SoFISHticated policy – social perspectives on the fish conflict in the Northeast Atlantic

Ecosystem changes currently question the traditional allocation of fishing rights and quotas in the fishery of Northeast Atlantic mackerel and Norwegian spring-spawning herring in the Northeast Atlantic. Variability in the distribution of these highly migratory species escalated in a political conflict between member states of the European Union, Iceland, the Faroe Islands and Norway, which is a driving force for unsustainable fishery. The aim of this paper is to investigate this conflict by outlining the social understandings of diverse stakeholders by using the Q methodology. The method reduced the complexity of numerous opinions, detected four distinct perspectives and simultaneously categorised the participating stakeholders. Although the perspectives differ in various elements, the protection of economic interests seems to dominate over the quest for sustainability. The call of all stakeholders in this study to clarify the fishing rights in the Northeast Atlantic reveals a clear deficiency of the current international fishery management in handling abrupt ecological changes and the necessity to acknowledge this as a complex adaptive system.     

层式滤波隔震垫

根据震相图展示的地震波运动规律,利用滚动摩擦阻力只相当滑动摩擦阻力1／40～1／60的力学效应,将轴承钢球呈层状重叠组合成隔震垫,可获得重复滤波效果,能高效降低地震对房屋建筑的作用力,达到提高地震安全度,降低建筑成本、节约建筑资源的目的。     

不同过滤条件下黄色物质的吸收光谱演化特征分析

在实验室对青岛近岸现场水样进行处理、分析,获得了原始状态下水样和被过滤（0.22μm,0.45μm）黄色物质水样的吸收光谱数据,除进一步验证了黄色物质光谱分布模式外,还测得黄色物质光吸收随滤膜孔径的变化。30个现场样品在波长380nm处吸收系数统计平均值^—Ay（380）分析表明,当使用0.45μm过滤器对水样进行处理,所获得的^—Ay（380）值要比未经过处理的现场水样所测得的^—Ay（380）值高约2.1％;而用0.22μm过滤器对现场水样进行处理,所获得的^—Ay（380）值要比未经过处理的现场样品所测得的^—Ay（380）值高约12．6％,要比用0．45μm过滤器处理的现场样品所测得的^—Ay（380）值高约10.3％,这些数据的获得为现有已获黄色物质数据的“归一化”和统一评判以及未来黄色物质测量数据的准确性判断与比较提供了参考。     

The Marine Planning Framework for South Australia: A new ecosystem-based zoning policy for marine management

The Marine Planning Framework for South Australia is a new large-scale, ecosystem-based zoning policy for management of development and use in the marine environment. Utilising the Geographic Information System (GIS), the model establishes four ecologically rated zones, derived from known ecological criteria. A series of goals, objectives and strategies represent the desired outcomes for each of the ecologically rated zones. A Performance Assessment System (PAS) will subsequently evaluate the success of the marine plans. Implementation will be supported by a collaborative whole-of-government approach. Marine plans will facilitate the delivery of long-term protection to the marine environment as a whole ecosystem, whilst enabling a broad range of activities to occur in a sustainable manner.     

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.     

Nonlinear adaptive control of an underwater towed vehicle

This paper addresses the problem of simultaneous depth tracking and attitude control of an underwater towed vehicle. The system proposed uses a two-stage towing arrangement that includes a long primary cable, a gravitic depressor, and a secondary cable. The towfish motion induced by wave driven disturbances in both the vertical and horizontal planes is described using an empirical model of the depressor motion and a spring-damper model of the secondary cable. A nonlinear, Lyapunov-based, adaptive output feedback control law is designed and shown to regulate pitch, yaw, and depth tracking errors to zero. The controller is designed to operate in the presence of plant parameter uncertainty. When subjected to bounded external disturbances, the tracking errors converge to a neighbourhood of the origin that can be made arbitrarily small. In the implementation proposed, a nonlinear observer is used to estimate the linear velocities used by the controller thus dispensing with the need for costly sensor suites. The results obtained with computer simulations show that the controlled system exhibits good performance about different operating conditions when subjected to sea-wave driven disturbances and in the presence of sensor noise. The system holds promise for application in oceanographic missions that require depth tracking or bottom-following combined with precise vehicle attitude control.     

Multi-variable adaptive back-stepping control of submersibles using SDU decomposition

A multi-variable adaptive autopilot for the dive-plane control of submarines is designed. The vehicle is equipped with bow and stern hydroplanes for maneuvering. It is assumed that the system parameters are not known, and the disturbance force is acting on the vehicle. Based on a back-stepping design approach, an adaptive control law is derived for the trajectory control of the depth and the pitch angle. To prevent singularity in the control law, the SDU decomposition of the high-frequency gain matrix is used for the design. In the closed-loop system, asymptotic tracking of the reference depth and pitch angle trajectories is accomplished. Simulation results are presented which show that the submarine performs dive-plane maneuvers in spite of the uncertainties in the system parameters and disturbance forces.     

Alternative Fuzzy Cluster segmentation of remote sensing images based on Adaptive Genetic Algorithm

Remote sensing image segmentation is the basis of image understanding and analysis. However, the precision and the speed of segmentation can not meet the need of image analysis, due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm (AGA) and Alternative Fuzzy C-Means (AFCM). Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased, and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means (FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.     

Estimating sagebrush cover in semi-arid environments using Landsat Thematic Mapper data

Sagebrush ecosystems of the western US provide important habitat for several ungulate and vertebrate species. As a consequence of energy development, these ecosystems in Wyoming have been subjected to a variety of anthropogenic disturbances. Land managers require methodology that will allow them to consistently catalog sagebrush ecosystems and evaluate potential impact of proposed anthropogenic activities. This study addresses the utility of remotely sensed and ancillary geospatial data to estimate sagebrush cover using ordinal logistic regression. We demonstrate statistically significant prediction of ordinal sagebrush cover categories using spectral (χ² = 113; p < 0.0001) and transformed indices (χ² = 117; p < 0.0001). Both Landsat spectral bands (c-value = 0.88) and transformed indices (c-value = 0.89) can distinguish sites with closed, moderate and open cover sagebrush cover categories from no cover. The techniques described in this study can be used for estimating categories of sagebrush cover in arid ecosystems.     

基于AR（p）的卡尔曼滤波在GPS变形监测中的应用

由于AR（p）模型结构比较简单且计算比较方便,在变形分析中,目前常采用此模型建立变形模型。然而单纯的AR模型把模型参数作为定值,变形数据拟合误差及变形预测误差可能会比较大。介绍了将卡尔曼滤波引入AR模型,利用观测数据建立AR模型,即建立观测方程;以AR模型的参数为状态向量建立状态方程。从而形成动态系统的卡尔曼滤波函数模型,动态计算出AR模型的参数以便预测。此方法快速、实时,且占有较少内存,充分利用了AR模型和卡尔曼滤波二者的优点。