Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations

The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work is to develop a more general model that, in addition to patient anatomy, also considers variable beam configurations, to achieve a more comprehensive automatic planning with a potentially easier clinical implementation, without the need of training specific models for different beam settings

Continuous optimization via simulation using Golden Region search

Simulation Optimization (SO) is the use of mathematical optimization techniques in which the objective function (and/or constraints) could only be numerically evaluated through simulation. Many of the proposed SO methods in the literature are rooted in or originally developed for deterministic optimization problems with available objective function. We argue that since evaluating the objective function in SO requires a simulation run which is more computationally costly than evaluating an available closed form function, SO methods should be more conservative and careful in proposing new candidate solutions for objective function evaluation. Based on this principle, a new SO approach called Golden Region (GR) search is developed for continuous problems. GR divides the feasible region into a number of (sub) regions and selects one region in each iteration for further search based on the quality and distribution of simulated points in the feasible region and the result of scanning the response surface through a metamodel. The experiments show the GR method is efficient compared to three well-established approaches in the literature. We also prove the convergence in probability to global optimum for a large class of random search methods in general and GR in particular

Generic Architecture for Predictive Computational Modelling with Application to Financial Data Analysis: Integration of Semantic Approach and Machine Learning

The PhD thesis introduces a Generic Architecture for Predictive Computational Modelling capable of automating analytical conclusions regarding quantitative data structured as a data frame. The model involves heterogeneous data mining based on a semantic approach, graph-based methods (ontology, knowledge graphs, graph databases) and advanced machine learning methods. The main focus of my research is data pre-processing aimed at a more efficient selection of input features to the computational model. Since the model I propose is generic, it can be applied for data mining of all quantitative datasets (containing two-dimensional, size-mutable, heterogeneous tabular data); however, it is best suitable for highly interconnected data. To adapt this generic model to a specific use case, an Ontology as the formal conceptual representation for the relevant domain knowledge is needed. I have determined to use financial/market data for my use cases. In the course of practical experiments, the effectiveness of the PCM model application for the UK companies’ financial risk analysis and the FTSE100 market index forecasting was evaluated. The tests confirmed that the PCM model has more accurate outcomes than stand-alone traditional machine learning methods. By critically evaluating this architecture, I proved its validity and suggested directions for future research

An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy

A new software package allows for dense electron microscopy reconstructions of neuronal networks in the fruit fly brain, and reveals specific differences in microcircuits between insects and vertebrates

Geodetic Sciences

Space geodetic techniques, e.g., global navigation satellite systems (GNSS), Very Long Baseline Interferometry (VLBI), satellite gravimetry and altimetry, and GNSS Reflectometry & Radio Occultation, are capable of measuring small changes of the Earth�s shape, rotation, and gravity field, as well as mass changes in the Earth system with an unprecedented accuracy. This book is devoted to presenting recent results and development in space geodetic techniques and sciences, including GNSS, VLBI, gravimetry, geoid, geodetic atmosphere, geodetic geophysics and geodetic mass transport associated with the ocean, hydrology, cryosphere and solid-Earth. This book provides a good reference for geodetic techniques, engineers, scientists as well as user community

Of Single Nucleotides and Single Cells: Charting the Genotype-Phenotype Map at High Resolution Using \u3ci\u3eDrosophila melanogaster\u3c/i\u3e

Understanding the mechanisms by which genetic variation brings about phenotypic variation is essential for understanding variation in complex traits. Drosophila melanogaster is a powerful model organism for such studies. Flies are easy to raise in the laboratory under controlled genetic and environmental conditions and many genetic tools are widely available. To chart the genotype-phenotype map, we need to study how individual genetic variants contribute to phenotypic variation, as well as how environmental perturbations influence gene expression. Regarding the former, I generated single nucleotide substitutions in Obp56h in a common genetic background. Obp56h, a member of the Odorant binding protein multigene family, is a small gene in a favorable genomic location for CRISPR-Cas9 mediated deletion. After deletion, I reinserted the gene at the endogenous locus with individual allelic variants chosen from those segregating in a wild-derived inbred population to produce five lines varying at single nucleotides in a common genetic background. Different alleles, both within and near the gene (potentially regulatory) and both common and rare, have different, large effects on organismal fitness traits as well as on genome-wide coregulated ensembles of transcripts. These effects are at the level of mean and microenvironmental variance in both fitness traits and the transcriptome. However, these alleles have only small effects on fitness traits in the wild-derived inbred population indicating that the effects of individual alleles can be context-specific and are perhaps suppressed in natural populations via epistatic interactions. Next, I studied how acute cocaine consumption and developmental alcohol exposure affect the transcriptome at single-cell resolution. The Drosophila brain is small, allowing for comprehensive whole-brain studies. Further, previous studies have characterized effects of acute cocaine consumption and developmental alcohol exposure on flies, which resemble those in humans. Single-cell RNA sequencing revealed that the transcriptomes of cells in the fly brain are affected in a cell-type and sex-dependent manner after the flies consumed fixed amounts of cocaine or are exposed to developmental alcohol exposure. These effects are sexually dimorphic, with males showing a greater degree of differential expression and are particularly prominent in glial and mushroom body cells. Developmental alcohol exposure leads to a similar, but different, sexually dimorphic and cell-type dependent pattern of differential expression as cocaine consumption. Some mechanisms are shared between the experimental paradigms indicating common processes. The strategies used in the studies described in this dissertation can be generally applied to explore genotype-phenotype relationships at high resolution

Integration of GIS and DSS: a methodology to evaluate low carbon strategies in a smart urban metabolism context

An Urban Metabolism system can be examined by evaluating the incoming and outgoing energy flows of a city. Academics and researchers have utilized Urban Metabolism framework to analyze different urban areas and have begun to extend the framework beyond the city-region unit of analysis to inform related aspects of the Urban Metabolism: in this context UM framework is a tool that can be useful in the decision making process. This study aims to be an opportunity and an example of environmental analysis of UM, from the point of view of CO2eq emissions and absorptions. A multi-objective Decision Support System is developed with the aim of minimizing the environmental, social and economic impacts of the CO2eq emissions at the municipal level. The Decision Support System has been implemented and a few scenario analyses were developed: enhancement of energy efficiency of residential and industrial buildings, increase of green areas, production of electricity by means of photovoltaic installation on site, efficiency of the vehicle fleet and finally, proper recycling of waste. The municipality of Tavagnacco recognizes this approach as a new perspective of analysis for a future comparison project with other municipalities. From this comparison it is expected to get results that can accredit the most convenient method from the environmental, social and economic point of view, and can offer the basis for the improvement of energy efficiency. Results of this work can provide evidence in support of an increased awareness in issues related to the CO2eq reduction.Il metabolismo di un sistema urbano pu`o essere esaminato cercando di sviluppare e comprendere i flussi energetici in ingresso e in uscita dalla citt`a. Accademici e ricercatori hanno utilizzato questo approccio al fine di valutare diverse aree urbane e hanno recentemente esteso il quadro di indagine al di l`a dell\u2019unit`a di citt`a-regione al fine di utilizzare questo strumento nell\u2019ambito del processo decisionale di pianificazione del territorio. Questo percorso vuole definire una possibile metodologia e un esempio di approccio spaziale ad un\u2019analisi di bilancio comunale di CO2eq. E\u2019 stato sviluppato un Sistema di Supporto alle Decisioni multiobiettivo, con il fine di minimizzare l\u2019impatto ambientale oltre a quello sociale e quello economico delle emissioni di CO2eq su scala comunale. Il Sistema di Supporto alle Decisioni ha previsto l\u2019implementazione di alcuni scenari di analisi quali l\u2019incentivazione dell\u2019efficientamento energetico degli edi- fici residenziali ma anche industriali, l\u2019aumento delle aree a verde, la produzione di energia elettrica in loco mediante impianto fotovoltaico, l\u2019efficientamento del parco veicolare e infine una valida raccolta differenziata. Il comune di Tavagnacco conosce le sfide future in merito ai problemi ambientali e si impegna in un progetto pilota di valutazione delle emissioni di CO2eq. In un prossimo futuro si delinea un lavoro di confronto tra comuni che utilizzano metodi di abbattimento delle emissioni. Da questo confronto ci si aspetta di ottenere risultati che possano accreditare il metodo pi`u conveniente dal punto di vista ambientale, economico e sociale, e quindi offrire delle basi per una valutazione sull\u2019opportunit`a di miglioramento ed efficientamento energetico a livello comunale e sovracomunale. Si auspica che i risultati di questo lavoro possano offrire elementi convincenti a supporto di un atteggiamento sempre pi`u attento alle problematiche legate alla riduzione delle emissioni di CO2eq

Operational Research and Machine Learning Applied to Transport Systems

The New Economy, environmental sustainability and global competitiveness drive inno- vations in supply chain management and transport systems. The New Economy increases the amount and types of products that can be delivered directly to homes, challenging the organisation of last-mile delivery companies. To keep up with the challenges, deliv- ery companies are continuously seeking new innovations to allow them to pack goods faster and more efficiently. Thus, the packing problem has become a crucial factor and solving this problem effectively is essential for the success of good deliveries and logistics. On land, rail transportation is known to be the most eco-friendly transport system in terms of emissions, energy consumption, land use, noise levels, and quantities of people and goods that can be moved. It is difficult to apply innovations to the rail industry due to a number of reasons: the risk aversion nature, the high level of regulations, the very high cost of infrastructure upgrades, and the natural monopoly of resources in many countries. In the UK, however, in 2018 the Department for Transport published the Joint Rail Data Action Plan, opening some rail industry datasets for researching purposes. In line with the above developments, this thesis focuses on the research of machine learning and operational research techniques in two main areas: improving packing operations for logistics and improving various operations for passenger rail. In total, the research in this thesis will make six contributions as detailed below. The first contribution is a new mathematical model and a new heuristic to solve the Multiple Heterogeneous Knapsack Problem, giving priority to smaller bins and consid- ering some important container loading constraints. This problem is interesting because many companies prefer to deal with smaller bins as they are less expensive. Moreover, giving priority to filling small bins (rather than large bins) is very important in some industries, e.g. fast-moving consumer goods. The second contribution is a novel strategy to hybridize operational research with ma- chine learning to estimate if a particular packing solution is feasible in a constant O(1) computational time. Given that traditional feasibility checking for packing solutions is an NP-Hard problem, it is expected that this strategy will significantly save time and computational effort. The third contribution is an extended mathematical model and an algorithm to apply the packing problem to improving the seat reservation system in passenger rail. The problem is formulated as the Group Seat Reservation Knapsack Problem with Price on Seat. It is an extension of the Offline Group Seat Reservation Knapsack Problem. This extension introduces a profit evaluation dependent on not only the space occupied, but also on the individual profit brought by each reserved seat. The fourth contribution is a data-driven method to infer the feasible train routing strate- gies from open data in the United Kingdom rail network. Briefly, most of the UK network is divided into sections called berths, and the transition point from one berth to another is called a berth step. There are sensors at berth steps that can detect the movement when a train passes by. The result of the method is a directed graph, the berth graph, where each node represents a berth and each arc represents a berth-step. The arcs rep- resent the feasible routing strategies, i.e. where a train can move from one berth. A connected path between two berths represents a connected section of the network. The fifth contribution is a novel method to estimate the amount of time that a train is going to spend on a berth. This chapter compares two different approaches, AutoRe- gressive Moving Average with Recurrent Neural Networks, and analyse the pros and cons of each choice with statistical analyses. The method is tested on a real-world case study, one berth that represent a busy junction in the Merseyside region. The sixth contribution is an adaptive method to forecast the running time of a train journey using the Gated Recurrent Units method. The method exploits the TD’s berth information and the berth graph. The case-study adopted in the experimental tests is the train network in the Merseyside region