Laboratoire Bourguignon des Matériaux et Procédés

The timber construction industry mainly uses softwood as structural products, owing to the slenderness of the trees and rather homogeneous nature of the timber, which makes it easier and more cost-effective to process compared to hardwood. By using narrower and shorter components than for softwood, it might be technically and economically possible to produce finger-jointed, edge-glued oak laminations to be used in glued laminated products. This study proposes to explore the impact of the shorter size of components and thus greater heterogeneity of oak edge-glued laminations on their structural properties. Homogenizing the elastic modulus of the components of the laminations through preliminary sorting appeared to greatly improve the strength of the edge-glued laminations, whereas the sole strength grading with no further control of elastic modulus variability could lead to low strengths with disadvantageous system effect. The local evaluation of the ratio between the lowest and highest MoE values of sub-boards in parallel across the lamination width proved to be correlated to tensile strength, with a coefficient of determination of $$R^2$$ = 0.30. Moreover, homogenizing the elastic modulus of the laminations led to strengths corresponding to system factor values $$k_{sys}$$ exceeding 1.1, as recommended by the standard. A better control of variability of the mechanical properties of hardwood products, seems offering a pathway to more efficient utilization of the hardwood resource.

Abstract In the era of high-throughput biology, molecular phenotypes have proven effective in predicting disease states and future trajectories. Transcriptomics, in particular, has enabled the dissection of complex diseases with heterogeneous genetic and environmental aetiology, both aiding diagnosis and augmenting treatment. As improving technology has led to measurements of gene expression at increasing granularity, it has become progressively feasible to resolve disease traits that present locally or with spatial heterogeneity. Principal among these are cancers in which tumour gene expression, while itself heterogenous, exhibits a distinct signature from that of the surrounding tissue. Identifying this signature through molecular phenotyping facilitates specific cancer diagnosis and treatment. Here, we introduce AGP-Net, a multi-modal foundation framework capable of predicting gene expression from histopathology images. Rather than produce an aggregated estimate of expression for each gene, AGP-Net disaggregates images into spots and attempts to resolve the variation in gene expression across them, thereby providing coarse spatial transcriptomic predictions across the tumour slice and surrounding region. The challenge in doing so is due to data sparsity relative to the dimensionality of the problem: the number of genes and their contextual heterogeneity within and between tissue and cancer types makes it difficult to train a model on the limited data available. The innovation of AGP-Net lies in borrowing strength across similar genes as defined by their textual language descriptions. Our AGP-Net supports datasets with varying gene coverage and facilitates the prediction of gene expression for previously unseen genes based on their textual descriptions. Trained on millions of spots from diverse dataset sources, AGP-Net establishes state-of-the-art performance in zero-shot spatial gene expression prediction, demonstrating its adaptability to generalize across novel scenarios.

Wood density measurement plays a crucial role in assessing wood quality and predicting its mechanical performance. This dataset was collected to compare the accuracy and reliability of two non-destructive techniques, X-rays and terahertz waves, for measuring wood density. While X-rays have been commonly used in the industry due to their effectiveness, they pose health risks due to ionizing radiation. Terahertz waves, on the other hand, are non-ionizing and offer high spatial resolution. This article presents a database of wood samples measurements obtained using both techniques, on the same 110 samples with a fine location of the measuring points, on a wide range of wood species (tropical and temperate ones) and densities, from 111 kg·m−3 to 1086 kg·m−3. The database includes X-ray and terahertz scans, sample dimensions, moisture content, and color photographs.

In many foundries, numerical simulation is used to determine the origins of different defects as this tool allows the acceleration of the design process. However, the databases provided by different software do not seem to tally with the actual properties of the material. In fact, every foundry uses a different grade of steel and varying mixtures of sand. An evaluation of the impact of different material properties showed the importance of measuring every physical property to improve the database of the software. Following this, an experiment was conducted to evaluate the gap between numerical simulations and the results obtained through experimentation. This experiment, called thermal analysis, consists in measuring the solidification and cooling of a cylinder filled with liquid steel. After the calculation of the steel properties and a simulation with real experimental parameters, a comparison between each cooling curve was realized. This comparison shows that the calculated properties provide a simulated cooling curve which is closer to the experimental curve than the properties in the original database. We did not explore all of the metal properties in this study, but the modification of the sand properties was explored, together with the thermal conductivity of the steel and sand. These other measurements will be obtained in a future study.