Low Error Research Articles

Novel sulfonic groups grafted sugarcane bagasse biosorbent for efficient removal of cationic dyes from wastewater

In this research, we reported the synthesis of effective sulphonated sugarcane bagasse (SCB@SA) biosorbent based on agriculture waste materials via a simple diazotization strategy for the removal of methylene blue (MB) and Bismarck Brown R(BB) dyes from waste water samples. First, the sugarcane bagasse (SCB) waste was collected, grinded, and sieved to obtain the desired size. Secondly, the SCB powder is modified with sulfanilinic acid (SA) via the formation of its diazonium salt to introduce sulfonic groups on the SCB surface. Different advanced techniques were applied to characterize the prepared materials before and after the adsorption process viz. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Energy-dispersive X-ray spectroscopy (EDS) and Thermogravimetric analysis (TGA). Different parameters affecting the adsorption process of both MB and BB were studied. Because of the higher correlation coefficient (R2 ≥ 0.999) and lower error functions, the equilibrium MB and BB adsorption isotherms for a single-dye system fit Langmuir with maximum adsorption capacity reaching to 127.48 and 166.75 mg/g for MB and BB, respectively. Moreover, the RL values obtained for both dyes lie between 0 and 1, indicating that MB and BB adsorption by SCB@SA is a favorable process. Besides, the error functions’ values of the pseudo-2nd-order are significantly lower than those of the pseudo-1st-order, implying that the adsorption MB and BB onto SCB@SA biosorbent fitted the pseudo-2nd-order kinetic model in a chemosorption manner. In the thermodynamic studies, the adsorption process is spontaneous, exothermic, and has less randomness. In addition, the SCB@SA biosorbent could be reused in five cycles maintaining on suitable adsorption efficiency. Finally, the MB and BB dyes could be adsorbed on the SCB@SA biosorbent via three mechanisms including π-π stacking, columbic attraction, and hydrogen bonding.

Understanding the Dynamics of Ocean Wave-Current Interactions Through Multivariate Multi-Step Time Series Forecasting

ABSTRACT Understanding ocean wave-current interactions’ complex dynamics is crucial for coastal engineering, marine operations, and climate research applications. This study introduces a pioneering data-driven approach by employing advanced deep learning techniques, specifically Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) models, to forecast both wave and current parameters at varying depths. The models are designed to capture the complex temporal relationships inherent in ocean dynamics, considering wave speed and direction, current speed, and direction as multivariate time series inputs. Two comprehensive experiments are conducted, one utilizing historical values of all parameters and another focusing on using wave parameters to forecast current parameters. Model performance is rigorously evaluated across forecast horizons of 5, 12, and 24 hours ahead using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). BiLSTM emerges as the superior model, demonstrating lower errors, particularly at higher depths, while nearshore predictions reveal challenges in shallower waters. Furthermore, the methodology incorporates hyperparameter optimization and cross-validation techniques to enhance the model’s robustness. Ultimately, this work represents a transformative leap toward smarter oceans, emphasizing the fusion of fluid dynamics and bathymetry to advance our understanding of coupled wave-current dynamics. The results showcase high accuracy and reliability across various forecast horizons and depths, signifying the method’s potential applications in oceanography, hydrodynamics, coastal engineering, and ocean renewable energy.

Novel multi-degree-of-freedom force–displacement mixed control framework for low loading rate substructure hybrid testing

In substructure hybrid tests, imposing realistic multi-degree-of-freedom (MDOF) displacement and force boundary conditions on a specimen involves controlling several actuators to emulate the MDOF boundary conditions of the experimental substructure. This setup presents significant challenges for decoupling control in MDOF systems. Due to the typically high stiffness of the specimen in the axial direction, force control is preferred over displacement control, which has limited accuracy in this direction. Typically, force control in the axial direction is combined with simultaneous displacement control in the softer direction, forming an MDOF force-displacement mixed control scheme. This scheme transforms force control into displacement control in the inner loop in the axial direction to ensure safe loading. The scheme involves addressing two major challenges: the nonlinear force–displacement transformation and the nonlinear coordinate transformation from the local actuator space (LAS) to the global Cartesian coordinate (GCC) system. To address these challenges, the Modified Newton–Raphson iteration integrated with proportional integral plus feed-forward (MNR-PIFF) and incremental kinematic transformation integrated with proportional integral plus feed-forward (IKT-PIFF) methods are proposed. Based on these methods, a novel MDOF force–displacement mixed control (NMFDC) framework is developed for substructure hybrid testing. A 6-DOF cyclic test simulation using the NMFDC framework was conducted to demonstrate its superiority. Furthermore, a 6-DOF cyclic test and substructure hybrid test at low loading rates were performed to validate the NMFDC framework because of the loading equipment limit. Experimental results and numerical simulations indicate that the NMFDC framework achieves swifter response with lower error levels. For applications in fast and real-time hybrid testing, dynamic loading equipment need to be used, the PIFF controller in the NMFDC framework should be replaced by adaptive time delay compensation in future developments.

Using quantile mapping and random forest for bias‐correction of high‐resolution reanalysis precipitation data and CMIP6 climate projections over Iran

AbstractClimate change is expected to cause important changes in precipitation patterns in Iran until the end of 21st century. This study aims at evaluating projections of climate change over Iran by using five climate model outputs (including ACCESS‐ESM1‐5, BCC‐CSM2‐MR, CanESM5, CMCC‐ESM2 and MRI‐ESM2‐0) of the Coupled Model Intercomparison Project phase 6 (CMIP6), and performing bias‐correction using a novel combination of quantile mapping (QM) and random forest (RF) between the years 2015 and 2100 under three shared socioeconomics pathways (SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). First, bias‐correction was performed on ERA5‐Land reanalysis data as reference period (1990–2020) using the QM method, then the corrected ERA5‐Land reanalysis data was considered as measured data. Based on the corrected ERA5‐Land reanalysis data (1990–2020) and historical simulations (1990–2014), the future projections (2015–2100) were also bias‐corrected utilizing the QM method. Next, the accuracy of the QM method was validated by comparing the corrected ERA5‐Land reanalysis data with model outputs for overlapping years between 2015 and 2020. This comparison revealed persistent biases; hence, a combination of QM‐RF method was applied to rectify future climate projections until the end of the 21st century. Based on the QM result, CMCC‐ESM2 revealed the highest RMSE in both SSP2‐4.5 and SSP3‐7.0 amounting to 331.74 and 201.84 mm·year−1, respectively. Particularly, the exclusive use of the QM method displayed substantial errors in projecting annual precipitation based on SSP5‐8.5, notably in the case of ACCESS‐ESM1‐5 (RMSE = 431.39 mm·year−1), while the RMSE reduced after using QM‐RF method (197.75 mm·year−1). Obviously, a significant enhancement in results was observed upon implementing the QM‐RF combination method in CMCC‐ESM2 under both SSP2‐4.5 (RMSE = 139.30 mm·year−1) and SSP3‐7.0 (RMSE = 151.43 mm·year−1) showcasing approximately reduction in RMSE values by 192.43 and 50.41 mm·year−1, respectively. Although each bias‐corrected model output was evaluated individually, multi‐model ensemble (MME) was also created to project the annual future precipitation pattern in Iran. By considering that combination of QM‐RF method revealed the lower errors in correcting model outputs, we used the QM‐RF technique to create the MME. Based on SSP2‐4.5, the MME climate projections highlight imminent precipitation reductions (>10%) across large regions of Iran, conversely projecting increases ranging from 10% to over 20% in southern areas under SSP3‐7.0. Moreover, MME projected dramatic declines under SSP5‐8.5, especially impacting central, eastern, and northwest Iran. Notably, the most pronounced possibly decline patterns are projected for arid regions (central plateau) and eastern areas under SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5.

The influence of radiation modeling on flame characteristics and emissions prediction in microcombustors with Ammonia/Hydrogen blends

This study advances numerical methodologies for designing microcombustor-based thermophotovoltaic systems by incorporating comprehensive analyses of radiation models and absorption coefficients. It examines the impact of non-premixed hydrogen-ammonia-air fuel blends on the micro combustion process using three-dimensional CFD simulations on a Y-shaped microcombustor under atmospheric conditions.Key to this research is modeling radiative heat transfer from multiple species, including ammonia (NH3), nitric oxide (NO), and water vapor, ensuring accurate predictions and optimization of thermal behavior and efficiency. Three modeling approaches were compared: a multispecies Planck-mean approximation radiation model (PMAC), a narrowband model for water vapor, and a scenario excluding radiation effects.The PMAC model, incorporating the absorption coefficients of ammonia and its products, consistently produced lower errors in flame length compared to the NB method across all equivalence ratios, with errors ranging from 0 % to 7 % for PMAC and 10 % to 30 % for NB, with the lowest error observed at Φ = 1.0. Additionally, the prediction of laminar burning velocity (LBV) differed by approximately 4 % between the two models.Considering different fuel blends, two scenarios were compared: constant input thermal power and constant fuel flow rate. Efficiency in the constant input thermal power scenario maintained higher radiation efficiency, in the range of 32–38%. Additionally, radiation efficiency initially increased with increasing H2 content but subsequently decreased for high H2 values due to effects on flame area, flame position, and temperature.

A comparison of occupancy-sensing and energy-saving performance: CO2 sensors versus fisheye cameras

A number of occupancy-sensing methods have been proposed to date with the goal of saving HVAC energy in commercial spaces by means of demand-controlled ventilation. Currently, CO2 measurement is the most common approach deployed in practice. However, recently a number of other approaches have been proposed, such as using surveillance cameras, depth sensors, thermal imagers, etc. In this paper, we compare the occupancy-sensing performance of high-accuracy commercial CO2 sensors and overhead fisheye cameras supported by deep-learning algorithms. Our experiments are conducted over 3 days in a large university classroom with highly-dynamic occupancy. First, we assess the impact of parameter selection and sensor placement on CO2-to-occupancy conversion accuracy. Then, for the best-performing setup, we compare this accuracy against that of fisheye cameras. Subsequently, we estimate the potential energy savings offered by both systems. Our results show that overhead fisheye cameras produce on average a 20% lower occupancy-estimation error than CO2 sensors in steady-state periods, and almost 70% lower error in transient periods across 4 performance metrics used in this work. In terms of potential energy savings, fisheye cameras offer on average 6 percentage points of additional savings compared to CO2 sensors. Given their relatively low cost and implementation simplicity, CO2 sensors are an attractive approach to saving HVAC energy, however their occupancy-estimation accuracy is highly sensitive to parameter selection that is challenging in practice. Occupancy sensing based on fisheye cameras is not subject to similar parameterization sensitivity and in addition to outperforming CO2 sensors allows precise localization of occupants in a space thus potentially enabling additional space-management and safety/security services, that CO2 sensors cannot offer.

AI-based non-linear models for mechanical and toughness properties of sustainable fiber-reinforced geopolymer concrete (FRGPC)

The incorporation of fibers into geopolymer concrete (GPC) produces FRGPC which mitigates brittle failure and restrains macro crack propagation; however, research on predicting the mechanical and toughness properties of FRGPC remains limited. This study addresses this gap by developing prediction models for compressive strength (CS), flexural strength (FS), flexural toughness (FT), and fracture toughness (FR). A dataset of 600 data points was compiled from the published literature, encompassing various constituent proportions, fiber shapes, fiber dosages, aspect ratios, and curing conditions. Employing an artificial neural network (ANN) methodology, 10 independent variables (g1, g2, …., g10) were utilized to predict four dependent variables (CS, FS, FT, and FR), resulting in the development of eight non-linear ANN models for both straight fibers (SFs) and hooked fibers (HFs). Each model has shown a higher R 2 value and lower root mean square error (RMSE) for training (70%), testing (15%), and validation (15%) datasets. The prediction model for the CS of FRGPC with HFs (CS-HF) and SF (CS-SF) showed R 2 values of 0.983 and 0.973, and RMSE of 2.088 and 2.435 MPa highlighting the accuracy of the ANN models. Similarly, the comparative analysis for FR models exhibited R 2 values of 0.997 and 0.987, and RMSE values of 0.045 Mpa √m and 0.032 Mpa √m for FR-HF and FR-SF, highlighting that fiber addition strongly impacts improving toughness properties. To identify the most influential independent variable(s), sensitivity analysis revealed g10, g1, g8, and g9 as the most influential parameters for mechanical and toughness properties with SFs. For HFs, g1, g2, and g3 were most influential for mechanical properties, and g8, g9, and g10 for toughness. This study also presented the mathematical formulation of the developed models for better interpretability and to facilitate optimal and economical FRGPC mix design selection, highlighting the potential of AI-based models in advancing sustainable construction materials.

Numerical and machine learning modeling of GFRP confined concrete-steel hollow elliptical columns

This article investigates the behavior of hybrid FRP Concrete-Steel columns with an elliptical cross section. The investigation was carried out by gathering information through literature and conducting a parametric study, which resulted in 116 data points. Moreover, multiple machine learning predictive models were developed to accurately estimate the confined ultimate strain and the ultimate load of confined concrete at the rupture of FRP tube. Decision Tree (DT), Random Forest (RF), Adaptive Boosting (ADAB), Categorical Boosting (CATB), and eXtreme Gradient Boosting (XGB) machine learning techniques were utilized for the proposed models. Finally, these models were visually and quantitatively verified and evaluated. It was concluded that the CATB and XGB are standout models, offering high accuracy and strong generalization capabilities. The CATB model is slightly superior due to its consistently lower error rates during testing, indicating it is the best model for this dataset when considering both accuracy and robustness against overfitting.

Better representation of vegetation phenology improves estimations of annual gross primary productivity

Carbon uptake by vegetation plays a vital role in the global carbon cycle. Annual gross primary productivity (AGPP) represents the total amount of carbon compounds produced by vegetation photosynthesis over a year and is a crucial metric for quantifying vegetation carbon uptake. Although some theories have been developed to explain the spatiotemporal variation in AGPP, they often overlook the seasonal differences in photosynthesis across vegetation phenological periods. This gap highlights the need for a more reasonable representation of vegetation phenology in AGPP modelling. Therefore, we developed a novel theoretical AGPP model that decomposes AGPP into detailed vegetation phenological periods (green-up, maturation, and senescence) and investigated the effects of climatic factors on the seasonal components of AGPP. Compared with existing models, our model considers the length of the multiple vegetation phenological periods, rather than just the length of the carbon uptake period. When evaluated against flux tower data, our model outperformed the comparative model, with a higher determination coefficient and lower root mean square error. The analysis also demonstrated that the developed model can reproduce spatial and temporal variations in satellite-based global AGPP. In addition, we identified distinct differences in the responses of AGPP's seasonal components to climatic factors: shortwave radiation predominantly affected the seasonal component of AGPP during senescence, air temperature during green-up, and vapor pressure deficit during maturation. Our study proposes a potential mechanism for AGPP estimation and highlights the importance of accurately representing vegetation phenology in AGPP modelling.

Prediction Of Student Achievement Using Artificial Neural Network And Support Vector Regression At SMK TELKOM Lampung

The analysis of student performance is crucial in vocational schools because it helps identify the challenges students face in preparing themselves for the workforce. By integrating data mining techniques such as Artificial Neural Networks (ANN), educators can enhance their understanding of factors that improve student learning outcomes. An artificial neural network (ANN) is composed of interconnected artificial neurons that can learn from input data and make complex predictions, including academic achievements. The structure and function of the human brain inspire ANN. This study compares the effec- tiveness of the artificial neural network (ANN) method with other methodologies, such as support vector regression (SVR), to predict student achievement at SMK Telkom Lampung. Primary data collected from SMK Telkom Lampung includes 4939 examples with 550 cases, 26 features, and 4 meta-attributes. Performance evaluation involves metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). The coefficient of determination (R2) value of the Neural Network at 0.001 is higher than the R2 value of SVR, which reaches -0.036. Research find- ings indicate that the Artificial Neural Network model slightly outperforms the Support Vector Regression model, with lower prediction error rates and better ability to explain data variability.

Effects of slow dynamic, fast dynamic, and static stretching on recovery of performance, range of motion, balance, and joint position sense in healthy adults

IntroductionConsidering the effects of fatigue on athletic performance and the subsequent increase in the probability of injury, the purpose of this study was to compare the effects of slow dynamic, fast dynamic, and static stretching on the recovery of performance, range of motion (ROM), balance, and joint position sense.MethodsFifteen collegiate healthy females were involved in four separate sessions of slow dynamic stretching (SDS), fast dynamic stretching (FDS), static stretching (SS), and control condition (CC; without stretching), in a random order with at least 48 h of rest between sessions. After warming up, the individuals performed ROM, balance, joint position sense (JPS) maximum voluntary isometric contraction (MVIC) force as well as countermovement (CMJ) and squat jump (SJ) as pre-tests. After performing the knee fatigue protocol of 4 sets of knee extension and flexion at 60% of 1 repetition maximum (RM) to exhaustion (CC; without stretching) or stretching programs (SDS or FDS or SS), the subjects repeated all the tests at post-test 1 (after 5 min) and post-test 2 (after 60 min).ResultsA significantly lower JPS error was detected with SDS while JPS error increased in the SS and control conditions (p < 0.0001). MVIC force significantly increased with SDS and FDS but decreased in control and SS conditions (p < 0.0001). Moreover, a significant decrease in CMJ and SJ height in SS and control conditions was revealed (p < 0.0001). Also, a significant decrease in balance with the control condition was revealed. But only SDS minimized fatigue-induced balance decrements (p < 0.0001). Additionally, the control condition experienced a significant decrease in knee extensor ROM, which contrasted with the significant increase in the quadriceps flexibility with the stretching conditions.ConclusionsThe present results support the idea that SDS may increase quadriceps MVIC force, knee extensor ROM and knee JPS. So according to the present results, it is suggested that the SDS could be implemented and incorporated into a regular recovery program.

Prediction of flash flood peak discharge in hilly areas with ungauged basins based on machine learning

ABSTRACT Peak discharge is an essential element of hydrological forecasting. Due to rapid outbreaks of flash floods in hilly areas and the lack of measured data, the fast and accurate estimation of peak discharge is crucial for flash flood hazard management. Three machine learning algorithms were applied to estimate peak discharge; this estimation was compared with the results of hydrological–hydraulic models, and the results were verified with measured watershed data. In this paper, 10 hydrological and geomorphological parameters were selected to predict the flood peak discharge in 103 watersheds in Taiyi Mountain North District. The results show that the particle swarm optimization backpropagation (PSO-BP) neural network model outperforms the BP neural network and random forest regression in prediction performance. PSO-BP has a lower mean absolute error (2.51%), root mean square error (3.74%), and mean absolute percentage error (2.74%) than the other models, which indicates that PSO-BP has high prediction accuracy. Importance analysis revealed that rainfall, early impact rainfall, catchment area, and rain intensity are the key input parameters of PSO-BP. The proposed method was confirmed to be a fast and relatively accurate algorithm for estimating the peak discharge of flash floods in ungauged basins.

Lineage-informative microhaplotypes for recurrence classification and spatio-temporal surveillance of Plasmodium vivax malaria parasites

Challenges in classifying recurrent Plasmodium vivax infections constrain surveillance of antimalarial efficacy and transmission. Recurrent infections may arise from activation of dormant liver stages (relapse), blood-stage treatment failure (recrudescence) or reinfection. Molecular inference of familial relatedness (identity-by-descent or IBD) can help resolve the probable origin of recurrences. As whole genome sequencing of P. vivax remains challenging, targeted genotyping methods are needed for scalability. We describe a P. vivax marker discovery framework to identify and select panels of microhaplotypes (multi-allelic markers within small, amplifiable segments of the genome) that can accurately capture IBD. We evaluate panels of 50–250 microhaplotypes discovered in a global set of 615 P. vivax genomes. A candidate global 100-microhaplotype panel exhibits high marker diversity in the Asia-Pacific, Latin America and horn of Africa (median HE = 0.70–0.81) and identifies 89% of the polyclonal infections detected with genome-wide datasets. Data simulations reveal lower error in estimating pairwise IBD using microhaplotypes relative to traditional biallelic SNP barcodes. The candidate global panel also exhibits high accuracy in predicting geographic origin and captures local infection outbreak and bottlenecking events. Our framework is open-source enabling customised microhaplotype discovery and selection, with potential for porting to other species or data resources.

Computational intelligent techniques for predicting optical behavior of different materials

The current research introduces a comparison study of the utilization of artificial neural networks (ANN) and genetic programming (GP) for predicting the optical behavior of different materials. The experimental data for a variety of materials, including semiconductors, insulators, oxides, and halides are extracted and utilized in ANN and GP as inputs. Simulation and prediction processes are carried out based on ANN and GP techniques. The most important aim presented in the current research is to obtain two equations of n as a function of Eg by the two based on ANN and GP models. The first numerical equation is obtained based on the ANN model exhibiting mean squared error (MSE) does not exceed 10−1. The second nonlinear equation is obtained based on the GP model with an acceptable fitness value. The estimated results based on the proposed approaches ANN and GP presented a great match with their targets. It demonstrated that the trained results including simulated and predicted results based on ANN and GP introduce excellent fitting compared with alike results obtained based on the conventional theoretical techniques. The mean absolute percentage error values prove that the ANN model is significantly more accurate than the GP model. Since lower error values suggest better prediction, hence it is clear that ANN performed better than GP. The equation derived by the ANN model is utilized to predict the refractive index for binary and ternary compounds. The values of the refractive index have been predicted for materials for which measurements have been made practically as a test step to ensure the accuracy of the results obtained through the deduced mathematical equations. Then, the application of these equations was generalized to materials that were not measured experimentally. A detailed discussion of the modeling results is introduced and proved that ANN and GP models are effective and successful tools for predicting the refractive index for different types of materials.

Forecasting CO2 emissions of fuel vehicles for an ecological world using ensemble learning, machine learning, and deep learning models.

The continuous increase in carbon dioxide (CO2) emissions from fuel vehicles generates a greenhouse effect in the atmosphere, which has a negative impact on global warming and climate change and raises serious concerns about environmental sustainability. Therefore, research on estimating and reducing vehicle CO2 emissions is crucial in promoting environmental sustainability and reducing greenhouse gas emissions in the atmosphere. This study performed a comparative regression analysis using 18 different regression algorithms based on machine learning, ensemble learning, and deep learning paradigms to evaluate and predict CO2 emissions from fuel vehicles. The performance of each algorithm was evaluated using metrics including R2, Adjusted R2, root mean square error (RMSE), and runtime. The findings revealed that ensemble learning methods have higher prediction accuracy and lower error rates. Ensemble learning algorithms that included Extreme Gradient Boosting (XGB), Random Forest, and Light Gradient-Boosting Machine (LGBM) demonstrated high R2 and low RMSE values. As a result, these ensemble learning-based algorithms were discovered to be the most effective methods of predicting CO2 emissions. Although deep learning models with complex structures, such as the convolutional neural network (CNN), deep neural network (DNN) and gated recurrent unit (GRU), achieved high R2 values, it was discovered that they take longer to train and require more computational resources. The methodology and findings of our research provide a number of important implications for the different stakeholders striving for environmental sustainability and an ecological world.

Formation energy prediction of neutral single-atom impurities in 2D materials using tree-based machine learning

Abstract We applied tree-based machine learning algorithms to predict the formation energy of impurities in 2D materials, where adsorbates and interstitial defects are investigated. Regression models based on random forest, gradient boosting regression, histogram-based gradient-boosting regression, and light gradient-boosting machine algorithms are employed for training, testing, cross validation, and blind testing. We utilized chemical features from fundamental properties of atoms and supplemented them with structural features from the interaction of the added chemical element with its neighboring host atoms via the Jacobi–Legendre (JL) polynomials. Overall, the prediction accuracy yields optimal MAE ≈ 0.518 , RMSE ≈ 1.14 , and R 2 ≈ 0.855 . When trained separately, we obtained lower residual errors RMSE and MAE, and higher R 2 value for predicting the formation energy in the adsorbates than in the interstitial defects. In both cases, the inclusion of the structural features via the JL polynomials improves the prediction accuracy of the formation energy in terms of decreasing RMSE and MAE, and increasing R 2. This work demonstrates the potential and application of physically meaningful features to obtain physical properties of impurities in 2D materials that otherwise would require higher computational cost.

A hierarchical estimation of road grade based on tire force observation

Road grade is important for autonomous vehicles, but it is difficult to measure directly. To address this issue, a hierarchical estimation of road grade is suggested based on the observation of tire forces. First, a 7-degree-of-freedom (DOF) dynamics model, including vehicle longitudinal, lateral, and yaw motions together with wheel rotations, is developed while considering the road grade. Subsequently, a dual-layer road grade estimation strategy is proposed based on an unscented Kalman filter (UKF). The lower-layer UKF estimates the longitudinal and lateral tire forces for road grade observation, and the upper-layer UKF is employed to estimate the road grade by considering the vehicle’s lateral acceleration and yaw rate. Finally, CarSim and MATLAB joint simulations and road tests are performed under different conditions to validate the correctness and effectiveness of the proposed estimation method. The results show that the proposed tire force observation-based estimator exhibits a lower mean absolute error and root mean square error on sloping roads and combined curved and sloping roads, and presents a better overall estimation performance on road grade compared with the widely used kinematics and dynamics model-based estimators.

Comparison of enhanced neural network and response surface models in predicting bio-dissolution of aluminum and vanadium from bauxite residue by isolated Aspergillus niger strains

Background: Recovery of Al and V from red mud, a hazardous residue of the Bayer process, using bioleaching is a nature-based waste management solution that simultaneously improves environment protection and metallurgical recoveries.Methods: For the first time, a novel hybrid multilayer Perceptron (MLP) network enhanced by an Imperialist Competitive Algorithm (ICA), and a response surface developed based on a factorial experiment design methodology (RSM) were employed and compared for, prediction optimization and optimization prediction of Al and V bioleaching by strains of A. Niger microorganisms isolated from pistachio shells and grape skins. The controlling variables were fungi source (A), adoption strategy (B), solid activation (C), solid percent (D), and bioleaching time. Considering the stochastic nature of ICA, a multi-criteria-ranking system based on accuracy and error indices was developed to select the best MLP. Probability value at 95% confidence interval, lack-of-fit, analysis of variance, R2, Adj. R2, and predicted R2 were the judges for the determination of the developed model's statistical significance.Significant Findings: Based on ANOVA, between A, B, C, and D, the effectiveness orders of A > D > C>AC>AD on Al, and A>AD>C>AC>D on V bioleaching were obtained. The superiority of the developed hybrid ICA-MLP model over the developed RSM model in predicting Al and V dissolution was determined by NSE, RSME, MAE, and MEDAE. Parameters optimization and consequently evaluating optimum condition repeatability through three repeating experiments resulted in maximum dissolution recoveries of RSM: 96.5% for Al and 91.2% for V, and ICA-MLP: 97.1% for Al and 90.3% for V. Considering the lower relative errors of repeating validation tests, it can be concluded that, although both models provide reasonable results, but the ICA-MLP methodology is more reliable (relative error <1.8%).

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Background and ObjectivesTechniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. MethodsTo construct the cGAN training and simulated test sets, we generated 30,000 artificial elasticity images using a parametric model and computed the corresponding phase difference images using finite element analysis to simulate compression applied to the artificial samples. We also imaged real MCF7 breast tumor spheroids embedded in hydrogel using mechano-microscopy to construct the experimental test set and evaluated the cGAN using the algebraic elasticity images and co-registered OCM and confocal fluorescence microscopy (CFM) images. ResultsComparison with the simulated test set ground truth elasticity images shows the cGAN produces a lower root mean square error (median: 3.47 kPa, 95 % confidence interval (CI) [3.41, 3.52]) than the algebraic method (median: 4.91 kPa, 95 % CI [4.85, 4.97]). For the experimental test set, the cGAN elasticity images contain features resembling stiff nuclei at locations corresponding to nuclei seen in the algebraic elasticity, OCM, and CFM images. Furthermore, the cGAN elasticity images are higher resolution and more robust to noise than the algebraic elasticity images. ConclusionsThe cGAN elasticity images exhibit better accuracy, spatial resolution, sensitivity, and robustness to noise than the algebraic elasticity images for both simulated and real experimental data.

Fusing multi-scale functional connectivity patterns via Multi-Branch Vision Transformer (MB-ViT) for macaque brain age prediction

Brain age (BA) is defined as a measure of brain maturity and could help characterize both the typical brain development and neuropsychiatric disorders in mammals. Various biological phenotypes have been successfully applied to predict BA of human using chronological age (CA) as label. However, whether the BA of macaque, one of the most important animal models, can also be reliably predicted is largely unknown. To address this question, we propose a novel deep learning model called Multi-Branch Vision Transformer (MB-ViT) to fuse multi-scale (i.e., from coarse-grained to fine-grained) brain functional connectivity (FC) patterns derived from resting state functional magnetic resonance imaging (rs-fMRI) data to predict BA of macaques. The discriminative functional connections and the related brain regions contributing to the prediction are further identified based on Gradient-weighted Class Activation Mapping (Grad-CAM) method. Our proposed model successfully predicts BA of 450 normal rhesus macaques from the publicly available PRIMatE Data Exchange (PRIME-DE) dataset with lower mean absolute error (MAE) and mean square error (MSE) as well as higher Pearson's correlation coefficient (PCC) and coefficient of determination (R2) compared to other baseline models. The correlation between the predicted BA and CA reaches as high as 0.82 of our proposed method. Furthermore, our analysis reveals that the functional connections predominantly contributing to the prediction results are situated in the primary motor cortex (M1), visual cortex, area v23 in the posterior cingulate cortex, and dysgranular temporal pole. In summary, our proposed deep learning model provides an effective tool to accurately predict BA of primates (macaque in this study), and lays a solid foundation for future studies of age-related brain diseases in those animal models.