For long flights, the cruise is the longest phase and where the largest amount of fuel is consumed. An in-cruise optimization method has been implemented to calculate the optimal trajectory that reduces the flight cost. A three-dimensional grid has been created, coupling lateral navigation and vertical navigation profiles. With a dynamic analysis of the wind, the aircraft can perform a horizontal deviation or change altitudes via step climbs to reduce fuel consumption. As the number of waypoints and possible step climbs is increased, the number of flight trajectories increases exponentially; thus, a genetic algorithm has been implemented to reduce the total number of calculated trajectories compared to an exhaustive search. The aircraft’s model has been obtained from a performance database, which is currently used in the commercial flight management system studied in this paper. A 5% average flight cost reduction has been obtained.
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In flexible education, recommender systems that support course selection, are considered a viable means to help students in making informed course selections, especially where curricula offer greater flexibility. However, these recommender systems present both potential benefits and looming risks, such as overdependence on technology, biased recommendations, and privacy issues. User control mechanisms in recommender interfaces (or algorithmic affordances) might offer options to address those risks, but they have not been systematically studied yet. This paper presents the outcomes of a design session conducted during the INTERACT23 workshop on Algorithmic Affordances in Recommender Interfaces. This design session yielded insights in how the design of an interface, and specifically the algorithmic affordances in these interfaces, may address the ethical risks and dilemmas of using a recommender in such an impactful context by potentially vulnerable users. Through design and reflection, we discovered a host of design ideas for the interface of a flexible education interface, that can serve as conversation starters for practitioners implementing flexible education. More research is needed to explore these design directions and to gain insights on how they can help to approximate more ethically operating recommender systems.
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Twirre is a new architecture for mini-UAV platforms designed for autonomous flight in both GPS-enabled and GPS-deprived applications. The architecture consists of low-cost hardware and software components. High-level control software enables autonomous operation. Exchanging or upgrading hardware components is straightforward and the architecture is an excellent starting point for building low-cost autonomous mini-UAVs for a variety of applications. Experiments with an implementation of the architecture are in development, and preliminary results demonstrate accurate indoor navigation
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Privacy concerns can potentially make camera-based object classification unsuitable for robot navigation. To address this problem, we propose a novel object classification system using only a 2D-LiDAR sensor on mobile robots. The proposed system enables semantic understanding of the environment by applying the YOLOv8n model to classify objects such as tables, chairs, cupboards, walls, and door frames using only data captured by a 2D-LiDAR sensor. The experimental results show that the resulting YOLOv8n model achieved an accuracy of 83.7% in real-time classification running on Raspberry Pi 5, despite having a lower accuracy when classifying door-frames and walls. This validates our proposed approach as a privacy-friendly alternative to camera-based methods and illustrates that it can run on small computers onboard mobile robots.
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Innovations are required in urban infrastructures due to the pressing needs for mitigating climate change and prevent resource depletion. In order to address the slow pace of innovation in urban systems, this paper analyses factors involved in attempts to introduce novel sanitary systems. Today new requirements are important: sanitary systems should have an optimal energy/climate performance, with recovery of resources, and with fewer emissions. Anaerobic digestion has been suggested as an alternative to current aerobic waste water treatment processes. This paper presents an overview of attempts to introduce novel anaerobic sanitation systems for domestic sanitation. The paper identifies main factors that contributed to a premature termination of such attempts. Especially smaller scale anaerobic sanitation systems will probably not be able to compete economically with traditional sewage treatment. However, anaerobic treatment has various advantages for mitigating climate change, removing persistent chemicals, and for the transition to a circular economy. The paper concludes that loss avoidance, both in the sewage system and in the waste water treatment plants, should play a key role in determining experiments that could lead to a transition in sanitation. http://dx.doi.org/10.13044/j.sdewes.d6.0214 LinkedIn: https://www.linkedin.com/in/karel-mulder-163aa96/
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This exploratory study investigates the rationale behind categorizing algorithmic controls, or algorithmic affordances, in the graphical user interfaces (GUIs) of recommender systems. Seven professionals from industry and academia took part in an open card sorting activity to analyze 45 cards with examples of algorithmic affordances in recommender systems’ GUIs. Their objective was to identify potential design patterns including features on which to base these patterns. Analyzing the group discussions revealed distinct thought processes and defining factors for design patterns that were shared by academic and industry partners. While the discussions were promising, they also demonstrated a varying degree of alignment between industry and academia when it came to labelling the identified categories. Since this workshop is part of the preparation for creating a design pattern library of algorithmic affordances, and since the library aims to be useful for both industry and research partners, further research into design patterns of algorithmic affordances, particularly in terms of labelling and description, is required in order to establish categories that resonate with all relevant parties
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We are currently in a transition moving from a linear economy grounded on economic value maximization based on material transformation to a circular economy. Core of this transition is organising value preservation from various yet interlinked perspectives. The underlying fundamental shift is to move away from mere financial value maximization towards multiple value creation (WCED, 1987; Jonker, 2014; Raworth, 2017). This implies moving from mere economic value creation, to simultaneously and in a balanced way creating ecological and social value. A parallel development supporting this transition can be observed in accounting & control. Elkington (1994) introduced the triple bottom line (TBL) concept, referring to the economic, ecological and social impact of companies. The TBL should be seen more as a conceptual way of thinking, rather than a practical innovative accounting tool to monitor and control sustainable value (Rambaud & Richard, 2015). However, it has inspired accounting & control practitioners to develop accounting tools that not only aim at economic value (‘single capital’ accounting) but also at multiple forms of capital (‘multi capital’ accounting or integrated reporting). This has led to a variety of integrated reporting platforms such as Global Reporting Initiative (GRI), International Integrated Reporting Framework (IIRC), Dow Jones Sustainable Indexes (DJSI), True Costing, Reporting 3.0, etc. These integrated reporting platforms and corresponding accounting concepts, can be seen as a fundament for management control systems focussing on multiple value creation. This leads to the following research question: How are management control systems designed in practice to drive multiple value creation?
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The methodology of biomimicry design thinking is based on and builds upon the overarching patterns that all life abides by. “Cultivating cooperative relationships” within an ecosystem is one such pattern we as humans can learn from to nurture our own mutualistic and symbiotic relationships. While form and process translations from biology to design have proven accessible by students learning biomimicry, the realm of translating biological functions in a systematic approach has proven to be more difficult. This study examines how higher education students can approach the gap that many companies in transition are struggling with today; that of thinking within the closed loops of their own ecosystem, to do good without damaging the system itself. Design students should be able to assess and advise on product design choices within such systems after graduation. We know when tackling a design challenge, teams have difficulties sifting through the mass of information they encounter, and many obstacles are encountered by students and their professional clients when trying to implement systems thinking into their design process. While biomimicry offers guidelines and methodology, there is insufficient research on complex, systems-level problem solving that systems thinking biomimicry requires. This study looks at factors found in course exercises, through student surveys and interviews that helped (novice) professionals initiate systems thinking methods as part of their strategy. The steps found in this research show characteristics from student responses and matching educational steps which enabled them to develop their own approach to challenges in a systems thinking manner. Experiences from the 2022 cohort of the semester “Design with Nature” within the Industrial Design Engineering program at The Hague University of Applied Sciences in the Netherlands have shown that the mixing and matching of connected biological design strategies to understand integrating functions and relationships within a human system is a promising first step. Stevens LL, Whitehead C, Singhal A. Cultivating Cooperative Relationships: Identifying Learning Gaps When Teaching Students Systems Thinking Biomimicry. Biomimetics. 2022; 7(4):184. https://doi.org/10.3390/biomimetics7040184
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and metereological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be GatedRecurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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Clinical decision support systems (CDSSs) have gained prominence in health care, aiding professionals in decision-making and improving patient outcomes. While physicians often use CDSSs for diagnosis and treatment optimization, nurses rely on these systems for tasks such as patient monitoring, prioritization, and care planning. In nursing practice, CDSSs can assist with timely detection of clinical deterioration, support infection control, and streamline care documentation. Despite their potential, the adoption and use of CDSSs by nurses face diverse challenges. Barriers such as alarm fatigue, limited usability, lack of integration with workflows, and insufficient training continue to undermine effective implementation. In contrast to the relatively extensive body of research on CDSS use by physicians, studies focusing on nurses remain limited, leaving a gap in understanding the unique facilitators and barriers they encounter. This study aimed to explore the facilitators and barriers influencing the adoption and use of CDSSs by nurses in hospitals, using an extended Fit Between Individuals, Tasks, and Technology (FITT) framework.
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