Multiple agents routing and scheduling algorithms for network-based transportation systems.

This research attempts to develop effective and practical algorithms that enable multiple agents to address routing and scheduling problems simultaneously: given a set of initial points and final points for multiple agents in a route network, separation-compliant routes and speed profiles are to be found for every agent while maximising a performance index subject to satisfy operational constraints. The algorithms are applicable to many transportation systems that consider many operational factors such as flight planning problems in the Air Traffic Management (ATM) system, and analysing urban airspace structure for an Unmanned Aircraft System (UAS) Traffic Management (UTM) system. This thesis focuses on an investigation of a new horizontal Routing and Scheduling (R&S) algorithm for homogeneous multiple arrivals at a single airport. Importantly, this study is the first to investigate the routing problem and scheduling problem simultaneously in the ATM domain, and it is found that a time-based separation concept and a flight time weighting scheme applied in the proposed algorithm allows for horizontal separation-compliant routing and scheduling for each flight. Simulation results show that the current flight planning approach would benefit from the proposed R&S algorithm that provides detailed flight plans in a less computation time. Another part of this thesis focuses on the extension of the R&S algorithm to deal with multiple heterogeneous aircraft arriving at multiple airports, and also to cope with three-dimensional route network. With these extensions, the proposed R&S algorithm can be adopted to handle a wider range of operational conditions represented by various combinations of aircraft types in a fleet and neighbour-dependent separation requirements. Numerical simulation using a simple route network model shows that the R&S algorithm can find the near-optimal route and schedule within polynomial time. As a more realistic case study, we tested the algorithm into the London Terminal Manoeuvring Area (LTMA). The numerical experiment shows that the algorithm provides a separation-compliant route and schedule for multiple heterogeneous aircraft in the three-dimensional LTMA efficiently. By modifying the proposed algorithm, we address flight planning problems that arise in drone delivery, which is one of the most promising applications of the UTM system. As a preliminary study, we demonstrate two last-mile delivery cases (1-to-M) and one first-mile delivery case (M-to-1) within a route network over roads. The results of each case show that detailed flight plans could support analysis of the route network capacity and help to establish requirements for safe and efficient operations. On the basis of this observation, the analysis of the structured urban airspace capacity is performed for four different types of drone delivery operation (1-to-M, M-to-1, N - to-M, and M-to-N ) using the proposed algorithms, where we suggest four intuitive metrics calculated from the detailed flight plans. We apply two different sequencing algorithms (First Come First Served algorithm and Last Come First Served algorithm) - an outer loop of the R&S algorithms - for each operation type. Monte Carlo simulation results suggest to use either more efficient sequencing algorithm or both of the algorithms together in a timely manner for each operation type. From the simulation results, we could expect that the proposed algorithms provide the analysis and suggestions for designing urban airspace to support designers, regulators, and policymakers. Collectively, the algorithms proposed in this thesis may play a key role in many network-based transport planning problems regarding effective and safe operations, along with future works on extension of the algorithm to real-time planning algorithms and to other transportation systems.PhD in Aerospac

A new multiple flights routing and scheduling algorithm in terminal manoeuvring area

We address multiple flights planning problems from its initial waypoint to its destination while satisfying the minimum separation requirement between each aircraft at all times in a Terminal Manoeuvring Area (TMA) to maintain or increase runway throughput. Due to operational constraints for safety, most of the current aircraft fly over or by waypoints, and along nominal routes in the airspace. Where the waypoints and routes in the airspace can be modelled as a weighted digraph, called airspace graph. We propose a problem that consists of determining a flight path (routing problem) and its speed profile (scheduling problem) in a given airspace graph in which a time-based weighting scheme of the airspace graph is proposed to reflect a speed-limitation-compliant schedule that satisfy the minimum separation requirement. For multiple flights cases, the flight paths and schedules are obtained by iteratively solving the problem for each flight by applying the First Come First Served (FCFS) algorithm to determine an arrival sequence. The main contributions of this paper are increasing a solution search space by solving two problems simultaneously, efficient computational time, and providing the separation-compliant flight path and speed profile within the speed limitation for each flight. We demonstrate the advantages of the proposed approach through a case study in which multiple flights arrive at a single airport, and we compare the results with Regulated Tactical Flight Model (RTFM) obtained from EUROCONTROL Demand Data Repository 2 (DDR2). Although, we consider only a single airport and make an assumption to simplify flight routes from holding stacks to a Final Approach Fix (FAF), the results show the potential usage of the proposed algorithm as a Decision Support Tool (DST) for Air Traffic Controllers (ATCOs) if the following considerations are taken into account: detailed routes-based flights after the holding stacks, multiple airports, departing aircraft, all possibe aircraft types, and uncertainties produced by external sources

Structured urban airspace capacity analysis: four drone delivery cases

A route network-based urban airspace is one of the initial operational concepts of managing the high-density very low-level (VLL) urban airspace for unmanned aircraft system (UAS) traffic management (UTM). For the conceptual urban airspace, it is necessary to perform a quantitative analysis of urban airspace to stakeholders for designing rules and regulations. This study aims to discuss the urban airspace capacity for four different operation types by applying different sequencing algorithms and comparing its results to provide insight and suggestions for different operation cases to assist airspace designers, regulators, and policymakers. Four drone delivery operation types that can be applied in the high-density VLL urban airspace are analysed using the suggested four metrics: total flight time; total flight distance; mission completion time; the number of conflicts. The metrics can be calculated from a flight planning algorithm that we proposed in our previous studies. The algorithm for multiple agents flight planning problems consists of an inner loop algorithm, which calculates each agent’s flight plan, and an outer loop algorithm, which determines the arrival and departure sequences. For each operation type, we apply two different outer loops with the same inner loop to suggest an appropriate sequencing algorithm. Numerical simulation results show tendencies for each type of operation with regard to the outer loop algorithms and the number of agents, and we analyse the results in terms of airspace capacity, which could be utilised for designing structures depending on urban airspace situations and environments. We expect that this study could give some intuition and support to policymakers, urban airspace designers, and regulators

A Case of Primary Diffuse Large B Cell Lymphoma of the Maxillary Sinus Presenting as Epiphora

Primary sinusoidal non-Hodgkin’s lymphoma (NHL) is a very rare disease. The main symptoms of sinusoidal NHL are rhinorrhea, nasal obstruction, and post-nasal drip. Symptoms such as eye protrusion, diplopia, trismus, and periorbital pain can also occur. Epiphora is a very rare symptom of sinusoidal NHL, which can lead to a misdiagnosis of dacryocystitis or dacryostenosis. The authors report the case of a 46-year-old female patient who visited hospital for symptoms of epiphora, which did not improve even after 3 months of eye treatment, leading to a final diagnosis of maxillary NHL

Theoretical and Experimental Studies of the Dechlorination Mechanism of Carbon Tetrachloride on a Vivianite Ferrous Phosphate Surface

Chlorinated organics are the principal and most frequently found contaminants in soil and groundwater, generating significant environmental problems. Over the past several decades, Fe-containing minerals naturally occurring in aquatic and terrestrial environments have been used as natural electron donors, which can effectively dechlorinate a variety of chlorinated organics. However, a full understanding of the reaction mechanism of the dechlorination pathway cannot be obtained by experimental investigations alone, due to the immeasurability of chemical species formed over a short reaction time. In this report, we describe experiments and density functional theory (DFT) calculations carried out to investigate the complex reduction pathway of carbon tetrachloride (CT) on a vivianite (Fe^(II)_3(PO_4)_2·8H_2O) surface. Our results indicate that chloroform (HCCl_3) and formate are the primary transformation products. The experimental results reveal that the reduction kinetics of CCl_4 can be dramatically accelerated as the pH is increased from 3 to 11. On the basis of the DFT calculations, we found that HCCl_3 can be formed by ^•CCl_3 and :CCl_3^(–*) on a deprotonated vivianite surface (an adsorbate on vivianite is denoted using an asterisk). In addition, :CCl_3^(–*) can be nonreductively dechlorinated to form :CCl_2^* followed by sequential nucleophilic attack by OH^(–*), resulting in the formation of :CCl(OH)^* and :C(OH)_2^*, which are responsible for production of CO and formate, respectively. The results obtained from this study can facilitate the modeling of systems of other halogenated species and minerals, which will provide fundamental insight into their corresponding reaction mechanisms

Clinical Characteristics of Patients Diagnosed With Odontogenic Rhinosinusitis After Dental Implants

Background and Objectives With the ongoing development of intraoral surgical treatment and invasive dental treatments such as implants, odontogenic rhinosinusitis (ORS) is on the rise. ORS related to dental implants accounts for 8% to 37% of cases. The purpose of this study is to define the characteristics of patients with ORS related to dental implants. Methods From 2015 to 2019, the medical records of 15 patients who developed maxillary sinus disease after receiving dental implants were retrospectively analyzed among patients who visited the ear nose and throat and dentistry departments. We reviewed the chief complaint, assessment, diagnosis, treatment and prognosis of these patients. Results Of the 15 patients, all were diagnosed with ORS. One patient with a post-operative cheek cyst, 1 with fungal sinusitis, 1 with an inverted papilloma, 1 with chronic rhinosinusitis, and 1 with a radicular cyst were diagnosed after surgery. Endoscopic sinus surgery was performed in 14 patients and 2 patients underwent a combined operation. One patient improved after medical treatment. The follow-up period was about 8.6 months. No recurrence was found in any of the patients. Conclusion If an implant problem is suspected based on history-taking and physical examination, active consultation with dentistry is needed to diagnose ORS

Molecular Dynamics Study on Crack Propagation in Al Containing Mg–Si Clusters Formed during Natural Aging

The crack propagation behavior of Al containing Mg–Si clusters is investigated using molecular dynamics (MD) simulations to demonstrate the relationship between the natural aging time in Al–Si–Mg alloys and ductility. Experimental results show that the elongation at failure decreases with natural aging. There are few studies on the relationship between natural aging and ductility because of the difficult observation of Mg–Si clusters. To solve the difficulty, cracked Al containing Mg–Si clusters of varying sizes are assumed for the MD simulations. A larger Mg–Si cluster in Al results in earlier crack opening and dislocation emission. Moreover, as the Mg–Si cluster size increases, the stress near the crack tip becomes more concentrated. This causes rapid crack propagation, a similar effect to that of crack tip sharpening. As a result of long-term natural aging, the cracks expand rapidly. The influence of geometry is also investigated. Crack lengthening and thickness reduction negatively impact the fracture toughness, with the former having a larger impact than the latter. Although there are several discrepancies in the practical deformation conditions, the simulation results can help to more thoroughly understand natural aging in Al–Si–Mg alloys

Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach

With the increasing number of fast-electric vehicle charging stations (fast-EVCSs) and the popularization of information technology, electricity price competition between fast-EVCSs is highly expected, in which the utilization of public and/or privacy-preserved information will play a crucial role. Self-interest electric vehicle (EV) users, on the other hand, try to select a fast-EVCS for charging in a way to maximize their utilities based on electricity price, estimated waiting time, and their state of charge. While existing studies have largely focused on finding equilibrium prices, this study proposes a personalized dynamic pricing policy (PeDP) for a fast-EVCS to maximize revenue using a reinforcement learning (RL) approach. We first propose a multiple fast-EVCSs competing simulation environment to model the selfish behavior of EV users using a game-based charging station selection model with a monetary utility function. In the environment, we propose a Q-learning-based PeDP to maximize fast-EVCS' revenue. Through numerical simulations based on the environment: (1) we identify the importance of waiting time in the EV charging market by comparing the classic Bertrand competition model with the proposed PeDP for fast-EVCSs (from the system perspective); (2) we evaluate the performance of the proposed PeDP and analyze the effects of the information on the policy (from the service provider perspective); and (3) it can be seen that privacy-preserved information sharing can be misused by artificial intelligence-based PeDP in a certain situation in the EV charging market (from the customer perspective)

A new graph-based flight planning algorithm for unmanned aircraft system traffic management

To efficiently and safely provide various types of services, small Unmanned Aircraft System (sUAS) are envisioned to be integrated with other airspace users. sUAS operation types such as route network, free flight, free routing can be determined depending on services, operating environments, etc. This paper addresses a route network-based flight planning problem that includes separation considered routing and scheduling for multiple sUASs. We propose an algorithm that generates each a route and schedule for each flight from its origin to its destination to minimise each sUAS' flight time while satisfying the minimum separation requirement at all times. The algorithm consists of an inner loop and an outer loop. In the inner loop each sUAS optimises its flight plan by solving its unique shortest path problem in a decentralised way. In the outer loop one of the flights is allocated using a centralised algorithm in each outer loop. The algorithm continues until all flights are allocated. As a preliminary study, we demonstrate the proposed algorithm through case studies for “last-mile delivery”, and “first-mile delivery”. The main contributions of this paper are as follows: increasing a solution search space by solving routing and scheduling problems simultaneously with separation assurance; low computational time. The proposed algorithm can be potentially applied to airspace capacity estimation and throughput of service points

Influence of the Composition and Vacancy Concentration on Cluster Decomposition Behavior in Al–Si–Mg Alloy: A Kinetic Monte Carlo Study

The influence of cluster composition and the addition of vacancies on the decomposition behavior of clusters during artificial aging in Al–Si–Mg alloys were analyzed according to the kinetic Montel Carlo model. Clusters with a balanced composition (Mg/(Mg + Si) = 0.5) were the most difficult to decompose. In addition, the cluster decomposition was slower when more vacancies were added to the cluster. Among Si, Mg, and vacancies, vacancies most significantly affect decomposition. The clusters with Mg/(Mg + Si) ≤ 0.4 strongly trap vacancies, which can be classified as hardly decomposable vacancy-rich clusters. The clustering behavior during natural aging and the effect of pre-aging were analyzed using the Kinetic Monte Carlo model. Pre-aging slows down cluster formation due to the lowered vacancy concentration. In addition, the overall composition of the clusters changes to easily decomposable clusters after pre-aging. Thus, not only is the number of clusters reduced but also the clusters are more easily decomposable when pre-aging is performed