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No full textPE, one of the main materials for the general-purpose separator of lithium-ion battery, causes heat shrinkage of about 10% at 120??C and up to 37% at 160??C, so it is regarded as one of the most important factors affecting safety, such as battery short circuit. Among several reported separator materials that could replace commercial separators of lithium-ion batteries(LIBs), aramid nanofiber (ANF) is considered one of the promising separator materials due to its excellent mechanical properties and thermal stability.
In this study, an aramid separator was prepared by applying the electrospinning method. Polyethylene oxide (PEO) was mixed as a carrier-polymer because the aramid solution has high viscosity and is difficult to electrospun alone. Solubility was evaluated for the homogeneity of the solution, and the rheological characteristics of the solution were evaluated to determine the supply ratio suitable for electrospinning. As a result, a porous membrane structure made of aramid composite nanofibers was fabricated
Deep learning-based super-resolution for harmful algal bloom monitoring of inland water
No full textInland water frequently occurs during harmful algal blooms (HABs), rendering it challenging to comprehend the spatiotemporal features of algal dynamics. Recently, remote sensing has been applied to effectively detect the algal spatiotemporal behaviors in expensive water bodies. However, image sensor resolution limitation can render the understanding of spatiotemporal features of relatively small water bodies challenging. In addition, few studies have improved the resolution of remote sensing images to investigate inland water quality, owing to the image sensor resolution limitations. Therefore, this study applied deep learning-based Super-resolution for transforming satellite imagery of 20 m to airborne imagery of 5 m. After performing atmospheric correction for the acquired images, we adopted super-resolution (SR) methodologies using a super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial networks (SRGAN) to estimate the Chlorophyll-a (Chl-a) concentration in the Geum River of South Korea. Both methods generated SR images with water reflectance at 665, 705, and 740 nm. Then, two band-ratio algorithms at 665 and 740 nm wavelengths were applied to the reflectance images to estimate the Chl-a concentration maps. The SRCNN model outperformed SRGAN and bicubic interpolation with peak signal-to-noise ratios (PSNR), mean square errors (MSE), and structural similarity index measures (SSIM) for the validation dataset of 24.47 (dB), 0.0074, and 0.74, respectively. SR maps from the SRCNN provided more detailed spatial information on Chl-a in the Geum River compared to the information obtained from satellite images. Therefore, these findings showed the potential of deep learning-based SR algorithms by providing further information according to the algal dynamics for inland water management with remote sensing images
High-Efficiency Photodetector Based on a CVD-Grown WS2 Monolayer
No full textFuture generation technologies demand high efficiencyphotodetectorsto enable sensing and switching devices for ultrafast communicationand machine vision. This requires direct-band gap materials with highphotosensitivity, high detectivity, and high quantum efficiency. Monolayeredtwo-dimensional-semiconductor-based photodetectors are the most promisingmaterials for such applications, although experimental realizationhas been limited due to the unavailability of a high-quality sample.In the current paper, we report about a WS2-based photodetectorhaving a sensitivity of 290 A W-1 upon 405 nm excitationand an incident power density as low as 0.06 mW/cm(2). Thefabricated device shows a detectivity of 52 x 10(14) with an external quantum efficiency of 89 x 10(3) %.The observed superior photoresponse parameters of the CVD-grown WS2-based photodetector as compared to Si-detectors establishits capability to replace the Si-photodetectors with monolayered ultrathindevice having superior performance parameters
Transformer-based Unified Recognition of Two Hands Manipulating Objects
No full textUnderstanding the hand-object interactions from an egocentric video has received a great attention recently. So far, most approaches are based on the convolutional neural network (CNN) features combined with the temporal encoding via the long short-term memory (LSTM) or graph convolution network (GCN) to provide the unified understanding of two hands, an object and their interactions. In this paper, we propose the Transformer-based unified framework that provides better understanding of two hands manipulating objects. In our framework, we insert the whole image depicting two hands, an object and their interactions as input and jointly estimate 3 information from each frame: poses of two hands, pose of an object and object types. Afterwards, the action class defined by the hand-object interactions is predicted from the entire video based on the estimated information combined with the contact map that encodes the interaction between two hands and an object. Experiments are conducted on H2O and FPHA benchmark datasets and we demonstrated the superiority of our method achieving the state-of-the-art accuracy. Ablative studies further demonstrate the effectiveness of each proposed module
GABAergic-like dopamine synapses in the brain
No full textDopamine synapses play a crucial role in volitional movement and reward-related behaviors, while dysfunction of dopamine synapses causes various psychiatric and neurological disorders. Despite this significance, the true biological nature of dopamine synapses remains poorly understood. Here, we show that dopamine transmission is strongly correlated with GABA co-transmission across the brain and dopamine synapses are structured and function like GABAergic synapses with marked regional heterogeneity. In addition, GABAergic-like dopamine synapses are clustered on the dendrites, and GABA transmission at dopamine synapses has distinct physiological properties. Interestingly, the knockdown of neuroligin-2, a key postsynaptic protein at GABAergic synapses, unexpectedly does not weaken GABA co-transmission but instead facilitates it at dopamine synapses in the striatal neurons. More importantly, the attenuation of GABA co-transmission precedes deficits in dopaminergic transmission in animal models of Parkinson???s disease. Our findings reveal the spatial and functional nature of GABAergic-like dopamine synapses in health and disease
Mass deformations of brane brick models
No full textWe investigate a class of mass deformations that connect pairs of 2d (0, 2) gauge theories associated to different toric Calabi-Yau 4-folds. These deformations are generalizations to 2d of the well-known Klebanov-Witten deformation relating the 4d gauge theories for the ???2/???2 ?? ??? orbifold and the conifold. We investigate various aspects of these deformations, including their connection to brane brick models and the relation between the change in the geometry and the pattern of symmetry breaking triggered by the deformation. We also explore how the volume of the Sasaki-Einstein 7-manifold at the base of the Calabi-Yau 4-fold varies under deformation, which leads us to conjecture that it quantifies the number of degrees of freedom of the gauge theory and its dependence on the RG scale
TET family proteins are dispensable for the structure and physiological function of dopamine neurons in health and Parkinson???s disease
No full textDNA undergoes demethylation via the oxidation of 5-methylcytosine (5mC), which is mediated by the Ten Eleven Translocation (TET) family of proteins. Notably, 5hmC is higher in the brain than in other tissues and is dynamically regulated according to development, aging, and brain diseases. In addition, accumulating evidence has recently revealed that 5-hmC and TETs play a significant role in synaptic functions, anxiety, addiction, and cognition in several brain regions. Furthermore, TET enzymes have turned out to be essential for diverse types of neurons in health and brain disorders. In this study, by generating triple knockout (TKO) mice of TET family proteins (TET1, 2, and 3) selectively in dopamine (DA) neurons, we investigated the roles of TET proteins in the structure and the function of DA neurons, which are pivotal for voluntary movement, reward-related behaviors, and motivation. We revealed that DA neuron-specific TET1, 2, 3 TKO does not alter the cellular structure and survival of DA neurons. Furthermore, DA neurons from substantia nigra pars compacta (SNc) show that intrinsic properties and synaptic transmission of DA neurons are unchanged by disruption of TET family proteins. Moreover, we revisited the pathophysiological importance of TET enzymes in Parkinson???s disease(PD) by utilizing pharmacological and genetic mouse models of PD. Against our expectation, however, we found that PD pathology induced by two types of PD models is unaffected by disruption of TET family proteins, which suggests that the role of TET family proteins in the pathophysiology of PD can be insignificant. Thus, contrary to the previous reports, TET family enzymes may be dispensable for the structure and function of specific neurons in health and disease
