Best Combination Of Variables Research Articles

AKIMLpred: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study

BackgroundEarly recognition and timely intervention for AKI in critically ill patients were crucial to reduce morbidity and mortality. This study aimed to use biomarkers to construct a optimal machine learning model for early prediction of AKI in critically ill patients within seven days. MethodsThe prospective cohort study enrolled 929 patients altogether who were admitted in ICU including 680 patients in training set (Jiefang Campus) and 249 patients in external testing set (Binjiang Campus). After performing strict inclusion and exclusion criteria, 421 patients were selected in training set for constructing predictive model and 167 patients were selected in external testing for evaluating the predictive performance of resulting model. Urine and blood samples were collected for kidney injury associated biomarkers detection. Baseline clinical information and laboratory data of the study participants were collected. We determined the average prediction efficiency of six machine learning models through 10-fold cross validation. ResultsIn total, 78 variables were collected when admission in ICU and 43 variables were statistically significant between AKI and non-AKI cohort. Then, 35 variables were selected as independent features for AKI by univariate logistic regression. Spearman correlation analysis was used to remove two highly correlated variables. Three ranking methods were used to explore the influence of 33 variables for further determining the best combination of variables. The gini importance ranking method was found to be applicable for variables filtering. The predictive performance of AKIMLpred which constructed by the XGBoost algorithm was the best among six machine learning models. When the AKIMLpred included the nine features (NGAL, IGFBP7, sCysC, CAF22, KIM-1, NT-proBNP, IL-6, IL-18 and L-FABP) with the highest influence ranking, its model had the best prediction performance, with an AUC of 0.881 and an accuracy of 0.815 in training set, similarly, with an AUC of 0.889 and an accuracy of 0.846 in validation set. Moreover, the performace was slightly outperformed in testing set with an AUC of 0.902 and an accuracy of 0.846. The SHAP algorithm was used to interpret the prediction results of AKIMLpred. The web-calculator of AKIMLpred was shown for predicting AKI with more convenient(https://www.xsmartanalysis.com/model/list/predict/model/html?mid=8065&symbol=11gk693982SU6AE1ms21). AKIMLpred was better than the optimal model built with only routine tests for predicting AKI in critically ill patients within 7 days. ConclusionThe model AKIMLpred constructed by the XGBoost algorithm with selecting the nine most influential biomarkers in the gini importance ranking method had the best performance in predicting AKI in critically ill patients within 7 days. This data-driven predictive model will help clinicians to make quick and accurate diagnosis.

Preoperative nomogram for predicting spread through air spaces in clinical-stage IA non-small cell lung cancer using 18F-fluorodeoxyglucose positron emission tomography/computed tomography

PurposeThis study aims to assess the predictive value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiological features and the maximum standardized uptake value (SUVmax) in determining the presence of spread through air spaces (STAS) in clinical-stage IA non-small cell lung cancer (NSCLC).MethodsA retrospective analysis was conducted on 180 cases of NSCLC with postoperative pathological assessment of STAS status, spanning from September 2019 to September 2023. Of these, 116 cases from hospital one comprised the training set, while 64 cases from hospital two formed the testing set. The clinical information, tumor SUVmax, and 13 related CT features were analyzed. Subgroup analysis was carried out based on tumor density type. In the training set, univariable and multivariable logistic regression analyses were employed to identify the most significant variables. A multivariable logistic regression model was constructed and the corresponding nomogram was developed to predict STAS in NSCLC, and its diagnostic efficacy was evaluated in the testing set.ResultsSUVmax, consolidation-to-tumor ratio (CTR), and lobulation sign emerged as the best combination of variables for predicting STAS in NSCLC. Among these, SUVmax and CTR were identified as independent predictors for STAS prediction. The constructed prediction model demonstrated area under the curve (AUC) values of 0.796 and 0.821 in the training and testing sets, respectively. Subgroup analysis revealed a 2.69 times higher STAS-positive rate in solid nodules compared to part-solid nodules. SUVmax was an independent predictor for predicting STAS in solid nodular NSCLC, while CTR and an emphysema background were independent predictors for STAS in part-solid nodular NSCLC.ConclusionOur nomogram based on preoperative 18F-FDG PET/CT radiological features and SUVmax effectively predicts STAS status in clinical-stage IA NSCLC. Furthermore, our study highlights that metabolic parameters and CT variables associated with STAS differ between solid and part-solid nodular NSCLC.

Optimization of Rituximab Therapy in Adult Patients With PLA2R1-Associated Membranous Nephropathy With Artificial Intelligence

IntroductionRituximab is a first-line treatment for membranous nephropathy. Nephrotic syndrome limits rituximab exposure due to urinary drug loss. Rituximab underdosing (serum level< 2 μg/mL at month-3) is a risk factor for treatment failure. We developed a machine learning algorithm to predict the risk of underdosing based on patients’ characteristics at rituximab infusion. We investigated the relationship between the predicted risk of underdosing and the cumulative dose of rituximab required to achieve remission. MethodsRituximab concentrations were measured at month-3 in 92 sera from adult patients with primary membranous nephropathy, split into a training (75%) and a testing set (25%). A forward-backward machine-learning procedure determined the best combination of variables to predict rituximab underdosing in the training dataset, which was tested in the test set. The performances were evaluated for accuracy, sensitivity and specificity in ten-fold cross-validation training and test sets. ResultsThe best variables combination to predict rituximab underdosing included: age, sex, body surface area, anti-PLA2R1 antibody titer at day-0, serum albumin at day-0 and day-15 and serum creatinine at day-0 and day-15. The accuracy, sensitivity and specificity were respectively 79.4%, 78.7% and 81.0% (training dataset), and 79.2%, 84.6% and 72.7% (testing dataset). In both sets, the algorithm performed significantly better than chance (p<0.05). Patients with an initial high probability of underdosing experienced a longer time to remission with higher rituximab cumulative doses required to achieved remission. ConclusionThis algorithm could allow for early intensification of rituximab regimen in patients at high estimated risk of underdosing to increase the likelihood of remission.

Artificial neural networks and adaptive neuro-fuzzy inference systems for prediction of soil respiration in forested areas southern Brazil.

The purpose of this study was to estimate the temporal variability of CO2 emission (FCO2) from O2 influx into the soil (FO2) in a reforested area with native vegetation in the Brazilian Cerrado, as well as to understand the dynamics of soil respiration in this ecosystem. The database is composed of soil respiration data, agroclimatic variables, improved vegetation index (EVI), and soil attributes used to train machine learning algorithms: artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The predictive performance was evaluated based on the mean absolute error (MEA), root mean square error (RMSE), mean absolute percentage error (MAPE), agreement index (d), confidence coefficient (c), and coefficient of determination (R2). The best estimation results for validation were FCO2 with multilayer perceptron neural network (MLP) (R2 = 0.53, RMSE = 0.967µmolm-2s-1) and radial basis function neural network (RBF) (R2 = 0.54, RMSE = 0.884µmolm-2s-1) and FO2 with MLP (R2 = 0.45, RMSE = 0.093mgm-2s-1) and RBF (R2 = 0.74, 0.079mgm-2s-1). Soil temperature and macroporosity are important predictors of FCO2 and FO2. The best combination of variables for training the ANFIS was selected based on trial and error. The results were as follows: FCO2 (R2 = 16) and FO2 (R2 = 29). In all models, FCO2 outperformed FO2. A primary factor analysis was performed, and FCO2 and FO2 correlated best with the weather and soil attributes, respectively.

Early Warning Indicators of Groundwater Drought in Mountainous Regions

AbstractAquifers in mountainous regions are susceptible to drought. However, the diverse hydroclimatology, the small and responsive aquifers, and the varied nature of interactions between groundwater and surface water lead to complex groundwater level responses that are challenging to interpret for understanding groundwater drought. In this study, generalized additive models (GAMs) are used to explore the sensitivity of summer groundwater levels to various climate and hydrological predictor variables (indicators) in each of a snowmelt‐ and rainfall‐dominated hydroclimatic regime in British Columbia, Canada. A sensitivity analysis explores individual seasonal predictor variables, station versus gridded climate data, teleconnection indices, and time series length. GAMs are then generated for different combinations of predictor variables to identify the best combination for each region. In the snowmelt‐dominated regime, maximum spring temperature, maximum snow water equivalent, and the winter Nino 3.4 index is the best combination of variables for predicting summer groundwater levels. In the rainfall‐dominated regime, maximum spring temperature, winter precipitation, and spring streamflow is the best combination. The unique combinations of predictor variables for each region can be used as early warning indicators for groundwater drought preparedness by water managers prior to the beginning of the summer. The Standardized Groundwater Level Index (SGI) is also effective at indicating which wells had pronounced responses to periods of drought in each region. However, the SGI differed among aquifers of similar type, suggesting other factors such as aquifer response mechanism and groundwater pumping may have an important influence on the SGI.

Local Climate Zone Classification Using Daytime Zhuhai-1 Hyperspectral Imagery and Nighttime Light Data

The tremendous advancement of cities has caused changes to the urban subsurface. Urban climate problems have become increasingly prominent, especially with regard to the intensification of the urban heat island (UHI) effect. The local climate zone (LCZ) is a new quantitative method for analyzing urban climate that is based on the kind of urban surface and can effectively deal with the problem of the hazy distinction between urban and rural areas in UHI effect research. LCZs are widely used in regional climate modeling, urban planning, and thermal comfort surveys. Existing large-scale LCZ classification methods usually use visual features of optical images, such as spectral and textural features. There are many problems with hyperspectral LCZ extraction over large areas. LCZ is an integrated concept that includes features of the geography, society, and economy. Consequently, it makes sense to consider the characteristics of human activity and the visual features of the images to interpret them accurately. ALOS_DEM data can depict the city’s physical characteristics; however, images of nighttime lights are crucial indicators of human activity. These three datasets can be used in combination to portray the urban environment. Therefore, this study proposes a method for fusing daytime and nighttime data for LCZ mapping, i.e., fusing daytime Zhuhai-1 hyperspectral images and their derived feature indices, ALOS_DEM data, and nighttime light data from Luojia-1. By combining daytime and nighttime information, the proposed approach captures the temporal dynamics of urban areas, providing a more complete representation of their characteristics. The integration of the data allows for a more refined identification and characterization of urban land cover. It comprehensively integrates daytime and nighttime data, exploits synergistic information from multiple sources, and provides higher accuracy and resolution for LCZ mapping. First, we extracted various features, namely spectral, red-edge, and textural features, from the Zhuhai-1 images, ALOS_DEM data, and nighttime light data from Luojia-1. Random forest (RF) and XGBoost classifiers were used, and the average impurity reduction method was employed to assess the significance of the variables. All the input variables were optimized to select the best combination of variables. The results from a study of the 5th ring road area of Beijing, China, revealed that the technique achieved LCZ mapping with good precision, with a total accuracy of 87.34%. In addition, to examine and contrast the effects of various feature indices on the LCZ classification accuracy, feature combination methods were used. The results of the study showed that the accuracies of LCZ classification in terms of spectral and textural were improved by 2.33% and 2.19% using the RF classifier, respectively. The radiation brightness value (RBV) (GI value = 0.0212) attained the classification’s highest variable importance value; the DEM also produced a high GI value (0.0159), indicating that night lighting and landform features strongly influence LCZ classification.

Femoral and acetabular features explain acetabular contact pressure sensitivity to hip internal rotation in persons with cam morphology: A finite element analysis

BackgroundFemoroacetabular impingement is characterized by premature contact between the proximal femur and acetabulum. The loss of femoral head-neck concavity associated with cam morphology leads to mechanical impingement during hip flexion and internal rotation. Other femoral and acetabular features have been linked with mechanical impingement but have not been comprehensively investigated. This study sought to determine which bony features are most influential in contributing to mechanical impingement in persons with a cam morphology. MethodsTwenty individuals (10 female, 10 male) with a cam morphology participated. Finite element analyses incorporating subject-specific bony geometry derived from computed tomography scans were used to determine which femoral (alpha angle and femoral neck-shaft angle) and acetabular (anteversion angle, inclination angle, depth, and lateral center-edge angle) features accentuate acetabular contact pressure with increasing degrees of hip internal rotation with the hip flexed to 90°. To determine the best predictors of acetabular contact pressure sensitivity to internal rotation, all morphological variables were included in a stepwise regression with the final model subjected to a bootstrapping procedure. FindingsThe stepwise regression revealed that femoral neck-shaft angle, acetabular anteversion angle, acetabular inclination angle, and acetabular depth were the best combination of variables to predict contact pressure sensitivity to internal rotation, explaining 55% of the variance. Results of the bootstrap analysis revealed that a median value of 65% [37%, 89%] variance in sensitivity could be explained by these morphological variables. InterpretationMechanical impingement and the concomitant acetabular contact pressure are modulated by multiple femoral and acetabular features in persons with a cam morphology.

137 Parturition prediction in mares using gestation length, and pH and hardness of preparturient mammary secretions

Due to variability in gestation length, attention is placed on predictive variables of parturition. Previous studies have focused on variables of days in gestation (DIG), and pH or hardness (Hd) of preparturient mammary secretions. Research on the added benefit of using multiple variables simultaneously is limited. We hypothesized that a combination of mammary secretion pH, Hd, and DIG would improve parturition predictions within 24, 48, and 72h of birth. Our objectives were to identify the best combination of variables that provided probability estimates of parturition and to determine optimal cutoff probabilities for predicting parturition. Diagnostic criteria were compared with previously published criteria using samples collected in this study (Table 1). Mammary secretions (n = 1,524) were collected from 36 mares before 135 births from 2014 to 2021. The pH was measured with narrow range (pH 6.0–7.7, 0.3 precision) pH strips, Hd with the Predict-a-Foal Kit (0–5 squares, 0.5 precision), and DIG was calculated based on the last day bred. Backward stepwise selection with mixed logistic regression was used to select the best combination of variables to estimate probability of whether parturition would occur. Bootstrapping with 10,000 replications and data clustered by foal was utilized to improve precision of coefficients used to determine estimated probability. Decision curve analysis (DCA) was utilized to compare the net benefits of each diagnostic criteria. The best combination of variables for prediction within 24h (8 ± 4h)were pH and DIG, while prediction within 48h (32 ± 4h) and 72h (56 ± 4h) added the variable of Hd. Optimal cutoff probabilities that maximized the sensitivity (SN) and specificity (SP) of parturition diagnosis were 13.8, 10.2, and 12.7% for 24, 48, and 72h. The sum of the SN, positive predictive value (PPV), SP, and negative predictive value (NPV) illustrates how the combination is optimized over any single diagnostic criteria (Table 1). The DCA indicates greater net benefit for predictions using the combination over single diagnostic criteria within realistic threshold probabilities. Using a combination of variables to predict if parturition will occur will reduce false positives without increasing false negatives.

Predicting depression and anxiety of Chinese population during COVID-19 in psychological evaluation data by XGBoost

BackgroundDue to the onset of sudden stress, COVID-19 has greatly impacted the incidence of depression and anxiety. However, challenges still exist in identifying high-risk groups for depression and anxiety during COVID-19. Studies have identified how resilience and social support can be employed as effective predictors of depression and anxiety. This study aims to select the best combination of variables from measures of resilience, social support, and alexithymia for predicting depression and anxiety. MethodsThe eXtreme Gradient Boosting (XGBoost11The eXtreme Gradient Boosting.) model was applied to a dataset including data on 29,841 participants that was collected during the COVID-19 pandemic. Discriminant analyses on groups of participants with depression (DE22Depression.), anxiety (AN33Anxiety.), comorbid depression and anxiety (DA44Depression and anxiety comorbidity.), and healthy controls (HC55Health control.), were performed. All variables were selected according to their importance for classification. Further, analyses were performed with selected features to determine the best variable combination. ResultsThe mean accuracies achieved by three classification tasks, DE vs HC, AN vs HC, and DA vs HC, were 0.78, 0.77, and 0.89. Further, the combination of 19 selected features almost exhibited the same performance as all 56 variables (accuracies = 0.75, 0.75, and 0.86). ConclusionsResilience, social support, and some demographic data can accurately distinguish DE, AN, and DA from HC. The results can be used to inform screening practices for depression and anxiety. Additionally, the model performance of a limited scale including only 19 features indicates that using a simplified scale is feasible.

Developing a machine learning prediction algorithm for early differentiation of urosepsis from urinary tract infection.

Early recognition and timely intervention for urosepsis are key to reducing morbidity and mortality. Blood culture has low sensitivity, and a long turnaround time makes meeting the needs of clinical diagnosis difficult. This study aimed to use biomarkers to build a machine learning model for early prediction of urosepsis. Through retrospective analysis, we screened 157 patients with urosepsis and 417 patients with urinary tract infection. Laboratory data of the study participants werecollected, including data on biomarkers, such as procalcitonin, D-dimer, and C-reactive protein. We split the data into training (80%) and validation datasets (20%) and determined the average model prediction accuracy through cross-validation. In total, 26 variables were initially screened and 18 were statistically significant. The influence of the 18 variables was sorted using three ranking methods to further determine the best combination of variables. The Gini importance ranking method was found to be suitable for variable filtering. The accuracy rates of the six machine learning models in predicting urosepsis were all higher than 80%, and the performance of the artificial neural network (ANN) was the best among all. When the ANN included the eight biomarkers with the highest influence ranking, its model had the best prediction performance, with an accuracy rate of 92.9% and an area under the receiver operating characteristic curve of 0.946. Urosepsis can be predicted using only the top eight biomarkers determined by the ranking method. This data-driven predictive model will enable clinicians to make quick and accurate diagnoses.

QUAD FLAT NO-LEAD PACKAGE WITH DIFFERENT THERMAL PROFILES AND SOLDER PAD FINISHING FOR VOID MINIMIZATION

Voiding in solder joints is a common problem that occur in electronic packaging, specifically for bottom terminated component (BTC). Quad Flat No-Lead (QFN) is a good example of BTC component that susceptible to void. Thus, QFN component thermal pads were used as the test vehicle in this voiding assessment. The objectives of this study are to analyze the influence of the variables from every parameter evaluated in the experiment on voiding area in QFN thermal pad solder joint and obtain the best combination of variables that can minimize the total void area percentage inside QFN thermal pad solder joint down to below 5%. In this study, several variables and their impacts on voiding based on the literature review were studied. No clean lead-free solder paste of SAC305 composition was employed in this experiment. Various stencil patterns were tested, including Window-Pane, Round, and Triangle/Polygon. The influence of pad surface finishing of the test board on the voiding reduction was analyzed in this study. The effects of convection reflow of various profiles and atmospheres were also considered. All the variables were setup for the experiment execution which begins by solder paste printing, solder paste inspection, component pick &amp; place, convection reflow soldering, Xray inspection, void data extraction &amp; analysis, and finally void results compilation. The results shown that reflow profile Ramp-To-Spike (RTS) Long Reflow Time High Peak Temperature gave lesser void area % than Ramp-Soak-Spike (RSS) Long Soak and RTS Normal profiles.

Prediction of Noninvasive Ventilation Failure in COVID-19 Patients: When Shall We Stop?

In coronavirus disease 2019 (COVID-19), there are no tools available for the difficult task of recognizing which patients do not benefit from maintaining respiratory support, such as noninvasive ventilation (NIV). Identifying treatment failure is crucial to provide the best possible care and optimizing resources. Therefore, this study aimed to build a model that predicts NIV failure in patients who did not progress to invasive mechanical ventilation (IMV). This retrospective observational study included critical COVID-19 patients treated with NIV who did not progress to IMV. Patients were admitted to a Portuguese tertiary hospital between October 1, 2020, and March 31, 2021. The outcome of interest was NIV failure, defined as COVID-19-related in-hospital death. A binary logistic regression was performed, where the outcome (mortality) was the dependent variable. Using the independent variables of the logistic regression a decision-tree classification model was implemented. The study sample, composed of 103 patients, had a mean age of 66.3 years (SD=14.9), of which 38.8% (40 patients) were female. Most patients (82.5%) were autonomous for basic activities of daily living. The prediction model was statistically significant with an area under the curve of 0.994 and a precision of 0.950. Higher age, a higher number of days with increases in the fraction of inspired oxygen (FiO2), a higher number of days of maximum expiratory positive airway pressure, a lower number of days on NIV, and a lower number of days from disease onset to hospital admission were, with statistical significance, associated with increased odds of death. A decision-tree classification model was then obtained to achieve the best combination of variables to predict the outcome of interest. This study presents a model to predict death in COVID-19 patients treated with NIV in patients who did not progress to IMV, based on easily applicable variables that mainly reflect patients' evolution during hospitalization. Along with the decision-tree classification model, these original findings may help clinicians define the best therapeutical approach to each patient, prioritizing life-comforting measures when adequate, and optimizing resources, which is crucial within limited or overloaded healthcare systems. Further research is needed on this subject of treatment failure, not only to understand if these results are reproducible but also, in a broader sense, helping to fill this gap in modern medicine guidelines.

Contrasting occupancy models with presence-only models: Does accounting for detection lead to better predictions?

Species distribution models are popular statistical tools for inferring potential distribution range of species across space and time and are extensively used in conservation planning. Models based on presence-only data (e.g., MaxEnt) are widely used; however, these models assume perfect species detectability. Occupancy modelling is considered a better modelling technique since it accounts for species detectability. Presence-only models are relatively simpler, requiring only presence locations, while occupancy models are data hungry models requiring detection/non-detection data from multiple visits to the survey sites. We utilized data from the Kerala Bird Atlas (India) and modelled current distribution for 109 species using MaxEnt and occupancy approaches. MaxEnt performed well even with less occurrences, while occupancy model failed for species with fewer than 40 records. In terms of evaluation metrics, AUC and Root Mean Square Error, both models performed relatively better for species with low occurrences than those with high occurrences (generalist species). The comparison metrics (Relative-rank scores, Root Mean Square Error, Hellinger distance and Expectation of Shared Presences) were significantly correlated with the number of occurrences; MaxEnt and occupancy based SDMs for widespread species had more concordance than SDMs of narrowly distributed species. There was some discordance between algorithms with regards to diversity hotspot. Selection of best combination of variables and correction for overprediction can help improve performances of both models and improve consistency between them. Given the data hungry nature of occupancy models and marginal difference with the MaxEnt models; it appears that latter is better suited for predicting the distribution of rare species and studies dealing with cumulative data from multiple species.

Tortillas made from nixtamalized maize and extruded chickpea flours: A product with improved in vitro nutritional and antihypertensive properties

AbstractBackground and ObjectivesIn this study, tortillas with improved in vitro nutritional and antihypertensive properties were developed using flours produced from nixtamalized maize (NixM) and extruded chickpea (ExtCP). The aims were (1) to find the optimal extrusion conditions to produce optimized ExtCP flour with high values of antioxidant activity (AoxA), total phenolic compounds (TPC), and in vitro protein digestibility (IVPD), and (2) to evaluate the addition effect of 30% of optimized ExtCP flour over the nutritional, technological, sensory y nutraceutical properties of tortillas prepared from commercial NixM flour. A rotatable composite central experimental design with two factors (extrusion temperature [ET] = 50°C–170°C; screw speed (SS) = 50–240 rpm) and five levels was used to optimize the extrusion process.FindingsThe best combination of variables of the extrusion process to produce ExtCP flour was: ET = 143°C/SS = 138 rpm. The tortillas added with ExtCP flour had: proteins = 13.27%, total dietary fiber = 13.40%, protein chemical score = 85.42%, IVPD = 82.01%, calculated protein efficiency ratio (C‐PER) = 1.84, AoxA = 8031 µmol TE/100 g, and sensory acceptability = 8.51 (value located between I like it very much and I like it extremely).ConclusionsThe tortillas made from nixtamalized maize and ExtCP flours have better in vitro nutritional and antihypertensive properties than tortillas prepared from only nixtamalized maize flour.Significance and NoveltyThe addition of optimized ExtCP flour to commercial NixM flour allows obtaining functional tortillas with improved and sensory acceptable in vitro nutritional and antihypertensive properties.

Integrating Angular Backscatter Response Analysis Derivatives Into a Hierarchical Classification for Habitat Mapping

Accurate maps of biological communities are essential for monitoring and managing marine protected areas but more information on the most effective methods for developing these maps is needed. In this study, we use Wilsons Promontory Marine National Park in southeast Australia as a case study to determine the best combination of variables and scales for producing accurate habitat maps across the site. Wilsons Promontory has full multibeam echosounder (MBES) coverage coupled with towed video, remotely operated underwater vehicle (ROV) and drop video observations. Our study used an image segmentation approach incorporating MBES backscatter angular response curve and bathymetry derivatives to identify benthic community types using a hierarchical habitat classification scheme. The angular response curve data were extracted from MBES data using two different methods: 1) angular range analysis (ARA) and 2) backscatter angular response (AR). Habitat distributions were predicted using a supervised Random Forest approach combining bathymetry, ARA, and AR derivatives. Variable importance metrics indicated that ARA derivatives, such as grain size, impedance and volume heterogeneity were more important to model performance than AR derivatives mean, skewness, and kurtosis. Additionally, this study investigated the impact of segmentation software settings when creating segmented surfaces and their impact on overall model accuracy. We found using fine scale segmentation resulted in the best model performance. These results indicate the importance of incorporating backscatter derivatives into biological habitat maps and the need to consider scale to increase the accuracy of the outputs to help improve the spatial management of marine environments.

Statistical approach to optimize crashworthiness of thermoplastic commingled composites

A design of experiments analysis was developed to relate the processing conditions to the impact energy absorption capability of thermoplastic commingled composites. Based on low velocity impact test responses and statistical modeling, a factorial design was implemented to investigate the interaction of processing parameters with the polymer viscosity. The influence of thermal consolidation on materials performances were evaluated and quantified. The dataset was obtained according to the impact energy capability of carbon fiber/polyamide and carbon fiber/poly (ether-ether-ketone) commingled composites. The results identified the best combination of variables which improves crashworthiness of structural composites and leads to high impact energy absorption capabilities. The results revealed that the commingled composites crashworthiness strongly depends on the polymer yarn dispersion in the carbon fiber tow, that was identified and classified in different distribution patterns. This work represents a significant step towards manufacturing of thermoplastics composites with target structural performance.

Development of statistical and machine learning models to predict the occurrence of radiation fog in Japan

AbstractThis study develops statistical and machine learning models based on discriminant analysis, logistic regression analysis, and a support vector machine (SVM) to predict the occurrence of radiation fog in Japan. The selection of a suitable set of explanatory variables for the models was made using the Akaike information criterion (AIC). The accuracies of the three models were measured and compared. To determine the optimum combination of explanatory variables, temperature, humidity, wind speed, precipitation, sunshine, and visibility data were considered. Based on the root mean square error (RMSE) and AIC values, the best combination of variables was found to include: the presence of precipitation, mean wind speed during the night, minimum temperature during the night, the amount of temperature cooling during the night, the minimum humidity during the previous day, and visibility at 18:00. A comparison of the predictive accuracies of the three models using the selected variable combination showed that the discriminant model produced a critical success index (CSI, or threat score) of 22.5, while the logistic regression model and the SVM model both produced better CSI results—with scores of 33.8 and 38.2, respectively.

BIM and BEM Methodologies Integration in Energy-Efficient Buildings Using Experimental Design

Linking Building Information Modelling and Building Energy Modelling methodologies appear as a tool for the energy performance analysis of a dwelling, being able to build the physical model via Autodesk Revit and simulating the energy modeling with its complement Autodesk Insight. A residential two-story house was evaluated in five different locations within distinct climatic zones to reduce its electricity demand. Experimental Design is used as a methodological tool to define the possible arrangement of results emitted via Autodesk Insight that exhibits the minor electric demand, considering three variables: Lighting efficiency, Plug-Load Efficiency, and HVAC systems. The analysis concluded that while the higher the efficiency of lighting and applications, the lower the electric demand. In addition, the type of climate and thermal characteristics of the materials that conform to the building envelope have significant effects on the energetic performance. The adjustment of different energetic measures and its comparison with other climatic zones enable decision-makers to choose the best combination of variables for developing strategies to lower the electric demand towards energy-efficient buildings.

Multiobjective Optimization Design of Outer Rotor Coreless Bearingless Permanent Magnet Synchronous Motor

The outer rotor coreless bearingless permanent magnet synchronous motor (ORCBPMSM) is suitable for flywheel energy storage systems because of the advantages of no core loss and high efficiency. To improve the output torque and suspension forces of the motor, a Taguchi-response surface method (T-RSM) is proposed to conduct multiobjective optimization design of the motor. First, the basic structure and working principle of the proposed motor are expounded, and the basic design parameters of the motor are given. Then, the orthogonal matrix is established according to the number of factor levels, and the finite element analyze (FEA) is conducted to obtain each set of test results. After that, the sensitivity analysis is conducted and the best combination of variables can be achieved. Aiming at improving the accuracy of the Taguchi method, the fitting functions are established among the targets and conflicting variables, and the optimal solution is obtained by RSM. Finally, the optimal motor is manufactured, and the performance of the motor is verified. The experimental results show that stable operation can be obtained and the operating performance of the proposed motor is improved after optimization.

Exploring the effects of different combinations of predictor variables for the treatment of wastewater by microalgae and biomass production

Microalgae provide an alternative and cost-efficient method for treating wastewater with simultaneous recovery of resources in the form of biomass. In order to increase the biomass productivity and wastewater treatment capability of microalgae, it is essential to provide the right combination of predictor variables. However, it is practically impossible to determine the best combination of variables through conventional methods from a large dataset of published research. Machine learning algorithms can easily detect the pattern in the large dataset and provide the best combination as desired. In the present analysis, decision tree algorithm was used to determine the effects and the best combination of predictor variables, including microalgae class, pre-cultivation stage deciding factors and operating variables, resulting in high biomass productivity and wastewater treatment capability. Decision tree analysis detected 10 different combinations of predictor variables leading to high nitrogen removal efficiency, 10 combinations for high phosphorus removal efficiency and 8 combinations for increased biomass production. These combinations were tested on recently published experimental findings and nearly 80% accuracy was obtained. The results obtained through machine learning analysis can be used in constructing high throughput experimental designs, which may assist in carrying out the efficient wastewater treatment at large scale.