Mass sensing using an amorphous silicon MEMS resonator

Bilayer micro-bridge resonators of amorphous silicon and titanium were fabricated on a glass substrate at 100°C by surface micromachining using an aluminum sacrificial layer. Au square patterns with sub-micrometer dimensions were patterned on top of the microresonators. These Au squares allowed specific immobilization of thiolated single strand DNA probe oligonucleotides. The frequency response of the electrostatically-actuated resonators was measured optically. The mass loading effect of the Au squares and of the immobilized ssDNA probes were determined by the shift in the resonance frequency of the micro-bridge resonators

a promising new technique to advance bioelectronic medicines

© 2021. The Author(s).Nerve conduction block is an appealing way to selective target the nervous system for treating pathological conditions. Several modalities were described in the past, with the kilohertz frequency stimulation generating an enormous interest and tested successfully in clinical settings. Some shortcomings associated with different modalities of nerve blocking can limit its clinical use, as the "onset response", the high demand of energy supply, among others. A recent study by Muzquiz and colleagues describes the efficacy and reversibility of low frequency alternating currents in blocking the cervical vagus in the pig, in the absence of an onset effect and apparent lack of neuronal damage.publishersversionpublishe

D3-D5 theories with unquenched flavors

We construct the string duals of the defect theories generated when N_f flavor D5-branes intersect N_c color D3-branes along a 2+1 dimensional subspace. We work in the Veneziano limit in which N_c and N_f are large and N_f/N_c is fixed. By smearing the D5-branes, we find supergravity solutions that take into account the backreaction of the flavor branes and preserve two supercharges. When the flavors are massless the resulting metric displays an anisotropic Lifshitz-like scale invariance. The case of massive quarks is also considered.Comment: 29 pages, 1 figure; v2: references adde

Enhanced magnetocaloric response in Cr/Mo containing Nanoperm-type amorphous alloys

The magnetocaloric effect of Fe76Cr8−xMoxCu1B15 (x =0,4) alloys is studied. Although the combined addition of Cr and Mo is more efficient in tuning the Curie temperature of the alloy, the Mo-free alloy presents a higher magnetocaloric response. The refrigerant capacity (RC) for the Mo-containing alloy is comparable to that of Gd5Ge1.9Si2Fe0.1 (for a field of 50 kOe, RC = 273 J kg−1 for the Mo alloy vs 240 J kg−1 for the Gd-based one), with a larger temperature span of the optimal refrigeration cycle (250 K vs 90 K, respectively). The restriction of the temperature span to 90 K gives RC = 187 J kg−1 for the Mo alloy. A master curve behavior for the magnetic entropy change is also evidenced

Rapid Deforestation and Burned Area Detection using Deep Multimodal Learning on Satellite Imagery

Deforestation estimation and fire detection in the Amazon forest poses a significant challenge due to the vast size of the area and the limited accessibility. However, these are crucial problems that lead to severe environmental consequences, including climate change, global warming, and biodiversity loss. To effectively address this problem, multimodal satellite imagery and remote sensing offer a promising solution for estimating deforestation and detecting wildfire in the Amazonia region. This research paper introduces a new curated dataset and a deep learning-based approach to solve these problems using convolutional neural networks (CNNs) and comprehensive data processing techniques. Our dataset includes curated images and diverse channel bands from Sentinel, Landsat, VIIRS, and MODIS satellites. We design the dataset considering different spatial and temporal resolution requirements. Our method successfully achieves high-precision deforestation estimation and burned area detection on unseen images from the region. Our code, models and dataset are open source: https://github.com/h2oai/cvpr-multiearth-deforestation-segmentationComment: CVPR 2023 Workshop on Multimodal Learning for Earth and Environment (MultiEarth