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On the Local Ultrametricity of Finite Metric Data
Get PDFNew local ultrametricity measures for finite metric data are proposed through the viewpoint that their Vietoris-Rips corners are samples from p-adic Mumford curves endowed with a Radon measure coming from a regular differential 1-form. This is experimentally applied to the iris dataset
Classroom Disruptions and Classroom Management in Learning Factory Settings at Vocational Schools
Get PDFContext: As part of vocational education and training, learning factories are a new, hands-on learning setting in which students can create products with realistic digital manufacturing equipment while still in vocational school. Given their novelty, learning factories have not yet been studied with respect to whether special classroom management may be needed. One key aspect of classroom management for teachers is the dealing with classroom disruptions. The aim of this study is to investigate what types of classroom disruptions occur in learning factories and how teachers deal with them.
Methods: To close the existing research gap, a guideline-based, semi-structured interview study with seven teachers from the federal state of Baden-Württemberg, Germany, was conducted. The interviews were analyzed with a qualitative content analysis using the software MAXQDA.
Findings: The findings show that in this new setting, established strategies for mitigating classroom disruptions can be adapted and applied. Teachers were found to use and optimize their existing abilities to ensure learning success and were able to protect the monetary value of the factory against certain disruptions. Mutual trust between teachers and students, as well as teachers utilizing strategies according to their personality, were mentioned as the most important factors in ensuring success in this context.
Conclusion: Learning factories as a new learning environment in vocational schools do not seem to require specific classroom management approaches. As a result, their use can be safely expanded. Teachers value the possibility of teaching in this special setting while seeing that there are new possible ways of disruptions. Nevertheless, the interviewees feel themselves prepared for these new challenges, using their already established repertoire of strategies, adapting them, if necessary, to this new setting. To do this, teachers need to systemize and understand disruptions inside their classrooms. So far, research is lacking systemizations for classroom disruptions in digital settings like learning factories. This study extends the research landscape with an adaption of an already existing construct
Development of an expert system for the deformation monitoring of historical sites using Artificial Intelligence (AI)
Get PDFA hierarchical approach for near real-time 3D surface change analysis of permanent laser scanning point clouds
Get PDFExplainability Versus Accuracy of Machine Learning Models: The Role of Task Uncertainty and Need for Interaction with the Machine Learning Model
Get PDFThis paper investigates the importance of explainability versus accuracy of machine learning
(ML) models. We propose that greater task uncertainty makes people want to interact more with the ML
model, which increases the importance of explainability relative to accuracy. We focus on the use of ML
models for product cost estimation during new product development. The paper provides mixed-methods
evidence on the trade-off between explainability and accuracy of ML models. Specifically, we find support for an inverse relationship between explainability and accuracy from the perspective of cost experts. We also find that the accurate but complex and less explainable ML model of gradient boosted regression (GBR) was preferred in only a few situations; mostly, the more basic, better explainable models of multiple linear regression (MLR) and case-based reasoning (CBR) were preferred, although these were less accurate. This suggests that lack of explainability can indeed be a major limitation for the application of ML models. Furthermore, we investigate specific characteristics that could increase task uncertainty and the importance of explainability in our context: project unpredictability, product cost granularity, predecessor product availability, target cost gap, and product development phase
CriticS: a resource criticality characterization method for life cycle assessment considering stakeholders’ perspectives
Get PDFPurpose
Assessing the supply risk of critical raw materials (CRMs) is crucial for supporting green transition strategies. Combining criticality assessment with life cycle sustainability assessment (LCSA) helps to link business actions to supply risks. However, these assessments are characterized by a variety in context, scope, and stakeholder influence, as well as a lack of method standardization. Currently, no operational quantitative method applied to LCSA includes diverse stakeholder perspectives.
Methods
This study proposes a novel fit-for-purpose criticality assessment methodology leveraging existing criticality values from the study on CRMs from the European Commission (EC) while considering different stakeholder perspectives. In this research, several sets of characterization factors (CFs) are proposed, in which the values for supply risk and economic importance from the EC study on CRMs are combined in different ways, in some cases also with further parameters such as material prices. The methodology, called CriticS, guides stakeholders in defining the goal and scope, choosing sets of CFs, and operationalizing the assessment using a product’s bill of materials (BoM) or its life cycle inventory (LCI). Specific sets of CFs are tested in a proof-of-concept case study of a laptop by using its BoM and LCI.
Results and discussion
Supply risk and economic importance values were used to create 19 different sets of CFs. All sets of CFs of the CriticS are organized in a decision tree framework, helping stakeholders to select the most appropriate set of CFs that meets their needs. The CFs are linked to elementary flows in the ecoinvent® database, creating an operationalized model. The results of the proof-of-concept study highlight the benefits and the challenges in applying the CriticS methodology. These challenges are discussed, and potential solutions are identified.
Conclusions
The results demonstrated the usefulness of the CriticS method with regard to the selection of the set of CFs using the decision tree, taking into account a given stakeholder’s perspective. Future research should focus on refining the CF-elementary flow links, integrating CriticS into LCA software, and interpreting the results of the CriticS method together with those of life cycle sustainability assessment
Search for light long-lived particles decaying to displaced jets in proton-proton collisions at √s = 13.6 TeV
Get PDFA search for light long-lived particles (LLPs) decaying to displaced jets is presented, using a
data sample of proton–proton collisions at a center-of-mass energy of 13.6 TeV, corresponding
to an integrated luminosity of 34.7 fb, collected with the CMS detector at the CERN LHC in
2022. Novel trigger, reconstruction, and machine-learning techniques were developed for and
employed in this search. After all selections, the observations are consistent with the background
predictions. Limits are presented on the branching fraction of the Higgs boson to LLPs that
subsequently decay to quark pairs or tau lepton pairs. An improvement by up to a factor of 10 is
achieved over previous limits for models with LLP masses smaller than 60 GeV and proper
decay lengths smaller than 1 m. The first constraints are placed on the fraternal twin Higgs
(FTH) and folded supersymmetry (FSUSY) models, where the lower bounds on the top quark
partner mass reach up to 350 GeV for the FTH model and 250 GeV for the FSUSY model