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小肠结节灌注与尤斯灌流技术在猪肠道功能研究方面的应用

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农业现代化研究 2012年第6期33卷 741-744页

作者：唐宇龙何流琴 c.m.nyachoti 印遇龙中国科学院亚热带农业生态研究所中国科学院亚热带农业生态过程重点实验室湖南省畜禽健康养殖工程技术中心农业部中南动物营养与饲料科学观测实验站湖南长沙410125 中国科学院研究生院北京100039 Department of Animal Science University of ManitobaWinnipeg

小肠灌注(Small Intestinal Segment Perfusion)技术是通过建立仔猪的原位模型,在病原菌的感染下,检测小肠的净流量和电解质浓度,从而鉴定不同益生物质在腹泻上的作用;尤斯灌流(Ussing chamber)技术通过微电极检测整个肠道细胞膜离子通... 详细信息

小肠灌注(Small Intestinal Segment Perfusion)技术是通过建立仔猪的原位模型,在病原菌的感染下,检测小肠的净流量和电解质浓度,从而鉴定不同益生物质在腹泻上的作用;尤斯灌流(Ussing chamber)技术通过微电极检测整个肠道细胞膜离子通道变化的电流信号,研究其药物吸收、通透性和分泌情况的变化。本文就这两种技术的发明,主要步骤和在肠道上的应用进行详细的介绍。

关键词：小肠灌注技术尤斯灌流技术肠道研究

日粮中添加腰果粉对蛋鸡生长性能及免疫器官和内脏重量的影响

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日粮中添加腰果粉对蛋鸡生长性能及免疫器官和内脏重量的影响

中国畜牧兽医学会动物营养学分会第十一次全国动物营养学术研讨会

作者：刘刚 Y.m.Aguilar c.m.nyachoti 任文凯李颖慧印遇龙农业部中南动物营养与饲料科学观测实验站湖南省畜禽健康养殖工程技术研究中心中国科学院亚热带农业生态过程重点实验室中国科学院亚热带农业生态研究所 Department of Animal Science University of Manitoba

本研究旨在测定在日粮中添加腰果粉末对蛋鸡生长性能及免疫器官和内脏重量的影响。选取1日龄白来航母鸡320只,随机分为4组(每组4个重复,每个重复30只):对照组饲喂基础日粮,试验组饲喂分别添加0.5%、1.5%和2.5%腰果粉的试验日粮,试验期5... 详细信息

本研究旨在测定在日粮中添加腰果粉末对蛋鸡生长性能及免疫器官和内脏重量的影响。选取1日龄白来航母鸡320只,随机分为4组(每组4个重复,每个重复30只):对照组饲喂基础日粮,试验组饲喂分别添加0.5%、1.5%和2.5%腰果粉的试验日粮,试验期56天。结果显示,35天时,日粮中补充0.5%腰果粉能够增加体重(Body Weight),法氏囊和盲肠相对重量。然而,日粮中补充1.5%和2.5%腰果粉处理组的生产指标则有所降低(P<0.05)。此外,56天试验组胸腺和胃的相对重量变化趋势与35天时有所不同。在第35天时,添加腰果粉并不影内脏的相对重量,然而在第56天时对照组胰腺和肾脏相对重量比试验组重,添加1.5%和2.5%腰果粉的试验组肠道pH值增加(P<0.05)。结果表明,日粮中添加0.5%的腰果粉对母鸡的生长性能,免疫和内脏器官有一定好处。

关键词：腰果免疫器官母鸡内脏

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菜籽粕在猪营养上的应用

国外畜牧学（猪与禽） 2018年第8期38卷 5-7页

作者：刘文峰(译) 陈建康(校) N.Sanjayan c.m.nyachoti I.H.Kim 谷实农牧集团股份有限公司加拿大曼尼托巴大学韩国檀国大学不详

菜籽粕可作为猪日粮中其他蛋白质饲料(如豆粕)的廉价替代物。本文将概述最新的研究进展。

关键词：菜籽粕猪日粮应用营养蛋白质饲料替代物豆粕

consumer transparency in the production chain for plant varieties produced using new genomic techniques

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aBIOTEcH 2024年第2期5卷 239-246页

作者： J.m.Lukasiewicz c.c.m.van de Wiel L.A.P.Lotz m.J.m.Smulders Wageningen University&Research P.O.Box 386NL-6700 AJ WageningenThe Netherlands

Plants edited with new genomic techniques(NGTs)currently fall under the Genetically modified Organisms Directive(2001/18/Ec)in the European *** the proposal of the European commission,NGT plants are partially exempted from the regulations of this *** proposal makes a distinction between two categories of NGT plants:NGT-1 and ***-1 category plants are considered equal to plants obtained through conventional breeding *** plants will not be labelled for the consumer,although they will be labelled as ***-2 category plants may be labelled with additional information as a positive *** of seeds of varieties made with gene editing,but not the products,would mean that most steps in the production chain are transparent,but not the last step towards ***“right to know”and increasing knowledge of gene-edited food is a common theme in food labelling towards ***,we describe current labelling regimes and registers and how these may be applied to provide transparency on gene-edited products to ***,we also look into consumer studies,which indicate a greater acceptance of gene-edited food among consumers,especially when additional benefits such as sustainability are mentioned.

关键词： Labelling New genomic techniques consumer transparency Plant products

Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields

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Petroleum 2024年第1期10卷 150-164页

作者： Leonardo Goliatt c.m.Saporetti L.c.Oliveira E.Pereira Department of computational and Applied mechanics Federal University of Juiz de ForaJuiz de ForaBrazil Department of computational modeling Polytechnic InstituteRio de Janeiro State UniversityNova FriburgoBrazil Petrobras Rio de JaneiroBrazil Department of Stratigraphy and Paleontology Rio de Janeiro State UniversityRio de JaneiroBrazil

Rock samples'TOc content is the best indicator of the organic matter in source *** origin rock samples’analysis is used to calculate it manually by *** method requires time and resources because it relies on samples from many well intervals in source ***,research has been done to aid this *** learning algorithms can estimate total organic carbon instead of well logs and stratigraphic *** light of these efforts,the current work present a study on automating the total organic carbon estimation using machine learning approaches improved by an evolutionary methodology to give the model flexibility and *** algorithms,differential evolution,particle swarm optimization,grey wolf optimization,artificial bee colony,and evolution strategies were used to improve machine learning models to predict *** six metaheuristics were integrated into four machine learning methods:extreme learning machine,elastic net linear model,linear support vector regression,and multivariate adaptive regression *** samples from the YuDong-Nan shale gas field,located in the Sichuan basin,were used to evaluate the hybrid *** findings show that combining machine learning models with an evolutionary algorithms in a hybrid fashion produce flexible models that accurately predict *** results show that,independent of the metaheuristic used to guide the model selection,optimized extreme learning machines attained the best performance scores according to six *** hybrid models can be used in exploratory geological research,particularly for unconventional oil and gas resources.

关键词： Total organic carbon Hybrid models Automated machine learning Evolutionary algorithms Geology

An improved pulse coupled neural networks model for semantic IoT

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Digital communications and Networks 2024年第3期10卷 557-567页

作者： Rong ma Zhen Zhang Yide ma Xiping Hu Edith c.H.Ngai Victor c.m.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China college of computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse coupled Neural Network(PcNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PcNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse coupled Neural Networks(IPcNN)model is proposed in this *** IPcNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPcNN on the gray images and natural images from the matlab and Berkeley Segmentation *** IPcNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

Semi-analytical investigation of heat transfer in a porous convective radiative moving longitudinal fin exposed to magnetic field in the presence of a shape-dependent trihybrid nanofluid

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Applied mathematics and mechanics(English Edition) 2024年第1期45卷 197-216页

作者： c.G.PAVITHRA B.J.GIREESHA m.L.KEERTHI Department of PG Studies and Research in mathematics Kuvempu UniversityShankaraghatta 577451India Department of mathematics and Statistics M.S.Ramaiah University of Applied SciencesBengaluru 560058India

The thermal examination of a non-integer-ordered mobile fin with a magnetism in the presence of a trihybrid nanofluid(Fe_3O_4-Au-Zn-blood) is carried out. Three types of nanoparticles, each having a different shape, are considered. These shapes include spherical(Fe_3O_4), cylindrical(Au), and platelet(Zn) configurations. The combination approach is utilized to evaluate the physical and thermal characteristics of the trihybrid and hybrid nanofluids, excluding the thermal conductivity and dynamic viscosity. These two properties are inferred by means of the interpolation method based on the volume fraction of nanoparticles. The governing equation is transformed into a dimensionless form, and the Adomian decomposition Sumudu transform method(ADSTm) is adopted to solve the conundrum of a moving fin immersed in a trihybrid nanofluid. The obtained results agree well with those numerical simulation results, indicating that this research is reliable. The influence of diverse factors on the thermal overview for varying noninteger values of γ is analyzed and presented in graphical representations. Furthermore, the fluctuations in the heat transfer concerning the pertinent parameters are studied. The results show that the heat flux in the presence of the combination of spherical, cylindrical, and platelet nanoparticles is higher than that in the presence of the combination of only spherical and cylindrical nanoparticles. The temperature at the fin tip increases by 0.705 759% when the value of the Peclet number increases by 400%, while decreases by 11.825 13% when the value of the Hartman number increases by 400%.

关键词： convection radiation moving longitudinal fin Adomian decomposition Sumudu transform method(ADSTm) trihybrid nanofluid magnetic field

日粮不同蛋白水平对感染大肠杆菌断奶仔猪的生长性能、肠道健康和肠道微生物区系的影响效应

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饲料与畜牧（新饲料） 2009年第8期 63-63页

作者：刘冰 F.O.Opapeju D.O.Krause R.L.Payne m.Rademacher c.m.nyachoti

本文研究日粮中不同粗蛋白水平对感染产肠毒素大肠杆菌（ETEc）K88的断奶仔猪生长性能、肠道健康、肠道微生物区系的影响效应，试验期14d。供试仔猪40只（5．32±0．24kg），2个日粮组，每组5个重复，每个重复4只：①粗蛋白22．5％；②... 详细信息

本文研究日粮中不同粗蛋白水平对感染产肠毒素大肠杆菌（ETEc）K88的断奶仔猪生长性能、肠道健康、肠道微生物区系的影响效应，试验期14d。供试仔猪40只（5．32±0．24kg），2个日粮组，每组5个重复，每个重复4只：①粗蛋白22．5％；②粗蛋白17．6％＋氨基酸。基于理想蛋白比率，2组日粮含有相同的代谢能和标准回肠消化赖氯酸、蛋氨酸＋胱氨酸、苏氨酸、色氨酸。低蛋白日粮中添加异亮氨酸和缬氨酸使粗蛋白水平达到22．5％。断奶后第8d，强饲6mLETEc悬浮液（10^10cfu／mL）。第7d、9d、10d、12d测定饲料消化和体重情况，14d测定平均日增重、平均日采食量和料重比。在感染的前1d、感染后第3d和第7d每个重复屠宰1头试验猪（屠宰10头猪／d）来评定肠道形态、肠道微生物区系和代谢情况。

关键词：大肠杆菌K88 肠道形态生长性能蛋白断奶仔猪

Optimization of a photovoltaic-battery system using deep reinforcement learning and load forecasting

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Energy and AI 2024年第2期16卷 168-183页

作者： António corte Real G.Pontes Luz J.m.c.Sousa m.c.Brito S.m.Vieira IDmEc Instituto Superior TécnicoUniversidade de LisboaLisboa1049-001Portugal Instituto Dom Luiz Faculdade de CiênciasUniversidade de LisboaCampo Grande1749-016LisboaPortugal

Home Energy management Systems(HEmS)are increasingly relevant for demand-side management at the residential level by collecting data(energy,weather,electricity prices)and controlling home appliances or storage *** control can be performed with classical models that find optimal solutions,with high real-time computational cost,or data-driven approaches,like Reinforcement Learning,that find good and flexible solutions,but depend on the availability of load and generation data and demand high computational resources for *** this work,a novel HEmS is proposed for the optimization of an electric battery operation in a real,online and data-driven environment that integrates state-of-the-art load forecasting combining cNN and LSTm neural networks to increase the robustness of *** Reinforcement Learning agents are trained with different algorithms(Double DQN,Dueling DQN,Rainbow and Proximal Policy Optimization)in order to minimize the cost of electricity purchase and to maximize photovoltaic self-consumption for a PV-Battery residential *** show that the best Reinforcement Learning agent achieves a 35%reduction in total cost when compared with an optimization-based agent.

关键词： Energy management system Deep reinforcement learning Demand-side management Load forecasting Deep learning Energy storage systems