Psychological Status of High School Students 1 Year After the COVID-19 Emergency

Background: With the control of the epidemic, adolescents\u27 mental outlook might have improved. However, little evidence existed with regard to the psychological status of adolescents in post-COVID-19 era. This present study aimed to explore the psychological status of high school students after the epidemic getting eased. Methods: A web-based cross-sectional survey was used to obtain data from three high schools, including the demographic information, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), the Self-Rating Scale of Sleep (SRSS), and self-designed general recent-status questionnaire. Correlation analysis was performed to explore potential associations between the depression symptoms, anxiety symptoms, and sleep status. The PHQ-9 and GAD-7 differences between nowadays data and the data enrolled 12 months before were also compared. Result: A total of 1,108 qualified questionnaires were obtained. The prevalence of depressive and anxious symptoms was 27.5 and 21.3%, respectively, from mild to severe in all students, while 11.8% of these high students got sleep disturbances. Both the rate and the severity of depression, anxiety and sleep problems of female students were higher than male students. Grade three students suffered higher prevalence and severer mental disturbances than the other two grades. There were significant correlations between the depression symptoms, anxiety symptoms, and sleep status. The psychological status has been improved in nowadays high school students compared with the sample enrolled 12 months before. Conclusion: As a supplement to our former study, this present research provided a perspective on the psychological status of high school students 1 year after the COVID-19 pandemic being well controlled. We should pay attention to the psychological status of high school students, and should also notice the progresses made by this special group after the epidemic

Robot-Assisted Deep Venous Thrombosis Ultrasound Examination using Virtual Fixture

Deep Venous Thrombosis (DVT) is a common vascular disease with blood clots inside deep veins, which may block blood flow or even cause a life-threatening pulmonary embolism. A typical exam for DVT using ultrasound (US) imaging is by pressing the target vein until its lumen is fully compressed. However, the compression exam is highly operator-dependent. To alleviate intra- and inter-variations, we present a robotic US system with a novel hybrid force motion control scheme ensuring position and force tracking accuracy, and soft landing of the probe onto the target surface. In addition, a path-based virtual fixture is proposed to realize easy human-robot interaction for repeat compression operation at the lesion location. To ensure the biometric measurements obtained in different examinations are comparable, the 6D scanning path is determined in a coarse-to-fine manner using both an external RGBD camera and US images. The RGBD camera is first used to extract a rough scanning path on the object. Then, the segmented vascular lumen from US images are used to optimize the scanning path to ensure the visibility of the target object. To generate a continuous scan path for developing virtual fixtures, an arc-length based path fitting model considering both position and orientation is proposed. Finally, the whole system is evaluated on a human-like arm phantom with an uneven surface.Comment: Accepted Paper IEEE T-AS

Prevalence of Depression and Anxiety Symptoms of High School Students in Shandong Province During the COVID-19 Epidemic

© Copyright © 2020 Zhang, Zhai, Yang, Zhang, Zhou, Yang, Duan and Zhou. Background: The coronavirus disease 2019 (covid-19) has brought physical risks as well as psychological challenges to the whole world. High school students are a special group suffering from both the academic pressure and the threat of the epidemic. The present study aims to conduct an online survey to investigate the psychological status of high school students in Shandong Province. Methods: Using a web-based cross-sectional survey, data was collected from 1,018 voluntary high school students assessed with demographic information, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7) and a self-designed online-study effect survey. Correlation analysis was performed to explore the relationships between depression symptoms, anxiety symptoms, and study effect. Result: The prevalence of depressive symptoms, anxiety symptoms, and a combination of depressive and anxiety symptoms was 52.4, 31.4, and 26.8%, respectively, among high school students in Shandong Province during the COVID-19 epidemic. And from moderate to severe severity level, the rates of depressive symptoms and anxious symptoms were 17.6 and 4.6%. Female students exhibited a higher rate and severity of mental symptoms than male, and grade one senior high school students got a higher rate and severity of mental symptoms than the other two grades. Nearly half of the students were not satisfied with their online-study effect. The PHQ-9 score had a strong positive correlation with the GAD-7 score. Both the PHQ-9 score the GAD-7 score had a negative correlation with the study-effect survey score. Conclusion: Quite a number of high school students suffered from depression and anxiety symptoms during the COVID-19 epidemic. Sufficient attentions should be paid, and necessary supports should be provided, to protect the mental health of this special group

Ultra-Low-Frequency Radio Astronomy Observations from a Selenocentric Orbit: first results of the Longjiang-2 experiment

This paper introduces the first results of observations with the Ultra-Long-Wavelength (ULW) -- Low Frequency Interferometer and Spectrometer (LFIS) on board the selenocentric satellite Longjiang-2. We present a brief description of the satellite and focus on the LFIS payload. The in-orbit commissioning confirmed a reliable operational status of the instrumentation. We also present results of a transition observation, which offers unique measurements on several novel aspects. We estimate the RFI suppression required for such a radio astronomy instrumentation at the Moon distances from Earth to be of the order of 80 dB. We analyse a method of separating Earth- and satellite-originated radio frequency interference (RFI). It is found that the RFI level at frequencies lower than a few MHz is smaller than the receiver noise floor.Comment: Accepted for publication in Experimental Astronomy; 22 pages, 11 figure

Search for light dark matter from atmosphere in PandaX-4T

We report a search for light dark matter produced through the cascading decay of $\eta$ mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduce a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasi-elastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne$\cdot$year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross-section is set at $5.9 \times 10^{-37}{\rm cm^2}$ for dark matter mass of $0.1$ MeV$/c^2$ and mediator mass of 300 MeV$/c^2$. The lowest upper limit of $\eta$ to dark matter decay branching ratio is $1.6 \times 10^{-7}$

A Search for Light Fermionic Dark Matter Absorption on Electrons in PandaX-4T

We report a search on a sub-MeV fermionic dark matter absorbed by electrons with an outgoing active neutrino using the 0.63 tonne-year exposure collected by PandaX-4T liquid xenon experiment. No significant signals are observed over the expected background. The data are interpreted into limits to the effective couplings between such dark matter and electrons. For axial-vector or vector interactions, our sensitivity is competitive in comparison to existing astrophysical bounds on the decay of such dark matter into photon final states. In particular, we present the first direct detection limits for an axial-vector (vector) interaction which are the strongest in the mass range from 25 to 45 (35 to 50) keV/c$^2$

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5

Self-Organizing Map Analysis on Peanut Yield and Agronomy Characteristics

International audienceThe model system between peanut yield and agronomy characteristics which is nonlinear, irreversible and dissipative. The objective in the study was the peanut cultivated in the different ecological regions in Shandong province, aimed to establish the new non-nonlinear model based on Self-Organizing Maps (SOM) to improve the cultivation information of peanut growth process. In the article, applying SOM network achieved the cluster between peanut yield and agronomy characteristics about 4 variables, involved in plant height, branches, full pods and peanut yield ratio. MATLAB 7 software is used to classify 60 samplings of peanut yield and agronomy characteristics. It is concluded that the SOM network can respond the complicated information classification among each peanut yield, during the analysis, the results also showed SOM method is the most suitable for peanut yield and characteristics classification, especially analysis of clusters on basis of peanut agronomy parameters, so the study can be applied on agronomy characteristics and peanut yield of the different ecological regions in Shandong province