Use of stochastic simulations to investigate the power and design of a whole genome association study using single nucleotide polymorphism arrays in farm animals

This paper presents a quick, easy to implement and versatile way of using stochastic simulations to investigate the power and design of using single nucleotide polymorphism (SNP) arrays for genome-wide association studies in farm animals. It illustrates the methodology by discussing a small example where 6 experimental designs are considered to analyse the same resource consisting of 6006 animals with pedigree and phenotypic records: (1) genotyping the 30 most widely used sires in the population and all of their progeny (515 animals in total), (2) genotyping the 100 most widely used sires in the population and all of their progeny (1102 animals in total), genotyping respectively (3) 515 and (4) 1102 animals selected randomly or genotyping respectively (5) 515 and (6) 1102 animals from the tails of the phenotypic distribution. Given the resource at hand, designs where the extreme animals are genotyped perform the best, followed by designs selecting animals at random. Designs where sires and their progeny are genotyped perform the worst, as even genotyping the 100 most widely used sires and their progeny is not as powerful of genotyping 515 extreme animals.

Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G.Besides,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective solutions.To this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground users.The goal was to mitigate co-channel interference while maximizing long-term system utility.The problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this problem.Extensive simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.

User Association and Power Allocation for UAV-Assisted Networks: A Distributed Reinforcement Learning Approach

Unmanned aerial vehicles(UAVs)can be employed as aerial base stations(BSs)due to their high mobility and flexible deployment.This paper focuses on a UAV-assisted wireless network,where users can be scheduled to get access to either an aerial BS or a terrestrial BS for uplink transmission.In contrast to state-of-the-art designs focusing on the instantaneous cost of the network,this paper aims at minimizing the long-term average transmit power consumed by the users by dynamically optimizing user association and power allocation in each time slot.Such a joint user association scheduling and power allocation problem can be formulated as a Markov decision process(MDP).Unfortunately,solving such an MDP problem with the conventional relative value iteration(RVI)can suffer from the curses of dimensionality,in the presence of a large number of users.As a countermeasure,we propose a distributed RVI algorithm to reduce the dimension of the MDP problem,such that the original problem can be decoupled into multiple solvable small-scale MDP problems.Simulation results reveal that the proposed algorithm can yield lower longterm average transmit power consumption than both the conventional RVI algorithm and a baseline algorithm with myopic policies.

SHEsis,a powerful software platform for analyses of linkage disequi-librium,haplotype construction,and genetic association at polymor-phism loci

The authors want to changed the web link of the software platform in this Briefing.Page 97,section 'INTRODUCTION',the web link of SHEsis is changed from http://www.nhgg.org/analysis tohttp://analysis.bio-x.

A practical approach to improve alarm system performance: Application to power plant

Process safety in chemical industries is considered to be one of the important goals towards sustainable development. This is due to the fact that, major accidents still occur and continue to exert significant reputational and financial impacts on process industries. Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal conditi on in the plant. Therefore, if deployed properly, alarm systems can play a critical role in helping plant operators ensure process safety and profitability. How-ever, in practice, many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety. A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues. In this article, we provide a simple, practical, systematic approach that can be applied by plant engineers (i.e., non-experts) to improve industrial alarm system performance. The proposed approach is demonstrated using an industrial power plant case study.

A New Approach of Time Series Variation Based on Power Links and Field Association Words

This paper has proposed a new methodology extracting stability classes of field association words depending on automatically power link analysis to enhance the precision of decision tree. In this paper, we have studied the effects of the time variation based on the frequencies of specific words called field association words that connected to documents using power link in a specific period. The stability classes have referred to the popularity of field association words based on the change of time in a given period. The new approach has evaluated by conducting experiments simulating results of 1575 files (about 5.16 MB). Based on these experiments, it has turned out that, the F-measure for ascending, stable and descending classes have achieved 93.6%, 99.8% and 75.7%, respectively. These results mean that F-measure was increasing by 12%, 4% and 34% than traditional methods because of the power link analysis.

J.D.Power & Association年度车载导航系统用户满意度调查

车载导航系统是全球卫星定位技术(Global Positioning System,简称GPS)应用于汽车定位导航的电子产品.车载导航仪内部装有储存大量电子地图信息的存储器,通过GPS卫星信号确定的位置坐标与此相匹配,便可确定汽车在电子地图中的准确位置.在此基础上,可实现行车导航、路线推荐、信息查询、播放AV/TV等多种功能.驾驶者只须通过观看显示器上的画面、收听语音提示,操纵手中的遥控器即可实现上述功能,从而轻松自如地驾车.自1990年世界第一台车用导航系统问世以来,车用导航系统在全球迅速普及,增长势头锐不可挡.

基于碳捕集-电转气的矿区综合能源系统协同优化调度

“双碳”战略目标下,为促进风光消纳与能源电力低碳转型、提高矿区能源利用率,提出一种含伴生能源和碳捕集与电转气耦合的矿区综合能源系统低碳经济调度模型。首先,综合考虑瓦斯、乏风、涌水等矿区伴生能源利用,建立矿区综合能源系统基本模型,并以碳捕集和电转气设备为耦合单元,促进节能减排与可再生能源消纳。然后,引入奖惩阶梯式碳交易机制,以矿区综合能源系统运行成本最小为目标,建立矿区综合能源系统协同优化调度模型。最后,以中国云南某大型煤矿为例,通过设置不同场景进行仿真分析。结果表明,所提模型能够促进矿区综合能源系统低碳经济运行,提高风光消纳率。

广西十万大山山地常绿阔叶林优势种空间分布格局与关联性

【目的】山地常绿阔叶林是十万大山的主要植被类型,揭示其群落种群空间分布格局与关联性特征,有利于探讨群落物种竞争关系与维持机制。【方法】以十万大山1 hm^(2)山地常绿阔叶林样地重要值排名前6位的树种为研究对象,采用g(r)函数、方差比率法、Spearman秩相关系数分析种群空间分布格局和种内、种间相关性。【结果】以完全随机模型对样地群落优势种空间分布格局进行分析,发现优势种在1~11 m尺度上均呈聚集分布,云南桤叶树Clethra delavayi、银木荷Schima argentea、柔毛紫茎Stewartia villosa在1~25 m尺度上呈聚集分布;以异质泊松模型分析时,发现优势种在10~25 m尺度上均呈随机分布,表明优势种空间分布格局受生境异质性影响,且不同种群受影响的程度不同;样地微地形因子中海拔和坡度对优势种的格局分布影响较大;不同种群的龄级空间分布格局受个体密度影响较大,增长型种群幼龄个体聚集分布,随着龄级的增加其空间分布趋于随机分布,衰退型种群成年个体小尺度上聚集分布,幼龄个体随机分布;样地群落优势种的幼树、小树、成年树之间无种内竞争排斥关系,种内关联性均为正相关或无关联,种内关联性与尺度相关;群落优势种种间关联性总体呈正关联,种间正相关和无关联种对多于负相关种对,表明群落中优势种的种间竞争关系较弱,优势种间相互独立,利于物种共存。【结论】十万大山山地常绿阔叶林优势种的分布格局受生境异质性和密度制约的影响,且分布格局随尺度和发育阶段而变化。种内、种间关系多为正相关或无关联,群落优势种排斥竞争关系不显著,群落种群结构稳定。

基于Spark集群的火电机组经济性挖掘

以电厂经济性为挖掘目标,把供电煤耗率作为机组的评价指标,通过最大信息系数法对影响机组煤耗率的运行参数进行特征选择。对基于Spark的并行Mini Batch K-means算法对外部约束条件进行工况划分以及数据离散化,采用并行FP-growth算法挖掘出机组全工况的强关联规则,进而得到机组重要参数的运行优化指导方案。以某电厂机组为例,结果表明,该方法能够提高数据挖掘效率并完成电厂参数最优值的确定,根据挖掘结果可以更好地对机组人员进行指导。

代表性春大豆种质资源叶片蔗糖含量全基因组关联分析

蔗糖是植物糖类源库运输的主要形式,是植物生长中重要的能源物质。本研究利用196份已重测序的国内外品种构成的自然群体为试验材料,测定该群体苗期叶片蔗糖含量,结合混合线性模型(Mixed Linear Model)进行全基因组关联分析。结果显示:根据阈值筛选得到5个显著相关SNP位点,分别位于第15和20号染色体上,且在SNP位点上下游各100 kb搜索到27个相关基因。通过GO功能富集分析、KEGG代谢通路富集分析及基因功能注释,筛选得到5个可能与大豆叶片蔗糖含量相关的候选基因,并通过相对表达量分析鉴定得到Glyma.15G023800、Glyma.15G024000、Glyma.15G024600共3个与大豆叶片蔗糖含量相关基因。研究结果为探究大豆叶片蔗糖含量遗传机理提供理论参考。

自动激活的长时联结表征对工作记忆的促进效应

本研究通过让被试学习具有语义关联性的图片对,以达到在短期学习的基础上迅速建立自动激活的长时记忆联结表征。在此基础上,通过3个实验探讨了自动激活的长时记忆联结表征对工作记忆的影响机制。实验1控制呈现方式(联结/独立)与记忆方式(学习/无学习),结果表明学习条件下,联结条件的正确率与记忆容量显著高于独立条件,说明自动激活的联结表征促进了当前工作记忆的加工;实验2控制呈现时间(500ms/1000ms)与呈现方式(联结/独立),结果显示两种呈现时间下,联结条件相较于独立条件,在正确率与记忆容量方面均有显著提升,从而排除了促进作用是由编码速度提升所导致的可能性;实验3进一步采用alpha震荡作为脑电指标,探究长时联结表征促进工作记忆的机制,结果发现,联结表征增加了工作记忆的维持数量。3个实验结果表明,自动激活的长时记忆联结表征对当前的工作记忆任务具有促进作用,并且这种促进作用是来自联结关系辅助了工作记忆的维持加工,而不是压缩的简化作用。

福建大嵛山岛常绿阔叶林木本植物优势种的生态位和种间联结

采用典型样地调查法对福建大嵛山岛常绿阔叶林木本植物进行群落调查,采用Levins指数、Shannon-Wiener指数、Pianka指数、Schoener指数对木本植物优势种(重要值大于1.5%)的生态位宽度、生态位重叠、生态位相似性进行分析,并采用方差比率、χ^(2)检验、种间联结系数、Jaccard指数分析联结性特征。结果显示:在福建大嵛山岛常绿阔叶林木本植物中筛选出16个优势种,其中,樟(Camphora officinarum Nees)的重要值和生态位宽度最大。木本植物优势种的120个种对中,Pianka指数和Schoener指数在[0.0,0.5)范围内的种对分别有78和91对,占总种对数的65.0%和75.8%,表明多数种对生态位重叠程度和生态位相似性较低,即优势种分布相对独立,种间关系较稳定。优势种间的方差比率为2.37,统计量为37.89,表明总体联结性为显著正联结。χ^(2)检验结果显示:75个种对呈正联结,41个种对呈负联结,4个种对无联结。联结系数分析结果显示:79个种对呈正联结,42个种对呈负联结。80个种对的Jaccard指数在[0.43,1.00]范围内,表明多数种对的联结性较强。回归分析结果显示:联结系数和Jaccard指数与Pianka指数和Schoener指数均在0.001水平上显著正相关。综上所述,福建大嵛山岛常绿阔叶林木本植物优势种间的生态位重叠程度和生态位相似性较低,多数种对呈正联结,且联结性较强;目前大嵛山岛常绿阔叶林接近群落演替发展的中后期阶段,物种组成和群落结构较为稳定。

体育强国进程中我国运动项目发展的历史成效、现实困囿与优化路径

在加快推进体育强国建设进程中,系统总结我国运动项目发展的历史成效,探索运动项目发展的现实困囿与优化路径,对于提升运动项目发展水平具有现实意义。研究认为,我国运动项目伴随体育事业发展取得了显著历史成效,主要体现在运动项目发展方式不断创新,协会实体化改革深入推进;运动项目活动广泛开展,形成相对稳定的赛事体系;运动项目服务奥运战略作用明显,综合实力不断增强;运动项目产业效益显著提升,项目文化影响力不断扩大;运动项目育人功能不断挖掘,依托项目培养大量优秀竞技人才。同时,我国运动项目发展存在单项协会自治能力不足,协会实体化改革亟待进一步深化;运动项目发展不均衡,整体竞技水平和综合竞争力不强;运动项目文化建设相对滞后,缺乏多样性的运动项目标准体系;运动项目基层组织和赛事体系不健全,社会化、市场化开发不足等现实问题。研究提出,健全运动项目协会治理体系,分类深化运动项目协会实体化改革;深入推进运动项目文化建设,打造多样性的运动项目标准化体系;完善运动项目竞赛体系,推动运动项目特色化、社会化发展;优化运动项目结构布局,提升运动项目综合竞争力和国际影响力。

地方知识与海岸带规划——一个东南渔村环境抗争的人类学分析

随着全球环境危机的加剧,通过单一的规范性和普遍化知识应对环境冲突面临着多方面的困境和风险。因此,在“绿水青山就是金山银山”的新时代生态文明建设理念下,环境冲突解决机制应朝着多元主义转向,重点关注地方知识系统与科学知识体系在环境运动中的协同价值。文章以一个东南滨海渔村的环境抗争过程为实证案例,考察地方组织、地景叙事等地方知识传统与海岸带规划、环境政策等规范性知识不断博弈互动,以及与普遍性科学知识嵌入重组的地方实践,呈现环境议题的复杂性。在此基础上,新时代环境冲突的研究应关注不同利益主体在多元文化情景中生成的地方知识与科学知识之间的互动逻辑,转向多元复合型的环境冲突解决机制。

基于关联规则与离群点的新能源汽车动力域入侵检测

为提高新能源汽车动力域中针对篡改攻击的入侵检测系统效果,建立包括关联规则检测和离群点检测的动力域防护模型,通过实车采集固定工况下的动力域报文数据,基于关联规则算法建立规则库检测篡改攻击;在关联规则检测的基础上通过离群点检测,检测复杂类型的篡改攻击。仿真结果表明,该方法相比于传统的关联规则方法检测准确率提高5.83%,能有效检测针对新能源汽车动力域的篡改攻击。

基于知识图谱技术的电网项目数据关联分析

为解决电网项目数据关联难度大的问题,提出了基于知识图谱技术的电网项目数据关联分析。分析了历史项目特征提取和新增储备项目特征提取方法,论述了基于规则的信息抽取和基于序列标注的信息提取方法,以及电力项目知识图谱的基本构成和构建方法,提出了项目多维关联分析平台,论述了分析方法和分析流程,并提出了系统网络结构,结合实际应用说明了所提系统的应用效果。

基于TCP/IP协议与关联规则的电力监控系统多线程通信方法

电力监控系统环境中存在过多噪声因素干扰,导致通信效率和质量低。为此,提出一种基于传输控制协议/网际协议(transmission control protocol/internet protocol,TCP/IP)与关联规则的多线程通信算法。将背景噪声看作电力监控信号的突变现象,查找回应突变函数的信号值,利用谐波分离算法,去除背景噪声。根据不同线程的传输特点,采用TCP/IP协议建立通信程序包,分别设置句柄、终止、挂起以及执行函数,为不同线程的通信数据,匹配不同的通信协议。试验结果证明:对电力监控系统源设备的传输信号多线程通信时,通信信号波频变化最为平稳,在0~2000 s的采样区间内,未出现传输为0现象;对背景噪声去噪后,波形相比原始信号变化明显较为稳定,没有出现过高或过低的幅值变化。所提方法通信信号表达平稳、效率较高,对原始信号的保留效果较好,去噪能力很强。

基于大数据技术的电力稽查数据关联挖掘方法分析

阐述大数据技术的特点,采用电力营销稽查数据采集、数据抽取与清洗处理、数据质心收敛与关联挖掘,探讨稽查数据关联挖掘方法的设计。结果表明,对电力营销稽查数据的关联挖掘,可以提高挖掘后数据与预设规则的关联度。

“物联网+VOCs”发电及余热综合利用

牛庄油田牛871区块属于高压低渗油藏,气油比高,油井生产过程中,因受井底压力变化影响,从原油中分离出大量伴生挥发性有机物(VOCs),并积聚在油套形空间,如不及时排放,将使套压上升、降低液面、产生气锁,造成减产。一方面,利用伴生气燃烧加热水套炉和多功能储油罐,剩余的火炬燃烧或释放掉,造成能源浪费、污染环境;另外抽油机和电磁加热装置消耗了大量电能。针对上述问题,为了接轨油田“四化”建设,研发出“物联网+VOCs”发电及余热综合利用系统,成功在牛871斜50丛式井组开展试验,回收利用伴生气20.7×10^(4)m^(3)/a,发电95.2×10^(4)kW·h/a,利用余热2580 GJ/a,完全满足牛871斜50丛式井组供电和加热需求,年减排硫化物104.937 kg,实现VOCs治理和综合利用,环保效益和经济效益双丰收。