Presenting techniques, case-studies and methodologies that combine the use of simulation approaches with optimization techniques for facing problems in manufacturing, logistics, or aeronautical problems, this book provides solutions to common industrial problems in several fields, which range from manufacturing to aviation problems, where the common denominator is the combination of simulation’s flexibility with optimization techniques’ robustness.
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Background: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.
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This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for riving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.
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Mycelium biocomposites (MBCs) are a fairly new group of materials. MBCs are non-toxic and carbon-neutral cutting-edge circular materials obtained from agricultural residues and fungal mycelium, the vegetative part of fungi. Growing within days without complex processes, they offer versatile and effective solutions for diverse applications thanks to their customizable textures and characteristics achieved through controlled environmental conditions. This project involves a collaboration between MNEXT and First Circular Insulation (FC-I) to tackle challenges in MBC manufacturing, particularly the extended time and energy-intensive nature of the fungal incubation and drying phases. FC-I proposes an innovative deactivation method involving electrical discharges to expedite these processes, currently awaiting patent approval. However, a critical gap in scientific validation prompts the partnership with MNEXT, leveraging their expertise in mycelium research and MBCs. The research project centers on evaluating the efficacy of the innovative mycelium growth deactivation strategy proposed by FC-I. This one-year endeavor permits a thorough investigation, implementation, and validation of potential solutions, specifically targeting issues related to fungal regrowth and the preservation of sustained material properties. The collaboration synergizes academic and industrial expertise, with the dual purpose of achieving immediate project objectives and establishing a foundation for future advancements in mycelium materials.
Nano and micro polymeric particles (NMPs) are a point of concern by environmentalists and toxicologist for the past years. Their presence has been detected in many environmental bodies and even in more recently human blood as well. One of the most common paths these particles take to enter living organisms is via water consumption. However, despite the efforts of different academic and other knowledge groups, there is no consensus about standards methods which can be used to qualifying and quantifying these particles, especially the submicrometric ones. Many different techniques have been proposed like field flow fractionation (FFF) followed by multi angle laser scattering (MALS), pyrolysis-GC and scanning electron microscopy (SEM). Additionally, the sampling collection and preparation is also considered a difficult step, as such particles are mostly present in very low concentration. Nanocatcher proposes the use of submerged drones as a sampling collection tool to monitor the presence of submicrometric polymeric particles in water bodies. The sample collections will be done using special membrane systems specially designed for the drone. After collected, the samples will be analysed using FFF+MALS, SEM and Py-GC. If proven successful, the use of submerged drones can strongly facilitate sampling and mapping of submicrometric polymeric particles in water bodies and will provide an extensive and comprehensive map of the presence of these particles in such environment.
To optimize patient care, it is vital to prevent infections in healthcare facilities. In this respect, the increasing prevalence of antibiotic-resistant bacterial strains threatens public healthcare. Current gold standard techniques are based on classical microbiological assays that are time consuming and need complex expensive lab environments. This limits their use for high throughput bacterial screening to perform optimal hygiene control. The infection prevention workers in hospitals and elderly nursing homes underline the urgency of a point-of-care tool that is able to detect bacterial loads on-site in a fast, precise and reliable manner while remaining with the available budgets. The aim of this proposal titled SURFSCAN is to develop a novel point-of-care tool for bacterial load screening on various surfaces throughout the daily routine of professionals in healthcare facilities. Given the expertise of the consortium partners, the point-of-care tool will be based on a biomimetic sensor combining surface imprinted polymers (SIPs), that act as synthetic bacterial receptors, with a thermal read-out strategy for detection. The functionality and performance of this biomimetic sensor has been shown in lab conditions and published in peer reviewed journals. Within this proposal, key elements will be optimized to translate the proof of principle concept into a complete clinical prototype for on-site application. These elements are essential for final implementation of the device as a screening and assessment tool for scanning bacterial loads on surfaces by hospital professionals. The research project offers a unique collaboration among different end-users (hospitals and SMEs), and knowledge institutions (Zuyd University of Applied Sciences, Fontys University of Applied Sciences and Maastricht Science Programme, IDEE-Maastricht University), which guarantees transfer of fundamental knowledge to the market and end-user needs.
