Plastic deformation behavior of Cu thin films during fatigue testing

AbstractPlastic deformation behavior of electrodeposited copper films with thickness of 12 μm was investigated by using a testing system employed an electro-dynamic actuator. During the fatigue tests, cyclic plastic strain and ratcheting strain were measured continuously with high precision by using the capacitance extensometer. Since the displacement gage is stable and its response is so fast, the deformation can be measured in real time during fatigue tests as well as tensile tests. The cyclic plastic strain range and monotonic plastic strain increase with loading cycles and they are similar to the conventional creep curve. The monotonic plastic strain at fracture is nearly constant, irrespective of mean stress, indicating that the monotonic plastic strain can be used as an indicator of damage. Based on these results, a prediction method for the monotonic plastic strain is proposed. It could be found from the prediction results that the monotonic plastic strain is well predicted

ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation

We present ArtiGrasp, a novel method to synthesize bi-manual hand-object interactions that include grasping and articulation. This task is challenging due to the diversity of the global wrist motions and the precise finger control that are necessary to articulate objects. ArtiGrasp leverages reinforcement learning and physics simulations to train a policy that controls the global and local hand pose. Our framework unifies grasping and articulation within a single policy guided by a single hand pose reference. Moreover, to facilitate the training of the precise finger control required for articulation, we present a learning curriculum with increasing difficulty. It starts with single-hand manipulation of stationary objects and continues with multi-agent training including both hands and non-stationary objects. To evaluate our method, we introduce Dynamic Object Grasping and Articulation, a task that involves bringing an object into a target articulated pose. This task requires grasping, relocation, and articulation. We show our method's efficacy towards this task. We further demonstrate that our method can generate motions with noisy hand-object pose estimates from an off-the-shelf image-based regressor.Comment: 3DV-2024 camera ready. Project page: https://eth-ait.github.io/artigrasp

Influence of Carbon Content and Isothermal Heat Treatment Temperature on the Microstructure and Mechanical Properties of Ultra-High Strength Bainitic Steels

The effect of carbon content and isothermal heat treatment conditions on the microstructure evolution and mechanical properties of ultra-high strength bainitic steels was investigated. A reduction in carbon content from 0.8 wt% to 0.6 wt% in super-bainite steel with typical chemistry effectively improved not only the Charpy impact toughness but also the strength level. This suggests that reducing the carbon content is a very promising way to obtain better mechanical balance between strength and impact toughness. The higher Charpy impact toughness at a lower carbon content of 0.6 wt% is thought to result from a reduction in austenite fraction, and refinement of the austenite grain. The coarse austenite grains have a detrimental effect on impact toughness, by prematurely transforming to deformation-induced martensite during crack propagation. Mechanical properties were also affected by the isothermal treatment temperature. The lower isothermal temperature enhanced the formation of bainitic ferrite with a refined microstructure, which has a beneficial influence on strength, but reduces impact toughness. The lower impact toughness at lower isothermal temperature is attributed to the sluggish redistribution of carbon from the bainitic ferrite into the surrounding austenite. Higher solute carbon in the bainitic ferrite contributes to an increase of strength, but at the same time, encourages a propensity to cleavage fracture.11Ysciescopuskc

Identifying novel genetic variants for brain amyloid deposition: a genome-wide association study in the Korean population

Background: Genome-wide association studies (GWAS) have identified a number of genetic variants for Alzheimer's disease (AD). However, most GWAS were conducted in individuals of European ancestry, and non-European populations are still underrepresented in genetic discovery efforts. Here, we performed GWAS to identify single nucleotide polymorphisms (SNPs) associated with amyloid β (Aβ) positivity using a large sample of Korean population. Methods: One thousand four hundred seventy-four participants of Korean ancestry were recruited from multicenters in South Korea. Discovery dataset consisted of 1190 participants (383 with cognitively unimpaired [CU], 330 with amnestic mild cognitive impairment [aMCI], and 477 with AD dementia [ADD]) and replication dataset consisted of 284 participants (46 with CU, 167 with aMCI, and 71 with ADD). GWAS was conducted to identify SNPs associated with Aβ positivity (measured by amyloid positron emission tomography). Aβ prediction models were developed using the identified SNPs. Furthermore, bioinformatics analysis was conducted for the identified SNPs. Results: In addition to APOE, we identified nine SNPs on chromosome 7, which were associated with a decreased risk of Aβ positivity at a genome-wide suggestive level. Of these nine SNPs, four novel SNPs (rs73375428, rs2903923, rs3828947, and rs11983537) were associated with a decreased risk of Aβ positivity (p < 0.05) in the replication dataset. In a meta-analysis, two SNPs (rs7337542 and rs2903923) reached a genome-wide significant level (p < 5.0 × 10-8). Prediction performance for Aβ positivity increased when rs73375428 were incorporated (area under curve = 0.75; 95% CI = 0.74-0.76) in addition to clinical factors and APOE genotype. Cis-eQTL analysis demonstrated that the rs73375428 was associated with decreased expression levels of FGL2 in the brain. Conclusion: The novel genetic variants associated with FGL2 decreased risk of Aβ positivity in the Korean population. This finding may provide a candidate therapeutic target for AD, highlighting the importance of genetic studies in diverse populations

D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions

We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose. This is challenging, because it requires reasoning about the complex articulation of the human hand and the intricate physical interaction with the object. We propose a novel method that frames this problem in the reinforcement learning framework and leverages a physics simulation, both to learn and to evaluate such dynamic interactions. A hierarchical approach decomposes the task into low-level grasping and high-level motion synthesis. It can be used to generate novel hand sequences that approach, grasp, and move an object to a desired location, while retaining human-likeness. We show that our approach leads to stable grasps and generates a wide range of motions. Furthermore, even imperfect labels can be corrected by our method to generate dynamic interaction sequences. Video and code are available at: https://eth-ait.github.io/d-grasp/

Identification of hyperparameters with high effects on performance of deep neural networks: application to clinicopathological data of ovarian cancer

Recent advances in deep learning have emerged as an effective approach for precision medicine. The applications of deep learning to medicine have been applied mainly to medical image data but not clinicopathological data. One of challenges of deep learning model to clinicopathological data is to optimize hyperparameters to get high predictive power. In this study, we identified hyperparameters of deep learning model that have large effects on power. Specifically, we focused on predicting platinum-based chemotherapy response for ovarian cancer patients. As a performance metric, we used the area under the curve. We optimized six hyperparameters: the number of hidden layers, number of hidden units, optimization algorithm, weight initialization, activation function, and dropout rate. We also identified significant interaction effects between hyperparameters. We successfully found the combination of hyperparameters having large effects on prediction. These optimal combinations are expected to increase the prediction accuracy for the response to chemotherapy for a variety of cancer patients.N