Mexico City airport is located close to the center ofthe city and is Mexico’s busiest airport which is consideredcongested. One of the consequences of airport congestion areflight delays which in turn decrease costumer’s satisfaction. Airtraffic control has been using a ground delay program as a toolfor alleviating the congestion problems, particularly in the mostcongested slots of the airport. This paper uses a model-basedapproach for analyzing the effectiveness of the ground delayprogram and rules. The results show that however the rulesapplied seem efficient, there is still room for improvement inorder to make the traffic management more efficient.
MULTIFILE
Airport management is frequently faced with a problem of assigning flights to available stands and parking positions in the most economical way that would comply with airline policies and suffer minimum changes due to any operational disruptions. This work presents a novel approach to the most common airport problem – efficient stand assignment. The described algorithm combines benefits of data-mining and metaheuristic approaches and generates qualitative solutions, aware of delay trends and airport performance perturbations. The presented work provides promising solutions from the starting moments of computation, in addition, it delivers to the airport stakeholders delay-aware stand assignment, and facilitates the estimation of risk and consequences of any operational disruptions on the slot adherence.
MULTIFILE
For delayed and long-term students, the education process is often a lonely journey. The main conclusion of this research is that learning should not be an individual process of the student connected to one lecturer, but rather a community where learning is a collective journey. The social interaction between lecturers, groups of delayed students and other actors is an important engine for arriving at the new knowledge, insights and expertise that are important to reach their final level. This calls for the design of social structures and the collaboration mechanism that enable the bonding of all members in the community. By making use of this added value, new opportunities for the individual are created that can lead to study success. Another important conclusion is that in the design and development of learning communities, sufficient attention must be paid to cultural characteristics. Students who delay are faced with a loss of self-efficacy and feelings of shame and guilt. A learning community for delayed students requires a culture in which students can turn this experience into an experience of self-confidence, hope and optimism. This requires that the education system pays attention to language use, symbols and rituals to realise this turn. The model ‘Building blocks of a learning environment for long-term students’ contains elements that contribute to the study success of delayed and long-term students. It is the challenge for every education programme to use it in an appropriate way within its own educational context. Each department will have to explore for themselves how these elements can be translated into the actions, language, symbols and rituals that are suitable for their own target group.
DOCUMENT
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
Predictive maintenance, using data of thousands of sensors already available, is key for optimizing the maintenance schedule and further prevention of unexpected failures in industry.Current maintenance concepts (in the maritime industry) are based on a fixed maintenance interval for each piece of equipment with enough safety margin to minimize incidents. This means that maintenance is most of the time carried out too early and sometimes too late. This is in particular true for maintenance on maritime equipment, where onshore maintenance is strongly preferred over offshore maintenance and needs to be aligned with the vessel’s operations schedule. However, state-of-the-art predictive maintenance methods rely on black-box machine learning techniques such as deep neural networks that are difficult to interpret and are difficult to accept and work with for the maintenance engineers. The XAIPre project (pronounce Xyper) aims at developing Explainable Predictive Maintenance algorithms that do not only provide the engineers with a prediction but in addition, with a risk analysis on the components when delaying the maintenance, and what the primary indicators are that the algorithms use to create inference. To use predictive maintenance effectively in Maritime operations, the predictive models and also the optimization of the maintenance schedule using these models, need to be aware of the past and planned vessel activities, since different activities affect the lifetime of the machines differently. For example, the degradation of a hydraulic pump inside a crane depends on the type of operations the crane but also the vessel is performing. Thus the models do not only need to be explainable but they also need to be aware of the context which is in this case the vessel and machinery activity. Using sensor data processing and edge-computing technologies that will be developed and applied by the Hanze University of Applied Sciences in Groningen (Hanze UAS), context information is extracted from the raw sensor data. The XAIPre project combines these Explainable Context Aware Machine Learning models with state-of-the-art optimizers, that are already developed and available from the NWO CIMPLO project at LIACS, in order to develop optimal maintenance schedules for machine components. The resulting XAIPre prototype offers significant competitive advantages for maritime companies such as Heerema, by increasing the longevity of machine components, increasing worker safety and decreasing maintenance costs.
Predictive maintenance, using data of thousands of sensors already available, is key for optimizing the maintenance schedule and further prevention of unexpected failures in industry. Current maintenance concepts (in the maritime industry) are based on a fixed maintenance interval for each piece of equipment with enough safety margin to minimize incidents. This means that maintenance is most of the time carried out too early and sometimes too late. This is in particular true for maintenance on maritime equipment, where onshore maintenance is strongly preferred over offshore maintenance and needs to be aligned with the vessel’s operations schedule. However, state-of-the-art predictive maintenance methods rely on black-box machine learning techniques such as deep neural networks that are difficult to interpret and are difficult to accept and work with for the maintenance engineers. The XAIPre project (pronounce “Xyper”) aims at developing Explainable Predictive Maintenance (XPdM) algorithms that do not only provide the engineers with a prediction but in addition, with 1) a risk analysis on the components when delaying the maintenance, and 2) what the primary indicators are that the algorithms used to create inference. To use predictive maintenance effectively in Maritime operations, the predictive models and the optimization of the maintenance schedule using these models, need to be aware of the past and planned vessel activities, since different activities affect the lifetime of the machines differently. For example, the degradation of a hydraulic pump inside a crane depends on the type of operations the crane performs. Thus, the models do not only need to be explainable but they also need to be aware of the context which is in this case the vessel and machinery activity. Using sensor data processing and edge-computing technologies that will be developed and applied by the Hanze UAS in Groningen, context information is extracted from the raw sensor data. The XAIPre project combines these Explainable Context Aware Machine Learning models with state-of-the-art optimizers, that we already developed in the NWO CIMPLO project at LIACS, in order to develop optimal maintenance schedules for machine components. The optimizers will be adapted to fit within XAIPre. The resulting XAIPre prototype offers significant competitive advantages for companies such as Heerema, by increasing the longevity of machine components, increasing worker safety, and decreasing maintenance costs. XAIPre will focus on the predictive maintenance of thrusters, which is a key sub-system with regards to maintenance as it is a core part of the vessels station keeping capabilities. Periodic maintenance is currently required in fixed intervals of 5 years. XPdM can provide a solid base to deviate from the Periodic Maintenance prescriptions to reduce maintenance costs while maintaining quality. Scaling up to include additional components and systems after XAIPre will be relatively straightforward due to the accumulated knowledge of the predictive maintenance process and the delivered methods. Although the XAIPre system will be evaluated on the use-cases of Heerema, many components of the system can be utilized across industries to save maintenance costs, maximize worker safety and optimize sustainability.
