Multistaged discharge constructing heterostructure with enhanced solid-solution behavior for long-life lithium-oxygen batteries.

Inferior charge transport in insulating and bulk discharge products is one of the main factors resulting in poor cycling stability of lithium-oxygen batteries with high overpotential and large capacity decay. Here we report a two-step oxygen reduction approach by pre-depositing a potassium carbonate layer on the cathode surface in a potassium-oxygen battery to direct the growth of defective film-like discharge products in the successive cycling of lithium-oxygen batteries. The formation of defective film with improved charge transport and large contact area with a catalyst plays a critical role in the facile decomposition of discharge products and the sustained stability of the battery. Multistaged discharge constructing lithium peroxide-based heterostructure with band discontinuities and a relatively low lithium diffusion barrier may be responsible for the growth of defective film-like discharge products. This strategy offers a promising route for future development of cathode catalysts that can be used to extend the cycling life of lithium-oxygen batteries

ClockViz: Designing Public Visualization for Coping with Collective Stress in Teamwork

The intervention solutions for coping with collective stress have been neglected in interaction design because of limited scalability of the physiological measuring methods. This paper focuses on exploring visual biofeedback design for collective stress in the context of teamwork. We design ClockViz, an augmented reality installation overlaid with static or dynamic projection to visualize three different extents of collective stress on a clock. Results of a 16-participant study show that ClockViz is useful to provide biofeedback data, change their internal status, and increase their mindfulness. Based on the results, we also discussed the potential solutions to collective stress sensing for designers to apply into their interactive design intervention

US-SFNet: A Spatial-Frequency Domain-based Multi-branch Network for Cervical Lymph Node Lesions Diagnoses in Ultrasound Images

Ultrasound imaging serves as a pivotal tool for diagnosing cervical lymph node lesions. However, the diagnoses of these images largely hinge on the expertise of medical practitioners, rendering the process susceptible to misdiagnoses. Although rapidly developing deep learning has substantially improved the diagnoses of diverse ultrasound images, there remains a conspicuous research gap concerning cervical lymph nodes. The objective of our work is to accurately diagnose cervical lymph node lesions by leveraging a deep learning model. To this end, we first collected 3392 images containing normal lymph nodes, benign lymph node lesions, malignant primary lymph node lesions, and malignant metastatic lymph node lesions. Given that ultrasound images are generated by the reflection and scattering of sound waves across varied bodily tissues, we proposed the Conv-FFT Block. It integrates convolutional operations with the fast Fourier transform to more astutely model the images. Building upon this foundation, we designed a novel architecture, named US-SFNet. This architecture not only discerns variances in ultrasound images from the spatial domain but also adeptly captures microstructural alterations across various lesions in the frequency domain. To ascertain the potential of US-SFNet, we benchmarked it against 12 popular architectures through five-fold cross-validation. The results show that US-SFNet is SOTA and can achieve 92.89% accuracy, 90.46% precision, 89.95% sensitivity and 97.49% specificity, respectively

U-SEANNet: A Simple, Efficient and Applied U-Shaped Network for Diagnosis of Nasal Diseases on Nasal Endoscopic Images

Numerous studies have affirmed that deep learning models can facilitate early diagnosis of lesions in endoscopic images. However, the lack of available datasets stymies advancements in research on nasal endoscopy, and existing models fail to strike a good trade-off between model diagnosis performance, model complexity and parameters size, rendering them unsuitable for real-world application. To bridge these gaps, we created the first large-scale nasal endoscopy dataset, named 7-NasalEID, comprising 11,352 images that contain six common nasal diseases and normal samples. Subsequently, we proposed U-SEANNet, an innovative U-shaped architecture, underpinned by depth-wise separable convolution. Moreover, to enhance its capacity for detecting nuanced discrepancies in input images, U-SEANNet employs the Global-Local Channel Feature Fusion module, enabling it to utilize salient channel features from both global and local contexts. To demonstrate U-SEANNet's potential, we benchmarked U-SEANNet against seventeen modern architectures through five-fold cross-validation. The experimental results show that U-SEANNet achieves a commendable accuracy of 93.58%. Notably, U-SEANNet's parameters size and GFLOPs are only 0.78M and 0.21, respectively. Our findings suggest U-SEANNet is the state-of-the-art model for nasal diseases diagnosis in endoscopic images.Comment: This manuscript has been submitted to ICASSP 202

The Origin of the Prompt Emission for Short GRB 170817A: Photosphere Emission or Synchrotron Emission?

The first gravitational-wave event from the merger of a binary neutron star system (GW170817) was detected recently. The associated short gamma-ray burst (GRB 170817A) has a low isotropic luminosity (~1047 erg s−1) and a peak energy E p ~ 145 keV during the initial main emission between −0.3 and 0.4 s. The origin of this short GRB is still under debate, but a plausible interpretation is that it is due to the off-axis emission from a structured jet. We consider two possibilities. First, since the best-fit spectral model for the main pulse of GRB 170817A is a cutoff power law with a hard low-energy photon index ($\alpha =-{0.62}_{-0.54}^{+0.49}$), we consider an off-axis photosphere model. We develop a theory of photosphere emission in a structured jet and find that such a model can reproduce a low-energy photon index that is softer than a blackbody through enhancing high-latitude emission. The model can naturally account for the observed spectrum. The best-fit Lorentz factor along the line of sight is ~20, which demands that there is a significant delay between the merger and jet launching. Alternatively, we consider that the emission is produced via synchrotron radiation in an optically thin region in an expanding jet with decreasing magnetic fields. This model does not require a delay of jet launching but demands a larger bulk Lorentz factor along the line of sight. We perform Markov Chain Monte Carlo fitting to the data within the framework of both models and obtain good fitting results in both cases