Portail HAL Nantes Université
Not a member yet
120169 research outputs found
Sort by
Machine Learning Based Environmental Impact Prediction of Construction Products Using EPD Data
No full textInternational audienceThe construction sector significantly contributes to global greenhouse gas emissions, creating an urgent need for efficient environmental impact assessment methods. This study evaluates machine learning (ML) models to rapidly predict the Global Warming Potential (GWP) of construction products using standardized Environmental Product Declaration (EPD) data from the ÖKOBAUDAT database. Five regression models - Artificial Neural Network (ANN), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) were trained, optimized, and compared. Data preprocessing involved careful feature selection through correlation analysis, management of missing values, and outlier removal. Model performance was assessed using the coefficient of determination R2, root mean square error (RMSE), and mean absolute error (MAE). Results demonstrated that ensemble tree-based models (RF and XGB) performed best achieving R2 ~ 0.90 on held-out dataset and retaining R2 ~ 0.81–0.83 on an independent external EPD validation set with mean error of almost 10% of a typical product footprint. These findings demonstrate ML’s capability to effectively approximate product life-cycle impacts, providing rapid estimates for early-stage sustainability decision-making. However, detailed Life Cycle Assessments (LCA) remain essential for critical decisions
Residual stresses and strains during laser assisted tape placement of thermoplastic composite: Multi-physical modelling and experimental validation
No full textInternational audienceThe prediction of residual stresses and strains during laser-assisted tape placement of thermoplastic composite has been investigated by numerous studies in the literature. They are however rarely validated compared to experimental results and based on simplifications of the material thermomechanical behaviour. This study proposes a multi-physical model to address the heat transfer, crystallization kinetics, the induced thermal and crystallization strains as well as the mechanical behaviour of the material during processing. The thermal and crystallization models were already validated in a previous study, and the mechanical behaviour is described with an incremental linear elastic constitutive law and the Classical Lamination Theory. The thermomechanical properties are based on values from the literature and the supplied datasheet. Without any fine tuning of the model, predicted curvatures of cross ply laminates are well described, as well as their evolution with the temperature after manufacturing and during annealing. The measured and calculated curvatures are found in excellent agreement with errors comprised between 2% and 13%. A sensitivity analysis demonstrates that the developed model is more able to correctly reproduce the experimentally observed material behaviour compared to simplified approaches found in the literature
Ultra-sensitive radon assay using an electrostatic chamber in a recirculating system
No full textInternational audienceRare event searches such as neutrinoless double beta decay and Weakly Interacting Massive Particle detection require ultra-low background detectors. Radon contamination is a significant challenge for these experiments, which employ highly sensitive radon assay techniques to identify and select low-emission materials. This work presents the development of ultra-sensitive electrostatic chamber (ESC) instruments designed to measure radon emanation in a recirculating gas loop, for future lower background experiments. Unlike traditional methods that separate emanation and detection steps, this system allows continuous radon transport and detection. This is made possible with a custom-built recirculation pump. A Python-based analysis framework, PyDAn, was developed to process and fit time-dependent radon decay data. Radon emanation rates are given for various materials measured with this instrument. A radon source of known activity provides an absolute calibration, enabling statistically-limited minimal detectable activities of 20 Bq. These devices are powerful tools for screening materials in the development of low-background particle physics experiments
Investigating the influence of affinity on the gaze behavior of individuals with Autism Spectrum Disorder (ASD) based on their unique autistic traits
No full textInternational audienceAutism spectrum is very wide, but autistic people share some traits such as social or language impairments. Another common characteristic is a strong passion, called an affinity, which can be anything from a movie character to a topic like history, or even a specific object. Numerous testimonies attest to the support provided by affinities for autistic individuals, offering a reassuring space in a sometimes frightening world. Clinical psychologists consider affinities as keys that can unlock language and learning for people with ASD. However, objective evidence of this role is still lacking. In this study, we used eye-tracking technology to explore the visual attention patterns of autistic individuals when presented with their affinity compared to neutral stimuli. We recruited 52 autistic participants and showed them 38 images: 10 featuring their affinity and 28 neutral ones, while recording their eye movements. Eye-tracking data provided crucial insights into how visual attention is modulated by affinities. Our results reveal significant variability in visual engagement depending on the specific autistic traits and affinities of the participants. Some showed heightened visual engagement with affinity images, while others withdrew their gaze. Some exhibited a mixed response, with both increased engagement and gaze withdrawal, and a few showed no difference between the two sets of images. These findings highlight the complex relationship between visual attention and affinities in autistic individuals, highlighting the potential of eye-tracking as a tool for understanding and leveraging these affinities in therapeutic and educational settings
Cramér–Rao bound analysis of nested arrays under impulsive noise with coarrays and FLOSs
No full textInternational audienceNested arrays are extensively employed in array signal processing to augment the degrees of freedom and enhance estimation precision, and the Cramér–Rao Bound (CRB) for nested arrays in a Gaussian noise environment has been established. Nevertheless, in a practical wireless communication environment, noise usually exhibits an impulsive characteristic. The impulsive noise applied in a uniform linear array (ULA) has been extensively studied in the literature, but only closed-form expressions of CRB with Cauchy and Gaussian noise distributions are given. Although nested arrays have garnered significant attention recently, research on the CRB under impulsive noise conditions remains scarce. In this paper, we provide the CRB expression for direction of arrival (DOA) estimation with nested arrays in an impulsive noise environment, which indicates that the CRB is formulated in terms of the fractional low-order statistics (FLOSs) of received data. Moreover, we also calculate the CRB results for different FLOSs as well as various sparse arrays and validate that the derived CRB makes an important contribution to the performance analysis of sparse arrays in impulsive noise
Probing of EoS with clusters and hypernuclei
No full textInternational audienceThe study of the nuclear equation-of-state (EoS) is a one of the primary goals of experimental and theoretical heavy-ion physics. The comparison of recent high statistics data from the STAR Collaboration with transport models provides a unique possibility to address this topic in a yet unexplored energy domain. Employing the microscopic n-body Parton-Hadron-Quantum-Molecular Dynamics (PHQMD) transport approach, which allows to describe the propagation and interactions of hadronic and partonic degrees of freedom including cluster and hyper-nucleus formation and dynamics, we investigate the influence of different EoS on bulk observables, the multiplicity, and rapidity distributions of protons, s and clusters up to A=4 as well as their influence on the collective flow. We explore three different EoS: two static EoS, dubbed 'soft' and 'hard', which differ in the compressibility modulus, as well as a soft momentum dependent EoS. We find that a soft momentum dependent EoS reproduces most baryon and cluster observables, including the flow observables, quantitatively