IDENTIFICATION OF RISK FACTORS ASSOCIATED WITH FOOD WASTE REDUCTION

Food waste occurs from initial production all the way to consumption. Whilst different tactics are implemented to reduce food waste among the industry and consumers, changes in production and distribution methods change the sources and degree of risk. Understanding and identifying risk factors that might be introduced through changes, such as product handling and pattern of use, are needed to ensure food safety. The use of outgraded produce (i.e., visually unacceptable for the market) and the proposal of ignoring shelf life date labels have gained attention as solutions to food waste in both the U.S. and Europe. This study suggests that outgraded produce with physical damage (i.e., open lesions) retains more pathogens following disinfection treatment compared to ones with physiological defects (i.e., wounds with intact surface). However, rapid growth of spoilage microbiota limits the shelf life of outgraded produce with physical damage, and thus makes the survival and growth of retained pathogens, during post-harvest storage, irrelevant to food safety. In contrast to whole fruits and vegetables, the growth of L. monocytogenes in physically damaged produce became problematic before consumers could detect the sensory deterioration on RTE foods under both strict and abuse refrigeration temperatures. Therefore, the quality deteriorations, such as off odor, sliminess and fungal growth, should not be used as fail-safe indicators considering shelf-life limitation for L. monocytogenes growth. This study addresses food safety concerns associated with waste reduction and provides a quantitative framework for the development of risk management decisions

Near-Infrared Alcohol Detection Circuit Based On Multisim

Because the number of private cars has expanded, drunk driving has become more and more frequent. The detection of a driver’s alcohol concentration has become the focus of attention. Therefore, infrared alcohol detection was studied. The principle of infrared blood glucose noninvasive detection was investigated, and it was compared with infrared spectrum detection. Finally, using transmission technology and an infrared emitter and receiver, an infrared alcohol identification circuit was designed by NBohr’s Law and the Correcting Beer-Lambert Law. It was composed of an infrared acquisition circuit, an infrared electronic filter circuit, and an infrared amplifier circuit. And the infrared alcohol identification circuit was composed of multiple circuits in series and parallel. At various pins on the first AD844AN, the infrared electronic filter circuit receives an alternating current source voltage of 1000V with a basic signal frequency of 60 Hz. At the input end, the infrared amplifier circuit receives a current signal with a frequency of 1 Hz and an amplitude of 5 uA and performs the reproduction experiment using Multisim. As a result of the signal being upgraded to fulfill the objective of recognition, distinct information reappears and exhibits different waveforms

A new fracture permeability model of CBM reservoir with high-dip angle in the southern Junggar Basin, NW China

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the National Major Research Program for Science and Technology of China (2016ZX05043-001), the National Natural Science Fund of China (grant nos. 41602170 and 41772160), the Royal Society International Exchanges-China NSFC Joint Project (grant nos. 4161101405 and RG13991-10), and Key Research and Development Projects of the Xinjiang Uygur Autonomous Region (2017B03019-01).Peer reviewedPublisher PD

Genomic characterization of polyextremotolerant black yeasts isolated from food and food production environments

Black yeasts have been isolated from acidic, low water activity, and thermally processed foods as well as from surfaces in food manufacturing plants. The genomic basis for their relative tolerance to food-relevant environmental stresses has not been well defined. In this study, we performed whole genome sequencing (WGS) on seven black yeast strains including Aureobasidium (n=5) and Exophiala (n=2) which were isolated from food or food production environments. These strains were previously characterized for their tolerance to heat, hyperosmotic pressure, high pressure processing, hypochlorite sanitizers, and ultraviolet light. Based on the WGS data, three of the strains previously identified as A. pullulans were reassigned as A. melanogenum. Both haploid and diploid A. melanogenum strains were identified in this collection. Single-locus phylogenies based on beta tubulin, RNA polymerase II, or translation elongation factor protein sequences were compared to the phylogeny produced through SNP analysis, revealing that duplication of the fungal genome in diploid strains complicates the use of single-locus phylogenetics. There was not a strong association between phylogeny and either environmental source or stress tolerance phenotype, nor were trends in the copy numbers of stress-related genes associated with extremotolerance within this collection. While there were obvious differences between the genera, the heterogenous distribution of stress tolerance phenotypes and genotypes suggests that food-relevant black yeasts may be ubiquitous rather than specialists associated with particular ecological niches. However, further evaluation of additional strains and the potential impact of gene sequence modification is necessary to confirm these findings

Nano-Hertz gravitational waves from collapsing domain walls associated with freeze-in dark matter in light of pulsar timing array observations

Evidence for a stochastic gravitational wave background in the nHz frequency band is recently reported by four pulsar timing array collaborations NANOGrav, EPTA, CPTA, and PPTA. It can be interpreted by gravitational waves from collapsing domain walls in the early universe. We assume such domain walls arising from the spontaneous breaking of a $Z_2$ symmetry in a scalar field theory, where a tiny $Z_2$-violating potential is required to make domain walls unstable. We propose that this $Z_2$-violating potential is radiatively induced by a feeble Yukawa coupling between the scalar field and a fermion field, which is also responsible for dark matter production via the freeze-in mechanism. Combining the pulsar timing array data and the observed dark matter relic density, we find that the model parameters can be narrowed down to small ranges.Comment: 18 pages, 6 figure

Robust Mixture-of-Expert Training for Convolutional Neural Networks

Sparsely-gated Mixture of Expert (MoE), an emerging deep model architecture, has demonstrated a great promise to enable high-accuracy and ultra-efficient model inference. Despite the growing popularity of MoE, little work investigated its potential to advance convolutional neural networks (CNNs), especially in the plane of adversarial robustness. Since the lack of robustness has become one of the main hurdles for CNNs, in this paper we ask: How to adversarially robustify a CNN-based MoE model? Can we robustly train it like an ordinary CNN model? Our pilot study shows that the conventional adversarial training (AT) mechanism (developed for vanilla CNNs) no longer remains effective to robustify an MoE-CNN. To better understand this phenomenon, we dissect the robustness of an MoE-CNN into two dimensions: Robustness of routers (i.e., gating functions to select data-specific experts) and robustness of experts (i.e., the router-guided pathways defined by the subnetworks of the backbone CNN). Our analyses show that routers and experts are hard to adapt to each other in the vanilla AT. Thus, we propose a new router-expert alternating Adversarial training framework for MoE, termed AdvMoE. The effectiveness of our proposal is justified across 4 commonly-used CNN model architectures over 4 benchmark datasets. We find that AdvMoE achieves 1% ~ 4% adversarial robustness improvement over the original dense CNN, and enjoys the efficiency merit of sparsity-gated MoE, leading to more than 50% inference cost reduction. Codes are available at https://github.com/OPTML-Group/Robust-MoE-CNN.Comment: ICCV 202

Treatment of engineering waste slurries by microbially induced struvite precipitation mechanisms

With societal development, the growing scale of engineering construction, and the increase in environmental protection requirements, the necessity of engineering waste mud disposal is becoming increasingly prominent. In this study, microbially induced struvite precipitation (MISP) was introduced to treat engineering waste mud. The study mainly focused on: i) the optimal mineralization scheme for microbially induced struvite precipitation, ii) the feasibility of the process and the effect of reaction parameters on treating engineering waste mud with microbially induced struvite precipitation, and iii) the mechanism of microbially induced struvite precipitation in treating engineering waste mud. The results showed that the waste mud could be well treated with 8.36×106 cell⋅mL−1 bacteria, 10 mM urea, 20 mM phosphate buffer, and 25 mM MgCl2 at pH 7. The kaolin suspension could be effectively flocculated. The flocculation rate reached approximately 87.2% under the optimum mineralization conditions. The flocculation effect was mainly affected by the concentrations of reactants and heavy metals and the suspension pH. The X-ray diffraction (XRD) patterns showed a strong struvite (MAP) diffraction peak. Scanning electron microscopy (SEM) images indicated that under the optimal mineralization conditions, the crystals were large and showed prismatic shapes tilted at both ends with adhered kaolin particles. In summary, this manuscript provides an effective way to treat engineering waste mud, and the findings should have a positive effect on enhancing soil fertility and preventing secondary pollution