An Algorithm for Short-Circuit Current Interval in Distribution Networks with Inverter Type Distributed Generation Based on Affine Arithmetic

During faults in a distribution network,the output power of a distributed generation(DG)may be uncertain.Moreover,the output currents of distributed power sources are also affected by the output power,resulting in uncertainties in the calculation of the short-circuit current at the time of a fault.Additionally,the impacts of such uncertainties around short-circuit currents will increase with the increase of distributed power sources.Thus,it is very important to develop a method for calculating the short-circuit current while considering the uncertainties in a distribution network.In this study,an affine arithmetic algorithm for calculating short-circuit current intervals in distribution networks with distributed power sources while considering power fluctuations is presented.The proposed algorithm includes two stages.In the first stage,normal operations are considered to establish a conservative interval affine optimization model of injection currents in distributed power sources.Constrained by the fluctuation range of distributed generation power at the moment of fault occurrence,the model can then be used to solve for the fluctuation range of injected current amplitudes in distributed power sources.The second stage is implemented after a malfunction occurs.In this stage,an affine optimization model is first established.This model is developed to characterizes the short-circuit current interval of a transmission line,and is constrained by the fluctuation range of the injected current amplitude of DG during normal operations.Finally,the range of the short-circuit current amplitudes of distribution network lines after a short-circuit fault occurs is predicted.The algorithm proposed in this article obtains an interval range containing accurate results through interval operation.Compared with traditional point value calculation methods,interval calculation methods can provide more reliable analysis and calculation results.The range of short-circuit current amplitude obtained by this algorithm is slightly larger than those obtained using the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Therefore,the proposed algorithm has good suitability and does not require iterative calculations,resulting in a significant improvement in computational speed compared to the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Furthermore,the proposed algorithm can provide more reliable analysis and calculation results,improving the safety and stability of power systems.

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.

Singular optical propagation properties of two types of one-dimensional anti-PT-symmetric periodic ring optical waveguide networks

Two types of one-dimensional(1D)anti-PT-symmetric periodic ring optical waveguide networks,consisting of gain and loss materials,are constructed.The singular optical propagation properties of these networks are investigated.The results show that the system composed of gain materials exhibits characteristics of ultra-strong transmission and bidirectional reflection.Conversely,the system composed of loss materials demonstrates equal transmittance and reflectance at some frequencies.In both the systems,a new type of total reflection phenomenon is observed.When the imaginary part of the refractive indices of waveguide segments is smaller than 10-5,the system shows bidirectional transparency with the transmittance tending to be 1 and reflectivity to be smaller than 10-8 at some bands.When the refractive indices of the waveguide segments are real,the system will be bidirectional transparent at the full band.These findings may deepen the understanding of anti-PT-symmetric optical systems and optical waveguide networks,and possess potential applications in efficient optical energy storage,ultra-sensitive optical filters,ultra-sensitive all-optical switches,integrated optical chips,stealth physics,and so on.

Coexistence behavior of asymmetric attractors in hyperbolic-type memristive Hopfield neural network and its application in image encryption

The neuron model has been widely employed in neural-morphic computing systems and chaotic circuits.This study aims to develop a novel circuit simulation of a three-neuron Hopfield neural network(HNN)with coupled hyperbolic memristors through the modification of a single coupling connection weight.The bistable mode of the hyperbolic memristive HNN(mHNN),characterized by the coexistence of asymmetric chaos and periodic attractors,is effectively demonstrated through the utilization of conventional nonlinear analysis techniques.These techniques include bifurcation diagrams,two-parameter maximum Lyapunov exponent plots,local attractor basins,and phase trajectory diagrams.Moreover,an encryption technique for color images is devised by leveraging the mHNN model and asymmetric structural attractors.This method demonstrates significant benefits in correlation,information entropy,and resistance to differential attacks,providing strong evidence for its effectiveness in encryption.Additionally,an improved modular circuit design method is employed to create the analog equivalent circuit of the memristive HNN.The correctness of the circuit design is confirmed through Multisim simulations,which align with numerical simulations conducted in Matlab.

Coordinated Voltage Control of Distribution Network ConsideringMultiple Types of Electric Vehicles

The couple between the power network and the transportation network(TN)is deepening gradually with the increasing penetration rate of electric vehicles(EV),which also poses a great challenge to the traditional voltage control scheme.In this paper,we propose a coordinated voltage control strategy for the active distribution networks considering multiple types of EV.In the first stage,the action of on-load tap changer and capacitor banks,etc.,are determined by optimal power flow calculation,and the node electricity price is also determined based on dynamic time-of-use tariff mechanism.In the second stage,multiple operating scenarios of multiple types of EVs such as cabs,private cars and buses are considered,and the scheduling results of each EV are solved by building an optimization model based on constraints such as queuing theory,Floyd-Warshall algorithm and traffic flow information.In the third stage,the output power of photovoltaic and energy storage systems is fine-tuned in the normal control mode.The charging power of EVs is also regulated in the emergency control mode to reduce the voltage deviation,and the amount of regulation is calculated based on the fair voltage control mode of EVs.Finally,we test the modified IEEE 33-bus distribution system coupled with the 24-bus Beijing TN.The simulation results show that the proposed scheme can mitigate voltage violations well.

Fracture network types revealed by well test curves for shale reservoirs in the Sichuan Basin,China

Pressure buildup testing can be used to analyze fracture network characteristics and conduct quantitative interpretation of relevant parameters for shale gas wells,thus providing bases for assessing the well productivity and formulating proper development strategies.This study establishes a new well test interpretation model for fractured horizontal wells based on seepage mechanisms of shale reservoirs and proposes a method for identifying fracturing patterns based on the characteristic slopes of pressure buildup curves and curve combination patterns.The pressure buildup curve patterns are identified to represent three types of shale reservoirs in the Sichuan Basin,namely the moderately deep shale reservoirs with high pressure,deep shale reservoirs with ultra-high pressure,and moderately deep shale reservoirs with normal pressure.Based on this,the relationship between the typical pressure buildup curve patterns and the fracture network types are put forward.Fracturing effects of three types of shale gas reservoir are compared and analyzed.The results show that typical flow patterns of shale reservoirs include bilinear flow in primary and secondary fractures,linear flow in secondary fractures,bilinear flow in secondary fractures and matrix,and linear flow in matrix.The fracture network characteristics can be determined using the characteristic slopes of pressure buildup curves and curve combinations.The linear flow in early secondary fractures is increasingly distinct with an increase in primary fracture conductivity.Moreover,the bilinear flow in secondary fractures and matrix and the subsequent linear flow in the matrix occur as the propping and density of secondary fractures increase.The increase in the burial depth,in-situ stress,and stress difference corresponds to a decrease in the propping of primary fractures that expand along different directions in the shale gas wells in the Sichuan Basin.Four pressure buildup curve patterns exist in the Sichuan Basin and its periphery.The pattern of pressure buildup curves of shale reservoirs in the Yongchuan area can be described as 1/2/→1/4,indicating limited stimulated reservoir volume,poorly propped secondary fractures,and the forming of primary fractures that extend only to certain directions.The pressure buildup curves of shale reservoirs in the main block of the Fuling area show a pattern of 1/4/→1/2 or 1/2,indicating greater stimulated reservoir volume,well propped secondary fractures,and the forming of complex fracture networks.The pattern of pressure buildup curves of shale reservoirs in the Pingqiao area is 1/2/→1/4→/1/2,indicating a fracturing effect somewhere between that of the Fuling and Yongchuan areas.For reservoirs with normal pressure,it is difficult to determine fracture network characteristics from pressure buildup curves due to insufficient formation energy and limited liquid drainage.

The Role and Place of Artificial Neural Network Architectures Structural Redundancy in the Input Data Prototypes and Generalization Development

Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.

Effects of exercise by type and duration on quality of life in patients with digestive system cancers:A systematic review and network meta-analysis

Background:There is scant evidence regarding the effects of exercise type and duration on quality of life(QoL)in digestive system cancer(DSC)survivors.We aim to investigate the optimal type and duration of exercise to improve QoL for DSC survivors through a systematic review and network meta-analysis.Methods:A systematic literature search of PubMed,Embase,and Web of Science was performed.Eligibility for study inclusion was limited to studies that were randomized controlled trials involving all kinds of exercise in adult patients with DSCs,and the comparator was in standard care or other types of exercise.The primary outcome was QoL,including general health,physical health,mental health,and role function.Secondary outcomes included cancer-related symptoms such as fatigue,insomnia,depression,anxiety,and duration of hospital stay.The network meta-analyses were performed using a random-effect model.Results:The analysis included 32 eligible articles and a total of 2558 participants.Our primary outcome indicated that short-term aerobic exercise significantly enhanced general health(standardized mean difference(SMD)=0.66,95%credible intervals(Crls):0.05 to 1.30),and also contributed to a better mental health(SMD=0.38,95%CrI:-0.05 to 0.81)and role function(SMD=0.48,95%CrI:-0.27 to 1.20).Although without significant changes,short-term resistance exercise tended to increase the physical health of patients with DSCs(SM=0.69,95%CrI:-0.07 to 1.50)and effective in alleviating fatigue(SMD=-0.77,95%CrI:-1.50 to 0.01).Short-term aerobic exercise was related to a lower score of insomnia(SMD=-1.20,95%CrI:-2.40 to 0.06),depression(SMD=-0.51,95%CrI:-1.50 to 0.45),and anxiety(SMD=-0.45,95%CrI:-1.30 to 0.34).All types of exercise related to a trend of declined hospital stays(-0.87 to-5.00 day).Long-term resistance exercise,however,was negatively associated with general health(SMD=-0.33,95%CrI:-1.70 to 1.00),physical health(SMD=-0.18,95%CrI:-1.30to 0.90),and role function(SMD=-1.20,95%CrI:-2.50 to 0.11).Conclusion:This study suggests that short-term aerobic exercise,with or without resistance exercise programs,enhances QoL(especially for general health)as well as relieves cancer-related symptoms for DSC survivors,while long-term resistance exercise may have negative effects,and thus should be adopted cautiously.These results provide important evidence for the management of DSCs.

Acute liver failure:A systematic review and network meta-analysis of optimal type of stem cells in animal models

BACKGROUND The therapeutic effects of various stem cells in acute liver failure(ALF)have been demonstrated in preclinical studies.However,the specific type of stem cells with the highest therapeutic potential has not been determined.AIM To validate the efficacy of stem cells in ALF model and to identify the most promising stem cells.METHODS A search was conducted on the PubMed,Web of Science,Embase,Scopus,and Cochrane databases from inception to May 3,2022,and updated on November 16,2022 to identify relevant studies.Two independent reviewers performed the literature search,identification,screening,quality assessment,and data extraction.RESULTS A total of 89 animal studies were included in the analysis.The results of traditional meta-analysis showed that stem cell therapy could significantly reduce the serum levels of alanine aminotransferase[weighted mean difference(WMD)=-181.05(-191.71,-170.39)],aspartate aminotransferase[WMD=-309.04(-328.45,-289.63)],tumor necrosis factor-alpha[WMD=-8.75(-9.93,-7.56)],and interleukin-6[WMD=-10.43(-12.11,-8.76)]in animal models of ALF.Further subgroup analysis and network meta-analysis showed that although mesenchymal stem cells are the current research hotspot,the effect of liver stem cells(LSCs)on improving liver function is significantly better than that of the other five types of stem cells.In addition,the ranking results showed that the possibility of LSCs improving liver function ranked first.This fully proves the great therapeutic potential of LSCs,which needs to be paid more attention in the future.CONCLUSION LSCs may have a higher therapeutic potential.Further high-quality animal experiments are needed to explore the most effective stem cells for ALF.

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.

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.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Social-ecological perspective on the suicidal behaviour factors of early adolescents in China:a network analysis

Background In early adolescence,youth are highly prone to suicidal behaviours.Identifying modifiable risk factors during this critical phase is a priority to inform effective suicide prevention strategies.Aims To explore the risk and protective factors of suicidal behaviours(ie,suicidal ideation,plans and attempts)in early adolescence in China using a social-ecological perspective.Methods Using data from the cross-sectional project‘Healthy and Risky Behaviours Among Middle School Students in Anhui Province,China',stratified random cluster sampling was used to select 5724 middle school students who had completed self-report questionnaires in November 2020.Network analysis was employed to examine the correlates of suicidal ideation,plans and attempts at four levels,namely individual(sex,academic performance,serious physical llness/disability,history of self-harm,depression,impulsivity,sleep problems,resilience),family(family economic status,relationship with mother,relationship with father,family violence,childhood abuse,parental mental illness),school(relationship with teachers,relationship with classmates,school-bullying victimisation and perpetration)and social(social support,satisfaction with society).Results In total,37.9%,19.0%and 5.5%of the students reported suicidal ideation,plans and attempts in the past 6 months,respectively.The estimated network revealed that suicidal ideation,plans and attempts were collectively associated with a history of self-harm,sleep problems,childhood abuse,school bullying and victimisation.Centrality analysis indicated that the most influential nodes in the network were history of self-harm and childhood abuse.Notably,the network also showed unique correlates of suicidal ideation(sex,weight=0.60;impulsivity,weight=0.24;family violence,weight=0.17;relationship with teachers,weight=-0.03;school-bullying perpetration,weight=0.22),suicidal plans(social support,weight=-0.15)and suicidal attempts(relationship with mother,weight=-0.10;parental mental llness,weight=0.61).Conclusions This study identified the correlates of suicidal ideation,plans and attempts,and provided practical implications for suicide prevention for young adolescents in China.Firstly,this study highlighted the importance of joint interventions across multiple departments.Secondly,the common risk factors of suicidal ideation,plans and attempts were elucidated.Thirdly,this study proposed target interventions to address the unique influencing factors of suicidal ideation,plans and attempts.

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.

Evaluation of the efficacy and safety of endoscopic band ligation in the treatment of bleeding from mild to moderate gastric varices type 1

BACKGROUND According to practice guidelines,endoscopic band ligation(EBL)and endoscopic tissue adhesive injection(TAI)are recommended for treating bleeding from esophagogastric varices.However,EBL and TAI are known to cause serious complications,such as hemorrhage from dislodged ligature rings caused by EBL and hemorrhage from operation-related ulcers resulting from TAI.However,the optimal therapy for mild to moderate type 1 gastric variceal hemorrhage(GOV1)has not been determined.Therefore,the aim of this study was to discover an individualized treatment for mild to moderate GOV1.AIM To compare the efficacy,safety and costs of EBL and TAI for the treatment of mild and moderate GOV1.METHODS A clinical analysis of the data retrieved from patients with mild or moderate GOV1 gastric varices who were treated under endoscopy was also conducted.Patients were allocated to an EBL group or an endoscopic TAI group.The differences in the incidence of varicose relief,operative time,operation success rate,mortality rate within 6 wk,rebleeding rate,6-wk operation-related ulcer healing rate,complication rate and average operation cost were compared between the two groups of patients.RESULTS The total effective rate of the two treatments was similar,but the efficacy of EBL(66.7%)was markedly better than that of TAI(39.2%)(P<0.05).The operation success rate in both groups was 100%,and the 6-wk mortality rate in both groups was 0%.The average operative time(26 min)in the EBL group was significantly shorter than that in the TAI group(46 min)(P<0.01).The rate of delayed postoperative rebleeding in the EBL group was significantly lower than that in the TAI group(11.8%vs 45.1%)(P<0.01).At 6 wk after the operation,the healing rate of operation-related ulcers in the EBL group was 80.4%,which was significantly greater than that in the TAI group(35.3%)(P<0.01).The incidence of postoperative complications in the two groups was similar.The average cost and other related economic factors were greater for the EBL than for the TAI(P<0.01).CONCLUSION For mild to moderate GOV1,patients with EBL had a greater one-time varix eradication rate,a greater 6-wk operation-related ulcer healing rate,a lower delayed rebleeding rate and a lower cost than patients with TAI.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Quantitative effect of kerogen type on the hydrocarbon generation potential of Paleogene lacustrine source rocks,Liaohe Western Depression,China

Kerogen types exert a decisive effect on the onset and capacity of hydrocarbon generation of source rocks.Lacustrine source rocks in the Liaohe Western Depression are characterized by thick deposition,high total organic carbon(TOC)content,various kerogen types,and a wide range of thermal maturity.Consequently,their hydrocarbon generation potential and resource estimation can be misinterpreted.In this study,geochemical tests,numerical analysis,hydrocarbon generation kinetics,and basin modeling were integrated to investigate the differential effects of kerogen types on the hydrocarbon generation potential of lacustrine source rocks.Optimized hydrocarbon generation and expulsion(HGE)models of different kerogen types were established quantitatively upon abundant Rock-Eval/TOC/vitrinite reflectance(R_(o))datasets.Three sets of good-excellent source rocks deposited in the fourth(Es4),third(Es3),and first(Es1)members of Paleogene Shahejie Formation,are predominantly types I-II_(1),II_(1)-II_(2),and II-III,respectively.The activation energy of types I-II_(2)kerogen is concentrated(180-230 kcal/mol),whereas that of type III kerogen is widely distributed(150-280 kcal/mol).The original hydrocarbon generation potentials of types I,II_(1),II_(2),and III kerogens are 790,510,270,and 85 mg/g TOC,respectively.The Ro values of the hydrocarbon generation threshold for type I-III source rocks gradually increase from 0.42%to 0.74%,and Ro values of the hydrocarbon expulsion threshold increase from 0.49%to 0.87%.Types I and II_(1)source rocks are characterized by earlier hydrocarbon generation,more rapid hydrocarbon expulsion,and narrower hydrocarbon generation windows than types II_(2)and III source rocks.The kerogen types also affect the HGE history and resource potential.Three types(conventional,tight,and shale oil/gas)and three levels(realistic,expected,and prospective)of hydrocarbon resources of different members in the Liaohe Western Depression are evaluated.Findings suggest that the Es3 member has considerable conventional and unconventional hydrocarbon resources.This study can quantitatively characterize the hydrocarbon generation potential of source rocks with different kerogen types,and facilitate a quick and accurate assessment of hydrocarbon resources,providing strategies for future oil and gas exploration.

Analysis of the influencing factors and clinical related characteristics of pulmonary tuberculosis in patients with type 2 diabetes mellitus

BACKGROUND In China,the prevalence of type 2 diabetes mellitus(T2DM)among diabetic patients is estimated to be between 90%-95%.Additionally,China is among the 22 countries burdened by a high number of tuberculosis cases,with approximately 4.5 million individuals affected by active tuberculosis.Notably,T2DM poses a significant risk factor for the development of tuberculosis,as evidenced by the increased incidence of T2DM coexisting with pulmonary tuberculosis(T2DMPTB),which has risen from 19.3%to 24.1%.It is evident that these two diseases are intricately interconnected and mutually reinforcing in nature.AIM To elucidate the clinical features of individuals diagnosed with both T2DM and tuberculosis(T2DM-PTB),as well as to investigate the potential risk factors associated with active tuberculosis in patients with T2DM.METHODS T2DM-PTB patients who visited our hospital between January 2020 and January 2023 were selected as the observation group,Simple DM patients presenting to our hospital in the same period were the control group,Controls and case groups were matched 1:2 according to the principle of the same sex,age difference(±3)years and disease duration difference(±5)years,patients were investigated for general demographic characteristics,diabetes-related characteristics,body immune status,lifestyle and behavioral habits,univariate and multivariate analysis of the data using conditional logistic regression,calculate the odds ratio(OR)values and 95%CI of OR values.RESULTS A total of 315 study subjects were included in this study,including 105 subjects in the observation group and 210 subjects in the control group.Comparison of the results of both anthropometric and biochemical measures showed that the constitution index,systolic blood pressure,diastolic blood pressure and lymphocyte count were significantly lower in the case group,while fasting blood glucose and high-density lipoprotein cholesterol levels were significantly higher than those in the control group.The results of univariate analysis showed that poor glucose control,hypoproteinemia,lymphopenia,TB contact history,high infection,smoking and alcohol consumption were positively associated with PTB in T2DM patients;married,history of hypertension,treatment of oral hypoglycemic drugs plus insulin,overweight,obesity and regular exercise were negatively associated with PTB in T2DM patients.Results of multivariate stepwise regression analysis found lymphopenia(OR=17.75,95%CI:3.40-92.74),smoking(OR=12.25,95%CI:2.53-59.37),history of TB contact(OR=6.56,95%CI:1.23-35.03)and poor glycemic control(OR=3.37,95%CI:1.11-10.25)was associated with an increased risk of developing PTB in patients with T2DM,While being overweight(OR=0.23,95%CI:0.08-0.72)and obesity(OR=0.11,95%CI:0.02-0.72)was associated with a reduced risk of developing PTB in patients with T2DM.CONCLUSION T2DM-PTB patients are prone to worse glycemic control,higher infection frequency,and a higher proportion of people smoking,drinking alcohol,and lack of exercise.Lymphopenia,smoking,history of TB exposure,poor glycemic control were independent risk factors for T2DM-PTB,and overweight and obesity were associated with reduced risk of concurrent PTB in patients with T2DM.

Biodiversity metrics on ecological networks: Demonstrated with animal gastrointestinal microbiomes

Biodiversity has become a terminology familiar to virtually every citizen in modern societies.It is said that ecology studies the economy of nature,and economy studies the ecology of humans;then measuring biodiversity should be similar with measuring national wealth.Indeed,there have been many parallels between ecology and economics,actually beyond analogies.For example,arguably the second most widely used biodiversity metric,Simpson(1949)’s diversity index,is a function of familiar Gini-index in economics.One of the biggest challenges has been the high“diversity”of diversity indexes due to their excessive“speciation”-there are so many indexes,similar to each country’s sovereign currency-leaving confused diversity practitioners in dilemma.In 1973,Hill introduced the concept of“numbers equivalent”,which is based on Renyi entropy and originated in economics,but possibly due to his abstruse interpretation of the concept,his message was not widely received by ecologists until nearly four decades later.What Hill suggested was similar to link the US dollar to gold at the rate of$35 per ounce under the Bretton Woods system.The Hill numbers now are considered most appropriate biodiversity metrics system,unifying Shannon,Simpson and other diversity indexes.Here,we approach to another paradigmatic shift-measuring biodiversity on ecological networks-demonstrated with animal gastrointestinal microbiomes representing four major invertebrate classes and all six vertebrate classes.The network diversity can reveal the diversity of species interactions,which is a necessary step for understanding the spatial and temporal structures and dynamics of biodiversity across environmental gradients.

Identification of Type of a Fault in Distribution System Using Shallow Neural Network with Distributed Generation

A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.