大珠母贝人工育苗技术的研究

研究大珠母贝人工育苗若干技术,提出一些技术措施：亲贝强化培育促熟,人工诱导排放精卵,优选幼虫,投喂适口的饵料,适时倒池以及投放合适的采苗器等。结果表明,平均单产壳高2-3mm的幼苗8．1万粒／m3,最高可达9．3万粒／m3。     

湿地植物固体碳源养分释放动态及其对人工湿地水体脱氮效率的影响

污水低碳氮比(C/N)是影响人工湿地氮去除效果限制因素,传统的碳源添加及利用存在成本较高、补充困难的局限性.本研究契合“就地取材,原位处理”的废物利用原则,选择人工湿地常见的水生植物和农业废弃物(玉米芯、稻草)作为对照,对比了挺水植物(香蒲、美人蕉)、浮叶植物(莲)、沉水植物(菹草)、湿生植物(南荻、短尖苔草)的释碳能力,初步确定了以香蒲、美人蕉、南荻为代表的植物具有较好的释放碳能力,在中国湿地中分布广泛,且均对水体二次污染较小.以南荻、美人蕉、玉米芯作为碳源添加的模拟人工湿地实验验证表明,在低碳氮比的模拟表流人工湿地投入植物碳源,能有效提高系统的脱氮效率,对照组、南荻组、美人蕉组和玉米芯组的出水总氮浓度分别为(5.24±0.07)、(4.50±0.10)、(3.75±0.17)和(2.97±0.18) mg/L,对应的去除率分别为58%、64%、70%和76%,确定南荻和美人蕉植物残体以及改性材料均残体和改性材料适合作为人工湿地中原位利用的外加碳源.本研究探索了通过湿地植物配置解决人工湿地水体低C/N比的问题,为提高湿地脱氮效果提供了有意义的新途径.     

支持向量机、随机森林和人工神经网络机器学习算法在地球化学异常信息提取中的对比研究

地球化学勘查是通过发现异常、解释评价异常进行找矿的。因此,地球化学异常识别对矿产资源的定位、定量预测具有重要的的指示作用。在大数据时代的背景下,机器学习方法不要求数据满足正态分布的分布形式,且具有非线性以及泛化能力强等特点,因而逐渐地被应用于矿产资源的定量预测评价,如神经网络、支持向量机、贝叶斯网络、随机森林、受限玻尔兹曼机、极限学习机等。本文通过设计理论实验,可视化了不同算法,提出了不同机器学习方法在不同地区的地球化学异常信息提取中的效果存在不一致性的假设。在此基础上,以湖南香花岭锡多金属矿整装勘查区及甘肃合作金矿整装勘查区的地球化学异常提取为研究内容,将人工神经网络、随机森林以及支持向量机应用于研究区地球化学异常信息的提取与识别工作。在香花岭研究区,人工神经网络的结果较好,在合作研究区,随机森林的结果较好,从而验证了上述假设。通过生成两研究区的地球化学异常图,讨论了该方法在两研究区地球化学异常的地质意义和该方法的可靠性与实用性。此外,还完善了基于多种监督机器学习方法的地球化学异常信息提取流程,为软件开发提供了一定的理论依据。     

Artificial intelligence in seismology:Advent,performance and future trends

Realistically predicting earthquake is critical for seismic risk assessment,prevention and safe design of major structures.Due to the complex nature of seismic events,it is challengeable to efficiently identify the earthquake response and extract indicative features from the continuously detected seismic data.These challenges severely impact the performance of traditional seismic prediction models and obstacle the development of seismology in general.Taking their advantages in data analysis,artificial intelligence(AI) techniques have been utilized as powerful statistical tools to tackle these issues.This typically involves processing massive detected data with severe noise to enhance the seismic performance of structures.From extracting meaningful sensing data to unveiling seismic events that are below the detection level,AI assists in identifying unknown features to more accurately predicting the earthquake activities.In this focus paper,we provide an overview of the recent AI studies in seismology and evaluate the performance of the major AI techniques including machine learning and deep learning in seismic data analysis.Furthermore,we envision the future direction of the AI methods in earthquake engineering which will involve deep learning-enhanced seismology in an internet-of-things(IoT) platform.     

制约卫星轨道寿命的另一种机制

近点共振会导致太阳系小天体(小行星,自然卫星以及大行星和月球的人造卫星)的轨道偏心率出现变幅较大的长周期变化,特别是以月球和大行星为中心天体的大倾角轨道(确切地说是倾角接近90°的极轨道)卫星,由于类似的原因,偏心率的增大而导致近星距rp=a(1-e)≤ae(ae是中心天体的赤道半径),使其落到中心天体上,结束轨道寿命,这与耗散机制大不相同,因此将对其作理论分析,并以计算实例加以证实.     

中国鲎(Tachypleus tridentatus)人工授精育苗的初步研究

用厦门海滩捕获的成熟雌雄中国鲎进行人工授精育苗试验，并对受精率、孵化率、幼鲎饲养成活率进行测定。结果表明，受精率为95％，孵化率为85％，幼鲎成活率为100％；1996年7月29日人工授精的鲎，至1997年7月11日仍健康生长，并已蜕第一次壳。说明中国鲎用人工授精的方法大量人工育苗是可行的。要提高受精率和育苗效果，应在5～6月为佳。     

Wave hindcasting by coupling numerical model and artificial neural networks

By coupling numerical wave model (NWM) and artificial neural networks (ANNs), a new procedure for wave prediction is proposed. In many situations, numerical wave modeling is not justified due to economical consideration. Although incorporation of an ANN model is inexpensive, such a model needs a long time period of wave data for training, which is generally inconvenient to achieve. A proper combination of these two methods could carry the potentials of both. Based on the proposed approach, wave data are generated by a NWM by means of a short period of assumed winds at a concerned point. Then, an ANN is designed and trained using the above-mentioned generated wind-wave data. This ANN model is capable of mapping wind-velocity time series to wave height and period time series with low cost and acceptable accuracy. The method was applied for wave hindcasting to two different sites; Lake Superior and the Pacific Ocean. Simulation results show the superiority of the proposed approach.     

Estimation of submarine groundwater discharge in Plover Cove, Tolo Harbour, Hong Kong by Rn

Algal blooms in Tolo Harbour, Hong Kong have received much attention and submarine groundwater discharge is speculated to be a significant pathway carrying nutrients into the constricted estuary. Plover Cove, a small cove in the Harbour, was selected for SGD analysis using ²²²Rn budget. The volumetric SGD rates are estimated to be about 8000 m³/day for neap tide and about 17,000 m³/day for spring tide. Result of nutrient analysis of the porewater indicates that the nutrient loading through this pathway is speculated to be crucial for eutrophication in Tolo Harbour. Current practice for the management of algal blooms in Hong Kong, in which nutrient loading through SGD was ignored, has to be reviewed and the control measures of groundwater contamination are obviously required.     

Radiation of lamp and optimized experiment using artificial light in the Arctic Ocean

A winter optical experiment by an artificial lamp was conducted in the Amundsen Bay of Arctic Ocean from November of 2007 to January of 2008.The radiation field emitted from an artificial lamp was measured and is introduced in this paper ,and the optimized experiment project is discussed.It is demonstrated that the minimum size allowed of the lamp is determined by both the field of view(FOV) of optical instrument and the measuring distance from the lamp.Some problems that might influence on the experimen...     

自动观测与人工观测数据差异的初步分析

利用2004—2007年内蒙古地区50个地面自动气象站与人工平行观测期间的数据,对自动与人工观测的气温、相对湿度、地表温度进行了差异分析,统计了两种观测之间的对比差值。对各要素观测差异在全区的分布特点进行了分析。结果表明:自动观测与人工观测各气象要素均存在一定的差异,但大部分地区各要素的差异都在自动站误差允许范围之内;造成差异的原因是多方面的,包括仪器本身存在缺陷及观测方法不一致等。