Publications associées à Predictive models for assessing the passive solar and daylight potential of neighborhood designs: A comparative proof-of-concept study

Publications associées (32)

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Mohammad Khaja Nazeeruddin

Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

Elsevier Science Sa2024

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

Dimitrios Kyritsis, Jinzhi Lu, Xiaochen Zheng

The aircraft assembly system is highly complex involving different stakeholders from multiple domains. The design of such a system requires comprehensive consideration of various industrial scenarios aiming to optimize key performance indicators. Tradition ...

Elsevier Sci Ltd2024

Decomposition method to evaluate district heating/cooling network potential at urban scale

Ana Catarina Gouveia Braz, François Maréchal, Luc Girardin, Bingqian Liu

In this work, a tool to design district heating networks (DHN) is presented and applied to the city of Lausanne as a case study. The evaluation of the buildings’ heat/cooling demand is performed using a Geographic Information System (GIS) database, built f ...

Associazione Italiana Di Ingegneria Chimica2024

An explorative method to support design decisions based on carbon constraints and daylight sufficiency needs

Nazanin Rezaei Oghazi

The global construction industry contributes to 37% of carbon emissions associated to both building operations and construction. To help achieve the net-zero targets set by 2050, it is mandated to achieve a 50% reduction in carbon emissions by 2030. As we ...

EPFL2024

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Paolo Pegolo

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

Amer Chemical Soc2024

Degradable and Printed Microstrip Line for Chipless Temperature and Humidity Sensing

Danick Briand, Jaemin Kim, James Alec Pierce Bourely

Research on chipless and passive architectures for environmental sensing is generating high interest because they do not require any semiconductor components or batteries to operate, thus resulting in an eco-friendlier footprint. This study demonstrates a ...

2024

Flash Melting Amorphous Ice

Marcel Drabbels, Ulrich Lorenz, Gabriele Bongiovanni, Constantin Richard Krüger, Nathan Junior Mowry

Water can be vitrified if it is cooled at high rates, which makes it possible to outrun crystallization in so-called no man’s land, a range of deeply supercooled temperatures where water crystallizes rapidly. Here, we study the reverse process in pure wate ...

2024

Modified capillary number to standardize droplet generation in suction-driven microfluidics

Jatin Panwar

In droplet microfluidic devices with suction-based flow control, the microchannel geometry and suction pressure at the outlet govern the dynamic properties of the two phases that influence the droplet generation. Therefore, it is critical to understand the ...

Springer Heidelberg2024

Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion

Minh Hieu Lê

Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. In this study, we developed a fully computer-supported pipeline to predict both the cage height and the degree of lumbar lordosis subtract ...

2024

RE:SLAB — a load bearing system for open-ended component reuse in building structures

Corentin Jean Dominique Fivet, Jan Friedrich Georg Brütting, Dario Redaelli, Alex-Manuel Muresan, Edisson Xavier Estrella Arcos

The construction industry plays a major role in the high levels of greenhouse gas emissions, resource consumption, and waste generation observed nowadays. Key to the circular economy, structural component reuse arises as a promising solution to divert cons ...

2024

Shaped Laser Pulses for Microsecond Time-Resolved Cryo-EM: Outrunning Crystallization during Flash Melting

Marcel Drabbels, Ulrich Lorenz, Constantin Richard Krüger, Nathan Junior Mowry

Water vitrifies if cooled at rates above 3 × 105 K/s. In contrast, when the resulting amorphous ice is flash heated, crystallization occurs even at a more than 10 times higher heating rate, as we have recently shown. This may present an issue for microseco ...

2024

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Florent Evariste Forest, Yunhong Che

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions ...

Ieee-Inst Electrical Electronics Engineers Inc2024

On distributional autoregression and iterated transportation

Victor Panaretos, Laya Ghodrati

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

Wiley2024

Random matrix methods for high-dimensional machine learning models

Antoine Philippe Michel Bodin

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

EPFL2024

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Michele Bianco, Tianyue Chen

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Oxford Univ Press2024

Iterative removal of sources to model the turbulent electromotive force

Salim Bennouna, Abhijit Bhausaheb Bendre

We describe a novel method to compute the components of dynamo tensors from direct magnetohydrodynamic (MHD) simulations. Our method relies upon an extension and generalization of the standard H & ouml;gbom CLEAN algorithm widely used in radio astronomy to ...

Oxford Univ Press2024

Models implemented in the methodological approach to design the initial STEP first wall contour

Kevin Henricus Annemarie Verhaegh, Davide Galassi, Filippo Bagnato, Dario Vaccaro

The official Spherical Tokamak for Energy Production mission aims to demonstrate the ability to generate net electricity from fusion with the STEP Prototype Power plant. One of the key technological and engineering challenges in fusion power plants is mana ...

Elsevier Science Sa2024

Oligolithic Cross-task Optimizations across Isolated Workloads

Anastasia Ailamaki, Panagiotis Sioulas, Eleni Zapridou

Enterprises collect data in large volumes and leverage them to drive numerous concurrent decisions and business processes. Their teams deploy multiple applications that often operate concurrently on the same data and infrastructure but have widely differen ...

2024

Dynamical simulation via quantum machine learning with provable generalization

Zoë Holmes

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we d ...

Amer Physical Soc2024