A Semantic Collaboration Method Based on Uniform Knowledge Graph

The Semantic Internet of Things is the extension of the Internet of Things and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the Internet of Things. However, the Semantic Internet of Things has the characteristics of both the Internet of Things and the Semantic Web environment, and the corresponding semantic data presents many new data features. In this study, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph, allowing us to be applied to the environment of the Semantic Internet of Things better. Here, we design a semantic collaboration method based on a uniform knowledge graph. It can take the uniform knowledge graph as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the Semantic Internet of Things. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes

A 3D model of ovarian cancer cell lines on peptide nanofiber scaffold to explore the cell–scaffold interaction and chemotherapeutic resistance of anticancer drugs

RADA16-I peptide hydrogel, a type of nanofiber scaffold derived from self-assembling peptide RADA16-I, has been extensively applied to regenerative medicine and tissue repair in order to develop novel nanomedicine systems. In this study, using RADA16-I peptide hydrogel, a three-dimensional (3D) cell culture model was fabricated for in vitro culture of three ovarian cancer cell lines. Firstly, the peptide nanofiber scaffold was evaluated by transmission electron microscopy and atom force microscopy. Using phase contrast microscopy, the appearance of the representative ovarian cancer cells encapsulated in RADA16-I peptide hydrogel on days 1, 3, and 7 in 24-well Petri dishes was illustrated. The cancer cell–nanofiber scaffold construct was cultured for 5 days, and the ovarian cancer cells had actively proliferative potential. The precultured ovarian cancer cells exhibited nearly similar adhesion properties and invasion potentials in vitro between RADA16-I peptide nanofiber and type I collagen, which suggested that RADA16-I peptide hydrogel had some similar characteristics to type I collagen. The precultured ovarian cancer cells had two-fold to five-fold higher anticancer drug resistance than the conventional two-dimensional Petri dish culture. So the 3D cell model on peptide nanofiber scaffold is an optimal type of cell pattern for anticancer drug screening and tumor biology

Pore Characteristics and Computed Permeability of Pastes Subject to Freeze-thaw Cycles at Very Early Ages by X-ray CT

Freeze-thaw cycling can damage microstructure of concrete and reduce the service life of concrete structures. This is especially detrimental for concrete subject to freeze-thaw cycles at very early-ages, such as within the first few days after constructions. Typical low and high water-to-cement (w/c) ratio pastes are investigated experimentally and numerically in this study. The pastes are subject to freeze-thaw cycles at the age of 1, and 7 days and then sealed-cured to the age of 45 days. The pore size distribution is measured and quantified by X-ray computer tomography (CT) with high resolution. The permeability of the corresponding pastes is predicted numerically based on the reconstructed microstructure results obtained from the CT measurements. The pore size distribution and the permeability variations of different pastes are investigated, and the key affecting factors are recognized. The Katz-Thompson and Navier-Stokes methods for computing permeability of paste are compared. The detectable pore size range by MIP, BSE, and X-ray CT in hardened paste is identified. The results of this study will offer suggestions for material design and curing strategies for concrete structures which are prone to experiencing freeze-thaw cycles at very early-ages

Improving energy capture and power quality of power electronic connected generation

Power electronic converter is a significant intermediate media for electric renewable energy systems when integrated into the utility grid. Renewable energy systems such as wind, solar and wave energy systems usually operate with irregular natural energy sources. Advanced energy conversion interfaces are therefore highly desirable for stable power supply, good system reliability and high energy extraction efficiency. This thesis investigates the power generation and conversion systems, with the concentrations on the long-term operation cost, full-power-range efficiency and power quality of power electronic converters, for wind, solar and wave energy applications. The story starts with a hybrid wind-solar energy system design targeting at improving energy yield and system reliability. Wind energy and solar energy, as two complementary energy resources, are combined in a single energy system that features improved energy supply stability and reduced energy storage requirement. Special adaptive energy extraction maximisation algorithms are developed for energy generators in order to increase the energy extraction efficiency. The overall energy cogeneration system can offer high productivity and robustness under varying weather conditions. In the second part of this thesis, a bidirectional DC-AC converter based on the well-established Silicon (Si) based two-level circuit and the emerging Silicon Carbide (SiC) based three-level circuit is investigated, with the motivation to enhance the full-power-range efficiency in renewable energy generation and conversion systems. The SiC based circuit is advantageous especially under low-power conditions due to its low switching losses. The costs of power electronics, especially the power semiconductor devices, are taken into account. The Si based circuit provides a more cost-effective option and lower conduction losses under high-power conditions to further improve the overall energy conversion efficiency. All these benefits are integrated in a single converter called hybrid level-matching (HLM) converter, which is comprised of parallel-connected SiC and Si based circuits. A model predictive control (MPC) algorithm is developed to assist the switching state selection for minimised power losses across the full power range. The proposed HLM converter shows similar power control quality and better full-power-range efficiency compared to its conventional counterparts. The operation of the HLM converter under the proposed MPC controller is experimentally verified by a lab-scale demonstrator. The final part of this thesis focuses on the control of an existing flying capacitor based multilevel converter known as stacked multicell converter (SMC). Considered as a superior DC-AC converter candidate in renewable energy standalone load applications, SMC can be controlled under different capacitor voltage ratios to increase the output voltage resolution. This is studied to explore the potential to improve power control quality within the same SMC circuit by applying different capacitor voltage set-points. The capacitor voltage balancing and the basic three-phase current control are achieved by means of a space vector based MPC algorithm. A method to reduce the computational burden by shrinking the space vector candidate size is proposed. The trade-off between capacitor voltage balancing and current reference tracking poses a major challenge to the SMC in its flexibility in capacitor voltage ratio choice. This is investigated in detail to verify the feasibility to reduce load harmonic distortion by modifying the traditional capacitor voltage ratio in a SMC with three stacked cells

TPGNN: Learning High-order Information in Dynamic Graphs via Temporal Propagation

Temporal graph is an abstraction for modeling dynamic systems that consist of evolving interaction elements. In this paper, we aim to solve an important yet neglected problem -- how to learn information from high-order neighbors in temporal graphs? -- to enhance the informativeness and discriminativeness for the learned node representations. We argue that when learning high-order information from temporal graphs, we encounter two challenges, i.e., computational inefficiency and over-smoothing, that cannot be solved by conventional techniques applied on static graphs. To remedy these deficiencies, we propose a temporal propagation-based graph neural network, namely TPGNN. To be specific, the model consists of two distinct components, i.e., propagator and node-wise encoder. The propagator is leveraged to propagate messages from the anchor node to its temporal neighbors within $k$-hop, and then simultaneously update the state of neighborhoods, which enables efficient computation, especially for a deep model. In addition, to prevent over-smoothing, the model compels the messages from $n$-hop neighbors to update the $n$-hop memory vector preserved on the anchor. The node-wise encoder adopts transformer architecture to learn node representations by explicitly learning the importance of memory vectors preserved on the node itself, that is, implicitly modeling the importance of messages from neighbors at different layers, thus mitigating the over-smoothing. Since the encoding process will not query temporal neighbors, we can dramatically save time consumption in inference. Extensive experiments on temporal link prediction and node classification demonstrate the superiority of TPGNN over state-of-the-art baselines in efficiency and robustness.Comment: Under revie

Deep insight into charge equilibration and the effects on producing neutron-rich isotopes around N = 126 in the multinucleon transfer reactions

The dynamics of the charge equilibration (CE) and the effects on the production of the neutron-rich isotopes around $N = 126$ in multinucleon transfer reactions are still not well understood. In this Letter, we investigate the mechanism of the CE from different viewpoints by using the extended version of the dinuclear system model (DNS-sysu) and the improved quantum molecular dynamics (ImQMD) model. From the macroscopic and microscopic dynamical viewpoints, we find incomplete CE for the mass asymmetry reaction systems even in the very deep collisions, and the behavior of "inverse CE" that the tendency of the fragments is away from the $N/Z$ value of the compound system in the reaction $^{140}$Xe + $^{198}$Pt. Unlike the slow process presented in the ImQMD model, the behavior of fast equilibration with the characteristic time $\sim$ 0.1 zs is obtained based on the DNS-sysu model, which is consistent with the experimental data. By performing the systematic calculation, the correlation between the CE and the mass asymmetry of the reaction systems is clarified, which not only accounts for the observed intriguing phenomena of the CE but also provides essential information for producing the neutron-rich isotopes around $N = 126$.Comment: 8 pages, 5 figure