A Novelty Method for Identifying Risk Factors of Sudden Food Safety Event

Food is the basic material basis for human survival. Sudden food safety event risks mainly derive from accidental or natural food safety risks, poor food storage environments, and inefficient government regulation policies. The factor identification of sudden food safety risks is the key to controlling such risks. Therefore, the efficient and scientific identification of risk sources and types will be very important in managing sudden food safety risks. In this study, 16 sudden food safety event risk factors were identified through a literature review, and their interactive relationships were clarified using an interpretive structural model (ISM). Then, the weights of influencing factors were calculated through the analytic hierarchy process (AHP), and the combined weight of indices was determined. Results show that the 16 sudden food safety event risk factors can be divided into four levels. The quality standard for food safety (S5) and food storage (S14) is at the bottom layer of risks of sudden food safety events (the first-layer index weight is 36.899%). The judgment matrices at the four levels passed the consistency check. The influence weight of the factor "whether it contains transgenic raw materials" (S9) ranks second (the total weight is 18.151%). This index system for sudden food safety event risk factors is highly effective, with good operability for managing sudden food safety event risks. The obtained conclusions are important reference values for identifying the factors influencing food safety risk management, determining the emphasis of food safety supervision, realizing food risk prevention and control, and strengthening and guaranteeing the food safety level

Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI

Objective. Imaging dynamic object with high temporal resolution is challenging in magnetic resonance imaging (MRI). Partial separable (PS) model was proposed to improve the imaging quality by reducing the degrees of freedom of the inverse problem. However, PS model still suffers from long acquisition time and even longer reconstruction time. The main objective of this study is to accelerate the PS model, shorten the time required for acquisition and reconstruction, and maintain good image quality simultaneously. Approach. We proposed to fully exploit the dimension reduction property of the PS model, which means implementing the optimization algorithm in subspace. We optimized the data consistency term, and used a Tikhonov regularization term based on the Frobenius norm of temporal difference. The proposed dimension-reduced optimization technique was validated in free-running cardiac MRI. We have performed both retrospective experiments on public dataset and prospective experiments on in-vivo data. The proposed method was compared with four competing algorithms based on PS model, and two non-PS model methods. Main results. The proposed method has robust performance against shortened acquisition time or suboptimal hyper-parameter settings, and achieves superior image quality over all other competing algorithms. The proposed method is 20-fold faster than the widely accepted PS+Sparse method, enabling image reconstruction to be finished in just a few seconds. Significance. Accelerated PS model has the potential to save much time for clinical dynamic MRI examination, and is promising for real-time MRI applications.Comment: 23 pages, 11 figures. Accepted as manuscript on Physics in Medicine & Biolog

Creation of NV centers over a millimeter-sized region by intense single-shot ultrashort laser irradiation

一つの超短レーザーパルスでダイヤモンド量子センサ源を広領域で作製 --超短時間でダイヤモンドを超高感度量子センサに--. 京都大学プレスリリース. 2023-03-15.Recently, ultrashort laser processing has attracted attention for creating nitrogen-vacancy (NV) centers because this method can create single NV centers in spatially-controlled positions, which is an advantage for quantum information devices. On the other hand, creating high-density NV centers in a wide region is also important for quantum sensing because the sensitivity is directly enhanced by increasing the number of NV centers. A recent study demonstrated the creation of high-density NV centers by irradiating femtosecond laser pulses, but the created region was limited to micrometer size, and this technique required many laser pulses to avoid graphitization of diamond. Here, we demonstrate the creation of NV centers in a wide region using only an intense single femtosecond laser pulse irradiation. We irradiated a diamond sample with a femtosecond laser with a focal spot size of 41 µm and a laser fluence of up to 54 J/cm², which is much higher than the typical graphitization threshold in multi-pulse processing. We found that single-pulse irradiation created NV centers without post-annealing for a laser fluence higher than 1.8 J/cm², and the region containing NV centers expanded with increasing laser fluence. The diameter of the area was larger than the focal spot size and reached over 100 µm at a fluence of 54 J/cm². Furthermore, we demonstrated the NV centers' creation in a millimeter-sized region by a single-shot defocused laser pulse over 1100 µm with a fluence of 33 J/cm². The demonstrated technique will bring interest in the fundamentals and applications of fabricating ultrahigh-sensitivity quantum sensors

New Subquadratic Algorithms for Constructing Lightweight Hadamard MDS Matrices (Full Version)

Maximum Distance Separable (MDS) Matrix plays a crucial role in designing cryptosystems. In this paper we mainly talk about constructing lightweight Hadamard MDS matrices based on subquadratic multipliers over $GF(2^4)$. We firstly propose subquadratic Hadamard matrix-vector product formulae (HMVP), and provide two new XOR count metrics. To the best of our knowledge, subquadratic multipliers have not been used to construct MDS matrices. Furthermore, combined with HMVP formulae we design a construction algorithm to find lightweight Hadamard MDS matrices under our XOR count metric. Applying our algorithms, we successfully find MDS matrices with the state-of-the-art fewest XOR counts for $4 \times 4$ and $8 \times 8$ involutory and non-involutory MDS matrices. Experiment results show that our candidates save up to $40.63\%$ and $10.34\%$ XOR gates for $8 \times 8$ and $4 \times 4$ matrices over $GF(2^4)$ respectively

Most Lithium-rich Low-mass Evolved Stars Revealed as Red Clump stars by Asteroseismology and Spectroscopy

Lithium has confused scientists for decades at almost each scale of the universe. Lithium-rich giants are peculiar stars with lithium abundances over model prediction. A large fraction of lithium-rich low-mass evolved stars are traditionally supposed to be red giant branch (RGB) stars. Recent studies, however, report that red clump (RC) stars are more frequent than RGB. Here, we present a uniquely large systematic study combining the direct asteroseismic analysis with the spectroscopy on the lithium-rich stars. The majority of lithium-rich stars are confirmed to be RCs, whereas RGBs are minor. We reveal that the distribution of lithium-rich RGBs steeply decline with the increasing lithium abundance, showing an upper limit around 2.6 dex, whereas the Li abundances of RCs extend to much higher values. We also find that the distributions of mass and nitrogen abundance are notably different between RC and RGB stars. These findings indicate that there is still unknown process that significantly affects surface chemical composition in low-mass stellar evolution.Comment: 27 pages, 10 figures, 3 table