Shandong Normal University

About: Shandong Normal University is a education organization based out in Jinan, Shandong, China. It is known for research contribution in the topics: Laser & Catalysis. The organization has 12378 authors who have published 12576 publications receiving 174572 citations.

Plant Unsaturated Fatty Acids: Biosynthesis and Regulation.

Abstract: In most plants, major unsaturated fatty acids (UFAs) are three C18 species, namely, oleic (18:1), linoleic (18:2), and α-linolenic (18:3) acids. These simple compounds play multiple crucial roles in planta and are also important economic traits of oil crops. The enzymatic steps of C18 UFA biosynthesis have been well established. However, the associated FA/lipid trafficking between the plastid and the endoplasmic reticulum remains largely unclear, as does the regulation of the expression and activities of the involved enzymes. In this review, we will revisit the biosynthesis of C18 UFAs with an emphasis on the trafficking, and present an overview of the key enzymes and their regulation. Of particular interest is the emerging regulatory network composed of transcriptional factors and upstream signaling pathways. The review thereby provides the promise of using physical, biochemical and/or genetic means to manipulate FA composition and increase oil yield in crop improvement.

The Maximum Principles for Stochastic Recursive Optimal Control Problems Under Partial Information

Abstract: A maximum principle for partially observed stochastic recursive optimal control problems is obtained under the assumption that control domains are not necessarily convex and forward diffusion coefficients do not contain control variables Such kind of recursive optimal control problems have wide applications in finance and economics such as recursive utility optimization and principal-agent problems By virtue of a classical spike variational approach and a filtering technique, the maximum principle is obtained, and the related adjoint processes are characterized by the solutions of forward-backward stochastic differential equations in finite-dimensional spaces Then our theoretical results are applied to study a partially observed linear-quadratic recursive optimal control problem In addition, for the case with initial and terminal state constraints, the corresponding maximum principle is also obtained by using Ekeland's variational principle

Interest-aware Message-Passing GCN for Recommendation

Abstract: Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the collaborative signals from the high-order neighbors. Like other GCN models, the GCN based recommendation models also suffer from the notorious over-smoothing problem – when stacking more layers, node embeddings become more similar and eventually indistinguishable, resulted in performance degradation. The recently proposed LightGCN and LR-GCN alleviate this problem to some extent, however, we argue that they overlook an important factor for the over-smoothing problem in recommendation, that is, high-order neighboring users with no common interests of a user can be also involved in the user’s embedding learning in the graph convolution operation. As a result, the multi-layer graph convolution will make users with dissimilar interests have similar embeddings. In this paper, we propose a novel Interest-aware Message-Passing GCN (IMP-GCN) recommendation model, which performs high-order graph convolution inside subgraphs. The subgraph consists of users with similar interests and their interacted items. To form the subgraphs, we design an unsupervised subgraph generation module, which can effectively identify users with common interests by exploiting both user feature and graph structure. To this end, our model can avoid propagating negative information from high-order neighbors into embedding learning. Experimental results on three large-scale benchmark datasets show that our model can gain performance improvement by stacking more layers and outperform the state-of-the-art GCN-based recommendation models significantly.

Strong Exciton–Photon Coupling in Hybrid Inorganic–Organic Perovskite Micro/Nanowires

Abstract: Recently, inorganic–organic perovskite nanowires with strong photon confinement and isotropy have attracted considerable attention for advanced applications in optoelectronic devices from lasers and photodetectors to transistors. Moreover, their high exciton oscillation strength and binding energy make them very promising for polariton devices in the strong light–matter interaction region. This study presents the strong exciton–photon coupling in hybrid inorganic–organic CH3NH3PbBr3 micro/nanowire cavities at room temperature. Clear anticrossing feature is observed by using remote excitation photoluminescence emission spectroscopy with vacuum Rabi splitting energy up to 390 meV. The observed vacuum Rabi splitting energy of up to ≈390 meV (0.32 × 3.66 µm2) is attributed to large oscillator strength and photon confinement in reduced dimension of the micro/nanowire based Fabry–Perot cavities. With increasing pump fluence, the exciton–photon coupling is weakened because of carrier screening effect, which leads to the occurrence of photonic lasing instead of polariton lasing. The demonstrated strong exciton–photon coupling in perovskite micro/nanowire cavities is significant for the development of high performance polariton-based incoherent and coherent light sources, nonlinear optics, and slow light applications.