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• Deep learning elucidates the shape factor in EMT for thermal conductivity estimates. • The ratio of an inclusion's projected areas is closely related to the shape factor. • Transfer learning extends the original EMT for new thermal transport problems. The effective medium theories (EMTs) can reliably approximate the property of a composite using properties of the inclusion and matrix phase. However, their inherent assumptions and the availability of mathematical forms for describing the inclusion structure limit their accuracy and applicability. In this work, we utilize the capabilities of a deep learning method to ameliorate the latter restriction for a particular EMT formulation. Our deep learning models elucidate the inclusion structure using several physics-based descriptors and can be easily adapted for other inclusion shapes through transfer learning. Using our models, we shed light on the interpretation of the shape factor in the chosen EMT. More importantly, we extend, not replace, the EMT for cases beyond its original formulation. Our proposed transfer learning approach requires relatively low computation cost and a small sample number, making it especially useful when new data is limited. [ABSTRACT FROM AUTHOR]

Analyzing and presenting data from multiple groups are much more informative than that from two groups. However, common tools such as S plot and volcano plot are only available for identifying the significant features between two groups and are restricted to multiple-group comparisons. This study proposed novel visualization plots which not only overcame the restrictions of the above methods but also utilized the p values of multiple tests as the x-axis. The novel visualization plots included a parametric method and a nonparametric method. The parametric method was a combination of an analysis of variance and Welch's analysis of variance; the nonparametric method used the Kruskal-Wallis test. During the selection of significant features, machine learning algorithms were used to determine the cutting points of the x-axis. As a proof of concept, the real data from the experiments of 4-MeO-α-PVP metabolites and fish spoilage metabolomics were illustrated via our visualization method. The results showed that the novel visualization plots were much efficiently presented to identify significant metabolites in multiple-group comparisons. Especially, the positive predicted values of the nonparametric method and the cutting points determined by logistic regression were higher than those of other machine learning algorithms in determining the cutting points for multiple groups. • New visualization plots outweigh volcano plot and S plot for multiple-group study. • Parametric method requires normality of data and Bonferroni's adjustment is suggested to utilize on cut point of x-axis. • Nonparametric method is flexible on data type and machine learning method is suggested to use on cut point of x-axis. • As proof-of-concept, two methods perform well for multiple-group comparisons. [ABSTRACT FROM AUTHOR]

Experiences of childhood trauma (CT) would lead to serious mental problems, especially depression. Therefore, it becomes crucial to identify influential factors related to depression and explore their associations. The objectives were to 1) identify critical depression-related factors using the extreme gradient boosting (XGBoost) method from a large-scale survey data; 2) explore associations between these factors for targeted interventions and treatments. A large-scale epidemiological study covering 63 universities was conducted in Jilin Province, China. The XGBoost model was trained and tested to classify young adults with CT experiences who had or did not have depression (N = 27,671). The essential factors were selected by SHapley Additive exPlanations (SHAP) value. Multiple logistic regression analyses were conducted for validation. The associations between these depression-related factors were further explored using network analysis. The XGBoost model selected the top 10 features associated with depression with satisfactory performance (AUC = 0.91; sensitivity = 0.88 and specificity = 0.76). These factors significantly differed between depression and non-depression groups (p < 0.001). There are strong positive associations between anxiety and obsessive-compulsive disorder (OCD), anxiety and post-traumatic stress disorder (PTSD), social anxiety disorder (SAD) and appearance anxiety, and negative associations between sleep quality and anxiety, sleep quality and PTSD among CT participants with depression. The cross-sectional design cannot draw causality, and biases in self-report measurements cannot be ignored. XGBoost model and network analysis were useful methods for discovering and understanding depression-related factors in this epidemiological study. Moreover, these essential factors could offer insights into future interventions and treatments for depressed young adults with CT experiences. • The XGBoost model identified the top 10 depression-related factors, including anxiety, sleep quality, PTSD, loneliness, and OCD, among others. • These selected factors exhibited variations between the depression and non-depression groups. • Specifically, anxiety emerged as the leading risk factor for depression. • Furthermore, among CT participants with depression, strong positive associations were observed between anxiety, OCD, PTSD, and SAD. [ABSTRACT FROM AUTHOR]

The cell surface hydrophobicity (CSH) is an assessable physicochemical property used to evaluate the microbial adhesion to the surface of biomaterials, which is an essential step in the microbial biofilm formation and pathogenesis. For the present in vitro fermentation experiment, the CSH of ruminal mixed microbes was considered, along with other data records of pH, ammonia-nitrogen concentration, and neutral detergent fibre digestibility, conditions of surface tension and specific surface area in two different time scales. A dataset of 170,707 perturbations of input variables, grouped into two blocks of data, was constructed. Next, Expected Measurement Moving Average – Machine Learning (EMMA-ML) models were developed in order to predict CSH after perturbations of all input variables. EMMA-ML is a Perturbation Theory method that combines the ideas of Expected Measurement, Box-Jenkins Operators/Moving Average, and Time Series Analysis. Seven regression methods have been tested: Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, Elastic Net regression, Neural Networks regression, and Random Forests (RF). The best regression performance has been obtained with RF (EMMA-RF model) with an R-squared of 0.992. The model analysis has shown that CSH values were highly dependent on the in vitro fermentation parameters of detergent fibre digestibility, ammonia – nitrogen concentration, and the expected values of cell surface hydrophobicity in the first time scale. [ABSTRACT FROM AUTHOR]

It has long been a challenge in the design of cutter suction dredgers (CSD) that the measurement of mud concentration lags behind other indicators like vacuum and flow rate. This phenomenon is named the time-lag effect. It significantly hinders the optimization and automation of CSD operation, for the mud concentration is a primary reference during CSD operation. As such, this study presented a data mining-based solution for short-term forecasting of mud concentrations. To accomplish this goal, first, an integration method based on hydraulics principles was proposed to align mud concentrations with other indicators over time. Second, normal construction identification, data filtering, and data smoothing were used to preprocess data. Third, a feature selection process, named "indicator class", was applied regarding the construction technology and the characteristics of the pump-pipeline system of CSDs. Last, a short-term forecasting algorithm was presented based on a hybrid ensemble strategy; this established the relationship between the actual mud concentration and the selected indicators. The validity of this algorithm was tested by a case study that collected a group of raw data logged by a CSD monitoring system. The result showed that the real-time prediction could reach a high accuracy where the coefficient of determination (R2) is 0.886 and the mean square error (MSE) is 4.708, and the short-time forecasting result within 12s is consistent with the actual trend of the mud concentration. The proposed approach has a significant deal of potential to enhance the quality and efficiency of operations by providing operators with essential references to real-time mud concentrations. • The "time-lag effect" of the measured mud concentration of CSD construction is addressed. • A framework using realtime monitored indicators to forecast mud concentration is proposed. • The key techniques include indicator selection, time alignment, and algorithm forecasting. • A hybrid algorithm is proposed to fit the relationship between mud concentration and key indicators. • The effectivenesses of the real-time mode and short-term mode of the proposed method are verified. [ABSTRACT FROM AUTHOR]