H2 production via water electrolysis is of great significance in clean energy production, which, however, suffers from the sluggish kinetics of the anodic oxygen evolution reaction (OER). Moreover, the anode product, O2, which is of rather low value, may lead to dangerous explosions and the generation of membrane‐degrading reactive oxygen species. Herein, to address these issues of electrocatalytic H2 production, we summarize the most recent advances in three stages based on the benefit increments and various electron donation routes, which are: 1) electron donation by traditional OER: developing efficient catalysts for water oxidation to promote H2 production; 2) electron donation by the oxidation of sacrificial agents: using sacrificial agents to assist H2 production; 3) electron donation by electrosynthesis reaction: achieving electrosynthesis in parallel with cathodic H2 production. Present challenges and related prospects will also be discussed, hopefully to benefit the further progress of electrocatalytic H2 generation.

The cyclic alkyl(amino) carbene (cAAC:)-stabilized acyclic germylones (Me2-cAAC:)2Ge (1) and (Cy2-cAAC:)2Ge (2) were prepared utilizing a one-pot synthesis of GeCl2(dioxane), cAAC:, and KC8 in a 1:2:2.1 molar ratio. Dark green crystals of compounds 1 and 2 were produced in 75 and 70% yields, respectively. The reported methods for the preparation of the corresponding silicon compounds turned out to be not applicable in the case of germanium. The single-crystal X-ray structures of 1 and 2 feature the C-Ge-C bent backbone, which possesses a three-center two-electron π-bond system. Compounds 1 and 2 are the first acyclic germylones containing each one germanium atom and two cAAC: molecules. EPR measurements on compounds 1 and 2 confirmed the singlet spin ground state. DFT calculations on 1/2 revealed that the singlet ground state is more stable by ~16 to 18 kcal mol(-1) than that of the triplet state. First and second proton affinity values were theoretically calculated to be of 265.8 (1)/267.1 (2) and 180.4 (1)/183.8 (2) kcal mol(-1), respectively. Further calculations, which were performed at different levels suggest a singlet diradicaloid character of 1 and 2. The TD-DFT calculations exhibit an absorption band at ~655 nm in n-hexane solution that originates from the diradicaloid character of germylones 1 and 2.

The Kuroshio intrusion from the West Philippine Sea (WPS) and mesoscale eddies are important hydrological features in the northern South China Sea (SCS). In this study, absorption and fluorescence of dissolved organic matter (CDOM and FDOM) were determined to assess the impact of these hydrological features on DOM dynamics in the SCS. DOM in the upper 100 m of the northern SCS had higher absorption, fluorescence, and degree of humification than in the Kuroshio Current of the WPS. The results of an isopycnal mixing model showed that CDOM and humic‐like FDOM inventories in the upper 100 m of the SCS were modulated by the Kuroshio intrusion. However, protein‐like FDOM was influenced by in situ processes. This basic trend was modified by mesoscale eddies, three of which were encountered during the fieldwork (one warm eddy and two cold eddies). DOM optical properties inside the warm eddy resembled those of DOM in the WPS, indicating that warm eddies could derive from the Kuroshio Current through Luzon Strait. DOM at the center of cold eddies was enriched in humic‐like fluorescence and had lower spectral slopes than in eddy‐free waters, suggesting inputs of humic‐rich DOM from upwelling and enhanced productivity inside the eddy. Excess CDOM and FDOM in northern SCS intermediate water led to export to the Pacific Ocean interior, potentially delivering refractory carbon to the deep ocean. This study demonstrated that DOM optical properties are promising tools to study active marginal sea‐open ocean interactions.

After two cycles of marker-assisted breeding on three loci, lines with transgressive segregation of 8.22-9.32 % protein content were developed based on four original soybean parents with 35.35-44.83 % protein content. Marker-assisted breeding has been an innovative approach in conventional breeding, which is to be further demonstrated, especially for quantitative traits. A study on continuous transgressive breeding for seed protein content (SPC) in soybean using marker-assisted procedures is reported here. The SPC of the recombinant inbred line (RIL) population XG varied in 38.04-47.54 % under five environments with P 1 of 35.35 %, P 2 of 44.34 % and total heritability of 89.11 %. A transgressive segregant XG30 with SPC 45.53 % was selected for further improvement. The linkage mapping of XG showed its genetic constitution composed of five additive QTL (32.16 % of phenotypic variation or PV) and two pairs of epistatic QTL (2.96 % PV) using 400 SSR markers with the remnant heritability 53.99 % attributed to the undetected collective of minor QTL. Another transgressive segregant WT133 with SPC 48.39 % was selected from the RIL population WT (44.83 % SPC for both parents). XG30 and WT133 were genotyped on the three major additive QTL (Prot-08-1, Prot-14-1 and Prot-19-2) as A 2 A 2 B 2 B 2 L 1 L 1 and A 1 A 1 B 1 B 1 L 2 L 2 , respectively. From WT133×XG30, surprising transgressive progenies were obtained, among which the recombinants with all three positive alleles A 2 _B 2 _L 2 _ performed the highest SPC, especially that of Prot-08-1. The five F 2-derived superior families showed their means higher than the high parent value in F 2:3 and F 2:4 and more transgressive effect in F 2:5:6, with the highest as high as 54.15 %, or 4.82 and 9.32 % more than WT133 and its original high parent, respectively. This study demonstrated the efficiency of marker-assisted procedure in breeding for transgressive segregation of quantitative trait.

Reaction of the monoanionic radical salt IP˙(-)K(+) (IP = (Py)CH(=NR); Py = C5H4N, R = 2,6-iPr2C6H3; α-iminopyridine) with GeCl2(dioxane) afforded compound (IPGeCl)2 (1) which produced red blocks of IPGe: (2), when treated with KC8 in toluene. 1 is a digermylene formed via C-C coupling between two carbon-centered radicals. 2 can be considered as an analogue of a N-heterocyclic carbene, which exhibits a five-membered GeC2N2 ring with one C=C double bond. 2 is formed by two-electron reduction of 1 with cleavage of the two Ge-Cl bonds and the central C-C single bond.

Synthetic spin-orbit (SO) coupling, an important ingredient for quantum simulation of many exotic condensed matter physics, has recently attracted considerable attention. The static and dynamic properties of a SO coupled Bose-Einstein condensate (BEC) have been extensively studied in both theory and experiment. Here we numerically investigate the generation and propagation of a dynamical spin-density wave (SDW) in a SO coupled BEC using a fast moving Gaussian-shaped barrier. We find that the SDW wavelength is sensitive to the barrier's velocity while varies slightly with the barrier's peak potential or width. We qualitatively explain the generation of SDW by considering a rectangular barrier in a one dimensional system. Our results may motivate future experimental and theoretical investigations of rich dynamics in the SO coupled BEC induced by a moving barrier.

Given the growing popularity of nonprobability samples as a cost- and time-efficient alternative to probability sampling, a variety of adjustment approaches have been proposed to correct for self-selection bias in nonrandom samples. Popular methods such as inverse propensity-score weighting (IPSW) and propensity-score (PS) adjustment by subclassification (PSAS) utilize a probability sample as a reference to estimate pseudo-weights for the nonprobability sample based on PSs. A recent contribution, kernel weighting (KW), has been shown to be able to improve over IPSW and PSAS with respect to mean squared error. However, the effectiveness of these methods for reducing bias critically depends on the ability of the underlying propensity model to reflect the true (self-)selection process, which is a challenging task with parametric regression. In this study, we propose a set of pseudo-weights construction methods, KW-ML, utilizing both machine learning (ML) methods (to estimate PSs) and KW (to construct pseudo-weights based on the ML-estimated PSs), which provides added flexibility over logistic regression-based methods. We compare the proposed KW-ML pseudo-weights that are based on model-based recursive partitioning, conditional random forests, gradient tree boosting, and model-based boosting, with KW pseudo-weights based on parametric logistic regression in population mean estimation via simulations and a real data example. Our results indicate that particularly boosting methods represent promising alternatives to logistic regression and result in KW estimates with lower bias in a variety of settings, without increasing variance.

On Twitter, people often use hashtags to mark the subject of a tweet. Tweets have specific themes or content that are easy for people to manage. With the increase in the number of tweets, how to automatically recommend hashtags for tweets has received wide attention. The previous hashtag recommendation methods were to convert the task into a multi-class classification problem. However, these methods can only recommend hashtags that appeared in historical information, and cannot recommend the new ones. In this work, we extend the self-attention mechanism to turn the hashtag recommendation task into a sequence labeling task. To train and evaluate the proposed method, we used the real tweet data which is collected from Twitter. Experimental results show that the proposed method can be significantly better than the most advanced method. Compared with the state-of-the-art methods, the accuracy of our method has been increased 4%.