Trace metals in seawater and copepods in the ocean outfall area off the northern Taiwan coast

The distribution, partitioning and concentrations of trace metals (Cd, Cr, Cu, Fe, Mn, Pb and Zn) in seawater, including dissolved and particulate phases, and in copepods in the ocean outfall area off the northern coast of Taiwan were investigated. Normalization of metal concentrations to the background metal concentration to yield relative enrichment factors (EF), which were used to evaluate the contamination of dissolved and particulate trace metals in seawater around the ocean outfall. The EF results indicated that the outfall area was significantly contaminated by dissolved Fe and Zn, and by particulate Fe, Cr, Cu, Pb and Zn. In addition, most trace metals were chiefly in the particulate phase. The average percentage of total metal concentrations (dissolved plus particulate phases) bound by suspended particulate matter followed the sequence Al(95%) = Mn(95%) > Pb(88%) > Cu(86%) > Fe(72%) > Zn(32%) > Cr(17.5%) > Cd(3.4%). Therefore, metal contamination is better evaluated in solid phase than in the dissolved phase. The concentration ranges of trace metals in the copepods, Temora turbinata, Oncaea venusta and Euchaeta rimana, near the outfall were: Cd, 0.23-1.81 microg g(-1); Cr, 16.5-195 microg g(-1); Cu, 14-160 microg g(-1); Fe, 256-7255 microg g(-1); Mn, 5.5-80.8 microg g(-1); Pb, 2.6-56.2 microg g(-1); Zn, 132-3891 microg g(-1); and Al, 0.21-1.13%. Aluminum, and probably Fe, seemed to be the major elements in copepods. The concentrations of trace metals in copepods, especially Temora turbinata, near the outfall were generally higher than those obtained in the background station. The mean increase in bioconcentration factor of metals in copepods ranged from 4 to 7 and followed the sequence Al(6.4) > Cu(6.2) > Fe(6.0) > Zn(5.7) > Pb(5.6) > Cr(5.5) > Cd(5.1) > Mn(4.7). Therefore, marine copepods in the waters of northern Taiwan can accumulate trace metals over background concentrations and act as contamination indicators.     

Predicting Sea Surface Salinity Using an Improved Genetic Algorithm Combining Operation Tree Method

The purpose of this study is to demonstrate the use of an improved genetic algorithm combining operation tree method (IGAOT) and apply it to monitor the salinity of the Taiwan Strait by using remote-sensing data. The genetic algorithm combining operation tree (GAOT) is a data mining method used to automatically discover relationships among nonlinear systems. Based on genetic algorithms (GAs), the relationships between input and output can be expressed as parse trees. The GAOT method typically has the disadvantages of premature convergence, which means it cannot produce satisfying solutions and performs satisfactorily when applied to only low-dimensional problems. Therefore, the GAOT method is enhanced using an automatic incremental procedure to improve the search ability of the method and avoid trapping in a local optimum. In this case study, an IGAOT is used to determine the relationship between the in situ data on the salinity of the Taiwan Strait and the data on the spectral parameters, seven wavebands, of a Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. The results indicate that the IGAOT model performs more favorably than do the GAOT and linear regression (LR₁ and LR₂) models, exhibits higher correlation coefficients, and involves fewer estimating errors. The results of this study indicate that the proposed technique is useful for estimating the Taiwan Strait salinity.     

西影井水位特异动态的成因初析

西影井水位自1974－1978年三次井溢后，1989年9月以来又出现了一次大起大落的特异变化。很多学者都十分关注这罕见的现象，并认为是区域应力增强所致的。经过多年的调查与分析，笔者认为是大量抽取地下水引起的地面沉降与地裂并由此导致地下岩层的层间错动与井管变形破坏的结果。     

矿产地质勘查项目研判ABC

矿产地质勘查是高投入、高风险、高回报的投资活动,如何引进经济、优质的矿权是矿产地质勘查的先决条件和企业赖以发展的基础.对项目高效、快速、准确的研判是项目研发最重要的一步.文章把矿权分为3种矿权来源和4种项目类型.通过3种矿权来源点评和4种项目类型的剖析,探讨了项目研判的内容、研判的准则、研判的工作程序,把研判过程分为初判、普判、详判3个阶段.在此基础上研究了研判各阶段时间安排与具体工作内容,普判阶段编图方法、取样间距和取样方法,普判阶段工作量及费用概略估计,以及详判阶段的几个关键问题.     

无人机影像地图制作实验研究

首先介绍了无人机低空航测系统的组成,然后针对无人机影像分辨率高、像幅小的特点提出了3种影像地图的制作方法,即单张影像几何纠正法、拼接后纠正法及空中三角测量法.文中着重对3种方法的关键性技术问题进行了论述,并从成图效果、精度及效率等方面对每种制图方法进行了综合分析和实验验证.结果表明,拼接后纠正法效率高,空中三角测量法精...     

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image

Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

海岸侵蚀灾害管理中的几项基础工作

讨论了作为海岸带管理工作中一项重要管理内容──海岸侵蚀灾害管理中的几项基础性工作,主要讨论了灾害评估的意义;海岸侵蚀灾害管理基础图件和专业图件的名称及主要内容;建立海岸侵蚀灾害信息系统的意义和主要内容;关于海岸侵蚀灾害管理法的意义和急迫性;提出了建立禁建区和禁采区的意义和紧迫性,并指出在海岸带开发建设中要统一管理,全面规划,注意到海岸带生态环境的特殊性。总之在海岸侵蚀灾害管理中,首先要做好基础工作,在此基础上做好法制建设和依法管理。     

Unconventional geometric phase gate and multiqubit entanglement for hot ions with a frequency-modulated field

We present an alternative scheme for implementing the unconventional geometric two-qubit phase gate and preparing multiqubit entanglement by using a frequency-modulated laser field to simultaneously illuminate all ions. Selecting the index of modulation yields selective mechanisms for coupling and decoupling between the internal and the external states of the ions. By the selective mechanisms, we obtain the unconventional geometric two-qubit phase gate, multiparticle Greenberger--Horne--Zeilinger states and highly entangled cluster states. Our scheme is insensitive to the thermal motion of the ions.     

Effects of degree of saturation on shallow landslides triggered by rainfall

The empirical rainfall threshold concept and the physical-based model are two commonly used approaches for the assessment of shallow landslides triggered by rainfall. To investigate in detail the rainfall-triggered shallow landslides, many physical-based models coupling the infinite slope stability analysis with the rainfall infiltration modeling in variably saturated soil were developed. However, in those physical-based shallow landslide models, the unit weight and the unsaturated shear strength were assumed constant rather than depending on the degree of saturation. In this study, the effects of the unit weight and the unsaturated shear strength as function of degree of saturation on rainfall-triggered shallow landslides are examined. Several designed scenarios and a real case scenario are used to conduct the examinations. The results show that not only the occurrence of shallow landslides but also the failure depth and the time to failure could be misassessed if the influences of degree of saturation on the unit weight and the unsaturated shear strength are neglected.