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

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

The Performance Test of Three Different Horizontal Axis Wind Turbine (HAWT) Blade Shapes Using Experimental and Numerical Methods

Three different horizontal axis wind turbine (HAWT) blade geometries with the same diameter of 0.72 m using the same NACA4418 airfoil profile have been investigated both experimentally and numerically. The first is an optimum (OPT) blade shape, obtained using improved blade element momentum (BEM) theory. A detailed description of the blade geometry is also given. The second is an untapered and optimum twist (UOT) blade with the same twist distributions as the OPT blade. The third blade is untapered and untwisted (UUT). Wind tunnel experiments were used to measure the power coefficients of these blades, and the results indicate that both the OPT and UOT blades perform with the same maximum power coefficient, Cp = 0.428, but it is located at different tip speed ratio, λ = 4.92 for the OPT blade and λ = 4.32 for the UOT blade. The UUT blade has a maximum power coefficient of Cp = 0.210 at λ = 3.86. After the tests, numerical simulations were performed using a full three-dimensional computational fluid dynamics (CFD) method using the k-ω SST turbulence model. It has been found that CFD predictions reproduce the most accurate model power coefficients. The good agreement between the measured and computed power coefficients of the three models strongly suggest that accurate predictions of HAWT blade performance at full-scale conditions are also possible using the CFD method

Accurate real-time sensing tip for aqueous NO with optical fibers embedded in active hydrogel waveguide

A NO sensing tip is made by inserting two parallel optical fibers inside a poly 2-hydroxyethyl methacrylate (PolyHEMA) hydrogel waveguide mixed with the probe molecule 1, 2-Diaminoanthraquinone (DAQ). There is a length difference of 1 mm between the two fibers, and the light has to propagate through the difference from the short fiber to the long fiber. The total cross section area of the active hydrogel waveguide embedded with the fibers is only 3mm x 1.2 mm. For practical use the tip is housed in a needle for mechanical protection and the sensing tip is able to detect aqueous NO concentration around 1 μM with time resolution about 5 minutes. Such a sensing tip can be used to monitor the medical conditions inside the brain after a stroke or a brain injury