Resistive Switching Memory of TiO2 Nanowire Networks Grown on Ti Foil by a Single Hydrothermal Method

The resistive switching characteristics of TiO_2 nanowire networks directly grown on Ti foil by a single-step hydrothermal technique are discussed in this paper. The Ti foil serves as the supply of Ti atoms for growth of the TiO_2 nanowires, making the preparation straightforward. It also acts as a bottom electrode for the device. A top Al electrode was fabricated by e-beam evaporation process. The Al/TiO_2 nanowire networks/Ti device fabricated in this way displayed a highly repeatable and electroforming-free bipolar resistive behavior with retention for more than 10~4 s and an OFF/ON ratio of approximately 70. The switching mechanism of this Al/TiO_2 nanowire networks/Ti device is suggested to arise from the migration of oxygen vacancies under applied electric field. This provides a facile way to obtain metal oxide nanowire-based Re RAM device in the future.

Meta-QTL analysis for mining of candidate genes and constitutive gene network development for fungal disease resistance in maize(Zea mays L.)

The development of resistant maize cultivars is the most effective and sustainable approach to combat fungal diseases.Over the last three decades,many quantitative trait loci(QTL)mapping studies reported numerous QTL for fungal disease resistance(FDR)in maize.However,different genetic backgrounds of germplasm and differing QTL analysis algorithms limit the use of identified QTL for comparative studies.The meta-QTL(MQTL)analysis is the meta-analysis of multiple QTL experiments,which entails broader allelic coverage and helps in the combined analysis of diverse QTL mapping studies revealing common genomic regions for target traits.In the present study,128(33.59%)out of 381 reported QTL(from 82 studies)for FDR could be projected on the maize genome through MQTL analysis.It revealed 38 MQTL for FDR(12 diseases)on all chromosomes except chromosome 10.Five MQTL namely 1_4,2_4,3_2,3_4,and 5_4 were linked with multiple FDR.Total of 1910 candidate genes were identified for all the MQTL regions,with protein kinase gene families,TFs,pathogenesis-related,and disease-responsive proteins directly or indirectly associated with FDR.The comparison of physical positions of marker-traits association(MTAs)from genome-wide association studies with genes underlying MQTL interval verified the presence of QTL/candidate genes for particular diseases.The linked markers to MQTL and putative candidate genes underlying identified MQTL can be further validated in the germplasm through marker screening and expression studies.The study also attempted to unravel the underlying mechanism for FDR resistance by analyzing the constitutive gene network,which will be a useful resource to understand the molecular mechanism of defense-response of a particular disease and multiple FDR in maize.

Incorporation of Mg-phenolic networks as a protective coating for magnesium alloy to enhance corrosion resistance and osteogenesis in vivo

Magnesium(Mg) and its alloys have been intensively studied to develop the next generation of bone implants recently, but their clinical application is restricted by rapid degradation and unsatisfied osteogenic effect in vivo. A bioactive chemical conversion Mg-phenolic networks complex coating(e EGCG) was stepwise incorporated by epigallocatechin-3-gallate(EGCG) and exogenous Mg^(2+)on Mg-2Zn magnesium alloy. Simplex EGCG induced chemical conversion coating(c EGCG) was set as compare group. The in vitro corrosion behavior of Mg-2Zn alloy, c EGCG and e EGCG was evaluated in SBF using electrochemical(PDP, EIS) and immersion test. The cytocompatibility was investigated with rat bone marrow mesenchymal stem cells(r BMSCs). Furthermore, the in vivo tests using a rabbit model involved micro computed tomography(Micro-CT) analysis, histological observation, and interface analysis. The results showed that the e EGCG is Mgphenolic multilayer coating incorporated Mg-phenolic networks, which is rougher, more compact and much thicker than c EGCG. The e EGCG highly improved the corrosion resistance of Mg-2Zn alloy, combined with its lower average hemolytic ratios, continuous high scavenging effect ability and relatively moderate contact angle features, resulting in a stable and suitable biological environment, obviously promoted r BMSCs adhesion and proliferation. More importantly, Micro-CT, histological and interface elements distribution evaluations all revealed that the e EGCG effectively inhibited degradation and enhanced bone tissue formation of Mg alloy implants. This study puts forward a promising bioactive chemical conversion coating with Mg-phenolic networks for the application of biodegradable orthopedic implants.

Performance comparison of training algorithms for the estimation of B?hme abrasion resistance using neural networks

Natural stones used as floor and wall coverings are exposed to many different abrasive forces,so it is essential to choose suitable materials for wear resistance in terms of the life of the structure.The abrasion resistance of natural stones can be determined in the laboratory by applying the B?hme abrasion resistance(BAR)test.However,the direct analysis of BAR in the laboratory has disadvantages such as wasting time and energy,experimental errors,and health impacts.To eliminate these disadvantages,the estimation of BAR using artificial neural networks(ANN)was proposed.Different natural stone samples were collected from Türkiye,and uniaxial compressive strength(UCS),flexural strength(FS),water absorption rate(WA),unit volume weight(UW),effective porosity(n),and BAR tests were carried out.The outputs of these tests were gathered and a data set,consisting of a total of 105 data,was randomly divided into two groups:testing and training.In the current study,the success of three different training algorithms of Levenberg-Marquardt(LM),Bayesian regularization(BR),and scaled conjugate gradient(SCG)were compared for BAR prediction of natural stones.Statistical criteria such as coefficient of determination(R~2),mean square error(MSE),mean square error(RMSE),and mean absolute percentage error(MAPE),which are widely used and adopted in the literature,were used to determine predictive validity.The findings of the study indicated that ANN is a valid method for estimating the BAR value.Also,the LM algorithm(R~2=0.9999,MSE=0.0001,RMSE=0.0110,and MAPE=0.0487)in training and the BR algorithm(R~2=0.9896,MSE=0.0589,RMSE=0.2427,and MAPE=1.2327)in testing showed the best prediction performance.It has been observed that the proposed method is quite practical to implement.Using the artificial neural networks method will provide an advantage in similar laborintensive experimental studies.

High Water Resistance and Enhanced Mechanical Properties of Bio-Based Waterborne Polyurethane Enabled by in-situ Construction of Interpenetrating Polymer Network

In this study,acrylic acid was used as a neutralizer to prepare bio-based WPU with an interpenetrating polymer network structure by thermally induced free radical emulsion polymerization.The effects of the content of acrylic acid on the properties of the resulting waterborne polyurethane-poly(acrylic acid)(WPU-PAA)dispersion and the films were systematically investigated.The results showed that the cross-linking density of the interpenetrating network polymers was increased and the interlocking structure of the soft and hard phase dislocations in the molecular segments of the double networks was tailored with increasing the content of acrylic acid,leading to enhancement of the mechanical properties and water resistance of WPU-PAA films.Notably,with the increase in content of acrylic acid,the tensile strength,Young’s modulus,and toughness of the WPU-PAA-110 film increased by 3 times,and 8 times,and 2.4 times compared with WPU-PAA-80,respectively.The WPU-PAA-100 film showed the best water resistance,and the water absorption rate at 96 h was only 3.27%.This work provided a new design scheme for constructing bio-based WPU materials with excellent properties.

Simiao Wan alleviates obesity-associated insulin resistance via PKCε/IRS-1/PI3K/Akt signaling pathway based on network pharmacology analysis and experimental validation

Background:The purpose of the study was to investigatethe active ingredients and potential biochemicalmechanisms of Simiao Wan(SMW)in obesity-associated insulin resistance.Methods:An integrated network pharmacology method to screen the active compoundsand candidate targets,construct the protein-protein-interaction network,and ingredients-targets-pathways network was constructed for topological analysis to identify core targets and main ingredients.To find the possible signaling pathways,enrichment analysis was performed.Further,a model of insulin resistance in HL-7702 cells was established to verify the impact of SMW and the regulatory processes.Results:An overall of 63 active components and 151 candidate targets were obtained,in which flavonoids were the main ingredients.Enrichment analysis indicated that the PI3K-Akt signaling pathway was the potential pathway regulated by SMW in obesity-associated insulin resistance treatment.The result showed that SMW could significantly ameliorate insulin sensitivity,increase glucose synthesis and glucose utilization and reduce intracellular lipids accumulation in hepatocytes.Also,SMW inhibited diacylglycerols accumulation-induced PKCεactivity and decreased its translocation to the membrane.Conclusion:SMW ameliorated obesity-associated insulin resistance through PKCε/IRS-1/PI3K/Akt signaling axis in hepatocytes,providing a new strategy for metabolic disease treatment.

THEORY AND APPLICATION OF πBRIDGE HYDRAULIC RESISTANCE NETWORKS

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Quality estimation of resistance spot welding of stainless steel based on BP neural network

The end value of the dynamic resistance curve of stainless steel was proved to have strong correlation with nugget size by experiments, so it was an important factor for estimation of weld quality. BP neural network was employed to estimate the weld quality, The end value of the dynamic resistance curve, welding current and welding time were selected as the input variables while the nugget diameter, which is closely related to weld quality, was selected as the output variable. Testing results shows that such network has fine fault tolerance and real-time quality estimation is possible.

Application of an Artificial Neural Network Method for the Prediction of the Tube-Side Fouling Resistance in a Shell-And-Tube Heat Exchanger

The accumulation of undesirable deposits on the heat exchange surface represents a critical issue in industrial heat exchangers.Taking experimental measurements of the fouling is relatively difficult and,often,this method does not lead to precise results.To overcome these problems,in the present study,a new approach based on an Artificial Neural Network(ANN)is used to predict the fouling resistance as a function of specific measurable variables in the phosphoric acid concentration process.These include:the phosphoric acid inlet and outlet temperatures,the steam temperature,the phosphoric acid density,the phosphoric acid volume flow rate circulating in the loop.Some statistical accuracy indices are employed simultaneously to justify the interrelation between these independent variables and the fouling resistance and to select the best training algorithm allowing the determination of the optimal number of hidden neurons.In particular,the BFGS quasi-Newton back-propagation approach is found to be the most performing of the considered training algorithms.Furthermore,the best topology ANN for the shell and tube heat exchanger is obtained with a network consisting of one hidden layer with 13 neurons using a tangent sigmoid transfer function for the hidden and output layers.This model predicts the experimental values of the fouling resistance with AARD%=0.065,MSE=2.168×10^(−11),RMSE=4.656×10^(−6)and r^(2)=0.994.

Junction Pressure Composing MethodCalculating the Total Resistance of Mine Ventilation Network

In mine ventilation network calculation, the total ventilatiou perameters, such as total specific resistance and total natural veutilatiou pressure of an overall mine ventilation system, play an important role on selecting main fan and regulating its operating point. This paper explains the critical effect of network’ s total parameter calculation on the above two aspects and presents a new method, the junction pressure composing method(JPC method), which can be applied to calculate the total resistance.of an overall, complex and multi-fan ventilation network. Based on the total ressistance and airflow rate of main fan, total specific resistance of a natwork is easily calculated. This method gets rid of those shortcomings in the route airflow working mathod(RAW method), greatly improves computing speed and adaptability, and can calculate the total parameters of a mine ventilation network rapidly and conveniently. This method is proved to be correct and reliable by example tests.

Neural network prediction of the shunt current in resistance spot welding

An error back propagation （BP） neural network prediction model was established for the shunt current compensation in series resistance spot welding. The input variables for the neural network consist of the resistivity of the material, the thickness of workpiece and the spot spacing, and the shunt rate is outputted. A simplified calculation for the shunt rate was presented based on the feature of the constant-current resistance spot welding and the variation of the resistance in resistance spot welding process, and then the data generated by simplified calculation were used to train and adjust the neural network model. The neural network model proposed was used to predict the shunt rate in the spot welding of 20# mlid steel （in Chinese classification） （in 2. 0 mm thickness） and 10# mild steel （in 1.5 mm and 1.0 mm thickness）. The maximum relative prediction errors are, respectively, 2. 83%, 1.77% and 3.67%. Shunt current compensation experiments were peoCormed based on the neural network prediction model proposed to check the diameter difference of nuggets. Experimental results show that maximum nugget diameter deviation is less than 4% for both 10# and 20# mlid steels with spot spacing of 30 mm and 50 mm.

Modeling and analysis of resistance spot welding based on neural network

A numerical study on the multi-parameter control method based on nonlinear auto-regressive with exogenous input neural network （NARX） is presented here. Welding current was set as the input parameter; electrode displacement and dynamic resistance were set us the output parameters. The NARX model using these parameters was set up to simulate the multi-parameter resistance spot welding process. By comparing actual experimental data and NARX model output data, it was validated that the results from the model reflect the relationship between input parameter and output parameters correctly under the influence of many affecting factors.

Comparative transcriptome analysis of Ts(Resistant genotype)and Ma(Susceptible genotype)marigold(Tagetes erecta L.)leaves in response to Alternaria tagetica

Marigold black spot caused by Alternaria tagetica is a major disease that can decrease marigold production by 40%,resulting in serious economic losses.In this study,we identified many genes responsive to A.tagetica in the resistant and susceptible marigold genotypes.Analyses of differentially expressed genes,expression trends,and a weighted gene co-expression network revealed a series of hub genes with key roles in different A.tagetica infection stages.Additionally,1216 unigenes encoding transcription factors from eight families were differentially expressed between Ts and Ma.Moreover,R genes fromvarious families(e.g.,N,NL,RLP,and TNL)were differentially expressed in the twomarigold genotypes before and after the inoculation with A.tagetica.Pathway diagrams were used to visualize the leaf transcriptional changes in the two marigold genotypes infected by A.tagetica to clarify the effects of A.tagetica on the expression patterns of genes involved in phosphatidylinositol signaling,plant–pathogen interactions,and plant hormone signal transduction.We identified candidate genes related to disease resistance and generated valuable resources for analyzing the candidate gene functions related to black spot resistance in marigold.The study data may be useful for the molecular marker-assisted screening and breeding of marigold lines with increased disease resistance.

Data-driven short circuit resistance estimation in battery safety issues

Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.

Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils.

Modeling of thermal conductivity for disordered carbon nanotube networks

Several theoretical models have been developed so far to predict the thermal conductivities of carbon nanotube(CNT)networks.However,these models overestimated the thermal conductivity significantly.In this paper,we claimed that a CNT network can be considered as a contact thermal resistance network.In the contact thermal resistance network,the temperature of an individual CNT is nonuniform and the intrinsic thermal resistance of CNTs can be ignored.Compared with the previous models,the model we proposed agrees well with the experimental results of single-walled CNT networks.

Network pharmacology-based screening of the active ingredients and mechanisms of Cymbaria daurica against diabetes mellitus

Cymbaria daurica L.has a long history as a folk medicine and tea for the treatment of diabetes.However,the biological activity and mechanism of its hypoglycemic effect have not been fully elucidated.In this study,the potential mechanism of C.daurica against type 2 diabetes mellitus(T2DM)was postulated via pharmacological network analysis.Based on data mining techniques involving topological parameters,gene ontology,and pathway enrichment,we established a compound-target,protein-protein interaction,and target-pathway network to identify central targets and pathways.Pathway enrichment analysis revealed that the most important pathway associated with C.daurica in treating T2DM is the PI3K-Akt signaling pathway.Molecular docking was performed to validate the predicted results.Then,a HepG2 cell insulin resistance model and a high-fat,high-glucose diet combined with a streptozotocin-induced T2DM rat model was established to assess the fasting glucose changes and lipid profile after C.daurica treatment,respectively.Finally,real-time PCR and western blotting were used to verify changes in key targets.The anti-diabetic mechanism of the active ingredient in C.daurica may involve the regulation of IRS-2,Akt1,GLUT4,and GSK3β.

Identification of watered-out zones in carbonate reservoirs using resistivity curve reconstruction based on reservoir classification

The KT-II layer in the Zananor Oilfield,Caspian Basin,Kazakhstan,contains carbonate reservoirs of various types.The complex pore structure of the reservoirs have made it difficult to identify watered-out zones with traditional logging interpretation methods.This study classifies the reservoirs on the basis of core analysis and establishes an identification model for watered-out layers in the field to effectively improve the interpretation accuracy.Thin section analysis shows that there are three types of pores in the reservoirs,i.e.,the matrix pore,fracture and dissolution vug.A triple porosity model is used to calculate the porosities of the reservoirs and the results are combined with core analysis to classify the reservoirs into the fractured,matrix pore,fracture-pore as well as composite types.A classification standard is also proposed.There are differences in resistivity logging responses from the reservoirs of different types before and after watering-out.The preewatering-out resistivities are reconstructed using generalized neural network for different types of reservoirs.The watered-out layers can be effectively identified according to the difference in resistivity curves before and after watering-out.The results show that the watered-out layers identified with the method are consistent with measured data,thus serving as a reference for the evaluation of watered-out layers in the study area.

A General Study on Equivalent Resistance of Three-Terminal Ladder Network

In view of the application importance of resistance network in modern science and technology, this paper presents the basic structure of a three terminals ladder shaped resistance network, for which, to study in- depth the equivalent resistance, carry out network analysis by applying virtual current method and construct a model of two elements three orders differential equation. Based on different marginal conditions, two general adaptive rules for the three-terminal ladder shaped inlet resistance, as well as two ultimate rules for the equiva- lent resistance of three-terminal infinite ladder shaped were given.

Estimation of equivalent internal-resistance of PEM fuel cell using artificial neural networks

A practical method of estimation for the internal-resistance of polymer electrolyte membrane fuel cell (PEMFC) stack was adopted based on radial basis function (RBF) neural networks. In the training process, k-means clustering algorithm was applied to select the network centers of the input training data. Furthermore, an equivalent electrical-circuit model with this internal-resistance was developed for investigation on the stack. Finally using the neural networks model of the equivalent resistance in the PEMFC stack, the simulation results of the estimation of equivalent internal-resistance of PEMFC were presented. The results show that this electrical PEMFC model is effective and is suitable for the study of control scheme, fault detection and the engineering analysis of electrical circuits.