Modelling process knowledge in architectural design: A case-based approach

The paper presents on-going research aimed at the understanding and support of process knowledge in architectural design, from early and not sufficiently defined, to satisfactorily-defined phases. Today, technical, planning, management and environmental issues have created a scenario of such complexity that traditionally efficient control tools (e.g. technical manuals) are inadequate and there is a demand for new, integrated instruments to handle the decision process underlying architectural design. We assume design as a recursive and incrementally specified intentional planning activity, involving goals, constraints and their relationships. The essence of architectural design is thus encapsulated in the continual recursive transformation of the initial model, in order to map the desired state onto the enacted one. On the basis of this concept of design we describe the model of an environment aimed at progressively representing the enlarging space of acquired knowledge, and at supporting the designer's central role in the management of complexity

Advancing functional connectivity research from association to causation

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures

Guard-Function-Constraint-Based Refinement Method to Generate Dynamic Behaviors of Workflow Net with Table

In order to model complex workflow systems with databases, and detect their data-flow errors such as data inconsistency, we defined Workflow Net with Table model (WFT-net) in our previous work. We used a Petri net to describe control flows and data flows of a workflow system, and labeled some abstract table operation statements on transitions so as to simulate database operations. Meanwhile, we proposed a data refinement method to construct the state reachability graph of WFT-nets, and used it to verify some properties. However, this data refinement method has a defect, i.e., it does not consider the constraint relation between guard functions, and its state reachability graph possibly has some pseudo states. In order to overcome these problems, we propose a new data refinement method that considers some constraint relations, which can guarantee the correctness of our state reachability graph. What is more, we develop the related algorithms and tool. We also illustrate the usefulness and effectiveness of our method through some examples