Exploring SMEFT Induced Non-Standard Interactions from COHERENT to Neutrino Oscillations

We investigate the prospects of next-generation neutrino oscillation experiments DUNE, T2HK and JUNO including TAO within Standard Model Effective Field Theory (SMEFT). We also re-interpret COHERENT data in this framework. Considering both charged and neutral current neutrino Non-Standard Interactions (NSIs), we analyse dimension-6 SMEFT operators and derive lower bounds to UV scale $\Lambda$. The most powerful probe is obtained on ${\cal O}_{{ledq}_{1211}}$ with $\Lambda \gtrsim$ 450 TeV due to the electron neutrino sample in T2HK near detector. We find DUNE and JUNO to be complementary to T2HK in exploring different subsets of SMEFT operators at about 25 TeV. We conclude that near detectors play a significant role in each experiment. We also find COHERENT with CsI and LAr targets to be sensitive to new physics up to $\sim$900 GeV.Comment: 48 pages, 9+9 figures, 7+5 tables, updated to account for CKM elements extracted from data properl

Association between Non-Suicidal Self-Injuries and Suicide Attempts in Chinese Adolescents and College Students: A Cross-Section Study

This study examined the association between non-suicidal self-injury (NSSI) and suicide attempts among Chinese adolescents and college students.A total sample of 2013 Chinese students were randomly selected from five schools in Wuhan, China, including 1101 boys and 912 girls with the age ranging between 10 and 24 years. NSSI, suicidal ideation, suicide attempts and depressive symptoms were measured by self-rated questionnaires. Self-reported suicide attempts were regressed on suicidal ideation and NSSI, controlling for participants' depressive symptoms, and demographic characteristics.The self-reported prevalence rates of NSSI, suicidal ideation, suicide attempts were 15.5%, 8.8%, and 3.5%, respectively. Logistic regression analyses indicated that NSSI was significantly associated with self-reported suicide attempts. Analyses examining the conditional association of NSSI and suicidal ideation with self-reported suicide attempts revealed that NSSI was significantly associated with greater risk of suicide attempts in those not reporting suicidal ideation than those reporting suicidal ideation in the past year.These findings highlight the importance of NSSI as a potentially independent risk factor for suicide attempts among Chinese/Han adolescents and college students

Establishment of an Efficient in Vitro Propagation System for Iris Sanguinea

Iris sanguinea is a perennial flowering plant that is typically cultivated through seeds or bulbs. However, due to limitations in conventional propagation, an alternate regeneration system using seeds was developed. The protocol included optimization of sterilization, stratification and scarification methods as iris seeds exhibit physiological dormancy. In addition to chlorine-based disinfection, alkaline or heat treatment was used to break seed dormancy and reduce contamination. When seeds were soaked in water at 80 °C overnight, and sterilized with 75% EtOH for 30 s and 4% NaOCl solution for 20 minutes, contamination was reduced to 10% and a 73.3% germination was achieved. The germinated seedlings with 2-3 leaves and radicle were used as explants to induce adventitious buds. The optimal MS medium with 0.5 mg L−1 6-benzylaminopurine, 0.2 mg L−1 NAA, and 1.0 mg L−1 kinetin resulted in 93.3% shoot induction and a proliferation coefficient of 5.30. Medium with 0.5 mg L−1 NAA achieved 96.4% rooting of the adventitious shoots. The survival rate was more than 90% after 30 days growth in the cultivated matrix. In conclusion, a successful regeneration system for propagation of I. sanguinea was developed using seeds, which could be utilized for large-scale propagation of irises of ecological and horticultural importance

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction

Detection of Linkage Between Solar and Lunar Cycles and Runoff of the World\u27s Large Rivers

It is an ongoing concern that global hydrological cycle can be likely intensified under context of climate change and anthropogenic actions. Here, our results show that the solar and lunar periodic motions (SLPMs) have substantial impact on the runoff of the world\u27s large rivers. We estimate that SLPMs can produce a change of the world\u27s large rivers runoff by as much as 6.7%. Although climate models suggest that the increased frequency of extreme events is attributed to anthropogenic activities, it is out of our expectation that as much as 73% and 85% of the extreme flood and drought events (based on runoff discharged to the ocean) appear in resonance with SLPMs, respectively. A reevaluation of impacts of SLPMs on changes in the world\u27s river runoff is urgently needed, especially when extreme drought and flood events are on the rise

catena-Poly[bis­[octa­kis(dimethyl sulf­oxide)praseodymium(III)] hexa-μ3-sulfido-dodeca-μ2-sulfido-hexa­sul­fido­hexa­silverhexa­molybdenum]

The title compound, {[Pr(C2H6OS)8]2[Mo6Ag6S24]}n, contains polymeric Mo6S24Ag6 2− anions and [Pr(Me2SO)8]3+ cations, forming a one-dimensional polymeric Mo/S/Ag cluster. The anion assumes the conformation of a zigzag chain. The trivalent cations are arrayed amongst the anionic chains and are well separated from each other. Each Mo and Ag atom is coordinated by four S atoms in a distorted tetra­hedral geometry. The Pr3+ atom is coordinated by eight dimethyl sulfoxide ligands, forming a polyhedron-shaped distorted square anti­prism

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation

With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently. In this paper, we focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. First and foremost, we identify and formulate the lifelong sequential behavior incomprehension problem for LLMs in recommendation domains, i.e., LLMs fail to extract useful information from a textual context of long user behavior sequence, even if the length of context is far from reaching the context limitation of LLMs. To address such an issue and improve the recommendation performance of LLMs, we propose a novel framework, namely Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings. For zero-shot recommendation, we perform semantic user behavior retrieval (SUBR) to improve the data quality of testing samples, which greatly reduces the difficulty for LLMs to extract the essential knowledge from user behavior sequences. As for few-shot recommendation, we further design retrieval-enhanced instruction tuning (ReiT) by adopting SUBR as a data augmentation technique for training samples. Specifically, we develop a mixed training dataset consisting of both the original data samples and their retrieval-enhanced counterparts. We conduct extensive experiments on a real-world public dataset (i.e., MovieLens-1M) to demonstrate the superiority of ReLLa compared with existing baseline models, as well as its capability for lifelong sequential behavior comprehension.Comment: Under Revie

Separate density and viscosity measurements of unknown liquid using quartz crystal microbalance

Aqueous liquids have a wide range of applications in many fields. Basic physical properties like the density and the viscosity have great impacts on the functionalities of a given ionic liquid. For the millions kinds of existing liquids, only a few have been systematically measured with the density and the viscosity using traditional methods. However, these methods are limited to measure the density and the viscosity of an ionic liquid simultaneously especially in processing micro sample volumes. To meet this challenge, we present a new theoretical model and a novel method to separate density and viscosity measurements with single quartz crystal microbalance (QCM) in this work. The agreement of experimental results and theocratical calculations shows that the QCM is capable to measure the density and the viscosity of ionic liquid