Ex vivo 3D osteocyte network construction with primary murine bone cells

Osteocytes reside as three-dimensionally（3D） networked cells in the lacunocanalicular structure of bones and regulate bone and mineral homeostasis. Despite of their important regulatory roles, in vitro studies of osteocytes have been challenging because：（1） current cell lines do not sufficiently represent the phenotypic features of mature osteocytes and（2） primary cells rapidly differentiate to osteoblasts upon isolation. In this study, we used a 3D perfusion culture approach to：（1） construct the 3D cellular network of primary murine osteocytes by biomimetic assembly with microbeads and（2） reproduce ex vivo the phenotype of primary murine osteocytes, for the first time to our best knowledge. In order to enable 3D construction with a sufficient number of viable cells, we used a proliferated osteoblastic population of healthy cells outgrown from digested bone chips. The diameter of microbeads was controlled to：（1） distribute and entrap cells within the interstitial spaces between the microbeads and（2） maintain average cell-to-cell distance to be about 19 mm. The entrapped cells formed a 3D cellular network by extending and connecting their processes through openings between the microbeads. Also, with increasing culture time, the entrapped cells exhibited the characteristic gene expressions（SOST and FGF23） and nonproliferative behavior of mature osteocytes. In contrast, 2D-cultured cells continued their osteoblastic differentiation and proliferation. This 3D biomimetic approach is expected to provide a new means of：（1） studying flow-induced shear stress on the mechanotransduction function of primary osteocytes,（2） studying physiological functions of 3D-networked osteocytes with in vitro convenience,and（3） developing clinically relevant human bone disease models.

The Budget Constrained Multi-product Newsboy Problem with Reactive Production:A Problem from Entrepreneurial Network Construction

This paper develops an extended newsboy model and presents a formula- tion for this model. This new model has solved the budget contained multi-product newsboy problem with the reactive production. This model can be used to describe the status of entrepreneurial network construction. We use the Lagrange multiplier procedure to deal with our problem, but it is too complicated to get the exact solu-tion. So we introduce the homotopy method to deal with it. We give the flow chart to describe how to get the solution via the homotopy method. We also illustrate our model in both the classical procedure and the homotopy method. Comparing the two methods, we can see that the homotopy method is more exact and efficient.

ConGCNet:Convex geometric constructive neural network for Industrial Internet of Things

The intersection of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)has garnered ever-increasing attention and research interest.Nevertheless,the dilemma between the strict resource-constrained nature of IIoT devices and the extensive resource demands of AI has not yet been fully addressed with a comprehensive solution.Taking advantage of the lightweight constructive neural network(LightGCNet)in developing fast learner models for IIoT,a convex geometric constructive neural network with a low-complexity control strategy,namely,ConGCNet,is proposed in this article via convex optimization and matrix theory,which enhances the convergence rate and reduces the computational consumption in comparison with LightGCNet.Firstly,a low-complexity control strategy is proposed to reduce the computational consumption during the hidden parameters training process.Secondly,a novel output weights evaluated method based on convex optimization is proposed to guarantee the convergence rate.Finally,the universal approximation property of ConGCNet is proved by the low-complexity control strategy and convex output weights evaluated method.Simulation results,including four benchmark datasets and the real-world ore grinding process,demonstrate that ConGCNet effectively reduces computational consumption in the modelling process and improves the model’s convergence rate.

Optimal blockchain network construction methodology based on analysis of configurable components for enhancing Hyperledger Fabric performance

Presently,blockchain technology has been widely applied in various application domains such as the Internet of Things(IoT),supply chain management,healthcare,etc.So far,there has been much confusion about whether blockchain performs with scale,and admittedly,a lack of information about best practices that can improve the performance and scale.This paper proposes a novel blockchain network construction methodology to improve the performance of Hyperledger Fabric.As a highly scalable permissioned blockchain platform,Hyperledger Fabric supports a wide range of enterprise use cases from finance to governance.A comprehensive evaluation is performed by observing various configurable network components that can affect the blockchain performance.To demonstrate the significance of the proposed methodology,we set up the experiment environment for the baseline and the test network using optimized parameters,respectively.The experimental results indicate that the test network's performance is enhanced effectively compared to the baseline in transaction throughput and transaction latency.

Improvement of tree-like network constructal method for heat conduction optimization

The analysis of the “tree-like network” construct method has been repeated. The high effective conduction channel distribution has been optimized again, without the premise that the new order assembly construct must be assembled by the optimized last order construct. It is proved that the “tree-like network” construct method is faultiness. A more optimal construct is obtained,; when the thermal conductivity; the proportion of the two heat conduction materials are constant, the limit of the minimum heat resistance is derived. All these conclusions can be used to guide the engineering application.

A DATA MINING METHOD BASED ON CONSTRUCTIVE NEURAL NETWORKS

In this letter,Constructive Neural Networks (CNN) is used in large-scale data mining. By introducing the principle and characteristics of CNN and pointing out its deficiencies,fuzzy theory is adopted to improve the covering algorithms. The threshold of covering algorithms is redefined. "Extended area" for test samples is built. The inference of the outlier is eliminated. Furthermore,"Sphere Neighborhood (SN)" are constructed. The membership functions of test samples are given and all of the test samples are determined accordingly. The method is used to mine large wireless monitor data (about 3×107 data points),and knowledge is found effectively.

Construction of NIM sub-network regulated by Liuwei Dihuang decoction and its characteristics

OBJECTIVE To construct the neuroendocrine immunomodulation(NIM) sub-network regulated by Liuwei Dihuang decoction(LW) and analyze its characteristics.METHODS We took the GSE57273 in GEO database and screened the differentially expressed genes(DEGs)(P<0.01) by the GEO2 R tool as gene expression signature of LW.The global PPI network was constructed in the context of whole PPI network through direct interaction algorithm and forest algorithm respectively.Then the enrichment and the topological characteristics of NIM signaling molecules were evaluated by permutation test.Finally,we abstracted the NIM sub-network by NIMNT,which combined the NIM molecular network and forest algorithm,and analyzed the topological characteristics of it by the Network Analyzer(release 2.7) plugin in Cytoscape v3.5.1.RESULTS We got 2468 DEGs in the gene expression signature of LW.After analyzing the global PPI network of LW got by two kinds of algorithms,we found that the NIM signaling molecules significantly enriched and located in important positions in the global PPI network.The NIM sub-network regulated by LW contained 1099 nodes and 1056 edges.We screened out 22 hub nodes(Degree>10).Among them,there were 19 NIM signaling molecules in which only ESR1 changed significantly and 3 non-DEGs(NFATC2,RARA,TP53).However,the down.stream of the hub nodes were significantly changes.CONCLUSION The results suggested that LW might mainly regulate the non-hub nodes to recovery of the network imbalance of the body in the state of disease.

Research and Design of Network Management Construction Based on Mobile AGENT

The technical characters of mobile agent （MA） originated in the distributional artificial intelligence domain is introduced. A network management construction based on agent （NMCA） is then proposed. The NMCA structure features are elaborated in detail. A prototype design of NMCA is given by using the jKQML programming. The establishment of NMCA platform will be helpful to reduce the correspondence load of network management and improves the efficiency and the expansion ability of network management systems.

Self-Constructing Neural Network Modeling and Control of an AGV

Tracking precision of pre-planned trajectories is essential for an auto-guided vehicle (AGV). The purpose of this paper is to design a self-constructing wavelet neural network (SCWNN) method for dynamical modeling and control of a 2-DOF AGV. In control systems of AGVs, kinematical models have been preferred in recent research documents. However, in this paper, to enhance the trajectory tracking performance through including the AGV’s inertial effects in the control system, a learned dynamical model is replaced to the kinematical kind. As the base of a control system, the mathematical models are not preferred due to modeling uncertainties and exogenous inputs. Therefore, adaptive dynamic and control models of AGV are proposed using a four-layer SCWNN system comprising of the input, wavelet, product, and output layers. By use of the SCWNN, a robust controller against uncertainties is developed, which yields the perfect convergence of AGV to reference trajectories. Owing to the adaptive structure, the number of nodes in the layers is adjusted in online and thus the computational burden of the neural network methods is decreased. Using software simulations, the tracking performance of the proposed control system is assessed.

Discussion on Construction of CDMA2000 1xEV-D0 Network

The research advances on 3G CDMA standards are outlined and the network architecture and networking characteristics of CD- MA2000 1x EV-DO and CDMA2000 1x EV-DV are described.Em- phasis is put on the key technical issues that may occur when China Unicom upgrades CDMA2000 1x to CDMA2000 1x EV-DO.

Improving the Input of Classified Neural Networks Through Feature Construction

A general classification algorithm of neural networks is unable to obtain satisfied results because of the uncertain problems existing among the features in moot classification programs, such as interaction. With new features constructed by optimizing decision trees of examples, the input of neural networks is improved and an optimized classification algorithm based on neural networks is presented. A concept of dispersion of a classification program is also introduced too in this paper. At the end of the paper, an analysis is made with an example.

Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine

Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.

Predicting Compressive Strength of Recycled Concrete for Construction 3D Printing Based on Statistical Analysis of Various Neural Networks

Construction 3D printing is changing construction industry, but for its immaturity, there are still many problems to be solved. One of the major problems is to study materials for construction 3D printing. Because printed buildings are very different from traditional buildings, there are special requirements for printing materials. Based on environmental and cost considerations, the recycled concrete as printing material is a perfect choice. In order to study and develop the construction 3D printing materials, it is necessary to predict the properties of them. As one of the most effective artificial intelligence algorithms, artificial neural network can deal with multi-parameter and nonlinear problems, and it can provide useful reference to predict the performance of recycled concrete for 3D printing. However, since there are many types and parameters for neural network, it is difficult to select the optimal neural network with excellent prediction performance. In this paper, by comparing different types of neural networks and statistically analyzing the distribution of the root-mean-square error (RMSE) and the coefficient of determination (R2) of these neural networks, we can determine the best performance among four neural networks and finally select the suitable one to predict the performance of 3D printing concrete.

Present situation and prospects of forest ecological network in Jilin Province, China

This paper makes a brief introduction of the ecological environment, forestry achievements, and the existing questions of Jilin Province. The task of forest ecological network and eight questions demanding prompt solution were discussed based on the present situation of forestry in Jilin Province. The author also made prospects for future application of bio-technique, infor-mation technology, new material technology and nuisance-free forest health technology in forest ecological network.

融合视觉特征与运动特征的众源轨迹数据道路交叉口识别方法

随着移动定位技术的快速发展,众源车辆轨迹数据已成为导航路网地图构建与实时更新的重要数据源。道路交叉口是路网地图和路径规划的关键结点,准确识别轨迹数据中的道路交叉口是基于众源轨迹数据构建导航路网地图的重要基础。目前基于众源轨迹数据的道路交叉口识别方法主要分为基于运动特征、视觉特征和深度学习的方法。然而,由于交叉口形状、大小的差异性及轨迹数据密度分布的异质性,采用单一策略或方法难以满足不同轨迹数据场景下(如轨迹稀疏区域和交叉口分布密集区域)的道路交叉口准确完整提取,而导致交叉口的漏提取或错误识别等问题。为此,本文基于组合优化思想,提出了一种融合视觉特征与运动特征的众源轨迹数据道路交叉口识别方法。该方法在提取车辆运动特征识别道路交叉口的基础上,结合人类在观察轨迹数据时的视觉认知过程,通过融合轨迹数据的运动特征与视觉特征,实现不同复杂场景下的道路交叉口识别与结果优化。采用成都市网约车轨迹数据和武汉市出租车轨迹数据进行试验与对比分析,结果表明相比于现有代表性方法,本文方法对道路交叉口识别精度和召回率均具有显著提升。