How do stream processes affect hazard exposure on alluvial fans? Insights from an experimental study

Alluvial fans are among the most privileged settlement areas in many mountain regions. These landforms are particularly dynamic being episodically affected by distributary processes generated by extreme flood events. Addressing risk assessment entails determining hazard exposure and unravelling how it might be related to process loading and to process dynamics once the flow becomes unconfined on the surface of alluvial fans. By following a ‘similarity of process concept’, rather than by attempting to scale a real-world prototype, we performed a set of 72 experimental runs on an alluvial fan model. Thereby, we considered two model layouts, one without a guiding channel and featuring a convex shape and the other one with a guiding channel, a bridge, and inclined but planar overland flow areas as to mirror an anthropic environment. Process magnitude and intensity parameters were systematically varied, and the associated biphasic distributary processes video recorded. For each experiment, the exposure was detected by mapping the exposed area in a GIS, thereby discerning between areas exposed to biphasic flows and the associated depositional phenomena or to the liquid flow phase only. Our results reveal that total event volume, sediment availability and stream power in the feeding channel, as well as depositional effects, avulsion, and channelization on the alluvial fan concur to determine the overall exposure. Stream process loading alone, even when rigorously defined in terms of its characterizing parameters, is not sufficient to exhaustively determine exposure. Hence, further developing reliable biphasic simulation models for hazard assessment on settled alluvial fans is pivotal.

An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

基于模糊群决策的绿色供应商选择和订单分配方法

随着市场竞争愈发激烈、全球环境日益恶化以及公众环保意识的提高,绿色采购问题受到了广泛关注。现有研究绝大多数聚焦于考虑绿色准则的供应商选择问题,以实现可持续经营。然而,在综合考虑时间、价格和成本等因素的前提下,从适应性强的供应商处获得适当的产品,对于企业来说更为可行和经济,即供应商选择和订单分配。为了解决上述关键问题,提出了一种模糊环境下的多准则群体决策方法。提出了一种基于理想解相似性偏好排序和层次分析法(technique for order of preference by similarity to ideal solution and analytic hierarchy process,TOPSIS-AHP)的模糊排序方法,该方法可根据企业战略灵活调整传统准则和绿色准则的权重。在双目标优化模型中引入供应商排名进行订货,在实现采购绩效最大化的同时,最大限度地降低采购成本。结果表明:所提方法能够有效评价供应商绩效,并优化候选供应商之间的订单分配。

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

REVISITING THE SIMILAR PROCESS TO ENGINEER THE CONTEMPORARY SYSTEMS

This paper addresses the present-day context of Systems Engineering, revisiting and setting up an updated framework for the SIMILAR process in order to use it to engineer the contemporary systems. The contemporary world is crowded of large interdisciplinary complex systems made of other systems, personnel, hardware, software, information, processes, and facilities. An integrated holistic approach is crucial to develop these systems and take proper account of their multifaceted nature and numerous interrelationships. As the system＇s complexity and extent grow, the number of parties involved （stakeholders and shareholders） usually also raises, bringing to the interaction a considerable amount of points of view, skills, responsibilities, and interests. The Systems Engineering approach aims to tackle the complex and interdisciplinary whole of those socio-technical systems, providing the means to enable their successful realization. Its exploitation in our modern world is assuming an increasing relevance noticeable by emergent standards, academic papers, international conferences, and post-graduate programmes in the field. This work aims to provide ＂the picture＂ of modern Systems Engineering, and to update the context of the SIMILAR process model in order to use this renewed framework to engineer the challenging contemporary systems. The emerging trends in the field are also pointed-out with particular reference to the Model-Based Systems Engineering approach.