of value sampling, transmission latencies, and packet losses. This calls for deterministic protocols as well as for real-time-capable implementations of these protocols. In this paper, we present the protocol framework BiPS (Black-burst integrated Protocol Stack), which provides real-time protocol and operating system functionalities, and its implementation on the Imote 2 hardware platform hosting the transceiver CC 2420. Furthermore, we present the application and deployment of BiPS in an industrial environment.

Spectrum sensing using energy detectors is a promising, simple and flexible approach for cognitive radio networks to detect primary users. In this paper, we present a new energy detector, called Selective Clustering Energy Detector (SCED. This detector determines dynamic thresholds based on clustering and aggregating selected sequences of energy measurements obtained from a software-defined radio. We assess SCED in a series of controlled experiments on real hardware, and compare its performance and reliability to reference cell based energy detectors reported in the literature. We show that SCED surpasses these detectors in all experiments, in particular in scenarios with non-transmission-free reference cells and during the startup phase.

Cognitive radio networks provide solutions for the coexistence of primary users (license holders) and secondary users within a licensed spectrum. In this paper, we present and assess a three-dimensional stabilization protocol for cognitive radio networks, addressing the dimensions time, channel, and space. For stabilization in time and space, nodes agree on a global time structure consisting of superslots and macroslots. For stabilization of network-wide channel usage, nodes determine and share global schedules, which assign channels of sufficient quality to macroslots. The stabilization protocol realizes a conservative strategy, whereby nodes only transmit on channels that have a sufficient locally measured quality and have been assigned in a recent schedule. Furthermore, the protocol is robust against topology changes. To assess the stabilization capability of the protocol, we introduce the notion of schedule-consistency, and evaluate the protocol performance in a series of ns-3 simulations.

This paper discusses the robustness properties of a novel, model-based HVDC fault localization algorithm. The approach utilizes a bank of Kalman filters to rank the fit of a discrete set of line fault models to measured data. The best fitting model indicates the fault position and fault type. The robustness of the algorithm in the presence of disturbances and unmodeled dynamics is demonstrated using synthetic data from high fidelity simulations as well as field data from a real HVDC transmission system.

To evaluate whether extracorporeal carbon dioxide removal by means of a pumpless extracorporeal lung-assist (PECLA) device could be an effective and safe alternative to invasive mechanical ventilation in patients with chronic pulmonary disease and acute hypercapnic ventilatory failure not responding to noninvasive ventilation (NIV).In this multicentre, retrospective study, 21 PECLA patients were compared with respect to survival and procedural outcomes to 21 matched controls with conventional invasive mechanical ventilation. Matching criteria were underlying diagnosis, age, Simplified Acute Physiology Score II and pH at ICU admission.Of the 21 patients treated with PECLA, 19 (90 %) did not require intubation. Median PaCO(2) levels and pH in arterial blood prior to PECLA were 84.0 mmHg (54.2-131.0) and 7.28 (7.10-7.41), respectively. Within 24 h, median PaCO(2) levels and pH had significantly improved to 52.1 (33.0-70.1; p0.001) and 7.44 (7.27-7.56; p0.001), respectively. Two major and seven minor bleeding complications related to the device occurred. Further complications were one pseudoaneurysm and one heparin-induced thrombocytopenia type 2. Compared to the matched control group, there was a trend toward a shorter hospital length of stay in the PECLA group (adjusted p = 0.056). There was no group difference in the 28-day (24 % vs. 19 %, adjusted p = 0.845) or 6-month mortality (33 % vs. 33 %).In this study the use of extracorporeal carbon dioxide removal allowed avoiding intubation and invasive mechanical ventilation in the majority of patients with acute on chronic respiratory failure not responding to NIV. Compared to conventional invasive ventilation, short- and long-term survivals were similar.

Purpose: Computed tomography (CT) guided minimally invasive interventions such as biopsies or ablation therapies often involve insertion of a needle-shaped instrument into the target organ (e.g., the liver). Today, these interventions still require manual planning of a suitable trajectory to the target (e.g., the tumor) based on the slice data provided by the imaging modality. However, taking into account the critical structures and other parameters crucial to the success of the intervention—such as instrument shape and penetration angle—is challenging and requires a lot of experience. Methods: To overcome these problems, we present a system for the automatic or semiautomatic planning of optimal trajectories to a target, based on 3D reconstructions of all relevant structures. The system determines possible insertion zones based on so-calledhard constraints and rates the quality of these zones by so-called soft constraints. The concept of pareto optimality is utilized to allow for a weight-independent proposal of insertion trajectories. In order to demonstrate the benefits of our method, automatic trajectory planning was applied retrospectively to n = 10 data sets from interventions in which complications occurred. Results: The efficient (graphics processing unit-based) implementation of the constraints results in a mean overall planning time of about 9 s. The examined trajectories, originally chosen by the physician, have been rated as follows: in six cases, the insertion point was labeled invalid by the planning system. For two cases, the system would have proposed points with a better rating according to thesoft constraints. For the remaining two cases the system would have indicated poor rating with respect to one of the soft constraints. The paths proposed by our system were rated feasible and qualitatively good by experienced interventional radiologists. Conclusions: The proposed computer-assisted trajectory planning system is able to detect unsafe and propose safe insertion trajectories and may especially be helpful for interventional radiologist at the beginning or during their interventional training.

Invasive mechanical ventilation is known to be detrimental to patients with respiratory failure due to acute exacerbation of chronic obstructive pulmonary disease (AECOPD). If hypercapnic respiratory failure and acidosis cannot be controlled by noninvasive mechanical ventilation, extracorporeal carbon dioxide removal (ECCO2 R) serves as an alternative option. Currently applied systems like extracorporeal membrane oxygenation (ECMO) or pumpless extracorporeal lung assist (PECLA) are associated with potentially significant bleeding complications and require a very high nursing standard. We report a case of AECOPD with hypercapnic coma and failure of noninvasive ventilation for which we used a novel low-flow ECCO2 R device, called the Hemolung Respiratory Assist System. This device requires only a single 15.5 French double-lumen venous catheter and operates at blood flows of 350 mL/min to 550 mL/min. Use of this device enabled the patient to avoid general anesthesia and invasive mechanical ventilation without adverse events. In addition, weaning from noninvasive mechanical ventilation, early mobilization, communication and nutrition were facilitated. CO2 removal with low extracorporeal blood flow avoided intubation in the treatment of hypercapnic coma with failure of non-invasive ventilation.

Background To evaluate the economic implications of the pre-emptive use of extracorporeal carbon dioxide removal (ECCO2R) to avoid invasive mechanical ventilation (IMV) in patients with hypercapnic ventilatory insufficiency failing non-invasive ventilation (NIV). Methods Retrospective ancillary cost analysis of data extracted from a recently published multicentre case–control-study (n = 42) on the use of arterio-venous ECCO2R to avoid IMV in patients with acute on chronic ventilatory failure. Cost calculations were based on average daily treatment costs for intensive care unit (ICU) and normal medical wards as well as on the specific costs of the ECCO2R system. Results In the group treated with ECCO2R IMV was avoided in 90 % of cases and mean hospital length of stay (LOS) was shorter than in the matched control group treated with IMV (23.0 vs. 42.0 days). The overall average hospital treatment costs did not differ between the two groups (41.134 vs. 39.366 €, p = 0.8). A subgroup analysis of patients with chronic obstructive pulmonary disease (COPD) revealed significantly lower median ICU length of stay (11.0 vs. 35.0 days), hospital length of stay (17.5 vs. 51.5 days) and treatment costs for the ECCO2R group (19.610 vs. 46.552 €, p = 0.01). Conclusions Additional costs for the use of arterio-venous ECCO2R to avoid IMV in patients with acute-on-chronic ventilatory insufficiency failing NIV may be offset by a cost reducing effect of a shorter length of ICU and hospital stay. peerReviewed

This paper discusses a novel protection technique for DC line faults in multi-terminal HVDC (MTDC) systems. Hereby a bank of Kalman filters ranks the fit of a discrete set of line fault models to measured data. The best fitting model indicates the fault location and fault type. The suitability of the technique is discussed for fast on-line fault localization and exact off-line fault localization and fault identification. Experimental results are shown to validate the approach based on data from a high fidelity simulation of a 3-terminal HVDC system.

BBS (Black Burst Synchronization) is a synchronization protocol for multi-hop wireless ad-hoc networks providing deterministic upper bounds for tick offset and convergence delay. General bounds can be determined analytically, and depend on parameters such as maximum network diameter and maximum clear channel assessment delay. From the general bounds, concrete bounds can be derived by inserting hardware-specific values. Certainly, it is crucial that the platform-specific values are accurate, and that all sources of delay are considered. In this paper, we report on the experimental validation and derivation of timing constraints of BBS for an implementation on the Imote 2 platform, using a Software Defined Radio (SDR) for some measurements. In particular, we identify sources of delay that have an impact on the upper bounds for tick offset and convergence delay, and devise and conduct experiments to measure these delays. As it turns out, the timing constraints for BBS reported in the original work need several refinements. Also, the jitter introduced by optimization techniques of the hardware platform like instruction and data caches needs careful consideration. We have applied these insights gained from the experiments to improve the design and implementation of BBS.

BACKGROUND Systematic data on clinical outcome in patients with liver failure due to cardiogenic shock are scarce. METHODS We performed a monocentric retrospective data analysis in 197 cardiogenic shock patients with serum bilirubin levels above 102 µmol/L receiving molecular adsorbent recirculating system (MARS). We assessed clinical outcome, recorded laboratory parameters, and tried to assess risk factors for survival. RESULTS The median duration of MARS was 87 hours (range, 20-315 hours) during a median time period of 9 days (range, 3-736 days). During MARS, 48 to 75% of patients developed infections and gastrointestinal, respiratory, and neurological complications, respectively. Inhospital mortality was 66% (n = 129). Baseline bilirubin levels were comparable between survivors and non-survivors. During MARS, bilirubin values decreased significantly in survivors but not in non-survivors. Of various clinical and biochemical parameters assessed at baseline, the sepsis-related Organ Failure Assessment score remained the only independent predictor of inhospital mortality. CONCLUSION Inhospital mortality is still unsatisfyingly high in cardiogenic shock patients with liver failure. Future studies should clarify whether MARS can definitively improve survival in these patients.

Computed tomography (CT) guided minimally invasive interventions such as biopsies or ablation therapies often involve insertion of a needle-shaped instrument into the target organ (e.g., the liver). Today, these interventions still require manual planning of a suitable trajectory to the target (e.g., the tumor) based on the slice data provided by the imaging modality. However, taking into account the critical structures and other parameters crucial to the success of the intervention--such as instrument shape and penetration angle--is challenging and requires a lot of experience.To overcome these problems, we present a system for the automatic or semiautomatic planning of optimal trajectories to a target, based on 3D reconstructions of all relevant structures. The system determines possible insertion zones based on so-called hard constraints and rates the quality of these zones by so-called soft constraints. The concept of pareto optimality is utilized to allow for a weight-independent proposal of insertion trajectories. In order to demonstrate the benefits of our method, automatic trajectory planning was applied retrospectively to n = 10 data sets from interventions in which complications occurred.The efficient (graphics processing unit-based) implementation of the constraints results in a mean overall planning time of about 9 s. The examined trajectories, originally chosen by the physician, have been rated as follows: in six cases, the insertion point was labeled invalid by the planning system. For two cases, the system would have proposed points with a better rating according to the soft constraints. For the remaining two cases the system would have indicated poor rating with respect to one of the soft constraints. The paths proposed by our system were rated feasible and qualitatively good by experienced interventional radiologists.The proposed computer-assisted trajectory planning system is able to detect unsafe and propose safe insertion trajectories and may especially be helpful for interventional radiologist at the beginning or during their interventional training.

Der Telemanipulator daVinci (Intuitive Surgical, Sunnyvale, Kalifornien) ist ein M aster-Slave System fur roboterassistierte minimalinvasive Chirurgie. Da er uber integrierte Gelenksensoren verfugt, kann er unter Verwendung der daVinci-API als mechanisches Trackingsystem verwendet werden. In dieser Arbeit evaluieren wir die Prazision und Genauigkeit eines daVinci mit Hilfe eines Genauigkeitsphantoms mit bekannten Masen. Der ermittelte Positionierungsfehler liegt in der Grosenordnung von 6 mm und ist somit fur einen Grosteil der medizinischen Fragestellungen zu hoch. Zur Reduktion des Fehlers schlagen wir daher eine Kalibrierung der Gelenksensoren vor.