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CLIMATE-CHANGE-INDUCED TEMPORAL VARIATION IN PRECIPITATION INCREASES NITROGEN LOSSES FROM INTENSIVE CROPPING SYSTEMS: ANALYSIS WITH A toy model

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Frontiers of Agricultural Science and Engineering 2022年第3期9卷 457-464页

作者： Peter MVITOUSEK Xinping CHEN Zhenling CUI Xuejun LIU Pamela AMATSON Ivan ORTIZ-MONASTERIO GPhilip ROBERTSON Fusuo ZHANG Department of Biology Stanford UniversityStanfordCA 94305USA College of Resources and Environment and Academy of Agricultural SciencesSouthwest UniversityChongqing 400716China College of Resources&Environmental Sciences China Agricultural UniversityBeijing 100193China Department of Earth System Science Stanford UniversityStanfordCA 94305USA International Maize and Wheat Improvement Center(CIMMYT) El BatanTexcoco 56237Mexico W.K.Kellogg Biological Station and Department of PlantSoil and Microbial SciencesMichigan State UniversityHickory CornersMI 49060USA.

A simple‘toy’model of productivity and nitrogen and phosphorus cycling was used to evaluate how the increasing temporal variation in precipitation that is predicted(and observed)to occur as a consequence of greenhouse-gasinduced climate change will affect crop yields and losses of reactive N that can cause environmental damage and affect human *** model predicted that as temporal variability in precipitation increased it progressively reduced yields and increased losses of reactive N by disrupting the synchrony between N supply and plant N ***,increases in the temporal variation of precipitation increased the frequency of floods and *** of this model indicate that climate-change-driven increases in temporal variation in precipitation in rainfed agricultural ecosystems will make it difficult to sustain cropping systems that are both high-yielding and have small environmental and human-health footprints.

关键词： crop yield fertilizer timing nitrogen loss precipitation variability toy model

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Using a toy model to Improve the Quantization of Gravity and Field Theories

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Journal of High Energy Physics, Gravitation and Cosmology 2022年第2期8卷 303-308页

作者： John R. Klauder Department of Physics and Department of Mathematics University of Florida Gainesville USA

A half-harmonic oscillator, which gets its name because the position coordinate is strictly positive, has been quantized and determined that it was a physically correct quantization. This positive result was found using affine quantization (AQ). The main purpose of this paper is to compare results of this new quantization procedure with those of canonical quantization (CQ). Using Ashtekar-like classical variables and CQ, we quantize the same toy model. While these two quantizations lead to different results, they both would reduce to the same classical Hamiltonian if ħ→ 0. Since these two quantizations have differing results, only one of the quantizations can be physically correct. Two brief sections also illustrate how AQ can correctly help quantum gravity and the quantization of most field theory problems.

关键词： toy model Affine Quantization (AQ) Canonical Quantization (CQ)

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Respective Roles of Short-and Long-Range Interactions in Protein Folding

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Wuhan University Journal of Natural Sciences 2004年第6期9卷 962-966页

作者： WANGLong-hui HUMin ZHOUHuai-bei LIUJuan SchoolofComputerScience WuhanUniversityWuhan430072HubeiChina WuhanUniversityJournalsPress WuhanUniversityWuhan430072HubeiChina

A new method was presented to discuss the respective roles of short- and long-range interactions in protein folding. It's based on an off-lattice model, which is also being called as toy model. Simulated annealing algorithm was used to search its native conformation. When it is applied to analysis proteins 1agt and 1aho, we find that helical segment cannot fold into native conformation without the influence of long-range interactions. That's to say that long-range interactions are the main determinants in protein folding. Key words toy model - protein folding - simulated annealing algorithm - short and long range interactions CLC number O 242.28 - Q71 Foundation item: Supported by the National Natural Science Foundation of China((60301009)Biography: WANG Long-hui (1976-), female, Ph. D candidate, research direction: machine learning, bioinformatics.

关键词： toy model protein folding simulated annealing algorithm short and long range interactions

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