Understanding water pollution management: Evidence and insights from incorporating cultural theory in social network analysis

Social network analysis gives a rigid assessment of network structure while cultural theory helps explain management outcomes in more detail. The combination of these methods provides significant and alternative insights about the complexity of water pollution management. Applied in the case study of the Calumpang Watershed in the Philippines, two types of relations were evaluated, namely, resource sharing and cooperative activities. Factors affecting cooperation and the creation of bridges were assessed in social network analysis, while cultural theory was used to reveal the underlying views of key actors on the nature of the river and its management. Evidence showed that cooperation was significantly influenced by resource sharing regardless of institutional affiliation, while bridges were created by becoming the pools of information and resources in the network. Using cultural theory, this study found out that local government officials dominantly have a mixture of hierarchist and egalitarian views characterizing belief in both rule-bound and group-motivated actions to solve the water pollution problem. However, their egalitarian views on management did not manifest in the current network. The study concludes that collaborative partnerships for water pollution management can be facilitated by promoting resource flow, utilizing influential bridges in the network, and understanding the unconventional values of government actors. Combining social network analysis and cultural theory is recommended to generate a more critical assessment of the institutional complexity of pollution and other water issues in watersheds.     

传感器网络环境中基于采样数据分布式滤波的研究综述

近年来,由大量具有感知、计算和通信能力的微型传感器组成的传感器网络越来越广泛地应用在生产及生活的方方面面.另一方面,随着微电子及数字信号技术的发展,利用采样数据的离散化的数字控制器或滤波器被普遍使用.为了反映这个新兴领域的最新进展,本文对传感器网络环境中基于采样数据分布式滤波的研究展开了综述.首先,综述了传感器网络中几种分布式滤波方法的研究进展.然后,针对不同的采样方式,详细总结了采样数据系统的控制与滤波问题的研究工作.随后,对目前已有的传感器网络基于采样数据的分布式滤波的研究结果进行了介绍.最后,对传感器网络环境中基于采样数据的分布式滤波方面未来可能的研究课题进行了展望.     

A simple method for calibrating optical backscatter sensors in high concentrations of non-cohesive sediments

A new method is introduced for calibrating optical backscatter sensors for suspended quartz sand concentrations of up to 200 kg m⁻³. Due to the high settling velocity of quartz sand in water, considerable difficulties have arisen in the past to maintain a spatially and temporally homogeneous suspension suitable for calibration. Traditional methods are clumsy and prone to errors. Here, the sediment is calibrated in glycerol, a clear fluid with a higher viscosity than water. The settling velocity is reduced by three orders of magnitude. An empirical relationship is obtained which is used to correct for any optical differences in response of the sensors in the two fluids. Any extra errors introduced by calibrating with a different fluid from that found in the field are outweighed by the simplicity and reliability of this method.     

Artificial neural network to translate offshore satellite wave data to coastal locations

Owing to the spatial averaging involved in satellite sensing, use of observations so collected is often restricted to offshore regions. This paper discusses a technique to obtain significant wave heights at a specified coastal site from their values gathered by a satellite at deeper offshore locations. The technique is based on the approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) type. The satellite-sensed data of significant wave height; average wave period and the wind speed were given as input to the network in order to obtain significant wave heights at a coastal site situated along the west coast of India. Qualitative as well as quantitative comparison of the network output with target observations showed usefulness of the selected networks in such an application vis-à-vis simpler techniques like statistical regression. The basic FFBP network predicted the higher waves more correctly although such a network was less attractive from the point of overall accuracy. Unlike satellite observations collection of buoy data is costly and hence, it is generally resorted to fewer locations and for a smaller period of time. As shown in this study the network can be trained with samples of buoy data and can be further used for routine wave forecasting at coastal locations based on more permanent flow of satellite observations.     

Dynamic model of manoeuvrability using recursive neural networks

This paper presents a Recursive Neural Network (RNN) manoeuvring simulation model for surface ships. Inputs to the simulation are the orders of rudder angle and ship’s speed and also the recursive outputs velocities of sway and yaw. This model is used to test the capabilities of artificial neural networks in manoeuvring simulation of ships. Two manoeuvres are simulated: tactical circles and zigzags. The results between both simulations are compared in order to analyse the accuracy of the RNN. The simulations are performed for the Mariner hull. The data generated to train the network are obtained from a manoeuvrability model performing the simulation of different manoeuvring tests. The RNN proved to be a robust and accurate tool for manoeuvring simulation.     

多重贸易网络的空间演化特征及其影响因素——基于货物、服务和增加值贸易的比较

基于复杂网络分析与贸易增加值核算方法,借助ArcGIS、Ucinet和MATLAB等工具,对全球64个主要经济体货物、服务和增加值多重贸易网络的空间演化及其影响因素进行综合对比分析,结果表明：① 全球货物、服务和增加值贸易网络的紧密性和通达效率均呈现稳步提升趋势,服务贸易的网络化趋势更为显著;金融危机对网络的“互惠性”“异配性”产生较大影响;② 空间格局上均具有显著的“核心?边缘”等级性,区域层面上形成了亚太和欧盟地区两大“派系”,美、中、日、德为两大“派系”联系的枢纽节点;③ Top1网络表现出不同的演化特征,其中货物、服务和增加值贸易网络分别呈现“星型”“雪花型”和“星型+链式”空间组织结构。3类网络演化过程中中国的崛起态势令人瞩目;④ QAP（二次指派程序）分析显示,传统的GDP、地理距离、共同语言等因素对于3类网络的形成具有相似的较强解释力,要素禀赋差异和贸易制度是影响它们之间差异性的主要因素,但其显著性水平不高且趋于弱化。     

A metaheuristic approach for efficient and effective sketch-to-metric map alignment

Sketching as a natural mode for human communication and creative processes presents opportunities for improving human–computer interaction in geospatial information systems. However, to use a sketch map as user input, it must be localized within the underlying spatial data set of the information system, the base metric map. This can be achieved by a matching process called qualitative map alignment in which qualitative spatial representations of the two input maps are used to establish correspondences between each sketched object and one or more objects in the metric map. The challenge is that, to the best of our knowledge, no method for matching qualitative spatial representations suggested so far is applicable in realistic scenarios due to excessively long runtimes, incorrect algorithm design or the inability to use more than one spatial aspect at a time. We address these challenges with a metaheuristic algorithm which uses novel data structures to match qualitative spatial representations of a pair of maps. We present the design, data structures and performance evaluation of the algorithm using real-world sketch and metric maps as well as on synthetic data. Our algorithm is novel in two main aspects. Firstly, it employs a novel system of matrices known as local compatibility matrices, which facilitate the computation of estimates for the future size of a partial alignment and allow several types of constraints to be used at the same time. Secondly, the heuristic it computes has a higher accuracy than the state-of-the-art heuristic for this task, yet requires less computation. Our algorithm is also a general method for matching labelled graphs, a special case of which is the one involving complete graphs whose edges are labelled with spatial relations. The results of our evaluation demonstrate practical runtime performance and high solution quality.     

Prediction of uniaxial compressive strength of sandstones using petrography-based models

The uniaxial compressive strength of intact rock is the main parameter used in almost all engineering projects. The uniaxial compressive strength test requires high quality core samples of regular geometry. The standard cores cannot always be extracted from weak, highly fractured, thinly bedded, foliated and/or block-in-matrix rocks. For this reason, the simple prediction models become attractive for engineering geologists. Although, the sandstone is one of the most abundant rock type, a general prediction model for the uniaxial compressive strength of sandstones does not exist in the literature. The main purposes of the study are to investigate the relationships between strength and petrographical properties of sandstones, to construct a database as large as possible, to perform a logical parameter selection routine, to discuss the key petrographical parameters governing the uniaxial compressive strength of sandstones and to develop a general prediction model for the uniaxial compressive strength of sandstones. During the analyses, a total of 138 cases including uniaxial compressive strength and petrographic properties were employed. Independent variables for the multiple prediction model were selected as quartz content, packing density and concavo–convex type grain contact. Using these independent variables, two different prediction models such as multiple regression and ANN were developed. Also, a routine for the selection of the best prediction model was proposed in the study. The constructed models were checked by using various prediction performance indices. Consequently, it is possible to say that the constructed models can be used for practical purposes.     

An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps

The main purpose of this study is to highlight the conceptual differences of produced susceptibility models by applying different sampling strategies: from all landslide area with depletion and accumulation zones and from a zone which almost represents pre-failure conditions. Variations on accuracy and precision values of the models constructed considering different algorithms were also investigated. For this purpose, two most popular techniques, logistic regression analysis and back-propagation artificial neural networks were taken into account. The town Ispir and its close vicinity (Northeastern part of Turkey), suffered from landsliding for many years was selected as the application site of this study. As a result, it is revealed that the back-propagation artificial neural network algorithms overreact to the samplings in which the presence (1) data were taken from the landslide masses. When the generalization capacities of the models are taken into consideration, these reactions cause imprecise results, even though the area under curve (AUC) values are very high (0.915 < AUC < 0.949). On the other hand, the susceptibility maps, based on the samplings in which the presence (1) data were taken from a zone which almost represents pre-failure conditions constitute more realistic susceptibility evaluations. However, considering the spatial texture of the final susceptibility values, the maps produced using the outputs of the back-propagation artificial neural networks could be interpreted as highly optimistic, while of those generated using the resultant probabilities of the logistic regression equations might be evaluated as pessimistic. Consequently, it is evident that, there are still some needs for further investigations with more realistic validations and data to find out the appropriate accuracy and precision levels in such kind of landslide susceptibility studies.     

The social networks of British and Indian expatriate scientists in Boston

This paper argues that although the social networks of highly skilled migrants are important, British and Indian scientists in Boston tend not to use expatriate social networks. I demonstrate that wider differences in cultural norms to the indigenous population, homogeneity within a migrant group and length of time spent in the US are not the only factors that affect the participation of highly skilled migrants in expatriate social networks. Other factors such as the size of an expatriate group, the annual flow of migrants to a region as well as the propensity of expatriates to participate in indigenous social networks will also affect the extent to which they participate in these networks.