Abstract Accurate estimates of aerosol refractive index (RI) are critical for modeling aerosol-radiation interaction, yet this information is limited for ambient organic aerosols, leading to large uncertainties in estimating aerosol radiative effects. We present a semi-empirical model that predicts the real RI n of organic aerosol material from its widely measured oxygen-to-carbon (O:C) and hydrogen-to-carbon (H:C) elemental ratios. The model was based on the theoretical framework of Lorenz-Lorentz equation and trained with n-values at 589 nm () of 160 pure compounds. The predictions can be expanded to predict n-values in a wide spectrum between 300 and 1,200 nm. The model was validated with newly measured and literature datasets of n-values for laboratory secondary organic aerosol (SOA) materials. Uncertainties of predictions for all SOA samples are within 5%. The model suggests that -values of organic aerosols may vary within a relatively small range for typical O:C and H:C values observed in the atmosphere.
As the popularity of hierarchical point forecast reconciliation methods increases, there is a growing interest in probabilistic forecast reconciliation. Many studies have utilized machine learning or deep learning techniques to implement probabilistic forecasting reconciliation and have made notable progress. However, these methods treat the reconciliation step as a fixed and hard post-processing step, leading to a trade-off between accuracy and coherency. In this paper, we propose a new approach for probabilistic forecast reconciliation. Unlike existing approaches, our proposed approach fuses the prediction step and reconciliation step into a deep learning framework, making the reconciliation step more flexible and soft by introducing the Kullback-Leibler divergence regularization term into the loss function. The approach is evaluated using three hierarchical time series datasets, which shows the advantages of our approach over other probabilistic forecast reconciliation methods.
Numerous functional magnetic resonance imaging (fMRI) studies have examined the neural mechanisms of negative emotional words, but scarce evidence is available for the interactions among related brain regions from the functional brain connectivity perspective. Moreover, few studies have addressed the neural networks for negative word processing in bilinguals. To fill this gap, the current study examined the brain networks for processing negative words in the first language (L1) and the second language (L2) with Chinese-English bilinguals. To identify objective indicators associated with negative word processing, we first conducted a coordinate-based meta-analysis on contrasts between negative and neutral words (including 32 contrasts from 1589 participants) using the activation likelihood estimation method. Results showed that the left medial prefrontal cortex (mPFC), the left inferior frontal gyrus (IFG), the left posterior cingulate cortex (PCC), the left amygdala, the left inferior temporal gyrus (ITG), and the left thalamus were involved in processing negative words. Next, these six clusters were used as regions of interest in effective connectivity analyses using extended unified structural equation modeling to pinpoint the brain networks for bilingual negative word processing. Brain network results revealed two pathways for negative word processing in L1: a dorsal pathway consisting of the left IFG, the left mPFC, and the left PCC, and a ventral pathway involving the left amygdala, the left ITG, and the left thalamus. We further investigated the similarity and difference between brain networks for negative word processing in L1 and L2. The findings revealed similarities in the dorsal pathway, as well as differences primarily in the ventral pathway, indicating both neural assimilation and accommodation across processing negative emotion in two languages of bilinguals.
Summary Despite the unique switching characteristics of CO2-responsive foaming, its stability remains questionable. In this protocol, we describe steps to synthesize a stable CO2-responsive foam by adding the preferably selected hydrophilic nanoparticle N20 into the surfactant C12A. We detail the selection of the most suitable nanoparticles for the surfactant by measuring the foaming volume and half-life of the dispersion. The protocol can be extended to manufacture with other types of responsive foams (e.g., light responsive foams, magnetic responsive foams). For complete details on the use and execution of this protocol, please refer to Li et al. (2022).1
Ozone reactions on human body surfaces produce volatile organic compounds (VOCs) that influence indoor air quality. However, the dependence of VOC emissions on the ozone concentration has received limited attention. In this study, we conducted 36 sets of single-person chamber experiments with three volunteers exposed to ozone concentrations ranging from 0 to 32 ppb. Emission fluxes from human body surfaces were measured for 11 targeted skin-oil oxidation products. For the majority of these products, the emission fluxes linearly correlated with ozone concentration, indicating a constant surface yield (moles of VOC emitted per mole of ozone deposited). However, for the second-generation oxidation product 4-oxopentanal, a higher surface yield was observed at higher ozone concentrations. Furthermore, many VOCs have substantial emissions in the absence of ozone. Overall, these results suggest that the complex surface reactions and mass transfer processes involved in ozone-dependent VOC emissions from the human body can be represented using a simplified parametrization based on surface yield and baseline emission flux. Values of these two parameters were quantified for targeted products and estimated for other semiquantified VOC signals, facilitating the inclusion of ozone/skin oil chemistry in indoor air quality models and providing new insights on skin oil chemistry.
Adaptive radiative cooling offers smart thermal regulation that saves energy for conditioning regardless of the variation in environment or requirements. In a recent study by Banerjee and colleagues, an electrochromic device based on the redox process of PEDOT was developed, enabling tunable surface temperature by applied electrical bias at ambient conditions.
Emerging organic pollutants (EOPs) in water are of great concern due to their high environmental risk, so urgent technologies are needed for effective removal of those pollutants. Herein, a heterogeneous advanced oxidation process (AOP) of peroxymonosulfate (PMS) activation by functional material was developed for degradation of a typical antibiotic, gatifloxacin (GAT). The reactive species including sulfate radical (SO4•−) and singlet oxygen (1O2) in this AOP were regulated by interlayered ions (Na+/H+) of titanate nanotubes that supported on Co(OH)2 hollow microsphere. Both the Na-type (NaTi-CoHS) and H-type (HTi-CoHS) materials achieved efficient PMS activation for GAT degradation, and HTi-CoHS even exhibited a relatively high degradation efficiency of 96.6% within 5 min. Co(OH)2 was considered the key component for generation of SO4•− after PMS activation, while hydrogen titanate nanotubes (H-TNTs) promoted the transformation of peroxysulfate radical (SO5•−) to 1O2 by hydrogen bond interaction. Therefore, when the interlayer ion of TNTs transformed from Na+ to H+, more 1O2 was produced for organic pollutant degradation. H-TNTs with lower symmetry preferred to adsorb PMS molecules to achieve interlayer electron transport through hydrogen bonding, rather than electrostatic interaction of Na+ for Na-TNTs. In addition, the degradation pathway of GAT mainly proceeded by the cleavage of C–N bond at the 8 N site of the piperazine ring, which was confirmed by condensed Fukui index and mass spectrographic analysis. This work gives new sights into the regulation of reactive species in AOPs by the composition of material and promotes the understanding of pollutant degradation mechanisms in water treatment process.
Passive millimeter-wave (PMMW) imaging technology is widely used in civilian and military applications. However, there are reflections similar to the optical band in PMMW images, which have negative influence on the target detection and recognition. In this paper, we present a reflection-based method to enhance the target features in PMMW images. The dividing line between target and reflection is obtained by the similarity of brightness temperature (TB). By combining the similarity and reflection principle, we propose a new method to obtain the feature points of target and reflection for registration. Then, the weighted method based on region TB is used to fusion target and reflection. Finally, to avoid interference with target detection and recognition, the reflection is removed. The experimental results show that the method can obtain higher contrast and more accurate target information.
Problematic internet use (PIU) is a concerning issue worldwide, and a considerable body of knowledge has accrued from research on the predictors of PIU; however, few studies have investigated the dynamic process by which the social environment impacts individuals’ PIU. Integrating a person–environment interactionist perspective with self-determination theory, we investigate how relational mobility impacts PIU by proposing a “permeating” mechanism of social interactions (i.e., interpersonal sensitivity) and basic psychological needs (i.e., relatedness satisfaction). In Study 1, using a large data set from the Chinese General Social Survey (N = 2,192), we found that relational mobility was negatively related to PIU. In Study 2, using a new sample (N = 392), we found that relational mobility alleviated PIU through interpersonal sensitivity. In Study 3, using a cross-lagged design and two-wave data (N = 298), we confirmed the chain-mediating roles of interpersonal sensitivity and relatedness satisfaction in the relationship between relational mobility and PIU.
Soil microbes assemble in highly complex and diverse microbial communities, and microbial diversity patterns and their drivers have been studied extensively. However, diversity correlations and co-occurrence patterns between bacterial, fungal, and archaeal domains and between microbial functional groups in arid regions remain poorly understood. Here we assessed the relationships between the diversity and abundance of bacteria, fungi, and archaea and explored how environmental factors influence these relationships. We sampled soil along a 1500-km-long aridity gradient in temperate grasslands of Inner Mongolia (China) and sequenced the 16S rRNA gene of bacteria and archaea and the ITS2 gene of fungi. The diversity correlations and co-occurrence patterns between bacterial, fungal, and archaeal domains and between different microbial functional groups were evaluated using α-diversity and co-occurrence networks based on microbial abundance. Our results indicate insignificant correlations among the diversity patterns of bacterial, fungal, and archaeal domains using α-diversity but mostly positive correlations among diversity patterns of microbial functional groups based on α-diversity and co-occurrence networks along the aridity gradient. These results suggest that studying microbial diversity patterns from the perspective of functional groups and co-occurrence networks can provide additional insights on patterns that cannot be accessed using only overall microbial α-diversity. Increase in aridity weakens the diversity correlations between bacteria and fungi and between bacterial and archaeal functional groups, but strengthens the positive diversity correlations between bacterial functional groups and between fungal functional groups and the negative diversity correlations between bacterial and fungal functional groups. These variations of the diversity correlations are associated with the different responses of microbes to environmental factors, especially aridity. Our findings demonstrate the complex responses of microbial community structure to environmental conditions (especially aridity) and suggest that understanding diversity correlations and co-occurrence patterns between soil microbial groups is essential for predicting changes in microbial communities under future climate change in arid regions.