491 research outputs found
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 . 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
, and peak luminosity of which makes the source the first Galactic ultraluminous
X-ray source hosting a neutron star.Comment: publishe
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