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.

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.

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。

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%。

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。

Long-lasting,reinforced electrical networking in a high-loading Li_(2)S cathode for high-performance lithium–sulfur batteries

Realizing a lithium sulfide(Li_(2)S)cathode with both high energy density and a long lifespan requires an innovative cathode design that maximizes electrochemical performance and resists electrode deterioration.Herein,a high-loading Li_(2)S-based cathode with micrometric Li_(2)S particles composed of two-dimensional graphene(Gr)and one-dimensional carbon nanotubes(CNTs)in a compact geometry is developed,and the role of CNTs in stable cycling of high-capacity Li–S batteries is emphasized.In a dimensionally combined carbon matrix,CNTs embedded within the Gr sheets create robust and sustainable electron diffusion pathways while suppressing the passivation of the active carbon surface.As a unique point,during the first charging process,the proposed cathode is fully activated through the direct conversion of Li_(2)S into S_(8) without inducing lithium polysulfide formation.The direct conversion of Li_(2)S into S_(8) in the composite cathode is ubiquitously investigated using the combined study of in situ Raman spectroscopy,in situ optical microscopy,and cryogenic transmission electron microscopy.The composite cathode demonstrates unprecedented electrochemical properties even with a high Li_(2)S loading of 10 mg cm^(–2);in particular,the practical and safe Li–S full cell coupled with a graphite anode shows ultra-long-term cycling stability over 800 cycles.

非奈利酮与SGLT2抑制剂对2型糖尿病和/或慢性肾脏病患者心血管事件的影响

目的比较非奈利酮与钠-葡萄糖共转运蛋白-2(sodium-glucose cotransporter-2,SGLT2)抑制剂对2型糖尿病和/或慢性肾脏病患者心血管事件的影响。方法检索PubMed、Cochrane Library、Web of Science和Embase数据库关于2型糖尿病和/或慢性肾脏病患者的随机对照试验,时间为建库至2023年7月3日。基于频率模型,使用STATA 17.0软件进行网状荟萃分析(network meta-analysis,NMA)。结果共纳入7项随机对照试验,包括33206例患者。涉及的治疗方式包括非奈利酮和SGLT2抑制剂,其中SGLT2抑制剂包含恩格列净、卡格列净、达格列净和索格列净(双重SGLT抑制剂)。在心血管复合事件方面,根据累计曲线下的概率面积(surface under the cumulative ranking area,SUCRA)排序,索格列净最有效。在心血管死亡方面,根据SUCRA排序,恩格列净最有效。在心力衰竭住院方面,根据SUCRA排序,卡格列净最有效。在全因死亡方面,根据SUCRA排序,达格列净最有效。非奈利酮和SGLT2抑制剂在不良事件、严重不良事件和急性肾损害的安全性方面比较,差异均无统计学意义(均P>0.05)。与采用非奈利酮治疗的患者相比,采用SGLT2抑制剂治疗的患者高钾血症发生率更低(RR=0.41,95%CI 0.32~0.52)。结论与非奈利酮相比,SGLT2抑制剂能更好地降低心血管事件的发生率,可作为2型糖尿病和/或慢性肾脏病患者的基础治疗,帮助预防或减少心血管事件。

Fang-Xia-Dihuang decoction inhibits breast cancer progression induced by psychological stress via down-regulation of PI3K/AKT and JAK2/STAT3 pathways:An in vivo and a network pharmacology assessment

Background:The development and prognosis of breast cancer are intricately linked to psychological stress.In addition,depression is the most common psychological comorbidity among breast cancer survivors,and reportedly,Fang-Xia-Dihuang decoction(FXDH)can effectively manage depression in such patients.However,its pharmacological and molecular mechanisms remain obscure.Methods:Public databases were used for obtaining active components and related targets.Main active components were further verified by ultra-high-performance liquid chromatography-high-resolution mass spectrometry(UPLC-HRMS).Protein–protein interaction and enrichment analyses were taken to predict potential hub targets and related pathways.Molecule docking was used to understand the interactions between main compounds and hub targets.In addition,an animal model of breast cancer combined with depression was established to evaluate the intervention effect of FXDH and verify the pathways screened by network pharmacology.Results:174 active components of FXDH and 163 intersection targets of FXDH,breast cancer,and depression were identified.Quercetin,methyl ferulate,luteolin,ferulaldehyde,wogonin,and diincarvilone were identified as the principal active components of FXDH.Protein–protein interaction and KEGG enrichment analyses revealed that the phosphoinositide-3-kinase–protein kinase B(PI3K/AKT)and Janus kinase/signal transducer and activator of transcription(JAK2/STAT3)signaling pathways played a crucial role in mediating the efficacy of FXDH for inhibiting breast cancer progression induced by depression.In addition,in vivo experiments revealed that FXDH ameliorated depression-like behavior in mice and inhibited excessive tumor growth in mice with breast cancer and depression.FXDH treatment downregulated the expression of epinephrine,PI3K,AKT,STAT3,and JAK2 compared with the control treatment(p<0.05).Molecular docking verified the relationship between the six primary components of FXDH and the three most important targets,including phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha(PIK3CA),AKT,and STAT3.Conclusion:This study provides a scientific basis to support the clinical application of FXDH for improving depression-like behavior and inhibiting breast cancer progression promoted by chronic stress.The therapeutic effects FXDH may be closely related to the PI3K/AKT and JAK2/STAT3 pathways.This finding helps better understand the regulatory mechanisms underlying the efficacy of FXDH.

燃烧机理构建的极小化反应网络方法:C_(2)燃料燃烧

针对目前C_(2)燃料反应机理中存在的诸多问题,本文采用极小化反应网络方法,在特定化学分辨率条件下构建了乙烯、乙烷和乙醇等典型C_(2)燃料的燃烧反应机理.构建的C_(2)单组分机理具有简洁的反应网络,采用可逆反应形式,并对反应方向在形式上进行了统一.在动力学参数方面,采用经典Arrhenius方程(A,Ea)双参数形式,在保证物理意义的同时有利于参数优化.C_(2)机理构建过程中避免了机理简化步骤,能够大幅减少物种数和反应数.动力学模拟结果表明,构建的C_(2)燃烧反应机理在保持较小尺寸的同时有较高的可靠性,并为机理的实际工程应用降低计算成本带来便利.

电力行业CO_(2)排放量预测及减排路径——以徐州市为例

基于1996—2022年《徐州统计年鉴》数据,分析徐州市电力行业CO_(2)排放特征,参考宾婵佳等的方法测算徐州市电力行业CO_(2)排放量,运用BP神经网络模型对2022—2030年徐州市电力行业CO_(2)排放量进行预测.结果表明:1995—2021年徐州市电力行业CO_(2)排放量为1002.684~4462.032万t;单位煤耗CO_(2)排放系数从1995年的1.027 t/MWh上升到1998年的1.043 t/MWh,再下降到2021年的0.820 t/MWh.不同情景下,徐州市电力行业CO_(2)排放量的预测值与实际值的独立样本t检验结果表明,运用BP神经网络模型预测电力行业CO_(2)排放量是可行的.预测结果显示,到2030年,徐州市电力行业CO_(2)排放量在基准情景下为5382.358万t,低碳情景下为4481.523万t,强化低碳情景下为4077.167万t.提出了徐州市电力行业可从发电端、电网端和消费端实施碳减排措施.

2型糖尿病视网膜病变患者静息态功能磁共振认知控制网络研究

目的 探讨2型糖尿病视网膜病变(T2DR)患者在静息状态下背外侧前额叶(DLPFC)的功能连接(FC)改变特征。方法 纳入2021年8月—2022年8月中国中医科学院眼科医院诊治的T2DR患者30例作为观察组,将通过广告宣传招募的30例健康受试者作为对照组。2组受试者均测定空腹状态下血糖(GLU)、糖化血红蛋白(HbA1c)、血清C肽指标,进行静息态脑功能磁共振(rs-fMRI)扫描,通过分析DLPFC与全脑的FC,比较2组受试者FC差异,将差异脑区FC值与临床量表评分进行Pearson相关分析,并完成蒙特利尔认知评估量表(MoCA)、简易智力状态检查(MMSE)的评定。结果 (1)糖尿病相关指标:观察组患者GLU、HbA1c水平高于对照组(t_(GLU)=11.601、t_(HbA1c)=13.837,均P=0.000),MoCA得分、MMSE得分低于对照组(t_(MoCA)=-12.827、t_(MMSE)=-13.395,均P=0.000),差异均有统计学意义。2组血清C肽浓度比较,差异无统计学意义(P>0.05)。(2)FC值比较:以年龄、性别、受教育程度及头动作为协变量,观察组患者以左侧DLPFC为种子点,观察组在左侧顶下小叶、右侧中央后回的FC均高于对照组(t_(左侧顶下小叶)=6.785、t_(右侧中央后回)=5.126,均P=0.000);以右侧DLPFC为种子点,观察组在左侧顶下小叶、左侧额下三角回及小脑后叶的FC均高于对照组(t_(左侧顶下小叶)=4.274,t_(左侧额下三角回)=3.991、t_(小脑后叶)=4.227,均P=0.000),阈值体素水平数值满足要求,差异均有统计学意义。(4)相关性分析:观察组以右侧DLPFC为种子点,左侧顶下小叶FC值与MoCA量表评分呈正相关(r=0.494,P=0.005),有统计学意义。结论 T2DR患者存在认知控制网络(CCN)与默认网络、感觉运动网络和小脑网络异常的FC,这可能是T2DR重要的神经病理机制。

响应面法和人工神经网络对亚临界CO_(2)萃取红花籽油的建模与优化

本文旨在寻找有效建模方法以预测亚临界CO_(2)萃取红花籽油的萃取率,优化其萃取工艺条件。以单因素实验为基础,采用Box-Behnken试验设计,研究了萃取压力、分离温度、萃取时间对红花籽油萃取率的影响,并采用响应面法(RSM)和人工神经网络(ANN)两种方法分别对同一实验进行建模分析,通过RSM数值优化、人工神经网络和遗传算法结合(ANN-GA)两种方法优化其工艺条件。结果表明,RSM与ANN两种模型均能较为精准预测,但通过两种模型的决定系数(R^(2))、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)、均方根误差(RMSE)值比较,得出ANN模型(R^(2)=0.9966)的预测效果较优于RSM模型(R^(2)=0.9950)。ANN-GA确定的最佳萃取条件及萃取率分别为:萃取压力19.04 MPa、分离温度55.50℃、萃取时间134.98 min、萃取率23.52%。综上,RSM和ANN两种方法均可用于亚临界CO_(2)萃取带壳红花籽油的建模与优化,但ANN的预测准确度及拟合能力更为优秀。

A UAV-Assisted V2X Network Architecture with Separated Data Transmission and Network Control

With the explosive increasing number of connecting devices such as smart phones, vehicles,drones, and satellites in the wireless networks, how to manage and control such a huge number of networking nodes has become a great challenge. In this paper, we combine the advantages of centralized networks and distributed networks approaches for vehicular networks with the aid of Unmanned Aerial Vehicle(UAV), and propose a Center-controlled Multihop Wireless(CMW) networking scheme consisting of data transmission plane performed by vehicles and the network control plane implemented by the UAV.Besides, we jointly explore the advantages of Medium Access Control(MAC) protocols in the link layer and routing schemes in the network layer to facilitate the multi-hop data transmission for the ground vehicles.Particularly, the network control plane in the UAV can manage the whole network effectively via fully exploiting the acquired network topology information and traffic requests from each vehicle, and implements various kinds of control based on different traffic demands, which can enhance the networking flexibility and scalability significantly in vehicular networks.Simulation results validate the advantages of the proposed scheme compared with existing methods.