Sum Of Squared Errors Research Articles

Application of GIS and feedforward back-propagated ANN models for predicting the ecological and health risk of potentially toxic elements in soils in Northwestern Nigeria.

Potentially toxic elements (PTEs) occur naturally in most geologic materials. However, recent anthropogenic disturbances such as ore mining have contributed significantly to their enrichment in soils. Their occurrence in soil may portend a myriad of related risks to the environment and biota. Most traditional soil quality evaluation methods involve comparing the background values of the elements to the established guideline values, which is often time-consuming and fraught with computational errors. As a result, to conduct a comprehensive and unbiased evaluation of soil quality and its effects on the ecosystem and human health, this research combined geochemical, numerical, and GIS data for a composite health risk zonation of the entire study area. Furthermore, the multilayer perceptron artificial neural network (MLP-NN) was used to forecast the most important toxic components influencing soil quality. Geochemical, statistical, and quantitative soil pollution evaluation (pollution index and ecological risk index) showed that apart from mining, the spread and association of trace elements and oxides occur as a consequence of surface environmental conditions (e.g., leaching, weathering, and organo-metallic complexation). The hazard quotients (HQs) and hazard index (HI) of all PTEs were greater than one. This indicates that residents (particularly children) are more susceptible to risks from toxic element ingestion than dermal exposure and inhalation. Ingestion of As and Cr resulted in higher cancer risks and lifetime cancer risk levels (> 1.0E 04), with risk levels increasing toward the northeastern, western, and southeastern directions of the study area. The low modeling errors observed from the sum of square errors, relative errors, and coefficient of determination confirmed the efficiency of the MLP-NN in pollution load prediction. Based on the sensitivity analysis, Hg, Sr, Zn, Ba, As, and Zr showed the greatest influence on soil quality. Focus on remediation should therefore be placed on the removal of these elements from the soil.

Phase equilibria of “syngas” components, aiming for separation by flash distillation

Syngas is a mixture containing methane, hydrogen, carbon dioxide, and carbon monoxide, and it is generated in various processes, such as gasification or methane production via steam reforming. It is desired to separate the syngas to its components in order to create a reliable clean source of energy from methane and hydrogen and to reduce emissions by capturing carbon dioxide. As for today, there is no available data on phase equilibrium of syngas in the literature, which is essential for developing separation processes by distillation. The composition of the syngas depends on its source; therefore, the method for calculating the phase equilibrium shall support any initial composition of the syngas. In the current research, we perform phase stability analysis, using tangent plane distance function and minimizing Helmholtz free energy of the system using two mixing rules: van der Waals and Wong and Sandler. The main strength of the suggested method is its ability to predict any number of phases, avoiding the need for prior knowledge. The current manuscript presents an analysis of binary mixtures of the syngas components, CH4 + CO2, CH4 + CO, CH4 + H2, CO2 + CO, CO2 + H2, and CO + H2, in order to fit the model's parameters to experimental data from the literature, at various temperatures. The aim of this research is to determine which mixing rule shall be further used and to determine the temperature dependent parameters. The parameters fitting is done by minimizing the sum of square errors using “steepest decent” and Fibonacci search method.

Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

AbstractAccurate effort estimation is necessary for efficient management of software development projects, as it relates to human resource management. Ensemble methods, which employ multiple statistical and machine learning techniques, are more robust, reliable, and accurate effort estimation techniques. This study develops a stacking ensemble model based on optimization correction factors by integrating seven statistical and machine learning techniques (K‐nearest neighbor, random forest, support vector regression, multilayer perception, gradient boosting, linear regression, and decision tree). The grid search optimization method is used to obtain valid search ranges and optimal configuration values, allowing more accurate estimation. We conducted experiments to compare the proposed method with related methods, such as use case points‐based single methods, optimization correction factors‐based single methods, and ensemble methods. The estimation accuracies of the methods were evaluated using statistical tests and unbiased performance measures on a total of four datasets, thus demonstrating the effectiveness of the proposed method more clearly. The proposed method successfully maintained its estimation accuracy across the four experimental datasets and gave the best results in terms of the sum of squares errors, mean absolute error, root mean square error, mean balance relative error, mean inverted balance relative error, median of magnitude of relative error, and percentage of prediction (0.25). The p‐value for the t‐test showed that the proposed method is statistically superior to other methods in terms of estimation accuracy. The results show that the proposed method is a comprehensive approach for improving estimation accuracy and minimizing project risks in the early stages of software development.

AN APPLICATION OF TIME DEPENDENT FOURIER AMPLITUDE MODEL ON FORECASTING THE UNITED STATE POPULATION

This study smeared the Time Dependent Fourier Amplitude Model Approach to forecast the Population of the United States of America from 1790 to 2020 on a 10-year interval using Number Crunches Statistical software (NCSS). Results obtained using this procedure was matched with the results obtained in the other models: Malthusian, Logistics, and Logistics (Least Squares) Model. These models were matched using the goodness of fit (the coefficient of determination (R2), the sum of square error (SSE)), the Akaike information criterion (AIC), Bayesian information criterion (BIC), Mean Absolute Deviation (MAD), Mean Error (ME), and Mean Sum of square Error (MSSE), Results displays that the Time Dependent Fourier Amplitude Model has the highest R2 and has the lowest SSE, AIC, BIC, ME, MAD, and MSSE. The normal probability plot of residual also forms a lined pattern. The Time Dependent Fourier Amplitude Model gives a statistically significant development in the data sets as compared to the earlier models and also is a suitable model for forecasting the United States population.

Mechanical Properties Variation in Wood-Plastic Composites with a Mixed Wood Fiber Size.

In this study, the influence of fiber particle size on the mechanical properties of a wood--plastic composite (WPC) was investigated using a combination of experimental measurements and numerical modeling. Four different sizes of wood fibers (10-20 mesh, 20-40 mesh, 40-80 mesh, and 80-120 mesh) were used to reinforce high-density polyethylene (HDPE), either separately or in combination. The different sizes of fibers produced varying properties in the resulting composites. The smallest fiber size (80-120 mesh) resulted in the lowest flexural and tensile properties, but the highest impact strength (15.79 kJ/m2) compared to the other three sizes (12.18-14.29 kJ/m2). Using a blend of fiber sizes resulted in improved mechanical properties. Composites containing a mix of 20-40 mesh and 40-80 mesh fibers exhibited the best flexural (strength 74.16 MPa, modulus 5.35 GPa) and tensile performance (strength 48.27 MPa, modulus 4.30 GPa), while composites containing a mix of all four fiber sizes had the highest impact-resistant strength (16.08 kJ/m2). Several models, including the Rule of Mixtures (ROM), the Inverse Rule of Mixtures (IROM), and the Hirsch models, were used to predict the performance of WPCs. The ROM model was found to be the most accurate in describing the mechanical properties of WPCs reinforced with multi-size wood fibers, based on the sum squared error (SSE) analysis.

Microwave-assisted drying of Ocimum sanctum leaves: Analysis of moisture content, drying kinetic model, and techno-economics

This study aims to evaluate the drying kinetics of basil leaves using microwave-assisted drying (MAD) to determine moisture content, moisture ratio, drying rate, and economic analysis at various microwave power levels (136, 264, 440, and 616 W). This study discusses the economic analysis between microwave versus oven drying and this study has not been discussed in other studies. The leaves of basil are: 2.83 ± 0.1831 × 1.42 ± 0.1398 cm (small leaves); 4.08 ± 0.1619 × 1.83 ± 0.1059 cm (medium leaves); 6.16 ± 0.1713 × 2.70 ± 0.2357 cm (large leaves). They evaluated each condition using five thin layer models: Henderson and Pabis, Midilli et al., Hii et al., Verma et al., and Diffusion Approximation. Coefficient of correlation (R2), Chi-square (X2), Sum of Square Error (SSE), Root Mean Square Error (RMSE), and Mean Square Error (MSE) were used as the statistical parameters to determine the best drying kinetics model. The results show that the Hii et al. model is the most appropriate. An economic study was conducted between the selected microwave-assisted drying kinetics model and the optimum oven drying conditions from previous research results. Using 17 microwaves resulted in a Rate of Investment (ROI) of 46.9188%, Break Even Point (BEP) of 37.1189%, and interest rate (i) of 0.0094%. While using the 17 ovens resulted in an ROI calculation of 30.5957%, BEP of 43.6197%, and interest rate (i) of 0.0465%. Thus the microwave-assisted drying is more optimum than the oven drying. This research is expected to be useful for industry or further research in food science and the food industry.

A novel model for predicting tenacity and unevenness of ring-spun yarn: a special case in textile engineering

This study aimed to model the unevenness and tenacity of ring-spun yarn in a special case in textile engineering using response surface methodology. Yarn number and front roll speed were input variables, while yarn tenacity and unevenness were response/output variables. This study showed that the response surface methodology (RSM) could predict the yarn’s tenacity and unevenness with the yarn coefficient of determination (R2) values of 0.99 and 0.98 and with the error sum of square (SS residual) values 0.00187 and 0.003215, respectively. We also found that an artificial neural network (ANN) could predict the yarn's tenacity and unevenness with the yarn coefficient of determination (R2) values of 0.51 and 0.63 and with the error sum of square (SS residual) values 1.48 and 0.856, respectively. It was concluded that the response surface methodology (RSM) and artificial neural network (ANN) could predict the yarn's tenacity and unevenness. Response surface methodology (RSM) predicts yarn characteristics better than ANN with MIMO (multiple inputs, multiple outputs) modeling. The novelty of this study is that we used RSM and ANN for the first time to obtain the tenacity and unevenness of ring-spun yarn accurately. A simpler approach was employed in this study for predicting tenacity and unevenness using RSM and ANN; however, future research holds the potential for incorporating advanced mathematical models to enhance the prediction. This research suggests that RSM and ANN can be applied to predicting the tenacity and unevenness of ring-spun yarn. The scientific application of this research is that the investigation will benefit practitioners in the textile industry to optimize yarn parameters by ring spinning machines.

An improved clustering based on K-means for hotspots data

Riau province is one of the provinces in Indonesia where forest fires frequently occur every year. Hotspot data is geothermal points and they can be utilized as an indicator of forest fires. Clustering’s method can be used to analyze potential forest fires from hotspot data’s cluster pattern. In this study, hybrid genetic algorithm polygamy with K-means (GAP K-means) was used for hotspot data clustering. GA polygamy was used to determine the initial centroid of K-means. It was used to solve the sensitivity of K-means to the initial centroid, and to find the optimal solution faster. Experimentally compared the performance of GAP K-means, GA K-means, and K-means on the hotspots data, two artificial datasets, and three real-life datasets. Sum square error (SSE), davies bouldin index (DBI), silhouette coefficient (SC) and F-measure are used to evaluation clustering. Based this experiment, GAP K-means outperforms than K-means but GAP K-means still not fast to achieve convergent than GA K-means.

Modelling of Nano Silica Formation from Geothermal Silica Using Co-Precipitation Method

Nanosilica is a nanotechnology product with many substantial functions in many industries. Previous research showed that nano silica can be synthesized from geothermal silica, that caused silica scaling in injection well and turbine in geothermal power plant, reducing its power plant capacity by 40%. This research used geothermal silica as a precursor for nanosilica production with co-precipitation to recover amorphous silica from silica geothermal to reduce scaling while also increasing its economic value. The objectives of this study were to determine the model to represent the co-precipitation method for nano-silica by using nucleation and growth modelling, therefore help the scale-up process of nanosilica production. The experiment was conducted in four steps. (1) Silica washing utilized distillate water with a ratio of water to silica of 10:1 and sulfuric acid of 20% with a ratio of acid to silica of 4:1, both as a washing agent. (2) Preparation of HCl with concentrations of 3% and 9%. (3) Preparation of sodium silicate solution by reacting washed silica and NaOH to obtain sodium silicate. (4) Preparation of primary and secondary sodium silicate by diluting sodium silicate by two and four times to obtain primary and secondary sodium silicate, respectively. (5) Precipitation of sodium silicate with HCl consists of two steps using primary and secondary sodium silicate. The result showed that the model fit concentration data, with Sum of Squared Error (SSE) 1.9297.10-4, mass transfer coefficient rate is 9.8.10-3 dm/min, and the average relative error is 3.5%.

RSM optimization studies for cadmium ions adsorption onto pristine and acid-modified kaolinite clay

Clay has been reported as an active absorbent for the removal of toxic heavy metals from aqueous medium. In this study, pristine and acid modified kaolinite clays (PKC and AMKC) were prepared, characterized using various analyses, and tested for Cd2+ ion adsorption from textile industry wastewater. After acid modification, the specific surface area of clay increased from 84.2 to 389.4 m2/g. Adsorption isotherm, kinetics and thermodynamics behaviour process were examined. The pH at (pHpzc) of 8.5 indicate that AMKC surface is positively charged for pH below the pHpzc attracting anions. Response surface methodology was used to investigate the effect of adsorption process factors on Cd2+ ion removal uptake. At the optimum process conditions of 45.3 °C temperature, 0.63 g/L adsorbent dosage, and 120.9 min contact time, the percentages of Cd2+ adsorbed by PKC and AMKC were 77.82% and 99.19%, respectively. Various models were employed to analyzed the kinetic and equilibrium data. The Pseudo-first order, Pseudo-second order and Intra-particle diffusion were used to evaluate the kinetic data, while the Langmuir, Freundlich and Temkin isotherm models were applied to analyzed the equilibrium data. The sorption kinetics was found to be best described by Pseudo-second order considering the high correlation coefficient (R2), smaller Chi-square (ᵪ2) and sum of square error (SSE). The Freundlich model was the most accurate in describing the equilibrium data followed by Langmuir and Temkin respectively. The thermodynamic reveal that the reaction is spontaneous and endothermic in nature, and increase in randomness between the adsorbent and adsorbate. The obtained activation energy (Ea) value suggest that the adsorption mechanism of Cd(II) is a physisorption dominated.

T-S Fuzzy Algorithm Optimized by Genetic Algorithm for Dry Fermentation pH Control

In the process of anaerobic dry fermentation to produce biogas, maintaining a suitable pH in the environment is more conducive to the degradation of crop straw. When the pH in the fermentation environment is too low, the process of anaerobic digestion by anaerobic bacteria is inhibited. Therefore, it is necessary to quickly adjust the pH. In this work, we studied the control technology of a pH regulation system and then constructed a T-S fuzzy controller. Upon simplifying the T-S fuzzy controller, the system delay time was reduced, and two genetic algorithms with different fitness performance indicators were used to optimize the T-S fuzzy control. The simulation experiment in this study was designed through simulation software, and the results show that the improved control method has a fast regulation ability. Finally, on-site experiments were conducted using the four control methods under the acidification conditions set in the experimental device. The results show that the control method used in this study to improve performance by integrating the error sum of squares has a short control time and small oscillation and overshoot, and it can better regulate the environmental pH to achieve appropriate conditions when acidification occurs during anaerobic dry fermentation.

Growth and carbon sequestration in biomass of Cordia alliodora in Andean agroforestry systems with coffee

Timber production and carbon sequestration in trees in agroforestry systems (AFS) are key to productivity and climate change mitigation. There are no studies about dynamics of growth and carbon sequestration of Cordia alliodora during all plantation cycle. The objective of this study was to develop models for diametric growth and carbon sequestration in aboveground biomass of C. alliodora in AFS with coffee in Líbano, Tolima, Colombia. Nonlinear models of growth and carbon sequestration in aboveground biomass of C. alliodora in AFS with coffee were developed. A total of 90 trees, ranging in age from 1 to 19 years, were randomly selected in farms and measured (diameter at breast height -dbh- and total height -h) in AFS with a basal area of C. alliodora between 0.22 and 17.8 m2/ha. Timber volume and aboveground biomass were estimated with allometric models, while carbon was estimated by multiplying aboveground biomass by 0.47. The best-fit models were selected according to the coefficient of determination (R2), Akaike's information criterion (AIC), predicted residual error sum of squares (PRESS), biological logic and a residual analysis. The highest growth rate of this species was reached at 4–6 years for dbh and h (3.6 cm/year and 2.9 m/year, respectively) and at 20 years for timber and carbon (0.60 m3/tree/year and 88.9 kg C/tree/year, respectively). In 20 years, a C. alliodora tree would store 1.1 Mg C and a AFS with 60 trees/ha would sequester between 260 Mg CO2/ha in aboveground biomass. The results show that C. alliodora trees could be maintained in the field for more than 20 years, thus increasing the volume per individual and carbon sequestration for a longer time. This demonstrates the importance of this species mainly when timber production and carbon sequestration are priorities for its profitability.

Power-Weighted Prediction of Photovoltaic Power Generation in the Context of Structural Equation Modeling

With the popularization of solar energy development and utilization, photovoltaic power generation is widely used in countries around the world and is increasingly becoming an important part of new energy generation. However, it cannot be ignored that changes in solar radiation and meteorological conditions can cause volatility and intermittency in power generation, which, in turn, affects the stability and security of the power grid. Therefore, many studies aim to solve this problem by constructing accurate power prediction models for PV plants. However, most studies focus on adjusting the photovoltaic power station prediction model structure and parameters to achieve a high prediction accuracy. Few studies have examined how the various parameters affect the output of photovoltaic power plants, as well as how significantly and effectively these elements influence the forecast accuracy. In this study, we evaluate the correlations between solar irradiance intensity (GHI), atmospheric density (ρ), cloudiness (CC), wind speed (WS), relative humidity (RH), and ambient temperature (T) and a photovoltaic power station using a Pearson correlation analysis and remove the factors that have little correlation. The direct and indirect effects of the five factors other than wind speed (CC) on the photovoltaic power station are then estimated based on structural equation modeling; the indirect effects are generated by the interaction between the variables and ultimately have an impact on the power of the photovoltaic power station. Particle swarm optimization-based support vector regression (PSO-SVR) and variable weights utilizing the Mahalanobis distance were used to estimate the power of the photovoltaic power station over a short period of time, based on the contribution of the various solar radiation and climatic elements. Experiments were conducted on the basis of the measured data from a distributed photovoltaic power station in Changzhou, Jiangsu province, China. The results demonstrate that the short-term power of a photovoltaic power station is significantly influenced by the global horizontal irradiance (GHI), ambient temperature (T), and atmospheric density (ρ). Furthermore, the results also demonstrate how calculating the relative importance of the various contributing factors can help to improve the accuracy when estimating how powerful a photovoltaic power station will be. The multiple weighted regression model described in this study is demonstrated to be superior to the standard multiple regression model (PSO-SVR). The multiple weighted regression model resulted in a 7.2% increase in R2, a 10.7% decrease in the sum of squared error (SSE), a 2.2% decrease in the root mean square error (RMSE), and a 2.06% decrease in the continuous ranked probability score (CRPS).

Investigations of synoptic wind profile patterns in complex urban areas based on LiDAR measurements

Accurately assessing the variations in wind profiles is very significant for wind engineering and atmospheric research. The complexity of topography poses challenges to accurately predicting wind profile patterns. To accurately evaluate the impact of the heterogeneity of the underlying urban surface and mountainous terrain on the synoptic wind profiles, the vertical profiles of synoptic winds below 1032 m in Shenzhen were investigated for six months based on a high-spatiotemporal-resolution light detection and ranging (LiDAR) system. Specifically, the wind speed and direction profile patterns were summarized and analyzed using a self-organizing map (SOM) clustering technique, considering all possible atmospheric stability conditions. The results indicate that SOMs are effective tools for extracting synoptic wind speed and direction profile patterns based on the sum of squared errors (SSE) and variance ratio criterion (VRC) values. Additionally, the heterogeneity of the underlying urban surface and mountainous terrain has considerable effects on the wind speed and direction profile patterns. The wind speed and wind direction profiles can be divided into three distinct categories. Due to the presence of mountainous terrain, gradient winds can rise to altitudes of approximately 800 m, causing the mean wind twist angles to exceed 45° at heights below 1 km. The maximum wind twist angles can reach 100°. Moreover, this study offers improved models for describing diverse wind speed and direction profile patterns. These models may be beneficial in future research and engineering applications.

Comparison of various metaheuristic algorithms to extract the optimal PEMFC modeling parameters

In this study, operating parameters of a semi-empirical mathematical model of a proton-exchange membrane fuel cell (PEMFC) are extracted and compared using various metaheuristic algorithms to analyze its behavior in a fast and economical manner before setting it up for application. Sum-squared error is employed as the objective function to find the values of seven unknown modeling parameters. The parameter extraction is carried out by using the polarization curve data of a 1000 W commercial PEMFC. Three different multi-attribute decision-making methods are used to rank the metaheuristic algorithms in terms of performance. The findings indicate that Moth Flame Algorithm is ranked first considering the sum-squared error (0.4389) and runtime (1054 s); whereas Grey Wolf Optimizer gives the best objective function efficiency (98.6034%) and lowest error rate (0.1096). It was also found that JAYA and Cuckoo Search are not convenient for parameter extraction of the specified fuel cell. Overall, the distinctive features of the study provide valuable insights and advancements in the field of fuel cell modeling and optimization.

A New Adaptive Analytical Model for the Spherical Reluctance Motor Based on Hybrid Trigonometric Function–Power Function

To realize the multiple-degree-of-freedom (multi-DOF) motion, this paper proposes a new spherical reluctance motor (SPRM). For the nonlinear characteristics of flux linkage and excessively complex torque calculation, this paper proposes a new adaptive analytical model for SPRM based on hybrid trigonometric function-power function. A sixth-degree polynomial is used to represent the five coefficients of this model, and these coefficients are calculated from flux linkage-rotor angle-phase current data using numerical curve fitting with nonlinear least square. Based on the criterion of minimizing the Sum of Squared Error (SSE) between the flux linkage obtained by Finite Element Analysis (FEA) and the flux linkage calculated by analytical model, the optimal trigonometric function angular frequency coefficient and the optimal power function exponent are adaptively obtained by iterative algorithm. The feasibility and accuracy of this model is validated by comparing with the existing models. Furthermore, the experimental results show that the calculated torque has a good agreement with the measured torque, which further verifies the validity of the proposed analytical model. The versatility of the proposed model for SPRMs with different structure parameters is discussed as well.

Fuel-cell parameter estimation based on improved gorilla troops technique

The parameter extraction of the proton exchange membrane fuel cells (PEMFCs) is an active study area over the past few years to achieve accurate current/voltage (I/V) curves. This work proposes an advanced version of an improved gorilla troops technique (IGTT) to precisely estimate the PEMFC’s model parameters. The GTT's dual implementation of the migration approach enables boosting the exploitation phase and preventing becoming trapped in the local minima. Besides, a Tangent Flight Strategy (TFS) is incorporated with the exploitation stage for efficiently searching the search space. Using two common PEMFCs stacks of BCS 500W, and Modular SR-12, the developed IGTT is effectively applied. Furthermore, the two models are evaluated under varied partial temperature and pressure. In addition to this, different new recently inspired optimizers are employed for comparative validations namely supply demand optimization (SDO), flying foxes optimizer (FFO) and red fox optimizer (RFO). Also, a comparative assessment of the developed IGTT and the original GTT are tested to ten unconstrained benchmark functions following to the Congress on Evolutionary Computation (CEC) 2017. The proposed IGTT outperforms the standard GTT, grey wolf algorithm (GWA) and Particle swarm optimizer (PSO) in 92.5%, 87.5% and 92.5% of the statistical indices. Moreover, the viability of the IGTT is proved in comparison to various previously published frameworks-based parameter's identification of PEMFCs stacks. The obtained sum of squared errors (SSE) and the standard deviations (STD) are among the difficult approaches in this context and are quite competitive. For the PEMFCs stacks being studied, the developed IGTT achieves exceedingly small SSE values of 0.0117 and 0.000142 for BCS 500 and SR-12, respectively. Added to that, the IGTT gives superior performance compared to GTT, SDO, FFO and RFO obtaining the smallest SSE objective with the least STD ever.

Novel Process Modeling of Magnetic-Field Assisted Finishing (MAF) with Rheological Properties

The performance of a magnetic-field-assisted finishing (MAF) process, an advanced surface finishing process, is severely affected by the rheological properties of an MAF brush. The yield stress and viscosity of the MAF brush, comprising iron particles and abrasives mixed in a liquid carrier medium, change depending on the brush’s constituents and the applied magnetic field, which in turn affect the material removal mechanism and the corresponding final surface roughness after the MAF. A series of experiments was conducted to delineate the effect of MAF processing conditions on the yield stress of the MAF brush. The experimental data were fitted into commonly used rheology models. The Herschel–Bulkley (HB) model was found to be the most suitable fit (lowest sum of square errors (SSE)) for the shear stress–shear rate data obtained from the rheology tests and used to calculate the yield stress of the MAF brush. Processing parameters, such as magnetic flux density, weight ratio of iron and abrasives, and abrasive (black ceramic in this study) size, with p-values of 0.031, 0.001 and 0.037, respectively, (each of them lower than the significance level of 0.05), were all found to be statistically significant parameters that affected the yield stress of the MAF brush. Yield stress increased with magnetic flux density and the weight ratio of iron to abrasives in MAF brush and decreased with abrasive size. A new process model, a rheology-integrated model (RM), was formulated using the yield stress data from HB model to determine the indentation depth of individual abrasives in the workpiece during the MAF process. The calculated indentation depth enabled us to predict the material removal rate (MRR) and the instantaneous surface roughness. The predicted MRR and surface roughness from the RM model were found to be a better fit with the experimental data than the pre-existing contact mechanics model (CMM) and wear model (WM) with a R2 of 0.91 for RM as compared to 0.76 and 0.78 for CMM and WM. Finally, the RM, under parametric variations, showed that MRR increases and roughness decreases as magnetic flux density, rotational speed, weight ratio of iron to abrasive particles in MAF brush, and initial roughness increase, and abrasive size decreases.

Modeling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion

Fires in buildings are significant public safety hazards and can result in fatalities and substantial financial losses. Studies have shown that the socioeconomic makeup of a region can impact the occurrence of building fires. However, existing models based on the classical stepwise regression procedure have limitations. This paper proposes a more accurate predictive model of building fire rates using a set of socioeconomic variables. To improve the model’s forecasting ability, a backward elimination by robust final predictor error (RFPE) criterion is introduced. The proposed approach is applied to census and fire incident data from the South East Queensland region of Australia. A cross-validation procedure is used to assess the model’s accuracy, and comparative analyses are conducted using other elimination criteria such as p-value, Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and predicted residual error sum of squares (PRESS). The results demonstrate that the RFPE criterion is a more accurate predictive model based on several goodness-of-fit measures. Overall, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modeling of building fires.

Combination Prediction of Income Gap between Urban and Rural Residents in China Based on IOWA Operator

Aims: Based on the income data of urban and rural residents in China from 1998 to 2021, the income gap variables of urban and rural residents were constructed, and the combination prediction method was used to predict the income gap between urban and rural residents in China.
 Methodology: Grey prediction model GM (1,1), Holt-winter seasonless exponential smoothing model and autoregressive moving average ARIMA model were used to construct an order weighted arithmetic mean combination model induced by IOWA with the minimum sum of error squares. Then, by building new weights, three individual forecasting models and combination forecasting models are used to forecast the income gap between urban and rural residents in the next five years.
 Results: The results show that the accuracy of the combined prediction model is significantly better than that of the single prediction model.
 Conclusion: It can be known that the income gap between urban and rural residents will widen further in the future, with an average growth rate of 4.55%.