Nonlinear Regression Equations Research Articles

Theoretical derivation and analysis of strength calculation formulas of internal gear pair with double contact points

To describing the strength conditions of tooth surfaces of internal gear pair with double contact points (IGPDCP) accurately, theoretical calculation method of the strength of tooth surfaces is proposed in this paper. Theoretical derivation and analysis of strength calculation formulas of the IGPDCP drive are carried out. For contact strength analysis, based on Hertz point contact theory, general calculation idea of contact strength is provided. The main curvature of the active contact line on tooth surfaces of the pinion and internal gear are derived, respectively. Furtherly, according to theoretical calculation and derivation, the formula for calculating the contact strength of the IGPDCP drive is obtained. Orthogonal test method is used to derive the calculation formula of bending strength of the IGPDCP drive, considering test factors include the modulus, tooth number of gear, transmission ratio, helix angle, contact angle, pressure angle, and torque. Nonlinear regression equations of bending stress of the pinion and internal gear are derived, respectively. The discussion of results of contact and bending strength between formula calculation and finite element analysis are present. The conclusions show that the proposed formulas can satisfy theoretical analysis and engineering application.

New 2D roughness parameters with geometric and physical meanings for rock joints and their correlation with joint roughness coefficient

Determining the joint roughness accurately will better serve the peak shear strength estimation models of rock joints used for stability assessment of rock masses. Considering the defects of the existing quantitative characterization parameters for two-dimensional (2D) joint roughness, especially the lack of explicit geometric and physical meaning, we proposed two new 2D roughness parameters, θ2D and h2D. The former, θ2D, represents the average inclination angle of all potential contact asperities over the entire joint profile, while the latter, h2D, characterizes their average undulation height. Both parameters are closely related to the shear strength of rock joints. Then, the roughness parameters θ2D and h2D of 102 rock joint profiles digitized at 0.5 mm sampling interval were calculated, and a new nonlinear regression equation for the determination of the 2D joint roughness coefficient (JRC) was established by combining the calculated results of the two roughness parameters. It was verified that the proposed equation could give accurate JRC estimation values of the 10 standard profiles of rock joints. Through the comparative analysis of the experimental data collected from earlier studies for the peak shear strength of 73 rock joint samples and corresponding estimated values, the equation was further verified to be applicable and accurate for estimating the JRC values of rock joints. Furthermore, we discussed the effects of shear direction and sampling interval on roughness and further provided another equation that could be applied to estimate the JRC values of joint profiles at the sampling interval of 1.0 mm.

Influence of Weather Factors on the Incidence of Major Insect Pests of Sesame (Sesamum indicum L.)

Population dynamics of major pests viz., web worm, Antigastra catalaunalis (Duponchel), leaf hoppers Orosius albicinctus (Distant) and gallfly Asphondilia sesami (Felt) infesting sesame was studied in Coimbatore, Tamil Nadu, India during Rabi and Kharif seasons of the year 2020. Correlation between the pest population, percent damage and weather parameters was analyzed and regression equations were developed. The incidence of leaf hoppers, web worm and gallfly on sesame crop during Rabi season started on 4th, 3rd and 7th Standard meteorological week (SMW) respectively. The peak attained on 8th,11th, and 11th SMW with population of leafhoppers and damage by web worm and gallfly (2.01/plant,60.04 percent and 49.68 percent) respectively during Rabi season. In case of leafhopper and web worm, minimum temperature (r=0.031r=0.373) had non-significant positive correlation whereas mean relative humidity (r=-0.02) registered non-significant negative correlation with leaf hopper and web worm significant negative correlation Gall fly incidence showed significant positive correlation with maximum and minimum temperature. Whereas in Kharif season, incidence of leafhoppers, web worm and gallfly on sesame crop started on 33rd, 32nd and 37th SMW respectively. The peak activity and incidence was noticed during 37th, 39th and 40th SMW for leafhoppers, web worm and gallfly respectively. The correlation between weather parameters and incidence of major pests followed a similar pattern to kharif season. Regression analysis data revealed that the multiple non-linear regression equations are sufficient to predict the pest population with prevailing weather parameters.

Study on the Consumption Mechanism and Lubrication of Mold Powder Based on Non-Sinusoidal Oscillation Mode

A two-dimensional mold model coupled multiphase flow, heat transfer, solidification and mold oscillation was established based on the casting parameters of the mold of plant. The accuracy of the model was verified by comparing the measured by plant and calculated mold powder consumption under the same casting conditions. The mechanism of mold powder consumption and lubrication was analyzed based on the non-sinusoidal oscillation mode, and the effect of non-sinusoidal oscillation parameters on mold powder consumption was discussed. Mold powder consumption was determined by the downward flow velocity of liquid mold powder and the thickness of liquid mold powder film, the liquid mold powder consumption decreased with the decrease of those. When the mold moved downward, the mold powder thickness and downward flow velocity decreased, the minimum mold powder consumption reached at the middle of the negative strip time, and the variation was to opposite when the mold moved upward, the maximum mold powder consumption appeared during the positive strip time. With the decrease of casting speed and modification ratio, and increase of oscillation frequency and oscillation amplitude, the mold powder consumption had the tendency to increase. The nonlinear regression equation was fitted by the Levenberg–Marquardt method combined with the universal global optimization method to evaluate mold powder consumption.

Structure-Function Relationship in Keratoconus: Spatial and Depth Vision.

The purpose of this study was to determine changes in spatial and depth vision with increasing severity of keratoconus and to model the structure-function relationship to identify distinct phases of loss in visual function with disease severity. Best-spectacle corrected, monocular high-contrast visual acuity, contrast sensitivity function (CSF) and stereoacuity of 155 cases (16-31years) with mild to advanced bilateral keratoconus was determined using standard psychophysical tests. Disease severity was quantified using the multimetric D-index. The structure-function relationship was modeled using linear, positive exponential, negative exponential, and logistic nonlinear regression equations. The logistic regression model explained the highest proportion of variance for spatial vision, without bias in the residual plots (R2 ≥ 66%, P < 0.001). Visual acuity showed a distinct ceiling phase and a steeper loss rate with increasing D-index (1.8units/D-index) in this model. The area under the CSF lacked this ceiling phase and had a shallower loss rate (0.28units/D-index). Stereoacuity loss with D-index was poorly explained by all models tested (P ≤ 0.2). Cases with lower and bilaterally symmetric D-index had better stereoacuity (181.6-376 arc seconds) than those with higher D-index (>400 arc second); both were significantly poorer than controls (approximately 30 arc second). Vision loss in keratoconus varies with the visual function parameter tested. Contrast sensitivity may be an earlier indicator of spatial vision loss than visual acuity. Depth perception is significantly deteriorated from very early stages of the disease. The study outcomes may be used to forecast longitudinal vision loss in keratoconus and to apply appropriate interventions for timely preservation/enhancement of vulnerable visual functions.

Achieving Real-World Saturated Hydraulic Conductivity: Practical and Theoretical Findings from Using an Exponential One-Phase Decay Model

Obtaining accurate values of saturated hydraulic conductivity (Ksat) is very important for managing all natural or artificial processes involving water flow into soils. Double-ring infiltration (DRI) is one of the easiest-to-work-with techniques commonly used for Ksat determination. Unfortunately, when improperly used, it leads to important variations and inaccurate results. This study was designed to investigate the necessary conditions to reach the true-value or real-world saturated hydraulic conductivity (Ksat-real-world) in the field. For this purpose, the effects of two factors—namely, the measured infiltration data type (cumulative, instant rate, and average rate) and the related non-linear regression equation type—were analyzed. Measurements with DRI were performed with samples from 106 locations in three West African countries, namely, Burkina Faso, Mali, and Cote d’Ivoire. The soils were composed of loam, sandy loam, and sandy clay loam. The results show that when infiltration rates are used rather than cumulative infiltration non-linear regression curves, the variability between the measured Ksat and the real-world saturated hydraulic conductivity (Ksat-real-world) could reach from 2.2% to 58.8%. This variability was caused by the approximate amplification—according to the procedure used—of time-increment measurement errors. Extending the test duration to more than 4 h, especially when clay soils were involved, and using the exponential one-phase decay non-linear regression of the cumulative infiltration data based on a clear measurement protocol provided the Ksat values that were closest to Ksat-real-world.

Research on the Development of Wordle Based on Multi-Model Analysis

Wordle is becoming more and more popular around the world, so it is of great significance to study its healthy development. This paper predicts the number of Wordle 's report results and predicts the percentage of attempts for different words. Firstly, this paper establishes a prediction model of the number of ARIMA report results, and analyzes the relationship between word attributes and the percentage of players in difficult mode. This article collects data on the number of Wordle 's daily reporting results from January 7 to December 31, 2022, and predicts that the number of reporting results in 2023 will be: 10220-10637. This paper constructs three indicators to measure word attributes: the number of vowel letters, the number of affixes and the number of repeated letters. Using Pearson correlation coefficient method and AIC information criterion, according to the correlation coefficient of the three indicators, it is analyzed that there is no relationship between word attributes and the percentage of players in difficult mode. Then, this paper establishes a prediction model of the distribution of the number of attempts, and accurately predicts the percentage of attempts of EERTE words. This paper constructs the data into a lexicon and quantifies the letters. Multiple linear regression equation and multiple nonlinear regression equation were established by using the number of vowel letters, the number of affixes and the number of repeated letters corresponding to each letter in the word. The average number of guessing words and variance were fitted. It was found that the fitting effect of multiple nonlinear regression was better, was 0.805 and 0.821. Finally, the related attributes of “EERIE” were counted, and its distribution percentage was obtained. The results were 0, 1 %, 16 %, 49 %, 29 % and 3 %. The model constructed in this paper can provide some theoretical support for the good development of Wordle.

Multivariate nonlinear regression analysis of hydraulic fracturing parameters based on hybrid FEM–DEM

PurposeThis paper aims to employ a multivariate nonlinear regression analysis to establish a predictive model for the final fracture area, while accounting for the impact of individual parameters.Design/methodology/approachThis analysis is based on the numerical simulation data obtained, using the hybrid finite element–discrete element (FE–DE) method. The forecasting model was compared with the numerical results and the accuracy of the model was evaluated by the root mean square (RMS) and the RMS error, the mean absolute error and the mean absolute percentage error.FindingsThe multivariate nonlinear regression model can accurately predict the nonlinear relationships between injection rate, leakoff coefficient, elastic modulus, permeability, Poisson’s ratio, pore pressure and final fracture area. The regression equations obtained from the Newton iteration of the least squares method are strong in terms of the fit to the six sensitive parameters, and the model follow essentially the same trend with the numerical simulation data, with no systematic divergence detected. Least absolutely deviation has a significantly weaker performance than the least squares method. The percentage contribution of sensitive parameters to the final fracture area is available from the simulation results and forecast model. Injection rate, leakoff coefficient, permeability, elastic modulus, pore pressure and Poisson’s ratio contribute 43.4%, −19.4%, 24.8%, −19.2%, −21.3% and 10.1% to the final fracture area, respectively, as they increased gradually. In summary, (1) the fluid injection rate has the greatest influence on the final fracture area. (2)The multivariate nonlinear regression equation was optimally obtained after 59 iterations of the least squares-based Newton method and 27 derivative evaluations, with a decidability coefficient R2 = 0.711 representing the model reliability and the regression equations fit the four parameters of leakoff coefficient, permeability, elastic modulus and pore pressure very satisfactorily. The models follow essentially the identical trend with the numerical simulation data and there is no systematic divergence. The least absolute deviation has a significantly weaker fit than the least squares method. (3)The nonlinear forecasting model of physical parameters of hydraulic fracturing established in this paper can be applied as a standard for optimizing the fracturing strategy and predicting the fracturing efficiency in situ field and numerical simulation. Its effectiveness can be trained and optimized by experimental and simulation data, and taking into account more basic data and establishing regression equations, containing more fracturing parameters will be the further research interests.Originality/valueThe nonlinear forecasting model of physical parameters of hydraulic fracturing established in this paper can be applied as a standard for optimizing the fracturing strategy and predicting the fracturing efficiency in situ field and numerical simulation. Its effectiveness can be trained and optimized by experimental and simulation data, and taking into account more basic data and establishing regression equations, containing more fracturing parameters will be the further research interests.

Analysis of left-turn behaviors of non-motorized vehicles and vehicle-bicycle conflicts.

In order to further study the expansion characteristics of left-turning non-motorized vehicles at intersections and the relationship between expansion characteristics and vehicle-bicycle conflicts, the trajectory point data of left-turning non-motorized vehicles are extracted using video trajectory tracking technology, and construct the cubic curve expansion envelope equation with the highest fitting degree. For the purpose of quantifying the expansion degree of non-motor vehicles after starting, two intersections in Guangxi Zhuang Autonomous Region were selected for case analysis, and the numerical range of expansion degree of the intersection with a left-turn waiting area and the intersection without a left-turn waiting area was obtained. Study the mathematical relationship between the expansion degree and its influencing factors, and establish the multivariate nonlinear regression equation between the expansion degree and the left-turn non-motorized vehicle flow, the number of parallel non-motorized vehicles, and the left-turn green light time. Analyze the vehicle-bicycle conflicts caused by the expansion of left-turning non-motorized vehicles, determine the essential factors affecting the number of non-motorized vehicles, and establish the multiple linear regression equation between the number of non-motorized vehicles and the number of left-turning non-motorized vehicles, the expansion degree, and the number of parallel non-motorized vehicles, the results show that the model has high accuracy. By analyzing the expansion characteristics of left-turning non-motorized vehicles at intersections, the relationship between different influencing factors and the expansion degree is obtained. Then the vehicle-bicycle conflicts under the influence of expansion characteristics is analyzed, providing theoretical ideas for improving traffic efficiency and optimizing traffic organization at intersections.

Influence of initiation conditions of methane-air mixture on the explosion parameters in the degasation pipeline

Purpose. Increasing the safety of personnel at underground works in case of the threat of gas explosions, including explosion of degassing pipelines by taking into account the influence of the place of initiation of the explosion and the mode of combustion when calculating the parameters of the explosion. Mathematical modeling of the process of ignition and combustion of the methane-air mixture in the degassing pipeline. Research methods. nalysis and generalization of theoretical studies, numerical simulation of gas-dynamic detonation combustion of a gas-air mixture, including a model of chemical combustion kinetics, which, on the whole, allows one to trace the dynamics of formation of shock air waves in conditions of mine workings. Mathematic processing of the results of the experiment by the method of least squares. When solving the nonlinear regression equations, linearization was used by the method of logarithm. Results. It is established that the combustion dynamics of the gas-air mixture has a decisive influence on the formation of UVB: in the deflagration combustion mode of the gas-air mixture, with the initiation at the edge of the cloud, the length of the blast wave is increased 3.6 times in the direction opposite to combustion, and the wave amplitude by 10% spread of flame. In the detonation mode, the effect of a directed explosion is observed, in which the amplitude of the shock air wave is opposite to the motion of the detonation wave 5 times less than in the direct shock air wave. The model of an instantaneous volumetric explosion, in comparison with combustion models, yields an underestimated amplitude value. Scientific novelty. As a result of numerical modeling of the ignition and combustion of the sealed section of the mine atmosphere, regularities in the formation and propagation of shock air waves, the dependence of their parameters on the combustion regime and the location of the initiation of the gas-air mixture were obtained. Practical value. The conducted research allows to improve the method of calculating the resistance of degassing pipelines and their elements to explosive load.

Improvement of the metod of protein content prediction in wheat kernels

The study was conducted in order to expand the possibilities of using the developed multiple nonlinear regression equation, re ecting the dependence of the protein content in wheat grain (Y, %) on the content of raw gluten (X1, %) and the mass of 1000 grains (X2, g). The possibility of practical use of the developed and three improved equations for predicting the protein content of wheat grain is shown. An algorithm and results of checking the prognostic capabilities and accuracy of predict equations from independent data are presented. Generalization of experimental data from 288 literature sources of domestic and foreign authors with a total number of observations n = 4604 on more than three hundred varieties of winter and spring soft and durum wheat grown in the period from 1959 to 2020 in Russia and abroad (Albania, Belarus, Bulgaria, Egypt, Kazakhstan, Lithuania, Poland, Slovakia and Ukraine) showed that the number of values that go beyond to limits regulated by GOST 10846-91 «Protein determination method» was 755 or 16.4 %. At the same time, the accuracy of prediction the protein content reached 83.6 %. The developed equations can be used to predict of protein content in almost all cases: when the results of the analysis of protein content and raw gluten in wheat kernel, as well the 1000-kernels weight are given at actual or fxed moisture content, or in terms of dry matter

Flow boiling heat transfer enhancement in vertical minichannel heat sink with non-uniform microcavity arrays under electric field

Effects of electrohydrodynamics (EHD) on the bubble behavior and enhanced heat dissipation mechanism for four surface configurations and various heat fluxes in an upward flowing minichannel heat sink were studied experimentally. Three different minichannel configurations with microcavities array were fabricated using direct metal laser sintering. Flow boiling experiments of nanofluid (SiO2-R141b) under different nonuniform electric fields were performed. Homogeneity test of Variance Analysis was introduced to analyze the effect of design and operation parameters on heat transfer. Coupled effect of microcavity and electric field was further found to improve boiling heat transfer efficiency. Visualization shows that the diameter of small bubbles leaving microcavity increased under electric field. The bubbles wiggled between wire electrode and heated wall by action of electric field in bubbly flow district. Furthermore, the mechanism of perturbation of small bubbles flowing along the heating wall under electric field was discussed. It was revealed that the micro-concave cavity array distributed dense downstream and sparse upstream had a large impact on heat transfer coefficient (HTC), especially under electric field. The maximum synthetic enhancement factor of 2.11 was obtained for given flow conditions. Furthermore, a new predictive equation was proposed by a nonlinear regression equation to consider the effects of micro-concave cavity distribution and electric field factors on thermal transfer performance, and this correlation can predict the HTC affected by non-uniform micro-concave arrays well within the ± 10% error band.

The effect of increasing work rate amplitudes from a common metabolic baseline on the kinetic response of V̇O2p, blood flow, and muscle deoxygenation.

A step-transition in external work rate (WR) increases pulmonary O2 uptake (V̇O2p) in a monoexponential fashion. While the rate of this increase, quantified by the time constant (τ), has frequently been shown to be similar between multiple different WR amplitudes (ΔWR), the adjustment of O2 delivery to the muscle (via blood flow; BF), a potential regulator of V̇O2p kinetics, has not been extensively studied. To investigate the role of BF on V̇O2p kinetics, ten participants performed step-transitions on a knee-extension ergometer from a common baseline WR (3 W) to: 24, 33, 45, 54, and 66 W. Each transition lasted 8 min and was repeated 4-6 times. Volume turbinometry and mass spectrometry, Doppler ultrasound, and near-infrared spectroscopy were used to measure V̇O2p, BF, and muscle deoxygenation (deoxy[Hb+Mb]), respectively. Like transitions were ensemble-averaged and phase II V̇O2p, BF, and deoxy[Hb+Mb] were fit with a monoexponential non-linear least squares regression equation. With increasing ΔWR, τV̇O2p became larger at the higher ΔWRs (p < 0.05), while τBF did not change significantly, and the mean response time (MRT) of deoxy[Hb+Mb] became smaller. These findings that V̇O2p kinetics become slower with increasing ΔWR, while BF kinetics are not influenced by ΔWR, suggest that O2 delivery could not limit V̇O2p in this situation. However, the speeding of deoxy[Hb+Mb] kinetics with increasing ΔWR does imply that the O2 delivery-to-O2 utilization of the microvasculature decreases at higher ΔWRs. This suggests that the contribution of O2 delivery and O2 extraction to V̇O2 in the muscle change with increasing ΔWR.

The use of response surface methodology (RSM) for improving Freezing point, Cloud point and Pour point of common carp (Cyprinus carpio L.) fish viscera oil extracted by infrared ray

    As a result of the special advantages of infrared such as ambient does not affect by IR heating, being environmentally friendly, reducing the size of equipment used in food heating and ease of control. Moreover, it is one of the most promising techniques, so the current study aimed to investigate the application of infrared ray in extracting the oil of common carp fish wastes (Cyprinus carpio L.), and the study of the effect of the independent factors, which are: power (130, 190, 250 watts), temperature (50, 60, 70 ºC), the distance between the radiation source and the surface of the waste (1, 15.5, 30 cm) on the freezing point, cloud point and pour of carp waste oil using response surfaces methodology. The results showed that the lowest practical and predicted values for the above traits were -4.6, -3.1, 11.9, 12.2, 6.8, and 7.01, respectively, and that the increase in power and temperature had a negative effect, as it led to an increase in the cloud point. The results illustrated that the interaction between distance, power and temperature led to a decrease in the freezing point. The results also clarified that the increase in power with a decrease in temperature and stability of the distance led to an increase in the pour point. The results indicated that the optimal conditions were the power 315.94W, the temperature 66.14° C, and the distance is 20.02 cm. The results of the statistical showed that the nonlinear multiple regression equation was used to predict the values of the cloud point, while the multiple linear regression equation was used to predict the pour point values. The nonlinear multiple regression equation had a non-significant effect to predict the freezing point values, with an error rate ranging from 0.32% to 30%.

Numerical simulation analysis and orthogonal experiment optimization of the factors affecting plasma gasification of oil-based drilling cuttings

Plasma gasification technology is a harmless and resourceful solid waste treatment method. As a hazardous waste, studying the plasma gasification treatment of oil-based drilling cuttings has practical significance. Therefore, this paper uses Aspen Plus to establish a plasma gasification model. Based on model validation, the effects of input power, oxygen equivalence ratio, and carrier gas type on the plasma gasification process of oil-based drilling cuttings were discussed. In addition, by designing a quadratic regression orthogonal experiment, a nonlinear quadratic regression equation was established between the three factors of gasification pressure, input power, and equivalence ratio, as well as different gasification indicators, namely gas yield, lower heating value, and gasification efficiency. By solving the equation, it was found that when the gasification power is 70 kW, the gasification pressure is 10 atm, and the equivalence ratio is 0.1, the gas yield reaches its maximum of 0.74 kg/m3; When the gasification power is 15 kW, the gasification pressure is 10 atm, and the equivalence ratio is 0.1, the lower heating value of gas production reaches its maximum of 9.68 MJ/m3; When the gasification power is 15 kW, the gasification pressure is 1 atm, and the equivalence ratio is 0.1, the gasification efficiency reaches its maximum of 37.6%. At the same time, it was found that the influence of gasification pressure, input power, and equivalence ratio on gasification indicators was ranked in descending order as equivalence ratio, input power, and gasification pressure. The above research provides certain reference values for the practical operation of plasma gasification of oil-based drilling cuttings.

Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

Convenience store chains are many people’s top choice for dining and leisure and have logistics procedures that involve each store receiving multiple deliveries because of the varying delivery periods and suitable temperatures for different goods. The estimated arrival time for each delivery has a huge impact on the route arrangement and convenience store preparation for dispatchers to schedule future deliveries. This study collected global positioning system travel data from a fleet of one of the top convenience store chains in Taiwan between April 2021 and March 2022 and proposed machine learning to establish a model to predict travel times. For unavailable data, we proposed the nonlinear regression equation to fill in the missing GPS data. Moreover, the study used the data between April 2022 and September 2022 with mean absolute percentage error to validate the prediction effects exceeding 97%. Therefore, the proposed model based on historical data and the machine learning algorithm in this study can help logistics fleets estimate accurate travel times for their scheduling of future delivery tasks and arranging routes.

LABOUR PRODUCTIVITY ANALYSIS OF MANUFACTURING SECTOR IN TURKEY AGAINST EU

This study offers an in-depth analysis of labour productivity of manufacturing sector in Turkey and provides a comparison with EU27 and EA19 countries utilizing Eurostat time series data of 63 quarters covering 2005/first quarter-2020/third quarter time interval. Productivity trends are identified and interpreted by relating them with the key macroeconomic events and factors. Multiple linear and non-linear regression equations, and ARIMA model with different parameters are applied to the time series data considering the periods with and without covid effect. Future projections are made for the periods 2020–2023 for Turkey manufacturing sector based on the best fitting regression and ARIMA solutions and they are compared. Findings revealed that extreme covid conditions of even two quarters of data have significant impact on the forecasted values for Turkey, EU27 and EA19 countries. ARIMA analysis with 12 different parameter settings provided accurate results, supported by Thiel’s inequality coefficients and standard error measures. Analysis has shown consistent patterns between EA19 and EU27 countries. ARIMA results represent better compatibility with the regression results for Turkey. Study is valuable by providing comprehensive and comparative analysis, revealing future forecasts and covid effect and degree of recovery from the pandemic.

Box-Behnken design optimization of xylanase and cellulase production by Aspergillus fumigatus on Stipa tenacissima biomass

ABSTRACT Optimization of xylanase and cellulase production by a newly isolated Aspergillus fumigatus strain grown on Stipa tenacissima (alfa grass) biomass without pretreatment was carried out using a Box-Behnken design. First, the polysaccharides of dried and ground alfa grass were characterized using chemical methods (strong and diluted acid). The effect of substrate particle size on xylanase and carboxymethylcellulase (CMCase) production by the selected and identified strain was then investigated. Thereafter, experiments were statistically planned with a Box-Behnken design to optimize initial pH, cultivation temperature, moisture content, and incubation period using alfa as sole carbon source. The effect of these parameters on the two enzyme production was evaluated using the response surface method. Analysis of variance was also carried out, and production of the enzymes was expressed using a mathematical equation depending on the influencing factors. The effects of individual, interaction, and square terms on production of both enzymes were represented using the nonlinear regression equations with significant R 2 and P-values. Xylanase and CMCase production levels were enhanced by 25% and 27%, respectively. Thus, this study demonstrated for the first time the potential of alfa as a raw material to produce enzymes without any pretreatment. A set of parameter combinations was found to be effective for the production of xylanase and CMCase by A. fumigatus in an alfa-based solid-state fermentation.

Research on Calculating Traffic Capacity in Extra-Long Subsea Tunnels—A Case Study of the Qingdao Jiaozhou Bay Subsea Tunnel

Analyzing the traffic capacity of extra-long tunnels is crucial in assessing their sustainable capacity. However, previous studies on tunnel capacity mainly considered the influence of a single factor, ignoring the interaction between multiple factors, which cannot reflect the actual tunnel capacity. Therefore, considering the influence of multiple factors, this paper constructs an actual capacity calculation model for extra-long tunnels. Firstly, by combining hierarchical analysis and the entropy method, we determined the key factors that influence the capacity of extra-long tunnels. Secondly, based on the constructed traffic simulation model, we constructed an actual capacity model of extra-long tunnels by using multiple non-linear regression equations and tested the goodness of fit with the help of the misfit term. Finally, we determined the key correction coefficients of the model using the difference proportion method. Taking Qingdao Jiaozhou Bay undersea Tunnel as an example, the research results show that the method proposed in this paper can accurately determine the tunnel capacity with an error of less than 4%, providing a theoretical basis and practical guidance for the management and control of the tunnel’s sustainable carrying capacity after traffic congestion.

Approximation of aeration efficiency at sharp-crested weirs using metaheuristic regression approaches

Abstract This paper explores the ability of multivariate adaptive regression splines, decision trees, Gaussian processes, and multiple non-linear regression equation approaches to predict the aeration efficiency at various weirs and discusses their results. In total, 126 experimental observations were collected in the laboratory, of which 88 were arbitrarily selected for model training, and the rest were used for model validation. Various graphical presentations and goodness-of-fit parameters were used to assess the performance of the models. Performance evaluation results, Whisker plot, and Taylor's diagram indicated that the GP_rbf-based model was superior to other implemented models in predicting the aeration efficiency of weirs with CC (0.9961 and 0.9973), MAE (0.0079 and 0.0195), RMSE (0.0122 and 0.0251), scattering index (0.0594 and 0.1238), and Nash Sutcliffe model efficiency (0.9923 and 0.9564) values in the training and validating stages, respectively. The predicted values by GP_rbf lie within the ±30% error line in the training and validating stages, with most of it lying at/close to the line of agreement. The random forest model had better predictability than other decision tree models implied. The sensitivity analysis of parameters suggests shape factor and drop height as major influencing factors in predicting the aeration efficiency.