On the Evolution of CAE Research

Less than a decade ago it seemed that a new paradigm of engineering–called computer-aided engineering (CAE) – was emerging. This emergence was driven in part by the success of computer support for the tasks of engineering analysis and in part by a new understanding of how computational ideas largely rooted in artificial intelligence (AI) could perhaps improve the practice of engineering, especially in the area of design synthesis. However, while this “revolution” has failed to take root or flourish as a separate discipline, it has spawned research that is very different from traditional engineering research. To the extent that such CAE research is different in style and paradigm, it must also be evaluated according to different metrics. Some of the metrics that can be used are suggested, and some of the evaluation issues that remain as open questions are pointed out

Knowledge-Based Support for Management of Concurrent, Multidisciplinary Design

Artificial intelligence (AI) applications to design have tended to focus on modeling and automating aspects of single discipline design tasks. Relatively little attention has thus far been devoted to representing the kinds of design \u27metaknowledge\u27 needed to manage the important interface issues that arise in concurrent design, that is, multidisciplinary design decision-making. This paper provides a view of the process and management of concurrent design and evaluates the potential of two AI approaches—blackboard architectures and co-operative distributed problem-solving (CDPS)—to model and support the concurrent design of complex artifacts. A discussion of the process of multidisciplinary design highlights elements of both sequential and concurrent design decision-making. We identify several kinds of design metaknowledge used by expert managers to: partition the design task for efficient execution by specialists; set appropriate levels of design conservatism for key subsystem specifications; evaluate, limit and selectively communicate design changes across discipline boundaries; and control the sequence and timing of the key (highly constrained and constraining) design decisions for a given type of artifact. We explore the extent to which blackboard and CDPS architectures can provide valid models of and potential decision support for concurrent design by (1) representing design management metaknowledge, and (2) using it to enhance both horizontal (interdisciplinary) and vertical (project life cycle) integration among product design, manufacturing and operations specialists

Effect of Rosuvastatin on Acute Kidney Injury in Sepsis-Associated Acute Respiratory Distress Syndrome.

Background:Acute kidney injury (AKI) commonly occurs in patients with sepsis and acute respiratory distress syndrome (ARDS). Objective:To investigate whether statin treatment is protective against AKI in sepsis-associated ARDS. Design:Secondary analysis of data from Statins for Acutely Injured Lungs in Sepsis (SAILS), a randomized controlled trial that tested the impact of rosuvastatin therapy on mortality in patients with sepsis-associated ARDS. Setting:44 hospitals in the National Heart, Lung, and Blood Institute ARDS Clinical Trials Network. Patients:644 of 745 participants in SAILS who had available baseline serum creatinine data and who were not on chronic dialysis. Measurements:Our primary outcome was AKI defined using the Kidney Disease Improving Global Outcomes creatinine criteria. Randomization to rosuvastatin vs placebo was the primary predictor. Additional covariates include demographics, ARDS etiology, and severity of illness. Methods:We used multivariable logistic regression to analyze AKI outcomes in 511 individuals without AKI at randomization, and 93 with stage 1 AKI at randomization. Results:Among individuals without AKI at randomization, rosuvastatin treatment did not change the risk of AKI (adjusted odds ratio: 0.99, 95% confidence interval [CI]: 0.67-1.44). Among those with preexisting stage 1 AKI, rosuvastatin treatment was associated with an increased risk of worsening AKI (adjusted odds ratio: 3.06, 95% CI: 1.14-8.22). When serum creatinine was adjusted for cumulative fluid balance among those with preexisting stage 1 AKI, rosuvastatin was no longer associated worsening AKI (adjusted odds ratio: 1.85, 95% CI: 0.70-4.84). Limitations:Sample size, lack of urine output data, and prehospitalization baseline creatinine. Conclusion:Treatment with rosuvastatin in patients with sepsis-associated ARDS did not protect against de novo AKI or worsening of preexisting AKI

Direct health care costs of treating seasonal affective disorder: a comparison of light therapy and fluoxetine.

Objective. To compare the direct mental health care costs between individuals with Seasonal Affective Disorder randomized to either fluoxetine or light therapy. Methods. Data from the CANSAD study was used. CANSAD was an 8-week multicentre double-blind study that randomized participants to receive either light therapy plus placebo capsules or placebo light therapy plus fluoxetine. Participants were aged 18-65 who met criteria for major depressive episodes with a seasonal (winter) pattern. Mental health care service use was collected for each subject for 4 weeks prior to the start of treatment and for 4 weeks prior to the end of treatment. All direct mental health care services costs were analysed, including inpatient and outpatient services, investigations, and medications. Results. The difference in mental health costs was significantly higher after treatment for the light therapy group compared to the medication group-a difference of $111.25 (z = -3.77, P = 0.000). However, when the amortized cost of the light box was taken into the account, the groups were switched with the fluoxetine group incurring greater direct care costs-a difference of$75.41 (z = -2.635, P = 0.008). Conclusion. The results suggest that individuals treated with medication had significantly less mental health care cost after-treatment compared to those treated with light therapy

Modeling 21st century project teams: docking workflow and knowledge network computational models

This paper reports on an attempt to integrate and extend two established computational organizational models\u2014SimVision\uae and Blanche\u2014to examine the co-evolution of workflow and knowledge networks in 21st century project teams. Traditionally, workflow in project teams has been modeled as sets of sequential and/or parallel activities each assigned to a responsible participant, organized in a fixed structure. In the spirit of Jay Galbraith\u2019s (1973) information processing view of organizations, exceptions\u2014situations in which participants lack the required knowledge to complete a task\u2014are referred up the hierarchy for resolution. However, recent developments in digital technologies have created the possibility to design project teams that are more flexible, self-organizing structures, in which exceptions can be resolved much more flexibly through knowledge networks that extend beyond the project or even the company boundaries. In addition to seeking resolution to exceptions up the hierarchy, members of project teams may be motivated to retrieve the necessary expertise from other knowledgeable members in the project team. Further, they may also retrieve information from non-human agents, such as knowledge repositories or databases, available to the project team. Theories, such as Transactive Memory, Public Goods, Social Exchange and Proximity may guide their choice of retrieving information from a specific project team member or database. This paper reports on a \u201cdocked\u201d computational model that can be used to generate and test hypotheses about the co-evolution of workflow and knowledge networks of these 21st century project teams in terms of their knowledge distribution and performance. The two computational models being docked are SimVision (Jin & Levitt, 1999) which has sophisticated processes to model organizations executing project-oriented workflows, and Blanche (Hyatt, Contractor, & Jones, 1997), a multiagent computational network environment, which models multitheoretical mechanisms for the retrieval and allocation of information in knowledge networks involving human and non-human agents. This paper was supported in part by a grant from the U.S. National Science Foundation for the project \u201cCo-Evolution of Knowledge Networks and 21st Century Organizational Forms (IIS- 9980109)

Experimental Implementation of a Codeword Stabilized Quantum Code

A five-qubit codeword stabilized quantum code is implemented in a seven-qubit system using nuclear magnetic resonance (NMR). Our experiment implements a good nonadditive quantum code which encodes a larger Hilbert space than any stabilizer code with the same length and capable of correcting the same kind of errors. The experimentally measured quantum coherence is shown to be robust against artificially introduced errors, benchmarking the success in implementing the quantum error correction code. Given the typical decoherence time of the system, our experiment illustrates the ability of coherent control to implement complex quantum circuits for demonstrating interesting results in spin qubits for quantum computing

Agent-Based Modeling of Knowledge Dynamics

Abstract Knowledge is distributed unevenly through most enterprises. Hence, flows of knowledge (e.g., across time, people, locations, organizations) are critical to organizational efficacy and performance under a knowledge-based view of the firm. However, supported principally by narrative textual theory in the emerging knowledge management (KM) field, the researcher has difficulty describing how different kinds of knowledge will flow through various parts of an organization. This causes difficulty also for predicting the effects of alternate approaches to dispersing knowledge that 'clumps' in various areas. This problem is also manifest for the KM professional, who lacks clear theory or tools to anticipate how any particular information technology or other managerial intervention may enhance or impede specific knowledge flows in the enterprise. In this expository article, we build upon a steady stream of research in computational organization theory to develop agent-based models of knowledge dynamics. This work draws from emerging theory for multidimensional representation of the knowledge-flow phenomenon, which enables the dynamics of enterprise knowledge flows to be formalized and emulated through computational models. This approach provides the means for knowledge-flow processes to be visualized and analyzed in new ways. Computational experimentation enables the performance of many alternate process designs and technological interventions to be compared through examination of dynamic models, before committing to a specific approach in practice. We illustrate this research method and modeling environment through semi-formal representation and agent-based emulation of several knowledge-flow processes from the domain of software development. We also outline key directions for the new kinds of KM research and practice elucidated by this work