Technologies, markets and challenges for development of the Canadian Oil Sands industry

This paper provides an overview of the current status of development of the Canadian oil sands industry, and considers possible paths of further development. We outline the key technology alternatives, critical resource inputs and environmental challenges and strategic options both at the company and government level. We develop a model to calculate the supply cost of bitumen and synthetic crude oil using the key technologies. Using the model we evaluate the sensitivity of the supply costs to the critical model inputs

ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements?

Evaluating the accuracy of outputs generated by Large Language Models (LLMs) is especially important in the climate science and policy domain. We introduce the Expert Confidence in Climate Statements (ClimateX) dataset, a novel, curated, expert-labeled dataset consisting of 8094 climate statements collected from the latest Intergovernmental Panel on Climate Change (IPCC) reports, labeled with their associated confidence levels. Using this dataset, we show that recent LLMs can classify human expert confidence in climate-related statements, especially in a few-shot learning setting, but with limited (up to 47%) accuracy. Overall, models exhibit consistent and significant over-confidence on low and medium confidence statements. We highlight implications of our results for climate communication, LLMs evaluation strategies, and the use of LLMs in information retrieval systems.Comment: Tackling Climate Change with Machine Learning workshop at NeurIPS 202

AdsorbRL: Deep Multi-Objective Reinforcement Learning for Inverse Catalysts Design

A central challenge of the clean energy transition is the development of catalysts for low-emissions technologies. Recent advances in Machine Learning for quantum chemistry drastically accelerate the computation of catalytic activity descriptors such as adsorption energies. Here we introduce AdsorbRL, a Deep Reinforcement Learning agent aiming to identify potential catalysts given a multi-objective binding energy target, trained using offline learning on the Open Catalyst 2020 and Materials Project data sets. We experiment with Deep Q-Network agents to traverse the space of all ~160,000 possible unary, binary and ternary compounds of 55 chemical elements, with very sparse rewards based on adsorption energy known for only between 2,000 and 3,000 catalysts per adsorbate. To constrain the actions space, we introduce Random Edge Traversal and train a single-objective DQN agent on the known states subgraph, which we find strengthens target binding energy by an average of 4.1 eV. We extend this approach to multi-objective, goal-conditioned learning, and train a DQN agent to identify materials with the highest (respectively lowest) adsorption energies for multiple simultaneous target adsorbates. We experiment with Objective Sub-Sampling, a novel training scheme aimed at encouraging exploration in the multi-objective setup, and demonstrate simultaneous adsorption energy improvement across all target adsorbates, by an average of 0.8 eV. Overall, our results suggest strong potential for Deep Reinforcement Learning applied to the inverse catalysts design problem.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023), AI for Accelerated Materials Design Worksho

U.S. response to an Oil Import Disruption Role of the Federal Government in Light Duty Vehicle Transportation

This report analyzes technological and policy options for the U.S. federal government response within the light duty vehicle (LDV) sector in the event of a 5 year sustained U.S. oil import curtailment of 5 MMB/D and a global supply disruption of roughly 18 MMB/D. The cause of the oil disruption is damage to the oil production infrastructure in the Middle East; therefore, it is public knowledge that the disruption will be sustained

Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning

Deep learning in computational biochemistry has traditionally focused on molecular graphs neural representations; however, recent advances in language models highlight how much scientific knowledge is encoded in text. To bridge these two modalities, we investigate how molecular property information can be transferred from natural language to graph representations. We study property prediction performance gains after using contrastive learning to align neural graph representations with representations of textual descriptions of their characteristics. We implement neural relevance scoring strategies to improve text retrieval, introduce a novel chemically-valid molecular graph augmentation strategy inspired by organic reactions, and demonstrate improved performance on downstream MoleculeNet property classification tasks. We achieve a +4.26% AUROC gain versus models pre-trained on the graph modality alone, and a +1.54% gain compared to recently proposed molecular graph/text contrastively trained MoMu model (Su et al. 2022).Comment: 2023 ICML Workshop on Computational Biolog

Pour une politique ambitieuse des données publiques

Ce rapport présente une étude sur la réutilisation des données publiques, menée pour la Délégation aux usages de l’Internet du Ministère de l’Enseignement supérieur et de la Recherche dans le cadre du Master d’Action Publique de l’École des Ponts ParisTech. Il met en perspective la problématique et les enjeux de l’Open Data, propose un état des lieux de la réutilisation des données publiques en France, et dessine trois scénarios prospectifs pour l’évolution future de ce mouvement. Elle présente seize propositions pour une politique nationale ambitieuse d’ouverture et de réutilisation des données publiques. Quatre élèves de l’École des Ponts ParisTech, Pierre-Henri Bertin, Romain Lacombe, François Vauglin et Alice Vieillefosse ont mené cette analyse de septembre 2010 à janvier 2011, en rencontrant les acteurs clés de la réutilisation des données publiques, en prenant part à des colloques internationaux, et en s’appuyant sur la bibliographie existante

FLIM FRET Technology for Drug Discovery: Automated Multiwell-Plate High-Content Analysis, Multiplexed Readouts and Application in Situ**

A fluorescence lifetime imaging (FLIM) technology platform intended to read out changes in Förster resonance energy transfer (FRET) efficiency is presented for the study of protein interactions across the drug-discovery pipeline. FLIM provides a robust, inherently ratiometric imaging modality for drug discovery that could allow the same sensor constructs to be translated from automated cell-based assays through small transparent organisms such as zebrafish to mammals. To this end, an automated FLIM multiwell-plate reader is described for high content analysis of fixed and live cells, tomographic FLIM in zebrafish and FLIM FRET of live cells via confocal endomicroscopy. For cell-based assays, an exemplar application reading out protein aggregation using FLIM FRET is presented, and the potential for multiple simultaneous FLIM (FRET) readouts in microscopy is illustrated

Bone Marrow Transplant

Mucopolysaccharidosis type I-H (MPS I-H) is a rare lysosomal storage disorder caused by α-L-Iduronidase deficiency. Early haematopoietic stem cell transplantation (HSCT) is the sole available therapeutic option to preserve neurocognitive functions. We report long-term follow-up (median 9 years, interquartile range 8-16.5) for 51 MPS I-H patients who underwent HSCT between 1986 and 2018 in France. 4 patients died from complications of HSCT and one from disease progression. Complete chimerism and normal α-L-Iduronidase activity were obtained in 84% and 71% of patients respectively. No difference of outcomes was observed between bone marrow and cord blood stem cell sources. All patients acquired independent walking and 91% and 78% acquired intelligible language or reading and writing. Intelligence Quotient evaluation (n = 23) showed that 69% had IQ ≥ 70 at last follow-up. 58% of patients had normal or remedial schooling and 62% of the 13 adults had good socio-professional insertion. Skeletal dysplasia as well as vision and hearing impairments progressed despite HSCT, with significant disability. These results provide a long-term assessment of HSCT efficacy in MPS I-H and could be useful in the evaluation of novel promising treatments such as gene therapy

Sifflement de diaphragmes en conduit soumis à un écoulement subsonique turbulent

Orifices used as pressure drop devices in pipes of power plants can cause tonal noise. The consequences of whistling are noise and vibration levels higher than what is acceptable. The purpose of the present works is to study the whistling phenomenon with experiments and numeric in order to propose comprehension and prediction tools. One of the results of the study is the experimental and numerical identification of the acoustic amplification conditions at the orifice, which is a necessary phenomenon for whistling. The experiments show that the whistling ranges, expressed in a Strouhal number function of the orifice thickness and the flow velocity inside the orifice, lie between 0.2 and 0.4 and between 0.7 and 0.9 and that they are independent of the Reynolds number. The whistling ability of orifices has also been defined with numerical simulations. Two approaches are used, the first consisting of incompressible U-RANS calculations, the second based on compressible LES. The numerical simulations are able to capture the acoustic amplification at the orifice, for a spatial discretization small enough at the upstream edge of the orifice. Another result of the study is the definition of the parameters controlling the whistling features when acoustic reflections are present. A linear stability analysis is able to predict the whistling frequency, and it is shown that the whistling amplitude is maximum at a Strouhal number of 0.25 and that it increases with the global reflection surrounding the orifice.Les diaphragmes utilisés comme organes de perte de charge à l'intérieur des tuyauteries de centrales électriques ont été mis en cause dans la création de sifflement. Les conséquences de ces phénomènes sont des niveaux de bruit et de vibration pouvant dépasser les valeurs admissibles. L'objectif de la thèse est d'étudier le sifflement sur la base d'expérimentations et de calculs numériques afin de proposer des outils de compréhension et de prédiction. Un résultat de la thèse correspond à l’identification expérimentale et numérique des conditions d’amplification acoustique au niveau de diaphragmes, phénomène nécessaire au sifflement. Les expériences montrent que les plages de sifflement, exprimées sous la forme d’un nombre de Strouhal fonction de l’épaisseur du diaphragme et de la vitesse dans l’orifice, s’étendent de 0,2 à 0,4 et de 0,7 à 0,9 et sont indépendantes du nombre de Reynolds. Le potentiel de sifflement de diaphragmes est également caractérisé à l’aide de simulations numériques. Deux approches sont utilisées avec des calculs U-RANS incompressibles et des simulations LES compressibles. Il apparaît que la simulation numérique permet de reproduire l’effet d’amplification acoustique à l’origine du sifflement, pour des pas de discrétisation spatial au coin amont de l’orifice suffisamment petit. Un autre résultat de la thèse est la définition des paramètres contrôlant les caractéristiques du sifflement en présence de réflexions acoustiques. Une analyse de stabilité linéaire prédit l’apparition d’un sifflement et sa fréquence. L’amplitude de sifflement est maximum pour un nombre de Strouhal autour de 0,25 et augmente avec le taux de réflexion autour du diaphragme

Whistling of orifices in duct under turbulent subsonic flow

Les diaphragmes utilisés comme organes de perte de charge à l'intérieur des tuyauteries de centrales électriques ont été mis en cause dans la création de sifflement. Les conséquences de ces phénomènes sont des niveaux de bruit et de vibration pouvant dépasser les valeurs admissibles. L'objectif de la thèse est d'étudier le sifflement sur la base d'expérimentations et de calculs numériques afin de proposer des outils de compréhension et de prédiction. Un résultat de la thèse correspond à l’identification expérimentale et numérique des conditions d’amplification acoustique au niveau de diaphragmes, phénomène nécessaire au sifflement. Les expériences montrent que les plages de sifflement, exprimées sous la forme d’un nombre de Strouhal fonction de l’épaisseur du diaphragme et de la vitesse dans l’orifice, s’étendent de 0,2 à 0,4 et de 0,7 à 0,9 et sont indépendantes du nombre de Reynolds. Le potentiel de sifflement de diaphragmes est également caractérisé à l’aide de simulations numériques. Deux approches sont utilisées avec des calculs U-RANS incompressibles et des simulations LES compressibles. Il apparaît que la simulation numérique permet de reproduire l’effet d’amplification acoustique à l’origine du sifflement, pour des pas de discrétisation spatial au coin amont de l’orifice suffisamment petit. Un autre résultat de la thèse est la définition des paramètres contrôlant les caractéristiques du sifflement en présence de réflexions acoustiques. Une analyse de stabilité linéaire prédit l’apparition d’un sifflement et sa fréquence. L’amplitude de sifflement est maximum pour un nombre de Strouhal autour de 0,25 et augmente avec le taux de réflexion autour du diaphragme.Orifices used as pressure drop devices in pipes of power plants can cause tonal noise. The consequences of whistling are noise and vibration levels higher than what is acceptable. The purpose of the present works is to study the whistling phenomenon with experiments and numeric in order to propose comprehension and prediction tools. One of the results of the study is the experimental and numerical identification of the acoustic amplification conditions at the orifice, which is a necessary phenomenon for whistling. The experiments show that the whistling ranges, expressed in a Strouhal number function of the orifice thickness and the flow velocity inside the orifice, lie between 0.2 and 0.4 and between 0.7 and 0.9 and that they are independent of the Reynolds number. The whistling ability of orifices has also been defined with numerical simulations. Two approaches are used, the first consisting of incompressible U-RANS calculations, the second based on compressible LES. The numerical simulations are able to capture the acoustic amplification at the orifice, for a spatial discretization small enough at the upstream edge of the orifice. Another result of the study is the definition of the parameters controlling the whistling features when acoustic reflections are present. A linear stability analysis is able to predict the whistling frequency, and it is shown that the whistling amplitude is maximum at a Strouhal number of 0.25 and that it increases with the global reflection surrounding the orifice