Optimization of 3D Cooling Channels in Injection Molding using DRBEM and Model Reduction

Issu de : ESAFORM 2009 - 12th ESAFORM Conference on material forming, Enschede, THE NETHERLANDS, 27-29 April 2009International audienceToday, around 30% of manufactured plastic goods rely on injection moulding. The cooling time can represent more than 70% of the injection cycle. In this process, heat transfer during the cooling step has a great influence both on the quality of the final parts that are produced, and on the moulding cycle time. In the numerical solution of three-dimensional boundary value problems, the matrix size can be so large that it is beyond a computer capacity to solve it. To overcome this difficulty, we develop an iterative dual reciprocity boundary element method (DRBEM) to solve Poisson’s equation without the need of assembling a matrix. This yields a reduction of the computational space dimension from 3D to 2D, avoiding full 3D remeshing. Only the surface of the cooling channels needs to be remeshed at each evaluation required by the optimisation algorithm. For more efficiency, DRBEM computing results are extracted stored and exploited in order to construct a model with very few degrees of freedom. This approach is based on a model reduction technique known as proper orthogonal (POD) or Karhunen-Loève decompositions. We introduce in this paper a practical methodology to optimise both the position and the shape of the cooling channels in 3D injection moulding processes. First, we propose an implementation of the model reduction in the 3D transient BEM solver. This reduction permits to reduce considerably the computing time required by each direct computation. Secondly, we present an optimisation methodology applied to different injection cooling problems. For example, we can minimize the maximal temperature on the cavity surface subject to a temperature uniformityconstraint. Thirdly, we compare our results obtained by our approach with experimental results to show that our optimisation methodology is viable

Spleen-Resident CD4+ and CD4− CD8α− Dendritic Cell Subsets Differ in Their Ability to Prime Invariant Natural Killer T Lymphocytes

One important function of conventional dendritic cells (cDC) is their high capacity to capture, process and present Ag to T lymphocytes. Mouse splenic cDC subtypes, including CD8α+ and CD8α− cDC, are not identical in their Ag presenting and T cell priming functions. Surprisingly, few studies have reported functional differences between CD4− and CD4+ CD8α− cDC subsets. We show that, when loaded in vitro with OVA peptide or whole protein, and in steady-state conditions, splenic CD4− and CD4+ cDC are equivalent in their capacity to prime and direct CD4+ and CD8+ T cell differentiation. In contrast, in response to α-galactosylceramide (α-GalCer), CD4− and CD4+ cDC differentially activate invariant Natural Killer T (iNKT) cells, a population of lipid-reactive non-conventional T lymphocytes. Both cDC subsets equally take up α-GalCer in vitro and in vivo to stimulate the iNKT hybridoma DN32.D3, the activation of which depends solely on TCR triggering. On the other hand, and relative to their CD4+ counterparts, CD4− cDC more efficiently stimulate primary iNKT cells, a phenomenon likely due to differential production of co-factors (including IL-12) by cDC. Our data reveal a novel functional difference between splenic CD4+ and CD4− cDC subsets that may be important in immune responses

Revisiting the B-cell compartment in mouse and humans: more than one B-cell subset exists in the marginal zone and beyond.

International audienceABSTRACT: The immunological roles of B-cells are being revealed as increasingly complex by functions that are largely beyond their commitment to differentiate into plasma cells and produce antibodies, the key molecular protagonists of innate immunity, and also by their compartmentalisation, a more recently acknowledged property of this immune cell category. For decades, B-cells have been recognised by their expression of an immunoglobulin that serves the function of an antigen receptor, which mediates intracellular signalling assisted by companion molecules. As such, B-cells were considered simple in their functioning compared to the other major type of immune cell, the T-lymphocytes, which comprise conventional T-lymphocyte subsets with seminal roles in homeostasis and pathology, and non-conventional T-lymphocyte subsets for which increasing knowledge is accumulating. Since the discovery that the B-cell family included two distinct categories - the non-conventional, or extrafollicular, B1 cells, that have mainly been characterised in the mouse; and the conventional, or lymph node type, B2 cells - plus the detailed description of the main B-cell regulator, FcγRIIb, and the function of CD40+ antigen presenting cells as committed/memory B-cells, progress in B-cell physiology has been slower than in other areas of immunology. Cellular and molecular tools have enabled the revival of innate immunity by allowing almost all aspects of cellular immunology to be re-visited. As such, B-cells were found to express "Pathogen Recognition Receptors" such as TLRs, and use them in concert with B-cell signalling during innate and adaptive immunity. An era of B-cell phenotypic and functional analysis thus began that encompassed the study of B-cell microanatomy principally in the lymph nodes, spleen and mucosae. The novel discovery of the differential localisation of B-cells with distinct phenotypes and functions revealed the compartmentalisation of B-cells. This review thus aims to describe novel findings regarding the B-cell compartments found in the mouse as a model organism, and in human physiology and pathology. It must be emphasised that some differences are noticeable between the mouse and human systems, thus increasing the complexity of B-cell compartmentalisation. Special attention will be given to the (lymph node and spleen) marginal zones, which represent major crossroads for B-cell types and functions and a challenge for understanding better the role of B-cell specificities in innate and adaptive immunology

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Reviewing the use of resilience concepts in forest sciences

Purpose of the review Resilience is a key concept to deal with an uncertain future in forestry. In recent years, it has received increasing attention from both research and practice. However, a common understanding of what resilience means in a forestry context, and how to operationalise it is lacking. Here, we conducted a systematic review of the recent forest science literature on resilience in the forestry context, synthesising how resilience is defined and assessed. Recent findings Based on a detailed review of 255 studies, we analysed how the concepts of engineering resilience, ecological resilience, and social-ecological resilience are used in forest sciences. A clear majority of the studies applied the concept of engineering resilience, quantifying resilience as the recovery time after a disturbance. The two most used indicators for engineering resilience were basal area increment and vegetation cover, whereas ecological resilience studies frequently focus on vegetation cover and tree density. In contrast, important social-ecological resilience indicators used in the literature are socio-economic diversity and stock of natural resources. In the context of global change, we expected an increase in studies adopting the more holistic social-ecological resilience concept, but this was not the observed trend. Summary Our analysis points to the nestedness of these three resilience concepts, suggesting that they are complementary rather than contradictory. It also means that the variety of resilience approaches does not need to be an obstacle for operationalisation of the concept. We provide guidance for choosing the most suitable resilience concept and indicators based on the management, disturbance and application context

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing