Optimizing Average Electric Power During the Charging of Lithium-Ion Batteries Through the Taguchi Method

In recent times, lithium-ion batteries have been widely used owing to their high energy density, extended cycle lifespan, and minimal self-discharge rate. The design of high-speed rechargeable lithium-ion batteries faces a significant challenge owing to the need to increase average electric power during charging. This challenge results from the direct influence of the power level on the rate of chemical reactions occurring in the battery electrodes. In this study, the Taguchi optimization method was used to enhance the average electric power during the charging process of lithium-ion batteries. The Taguchi technique is a statistical strategy that facilitates the systematic and efficient evaluation of numerous experimental variables. The proposed method involved varying seven input factors, including positive electrode thickness, positive electrode material, positive electrode active material volume fraction, negative electrode active material volume fraction, separator thickness, positive current collector thickness, and negative current collector thickness. Three levels were assigned to each control factor to identify the optimal conditions and maximize the average electric power during charging. Moreover, a variance assessment analysis was conducted to validate the results obtained from the Taguchi analysis. The results revealed that the Taguchi method was an eff ective approach for optimizing the average electric power during the charging of lithium-ion batteries. This indicates that the positive electrode material, followed by the separator thickness and the negative electrode active material volume fraction, was key factors significantly infl uencing the average electric power during the charging of lithium-ion batteries response. The identification of optimal conditions resulted in the improved performance of lithium-ion batteries, extending their potential in various applications. Particularly, lithium-ion batteries with average electric power of 16 W and 17 W during charging were designed and simulated in the range of 0-12000 s using COMSOL Multiphysics software. This study efficiently employs the Taguchi optimization technique to develop lithium-ion batteries capable of storing a predetermined average electric power during the charging phase. Therefore, this method enables the battery to achieve complete charging within a specific timeframe tailored to a specificapplication. The implementation of this method can save costs, time, and materials compared with other alternative methods, such as the trial-and-error approach.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Quantifying the impact of earthquakes and geological factors on spatial heterogeneity of debris-flow prone areas:A case study in the Hengduan Mountains

Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significance of this knowledge, a comprehensive quantification of the influence of regional topographical and geological factors on the spatial heterogeneity of debris-flow-prone areas has been lacking. This study selected the Hengduan Mountains, an earthquake-prone region characterized by diverse surface conditions and complex landforms, as a representative study area. An improved units zoning and objective factors identification methodology was employed in earthquake and fault analysis to assess the impact of seismic activity and geological factors on spatial heterogeneity of debrisflow prone areas. Results showed that the application of GIS technology with hydrodynamic intensity and geographical units analysis can effectively analyze debris-flow prone areas. Meanwhile, earthquake and fault zones obviously increase the density of debrisflow prone catchments and make them unevenly distributed. The number of debris-flow prone areas shows a nonlinear variation with the gradual increase of geomorphic factor value. Specifically, the area with 1000 m-2500 m elevation difference, 25°-30° average slope, and 0.13-0.15 land use index is the most favorable conditions for debris-flow occurrence;The average annual rainfall from 600 to 1150 mm and landslides gradient from 16° to 35° are the main causal factors to trigger debris flow. Our study sheds light on the quantification of spatial heterogeneity in debris flow-prone areas in earthquake-prone regions, which can offer crucial support for post-debris flow risk management strategies.

A Calculation Method of Double Strength Reduction for Layered Slope Based on the Reduction of Water Content Intensity

The calculation of the factor of safety(FOS)is an important means of slope evaluation.This paper proposed an improved double strength reductionmethod(DRM)to analyze the safety of layered slopes.The physical properties of different soil layers of the slopes are different,so the single coefficient strength reduction method(SRM)is not enough to reflect the actual critical state of the slopes.Considering that the water content of the soil in the natural state is the main factor for the strength of the soil,the attenuation law of shear strength of clayey soil changing with water content is fitted.This paper also establishes the functional relationship between different reduction coefficients.Then,a USDFLD subroutine is programmed using the secondary development function of finite element software.Controlling the relationship between field variables and calculation time realizes double strength reduction applicable to the layered slope.Finally,by comparing the calculation results of different examples,it is proved that the stress and displacement distribution of the critical slope state obtained by the improved method is more realistic,and the calculated safety factor is more reliable.The newly proposedmethod considers the difference of intensity attenuation between different soil layers under natural conditions and avoids the disadvantage of the strength reduction method with uniform parameters,which provides a new idea and method for stability analysis of layered and complex slopes.

Effects of Different Grinding Methods on the Quality of Soybean Bean Milk

[Objectives] This study was conducted to improve the nutritional value of soybean milk, enrich the variety and taste of soybean milk, and find healthy food that is more conducive to people s nutritional needs. [Methods] Whole soybean milk was prepared by grinding with a grinding wheel at a low concentration (low-concentration grinding) and a stainless steel mill at a high concentration (high-concentration grinding). The sensory, physical and chemical characteristics and anti-nutritional factors of whole soybean milk produced by different grinding methods were studied. [Results] Compared with low-concentration grinding, the protein content in soybean milk prepared by high-concentration grinding increased by 24%, and the dietary fiber content increased by 74.7%. Before and after high-pressure homogenization, the particle size D(4, 3) of soybean milk prepared by low-concentration grinding was 212.1 and 93.59 μm, respectively, and the particle size D(4, 3) of soybean milk prepared by high-concentration grinding was 134.0 and 64.64 μm, respectively. The trypsin inhibitor activity and phytic acid content of soybean milk prepared by high-concentration grinding were significantly lower than those of soybean milk prepared by low-concentration grinding. [Conclusions] This study improves the diet structure of the broad masses of people, strengthens people s physique, and provides a new idea for the implementation and development of China s "Soybean Action Programme".

The Factors of the Failure in the Treatment of Congenital Equinus Clubfoot Varus by the Ponseti Method at the Reference Health Care Center (CSREF) of Commune III of Bamako

Clubfoot malformation is the most common serious congenital anomaly affecting the foot in children. Its treatment by the Ponseti method is simple, profitable and widely used in the world. Objective: The objective of this study was to identify the factors of the failure of its treatment by the Ponseti method. Material and Method: We carried out a retrospective and descriptive study of cases of congenital equinus clubfoot varus at the Reference Health Care Center of Commune III of Bamako over 26 months from September 2020 to November 2022. Data were treated with the utmost anonymity. Result: This study was performed on 44 children seen for clubfoot: male (68%) and female (32%), with a sex ratio of 2.1. We obtained 13 cases of recidivism including 7 boys and 6 girls. We found 21 cases of unilateral and 23 bilateral;among which 9 recurrences were found against 4 in the unilateral forms. 85% of recurrences did not have good adherence to the splint and 62% did not come regularly for follow-up consultation. We obtained 33 children with idiopathic clubfeet (75%) with a recurrence of 24%, and 7 children with secondary clubfeet with 71 % recurrence. There was no recurrence in the postural type. Among the recurrences, 38.5% started treatment between 1 and 6 months, 23.1% from 0 to 1 month and 15.4% at 2 years and more. 85% of recurrences had a Pirani score between 4.5 to 6 at the start of treatment and 15% at a score of 2.5 to 4. Conclusion: The factors of the failure of the Ponseti method are mainly non-compliance with treatment, secondary clubfeet, and a high Pirani score at the start of treatment.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

Average vector field methods for the coupled Schrdinger KdV equations

The energy preserving average vector field （AVF） method is applied to the coupled Schr6dinger-KdV equations. Two energy preserving schemes are constructed by using Fourier pseudospectral method in space direction discretization. In order to accelerate our simulation, the split-step technique is used. The numerical experiments show that the non-splitting scheme and splitting scheme are both effective, and have excellent long time numerical behavior. The comparisons show that the splitting scheme is faster than the non-splitting scheme, but it is not as good as the non-splitting scheme in preserving the invariants.

Rutting influencing factors and prediction model for asphalt pavements based on the factor analysis method

To clarify the importance of various influencing factors on asphalt pavement rutting deformation and determine a screening method of model indicators,the data of the RIOHTrack full-scale track were examined using the factor analysis method(FAM).Taking the standard test pavement structure of RIOHTrack as an example,four rutting influencing factors from different aspects were determined through statistical analysis.Furthermore,the common influencing factors among the rutting influencing factors were studied based on FAM.Results show that the common factor can well characterize accumulative ESALs,center-point deflection,and temperature,besides humidity,which indicates that these three influencing factors can have an important impact on rutting.Moreover,an empirical rutting prediction model was established based on the selected influencing factors,which proved to exhibit high prediction accuracy.These analysis results demonstrate that the FAM is an effective screening method for rutting prediction model indicators,which provides a reference for the selection of independent model indicators in other rutting prediction model research when used in other areas and is of great significance for the prediction and control of rutting distress.

Aggregation operators and CRITIC-VIKOR method for confidence complex q-rung orthopair normal fuzzy information and their applications

Supply chain management is an essential part of an organisation's sustainable programme.Understanding the concentration of natural environment,public,and economic influence and feasibility of your suppliers and purchasers is becoming progressively familiar as all industries are moving towards a massive sustainable potential.To handle such sort of developments in supply chain management the involvement of fuzzy settings and their generalisations is playing an important role.Keeping in mind this role,the aim of this study is to analyse the role and involvement of complex q-rung orthopair normal fuzzy(CQRONF)information in supply chain management.The major impact of this theory is to analyse the notion of confidence CQRONF weighted averaging,confidence CQRONF ordered weighted averaging,confidence CQRONF hybrid averaging,confidence CQRONF weighted geometric,confidence CQRONF ordered weighted geometric,confidence CQRONF hybrid geometric operators and try to diagnose various properties and results.Furthermore,with the help of the CRITIC and VIKOR models,we diagnosed the novel theory of the CQRONF-CRITIC-VIKOR model to check the sensitivity analysis of the initiated method.Moreover,in the availability of diagnosed operators,we constructed a multi-attribute decision-making tool for finding a beneficial sustainable supplier to handle complex dilemmas.Finally,the initiated operator's efficiency is proved by comparative analysis.

Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation

In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.

Study on the Carbon Emission Factors in Guangdong Province Based on Divisia Decomposition Method

[Objective] By decomposing and studying the relative factors of carbon emissions in Guangdong Province,the policy and suggestion on further keeping the sustainable development were put forward,which provided the reference for the carbon emission reduction in other provinces.[Method] Based on the carbon emissions formula which was put forward by Johan,three factors(the energy structure,energy efficiency and economy development) which affected the carbon emissions during 1996-2009 in Guangdong Province were studied by using Divisia decomposition method of logarithmic mean weight(LMD).[Result] The economy development was the main reason that caused the continuous significant increase of carbon emissions in Guangdong Province.The improvement of energy efficiency was the important manner for decreasing the energy consumption and the carbon emissions.The adjustment and optimization of energy consumption structure had the huge potential for reducing the carbon emissions in Guangdong Province.[Conclusion] The carbon emissions in Guangdong Province would continue to increase in the future for a long time.When formulated the development strategy in the future,it needed pay special attention to keep the accord development of economy and environment.

Quantitative analysis on influencing factors for interface propagation-based thermal conductivity measurement method during solid-liquid transition

The recently proposed interface propagation-based method has shown its advantages in obtaining the thermal conductivity of phase change materials during solid-liquid transition over conventional techniques. However, in previous investigation, the analysis on the measurement error was qualitative and only focused on the total effects on the measurement without decoupling the influencing factors. This paper discusses the effects of influencing factors on the measurement results for the interface propagation-based method. Numerical simulations were performed to explore the influencing factors, namely model simplification, subcooling and natural convection, along with their impact on the measurement process and corresponding measurement results. The numerical solutions were provided in terms of moving curves of the solid-liquid interface and the predicted values of thermal conductivity. Results indicated that the impact of simplified model was strongly dependent on Stefan number of the melting process. The degree of subcooling would lead to underestimated values for thermal conductivity prediction. The natural convection would intensify the heat transfer rate in the liquid region, thereby overestimating the obtained results of thermal conductivity. Correlations and experimental guidelines are provided. The relative errors are limited in ±1.5%,±3%and ±2% corresponding to the impact of simplified model, subcooling and natural convection, respectively.

Solving Multi-Objective Linear Programming Problem by Statistical Averaging Method with the Help of Fuzzy Programming Method

A multi-objective linear programming problem is made from fuzzy linear programming problem. It is due the fact that it is used fuzzy programming method during the solution. The Multi objective linear programming problem can be converted into the single objective function by various methods as Chandra Sen’s method, weighted sum method, ranking function method, statistical averaging method. In this paper, Chandra Sen’s method and statistical averaging method both are used here for making single objective function from multi-objective function. Two multi-objective programming problems are solved to verify the result. One is numerical example and the other is real life example. Then the problems are solved by ordinary simplex method and fuzzy programming method. It can be seen that fuzzy programming method gives better optimal values than the ordinary simplex method.

Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

Readout errors caused by measurement noise are a significant source of errors in quantum circuits,which severely affect the output results and are an urgent problem to be solved in noisy-intermediate scale quantum(NISQ)computing.In this paper,we use the bit-flip averaging(BFA)method to mitigate frequent readout errors in quantum generative adversarial networks(QGAN)for image generation,which simplifies the response matrix structure by averaging the qubits for each random bit-flip in advance,successfully solving problems with high cost of measurement for traditional error mitigation methods.Our experiments were simulated in Qiskit using the handwritten digit image recognition dataset under the BFA-based method,the Kullback-Leibler(KL)divergence of the generated images converges to 0.04,0.05,and 0.1 for readout error probabilities of p=0.01,p=0.05,and p=0.1,respectively.Additionally,by evaluating the fidelity of the quantum states representing the images,we observe average fidelity values of 0.97,0.96,and 0.95 for the three readout error probabilities,respectively.These results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.

Exponential Time Differencing Method for a Reaction-Diffusion System with Free Boundary

For reaction-diffusion equations in irregular domains with moving boundaries,the numerical stability constraints from the reaction and diffusion terms often require very restricted time step sizes,while complex geometries may lead to difficulties in the accuracy when discretizing the high-order derivatives on grid points near the boundary.It is very challenging to design numerical methods that can efficiently and accurately handle both difficulties.Applying an implicit scheme may be able to remove the stability constraints on the time step,however,it usually requires solving a large global system of nonlinear equations for each time step,and the computational cost could be significant.Integration factor(IF)or exponential time differencing(ETD)methods are one of the popular methods for temporal partial differential equations(PDEs)among many other methods.In our paper,we couple ETD methods with an embedded boundary method to solve a system of reaction-diffusion equations with complex geometries.In particular,we rewrite all ETD schemes into a linear combination of specificФ-functions and apply one state-of-the-art algorithm to compute the matrix-vector multiplications,which offers significant computational advantages with adaptive Krylov subspaces.In addition,we extend this method by incorporating the level set method to solve the free boundary problem.The accuracy,stability,and efficiency of the developed method are demonstrated by numerical examples.

The Method of Calculating Average Skidding Distance

By analyzing the existing average skidding distance formulac and the shape of the landing area, theauthors put forward that the average skidding distance is the shortest when the ratio of length and width is 1, and the landing collectioll area is in proportion to of average geometrical skidding distance. The new models of calculating average distance are presented.

Negative Factors in Higher Vocational Colleges'English Teaching and the Methods to It

This thesis will analysis the negative factors in higher vocational colleges'English teaching and try to find effective methods to it.

How can technology and efficiency alleviate the dilemma of economic growth and carbon emissions in China's industrial economy? A metafrontier decoupling decomposition analysis

This paper attempts to explore the decoupling relationship and its drivers between industrial economic increase and energy-related CO_(2) emissions(ICE). Firstly, the decoupling relationship was evaluated by Tapio index. Then, based on the DEA meta-frontier theory framework which taking into account the regional and industrial heterogeneity and index decomposition method, the driving factors of decoupling process were explored mainly from the view of technology and efficiency. The results show that during2000-2019, weak decoupling was the primary state. Investment scale expansion was the largest reason hindering decoupling process of industrial increase from ICE. Both energy saving and production technology achieved significant progress, which facilitated the decoupling process. Simultaneously, the energy technology gap and production technology gap among regions have been narrowed, and played a role in promoting decoupling process. On the contrary, both scale economy efficiency and pure technical efficiency have inhibiting effects on decoupling process. The former indicates that the scale economy of China's industry was not conducive to improve energy efficiency and production efficiency, while the latter indicates that resource misallocation problem may exist in both energy market and product market.

Evaluation of soil erosion vulnerability in Hubei Province of China using RUSLE model and combination weighting method

Soil erosion has been recognized as a critical environmental issue worldwide.While previous studies have primarily focused on watershed-scale soil erosion vulnerability from a natural factor perspective,there is a notable gap in understanding the intricate interplay between natural and socio-economic factors,especially in the context of spatial heterogeneity and nonlinear impacts of human-land interactions.To address this,our study evaluates the soil erosion vulnerability at a provincial scale,taking Hubei Province as a case study to explore the combined effects of natural and socio-economic factors.We developed an evaluation index system based on 15 indicators of soil erosion vulnerability:exposure,sensitivity,and adaptability.In addition,the combination weighting method was applied to determine index weights,and the spatial interaction was analyzed using spatial autocorrelation,geographical temporally weighted regression and geographical detector.The results showed an overall decreasing soil erosion intensity in Hubei Province during 2000 and 2020.The soil erosion vulnerability increased before 2000 and then.The areas with high soil erosion vulnerability were mainly confined in the central and southern regions of Hubei Province(Xiantao,Tianmen,Qianjiang and Ezhou)with obvious spatial aggregation that intensified over time.Natural factors(habitat quality index)had negative impacts on soil erosion vulnerability,whereas socio-economic factors(population density)showed substantial spatial variability in their influences.There was a positive correlation between soil erosion vulnerability and erosion intensity,with the correlation coefficients ranging from-0.41 and 0.93.The increase of slope was found to enhance the positive correlation between soil erosion vulnerability and intensity.