Radio Frequency (RF) heating has been used widely in the food industry for the last decades. However, there is limited knowledge about RF inactivation mechanisms in the literature. The present study investigates the R...
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Metal nanoparticles were demonstrated to be favorable antibacterial agents to overcome antibacterial resistance in recent studies. Copper oxide possess various benefits, especially with respect to photocatalytic activ...
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Modelling microbial inactivation during radio frequency (RF) heating of food products has been proven challenging due to the significant variability in temperature profiles among RF processing runs. A specifically-tai...
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To monitor industrial processes properly, soft-sensors are widely used to predict significant but difficult-to-measure quality variables. However, the prediction performances of traditional data-driven soft-sensors ar...
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Quorum sensing (QS) enables bacterial communities to coordinate behaviors essential for survival and competitiveness. However, QS systems are vulnerable to exploitation, where an exploitative strain manipulates QS sig...
Quorum sensing (QS) enables bacterial communities to coordinate behaviors essential for survival and competitiveness. However, QS systems are vulnerable to exploitation, where an exploitative strain manipulates QS signals without incurring metabolic costs. Here, we introduce a reduced-complexity mathematical framework to examine how metabolic investments in signaling by the focal QS strain can influence its population fitness under exploitation. Our results demonstrate that intermediate metabolic investment in QS signaling optimizes population fitness, preventing premature activation of costly traits while maintaining competitive resilience. Moreover, the numerical analysis identifies robust “no-loss” regions where focal QS strain sustains its fitness despite exploitation. Finally, the game-theoretic analysis reveals that an evolutionarily stable state (ESS) emerges when both the QS and exploitative strain adopt intermediate investment levels. This work provides a quantitative framework for understanding microbial signaling dynamics, offering insights into designing synthetic QS-based microbial systems for antimicrobial and biotechnological applications.
Sustainable food systems embrace a range of aspects such as security of the supply of food, health, safety, affordability, quality, a strong food industry in terms of jobs and growth, and environmental sustainability ...
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Sustainable food systems embrace a range of aspects such as security of the supply of food, health, safety, affordability, quality, a strong food industry in terms of jobs and growth, and environmental sustainability in terms of issues such as climate change, biodiversity, water and soil quality. In recent years, quantitative modelling and engineering tools are being developed to better cope with these challenges at the level of all stakeholders involved, including industry, government and regulatory agencies. For example, Life Cycle Assessment (LCA) and related concepts (such as carbon or water footprints) are being exploited within a multi-objective food chain optimization framework. A well-balanced pan-European MSc programme “Sustainable Food Systems Engineering, technology and Business” (FOOD4S ‘ food force ’) 2020-2026 (2029), with a specific integrated and international outlook, fills an increasing need in the transfer of knowledge, experience and standards to developing countries in particular, while contributing to the necessary transformation towards social, environmental, and economic sustainability in food systems. The purpose of this paper is to address the nature of the challenges facing agriculture and food systems, to provide knowledge about the threats and to indicate possibilities of knowledge transfer by education and research.
The demand for sustainable replacements for fossil-based products is steadily increasing, especially now that the effects of climate change are becoming more prominent. Lignocellulose, which is a sustainable and abund...
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The demand for sustainable replacements for fossil-based products is steadily increasing, especially now that the effects of climate change are becoming more prominent. Lignocellulose, which is a sustainable and abundant carbon source, is dubbed to be the perfect replacement. Lignocellulose consists of lignin, hemicellulose, and cellulose. During the Simultaneous Saccharification and Fermentation (SSF) of cellulose, the hydrolysis and fermentation of the produced C6-sugars occurs simultaneously in the same vessel. The SSF process has mainly been developed to circumvent inhibitory effect and increase the overall product yield. Although the concept of the SSF process is promising, the applications are still limited. This contribution presents the trade-off-based multi-objective optimisation of an SSF process. Multi-objective optimisation allows for optimising (bio-)process with respect to multiple, and often conflicting, objectives. These optimisation problems do not render a unique optimal solution but instead an infinite set of so-called Pareto-optimal solutions, the Pareto front. From the Pareto front, the decision maker should select one working point. To aid decision makers in this selection process, the application of a novel genetic optimisation algorithm is presented in this contribution, i.e., tDOM, that is capable of filtering solutions using t-domination. This results in a less dense Pareto front that only contains solutions that are of interest for the decision maker. Additionally, by extending the t-domination concept to two subsequent solution populations, a novel problem-relevant stopping criterion is developed, resulting in a significant gain in the required computational time. A comparison to the well known NSGA-II is provided.
Multi-objective optimisation problems (MOOPs) consider multiple objectives simultaneously. Solving these problems does not render one unique solution but instead a set of equally optimal solutions, i.e., the Pareto fr...
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Multi-objective optimisation problems (MOOPs) consider multiple objectives simultaneously. Solving these problems does not render one unique solution but instead a set of equally optimal solutions, i.e., the Pareto front. The goal of solving a MOOP is to accurately and efficiently approximate the Pareto front. The use of evolutionary optimisation algorithms is widespread in this discipline. During each iteration, parent solutions are combined and mutated to create new offspring solutions. Both populations are subsequently combined and sorted. Only the N fittest solutions of the combined set are selected as the parent solutions for the subsequent iteration. The fitness of a solution is defined by its convergence to the Pareto front and its contribution to the overall solution diversity. Widely used evolutionary algorithms, like NSGA-II (Deb et al., 2002), use non-dominated sorting to assess the convergence of solutions and the concept of crowding distance to ensure a high solution diversity. Both concepts, however, require that all N solutions of the population are compared with all other ( N — 1) solutions for both aspects, and this for all M objectives. This results in a computational complexity of O(MN 2 ). In this contribution, a novel evolutionary algorithm is presented, boasting a significantly lower computational complexity of O(N log (N)). This is achieved by subdividing the feasible space into angular sections. Solutions are scored based on their distance from the current Utopia point and the overall crowdedness of their respective section. Sorting the population based on the attributed scores allows the selection of the N fittest solutions, without having to mutually compare them.
The avour of beer and its stability in time is inuenced by the presence of staling aldehydes. Understanding the conditions that inuence the formation and release of aldehydes during the beer production and storage is ...
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Anaerobic digestion is a widely employed technique that converts waste into biogas, which can be employed as renewable energy. To improve its efficiency, but also to treat recalcitrant waste, mixes involving several t...
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Anaerobic digestion is a widely employed technique that converts waste into biogas, which can be employed as renewable energy. To improve its efficiency, but also to treat recalcitrant waste, mixes involving several types of feedstock with different biodegradability rates are fed to the process. Finding the proper mix is difficult and involves extensive, long-lasting experimental work. This paper presents an approach which allows determining the mix with desired properties based on biochemical Methane Potential (BMP) tests, chemical composition of the feedstocks and optimization. This procedure is illustrated based on experimental data gathered from BMP tests on leaves of linden, oak and maple, and meadow. Subsequently, a simple model is used to simulate the continuous operation of the process converting the proposed mixes into methane.
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