<p>Ensemble based data assimilation approaches, such as the Ensemble Kalman Filter (EnKF), have been widely and successfully implemented to combine observations with dynamic models to investigate the evolution of a system&#8217;s state. Such inversions are powerful tools for providing forecasts as well as &#8220;hindcasting&#8221; events such as volcanic eruptions to investigate source parameters and triggering mechanisms. In this study, a high performance computing (HPC) adaptation of the EnKF is used to assimilate ground deformation observations from interferometric synthetic-aperture radar (InSAR) into high-fidelity, multiphysics finite element models to evaluate the prolonged unrest and June 26, 2018 eruption of Sierra Negra volcano, Gal&#225;pagos. The stability of the Sierra Negra magma system is evaluated at each time step by estimating variations in reservoir overpressure, Mohr-Coulomb failure in the host rock, and tensile stress and failure along the reservoir boundary. The deformation of Sierra Negra is tracked over a decade, during which almost 5 meters of surface uplift has been recorded. The EnKF reveals that the evolution of the stress state in the host rock surrounding the Sierra Negra magma reservoir likely controlled the timing of the eruption. While increases in magma reservoir overpressure remained modest (< 10 MPa) throughout the data assimilation time period, significant Mohr-Coulomb failure is indicated in the lead up to the eruption coincident with increased seismicity along both trapdoor faults within Sierra Negra&#8217;s caldera and along the caldera&#8217;s ring faults. During the final stages of pre-eruptive unrest, the EnKF models indicate limited tensile failure, with no tensile failure along the northern portion of the magma system where the eruption commenced. Most strikingly, model calculations of significant through-going Mohr-Coulomb failure correspond in space and time with a Mw 5.4 earthquake recorded in the hours preceding the 2018 eruption. Subsequent stress modeling implicates the Mw 5.4 earthquake along the southern intra-caldera trapdoor fault as the potential catalyst for tensile failure and dike initiation along the reservoir to the north. In conclusion, the volcano EnKF approach successfully tracked the evolving stability of Sierra Negra, indicating great potential for future forecasting efforts.</p>

A proper design of the mooring systems for Wave Energy Converters (WECs) requires an accurate investigation of both operating and extreme wave conditions. A careful analysis of these systems is required to design a mooring configuration that ensures station keeping, reliability, maintainability, and low costs, without affecting the WEC dynamics. In this context, an experimental campaign on a 1:20 scaled prototype of the ISWEC (Inertial Sea Wave Energy Converter), focusing on the influence of the mooring layout on loads in extreme wave conditions, is presented and discussed. Two mooring configurations composed of multiple slack catenaries with sub-surface buoys, with or without clump-weights, have been designed and investigated experimentally. Tests in regular, irregular, and extreme waves for a moored model of the ISWEC device have been performed at the University of Naples Federico II. The aim is to identify a mooring solution that could guarantee both correct operation of the device and load carrying in extreme sea conditions. Pitch motion and loads in the rotational joint have been considered as indicators of the device hydrodynamic behavior and mooring configuration impact on the WEC.

We have read with great interest manuscript by Piccinni et al. in which they demonstrated significant prevalence of liver steatosis in patients with compensated advanced chronic liver disease (cACLD), previously assumed to decrease in advanced fibrosis, as well as very good performance of Controlled attenuation parameter (CAP) in this cohort of patients (1,2).

The generalizability of empirical research depends on the reproduction of findings across settings and populations. Consequently, generalizations demand resources beyond that which is typically available to any one laboratory. With collective interest in the joint Simon effect (JSE)-a phenomenon that suggests people work more effectively with humanlike (as opposed to mechanomorphic) robots -we pursued a multi-institutional research cooperation between robotics researchers, social scientists, and software engineers. To evaluate the robustness of the JSE in dyadic human-robot interactions, we constructed an experimental infrastructure for exact, lab-independent reproduction of robot behavior. Deployment of our infrastructure across three institutions with distinct research orientations (well-resourced versus resource-constrained) provides initial demonstration of the success of our approach and the degree to which it can alleviate technical barriers to HRI reproducibility. Moreover, with the three deployments situated in culturally distinct contexts (Germany, the U.S. Midwest, and the Mexico-U.S. Border), observation of a JSE at each site provides evidence its generalizability across settings and populations. CCS CONCEPTS •Human-centered computing →Empirical studies in HCI. ACM Reference Format: Megan Strait, Florian Lier, Jasmin Bernotat, Sven Wachsmuth, Friederike Eyssel, Robert Goldstone, and Selma Šabanović. 2020. A Three-Site Reproduction of the Joint Simon Effect with the NAO Robot. In Proceedings of the 2020 ACM/IEEE Intemational Conference on Human-Robot Interaction (HRI ’ 20), March 23-26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3319502.3374783

In a rapidly developing IT business environment, quality management practices in various forms are inevitable. Information Technology industry is experiencing the fastest growth in the Bosnian economy over the past five years. Therefore, they are constantly adapting to meet the challenges of digital transformation and to satisfy the expectations of today’s customers. The best way in which an organization undertakes business activities is through quality management practices and organizational learning, which improves product quality and reduce product returns and the cost of servicing dissatisfied customers. This approach ultimately leads to an improvement in the company’s performance. This study proposes a research model based on extensive literature review. This model may serve as a good basis to investigate interrelationships between TQM practices, organizational learning, and organizational performance. It may also help to determine if organizational learning fosters plays a mediating role between TQM practices and performance in IT Sector. Further validation of the model is strongly recommended to future researchers.

Mathematical modelling to compute ground truth from 3D images is an area of research that can strongly benefit from machine learning methods. Deep neural networks (DNNs) are state-of-the-art methods design for solving these kinds of difficulties. Convolutional neural networks (CNNs), as one class of DNNs, can overcome special requirements of quantitative analysis especially when image segmentation is needed. This article presents a system that uses a cascade of CNNs with symmetric blocks of layers in chain, dedicated to 3D image segmentation from microscopic images of 3D nuclei. The system is designed through eight experiments that differ in following aspects: number of training slices and 3D samples for training, usage of pre-trained CNNs and number of slices and 3D samples for validation. CNNs parameters are optimized using linear, brute force, and random combinatorics, followed by voter and median operations. Data augmentation techniques such as reflection, translation and rotation are used in order to produce sufficient training set for CNNs. Optimal CNN parameters are reached by defining 11 standard and two proposed metrics. Finally, benchmarking demonstrates that CNNs improve segmentation accuracy, reliability and increased annotation accuracy, confirming the relevance of CNNs to generate high-throughput mathematical ground truth 3D images.

We present 783 surgical resections of typical and atypical carcinoid tumors of the lung identified in the pathology files of 20 different pathology departments. All cases were critically reviewed for clinical and pathological features and further correlated with clinical outcome. Long-term follow-up was obtained in all the patients and statistically analyzed to determine significance of the different parameters evaluated. Of the histopathological features analyzed, the presence of mitotic activity of 4 mitoses or more per 2mm2, necrosis, lymphatic invasion and lymph node metastasis were identified as statistically significant. Tumors measuring 3 cm or more were also identified as statistically significant and correlate with clinical outcome. Based on our analysis, we consider that the separation of low and intermediate grade neuroendocrine neoplasms of the lung needs to be readjusted in terms of mitotic count as the risk of over grading these neoplasms exceeds 10% under the current criteria. We also consider that tumor size is an important feature to be considered in the assessment of these neoplasms and together with the histological grade of the tumor offers important features that can be correlated with clinical outcome.

In this paper, two approaches are evaluated using the Full Error Detection and Correction (FEDC) method for a pipelined structure. The approaches are referred to as Full Duplication with Comparison...