Effect of Vent Mode on the Differential Pressure Pre-cooling Efficiency of Layered Peaches

The objective of this study was to explore the effect of the vent mode of corrugated boxes universally used in the market on the precooling performance of layered peaches and to determine the functional relationship between the precooling environmental parameters and the precooling efficiency and the optimal vent mode under different differential pressure pre-cooling working conditions in order to realize the rapid energy-saving precooling of peaches after harvest. A numerical model of heat and mass transfer during differential pressure precooling with circular and rectangular vents (abbreviated as CV and RV, respectively) was established based on computational fluid dynamics. By comparing and analyzing the experimental and simulated data, it was found that the maximum root mean square error and mean absolute percentage error between the two vent designs were 0.799 ℃ and 6.6%, respectively, which fully verified that this numerical model had high prediction accuracy. Through in-depth exploration of the temperature and flow field distribution in different vent modes, it was found that CV exhibited inferior precooling uniformity when compared with RV. Nevertheless, CV demonstrated a notable reduction in precooling time by 30%–40% and a decrease in fan energy consumption by 50%. Additionally, their relationships with differential pressure were described by. Based on these obtained results, the precooling quality of peaches could be improved by using RV, and the precooling cost could be reduced by using CV. To simultaneously achieve these two goals, the diameter of CV should be greater than 35 mm. This study provides a theoretical reference for the reasonable selection of vent parameters and accurate monitoring of fruit precooling performance in small and medium-sized orchards

STUDY ON MAIZE LEAF MORPHOLOGICAL MODELING AND MESH SIMPLIFICATION OF SURFACE

Abstract: According to the need of canopy visualization calculation in the digital plant research, we introduced a method, using Non-Uniform Rational B-Splines (NURBS) interpolation and multi-line segment splitting algorithm, to reconstruct the 3D morphological structure of maize leaf with a complexity controllable mesh. Using the data cloud obtained by digitizer, construct the surface of maize leaf by calculating the knot vectors and reverse calculating surface control points by difference calculation. The final visualization effect is realistic. According to leaf morphological characteristics, leaf surface mesh can be simplified by using inverse calculation of multi-line segment splitting algorithm, and the surface main characteristics can be maintained simultaneously. This method can be used in canopy visualization calculation and light distribution calculation. Results showed that it can improve the calculation efficiency obviously without increase the calculation error

Bioinspired bright noniridescent photonic melanin supraballs

Structural colors enable the creation of a spectrumof nonfading colors without pigments, potentially replacing toxic metal oxides and conjugated organic pigments. However, significant challenges remain to achieve the contrast needed for a complete gamut of colors and a scalable process for industrial application. We demonstrate a feasible solution for producing structural colors inspired by bird feathers. We have designed core-shell nanoparticles using high-refractive index (RI) (similar to 1.74) melanin cores and low-RI (similar to 1.45) silica shells. The design of these nanoparticles was guided by finite-difference time-domain simulations. These nanoparticles were self-assembled using a one-pot reverse emulsion process, which resulted in bright and noniridescent supraballs. With the combination of only two ingredients, synthetic melanin and silica, we can generate a full spectrum of colors. These supraballs could be directly added to paints, plastics, and coatings and also used as ultraviolet-resistant inks or cosmetics

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe

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