Fluorescent Double Network Hydrogels with Ionic Responsiveness and High Mechanical Properties for Visual Detection

We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,while the ionic cross-linked hydrogel of terbium ions and sodium alginate serves as the second network.The double-network structure,the introduction of nanoparticles and the reversible ionic crosslinked interactions confer high mechanical properties to the hydrogel.Terbium ions are not only used as the ionic cross-linked points,but also used as green emitters to endow hydrogels with fluorescent properties.On the basis of the “antenna effect” of terbium ions and the ion exchange interaction,the fluorescence of the hydrogels can make selective responses to various ions(such as organic acid radical ions,transition metal ions) in aqueous solutions,which enables a convenient strategy for visual detection toward ions.Consequently,the fluorescent double network hydrogel fabricated in this study is promising for use in the field of visual sensor detection.

Supramolecular polymer-based gel fracturing fluid with a double network applied in ultra-deep hydraulic fracturing

A gel based on polyacrylamide,exhibiting delayed crosslinking characteristics,emerges as the preferred solution for mitigating degradation under conditions of high temperature and extended shear in ultralong wellbores.High viscosity/viscoelasticity of the fracturing fluid was required to maintain excellent proppant suspension properties before gelling.Taking into account both the cost and the potential damage to reservoirs,polymers with lower concentrations and molecular weights are generally preferred.In this work,the supramolecular action was integrated into the polymer,resulting in significant increases in the viscosity and viscoelasticity of the synthesized supramolecular polymer system.The double network gel,which is formed by the combination of the supramolecular polymer system and a small quantity of Zr-crosslinker,effectively resists temperature while minimizing permeability damage to the reservoir.The results indicate that the supramolecular polymer system with a molecular weight of(268—380)×10^(4)g/mol can achieve the same viscosity and viscoelasticity at 0.4 wt%due to the supramolecular interaction between polymers,compared to the 0.6 wt%traditional polymer(hydrolyzed polyacrylamide,molecular weight of 1078×10^(4)g/mol).The supramolecular polymer system possessed excellent proppant suspension properties with a 0.55 cm/min sedimentation rate at 0.4 wt%,whereas the0.6 wt%traditional polymer had a rate of 0.57 cm/min.In comparison to the traditional gel with a Zrcrosslinker concentration of 0.6 wt%and an elastic modulus of 7.77 Pa,the double network gel with a higher elastic modulus(9.00 Pa)could be formed only at 0.1 wt%Zr-crosslinker,which greatly reduced the amount of residue of the fluid after gel-breaking.The viscosity of the double network gel was66 m Pa s after 2 h shearing,whereas the traditional gel only reached 27 m Pa s.

Improved Double Deep Q Network Algorithm Based on Average Q-Value Estimation and Reward Redistribution for Robot Path Planning

By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots.However,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data.Targeting those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed.First,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q network.Next,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory information.Additionally,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality data.Finally,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random environments.The experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average steps.The performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random environments.It not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.

Double Glow Plasma Surface Alloying Process Modeling Using Artificial Neural Networks

A model is developed for predicting the correlation between processing parameters and the technical target of double glow by applying artificial neural network (ANN). The input parameters of the neural network (NN) are source voltage, workplace voltage, working pressure and distance between source electrode and workpiece. The output of the NN model is three important technical targets, namely the gross element content, the thickness of surface alloying layer and the absorption rate (the ratio of the mass loss of source materials to the increasing mass of workpiece) in the processing of double glow plasma surface alloying. The processing parameters and technical target are then used as a training set for an artificial neural network. The model is based on multiplayer feedforward neural network. A very good performance of the neural network is achieved and the calculated results are in good agreement with the experimental ones.

A Routing Algorithm for Distributed Optimal Double Loop Computer Networks

A routing algorithm for distributed optimal double loop computer networks is proposed and analyzed. In this paper, the routing algorithm rule is described, and the procedures realizing the algorithm are given. The proposed algorithm is shown to be optimal and robust for optimal double loop. In the absence of failures,the algorithm can send a packet along the shortest path to destination; when there are failures,the packet can bypasss failed nodes and links.

A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.

Double Pruning Structure Design for Deep Stochastic Configuration Networks Based on Mutual Information and Relevance

Deep stochastic configuration networks(DSCNs)produce redundant hidden nodes and connections during training,which complicates their model structures.Aiming at the above problems,this paper proposes a double pruning structure design algorithm for DSCNs based on mutual information and relevance.During the training process,the mutual information algorithm is used to calculate and sort the importance scores of the nodes in each hidden layer in a layer-by-layer manner,the node pruning rate of each layer is set according to the depth of the DSCN at the current time,the nodes that contribute little to the model are deleted,and the network-related parameters are updated.When the model completes the configuration procedure,the correlation evaluation strategy is used to sort the global connection weights and delete insignificance connections;then,the network parameters are updated after pruning is completed.The experimental results show that the proposed structure design method can effectively compress the scale of a DSCN model and improve its modeling speed;the model accuracy loss is small,and fine-tuning for accuracy restoration is not needed.The obtained DSCN model has certain application value in the field of regression analysis.

Eigenvalue Spectrum and Synchronizability of Double-Layer Directed Ring Networks

The synchronizability of multiplex undirected regular networks has been intensively studied based on the study of the synchronizability of single-layer networks. However, most real networks are characterized by some degree of directionality. So multiplex directed networks can better explain the synchronizability phenomenon. Here, based on the theory of master stability function (MSF), we study the eigenvalue spectrum and synchronizability of double-layer inter-layer directed ring networks (Networks-A) and double-layer intra-layer directed ring networks (Networks-B). The eigenvalue spectrum of the supra-Laplacian matrix of the networks is rigorously derived, and the influence of the networks structure parameters on the network’s synchronizability is analyzed. The correctness of the theory is further verified by numerical simulation analysis. Finally, the synchronizability of four kinds of double-layer ring networks with different coupling modes, namely, Networks-A, Networks-B, Networks-C (double-layer undirected ring networks), and Networks-D (double-layer undirected inter-layer random-added-edge ring networks), is compared and the results can provide guidance for constructing the optimal synchronization network.

AN ASYMPTOTIC PROPERTY OF THE NUMBER OF SPANNING TREES OF DOUBLE FIXED STEP LOOP NETWORKS

Let T(C(p,1,q)) be the number of spanning trees of circulant graph C(p,1,q). In this note we show that T(C(p+1,1,q))T(C(p,1,q))→2, p→+∞.

基于Dueling Double DQN的交通信号控制方法

为了提高交叉口通行效率缓解交通拥堵,深入挖掘交通状态信息中所包含的深层次隐含特征信息,提出了一种基于Dueling Double DQN(D3QN)的单交叉口交通信号控制方法;构建了一个基于深度强化学习Double DQN(DDQN)的交通信号控制模型,对动作-价值函数的估计值和目标值迭代运算过程进行了优化,克服基于深度强化学习DQN的交通信号控制模型存在收敛速度慢的问题;设计了一个新的Dueling Network解耦交通状态和相位动作的价值,增强Double DQN(DDQN)提取深层次特征信息的能力;基于微观仿真平台SUMO搭建了一个单交叉口模拟仿真框架和环境,开展仿真测试;仿真测试结果表明,与传统交通信号控制方法和基于深度强化学习DQN的交通信号控制方法相比,所提方法能够有效减少车辆平均等待时间、车辆平均排队长度和车辆平均停车次数,明显提升交叉口通行效率。

A Double Network Hydrogel with High Mechanical Strength and Shape Memory Properties

Double network(DN)hydrogels as one kind of tough gels have attracted extensive at-tention for their potential applications in biomedical and load-bearing fields.Herein,we import more functions like shape memory into the conventional tough DN hydro-gel system.We synthesize the PEG-PDAC/P(AAm-co-AAc)DN hydrogels,of which the first network is a well-defined PEG(polyethylene glycol)network loaded with PDAC(poly(acryloyloxyethyltrimethyl ammonium chloride))strands,while the second network is formed by copolymerizing AAm(acrylamide)with AAc(acrylic acid)and cross-linker MBAA(N;N′-methylenebisacrylamide).The PEG-PDAC/P(AAm-co-AAc)DN gels exhibits high mechanical strength.The fracture stress and toughness of the DN gels reach up to 0.9 MPa and 3.8 MJ/m^3,respectively.Compared with the conventional double network hydrogels with neutral polymers as the soft and ductile second network,the PEG-PDAC/P(AAm-co-AAc)DN hydrogels use P(AAm-co-AAc),a weak polyelectrolyte,as the second network.The AAc units serve as the coordination points with Fe^3+ions and physically crosslink the second network,which realizes the shape memory property activated by the reducing ability of ascorbic acid.Our results indicate that the high mechanical strength and shape memory properties,probably the two most important characters related to the potential application of the hydrogels,can be introduced simultaneously into the DN hydrogels if the functional monomer has been integrated into the network of DN hydrogels smartly.

Transcriptome and morphological analyses of double flower formation in Dianthus chinensis

The double flower developmental process is regulated via a complex transcriptional regulatory network.To understand this highly dynamic and complex developmental process of Dianthus spp.,we performed a comparative analysis of floral morphology and transcriptome dynamics in simple flowers and double flowers.We found that the primordium of double flowers of‘X’was larger in size compared to that of simple flowers of‘L’in Dianthus chinensis.RNA-seq and Weighted Gene Co-expression Network Analysis(WGCNA)during flower development,identified stage-specific gene network modules.Expression analysis by RNA-seq indicated that a group of genes related to floral meristem identity,primordia position and polarity were highly expressed in double flowers genotypes compared to simple flowers genotypes,suggesting their roles in double-petal formation.A total of 21 DEGs related to petal number were identified between simple and double flowers.The experiments of in situ hybridization revealed that DcaAP2L,DcaLFY and DcaUFO genes were expressed in the intra-sepal boundary and petal boundary.We proposed a potential transcriptional regulatory network for simple and double flower development.This study provides novel insights into the molecular mechanism underlying double flower formation in Dianthus spp.

Simultaneous Determination of Gold and Platinum by Double Artificial Neural Network Analysis with Flow-injection Chemiluminescence

A highly sensitive double artificial neural network （DANN） analysis with flow-injection chemiluminescence （FI-CL） has been developed to simultaneously determine the trace amounts of the gold and platinum in simulated mixed samples, without the boring process.

Prediction model of surrounding rock deformation in doublecontinuous-arch tunnel based on the ABC-WNN

Purpose–The wavelet neural network(WNN)has the drawbacks of slow convergence speed and easy falling into local optima in data prediction.Although the artificial bee colony(ABC)algorithm has strong global optimization ability and fast convergence speed,it also has the drawbacks of slow speed while finding the optimal solution and weak optimization ability in the later stage.Design/methodology/approach–This article uses an ABC algorithm to optimize the WNN and establishes an ABC-WNN analysis model.Based on the example of the Jinan Yuhan underground tunnel project,the deformation of the surrounding rock of the double-arch tunnel crossing the fault fracture zone is predicted and analyzed,and the analysis results are compared with the actual detection amount.Findings–The comparison results show that the predicted values of the ABC-WNN model have a high degree of fitting with the actual engineering data,with a maximum relative error of only 4.73%.On this basis,the results show that the statistical features of ABC-WNN are the lowest,with the errors at 0.566 and 0.573,compared with the single back propagation(BP)neural network model and WNN model.Therefore,it can be derived that the ABC-WNN model has higher prediction accuracy,better computational stability and faster convergence speed for deformation.Originality/value–This article uses firstly the ABC-WNN for the deformation analysis of double-arch tunnels.This attempt laid the foundation for artificial intelligence prediction in deformation analysis of multiarch tunnels and small clearance tunnels.It can provide a new and effective way for deformation prediction in similar projects.

基于Double Deep Q Network的无人机隐蔽接敌策略

基于深度强化学习的连续状态空间无人机隐蔽接敌问题,提出了基于马尔可夫决策过程的隐蔽接敌双深度Q网络(DDQN)方法。利用DDQN生成目标值函数的方法解决了传统DQN的过拟合问题;采用按优先级随机抽样的方法获取训练样本,加速了神经网络的训练速度;设定贪婪系数按照指数下降的方法,解决了传统强化学习的“探索利用窘境”;在势函数奖赏函数设计中引入角度因子,使其更加符合实际作战情况。仿真实验结果表明,DDQN具有较好的收敛性,能有效生成隐蔽接敌策略。

Analysis on Connectivity of Inter-Orbit-Links in a MEO/LEO Double-Layer Satellite Network

As an important scheme of future global mobile satellite communication systems to provide multimedia service, a Double-Layer Satellite Network （DLSN） with MEO satellites and LEO satellites is proposed. The Inter-Orbit-Links （IOLs） between layers is an essential factor, which affects the performances of the DLSN systems. Considering certain constellation parameters, the geometric characteristics of IOLs are described and the connectivity of MEO satellites and LEO satellites in the DLSN is analyzed. By computer simulation, the results show that IOLs should be selectively established according to certain parameters rather than the simple in-sight principle.

A new algorithm for diameter of double loop network

Presents a new algorithm for diameter of double loop network(DLN) by which two new classes of infinite families of tight DLN of the known twelve infinie families of tight DLNs, under 3.3 in [1] including eleven are constructed.

Cascading Delays for the High-Speed Rail Network Under Different Emergencies:A Double Layer Network Approach

High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.

A double-network hydrogel for the dynamic compression of the lumbar nerve root

Current animal models of nerve root compression due to lumbar disc herniation only assess the mechanical compression of nerve roots and the inflammatory response. Moreover, the pressure applied in these models is static, meaning that the nerve root cannot be dynamically compressed. This is very different from the pathogenesis of lumbar disc herniation. In this study, a chitosan/polyacrylamide double-network hydrogel was prepared by a simple two-step method. The swelling ratio of the double-network hydrogel increased with prolonged time, reaching 140. The compressive strength and compressive modulus of the hydrogel reached 53.6 and 0.34 MPa, respectively. Scanning electron microscopy revealed the hydrogel's crosslinked structure with many interconnecting pores. An MTT assay demonstrated that the number of viable cells in contact with the hydrogel extracts did not significantly change relative to the control surface. Thus, the hydrogel had good biocompatibility. Finally, the double-network hydrogel was used to compress the L4 nerve root of male sand rats to simulate lumbar disc herniation nerve root compression. The hydrogel remained in its original position after compression, and swelled with increasing time. Edema appeared around the nerve root and disappeared 3 weeks after operation. This chitosan/polyacrylamide double-network hydrogel has potential as a new implant material for animal models of lumbar nerve root compression. All animal experiments were approved by the Animal Ethics Committee of Neurosurgical Institute of Beijing, Capital Medical University, China(approval No. 201601006) on July 29, 2016.

DISTRIBUTED OPTIMAL LOCAL DOUBLE LOOP NETWORK

A distibuted optimal local double loop(DOLDL) network is presented. Emphasis is laid on the topology and distributed routing algorithms for the DOLDL. On the basis of building an abstract model, a set of definitions and theorems are described and proved. An algorithm which can optimize the double loop networks is presented. The optimal values of the topologic parameters for the DOLDL have been obtained by the algorithm, and these numerical results are analyzed. The study shows that the bounds of the optimal diameter (d) and average hop distance (a) for this class of networks are [square-root 3N -2] less-than-or-equal-to d less-than-or-equal-to [square-root 3N+1] and (5N/9(N-1)) (square-root 3N-1.8) < a < (5N/9 (N-1)). (square-root 3N - 0.23), respectively (N is the number of nodes in the network. (3 less-than-or-equal-to N less-than-or-equal-to 10(4)). A class of the distributed routing algorithms for the DOLDL and the implementation procedure of an adaptive fault-tolerant algorithm are proposed. The correctness of the algorithm has been also verified by simulating.