THE RELATIONSHIP BETWEEN GLUTEUS MEDIUS ACTIVATION AND FRONTAL PLANE KNEE STABILITY

Excessive knee abduction moment and knee valgus in a weight bearing limb are well known biomechanical risk factors of chronic knee pain such as patellofemoral pain (PFP) or knee osteoarthritis (OA). Neuromuscular control of the hip abductors is important to prevent excessive knee abduction moment and knee valgus. Potential associations between altered neuromuscular control of gluteus medius (GMED) and PFP has been frequently suggested; however, there is limited literature on how neuromuscular control of the GMED is related to the knee abduction moment or knee valgus. The primary objective of the present study was to examine whether GMED onset and activation magnitude are related to the knee abduction moment and knee valgus. The secondary objective was to investigate the relationship between hip abductor strength and knee abduction moment and valgus. 20 healthy females (22.6 ± 2.5 yrs) performed 15 Single Limb Mini Squats (SLMS) on each leg. Correlations between the GMED activation parameters, hip abductor strength, and frontal plane knee angle and moment were examined separately for each limb in three different phases of the SLMS: Double to single limb transition, single limb stabilization, and descending phase. As secondary analyses, the relationships among frontal plane hip kinematics, kinetics, pelvic obliquity, and frontal plane knee angle and moment were examined separately for each limb in the specific movement phases. Greater GMED activation magnitude was significantly correlated with a decrease of the knee abduction moment during the single limb stabilization phase in the non-dominant limb only. The non-dominant limbs experienced significantly greater reduction of the knee abduction moment than the dominant limbs during the single limb stabilization phase. Greater hip abduction strength was correlated with less knee valgus only in the dominant limb during the double to single limb transition phase. Limb dominance may be an important factor when considering the neuromuscular control of GMED for controlling knee abduction moment. These results can provide useful insights for developing strategies for preventing chronic knee pain

Effect of pre-deformation on age-hardening behaviors in an Al-Mg-Cu alloy

The effects of 3%–50% pre-deformation following solution heat treatment on the age hardening of an Al-3Mg-1Cu alloy have been investigated by Vickers microhardness measurement, tensile tests, differential scanning calorimetry, scanning electron microscopy, and transmission electron microscopy. Pre-deformation has a strong effect on subsequent age-hardening behavior. The precipitation was accelerated, hardness peaks appeared earlier, formation of clusters was inhibited, and a larger fraction of precipitates was observed along the dislocation lines. The contribution of the precipitates to the hardness was evaluated by dissolution tests. It was found that pre-deformation followed by artificial aging resulted in a good strength-elongation balance. The results are significant for the development of combined mechanical deformation and heat treatment processes.publishedVersio

Real-space imaging of acoustic plasmons in large-area CVD graphene

An acoustic plasmonic mode in a graphene-dielectric-metal heterostructure has recently been spotlighted as a superior platform for strong light-matter interaction. It originates from the coupling of graphene plasmon with its mirror image and exhibits the largest field confinement in the limit of a nm-thick dielectric. Although recently detected in the far-field regime, optical near-fields of this mode are yet to be observed and characterized. Direct optical probing of the plasmonic fields reflected by the edges of graphene via near-field scattering microscope reveals a relatively small damping rate of the mid-IR acoustic plasmons in our devices, which allows for their real-space mapping even with unprotected, chemically grown, large-area graphene at ambient conditions. We show an acoustic mode that is twice as confined - yet 1.4 times less damped - compared to the graphene surface plasmon under similar conditions. We also image the resonant acoustic Bloch state in a 1D array of gold nanoribbons responsible for the high efficiency of the far-field coupling. Our results highlight the importance of acoustic plasmons as an exceptionally promising platform for large-area graphene-based optoelectronic devices operating in mid-IR

VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting

Label-efficient and reliable semantic segmentation is essential for many real-life applications, especially for industrial settings with high visual diversity, such as waste sorting. In industrial waste sorting, one of the biggest challenges is the extreme diversity of the input stream depending on factors like the location of the sorting facility, the equipment available in the facility, and the time of year, all of which significantly impact the composition and visual appearance of the waste stream. These changes in the data are called ``visual domains'', and label-efficient adaptation of models to such domains is needed for successful semantic segmentation of industrial waste. To test the abilities of computer vision models on this task, we present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting. Our challenge incorporates a fully-annotated waste sorting dataset, ZeroWaste, collected from two real material recovery facilities in different locations and seasons, as well as a novel procedurally generated synthetic waste sorting dataset, SynthWaste. In this competition, we aim to answer two questions: 1) can we leverage domain adaptation techniques to minimize the domain gap? and 2) can synthetic data augmentation improve performance on this task and help adapt to changing data distributions? The results of the competition show that industrial waste detection poses a real domain adaptation problem, that domain generalization techniques such as augmentations, ensembling, etc., improve the overall performance on the unlabeled target domain examples, and that leveraging synthetic data effectively remains an open problem. See https://ai.bu.edu/visda-2022/Comment: Proceedings of Machine Learning Researc