Barium (Ba) isotopes can be used as potential tracers for crustal material recycling in the mantle. Determination of the Ba isotope composition of the depleted mantle is essential for such applications. However, Ba isotope data for mantle-derived basalts are still rare. In this study, we reported high-precision Ba isotope data of 30 oceanic basalts including 25 mid-ocean ridge basalts (MORBs) from geochemically and geologically diverse mid-ocean ridge segments and five back-arc basin basalts. The δ^138/134Ba values of these samples varied from −0.06‰ to +0.11‰, with no systematic cross-region variation. Together with published data, we constrained the average δ^138/134Ba of global MORBs to +0.05‰±0.09‰ (2 standard deviation, n = 51). Based on depleted MORBs that have (La/Sm)_N < 0.8, low ⁸⁷Sr/⁸⁶Sr (< 0.70263), and low Ba/Th < 71.3, we estimated the average δ^138/134Ba of the depleted MORB mantle (DMM) as + 0.05‰ ± 0.05‰ (2SD, n = 16) that is significantly lower than the DMM (≈ 0.14‰) reported previously. If a new estimation of the DMM is applied, it is unreasonable to infer that the Ba isotope signatures of the “enriched-type” MORBs (E-MORBs) could be attributed to pervasive sediment recycling in the upper mantle. We, therefore, conclude that the Ba isotope compositions of the E-MORBs could be sourced from the incorporation of subducted altered oceanic crust and/or sediments depending on the Ba isotope composition and other geochemical information of the local mantle.

High-fidelity quantum operation of qubits plays an important role in magnetometry based on nitrogen-vacancy (NV) centers in diamonds. However, the nontrivial spin-spin coupling of the NV center decreases signal contrast and sensitivity. Here, we overcome this limitation by exploiting the amplitude modulation of microwaves, which allows us to perfectly detect magnetic signals at low fields. Compared with the traditional double-quantum sensing protocol, the full contrast of the detection signal was recovered, and the sensitivity was enhanced three times in the experiment. Our method is applicable to a wide range of sensing tasks, such as temperature, strain, and electric field.

By optimization methods on Riemannian submanifolds, we establish two inequalities between the intrinsic and extrinsic invariants, for generalized normalized δ-Casorati curvatures of warped product submanifolds in a Riemannian manifold of quasi-constant curvature. We generalize the conclusions of the optimal inequalities of submanifolds in real space forms.

The data-driven conditional multinomial logit choice model with customer features performs well in the assortment personalization problem when the low-rank structure of the parameter matrix is considered. However, despite recent theoretical and algorithmic advances, parameter estimation in the choice model still poses a challenging task, especially when there are more predictors than observations. For this reason, we suggest a penalized likelihood approach based on a feature matrix to recover the sparse structure from populations and products toward the assortment. Our proposed method considers simultaneously low-rank and sparsity structures, which can further reduce model complexity and improve its estimation and prediction accuracy. A new algorithm, sparse factorial gradient descent (SFGD), was proposed to estimate the parameter matrix, which has high interpretability and efficient computing performance. As a first-order method, the SFGD works well in high-dimensional scenarios because of the absence of the Hessian matrix. Simulation studies show that the SFGD algorithm outperforms state-of-the-art methods in terms of estimation, sparsity recovery, and average regret. We also demonstrate the effectiveness of our proposed method using advertising behavior data analysis.

The rapid development of social media leads to the spread of a large amount of false news, which not only affects people’s daily life but also harms the credibility of social media platforms. Therefore, detecting Chinese fake news is a challenging and meaningful task. However, existing fake news datasets from Chinese social media platforms have a relatively small amount of data and data collection in this field is relatively old, thus being unable to meet the requirements of further research. In consideration of this background, we release a new Chinese Weibo Fake News dataset, which contains 26320 fake news data collected from Weibo. In addition, we propose a fake news detection model based on data augmentation that can effectively solve the problem of a lack of fake news, and we improve the generalization ability and robustness of the model. We conduct numerous experiments on our Chinese Weibo Fake News dataset and successfully deploy the model on the web page. The experimental performance proves the effectiveness of the proposed end-to-end model for detecting fake news on social media platforms.

An eco-friendly and bio-based ternary hybrid gel consisting of alginate, sepiolite, and ammonium polyphosphate (APP) was fabricated via a facile one-pot method. Rheological tests showed that this ternary hybrid hydrogel exhibited shear-thinning behavior. Firefighting experiments showed that a burning cotton bale extinguished by using water re-ignited, whereas the ternary hybrid gel effectively prevented smoldering and re-ignition of the cotton bale because of the firm adhesion of the hybrid gel to the surface of the cotton bale. Firefighting experiments also showed that the hybrid gel only covered the upper layer of a rice pile after firefighting efforts, whereas water ruined the grains completely, making them inedible and suitable for use only as a feed or for discard with the burnt grains. The firefighting mechanism of this hybrid gel involved multiple modes of action: volatilization of the large amount of water in the gel absorbed much heat (cooling the combustion zone), APP decomposed into non-flammable ammonia when heated (dilution of flammable volatiles and oxygen), and APP and sepiolite were conducive to forming a continuous and dense char layer (insulation of the exchange of combustible gas, heat, and oxygen). This work provides an environmentally friendly, cost-effective, and bio-based hybrid gel for firefighting in grain and cotton reserves.