Newborn skin maturity models for gestational age prediction

A multicenter clinical trial evaluated the accuracy of a novel device to detect preterm newborns. A portable multiband reflectance photometric device assessed 781 newborns’ skin maturity and used machine learning models to predict reference gestational age, adjusting it to birth weight and antenatal corticosteroid therapy exposure. The day difference between the reference and the test had a median of -1.4 (IQR: -2.1). Using established methods such as comparator ultrasound and last menstrual period (LMP), the medians were 0 (IQR: 4) and 0.01 (IQR: 4), respectively. For prematurity discrimination, the area under the receiver operating characteristic curve (AUROC) was 0.986 (95% CI: 0.977 to 0.994). In newborns with absent or unreliable LMP, the intent-to-discriminate analysis showed that the test generated correct classifications 95.8% of the time. The assessment of the newborn's skin maturity adjusted by learning models promises accurate pregnancy dating at birth without the use of antenatal ultrasound reference.

Newborn skin maturity models for gestational age prediction

A multicenter clinical trial evaluated the accuracy of a novel device to detect preterm newborns. A portable multiband reflectance photometric device assessed 781 newborns’ skin maturity and used machine learning models to predict reference gestational age, adjusting it to birth weight and antenatal corticosteroid therapy exposition. The day difference between the reference and the test had a median of -1.4 (IQR: -2.1). Using established methods such as comparator ultrasound and last menstrual period (LMP), the medians were 0 (IQR: 4) and 0.01 (IQR: 4), respectively. For prematurity discrimination, the area under the receiver operating characteristic curve (AUROC) was 0.986 (95% CI: 0.977 to 0.994). In newborns with absent or unreliable LMP, the intent-to-discriminate analysis showed that the test generated correct classifications 95.8% of the time. The assessment of the newborn's skin maturity adjusted by learning models promises accurate pregnancy dating at birth without the use of antenatal ultrasound reference.

Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal

Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.

Nonstructural carbohydrates, carbon and nitrogen concentrations in fine roots of Quercus variabilis secondary forests after two different periods of regeneration

Aim of study: Quercus variabilis is a sclerophyllous oak with strong resprouting capabilities and whose regeneration is facilitated by the development of stump shoots following disturbance. During secondary forest regeneration, fine roots are important organs relative to changes in stand characteristics. Here, we aimed to provide novel insights into the chemical composition variations in roots with seasonality and root order hierarchy in a Q. variabilis forest at different periods of regeneration.Area of study: The forest is located next to the Baxianshan National Reserve in the southern part of the Yanshan Mountains, Tianjin, China.Materials and methods: Six plots were established in stands with either eight or 40 years of regeneration for the repeated sampling of fine roots during the growing season of 2019. All roots were classified by branch order. The first three root orders were collected to analyse the concentrations of nonstructural carbohydrate, carbon, and nitrogen.Main results: Short-term regeneration stands showed a reduction in soil moisture and an increase in soil temperature because of the lower canopy cover, compared to long-term stands. Soluble sugar and starch were lower in roots of short-term stands than in those of long-term stands, and the decreasing ratio of both parameters was observed in short-term stands. Less carbon and greater nitrogen concentrations of fine roots were found in short-term stands than in long-term stands, which resulted in weaker C/N ratio values. Nonstructural carbohydrate was stored more in higher order roots than terminal roots and presented greater sensitivity to forest regeneration. Redundancy discriminate analysis demonstrated that the nonstructural carbohydrate concentrations in roots were affected positively by canopy cover and negatively by soil temperature.Research highlights: The seasonal dynamics and branch allocation of chemical reserves in fine roots varied in the different periods of forest regeneration because of the discrepancy between the canopy cover and soil traits. Less nonstructural carbohydrate and a lower C/N ratio at the onset of forest regeneration may elevate the risk of root death.Keywords: soluble sugar; starch; forest regeneration; root order; C/N ratio; redundancy discriminate analysis.

Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines

Mine water inrush seriously threatens the safety of coal mine production. Quick and accurate identification of mine water inrush sources is of great significance to preventing mine water hazards. This paper combined partial least squares-discriminate analysis (PLS-DA) with inrush water chemical composition to identify the source of water inrush from multiple aquifers in mines. The Renlou Coal Mine in the Linhuan mining area was selected for this study, and seven conventional water chemical compositions from 54 water samples in three aquifers were collected and tested, of which 45 water samples were used to establish the PLS-DA discriminant model, and nine were used to test the prediction effect. To improve model accuracy and predictive ability, hierarchical clustering analysis method was used to eliminate seven unqualified water samples to reduce the errors caused by improper data. PCA and PLS-DA methods were used to analyze and process the remaining water sample data, and on the basis of PCA analysis, the remaining 38 water samples were used to establish the PLS-DA discriminant model. The model was validated using permutation and external prediction tests. The research shows the following results: (1) Both PCA and PLS-DA methods can distinguish water samples from three different water sources, but the classification effect of PLS-DA was better than PCA because it can strengthen the difference of water chemical composition between different water sources. (2) The correct discrimination rate of the PLS-DA discriminant model was as high as 100%, and permutation tests showed that the model was not overfit. External validation found that the model had good stability and discrimination. (3) HCO3- and total dissolved solids (TDS) were the most important differential marker compositions that affected the discrimination results based on Variable Importance for the Projection (VIP) scores. The discriminant model established in this study combined the advantages of principal component analysis and multiple regression analysis, providing a new method for accurately identifying the sources of water inrush in mines.

Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

The diagnostic analytic of harmonic source is crucial research due to identify and diagnose the harmonic source in the power system. This paper presents a comparison of machine learning (ML) algorithm known as linear discriminate analysis (LDA) and k-nearest neighbor (KNN) in identifying and diagnosing the harmonic sources. Voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for ML. Several unique cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, each ML algorithm is executed 10 times due to prevent any overfitting result and the performance criteria are measured consist of the accuracy, precision, geometric mean, specificity, sensitivity, and F measure are calculated.

Exploring the Experience of Creative Tourism in the Northern Region of Portugal—A Gender Perspective

Creative tourism is a quite recent tourism segment that has been rapidly diffused all over the world. Nevertheless, studies on this segment were not concerned, until present, with the differences in gender intention, evaluation and the overall satisfaction regarding creative tourism activities. For that, this paper examines these three components from a gender perspective regarding the creative tourism activities developed by CREATOUR pilots in the northern region of mainland Portugal between 2017 and 2019. The methods used were quantitative in nature. Five hundred and ninety-five questionnaires were applied to the participants in the 45 creative tourism activities developed by the 10 pilot institutions selected to join the CREATOUR project (Creative Tourism Destination Development in Small Cities and Rural Areas). The questionnaire used consisted of 31 closed questions aimed at the profile, the motivations, the perception and the evaluation of activities by the participants. It used descriptive statistics and discriminate analysis. The main results show that men and women had similar demographic characteristics (e.g., age and educational level), but they were significantly different in some variables, such as their intention to participate in creative activities, and their evaluation and overall satisfaction with their personal experiences. It is statistically confirmed that, based on their experiences in creative tourism, men and women fall into different clusters.

Naïve Bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification

<span>The diagnostic analytic type of harmonic source is a vital research due to diagnose and identify type of harmonic source that exist in the power system. This paper presents a comparison of machine learning (ML) algorithm namely as the Naïve Bayes (NB) and linear discriminate analysis (LDA) in identifying and diagnosing the harmonic sources. The MLs inputs are the voltage and current feature sets that estimated from the time-frequency representation (TFR) of S-transform analysis. Four specific cases of harmonic source location are considered in this research, whereas harmonic voltage (H_V) and harmonic current (H_C) source type-load are used in the diagnosing process. The sufficiency of the proposed methodology is tested and verified on the IEEE 4-bust test feeder, and to prevent overfitting, the K-fold cross-validation technique is implemented for performance evaluation. To identify the best ML, the performance measurement consist of the accuracy, precision, geometric mean, F-measure, sensitivity, and specificity are conducted.</span>