Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm - support vector regression model

Dragonfly algorithm-support vector machine approach for prediction the optical properties of blood

Knowledge of the optical properties of blood plays important role in medical diagnostics and therapeutic applications in laser medicine. In this paper, we present a very rapid and accurate artificial intelligent approach using Dragonfly Algorithm/Support Vector Machine models to estimate the optical properties of blood, specifically the absorption coefficient, and the scattering coefficient using key parameters such as wavelength (nm), hematocrit percentage (%), and saturation of oxygen (%), in building very highly accurate Dragonfly Algorithm-Support Vector Regression models (DA-SVR). 1000 training and testing sets were selected in the wavelength range of 250-1200 nm and the hematocrit of 0-100%. The performance of the proposed method is characterized by high accuracy indicated in the correlation coefficients (R) of 0.9994 and 0.9957 for absorption and scattering coefficients, respectively. In addition, the root mean squared error values (RMSE) of 0.972 and 2.9193, as well as low mean absolute error values (MAE) of 0.2173 and 0.2423, this result showed a strong match with the experimental data. The models can be used to accurately predict the absorption and scattering coefficients of blood, and provide a reliable reference for future studies on the optical properties of human blood.

Optimizing and hyper-tuning machine learning models for the water absorption of eggshell and glass-based cementitious composite

Cementitious composites' performance degrades in extreme conditions, making it more important to enhance its resilience. To further the adaptability of eco-friendly construction, waste materials are increasingly being repurposed. Cementitious composites deteriorate in both direct and indirect ways due to the facilitation of hostile ion transport by water. The effects of using eggshell and glass powder as partial substitutes for cement and sand in mortar on the water-absorption capacity were investigated using machine learning (ML) modeling techniques such as Gene Expression Programming (GEP) and Multi Expression Programming (MEP). To further assess the importance of inputs, sensitivity analysis and interaction research were carried out. The water absorption property of cementitious composites was precisely estimated by the generated ML models. It was noted that the MEP model, with an R2 of 0.90, and the GEP model, with an R2 of 0.88, accurately predicted results. The sensitivity analysis revealed that the absorption capacity of the mortar was most affected by the presence of eggshell powder, sand, and glass powder. GEP and MEP model's significance lies in the fact that they offer one-of-a-kind mathematical formulas that can be applied to the prediction of features in another database. The mathematical models resulting from this study can help scientists and engineers rapidly assess, enhance, and rationalize mixture proportioning. The built models can theoretically compute the water absorption of cement mortar made from eggshell powder and glass powder based on varied input parameters, resulting in cost and time savings.

Bio-inspired based meta-heuristic approach for predicting the strength of fiber-reinforced based strain hardening cementitious composites

A recently introduced bendable concrete having hundred times greater strain capacity provides promising results in repair of engineering structures, known as strain hardening cementitious composites (SHHCs). The current research creates new empirical prediction models to assess the mechanical properties of strain-hardening cementitious composites (SHCCs) i.e., compressive strength (CS), first crack tensile stress (TS), and first crack flexural stress (FS), using gene expression programming (GEP). Wide-ranging records were considered with twelve variables i.e., cement percentage by weight (C%), fine aggregate percentage by weight (Fagg%), fly-ash percentage by weight (FA%), Water-to-binder ratio (W/B), super-plasticizer percentage by weight (SP%), fiber amount percentage by weight (Fib%), length to diameter ratio (L/D), fiber tensile strength (FTS), fiber elastic modulus (FEM), environment temperature (ET), and curing time (CT). The performance of the models was deduced using correlation coefficient (R) and slope of regression line. The established models were also assessed using relative root mean square error (RRMSE), Mean absolute error (MAE), Root squared error (RSE), root mean square error (RMSE), objective function (OBF), performance index (PI) and Nash-Sutcliffe efficiency (NSE). The resulting mathematical GP-based equations are easy to understand and are consistent disclosing the originality of GEP model with R in the testing phase equals to 0.8623, 0.9269, and 0.8645 for CS, TS and FS respectively. The PI and OBF are both less than 0.2 and are in line with the literature, showing that the models are free from overfitting. Consequently, all proposed models have high generalization with less error measures. The sensitivity analysis showed that C%, Fagg%, and ET are the most significant variables for all three models developed with sensitiveness index higher than 10 %. The result of the research can assist researchers, practitioners, and designers to assess SHCC and will lead to sustainable, faster, and safer construction from environment-friendly waste management point of view.

Advanced investigation of a putative adsorption process of nine non key food odorants (non-KFOs) on the broadly tuned human olfactory receptor OR2W1: Statistical physics modeling and molecular docking study

In the present paper, statistical physics formalism was used to understand the olfactory perception via the investigation of dose-olfactory response curves of a putative adsorption process of nine non key food odorants (non-KFOs) on the broadly tuned human olfactory receptor OR2W1, in order to quantitative characterize the interactions between the nine studied non-KFOs, i. e., furfuryl sulfide, furfuryl disulfide, benzyl methyl disulfide, furfuryl methyl disulfide, benzyl methyl sulfide, 1-phenylethanethiol, benzyl mercaptan, furfuryl methyl sulfide and 3-phenylpropanol molecules and OR2W1 binding sites at a molecular level. Two advanced adsorption models have been proposed: the advanced monolayer monoenergy model (monolayer model with identical and independent olfactory receptor binding sites) (Model 1) and the advanced monolayer model with two independent types of olfactory receptor binding sites (Model 2). It was concluded that the monolayer monoenergy model was selected as the most adequate model to fit the experimental dose-olfactory response curves tabulated in literature. Actually, the numerical values of the three fitted physico-chemical parameters (RM1, n and C1) were obtained by a non-linear regression. Indeed, modeling results suggested that the number of docked non-KFOs per OR2W1 binding site n values (1.24 < n < 1.94) was always superior to 1, which indicated the non-parallel orientation of the studied odorants on the olfactory receptor and the multi-molecular adsorption mechanism. The estimated molar adsorption energy ΔEa values (ranged from 6.07 to 12.16 kJ/mol) for the nine olfactory systems confirmed the physical the exothermic characters of the adsorption process since ΔEa values were lower than 40 kJ/mol and positive. Furthermore, these estimated parameters were applied to characterize stereographically and energetically the interaction between the nine non-KFOs and OR2W1 through the determination of the human receptor binding site size distributions (RSDs) and the adsorption energy distributions (AEDs), which were spread out from 0.25 to 6.50 nm and from 0 to 22.50 kJ/mol, respectively. The docking computation between these nine non-KFOs and OR2W1 proved that the estimated binding affinities were belonged to the adsorption energies spectrum in general and the specific adsorption energy band or the molecular vibration modes limited spectrum (between 2.50 kJ/mol and 17 kJ/mol) (approximate olfactory band).

High-performance surface plasmon resonance fiber sensor based on cylindrical vector modes

Cylindrical vector modes with azimuthal polarization and low transmission loss are proposed for the first time to be utilized in a novel design of a surface plasmon resonance (SPR) sensor based on a circular photonic crystal fiber (C-PCF). A C-PCF with a ring of air holes in the cladding is designed where a gold layer with a thickness of 44 nm is coated on the outer cladding surface. The optimal geometric parameters are determined using the finite-element method (FEM) for a high-quality TE₀₁ mode and high sensitivity of the sensor. The proposed SPR sensor shows high sensitivity for analyte refractive index (RI) ranging from n_a = 1.29 to 1.34 over the wavelength range of 1400-2000 nm. It is expected that the proposed sensor can sense low concentrations of hemoglobin, lymphocytes and monocytes of red and white blood cells which are effective in diagnosing the progress of cancer tumors. The maximum sensitivity of 13,800 nm/RIU is obtained in the refractive index environment of 1.33-1.34. The sensor resolution is of the order of 10^-6 and the amplitude sensitivity reaches its maximum of 2380 RIU^-1 at n_a = 1.30 which is the highest value ever reported. Our proposed sensor shows high sensitivity and simultaneously simple design with high performance.

Theoretical study of the olfactory perception of floral odorant on OR10J5 and Olfr16 using the grand canonical ensemble in statistical physics approach

In this work, two experimental dose-response curves of lyral molecules on the OR10J5 and the Olfr16 were employed in order to examine the evolution of physico-chemical parameters involved in the selected statistical physics model(s) to investigate the human and the mouse smelling of a floral scent. Indeed, one layer adsorption model on one type of sites with one energy (1LAM1T1E) and one layer adsorption model on two types of sites with two energies (1LAM2T2E), considered as appropriate models for the adsorption of lyral molecules on the OR10J5 and Olfr16, respectively, have been applied to fit the experimental data. Stereographic and energetic physico-chemical parameters, namely: the maximum response(s) at saturation, the number of docked molecules per olfactory receptor binding site and the concentration(s) at half saturation, were investigated to retrieve helpful information to describe the adsorption process putatively introduced in the olfaction perception. Thus, the advanced modeling results indicated that the studied molecules were docked with a non-parallel orientation (n > 1). Furthermore, for the two olfactory systems, the molar adsorption energies estimated from curves modeling were inferior to 11 kJ/mol, which showed the physisorption process of the adsorption of lyral molecules on OR10J5 and Olfr16. The 1LAM2T2E and the 1LAM1T1E were applied to estimate the OR10J5 and the Olfr175 RSDs, respectively. Hence, lyral RSDs were spread out from 0.7 to 20 nm with maximums at about 4 nm for OR10J5 and at about 3.65 nm for Olfr16. In addition, by using the two advanced models, the olfactory responses of lyral on OR10J5 and Olfr16 can be used for the energetic characterization of the lyral-OR10J5/Olfr16 binding sites interactions and allowed access to the adsorption energy distributions (AEDs). Then, two approximate olfactory bands can be determined for lyral molecules docked on OR10J5 and Olfr16, which are defined between 3 and 15.5 kJ/mol and between 3.5 and 13.5 kJ/mol, respectively. Lastly, thanks to the proposed models the adsorption entropy of the studied systems can be calculated to describe the disorder and the order on OR10J5 and Olfr16 surfaces (disorder peak of the two olfactory systems was attained when the equilibrium concentration was equal to the concentration at half saturation). Furthermore, the Gibbs free enthalpy and the internal energy were estimated and their negative values indicated that the adsorption phenomenon involved in the olfactory perception was spontaneous and exothermic nature.

Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models

Hemoglobin is one of the most important blood elements, and its optical properties will determine all other optical properties of human blood. Since the refractive index (RI) of hemoglobin plays a vital role as a non-invasive indicator of some illnesses, accurate calculation of it would be of great importance. Moreover, measurement of the RI of hemoglobin in the laboratory is time-consuming and expensive; thus, developing a smart approach to estimate this parameter is necessary. In this research, four viable strategies were used to make a quantitative correlation between the RI of hemoglobin and its influencing parameters including the concentration, wavelength, and temperature. First, alternating conditional expectations (ACE), a statistical approach, was employed to generate a correlation to predict the RI of hemoglobin. Then, three different optimized intelligent techniques-optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)-were used to model the RI. A bat-inspired (BA) algorithm was embedded in the formulation of intelligent models to obtain the optimal values of weights and biases of an artificial neural network, membership functions of the fuzzy inference system, and free parameters of support vector regression. The coefficient of determination, root-mean-square error, average absolute relative error, and symmetric mean absolute percentage error for each of the ACE, ONN, OFIS, and OSVR were found as the measure of each model's accuracy. Results showed that ACE and optimized models (ONN, OFIS, and OSVR) have promising results in the estimation of hemoglobin's RI. Collectively, ACE outperformed ONN, OFIS, and OSVR, while sensitivity analysis indicated that the concentration, wavelength, and, lastly, temperature would have the highest impact on the RI.

Intelligent modeling of dye removal by aluminized activated carbon

Methylene blue (MB) is an important compound in textile and wood processing industries as well as in medical research for combating malaria parasites. Despite these versatilities, direct contact with human beings results in adverse health challenges, and contamination of water bodies affects aquatic biotas. Hence, it is important to treat MB-contaminated wastewaters before disposal into water bodies. Adsorption, which depends on some parameters, proves to be an easy, cheap, and efficient technique to remove pollutants in wastewater. However, investigating these parameters experimentally is a laborious, expensive, and time-consuming process whose efficiency is limited by the conditions imposed on the experiments. Herein, we developed polynomial multiple linear regression (MLR) and the three other machine learning models to study the interplay of five adsorption parameters (descriptors) and their effects on the removal of methylene blue from water using aluminized activated carbon (Al-AC). The optimized machine learning models, that is random forest (R = 0.9905), support vector regression (R = 0.9946), and multilayer perceptron (R = 0.9993), outperformed the best MLR model (R = 0.9845) by small margins. High statistical R and low error values are not enough to satisfactorily classify a model. Hence, the generalizability of the models was further determined under different experimental conditions, and the order of predictive accuracy of the models was established as ANN > SVR > RF > 2-degree MLR. Aluminum loading, adsorbent dosage, and initial adsorbate concentration are the most important factors affecting MB removal. The removal efficiency, which could reach 99.9% at optimum conditions, does not depend on the temperature thus eliminating the need to install temperature control apparatus for practical setup.

Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches

Stabilized aggregate bases are vital for the long-term service life of pavements. Their stiffness is comparatively higher; therefore, the inclusion of stabilized materials in the construction of bases prevents the cracking of the asphalt layer. The effect of wet−dry cycles (WDCs) on the resilient modulus (Mr) of subgrade materials stabilized with CaO and cementitious materials, modelled using artificial neural network (ANN) and gene expression programming (GEP) has been studied here. For this purpose, a number of wet−dry cycles (WDC), calcium oxide to SAF (silica, alumina, and ferric oxide compounds in the cementitious materials) ratio (CSAFRs), ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ3), and deviator stress (σ4) were considered input variables, and Mr was treated as the target variable. Different ANN and GEP prediction models were developed, validated, and tested using 30% of the experimental data. Additionally, they were evaluated using statistical indices, such as the slope of the regression line between experimental and predicted results and the relative error analysis. The slope of the regression line for the ANN and GEP models was observed as (0.96, 0.99, and 0.94) and (0.72, 0.72, and 0.76) for the training, validation, and test data, respectively. The parametric analysis of the ANN and GEP models showed that Mr increased with the DMR, σ3, and σ4. An increase in the number of WDCs reduced the Mr value. The sensitivity analysis showed the sequences of importance as: DMR > CSAFR > WDC > σ4 > σ3, (ANN model) and DMR > WDC > CSAFR > σ4 > σ3 (GEP model). Both the ANN and GEP models reflected close agreement between experimental and predicted results; however, the ANN model depicted superior accuracy in predicting the Mr value.

An Investigation on the Behaviour of Geosynthetic Reinforced Quarry Waste Bases (QWB) Under Vertical loading

The strength and rigidity of base course can significantly affect the performance of pavements. The rigidity of roadways relies on the infill material used in base layers which is interdependent on its thickness and quality. With the increase in the base thickness, the performance of the base course improves but the cost associated with it also increases. Since the natural aggregates are not adequately available, use of waste materials in road construction can prove economical and environmental friendly. In this study, efficacy of geosynthetic (geocells and non-woven geotextile) reinforced quarry waste as an alternative base course materials (BCM) were investigated under static loading conditions (plate load test-PLT). By increasing the geocell height from 100 to 150 mm, the bearing capacity (BC) increased from 450 to 840 kPa. Similarly due to combined use of geocell and geotextile, BC increased from 500 to 890 kPa. The experimental results depict that the geosynthetic reinforcement increases the load bearing capacity of QWB's by 85%. Moreover, the artificial neural network analysis (ANN) was performed to predict the deformation on top of footing while considering different influential parameters. The results obtained from the ANN analysis were in good fit.

Performance Evaluation of Solar Still in Veracruz, Mexico Gulf Coastline

Access to freshwater for rural populations is increasingly difficult worldwide. Even in coastal regions with abundantly available seawater, this is not suitable to meet the population’s basic needs. Desalination with solar stills represents a simple, inexpensive, and accessible alternative to obtaining freshwater. This research shows the results obtained with a mathematical model of a single slope solar still proposed by Dunkle, compared with experimental data. Field experiments were carried out in the coastal zone of the Gulf of Mexico to validate the mathematical model. Different operating parameters of the solar still and their performance depending on climatic variations of the study site were studied. The average yield of distilled water was 1.57 L/m2 d.

Hybrid support vector regression and crow search algorithm for modeling and multiobjective optimization of microalgae-based wastewater treatment

Microalgae-based wastewater treatment (and biomass production) is an environmentally benign and energetically efficient technique as compared to traditional practices. The present study is focused on optimization of the major treatment variables such as temperature, light-dark cycle (LD), and nitrogen (N)-to-phosphate (P) ratio (N/P) for the elimination of N and P from tertiary municipal wastewater utilizing Chlorella kessleri microalgae species. In this regard, a hybrid support vector regression (SVR) technique integrated with the crow search algorithm has been applied as a novel modeling/optimization tool. The SVR models were formulated using the experimental data, which were furnished according to the response surface methodology with Box-Behnken Design. Various statistical indicators, including mean absolute percentage error, Taylor diagram, and fractional bias, confirmed the superior performance of SVR models as compared to the response surface methodology (RSM) and generalized linear model (GLM). Finally, the best SVR model was hybridized with the crow search algorithm for single/multi-objective optimizations to acquire the global optimal treatment conditions for maximum N and P removal efficiencies. The best-operating conditions were found to be 29.3°C, 24/0 h/h of LD, and 6:1 of N/P, with N and P elimination efficiencies of 99.97 and 93.48%, respectively. The optimized values were further confirmed by new experimental data.

Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted N_st by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for N_lg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for N_st, and 0.1000, 0.2700, and 0.1500 for N_lg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.

Generalized prediction and optimal operating parameters of PCDD/F emissions by explainable Bayesian support vector regression

The current derived models for predicting polychlorinated dibenzo-p-dioxins and -furans (PCDD/F) emissions from incineration can only be applied to a specific incinerator due to high deviation or systematic errors. And the models fail to provide quantized guidance for the operation of full-scale municipal solid waste incinerators. To address the problem, explainable Bayesian support vector regression (E-BSVR) has been established to generalized predict and maximumly reduce the PCDD/F emissions. First, forty-two PCDD/F samples were determined from a whole year experiment in a full-scale incinerator. Meanwhile, 1,2,4-trichlorobenzene(1,2,4-TrCBz), carbon monoxide, sulfur dioxide, oxynitride, particulate matter, fluoride, and hydrogen chloride were measured, as input features. Second, after box-cox transformation normalization, and hyperparameters tuning, the R-Squared and root mean square error (RMSE) of the proposed method are 0.983 and 0.044, exhibiting high accuracy. The high accuracy (R-Squared = 0.992) and generalization are also proven on the dataset with high PCDD/F emissions. Then, the performances of BSVR are compared with kernel ridge regression, multiple linear regression, and unary linear regression, indicating afar smaller RMSE of BSVR. Finally, the optimal operating parameters are calculated through local interpretable model-agnostic explanations and the partial dependence plot. Results indicate that reducing the content of organic chlorine in municipal solid waste and inhibiting the deacon reaction are important methods for reducing PCDD/F emissions. The optimal operating parameters for the maximal reduction of PCDD/F emissions are 1,2,4-TrCBz < 0.098 ug/m³, fluoride > 0.452 mg/m³. As a whole, the E-BSVR method can be used as a reliable and accurate approach for the prediction and reduction of PCDD/F emissions.

Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP

This study presents the development of new empirical prediction models to evaluate swell pressure and unconfined compression strength of expansive soils (P_sUCS-ES) using three soft computing methods, namely artificial neural networks (ANNs), adaptive neuro fuzzy inference system (ANFIS), and gene expression programming (GEP). An extensive database comprising 168 P_s and 145 UCS records was established after a comprehensive literature search. The nine most influential and easily determined geotechnical parameters were taken as the predictor variables. The network was trained and tested, and the predictions of the proposed models were compared with the observed results. The performance of all the models was tested using mean absolute error (MAE), root squared error (RSE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), regression coefficient (R²) and relative root mean square error (RRMSE). The sensitivity analysis indicated that the increasing order of inputs importance in case of P_s followed the order: maximum dry density MDD (30.5%) > optimum moisture content OMC (28.7%) > swell percent SP (28.1%) > clay fraction CF (9.4%) > plasticity index PI (3.2%) > specific gravity G_s (0.1%), whereas, in case of UCS it followed the order: sand (44%) > PI (26.3%) > MDD (16.8%) > silt (6.8%) > CF (3%) > SP (2.9%) > G_s (0.2%) > OMC (0.03%). Parametric analysis was also performed and the resulting trends were found to be in line with findings of past literature. The comparison results reflected that GEP and ANN are efficacious and reliable techniques for estimation of P_sUCS-ES. The derived mathematical GP-based equations portray the novelty of GEP model and are comparatively simple and reliable. The R_overall values for P_sUCS-ES followed the order: ANN > GEP > ANFIS, with all values lying above the acceptable range of 0.80. Hence, all the proposed AI approaches exhibit superior performance, possess high generalization and prediction capability, and evaluate the relative importance of the input parameters in predicting the P_sUCS-ES. The GEP model outperformed the other two models in terms of closeness of training, validation and testing data set with the ideal fit (1:1) slope. Evidently the findings of this study can help researchers, designers and practitioners to readily evaluate the swell-strength characteristics of the widespread expansive soils thus curtailing their environmental vulnerabilities which leads to faster, safer and sustainable construction from the standpoint of environment friendly waste management.

Rapid detection of seven indexes in sheep serum based on Raman spectroscopy combined with DOSC-SPA-PLSR-DS model

Hepatic fascioliasis, ketosis of pregnancy, toxemia of pregnancy and other common sheep diseases will directly affect the concentration (/enzymatic activity) of seven indicators, such as cortisol and high-density lipoprotein cholesterol (HDL-C) in sheep serum. Whether the concentrations (/enzymatic activity) of these indicators can be detected quickly will directly affect the prevention of sheep diseases and the targeted adjustment of breeding methods, thereby affecting the economic benefits of sheep breeding. In this research, we established partial least square regression (PLSR), support vector regression based on genetic algorithm optimization (GA-SVR) and extreme learning machine (ELM) models. Due to the large differences in the content of different substances, it is difficult to directly use the RMSE to evaluate the quantitative effect of the model. This study is the first to propose conducting deviation standardization (DS) for the determination results of various substances. To further improve the performance of the model, we use the successive projections algorithm (SPA) to optimize feature extraction and combine it with the better-performing PLSR model for training. The results show that the optimized DOSC-SPA-PLSR-DS quantitative model has better determination results for 101 sheep serum samples. The average RMSE_p* of the concentration of the six substances decreased from 0.0408 to 0.0387, the R_p² increased from 0.9758 to 0.9846, and the running time was reduced from 0.1659 to 0.0008 s. And the determination performance of lipase (LPS) enzymatic activity has also been improved. The results of this research show that sheep serum Raman spectroscopy combined with DOSC-SPA-PLSR-DS optimization can efficiently monitor the concentration (/enzyme activity) of seven indicators in real time and provide a new strategy for future intelligent supervision of animal husbandry.

Modeling the viscosity of nanofluids using artificial neural network and Bayesian support vector regression

This study demonstrates the application of artificial neural networks (ANNs) and Bayesian support vector regression (BSVR) models for predicting the relative viscosity of nanofluids. The study examined 19 nanofluids comprising 1425 experimental datasets that were randomly split in a ratio of 70:30 as a training dataset and a testing dataset, respectively. To establish the inputs that will yield the best model prediction, we conducted a systematic analysis of the influence of volume fraction of nanoparticles, the density of nanoparticles, fluid temperature, size of nanoparticles, and viscosity of base fluids on the relative viscosity of the nanofluids. Also, we analyzed the results of all possible input combinations by developing 31 support vector regression models based on all possible input combinations. The results revealed that the exclusion of the viscosity of the base fluids (as a model input) leads to a significant improvement in the model result. To further validate our findings, we used the four inputs—volume fraction of nanoparticles, the density of nanoparticles, fluid temperature, and size of nanoparticles to build an ANN model. Based on the 428 testing datasets, the BSVR and ANN predicted the relative viscosity of nanofluids with an average absolute relative deviation of 3.22 and 6.64, respectively. This indicates that the BSVR model exhibits superior prediction results compared to the ANN model and existing empirical models. This study shows that the BSVR model is a reliable approach for the estimation of the viscosity of nanofluids. It also offers a generalization ability that is much better than ANN for predicting the relative viscosity of nanofluids.

Short-term prediction of safety and operation impacts of lane changes in oscillations with empirical vehicle trajectories

Lane changes made during traffic oscillations on freeways largely affect traffic safety and could increase collision potentials. Predicting the impacts of lane change can help to develop optimal lane change strategies of autonomous vehicles for safety improvement. The study aims at proposing a machine learning method for the short-term prediction of lane-changing impacts (LCI) during the propagation of traffic oscillations. The empirical lane-changing trajectory records were obtained from the Next Generation Simulation (NGSIM) platform. A support vector regression (SVR) model was trained in this study to predict the LCI on the crash risks and flow change using microscopic traffic variables such as individual speed, gap and acceleration on both original lanes and target lanes. Sensitivity analyses were conducted in the SVR to quantify the contributions of correlative lane changing factors. The results showed that the trained SVR model achieved an accuracy of 72.81 % for the risk of crashes and 95.34 % in predicting the flow change. The sensitivity analysis explored the optimal speed and acceleration for the lane changer to achieve the lowest time integrated time-to-collision (TIT) value for safety maximization. Finally, we compared the LCI for motorcycles, automobiles and trucks as well as the LCI for both lane-changing directions (from left to right and from right to left). It was found that motorcycles conducted lane changes with smaller gaps and larger speed differences, which brings the highest crash risks. Passenger cars were found to be the safest when they conduct lane changes. Lane changes to the right had more negative impacts on traffic flow and crash risks.

Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression

The specific heat capacity of nanofluids ( C P n f ) is a fundamental thermophysical property that measures the heat storage capacity of the nanofluids.C P n f is usually determined through experimental measurement. As it is known, experimental procedures are characterised with some complexities, which include, the challenge of preparing stable nanofluids and relatively long periods to conduct experiments. So far, two correlations have been developed to estimate theC P n f . The accuracies of these models are still subject to further improvement for many nanofluid compositions. This study presents a four-input support vector regression (SVR) model hybridized with a Bayesian algorithm to predict the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids. The bayesian algorithm was used to obtain the optimum SVR hyperparameters. 189 experimental data collected from published literature was used for the model development. The proposed model exhibits low average absolute relative deviation (AARD) and a high correlation coefficient (r) of 0.40 and 99.53 %, respectively. In addition, we analysed the accuracies of the existing analytical models on the considered nanofluid compositions. The model based on the thermal equilibrium between the nanoparticles and base fluid (model II) show good agreement with experimental results while the model based on simple mixing rule (model I) overestimated the specific heat capacity of the nanofluids. To further validate the superiority of the proposed technique over the existing analytical models, we compared various statistical errors for the three models. The AARD for the BSVR, model II, and model I are 0.40, 0.82 and 4.97, respectively. This clearly shows that the model developed has much better prediction accuracy than existing models in predicting the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids. We believe the presented model will be important in the design of nanofluid-based applications due to its improved accuracy.

Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy

Masson pine is widely planted in southern China, and moisture content of the pine seedling leaves is an important index for evaluating the vigor of seedlings. For precisely predicting leaf moisture content, near-infrared spectroscopy analysis is applied in the experiment, which is a cost-effective, high-speed, and noninvasive material content prediction tool. To further improve the spectroscopy analysis accuracy, in this study, a new analysis model is proposed which integrates a stacked autoencoder for extracting hierarchical output-related features layer by layer and a support vector regression model to leverage these features for precisely predicting moisture contents. Compared with traditional spectroscopy analysis method like partial least squares regression and basic support vector regression, the proposed model shows great superiority for leaf moisture content prediction, with R2 value 0.9946 and root-mean squared error (RMSE) value 0.1636 in calibration set and R2 value 0.9621 and RMSE 0.4249 in prediction set.