Weighted Unsupervised Domain Adaptation Considering Geometry Features and Engineering Performance of 3D Design Data

The product design process in manufacturing involves iterative design modeling and analysis to achieve the target engineering performance, but such an iterative process is time consuming and computationally expensive. Recently, deep learning-based engineering performance prediction models have been proposed to accelerate design optimization. However, they only guarantee predictions on training data and may be inaccurate when applied to new domain data. In particular, 3D design data have complex features, which means domains with various distributions exist. Thus, the utilization of deep learning has limitations due to the heavy data collection and training burdens. We propose a bi-weighted unsupervised domain adaptation approach that considers the geometry features and engineering performance of 3D design data. It is specialized for deep learning-based engineering performance predictions. Domain-invariant features can be extracted through an adversarial training strategy by using hypothesis discrepancy, and a multi-output regression task can be performed with the extracted features to predict the engineering performance. In particular, we present a source instance weighting method suitable for 3D design data to avoid negative transfers. The developed bi-weighting strategy based on the geometry features and engineering performance of engineering structures is incorporated into the training process. The proposed model is tested on a wheel impact analysis problem to predict the magnitude of the maximum von Mises stress and the corresponding location of 3D road wheels. This mechanism can reduce the target risk for unlabeled target domains on the basis of weighted multi-source domain knowledge and can efficiently replace conventional finite element analysis

Topology Optimization via Machine Learning and Deep Learning: A Review

Topology optimization (TO) is a method of deriving an optimal design that satisfies a given load and boundary conditions within a design domain. This method enables effective design without initial design, but has been limited in use due to high computational costs. At the same time, machine learning (ML) methodology including deep learning has made great progress in the 21st century, and accordingly, many studies have been conducted to enable effective and rapid optimization by applying ML to TO. Therefore, this study reviews and analyzes previous research on ML-based TO (MLTO). Two different perspectives of MLTO are used to review studies: (1) TO and (2) ML perspectives. The TO perspective addresses "why" to use ML for TO, while the ML perspective addresses "how" to apply ML to TO. In addition, the limitations of current MLTO research and future research directions are examined

Performance Comparison of Design Optimization and Deep Learning-based Inverse Design

Surrogate model-based optimization has been increasingly used in the field of engineering design. It involves creating a surrogate model with objective functions or constraints based on the data obtained from simulations or real-world experiments, and then finding the optimal solution from the model using numerical optimization methods. Recent advancements in deep learning-based inverse design methods have made it possible to generate real-time optimal solutions for engineering design problems, eliminating the requirement for iterative optimization processes. Nevertheless, no comprehensive study has yet closely examined the specific advantages and disadvantages of this novel approach compared to the traditional design optimization method. The objective of this paper is to compare the performance of traditional design optimization methods with deep learning-based inverse design methods by employing benchmark problems across various scenarios. Based on the findings of this study, we provide guidelines that can be taken into account for the future utilization of deep learning-based inverse design. It is anticipated that these guidelines will enhance the practical applicability of this approach to real engineering design problems

Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress

The impact performance of the wheel during wheel development must be ensured through a wheel impact test for vehicle safety. However, manufacturing and testing a real wheel take a significant amount of time and money because developing an optimal wheel design requires numerous iterative processes of modifying the wheel design and verifying the safety performance. Accordingly, the actual wheel impact test has been replaced by computer simulations, such as Finite Element Analysis (FEA), but it still requires high computational costs for modeling and analysis. Moreover, FEA experts are needed. This study presents an aluminum road wheel impact performance prediction model based on deep learning that replaces the computationally expensive and time-consuming 3D FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and barrier mass value used for wheel impact test are utilized as the inputs to predict the magnitude of maximum von Mises stress, corresponding location, and the stress distribution of 2D disk-view. The wheel impact performance prediction model can replace the impact test in the early wheel development stage by predicting the impact performance in real time and can be used without domain knowledge. The time required for the wheel development process can be shortened through this mechanism

Growth Pattern of Hepatic Metastasis as a Prognostic Index Reflecting Liver Metastasis-Associated Survival in Breast Cancer Liver Metastasis

Breast cancer with liver metastasis (BCLM) frequently cause hepatic failure owing to extensive liver metastasis compared to other cancers; however, there are no clinicopathologic or radiologic parameters for estimating BCLM prognosis. We analyzed the relationship between radiologic and clinicopathologic characteristics with survival outcomes in BCLM. During 2009–2019, baseline and final abdomen computed tomography or liver magnetic resonance imaging of BCLM patients were reviewed. Liver metastasis patterns were classified as oligometastasis (≤3 metastatic lesions), non-confluent or confluent mass formation, infiltration, and pseudocirrhosis. Thirty-one surgical or biopsy specimens for liver metastasis were immunostained for L1 adhesion molecule (L1CAM), Yes-associated protein 1/Transcriptional co-activator with PDZ-binding motif (YAP/TAZ), and β1-integrin. Out of 156 patients, 77 initially had oligometastasis, 58 had nonconfluent mass formation, 14 had confluent mass formation, and 7 had infiltrative liver metastasis. Confluent or infiltrative liver metastasis showed inferior liver metastasis-associated survival (LMOS) compared to others (p = 0.001). Positive staining for L1CAM and YAP/TAZ was associated with inferior survival, and YAP/TAZ was related to final liver metastasis. Initial hepatic metastasis was associated with LMOS, especially confluent mass formation, and infiltrative liver metastasis pattern was associated with poor survival. Positive staining for YAP/TAZ and L1CAM was associated with inferior LMOS, and YAP/TAZ was related to final liver metastasis

An Electrochemical Cell for Selective Lithium Capture from Seawater

Lithium (Li) is a core element of Li-ion batteries (LIBs). Recent developments in mobile electronics such as smartphones and tablet PCs as well as advent of large-scale LIB applications including electrical vehicles and grid-level energy storage systems have led to an increase in demand for LIBs, giving rise to a concern on the availability and market price of Li resources. However, the current Lime-Soda process that is responsible for greater than 80% of worldwide Li resource supply is applicable only in certain regions on earth where the Li concentrations are sufficiently high (salt lakes or salt pans). Moreover, not only is the process time-consuming (12-18 months), but post-treatments are also required for the purification of Li. Here, we have devised a location-independent electrochemical system for Li capture, which can operate within a short time period (a few hours to days). By engaging olivine LiFePO4 active electrode that improves interfacial properties via polydopamine coating, the electrochemical cell achieves 4330 times amplification in Li/Na ion selectivity (Li/Na molar ratio of initial solution = 0.01 and Li/Na molar ratio of final electrode = 43.3). In addition, the electrochemical system engages an I-/I-3(-) redox couple in the other electrode for balancing of the redox states on both electrode sides and sustainable operations of the entire cell. Based on the electrochemical results, key material and interfacial properties that affect the selectivity in Li capture are identified.

Induction of Fatty Acid Oxidation Underlies DNA Damage‐Induced Cell Death and Ameliorates Obesity‐Driven Chemoresistance

Abstract The DNA damage response is essential for preserving genome integrity and eliminating damaged cells. Although cellular metabolism plays a central role in cell fate decision between proliferation, survival, or death, the metabolic response to DNA damage remains largely obscure. Here, this work shows that DNA damage induces fatty acid oxidation (FAO), which is required for DNA damage‐induced cell death. Mechanistically, FAO induction increases cellular acetyl‐CoA levels and promotes N‐alpha‐acetylation of caspase‐2, leading to cell death. Whereas chemotherapy increases FAO related genes through peroxisome proliferator‐activated receptor α (PPARα), accelerated hypoxia‐inducible factor‐1α stabilization by tumor cells in obese mice impedes the upregulation of FAO, which contributes to its chemoresistance. Finally, this work finds that improving FAO by PPARα activation ameliorates obesity‐driven chemoresistance and enhances the outcomes of chemotherapy in obese mice. These findings reveal the shift toward FAO induction is an important metabolic response to DNA damage and may provide effective therapeutic strategies for cancer patients with obesity

An Electrochemical Cell for Selective Lithium Capture from Seawater

Lithium (Li) is a core element of Li-ion batteries (LIBs). Recent developments in mobile electronics such as smartphones and tablet PCs as well as advent of large-scale LIB applications including electrical vehicles and grid-level energy storage systems have led to an increase in demand for LIBs, giving rise to a concern on the availability and market price of Li resources. However, the current Lime-Soda process that is responsible for greater than 80% of worldwide Li resource supply is applicable only in certain regions on earth where the Li concentrations are sufficiently high (salt lakes or salt pans). Moreover, not only is the process time-consuming (12–18 months), but post-treatments are also required for the purification of Li. Here, we have devised a location-independent electrochemical system for Li capture, which can operate within a short time period (a few hours to days). By engaging olivine LiFePO₄ active electrode that improves interfacial properties via polydopamine coating, the electrochemical cell achieves 4330 times amplification in Li/Na ion selectivity (Li/Na molar ratio of initial solution = 0.01 and Li/Na molar ratio of final electrode = 43.3). In addition, the electrochemical system engages an I^–/I₃^– redox couple in the other electrode for balancing of the redox states on both electrode sides and sustainable operations of the entire cell. Based on the electrochemical results, key material and interfacial properties that affect the selectivity in Li capture are identified