Airports and surrounding airspaces are limited in terms of capacity and represent the major bottlenecks of the air traffic management system. This paper addresses the problems of terminal airspace management and airport congestion management at the macroscopic level through the integrated control of arrivals and departures. Conflict detection and resolution methods are applied to a predefined terminal route structure. Different airside components are modeled using network abstraction. Speed, arrival and departure times, and runway assignment are managed by using an optimization method. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on case studies of Paris Charles De-Gaulle airport show some potential improvements: First, when the airport capacity is decreased, until a certain threshold, the overload can be mitigated properly by adjusting the aircraft entry time in the Terminal Maneuvering Area and the pushback time. Second, landing and take-off runway assignments in peak hours with imbalanced runway throughputs can significantly reduce flight delays. A decrease of 37% arrival delays and 36% departure delays was reached compared to baseline case.
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Airports and surrounding airspaces are limited in terms of capacity and represent the major bottleneck in the air traffic management system. This paper proposes a two level model to tackle the integrated optimization problem of arrival, departure, and surface operations. The macroscopic level considers the terminal airspace management for arrivals and departures and airport capacity management, while the microscopic level optimizes surface operations and departure runway scheduling. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on real-world case studies of Paris Charles De-Gaulle airport, show the benefits of this integrated approach.
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Atherosclerosis is the development of lipid-laden plaques in arteries and is nowadays considered as an inflammatory disease. It has been shown that high doses of ionizing radiation, as used in radiotherapy, can increase the risk of development or progression of atherosclerosis. To elucidate the effects of radiation on atherosclerosis, we propose a mathematical model to describe radiation-promoted plaque evelopment. This model distinguishes itself from other models by combining plaque initiation and plaque growth, and by incorporating information from biological experiments. It is based on two consecutive processes: a probabilistic dose-dependent plaque initiation process, followed by deterministic plaque growth.
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The constant growth of air traffic, especially in Europe, is putting pressure on airports, which, in turn, are suffering congestion problems. The airspace surrounding airport, terminal manoeuvring area (TMA), is particularly congested, since it accommodates all the converging traffic to and from airports. Besides airspace, airport ground capacity is also facing congestion problems, as the inefficiencies coming from airspace operations are transferred to airport ground and vice versa. The main consequences of congestion at airport airspace and ground, is given by the amount of delay generated, which is, in turn, transferred to other airports within the network. Congestion problems affect also the workload of air traffic controllers that need to handle this big amount of traffic.This thesis deals with the optimization of the integrated airport operations, considering the airport from a holistic point of view, by including operations such as airspace and ground together. Unlike other studies in this field of research, this thesis contributes by supporting the decisions of air traffic controllers regarding aircraft sequencing and by mitigating congestion on the airport ground area. The airport ground operations and airspace operations can be tackled with two different levels of abstractions, macroscopic or microscopic, based on the time-frame for decision-making purposes. In this thesis, the airport operations are modeled at a macroscopic level.The problem is formulated as an optimization model by identifying an objective function that considers the amount of conflicts in the airspace and capacity overload on the airport ground; constraints given by regulations on separation minima between consecutive aircraft in the airspace and on the runway; decision variables related to aircraft entry time and entry speed in the airspace, landing runway and departing runway choice and pushback time. The optimization model is solved by implementing a sliding window approach and an adapted version of the metaheuristic simulated annealing. Uncertainty is included in the operations by developing a simulation model and by including stochastic variables that represent the most significant sources of uncertainty when considering operations at a macroscopic level, such as deviation from the entry time in the airspace, deviation in the average taxi time and deviation in the pushback time. In this thesis, optimization and simulation techniques are combined together by developing two methods that aim at improving the solution robustness and feasibility. The first method acts as a validation tool for the optimized solution, and it improves the robustness of solution by iteratively fine-tuning some of the optimization model input parameters. The second method embeds the optimization in a simulation environment by taking full advantage of the sliding window approach and creating a loop for a continuous improvement of the optimized solution at each window of the sliding window approach. Both methods prove to be effective by improving the performance, lowering the total amount of conflicts up to 23.33% for the first method and up to 11.2% for the second method, however, in contrast to the deterministic method, the two methods they are not able to achieve a conflict-free scenario due to the effect of uncertainty.In general, the research conducted in this thesis highlights that uncertainty is a factor that affects to a large extent the feasibility of optimized solution when applied to real-world instances, and it, moreover, confirms that using simulation together with optimization has the potentiality toivdeal with uncertainty. The framework developed can be potentially applied to similar problems and different optimization solving methods can be adapted to it.Keywords: Optimization, Simulation, Integrated airport operations, Uncertainty
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his paper develops a new, broader, and more realistic lens to study (lacking) linkages between government policy and school practices. Drawing on recent work in organization theory, we advance notions on cluster of organization routines and the logic of complementarities underlying organizational change. This lens allows looking at how schools do (not) change a cluster of organization routines in response to multiple, simultaneous demands posed by government policies. Thirteen purposively selected Dutch secondary schools responding to three central government policies calling for concurrent change were analyzed, taking the schedule of a school as an exemplary case of a cluster of organization routines. Five distinct responses were distinguished, which can be sorted according to their impact on the whole organization. The study fnds that ten of the thirteen schools did not change anything in response to at least one of the three policies we studied. However, all schools changed their cluster of organization routines, which impacted the whole organization in response to at least one of the three government policies. Therefore, looking at combinations of responses and considering the impact of change on school organizations qualifes ideas about schools being resistant to policy or unwilling to change and improve.
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From the article: "A facile approach for the fabrication of large-scale interdigitated nanogap electrodes (nanogap IDEs) with a controllable gap was demonstrated with conventional micro-fabrication technology to develop chemocapacitors for gas sensing applications. In this work, interdigitated nanogap electrodes (nanogap IDEs) with gaps from 50–250 nm have been designed and processed at full wafer-scale. These nanogap IDEs were then coated with poly(4-vinyl phenol) as a sensitive layer to form gas sensors for acetone detection at low concentrations. These acetone sensors showed excellent sensing performance with a dynamic range from 1000 ppm to 10 ppm of acetone at room temperature and the observed results are compared with conventional interdigitated microelectrodes according to our previous work. Sensitivity and reproducibility of devices are discussed in detail. Our approach of fabrication of nanogap IDEs together with a simple coating method to apply the sensing layer opens up possibilities to create various nanogap devices in a cost-effective manner for gas sensing applications"
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Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring.
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