Lower Root Mean Square Error Values Research Articles

Prediction of crippling load of I-shaped steel columns by using soft computing techniques

AbstractThis study is primarily aimed at creating three machine learning models: artificial neural network (ANN), random forest (RF), and k-nearest neighbour (KNN), so as to predict the crippling load (CL) of I-shaped steel columns. Five input parameters, namely length of column (L), width of flange (bf), flange thickness (tf), web thickness (tw) and height of column (H), are used to compute the crippling load (CL). A range of performance indicators, including the coefficient of determination (R2), variance account factor (VAF), a-10 index, root mean square error (RMSE), mean absolute error (MAE) and mean absolute deviation (MAD), are used to assess the effectiveness of the established machine learning models. The results show that all of the three ML (machine learning) models can accurately predict the crippling load, but the performance of ANN is superior: it delivers the highest value of R2 = 0.998 and the lowest value of RMSE = 0.008 in the training phase, as well as the highest value of R2 = 0.996 and the smaller value of RMSE = 0.012 in the testing phase. Additional methods, including rank analysis, reliability analysis, regression plot, Taylor diagram and error matrix plot, are employed to assess the models’ performance. The reliability index (β) of the models is calculated by using the first-order second moment (FOSM) technique, and the result is compared with the actual value. Additionally, sensitivity analysis is performed to check the impact of the input variables on the output (CL), finding that bf has the greatest impact on the crippling load, followed by tf, tw, H and L, in that order. This study demonstrates that ML techniques are useful for developing a reliable numerical tool for measuring the crippling load of I-shaped steel columns. It is found that the proposed techniques can also be used to predict other kinds of failures as well as different kinds of perforated columns.

Experimental study and mathematical modeling of drying kinetics of mint leaves under forced convection

In this study, we investigated the influence of temperature and air velocity on the drying behavior of Mentha spicata L. leaves using a forced convection dryer. This drying method was chosen for its cost-effectiveness and its ability to facilitate fast and efficient drying while preserving the quality of aromatic herbs. Our aim was to identify the most suitable mathematical model for accurately representing the drying process. We analyzed experimental data on moisture content obtained during thin-layer drying of Mentha spicata L. using various established empirical models from existing literature. Thirteen empirical models were applied to fit the drying curves obtained. Statistical analysis, employing key parameters such as the coefficient of determination (R2), reduced chi-square (χ2), and root mean square error (RMSE), indicated that the Midilli and Kucuk model provided the most accurate fit for the observed drying curves. This was evidenced by the highest R2 values (0.9994, 1.0000), lowest χ2 values (6.07 × 10−6, 6.28 × 10−5), and lowest RMSE values (2.17 × 10−3, 6.86 × 10−3). We observed an increasing trend in moisture diffusivity with temperature variations between 32 °C and 60 °C, while maintaining a constant airflow velocity of 1.2 m/s. Additionally, an increase in mean air velocity from 0.7 to 1.7 m/s correlated with higher moisture diffusivity. The effectiveness of the Arrhenius equation in representing the temperature dependence of diffusivity was confirmed, with an activation energy of 72 kJ/mol, consistent with previous literature findings.

Left, Right, Midpoint and Random Point Imputation Techniques for Weibull Regression Model with Right and Interval-Censored Data”

The research explores several imputation techniques, namely left, right, midpoint and random imputations for the MLE of the Weibull regression model with covariate for uncensored, right, and interval-censored data. A simulation study is conducted to obtain the parameter estimates of the model with different imputation techniques, sample sizes, and censoring proportions and its performance are evaluated using bias, standard error (SE), and root mean square error (RMSE). The simulation result indicates that midpoint imputation technique outperformed other techniques based on the lowest RMSE values. Finally, the model was fit to diabetic nephropathy data were fitted to the model using selected imputation techniques. The result concluded that the Weibull regression model may provide a good fit to the data and that the covariate, gender has a significant effect on the survival time of patient kidneys.

Design and Testing of a Fruit Tree Variable Spray System Based on ExG-AABB

This paper addresses the issue of pesticide waste and low utilization rates resulting from traditional plant protection via spraying operations, which apply equal dosages to different targets or to different parts of the same target. To tackle this problem, we designed a variable fruit tree spraying system based on the ExG-AABB (excess green and axis-aligned bounding box) algorithm. We used a Kinect depth camera to capture information about the fruit tree canopy and constructed a spray flow model using pulse width modulation and variable spray control technology. Variable multi-nozzle spraying was guided by combining this canopy data. We evaluated the accuracy of each model in calculating canopy volume by comparing the coefficient of determination (R2) and root mean square error (RMSE) of the ExG-AABB with the slice convex hull method, voxel method, three-dimensional alpha-shape method, and QuickHull method. The ExG-AABB algorithm had the highest R2 value (0.9334) and the lowest RMSE value (0.0353 m3) among the five models, indicating that it most accurately reflects the true volume of the fruit tree canopy. This validates the effectiveness of the ExG-AABB algorithm in calculating canopy volume. We established a correlation model between canopy volume and spray volume, designed a canopy-adaptive layering method based on point cloud processing, and achieved precise calculation of nozzle flow. Comparative field experiments were conducted to analyze the spray coverage rate and observed flow, thereby evaluating the spraying effect of this variable spraying system. The experimental results showed that compared to conventional continuous spraying, this variable spraying system not only achieves more uniform spray coverage but also significantly reduces pesticide usage by 48.1%. Furthermore, through system optimization, the average coverage rate of the middle layer of the canopy decreased by 17.53%, effectively reducing the phenomenon of overlapping spraying from multiple nozzles and improving spraying efficiency.

Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae

Enhancing biohydrogen production from microalgae is crucial in addressing environmental and energy challenges. It provides a sustainable, clean energy source while reducing greenhouse gas emissions. Moreover, it advances microalgae-based biotechnology, enabling innovative biofuel production and ecological revitalization. The main target of this study is to develop a robust ANFIS model to simulate the biohydrogen production process from microalgae within photobioreactors. The study focuses on enhancing hydrogen yield by optimizing three critical process parameters: sulfur concentration (%), run time (hours), and wet biomass concentration (g/L). Initially, an adaptive neuro-fuzzy inference system (ANFIS) model for biohydrogen production process is constructed based on empirical data. Subsequently, the red-tailed hawk algorithm (RTH) is used to determine the optimal values for the process parameters, corresponding to maximum hydrogen yield. The performance of ANFIS model in predicting hydrogen yield is assessed using root mean square error (RMSE) and coefficient-of-determination (R2) values. The obtained RMSE values for training and testing data are 2.8477 × 10−05 and 1.2807, respectively, while the corresponding R2 values are 1.0 and 0.9911 for training and testing. The introduction of fuzzy logic into the model significantly improves its predictive accuracy, as evidenced by the drop in RMSE from 10.79 with ANOVA to 0.7159 with ANFIS, representing a substantial 93.4 % decrease. The remarkable precision of the ANFIS model, indicated by its low RMSE and high R2 values, underscores the success of the modeling stage. The combination between ANFIS with the RTH technique yields impressive results, leading to a hydrogen yield enhancement of 6.87 % and 26.65 % when compared to both measured data and ANOVA.

Energy expenditure prediction in preschool children: a machine learning approach using accelerometry and external validation

Objective.This study aimed to develop convolutional neural networks (CNNs) models to predict the energy expenditure (EE) of children from raw accelerometer data. Additionally, this study sought to external validation of the CNN models in addition to the linear regression (LM), random forest (RF), and full connected neural network (FcNN) models published in Steenbocket al(2019J. Meas. Phys. Behav.294-102).Approach.Included in this study were 41 German children (3.0-6.99 years) for the training and internal validation who were equipped with GENEActiv, GT3X+, and activPAL accelerometers. The external validation dataset consisted of 39 Canadian children (3.0-5.99 years) that were equipped with OPAL, GT9X, GENEActiv, and GT3X+ accelerometers. EE was recorded simultaneously in both datasets using a portable metabolic unit. The protocols consisted of a semi-structured activities ranging from low to high intensities. The root mean square error (RMSE) values were calculated and used to evaluate model performances.Main results.(1) The CNNs outperformed the LM (13.17%-23.81% lower mean RMSE values), FcNN (8.13%-27.27% lower RMSE values) and the RF models (3.59%-18.84% lower RMSE values) in the internal dataset. (2) In contrast, it was found that when applied to the external Canadian dataset, the CNN models had consistently higher RMSE values compared to the LM, FcNN, and RF.Significance.Although CNNs can enhance EE prediction accuracy, their ability to generalize to new datasets and accelerometer brands/models, is more limited compared to LM, RF, and FcNN models.

A hybrid BOA-SVR approach for predicting aerobic organic and nitrogen removal in a gas-liquid-solid circulating fluidized bed bioreactor

This study introduces the hybrid of the Bayesian optimization algorithm and support vector regression (BOA-SVR) models to predict the removal of aerobic organic (total chemical oxygen demand, COD) and nitrogen compounds such as total Kjeldahl Nitrogen (TKN), ammonium nitrogen (NH4-N), and nitrate nitrogen (NO3-N) from municipal wastewater in a gas-liquid-solid circulating fluidized bed (GLSCFB) bioreactor. GLSCFB bioreactors treat wastewater by removing nutrients biologically. The downer of a GLSCFB bioreactor provided experimental data on TKN, NH4-N, NO3-N, and TCOD removal. The hybrid optimal intelligence algorithm (BOA-SVR) has improved model accuracy across multiple domains by combining BOA and SVR. The coefficient of determination (R2), residual, mean absolute error (MAE), root mean square error (RMSE), and fractional bias (FB) were used to analyze BOA-SVR model performance. The models match experimental data from four operational stages well, with R2 or adj R2 values above 0.99 for all responses. The model's accuracy was confirmed by relative deviations and residual plots showing dispersion around the zero-reference line. The BOA-SVR model consistently predicted dependent variables with low RMSE and MAE values (≤ 2.24 and 2.21, respectively) and near-zero FB. Computing efficiency was shown by optimizing TCOD, TKN, NH4-N, and NO3-N models in 70.61, 72.89, 74.37, and 54.07 s. A rigorous test on unseen data with different noise levels confirmed the model's stability. Furthermore, BOA-SVR performs better than traditional multiple linear regression (MLR). Overall, the BOA-SVR model predicts biological nutrient removal in municipal wastewater utilizing a GLSCFB bioreactor quickly, correctly, and efficiently, reducing experimental stress and resource use.

Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)

This paper sets pioneering research which investigates the parametric identification of thermoelectric modules (TEMs) through the employment of enhanced slime mould algorithm (ESMA). The proposed method incorporates a pair of modifications to the standard slime mould algorithm (SMA). Primary modification encloses computation of random average position between the slimes' current individual position and best individual position towards resolution of local optima issue. Subsequent modification then involves substitution of an exponential function to the existing tangent hyperbolic function within formula p of the standard SMA in enabling improved probability variants via the selection of updated equations. Competency of the proposed algorithm in generating the optimal parameters for TEMs was appraised based on 21 benchmarked design parameters, following the objective of root mean square error (RMSE) minimization between the temperature of both actual and estimated models. Acquired results which demonstrate lower values of RMSE and parameter deviation index against the standard SMA and other preceding algorithms such as particle swarm optimization, sine cosine algorithm, moth flame optimizer and ant lion optimizer ultimately verified ESMA’s efficacy as an effective approach for accurate model identification.

Modeling stage‐discharge and sediment‐discharge relationships in data‐scarce Himalayan River Basin Dhauliganga, Central Himalaya, using neural networks

AbstractThis study focuses on the hydro‐sedimentological characterization and modeling of the Dhauliganga River in Uttarakhand, India. Field data collected from 2018–2020, including stage, velocity, and suspended sediment concentration (SSC), showed notable variations influenced by melting snow, glaciers, and precipitation. Challenges in accurately modeling rivers with a topography and sparse gauging stations were addressed using artificial neural networks (ANN). The calibrated models precisely predicted stage‐discharge and sediment‐discharge relationships, demonstrating the effectiveness of machine learning, particularly ANN‐based modeling, in such challenging terrains. The model's performance was assessed using coefficient of determination (R2), root mean square error (RMSE), and mean square error (MSE). During the calibration phase, the model exhibited notable performance with R2 values of 0.96 for discharge and 0.63 for SSC, accompanied by low RMSE values of 5.29 cu m s–1 for discharge and 0.61 g for SSC. Subsequently, in the prediction phase, the model maintained its robustness, achieving R2 values of 0.97 for discharge and 0.63 for SSC, along with RMSE values of 5.67 cu m s–1 for discharge and 0.68 g for SSC. The study also found a strong agreement between water flow estimates derived from traditional methods, ANN, and actual measurements. The suspended sediment load, influenced by both water flow and SSC, varied annually, potentially modifying aquatic habitats through sediment deposition, and altering aquatic communities. These findings offer crucial insights into the hydro‐sedimentological dynamics of the studied river, providing valuable applications for sustainable water‐resource management in challenging terrains and addressing environmental concerns related to sedimentation, water quality, and aquatic ecosystem.

Data-driven performance analysis of an active chilled beam air conditioning system: A machine learning approach for energy efficiency and predictive maintenance

This study aims to enhance energy performance estimation and maintenance planning for office buildings in Tehran. Machine learning and data mining techniques were employed, utilizing Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Narrow Neural Networks, and optimizable SVMs. A comprehensive dataset of 2500 experimental samples was utilized with five input parameters (building size, occupancy patterns, environmental conditions, and ACB system parameters) and one output parameter (electricity consumption). GPR models performed better in estimating electricity consumption, achieving a Root Mean Square Error (RMSE) of 297.01 and an impressive R-squared value of 0.99. To optimize energy performance and design more energy-efficient buildings, this model accurately predicts energy consumption. Additionally, this study addressed maintenance planning for ACB systems, simulating maintenance issues with 2500 synthetic samples. The dataset included building age, size, number of floors, usage intensity, previous maintenance history, location, and environmental factors. Maintenance time was estimated using these features, and XGBoost was employed to predict ACB system maintenance time. Low MSE, RMSE, and R-squared values in the XGBoost model allow for accurate predictions. Using this model, facility managers can proactively plan maintenance activities, allocate resources, and minimize downtime. Through proactive measures, ACB performance and maintenance costs are optimized. The study accurately predicts ACB system energy consumption and maintenance time using the rational quadratic GPR model, applicable in Tehran and beyond. Additionally, MATLAB version R2022b was used for prediction, while DesignBuilder and EnergyPlus were utilized for technical analysis.

Monitoring and forecasting desertification and land degradation using remote sensing and machine learning techniques in Sistan plain, Iran

Monitoring and predicting desertification in arid regions are crucial for addressing environmental and societal challenges. Remote sensing is vital for tracking land surfaces and ecosystems changes. The study aims to use remote sensing-based data to monitor and predict desertification in the Sistan Plain through a data screening approach. The study's satellite data consisted of Landsat 5 and 8 images taken in June each year over 10 years (1990–2020). Remote sensing-based indices, including land use and land cover (LULC) map, normalized differential vegetation index (NDVI), improved vegetation index (EVI), vegetation condition index (VCI), surface temperature condition index (TCI), modified normalized differential water level index (MNDWI) and salinity index (SI) were used in the study. In addition to satellite data, environmental indices, including standardized precipitation index (SPI) and streamflow drought index (SDI), were used. The study employed the random forest (RF) method and the mixed model of automated cells and Markov chain (CA-Markov) to monitor desertification and quantitatively predict its condition in 2030. Root-mean-square error (RMSE) and mean-square error (MSE) indicators were used to evaluate the error. Based on the findings, the RF correlation coefficient (R2) and RMSE were obtained about 0.97 and 0.08, respectively. High coefficient values and low RMSE values indicate that the random forest model is highly efficient in assessing desertification for the study period from 1990 to 2020. The change detection method revealed that desertification increased from 1990 to 2010 but decreased from 2010 to 2020. The decreasing trend is expected to continue until 2030. The Kappa coefficient for the prediction of desertification in 2030 was found to be 0.94, which indicates a correct classification based on the collected samples. In addition, the study identified the SI and SDI as effective indices in the desertification process in the study area. Overall, this study provides valuable insights into monitoring and predicting desertification, which could help develop appropriate strategies for managing and controlling desertification in the Sistan Plain through remote sensing and machine learning techniques.

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.

A Hybrid Deep Learning Model to Estimate the Future Electricity Demand of Sustainable Cities

Rapid population growth, economic growth, and technological developments in recent years have led to a significant increase in electricity consumption. Therefore, the estimation of electrical energy demand is crucial for the planning of electricity generation and consumption in cities. This study proposes a hybrid deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) techniques, both of which are deep learning techniques, to estimate electrical load demand. A hybrid deep learning model and LSTM model were applied to a dataset containing hourly electricity consumption and meteorological information of a city in Türkiye from 2017 to 2021. The results were evaluated using mean absolute percent error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics. The proposed CNN-LSTM hybrid model was compared to the LSTM model, with lower MAPE, MAE, and RMSE values. Furthermore, the CNN-LSTM model exhibited superior prediction performance with an R2 value of 0.8599 compared to the LSTM model with an R2 value of 0.8086. These results demonstrate the effectiveness of the proposed deep learning model in accurately estimating future electrical load demand to plan electricity generation for sustainable cities.

Incorporating Honey Badger Algorithm in Estimating Gamma Distribution With Application to Stock Price Modelling

This study evaluates the performance of various estimation methods in stock price analysis across diverse parameters, focusing on the Honey Badger Algorithm (HBA). The purpose is to determine the most accurate and reliable method for parameter estimation. Methodologically, we analyze data spanning eight years from publicly traded Malaysian property companies, employing financial metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Our findings highlight HBA’s consistent precision in parameter estimation, with values closely aligning with initial parameters across different stock sizes. For example, HBA-Gamma model achieves an MAE of 0.0592 and an RMSE of 0.8458 for 13 stocks, demonstrating its proficiency in capturing stock price distributions in dynamic markets. In contrast, the Artificial Immune System (AIS) provides reasonable estimates but with higher variability. The Regression Method exhibits mixed outcomes, displaying accuracy in some cases but notable variability and reduced precision, especially with larger datasets. The Moment Method, while adequate, shows slightly higher variance compared to both HBA and AIS. Further analysis using Log Likelihood values confirms HBA’s superior fit to the data, consistently surpassing AIS, Regression Method, and Moment Method in likelihood maximization across various stock numbers. Specifically, HBA exhibits lower MAE and RMSE values of 0.1034 and 0.06723, respectively, for 26 stocks, further validating its effectiveness in parameter estimation and stock price prediction. These findings underscore the importance of integrated approaches that account for market nuances rather than relying solely on individual model forecasts. The results affirm HBA’s potential for informed investment decision-making, emphasizing its robust performance and enhanced predictive capabilities compared to alternative methodologies. However, further research is needed to assess the generalizability of these findings to other markets and contexts.

Optimizing flood predictions by integrating LSTM and physical-based models with mixed historical and simulated data

The current flood forecasting models heavily rely on historical measured data, which is often insufficient for robust predictions due to practical challenges such as high measurement costs and data scarcity. This study introduces a novel hybrid approach that synergistically combines the outputs of traditional physical-based models with historical data to train Long Short-Term Memory (LSTM) networks. Specifically, the NAM hydrological model and the HD hydraulic model are employed to simulate flood processes. Focusing on the Jinhua basin, a typical plains river area in China, this research evaluates the efficacy of LSTM models trained on measured, mixed, and simulated datasets. The LSTM architecture includes multiple layers, with optimized hyperparameters tailored for flood forecasting. Key performance indicators such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Peak-relative Error (PRE) are employed to assess the predictive accuracy of the models. The findings demonstrate that LSTM models trained on mixed datasets with a simulated-to-measured data ratio of less than 2:1 consistently achieve superior performance, exhibiting significantly lower RMSE and MAE values compared to models trained on mixed data with larger data ratios. This highlights the advantage of integrating measured and simulated data, leveraging the strengths of both data types to enhance model accuracy. Despite its advantages, the approach has limitations, including dependence on the quality of simulated data and potential computational complexity. However, the development of this hybrid model marks a significant advancement in flood forecasting, offering a promising solution to the challenges of computational efficiency and data scarcity. Potential applications of this approach include real-time flood prediction and risk management in other flood-prone regions, providing a robust framework for integrating diverse data sources to improve forecasting accuracy.

Patient-specific Implants Improve Volumetric Surgical Accuracy Compared to Stock Reconstruction Plates in Modern Paradigm Virtual Surgical Planning of Fibular Free Flaps for Head and Neck Reconstruction

BackgroundVirtual surgical planning (VSP) for composite microvascular free flaps has become standard of care for oncologic head and neck reconstruction. Controversy remains as to the use of three-dimensional (3D)-printed titanium patient specific implants (PSI) versus hand-bent stock reconstruction plates. Proponents of PSIs cite improved surgical accuracy, reduced operative times, and improved clinical outcomes. Detractors purport increased cost associated with PSIs and presumed equivalent accuracy with less expensive stock plates. PurposeThe study purpose was to measure and compare the 3D-volumetric accuracy of PSI versus stock reconstruction plates among subjects undergoing VSP-guided mandibular fibular free flap reconstruction. Study Design/Setting/SampleA retrospective cohort study of subjects undergoing VSP-guided fibular free flap reconstructions at Mayo Clinic between 2016-2023 was performed. Subjects were excluded for non-VSP guidance, midfacial reconstruction, non-fibular free flaps, and lack of requisite study variables. Predictor VariableThe primary predictor was the type of reconstruction plate utilized (PSI versus stock plate). Main Outcome VariableThe main outcome was volumetric surgical accuracy of the final reconstruction compared to the preoperative surgical plan by root mean square error (RMSE) calculation. Lower RMSE values indicated a higher surgical accuracy. CovariatesCovariates included age, sex, race, smoking status, ASA classification, Charlson Comorbidity Index, preoperative diagnosis, and number of fibular segments. AnalysisDifferences in surgical accuracy were assessed between pre- and post-operative segmented scans using volumetric overlays from which RMSE values were calculated. Univariate and multivariate modeling of plate type to RMSE calculation was performed. Statistical significance set to p<0.05. ResultsTotal of 130 subjects were identified, 105 PSI and 25 stock plates. Calculated mean RMSE in millimeters (mm) for stock plates was 1.46 (SD: 0.33) and 1.15 (SD: 0.36) for PSIs. Univariate modeling demonstrated a statistically significant difference in RMSE of 0.31 (95% CI: 0.16-0.47) (p<0.001) equating to a 21.2% (p<0.001) improved volumetric surgical accuracy for PSIs. The association of improved volumetric accuracy with PSIs maintained in all multivariate models controlling for confounding. Conclusion and RelevanceIn modern era VSP-guided head and neck fibular free flap reconstruction, patient-specific 3D-printed titanium implants confer a statistically significant improvement in volumetric surgical accuracy over stock reconstruction plates.

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

The development of fault detection methods in wind turbine (WT), especially for single fault detection, is continuously increasing. However, the rapid growth of fault detection in WT leads to another challenge where multiple faults can occur. The single fault detection method in WT is no longer reliable, especially when multiple faults occur simultaneously. Therefore, multiple faults detection in doubly-fed induction generators (DFIG) WT was proposed using an artificial neural networks (ANN) model. These multiple faults include internal and external stator faults happening simultaneously. Internal stator faults cover inter-turn short circuit faults and open circuit faults, while external stator faults cover loss of excitation and external short circuit faults. The performance of the developed multiple faults detection model was measured using accuracy and the root mean square error (RMSE) value. The results show that the developed model performs well with high accuracy and a low RMSE value. Thus, the developed model can accurately detect the coexistence of multiple faults in DFIG WT.

Texture prediction of natural soils in the Brazilian Amazon through proximal sensors

Proximal sensors provide fast, low-cost, environmentally friendly, and reliable analyses for the characterization of soils and other materials. Numerous studies have been conducted on soils in temperate regions, but there are knowledge gaps regarding the use of these devices in tropical soils, especially in the Amazon region. In this regard, this study utilized portable proximal sensors of X-ray fluorescence spectroscopy (pXRF) and diffuse reflectance spectroscopy in the visible to near-infrared region (Vis-NIR) for predicting the texture of natural soils in 61 municipalities in the state of Pará, Amazon region, Brazil. The objectives were: i) to investigate the accuracy of soil texture prediction based on data from sensors separately and sensor fusion (pXRF and Vis-NIR data) using two supervised algorithms (Random Forest, RF, and Support Vector Machine, SVM) and ii) to assess the effect of soil horizon (superficial and subsuperficial horizons, and their combination) in predicting the texture of tropical natural soils. In total, 233 soil samples were collected in the 0–20 cm and 80–100 cm depths, equivalent to superficial and subsuperficial horizons in areas with primary or secondary forest cover with at least 20 years of natural regeneration and approximately 20 ha of coverage area. The hydrometer method was used for soil texture analysis. In parallel, a portion of the soil samples was analyzed by pXRF and Vis-NIR, in triplicate, under laboratory conditions. The predictive models with RF were more robust compared to the models obtained with SVM, according to ratio performance interquartile distance (RPIQ), coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The R2 values obtained by pXRF, Vis-NIR, and sensor data combination were, respectively, 0.89, 0.87, and 0.93 for sand; 0.92, 0.90, and 0.93 for clay; and 0.91, 0.67, and 0.93 for silt. Overall, clay prediction models achieved higher R2 values compared to sand and silt models. Soil texture prediction using sensor fusion showed lower RMSE values and higher R2 and RPIQ values, respectively (sand: 7.79, 0.93, 4.69; clay: 5.58, 0.93, 3.86; and silt: 5.72, 0.92, 2.92) compared to the best-performing sensor individually (Vis-NIR). With regard to the optimal model utilizing individual sensor data, Vis-NIR models exhibited reduced error for clay and sand prediction. The effect of combining horizons to a single and bigger dataset was minimally important for the models. The results demonstrate confidence in the use of proximal sensors for soil texture assessment in natural Amazon soils, aiming to reduce costs and the time required for analyses.

Characterizing the Exponential Profile of W' Recovery Following Partial Depletion.

The aim of this study was to characterize W' recovery kinetics in response to a partial W' depletion. We hypothesized that W' recovery following a partial depletion would be better described by a biexponential than by a monoexponential model. Nine healthy men performed a ramp incremental exercise test, three to five constant load trials to determine critical power and W', and 10 experimental trials to quantify W' depletion. Each experimental trial consisted of two constant load work bouts (WB1 and WB2) interspersed by a recovery interval. WB1 was designed to evoke a 25% or 75% W' depletion (DEP 25% and DEP 75% ). Subsequently, participants recovered for 30, 60, 120, 300, or 600 s and then performed WB2 to exhaustion to calculate the observed W' recovery (W' OBS ). W' OBS data were fitted using monoexponential and biexponential models, both with a variable and with a fixed model amplitude. Root mean square error and Akaike information criterion (AIC c ) were calculated to evaluate the models' goodness-of-fit. The biexponential model fits were associated with overall lower root mean square error values (0.4% to 5.0%) when compared with the monoexponential models (2.9% to 8.0%). However, ΔAIC c resulted in negative values (-15.5 and -23.3) for the model fits where the amplitude was kept free, thereby favoring the use of a monoexponential model for both depletion conditions. For the model fits where the amplitude was fixed at 100%, ΔAIC c was negative for DEP 25% (-15.0) but positive for DEP 75% (11.2). W' OBS values were strongly correlated between both depletion conditions ( r = 0.92) and positively associated with V̇O 2peak , critical power, and gas exchange threshold ( r = 0.67 to 0.77). The present study results did not provide evidence in favor of a biexponential modeling technique to characterize W' recovery following a partial depletion. Moreover, we demonstrated that fixed time constants were insufficient to model W' recovery across different depletion levels, and that W' recovery was positively associated with aerobic fitness. These findings underline the importance of employing variable and individualized time constants in future predictive W' models.

Advancing Stock Market Predictions with Time Series Analysis including LSTM and ARIMA

Predicting stock market prices accurately is a major task for investors and traders seeking to optimize their decision-making processes. This research focuses on the comparative analysis of advanced machine learning (ML) techniques, particularly, the Long Short-Term Memory (LSTM) model and Autoregressive Integrated Moving Average (ARIMA) model for predicting stock market prices. The study enforces thorough data collection and preprocessing to ensure the quality and reliability of the historical stock price data, forming a robust foundation for the predictive models. The core contribution of this paper lies in its systematic and comparative analysis of these two models. A range of performance metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are employed to assess and contrast the predictive accuracy and efficiency of the LSTM and ARIMA models. The research findings indicate that the ARIMA model, contrary to expectations, outperforms the LSTM model in this study, achieving lower RMSE and MAE values. Specifically, the ARIMA model demonstrates a Test RMSE of 4.336 and a Test MAE of 3.45926, indicating its superior predictive accuracy compared to the LSTM model. Furthermore, the study sets its findings against the backdrop of existing literature by comparing the performance of its models with those reported in previous research. This comparison shows better results achieved by our stock market prediction models. By addressing limitations observed in prior studies and demonstrating practical applicability, this research contributes to advancing stock market prediction methodologies, offering valuable insights for investors and traders.