1st METECH workshop – From deep-sea to coastal zones: Methods and Techniques for studying Palaeoenvironments

Reconstructing past climate and past ocean circulation demands the highest possible precision and accuracy which urges the scientific community to look at different sediment records such as the ones from coastal zones to deep-sea with a more complete set of technical and methodological tools. However, the information given by each tool varies in precision, accuracy and in significance according to their environmental settings. It is therefore essential to compare tools. With that in mind, and as part of the International year of Planet Earth, a workshop entitled `From deep-sea to coastal zones: Methods and Techniques for studying palaeoenvironments' took place in Faro (Portugal), from 25–29 February 2008

Dynamique de propagation sur réseaux aléatoires : caractérisation de la transition de phase

Pour modéliser des systèmes complexes où un grand nombre d’éléments interagissent, la science des réseaux offre une approche systématique et universelle où les éléments sont représentés par des noeuds et les interactions par des liens. Cette science est devenu un incontournable pour l’étude des dynamiques stochastiques de propagation, servant à modéliser la transmission d’un virus ou quelconque type d’information qui se propage par contacts à l’intérieur d’une population. Un des aspects intéressants des dynamiques de propagation sur réseaux est l’émergence d’un phénomène collectif, prenant la forme d’une transition de phase au sens de la physique statistique, lorsque l’on varie le taux de transmission. Ce phénomène critique marque le moment où une fraction non nulle de la population sera affectée par le processus. Dans ce mémoire, on se consacre au développement de méthodes d’analyse pour caractériser la transition de phase des dynamiques de propagation sur réseaux. On s’intéresse plus particulièrement au modèle susceptible-infecté-susceptible sur réseaux aléatoires issus du modèle des configurations et variant temporellement. Nous proposons un cadre théorique pour l’étude de ce modèle, menant à une description autocohérente de l’état stationnaire du système. Cela nous permet d’obtenir plusieurs résultats analytiques associés au phénomène critique, notamment une expression implicite pour le seuil de transition de phase et des bornes pour la valeur des exposants critiques de certains observables. Ces résultats nous permettent de mieux comprendre le concept de transition de phase localisée et comment chaque classe de noeuds s’active au-delà du seuil d’épidémie.To study complex systems where a large number of elements interact with each other, network science gives a systematic and universal approach using nodes and edges to represent the elements and their interactions. This has become a must for the study of stochastic propagation dynamics, used to model the transmission of viruses or any kind of information propagating through contacts within a population. One interesting aspect of propagation dynamics on networks is the emergence of a collective phenomenon, a phase transition from the statistical physics’ perspective, as the transmission rate is varied. This critical phenomenon is associated with a non-vanishing fraction of the population affected by the process. The purpose of this work is to develop new analysis methods to characterize the phase transition of propagation dynamics on networks. We investigate more particularly the susceptible-infected-susceptible dynamics on time-varying configuration model networks. We propose a theoretical framework for the study of this model, leading to a self-consistent description of the stationary state of the system. This allows us to obtain a number of analytical results concerning the critical phenomenon, such as an implicit expression for the epidemic threshold and bounds for the critical exponents of various observables. These results help us to understand the concept of localized phase transition and how each class of nodes activates beyond the epidemic threshold

Variable Capacity Mini-Split Air Source Heat Pump Model for TRNSYS

RÉSUMÉ Les pompes à chaleur sont des appareils efficaces qui ont la capacité d’extraire la majorité de leur énergie de sources renouvelables (air, sol, eau). Les pompes à chaleur air-air (PACAA), en particulier, sont utilisées depuis longtemps dans le secteur résidentiel canadien. Elles sont faciles à installer, leur coût en capital est moindre que les pompes à chaleur utilisant le sol (géothermie) et elles représentent une solution efficace de chauffage résidentiel qui peut jouer un rôle important dans les efforts internationaux pour diminuer les émissions de gaz à effet de serre. Malgré ces avantages, les PACAA comportent leur part de contraintes qui limitent leur progression au pays. Elles sont reconnues pour multiplier les cycles marche/arrêt à charge partielle, ce qui affecte leur efficacité et cause de l’inconfort pour les usagers. Leurs performances chutent rapidement à basse température ambiante, au point où elles cessent complètement d’opérer dans les climats froids. Les économies sur les coûts peuvent alors devenir négligeables ou inexistantes dans certaines régions caractérisées par un climat froid et/ou un accès à de l’énergie abordable comme du gaz naturel. De récents progrès technologiques ont favorisé la venue des compresseurs entraînés par des moteurs à fréquence variable (inverter-driven compressors) dans les pompes à chaleur air-air à capacité variable (PACAACV). Ces compresseurs permettent d’accroître la capacité de chauffage en maintenant un Coefficient de Performance (COP) supérieur à 1.0 à basse température ambiante, tout en offrant plus de confort et d’efficacité à charge partielle. Cependant, certaines études rapportent que la technologie pourrait ne pas toujours livrer les performances (COP) annoncées sur le terrain. De plus, il y a présentement une pénurie de modèles de simulation disponibles pour aider les ingénieurs en bâtiment à mieux comprendre le comportement et estimer le potentiel d’économie énergétique des PACAACV. Les objectifs principaux de ce projet sont donc d’effectuer des tests de laboratoire sur des pompes à chaleur air-air bibloc à capacité variable pour obtenir une cartographie de performance complète, et d’utiliser ces résultats pour créer un modèle de PACAACV simple et accessible pour utilisation dans le logiciel de simulation TRNSYS. Des expériences détaillées ont été menées dans une chambre de test de contrôle climatique au laboratoire de CanmetÉNERGIE à Varennes.----------ABSTRACT Heat pumps are energy efficient devices that can extract the majority of their energy from a renewable cold source (air, ground, water). Air source heat pumps (ASHPs), in particular, have long been used in the Canadian residential sector. They are easy to install, their capital cost is lower than ground source heat pumps, and they represent an energy efficient space heating solution than can play a key role in international efforts to reduce GHG emissions. Despite these advantages, ASHPs also have limitations that hinder their adoption in the country. They are known to cycle at part load, causing discomfort for users and reducing their efficiency. Their performance also decreases significantly with outdoor temperature, to the point where they stop operating in colder climates. Therefore, utility cost savings can become negligible or non-existent in some regions characterized by cold climate and/or access to affordable energy sources like natural gas. Recent technological advancements have seen the use of inverter-driven compressors in variable capacity air source heat pumps (VCASHPs). Their benefit is to increase the heating capacity while maintaining an efficiency above 100 % at low ambient temperatures. They also maintain better comfort conditions at warmer temperatures by avoiding On/Off cycles. However, some studies report that the technology may not always deliver the advertised improved efficiency in the field. Moreover, there is a lack of simulation models available for building designers to better understand VCASHP behaviour and estimate their energy saving potential. The main objectives of this project are therefore to perform laboratory tests on a mini-split variable capacity air source heat pump to obtain a complete performance map, and use these results to create a simple and accessible VCASHP model in TRNSYS. Detailed experiments were conducted in an environmental controllable test chamber at the CanmetENERGY-Varennes laboratory. The test bench can provide a variety of heating loads and user-defined ambient temperatures, while explicitly measuring compressor frequency to help establish its impact on VCASHP performance. Testing was conducted from December 2016 to April 2017 with a commercially available ductless VCASHP system with a rated heating and cooling capacity of 4.0 kW (13,600 Btu/h) at 8.3 °C and 3.5 kW (12,000 Btu/h) at 35 °C, respectively

Processus de contagion sur réseaux complexes au-delà des interactions dyadiques

Alors que la pandémie de COVID-19 affecte le monde depuis presque deux ans, il va sans dire qu'une meilleure compréhension des processus de contagion, de leur évolution et des effets des mesures de contrôle est essentielle pour réduire leur impact sur la société. Le cadre théorique pour la modélisation des processus de contagion est très général et permet, bien entendu, de décrire la propagation des maladies infectieuses causées par des agents pathogènes (virus, bactéries, parasites, etc.), mais aussi la propagation des rumeurs et de la désinformation. Peu importe la nature du processus, la transmission s'effectue de proche en proche grâce aux interactions entre les individus. Par conséquent, la structure sociale complexe des populations, qui n'est ni parfaitement ordonnée, ni complètement aléatoire, joue un rôle de premier plan. Dans cette thèse, nous étudions les processus de contagion sur réseaux, où les individus et les interactions entre ces individus sont représentés par des nœuds et des liens respectivement. Nous utilisons une approche théorique principalement basée sur la physique statistique et la dynamique non linéaire. Nous nous concentrons plus spécifiquement sur les réseaux d'ordre supérieur, lesquels mettent les interactions de groupe à l'avant-plan. Notre analyse va donc au-delà des interactions dyadiques. Bien plus qu'une reformulation mathématique de la structure, cette perspective est primordiale pour obtenir une compréhension plus complète de la phénoménologie des processus de contagion. Nous démontrons l'importance des interactions de groupe à l'aide de trois résultats principaux. D'abord, nous caractérisons un phénomène de localisation mésoscopique : pour certaines structures hétérogènes, la propagation persiste uniquement dans les groupes de grande taille. Ce phénomène a notamment une incidence sur l'effet des mesures de contrôle visant à prohiber les regroupements au-delà d'une certaine taille, à l'instar de ce qui fut instauré pour endiguer la pandémie de COVID-19. Ensuite, nous étudions un modèle où les individus doivent accumuler une dose infectieuse minimale pour devenir infectés. Nous montrons qu'une structure d'ordre supérieur et des temps d'exposition hétérogènes induisent une probabilité d'infection non linéaire universelle. L'épidémie résultante peut alors croître de manière super-exponentielle en fonction du temps. Finalement, nous poussons plus en profondeur l'analyse des processus de contagion non linéaire. Dans ce contexte, nous montrons que les groupes peuvent avoir plus d'importance que les individus ultra-connectés pour qu'une épidémie ou un phénomène social envahissent le plus rapidement possible une population.After almost two years into the COVID-19 pandemic, it is clear that a better understanding of contagion processes, their evolution, and the impact of control measures is essential to reduce their burden on society. The theoretical framework for the modeling of contagion is quite general. It can describe the spread of pathogens causing diseases (viruses, bacteria, parasites, etc.), but also the spread of rumors and disinformation. Irrespective of the nature of the underlying process, the contagion evolves through local interactions between the individuals. Consequently, the complex social structure of populations, which is neither perfectly ordered nor completely random, plays a fundamental role in shaping spreading. In this thesis, we study contagion processes on networks where individuals and the interaction between them are represented by nodes and edges respectively. We use a theoretical approach based on statistical physics and nonlinear dynamics. We focus on higher-order networks, putting group interactions beyond pairwise interactions at the forefront. More than a mere mathematical generalization, we find this perspective is paramount to obtain a complete picture of the phenomenology of contagion dynamics. We demonstrate the importance of group interactions through three principal results. First, we characterize a mesoscopic localization phenomenon where the contagion thrives only in large groups for certain types of heterogeneous structure. This phenomenon significantly affects the results of interventions like the cancelation of events larger than a critical size, similar to the measures being used to limit the spreading of COVID-19. Second, we study a model where individuals must accumulate a minimal infective dose to become infected. We show that a higher-order structure and heterogeneous exposure induce a universal nonlinear infection probability. The epidemic size can then grow super-exponentially with time. Finally, with a more in-depth analysis of nonlinear contagions, we show that groups can be more influential than hubs (super-connected individuals) to maximize the early spread of an epidemic

High-resolution Late Pleistocene paleomagnetic secular variation record from Laguna Potrok Aike, Southern Patagonia (Argentina): preliminary results from the ICDP-PASADO drilling

PosterHere we present preliminary results of a high-resolution full vector paleomagnetic reconstruction from the southernmost continental scientific drilling site Laguna Potrok Aike, Argentina (52°S). Magnetic analyses of the long PASADO-ICDP composite record are currently underway at the Sedimentary paleomagnetism laboratory of the Institut des sciences de la mer de Rimouski (ISMER) and reveal high quality paleomagnetic dat

Ocean and climate changes in polar and sub-polar environments: proceedings from the 2010 IODP-Canada/ECORD summer school

The European Consortium for Ocean Drilling Program (ECORD), the Canadian Consortium for Ocean Drilling (CCOD), the Network of the Universités du Québec (UQ), the Université du Québec à Montréal (UQAM) and GEOTOP sponsored, in 2010, a summer school entitled Ocean and climate changes in polar and sub-polar environments. This summer school took place from 27 June to 12 July in Rimouski, Québec city and Montréal (Quebec, Canada) and was attended by nineteen students and postdoctoral fellows from seven countries: Canada, France, Germany, UK, Serbia, Portugal and the USA. Lectures, hands-on laboratory exercises and laboratory visits were conducted at the Institut des Sciences de la Mer de Rimouski (ISMER), Institut National de la Recherche Scientifique Centre Eau Terre Environnement (INRS-ETE) and UQAM, in addition to two field trips and a short geological and geophysical cruise on board the R/V Coriolis II in the St Lawrence Estuary and Saguenay Fjord. During the summer school, more than twenty researchers gave lectures on the use of several paleoceanographic and geophysical techniques to reconstruct ocean and climate changes in polar and sub-polar environments. Some of these lectures are presented as short review papers in this volume. They are intended to portray a brief, but state-of-the-art overview of an array of techniques applied to Arctic and sub-Arctic environments, as well as the geological background information needed by the summer school participants to put the scientific expedition and fieldwork into context. The volume begins with a view on the great challenges and key issues to be addressed in the Arctic Ocean (Stein) in the forthcoming years and is followed by a review (O'Regan) on Late Cenozoic paleoceanography of the Central Arctic. The two subsequent papers (St-Onge et al and de Vernal et al) deal with the oceanographic, paleoceanographic and geological context of the Saguenay Fjord, and St Lawrence Estuary and Gulf. The subsequent set of papers review the use of planktonic foraminifers (Eynaud), diatoms (Crosta) and dinocysts (de Vernal and Rochon) in polar or sub-polar environments. These articles are followed by a paper on transfer functions (Guiot) summarizing the different approaches used to reconstruct past environmental conditions from micropaleontological proxy data. Two papers on geochemical and isotopic proxies are then presented and related to either foraminifera isotopic records (Hillaire-Marcel) in high northern latitudes or changes in ocean circulation and weathering inputs derived from radiogenic isotopes (Frank). The volume concludes with a paper on the application of visible/near infrared derivative spectroscopy to Arctic sediments (Ortiz). All the papers published in this volume benefited from the reviews of at least two reviewers, whom we thank for their valuable time and comments. We also thank the crew of the Coriolis II, and the many scientists, participants and volunteers who contributed to the summer ..

Nonlinear bias toward complex contagion in uncertain transmission settings

Current epidemics in the biological and social domains are challenging the standard assumptions of mathematical contagion models. Chief among them are the complex patterns of transmission caused by heterogeneous group sizes and infection risk varying by orders of magnitude in different settings, like indoor versus outdoor gatherings in the COVID-19 pandemic or different moderation practices in social media communities. However, quantifying these heterogeneous levels of risk is difficult and most models typically ignore them. Here, we include these novel features in an epidemic model on weighted hypergraphs to capture group-specific transmission rates. We study analytically the consequences of ignoring the heterogeneous transmissibility and find an induced superlinear infection rate during the emergence of a new outbreak, even though the underlying mechanism is a simple, linear contagion. The dynamics produced at the individual and group levels are therefore more similar to complex, nonlinear contagions, thus blurring the line between simple and complex contagions in realistic settings. We support this claim by introducing a Bayesian inference framework to quantify the nonlinearity of contagion processes. We show that simple contagions on real weighted hypergraphs are systematically biased toward the superlinear regime if the heterogeneity of the weights is ignored, greatly increasing the risk of erroneous classification as complex contagions. Our results provide an important cautionary tale for the challenging task of inferring transmission mechanisms from incidence data. Yet, it also paves the way for effective models that capture complex features of epidemics through nonlinear infection rates.Comment: 19 pages, 5 figure

Adaptive hypergraphs and the characteristic scale of higher-order contagions using generalized approximate master equations

People organize in groups and contagions spread across them. A simple process, but complex to model due to dynamical correlations within groups and between groups. Groups can also change as agents join and leave them to avoid infection. To study the characteristic levels of group activity required to best model dynamics and for agents to adapt, we develop master equations for adaptive hypergraphs, finding bistability and regimes of detrimental, beneficial, and optimal rewiring, at odds with adaptation on networks. Our study paves the way for higher-order adaptation and self-organized hypergraphs

Phase transition of the susceptible-infected-susceptible dynamics on time-varying configuration model networks

We present a degree-based theoretical framework to study the susceptible-infected-susceptible (SIS) dynamics on time-varying (rewired) configuration model networks. Using this framework, we provide a detailed analysis of the stationary state that covers, for a given structure, every dynamic regimes easily tuned by the rewiring rate. This analysis is suitable for the characterization of the phase transition and leads to three main contributions. (i) We obtain a self-consistent expression for the absorbing-state threshold, able to capture both collective and hub activation. (ii) We recover the predictions of a number of existing approaches as limiting cases of our analysis, providing thereby a unifying point of view for the SIS dynamics on random networks. (iii) We reinterpret the concept of hub-dominated phase transition. Within our framework, it appears as a heterogeneous critical phenomenon : observables for different degree classes have a different scaling with the infection rate. This leads to the successive activation of the degree classes beyond the epidemic threshold.Comment: 14 pages, 11 figure