Study on the Impact of Massive Refracturing on the Fracture Network in Tight Oil Reservoir Horizontal Wells

Class III tight oil reservoirs have low porosity and permeability,which are often responsible for low production rates and limited recovery.Extensive repeated fracturing is a well-known technique to fix some of these issues.With such methods,existing fractures are refractured,and/or new fractures are created to facilitate communication with natural fractures.This study explored how different refracturing methods affect horizontal well fracture networks,with a special focus on morphology and related fluid flow changes.In particular,the study relied on the unconventional fracture model(UFM).The evolution of fracture morphology and flow field after the initial fracturing were analyzed accordingly.The simulation results indicated that increased formation energy and reduced reservoir stress differences can promote fracture expansion.It was shown that the length of the fracture network,the width of the fracture network,and the complexity of the fracture can be improved,the oil drainage area can be increased,the distance of oil and gas seepage can be reduced,and the production of a single well can be significantly increased.

Using Network Pharmacology to Explore the Mechanism of Capsicum in Treating Type 2 Diabetes Mellitus

[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.

From non-obese diabetic to Network for the Pancreatic Organ Donor with Diabetes: New heights in type 1 diabetes research

Since the discovery of therapeutic insulin in 1922 and the development of the non-obese diabetic spontaneous mouse model in 1980,the establishment of Network for Pancreatic Organ Donor with Diabetes(n POD) in 2007 is arguably the most important milestone step in advancing type 1 diabetes(T1D) research. In this perspective,we briefly describe how n POD is transforming T1 D research via procuring and coordinating analysis of disease pathogenesis directly in human organs donated by deceased diabetic and control subjects. The successful precedent set up by n POD is likely to spread far beyond the confines of research in T1 D to revolutionize biomedical research of other disease using high quality procured human cells and tissues.

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

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.

Stochastic Framework for Solving the Prey-Predator Delay Differential Model of Holling Type-Ⅲ

The current research aims to implement the numerical resultsfor the Holling third kind of functional response delay differential modelutilizing a stochastic framework based on Levenberg-Marquardt backpropagationneural networks (LVMBPNNs). The nonlinear model depends uponthree dynamics, prey, predator, and the impact of the recent past. Threedifferent cases based on the delay differential system with the Holling 3^(rd) type of the functional response have been used to solve through the proposedLVMBPNNs solver. The statistic computing framework is provided byselecting 12%, 11%, and 77% for training, testing, and verification. Thirteennumbers of neurons have been used based on the input, hidden, and outputlayers structure for solving the delay differential model with the Holling 3rdtype of functional response. The correctness of the proposed stochastic schemeis observed by using the comparison performances of the proposed and referencedata-based Adam numerical results. The authentication and precision ofthe proposed solver are approved by analyzing the state transitions, regressionperformances, correlation actions, mean square error, and error histograms.

Integration of network pharmacology with experimental verification reveals the hypoglycemic mechanism of coptisine in Jinqi Jiangtang tablets:inhibition of the FoxO1 signaling pathway and hepatic gluconeogenesis

Background:Jinqi Jiangtang tablets(JQJT)have been approved for the treatment of type 2 diabetes mellitus(T2DM)in China for many years.Exploring the effective substances and mechanisms of JQJT is important for its clinical application and further drug research and development.This study aimed to explore the chemical basis and mechanisms of JQJT in the treatment of T2DM.Methods:With network pharmacology,we screened substances in JQJT and their possible targets,then constructed the action network and enriched the biological functions and pathways associated with the active components,and identified the potential targets and mechanisms of JQJT in the treatment of T2DM.Based on the network pharmacology data,we explored the hypoglycemic mechanisms of coptisine in JQJT through western blot and quantitative real-time polymerase chain reaction.Results:Forty-three compounds with good pharmacokinetic properties were identified in JQJT,together with 146 potential biological targets.Among these potential targets,74 were associated with treatment of T2DM.A compound-target network of the 43 compounds against T2DM was constructed.Biological process and signal pathway enrichment analysis of the network highlighted the FoxO signaling pathway.Western blot and quantitative real-time polymerase chain reaction results showed that coptisine,but not epiberberine,significantly inhibited expression of key genes involved in hepatocyte gluconeogenesis by regulating the FoxO1 signaling pathway.Conclusion:Network pharmacology analysis and cell experiments showed that coptisine regulated glucose homeostasis by inhibiting the FoxO1 signaling pathway and hepatic gluconeogenesis,which may be one of the mechanisms of JQJT in the treatment of T2DM.

A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation data.The classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS domain.Recently,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual methods.The ability to extract important features is vital in making RST classification more accurate.This work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction models.We used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification performance.Comparative experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art models.The proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt roads.Moreover,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.

End-to-end aspect category sentiment analysis based on type graph convolutional networks

For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.

A systematic study of Erzhu Erchen decoction against damp-heat internalized type 2 diabetes based on data mining and experimental verification

Background:Erzhu Erchen decoction(EZECD),which is based on Erchen decoction and enhanced with Atractylodes lancea and Atractylodes macrocephala,is widely used for the treatment of dampness and heat(The clinical manifestations of Western medicine include thirst,inability to drink more,diarrhea,yellow urine,red tongue,et al.)internalized disease.Nevertheless,the mechanism of EZECD on damp-heat internalized Type 2 diabetes(T2D)remains unknown.We employed data mining,pharmacology databases and experimental verification to study how EZECD treats damp-heat internalized T2D.Methods:The main compounds or genes of EZECD and damp-heat internalized T2D were obtained from the pharmacology databases.Succeeding,the overlapped targets of EZECD and damp-heat internalized T2D were performed by the Gene Ontology,kyoto encyclopedia of genes and genomes analysis.And the compound-disease targets-pathway network were constructed to obtain the hub compound.Moreover,the hub genes and core related pathways were mined with weighted gene co-expression network analysis based on Gene Expression Omnibus database,the capability of hub compound and genes was valid in AutoDock 1.5.7.Furthermore,and violin plot and gene set enrichment analysis were performed to explore the role of hub genes in damp-heat internalized T2D.Finally,the interactions of hub compound and genes were explored using Comparative Toxicogenomics Database and quantitative polymerase chain reaction.Results:First,herb-compounds-genes-disease network illustrated that the hub compound of EZECD for damp-heat internalized T2D could be quercetin.Consistently,the hub genes were CASP8,CCL2,and AHR according to weighted gene co-expression network analysis.Molecular docking showed that quercetin could bind with the hub genes.Further,gene set enrichment analysis and Gene Ontology represented that CASP8,or CCL2,is negatively involved in insulin secretion response to the TNF or lipopolysaccharide process,and AHR or CCL2 positively regulated lipid and atherosclerosis,and/or including NOD-like receptor signaling pathway,and TNF signaling pathway.Ultimately,the quantitative polymerase chain reaction and western blotting analysis showed that quercetin could down-regulated the mRNA and protein experssion of CASP8,CCL2,and AHR.It was consistent with the results in Comparative Toxicogenomics Database databases.Conclusion:These results demonstrated quercetin could inhibit the expression of CASP8,CCL2,AHR in damp-heat internalized T2D,which improves insulin secretion and inhibits lipid and atherosclerosis,as well as/or including NOD-like receptor signaling pathway,and TNF signaling pathway,suggesting that EZECD may be more effective to treat damp-heat internalized T2D.

Network pharmacology-based approach to investigate the mechanism of Huang-Lian-Jie-Du-Decoction for treatment of type 2 diabetes mellitus

Background:Although the benefits of Huang-Lian-Jie-Du-Decoction(HLJDD)on type 2 diabetes mellitus are noted,the material base and action mechanism remain unknown.This paper aim is to reveal the material base and action mechanism of HLJDD against type 2 diabetes mellitus in a system pharmacology framework.Methods:The compounds in HLJDD were first retrieved from the Traditional Chinese Medicine Systems Pharmacology database and analysis platform.Once retrieved,they were fed into the SwissTargetPrediction database to predict the interacting targets.Meanwhile,a human expression profile dataset was analyzed in the Gene Expression Omnibus database,and subsequently,the differentially expressed genes were compared to the HLJDD-related targets.We conducted a protein-protein interaction analysis,Kyoto Encyclopedia of Genes and Genomes pathway analysis,and Gene Ontology analysis to identify the potential active compounds and targets.Lastly,to verify the binding affinities of those compounds and targets,we performed molecular docking.Results:We obtained 15 key compounds,such as quercetin,epiberberine,and berberine,and 10 hub genes,such as IκB kinase-βand phosphatidylinositol 3-kinase regulatory subunit alpha.The top 10 enriched pathways were also found to be tightly related to type 2 diabetes mellitus,including insulin resistance and FoxO signaling pathway.Moreover,all the key compounds were found to bind well to the hub genes.Particularly for the target of IκB kinase-β,11 out of 15 compounds bound to it with energies of<−9.0 kcal/mol.Conclusion:In summary,15 key compounds of HLJDD may affect type 2 diabetes mellitus development by multiple genes such as IκB kinase-βand phosphatidylinositol 3-kinase regulatory subunit alpha and signaling pathways such as insulin resistance and FoxO signaling pathway.

Study on the mechanism of Salvia miltiorrhiza-Pueraria lobata in the treatment of type 2 diabetes mellitus based on network pharmacology

Objective:To explore the synergistic mechanism of salvia miltiorrhiza and Pueraria lobata of multi-component,multi-target and multi-pathway in the treatment of type 2 diabetes mellitus(T2DM).Methods:The chemical components of Salvia miltiorrhiza and Pueraria lobata were queried and screened through the pharmacological database and analysis platform of traditional Chinese medicine system,and the chemical components were predicted by SwissTargetPrediction database.At the same time,the related targets of T2DM were searched and screened from GeneCards,TTD,DrugBank and Disgenet databases.The chemical composition targets and disease targets are intersected,and the PPI network of intersection targets is constructed by using STRING11.0 database,and the PPI network nodes are screened to get the key targets.The GO function and KEGG pathway enrichment analysis of the key targets are carried out.Results:A total of 70 chemical constituents and 51 key targets for the interaction between chemical components and diseases were obtained through retrieval and screening.After enrichment and analysis of 51 key targets,a total of 71 cellular components,85 molecular functions,559 biological processes and 137 signal pathways were obtained.The treatment of T2DM with Salvia miltiorrhiza and Pueraria lobata may be related to AGE-RAGE,Pl3K-Akt,ErbB,insulin resistance,HIF-1 and other signal pathways.Conclusion:This study preliminarily reveals the action mechanism of Salvia miltiorrhiza and Pueraria lobata on the treatment of T2DM with multi-components,multi-targets and multi-pathways,which provides a certain basis for the study of the molecular mechanism of Salvia miltiorrhiza and Pueraria lobata pairs.

Potential molecular mechanism of Euonymus alatus in the treatment of type 2 diabetes based on network pharmacology

Objective:To investigate the potential molecular mechanism of Guijianyu(Euonymus Alatus)in the treatment of type 2 diabetes by network pharmacology approach.Methods:Relevant literature and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP)were searched to screen the main active ingredients and targets of Guijianyu.The GeneCards database and OMIM database were searched to obtain target datasets for type 2 diabetes.And sorted out the intersection targets of drug and disease.The"drug-ingredient-target-disease"network model was constructed with the help of Cytoscape software,the protein-protein interaction(PPI)network was drawn using the STRING platform,and gene function and metabolic pathway enrichment analysis were also performed on the relevant targets of Guijianyu in the treatment of type 2 diabetes.Results:After the screening,8 main active ingredients and 154 drug-disease common targets were obtained,of which JUN,MAPK1,AKT1,RELA,and IL6 may be the key regulatory genes of Guijianyu in the treatment of type 2 diabetes.GO enrichment analysis yielded a total of 153 biological processes,mainly including nuclear receptor activity,transcription factor activity,direct ligand regulated sequence-specific DNA binding,steroid hormone receptor activity,cytokine receptor binding.KEGG analysis was enriched to a total of 156 significant pathways,mainly including AGE-RAGE signaling pathway in diabetic complications,fluid shear stress and atherosclerosis,hepatitis B,prostate cancer,kaposi sarcoma-associated herpesvirus infection.Conclusion:Studies based on network pharmacology show that various active ingredients such as quercetin and kaempferol in Guijianyu could act on multiple targets such as JUN,MAPK1,AKT1,RELA,and IL6.It may play a role in the treatment of type 2 diabetes and related complications by synergistically regulating glucose and lipid metabolism,reducing insulin resistance,protecting pancreatic beta-cell function,anti-oxidative stress,anti-inflammatory repair and so on.

Four Types of Percolation Transitions in the Cluster Aggregation Network Model

We study the percolation transition in a one-species cluster aggregation network model, in which the parameter α describes the suppression on the cluster sizes. It is found that the model can exhibit four types of percolation transitions, two continuous percolation transitions and two discontinuous ones. Continuous and discontinuous percolation transitions can be distinguished from each other by the largest single jump. Two types of continuous percolation transitions show different behaviors in the time gap. Two types of discontinuous percolation transitions are different in the time evolution of the cluster size distribution. Moreover, we also find that the time gap may also be a measure to distinguish different discontinuous percolations in this model.

Hydrothermal Synthesis and Crystal Structure of a New Sandwich-type Molybdenum Phosphate

The crystal structure of the title compound, [enH2][Fe{MoⅤ6O12(OH)3(HPO4)- (H2PO4)3}2]6en6H2O (en = H2NCH2CH2NH2), hydrothermally synthesized from a mixture of Na2MoO42H2O, Fe2(SO4)3, H3PO4, H2N(CH2)2NH2 and water, has been determined by single- crystal X-ray diffraction. The crystal is of triclinic, space group P?with a = 11.9014(1), b = 13.4246(2), c = 13.8719(2) , a = 87.465(1), b = 69.981(1), g = 64.960(1)? V = 1873.46(4) 3, Z = 1, Mr = 2997.89, F(000) = 1466, m = 2.427 mm-1 and Dc = 2.657 g/cm3. The final R = 0.0404 for 5570 observed reflections (I > 2s(I)). The structural analysis reveals that each cluster anion contains two coplanar {Mo6} rings of six edge-sharing Mo(O5OH) octahedra, and the two {Mo6} rings are linked together through one octahedral FeⅡ ion to generate a sandwich-type cluster with rigorous () symmetry. Moreover, these clusters are further linked into a three-dimensional frame- work by hydrogen bonds.

Active Ingredients and Mechanism of Action of Rhizoma Coptidis against Type 2 Diabetes Based on Network-Pharmacology and Bioinformatics

A pharmacological network of"component/target/pathway"for Rhizoma coptidis against type 2 diabetes(T2D)was established by network-pharmacology,and the active components of Rhizoma coptidis and its mechanism were explored.A literature-based and database study of the components of Rhizoma coptidis was carried out and screened by ADME paramcters.The targets of Rhizoma coptidis were predicted by the ligand similarity method.Related pathways were analyzed with databases,and software was used to construct a "component/target path" network.The mechanism was further confirmed by GEO database with R software.A total of 12 active components were screened from Rhizoma coptidis,involving 57 targets including MAPKI,STAT3,INSR,and 38 signaling pathways were associated with T2D.Related signaling pathways included essential pathways for T2D such as insulin resistance,and pathways that had indirect effect on T2D.It was suggested that Rhizoma coptidis may exert its effects against T2D through multi-component,multi-target,and multi-pathway forms.

BP neural network classification on passenger vehicle type based on GA of feature selection

This paper has concluded six features that belong to passenger vehicle types based on genetic algorithm(GA)of feature selection.We have obtained an optimal feature subset,including length,ratio of width and length,and ratio of height and length.And then we apply this optimal feature subset as well as another feature set,containing length,width and height,to the network input.Back-propagation(BP)neural network and support vector machine(SVM)are applied to classify the passenger vehicle type.There are four passenger vehicle types.This paper selects 400 samples of passenger vehicles,among which 320 samples are used as training set(each class has 80 samples)and the other 80 samples as testing set,taking the feature of the samples as network input and taking four passenger vehicle types as output.For the test,we have applied BP neural network to choose the optimal feature subset as network input,and the results show that the total classification accuracy rate can reach 96%,and the classification accuracy rate of first type can reach 100%.In this condition,we obtain a conclusion that this algorithm is better than the traditional ones[9].

Project of “Three Networks Greening” based on optimal allocation in the Yellow River Delta,China (Dongying section)

We have used the Yellow River Delta （Dongying section） as our study area to address the project of ＂Three Networks Greening＂ （TNG）. With the use of GIS technology and from an ecological point of view, an optimal allocation scheme of land resources is constructed and applied to guide the adjustment of land resources. Given this scheme, we have calculated that the area of land suitable for forest and shrubs without greening is 2256 km^2. Simultaneously, acting on the layout of the TNG project, afforestation site types are prepared and improved. Soil types, microrelief, salinity and underwater levels are combined as major classification factors and irrigation conditions as a reference to classify sites into eight types. In this way, land suitable for forest and grass is afforested given particular planting patterns. Finally, by overlaying this forestry site type map with the TNG plan map, some suggestions and strategies are proposed and used to direct the TNG project. An ecological oasis of the Yellow River Delta should be the result.

Chain-type wireless sensor network node scheduling strategy

In order to reduce power consumption of sensor nodes and extend network survival time in the wireless sensor network （WSN）, sensor nodes are scheduled in an active or dormant mode. A chain-type WSN is fundamental y different from other types of WSNs, in which the sensor nodes are deployed along elongated geographic areas and form a chain-type network topo-logy structure. This paper investigates the node scheduling prob-lem in the chain-type WSN. Firstly, a node dormant scheduling mode is analyzed theoretical y from geographic coverage, and then three neighboring nodes scheduling criteria are proposed. Sec-ondly, a hybrid coverage scheduling algorithm and dead areas are presented. Final y, node scheduling in mine tunnel WSN with uniform deployment （UD）, non-uniform deployment （NUD） and op-timal distribution point spacing （ODS） is simulated. The results show that the node scheduling with UD and NUD, especial y NUD, can effectively extend the network survival time. Therefore, a strat-egy of adding a few mobile nodes which activate the network in dead areas is proposed, which can further extend the network survival time by balancing the energy consumption of nodes.

Sensor deployment strategy for chain-type wireless underground mine sensor network

Wireless sensor networks (WSNs) are very important for monitoring underground mine safety. Sensor node deployment affects the performances of WSNs. In our study, a chain-type wireless underground mine sensor network (CWUMSN) is first pre- sented. A CWUMSN can monitor the environment and locate miners in underground mines. The lowest density deployment strate- gies of cluster head nodes are discussed theoretically. We prove that the lifetime of CWUMSN with a non-uniform deployment strategy is longer than with a uniform deployment strategy. Secondly, we present the algorithm of non-uniform lowest density de- ployment of cluster head nodes. Next, we propose a dynamic choice algorithm of cluster head nodes for CWUMSN which can im- prove the adaptability of networks. Our experiments of CWUMSN with both non-uniform lowest density and uniform lowest den- sity deployments are simulated. The results show that the lifetime of CWUMSN with non-uniform lowest density deployment is almost 2.5 times as long as that of the uniform lowest density deployment. This work provides a new deployment strategy for wire- less underground mine sensor networks and then effectively promotes the application of wireless sensor networks to underground mines.