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Addressing Socio-Economic and Environmental Challenges Linked to Water Temperatures in the Face of Global Change : Application at the Seine Hydrosystem
No full textInternational audienceTemperature is a critical factor at the interface between water and energy stakeholders. It plays a vital role in enabling them to sustain and develop their activities without competing for resources, particularly during periods of crisis. Both surface water bodies and subsurface compartments (<200 m), essential for maintaining aquatic ecosystems and supporting human adaptation to global changes, are utilized for a range of purposes. These include low-impact thermal energy production (e.g., river uses and shallow geothermal energy), drinking water supply, irrigation, and industrial applications.However, these diverse uses by water and energy stakeholders, along with their associated infrastructures, lead to thermal interferences. These interferences are superimposed on broader climatic variations and trends, further complicating resource management.In the Seine basin, observed trends are projected to persist and intensify. These include rising average temperatures, decreasing summer rainfall, and the increasing frequency and severity of extreme events such as floods, droughts, and heatwaves. The sustainable management of water resources will hinge on our collective ability to anticipate and mitigate the effects of these changes.To better predict the Seine basin’s responses to climate change, it is crucial to deepen our understanding of heat transfers between the atmosphere and the various compartments of the hydrosystem. This knowledge will be key to developing strategies that balance the needs of all stakeholders while preserving vital ecosystems and ensuring resilience against global change.In this presentation, we will present data collection, the development of numerocal tools, and the evaluation of the evolution of the Seine's temperatures over 100 years. Physical models and simulations help quantify thermal fluxes, highlighting the main sources of heat input and heat losses. The use of machine learning models in projecting the Seine's temperatures in Paris by 2100 adds a predictive dimension. Future developments to achieve modeling that allows us to produce numerical simulations necessary for the integrated management of surface and groundwater in quantitative, qualitative, and thermal terms will be presented
High-resolution building stock energy model for assessing the impact on the load curve of residential space heating electrification
No full textInternational audienc
Déploiement de la low-tech : une forme de diffusion par réinventions successives
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A predictor-corrector scheme for approximating signed distances using finite element methods
No full textIn this article, we introduce a finite element method designed for the robust computation of approximate signed distance functions to arbitrary boundaries in two and three dimensions. Our method employs a novel prediction-correction approach, involving first the solution of a linear diffusion-based prediction problem, followed by a nonlinear minimization-based correction problem associated with the Eikonal equation. The prediction step efficiently generates a suitable initial guess, significantly facilitating convergence of the nonlinear correction step.A key strength of our approach is its ability to handle complex interfaces and initial level set functions with arbitrary steep or flat regions, a notable challenge for existing techniques. Through several representative examples, including classical geometries and more complex shapes such as star domains and three-dimensional tori, we demonstrate the accuracy, efficiency, and robustness of the method, validating its broad applicability for reinitializing diverse level set functions.</p
Enjeux managériaux dans la Santé numérique : Analyse bibliométrique et analyse de réseau
No full textInternational audienceBackground Digital health has emerged as a transformative force in modern health care systems; these systems have witnessed a surge in technological innovations and solutions over the past 2 to 3 decades. Some studies have provided overviews of keywords and journals that shed light on the current state of digital health research, and there is an increasing focus on this field of study, even in the literature on business, management, and managerial challenges. Papers and reviews are needed on challenges in digital health related to organization, management, and adoption of technological innovations, as there are currently no formal analyses or structured reviews. Objective Given the existing focus of the business and management literature on digital health, there is a need to unravel managerial challenges in digital health. By highlighting foundational themes and challenges in management science for digital health, our objective is to contribute nuanced insights into influential studies and the structure of knowledge in this interdisciplinary domain. Methods To delineate the evolving landscape of digital health management and highlight the main challenges, we conducted a comprehensive bibliometric analysis. Our analysis considered peer-reviewed, English-language papers or reviews in the management field that focused on digital health or closely related concepts. To better understand the dataset, we conducted a performance analysis. Then, we created a co-citation network using BibExcel and analyzed it by clustering the papers using the Louvain algorithm in Gephi. Results Of 1186 papers about digital health or closely related concepts published between 1994 and 2022, 520 (43.8%) were included in the co-citation network and 468 (39.5%) were placed in 4 significant clusters (>1% of the total number of nodes). The mere existence of the clusters shows that different managerial challenges have distinct research communities. The 4 clusters were (1) user adoption and engagement in mHealth, (2) adoption and trust in mHealth services, (3) digital transformation in health care, and (4) implementation challenges and ethical considerations. There are interdependent managerial challenges in digital health, and a dynamic literature review provides a more precise understanding of what is at stake (eg, adoption studies) and upcoming challenges (eg, ethical considerations). Conclusions Our co-citation analysis unveiled evolving themes in the literature on digital health management. The exploration of clusters suggested dynamic shifts related to ethical considerations, health care organizations, and societal acceptance. We encourage further research on these topics and exploration of the intricate facets of the literature on digital health management. We hope that this study provides a more comprehensive understanding of the literature and will provide researchers insights into the principal challenges and unidentified gaps, such as the novel cluster on ethics, or the need for intercluster research to create links between research communities
Blockchain For Logistics 4.0: A Systematic Review and Prospects
No full textInternational audienceLogistics 4.0, with its supporting technologies, is gaining popularity with researchers and practitioners as a way to navigate the dynamic and demanding logistics environment. Blockchain technology is such a topical technology in logistics. The logistics industry benefits from blockchain as it enhances communication and collaboration among stakeholders through applications such as information sharing, goods monitoring and tracing, and executing financing and payments. However, the perceived lack of clarity on the adoption benefits has increased decision-maker’s hesitation to adopt and utilize blockchain technology in the logistics industry. To fully understand blockchain applications in the context of logistics, this paper conducts a systematic literature review (SLR) on blockchain research in logistics. A selection of 113 articles undergoes review based on a two-axis framework. The first axis lists the core blockchain technologies, such as decentralized ledger, consensus mechanism, and smart contract. It comprehensively reviews their interplay with the foundational logistics requirements, such as agility, collaboration, and resilience. The second axis reviews domains such as e-commerce logistics, business logistics, green logistics, and logistics financing, which are key avenues for blockchain applications. The key themes arising from the review are a precursor to deriving avenues for future research. Finally, we present Web 4.0 in logistics, the new wave of generative artificial intelligence (AI)-empowered blockchain innovation that can shape future logistics. More specifically, we explore the role of modern AI agents in driving future research for seamless blockchain integration to logistics, enabling logistics ESG (Environmental, Social, and Governance) and organizational adoption. Besides addressing a gap in existing literature, the concepts discussed in this study allow for a comprehensive understanding of blockchain adoption in logistics, thus encouraging wider practical application of novel technology. From a theoretical lens, this study helps comprehend blockchain technology and its significance to logistics research
Interpretable Power Grid Overload Detection with Information Flow-based Fuzzy Cognitive Maps
No full textInternational audienceThe increasing integration of renewable energysources (RES) brings significant challenges, such as grid overloading, which can escalate operational costs and brings a risk of system-wide blackouts. Traditional AI-based solutions often fall short due to their "black-box" nature, while many existing eXplainable AI (XAI) methods either provide unreliable post-hoc explanations or present compromised reliability and generalizability due to their vulnerability to spurious correlations. This study introduces a novel framework based on recently introduced Information Flow-based Fuzzy Cognitive Maps (IF-FCMs), a causal eXplainable Artificial Intelligence (CXAI) approach designed to overcome these limitations. By employing a two-layered architecture, this framework not only detects grid overloads but also identifies both local and global factors causing these events. Tested on the Medium Voltage Oberrhein network, IF-FCMs demonstrated predictive performance comparable to state-of-theart models such as Explainable Boosting Machines (EBM) and Logistic Regression (LR), while offering superior interpretability. These strengths establish IF-FCMs as a relia
Vulnerability Assessment of Charging Stations in the Electrified Road Network
No full textInternational audienceAs the adoption of electric vehicles (EVs) within electrified road networks (ERNs) continues to grow, the criticality of fast-charging stations (FCSs) to support this growth and alleviate range anxiety for EV users is increasingly evident. Due to the complex operating environment of FCSs, their reliable, safe operation faces significant challenges, from exposure to natural disasters, deliberate attacks, and technical failures. This leads to the need to analyze the vulnerability of FCSs within ERNs. This paper provides a mathematical framework to assess the vulnerability of the ERN to intentional attacks and identify critical FCSs within ERNs. We propose a System Optimal Dynamic Mixed Traffic Flow Assignment Model (SODTA) gametheoretical for an ERN where electric and fuel vehicles coexist. Building upon this model, an attacker-defender (AD) framework is established for assessing the vulnerability of FCSs within the ERN. We transform this AD problem into a mixed integer linear programming (MILP) one to solve it efficiently. Two indices are proposed to evaluate the vulnerability and the reallocation of charging loads within the ERN. Finally, we apply the proposed framework to conduct a vulnerability assessment of the ERN in North Carolina, USA. The key findings are as follows: 1) As the EV penetration increases, the studied ERN becomes more vulnerable to attacks. 2) With limited attack resources, mitigating performance losses over time is possible through charging load redistribution. 3) Variation in overall vulnerability levels with increasing attack resources reveals distinct patterns, ranging from mitigable to unmitigable vulnerability.Index Terms-Electric vehicles (EVs), electrified road network (ERNs), vulnerability assessment, fast-charging stations (FCSs), defender-attacker game model, system optimal dynamic mixed traffic flow assignment. I. INTRODUCTIONT HE introduction of electric vehicles (EVs) in electrified road networks (ERNs) is gaining momentum [1]. Despite</div
Le Golfe de Larchant : dalles de grès superposées, développement des chaos et altérations cryogéniques
No full textThe field trip has two objectives: (1) to dissect the geomorphology and the mutual relationships between the superimposed quartzitic sandstone slabs to seat their formation in a spatial dynamic; (2) to try to make a distinction between sandstone megaclatsts recently released from the escarpments and those which have been intensely weathered and which could possibly be chronological witnesses of the landscape evolution.La sortie a deux objectifs : (1) décortiquer les caractéristiques géomorphologiques et les relations mutuelles entre les dalles superposées pour replacer leur formation dans une dynamique spatiale ; (2) essayer de faire une distinction entre blocs de grès libérés récemment des escarpements et les blocs qui ont été profondément altérés par exposition aux intempéries et qui pourraient éventuellement être des témoins chronologique de l'évolution des paysages. Le livret d'accompagnement débute par le rappel de notions qui seront mises en avant pour argumenter les observations et leurs interprétations
Entropy Regularized Variational Dynamic Programming for Stochastic Optimal Control
No full textThis paper addresses stochastic optimal control, where the state-feedback control policies are probability distributions, with an additional entropy penalty on the control policy. We interpret the cost function as a Kullback-Leibler (KL) divergence between two joint distribu- tions, enabling the use of variational inference techniques. This approach leads to a dynamic programming principle, also defined in terms of KL divergence. In the linear case, we show the entropic penalty leads to Gaussian control policies, whose mean coincides with the linear quadratic regulator (LQR). Furthermore, when the state is not directly measured, we may prove a separation principle along the lines of linear quadratic Gaussian (LQG) control. In the case of nonlinear control systems being linear in the control inputs, with quadratic costs, we utilize our variational framework to approximate the optimal solution with Gaussian distributions. This yields closed-form recursive updates that extend traditional LQR control. We demonstrate the effectiveness of this new method in simulations