Value-Added Meat: Measuring Past Successes and Predicting Future Winners

Livestock industries are significantly affected by changes in consumer behaviour. In order to add value to meat and livestock production, many firms and farms are supporting the development of new products – these products can differ by credence attribute, by degree of processing and by marketing strategies. The literature suggests that one of the most important determinants of success in product innovation is an understanding of the market the product is introduced into. In this report consumer preferences for meat products, by animal species including minor meats, and by type of processing are examined. Responses to economic variables such as price, advertising and income are identified as are responses to food safety and meat related health issues. Interesting results include the fact that income elasticities of demand for meat products purchased at grocery stores are negative in this study and that own and cross price elasticities for certain meats, across processing levels for example, show strong substitutions. Thus introducing new meat products may not result in increased sales by animal species but may only result in substitution of one meat type product for another product of the same meat type. Successful new product introductions or changes in product quality are shown, in this report, to be accompanied by significant marketing/advertising investment and, either by design or by serendipity, to have credence attributes in line with consumer's changing concerns. An example of this is the response in branded chicken sales, at the time of BSE in Canada, for a brand that could advertise itself as being 100% grain fed. Marketing strategies, such as working with the Health Check™ program of the Heart and Stroke Foundation of Canada are shown to have an impact on firm level sales (as well as on sales at the individual product level), a halo effect, that may be of importance in the firm's development of other value-added products. Significant heterogeneity exists in consumer behaviour and it is important to recognize this heterogeneity in the development of value-added meat products. Added to the consumer heterogeneity, in general, is the heterogeneity in responses by meat type. What works for one sector, such as poultry, could be problematic in other sectors given differences in economic interrelationships reported in this study.meat, consumer behavior, value-added, strategic behaviour, Consumer/Household Economics, Food Consumption/Nutrition/Food Safety, Livestock Production/Industries, Marketing, D12, Q18,

SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction

3D occupancy prediction is an important task for the robustness of vision-centric autonomous driving, which aims to predict whether each point is occupied in the surrounding 3D space. Existing methods usually require 3D occupancy labels to produce meaningful results. However, it is very laborious to annotate the occupancy status of each voxel. In this paper, we propose SelfOcc to explore a self-supervised way to learn 3D occupancy using only video sequences. We first transform the images into the 3D space (e.g., bird's eye view) to obtain 3D representation of the scene. We directly impose constraints on the 3D representations by treating them as signed distance fields. We can then render 2D images of previous and future frames as self-supervision signals to learn the 3D representations. We propose an MVS-embedded strategy to directly optimize the SDF-induced weights with multiple depth proposals. Our SelfOcc outperforms the previous best method SceneRF by 58.7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes. SelfOcc produces high-quality depth and achieves state-of-the-art results on novel depth synthesis, monocular depth estimation, and surround-view depth estimation on the SemanticKITTI, KITTI-2015, and nuScenes, respectively. Code: https://github.com/huang-yh/SelfOcc.Comment: Code is available at: https://github.com/huang-yh/SelfOc

PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction

Semantic segmentation in autonomous driving has been undergoing an evolution from sparse point segmentation to dense voxel segmentation, where the objective is to predict the semantic occupancy of each voxel in the concerned 3D space. The dense nature of the prediction space has rendered existing efficient 2D-projection-based methods (e.g., bird's eye view, range view, etc.) ineffective, as they can only describe a subspace of the 3D scene. To address this, we propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively and a PointOcc model to process them efficiently. Considering the distance distribution of LiDAR point clouds, we construct the tri-perspective view in the cylindrical coordinate system for more fine-grained modeling of nearer areas. We employ spatial group pooling to maintain structural details during projection and adopt 2D backbones to efficiently process each TPV plane. Finally, we obtain the features of each point by aggregating its projected features on each of the processed TPV planes without the need for any post-processing. Extensive experiments on both 3D occupancy prediction and LiDAR segmentation benchmarks demonstrate that the proposed PointOcc achieves state-of-the-art performance with much faster speed. Specifically, despite only using LiDAR, PointOcc significantly outperforms all other methods, including multi-modal methods, with a large margin on the OpenOccupancy benchmark. Code: https://github.com/wzzheng/PointOcc.Comment: Code is available at https://github.com/wzzheng/PointOc

OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving

Understanding how the 3D scene evolves is vital for making decisions in autonomous driving. Most existing methods achieve this by predicting the movements of object boxes, which cannot capture more fine-grained scene information. In this paper, we explore a new framework of learning a world model, OccWorld, in the 3D Occupancy space to simultaneously predict the movement of the ego car and the evolution of the surrounding scenes. We propose to learn a world model based on 3D occupancy rather than 3D bounding boxes and segmentation maps for three reasons: 1) expressiveness. 3D occupancy can describe the more fine-grained 3D structure of the scene; 2) efficiency. 3D occupancy is more economical to obtain (e.g., from sparse LiDAR points). 3) versatility. 3D occupancy can adapt to both vision and LiDAR. To facilitate the modeling of the world evolution, we learn a reconstruction-based scene tokenizer on the 3D occupancy to obtain discrete scene tokens to describe the surrounding scenes. We then adopt a GPT-like spatial-temporal generative transformer to generate subsequent scene and ego tokens to decode the future occupancy and ego trajectory. Extensive experiments on the widely used nuScenes benchmark demonstrate the ability of OccWorld to effectively model the evolution of the driving scenes. OccWorld also produces competitive planning results without using instance and map supervision. Code: https://github.com/wzzheng/OccWorld.Comment: Code is available at: https://github.com/wzzheng/OccWorl

SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation

Depth estimation from images serves as the fundamental step of 3D perception for autonomous driving and is an economical alternative to expensive depth sensors like LiDAR. The temporal photometric constraints enables self-supervised depth estimation without labels, further facilitating its application. However, most existing methods predict the depth solely based on each monocular image and ignore the correlations among multiple surrounding cameras, which are typically available for modern self-driving vehicles. In this paper, we propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. We apply cross-view self-attention to efficiently enable the global interactions between multi-camera feature maps. Different from self-supervised monocular depth estimation, we are able to predict real-world scales given multi-camera extrinsic matrices. To achieve this goal, we adopt the two-frame structure-from-motion to extract scale-aware pseudo depths to pretrain the models. Further, instead of predicting the ego-motion of each individual camera, we estimate a universal ego-motion of the vehicle and transfer it to each view to achieve multi-view ego-motion consistency. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets DDAD and nuScenes.Comment: Accepted to CoRL 2022. Project page: https://surrounddepth.ivg-research.xyz Code: https://github.com/weiyithu/SurroundDept

Research Progress on the Stability of Edible Jiaosu Products

Edible jiaosu is one type of food with many bioactive substances, which is very beneficial to human health. The stability of edible jiaosu is the key and difficult point to the development to the new jiaosu products. Currently, many studies on edible jiaosu have been conducted both domestically and internationally. However, there has been no systematic induction and summary until now. Therefore, based on the existing research results, this article summarizes the detection models and evaluation indicators related to the stability of jiaosu products. It shows that the main models include the predicting shelf life dynamics models. The stability indicators mainly include the sensory evaluation, pH index, the functional evaluation, the content of activity, and the probiotic content, among which the sensory evaluation and the antioxidant activity are the main evaluation indicators for the most stability evaluation. The factors that affect the stability of edible jiaosu products include the production process conditions (breeding strain, and fermentation time), the post-processing and the storage environment. The summary shows that by selecting suitable strains or mixed strains for fermentation, reasonable adjustment of fermentation time, adopting appropriate sterilization and drying methods, other post-processing methods, and using the low temperature, dark, anaerobic conditions are all beneficial to improve the stability of jiaosu products. It is recommended to further explore the selection of advantageous strains, research on post processing techniques, and stability regulations for jiaosu processing in the future. In summary, this paper summarizes the evaluation models and indicators for the stability of edible jiaosu, pointed out that production the process conditions, the post-processing of edible enzyme products, and the storage environment have a significant impact on the stability of edible jiaosu, and proposed measures and suggestions to improve its stability. This work provides a theoretical reference basis and guidance for promoting the stability of edible jiaosu and the development of its related industries

Mesenchymal Stem Cells: A Double-edged Sword in Regulating Immune Responses

Mesenchymal stem cells (MSCs) have been employed successfully to treat various immune disorders in animal models and clinical settings. Our previous studies have shown that MSCs can become highly immunosuppressive upon stimulation by inflammatory cytokines, an effect exerted through the concerted action of chemokines and nitric oxide (NO). Here, we show that MSCs can also enhance immune responses. This immune-promoting effect occurred when proinflammatory cytokines were inadequate to elicit sufficient NO production. When inducible nitric oxide synthase (iNOS) production was inhibited or genetically ablated, MSCs strongly enhance T-cell proliferation in vitro and the delayed-type hypersensitivity response in vivo. Furthermore, iNOS-/- MSCs significantly inhibited melanoma growth. It is likely that in the absence of NO, chemokines act to promote immune responses. Indeed, in CCR5-/- CXCR3-/- mice, the immune-promoting effect of iNOS-/- MSCs is greatly diminished. Thus, NO acts as a switch in MSC-mediated immunomodulation. More importantly, the dual effect on immune reactions was also observed in human MSCs, in which indoleamine 2,3-dioxygenase (IDO) acts as a switch. This study provides novel information about the pathophysiological roles of MSCs. © 2012 Macmillan Publishers Limited All rights reserved

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech. Astron. arXiv admin note: text overlap with arXiv:1910.0443

Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst

The recently discovered neutron star transient Swift J0243.6+6124 has been monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT). Based on the obtained data, we investigate the broadband spectrum of the source throughout the outburst. We estimate the broadband flux of the source and search for possible cyclotron line in the broadband spectrum. No evidence of line-like features is, however, found up to $\rm 150~keV$. In the absence of any cyclotron line in its energy spectrum, we estimate the magnetic field of the source based on the observed spin evolution of the neutron star by applying two accretion torque models. In both cases, we get consistent results with $B\rm \sim 10^{13}~G$, $D\rm \sim 6~kpc$ and peak luminosity of $\rm >10^{39}~erg~s^{-1}$ which makes the source the first Galactic ultraluminous X-ray source hosting a neutron star.Comment: publishe