Marine Protected Area Costs as “Beneficial” Fisheries Subsidies: A Global Evaluation

Marine protected areas (MPAs) are now generally accepted as important tools in the protection of coastal biodiversity. It is also likely that they play a positive role in enhancing fisheries. Yet currently, less than 1% of the global oceans are protected, although international agreements have targets ranging from 10–30% coverage. Despite its minuscule size, we consider the current MPA “network” to be beneficial to fisheries, and its running or maintenance cost, therefore, to be a positive contribution to the sustainability of fisheries, or a “beneficial” subsidy (“harmful” subsidies enhance fishing capacity and effort). A method was derived from data in Balmford et al. (PNAS, 101: 9694–9697) to estimate the annual cost of maintaining MPAs as a function of their size, and of the degree of development of the country in question. We provide national costs of the 53 countries that jointly contribute 95% of global fisheries catch and, assuming that this type of subsidy, in a given country, cannot exceed 15% of the ex-vessel value of its fisheries catches, estimated a global MPA subsidy to fisheries of 870 million US$. Given that total subsidies to fisheries currently range from 30–34 billion US$ annually (without MPA costs), this amounts to only 2.5–2.8% of total subsidies to fisheries being devoted explicitly to the maintenance of the biodiversity that sustains them.     

A time‐staged multi‐objective network‐design model

A multi‐objective, time‐staged network‐design problem is formulated. Through transformation, the problem is decomposed into a set of single‐period, single‐objective problems. Lexicographic ordering is instrumental in effecting this transformation; it also allows a backward‐recursion algorithm to be applied using strong pruning criteria. Furthermore, monotonicity properties enable us to solve the problem using the familiar tree‐search algorithms. The solution method has several desirable properties — as shown by an example and a case study of Tripoli Province, Libya. First, the algorithm ensures continuity of project implementation over the multi time‐periods and provides optimality in later computational stages irrespective of the decision at an interim stage. Second, the algorithm tends to provide accessibility to unconnected regions in the study area at low user‐cost without employing weights to the two objective functions of accessibility and user‐cost efficiency. Such a property is deemed advantageous for suggesting transportation investments based purely on purchasing the greatest benefit for each dollar, with political neutrality strictly maintained.     

榆佳高速公路工程综合管理系统在过程管理中的实践与应用

对榆佳高速公路综合管理系统的功能以及在实践中的应用做了详细阐述,信息化管理有助于提高项目管理效率、提升形象、增进沟通、降低成本,从而提高企业核心竞争力.信息化管理推进了项目建设的规范化运作,有助于精品工程的实现.     

基于Agent控制器的单路口信号灯学习控制方法研究

将强化学习中的Q学习算法与Agent技术相结合,构成Agent控制器对单路口的信号灯进行控制.介绍了将经验知识与Q学习算法相结合实现的Agent学习机制,提出了一种适合交通环境的强化函数,以解决单路口的动态实时控制.经仿真软件Paramics对实际路口进行仿真实验,验证了该方法具有较好的控制效果.     

装有EPAS系统的汽车转向灵敏度仿真分析

描述了电动助力转向器的构造及其工作原理,对EPAS系统模型进行了简化,建立了EPAS机械动力学方程,推导出了电流比例控制策略下汽车转向灵敏度的传递函数方程,并在Matlab中进行了仿真计算,定性地阐述了电动机转动惯量等参数在汽车转向盘角阶跃输入下对系统响应特性的影响。     

一种新概率密度函数的提炼及在公路中的应用

利用汕梅高速公路混凝土路面标的施工试验数据,提炼出了一种新的非正态概率密度函数,并对施工结果进行了检验,得出了可靠的结果。     

基于Q学习的Agent在单路口交通控制中的应用

将Agent技术与Q学习算法相结合,应用到城市交通控制领域中,对单交叉口的交通流进行了控制研究,介绍了路口Agent的结构模型以及基于Q学习算法的学习机制的实现,提出了一种适用于交通控制的奖惩函数。即当红灯相位的饱和度大于绿灯相位的饱和度时,红灯相位的相对警界度在奖惩函数中占主导地位,此时大部分情况下会对Agent进行惩罚;在以后的决策过程中面对类似的交通状态Agent所选择的控制行为更倾向于将通行权切换给下一个相位,反之,Agent所选择的行为倾向于保持当前相位的通行权到下一决策时刻。并通过微观交通仿真软件Paramics对控制算法进行仿真研究,仿真结果表明该方法的控制效果优于定时控制,同时验证了奖惩函数的有效性。     

基于多层环路形态的上海中环路交通功能分析

借鉴多层环路系统已有研究成果．归纳其形成机理。结合上海市路网在中环浦西段建成前后流量对比．分析中环对于内环和外环的影响。并进一步依据环路功能划分．阐述上海市中环路所承担的主要功能。     

城市高快速路先行建设智能交通系统之我见

分析了城市高快速路现状普遍存在的突出问题及城市高快速路建设智能交通系统的意义，从实际需求和将来发展预留空间出发，结合工作实践，提出了城市高快速路智能交通系统的系统定位和功能架构。     

存在车辆干扰的车道线识别

为避免道路上行驶的其它车辆对车道线识别的干扰,提出了一种结合车辆识别的车道线识别方法。融合雷达数据,车辆识别模块首先在图像中识别出车辆占据的区域;对于每一个车道线识别模块挑出的车道线候选点进行判断,去除处于车辆区域的车道线点;如果有效车道线点数目不足,则利用卡尔曼滤波的跟踪结果,确定符合最小风险函数的车道线位置。经过多种工况下的试验验证,该方法能够稳定地对车道线进行识别,准确地提取车道线参数,并且算法对车辆干扰有良好的抵抗能力。