A Data Driven Tool supported CLD Creation Approach

The creation of Causal Loop Diagrams (CLDs) is a major phase in the System Dynamics (SD) life-cycle, since the created CLDs express dependencies and feedback in the system under study, as well as, guide modellers in building meaningful simulation models. The cre-ation of CLDs is still subject to the modeller's domain expertise (mental model) and her ability to abstract the system, because of the strong de-pendency on semantic knowledge. Since the beginning of SD, available system data sources (written and numerical models) have always been sparsely available, very limited and imperfect and thus of little benefit to the whole modelling process. However, in recent years, we have seen an explosion in generated data, especially in all business related domains that are analysed via Business Dynamics (BD). In this paper, we intro-duce a systematic tool supported CLD creation approach, which analyses and utilises available disparate data sources within the business domain. We demonstrate the application of our methodology on a given business use-case and evaluate the resulting CLD. Finally, we propose directions for future research to further push the automation in the CLD creation and increase confidence in the generated CLDs.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Equipping young people to engage with media driven science experiences: beyond science knowledge to science literacy and discerning consumers of news.

This paper reports on a study of a curricular intervention for pupils (age 10-13 years) in the UK aimed at supporting critical engagement with science based media reports. In particular the study focused on core elements of knowledge, skills and attitudes identified in previous studies that characterize critical consumers of science presented as news. This was an empirical study based on classroom observation. Data included responses from individual pupils, in addition video recording of group activity and intentional conversations between pupils and teachers were scrutinised. Analysis focused on core tasks relating to different elements of critical reading. Pupils demonstrated a grasp of questioning and evaluating text, however the capacity to translate this experience in support of a critical response to a media report with a science component is limited in assessing the credibility of text and as an element in critical reading.

Molecular characterization of immunoglobulin gene rearrangements in diffuse large B-cell lymphoma: antigen-driven origin and IGHV4-34 as a particular subgroup of the non-GCB subtype.

The pathogenesis of diffuse large B-cell lymphoma (DLBCL) remains partially unknown. The analysis of the B-cell receptor of the malignant cells could contribute to a better understanding of the DLBCL biology. We studied the molecular features of the immunoglobulin heavy chain (IGH) rearrangements in 165 patients diagnosed with DLBCL not otherwise specified. Clonal IGH rearrangements were amplified according to the BIOMED-2 protocol and PCR products were sequenced directly. We also analyzed the criteria for stereotyped patterns in all complete IGHV-IGHD-IGHJ (V-D-J) sequences. Complete V-D-J rearrangements were identified in 130 of 165 patients. Most cases (89%) were highly mutated, but 12 sequences were truly unmutated or minimally mutated. Three genes, IGHV4-34, IGHV3-23, and IGHV4-39, accounted for one third of the whole cohort, including an overrepresentation of IGHV4-34 (15.5% overall). Interestingly, all IGHV4-34 rearrangements and all unmutated sequences belonged to the nongerminal center B-cell-like (non-GCB) subtype. Overall, we found three cases following the current criteria for stereotyped heavy chain VH CDR3 sequences, two of them belonging to subsets previously described in CLL. IGHV gene repertoire is remarkably biased, implying an antigen-driven origin in DLBCL. The particular features in the sequence of the immunoglobulins suggest the existence of particular subgroups within the non-GCB subtype.

Advanced Business Simulations-Incorporating business and process execution data

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless , a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution & business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-case that involved verification of the automatically generated causal models by a domain expert.