Product Models in Network Based Co-operation in Structural Engineering

The Priority Programme ‘Network Based Co-operation in Structural Engineering’ of the ‘German Research Foundation’ (DFG) has been established in the year 2000. This paper describes and discusses the main research directions and first results of the workgroup ‘Distributed Product Models’. The five projects of the workgroup have developed completely different solutions for specific application domains. Each solution concept deals with a consistent product modeling and knowledge processing in a distributed environment in the planning process. The individual solution approaches of the projects are described and the underlying basic assumptions are discussed. A unified system architecture is described for all projects of the workgroup. Two different approaches (object-oriented and graph-based models) have been introduced for product and knowledge modeling. The common structure of these models will be explained to fully understand the differences of these modeling approaches. Finally the concepts for co-operative work and conflict management in a distributed environment are described: The solution approaches will be distinguished by classifying the supported co-operation according to time. A final scientific summary describes the state-of-the-art in network based co-operation in structural engineering: The role of research directions like knowledge modeling, standard product modeling and versioning in the distributed planning process will be explained

Using causal inference to avoid fallouts in data-driven parametric analysis: A case study in the architecture, engineering, and construction industry

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. Recognizing the limitations of isolated methodologies - namely, the lack of domain understanding in data-driven models, the subjective nature of empirical knowledge, and the idealized assumptions in first-principles simulations, we explore their synergetic integration. We showed the potential risk of biased results when using data-driven models without causal analysis. Through a case study on energy consumption in building design, we demonstrate how causal analysis significantly enhances the modeling process, mitigating biases and spurious correlations. We concluded that: (a) Sole data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Integrating causal analysis results aid to first-principles simulation design and parameter checking to avoid cognitive biases. We advocate for the routine integration of causal inference within data-driven models in engineering practices, emphasizing its critical role in ensuring the models' reliability and real-world applicability

Evaluating the effectiveness of System Engineering on Organization Management Using Structural Equations Modeling

System Engineering, as a new implication and major, plays an outstanding role in the organizations. This system pays attention to the project beneficiaries, product designing, data architecture, customers’ satisfaction, organizational integration, and applying organizational knowledge that has increased efficiency and productivity in an organization. Evaluating the effectiveness of system engineering on the organizational management targets (including product innovation, strategic planning) is considered to be one of the main issues engaged with the organizations. The purpose of this study is to evaluate the relation between system engineering, strategic planning, and product innovation engineering and to provide a comprehensive model for the execution of system engineering. For these purposes, specific processes have been identified for system engineering, strategic planning, and product innovation engineering. A questionnaire was prepared and distributed among the personnel at Iran Aircraft Manufacturing Co. Three assumptions were discussed (the influence of system engineering on strategic planning, the influence of system engineering on product innovation engineering, and the influence of strategic planning on product innovation engineering) and were evaluated by using of structural equations modeling (LISREL Software). The aforesaid influence was confirmed respectively with factor load of 5/10 and 2/84 and 3/66

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Preliminary Survey on Empirical Research Practices in Requirements Engineering

Context and Motivation:\ud Based on published output in the premium RE conferences and journals, we observe a growing body of research using both quantitative and qualitative research methods to help understand which RE technique, process or tool work better in which context. Also, more and more empirical studies in RE aim at comparing and evaluating alternative techniques that are solutions to common problems. However, until now there have been few meta studies of the current state of knowledge about common practices carried out by researchers and practitioners in empirical RE. Also, surprisingly little has been published on how RE researchers perceive the usefulness of these best practices.\ud \ud Objective:\ud The goal of our study is to improve our understanding of what empirical practices are performed by researchers and practitioners in RE, for the purpose of understanding the extent to which the research methods of empirical software engineering are adopted in the RE community.\ud \ud Method:\ud We surveyed the practices that participants of the REFSQ conference have been using in their empirical research projects. The survey was part of the REFSQ 2012 Empirical Track.\ud \ud Conclusions:\ud We found that there are 15 commonly used practices out of a set of 27. The study has two implications: first it presents a list of practices that are commonly used in the RE community, and a list of practices that still remain to be practiced. Researchers may now make an informed decision on how to extend the practices they use in producing and executing their research designs, so that their designs get better. Second, we found that senior researchers and PhD students do not always converge in their perceptions about the usefulness of research practices. Whether this is all right and whether something needs to be done in the face of this finding remains an open question

Conceptual Engineering, Topics, Metasemantics, and Lack of Control

Conceptual engineering is now a central topic in contemporary philosophy. Just 4-5 years ago it wasn’t. People were then engaged in the engineering of various philosophical concepts (in various sub-disciplines), but typically not self-consciously so. Qua philosophical method, conceptual engineering was under-explored, often ignored, and poorly understood. In my lifetime, I have never seen interest in a philosophical topic grow with such explosive intensity. The sociology behind this is fascinating and no doubt immensely complex (and an excellent case study for those interested in the dynamics of academic disciplines). That topic, however, will have to wait for another occasion. Suffice it to say that if Fixing Language (FL) contributed even a little bit to this change of focus in philosophical methodology, it would have achieved one of its central goals. In that connection, it is encouraging that the papers in this symposium are in fundamental agreement about the significance and centrality of conceptual engineering to philosophy. That said, the goal of FL was not only to advocate for a topic, but also to defend a particular approach to it: The Austerity Framework. These replies have helped me see clearer the limitations of that view and points where my presentation was suboptimal. The responses below are in part a reconstruction of what I had in mind while writing the book and in part an effort to ameliorate. I’m grateful to the symposiasts for helping me get a better grip on these very hard issue

Requirements Engineering as Creative Problem Solving: A Research Agenda for Idea Finding

This vision paper frames requirements engineering as a creative problem solving process. Its purpose is to enable requirements researchers and practitioners to recruit relevant theories, models, techniques and tools from creative problem solving to understand and support requirements processes more effectively. It uses 4 drivers to motivate the case for requirements engineering as a creative problem solving process. It then maps established requirements activities onto one of the longest-established creative problem solving processes, and uses these mappings to locate opportunities for the application of creative problem solving in requirements engineering. The second half of the paper describes selected creativity theories, techniques, software tools and training that can be adopted to improve requirements engineering research and practice. The focus is on support for problem and idea finding - two creative problem solving processes that our investigation revealed are poorly supported in requirements engineering. The paper ends with a research agenda to incorporate creative processes, techniques, training and tools in requirements projects