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.

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.

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.

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.

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

Conventional artificial neural networks used to solve electrical resistivity imaging （ERI） inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network （PBNN） nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter αk, which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.

A Detailed Thermal Resistance Network Analysis of FCBGA Package

Using thermal models to describe the heat dissipation process of FCBGA is a significant topic in the field of packaging.However,the thermal resistance model considering the structure of each part of the chip is still ambiguous and rare,but it is quite desirable in engineering.In this work,we propose a detailed thermal resistance network model,and describe it by using thermal conduction resistance and thermal spreading resistance.For a striking FCBGA case,we calculated the thermal resistance of each part of the structure according to the temperature field simulated by COMSOL.The thermal resistance network can be used to predict the temperatures in the chip under different conditions.For example,when the power changes by 40%,the relative error of junction temperature prediction is only 0.24%.The function of the detailed thermal resistance network in evaluating the optimization space and determining the optimization direction is clarified.This work illustrates a potential thermal resistance analysis method for electronic devices such as FCBGA.

Fire resistance evaluation through synthetic fire tests and generative adversarial networks

This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete columns.Our approach begins by creating deep learning models,namely generative adversarial networks and variational autoencoders,to learn the spatial distribution of real fire tests.We then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete columns.The generated data are employed to train state-of-the-art machine learning techniques,including Extreme Gradient Boost,Light Gradient Boosting Machine,Categorical Boosting Algorithm,Support Vector Regression,Random Forest,Decision Tree,Multiple Linear Regression,Polynomial Regression,Support Vector Machine,Kernel Support Vector Machine,Naive Bayes,and K-Nearest Neighbors,which can predict the fire resistance of the columns through regression and classification.Machine learning analyses achieved highly accurate predictions of fire resistance values,outperforming traditional models that relied solely on limited experimental data.Our study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments.

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.

PREPARATION AND CORROSION RESISTANCE OF NiP/TiO_2 COMPOSITE FILM ON CARBON STEEL IN SULFURIC ACID SOLUTION

A NiP/TiO2 composite film on carbon steel was prepared by electroless plating and sol-gel composite process. An artificial neural network was applied to optimize the prepared condition of the composite film. Corrosion behavior of the NiP/TiO2 composite film was investigated by polarization resistance measurement, anode polarization, ESEM （environmental scanning electron microscopy） and EIS （electrochemical impedance spectroscopy） measurements. Results showed that the NiP/ TiO2 composite film has a good corrosion resistance in 0.5mol/L H2SO4 solution. The element valence of the composite film was characterized by XPS （X-ray photoelectron spectroscopy） spectrum, and an anticorrosion mechanism of the composite film was discussed.

Quality estimation of the resistance spot welding based on pattern feature of the electrode displacement signal

The electrode displacement signal of the resistance spot welding process is monitored and mapped into a binary matrix. Some welded spots, from different welding current specifications, are classified into five classes according to the prototypes of the pattern matrices. A reliable quality classifier is developed based on Hopfield network when the tensile shear strength of the welded joint is measured as the quality indicator. The cross validation test results show that the method utilizing pattern matrix of the displacement signal to characterize nugget formation process is feasible and it can provide adequate quality information of the welded spot. At the same time, under small sample circumstance, the classifier presents good classification ability and it also can correctly estimate the weld quality in some abnormal welding process according to the pattern feature of the displacement signal.