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...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.展开更多
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) s...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.展开更多
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...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.展开更多
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 ext...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.展开更多
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...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。展开更多
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IN...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。展开更多
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...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.展开更多
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 ...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.展开更多
In highly dynamic and heterogeneous vehicular communication networks,it is challenging to efficiently utilize network resources and ensure demanding performance requirements of safetyrelated applications.This paper in...In highly dynamic and heterogeneous vehicular communication networks,it is challenging to efficiently utilize network resources and ensure demanding performance requirements of safetyrelated applications.This paper investigates machinelearning-assisted transmission design in a typical multi-user vehicle-to-vehicle(V2V)communication scenario.The transmission process proceeds sequentially along the discrete time steps,where several source nodes intend to deliver multiple different types of messages to their respective destinations within the same spectrum.Due to rapid movement of vehicles,real-time acquirement of channel knowledge and central coordination of all transmission actions are in general hard to realize.We consider applying multi-agent deep reinforcement learning(MADRL)to handle this issue.By transforming the transmission design problem into a stochastic game,a multi-agent proximal policy optimization(MAPPO)algorithm under a centralized training and decentralized execution framework is proposed such that each source decides its own transmission message type,power level,and data rate,based on local observations of the environment and feedback,to maximize its energy efficiency.Via simulations we show that our method achieves better performance over conventional methods.展开更多
Diabetes mellitus is a long-term condition characterized by hyperglycemia.It could lead to plenty of difficulties.According to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by...Diabetes mellitus is a long-term condition characterized by hyperglycemia.It could lead to plenty of difficulties.According to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by 2040,implying that one out of every ten persons will be diabetic.There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’lives.Due to its rapid development,deep learning(DL)was used to predict numerous diseases.However,DLmethods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization.Therefore,the selection of hyper-parameters is critical in improving classification performance.This study presents Convolutional Neural Network(CNN)that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm(BOA)has been employed for hyperparameters selection and parameters optimization.Two issues have been investigated and solved during the experiment to enhance the results.The first is the dataset class imbalance,which is solved using Synthetic Minority Oversampling Technique(SMOTE)technique.The second issue is the model’s poor performance,which has been solved using the Bayesian optimization algorithm.The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%,F1-score of 0.88.6,andMatthews Correlation Coefficient(MCC)of 0.88.6.展开更多
The coronavirus disease 2019(COVID-19)pandemic has greatly damaged human society,but the origins and early transmission patterns of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)pathogen remain unclea...The coronavirus disease 2019(COVID-19)pandemic has greatly damaged human society,but the origins and early transmission patterns of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)pathogen remain unclear.Here,we reconstructed the transmission networks of SARS-CoV-2 during the first three and six months since its first report based on ancestor-offspring relationships using BANAL-52-referenced mutations.We explored the position(i.e.,root,middle,or tip)of early detected samples in the evolutionary tree of SARS-CoV-2.In total,6799 transmission chains and 1766 transmission networks were reconstructed,with chain lengths ranging from 1-9 nodes.The root node samples of the 1766 transmission networks were from 58 countries or regions and showed no common ancestor,indicating the occurrence of many independent or parallel transmissions of SARS-CoV-2 when first detected(i.e.,all samples were located at the tip position of the evolutionary tree).No root node sample was found in any sample(n=31,all from the Chinese mainland)collected in the first 15 days from 24 December 2019.Results using six-month data or RaTG13-referenced mutation data were similar.The reconstruction method was verified using a simulation approach.Our results suggest that SARS-CoV-2 may have already been spreading independently worldwide before the outbreak of COVID-19 in Wuhan,China.Thus,a comprehensive global survey of human and animal samples is essential to explore the origins of SARS-CoV-2 and its natural reservoirs and hosts.展开更多
Background:1,2,3,4,6-penta-O-galloyl-beta-D-glucose(PGG)is a natural polyphenolic compound derived from multiple medicinal plants with favorable anticancer activity.Methods:In this study,the mechanisms of PGG against ...Background:1,2,3,4,6-penta-O-galloyl-beta-D-glucose(PGG)is a natural polyphenolic compound derived from multiple medicinal plants with favorable anticancer activity.Methods:In this study,the mechanisms of PGG against gastric cancer were explored through network pharmacology and molecular docking.First,the targets of PGG were searched in the Herbal Ingredients’Targets(HIT),Similarity Ensemble Approach(SEA),and Super-PRED databases.The potential targets related to gastric cancer were predicted from the Human Gene Database(GeneCards)and DisGeNET databases.The intersecting targets of PGG and gastric cancer were obtained by Venn diagram and then subjected to protein-protein interaction analysis to screen hub targets.Functional and pathway enrichment of hub targets were analyzed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases.The differential expression and survival analysis of hub targets in gastric cancer were performed based on The Cancer Genome Atlas database.Finally,the affinity of PGG with hub targets was visualized by molecular docking.Results:Three hub targets were screened,including mitogen-activated protein kinase 14(MAPK14),BCL2 like 1(BCL2L1),and vascular endothelial growth factor A(VEGFA).MAPK14 had a higher expression,while BCL2L1 and VEGFA had lower expression in gastric cancer than in normal conditions.Enrichment analysis indicated enrichment of these hub targets in MAPK,neurotrophin,programmed death-ligand 1(PD-L1)checkpoint,phosphatidylinositol 3-kinases/protein kinase B(PI3K-Akt),Ras,and hypoxia-inducible factor-1(HIF-1)signaling pathways.Conclusion:Therefore,network pharmacology and molecular docking analyses revealed that PGG exerts a therapeutic efficacy on gastric cancer by multiple targets(MAPK14,BCL2L1,and VEGFA)and pathways(MAPK,PD-L1 checkpoint,PI3K-Akt,Ras,and HIF-1 pathways).展开更多
In this work,we consider the performance analysis of state dependent priority traffic and scheduling in device to device(D2D)heterogeneous networks.There are two priority transmission types of data in wireless communi...In this work,we consider the performance analysis of state dependent priority traffic and scheduling in device to device(D2D)heterogeneous networks.There are two priority transmission types of data in wireless communication,such as video or telephone,which always meet the requirements of high priority(HP)data transmission first.If there is a large amount of low priority(LP)data,there will be a large amount of LP data that cannot be sent.This situation will cause excessive delay of LP data and packet dropping probability.In order to solve this problem,the data transmission process of high priority queue and low priority queue is studied.Considering the priority jump strategy to the priority queuing model,the queuing process with two priority data is modeled as a two-dimensionalMarkov chain.A state dependent priority jump queuing strategy is proposed,which can improve the discarding performance of low priority data.The quasi birth and death process method(QBD)and fixed point iterationmethod are used to solve the causality,and the steady-state probability distribution is further obtained.Then,performance parameters such as average queue length,average throughput,average delay and packet dropping probability for both high and low priority data can be expressed.The simulation results verify the correctness of the theoretical derivation.Meanwhile,the proposed priority jump queuing strategy can significantly improve the drop performance of low-priority data.展开更多
[Objectives] The paper was to explore the mechanism of capsicum ( Capsicum annuum L.) in treating type 2 diabetes mellitus (T2DM) and search for new targets. [Methods] The active ingredients of capsicum were queried f...[Objectives] The paper was to explore the mechanism of capsicum ( Capsicum annuum L.) in treating type 2 diabetes mellitus (T2DM) and search for new targets. [Methods] The active ingredients of capsicum were queried from TCMSP database to obtain the corresponding target proteins. The related targets of T2DM were screened from GeneCards database, and the target intersection of active ingredients of capsicum and diabetes mellitus was obtained via Venny software. The protein-protein interaction (PPI) network of the compounds was constructed using STRING database, and the GO bio-function and KEGG pathway enrichment were further analyzed using Metascape database. [Results] Through TCMSP database query and conditional screening, 14 candidate active molecules, 93 potential targets and 225 related pathways were obtained. [Conclusions] The results of GO and KEGG enrichment analysis show that the main active ingredients of capsicum play a role in the treatment of T2DM by regulating cancer pathways, chemical carcinogenesis—receptor activation, proteoglycans in cancer, and prostate cancer pathways, which will provide an important theoretical basis for subsequent research.展开更多
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...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.展开更多
基金supported in part by the National Natural Science Foundation of China(61901231)in part by the National Natural Science Foundation of China(61971238)+3 种基金in part by the Natural Science Foundation of Jiangsu Province of China(BK20180757)in part by the open project of the Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space,Ministry of Industry and Information Technology(KF20202102)in part by the China Postdoctoral Science Foundation under Grant(2020M671480)in part by the Jiangsu Planned Projects for Postdoctoral Research Funds(2020z295).
文摘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.
基金This work is funded by National Natural Science Foundation of China(Nos.42202292,42141011)the Program for Jilin University(JLU)Science and Technology Innovative Research Team(No.2019TD-35).The authors would also like to thank the reviewers and editors whose critical comments are very helpful in preparing this article.
文摘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.
基金This work is funded in part by the Science and Technology Development Fund,Macao SAR(Grant Nos.0093/2022/A2,0076/2022/A2 and 0008/2022/AGJ)in part by the National Nature Science Foundation of China(Grant No.61872452)+3 种基金in part by Special fund for Dongguan’s Rural Revitalization Strategy in 2021(Grant No.20211800400102)in part by Dongguan Special Commissioner Project(Grant No.20211800500182)in part by Guangdong-Dongguan Joint Fund for Basic and Applied Research of Guangdong Province(Grant No.2020A1515110162)in part by University Special Fund of Guangdong Provincial Department of Education(Grant No.2022ZDZX1073).
文摘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.
基金supported by Sichuan Province Science and Technology Support Program(NO.2020JDJQ0063,NO.2020YFS0566 and NO.2021JDKY0037,A-2021N-Z-5).
文摘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.
文摘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。
文摘针对火电机组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。
基金Korea Institute of Energy Technology Evaluation and Planning,Grant/Award Number:20214000000320Samsung Research Funding&Incubation Center of Samsung Electronics,Grant/Award Number:SRFC-MA1901-06。
文摘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.
基金supported in part by the National Natural Science Foundation of China under Grant 62071283,Grant 61771296,Grant 61872228 and Grant 62271513in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2018JQ6048 and Grant 2018JZ6006+3 种基金in part by Shaanxi Key Industrial Innovation Chain Project in Industrial Domain under Grant 2020ZDLGY15-09in part by Guang Dong Basic and Applied Basic Research Foundation under Grant 2021A1515012631in part by China Postdoctoral Science Foundation under Grant 2016M600761in part by the Fundamental Research Funds for the Central Universities under Grant GK202003075 and Grant GK202103016。
文摘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.
基金supported in part by the National Natural Science Foundation of China(62171322,62006173)the 2021-2023 China-Serbia Inter-Governmental S&T Cooperation Project(No.6)+1 种基金support of the Sino-German Center of Intelligent Systems,Tongji University。
文摘In highly dynamic and heterogeneous vehicular communication networks,it is challenging to efficiently utilize network resources and ensure demanding performance requirements of safetyrelated applications.This paper investigates machinelearning-assisted transmission design in a typical multi-user vehicle-to-vehicle(V2V)communication scenario.The transmission process proceeds sequentially along the discrete time steps,where several source nodes intend to deliver multiple different types of messages to their respective destinations within the same spectrum.Due to rapid movement of vehicles,real-time acquirement of channel knowledge and central coordination of all transmission actions are in general hard to realize.We consider applying multi-agent deep reinforcement learning(MADRL)to handle this issue.By transforming the transmission design problem into a stochastic game,a multi-agent proximal policy optimization(MAPPO)algorithm under a centralized training and decentralized execution framework is proposed such that each source decides its own transmission message type,power level,and data rate,based on local observations of the environment and feedback,to maximize its energy efficiency.Via simulations we show that our method achieves better performance over conventional methods.
基金This research/paper was fully supported by Universiti Teknologi PETRONAS,under the Yayasan Universiti Teknologi PETRONAS(YUTP)Fundamental Research Grant Scheme(015LC0-311).
文摘Diabetes mellitus is a long-term condition characterized by hyperglycemia.It could lead to plenty of difficulties.According to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by 2040,implying that one out of every ten persons will be diabetic.There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’lives.Due to its rapid development,deep learning(DL)was used to predict numerous diseases.However,DLmethods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization.Therefore,the selection of hyper-parameters is critical in improving classification performance.This study presents Convolutional Neural Network(CNN)that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm(BOA)has been employed for hyperparameters selection and parameters optimization.Two issues have been investigated and solved during the experiment to enhance the results.The first is the dataset class imbalance,which is solved using Synthetic Minority Oversampling Technique(SMOTE)technique.The second issue is the model’s poor performance,which has been solved using the Bayesian optimization algorithm.The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%,F1-score of 0.88.6,andMatthews Correlation Coefficient(MCC)of 0.88.6.
基金supported by the Ministry of Science and Technology of the People’s Republic of China(2021YFC0863400)Institute of Zoology,Chinese Academy of Sciences(E0517111,E122G611)。
文摘The coronavirus disease 2019(COVID-19)pandemic has greatly damaged human society,but the origins and early transmission patterns of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)pathogen remain unclear.Here,we reconstructed the transmission networks of SARS-CoV-2 during the first three and six months since its first report based on ancestor-offspring relationships using BANAL-52-referenced mutations.We explored the position(i.e.,root,middle,or tip)of early detected samples in the evolutionary tree of SARS-CoV-2.In total,6799 transmission chains and 1766 transmission networks were reconstructed,with chain lengths ranging from 1-9 nodes.The root node samples of the 1766 transmission networks were from 58 countries or regions and showed no common ancestor,indicating the occurrence of many independent or parallel transmissions of SARS-CoV-2 when first detected(i.e.,all samples were located at the tip position of the evolutionary tree).No root node sample was found in any sample(n=31,all from the Chinese mainland)collected in the first 15 days from 24 December 2019.Results using six-month data or RaTG13-referenced mutation data were similar.The reconstruction method was verified using a simulation approach.Our results suggest that SARS-CoV-2 may have already been spreading independently worldwide before the outbreak of COVID-19 in Wuhan,China.Thus,a comprehensive global survey of human and animal samples is essential to explore the origins of SARS-CoV-2 and its natural reservoirs and hosts.
基金supported by the Natural Science Foundation of Gansu Province[Grant Numbers 22JR5RA930,22JR5RA894]the Talent Project of Lanzhou Science and Technology Bureau[Grant Number 2022-3-44]+1 种基金the projects managed by the Administration of Traditional Chinese Medicine[Grant Number GZKG-2022-54]Intra Hospital Fund of the First Hospital of Lanzhou University[Grant Number ldyyyn2021101].
文摘Background:1,2,3,4,6-penta-O-galloyl-beta-D-glucose(PGG)is a natural polyphenolic compound derived from multiple medicinal plants with favorable anticancer activity.Methods:In this study,the mechanisms of PGG against gastric cancer were explored through network pharmacology and molecular docking.First,the targets of PGG were searched in the Herbal Ingredients’Targets(HIT),Similarity Ensemble Approach(SEA),and Super-PRED databases.The potential targets related to gastric cancer were predicted from the Human Gene Database(GeneCards)and DisGeNET databases.The intersecting targets of PGG and gastric cancer were obtained by Venn diagram and then subjected to protein-protein interaction analysis to screen hub targets.Functional and pathway enrichment of hub targets were analyzed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases.The differential expression and survival analysis of hub targets in gastric cancer were performed based on The Cancer Genome Atlas database.Finally,the affinity of PGG with hub targets was visualized by molecular docking.Results:Three hub targets were screened,including mitogen-activated protein kinase 14(MAPK14),BCL2 like 1(BCL2L1),and vascular endothelial growth factor A(VEGFA).MAPK14 had a higher expression,while BCL2L1 and VEGFA had lower expression in gastric cancer than in normal conditions.Enrichment analysis indicated enrichment of these hub targets in MAPK,neurotrophin,programmed death-ligand 1(PD-L1)checkpoint,phosphatidylinositol 3-kinases/protein kinase B(PI3K-Akt),Ras,and hypoxia-inducible factor-1(HIF-1)signaling pathways.Conclusion:Therefore,network pharmacology and molecular docking analyses revealed that PGG exerts a therapeutic efficacy on gastric cancer by multiple targets(MAPK14,BCL2L1,and VEGFA)and pathways(MAPK,PD-L1 checkpoint,PI3K-Akt,Ras,and HIF-1 pathways).
基金2020 MajorNatural Science Research Project of Jiangsu Province Colleges and Universities:Research on Forensic Modeling and Analysis of the Internet of Things(20KJA520004)2020 Open Project of National and Local Joint Engineering Laboratory of Radio Frequency Integration andMicro-assembly Technology:Research on the Security Performance of Radio Frequency Energy Collection Cooperative Communication Network(KFJJ20200201)+1 种基金2021 Jiangsu Police Officer Academy Scientific Research Project:Research on D2D Cache Network Resource Optimization Based on Edge Computing Technology(2021SJYZK01)High-level Introduction of Talent Scientific Research Start-up Fund of Jiangsu Police Institute(JSPI19GKZL407).
文摘In this work,we consider the performance analysis of state dependent priority traffic and scheduling in device to device(D2D)heterogeneous networks.There are two priority transmission types of data in wireless communication,such as video or telephone,which always meet the requirements of high priority(HP)data transmission first.If there is a large amount of low priority(LP)data,there will be a large amount of LP data that cannot be sent.This situation will cause excessive delay of LP data and packet dropping probability.In order to solve this problem,the data transmission process of high priority queue and low priority queue is studied.Considering the priority jump strategy to the priority queuing model,the queuing process with two priority data is modeled as a two-dimensionalMarkov chain.A state dependent priority jump queuing strategy is proposed,which can improve the discarding performance of low priority data.The quasi birth and death process method(QBD)and fixed point iterationmethod are used to solve the causality,and the steady-state probability distribution is further obtained.Then,performance parameters such as average queue length,average throughput,average delay and packet dropping probability for both high and low priority data can be expressed.The simulation results verify the correctness of the theoretical derivation.Meanwhile,the proposed priority jump queuing strategy can significantly improve the drop performance of low-priority data.
基金Supported by National Natural Science Foundation of China(81560702)Inner Mongolia Natural Science Foundation(2022LHMS08021).
文摘[Objectives] The paper was to explore the mechanism of capsicum ( Capsicum annuum L.) in treating type 2 diabetes mellitus (T2DM) and search for new targets. [Methods] The active ingredients of capsicum were queried from TCMSP database to obtain the corresponding target proteins. The related targets of T2DM were screened from GeneCards database, and the target intersection of active ingredients of capsicum and diabetes mellitus was obtained via Venny software. The protein-protein interaction (PPI) network of the compounds was constructed using STRING database, and the GO bio-function and KEGG pathway enrichment were further analyzed using Metascape database. [Results] Through TCMSP database query and conditional screening, 14 candidate active molecules, 93 potential targets and 225 related pathways were obtained. [Conclusions] The results of GO and KEGG enrichment analysis show that the main active ingredients of capsicum play a role in the treatment of T2DM by regulating cancer pathways, chemical carcinogenesis—receptor activation, proteoglycans in cancer, and prostate cancer pathways, which will provide an important theoretical basis for subsequent research.
基金supported by the Xiamen High-Level Health Talents Introduction and Training Project(Xiaweidang 2021-124)the National Natural Science Foundation of China(No.81774319).
文摘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.