The solar flare and cosmic gamma-ray burst experiment aboard the Ulysses spacecraft

The HUS-Ulysses team has prepared an instrument for the Ulysses spacecraft consisting of 2 Csi detectors and 2 Si surface barrier detectors for measuring x rays in the range 5 to 200 keV with up to 8 ms time resolution. The prime objectives of the experiment are the study of solar flares and cosmic gamma-ray bursts. The Ulysses mission will leave the ecliptic during the forthcoming solar maximum. The total time above ecliptic latitudes + or - 70 degrees is expected to be 230 days. The solar data can be used in conjunction with other experiments to measure the directivity of the emission and for correlative studies

Efficiently Dispersing Carbon Nanotubes in Polyphenylene Sulfide

Thermal plastics are replacing conventional metals in the aerospace, sporting, electronics, and other industries. Thermal plastics are able to withstand relatively high temperatures, have good fatigue properties, and are lighter than metals. Unfortunately, they are not very electrically conductive. However, adding carbon nanotubes to thermal plastics such as polyphenylene sulfide (PPS) can drastically increase the plastic\u27s conductivity at a low weight percent of nanotubes called the percolation threshold. The percolation threshold is the point where adding a little more carbon nanotubes brings together the network of nanotubes and greatly increases the conductivity. We need to learn how to increase the dispersion of nanotubes in PPS to reduce the amount of expensive nanotubes necesarry to reach the percolation threshold. Adding nanotubes to thermal plastics is a difficult procedure. A few different melting and mixing methods have been utilized in previous studies. Initially, we tested how to best disperse the nanotubes using an extruder after physically mixing the two components. We have determined that grinding the PPS pellets to 400 microns and smaller and then coating the PPS powder with the carbon nanotubes in a pulverizer reduces the size and number of carbon nanotube agglomerates in the PPS versus using pellets and mixing by hand. In addition, using moderate screw speeds such as 70 rpm in the extruder helped reduce agglomerates. These results will help us reach the percolation threshold of carbon nanotubes in polyphenylene sulfide while using a smaller amount of the costly nanotubes

Towards Context-aware Process Guidance in Cyber-Physical Systems with Augmented Reality

Assembly, configuration, maintenance, and repair processes in cyber-physical systems (e.g., a press line in a plant) comprise a multitude of complex tasks, whose execution needs to be controlled, coordinated and monitored. Amongst others, a process-centric guidance of users (e.g. service operators) is required, taking the high variability in the assembly of cyber-physical systems (e.g. press line variability) into account. Moreover, the tasks to be performed along these processes may be related to physical components, sensors and actuators, which need to be properly recognized, integrated and operated. In order to digitize cyber-physical processes as well as to guide users in a process-centric way, therefore, we suggest integrating process management technology, sensor/actuator interfaces, and augmented reality techniques. The paper discusses fundamental requirements for such an integration and presents an approach for process-centric user guidance that combines context and process management with augmented reality enhanced tasks. For evaluation purposes, we analyzed the cyber-physical processes of pharmaceutical packaging machines and implemented selected ones based on the approach. Overall, we are able to demonstrate the usefulness of context-aware process management for the flexible support of cyber-physical processes in the Industrial Internet of Things

Process-Driven and Flow-Based Processing of Industrial Sensor Data

For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results

Pain Management After Outpatient Anterior Cruciate Ligament Reconstruction: A Systematic Review of Randomized Controlled Trials.

BACKGROUND: Effective pain management after anterior cruciate ligament (ACL) reconstruction improves patient satisfaction and function. PURPOSE: To collect and evaluate the available evidence from randomized controlled trials (RCTs) on pain control after ACL reconstruction. STUDY DESIGN: Systematic review. METHODS: A systematic literature review was performed using PubMed, Medline, Google Scholar, UpToDate, Cochrane Reviews, CINAHL, and Scopus following PRISMA guidelines (July 2014). Only RCTs comparing a method of postoperative pain control to another method or placebo were included. RESULTS: A total of 77 RCTs met inclusion criteria: 14 on regional nerve blocks, 21 on intra-articular injections, 4 on intramuscular/intravenous injections, 12 on multimodal regimens, 6 on oral medications, 10 on cryotherapy/compression, 6 on mobilization, and 5 on intraoperative techniques. Single-injection femoral nerve blocks provided superior analgesia to placebo for up to 24 hours postoperatively; however, this also resulted in a quadriceps motor deficit. Indwelling femoral catheters utilized for 2 days postoperatively provided superior analgesia to a single-injection femoral nerve block. Local anesthetic injections at the surgical wound site or intra-articularly provided equivalent analgesia to regional nerve blocks. Continuous-infusion catheters of a local anesthetic provided adequate pain relief but have been shown to cause chondrolysis. Cryotherapy improved analgesia compared to no cryotherapy in 4 trials, while in 4 trials, ice water and water at room temperature provided equivalent analgesic effects. Early weightbearing decreased pain compared to delayed weightbearing. Oral gabapentin given preoperatively and oral zolpidem given for the first week postoperatively each decreased opioid consumption as compared to placebo. Ibuprofen reduced pain compared to acetaminophen. Oral ketorolac reduced pain compared to hydrocodone-acetaminophen. CONCLUSION: Regional nerve blocks and intra-articular injections are both effective forms of analgesia. Cryotherapy-compression appears to be beneficial, provided that intra-articular temperatures are sufficiently decreased. Early mobilization reduces pain symptoms. Gabapentin, zolpidem, ketorolac, and ibuprofen decrease opioid consumption. Despite the vast amount of high-quality evidence on this topic, further research is needed to determine the optimal multimodal approach that can maximize recovery while minimizing pain and opioid consumption. CLINICAL RELEVANCE: These results provide the best available evidence from RCTs on pain control regimens after ACL reconstruction

Youth Single-Sport Specialization in Professional Baseball Players.

Background: An increasing number of youth baseball athletes are specializing in playing baseball at younger ages. Purpose: The purpose of our study was to describe the age and prevalence of single-sport specialization in a cohort of current professional baseball athletes. In addition, we sought to understand the trends surrounding single-sport specialization in professional baseball players raised within and outside the United States (US). Study Design: Cross-sectional study; Level of evidence, 3. Methods: A survey was distributed to male professional baseball athletes via individual team athletic trainers. Athletes were asked if and at what age they had chosen to specialize in playing baseball at the exclusion of other sports, and data were then collected pertaining to this decision. We analyzed the rate and age of specialization, the reasons for specialization, and the athlete\u27s perception of injuries related to specialization. Results: A total of 1673 professional baseball athletes completed the survey, representing 26 of the 30 Major League Baseball (MLB) organizations. Less than half (44.5%) of professional athletes specialized in playing a single sport during their childhood/adolescence. Those who reported specializing in their youth did so at a mean age of 14.09 ± 2.79 years. MLB players who grew up outside the US specialized at a significantly earlier age than MLB players native to the US (12.30 ± 3.07 vs 14.89 ± 2.24 years, respectively; Conclusion: This study challenges the current trends toward early youth sport specialization, finding that the majority of professional baseball athletes studied did not specialize as youth and that those who did specialize did so at a mean age of 14 years. With the potential cumulative effects of pitching and overhead throwing on an athlete\u27s arm, the trend identified in this study toward earlier specialization within baseball is concerning

Low-Velocity Halo Clouds

Models that reproduce the observed high-velocity clouds (HVCs) also predict clouds at lower radial velocities that may easily be confused with Galactic disk (|z| < 1 kpc) gas. We describe the first search for these low-velocity halo clouds (LVHCs) using IRAS data and the initial data from the Galactic Arecibo L-band Feed Array survey in HI (GALFA-HI). The technique is based upon the expectation that such clouds should, like HVCs, have very limited infrared thermal dust emission as compared to their HI column density. We describe our 'displacement-map' technique for robustly determining the dust-to-gas ratio of clouds and the associated errors that takes into account the significant scatter in the infrared flux from the Galactic disk gas. We find that there exist lower-velocity clouds that have extremely low dust-to-gas ratios, consistent with being in the Galactic halo - candidate LVHCs. We also confirm the lack of dust in many HVCs with the notable exception of complex M, which we consider to be the first detection of warm dust in HVCs. We do not confirm the previously reported detection of dust in complex C. In addition, we find that most Intermediate- and Low-Velocity clouds that are part of the Galactic disk have a higher 60 micron/100 micron flux ratio than is typically seen in Galactic HI, which is consistent with a previously proposed picture in which fast-moving Galactic clouds have smaller, hotter dust grains.Comment: 30 pages, 7 figures. Accepted to the Ap

Role of Eddies in the Carbon Pump of Eastern Boundary Upwelling Systems, REEBUS, Cruise No. M156, 03.07. – 01.08.2019 Mindelo (Cap Verde) – Mindelo

Summary The major goal of the RV METEOR cruise M156 to Cape Verdian waters and the Mauritanian upwelling area off West Africa was to contribute to a better quantitative understanding of the effects of mesoscale eddies on CO2 source/sink mechanisms and the biological carbon pump in eastern boundary upwelling areas as well as their effects to the oligotrophic periphery including the deep-sea floor. The cruise M156 (MOSES Eddy Study I) was conducted within the framework of the BMBF funded REEBUS project (Role of Eddies in the Carbon Pump of Eastern Boundary Upwelling Systems) by a consortium of physical, biological (benthic microbiology, bacterial plankton, protists) and biogeochemical oceanographers. Specific aims were i. the quantification of solute and particle fluxes within and at the periphery of eddies; ii. to determine the turnover of carbon species, air-sea gas exchange of CO2, iii. the determination of the protistan and bacterial plankton community structures in the surface layers of an eddy, and iv. to quantify the magnitude and variability of material fluxes to the seabed and turnover in the sediment underneath the eddy passage. To achieve these aims, the cruise had two major observing strategies: i. an intense benthic/pelagic program along the zonal eddy passage at 18°N. Along this corridor ranging from 24°20’ to 16°30’W, five benthic/pelagic stations (E1 to E5) in different water depths and distances from the Mauritanian coast were performed. The motivation for this survey has been to resolve zonal gradients in pelagic element cycling as well as of organic matter degradation and burial in the seabed, which in turn could potentially be linked with changes in eddy induced primary- and export production. ii. the detailed investigation of an individual eddy to investigate physical, biogeochemical and biological processes on meso- to submeso-scales (100km to 10m). Satellite data analysis was performed before and during the cruise to identify a suitable eddy from a combination of sea-level anomaly, ocean color as Chl-a proxy, and sea-surface temperature supplemented with shipboard current velocity measurements. A total of 171 stations were sampled. The water column program consists of 59 CTD casts, 29 MSS and 20 Marine Snow Catcher deployments. For biogeochemical measurements at the sea surface two deployments of a Lagrangian Surface Drifter and one Waveglider deployment were conducted. At the seafloor, we conducted 10 BIGO deployments. Ten seafloor imaging surveys were performed using the towed camera system OFOS, supplemented with 7 Multibeam and 1 Sidescan surveys. In deviation from the cruise proposal, the planned long-term deployment of a Lander, which was planned to record a time series of oxygen fluxes during the passage of an eddy, was not deployed due to a major delay in its design and manufacturing. The planned AUV (Girona 500) deployments at the shallow E5 station close to the Mauritanian coast station did also not take place. Despite moderate weather conditions, all deployments were successful, hence all the data and sample material aimed for has been achieved. It is to expect that as planned all scientific questions can be addressed. Especially in the synthesis of all REEBUS cruises and the consideration of data from earlier cruises (MSM17/4, M107) into this region a high scientific potential can be expected

Artificial Intelligence Understands Peptide Observability and Assists With Absolute Protein Quantification

Targeted mass spectrometry has become the method of choice to gain absolute quantification information of high quality, which is essential for a quantitative understanding of biological systems. However, the design of absolute protein quantification assays remains challenging due to variations in peptide observability and incomplete knowledge about factors influencing peptide detectability. Here, we present a deep learning algorithm for peptide detectability prediction, d::pPop, which allows the informed selection of synthetic proteotypic peptides for the successful design of targeted proteomics quantification assays. The deep neural network is able to learn a regression model that relates the physicochemical properties of a peptide to its ion intensity detected by mass spectrometry. The approach makes use of experimentally detected deviations from the assumed equimolar abundance of all peptides derived from a given protein. Trained on extensive proteomics datasets, d::pPop's plant and non-plant specific models can predict the quality of proteotypic peptides for not yet experimentally identified proteins. Interrogating the deep neural network after learning from ~76,000 peptides per model organism allows to investigate the impact of different physicochemical properties on the observability of a peptide, thus providing insights into peptide observability as a multifaceted process. Empirical evaluation with rank accuracy metrics showed that our prediction approach outperforms existing algorithms. We circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the need for selecting the top most promising peptides for targeting a protein of interest. Further, we used an artificial QconCAT protein to experimentally validate the observability prediction. Our proteotypic peptide prediction approach not only facilitates the design of absolute protein quantification assays via a user-friendly web interface but also enables the selection of proteotypic peptides for not yet observed proteins, hence rendering the tool especially useful for plant research