Proppant transport in rough fracture networks using supercritical CO_(2)

Proppant transport within fractures is one of the most critical tasks in oil,gas and geothermal reservoir stimulation,as it largely determines the ultimate performance of the operating well.Proppant transport in rough fracture networks is still a relatively new area of research and the associated transport mechanisms are still unclear.In this study,representative parameters of rough fracture surfaces formed by supercritical CO_(2) fracturing were used to generate a rough fracture network model based on a spectral synthesis method.Computational fluid dynamics(CFD)coupled with the discrete element method(DEM)was used to study proppant transport in this rough fracture network.To reveal the turning transport mechanism of proppants into branching fractures at the intersections of rough fracture networks,a comparison was made with the behavior within smooth fracture networks,and the effect of key pumping parameters on the proppant placement in a secondary fracture was analyzed.The results show that the transport behavior of proppant in rough fracture networks is very different from that of the one in the smooth fracture networks.The turning transport mechanisms of proppant into secondary fractures in rough fracture networks are gravity-driven sliding,high velocity fluid suspension,and fracture structure induction.Under the same injection conditions,supercritical CO_(2)with high flow Reynolds number still has a weaker ability to transport proppant into secondary fractures than water.Thickening of the supercritical CO_(2)needs to be increased beyond a certain value to have a significant effect on proppant carrying,and under the temperature and pressure conditions of this paper,it needs to be increased more than 20 times(about 0.94 m Pa s).Increasing the injection velocity and decreasing the proppant concentration facilitates the entry of proppant into the branching fractures,which in turn results in a larger stimulated reservoir volume.The results help to understand the proppant transport and placement process in rough fracture networks formed by reservoir stimulation,and provide a theoretical reference for the optimization of proppant pumping parameters in hydraulic fracturing.

Porous Indium Nanocrystals on Conductive Carbon Nanotube Networks for High-Performance CO_(2)-to-Formate Electrocatalytic Conversion

Ever-increasing emissions of anthropogenic carbon dioxide(CO_(2))cause global environmental and climate challenges.Inspired by biological photosynthesis,developing effective strategies NeuNlto up-cycle CO_(2)into high-value organics is crucial.Electrochemical CO_(2)reduction reaction(CO_(2)RR)is highly promising to convert CO_(2)into economically viable carbon-based chemicals or fuels under mild process conditions.Herein,mesoporous indium supported on multi-walled carbon nanotubes(mp-In@MWCNTs)is synthesized via a facile wet chemical method.The mp-In@MWCNTs electrocatalysts exhibit high CO_(2)RR performance in reducing CO_(2)into formate.An outstanding activity(current density-78.5 mA cm^(-2)),high conversion efficiency(Faradaic efficiency of formate over 90%),and persistent stability(∼30 h)for selective CO_(2)-to-formate conversion are observed.The outstanding CO_(2)RR process performance is attributed to the unique structures with mesoporous surfaces and a conductive network,which promote the adsorption and desorption of reactants and intermediates while improving electron transfer.These findings provide guiding principles for synthesizing conductive metal-based electrocatalysts for high-performance CO_(2)conversion.

Graph neural network-based scheduling for multi-UAV-enabled communications in D2D networks

In this paper,we jointly design the power control and position dispatch for Multi-Unmanned Aerial Vehicle(UAV)-enabled communication in Device-to-Device(D2D)networks.Our objective is to maximize the total transmission rate of Downlink Users(DUs).Meanwhile,the Quality of Service(QoS)of all D2D users must be satisfied.We comprehensively considered the interference among D2D communications and downlink transmissions.The original problem is strongly non-convex,which requires high computational complexity for traditional optimization methods.And to make matters worse,the results are not necessarily globally optimal.In this paper,we propose a novel Graph Neural Networks(GNN)based approach that can map the considered system into a specific graph structure and achieve the optimal solution in a low complexity manner.Particularly,we first construct a GNN-based model for the proposed network,in which the transmission links and interference links are formulated as vertexes and edges,respectively.Then,by taking the channel state information and the coordinates of ground users as the inputs,as well as the location of UAVs and the transmission power of all transmitters as outputs,we obtain the mapping from inputs to outputs through training the parameters of GNN.Simulation results verified that the way to maximize the total transmission rate of DUs can be extracted effectively via the training on samples.Moreover,it also shows that the performance of proposed GNN-based method is better than that of traditional means.

Small but mighty:Empowering sodium/potassium-ion battery performance with S-doped SnO_(2) quantum dots embedded in N,S codoped carbon fiber network

SnO_(2) has been extensively investigated as an anode material for sodium-ion batteries(SIBs)and potassium-ion batteries(PIBs)due to its high Na/K storage capacity,high abundance,and low toxicity.However,the sluggish reaction kinetics,low electronic conductivity,and large volume changes during charge and discharge hinder the practical applications of SnO_(2)-based electrodes for SIBs and PIBs.Engineering rational structures with fast charge/ion transfer and robust stability is important to overcoming these challenges.Herein,S-doped SnO_(2)(S-SnO_(2))quantum dots(QDs)(≈3 nm)encapsulated in an N,S codoped carbon fiber networks(S-SnO_(2)-CFN)are rationally fabricated using a sequential freeze-drying,calcination,and S-doping strategy.Experimental analysis and density functional theory calculations reveal that the integration of S-SnO_(2) QDs with N,S codoped carbon fiber network remarkably decreases the adsorption energies of Na/K atoms in the interlayer of SnO_(2)-CFN,and the S doping can increase the conductivity of SnO_(2),thereby enhancing the ion transfer kinetics.The synergistic interaction between S-SnO_(2) QDs and N,S codoped carbon fiber network results in a composite with fast Na+/K+storage and extraordinary long-term cyclability.Specifically,the S-SnO_(2)-CFN delivers high rate capacities of 141.0 mAh g^(−1) at 20 A g^(−1) in SIBs and 102.8 mAh g^(−1) at 10 A g^(−1) in PIBs.Impressively,it delivers ultra-stable sodium storage up to 10,000 cycles at 5 A g^(−1) and potassium storage up to 5000 cycles at 2 A g^(−1).This study provides insights into constructing metal oxide-based carbon fiber network structures for high-performance electrochemical energy storage and conversion devices.

A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs

To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.

Combining GLP-1 receptor agonists and SGLT-2 inhibitors for cardiovascular disease prevention in type 2 diabetes:A systematic review with multiple network meta-regressions

BACKGROUND Glucagon-like peptide-1 receptor agonists(GLP-1RA)and sodium-glucose co-transporter-2 inhibitors(SGLT-2I)are associated with significant cardiovascular benefit in type 2 diabetes(T2D).However,GLP-1RA or SGLT-2I alone may not improve some cardiovascular outcomes in patients with prior cardiovascular co-morbidities.AIM To explore whether combining GLP-1RA and SGLT-2I can achieve additional benefit in preventing cardiovascular diseases in T2D.METHODS The systematic review was conducted according to PRISMA recommendations.The protocol was registered on PROSPERO(ID:42022385007).A total of 107049 participants from eligible cardiovascular outcomes trials of GLP-1RA and SGLT-2I were included in network meta-regressions to estimate cardiovascular benefit of the combination treatment.Effect modification of prior myocardial infarction(MI)and heart failure(HF)was also explored to provide clinical insight as to when the INTRODUCTION The macro-and micro-vascular benefits of glucagon-like peptide-1 receptor agonists(GLP-1RA)and sodium-glucose co-transporter-2 inhibitors(SGLT-2I)are independent of their glucose-lowering effects[1].In patients with type 2 diabetes(T2D),the major cardiovascular outcome trials(CVOT)showed that dipeptidyl peptidase-4 inhibitors(DPP-4I)did not improve cardiovascular outcomes[2],whereas cardiovascular benefit of GLP-1RA or SGLT-2I was significant[3,4].Further subgroup analyses indicated that the background cardiovascular risk should be considered when examining the cardiovascular outcomes of these newer glucose-lowering medications.For instance,prevention of major adverse cardiovascular events(MACE)was only seen in those patients with baseline atherosclerotic cardiovascular disease[3,4].Moreover,a series of CVOT conducted in patients with heart failure(HF)have demonstrated that(compared with placebo)SGLT-2I significantly reduced risk of hospitalization for HF or cardiovascular death,irrespective of their history of T2D[5-8].However,similar cardiovascular benefits were not observed in those with myocardial infarction(MI)[9,10].Cardiovascular co-morbidities are not only approximately twice as common but are also associated with dispropor-tionately worse cardiovascular outcomes in patients with T2D,compared to the general population[11].Therefore,it is of clinical importance to investigate whether the combination treatment of GLP-1RA and SGLT-2I could achieve greater cardiovascular benefit,particularly when considering patients with cardiovascular co-morbidities who may not gain sufficient cardiovascular protection from the monotherapies.This systematic review with multiple network meta-regressions was mainly aimed to explore whether combining GLP-1RA and SGLT-2I can provide additional cardiovascular benefit in T2D.Cardiovascular outcomes of these newer antidiabetic medications were also estimated under effect modification of prior cardiovascular diseases.This was to provide clinical insight as to when the combination treatment might be prioritized.

Mechanism of Gegen Qinlian Decoction in Treating Type 2 Diabetes Mellitus Complicated with NAFLD Based on Network Pharmacology

[Objectives]To explore the mechanism of Gegen Qinlian Decoction in treating type 2 diabetes mellitus(T2DM)complicated with non-alcoholic fatty liver disease(NAFLD)by analyzing the effective components of Gegen Qinlian Decoction.[Methods]TCMSP database was used to analyze the active components of Gegen Qinlian Decoction,and pubchem and Swiss ADME databases were also used to predict drug targets,extract T2DM complicated with NAFLD targets from OMIM and Genecards databases.Venny plot was drawn to obtain intersection targets,and finally Cytoscape was used to make core target maps and drug-target-disease network maps.Using DAVID and Metascape database to analyze the intersection targets,the gene ontology information of Go and KEGG was obtained.Microbial informatics technology was used to visualize GO,and Cytoscape was used to make drug-target-disease network map-enrichment pathway map.[Results]The network pharmacological analysis showed that Gegen Qinlian Decoction acted on the key targets of type 2 diabetes mellitus complicated with non-alcoholic fatty liver disease,such as ALB and ALT1,through many components,and achieved the purpose of treating this disease.The chemical constituents of the drug include formononetin,5-hydroxyisomucronulatol-2,5-2-O-glucoside,cholesteryl laurate,isoliquiritigenin,etc.[Conclusions]This study provides a new idea and theoretical support for future drug research and clinical practice.

Outage Probability Analysis for D2D-Enabled Heterogeneous Cellular Networks with Exclusion Zone:A Stochastic Geometry Approach

Interference management is one of the most important issues in the device-to-device(D2D)-enabled heterogeneous cellular networks(HetCNets)due to the coexistence of massive cellular and D2D devices in which D2D devices reuse the cellular spectrum.To alleviate the interference,an efficient interference management way is to set exclusion zones around the cellular receivers.In this paper,we adopt a stochastic geometry approach to analyze the outage probabilities of cellular and D2D users in the D2D-enabled HetCNets.The main difficulties contain three aspects:1)how to model the location randomness of base stations,cellular and D2D users in practical networks;2)how to capture the randomness and interrelation of cellular and D2D transmissions due to the existence of random exclusion zones;3)how to characterize the different types of interference and their impacts on the outage probabilities of cellular and D2D users.We then run extensive Monte-Carlo simulations which manifest that our theoretical model is very accurate.

Comparative study of anti-inflammatory effects of different processed products through the COX-2/PGE2 signaling pathway: based on network pharmacology and molecular docking

Background:Radix Aconiti Lateralis Preparata(Fu-zi)is a traditional Chinese medicinal herb,which has been widely used in the clinic and has potent anti-inflammatory activities.we aimed to explore the mechanisms of extract containing alkaloids from different Fu-zi Processed Products(FPP)in treating inflammation,especially rheumatoid arthritis(RA).Methods:Firstly,using network pharmacology technology,the ingredients,and targets of Fu-zi were obtained by searching and screening,the targets involving RA were acquired,the intersection targets were constructed a"component-target-pathway"network.A comprehensive investigation was conducted on the anti-rheumatoid arthritis mechanisms of 5 FPPs in lipopolysaccharide(LPS)induced RAW264.7 cells,which serve as a model for RA.The production of NO and inflammatory cytokines were measured by ELISA kit.Quantitative Real-time PCR(qRT-PCR)was utilized to measure the mRNA levels.COX-2/PGE2 signaling pathway-associated proteins were determined by western blot.Results:According to a network pharmacological study,16 chemical components and 43 common targets were found in Fu-zi and 6 key targets including PTGS2 were closely related to the mechanism of Fu-zi in treating RA.The in vitro study revealed that the levels of NO,TNF-α,and IL-1βwere substantially decreased by the 5 FPPs.The 5 FPPs significantly suppressed the expression of proteins COX-2,iNOS,and NF-κB,with particularly notable effects observed for PFZ and XFZ.Conclusion:Altogether,these results demonstrated that the 5 PPS containing alkaloids have a good anti-RA-related inflammatory effect,and the mechanism may be related to COX-2/PGE2 signaling pathway,particularly,Fu-zi prepared utilizing a traditional Chinese technique.

Prediction of SO_2 Concentration in Urban Atmosphere Based on B-P Neural Network

Base on the principle and method of B-P neural network,the prediction model of SO2 concentration in urban atmosphere was established by using the statistical data of a city in southwest China from 1991 to 2009,so as to forecast atmospheric SO2 concentration in a city of southwest China.The results showed that B-P neural network applied in the prediction of SO2 concentration in urban atmosphere was reasonable and efficient with high accuracy and wide adaptability,so it was worthy to be popularized.

基于抑菌实验和网络药理学探讨D-柠檬烯、2-茨醇对白色念珠菌的抑制作用

目的:采用抑菌实验研究蛇床子-冰片药对成分中的D-柠檬烯及2-茨醇的体外抗白色念珠菌作用,并运用网络药理学预测D-柠檬烯和2-茨醇治疗念珠菌病的核心靶点和通路。方法:以白色念珠菌为研究对象,K-B纸片扩散法分别测定0.5、1.0、1.5 mg的D-柠檬烯、2-茨醇、制霉菌素的药液抑菌圈直径;采用试管双倍稀释法和棋盘法,测定D-柠檬烯、2-茨醇的最低抑菌浓度(MIC)以及两两联用的MIC,计算出联合抑菌分数(FIC)。通过Pubchem、SwissTargetPrediction数据库预测D-柠檬烯、2-茨醇的有效靶点;通过GeneCards、OMIM数据库检索念珠菌病相关的疾病靶点;运用Venny软件获得两种化学成分和念珠菌病的共同靶点;运用Cytoscape 3.9. 0软件构建“成分-靶点-疾病”网络;利用STRING数据库构建蛋白互作PPI网络;利用R软件进行GO功能及KEGG通路富集分析。结果:D-柠檬烯的MIC为5 mg/mL,2-茨醇的MIC为2.5 mg/mL。D-柠檬烯与2-茨醇联用的FIC指数为0.75,呈相加作用。网络药理学筛选得到两种成分相关作用靶点152个,疾病靶点893个,两者交集靶点为24个;网络拓扑分析得到核心靶点为肿瘤坏死因子(TNF)、过氧化物酶体增殖物激活受体γ(PPARG)、雌激素受体(ESR1)等;KEGG分析得到核心通路为C型凝集素受体信号通路(C-type lectin receptor signaling pathway)、Fc epsilon RI信号通路(Fc epsilon RI signaling pathway)、催乳素信号通路(prolactin signaling pathway)等。结论:D-柠檬烯、2-茨醇对白色念珠菌均有抑制作用,且2种组分药物联合使用具有一定的协同作用。网络药理学预测初步提示D-柠檬烯、2-茨醇可能通过作用于TNF、PPARG、ESR1等核心靶点调控C型凝集素受体信号通路(C-type lectin receptor signaling pathway)、Fc epsilon RI信号通路(Fc epsilon RI signaling pathway)等以治疗念珠菌病。

DPP-4抑制剂治疗的2型糖尿病患者中焦虑和抑郁发生风险的Network meta分析

目的使用Network meta分析系统评价DPP-4抑制剂类降糖药致2型糖尿病(type 2 diabetes mellitus,T2DM)患者发生焦虑、抑郁的风险。方法系统检索Medline、Embase、Cochrane Library和Clinical Trials.gov网站数据库(截至2016年4月28日)中比较DPP-4抑制剂与其他降糖药发生焦虑、抑郁风险的随机对照试验(randomized controlled trial,RCT),采用传统meta分析和Network meta分析方法对纳入的RCT研究结果进行合并。结果共纳入44项研究,63 731例T2DM患者,包含9种干预措施:5种DPP-4抑制剂(西格列汀、维格列汀、沙格列汀、利格列汀和阿格列汀)、胰高糖素样肽1受体激动剂、磺脲类、噻唑烷二酮类和安慰剂。传统meta分析结果显示,分别与安慰剂、磺脲类、噻唑烷二酮类相比,DPP-4抑制剂导致焦虑、抑郁发生的风险均较低,但差异无显著性(P>0.05)。Network meta分析发现,与噻唑烷二酮类相比,维格列汀致T2DM患者发生焦虑、抑郁的风险较高,差异有显著性(P<0.05,OR=2.64,95%CI 1.03~6.79),与西格列汀相比,维格列汀致T2DM患者发生焦虑、抑郁的风险较高,差异有显著性(P<0.05,OR=2.42,95%CI 1.00~5.85)。与安慰剂或磺脲类比较,DPP-4抑制剂致T2DM患者发生焦虑、抑郁的风险相当,差异无显著性(P>0.05)。采用基于贝叶斯理论的Network meta分析对9种干预措施进行排序,结果显示西格列汀的风险最低。结论 DPP-4抑制剂不会加重T2DM患者罹患焦虑、抑郁等情绪问题的风险,但不同的DPP-4抑制剂导致的风险程度可能存在差异,建议今后开展的大型前瞻性研究应重视焦虑、抑郁等情绪问题的发生情况,以期为获得更加明确的结论提供证据支持。

不同剂量替西帕肽治疗2型糖尿病有效性和安全性的网状Meta分析

目的采用网状Meta分析评价不同剂量替西帕肽治疗2型糖尿病(Type 2 diabetes mellitus,T2DM)的有效性和安全性。方法检索PubMed、Embase、Cochrane Library、中国知网和万方数据库,收集不同剂量替西帕肽治疗T2DM的随机对照试验(Randomized controlled trials,RCTs),检索时限均从建库至2023年6月。由2名研究者独立筛选文献,提取文献并评价纳入文献的偏倚风险后,采用STATA17.0软件进行网状Meta分析。结果本研究共纳入10篇RCT,涉及6种干预措施,包括替西帕肽15 mg、替西帕肽10 mg、替西帕肽5 mg、胰岛素、胰高血糖素样肽-1受体激动剂(GLP-1RA)和安慰剂,涉及8524例糖尿病患者。网状Meta分析结果显示,药物有效性主要指标糖化血红蛋白(HbA1c)降低程度累积排序概率图曲线下面积(SUCRA)排序依次为替西帕肽15 mg>替西帕肽10 mg>替西帕肽5 mg>GLP-1RA>胰岛素。药物安全性方面,胰岛素、安慰剂、替西帕肽5 mg更有优势。结论大剂量下的替西帕肽可以更有效地降低T2DM患者的HbA1c、空腹血糖(FPG)、体重,但需要警惕其不良反应。

基于m×2正则化交叉验证的神经网络超参数调优方法

超参数调优是神经网络建模的关键问题。针对传统的超参数调优方法存在的问题,该文提出了一种基于m×2正则化交叉验证的超参数调优方法。目的是给出一种适用于复杂模型、大数据集背景下的计算开销较小且稳健的超参数调优方法。该方法的思想是从完整的数据集上选取少部分数据进行调优,避免模型在数据集较大时非常耗时的超参数调优难题;在m×2交叉验证的基础上设置正则化条件均衡训练集与验证集之间的分布差异,从而减少分布不一致带来的性能波动;使用信噪比作为调优的优化目标,从而可以综合考虑模型性能评价指标的均值和方差;并采用正交设计选择相关性较低的超参数组合以提高调优效率。以命名实体任务为例进行实验,在CoNLL 2003数据集上的实验结果显示,提出的调优方法能够选到和网格搜索性能上没有显著差异的超参数组合,且调优时间可显著降低约66%。

Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function(IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.

Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.

基于改进INFO-Bi-LSTM模型的SO_(2)排放质量浓度预测

针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。

Deadline-aware network coding for video on demand service over P2P networks

We are interested in providing Video-on-Demand (VoD) streaming service to a large population of clients using peer-to-peer (P2P) approach. Given the asynchronous demands from multiple clients, continuously changing of the buffered contents, and the continuous video display requirement, how to collaborate with potential partners to get expected data for future content delivery are very important and challenging. In this paper, we develop a novel scheduling algorithm based on deadline- aware network coding (DNC) to fully exploit the network resource for efficient VoD service. DNC generalizes the existing net- work coding (NC) paradigm, an elegant solution for ubiquitous data distribution. Yet, with deadline awareness, DNC improves the network throughput and meanwhile avoid missing the play deadline in high probability, which is a major deficiency of the con- ventional NC. Extensive simulation results demonstrated that DNC achieves high streaming continuity even in tight network conditions.

Wavelet Neural Network Based on NARMA-L2 Model for Prediction of Thermal Characteristics in a Feed System

Research of thermal characteristics has been a key issue in the development of high-speed feed system. Most of the work carried out thus far is based on the principle of directly mapping the thermal error against the temperature of critical machine elements irrespective of the operating conditions. But recent researches show that different sets of operating parameters generated significantly different error values even though the temperature of the machine elements generated was similar. As such, it is important to develop a generic thermal error model which is capable of evaluating the positioning error induced by different operating parameters. This paper ultimately aims at the development of a comprehensive prediction model that can predict the thermal characteristics under different operating conditions （feeding speed, load and preload of ballscrew） in a feed system. A novel wavelet neural network based on feedback linearization autoregressive moving averaging （NARMA-L2） model is introduced to predict the temperature rise of sensitive points and thermal positioning errors considering the different operating conditions as the model inputs. Particle swarm optimization（PSO） algorithm is brought in as the training method. According to ISO230-2 Positioning Accuracy Measurement and ISO230-3 Thermal Effect Evaluation standards, experiments under different operating conditions were carried out on a self-made quasi high-speed feed system experimental bench HUST-FS-001 by using Pt100 as temperature sensor, and the positioning errors were measured by Heidenhain linear grating scale. The experiment results show that the recommended method can be used to predict temperature rise of sensitive points and thermal positioning errors with good accuracy. The work described in this paper lays a solid foundation of thermal error prediction and compensation in a feed system based on varying operating conditions and machine tool characteristics.

A Combining Call Admission Control and Power Control Scheme for D2D Communications Underlaying Cellular Networks

As device-to-device(D2D) communications usually reuses the resource of cellular networks, call admission control(CAC) and power control are crucial problems. However in most power control schemes, total data rates or throughput are regarded as optimization criterion. In this paper, a combining call admission control(CAC) and power control scheme under guaranteeing QoS of every user equipment(UE) is proposed. First, a simple CAC scheme is introduced. Then based on the CAC scheme, a combining call admission control and power control scheme is proposed. Next, the performance of the proposed scheme is evaluated. Finally, maximum DUE pair number and average transmitting power is calculated. Simulation results show that D2 D communications with the proposed combining call admission control and power control scheme can effectively improve the maximum DUE pair number under the premise of meeting necessary QoS.