Optimising existing digital workflow for structural engineering organisations through the partnering of BIM and Lean processes

Building Information Modelling (BIM) is now seen as one of the leading transformative processes within the Architectural, Engineering and Construction (AEC) sector and has the potential to assist in streamlining the structural design process. However, its practical implementation can often add another layer to the existing workflow and can result, to its detriment, in the primary objective of optimising structural workflows being hindered. This can lead to structural organisations producing 3D models in tandem with traditional drawings, a lack of human intervention regarding software interoperability, and a reluctance to move away from conventional work methods. This paper will explore how a lean approach to BIM adoption can optimise the digital structural workflow, thereby enhancing BIM adoption. Although much research has been conducted on BIM as an enabler of Lean, there remains a gap regarding the synergies in how Lean tools can advance BIM adoption within the structural discipline. The closing of this knowledge gap will be advanced by comparing existing digital workflows within a structural organisation against a proposed integrated BIM workflow underpinned through Lean. The findings highlight that while BIM and Lean offer enhanced digital solutions to modernise structural design office workflows, the true capability of these tools will not be realised without a cultural change

Automated Game Design Learning

While general game playing is an active field of research, the learning of game design has tended to be either a secondary goal of such research or it has been solely the domain of humans. We propose a field of research, Automated Game Design Learning (AGDL), with the direct purpose of learning game designs directly through interaction with games in the mode that most people experience games: via play. We detail existing work that touches the edges of this field, describe current successful projects in AGDL and the theoretical foundations that enable them, point to promising applications enabled by AGDL, and discuss next steps for this exciting area of study. The key moves of AGDL are to use game programs as the ultimate source of truth about their own design, and to make these design properties available to other systems and avenues of inquiry.Comment: 8 pages, 2 figures. Accepted for CIG 201

Living with the Sea: Local Efforts Buffer Effects of Global Change

Living with the Sea examines the role of MMAs (Marine Managed Areas) in restoring and sustaining healthy oceans, particularly the importance of local management efforts. This document draws on MMA experiences worldwide by synthesizing results from over 25 natural science studies conducted over the past five years in 18 tropical countries in 48 MMAs. The analysis focuses on the role of MMAs in maintaining healthy oceans, showing that MMAs can be used to enhance fisheries outside their borders and safeguard threatened species. Conserving multiple habitats using MMAs can also protect diverse livelihoods and increase fisheries yields. Local protection of marine resources through the MMA process can provide strong local benefits to species, habitats, and people. Local protection buffers against global climate change impacts while maintaining the richness of marine life. Finally, MMAs benefit by using new scientific approaches and engaging citizen scientists

Communities in Networks

We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science. To help ease newcomers into the field, we provide a guide to available methodology and open problems, and discuss why scientists from diverse backgrounds are interested in these problems. As a running theme, we emphasize the connections of community detection to problems in statistical physics and computational optimization.Comment: survey/review article on community structure in networks; published version is available at http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

Logical analysis of data as a tool for the analysis of probabilistic discrete choice behavior

Probabilistic Discrete Choice Models (PDCM) have been extensively used to interpret the behavior of heterogeneous decision makers that face discrete alternatives. The classification approach of Logical Analysis of Data (LAD) uses discrete optimization to generate patterns, which are logic formulas characterizing the different classes. Patterns can be seen as rules explaining the phenomenon under analysis. In this work we discuss how LAD can be used as the first phase of the specification of PDCM. Since in this task the number of patterns generated may be extremely large, and many of them may be nearly equivalent, additional processing is necessary to obtain practically meaningful information. Hence, we propose computationally viable techniques to obtain small sets of patterns that constitute meaningful representations of the phenomenon and allow to discover significant associations between subsets of explanatory variables and the output. We consider the complex socio-economic problem of the analysis of the utilization of the Internet in Italy, using real data gathered by the Italian National Institute of Statistics