Corn Drying: Modelling the Quality Degradation

1994 ◽  
pp. 334-336 ◽  
Author(s):  
F. Courtois ◽  
A. Lebert ◽  
J. C. Lasseran ◽  
J. J. Bimbenet
Keyword(s):  
Quality Degradation

JENIS BURUNG DI KAMPUS UNIVERSITAS TANJUNGPURA

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Sutarto Kusuma Indra ◽  
Kustiati Kustiati ◽  
Rafdinal Rafdinal
Keyword(s):  
Habitat Loss ◽  
Human Life ◽  
Natural Habitat ◽  
Bird Species ◽  
Residential Land ◽  
Encounter Rates ◽  
Passer Montanus ◽  
Quality Degradation

Quality degradation, modification, and habitat loss are significant threats to bird species. The natural habitat of birds has been modified into residential land and facilities to meet the needs of human life as happened at Tanjungpura University. This study aims to determine of birds species at Tanjungpura University. Observations were carried out from January to March 2019. The method used in collecting the data from bird was “Encounter rates” which was conducted in the morning starting at 6 – 9 am and at 3 - 6 pm. The data obtained were analyzed with the formula of simple abundance scale and frequency of attendance. The birds found at Universitas Tanjungpura are 28 species classified into 23 genera, 17 families, and seven orders. Birds found to have an abundance order scale are classified into abundant, general, frequent and, unusual categories. Birds included in the abundant category are Collocalia fuciphaga and Passer montanus. The types of bird foods at Tanjungpura University consist of frugivore, insectivore, granivore, herbivore, carnivore, piscivore, omnivore, molluscivore, and nectarivore. The value of attendance frequency have range between 10-100%. The bird species with highest frequency of attendance’s value is Passer montanus, Pycnonotus aurigaster, Pycnonotus goiavier, Collocalia fuciphaga, and Anthreptes malacensis.

UV-C irradiation inhibits surface discoloration and delays quality degradation of fresh-cut stem lettuce

LWT ◽  
2021 ◽  
pp. 111533
Author(s):  
Cong Han ◽  
Wenna Zhen ◽  
Qingmin Chen ◽  
Maorun Fu
Keyword(s):  
Quality Degradation ◽  
Fresh Cut ◽  
Uv C

scholarly journals Soil quality degradation under horticulture practices in volcanic slope soil, East Java – Indonesia

2021 ◽  
Vol 648 (1) ◽  
pp. 012062
Author(s):  
S Kurniawan ◽  
M P Agustina ◽  
R A Wiwaha ◽  
A Y Wijaya ◽  
A D Fitria
Keyword(s):  
Soil Quality ◽  
Quality Degradation

The Virtual Water Gallery: a collaborative science and art project

2021 ◽  
Author(s):  
Louise Arnal ◽  
Martyn Clark ◽  
Stacey Dumanski ◽  
John Pomeroy
Keyword(s):  
Boreal Forests ◽  
Water Environment ◽  
Leading Edge ◽  
Virtual Water ◽  
Pilot Project ◽  
The Arctic ◽  
The Public ◽  
Science And Art ◽  
Quality Degradation ◽  
Collaborative Space

<p>Water is life and so water-related challenges, such as droughts, floods and water quality degradation, affect everyone. Conceptualizing water-related environmental and social problems in novel ways, with engagement between the public and science researchers, may lead to new and more comprehensive solutions to complex problems. A society that makes decisions informed by science and science that approaches problems in a transdisciplinary manner are key elements in finding creative and holistic solutions to the water-related challenges we all face. We believe that art can help co-establish new social norms to help us grasp and tackle water-related challenges in a more holistic manner.</p><p>The Virtual Water Gallery* is a science and art pilot project funded by Global Water Futures (GWF). GWF is a University of Saskatchewan-led research program that is funded in part by the Canada First Research Excellence Fund. Its overarching goal is to deliver risk management solutions, informed by leading-edge water science, to manage water futures in Canada and other cold regions where global warming is changing landscapes, ecosystems and the water environment. Launched in Summer 2020, the Virtual Water Gallery aims to provide a safe, inclusive and collaborative space for fully open discussions between scientists, artists, and a wider public, to explore past, present and future water challenges.</p><p>As part of this pilot project, 13 artists were paired with teams of GWF scientists to co-explore specific water challenges in various Canadian ecoregions and river basins, including the Arctic, the mountains, boreal forests, prairies, farmlands, lakes, rivers, and communities. These collaborations are leading to the co-creation of science and art pieces which will be exhibited online on a Virtual Water Gallery. By making this online exhibition accessible to a global audience, we hope that the co-created art pieces will open creative and informative discussions about urgent water challenges to a wider audience via the gallery space.</p><p>*More information about the Virtual Water Gallery on the GWF webpage: https://gwf.usask.ca/outreach/virtual-water-gallery.php</p>

Simulation of the transient thermally induced beam quality degradation in end-pumped slab Yb:YAG amplifiers of hundred-mJ-level

2017 ◽  
Vol 123 (9) ◽  
Author(s):  
Ye Lang ◽  
Jianguo Xin ◽  
Kamal Alameh ◽  
Zhongwei Fan ◽  
Yanzhong Chen ◽  
...  
Keyword(s):  
Beam Quality ◽  
Thermally Induced ◽  
Quality Degradation

scholarly journals Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

2018 ◽  
Vol 8 (8) ◽  
pp. 1258 ◽  
Author(s):  
Shuming Jiao ◽  
Zhi Jin ◽  
Chenliang Chang ◽  
Changyuan Zhou ◽  
Wenbin Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Frequency ◽  
Critical Issue ◽  
Compression Process ◽  
Compression Scheme ◽  
Quality Degradation ◽  
Digital Format ◽  
Enormous Amount ◽  
Frequency Features

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed “JPEG + deep learning” hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression.

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