Heat flux identification using reduced model and the adjoint method. Application to a brake disk rotating at variable velocity

International audienceIn previous works [1], reduced models have been used for solving inverse problems, characterized by a complex geometry requiring a large number of nodes and / or an objective of online identification. The treated application was a brake disc in two-dimensional representation, in rotation at variable speed. The dissipated heat flux at the pad-disk interface had been identified by Beck's method. We present here a similar application using the adjoint method. The modal reduction is done by using special bases (called branch bases) that offer the advantage of dealing with nonlinear problems and / or unsteady parameters. Adjoint method provides particularly accurate results in this configuration

North-eastern Morocco: a high geothermal prospect.

New temperature data obtained in 2007 in a hydrogeological borehole (1100 m deep) near Berkane have revealed an average geothermal gradient of about 126°C/km at depths greater than 300 m. This result confirms the average geothermal gradient estimated in a mining borehole located about 30km west of the Berkane borehole, in which water with temperatures as high as 96°C was reached at a depth of about 700 m. The new geothermal gradient, exceeding by far the ones already determined for this Moroccan area allows thinking about the possibility of programs for using high temperature waters in north-eastern Morocco

Healthcare access and mammography screening in Michigan: a multilevel cross-sectional study

Abstract Background Breast cancer screening rates have increased over time in the United States. However actual screening rates appear to be lower among black women compared with white women. Purpose To assess determinants of breast cancer screening among women in Michigan USA, focusing on individual and neighborhood socio-economic status and healthcare access. Methods Data from 1163 women ages 50-74 years who participated in the 2008 Michigan Special Cancer Behavioral Risk Factor Survey were analyzed. County-level SES and healthcare access were obtained from the Area Resource File. Multilevel logistic regression models were fit using SAS Proc Glimmix to account for clustering of individual observations by county. Separate models were fit for each of the two outcomes of interest; mammography screening and clinical breast examination. For each outcome, two sequential models were fit; a model including individual level covariates and a model including county level covariates. Results After adjusting for misclassification bias, overall cancer screening rates were lower than reported by survey respondents; black women had lower mammography screening rates but higher clinical breast examination rates than white women. However, after adjusting for other individual level variables, race was not a significant predictor of screening. Having health insurance or a usual healthcare provider were the most important predictors of cancer screening. Discussion Access to healthcare is important to ensuring appropriate cancer screening among women in Michigan

From Industrial Food Waste to Bioactive Ingredients: A Review on the Sustainable Management and Transformation of Plant-Derived Food Waste

According to the United Nations, approximately one-third of the food produced for human consumption is wasted. The actual linear "Take-Make-Dispose" model is nowadays obsolete and uneconomical for societies and the environment, while circular thinking in production systems and its effective adoption offers new opportunities and benefits. Following the "Waste Framework Directive" (2008/98/CE), the European Green Deal, and the actual Circular Economy Action Plan, when prevention is not possible, recovering an unavoidable food waste as a by-product represents a most promising pathway. Using last year's by-products, which are rich in nutrients and bioactive compounds, such as dietary fiber, polyphenols, and peptides, offer a wake-up call to the nutraceutical and cosmetic industry to invest and develop value-added products generated from food waste ingredients

Healthcare access and mammography screening in Michigan: a multilevel cross-sectional study

Abstract Background Breast cancer screening rates have increased over time in the United States. However actual screening rates appear to be lower among black women compared with white women. Purpose To assess determinants of breast cancer screening among women in Michigan USA, focusing on individual and neighborhood socio-economic status and healthcare access. Methods Data from 1163 women ages 50-74 years who participated in the 2008 Michigan Special Cancer Behavioral Risk Factor Survey were analyzed. County-level SES and healthcare access were obtained from the Area Resource File. Multilevel logistic regression models were fit using SAS Proc Glimmix to account for clustering of individual observations by county. Separate models were fit for each of the two outcomes of interest; mammography screening and clinical breast examination. For each outcome, two sequential models were fit; a model including individual level covariates and a model including county level covariates. Results After adjusting for misclassification bias, overall cancer screening rates were lower than reported by survey respondents; black women had lower mammography screening rates but higher clinical breast examination rates than white women. However, after adjusting for other individual level variables, race was not a significant predictor of screening. Having health insurance or a usual healthcare provider were the most important predictors of cancer screening. Discussion Access to healthcare is important to ensuring appropriate cancer screening among women in Michigan.http://deepblue.lib.umich.edu/bitstream/2027.42/112422/1/12939_2011_Article_270.pd

Two simple systems with cold atoms: quantum chaos tests and nonequilibrium dynamics

This article is an attempt to provide a link between the quantum nonequilibrium dynamics of cold gases and fifty years of progress in the lowdimensional quantum chaos. We identify two atomic systems lying on the interface: two interacting atoms in a harmonic multimode waveguide and an interacting two-component Bose-Bose mixture in a double-well potential. In particular, we study the level spacing distribution, the wavefunction statistics, the eigenstate thermalization, and the ability to thermalize in a relaxation process as such.Comment: 18 pages, 9 figure

Human body tracking by monocular vision

This article describes a tracking method of 3D articulated complex objects (for example, the human body), from a monocular sequence of perspective images . These objects and their associated articulations must be modelled . The principle of the method is based on the interpretation of image features as the 3D perspective projections points of the object model and an iterativ e Levenberg-Marquardt process to compute the model pose in accordance with the analysed image . This attitude is filtered (Kalman filter) to predict the model pose relative to the following image of the sequence . The image features are extracted locally according to the computed prediction . Tracking experiments, illustrated in this article by a cycling sequence, have been conducted to prove the validity of the approach.Cet article décrit une méthode de suivi d'objets 3D articulés, de forme complexe telle que les membres du corps humain, à partir d'une séquence monoculaire d'images perspectives. Ces objets devront être préalablement modélisés ainsi que les articulations associées. Le principe de la méthode est basé sur l'interprétation de points caractéristiques dans l'image comme étant les projections perspectives de points 3D liés au modèle de l'objet et d'un processus itératif fondé sur la méthode de Levenberg-Marquardt pour calculer l'attitude de ce modèle conforme à l'image analysée. Cette attitude et la covariance associée sont fournies à un filtre de Kalman de haut niveau pour prédire l'attitude du modèle relative à l'image suivante de la séquence. L'extraction de primitives s'effectue localement par prise en compte de la prédiction obtenue. Des expérimentations de suivi, illustrées dans cet article par une séquence de pédalage, ont été effectuées, prouvant ainsi la validité de l'approche

Dielectric spectroscopy of melt-mixed polypropylene and pyrolytically stripped carbon nanofiber composites

In this work, pyrolytically stripped carbon nanofiber (CNF) polypropylene (PP) composites were synthesized following a scalable melt-mixing method, and the effects of CNF weight concentrations on the electrical conductivity, dielectric permittivity, electrical modulus and electrical impedance of PP/CNF composites were studied. Quite unexpectedly, the electrical conductivity of PP/CNF composites improved only slightly as the incorporation of CNFs was raised, yielding a maximum of ~10−10 S m−1 for PP/CNF 5 wt. % composites. The increase corresponded to a gradual improvement of the dielectric constant up to a maximum of ~9 for PP/CNF 5 wt. % composites at 1 MHz, which was attributed to the raise of interface polarization effect. Moreover, the Cole–Cole model was used to analyze the effects of CNF concentrations on the dielectric relaxation of PP/CNF composites, from which was deduced that the incorporation of CNFs increases their dielectric strength and relaxation times. The analysis gathered here aims to provide a better insight into the enhanced dielectric properties observed in low-conducting polymer composites filled with CNFs.A. J. Paleo gratefully acknowledges support from FCT-Foundation for Science and Technology by the project UID/CTM/00264/2021 of 2C2T under the COMPETE and FCT/MCTES (PIDDAC) co-financed by FEDER through the PT2020 program and “plurianual” 2020–2023 Project UIDB/00264/2020

So you want your private LLM at home? : a survey and benchmark of methods for efficient GPTs

At least since the introduction of ChatGPT, the abilities of generative large language models (LLMs), sometimes called GPTs, are at the center of the attention of AI researchers, entrepreneurs, and others. However, for many applications, it is not possible to call an existing LLM service via an API due to data protection concerns or when no task-appropriate LLM exists. On the other hand, deploying or training a private LLM is often prohibitively computationally expensive. In this paper, we give an overview of the most important recent methodologies that help reduce the computational footprint of LLMs. We further present extensive benchmarks for seven methods from two of the most important areas of recent progress: model quantization and low-rank adapters, showcasing how it is possible to leverage state-of-the-art LLMs with limited resources. Our benchmarks include resource consumption metrics (e.g. GPU memory usage), a state-of-the-art quantitative performance evaluation as well as a qualitative performance study conducted by eight individual human raters. Our evaluations show that quantization has a profound effect on GPU memory requirements. However, we also show that these quantization methods, contrary to how they are advertised, cause a noticeable loss in text quality. We further show that low-rank adapters allow effective model fine-tuning with moderate compute resources. For methods that require less than 16 GB of GPU memory, we provide easy-to-use Jupyter notebooks that allow anyone to deploy and fine-tune state-of-theart LLMs on the Google Colab free tier within minutes without any prior experience or infrastructure