Oxidized sodium alginate/polyacrylamide hydrogels adhesive for promoting wheat growth.

Chemical modification of sodium alginate (SA) polymer chains can increase its functional group species. Sodium periodate (SP) was usually used to oxidize the hydroxyl groups on the chain of SA to aldehyde groups, the preparation of oxidized sodium alginate (OSA) using SP is not only complicated, also limits the variety of functional groups on the chain of OSA. By contrast, we have developed an innovative strategy for OSA, in which ammonium persulfate (APS) was used to oxidize SA, providing a clear elucidation of the oxidizing process and mechanism. OSA/PAM hydrogels were synthesized using OSA, the hydrogels possess excellent adhesion properties to various non-metallic and metallic substrates. Tensile and compression tests show that the cross-linked OSA/PAM hydrogels have superior mechanical properties. We exploit OSA/PAM hydrogels as soil adhesive and water-retaining agents for wheat growth. OSA/PAM hydrogels significantly improve the survival time of wheat grown in brown loam soil under a water-shortage environment, and slow down the wilting of wheat in a water-shortage environment and prolong the survival time of wheat in sandy soils. Our trials should make hydrogels important for wheat cultivation in brown loam soils and the development of desert areas.

Unraveling the Interfacial Polarization Effect between Pd and Polymeric Carbon Nitride toward Efficient CO2 Electroreduction to CO.

The efficient electrochemical conversion of carbon dioxide (CO2) to carbon monoxide (CO) using renewable energy is an effective route to pursue carbon neutrality. Optimizing the binding energy of CO on palladium (Pd) metal-based materials used in this process is to make sure the timely desorption of CO from their active sites is critical. Tuning the electronic structure of the Pd center is an effective strategy to optimize its catalytic performance. Herein, we rationally design Pd nanoparticles (NPs)/polymeric carbon nitride (CN) (Pd/CN) composite, which alters the electronic structure of Pd by introducing the interfacial polarization effect to accelerate CO desorption and improve CO selectivity of Pd catalyst. The optimized Pd/CN exhibits a CO Faradaic efficiency of 92.7% at -0.9 V versus reversible hydrogen electrode in CO2-saturated 0.1 M KHCO3 solution. Experimental investigations and theoretical calculations jointly confirm that the enhanced CO selectivity and stability originate from the electron transfer at the Pd/CN interface, and the weakened *CO adsorption on the palladium hydride surface.

3D Flower-Like Zinc Cobaltite for Electrocatalytic Reduction of Nitrate to Ammonia under Ambient Conditions.

Nitrate (NO3 - ) as a common pollutant of groundwater causes drinking water safety problems and seriously endangers people's health. Electrochemical reduction of nitrate to ammonia under ambient condition is a green and significant route to reduce the concentration of NO3 - and produce ammonia (NH3 ), known as a complement to the Haber-Bosch reaction. Currently, noble-metal electrocatalysts are often used in electrochemical reduction of NO3 - , but high cost and scarcity limited their application. Herein, three-dimensional (3D) flower-like zinc cobaltite (ZnCo2 O4 ) electrocatalyst was developed to convert nitrate into ammonia at room temperature. The NH3 yield rate could reach up to around 2100 µg mg-1 h-1 at a potential of -0.6 V vs. reversible hydrogen electrode (RHE), which was around 2.0 times higher than that of pristine Co3 O4 . In addition, the NH3 faradaic efficiency of ZnCo2 O4 electrocatalyst could reach around 95.4 % at potential of -0.4 V vs. RHE with good structural and morphological stability, which surpassed most reported non-noble metal-based electrocatalysts. Further studies concluded that the improved activity of electrocatalytic NO3 - reduction was ascribed to the existence of abundant active sites and the charge transfer from Co atoms to Zn atoms after Zn doping. Importantly, this work opens a new path for the development of Co-based materials as electrocatalysts for reducing nitrate to ammonia.

MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph.

To alleviate the data sparsity and cold start problems for collaborative filtering in recommendation systems, side information is usually leveraged by researchers to improve the recommendation performance. The utility of knowledge graph regards the side information as part of the graph structure and gives an explanation for recommendation results. In this paper, we propose an enhanced multi-task neighborhood interaction (MNI) model for recommendation on knowledge graphs. MNI explores not only the user-item interaction but also the neighbor-neighbor interactions, capturing a more sophisticated local structure. Besides, the entities and relations are also semantically embedded. And with the cross&compress unit, items in the recommendation system and entities in the knowledge graph can share latent features, and thus high-order interactions can be investigated. Through extensive experiments on real-world datasets, we demonstrate that MNI outperforms some of the state-of-the-art baselines both for CTR prediction and top-N recommendation.

KG2Vec: A node2vec-based vectorization model for knowledge graph.

Since the word2vec model was proposed, many researchers have vectorized the data in the research field based on it. In the field of social network, the Node2Vec model improved on the basis of word2vec can vectorize nodes and edges in social networks, so as to carry out relevant research on social networks, such as link prediction, and community division. However, social network is a network with homogeneous structure. When dealing with heterogeneous networks such as knowledge graph, Node2Vec will lead to inaccurate prediction and unreasonable vector quantization data. Specifically, in the Node2Vec model, the walk strategy for homogeneous networks is not suitable for heterogeneous networks, because the latter has distinguishing features for nodes and edges. In this paper, a Heterogeneous Network vector representation method is proposed based on random walks and Node2Vec, called KG2vec (Heterogeneous Network to Vector) that solves problems related to the inadequate consideration of the full-text semantics and the contextual relations that are encountered by the traditional vector representation of the knowledge graph. First, the knowledge graph is reconstructed and a new random walk strategy is applied. Then, two training models and optimizing strategies are proposed, so that the contextual environment between entities and relations is obtained, semantically providing a full vector representation of the Heterogeneous Network. The experimental results show that the KG2VEC model solves the problem of insufficient context consideration and unsatisfactory results of one-to-many relationship in the vectorization process of the traditional knowledge graph. Our experiments show that KG2vec achieves better performance with higher accuracy than traditional methods.

Online model checking for monitoring surrogate-based respiratory motion tracking in radiation therapy.

OBJECTIVE: Correlation between internal and external motion is critical for respiratory motion compensation in radiosurgery. Artifacts like coughing, sneezing or yawning or changes in the breathing pattern can lead to misalignment between beam and tumor and need to be detected to interrupt the treatment. We propose online model checking (OMC), a model-based verification approach from the field of formal methods, to verify that the breathing motion is regular and the correlation holds. We demonstrate that OMC may be more suitable for artifact detection than the prediction error. MATERIALS AND METHODS: We established a sinusoidal model to apply OMC to the verification of respiratory motion. The method was parameterized to detect deviations from typical breathing motion. We analyzed the performance on synthetic data and on clinical episodes showing large correlation error. In comparison, we considered the prediction error of different state-of-the-art methods based on least mean squares (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS), recursive least squares (RLSpred) and support vector regression (SVRpred). RESULTS: On synthetic data, OMC outperformed wLMS by at least 30 % and SVRpred by at least 141 %, detecting 70 % of transitions. No artifacts were detected by nLMS and RLSpred. On patient data, OMC detected 23-49 % of the episodes correctly, outperforming nLMS, wLMS, RLSpred and SVRpred by up to 544, 491, 408 and 258 %, respectively. On selected episodes, OMC detected up to 94 % of all events. CONCLUSION: OMC is able to detect changes in breathing as well as artifacts which previously would have gone undetected, outperforming prediction error-based detection. Synthetic data analysis supports the assumption that prediction is very insensitive to specific changes in breathing. We suggest using OMC as an additional safety measure ensuring reliable and fast stopping of irradiation.