Technische Hochschule Würzburg-Schweinfurt Publikationsserver OPUS
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Anwendung von mathematischer Optimierung für eine effiziente energetische Sanierungsplanung
No full textDie energetische Bestandssanierung hat für die Dekarbonisierung des Gebäudesektors eine entscheidende Funktion. Um Ressourcen bestmöglich einzusetzen, ist die ganzheitliche Quantifizierung und Optimierung von Sanierungsentscheidungen sinnvoll. Die Anwendung von mathematischen Optimierungsverfahren bietet Potenzial, das bisher vor allem im deutschsprachigen Raum kaum genutzt wird. Vorgestellt wird eine neue Methodik, die mittels eines mathematischen Ersatzmodells die Optimierung variabler Zielfunktionen erlaubt. Das Modell wird anhand von punktuellen Gebäudesimulationen kalibriert. Die Stärke der vorgestellten Methode liegt in der Analyse einer sehr großen Zahl von Sanierungskombinationen bei minimalem Rechenaufwand. Die Anwendbarkeit auf reale Objekte wird anhand von zwei unterschiedlichen exemplarischen Wohngebäudetypen demonstriert. Resultierende Sanierungsstrategien aus der Analyse werden beispielhaft für drei verschiedene Zielfunktionen mit Investitions- und Betriebskosten sowie CO2-Ausstoß bei unterschiedlichen Restriktionen dargestellt. Derzeit ist die vorgestellte Methode für die Bewertung und Optimierung des Heizwärmebedarfs von Wohngebäuden anwendbar. Es sind verschiedene Erweiterungen der Methodik angedacht
The HOMESIDE Study - A Research Project to Support People Living With Dementia and Their Family Caregivers: Preliminary Report on Reading Intervention
No full textThe behavioral and psychological symptoms of dementia (BPSD) can be challenging for family caregivers to cope with, leading to distress and fatigue. It is therefore important to offer effective strategies to reduce the impact of BPSD. The HOMESIDE randomized controlled trial (RCT) was testing purposefully developed interventions to improve the quality of life and wellbeing of dyads of people with dementia and family caregivers as a result of reduction of BPSD. HOMESIDE RCT was conducted in Australia, Germany, Norway, Poland and the United Kingdom between 2019 and 2022. The study design was a three-arm parallel-group single-blinded, pragmatic RCT with a sample size of 432 dyads. Dyads were randomly allocated to one of three treatment conditions: Music Intervention plus Standard Care; or Reading Intervention plus Standard Care; or Standard Care only. The Reading Intervention (RI) within the HOMESIDE RCT aimed to evoke shared discussion, reminiscence, meaningful shared experiences and consequently enrich everyday life, interaction and the emotional connection between the caregiver (CG) and carereceiver (CR); as well as to enhance activities of daily living and to promote relaxation or stimulation as appropriate. This paper describes the underlying conceptual framework, the content, and delivery of the Reading Intervention within the HOMESIDE RCT
IT-gestützte Datenanalyse und -visualisierung im Controlling
No full textDer Beitrag verdeutlicht die Möglichkeiten und den Nutzen der IT-Unterstützung für die Analyse und Visualisierung der Reportingdaten
The Relevance of Decolonizing Social Work: Critical Reflections on Colonial Pasts, Post-Colonial Presents and Decolonial Futures
No full textThe multiple and simultaneous realities of Social Work practice have always been more complex than its theories suggested. The knowledges, desires and perspectives of its addressees are much more nuanced than standardized empirical qualitative and quantitative research methodologies (that inform theorization) could possibly grasp. In consequence, many assumingly universal and evidence-based methods and instruments in Social Work do not lead towards anticipated outcomes that aim to support the social, political, ecological and economic needs of the addressees. Social Work theories in turn often lack the consideration of, under others, race, class and gender subordination and domination effects, which form integral parts of power dynamics between the oppressed and the oppressors at play in the 21st century. This leads to downplaying the need to theorize and support solidarity from below, as well as to strengthen relational accountability between those that want to advance social justice. In addition, structural elements “from above” such as political and welfare systems and development economies do not receive significant attention either. In consequence, many theories do not lead to concepts, approaches and praxis that sufficiently grasp the historical and contemporary situatedness in which Social Work operates
Self-employed and stressed out? The impact of stress and stress management on entrepreneurs’ mental health and performance
Get PDFIntroduction:
Entrepreneurs play a central role in economic and social stability, yet the start-up rate in Germany has declined in recent years, possibly due to the stress associated with entrepreneurial endeavors. Stressors such as financial uncertainty and time pressure are prevalent among entrepreneurs and negatively affect their psychological well-being. However, research on stress management strategies among self-employed individuals remains limited.
Methods:
This pilot study conducted a quantitative analysis with 117 self-employed participants in Germany. The study focused on typical entrepreneurial work demands and selected stress coping mechanisms.
Results:
The analysis revealed a significant correlation between quantitative demands and mental exhaustion. Furthermore, a high positive correlation between presenteeism and workload suggests that presenteeism may partially explain the variance in workload. These findings underscore how high job demands can lead to self-endangering behaviors that are detrimental to mental health.
Discussion:
Although no significant moderating effect of proactive coping on the relationship between job demands and mental exhaustion was observed, significant negative correlations between proactive coping and both job demands and mental exhaustion suggest a potential protective role of proactive coping against work-related stress. This study highlights the importance of understanding stress coping strategies among self-employed individuals and their impact on entrepreneurial success and mental well-being. Further research in this area is warranted to develop effective interventions to support the well-being and productivity of self-employed individuals in Germany
Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis
Get PDFIn the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time
G-code evaluation in CNC milling to predict energy consumption through Machine Learning
Get PDFComputerized Numeric Control (CNC) plays an essential role in highly autonomous manufacturing systems for interlinked process chains for machine tools. NC-programs are mostly written in standardized G-code. Evaluating CNC-controlled manufacturing processes before their real application is advantageous due to resource efficiency. One dimension is the estimation of the energy demand of a part manufactured by an NC-program, e.g. to discover optimization potentials. In this context, this paper presents a Machine Learning (ML) approach to assess G-code for CNC-milling processes from the perspective of the energy demand of basic G-commands. We propose Latin Hypercube Sampling as an efficient method of Design of Experiments to train the ML model with minimum experimental effort to avoid costly setup and implementation time of the model training and deployment
A simplified machine learning product carbon footprint evaluation tool
No full textOn the way to climate neutrality manufacturing companies need to assess the Carbon dioxide (CO2) emissions of their products as a basis for emission reduction measures. The evaluate this so-called Product Carbon Footprint (PCF) life cycle analysis as a comprehensive method is applicable, but means great effort and requires interdisciplinary knowledge. Nevertheless, assumptions must still be made to assess the entire supply chain. To lower these burdens and provide a digital tool to estimate the PCF with less input parameter and data, we make use of machine learning techniques and develop an editorial framework called MINDFUL. This contribution shows its realization by providing the software architecture, underlying CO2 factors, calculations and Machine Learning approach as well as the principles of its user experience. Our tool is validated within an industrial case study
