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

Automated Discovery of Loop Invariants for High-Assurance Programs Synthesized Using AI Planning Techniques

The discovery of loop invariants is a great challenge for the independent verification of automatically synthesized programs. This verification is needed to achieve high confidence in the correctness of the synthesized code, i.e., assurance that no latent defects in the synthesizer itself could have led to the synthesis of an incorrect program. To address this problem, we present an automated loop invariant discovery approach for programs synthesized using a combination of AI planning and component-based software development techniques. Specifically, a plan (denoting the synthesized code) is generated by an enhanced Graphplan planner first. The loop invariants can be automatically discovered based on the same planning graph used to synthesize the code. The correctness can be independently verified via standard loop invariant proof steps, including initialization, maintenance, and termination. The proposed approach not only has a rigorous theoretical basis, but is also guaranteed to produce accurate invariants by removing spurious invariants that are independent of the concerned loop. In combination with other loop invariant detection techniques, the proposed approach can produce loop invariants for complex programs and, thus, greatly facilitate high-confidence automated verification of synthesized systems

A spectral data release for 104 Type II Supernovae from the Tsinghua Supernova Group

We present 206 unpublished optical spectra of 104 type II supernovae obtained by the Xinglong 2.16m telescope and Lijiang 2.4m telescope during the period from 2011 to 2018, spanning the phases from about 1 to 200 days after the SN explosion. The spectral line identifications, evolution of line velocities and pseudo equivalent widths, as well as correlations between some important spectral parameters are presented. Our sample displays a large range in expansion velocities. For instance, the Fe~{\sc ii} $5169$ velocities measured from spectra at $t\sim 50$ days after the explosion vary from ${\rm 2000\ km\ s^{-1}}$to${\rm 5500\ km\ s^{-1}}$, with an average value of${\rm 3872 \pm 949\ km\ s^{-1}}$. Power-law functions can be used to fit the velocity evolution, with the power-law exponent quantifying the velocity decline rate. We found an anticorrelation existing between H$\beta$velocity at mid-plateau phase and its velocity decay exponent, SNe II with higher velocities tending to have smaller velocity decay rate. Moreover, we noticed that the velocity decay rate inferred from the Balmer lines (i.e., H$\alpha$and H$\beta$) have moderate correlations with the ratio of absorption to emission for H$\alpha$ (a/e). In our sample, two objects show possibly flash-ionized features at early phases. Besides, we noticed that multiple high-velocity components may exist on the blue side of hydrogen lines of SN 2013ab, possibly suggesting that these features arise from complex line forming region. All our spectra can be found in WISeREP and Zenodo

Strong convergence in stochastic averaging principle for two time-scales stochastic partial differential equations

AbstractThe theory of stochastic averaging principle provides an effective approach for the qualitative analysis of stochastic systems with different time-scales and is relatively mature for stochastic ordinary differential equations. In this paper, we study the averaging principle for a class of stochastic partial differential equations with two separated time scales driven by scalar noises. Under suitable assumptions it is shown that the slow component strongly converges to the solution of the corresponding averaged equation