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Ph.DDOCTOR OF PHILOSOPH

Robust Design for Intelligent Reflecting Surface-Assisted Secrecy SWIPT Network

This paper investigates the robust beamforming design in a secrecy multiple-input single-output (MISO) network aided by the intelligent reflecting surface (IRS) with simultaneous wireless information and power transfer (SWIPT). Specifically, by considering that the energy receivers (ERs) are potential eavesdroppers (Eves) and imperfect channel state information (CSI) of the direct and cascaded channels can be obtained, we investigate the max-min fairness robust secrecy design. The objective is to maximize the minimum robust information rate among the legitimate information receivers (IRs). To solve the formulated non-convex design problem in bounded and probabilistic CSI error models, we utilize the alternating optimization (AO) and successive convex approximation (SCA) methods to obtain an approximate problem. Then, an iteration-based algorithm framework was proposed, where the unit modulus constraint (UMC) of the IRS is handled by the penalty dual decomposition (PDD) method. Moreover, a stochastic SCA method is proposed to handle the outage constrained design with statistical CSI. Finally, simulation results validate the promising performance of the proposed design

Acceleration and displacement dynamic response laws of a shallow-buried bifurcated tunnel

In order to obtain the seismic dynamic laws of the acceleration and displacement of a shallow-buried bifurcated tunnel, the analysis of the numerical simulation was carried out by MIDAS-GTS/NX software. The results of the numerical simulation were verified by a shaking table model test. The results show that: (1) the numerical simulation and shaking table test coincide with each other in terms of variation law and peak value. The results of the numerical simulation are credible. (2) For different tunnel cross-section, the response of acceleration and displacement are significant difference. (3) The seismic response of the small distance tunnel (Section 6) is intense. The seismic response laws of the small distance tunnel are significant difference from other type tunnels. The seismic response of the measuring point at the middle rock column is intense. (4) Along the axis of the tunnel, the displacement of the tunnel firstly increases and then decreases. The displacement of the measuring point at the middle rock column is intense, which is in accordance with the law of the acceleration response. The seismic response laws of the tunnel are significantly affected by the middle rock column. The section structure size has a significant effect on the dynamic response of the tunnel

Cryo-EM structures of lipopolysaccharide transporter LptB2FGC in lipopolysaccharide or AMP-PNP-bound states reveal its transport mechanism

Lipopolysaccharides (LPS) of Gram-negative bacteria are critical for the defence against cytotoxic substances and must be transported from the inner membrane (IM) to the outer membrane (OM) through a bridge formed by seven membrane proteins (LptBFGCADE). The IM component LptB2FG powers the process through a yet unclarified mechanism. Here we report three high-resolution cryo-EM structures of LptB2FG alone and complexed with LptC (LptB2FGC), trapped in either the LPS- or AMP-PNP-bound state. The structures reveal conformational changes between these states and substrate binding with or without LptC. We identify two functional transmembrane arginine-containing loops interacting with the bound AMP-PNP and elucidate allosteric communications between the domains. AMP-PNP binding induces an inward rotation and shift of the transmembrane helices of LptFG and LptC to tighten the cavity, with the closure of two lateral gates, to eventually expel LPS into the bridge. Functional assays reveal the functionality of the LptF and LptG periplasmic domains. Our findings shed light on the LPS transport mechanism

Performance Management of Natural Resources: A Systematic Review and Conceptual Framework for China

In recent decades, the issue of “Performance management of natural resources” has received increasing attention. To explore the optimization of performance management of natural resources is of great significance to the sustainable development of a country’s society and economy. Based on the relevant literature of “Performance management of natural resources” and “performance management and evaluation of nature resources” in Web of Science from 1990 to 2021, this study reviews the research progress of performance management of natural resources (including water resources) with the help of the CiteSpace V. Through literature review and inductive analysis, the authors found that the pursuit of sustainable utilization and management of natural resources has become the frontier direction of research. However, performance management of natural resources still lacks a general conceptual interpretation and analysis framework, and its evaluation system and methods still need to be further improved. The existing research on influencing factors of natural resources management performance still lacks depth, and the application of quantitative models needs to be strengthened in the future. The combination of research and quantitative models also needs to be further strengthened. Based on the existing literature and the practical experience of countries all over the world, this study constructs the research framework of performance management of natural resources for China. On the basis of multiple evaluation objectives, subjects and means, the authors describes the process and mechanism of performance management of natural resources, and gives some feasible evaluation methods for the performance management of natural resources, in order to provide decision support for the sustainable utilization of natural resources for China

Knowledge Graph Grounded Goal Planning for Open-Domain Conversation Generation

Previous neural models on open-domain conversation generation have no effective mechanisms to manage chatting topics, and tend to produce less coherent dialogs. Inspired by the strategies in human-human dialogs, we divide the task of multi-turn open-domain conversation generation into two sub-tasks: explicit goal (chatting about a topic) sequence planning and goal completion by topic elaboration. To this end, we propose a three-layer Knowledge aware Hierarchical Reinforcement Learning based Model (KnowHRL). Specifically, for the first sub-task, the upper-layer policy learns to traverse a knowledge graph (KG) in order to plan a high-level goal sequence towards a good balance between dialog coherence and topic consistency with user interests. For the second sub-task, the middle-layer policy and the lower-layer one work together to produce an in-depth multi-turn conversation about a single topic with a goal-driven generation mechanism. The capability of goal-sequence planning enables chatbots to conduct proactive open-domain conversations towards recommended topics, which has many practical applications. Experiments demonstrate that our model outperforms state of the art baselines in terms of user-interest consistency, dialog coherence, and knowledge accuracy

Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma

Abstract Background About 10% of hematologic malignancies are multiple myeloma (MM), an untreatable cancer. Although lactate and branched-chain amino acids (BCAA) are involved in supporting various tumor growth, it is unknown whether they have any bearing on MM prognosis. Methods MM-related datasets (GSE4581, GSE136337, and TCGA-MM) were acquired from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Lactate and BCAA metabolism-related subtypes were acquired separately via the R package “ConsensusClusterPlus” in the GSE4281 dataset. The R package “limma” and Venn diagram were both employed to identify lactate-BCAA metabolism-related genes. Subsequently, a lactate-BCAA metabolism-related prognostic risk model for MM patients was constructed by univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. The gene set enrichment analysis (GSEA) and R package “clusterProfiler"were applied to explore the biological variations between two groups. Moreover, single-sample gene set enrichment analysis (ssGSEA), Microenvironment Cell Populations-counter (MCPcounte), and xCell techniques were applied to assess tumor microenvironment (TME) scores in MM. Finally, the drug’s IC50 for treating MM was calculated using the “oncoPredict” package, and further drug identification was performed by molecular docking. Results Cluster 1 demonstrated a worse prognosis than cluster 2 in both lactate metabolism-related subtypes and BCAA metabolism-related subtypes. 244 genes were determined to be involved in lactate-BCAA metabolism in MM. The prognostic risk model was constructed by CKS2 and LYZ selected from this group of genes for MM, then the prognostic risk model was also stable in external datasets. For the high-risk group, a total of 13 entries were enriched. 16 entries were enriched to the low-risk group. Immune scores, stromal scores, immune infiltrating cells (except Type 17 T helper cells in ssGSEA algorithm), and 168 drugs’IC50 were statistically different between two groups. Alkylating potentially serves as a new agent for MM treatment. Conclusions CKS2 and LYZ were identified as lactate-BCAA metabolism-related genes in MM, then a novel prognostic risk model was built by using them. In summary, this research may uncover novel characteristic genes signature for the treatment and prognostic of MM