Agile games bring people closer to agile techniques in an entertaining way.
With the Technical Debt Game, even technically inexperienced people can experience and understand the effects of technical debt on projects for themselves. While players had to come together in person for the original game, it can now also be played online. →
The project was developed in collaboration with voxelJet. The company specialises in the production of 3D printers for industrial 3D printing and also offers 3D printing as a service.
Like most people, you have probably never experienced a fire. As a result, many people are unaware of the correct behaviour to adopt in the event of a fire. With the right behaviour, you can drastically reduce the risk to yourself, your own family or your fellow human beings.
You need to know this: It is not only the fire that is dangerous, but above all the toxic smoke from the fire. Just a few breaths are enough for you to lose consciousness and die of smoke inhalation. →
Every great innovation needs a place where it can be created. →
Augsburg car-free! The task is the integral planning of an above-ground modular parking system, which is at the same time an electricity power plant and offers co-working spaces. →
As part of the Basic Subjects project, eleven students from the 4th semester of the Civil Engineering course became aware of the wastewater treatment plant in Otzing, Lower Bavaria, through their client Prof. Dr.-Ing. Rita Hilliges. →
Master Civil Engineering is accredited →
The project is part of the CARE REGIO joint project. As part of the sub-project "Digitisation of the care transition report (PÜB)", the student project is pursuing the application of deep learning techniques to advance the standardisation of PÜBs. →
Following the development of the prototype in the summer semester of 2023, the Pyramid app has now been officially published in the app stores. The app modernises communication between students and companies at Augsburg University of Applied Sciences' Pyramid Fair by supplementing existing sources of information such as the website, print and PDF catalogues. →
A machine learning application predicts patient volumes and thus facilitates nursing staff scheduling in order to efficiently manage staff shortages.
In cooperation with XITASO GmbH, an application was developed that uses machine learning to predict and visualise patient volumes in order to improve nursing staff scheduling. Based on historical data, the time series forecasting method is used, whereby models such as Random Forest, SARIMA and Holts Winters seasonal method are utilised. The model quality is evaluated by RMSE, MAPE and MAE. The predictions are presented in a user-friendly web interface, which enables care facilities to optimise staff planning in line with demand. →