Computational Science Hub (CSH)

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/16924

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Brewing a sustainable future: a firm-level analysis of sustainability initiatives in the coffee sector
(2025) Boller, Meta Leonie; Bosch, Christine; Heinzel, Kathleen; Birkenberg, Athena; Krupitzer, Christian
The coffee industry has long relied on third-party certification as their approach to sustainability, driven by customer demand and changing consumer behavior. Today, multiple forms of sustainability engagement have developed in the industry. This study uses a machine learning approach to analyze the engagement in sustainability initiatives of 100 firms active in the German market. Results reveal that company size and value chain position influence choice and engagement intensity in sustainability initiatives. A complementary literature analysis on policy recommendations to promote sustainability engagement in the coffee industry revealed a fragmented and insufficiently granular picture to address the diverse needs of stakeholders. While company characteristics significantly influence their choice of sustainability initiatives, policymakers often adopt generic approaches that do not reflect these nuances. Future research could extend this approach to deepen understanding or validate findings of policies for sustainable transformation in the coffee sector to other critical crops.
CortexVR: Immersive analysis and training of cognitive executive functions of soccer players using virtual reality and machine learning
(2022) Krupitzer, Christian; Naber, Jens; Stauffert, Jan-Philipp; Mayer, Jan; Spielmann, Jan; Ehmann, Paul; Boci, Noel; Bürkle, Maurice; Ho, André; Komorek, Clemens; Heinickel, Felix; Kounev, Samuel; Becker, Christian; Latoschik, Marc Erich
Goal: This paper presents an immersive Virtual Reality (VR) system to analyze and train Executive Functions (EFs) of soccer players. EFs are important cognitive functions for athletes. They are a relevant quality that distinguishes amateurs from professionals. Method: The system is based on immersive technology, hence, the user interacts naturally and experiences a training session in a virtual world. The proposed system has a modular design supporting the extension of various so-called game modes. Game modes combine selected game mechanics with specific simulation content to target particular training aspects. The system architecture decouples selection/parameterization and analysis of training sessions via a coaching app from an Unity3D-based VR simulation core. Monitoring of user performance and progress is recorded by a database that sends the necessary feedback to the coaching app for analysis. Results: The system is tested for VR-critical performance criteria to reveal the usefulness of a new interaction paradigm in the cognitive training and analysis of EFs. Subjective ratings for overall usability show that the design as VR application enhances the user experience compared to a traditional desktop app; whereas the new, unfamiliar interaction paradigm does not negatively impact the effort for using the application. Conclusion: The system can provide immersive training of EF in a fully virtual environment, eliminating potential distraction. It further provides an easy-to-use analyzes tool to compare user but also an automatic, adaptive training mode.
Integrating sensor data, laboratory analysis, and computer vision in machine learning-driven E-Nose systems for predicting tomato shelf life
(2025) Senge, Julia Marie; Kaltenecker, Florian; Krupitzer, Christian
Assessing the quality of fresh produce is essential to ensure a safe and satisfactory product. Methods to monitor the quality of fresh produce exist; however, they are often expensive, time-consuming, and sometimes require the destruction of the sample. Electronic Nose (E-Nose) technology has been established to track the ripeness, spoilage, and quality of fresh produce. Our study developed a freshness monitoring system for tomatoes, combining E-Nose technology with storage condition monitoring, color analysis, and weight-loss tracking. Different post-purchase scenarios were investigated, focusing on the influence of temperature and mechanical damage on shelf life. Support Vector Classifier (SVC) and k-Nearest Neighbor (kNN) were applied to classify storage scenarios and storage days, while Support Vector Regression (SVR) and kNN regression were used for predicting storage days. By using a data fusion approach with Linear Discriminant Analysis (LDA), the SVC achieved an accuracy of 72.91% in predicting storage days and an accuracy of 86.73% in distinguishing between storage scenarios. The kNN yielded the best regression results, with a Mean Absolute Error (MAE) of 0.841 days and a coefficient of determination of 0.867. The results highlight the method’s potential to predict storage scenarios and storage days, providing insight into the product’s remaining shelf life.

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Browsing Computational Science Hub (CSH) by Subject "Machine learning"