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Aromatic carbonyls (e.g., methoxybenzaldehydes), an important class of photosensitizers, are abundant in the atmosphere. Photosensitization and nitrate-mediated photo-oxidation can occur simultaneously, yet studies about their interactions, particularly for aqueous secondary organic aerosol (aqSOA) formation, remain limited. This study compared non-phenolic (3,4-dimethoxybenzaldehyde, DMB) and phenolic (vanillin, VL) methoxybenzaldehydes as photosensitizers for aqSOA formation via guaiacol (GUA) oxidation in the absence and presence of ammonium nitrate (AN) under atmospherically relevant cloud and fog conditions. GUA oxidation by triplet excited states of DMB (3DMB∗) (GUA + DMB) was ∼ 4 times faster and exhibited greater light absorption than oxidation by 3VL∗ (GUA + VL). Both GUA + DMB and GUA + VL formed aqSOA composed of oligomers, functionalized monomers, oxygenated ring-opening species, and N-containing products in the presence of AN. The observation of N-heterocycles such as imidazoles indicates the participation of ammonium in the reactions. The majority of generated aqSOA comprises potential brown carbon (BrC) chromophores. Oligomerization and functionalization dominated in GUA + DMB and GUA + VL, but functionalization appeared to be more important in GUA + VL due to contributions from VL itself. AN did not significantly affect the oxidation kinetics, but it had distinct effects on the product distributions, likely due to differences in the photosensitizing abilities and structural features of DMB and VL. In particular, the more extensive fragmentation in GUA + DMB than in GUA + VL likely generated more N-containing products in GUA + DMB + AN. In GUA + VL + AN, the increased oligomers may be due to VL-derived phenoxy radicals induced by ⚫OH or ⚫NO2 from nitrate photolysis. Furthermore, increased nitrated products observed in the presence of both DMB or VL and AN than in AN alone imply that photosensitized reactions may promote nitration. This work demonstrates how the structural features of photosensitizers affect aqSOA formation via non-carbonyl phenol oxidation. Potential interactions between photosensitization and AN photolysis were also elucidated. These findings facilitate a better understanding of photosensitized aqSOA formation and highlight the importance of AN photolysis in these reactions.

Drought is a major environmental stress that threatens crop production. Therefore, identification of genes involved in drought stress response is of vital importance to decipher the molecular mechanism of stress signal transduction and breed drought tolerance crops, especially for maize. Clade A PP2C phosphatases are core abscisic acid (ABA) signaling components, regulating ABA signal transduction and drought response. However, the roles of other clade PP2Cs in drought resistance remain largely unknown. Here, we discovered a clade F PP2C, ZmPP84, that negatively regulates drought tolerance by screening a transgenic overexpression maize library. Quantitative RT-PCR indicates that the transcription of ZmPP84 is suppressed by drought stress. We identified that ZmMEK1, a member of the MAPKK family, interacts with ZmPP84 by immunoprecipitation and mass spectrometry analysis. Additionally, we found that ZmPP84 can dephosphorylate ZmMEK1 and repress its kinase activity on the downstream substrate kinase ZmSIMK1, while ZmSIMK1 is able to phosphorylate S-type anion channel ZmSLAC1 at S146 and T520 in vitro. Mutations of S146 and T520 to phosphomimetic aspartate could activate ZmSLAC1 currents in Xenopus oocytes. Taken together, our study suggests that ZmPP84 is a negative regulator of drought stress response that inhibits stomatal closure through dephosphorylating ZmMEK1, thereby repressing ZmMEK1-ZmSIMK1 signaling pathway.

PurposeEmerging evidence regarding crowdfunding challenges long-standing “gender gap” views of traditional entrepreneurial financing and indicates that female entrepreneurs may have an advantage in crowdfunding. Yet, the literature primarily focuses on influences at the individual level, largely overlooking the interaction between gender and higher-level culture. Drawing on Hofstede's cultural dimensions, this paper aims to investigate the associations among entrepreneurs' gender, culture and crowdfunding performance, particularly in how entrepreneurs' gender and culture interact to affect crowdfunding performance.Design/methodology/approachLeveraging a sample of 21,730 Kickstarter crowdfunding campaigns and combining these data with data from Hofstede's study, the World Bank (WB) and the International Telecommunication Union (ITU), this study applies multilevel models to empirically investigate this question across 22 countries/regions.FindingsThis study confirms that the advantageous effect, that female entrepreneurs are likely to obtain better fundraising performance over their male counterparts, does exist in crowdfunding. Furthermore, the findings reveal that this advantageous effect of female entrepreneurs on crowdfunding performance would be reinforced when cultures of individualism and indulgence are high and culture of long-term orientation is low.Originality/valueThis study contributes to the literature on gender gaps in crowdfunding and entrepreneurial financing by adding an important culture-related boundary condition to the gender preference reported in earlier crowdfunding work. Moreover, the paper extends the knowledge about the impact of culture on crowdfunding performance and enlightens future research on leveraging multilevel modeling approach to examine the complex interplay between individuals and situations in crowdfunding.

Process complexities are characterized by strong nonlinearities, dynamics, and uncertainties. Monitoring such a complex process requires a high-quality model describing the corresponding nonlinear dynamic behavior. The proposed model is constructed using deep neural networks (DNNs) to represent the state transition and observation generation, both of which constitute a stochastic nonlinear state-space model. A new bidirectional recurrent neural network (RNN), creating a connection of the hidden layer between a forward RNN and a backward RNN, is proposed to generate the filtering estimation and the smoothing estimation of process states which further generate observations with DNN-based process models. The smoothing estimator and the process model are first learned offline with all collected samples. Then the filtering estimator is fine-tuned by the learned smoother and process models to achieve real-time monitoring since the filter state is estimated based on the past and the current observations. Two indices are designed based on the learned model for monitoring the process anomaly. The proposed process monitoring model can deal with complex nonlinearities, process dynamics, and process uncertainties, all of which can be very challenging for the existing methods, such as kernel mapping and stacked auto-encoder. Two case studies validate that the effectiveness of the proposed method outperforms the other comparative methods by at least 10% when using the averaged fault detection rate in the industrial experimental data.

Due to the existence of complex disturbances and frequent switching of operational conditions characteristics in the real industrial processes, the process data under different operational conditions subject to different distributions, which means there exist different manifold structures under broad operations. Globally, the entire process data are distributed in a multi-manifold structure. Nevertheless, the existing data-driven quality prediction methods do not consider the relationships among different manifolds of data and just treats the process data as a single manifold. How to extract effective multi-manifold structure feature representation from complex process data and enhance online prediction ability are still challenging in the field of real industrial processes. To this end, a novel stacked multi-manifold autoencoder (S-MMAE) is proposed for feature extraction and quality prediction. Specially, by introducing a new multi-manifold regularization into the original loss function of stacked autoencoder (SAE) at each layer, the intrinsic multi-manifold structure information of data is utilized to guide the feature learning procedure. In this way, the learned features can offer a more comprehensive representation of original data and help enhance the prediction performance. At last, the application results in a practical hydrocracking process demonstrate that the proposed S-MMAE can achieve excellent prediction accuracy, which outperforms other state-of-the-art methods.

Background: With the acceleration of urbanization, the demand for people to return to forests and their desire for nature is also increasing annually. However, whether the visual behaviors and aesthetic preferences of users vary with background attributes when viewing forest landscapes remains to be further explored. This information will help forest landscape planners and managers to create a forest landscape space suitable for different groups of people. Purpose: This study mainly discusses whether people’s professional background will affect their visual behavior, satisfaction preference and re-viewing intention of a landscape and discusses the relationship among them. Main results: (1) Under the background of an in-forest landscape, the visual behavior of users in different layouts presents great similarity. (2) Satisfaction preference for landscapes is not significantly influenced by the user’s background, but there is a significant linear relationship between satisfaction preference and re-viewing intention (Adj. R2 = 0.412 − 0.697, sig. = 0.000). (3) The spatial cognition that affects users’ visual behavior and satisfaction preference in a scene will change with the landscape layout and users’ professional background. Therefore, we suggest that landscape planners and managers should consider not only the spatial layout of the landscape itself but also the user’s own attributes (whether they have professional knowledge of the landscape) when optimizing the visual behavior and satisfaction preference of in-forest landscape space. This will ensure that users have a high sense of identity and attachment to the scene in a targeted way, thus arousing a greater “desire” to re-view the scene. In addition, our results can identify a more satisfactory course for tour routes according to the user’s professional background, thus enhancing the rate of tourists who decide to “visit again”.