This study examines Moisture Buffer Value (MBV) of interior finishing layers, which impacts buildings internal relative humidity, thus indoor environmental quality. The MBV depends on material’s moisture capacity and vapour diffusion resistance factor, both of which depends on relative humidity. The paper aims to (i) characterize a new recycled material plaster that includes construction and food industry waste through laboratory measurements, (ii) use dynamical building simulations to quantify the impact of the MBV of existing and the new interior plaster on relative humidity in real design scenarios, and (iii) evaluate how changing the definition of the MBV to consider its dependence on indoor air relative humidity can improve its accuracy. Results show that the plaster’s moisture buffering properties significantly reduce the variations of the relative humidity of interior climates compared to a vapour-tight finishing layer. The performances of the new plaster made with recycled materials are comparable to those of the other plasters. The new MBV definitions (“Dynamical MBV″ and “Summer/Winter MBV”) show a significantly improved correlation with the relative humidity variations of indoor climates of buildings, observed in dynamic simulations, with respect to the typically used practical MBV. The new definitions are therefore promising for practical applications.

A number of machine learning process models (SEMMA, KDD, CRISP-DM, CRISP-ML, Data-to-Value1 etc.) have been recently proposed to facilitate the development of machine learning models in their organizational context. While the existing proposals vary with respect to complexity and suitability for particular tasks, it would be desirable to have software tools that embody or support these process models, and make it easier for project teams to capture, share among team members and stakeholders and preserve the relevant project information pertaining to the various process stages. In particular, recorded past statistics may be applied to predict the duration of stages or the overall project effort.

Presently, to the best of our knowledge, no requirement analysis exists that stipulates the detailed needs. To this end, we present a first collection and analysis of a requirements document for the software tooling for machine learning process models. We describe the functional and non-functional requirements of a Computer-Aided Machine Learning Modeling (CAMLM) tool, the soft-computing world’s counter-part to a CASE (Computer Aided Software Engineering) tool.

Various software cover sub-areas such as team management and communication management (Confluence, Jira, Slack, Zoom...) or project management (CRISP-DM, Scrum, Kanban-Board...) or data and information management (model management [Weber, Christian; Hirmer, Pascal; Reimann, Peter; Schwarz, Holger (2019): A New Process Model for the Comprehensive Management of Machine Learning Models. In: Proceedings of the 21st International Conference on Enterprise Information Systems: SCITEPRESS - Science and Technology Publications.] ). What is not available to our knowledge, however, is software that covers the entire sub-areas and the entire life cycle of machine learning projects in detail.

In Zeiten zurückhaltender wirtschaftlicher Stimmungslage ist die Gründung einer Unternehmung/ der Schritt in die Selbstständigkeit durchaus risikobehaftet. Handelt es sich dabei zudem um eine Gründung/Selbstständigkeit mit Nachhaltigkeits-Fokus, so deuten Befragungen darauf hin, dass hierbei nochmals weitere spezifische Herausforderungen auftreten können. Gleichzeitig leisten nachhaltigkeitsorientierte Gründerinnen und Gründer jedoch einen wertvollen Beitrag zur Bewältigung wirtschaftlicher und gesamtgesellschaftlicher Herausforderungen unserer Zeit - auch und gerade in einer ländlich und industriell geprägten Region wie der Region Oberfranken/Südthüringen. Im Austausch mit unseren externen hochschulischen Praxispartnern (allen voran der IHK zu Coburg und der HWK Bayreuth), die traditionell in der Phase der Unternehmensgründung/des Sich-Selbstständigmachens unterstützen, entstand die Idee, eine Befragung regionaler nachhaltigkeitsorientierter Gründerinnen und Gründer durchzuführen, um deren spezifischen Unterstützungsbedarfe und -wünsche zu erfragen. Dies haben wir gemeinsam mit Studierenden eines interdisziplinären Seminars der Hochschule Coburg im Wintersemester 2023/2024 umgesetzt. Die Befragung wurde dabei mit den Studierenden in allen Phasen der Untersuchung partizipativ geplant, durchgeführt und ausgewertet. Die Ergebnisse der Befragung sollen vorrangig der Gründungsberatung dienen (einer Aufgabe, mit denen sich auch Hochschulen jüngst immer mehr auseinandersetzen müssen), gleichzeitig aber auch Implikationen für die Hochschullehre i.S. einer “Sustainable/Social Entrepreneurship Education” bieten. Im angehängten Ergebnisposter werden Ausgangslage und Zielstellung der Untersuchung, die Methodische Anlage sowie die Ergebnisse der Befragung von N=14 nachhaltigen Gründerinnen und Gründern der Region Coburg/Südthüringen vorgestellt und sich erste daraus ergebende Implikationen aufgezeigt.

This study investigates the social acceptance of integrated photovoltaic (IPV) systems in heritage and landscape contexts, focusing on Italian stakeholders in the construction sector. As part of the “BIPV meets History” research project, this study aims to identify barriers, potentials, drivers, and challenges for widespread PV technology adoption, considering heritage conservation, land preservation, energy production, and climate mitigation. A survey exploring opinions on PV technology integration was conducted. The survey was improved and extended to a total of 271 respondents, using the online method of Computer-Aided Web Interviewing (CAWI), to understand how perceptions of integrated photovoltaics have changed after COVID-19 and the European energy crisis, emphasizing aesthetic, environmental, economic, and personal aspects. The results indicate a general awareness of the technologies, with increasing acceptance in protected contexts, for historic buildings (from 51 to 68%) and especially landscapes (from 44 to 71%), driven by energy and environmental benefits. Cultural concerns, particularly the risk of impacting historical and natural identities, emerge as major barriers. Additionally, it is evident that awareness of PV panel recycling methods is still limited.