Breda University of Applied Sciences (BUas) adopts a comprehensive approach to AI integration, harmonizing technology with education, operations, and research. Our poster, "A Holistic AI Integration at BUas: Education, Operations, and Research," illustrates our innovative strategies across these domains. In education, we emphasize AI literacy, blending technical training with the application of AI in professional practices, fostering personalized learning and ethical innovation. Our operations approach is human-centered, focusing on enhancing efficiency while maintaining empathy and accountability, ensuring AI complements rather than replaces human roles. In research, we align AI with educational goals and industry needs, promoting ethical, data-focused research that contributes to societal advancement. This presentation offers insights into our balanced approach to AI, highlighting practical applications, ethical considerations, and the synergy between technology and human elements, providing valuable takeaways for implementing AI in diverse educational settings.
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This overview can be regarded as an atlas or travel guide with which the reader can follow a route along the various professorships. Chapter 2 centres on the professorships that are active in the field of Service Economy. Chapter 3 is dedicated to the professorships that are focussed on the field of Vital Region. Chapter 4 describes the professorships operating in the field of Smart Sustainable Industries. Chapter 5 deals with the professorships that are active in the field of the institution-wide themes of Design Based Education and Design Based Research. Lastly, in Chapter 6 we make an attempt to discover one or more connecting themes or procedures in the approach of the various professorships. This publication is not intended to give a definitive answer to the question as to what exactly NHL Stenden means by the concept of Design Based Research. The aim of this publication is to get an idea of everything that is happening in the NHL Stenden professorships and to pique one’s curiosity to find out more.
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One of the issues concerning the replacement of natural gas by green gas is the seasonal pattern of the gas demand. When constant production is assumed, this may limit the injected quantity of green gas into a gas grid to the level of the minimum gas demand in summer. A procedure was proposed to increase thegas demand coverage in a geographical region, i.e., the extent to which natural gas demand is replaced by green gas. This was done by modeling flexibility into farm-scale green gas supply chains. The procedure comprises two steps. In the first step, the types and number of green gas production units are determined,based on a desired gas demand coverage. The production types comprise time-varying biogas production, non-continuous biogas production (only in winter periods with each digester having a specified production time) and constant production including seasonal gas storage. In the second step locations of production units and injection stations are calculated, using mixed integer linear programming with cost price minimization being the objective. Five scenarios were defined with increasing gas demand coverage, representing a possible future development in natural gas replacement. The results show that production locations differ for each scenario, but are connected to a selection of injection stations, at least in the considered geographical region under the assumed preconditions. The cost price is mainly determined by the type of digesters needed. Increasing gas demand coverage does not necessarily mean a much higher cost price.
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Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
The CARTS (Collaborative Aerial Robotic Team for Safety and Security) project aims to improve autonomous firefighting operations through an collaborative drone system. The system combines a sensing drone optimized for patrolling and fire detection with an action drone equipped for fire suppression. While current urban safety operations rely on manually operated drones that face significant limitations in speed, accessibility, and coordination, CARTS addresses these challenges by creating a system that enhances operational efficiency through minimal human intervention, while building on previous research with the IFFS drone project. This feasibility study focuses on developing effective coordination between the sensing and action drones, implementing fire detection and localization algorithms, and establishing parameters for autonomous flight planning. Through this innovative collaborative drone approach, we aim to significantly improve both fire detection and suppression capabilities. A critical aspect of the project involves ensuring reliable and safe operation under various environmental conditions. This feasibility study aims to explore the potential of a sensing drone with detection capabilities while investigating coordination mechanisms between the sensing and action drones. We will examine autonomous flight planning approaches and test initial prototypes in controlled environments to assess technical feasibility and safety considerations. If successful, this exploratory work will provide valuable insights for future research into autonomous collaborative drone systems, currently focused on firefighting. This could lead to larger follow-up projects expanding the concept to other safety and security applications.
Cross-Re-Tour supports European tourism SME while implementing digital and circular economy innovations. The three year project promotes uptake and replication by tourism SMEs of tools and solutions developed in other sectors, to mainstream green and circular tourism business operations.At the start of the project existing knowledge-gaps of tourism SMEs will be researched through online dialogues. This will be followed by a market scan, an overview of existing state of the art solutions to digital and green constraints in other economic sectors, which may be applied to tourism SME business operations: water, energy, food, plastic, transport and furniture /equipment. The scan identifies best practices from other sectors related to nudging of clients towards sustainable behaviour and nudging of staff on how to best engage with new tourism market segments.The next stage of the project relates to two design processes: an online diagnostic tool that allows for measuring and assessing (160) SME’s potential to adapt existing solutions in digital and green challenges, developed in other economic sectors. Next to this, a knowledge hub, addresses knowledge constraints and proposes solutions, business advisory services, training activities to SMEs participating. The hub acts as a matchmaker, bringing together 160 tourism SMEs searching for solutions, with suppliers of existing solutions developed in other sectors. The next key activity is a cross-domain open innovation programme, that will provide 80 tourism SMEs with financial support (up to EUR 30K). Examples of partnerships could be: a hotel and a supplier of refurbished matrasses for hospitals; a restaurant and a supplier of food rejected by supermarkets, a dance event organiser and a supplier of refurbished water bottles operating in the cruise industry, etc.The 80 cross-domain partnerships will be supported through the knowledge hub and their business innovation advisors. The goal is to develop a variety of innovative partnerships to assure that examples in all operational levels of tourism SMEs.The innovation projects shall be presented during a show-and-share event, combined with an investors’ pitch. The diagnostic tool, market scan, knowledge hub, as well as the show and share offer excellent opportunities to communicate results and possible impact of open innovation processes to a wider international audience of destination stakeholders and non-tourism partners. Societal issueSupporting the implementation of digital and circular economy solutions in tourism SMEs is key for its transition towards sustainable low-impact industry and society. Benefit for societySolutions are already developed in other sectors but the cross-over towards tourism is not happening. The project bridges this gap.
