Raw Variables Research Articles

Muscular Performance of the Elbow Flexor and Extensor Muscles in Children With Myopathies: A Case-Control Study.

Children with myopathies often experience muscle weakness in their lower limbs. However, the upper limbs are also affected and, at the same time, play a key role in daily living activities as well as in transfers and assisted mobility using auxiliary devices. The objective was to assess the performance of the elbow flexor and extensor muscles through static and dynamic contractions in children with myopathies and in their typical peers. This was a case-control study. Seven children with different myopathies participated and were matched with typical children by sex and age (1:2). The muscle performance of elbow flexors (EFL) and elbow extensors (EEX) was assessed using an isokinetic dynamometer using isometric and isokinetic contractions at a speed of 120°s-1. The analyzed variables were peak torque (PT), total work (W), power (P), time to peak torque (TPT), acceleration time (AT), and deceleration time (DT). The raw variables were compared between groups using linear regression with mixed effects. A significance level of p<0.05 was adopted. Children with myopathies showed significantly lower values of PT, W, and P for both elbow muscle groups (p<0.05) compared to typically developing children; TPT showed no difference between groups; and AT and DT were higher in children with myopathies than in typical ones (p<0.05). Children with myopathies exhibited deficits in muscle performance, suggesting that the elbow muscles adopt atypical motor strategies, indicating impaired neuromuscular control. The isokinetic dynamometer is a device that can provide relevant information about muscle performance in this group of diseases.

Using lifestyle information in polygenic modeling of blood pressure traits: a simple method to reduce bias.

Complex traits are determined by the effects of multiple genetic variants, multiple environmental factors, and potentially their interaction. Predicting complex trait phenotypes from genotypes is a fundamental task in quantitative genetics that was pioneered in agricultural breeding for selection purposes. However, it has recently become important in human genetics. While prediction accuracy for some human complex traits is appreciable, this remains low for most traits. A promising way to improve prediction accuracy is by including not only genetic information but also environmental information in prediction models. However, environmental factors can, in turn, be genetically determined. This phenomenon gives rise to a correlation between the genetic and environmental components of the phenotype, which violates the assumption of independence between the genetic and environmental components of most statistical methods for polygenic modeling. In this work, we investigated the impact of including 27 lifestyle variables as well as genotype information (and their interaction) for predicting diastolic blood pressure, systolic blood pressure, and pulse pressure in older individuals in UK Biobank. The 27 lifestyle variables were included as either raw variables or adjusted by genetic and other non-genetic factors. The results show that including both lifestyle and genetic data improved prediction accuracy compared to using either piece of information alone. Both prediction accuracy and bias can improve substantially for some traits when the models account for the lifestyle variables after their proper adjustment. Our work confirms the utility of including environmental information in polygenic models of complex traits and highlights the importance of proper handling of the environmental variables.

Quarterly Percentual Change in Height, Weight, Body Fat and Muscle Mass in Young Football Players of Different Categories

Quarterly Percentual Change in Height, Weight, Body Fat and Muscle Mass in Young Football Players of Different Categories

Local scour depth at piles group exposed to regular waves: On the assessment of expressions based on classification concepts and evolutionary algorithms

An accurate estimation of local scour depth around piles group is inevitably essential to provide stability of marine structures. Over the past decades, many investigations have been made to understand the scouring process at piles group exposed to waves for field and experimental scales. This study aims to predict the wave-induced local scour depth by various robust Data Driven Models (DDMs) and Machine Learning Models (MLMs) developed by classification and evolutionary concepts: Model Tree (MT), Evolutionary Polynomial Regression (EPR), Multivariate Adaptive Regression Spline (MARS), and Gene-Expression Programming (GEP). From relevant literature, 125 data series were employed to provide empirical relationships for scour depth predictions. The raw variables were related to bed sediment, pile configuration, pile geometry, approaching flow, and wave characteristics. Non-dimensional parameters have been obtained through the Buckingham theorem in order to control local scour depth. In this way, the ratio of spacing between piles to pile diameter (G/D), sediment number (Ns), pile arrangement number (ratio of the number of piles parallel to the flow; m; to the number of piles normal to the flow; n), Shields parameter (θ), and Keulegan-Carpenter (KC) were considered. From the training and testing stages of Artificial Intelligence Models (AIMs), it was found that regression equation given by MARS model provided better performance (e.g., Correlation Coefficient [R] = 0.9297, Root Mean Square Error [RMSE] = 0.3489, and Scatter Index [SI] = 0.2765) than other AI models’ efficiency. Additionally, the performance of AI models was assessed for various ranges of dimensionless parameters (i.e., G/D, Ns, and θ) versus KC variation. MARS model had the best performance for G/D = 0–1 and KC < 10 (R = 0.9917 and RMSE = 0.2198) and 0.4≤θ < 0.5 and 15 < KC < 25 whereas EPR model had promising efficiency in the its highest level for Ns = 1–3 and 10 < KC ≤ 15 (R = 0.9941 and RMSE = 0.0771) than other AI models. For the sensitivity analysis, the mutation rate and number of chromosomes are examined for the GEP model, the K-fold number and number of parameters for the MARS model, and the number of algebraic terms and number of chromosomes for the EPR model. Furthermore, ranking analysis of AI models indicated that MARS model had the best performance (Ranking Performance Index [RPI] = 0.7348) and followed by GEP (0.3412), M5MT (0.2999), and EPR (0.2848).

Bayesian and AI Models for Evaluating the Economic Feasibility of Medicinal Herb Processing Facilities

The economic feasibility of medicinal herb processing facilities is critical to the sustainable growth of the herbal medicine industry. This study explores the application of Bayesian and artificial intelligence (AI) methodologies to evaluate the financial viability of such facilities. Bayesian models provide probabilistic assessments by incorporating prior knowledge and dynamically updating predictions based on new data, effectively addressing uncertainties in raw material costs, market demand, and production variables. AI models complement this approach by leveraging machine learning algorithms to analyze large datasets, identify patterns, and predict outcomes across various operational scenarios. The integration of these methodologies offers a comprehensive framework for evaluating feasibility, surpassing the limitations of traditional deterministic approaches. Findings highlight the importance of securing high-quality raw materials, adopting energy-efficient technologies, and diversifying market strategies to improve economic outcomes. The study emphasizes the value of scenario analysis, flexible business strategies, and advanced technologies in navigating the complex challenges of this industry. Recommendations include enhancing supply chain management, investing in automation, and aligning operations with emerging consumer trends. This research advances the understanding of economic evaluation in medicinal herb processing and provides actionable insights for stakeholders, paving the way for the sustainable development of the sector. Future research directions are proposed, including incorporating sustainability metrics and exploring blockchain technology for supply chain transparency.

Limb-specific isometric and isokinetic strength in adults: The potential role of regional bioelectrical impedance analysis-derived phase angle

Background & AimsIt is not yet known whether regional bioelectrical impedance (BIA) phase angle (PhA) may be informative of different types of strength performed by the lower and upper limbs, independently of lean soft tissue mass (LSTM). Using a sample of healthy adults, we aimed to examine the association and relevance of regional PhA relative to isometric and isokinetic strength of each limb. MethodsA total of 57 participants (32.7±12.9 years; 24.7±3.5 kg/m2) were included in the present investigation. Regional raw BIA variables were determined using a phase-sensitive BIA device. Dual-energy X-ray absorptiometry was used to evaluate LSTM. Absolute isometric and isokinetic (i.e., 60º/s and 180º/s) strength of each limb (extension and flexion) was assessed using an isokinetic dynamometer and used to calculate relative strength. ResultsIn absolute strength, only dominant leg PhA was associated with isometric extension strength (β=0.283) and isokinetic 180º/s flexion strength (β=0.354), regardless of LSTM (p<0.05). In relative strength, a significant association of regional PhA was found for dominant arm flexion isometric strength (β=0.336), and non-dominant arm and dominant leg extension isometric strength (β=0.377, β=0.565, respectively; p<0.05), independently of LSTM. Similarly, for isokinetic 180º/s strength, regional PhA significantly explained the variance in the relative strength of both arms and dominant leg (β=0.350 to 0.506), regardless of LSTM (p<0.05). Relative isokinetic 60º/s strength was not consistently associated with regional PhA (p≥0.05). ConclusionsRegional PhA significantly explained relative (isometric and 180º/s isokinetic strength of both arms and dominant leg), but not absolute muscle strength, independently of regional LSTM. Thus, after accounting for body size, regional PhA seems to have its own characteristics that explain relative strength independently of LSTM.

Using feature engineering and machine learning in FAO reference evapotranspiration estimation

Abstract The authors of this study investigated the use of machine learning (ML) and feature engineering (FE) techniques to accurately determine FAO reference evapotranspiration (ETo) with a minimal number of climate variables being measured. The recommended techniques for areas with insufficient measurements are based solely on daily temperature readings. Various ML methods were tested to evaluate how sophisticated an ML algorithm is for this task necessary. The main emphasis was on feature engineering, which involves converting raw variables into inputs better suited for ML algorithms, resulting in improved results. FE methods for estimating evapotranspiration include approximations of clear-sky solar radiation based on altitude and Julian day, approximate relative humidity and wind velocity, a categorical month variable, and variables interactions. The authors confirmed that the ability of ML in such tasks is not solely dependent on choosing the suitable algorithm but also on this frequently ignored step. The results of computational experiments are presented, accompanied by a comparison of the proposed method against standard ETo empiric equations. Machine learning methods, mainly due to the transformation of raw variables using FE, provided better results than traditional empirical methods and sophisticated ML algorithms without FE. In addition, the authors tested the applicability of the developed models in the broader area to evaluate the possibility of their generalizability. The potential of this approach to deliver improved predictions, reduced input requirements, and increased efficiency holds interesting promise for optimizing water management strategies, irrigation planning, and decision-making within the agricultural sector.

The Influence of Raw Material Inventory Control on the Production Process at PT. Dayup Indo in North Jakarta

This study aims to determine how much influence the control of raw material inventories on the production process at PT. Dayup Indo. The population in this study is the entire number of employees in the production and warehouse. This study uses a quantitative method. The sample in this study were 60 people with Non Probability Sampling technique, namely saturated Sampling. The independent variable in this study is the control of raw material inventory, while the dependent variable is the production process. Simple linear regression test results obtained Y = 4.215 + 0.728X which means that the value of 4.215 with 0.728 which states the effect of variable X on Y is positive. Based on regression analysis (t-test), it is known that t-value &gt; t-table is 9.757 &gt; 1.672 which means that raw material inventory control affects the production process at PT. Dayup Indo. With the coefficient of determination (R2) has a value of 0.621 indicates that the magnitude of the influence of raw material inventory control variables on variables of the production process is 62.1% while the remaining 37.9% is influenced by other objects that are not included in this study.

Eating in the city: Experimental effect of anthropogenic food resources on the body condition, nutritional status, and oxidative stress of an urban bioindicator passerine.

Urban areas provide a constant and predictable supply of anthropogenic processed food. The House Sparrow (Passer domesticus Linnaeus, 1758), a declining urban bioindicator species, has recently been reported to have a high level of oxidative stress, with urban diet or pollutants proposed as the potential cause. In this study, we aimed to experimentally determine the effects of two urban trophic resource types (bar snack food leftovers and pet food) on sparrows' physical condition, plasma biochemical nutritional parameters, and blood oxidative status in captivity. To exclude the potential previous effect of urban pollutants, 75 House Sparrows were captured from a rural area in SE Spain and kept in outdoor aviaries. Individuals were exposed to one of three diet treatments: control diet (fruit, vegetables, poultry grain mixture), bar snack diet (ultra-processed snacks), or cat food diet (dry pellets) for 20 days. Blood samples were collected before and after diet treatments to analyze the relative change rates of 12 variables, including physical condition, nutritional status, and oxidant-antioxidant status. A principal component analysis was run to identify gradients of variables covariation, and Generalized Linear Mixed Models were used to determine the effect of diets on each selected PC and on raw variables. The bar snack diet led to signs of anemia and malnutrition, and females tended to lose body condition. The cat food diet increased oxidative stress indicators and protein catabolism. Unbalanced urban diets can affect the body condition and nutritional physiology of House Sparrows and may also induce oxidative stress despite the absence of environmental pollution.

Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning–Based Modeling Study

Sepsis is a leading cause of death in patients with trauma, and the risk of mortality increases significantly for each hour of delay in treatment. A hypermetabolic baseline and explosive inflammatory immune response mask clinical signs and symptoms of sepsis in trauma patients, making early diagnosis of sepsis more challenging. Machine learning-based predictive modeling has shown great promise in evaluating and predicting sepsis risk in the general intensive care unit (ICU) setting, but there has been no sepsis prediction model specifically developed for trauma patients so far. To develop a machine learning model to predict the risk of sepsis at an hourly scale among ICU-admitted trauma patients. We extracted data from adult trauma patients admitted to the ICU at Beth Israel Deaconess Medical Center between 2008 and 2019. A total of 42 raw variables were collected, including demographics, vital signs, arterial blood gas, and laboratory tests. We further derived a total of 485 features, including measurement pattern features, scoring features, and time-series variables, from the raw variables by feature engineering. The data set was randomly split into 70% for model development with stratified 5-fold cross-validation, 15% for calibration, and 15% for testing. An Extreme Gradient Boosting (XGBoost) model was developed to predict the hourly risk of sepsis at prediction windows of 4, 6, 8, 12, and 24 hours. We evaluated model performance for discrimination and calibration both at time-step and outcome levels. Clinical applicability of the model was evaluated with varying levels of precision, and the potential clinical net benefit was assessed with decision curve analysis (DCA). A Shapley additive explanation algorithm was applied to show the effect of features on the prediction model. In addition, we trained an L2-regularized logistic regression model to compare its performance with XGBoost. We included 4603 trauma patients in the study, 1196 (26%) of whom developed sepsis. The XGBoost model achieved an area under the receiver operating characteristics curve (AUROC) ranging from 0.83 to 0.88 at the 4-to-24-hour prediction window in the test set. With a ratio of 9 false alerts for every true alert, it predicted 73% (386/529) of sepsis-positive timesteps and 91% (163/179) of sepsis events in the subsequent 6 hours. The DCA showed our model had a positive net benefit in the threshold probability range of 0 to 0.6. In comparison, the logistic regression model achieved lower performance, with AUROC ranging from 0.76 to 0.84 at the 4-to-24-hour prediction window. The machine learning-based model had good discrimination and calibration performance for sepsis prediction in critical trauma patients. Using the model in clinical practice might help to identify patients at risk of sepsis in a time window that enables personalized intervention and early treatment.

Forecasting and Optimizing Dual Media Filter Performance via Machine Learning

Four different machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and Gaussian Process Regressions (GPR), were applied to predict the performance of a multi-media filter operating as a function of raw water quality and plant operating variables. The models were trained using data collected over a seven year period covering water quality and operating variables, including true colour, turbidity, plant flow, and chemical dose for chlorine, KMnO4, FeCl3, and Cationic Polymer (PolyDADMAC). The machine learning algorithms have shown that the best prediction is at a 1-day time lag between input variables and unit filter run volume (UFRV). Furthermore, the RF algorithm with grid search using the input metrics mentioned above with a 1-day time lag has provided the highest reliability in predicting UFRV with a RMSE and R2 of 31.58 and 0.98, respectively. Similarly, RF with grid search has shown the shortest training time, prediction accuracy, and forecasting events using a ROC-AUC curve analysis (AUC over 0.8) in extreme wet weather events. Therefore, Random Forest with grid search and a 1-day time lag is an effective and robust machine learning algorithm that can predict the filter performance to aid water treatment operators in their decision makings by providing real-time warning of the potential turbidity breakthrough from the filters.

Evaluating the Variability Between 20-m Multistage Fitness Test Estimating Equations in Law Enforcement Recruits.

Campbell, P, Maupin, D, Lockie, RG, Dawes, JJ, Simas, V, Canetti, E, Schram, B, and Orr, R. Evaluating the variability between 20-m multistage fitness test estimating equations in law enforcement recruits. J Strength Cond Res 38(4): 742-748, 2024-The 20-m multistage fitness test (20MSFT) is commonly used by law enforcement agencies to measure aerobic fitness and to estimate maximal aerobic consumption (V̇ o2 max). These measures are an important occupational variable with aerobic fitness levels linked to employment status, occupational performance, and long-term health in law enforcement officers. There are a multitude of predictive equations used to provide an estimate of V̇ o2 max, with the extent of variability in the estimated V̇ o2 max currently unknown in this population. This has consequences for comparisons between, and across, differing agencies, and in capabilities of deriving normative data. The aim of this investigation was to compare the variability in estimated V̇ o2 max scores derived from different 20MSFT predictive equations. The 20-m multistage fitness test data from 1,094 law enforcement recruits (male n = 741, 25.2 ± 6.3 years; female: n = 353, 25.6 ± 5.6 years) from a single agency were retrospectively analyzed. The 20MSFT scores were transformed into estimated V̇ o2 max scores using 6 different predictive equations. Significance was set at p < 0.05. Results demonstrated significantly different V̇ o2 max scores between each predictive equation ( p < 0.001; d = 0.25-1.53) and between male and female recruits ( p < 0.001, r = 0.55). All estimated V̇ o2 max equations showed small to very strong correlations with each other ( p < 0.001; r = 0.32-0.99). The findings indicate considerable dispersion of V̇ o2 max scores when using differing equations, suggesting raw 20MSFT variables (e.g., shuttles or distance completed) should be preferred to measure and apply results from aerobic fitness tests if standardized approaches are not developed within law enforcement.

MULTIVARIATE MULTILEVEL MODELLING TO ASSESS FACTORS AFFECTING THE QUALITY OF VOCATIONAL HIGH SCHOOLS IN SOUTH SULAWESI PROVINCE

This study analyzes the quality of Vocational High Schools (VHS), which have a hierarchical data structure and have more than one response variable. Data gathered for this study is from the Basic Education Data (DAPODIK) in the form of raw data variables of several variables that characterize the quality of VHS and other independent variables in South Sulawesi for four years (2018 to 2021) from the Ministry of Finance Republic of Indonesia (KEMENKEU), and Statistics Indonesia (BPS). The explanatory variable at the regency level consists of four years (2018 to 2021), a multi-year and high-dimensional data structure. Therefore, Principal Component Analysis (PCA) is used to overcome this. The modelling is done by using multivariate multilevel modelling (MVMM) on one-level and two-level structures. This study aims to model the average National Examination and Accreditation scores of Vocational High School in South Sulawesi using MVMM modelling that considers the regency/city level and identifies the factors that influence the average National Examination and Accreditation scores. The results showed that the two-level multivariate model with a random intercept as a hierarchical component was better than the one-level multilevel model based on a minor Deviance Information Criterion (DIC) value. Simultaneously, at the 5% level of significance, variables that contribute significantly to the quality of Vocational High Schools in South Sulawesi Province are produced. The variables that have a significant effect on the quality of Vocational High Schools at the school level are the ratio of the number of students/pupils per study group, the percentage of certified teachers to the number of teachers, the ratio of the number of students/pupils per number of toilets, the ratio of laboratory availability, and the ratio of the availability of supporting rooms. Meanwhile, at the regency level, it was found that the percentage of poverty and Gross Regional Domestic Product (GRDP) had a significant effect on the quality of Vocational High Schools.

Feature engineering methodology for congestion forecasting

Short-term traffic forecasting is a key element in proactive traffic management, e.g., mitigating the negative effect of impending congestion through appropriate capacity allocation at signalized intersections. In this study, we develop a data-driven methodology for reliably and robustly predicting impending stable congestion. By incorporating feature engineering techniques into an iterative machine learning process, we develop a prediction model that can be intuitively understood by traffic experts and is amenable to diagnostics during implementation. Our iterative machine learning process combines the embedded and filter approaches for feature selection with the use of expert knowledge to create aggregative input variables. The embedded approach is represented by application of a decision tree algorithm, while the filter approach is reflected in use of the mean decrease in accuracy output of a random forest algorithm for identifying expressive variables. We tested the methodology by applying it to field data from a sub-network in Tel Aviv. We demonstrated a reduction in the number of decision tree input variables from 66 raw variables to the five most effective aggregative ones, while achieving statistically significant improvement in all performance indicators. The identification rate of stable congestion increased from 65% to 74% while the robustness of the results was enhanced: the standard deviations of the identification and false alarm rates fell from 8% to 3%, respectively, to 5% and 2%. • Short-term traffic forecasting is a key element in mitigating the negative effect of impending urban congestion. • A machine learning process for reliably predicting impending congestion was developed by using feature engineering. • Aggregative input variables were created by combining embedded and filter approaches with the use of expert knowledge. • The proposed methodology significantly increases both the congestion identification rate and the robustness of the results. • The final prediction model can be intuitively understood by traffic experts.

The Coffee Processing Method Had a More Pronounced Effect than Location and Production Systems on the Overall Quality of Kaffa Biosphere Reserve Coffees

A comprehensive examination of the physical and cup quality of Kafa Biosphere Reserve coffees was essential to identify the inherent qualities of the coffees in connection with the area’s soil physical and chemical characteristics. As a result, preliminary coffee quality data was acquired from bean physical and cup quality examination of coffees derived through a three-stage nested design combining districts (Gimbo, Gawata, and Decha), coffee production systems (forest, semiforest, and garden), and coffee processing methods (wet, semiwet, and dry). Representative soil samples were collected according to the sampling structure and analyzed following the standard procedures. Multiple factor analysis and Pearson’s correlation coefficient analysis were applied to the collected data. According to the results of multiple factor analysis, the Gimbo and Decha districts are not significantly different from each other in terms of coffee quality. However, they are substantially different from the Gawata district in terms of coffee quality. Similarly, within each district, there are no clear differences in coffee production systems. However, the production systems of districts varied significantly. The coffee processing method had a pronounced effect on the overall quality and preliminary grade, physical and raw quality variables of coffee. Screen retention (14) was correlated with soil iron, but it was related negatively to boron and sodium contents. A significant and positive relationship was observed between green coffee bean moisture content and soil potassium, calcium, magnesium, boron, sodium, organic carbon, silicon content, and soil pH. A positive and significant relationship between soil molybdenum and the coffee quality variable was observed across the studied districts whereas most soil micronutrients, specifically, zinc, sulfur, and manganese, were significantly and negatively related to the organoleptic qualities of the coffees. Further investigation that includes the effects of elevation is recommended in future studies.

Poor Nutritional Status and Dynapenia Are Highly Prevalent in Post-Acute COVID-19.

Poor nutritional status is common (estimated prevalence 5–69%) in acute coronavirus disease-2019 (COVID-19), and has been associated with hospitalization, the need for intensive care, and mortality. Body composition (BC) and muscle function have also been related in such patients to poor disease outcomes.As the evidence in the literature is limited, a cross-sectional study was carried out to determine the frequency of malnutrition in a cohort of post-acute COVID-19 patients referred to a rehabilitation center after hospital discharge. BC and muscle strength were assessed and the differences between bedridden and not bedridden patients were specifically evaluated.The study sample was composed of 144 post-acute COVID-19 patients (mean age 64.8 years; males = 95), 37% of whom were bedridden (males = 60%). Nutritional status was evaluated with Mini-Nutritional Assessment (MNA) and Controlling Nutritional status (CONUT). Fat-free mass (FFM) and skeletal muscle mass (SM) were estimated using bioelectrical impedance analysis (BIA). Raw BIA variables (phase angle = PhA and impedance ratios = IRs) were also determined and handgrip strength (HGS) was measured. Dynapenia was identified according to the 2019 EWGSOP criteria.According to MNA, 18% (n. 26) of patients were malnourished and 62% (n. 89) were at risk of malnutrition. As for CONUT, 21% (n. 31) of cases had moderate–severe malnutrition and 58% (n. 83) had light malnutrition. Abnormalities of raw BIA variables (low PhA and high IRs) and low HGS were more common in bedridden patients, in those who were malnourished, or had low FFM or SM. Dynapenic patients were 65% men and 47% women.In conclusion, malnutrition, BC alterations, and low HGS occur in post-acute COVID-19 patients and are more common in bedridden patients. Further studies are needed to identify reliable algorithms for assessing nutritional status in post-acute COVID-19 patients undergoing rehabilitation.

Critical Concurrent Feature Selection and Enhanced Heterogeneous Ensemble Learning Approach for Fault Detection in Industrial Processes

Machine learning approaches have been successfully applied to fault detection in complex industrial processes, while it is still challenging to design an optimal feature space and a stable model for various faults. To solve this problem, a hybrid approach with critical concurrent feature selection and enhanced heterogeneous ensemble learning is proposed. First, raw process variables from field instruments and slow features obtained by simple linear transformation are ingeniously combined to construct the concurrent feature space so as to contain static and dynamic information. Then, the critical concurrent feature space of each individual learner is automatically obtained by the optimal selection process, and the feature diversity of ensemble is realized simultaneously. Finally, an enhanced heterogeneous ensemble model is constructed by different optimized individual learners, which effectively improves the classification accuracy and stability in fault detection. The performance of the proposed approach is evaluated in a simulated Tennessee Eastman benchmark and a real-word three-phase flow process. The experimental results illustrate that the performance of each optimized individual learner outperforms classic random forest algorithm. The generalization and stability of the ensemble model are further improved. Compared with traditional classification algorithms, the proposed approach achieves superior performance with accuracy that exceeds 89% for the Tennessee Eastman process and exceeds 88% for the three-phase flow process respectively. Additionally, the selected critical concurrent features indicate that both static and dynamic information play important roles in fault detection of industrial processes.

How macro-variables drive crude oil volatility? Perspective from the STL-based iterated combination method

Based on the GARCH-MIDAS framework, this article mainly explores whether the Seasonal and Trend decomposition using Loess (STL decomposition) and the iterated combination approach can improve the prediction accuracy of crude oil price volatility by using macro variables. We introduce five macro variables, that is, consumer price index, interest rate, producer price index, industrial production index, and unemployment rate, into the long-term component in the GARCH-MIDAS model. Though the model including raw macro variables performs better than the model containing each STL-based single subsequence, we find that the iterated combination forecasts using the forecasts obtained by each subsequence are superior to the corresponding standard ones. Besides, when mean-variance investors perform asset allocation, they can obtain more certainty-equivalent-returns by applying our new iterated combination approach. Finally, various robustness checks can verify the above-mentioned findings. In short, this article may provide a new perspective for economic empirical applications and theoretical research.

Lack of general learning ability factor in a rat test battery measuring a wide spectrum of cognitive domains.

Objective: In the framework of a larger project aiming to test putative cognitive enhancer drugs in a system with improved translational validity, we established a rodent test battery, where different, clinically relevant cognitive domains were investigated in the same animal population. The aim of the current study was to check whether performances in the different tasks representing different cognitive functions are assay-specific or may originate in an underlying general learning ability factor. Methods: In the experiments 36 Long-Evans and 36 Lister Hooded rats were used. The test battery covered the following cognitive domains: attention and impulsivity (measured in the 5-choice serial reaction time task), spatial memory (Morris water-maze), social cognition (cooperation task), cognitive flexibility (attentional set shifting test), recognition memory (novel object recognition) and episodic memory (water-maze based assay). The outcome variables were analyzed by correlation analysis and principal component analysis (PCA). The datasets consisted of variables measuring learning speed and performance in the paradigms. From the raw variables composite variables were created for each assay, then from these variables a composite score was calculated describing the overall performance of each individual in the test battery. Results: Correlations were only found among the raw variables characterizing the same assay but not among variables belonging to different tests or among the composite variables. The PCAs did not reduce the dimensionality of the raw or composite datasets. Graphical analysis showed variable performance of the animals in the applied tests. Conclusions: The results suggests the assay outcomes (learning performance) in the system are based on independent cognitive domains.

Machine Learning-Based Prediction of Myocardial Recovery in Patients With Left Ventricular Assist Device Support.

Prospective studies demonstrate that aggressive pharmacological therapy combined with pump speed optimization may result in myocardial recovery in larger numbers of patients supported with left ventricular assist device (LVAD). This study sought to determine whether the use of machine learning (ML) based models predict LVAD patients with myocardial recovery resulting in pump explant. A total of 20 270 adult patients with a durable continuous-flow LVAD in the INTERMACS registry (Interagency Registry for Mechanically Assisted Circulatory Support) were included in the study. Ninety-eight raw clinical variables were screened using the least absolute shrinkage and selection operator for selection of features associated with LVAD-induced myocardial recovery. ML models were developed in the training data set (70%) and were assessed in the validation data set (30%) by receiver operating curve and Kaplan-Meier analysis. Least absolute shrinkage and selection operator identified 28 unique clinical features associated with LVAD-induced myocardial recovery, including age, cause of heart failure, psychosocial risk factors, laboratory values, cardiac rate and rhythm, and echocardiographic indices. ML models achieved area under the receiver operating curve of 0.813 to 0.824 in the validation data set outperforming logistic regression-based new INTERMACS recovery risk score (area under the receiver operating curve of 0.796) and previously established LVAD recovery risk scores (INTERMACS Cardiac Recovery Score and INTERMACS Recovery Score by Topkara et al) with area under the receiver operating curve of 0.744 and 0.748 (P<0.05). Patients who were predicted to recover by ML models demonstrated a significantly higher incidence of myocardial recovery resulting in LVAD explant in the validation cohort compared with those who were not predicted to recover (18.8% versus 2.6% at 4 years of pump support). ML can be a valuable tool to identify subsets of LVAD patients who may be more likely to respond to myocardial recovery protocols.