33,115 research outputs found

    Fully-Autonomous, Vision-based Traffic Signal Control: from Simulation to Reality

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    Ineffective traffic signal control is one of the major causes of congestion in urban road networks. Dynamically changing traffic conditions and live traffic state estimation are fundamental challenges that limit the ability of the existing signal infrastructure in rendering individualized signal control in real-time. We use deep reinforcement learning (DRL) to address these challenges. Due to economic and safety constraints associated training such agents in the real world, a practical approach is to do so in simulation before deployment. Domain randomisation is an effective technique for bridging the reality gap and ensuring effective transfer of simulation-trained agents to the real world. In this paper, we develop a fully-autonomous, vision-based DRL agent that achieve adaptive signal control in the face of complex, imprecise, and dynamic traffic environments. Our agent uses live visual data (i.e. a stream of real-time RGB footage) from an intersection to extensively perceive and subsequently act upon the traffic environment. Employing domain randomisation, we examine our agent’s generalisation capabilities under varying traffic conditions in both the simulation and the real-world environments. In a diverse validation set independent of training data, our traffic control agent reliably adapted to novel traffic situations and demonstrated a positive transfer to previously unseen real intersections despite being trained entirely in simulation

    Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process

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    In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.This work has received funding support from the SPRI-Basque Gov- ernment through the ELKARTEK program (OILTWIN project, ref. KK- 2020/00052)

    Structure and adsorption properties of gas-ionic liquid interfaces

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    Supported ionic liquids are a diverse class of materials that have been considered as a promising approach to design new surface properties within solids for gas adsorption and separation applications. In these materials, the surface morphology and composition of a porous solid are modified by depositing ionic liquid. The resulting materials exhibit a unique combination of structural and gas adsorption properties arising from both components, the support, and the liquid. Naturally, theoretical and experimental studies devoted to understanding the underlying principles of exhibited interfacial properties have been an intense area of research. However, a complete understanding of the interplay between interfacial gas-liquid and liquid-solid interactions as well as molecular details of these processes remains elusive. The proposed problem is challenging and in this thesis, it is approached from two different perspectives applying computational and experimental techniques. In particular, molecular dynamics simulations are used to model gas adsorption in films of ionic liquids on a molecular level. A detailed description of the modeled systems is possible if the interfacial and bulk properties of ionic liquid films are separated. In this study, we use a unique method that recognizes the interfacial and bulk structures of ionic liquids and distinguishes gas adsorption from gas solubility. By combining classical nitrogen sorption experiments with a mean-field theory, we study how liquid-solid interactions influence the adsorption of ionic liquids on the surface of the porous support. The developed approach was applied to a range of ionic liquids that feature different interaction behavior with gas and porous support. Using molecular simulations with interfacial analysis, it was discovered that gas adsorption capacity can be directly related to gas solubility data, allowing the development of a predictive model for the gas adsorption performance of ionic liquid films. Furthermore, it was found that this CO2 adsorption on the surface of ionic liquid films is determined by the specific arrangement of cations and anions on the surface. A particularly important result is that, for the first time, a quantitative relation between these structural and adsorption properties of different ionic liquid films has been established. This link between two types of properties determines design principles for supported ionic liquids. However, the proposed predictive model and design principles rely on the assumption that the ionic liquid is uniformly distributed on the surface of the porous support. To test how ionic liquids behave under confinement, nitrogen physisorption experiments were conducted for micro‐ and mesopore analysis of supported ionic liquid materials. In conjunction with mean-field density functional theory applied to the lattice gas and pore models, we revealed different scenarios for the pore-filling mechanism depending on the strength of the liquid-solid interactions. In this thesis, a combination of computational and experimental studies provides a framework for the characterization of complex interfacial gas-liquid and liquid-solid processes. It is shown that interfacial analysis is a powerful tool for studying molecular-level interactions between different phases. Finally, nitrogen sorption experiments were effectively used to obtain information on the structure of supported ionic liquids

    Probing the Intergalactic medium properties using X-ray absorption from multiple tracers

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    Based on the Lambda Cold Dark Matter concordance cosmological model (ΛCDM), the majority of baryons exist in the Intergalactic medium (IGM). It is extremely challenging to observationally trace the IGM, especially at higher temperatures and low densities. Post reionisation, the vast majority of hydrogen and helium is ionized in the IGM and therefore, the observation of metals is essential for parametrising the IGM properties. My hypothesis is that there is significant absorption in the diffuse highly ionisied IGM and that this IGM column density increases with redshift. I use X-ray absorption in multiple tracers which yields information on the total absorbing column density of the matter between the observer and the source. Clear IGM detections require tracer sources that are bright, distant, and common enough to provide a good statistical sample of IGM lines of sight (LOS). To more accurately isolate any IGM contribution to spectral absorption, I examine each tracer host type to realistically model it, in addition to using appropriate intrinsic continuum curvature models. I test the robustness of the result from a number of perspectives. I examine the impact of the key underlying assumptions that affect the column density calculations including metallicity, ionisation and location of absorption. I look for any evidence of evolution in the parameters. In Chapters 2, 3, 4 and 5, I use gamma-ray bursts (GRBs), blazars and quasars (QSOs) to estimate IGM baryon column densities, metallicity, temperature, ionisation parameters and redshift distributions. My results for each tracer are presented in each of the respective chapters and collectively in Chapter 5 which includes comparative analysis. In conclusion, through the work in this thesis I demonstrate a consistent case for strong X-ray absorption in the IGM on the LOS to three different tracer types and that it is related to redshift. The results are consistent with the ΛCDM model for density, temperature and metallicity. Given these results, I would recommend that studies of distant objects should not follow the convention of assuming all X-ray absorption in excess of our Galaxy is attributed to the host galaxy, that the host is neutral and has solar metallicity. Instead, particularly at higher redshift, absorption in the IGM should be accounted for to give more accurate results for the tracer host properties

    Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting

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    Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time-series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on LSTM, Stacked-LSTM, Bidirectional-LSTM Networks, XGBoost, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor were compared in the task of forecasting hourly rainfall volumes using time-series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models' performance. Results show that a Bidirectional-LSTM Network can be used as a rainfall forecast model with comparable performance to Stacked-LSTM Networks. Among all the models tested, the Stacked-LSTM Network with two hidden layers and the Bidirectional-LSTM Network performed best. This suggests that models based on LSTM-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget-wise rainfall forecast applications

    The applied psychology of addictive orientations : studies in a 12-step treatment context.

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    The clinical data for the studies was collected at The PROMIS Recovery Centre, a Minnesota Model treatmentc entre for addictions,w hich encouragesth e membership and use of the 12 step Anonymous Fellowships, and is abstinence based. The area of addiction is contextualised in a review chapter which focuses on research relating to the phenomenon of cross addiction. A study examining the concept of "addictive orientations" in male and female addicts is described, which develops a study conductedb y StephensonM, aggi, Lefever, & Morojele (1995). This presents study found a four factor solution which appeared to be subdivisions of the previously found Hedonism and Nurturance factors. Self orientated nurturance (both food dimensions, shopping and caffeine), Other orientated nurturance (both compulsive helping dimensions and work), Sensation seeking hedonism (Drugs, prescription drugs, nicotine and marginally alcohol), and Power related hedonism (Both relationship dimensions, sex and gambling. This concept of "addictive orientations" is further explored in a non-clinical population, where again a four factor solution was found, very similar to that in the clinical population. This was thought to indicate that in terms of addictive orientation a pattern already exists in this non-clinical population and that consideration should be given to why this is the case. These orientations are examined in terms of gender differences. It is suggested that the differences between genders reflect power-related role relationships between the sexes. In order to further elaborate the significance and meaning behind these orientations, the next two chapters look at the contribution of personality variables and how addictive orientations relate to psychiatric symptomatology. Personality variables were differentially, and to a considerable extent predictably involved with the four factors for both males and females.Conscientiousness as positively associated with "Other orientated Nurturance" and negatively associated with "Sensation seeking hedonism" (particularly for men). Neuroticism had a particularly strong association with the "Self orientated Nurturance" factor in the female population. More than twice the symptomatology variance was explained by the factor scores for females than it was for males. The most important factorial predictors for psychiatric symptomatology were the "Power related hedonism" factor for males, and "Self oriented nurturance" for females. The results are discussed from theoretical and treatment perspectives

    Reforming the United Nations

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    The thesis deals with the financial crisis that the United Nations faced starting in 1985 when the US Congress decided to withhold a significant part of the US contribution to the UN regular budget in order to force a greater say for the major contributors on budgetary issues, budgetary restraint and greater efficiency. The UN responded by the adoption of resolution 41/213 of 19 December 1986 that was based on the recommendations of a Group of High-level Intergovernmental Experts ("G-18") set up a year earlier. A new system was introduced regarding the formulation of the regular budget of the United Nations Organisation and a broader process of reform was initiated including a restructuring of the Secretariat and of the intergovernmental machinery in the economic and social fields. After an introductory chapter (Chapter I), the thesis examines the UN problems at the budgetary/financial and administrative/structural levels, the solutions proposed from within and without the United Nations established framework and the actual attempts at reform (Chapters II and ifi). The realisation that the implementation of reforms is rather disjointed and often unsuccessful (e.g. the failure to restructure the intergovernmental machi.neiy) prompts a search for the deeper causes of the UN problems at the political level and the attitudes of the main actors, namely the USA, the USSR, some up-and-coming states, notably Japan, the Third World states and, finally, of the UN Secretary-General and the Secretariat (Chapter 1V). Although the financial crisis may have subsided since 1988 and the USA seem committed to paying up their dues, the deeper UN crisis of identity has not been resolved and is expected to resurface if no bold steps are taken. In that direction, some possible alternative courses for the UN in the future are discussed drawing upon theory and practice (Chapte

    Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis

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    Amyotrophic lateral sclerosis (ALS) is a complex disease that leads to motor neuron death. Despite heritability estimates of 52%, genome-wide association studies (GWASs) have discovered relatively few loci. We developed a machine learning approach called RefMap, which integrates functional genomics with GWAS summary statistics for gene discovery. With transcriptomic and epigenetic profiling of motor neurons derived from induced pluripotent stem cells (iPSCs), RefMap identified 690 ALS-associated genes that represent a 5-fold increase in recovered heritability. Extensive conservation, transcriptome, network, and rare variant analyses demonstrated the functional significance of candidate genes in healthy and diseased motor neurons and brain tissues. Genetic convergence between common and rare variation highlighted KANK1 as a new ALS gene. Reproducing KANK1 patient mutations in human neurons led to neurotoxicity and demonstrated that TDP-43 mislocalization, a hallmark pathology of ALS, is downstream of axonal dysfunction. RefMap can be readily applied to other complex diseases
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