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  1. Chronic pain patients lack at-home pain assessment and management tools. The existing chronic-pain mobile applications are either solely relying on self-report pain levels or restricted to formal clinical settings. Our app, abbreviated from an NSF-funded project entitled Novel Computational Methods for Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS), is a multi-dimensional pain app that collects physiological signals to predict objective pain levels and trace daily at-home activities by incorporating a daily check-in section. We conducted a usability test with 33 healthy participants under pain conditions. The results provided initial support for the validity of the signals in predicting internalizing pain levels among the participants. With further development and testing, we believe the COMPASS app system has the potential to be used by both patients and clinicians as an additional tool to better assess and manage pain, especially for mobile healthcare applications.

     
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    Free, publicly-accessible full text available September 1, 2024
  2. The nitrogen cycle needed for scaled agriculture relies on energy- and carbon-intensive processes and generates nitrate-containing wastewater. Here we focus on an alternative approach—the electrified co-electrolysis of nitrate and CO2 to synthesize urea. When this is applied to industrial wastewater or agricultural runoff, the approach has the potential to enable low-carbon-intensity urea production while simultaneously providing wastewater denitrification. We report a strategy that increases selectivity to urea using a hybrid catalyst: two classes of site independently stabilize the key intermediates needed in urea formation, *CO2NO2 and *COOHNH2, via a relay catalysis mechanism. A Faradaic efficiency of 75% at wastewater-level nitrate concentrations (1,000 ppm NO3− [N]) is achieved on Zn/Cu catalysts. The resultant catalysts show a urea production rate of 16 µmol h−1 cm−2. Life-cycle assessment indicates greenhouse gas emissions of 0.28 kg CO2e per kg urea for the electrochemical route, compared to 1.8 kg CO2e kg−1 for the present-day route. 
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    Free, publicly-accessible full text available October 1, 2024
  3. Abstract

    Precomputed Radiance Transfer (PRT) remains an attractive solution for real‐time rendering of complex light transport effects such as glossy global illumination. After precomputation, we can relight the scene with new environment maps while changing viewpoint in real‐time. However, practical PRT methods are usually limited to low‐frequency spherical harmonic lighting. All‐frequency techniques using wavelets are promising but have so far had little practical impact. The curse of dimensionality and much higher data requirements have typically limited them to relighting with fixed view or only direct lighting with triple product integrals. In this paper, we demonstrate a hybrid neural‐wavelet PRT solution to high‐frequency indirect illumination, including glossy reflection, for relighting with changing view. Specifically, we seek to represent the light transport function in the Haar wavelet basis. For global illumination, we learn the wavelet transport using a small multi‐layer perceptron (MLP) applied to a feature field as a function of spatial location and wavelet index, with reflected direction and material parameters being other MLP inputs. We optimize/learn the feature field (compactly represented by a tensor decomposition) and MLP parameters from multiple images of the scene under different lighting and viewing conditions. We demonstrate real‐time (512 x 512 at 24 FPS, 800 x 600 at 13 FPS) precomputed rendering of challenging scenes involving view‐dependent reflections and even caustics.

     
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  4. Abstract Long-duration GRB 200829A was detected by Fermi-GBM and Swift-BAT/XRT, and then rapidly observed by other ground-based telescopes. It has a weak γ -ray emission in the very early phase and is followed by a bright spiky γ -ray emission pulse. The radiation spectrum of the very early emission is best fitted by a power-law function with index ∼−1.7. However, the bright spiky γ -ray pulse, especially the time around the peak, exhibits a distinct two-component radiation spectrum, i.e., Band function combined with a blackbody radiation spectrum. We infer the photospheric properties and reveal a medium magnetization at a photospheric position by adopting the initial size of the outflow as r 0 = 10 9 cm. It implies that the Band component in this pulse may be formed during the dissipation of the magnetic field. The power-law radiation spectra found in the very early prompt emission may imply the external-shock origination of this phase. Then, we perform the Markov Chain Monte Carlo method fitting on the light curves of this burst, where the jet corresponding to the γ -ray pulse at around 20 s is used to refresh the external shock. It is shown that the light curves of the very early phase and X-ray afterglow after 40 s, involving the X-ray bump at around 100 s, can be well modeled in the external-shock scenario. For the obtained initial outflow, we estimate the minimum magnetization factor of the jet based on the fact that the photospheric emission of this jet is missed in the very early phase. 
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  5. Optimization of pain assessment and treatment is an active area of research in healthcare. The purpose of this research is to create an objective pain intensity estimation system based on multimodal sensing signals through experimental studies. Twenty eight healthy subjects were recruited at Northeastern University. Nine physiological modalities were utilized in this research, namely facial expressions (FE), electroencephalography (EEG), eye movement (EM), skin conductance (SC), and blood volume pulse (BVP), electromyography (EMG), respiration rate (RR), skin temperature (ST), blood pressure (BP). Statistical analysis and machine learning algorithms were deployed to analyze the physiological data. FE, EEG, SC, BVP, and BP proved to be able to detect different pain states from healthy subjects. Multi-modalities proved to be promising in detecting different levels of painful states. A decision-level multi-modal fusion also proved to be efficient and accurate in classifying painful states. 
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