Potential of Geo-neutrino Measurements at JUNO

The flux of geoneutrinos at any point on the Earth is a function of the abundance and distribution of radioactive elements within our planet. This flux has been successfully detected by the 1-kt KamLAND and 0.3-kt Borexino detectors with these measurements being limited by their low statistics. The planned 20-kt JUNO detector will provide an exciting opportunity to obtain a high statistics measurement, which will provide data to address several questions of geological importance. This paper presents the JUNO detector design concept, the expected geo-neutrino signal and corresponding backgrounds. The precision level of geo-neutrino measurements at JUNO is obtained with the standard least-squares method. The potential of the Th/U ratio and mantle measurements is also discussed.Comment: 8 pages, 6 figures, an additional author added, final version to appear in Chin. Phys.

PiRL: Participant-Invariant Representation Learning for Healthcare

Due to individual heterogeneity, performance gaps are observed between generic (one-size-fits-all) models and person-specific models in data-driven health applications. However, in real-world applications, generic models are usually more favorable due to new-user-adaptation issues and system complexities, etc. To improve the performance of the generic model, we propose a representation learning framework that learns participant-invariant representations, named PiRL. The proposed framework utilizes maximum mean discrepancy (MMD) loss and domain-adversarial training to encourage the model to learn participant-invariant representations. Further, a triplet loss, which constrains the model for inter-class alignment of the representations, is utilized to optimize the learned representations for downstream health applications. We evaluated our frameworks on two public datasets related to physical and mental health, for detecting sleep apnea and stress, respectively. As preliminary results, we found the proposed approach shows around a 5% increase in accuracy compared to the baseline

A New ZrCuSiAs-Type Superconductor: ThFeAsN

We report the first nitrogen-containing iron-pnictide superconductor ThFeAsN, which is synthesized by a solid-state reaction in an evacuated container. The compound crystallizes in a ZrCuSiAs-type structure with the space group P4/nmm and lattice parameters a=4.0367(1) {\AA} and c=8.5262(2) {\AA} at 300 K. The electrical resistivity and dc magnetic susceptibility measurements indicate superconductivity at 30 K for the nominally undoped ThFeAsN.Comment: 6 pages, 4 figures, 1 tabl

P2RBox: A Single Point is All You Need for Oriented Object Detection

Oriented object detection, a specialized subfield in computer vision, finds applications across diverse scenarios, excelling particularly when dealing with objects of arbitrary orientations. Conversely, point annotation, which treats objects as single points, offers a cost-effective alternative to rotated and horizontal bounding boxes but sacrifices performance due to the loss of size and orientation information. In this study, we introduce the P2RBox network, which leverages point annotations and a mask generator to create mask proposals, followed by filtration through our Inspector Module and Constrainer Module. This process selects high-quality masks, which are subsequently converted into rotated box annotations for training a fully supervised detector. Specifically, we've thoughtfully crafted an Inspector Module rooted in multi-instance learning principles to evaluate the semantic score of masks. We've also proposed a more robust mask quality assessment in conjunction with the Constrainer Module. Furthermore, we've introduced a Symmetry Axis Estimation (SAE) Module inspired by the spectral theorem for symmetric matrices to transform the top-performing mask proposal into rotated bounding boxes. P2RBox performs well with three fully supervised rotated object detectors: RetinaNet, Rotated FCOS, and Oriented R-CNN. By combining with Oriented R-CNN, P2RBox achieves 62.26% on DOTA-v1.0 test dataset. As far as we know, this is the first attempt at training an oriented object detector with point supervision

The Ecological Restoration of Heavily Degraded Saline Wetland in the Yellow River Delta

As a result of discontinuous water flow, agriculture, and increasing urban use of fresh water affecting the natural wetlands of the Yellow River Delta, these areas have experienced significant degradation in the past two decades, ultimately diminishing the overall natural wetland land area in the region. This study aimed to address the issue of decreasing fresh water in the Yellow River Delta by studying the effects of three different approaches to restoration on long-term wetland recovery. The results of the study demonstrated that soil salt and available Na contents significantly decreased in response to all three restoration treatments. Impacts of the restoration treatments were more significant in 2009 than in 2010, as shown by the high rate of activity in the reed debris group. The highest phosphatase activity of the experimental period was also observed in the reed debris group. Meanwhile, a marked variation in soil nutrient elements (total carbon (TC), total nitrogen (TN), available phosphorus, and available potassium) was observed in the restoration treatment plots throughout the experimental period. TC and TN contents were generally higher in the restoration treatment groups than in the control group. Moreover, urease and phosphatase activity levels were highly correlated with one another, as well as with soil nutrient elements. In 2009, the yield of the Suaeda salsa plant was highest in the reed debris treatment group and lowest in the ploughing treatment group. The S. salsa plant did show a positive response to all of the different restoration treatments. Taken together, these results suggest that restoration approaches that implement ploughing techniques aided in the restoration of degraded saline wetlands.As a result of discontinuous water flow, agriculture, and increasing urban use of fresh water affecting the natural wetlands of the Yellow River Delta, these areas have experienced significant degradation in the past two decades, ultimately diminishing the overall natural wetland land area in the region. This study aimed to address the issue of decreasing fresh water in the Yellow River Delta by studying the effects of three different approaches to restoration on long-term wetland recovery. The results of the study demonstrated that soil salt and available Na contents significantly decreased in response to all three restoration treatments. Impacts of the restoration treatments were more significant in 2009 than in 2010, as shown by the high rate of activity in the reed debris group. The highest phosphatase activity of the experimental period was also observed in the reed debris group. Meanwhile, a marked variation in soil nutrient elements (total carbon (TC), total nitrogen (TN), available phosphorus, and available potassium) was observed in the restoration treatment plots throughout the experimental period. TC and TN contents were generally higher in the restoration treatment groups than in the control group. Moreover, urease and phosphatase activity levels were highly correlated with one another, as well as with soil nutrient elements. In 2009, the yield of the Suaeda salsa plant was highest in the reed debris treatment group and lowest in the ploughing treatment group. The S. salsa plant did show a positive response to all of the different restoration treatments. Taken together, these results suggest that restoration approaches that implement ploughing techniques aided in the restoration of degraded saline wetlands