2-(4-Chloro­anilino)-3-(2-hydroxy­ethyl)quinazolin-4(3H)-one

In the title mol­ecule, C16H14ClN3O2, the dihedral angle between the chloro­phenyl and pyrimidinone rings is 14.8 (1)°, while the dihedral angle between the fused benzene ring and the pyrimidinone ring is 3.8 (1)°. In the crystal structure, intra­molecular N—H⋯O hydrogen bonds, together with inter­molecular O—H⋯O hydrogen-bonding inter­actions, are present

Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer

Unsupervised/self-supervised representation learning in time series is critical since labeled samples are usually scarce in real-world scenarios. Existing approaches mainly leverage the contrastive learning framework, which automatically learns to understand the similar and dissimilar data pairs. Nevertheless, they are restricted to the prior knowledge of constructing pairs, cumbersome sampling policy, and unstable performances when encountering sampling bias. Also, few works have focused on effectively modeling across temporal-spectral relations to extend the capacity of representations. In this paper, we aim at learning representations for time series from a new perspective and propose Cross Reconstruction Transformer (CRT) to solve the aforementioned problems in a unified way. CRT achieves time series representation learning through a cross-domain dropping-reconstruction task. Specifically, we transform time series into the frequency domain and randomly drop certain parts in both time and frequency domains. Dropping can maximally preserve the global context compared to cropping and masking. Then a transformer architecture is utilized to adequately capture the cross-domain correlations between temporal and spectral information through reconstructing data in both domains, which is called Dropped Temporal-Spectral Modeling. To discriminate the representations in global latent space, we propose Instance Discrimination Constraint to reduce the mutual information between different time series and sharpen the decision boundaries. Additionally, we propose a specified curriculum learning strategy to optimize the CRT, which progressively increases the dropping ratio in the training process.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS

Explaining E-Tailers’ Source of Competitiveness: An Integrative Framework

E-tailers refer to small and medium size enterprises or individual entrepreneurs primarily conducting businesses on online shopping platforms. Although many works on e-marketplaces have been done, theory-driven studies that explain e-tailers’ source of competitiveness are relatively scarce. The current work developed an integrative theoretical model in which online social capital, structural assurance, and online word-of-month are proposed to affect e-tailers’ business performance. The current study offers implications on: 1) what are the unique sources of competitiveness for businesses operating in pure online environment; 2) how can the resource-scare e-tailers survive in their rivalry with large offline retailers

1-Benzyl­idene­amino-3-(4-methyl­phen­yl)thio­urea

In the title compound, C15H15N3S, the almost planar 2-benzyl­idenehydrazinecarbothio­amide unit (r.m.s. deviation = 0.0351 Å) is aligned at a dihedral angle of 64.42 (6)° with respect to the plane of the tolyl ring. The mol­ecule exhibits an E configuration for the azomethine linkage. In the crystal, inter­molecular N—H⋯S hydrogen bonds about centers of inversion lead to the formation of dimers

3-[(Furan-2-yl­methyl­idene)amino]-1-(4-methyl­phen­yl)thio­urea

There are two independent mol­ecules in the asymmetric unit of the title compound, C13H13N3OS, which was obtained from a condensation reaction of N-(p-tol­yl)hydrazinecarbothio­amide and furfural. The dihedral angles between the mean planes of the tolyl ring and the (furan-2-yl­methyl­ene)hydrazine unit are 39.83 (8) and 48.95 (7)° in the two mol­ecules. The mol­ecules both exhibit an E configuration. In the crystal, inter­molecular N—H⋯N and N—H⋯S hydrogen bonds connect the two independent mol­ecules

Effects of 2010–2045 climate change on ozone levels in China under carbon neutrality scenario: Key meteorological parameters and processes

We examined the effects of 2010–2045 climate change on ozone (O3) levels in China under carbon neutrality scenario using the Global Change and Air Pollution version 2.0 (GCAP 2.0). In eastern China (EC), GCAP 2.0 and other six models from Coupled Model Intercomparison Projection Phase 6 (CMIP6) all projected increases in daily maximum 2-m temperature (T2max), surface incoming shortwave radiation (SW), and planet boundary layer height, and decreases in relative humidity (RH) and sea level pressure. Future climate change is simulated by GCAP 2.0 to have large effects on O3 even under carbon neutrality pathway, with summertime regional and seasonal mean MDA8 O3 concentrations increased by 2.3 ppbv (3.9 %) over EC, 4.7 ppbv (7.3 %) over North China Plain, and 3.0 ppbv (5.1 %) over Yangtze River Delta. Changes in key meteorological parameters were found to explain 58–76 % of the climate-driven MDA8 O3 changes over EC. The most important meteorological parameters in summer are T2max and SW in northern and central EC and RH in southern EC. Analysis showed net chemical production was the most important process that increases O3, accounting for 34.0–62.5 % of the sum of all processes within the boundary layer. We also quantified the uncertainties in climate-induced MDA8 O3 changes by using CMIP6 multi-model projections of climate and a stepwise multiple linear regression model. GCAP 2.0 results are in the lower-end of the climate-induced increases in MDA8 O3 from the multi-models. These results have important implications for policy-making regarding emission controls under the background of climate warming