Improving the Systems Engineering Process with Multilevel Analysis of Interactions

The systems engineering V (SE-V) is an established process model to guide the development of complex engineering projects (INCOSE, 2011). The SE-V process involves decomposition and integration of system elements through a sequence of tasks that produce both a system design and its testing specifications, followed by successive levels of build, integration, and test activities. This paper presents a method to improve SE-V implementation by mapping multilevel data into design structure matrix (DSM) models. DSM is a representation methodology for identifying interactions either between components or tasks associated with a complex engineering project (Eppinger & Browning, 2012). Multilevel refers to SE-V data on complex interactions that are germane either at multiple levels of analysis, e.g. component versus subsystem conducted either within a single phase or across multiple time phases, e.g. early or late in the SE-V process. This method extends conventional DSM representation schema by incorporating multilevel test coverage data as vectors into the off diagonal cells. These vectors provide a richer description of potential interactions between product architecture and SE-V integration test tasks than conventional domain mapping matrices (DMMs). We illustrate this method with data from a complex engineering project in the offshore oil industry. Data analysis identifies potential for unanticipated outcomes based on incomplete coverage of SE-V interactions during integration tests. Additionally, assessment of multilevel features using maximum and minimum function queries isolates all the interfaces that are associated with either early or late revelations of integration risks based on the planned suite of SE-V integration tests

Performance of Coupled Product Development Activities with a Deadline

This paper explores the performance of coupled development tasks subject to a deadline constraint by proposing a performance generation model (PGM). The goal of the PGM is to develop insights about optimal strategies (i.e. sequential, concurrent, or overlapped) to manage coupled design tasks that share fixed amount of engineering resources subject to performance and deadline constraints. Model analysis characterizes the solution space for the coupled development problem. The solution space is used to explore the generation of product performance and the associated dynamic forces affecting concurrent development practices. We use these forces to explain conditions under which concurrency is a desirable strategy

Integrating intellectual property and sustainable business models: The SBM-IP canvas

Companies attempt to address global sustainability challenges through innovating products, services, and business models. This paper focuses on sustainable business model (SBM) innovations as a way to systemically transform businesses towards sustainability. It has been widely recognized that strategic approaches to using intellectual property (IP) need to be aligned with business model innovation for commercial success. Here we suggest that IP, aligned with SBMs, can also be used to create not only commercial, but also societal and environmental impact. Knowledge about how to best align IP with SBMs to drive sustainability transitions remains limited. We address this gap by developing an SBM-IP canvas that integrates IP considerations into each of the SBM canvas building blocks. We do this by employing relevant theoretical concepts from three literature streams, namely the business model (including SBM), IP, and innovation literature. We use case examples to illustrate different IP considerations that are relevant for the SBM-IP building blocks. These examples show that different IP types (e.g., patents, trademarks) and ways of using them (e.g., more or less restrictive licensing) are applied by companies in relation to the different building blocks. While covering new theoretical ground, the proposed SBM-IP canvas can help decision makers understand how they can use different IP types strategically to propose, create, deliver, and capture sustainable value for society, environment, and the business.</jats:p

Age differences in learning emerge from an insufficient representation of uncertainty in older adults

Healthy aging can lead to impairments in learning that affect many laboratory and real-life tasks. These tasks often involve the acquisition of dynamic contingencies, which requires adjusting the rate of learning to environmental statistics. For example, learning rate should increase when expectations are uncertain (uncertainty), outcomes are surprising (surprise) or contingencies are more likely to change (hazard rate). In this study, we combine computational modelling with an age-comparative behavioural study to test whether age-related learning deficits emerge from a failure to optimize learning according to the three factors mentioned above. Our results suggest that learning deficits observed in healthy older adults are driven by a diminished capacity to represent and use uncertainty to guide learning. These findings provide insight into age-related cognitive changes and demonstrate how learning deficits can emerge from a failure to accurately assess how much should be learned

Computational neuroscience across the lifespan: Promises and pitfalls

In recent years, the application of computational modeling in studies on age-related changes in decision making and learning has gained in popularity. One advantage of computational models is that they provide access to latent variables that cannot be directly observed from behavior. In combination with experimental manipulations, these latent variables can help to test hypotheses about age-related changes in behavioral and neurobiological measures at a level of specificity that is not achievable with descriptive analysis approaches alone. This level of specificity can in turn be beneficial to establish the identity of the corresponding behavioral and neurobiological mechanisms. In this paper, we will illustrate applications of computational methods using examples of lifespan research on risk taking, strategy selection and reinforcement learning. We will elaborate on problems that can occur when computational neuroscience methods are applied to data of different age groups. Finally, we will discuss potential targets for future applications and outline general shortcomings of computational neuroscience methods for research on human lifespan development

First identification of large electric monopole strength in well-deformed rare earth nuclei

Excited states in the well-deformed rare earth isotopes $^{154}$Sm and $^{166}$Er were populated via ``safe'' Coulomb excitation at the Munich MLL Tandem accelerator. Conversion electrons were registered in a cooled Si(Li) detector in conjunction with a magnetic transport and filter system, the Mini-Orange spectrometer. For the first excited $0^+$ state in $^{154}$Sm at 1099 keV a large value of the monopole strength for the transition to the ground state of $\rho^2(\text{E0}; 0^+_2 \to 0^+_\text{g}) = 96(42)\cdot 10^{-3}$ could be extracted. This confirms the interpretation of the lowest excited $0^+$ state in $^{154}$Sm as the collective $\beta$-vibrational excitation of the ground state. In $^{166}$Er the measured large electric monopole strength of $\rho^2(\text{E0}; 0^+_4 \to 0^+_1) = 127(60)\cdot 10^{-3}$ clearly identifies the $0_4^+$ state at 1934 keV to be the $\beta$-vibrational excitation of the ground state.Comment: submitted to Physics Letters

Monitoring of lung edema by microwave reflectometry during lung ischemia-reperfusion injury in vivo

It is still unclear whether lung edema can be monitored by microwave reflectometry and whether the measured changes in lung dry matter content (DMC) are accompanied by changes in PaO(2) and in pro-to anti-inflammatory cytokine expression (IFN-gamma and IL-10). Right rat lung hili were cross-clamped at 37 degrees C for 0, 60, 90 or 120 min ischemia followed by 120 min reperfusion. After 90 min (DMC: 15.9 +/- 1.4%; PaO(2): 76.7 +/- 18 mm Hg) and 120 min ischemia (DMC: 12.8 +/- 0.6%; PaO(2): 43 +/- 7 mm Hg), a significant decrease in DMC and PaO(2) throughout reperfusion compared to 0 min ischemia (DMC: 19.5 +/- 1.11%; PaO(2): 247 +/- 33 mm Hg; p < 0.05) was observed. DMC and PaO(2) decreased after 60 min ischemia but recovered during reperfusion (DMC: 18.5 +/- 2.4%; PaO(2) : 173 +/- 30 mm Hg). DMC values reflected changes on the physiological and molecular level. In conclusion, lung edema monitoring by microwave reflectometry might become a tool for the thoracic surgeon. Copyright (c) 2006 S. Karger AG, Basel