arXiv subjects

Yihui Liu

Publications and source records attributed to Yihui Liu.

13 recordsLinked to original sources

arXiv2026

Design and commissioning of a windowless gas-target system for high-current beams at JUNA

Windowless gas targets avoid the beam-energy loss and straggling introduced by entrance foils and are therefore well suited for direct measurements of low-energy nuclear reactions. A windowless gas-target system designed for operation with milliampere beams has been developed for the Jinping Underground Nuclear Astrophysics facility (JUNA). The system combines three-stage differential pumping, closed-loop gas recovery and purification, a constant-temperature power-compensation calorimeter, and a position-resolved target-thickness monitor based on secondary elastic scattering. Stable operation was achieved over a target-pressure range of 1-3 mbar, with pressure fluctuations below 1% during 8 h of continuous circulation, while the accelerator-side pressure was maintained at approximately $10^{-4}$ Pa. The closed-loop gas-circulation system maintained stable target conditions, while gas-transport calculations indicated that the axial pressure nonuniformity remained within approximately 1.6% under representative operating conditions. Calorimeter measurements were consistent with the thermal calculations, supporting the sensitivity correction used for beam-power determination. Beam commissioning with $^{14}\mathrm{N}(p,\gamma)^{15}\mathrm{O}$ and $^{12}\mathrm{C}(p,\gamma)^{13}\mathrm{N}$ at the 600 kV Cockcroft-Walton accelerator of the China Institute of Atomic Energy (CIAE) demonstrated stable operation of the gas-target and $\gamma$-ray detection systems and provided information on the influences of reaction position and beam heating. These results demonstrate the operating stability and diagnostic capability of the system for future high-current, low-energy nuclear-reaction measurements at JUNA.

astro-ph.GA↗

arXiv2025

Nuclear Physics at BRIF

The Beijing Radioactive Ion-beam Facility (BRIF), which is based on Isotope Separation On-Line (ISOL) technique, consists of a 100 MeV proton cyclotron as the driving accelerator, a two-stage ISOL system for ion separation, a 13-MV tandem accelerator for post-acceleration, a superconducting linac for further boosting beam energies. It is capable of providing ISOL beams in the energy range from 60 to 300 keV, and post-accelerated beams in the energy range from 3 to 10 MeV/u for nuclei with mass numbers of A < 80 by Isotope Separation On-Line (ISOL) technique. For nuclei with A up to 170, energies are still able to reach 3 MeV/u. This facility offers opportunities to address key questions of current interest in nuclear astrophysics, nuclear structure and reactions of unstable nuclei. In this review we present a comprehensive introduction to the BRIF and the typical experimental instruments installed on it, and then summarize current experimental results on unstable Na and Rb isotopes and future plan for further development of the BRIF to improve its performance.

physics.acc-ph↗

arXiv2023

GRB 220408B: A Three-Episode Burst from a Precessing Jet

Jet precession has previously been proposed to explain the apparently repeating features in the light curves of a few gamma-ray bursts (GRBs). In this {\it Letter}, we further apply the precession model to a bright GRB 220408B by examining both its temporal and spectral consistency with the predictions of the model. As one of the recently confirmed GRBs observed by our GRID CubeSat mission, GRB 220408B is noteworthy as it exhibits three apparently similar emission episodes. Furthermore, the similarities are reinforced by their strong temporal correlations and similar features in terms of spectral evolution and spectral lags. Our analysis demonstrates that these features can be well explained by the modulated emission of a Fast-Rise-Exponential-Decay (FRED) shape light curve intrinsically produced by a precessing jet with a precession period of $18.4 \pm 0.2$ seconds, a nutation period of $11.1 \pm 0.2$ seconds and viewed off-axis. This study provides a straightforward explanation for the complex yet similar multi-episode GRB light curves.

astro-ph.HE↗

arXiv2022

In-orbit Radiation Damage Characterization of SiPMs in the GRID-02 CubeSat Detector

Recently, silicon photomultipliers (SiPMs) have been used in several space-borne missions, owing to their solid state, compact size, low operating voltage, and insensitivity to magnetic fields. However, operating SiPMs in space results in radiation damage and degraded performance. In-orbit quantitative studies on these effects are limited. In this study, we present in-orbit SiPM characterization results obtained by the second detector of the Gamma-Ray Integrated Detectors (GRID-02), which was launched on 6 November 2020. An increase in dark current of $\sim$100 $μ$A/year per SiPM chip (model MicroFJ-60035-TSV) at 28.5 V and 5 $^{\circ}$C was observed. Consequently, the overall noise level (sigma) of the GRID-02 detector increased by $\sim$7.5 keV/year. The estimate of this increase is $\sim$40 $μ$A/year per SiPM chip at -20 $^{\circ}$C, highlighting the positive effect of using a cooling system.

physics.ins-det↗

arXiv2021

On-ground calibrations of the GRID-02 gamma-ray detector

The Gamma-Ray Integrated Detectors (GRID) are a space project to monitor the transient gamma-ray sky in the multi-messenger astronomy era using multiple detectors on-board CubeSats. The second GRID detector, GRID-02, was launched in 2020. The performance of the detector, including the energy response, effective area, angular response, and temperature-bias dependence, is calibrated in the laboratory and presented here. These measurements are compared with particle tracing simulations and validate the Geant4 model that will be used for generating detector responses.

astro-ph.IM↗

arXiv2021

GRB 210121A: A Typical Fireball Burst Detected by Two Small Missions

The Chinese CubeSat Mission, Gamma Ray Integrated Detectors (GRID), recently detected its first gamma-ray burst, GRB 210121A, which was jointly observed by the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM). This burst is confirmed by several other missions, including \fermi and \textit{Insight}-HXMT. We combined multi-mission observational data and performed a comprehensive analysis of the burst's temporal and spectral properties. Our results show that the burst is relatively special in its high peak energy, thermal-like low energy indices, and large fluence. By putting it to the $E_{\rm p}$-$E_{\rmγ, iso}$ relation diagram with assumed distance, we found this burst can be constrained at the redshift range of [0.3,3.0]. The thermal spectral component is also confirmed by the direct fit of the physical models to the observed spectra. Interestingly, the physical photosphere model also constrained a redshift of $z\sim$ 0.3 for this burst, which help us to identify a host galaxy candidate at such a distance within the location error box. Assuming the host galaxy is real, we found the burst can be best explained by the photosphere emission of a typical fireball with an initial radius of $r_0\sim$ 3.2 $\times 10^7$ cm.

astro-ph.HE↗

arXiv2014

Detecting adverse drug reactions for the drug Simvastatin

Adverse drug reactions (ADR) are widely concerning for public health issue. In this study we propose an original approach to detect ADRs using a feature matrix and feature selection. The experiments are carried out on the drug Simvastatin. Major side effects for the drug are detected and better performance is achieved compared to other computerized methods. Because currently the detected ADRs are based solely on computerized methods, further expert investigation is needed.

cs.CE↗

arXiv2014

Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database

Adverse drug reaction (ADR) is widely concerned for public health issue. ADRs are one of most common causes to withdraw some drugs from market. Prescription event monitoring (PEM) is an important approach to detect the adverse drug reactions. The main problem to deal with this method is how to automatically extract the medical events or side effects from high-throughput medical events, which are collected from day to day clinical practice. In this study we propose a novel concept of feature matrix to detect the ADRs. Feature matrix, which is extracted from big medical data from The Health Improvement Network (THIN) database, is created to characterize the medical events for the patients who take drugs. Feature matrix builds the foundation for the irregular and big medical data. Then feature selection methods are performed on feature matrix to detect the significant features. Finally the ADRs can be located based on the significant features. The experiments are carried out on three drugs: Atorvastatin, Alendronate, and Metoclopramide. Major side effects for each drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on computerized methods, further investigation is needed.

cs.LG↗

arXiv2013

Detect adverse drug reactions for drug Atorvastatin

Adverse drug reactions (ADRs) are big concern for public health. ADRs are one of most common causes to withdraw some drugs from markets. Now two major methods for detecting ADRs are spontaneous reporting system (SRS), and prescription event monitoring (PEM). The World Health Organization (WHO) defines a signal in pharmacovigilance as "any reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously". For spontaneous reporting systems, many machine learning methods are used to detect ADRs, such as Bayesian confidence propagation neural network (BCPNN), decision support methods, genetic algorithms, knowledge based approaches, etc. One limitation is the reporting mechanism to submit ADR reports, which has serious underreporting and is not able to accurately quantify the corresponding risk. Another limitation is hard to detect ADRs with small number of occurrences of each drug-event association in the database. In this paper we propose feature selection approach to detect ADRs from The Health Improvement Network (THIN) database. First a feature matrix, which represents the medical events for the patients before and after taking drugs, is created by linking patients' prescriptions and corresponding medical events together. Then significant features are selected based on feature selection methods, comparing the feature matrix before patients take drugs with one after patients take drugs. Finally the significant ADRs can be detected from thousands of medical events based on corresponding features. Experiments are carried out on the drug Atorvastatin. Good performance is achieved.

cs.CE↗

arXiv2013

Detect adverse drug reactions for drug Pioglitazone

In this study we propose a novel method to successfully detect the ADRs using feature matrix and feature selection. A feature matrix, which characterizes the medical events before patients take drugs or after patients take drugs, is created from THIN database. The feature selection method of Student's t-test is used to detect the significant features from thousands of medical events. The significant ADRs, which are corresponding to significant features, are detected. Experiments are performed on the drug Pioglitazone. Compared to other computerized methods, our proposed method achieves good performance.

cs.CE↗

arXiv2013

Detect adverse drug reactions for drug Alendronate

Adverse drug reaction (ADR) is widely concerned for public health issue. In this study we propose an original approach to detect the ADRs using feature matrix and feature selection. The experiments are carried out on the drug Simvastatin. Major side effects for the drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on the computerized method, further investigation is needed.

cs.CE↗

arXiv2013

Detect adverse drug reactions for the drug Pravastatin

cs.CE↗

arXiv2013

Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Biomarkers which predict patient's survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers of survival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time.

cs.NE↗