Amplification of velocity gradients, a key feature of turbulent flows, is affected by the non-local character of the incompressible fluid equations expressed by the second derivative (Hessian) of the pressure field. By analysing the structure of the flow in regions where the vorticity is the highest, we propose an approximate expression for the pressure Hessian in terms of the local vorticity, consistent with the existence of intense vortex tubes. Contrary to the often used simplification of an isotropic form for the pressure Hessian, which in effect inhibits vortex stretching, the proposed approximate form of the pressure Hessian enables much stronger vortex stretching. The prediction of the approximation proposed here is validated with results of direct numerical simulations of turbulent flows.

Autonomous fabric manipulation is a challenging task due to complex dynamics and potential self-occlusion during fabric handling. An intuitive method of fabric-folding manipulation first involves obtaining a smooth and unfolded fabric configuration before the folding process begins. However, the combination of quasi-static actions like pick & place and dynamic action like fling proves inadequate in effectively unfolding long-sleeved T-shirts with sleeves mostly tucked inside the garment. To address this limitation, this paper introduces an enhanced quasi-static action called pick & drag, specifically designed to handle this type of fabric configuration. Additionally, an efficient dual-arm manipulation system is designed in this paper, which combines quasi-static (including pick & place and pick & drag) and dynamic fling actions to flexibly manipulate fabrics into unfolded and smooth configurations. Subsequently, once it is confirmed that the fabric is sufficiently unfolded and all fabric keypoints are detected, the keypoint-based heuristic folding algorithm is employed for the fabric-folding process. To address the scarcity of publicly available keypoint detection datasets for real fabric, we gathered images of various fabric configurations and types in real scenes to create a comprehensive keypoint dataset for fabric folding. This dataset aims to enhance the success rate of keypoint detection. Moreover, we evaluate the effectiveness of our proposed system in real-world settings, where it consistently and reliably unfolds and folds various types of fabrics, including challenging situations such as long-sleeved T-shirts with most parts of sleeves tucked inside the garment. Specifically, our method achieves a coverage rate of 0.822 and a success rate of 0.88 for long-sleeved T-shirts folding. Supplemental materials and dataset are available on our project webpage at https://sites.google.com/view/fabricfolding.

The proton-boron (p 11 B) reaction is regarded as the holy grail of advanced fusion fuels, where the primary reaction produces 3 energetic α particles. However, due to the high nuclear bounding energy and bremsstrahlung energy losses, energy gain from the p 11 B fusion is hard to achieve in thermal fusion conditions. Owing to advances in intense laser technology, the p11 B fusion has drawn renewed attention by using an intense laser-accelerated proton beam to impact a boron-11 target. As one of the most influential works in this field, Labaune et al. first experimentally found that states of boron (solid or plasma) play an important role in the yield of α particles. This exciting experimental finding rouses an attempt to measure the nuclear fusion cross section in a plasma environment. However, up to now, there is still no quantitative explanation. Based on large-scale, fully kinetic computer simulations, the inner physical mechanism of yield increment is uncovered, and a quantitative explanation is given. Our results indicate the yield increment is attributed to the reduced energy loss of the protons under the synergetic influences of degeneracy effects and collective electromagnetic effects. Our work may serve as a reference for not only analyzing or improving further experiments of the p 11 B fusion but also investigating other beam-plasma systems, such as ion-driven inertial confinement fusions.

Sensory differences and anxiety disorders are highly prevalent in autistic individuals with and without ADHD. Studies have shown that sensory differences and anxiety are associated and that intolerance of uncertainty (IU) plays an important role in this relationship. However, it is unclear as to how different levels of the sensory processing pathway (i.e., perceptual, affective, or behavioral) contribute. Here, we used psychophysics to assess how alterations in tactile perception contribute to questionnaire measures of sensory reactivity, IU, and anxiety. Thirty-eight autistic children (aged 8-12 years; 27 with co-occurring ADHD) were included. Consistent with previous findings, mediation analyses showed that child-reported IU fully mediated an association between parent-reported sensory reactivity and parent-reported anxiety and that anxiety partially mediated an association between sensory reactivity and IU. Of the vibrotactile thresholds, only simultaneous frequency discrimination (SFD) thresholds correlated with sensory reactivity. Interestingly, we found that sensory reactivity fully mediated an association between SFD threshold and anxiety, and between SFD threshold and IU. Taken together, those findings suggest a mechanistic pathway whereby tactile perceptual alterations contribute to sensory reactivity at the affective level, leading in turn to increased IU and anxiety. This stepwise association can inform potential interventions for IU and anxiety in autism.

Competition among the two-plasmon decay (TPD) of backscattered light of stimulated Raman scattering (SRS), filamentation of the electron-plasma wave (EPW) and forward side SRS is investigated by two-dimensional particle-in-cell simulations. Our previous work [K. Q. Pan et al., Nucl. Fusion 58, 096035 (2018)] showed that in a plasma with the density near 1/10 of the critical density, the backscattered light would excite the TPD, which results in suppression of the backward SRS. However, this work further shows that when the laser intensity is so high ($>{10}^{16}$ W/cm2) that the backward SRS cannot be totally suppressed, filamentation of the EPW and forward side SRS will be excited. Then the TPD of the backscattered light only occurs in the early stage and is suppressed in the latter stage. Electron distribution functions further show that trapped-particle-modulation instability should be responsible for filamentation of the EPW. This research can promote the understanding of hot-electron generation and SRS saturation in inertial confinement fusion experiments.

A relation among invariants of filtered velocity gradients with two different filter sizes is derived. Based on this relation and physical reasoning, it is shown analytically that strain self-amplification contributes more to energy transfer than vortex stretching in homogeneous turbulence, as observed in recent numerical investigations of homogeneous isotropic turbulence. We note that the invariant relation studied and hence the inequality between strain self-amplification and vortex stretching apply to all homogeneous flows, not restricted to isotropic turbulence.

Background: Machine learning (ML) methods hold promise in allowing early detection of dementia. We performed a systematic review to assess the quality of published evidence for using ML methods applied to drawing tests of cognition, and to describe the accuracy of the methods. Methods: Embase, Medline, and Cochrane Central Library databases were searched for potential studies up to December 8, 2018 by four independent reviewers. Included articles satisfied the following criteria: 1) use of ML on 2) a drawing test in order to 3) assess cognition. The quality of evidence was then assessed using GRADE methodology. Results: The initial search yielded 4620 citations. Of these, 64 were eligible for full text review. 18 articles then met inclusion criteria. Median AUC across all models was 0.765, with certain ML algorithms performing better in terms of AUC or diagnostic accuracy. However, based on GRADE, the quality of evidence was deemed very low. Conclusions: ML has been applied by several groups to drawing tests of cognition. The quality of evidence is currently too low to make recommendations on their use. Future work must focus on improving reporting, and using standard algorithms and larger, more diverse datasets to improve comparability and generalizability.