Livelihood and seed mortality related vulnerability of carp seed traders of Bangladesh

Carp seed traders in Bangladesh are considered as one of the poorest, most vulnerable and least studied sub-population. Millions of fingerlings are transported annually to various parts of the country using bus tops and/or truck-beds in open and hand agitated method by the traders and as a result they encounter mass seed mortality. They are the key players in augmenting aquaculture activities by distributing fish fry and fingerlings to the fish farmers. Assessment of the seed sources and the livelihood of the traders are important to identify their point of entry to the aquaculture sector and further improvement. Status of household asset, income profiles, and risks involved were examined using DFID's sustainable livelihood approach (SLA) framework. Tools used to collect data included group discussions (GD), semi-structured and structured household (HH) survey. Samples (n=637) were randomly drawn purposefully from 10% of the carp seed traders within 11 districts. Sampled HH were classified into six land classes.

IREE: Automated Vision Tracking of Project Related Entities

The purpose of this supplemental project was to collect invaluable data from the large-scale construction sites of Egnatia Odos motorway needed to validate a novel automated vision-tracking method created under the parent grant. For this purpose, one US graduate and three US undergraduate students traveled to Greece for 4 months and worked together with 2 Greek graduate students of the local faculty collaborator. This team of students monitored project activities and scheduled data collection trips on a daily basis, setup a mobile video data collection lab on the back of a truck, and drove to various sites every day to collect hundreds of hours of video from multiple cameras on a large variety of activities ranging from soil excavation to bridge construction. The US students were underrepresented students from minority groups who had never visited a foreign country. As a result, this trip was a major life experience to them. They learned how to live in a non-English speaking country, communicate with Greek students, workers and engineers. They lead a project in a very unfamiliar environment, troubleshoot myriad problems that hampered their progress daily and, above all, how to collaborate effectively and efficiently with other cultures. They returned to the US more mature, with improved leadership and problem-solving skills and a wider perspective of their profession.

Implementation of trailer steering control on a multi-unit vehicle at high speeds

A high-speed path-following controller for long combination vehicles (LCVs) was designed and implemented on a test vehicle consisting of a rigid truck towing a dolly and a semitrailer. The vehicle was driven through a 3.5 m wide lane change maneuver at 80 km/h. The axles of the dolly and trailer were steered actively by electrically-controlled hydraulic actuators. Substantial performance benefits were recorded compared with the unsteered vehicle. For the best controller weightings, performance improvements relative to unsteered case were: lateral tracking error 75% reduction, rearward amplification (RA) of lateral acceleration 18% reduction, and RA of yaw rate 37% reduction. This represents a substantial improvement in stability margins. The system was found to work well in conjunction with the braking-based stability control system of the towing vehicle with no negative interaction effects being observed. In all cases, the stability control system and the steering system improved the yaw stability of the combination. © 2014 by ASME.

可重构装配系统开放式组织结构的研究

为了克服传统装配线的不足,在基于功能对象的产品族建模基础上,提出了主干线和装配模块相结合的可重构装配系统开放式组织结构。这种分形相似的组织结构具有能力柔性、鲁棒性、功能扩展能力等特点,并能通过模块的局部调整快速响应市场的需求。利用网络法,针对鲁棒性和增容性问题,提出了相应的重构优化算法。

泵车布料机构运动学分析及轨迹规划控制建模与仿真

给出了以混凝土泵车各臂油缸长度为参变量的布料机构浇筑过程的轨迹规划计算方法。在解决布料机构运动学分析的逆问题时 ,采用了基于多峰值并行搜索的遗传算法来求解最优控制优化目标函数 ,并对施工过程进行了仿真

混凝土泵车浇筑过程机器人化探讨

对混凝土泵车布料机构的运动姿态调整采取了最优控制 ,解决了泵车机器人化的运动分析的臂解问题 ,论述了泵车动态分析中应解决和注意的问题 ,给出了泵车控制自动化的程序流程图和控制系统图 ,从而为提高泵车施工过程的自动化和泵车的机器人化提出了新的思路

遥控移动式作业机器人实时多任务管理系统

本文介绍遥控移动式作业机器人实时多任务管理系统．该系统是在多总线和位总线相结合的计算机系统上，利用ｉＲＭＸⅡ实时多任务操作系统设计编制的实时多任务管理系统．该系统为遥控机器人提供了数据通讯，人机接口，任务作业等重要管理工作，使机器人能顺利地完成机械手操作和车体运动等重要控制功能。

Fuzzy Interpolation and Extrapolation: A Practical Approach

Z. Huang and Q. Shen. Fuzzy interpolative and extrapolative reasoning: a practical approach. IEEE Transactions on Fuzzy Systems, 16(1):13-28, 2008.

Angiosarcoma, porphyria cutanea tarda, and probable chloracne in a worker exposed to waste oil contaminated with 2,3,7,8-tetrachlorodibenzo-p-dioxin

A worker developed angiosarcoma, porphyria cutanea tarda, and skin lesions characteristic of mild chloracne. About 10 years earlier he had been employed at a truck terminal in Saint Louis, Missouri, at a time when it was sprayed with waste oil contaminated with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). The occurrence of these three rare conditions in a single exposed worker supports the aetiological relation between environmental exposure to TCDD and the subsequent development of soft tissue sarcoma and porphyria cutanea tarda.

Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchial knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples.

Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.

Unloading or not unloading? Sheep welfare implication of rest stop at control post after a 29h transport

In Europe, maximum journey time for transported sheep is set at 29. h (EC Regulation 1/2005), after which animals must be unloaded, fed and watered in control posts stopping for 24. h, as all other species, before continuing their journey. The industry considers these resting times too general, not taking into account the peculiar differences between species or age classes. Also, loading and unloading have been reported to be detrimental for the animals. Therefore, the industry pushes to reduce the times at control post and avoid unloading the animals from the truck. Since there is little information concerning the effect of resting in a stationary truck after long journeys, the present study aims to evaluate the effect of an 8. h rest stop on the truck for transported ewes compared to being unloaded for resting in a control post for the same amount of time, considering physiological and behavioural measures. Two groups of ewes were transported for 29. h, after which one was unloaded and housed in a pen (P) at the control post while the other was left inside the truck (T). After 8. h stop, a further 6. h travel was headed to the farm of origin. A third group (C) stayed at the farm as control. During the stop, standing, resting, moving and eating behaviour of all groups was recorded. Blood parameters, salivary and faecal cortisol were assessed at different stages. The behaviour of P animals during the resting period was more similar to C than to T ones, where feeding and lying behaviours were restricted by the limited space allowance on the truck. After returning to the farm of origin, both T and P animals showed different parameters' levels as compared to C. P ewes showed a mean loss weight of 2. kg not recorded in group T and showed higher signs of muscular damage compared to C group. It was concluded that, with so short resting times as 8. h, there is no clear advantages in terms of animal welfare for avoiding the unloading and loading of the animals in the control post after long journeys.

Sustainable non-automotive vehicles: The simulation challenges

Simulation is a well-established and effective approach to the development of fuel-efficient and low-emissions vehicles in both on-highway and off-highway applications.

The simulation of on-highway automotive vehicles is widely reported in literature, whereas research relating to non-automotive and off-highway vehicles is relatively sparse. This review paper focuses on the challenges of simulating such vehicles and discusses the differences in the approach to drive cycle testing and experimental validation of vehicle simulations. In particular, an inner-city diesel-electric hybrid bus and an ICE (Internal Combustion Engine) powered forklift truck will be used as case studies.

Computer prediction of fuel consumption and emissions of automotive vehicles on standardised drive cycles is well-established and commercial software packages such as AVL CRUISE have been specifically developed for this purpose. The vehicles considered in this review paper present new challenges from both the simulation and drive-cycle testing perspectives. For example, in the case of the forklift truck, the drive cycles involve reversing elements, variable mass, lifting operations, and do not specify a precise velocity-time profile. In particular, the difficulties associated with the prediction of productivity, i.e. the maximum rate of completing a series of defined operations, are discussed. In the case of the hybrid bus, the standardised drive cycles are unrepresentative of real-life use and alternative approaches are required in the development of efficient and low-emission vehicles.

Two simulation approaches are reviewed: the adaptation of a standard automotive vehicle simulation package, and the development of bespoke models using packages such as MATLAB/Simulink.

An heuristic approach for optimal location of gas supply units in transportation system

The best places to locate the Gas Supply Units (GSUs) on a natural gas systems and their optimal allocation to loads are the key factors to organize an efficient upstream gas infrastructure. The number of GSUs and their optimal location in a gas network is a decision problem that can be formulated as a linear programming problem. Our emphasis is on the formulation and use of a suitable location model, reflecting real-world operations and constraints of a natural gas system. This paper presents a heuristic model, based on lagrangean approach, developed for finding the optimal GSUs location on a natural gas network, minimizing expenses and maximizing throughput and security of supply.The location model is applied to the Iberian high pressure natural gas network, a system modelised with 65 demand nodes. These nodes are linked by physical and virtual pipelines – road trucks with gas in liquefied form. The location model result shows the best places to locate, with the optimal demand allocation and the most economical gas transport mode: by pipeline or by road truck.