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

Identification of Critical Areas of Openness–Vitality Intensity Imbalance in Waterfront Spaces and Prioritization of Interventions: A Case Study of Xiangjiang River in Changsha, China

To alleviate the contradiction between high-density urban spatial environments and high-frequency citizens’ activities, it is vital to determine the degree of openness of waterfront space, figure out the matching relationship between spatial openness and vitality intensity, identify imbalanced spatial zones and divide the order of intervention, and compensate for the limitations of subjective judgment in traditional planning decisions. This paper uses the Changsha Xiangjiang River waterfront space as a research sample based on multi-source data. It constructs the evaluation indicators system and research framework for the degree of openness of waterfront space. Then, by evaluating the openness and vitality intensity of the waterfront space and adopting the quadrant division method, waterfront space zones with a mismatched openness and vitality intensity were identified. Finally, planning interventions are prioritized based on a priority index. The results show the following: (1) The openness and vitality of the waterfront space of Xiangjiang River show the spatial distribution characteristics of “high in the middle and low in the north and south” and “high on the east bank and low on the west bank”. (2) Fifteen low-quality waterfront spatial zones with “low vitality intensity and low openness” and one with a severe imbalance of “low openness–high vitality intensity” were identified. These waterfront spatial zones cannot meet the requirements for the high-quality development of waterfront space. (3) The study delineates five priority levels for planning interventions. Among them, three waterfront space zones belong to priority V, mainly distributed north and south of the Xiangjiang River. Five waterfront spatial zones belonging to priority IV are concentrated in the middle of the Xiangjiang River. The above areas need to be prioritized for improvement to accurately promote the overall balanced development of the waterfront space

Using machine learning to determine age over 16 based on development of third molar and periodontal ligament of second molar

Abstract Background Having a reliable and feasible method to estimate whether an individual has reached 16 years of age would greatly benefit forensic analysis. The study of age using dental information has matured recently. In addition, machine learning (ML) is gradually being applied for dental age estimation. Aim The purpose of this study was to evaluate the development of the third molar using the Demirjian method (Demirjian3M), measure the development index of the third molar (I3M) using the method by Cameriere, and assess the periodontal ligament development of the second molar (PL2M). This study aimed to predict whether Chinese adolescents have reached the age of criminal responsibility (16 years) by combining the above measurements with ML techniques. Subjects & methods A total of 665 Chinese adolescents aged between 12 and 20 years were recruited for this study. The development of the second and third molars was evaluated by taking orthopantomographs. ML algorithms, including random forests (RF), decision trees (DT), support vector machines (SVM), K-nearest neighbours (KNN), Bernoulli Naive Bayes (BNB), and logistic regression (LR), were used for training and testing to determine the dental age. This is the first study to combine ML with an evaluation of periodontal ligament and tooth development to predict whether individuals are over 16 years of age. Results and conclusions The study showed that SVM had the highest Bayesian posterior probability at 0.917 and a Youden index of 0.752. This finding provides an important reference for forensic identification, and the combination of traditional methods and ML is expected to improve the accuracy of age determination for this population, which is of substantial significance for criminal litigation

Design and Optimization of NR-Based Stretchable Conductive Composites Filled with MoSi₂ Nanoparticles and MWCNTs: Perspectives from Experimental Characterization and Molecular Dynamics Simulations

Stretchable conductive composites play a pivotal role in the development of personalized electronic devices, electronic skins, and artificial implant devices. This article explores the fabrication and characterization of stretchable composites based on natural rubber (NR) filled with molybdenum disilicide (MoSi2) nanoparticles and multi-walled carbon nanotubes (MWCNTs). Experimental characterization and molecular dynamics (MD) simulations are employed to investigate the static and dynamic properties of the composites, including morphology, glass transition temperature (Tg), electrical conductivity, and mechanical behavior. Results show that the addition of MoSi2 nanoparticles enhances the dispersion of MWCNTs within the NR matrix, optimizing the formation of a conductive network. Dynamic mechanical analysis (DMA) confirms the Tg reduction with the addition of MWCNTs and the influence of MoSi2 content on Tg. Mechanical testing reveals that the tensile strength increases with MoSi2 content, with an optimal ratio of 4:1 MoSi2:MWCNTs. Electrical conductivity measurements demonstrate that the MoSi2/MWCNTs/NR composites exhibit enhanced conductivity, reaching optimal values at specific filler ratios. MD simulations further support experimental findings, highlighting the role of MoSi2 in improving dispersion and mechanical properties. Overall, the study elucidates the synergistic effects of nanoparticles and nanotubes in enhancing the properties of stretchable conductive composites

Additional file 1 of Using machine learning to determine age over 16 based on development of third molar and periodontal ligament of second molar

Additional file 1: Supplementary Table S1. List of the tuned hyperparameters for each Machine Learning algorithm. For each hyperparameter, the values inside square brackets were explored by Grid Search. Supplementary Table S2. Parameter estimates for logistic model for I3M

Relationships between the Water Uptake and Nutrient Status of Rubber Trees in a Monoculture Rubber Plantation

Rubber cultivation is primarily rainfed agriculture, which means that water supplies are not stable in most rubber cultivated areas. Therefore, improving the water use of rubber trees through fertilization management seems to be a breakthrough for enhancing the growth and latex yield of rubber trees and carrying out the intensive management of rubber agriculture. However, the relationships among the nutrient status of rubber trees, their water uptake, and soil resources, including water and nutrients, remain unclear. To address this issue, we measured C, N, P, K, Ca, and Mg concentrations in soil and leaves, stems, and roots in a monoculture rubber plantation and distinguished the water uptake depths based on stable isotope analysis throughout the year. We found that the rubber trees primarily absorbed water from the 5–50 cm depth layer, and soil water and nutrients (usually N, P, K) decreased with depth. In addition, the water uptake depth of rubber trees exhibited positive correlations with the nutrient status of their tissues. The more water the rubber trees absorb from the intermediate soil layer, the more nutrients they contain. Therefore, applying fertilizer to intermediate soil layers, especially those rich in C content, could greatly promote rubber tree growth

Relationships between the Water Uptake and Nutrient Status of Rubber Trees in a Monoculture Rubber Plantation

Rubber cultivation is primarily rainfed agriculture, which means that water supplies are not stable in most rubber cultivated areas. Therefore, improving the water use of rubber trees through fertilization management seems to be a breakthrough for enhancing the growth and latex yield of rubber trees and carrying out the intensive management of rubber agriculture. However, the relationships among the nutrient status of rubber trees, their water uptake, and soil resources, including water and nutrients, remain unclear. To address this issue, we measured C, N, P, K, Ca, and Mg concentrations in soil and leaves, stems, and roots in a monoculture rubber plantation and distinguished the water uptake depths based on stable isotope analysis throughout the year. We found that the rubber trees primarily absorbed water from the 5–50 cm depth layer, and soil water and nutrients (usually N, P, K) decreased with depth. In addition, the water uptake depth of rubber trees exhibited positive correlations with the nutrient status of their tissues. The more water the rubber trees absorb from the intermediate soil layer, the more nutrients they contain. Therefore, applying fertilizer to intermediate soil layers, especially those rich in C content, could greatly promote rubber tree growth

Integrating a Luminescent Porous Aromatic Framework into Indicator Papers for Facile, Rapid, and Selective Detection of Nitro Compounds

Porous aromatic framework materials with high stability, sensitivity, and selectivity have great potential to provide new sensors for optoelectronic/fluorescent probe devices. In this work, a luminescent porous aromatic framework material (LNU-23) was synthesized via the palladium-catalyzed Suzuki cross-coupling reaction of tetrabromopyrene and 1,2-bisphenyldiborate pinacol ester. The resulting PAF solid exhibited strong fluorescence emission with a quantum yield of 18.31%, showing excellent light and heat stability. Because the lowest unoccupied molecular orbital (LUMO) of LNU-23 was higher than that of the nitro compounds, there was an energy transfer from the excited LNU-23 to the analyte, leading to the selective fluorescence quenching with a limit of detection (LOD) ≈ 1.47 × 10−5 M. After integrating the luminescent PAF powder on the paper by a simple dipping method, the indicator papers revealed a fast fluorescence response to gaseous nitrobenzene within 10 s, which shows great potential in outdoor fluorescence detection of nitro compounds