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Anti-Asian prejudice increased during the COVID-19 pandemic, evidenced by a rise in physical attacks on individuals of Asian descent. Concurrently, as many governments enacted stay-at-home mandates, the spread of anti-Asian content increased in online spaces, including social media platforms such as Twitter. In the present study, we investigated temporal and geographic patterns in the prevalence of social media content relevant to anti-Asian prejudice within the U.S. and worldwide. Specifically, we used the Twitter Data Collection API to query over 13 million tweets posted during the first 15 months of the pandemic (i.e., from January 30, 2020 to April 30, 2021), for both negative (e.g., #kungflu) and positive (e.g., #stopAAPIhate) hashtags and keywords related to anti-Asian prejudice. Results of a range of exploratory and descriptive analyses offer novel insights. For instance, in the U.S., results from a burst analysis indicated that the prevalence of negative (anti-Asian) and positive (counter-hate) messages fluctuated over time in patterns that largely mirrored salient events relevant to COVID-19 (e.g., political tweets, highly-visible hate crimes targeting Asians). Other representative findings include geographic differences in the frequency of negative and positive keywords that shed light on the regions within the U.S. and the countries worldwide in which negative and positive messages were most frequent. Additional analyses revealed informative patterns in the prevalence of original tweets versus retweets, the co-occurrence of negative and positive content within a tweet, and fluctuations in content in relation to the number of new COVID-19 cases and reported COVID-related deaths. Together, 

Prejudice and hate directed toward Asian individuals has increased in prevalence and salience during the COVID-19 pandemic, with notable rises in physical violence. Concurrently, as many governments enacted stay-at-home mandates, the spread of anti-Asian content increased in online spaces, including social media. In the present study, we investigated temporal and geographical patterns in social media content relevant to anti-Asian prejudice during the COVID-19 pandemic. Using the Twitter Data Collection API, we queried over 13 million tweets posted between January 30, 2020, and April 30, 2021, for both negative (e.g., #kungflu) and positive (e.g., #stopAAPIhate) hashtags and keywords related to anti-Asian prejudice. In a series of descriptive analyses, we found differences in the frequency of negative and positive keywords based on geographic location. Using burst detection, we also identified distinct increases in negative and positive content in relation to key political tweets and events. These largely exploratory analyses shed light on the role of social media in the expression and proliferation of prejudice as well as positive responses online. 

A search for proton decay into $e^{+}/{\mu }^{+}$and a $\eta$meson has been performed using data from a $0.373\mathrm{Mton}·\mathrm{year}$exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intranuclear $\eta$interaction cross section, resulting in a factor of 2 reduction in uncertainties from this source and $\sim 10\%$increase in signal efficiency. No significant data excess was found above the expected number of atmospheric neutrino background events resulting in no indication of proton decay into either mode. Lower limits on the proton partial lifetime of $1.4\times {10}^{34}\mathrm{years}$for $p\to e^{+}\eta$and $7.3\times {10}^{33}\mathrm{years}$for $p\to {\mu }^{+}\eta$at the 90% CL were set. These limits are around 1.5 times longer than our previous study and are the most stringent to date. Published by the American Physical Society2024 

We present the results of the charge ratio ( $R$) and polarization ( $P_0^{\mu }$) measurements using decay electron events collected between September 2008 and June 2022 with the Super-Kamiokande detector. Because of its underground location and long operation, we are able to perform high-precision measurements by accumulating cosmic-ray muons. We measured the muon charge ratio to be $R=1.32\pm 0.02(\mathrm{stat}+\mathrm{syst})$at $E_{\mu }\cos {\theta }_{\text{Zenith}}={0.7}_{-0.2}^{+0.3}\mathrm{TeV}$, where $E_{\mu }$is the muon energy and ${\theta }_{\text{Zenith}}$is the zenith angle of incoming cosmic-ray muons. This result is consistent with the Honda flux model while indicating a tension with the $\pi K$model of $1.9\sigma$. We also measured the muon polarization at the production location to be $P_0^{\mu }=0.52\pm 0.02(\mathrm{stat}+\mathrm{syst})$at the muon momentum of ${0.9}_{-0.1}^{+0.6}\mathrm{TeV}/c$at the surface of the mountain; this also suggests a tension with the Honda flux model of $1.5\sigma$. This is the most precise measurement ever to experimentally determine the cosmic-ray muon polarization near $1\mathrm{TeV}/c$. These measurement results are useful to improve atmospheric neutrino simulations. Published by the American Physical Society2024 

Abstract Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to generate more than ten million events over 10 s in Super-Kamokande (SK). At such large event rates, the buffers of the SK analog-to-digital conversion board (QBEE) will overflow, causing random loss of data that are critical for understanding the dynamics of the supernova explosion mechanism. In order to solve this problem, two new data-acquisition (DAQ) modules were developed to aid in the observation of very nearby supernovae. The first of these, the SN module, is designed to save only the number of hit photomultiplier tubes during a supernova burst and the second, the Veto module, prescales the high-rate neutrino events to prevent the QBEE from overflowing based on information from the SN module. In the event of a very nearby supernova, these modules allow SK to reconstruct the time evolution of the neutrino event rate from beginning to end using both QBEE and SN module data. This paper presents the development and testing of these modules together with an analysis of supernova-like data generated with a flashing laser diode. We demonstrate that the Veto module successfully prevents DAQ overflows for Betelgeuse-like supernovae as well as the long-term stability of the new modules. During normal running the Veto module is found to issue DAQ vetos a few times per month resulting in a total dead-time less than 1 ms, and does not influence ordinary operations. Additionally, using simulation data we find that supernovae closer than 800 pc will trigger the Veto module, resulting in a prescaling of the observed neutrino data. 

Abstract Preceding a core-collapse supernova (CCSN), various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande (SK) via inverse beta decay interactions. Once these pre-supernova (pre-SN) neutrinos are observed, an early warning of the upcoming CCSN can be provided. In light of this, KamLAND and SK, both located in the Kamioka mine in Japan, have been monitoring pre-SN neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and SK on pre-SN neutrino detection. A pre-SN alert system combining the KamLAND detector and the SK detector was developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-SN neutrino signal from a 15M_⊙star within 510 pc of the Earth at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hr in advance.