Performance evaluation of polymer-filled metal fused filament fabrication tooling for profile extrusion

The application of additive manufacturing (AM) for tooling in the mould and die industry brings a disruptive potential in process performance, design flexibility and product enhancements. Maturing of existing AM technologies and emerging technologies such as metal-fused filament fabrication (metal FFF) can further support the applicability of AM tooling in polymer profile extrusion. This study provides a complete characterization of metal FFF 17–4 PH stainless-steel die inserts and evaluates their applicability in a polymer extrusion process chain. The presented experimental assessment pivots on the metrological characterization of the produced inserts and the impact of the insert characteristics on the final extrudates’ product. Considering a conventionally manufactured benchmark insert, produced via subtractive methods (CNC machining and electrical discharge machining), comparable results for AM tools in terms of extrudates’ quality and process repeatability are presented. It was found that despite significant higher average surface parameters for AM insert tools (Sa = 2–9 μm vs. Sa = 0.3–0.9 μm for dies manufactured by machining), a much smaller difference was observed in the resulting quality of polymer extrudates’ product. The roughness generation effect of polymer profile extrusion based on the different dies’ internal surface roughness topography and the effect on extrudates product was evaluated. Three-dimensional average roughness Sa on acrylonitrile butadiene styrene extrudate surfaces obtained from conventionally machined dies was in the range of 0.3 μm. For extrudates obtained from additively manufactured dies, their Sa was in the rage of 0.5 μm (despite the much higher surface roughness of FFF dies compared to machined dies). The results confirm that with suitable extrudates’ product requirement, it is feasible to apply metal FFF as the selected manufacturing method for tooling in polymer profile extrusion

Cumulative radiation exposure during thoracic endovascular aneurysm repair and subsequent follow-up

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A new neuropsychological instrument measuring effects of age and drugs on fitness to drive: development, reliability, and validity of MedDrive

Background: Current guidelines underline the limitations of existing instruments to assess fitness to drive and the poor adaptability of batteries of neuropsychological tests in primary care settings. Aims: To provide a free, reliable, transparent computer based instrument capable of detecting effects of age or drugs on visual processing and cognitive functions. Methods: Relying on systematic reviews of neuropsychological tests and driving performances, we conceived four new computed tasks measuring: visual processing (Task1), movement attention shift (Task2), executive response, alerting and orientation gain (Task3), and spatial memory (Task4). We then planned five studies to test MedDrive's reliability and validity. Study-1 defined instructions and learning functions collecting data from 105 senior drivers attending an automobile club course. Study-2 assessed concurrent validity for detecting minor cognitive impairment (MCI) against useful field of view (UFOV) on 120 new senior drivers. Study-3 collected data from 200 healthy drivers aged 20-90 to model age related normal cognitive decline. Study-4 measured MedDrive's reliability having 21 healthy volunteers repeat tests five times. Study-5 tested MedDrive's responsiveness to alcohol in a randomised, double-blinded, placebo, crossover, dose-response validation trial including 20 young healthy volunteers. Results: Instructions were well understood and accepted by all senior drivers. Measures of visual processing (Task1) showed better performances than the UFOV in detecting MCI (ROC 0.770 vs. 0.620; p=0.048). MedDrive was capable of explaining 43.4% of changes occurring with natural cognitive decline. In young healthy drivers, learning effects became negligible from the third session onwards for all tasks except for dual tasking (ICC=0.769). All measures except alerting and orientation gain were affected by blood alcohol concentrations. Finally, MedDrive was able to explain 29.3% of potential causes of swerving on the driving simulator. Discussion and conclusions: MedDrive reveals improved performances compared to existing computed neuropsychological tasks. It shows promising results both for clinical and research purposes

The Agile Alert System For Gamma-Ray Transients

In recent years, a new generation of space missions offered great opportunities of discovery in high-energy astrophysics. In this article we focus on the scientific operations of the Gamma-Ray Imaging Detector (GRID) onboard the AGILE space mission. The AGILE-GRID, sensitive in the energy range of 30 MeV-30 GeV, has detected many gamma-ray transients of galactic and extragalactic origins. This work presents the AGILE innovative approach to fast gamma-ray transient detection, which is a challenging task and a crucial part of the AGILE scientific program. The goals are to describe: (1) the AGILE Gamma-Ray Alert System, (2) a new algorithm for blind search identification of transients within a short processing time, (3) the AGILE procedure for gamma-ray transient alert management, and (4) the likelihood of ratio tests that are necessary to evaluate the post-trial statistical significance of the results. Special algorithms and an optimized sequence of tasks are necessary to reach our goal. Data are automatically analyzed at every orbital downlink by an alert pipeline operating on different timescales. As proper flux thresholds are exceeded, alerts are automatically generated and sent as SMS messages to cellular telephones, e-mails, and push notifications of an application for smartphones and tablets. These alerts are crosschecked with the results of two pipelines, and a manual analysis is performed. Being a small scientific-class mission, AGILE is characterized by optimization of both scientific analysis and ground-segment resources. The system is capable of generating alerts within two to three hours of a data downlink, an unprecedented reaction time in gamma-ray astrophysics.Comment: 34 pages, 9 figures, 5 table

The aerodynamics of the curled wake: a simplified model in view of flow control

When a wind turbine is yawed, the shape of the wake changes and a curled wake profile is generated. The curled wake has drawn a lot of interest because of its aerodynamic complexity and applicability to wind farm controls. The main mechanism for the creation of the curled wake has been identified in the literature as a collection of vortices that are shed from the rotor plane when the turbine is yawed. This work extends that idea by using aerodynamic concepts to develop a control-oriented model for the curled wake based on approximations to the Navier–Stokes equations. The model is tested and compared to time-averaged results from large-eddy simulations using actuator disk and line models. The model is able to capture the curling mechanism for a turbine under uniform inflow and in the case of a neutral atmospheric boundary layer. The model is then incorporated to the FLOw Redirection and Induction in Steady State (FLORIS) framework and provides good agreement with power predictions for cases with two and three turbines in a row.</p

Multimodal approach to predict neurological outcome after cardiac arrest: A single-center experience

Introduction: The aims of this study were to assess the concordance of different tools and to describe the accuracy of a multimodal approach to predict unfavorable neurological outcome (UO) in cardiac arrest patients. Methods: Retrospective study of adult (&gt;18 years) cardiac arrest patients who underwent multimodal monitoring; UO was defined as cerebral performance category 3-5 at 3 months. Predictors of UO were neurological pupillary index (NPi) 64 2 at 24 h; highly malignant patterns on EEG (HMp) within 48 h; bilateral absence of N20 waves on somato-sensory evoked potentials; and neuron-specific enolase (NSE) &gt; 75 \u3bcg/L. Time-dependent decisional tree (i.e., NPi on day 1; HMp on day 1-2; absent N20 on day 2-3; highest NSE) and classification and regression tree (CART) analysis were used to assess the prediction of UO. Results: Of 137 patients, 104 (73%) had UO. Abnormal NPi, HMp on day 1 or 2, the bilateral absence of N20 or NSE &gt;75 mcg/L had a specificity of 100% to predict UO. The presence of abnormal NPi was highly concordant with HMp and high NSE, and absence of N20 or high NSE with HMp. However, HMp had weak to moderate concordance with other predictors. The time-dependent decisional tree approach identified 73/103 patients (70%) with UO, showing a sensitivity of 71% and a specificity of 100%. Using the CART approach, HMp on EEG was the only variable significantly associated with UO. Conclusions: This study suggests that patients with UO had often at least two predictors of UO, except for HMp. A multimodal time-dependent approach may be helpful in the prediction of UO after CA. EEG should be included in all multimodal prognostic models