RXCJ1111.6+4050 galaxy cluster: the observational evidence of a transitional fossil group

We present a detailed kinematical and dynamical study of the galaxy cluster RXCJ1111.6+4050 (RXCJ1111), at z = 0.0756 using 104 new spectroscopic redshifts of galaxies observed at the TNG 3.5m telescope and SDSS DR16 public archive. Our analysis is performed in a multiwavelength context in order to study and compare mainly optical and X-ray properties using XMM-Newton data. We find that RXCJ1111 is a galaxy cluster showing a velocity distribution with clear deviations from Gaussianity, that we are able to explain by the presence of a substructure within the cluster. The two cluster components show velocity dispersions of $644 \pm 56$ km/s and $410 \pm 123$ km/s, which yield dynamical masses of M$_{200}$=$1.9 \pm 0.4 \times10^{14}$ M$_{\odot}$ and $0.6 \pm 0.4 \times 10^{14}$ M$_{\odot}$ for the main system and substructure, respectively. RXCJ1111 presents an elongation in the North-South direction and a gradient of 250-350 km/s/Mpc in the velocity field, suggest that the merger axis between the main system and substructure is slightly tilted with respect to the line-of-sight. The substructure is characterized by a magnitude gap $\Delta m_{12} \ge 1.8$, so it fits the "fossil-like" definition of a galaxy group. Mass estimates derived from X-ray and optical are in good agreement when two galaxy components are considered separately. We propose a 3D merging model and find that the fossil group is in an early phase of collision with the RXCJ1111 main cluster and almost aligned with the line-of-sight. This merging model would explain the slight increase found in the T$_X$ with respect to what we would expect for relaxed clusters. Due to the presence of several brightest galaxies, after this collision, the substructure would presumably lose its fossil condition. Therefore, RXCJ1111 represents the observational evidence that the fossil stage of a system can be temporary and transitional.Comment: 16 pages, 11 figures, 3 tables and 1 appendi

How frequent are close supermassive binary black holes in powerful jet sources?

24 pages, 36 figures. © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)Supermassive black hole binariesmay be detectable by an upcoming suite of gravitationalwave experiments. Their binary nature can also be revealed by radio jets via a short-period precession driven by the orbital motion as well as the geodetic precession at typically longer periods. We have investigated Karl G. Jansky Very Large Array and Multi-Element Radio Linked Interferometer Network (MERLIN) radio maps of powerful jet sources for morphological evidence of geodetic precession. For perhaps the best-studied source, Cygnus A, we find strong evidence for geodetic precession. Projection effects can enhance precession features, for which we find indications in strongly projected sources. For a complete sample of 33 3CR radio sources, we find strong evidence for jet precession in 24 cases (73 per cent). The morphology of the radio maps suggests that the precession periods are of the order of 10 6- 10 7 yr. We consider different explanations for the morphological features and conclude that geodetic precession is the best explanation. The frequently observed gradual jet angle changes in samples of powerful blazars can be explained by orbital motion. Both observations can be explained simultaneously by postulating that a high fraction of powerful radio sources have subparsec supermassive black hole binaries.We consider complementary evidence and discuss if any jetted supermassive black hole with some indication of precession could be detected as individual gravitational wave source in the near future. This appears unlikely, with the possible exception of M87.Peer reviewedFinal Published versio

Climate Change Technology R&D Portfolio Decision Making Under Uncertainty

In this project we have completed, or are in the process of, collecting and analyzing information on seven energy technologies – solar photovoltaics, nuclear power, carbon capture and storage, electricity from biomass, liquid bio-fuels, and batteries – in regards to their potential impact on reducing greenhouse gas emissions. We have collected expert elicitations, relating U.S. government funding trajectories to probabilities of success. We then used MiniCAM, a technologically-detailed Integrated Assessnent Model to determine the impact on the marginal cost of reducing greenhouse gas emissions, if the technologies were successful. Finally, we have performed initial analysis on portfolios of technologies. This project has partially supported nine papers, either published, under review, or under preparation for such journals as Energy Economics, The Energy Journal, Climatic Change, Management Science, and Transportation Research

Freshwater Ecosystems: From Models to Applications

Freshwater ecosystems—lakes and streams—are being endangered by agricultural, urban, and industrial pollution; hydraulic engineering; and overexploitation, which threaten their capacity to provide important services (recreation and supply of food and clean water, among others). Ecological modeling may be employed to estimate impacts and analyze mitigation strategies. Toy models are easy to construct, but applying them to real-world problems is often challenging. Here, we show in two case studies how the connection from model to application can be made. The first study analyzes whether and how the impact of climatic change on a mostly recreational fishery in an Alpine lake can be mitigated, while the second looks at restoring biodiversity after cleaning up pollution in a Korean river system, using aquatic insects, which play an essential functional role in aquatic food-webs and are very sensitive to water quality, as indicators of ecosystem health. These studies highlight the ability of process-based eco-evolutionary models to generate testable hypotheses and contribute solutions to real-world problems

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients\u27 AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices

VR-Fit: Walking-in-Place Locomotion with Real Time Step Detection for VR-Enabled Exercise

With recent advances in mobile and wearable technologies, virtual reality (VR) found many applications in daily use. Today, a mobile device can be converted into a low-cost immersive VR kit thanks to the availability of do-it-yourself viewers in the shape of simple cardboards and compatible software for 3D rendering. These applications involve interacting with stationary scenes or moving in between spaces within a VR environment. VR locomotion can be enabled through a variety of methods, such as head movement tracking, joystick-triggered motion and through mapping natural movements to translate to virtual locomotion. In this study, we implemented a walk-in-place (WIP) locomotion method for a VR-enabled exercise application. We investigate the utility of WIP for exercise purposes, and compare it with joystick-based locomotion in terms of step performance and subjective qualities of the activity, such as enjoyment, encouragement for exercise and ease of use. Our technique uses vertical accelerometer data to estimate steps taken during walking or running, and locomotes the user’s avatar accordingly in virtual space. We evaluated our technique in a controlled experimental study with 12 people. Results indicate that the way users control the simulated locomotion affects how they interact with the VR simulation, and influence the subjective sense of immersion and the perceived quality of the interaction. In particular, WIP encourages users to move further, and creates a more enjoyable and interesting experience in comparison to joystick-based navigation

Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia

Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoperative and battlefield settings given the variability of traditional vital signs, such as heart rate and blood pressure, and their inability to detect blood loss at an early stage. To this end, we acquired N = 58 photoplethysmographic (PPG) recordings from both trauma patients with suspected hemorrhage admitted to the hospital, and healthy volunteers subjected to blood withdrawal of 0.9 L. We propose four features to characterize each recording: goodness of fit (r2), the slope of the trend line, percentage change, and the absolute change between amplitude estimates in the heart rate frequency range at the first and last time points. Also, we propose a machine learning algorithm to distinguish between blood loss and no blood loss. The optimal overall accuracy of discriminating between hypovolemia and euvolemia was 88.38%, while sensitivity and specificity were 88.86% and 87.90%, respectively. In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn. The results suggest that the proposed features and algorithm are suitable for the automatic discrimination between hypovolemia and euvolemia, and can be beneficial and applicable in both intraoperative/emergency and combat casualty care

Acceptability of a Novel Smartphone Application for Rhythm Evaluation in Patients with Atrial Fibrillation

Background: Investigators at UMass Medical School and WPI co-developed a novel smartphone application (app), PULSESMART, that detects atrial fibrillation (AF). AF is the world’s most common, serious heart rhythm problem. In its early stages, most cases of AF are paroxysmal (pAF), making them difficult to identify early in the course of disease. Long-term cardiac monitoring is frequently needed to diagnose and prevent complications from AF, such as stroke. Home monitoring for AF can be clinically impactful but existing technologies have cost or methodological limitations. Data are needed on the potential acceptability and usability of heart rhythm monitoring applications. Aim: Our aim was to examine patient acceptability of using a pAF detection app. Methods: 52 patients with pAF underwent rhythm assessment using the app and completed a standardized questionnaire. We looked specifically at responses to 3 questions: 1) how easy was it to use? 2) How important could it be for you? And 3) to what extent does it fit into your daily life? Results: The mean age was 68.5 years and 69% female. The majority of patients reported the app was easy to use (73%), could be important to them and their health (84%), and would fit into their daily lives (78%). Conclusions: After use of the pAF detection app, most patients reported positively. The results suggest that older persons with, or at risk for, pAF may benefit from smartphone-based arrhythmia detection platforms. Further work is needed to assess the feasibility of at-home or in-clinic app use

The OPERA experiment Target Tracker

The main task of the Target Tracker detector of the long baseline neutrino oscillation OPERA experiment is to locate in which of the target elementary constituents, the lead/emulsion bricks, the neutrino interactions have occurred and also to give calorimetric information about each event. The technology used consists in walls of two planes of plastic scintillator strips, one per transverse direction. Wavelength shifting fibres collect the light signal emitted by the scintillator strips and guide it to both ends where it is read by multi-anode photomultiplier tubes. All the elements used in the construction of this detector and its main characteristics are described.Comment: 25 pages, submitted to Nuclear Instrument and Method