Sensitivity and uncertainties assessment in radiative forcing due to aerosol optical properties in diverse locations in China.

Aerosol optical properties play an important role in affecting direct aerosol radiative forcing (DARF). However, DARF estimation is still uncertain due to the complexity of aerosol optical properties. Therefore, in this study, the spatiotemporal distributions of aerosol properties and their effects on DARF in China from 2004 to 2020 are investigated using the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model. The results show that the aerosol optical parameters vary greatly and change with seasonal regularity, which is greatly affected by human activities. The control variable method was employed on aerosol optical properties for better estimation of DARF. Single scattering albedo (SSA) has the greatest effect on DARF, followed by aerosol optical depth (AOD) and the asymmetric factor (ASY) among the seven examined stations in China. The average DARF decreases by 4.2 % when the SSA increases by 0.3 % but increases by 34.7 % when the SSA decreases by 3 % in mainland China. When the AOD changes from -60 to +60 %, DARF changes from -54.7 % to +58.4 %. The variation in DARF is between -3 % and +3 % when the ASY varies from -30 % to +30 %. The instability in DARF resulted from the complicated and volatile nature of aerosol optical properties in the region; the aerosol optical properties are greatly affected by the aerosol types and relative humidity. The results of this study have important reference significance for understanding the variation of DARF and formulating pollution prevention and control policies in the region.

Incorporating Near-Pseudocapacitance Insertion Ni/Co-Based Hexacyanoferrate and Low-Cost Metallic Zn for Aqueous K-Ion Batteries.

The limited availability of cathode materials with high specific capacity and significant cycling stability for aqueous K-ion batteries (AKIBs) hinder their further development owing to the large radius of K+ (1.38 Å). Prussian blue and its analogs with a three-dimensional frame structure possessing special energy storage mechanism are promising candidates as cathode materials for AKIBs. In this study, K0.2 Ni0.68 Co0.77 Fe(CN)6 ⋅ 1.8H2 O (KNCHCF) was prepared as a cathode material for AKIBs. Both the electrochemical activity of Co ions and the near-pseudocapacitance intercalation of KNCHCF enhance K+ storage. Therefore, KNCHCF exhibits a superior capacity maintenance rate of 86 % after 1000 cycles at a high current density of 3.0 A g-1 . The storage mechanism of K+ in AKIBs was revealed through ex situ X-ray diffraction, ex situ Fourier transform infrared spectroscopy, and ex situ X-ray photoelectron spectroscopy measurements. Moreover, the assembled K-Zn hybrid battery showed good cycling stability with 93.1 % capacity maintenance at 0.1 A g-1 after 50 cycles and a high energy density of 96.81 W h kg-1 . Hence, KNCHCF may be a potential material for the development of AKIBs.

Assessment of Fatigue Using Wearable Sensors: A Pilot Study.

BACKGROUND: Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts patient health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are severely limited. METHODS: After using a recurrent neural network-based algorithm to impute missing time series data form a multisensor wearable device, we compared supervised and unsupervised machine learning approaches to gain insights on the relationship between self-reported non-pathological fatigue and multimodal sensor data. RESULTS: A total of 27 healthy subjects and 405 recording days were analyzed. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters, and daily questionnaires on fatigue. The best results were obtained when using the causal convolutional neural network model for unsupervised representation learning of multivariate sensor data, and random forest as a classifier trained on subject-reported physical fatigue labels (weighted precision of 0.70 ± 0.03 and recall of 0.73 ± 0.03). When using manually engineered features on sensor data to train our random forest (weighted precision of 0.70 ± 0.05 and recall of 0.72 ± 0.01), both physical activity (energy expenditure, activity counts, and steps) and vital signs (heart rate, heart rate variability, and respiratory rate) were important parameters to measure. Furthermore, vital signs contributed the most as top features for predicting mental fatigue compared to physical ones. These results support the idea that fatigue is a highly multimodal concept. Analysis of clusters from sensor data highlighted a digital phenotype indicating the presence of fatigue (95% of observations) characterized by a high intensity of physical activity. Mental fatigue followed similar trends but was less predictable. Potential future directions could focus on anomaly detection assuming longer individual monitoring periods. CONCLUSION: Taken together, these results are the first demonstration that multimodal digital data can be used to inform, quantify, and augment subjectively captured non-pathological fatigue measures.