Optimisation of panel component regions subject to hot stamping constraints using a novel deep-learning-based platform

The latest hot stamping processes can enable efficient production of complex shaped panel components with high stiffness-to-weight ratios. However, structural redesign for these intricate processes can be challenging, because compared to cold forming, the non-isothermal and dynamic nature of these processes introduces complexity and unfamiliarity among industrial designers. In industrial practice, trial-and-error approaches are currently used to update non-feasible designs where complicated forming simulations are needed each time a design change is made. A superior approach to structural redesign for hot stamping processes is demonstrated in this paper which applies a novel deep-learning-based optimisation platform. The platform consists of the interaction between two neural networks: a generator that creates 3D panel component geometries and an evaluator that predicts their post-stamping thinning distributions. Guided by these distributions the geometry is iteratively updated by a gradient-based optimisation technique. In the application presented in this paper, panel component geometries are optimised to meet imposed constraints that are derived from post-stamping thinning distributions. In addition, a new methodology is applied to select arbitrary geometric regions that are to be fixed during the optimisation. Overall, it is demonstrated that the platform is capable of optimising selective regions of panel component subject to imposed post-stamped thinning distribution constraints

In vitro-derived platelets: the challenges we will have to face to assess quality and safety.

Platelet transfusions are given to patients in hospital who have a low blood platelet count (thrombocytopenia) either because of major bleeding (following trauma or surgery) or because the bone marrow production of platelets is impaired often due to chemotherapy, infiltration with malignant cells, fibrosis or genetic disorders. We are currently entirely reliant on blood donors as a source of platelets in transfusion medicine. However, the demand for platelets continues to rise, driven by an aging population, advances in medical procedures and ever more aggressive cancer therapies, while the supply of blood donors continues to remain static. In recent years, several groups have made major advances toward the generation of platelets in vitro for human transfusion. Recent successes include results in both generating mature human megakaryocytes as well as in developing bioreactors for extracting platelets from these megakaryocytes. Platelets made in vitro could address several issues inherent to platelets derived from blood donors - the ability to scale up/down more flexibly according to demand and therefore less precarious supply line, reduction of the risk of exposure to infectious agents and finally the possibility of engineering stem cells to reduce immunogenicity. Here we define the quality control tools and suggest measures for implementation across the field for in vitro platelet genesis, to aid collaboration between laboratories and to aid production of the burdens of proof that will eventually be required by regulators for efficacy and biosafety. We will do this firstly, by addressing the quality control of the nucleated cells used to make the platelets with a particular emphasis to safety issues and secondly, we will look at how platelet function measurement are addressed particularly in the context of platelets derived in vitro.This work was supported by grants from the Rosetrees Trust (A1691), NHS Blood and Transplant and the European Union (SilkFusion: AMD-767309-3)

Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach

The novel non-isothermal Hot Forming and cold die Quenching (HFQ) process can enable the cost-effective production of complex shaped, high strength aluminium alloy panel components. However, the unfamiliarity of designing for the new process prevents its widescale adoption in industrial settings. Recent research efforts focus on the development of advanced material models for finite element simulations, used to assess the feasibility of new component designs for the HFQ process. However, FE simulations take place late in design processes, require forming process expertise and are unsuitable for early-stage design explorations. To address these limitations, this study presents a novel application of a Convolutional Neural Network (CNN) based surrogate as a means of rapid manufacturing feasibility assessment for components to be formed using the HFQ process. A diverse dataset containing variations in component geometry, blank shapes, and processing parameters, together with corresponding physical fields is generated and used to train the model. The results show that near indistinguishable full field predictions are obtained in real time from the model when compared with HFQ simulations. This technique provides an invaluable tool to aid component design and decision making at the onset of a design process for complex-shaped components formed under HFQ conditions

Implicit neural representations of sheet stamping geometries with small-scale features

Geometric deep learning models, like Convolutional Neural Networks (CNNs), show promise as surrogate models for predicting sheet stamping manufacturability but lack design variables essential for inverse problems like geometric optimisation. Recent developments in deep learning have enabled geometry generation from compact latent spaces that are suitable for optimisation. However, current methods do not accurately model small-scale geometric features that are crucial for stamping performance. This study proposes a new deep learning-based method to address this limitation and generate detailed stamping geometries for optimisation. Specifically, neural networks are trained to generate Signed Distance Fields (SDFs) for stamping geometries, where the zero-level-set of each SDF implicitly represents the generated geometry. A new training approach is proposed for generating SDFs of stamping geometries, which involves supervising geometric properties of the SDFs. A novel loss function is introduced that directly acts on the zero-level-set and places high emphasis on learning small-scale features. This approach is compared with the state-of-the-art approach DeepSDF by Park et al. (2019), which explicitly supervises SDF values using ground truth data. The geometry generation performance of networks trained using both approaches is evaluated quantitatively and qualitatively. The results demonstrate significantly greater geometric accuracy with the proposed approach, which can faithfully generate small-scale features. Further analysis of the new approach reveals an organised learned latent space and varying the network input generates high-quality geometries from this space. By integrating with CNN-based manufacturability surrogate models by Attar et al. (2021), this work could enable the first-ever manufacturability-constrained optimisation of arbitrary sheet stamping geometries, potentially reducing geometry design time and cost

Electromagnetic form factor of pion from N_f=2+1 dynamical flavor QCD

We present a calculation of the electromagnetic form factor of the pion in $N_f=2+1$ flavor lattice QCD. Calculations are made on the PACS-CS gauge field configurations generated using Iwasaki gauge action and Wilson-clover quark action on a $32^3\times64$ lattice volume with the lattice spacing estimated as $a=0.0907(13)$ fm at the physical point. Measurements of the form factor are made using the technique of partially twisted boundary condition to reach small momentum transfer as well as periodic boundary condition with integer momenta. Additional improvements including random wall source techniques and a judicious choice of momenta carried by the incoming and outgoing quarks are employed for error reduction. Analyzing the form factor data for the pion mass at $M_\pi \approx 411$ MeV and 296 MeV, we find that the NNLO SU(2) chiral perturbation theory fit yields $=0.441 \pm 0.046 {\rm fm}^2$ for the pion charge radius at the physical pion mass. Albeit the error is quite large, this is consistent with the experimental value of $0.452\pm 0.011 {\rm fm}^2$. Below $M_\pi\approx 300$ MeV, we find that statistical fluctuations in the pion two- and three-point functions become too large to extract statistically meaningful averages on a $32^3$ spatial volume. We carry out a sample calculation on a $64^4$ lattice with the quark masses close to the physical point, which suggests that form factor calculations at the physical point become feasible by enlarging lattice sizes to $M_\pi L\approx 4$.Comment: 28 pages, 14 figure

Improving engagement with healthcare in hepatitis C: a randomised controlled trial of a peer support intervention

National Institute for Health Research Policy Research Programme (‘Effectiveness of testing for treatment of hard-to-reach groups for latent tuberculosis, hepatitis B virus and hepatitis C virus in England: The HALT Study’, 015/0306

Experience of specialist DVA provision under COVID-19: listening to service user voices to shape future practice

In the context of high rates of domestic violence and abuse (DVA) during the pandemic, specialist DVA services have been required to adapt rapidly to continue to deliver essential support to women and children in both refuges and the community. This study examines service users’ experiences and views of DVA service provision under COVID-19 and discusses implications for future practice. Data are drawn from a wider evaluation of DVA services in five sites in England. Fifty-seven semi-structured interviews and five focus groups were conducted with 70 female survivors and seven children accessing DVA services during the pandemic. Analysis identified key themes in respect of the influence of COVID-19 on the experience of service delivery. COVID-19 restrictions had both positive and negative implications for service users. Remote support reduced face-to-face contact with services, but consistent communication counteracted isolation. Digital practices offered effective means of providing individual and group support, but there were concerns that not all children were able to access online support. Digital support offered convenience and control for survivors but could lack privacy and opportunities for relationship-building. The pivot to remote delivery suggests directions where DVA services can expand the range and nature of future service provision

Experience of specialist DVA provision under COVID-19: listening to service user voices to shape future practice

In the context of high rates of domestic violence and abuse (DVA) during the pandemic, specialist DVA services have been required to adapt rapidly to continue to deliver essential support to women and children in both refuges and the community. This study examines service users’ experiences and views of DVA service provision under COVID-19 and discusses implications for future practice. Data are drawn from a wider evaluation of DVA services in five sites in England. Fifty-seven semi-structured interviews and five focus groups were conducted with 70 female survivors and seven children accessing DVA services during the pandemic. Analysis identified key themes in respect of the influence of COVID-19 on the experience of service delivery. COVID-19 restrictions had both positive and negative implications for service users. Remote support reduced face-to-face contact with services, but consistent communication counteracted isolation. Digital practices offered effective means of providing individual and group support, but there were concerns that not all children were able to access online support. Digital support offered convenience and control for survivors but could lack privacy and opportunities for relationship-building. The pivot to remote delivery suggests directions where DVA services can expand the range and nature of future service provision

Sex Pheromone Evolution Is Associated with Differential Regulation of the Same Desaturase Gene in Two Genera of Leafroller Moths

Chemical signals are prevalent in sexual communication systems. Mate recognition has been extensively studied within the Lepidoptera, where the production and recognition of species-specific sex pheromone signals are typically the defining character. While the specific blend of compounds that makes up the sex pheromones of many species has been characterized, the molecular mechanisms underpinning the evolution of pheromone-based mate recognition systems remain largely unknown. We have focused on two sets of sibling species within the leafroller moth genera Ctenopseustis and Planotortrix that have rapidly evolved the use of distinct sex pheromone blends. The compounds within these blends differ almost exclusively in the relative position of double bonds that are introduced by desaturase enzymes. Of the six desaturase orthologs isolated from all four species, functional analyses in yeast and gene expression in pheromone glands implicate three in pheromone biosynthesis, two Δ9-desaturases, and a Δ10-desaturase, while the remaining three desaturases include a Δ6-desaturase, a terminal desaturase, and a non-functional desaturase. Comparative quantitative real-time PCR reveals that the Δ10-desaturase is differentially expressed in the pheromone glands of the two sets of sibling species, consistent with differences in the pheromone blend in both species pairs. In the pheromone glands of species that utilize (Z)-8-tetradecenyl acetate as sex pheromone component (Ctenopseustis obliquana and Planotortrix octo), the expression levels of the Δ10-desaturase are significantly higher than in the pheromone glands of their respective sibling species (C. herana and P. excessana). Our results demonstrate that interspecific sex pheromone differences are associated with differential regulation of the same desaturase gene in two genera of moths. We suggest that differential gene regulation among members of a multigene family may be an important mechanism of molecular innovation in sex pheromone evolution and speciation