Coefficient Of Determination Research Articles

The cortical neurophysiological signature of amyotrophic lateral sclerosis

Abstract The progressive loss of motor function characteristic of amyotrophic lateral sclerosis is associated with widespread cortical pathology extending beyond primary motor regions. Increasing muscle weakness reflects a dynamic, variably compensated brain network disorder. In the quest for biomarkers to accelerate therapeutic assessment, the high temporal resolution of magnetoencephalography is uniquely able to non-invasively capture micro-magnetic fields generated by neuronal activity across the entire cortex simultaneously. This study examined task-free magnetoencephalography to characterise the cortical oscillatory signature of amyotrophic lateral sclerosis for potential as a pharmacodynamic biomarker. Eight-ten minutes of magnetoencephalography in the task-free, eyes-open state was recorded in amyotrophic lateral sclerosis (n=36) and healthy age-matched controls (n=51), followed by a structural MRI scan for co-registration. Extracted magnetoencephalography metrics from the delta, theta, alpha, beta, low-gamma, high-gamma frequency bands included oscillatory power (regional activity), 1/f exponent (complexity) and amplitude envelope correlation (connectivity). Groups were compared using a permutations-based general linear model with correction for multiple comparisons and confounders. To test whether the extracted metrics could predict disease severity, a Random Forest Regression model was trained and evaluated using nested Leave-One-Out Cross-Validation. Amyotrophic lateral sclerosis was characterised by reduced sensorimotor beta band and increased high-gamma band power. Within the premotor cortex, increased disability was associated with a reduced 1/f exponent. Increased disability was more widely associated with increased global connectivity in the delta, theta, and high-gamma bands. Intra-hemispherically, increased disability scores were particularly associated with increases in temporal connectivity and inter-hemispherically with increases in frontal and occipital connectivity. The Random Forest model achieved a coefficient of determination (R2) of 0.24. The combined reduction in cortical sensorimotor beta and rise in gamma power is compatible with the established hypothesis of loss of inhibitory, GABAergic interneuronal circuits in pathogenesis. A lower 1/f exponent potentially reflects a more excitable cortex and a pathology unique to amyotrophic lateral sclerosis when considered with the findings published in other neurodegenerative disorders. Power and complexity changes corroborate with the results from paired-pulse transcranial magnetic stimulation. Increased magnetoencephalography connectivity in worsening disability is thought to represent compensatory responses to a failing motor system. Restoration of cortical beta and gamma band power has significant potential to be tested in an experimental medicine setting. Magnetoencephalography-based measures have potential as sensitive outcome measures of therapeutic benefit in drug trials and may have wider diagnostic value with further study, including as predictive markers in asymptomatic carriers of disease-causing genetic variants.

Comparative evaluation of two different dental age estimation methods using tooth pulp: A CBCT study.

To estimate the age of an individual based on 3D radiographic evaluation of pulp width of maxillary central incisor. This study included 185 CBCT images of individuals within age range of 14-64 years. Images were evaluated for maxillary central incisors and pulp width measurements were taken from cementoenamel junction and middle third of root. Obtained data was subjected to correlation and regression analysis from which the age of an individual was predicted. Results of the present study were compared with another study by the same authors. A negative linear relationship was obtained between age and pulp width. The standard error of estimate (SEE) in sagittal section was 11.36 years and that in coronal section was 11.23 years. The coefficient of determination for sagittal section was 0.107 and for coronal section was 0.127. An obtained regression formula was highly significant. Division of samples into various age groups reduced SEE drastically. It can be concluded that pulp width of maxillary central incisor is a reliable indicator of age estimation.

Development of Seismic Fragility Functions for Reinforced Concrete Buildings Using Damage-Sensitive Features Based on Wavelet Theory

In this study, wavelet-based damage-sensitive features are employed to derive the seismic fragility functions/curves for reinforced concrete moment-resisting frames. Two different wavelet transform functions, namely, Bior3.3 and Morlet mother wavelet families, were applied to absolute acceleration time histories of building frames to extract the wavelet-based and refined wavelet-based damage-sensitive features (i.e., DSF and rDSF). The accuracy of seismic assessments and certainty in predicting structural behavior strongly depend on the specific optimal intensity measures selected, reliability of wavelet-based damage-sensitive features, and some such intensity measures as PGA, PGV, PGD, Sa, and Sdi as the conventionally utilized measures to detect the damage state of a structure. These measures were examined against their statistical properties of efficiency, practicality, proficiency, coefficient of determination, and sufficiency to select the appropriate optimal intensity measures, which were then used to drive the fragility curves disclosing the failure or other damage states of interest. For the purposes of this study, three different concrete moment-resisting frames with four-, eight-, and twelve-story building frames were adopted for implementing the proposed approach. The findings demonstrate that the wavelet-based damage-sensitive features (DSFs/rDSF) simultaneously satisfy all the statistical properties cited above. This is evidenced by the low variance and dispersions observed in the frame damage state predictions by the fragility functions derived from the wavelet-based DSF when compared with those derived from the classical fragility analyses such as spectral acceleration at the first mode period of the structure. A final aspect of the study concerns the superior performance and efficiency of the fragility curves derived by the Bior3.3 wavelet-based DSF over those derived from Morlet wavelet-based DSF.

Effect of different constraining boundary conditions on simulated femoral stresses and strains during gait

Finite element analysis (FEA) is commonly used in orthopaedic research to estimate localised tissue stresses and strains. A variety of boundary conditions have been proposed for isolated femur analysis, but it remains unclear how these assumed constraints influence FEA predictions of bone biomechanics. This study compared the femoral head deflection (FHD), stresses, and strains elicited under four commonly used boundary conditions (fixed knee, mid-shaft constraint, springs, and isostatic methods) and benchmarked these mechanics against the gold standard inertia relief method for normal and pathological femurs (extreme anteversion and retroversion, coxa vara, and coxa valga). Simulations were performed for the stance phase of walking with the applied femoral loading determined from patient-specific neuromusculoskeletal models. Due to unrealistic biomechanics observed for the commonly used boundary conditions, we propose a novel biomechanical constraint method to generate physiological femur biomechanics. The biomechanical method yielded FHD (< 1 mm), strains (approaching 1000 µε), and stresses (< 60 MPa), which were consistent with physiological observations and similar to predictions from the inertia relief method (average coefficient of determination = 0.97, average normalized root mean square error = 0.17). Our results highlight the superior performance of the biomechanical method compared to current methods of constraint for both healthy and pathological femurs.

A comparison between geomembrane-sand tests and machine learning predictions

The interaction between soils and geosynthetics plays an important role in the applications of these materials for reinforcement in geotechnical engineering. The complexities of soil-geosynthetic interactions vary depending on the type and properties of both the geosynthetic and the soil. This paper introduces a machine learning approach, specifically a random forest algorithm, for predicting interface friction angles. The dataset comprises 495 interfaces involving geomembranes and sand, with fourteen influencing parameters recorded for each interface, influencing the shear strength outcome. In the analysis, Pearson's correlation coefficient is employed to measure the linear interdependence between each pair of input-input and input-output variables. Following the linear regression analysis, an optimized random forest is utilized to project the interface friction angle. The random forest algorithm divides the selected data into training and testing sets, and only 3% of the training set and 6% of the testing set exceed ±5° from the actual records. The coefficient of determination (R2) indicates strong agreement between the predicted and laboratory study friction angles, with R2 = 0.93 for the training set and R2 = 0.92 for the testing set. Consequently, the random forest algorithm demonstrates effectiveness in predicting interface friction angles.

Retrieval algorithm of chlorophyll-a concentration for coastal waters based on ridge regression

Abstract. In this paper, we use the multiple scattering near-infrared aerosol correction model of SeaDAS to execute the atmospheric correction for Terra/Aqua MODIS remote sensing data, and also use three standard operational chlorophyll-a concentration inversion algorithms built in SeaDAS, named OC2, OC3, and OC4, to carry out chlorophyll-a concentration inversion in the Yellow Sea and East China Sea. We validate the inversion results using in-situ measured chlorophyll-a concentration data collected from the Yellow Sea and East China Sea in 2003. The results show that the inversion results of OC3 and OC4 algorithm are significantly larger than in-situ measured values, and the results of the OC2 algorithm are closer to the measured values. In view of the shortcomings of these algorithms, we proposes a chlorophyll-a concentration inversion model based on ridge regression, and carry out chlorophyll-a concentration inversion test using MODIS images covering the Yellow Sea and East China Sea 2003. The results indicate that, the new inversion model can effectively overcome the deficiencies of OC2, OC3, and OC4 algorithms. The inversion model can effectively overcome the covariance problem of OC2, OC3 and OC4 algorithms on the multivariate linear regression model, and the model passed the F-test (F=25.893, p=0.000&lt;0.05), the mean absolute percentage error (MAPE) between the inversion values and the in-situ measured values was 21.8%, the root mean squared error (RMSE) was 0.325, and the coefficient of determination (R2) was 0.847. The accuracy and the fit degree of the new model were significantly better than those of the OC2, OC3 and OC4 algorithms. Therefore, the chlorophyll-a concentration inversion model based on ridge regression can effectively invert the chlorophyll-a concentration in the offshore.

Influence of operational excellence strategy on organisations’ competitiveness among tier III category of Commercial Banks in Kenya

Tier 3 Kenyan commercial banks face significant challenges, including intense competition, financial distress, and declining profits despite consistent loan growth. This study investigates the impact of operational excellence strategies on the competitiveness of these banks using a pragmatic research approach that combines qualitative and quantitative methods. The study is grounded in the dynamic capabilities theory and collected data from 288 respondents across various management roles within the banks through semi-structured questionnaires. The data were analyzed using thematic analysis for qualitative data and SPSS for quantitative data, including descriptive statistics, regression analysis, and ANOVA. The regression model revealed a moderate positive relationship between operational excellence and organizational competitiveness, with a correlation coefficient (R) of 0.685. The coefficient of determination (R Square) was 0.469, indicating that operational excellence accounts for 46.9% of the variability in competitiveness. The coefficient for Operational Excellence is 1.260 (standard error = 0.087, beta = 0.685), with a t-value of 14.493 and a significance level of 0.000. The constant coefficient is -1.565 (standard error = 0.353, t-value = -4.437). This study underscores the importance of strategic investments in operational excellence practices to enhance organizational efficiency and sustainable competitive advantages. However, the study's scope is limited to tier III commercial banks in Kenya, and future research should explore additional factors influencing competitiveness and include a broader sample for improved generalizability.

Experimental study on inhibition of coal spontaneous combustion by carbon dioxide and nitrogen

ABSTRACT Injecting CO2 and N2 to reduce the concentration of O2 in the air is a commonly used measure to prevent coal spontaneous combustion (CSC). To study the difference in inhibitory effects of CO2 and N2 on coal oxidation, experiments in this study were conducted using a setup of self-developed gas-bath coal oxidation for low temperatures. The oxidation process of the coal-oxygen system was then analyzed using reaction kinetics. Two different coal samples were tested in experiments under two inert gas environments, i.e. CO2 and N2 with various O2 concentrations, respectively. The changes in O2 and CO concentrations were measured during the oxidation process. By studying the standard oxygen consumption rate (SOCR) and the apparent activation energy of the coal-oxygen reaction system, inhibitory effects of CO2 and N2 on CSC were analyzed and compared. The results showed that the SOCR of the same coal sample remained consistent and independent of the O2 concentration within the same inert gas environment although under different O2 levels. In both CO2-O2 and N2-O2 environments, the SOCR of the coal sample exhibits exponential growth with temperature. The SOCR was found to have a functional connection with temperature, it can be represented as y = a ⋅ e bT , and the R 2 (determination coefficients) of the fitting formulas are all greater than 0.94. In the correlation, the inert gas environment has minimal impact on coefficient “b,” while in the CO2-O2 environment, reducing the value of coefficient “a” can effectively lower the coal sample’s SOCR. The GBMK sample and YMWK sample have an “a” value that is 20% of the value in the N2-O2 environment when oxidizing in the CO2-O2 environment. The SOCR in the CO2-O2 environment showed a decrement of between 26.0% and 52.1% when compared to that in the N2-O2 environment, whereas the activation energy showed an increment between 1.4% and 18.6%. The findings in this work provide an in-depth understanding of the prevention of CSC in the gob for risk mitigation.

Hourly near-ground NO2 concentration retrieval from geostationary satellite observations

Abstract. Nitrogen dioxide (NO2) is an important contributor to the formation of acid rain, photochemical smog and aerosol particles, which seriously endangers public health. At present, remote sensing of polar-orbiting satellites is a conventional means to obtain large-scale NO2 distribution, but it cannot capture the rapid change because of long revisit periods. The Advanced Himawari Imager (AHI) on the Himawari-8 geostationary satellite has the advantage of high time resolution, which makes it possible to realize near-real-time atmospheric monitoring. Here, based on the absorption characteristics of NO2 in infrared radiation, hourly near-surface NO2 concentrations are retrieved based on the brightness temperature from AHI and auxiliary information such as meteorology and aerosol. The results of 10-fold cross-validation show that NO2 estimations from satellite are in good agreement with in-situ measurements, and their determination coefficient (R2) can reach 0.79. Due to different emission and atmospheric diffusion conditions at different time, the model performance presents a diurnal variation of high accuracy in the noon and afternoon but low accuracy in the morning. Based on the retrieval dataset, it is found that high NO2 concentrations are mainly concentrated in the densely populated and industrial areas such as the North China area. In addition, NO2 pollution mainly occurs in autumn and winter, and the average NO2 concentration in winter is about 1.63 times that in summer in 2021. This study provides a new insight for satellite retrieval of NO2, which is of great significance for real-time pollution monitoring and public health protection.

Hectareage Prediction Models for Paddy Crop of Middle Gujarat

The present investigation was undertaken with a view to identify the models for predicting the hectareage of paddy crop of the middle Gujarat region. The investigation was carried out on the basis of secondary data covering the period of nineteen years, (1998-99 to 2016-17). The District level data relating to hectareage, production, productivity and farm harvest prices of paddy were obtained from the published and compiled information by Directorate of Agriculture, Gujarat State, Gandhinagar. The linear multiple regression technique (basically Nerlovian type) was employed. The eight single equation and four simultaneous equation (SE) models were tried for paddy crop, the following models were selected on the basis of the values of adjusted coefficient of multiple determination. SE model-III for paddy is given below.HEPD = 40960.532**** - 10.414*** HEBJ + 0.784 HEMZ - 1.187**** HEPDL + 3.720*** HEBJL + 5.588**** EYPD + 0.866 EYBJ - 6.205*** EYMZ- 6.833**** EPPD + 1.502 EPBJ (R2= 0.946)HEBJ = 3261.298 - 0.061 HEPD + 0.108 HEMZ - 0.093 HEPDL + 0.337 HEBJL + 0.441 EYPD + 0.220 EYBJ - 0.619 EYMZ - 0.594 EPPD + 0.227EPBJ (R2= 0.960)HEMZ = 1816.343 + 0.028 HEPD + 0.147 HEBJ + 0.220 HEBJL + 0.649 HEMZL - 0.120 EYPD - 0.176 EYBJ - 0.092 EYMZ - 0.226 EPMZ - 0.106EPBJ (R2= 0.850)*, **, ***, **** Significant at the 20, 10, 5, 1 percent level of significance, respectivelyFor the selected crops, SE model was recommended for prediction of the current hectareage on the basis of the adjusted coefficient of multiple determination ( R 2). For Paddy hectareage the main affecting factors viz., bajra hectareage, lagged hectareage of paddy, expected yield of maize and expected price of paddy. Expected yield and expected price of paddy were determining factors of bajra hectareage.

Soybean Saponin Content Detection Based on Spectral and Image Information Combination

Soybean saponin is a natural antioxidant and is anti-inflammatory. Hyperspectral analysis technology was applied to detect soybean saponin content rapidly and nondestructively in this paper. Firstly, spectral preprocessing methods were studied, and standard normal variable (SNV) was used to remove noise information. Secondly, a two-step hybrid variable selection approach based on synergy interval partial least squares (SiPLS) and iteratively retains informative variables (IRIV) was proposed to extract characteristic variables. Then, the ensemble learning model was constructed by back propagation neural network (BPNN), deep forest (DF), partial least squares regression (PLSR), and extreme gradient boosting (EXG). Finally, image information was combined into spectral data to improve model accuracy. The prediction coefficient of determination (R2) of the final model reached 0.9216. It can provide rapid, nondestructive, and accurate detection technology of soybean saponin content. A combination of spectral and image information will provide a new idea for application of hyperspectral.

Prognostic Value and Determinants of High-Sensitivity Cardiac Troponin T in Patients With a Systemic Right Ventricle: Insights From the SERVE Trial.

The determinants and prognostic value of high-sensitivity cardiac troponin T (hs-cTnT) among patients with a systemic right ventricle are largely unknown. Ninety-eight patients from the randomized controlled SERVE (Effect of Phosphodiesterase-5 Inhibition With Tadalafil on Systemic Right Ventricular Size and Function) trial were included. The correlation between baseline hs-cTnT concentrations and biventricular volumes and function quantified by cardiac magnetic resonance or cardiac multirow detector computed tomography was assessed by adjusted linear regression models. The prognostic value of hs-cTnT was assessed by adjusted Cox proportional hazards models, survival analysis, and concordance statistics. The primary outcome was time to the composite of clinically relevant arrhythmia, hospitalization for heart failure, or all-cause death. Median age was 39 (interquartile range, 32-48) years, and 32% were women. Median hs-cTnT concentration was 7 (interquartile range, 4-11) ng/L. Coefficients of determination for the relationship between hs-cTnT concentrations and right ventricular end-systolic volume index and right ventricular ejection fraction (RVEF) were +0.368 (P=0.046) and -0.381 (P=0.018), respectively. The sex- and age-adjusted hazard ratio for the primary outcome of hs-cTnT at 2 and 4 times the reference level (5 ng/L) were 2.89 (95% CI, 1.14-7.29) and 4.42 (95% CI, 1.21-16.15), respectively. The prognostic performance quantified by the concordance statistics for age- and sex-adjusted models based on hs-cTnT, right ventricular ejection fraction, and peak oxygen uptake predicted were comparable: 0.71% (95% CI, 0.61-0.82), 0.72% (95% CI, 0.59-0.84), and 0.71% (95% CI, 0.59-0.83), respectively. Hs-cTnT concentration was significantly correlated with right ventricular ejection fraction and right ventricular end-systolic volume index in patients with a systemic right ventricle. The prognostic accuracy of hs-cTnT was comparable to that of right ventricular ejection fraction and peak oxygen uptake predicted. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03049540.

Application of hybrid modeling to predict California bearing ratio of soil

California Bearing Ratio (CBR) is used to assess bearing capacity, deformation characteristics of roadbed soil, and base layer material in pavement structure. In general, CBR is often determined by laboratory or in-situ tests. However, it is time- and cost-consuming to conduct this experiment because this test requires cumbersome equipment such as a compressor. In this study, two Artificial Intelligence models are developed, including a simple model (Decision Tree Regression, DT) and a hybrid model (AdaBoost - Decision Tree, AB-DT). Using 214 data samples from Van Don - Mong Cai expressway, Vietnam, 10 input variables of the model were considered namely particle composition (content of gravel (X1), coarse sand (X2), fine sand (X3), silt clay (X4), organic (X5)), Atterberg limits (Liquid limit (X6), Plastic limit (X7), Plastic index (X8)), and compaction curve (optimum water content (X9) and maximum dry density (X10)). The developed models were evaluated by using a variety of statistical indicators, including coefficient of determination (R2­­), Root mean square error (RMSE), and Mean absolute error (MAE). The results show that AB-DT model has higher accuracy than the DT model. Moreover, the SHAP value analysis shows that the variable X10 influences the CBR value the most. Thus, it implies that applying the AB-DT model to effectively predict the CBR of the roadbed soil saves time and money for experiments.

The Influence of Motivation, Organizational Culture, and Transformational Leadership Style on Performance at Bhaktiasih General Hospital

This study investigates the influence of motivation, organizational culture, and transformational leadership style on performance at Bhaktiasih General Hospital. Utilizing quantitative analysis methods, data was collected from employees through surveys and analyzed using regression analysis. The results revealed significant positive effects of motivation, organizational culture, and transformational leadership style on employee performance with simultaneous test F count (318.779) &gt; F table (2.16), significance of 0.001 less than 0.10 and coefficient of determination of 0.823 or 82.30%. Specifically, higher levels of motivation, a strong organizational culture, and effective implementation of transformational leadership were associated with improved performance outcomes. These findings underscore the importance of fostering a motivational work environment, nurturing a positive organizational culture, and adopting transformational leadership practices to enhance performance in healthcare settings. Understanding these factors can inform management strategies aimed at improving overall performance and quality of care in hospitals like Bhaktiasih General Hospital.

Application of non parametric Bayesian methods in high dimensional data

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.

Optimizing properties of clayey soil using lime and waste marble powder: a sustainable approach for engineering applications

Several studies have explored the potential of waste marble powder (WMP) and lime (LM) as solutions for issues associated with clayey soils. While WMP enhances mechanical properties and addresses environmental concerns, LM effectively improves soil characteristics. This research investigates the efficacy of LM and WMP, both individually and in combination, in addressing challenges specific to clayey soils in Bouzaroura El Bouni, Algeria. These soils typically exhibit low load-bearing capacity, poor permeability, and erosion susceptibility. LM demonstrates promise in enhancing soil properties, while WMP not only addresses environmental concerns but also enhances mechanical characteristics, providing a dual benefit. The study utilizes a three-variable experiment employing Response Surface Methodology (RSM) Box-Behnken Design, with variations in clay content (88%–100%), LM treatment (1.5%–9%), and WMP inclusion (1.5%–9%). Statistical analysis, including ANOVA, reveals significant patterns with p-values &amp;lt;5%. Functional relationships between input variables (clay, LM, and WMP) and output variables (cohesion, friction angle, and unconfined compressive strength) are expressed through high determination coefficients (R2 = 99.84%, 77.83%, and 96.78%, respectively). Numerical optimization identifies optimal mixtures with desirability close to one (0.899–0.908), indicating successful achievement of the objective with 88% clay content, 3% LM, and 6% WMP. This study provides valuable insights into optimizing clay soil behavior for environmental sustainability and engineering applications, emphasizing the potential of LM and WMP as strategic additives.

An ensemble learning model for forecasting water-pipe leakage

Based on the benefits of different ensemble methods, such as bagging and boosting, which have been studied and adopted extensively in research and practice, where bagging and boosting focus more on reducing variance and bias, this paper presented an optimization ensemble learning-based model for a large pipe failure dataset of water pipe leakage forecasting, something that was not previously considered by others. It is known that tuning the hyperparameters of each base learned inside the ensemble weight optimization process can produce better-performing ensembles, so it effectively improves the accuracy of water pipe leakage forecasting based on the pipeline failure rate. To evaluate the proposed model, the results are compared with the results of the bagging ensemble and boosting ensemble models using the root-mean-square error (RMSE), the mean square error (MSE), the mean absolute error (MAE), and the coefficient of determination (R2) of the bagging ensemble technique, the boosting ensemble technique and optimizable ensemble technique are higher than other models. The experimental result shows that the optimizable ensemble model has better prediction accuracy. The optimizable ensemble model has achieved the best prediction of water pipe failure rate at the 14th iteration, with the least RMSE = 0.00231 and MAE = 0.00071513 when building the model that predicts water pipe leakage forecasting via pipeline failure rate.

Estimating Inhaled Nitrogen Dioxide from the Human Biometric Response

Breathing clean air is crucial for maintaining good human health. The air we inhale can significantly impact our physical and mental well-being, influenced by parameters such as particulate matter and gases (e.g. carbon dioxide, carbon monoxide, and nitrogen dioxide). Building on previous research that explored the effects of particulate matter (PM) in specific environments, analyzed using biometric indicators and machine learning models; this work focuses on the effects and estimation of inhaled nitrogen dioxide (NO&lt;sub&gt;2&lt;/sub&gt;). This study involved a cyclist equipped with sensors to monitor various biometric parameters. In addition, an electric car following the cyclist measured the ambient NO&lt;sub&gt;2&lt;/sub&gt; levels using an onboard sensor. A total of 329 biometric variables have been taken into account, of which 320 biometric variables are cognitive responses extracted using an electroencephalogram (EEG) and 9 biometric variables are physiological responses extracted using several sensors. Inhaled NO&lt;sub&gt;2&lt;/sub&gt; levels are first estimated initially by making use of all 329 variables, then using 9 physiological responses and finally using only 6 of the 9 physiological responses. The study also uses a ranking method to pinpoint which biometric variables most significantly estimate inhaled NO&lt;sub&gt;2&lt;/sub&gt; levels. Furthermore, it investigates the linear and non-linear relationship between certain variables and inhaled NO&lt;sub&gt;2&lt;/sub&gt;. The general precision of the prediction for the data set was moderate, as indicated by the coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) and the root mean square error (RMSE) between the true and estimated values of NO&lt;sub&gt;2&lt;/sub&gt; to be 0.35 and 5.41 ppb, respectively, in the test set. A higher accuracy in the prediction of lower values of NO&lt;sub&gt;2&lt;/sub&gt; levels was qualitatively observed using a scatter diagram and a Quantile-Quantile plot where the data were more plentiful. For more robust conclusions, additional data and refined machine learning models are necessary.

Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments

Hardness is one of the most crucial mechanical properties, serving as a key indicator of a material’s suitability for specific applications and its resistance to fracturing or deformation under operational conditions. Machine learning techniques have emerged as valuable tools for swiftly and accurately predicting material behavior. In this study, regression methods including decision trees, adaptive boosting, extreme gradient boosting, and random forest were employed to forecast Vickers hardness values based solely on scanned monochromatic images of indentation imprints, eliminating the need for diagonal measurements. The dataset comprised 54 images of D2 steel in various states, including commercial, quenched, tempered, and coated with Titanium Niobium Nitride (TiNbN). Due to the limited number of images, non-deep machine learning techniques were utilized. The Random Forest technique exhibited superior performance, achieving a Root Mean Square Error (RMSE) of 0.95, Mean Absolute Error (MAE) of 0.12, and Coefficient of Determination (R2) ≈ 1, surpassing the other methods considered in this study. These results suggest that employing machine learning algorithms for predicting Vickers hardness from scanned images offers a promising avenue for rapid and accurate material assessment, potentially streamlining quality control processes in industrial settings.

Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning

Reductions in energy consumption and greenhouse gas emissions are required globally. Under this background, the Multilayer Perceptron machine-learning algorithm was used to predict liquid natural gas consumption to improve energy consumption efficiency. Setting hyperparameters remains challenging in machine-learning-based prediction. Here, to improve prediction efficiency, hyperparameter autotuning via Bayesian optimization was used to identify the optimal combination of the eight key hyperparameters. The autotuned model was validated by comparing its predictive performance with that of a base model (with all hyperparameters set to the default values) using the coefficient of variation of root-mean-square error (CvRMSE) and coefficient of determination (R2) based on the Measurement and Verification Guideline evaluation metrics. To confirm the model’s industrial applicability, its predictions were compared with values measured at a small-to-medium-sized food factory. The optimized model performed better than the base model, achieving a CvRMSE of 12.30% and an R2 of 0.94, and achieving a predictive accuracy of 91.49%. By predicting energy consumption, these findings are expected to promote the efficient operation and management of energy in the food industry.