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

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We present 206 unpublished optical spectra of 104 type II supernovae obtained by the Xinglong 2.16m telescope and Lijiang 2.4m telescope during the period from 2011 to 2018, spanning the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

Dietary Modulation of Gut Microbiota Contributes to Alleviation of Both Genetic and Simple Obesity in Children

Gut microbiota has been implicated as a pivotal contributing factor in diet-related obesity; however, its role in development of disease phenotypes in human genetic obesity such as Prader–Willi syndrome (PWS) remains elusive. In this hospitalized intervention trial with PWS (n = 17) and simple obesity (n = 21) children, a diet rich in non-digestible carbohydrates induced significant weight loss and concomitant structural changes of the gut microbiota together with reduction of serum antigen load and alleviation of inflammation. Co-abundance network analysis of 161 prevalent bacterial draft genomes assembled directly from metagenomic datasets showed relative increase of functional genome groups for acetate production from carbohydrates fermentation. NMR-based metabolomic profiling of urine showed diet-induced overall changes of host metabotypes and identified significantly reduced trimethylamine N-oxide and indoxyl sulfate, host-bacteria co-metabolites known to induce metabolic deteriorations. Specific bacterial genomes that were correlated with urine levels of these detrimental co-metabolites were found to encode enzyme genes for production of their precursors by fermentation of choline or tryptophan in the gut. When transplanted into germ-free mice, the pre-intervention gut microbiota induced higher inflammation and larger adipocytes compared with the post-intervention microbiota from the same volunteer. Our multi-omics-based systems analysis indicates a significant etiological contribution of dysbiotic gut microbiota to both genetic and simple obesity in children, implicating a potentially effective target for alleviation

Weak order in averaging principle for stochastic differential equations with jumps

Abstract In this paper, we deal with the averaging principle for a two-time-scale system of jump-diffusion stochastic differential equations. Under suitable conditions, we expand the weak error in powers of timescale parameter. We prove that the rate of weak convergence to the averaged dynamics is of order 1. This reveals that the rate of weak convergence is essentially twice that of strong convergence

Design of lubricating oil temperature monitoring system of emulsion pump

In view of problem that current lubricating oil temperature monitoring method of emulsion pump can only identify oil temperature overrunning but cannot monitor abnormal temperature rise, an oil temperature monitoring method based on statistical process control was proposed according to statistical properties of oil temperature process data, and an implementation scheme of exponentially weighted moving average control chart applied to oil temperature monitoring was given. On the basis of the scheme, an oil temperature monitoring system of emulsion pump was designed. The practical application results show that the system can monitor oil temperature of emulsion pump in real time and send out fault alarm accurately

Switching control between variable frequency and power frequency of emulsion pump in coal mine

In view of problem of high impulse current when scheme of multiple pumps linkage with one frequency converter applied in coal mine emulsion pump station, asynchronous switching and synchronous switching control scheme between variable frequency and power frequency of emulsion pump were proposed. The field test results show that the synchronous switching control can avoid impulse current more effectively and truly realize free switching between variable frequency and power frequency

Using Artificial Neural Network To Determine Favorable Wheelchair Tilt and Recline Usage In People With Spinal Cord Injury Training ANN with Genetic Algorithm to Improve Generalization

Abstract-People with spinal cord injury (SCI) are at risk for pressure ulcers because of their poor motor function and consequent prolonged sitting in wheelchairs. The current clinical practice typically uses the wheelchair tilt and recline to attain specific seating angles (sitting postures) to reduce seating pressure in order to prevent pressure ulcers. The rationale is to allow the development of reactive hyperemia to re-perfuse the ischemic tissues. However, our study reveals that a particular tilt and recline setting may result in a significant increase of skin perfusion for one person with SCI, but may cause neutral or even negative effect on another person. Therefore, an individualized guidance on wheelchair tilt and recline usage is desirable in people with various levels of SCI. In this study, we intend to demonstrate the feasibility of using machine-learning techniques to classify and predict favorable wheelchair tilt and recline settings for individual wheelchair users with SCI. Specifically, we use artificial neural networks (ANNs) to classify whether a given tilt and recline setting would cause a positive, neutral, or negative skin perfusion response. The challenge, however, is that ANN is prone to overfitting, a situation in which ANN can perfectly classify the existing data while cannot correctly classify new (unseen) data. We investigate using the genetic algorithm (GA) to train ANN to reduce the chance of converging on local optima and improve the generalization capability of classifying unseen data. Our experimental results indicate that the GA-based ANN significantly improves the generalization ability and outperforms the traditional statistical approach and other commonly used classification techniques, such as BP-based ANN and support vector machine (SVM). To the best of our knowledge, there are no such intelligent systems available now. Our research fills in the gap in existing evidence