An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans

Along with the fast development of the marine economy and ever-increasing human activities, handy and reliable marine networking services are increasingly required in recent years. The ocean faces challenges to support cost-effective communication due to its special environments. Opportunistic networks with easy deployment and self-curing capability are expected to play an important role to adapt to such dynamic networking environments. In the literature, routing schemes for opportunistic networks mainly exploit node mobility and local relaying technologies. They did not take into account the impact of node behaviors on encountering opportunities and in case of no further relaying, network performance would be greatly degraded. To solve the problem, we propose an efficient routing scheme based on node attributes for opportunistic networks. We first construct delivery competency to predict the further relay nodes. Then a forwarding willingness mechanism is introduced to evaluate the relaying probability combining device capacity and movement behaviors of nodes. Finally, the utility metric is used to make decisions on message forwarding. The results show that the proposed scheme improves network performance in terms of delivery ratio, average latency, and overhead ratio as compared to other schemes.     

P213 BN: a novel large-cell boron nitride polymorph

A new boron nitride polymorph, P2₁3 BN (space group: P2₁3), is investigated by first-principles calculations, including its structural properties, stability, elastic properties, anisotropy and electronic properties. It is found that the new boron nitride polymorph P2₁3 BN is mechanically, dynamically and thermodynamically stable. The bulk modulus (B), shear modulus (G) and Young's modulus of P2₁3 BN are 91 GPa, 41 GPa and 107 GPa, respectively, all of which are larger than that of Y carbon and TY carbon. By comparing with c-BN, the Young's modulus, shear modulus and Poisson's ratio of P2₁3 BN show tiny anisotropy in the (001), (010), (100) and (111) planes. At the same time, in contrast with most boron nitride polymorphs, P2₁3 BN is a semiconductor material with a smaller band gap of 1.826 eV. The Debye temperature and the anisotropic sound velocities of P2₁3 BN are also investigated in this work.     

Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.     

Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation

For an industrial process, the estimation of feeding composition is important for analyzing production status and making control decisions. However, random errors or even gross ones inevitably contaminate the actual measurements. Feeding composition is conventionally obtained via discrete and low-rate artificial testing. To address these problems, a feeding composition estimation approach based on data reconciliation procedure is developed. To improve the variable accuracy, a novel robust M-estimator is first proposed. Then, an iterative robust hierarchical data reconciliation and estimation strategy is applied to estimate the feeding composition. The feasibility and effectiveness of the estimation approach are verified on a fluidized bed roaster. The proposed M-estimator showed better overall performance.     

FTRLIM: Distributed Instance Matching Framework for Large-Scale Knowledge Graph Fusion

Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects’ features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-Leader Instance Matching (FTRLIM), which matches instances between large-scale knowledge graphs with approximately linear time complexity. FTRLIM has participated in OAEI 2019 and achieved the best matching quality with significantly efficiency. In this research, we construct three data collections based on a real-world large-scale knowledge graph. Experiment results on the constructed data collections and two real-world datasets indicate that MultiObJ and FTRLIM outperform other state-of-the-art methods.     

Universality of entropy principle for a general diffeomorphism-covariant purely gravitational theory

Thermodynamics plays an important role in gravitational theories. It is a principle that is independent of gravitational dynamics, and there is still no rigorous proof to show that it is consistent with the dynamical principle. We consider a self-gravitating perfect fluid system with the general diffeomorphism-covariant purely gravitational theory. Based on the Noether charge method proposed by Iyer and Wald, considering static off/on-shell variational configurations, which satisfy the gravitational constraint equation, we rigorously prove that the extrema of the total entropy of a perfect fluid inside a compact region for a fixed total particle number demands that the static configuration is an on-shell solution after we introduce some appropriate boundary conditions, i.e., it also satisfies the spatial gravitational equations. This means that the entropy principle of the fluid stores the same information as the gravitational equation in a static configuration. Our proof is universal and holds for any diffeomorphism-covariant purely gravitational theories, such as Einstein gravity, \begin{document}$ f(R)$\end{document} gravity, Lovelock gravity, f(Gauss-Bonnet) gravity and Einstein-Weyl gravity. Our result indicates the consistency between ordinary thermodynamics and gravitational dynamics.     

Nonideal double-slope effect in organic field-effect transistors

With the development of device engineering and molecular design,organic field effect transistors(OFETs)with high mobility over 10 cm2 V-1-s-1 have been reported.However,the nonideal doubleslope effect has been frequently observed in some of these OFETs,which makes it difficult to extract the intrinsic mobility OFETs accurately,impeding the further application of them.In this review,the origin of the nonideal double-slope effect has been discussed thoroughly,with affecting factors such as contact resistance,charge trapping,disorder effects and coulombic interactions considered.According to these discussions and the understanding of the mechanism behind double-slope effect,several strategies have been proposed to realize ideal OFETs,such as doping,molecular engineering,charge trapping reduction,and contact engineering.After that,some novel devices based on the nonideal double-slope behaviors have been also introduced.     

Improved Deep Q-Network for User-Side Battery Energy Storage Charging and Discharging Strategy in Industrial Parks

Battery energy storage technology is an important part of the industrial parks to ensure the stable power supply, and its rough charging and discharging mode is difficult to meet the application requirements of energy saving, emission reduction, cost reduction, and efficiency increase. As a classic method of deep reinforcement learning, the deep Q-network is widely used to solve the problem of user-side battery energy storage charging and discharging. In some scenarios, its performance has reached the level of human expert. However, the updating of storage priority in experience memory often lags behind updating of Q-network parameters. In response to the need for lean management of battery charging and discharging, this paper proposes an improved deep Q-network to update the priority of sequence samples and the training performance of deep neural network, which reduces the cost of charging and discharging action and energy consumption in the park. The proposed method considers factors such as real-time electricity price, battery status, and time. The energy consumption state, charging and discharging behavior, reward function, and neural network structure are designed to meet the flexible scheduling of charging and discharging strategies, and can finally realize the optimization of battery energy storage benefits. The proposed method can solve the problem of priority update lag, and improve the utilization efficiency and learning performance of the experience pool samples. The paper selects electricity price data from the United States and some regions of China for simulation experiments. Experimental results show that compared with the traditional algorithm, the proposed approach can achieve better performance in both electricity price systems, thereby greatly reducing the cost of battery energy storage and providing a stronger guarantee for the safe and stable operation of battery energy storage systems in industrial parks.     

基于光声光谱技术的NO,NO2气体分析仪研究

NO, NO₂是大气污染源中的常见气体, 对环境具有严重的危害性. 为检测污染源中这两种气体的浓度, 构建了成本较低的基于红外热辐射光源的光声光谱气体检测系统. 分析计算得到了NO, NO₂ 在2500–6667 nm波段吸收谱线. 通过建立光声传输线RLC振荡电路模型和仿真得到品质因数、声压大小与谐振腔长、内腔半径以及调制频率的关系, 据此设计了光声池几何结构. 实验表明该系统所测得的光声信号与气体浓度有很好的线性关系, 并且对NO, NO₂气体极限检测灵敏度分别达到4.01 和1.07 μL. 通过调节激光发射波长和选取滤波片, 该系统还可用于其他微量气体的浓度检测. 关键词： 大气污染 光声光谱 气体检测