Tungsten-based bcc-superalloys

Applications from nuclear energy to rockets and jet engines are underpinned by advanced high temperature materials. Whilst state of the art, the performance of current nickel-based superalloys is fundamentally limited to Ni’s melting point, T. Here, we develop an analogous superalloy concept but with superior high temperature capability by transitioning to a bcc tungsten base, T. This strategy involves reinforcing bcc -W by TiFe intermetallic compound, which results in impressive high temperature compressive strengths of 500 MPa at . This bcc-superalloy design approach has wider applicability to other bcc alloy bases, including Mo, Ta, and Nb, as well as to refractory-metal high entropy alloys (RHEAs). By investigation of the underlying phase equilibria, thermodynamic modelling, characterisation and mechanical properties, we demonstrate the capability of ternary W-Ti-Fe tungsten-based bcc-superalloys as a new class of high temperature materials

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Explainable AI through the Learning of Arguments

earning arguments is highly relevant to the field of explainable artificial intelligence. It is a family of symbolic machine learning techniques that is particularly human-interpretable. These techniques learn a set of arguments as an intermediate representation. Arguments are small rules with exceptions that can be chained to larger arguments for making predictions or decisions. We investigate the learning of arguments, specifically the learning of arguments from a ‘case model’ proposed by Verheij [34]. The case model in Verheij’s approach are cases or scenarios in a legal setting. The number of cases in a case model are relatively low. Here, we investigate whether Verheij’s approach can be used for learning arguments from other types of data sets with a much larger number of instances. We compare the learning of arguments from a case model with the HeRO algorithm [15] and learning a decision tree

Explainable AI through the Learning of Arguments

Learning arguments is highly relevant to the field of explainable artificial intelligence. It is a family of symbolic machine learning techniques that is particularly human-interpretable. These techniques learn a set of arguments as an intermediate representation. Arguments are small rules with exceptions that can be chained to larger arguments for making predictions or decisions. We investigate the learning of arguments, specifically the learning of arguments from a 'case model' proposed by Verheij [34]. The case model in Verheij's approach are cases or scenarios in a legal setting. The number of cases in a case model are relatively low. Here, we investigate whether Verheij's approach can be used for learning arguments from other types of data sets with a much larger number of instances. We compare the learning of arguments from a case model with the HeRO algorithm [15] and learning a decision tree.Comment: Presented at the 33rd BeNeLux AI Conference (BNAIC/BENELEARN 2021

Pain Assessment for Older Persons in Nursing Home Care: An Evidence-Based Practice Guideline.

Up to 80% of nursing home residents are affected by pain. Pain assessment aims to determine pain intensity, quality, and course of pain to underpin diagnostic decision making. In the nursing home population, pain assessment is frequently compromised by cognitive impairment. Characteristics of the nursing home setting, such as resident's age, staff skill mix, and overall aims of the care provided, also need to be taken into account. Therefore, an interdisciplinary evidence-based clinical practice guideline for pain assessment in the nursing home setting was developed. A systematic literature search was carried out covering publications between 2003 and 2015. Thirty-nine studies were included in the preparation of this guideline, supplemented by 12 international reference guidelines. Recommendations were subjected to a structured consensus-finding process with representatives from 37 scientific and professional organizations and patient representatives. The guideline underwent independent peer review before finalization. It comprises 62 recommendations that are grouped into 4 chapters: (1) context of pain assessment in nursing home care; (2) screening; (3) focused assessment; and (4) reassessment/monitoring of pain. Main recommendations stipulate that clinicians should assess the patient's ability to provide self-report of pain when screening for pain and that each resident should be screened for the presence of pain. A focused assessment of pain, performed during rest and activities, should include pain intensity, changed behaviors, general mobility, pain history, comorbidities, and pain medication. Pain should be re-assessed at regular intervals using the same instruments that were used for the focused assessment. Guideline development demonstrated that many aspects of pain assessment in older persons have not received adequate research attention so far. Available studies predominantly possess only low levels of evidence. Therefore, research into this area needs to be systematically developed to address questions of clinical relevance to support patient care

P2-Type Layered High-Entropy Oxides as Sodium-Ion Cathode Materials

P2-type layered oxides with the general Na-deficient composition NaxTMO2 (x < 1, TM: transition metal) are a promising class of cathode materials for sodium-ion batteries. The open Na+ transport pathways present in the structure lead to low diffusion barriers and enable high charge/discharge rates. However, a phase transition from P2 to O2 structure occurring above 4.2 V, combined with metal dissolution at low potentials upon discharge, results in rapid capacity degradation. In this work, we demonstrate the positive effect of configurational entropy on the stability of the crystal structure during battery operation. Three different compositions of layered P2-type oxides were synthesized by solid-state chemistry, Na0.67(Mn0.55Ni0.21Co0.24)O2, Na0.67(Mn0.45Ni0.18Co0.24Ti0.1Mg0.03)O2, and Na0.67(Mn0.45Ni0.18Co0.18Ti0.1Mg0.03Al0.04Fe0.02)O2 with low, medium and high configurational entropy, respectively. The high-entropy cathode material shows lower structural transformation and Mn dissolution upon cycling in a wide voltage range from 1.5 to 4.6 V. Advanced operando techniques and post-mortem analysis were used to probe the underlying reaction mechanism thoroughly. Overall, the high-entropy strategy is a promising route for improving the electrochemical performance of P2 layered oxide cathodes for advanced sodium-ion battery applications