Variations In Features Research Articles

Algorithms of the Möbius function by random forests and neural networks

The Möbius function μ(n)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu (n)$$\\end{document} is known for containing limited information on the prime factorization of n. Its known algorithms, however, are all based on factorization and hence are exponentially slow on logn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\log n$$\\end{document}. Consequently, a faster algorithm of μ(n)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu (n)$$\\end{document} could potentially lead to a fast algorithm of prime factorization which in turn would throw doubt upon the security of most public-key cryptosystems. This research introduces novel approaches to compute μ(n)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu (n)$$\\end{document} using random forests and neural networks, harnessing the additive properties of μ(n)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu (n)$$\\end{document}. The machine learning models are trained on a substantial dataset with 317,284 observations (80%), comprising five feature variables, including values of n within the range of 4×109\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4\ imes 10^9$$\\end{document}. We implement the Random Forest with Random Inputs (RFRI) and Feedforward Neural Network (FNN) architectures. The RFRI model achieves a predictive accuracy of 0.9493, a recall of 0.5865, and a precision of 0.6626. On the other hand, the FNN model attains a predictive accuracy of 0.7871, a recall of 0.9477, and a precision of 0.2784. These results strongly support the effectiveness and validity of the proposed algorithms.

Interobserver delineation variability of computed tomography-based radiomic features of the parotid gland.

To assess the interobserver delineation variability of radiomic features of the parotid gland from computed tomography (CT) images and evaluate the correlation of these features for head and neck cancer (HNC) radiotherapy patients. Contrast-enhanced CT images of 20 HNC patients were utilized. The parotid glands were delineated by treating radiation oncologists (ROs), a selected RO and AccuContour auto-segmentation software. Dice similarity coefficients (DSCs) between each pair of observers were calculated. A total of 107 radiomic features were extracted, whose robustness to interobserver delineation was assessed using the intraclass correlation coefficient (ICC). Pearson correlation coefficients (r) were calculated to determine the relationship between the features. The influence of excluding unrobust features from normal tissue complication probability (NTCP) modeling was investigated for severe oral mucositis (grade ≥3). The average DSC was 0.84 (95% confidence interval, 0.83-0.86). Most of the shape features demonstrated robustness (ICC ≥0.75), while the first-order and texture features were influenced by delineation variability. Among the three observers investigated, 42 features were sufficiently robust, out of which 36 features exhibited weak correlation (|r|<0.8). No significant difference in the robustness level was found when comparing manual segmentation by a single RO or automated segmentation with the actual clinical contour data made by treating ROs. Excluding unrobust features from the NTCP model for severe oral mucositis did not deteriorate the model performance. Interobserver delineation variability had substantial impact on radiomic features of the parotid gland. Both manual and automated segmentation methods contributed similarly to this variation.

Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity.

Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity.

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF.

Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model's construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R2 of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang's agricultural economy, and also provides a reference for the prevention and control of regional soil salinization.

Spatial heterogeneity of low flow hydrological alterations in response to climate and land use within the Upper Mississippi River basin

The Upper Mississippi River Basin (UMRB), located in the Midwestern U.S., is a nationally recognized region of concern for water quality and aquatic biological health. At 500,000 km2, geospatial characteristics and climate vary widely within the basin and thus, hydrological behavior and associated effects on freshwater ecosystems may also dramatically vary. So far, previous studies of long-term hydrological behavior in the UMRB have mostly considered changes for the basin rather than changes at finer watershed scales. This study systematically evaluated long term trends in indices of hydrological alteration (IHA) for 118 of 131 watersheds of the UMRB and compared them with 1) variabilities in geospatial features (land use, soil type, and topography), 2) land use changes, 3) climate, and 4) evapotranspiration trends using hierarchical clustering and correlation analysis. Compared to climate trends, land use and evapotranspiration were found to have stronger impacts on trends in low flow IHA indices in the watershed. The results demonstrate that while overall, low flow has been increasing in the basin, there is considerable variability in alterations in low flow across watersheds. Specifically, the results indicate that in forested or mixed-use watersheds, low flow has been decreasing while in agricultural and urban watersheds low flow is increasing. These results together with the literature on water quality in the UMRB, suggest both stressors, water quantity and water quality exist in the UMRB and vary by land use and evapotranspiration, implying that effective watershed management plans are local not regional and should be directed toward reducing the specific local stressors.

Relationship between the variations in glacier features classified on a large scale with climate variables: a case study of Gangotri Glacier.

Changes in glacier area, glacial lakes, debris cover, and geomorphological features such as debris fans have a significant impact on glacial dynamics. Therefore, precise and timely observation and tracking of glacier surface changes is a necessity. The availability of high spatial resolution remote sensing images has made it viable to analyse the glacier surface changes at a local level. However, with an increase in spatial resolution, the spectral variability increases, giving rise to additional challenges (such as false changes and misregistration) in the change detection process. These challenges can preferably be dealt with using an object-based change detection (OBCD) approach rather than the conventional pixel-based change detection approach. Therefore, this study has proposed an OBCD methodology using high-spatial-resolution remote sensing images to detect changes in glacier features. Variability in glacier features has been further analysed by associating it with important climate variables, that is, air temperature and precipitation. As a case study, the changes in Gangotri Glacier (Uttarakhand Himalayas in India) features have been studied using high-spatial-resolution WorldView-2 and Linear Imaging Self-Scanning System (LISS)-4 images for a 3-year period 2011-2014. The spectral correspondences between glacier surface and non-glacier surface have been handled by considering brightness temperature and slope as ancillary data to improvise their distinction. A change detection accuracy of ~ 84% has been obtained using the OBCD approach. Results further show that the variations in glacier features are in congruence with the climatic observations.

Estimation of the Relative Chlorophyll Content of Carya illinoensis Leaves Using Fractional Order Derivative of Leaf and Canopy Scale Hyperspectral Data

Chlorophyll is a crucial physiological and biochemical indicator that impacts plant photosynthesis, accumulation of photosynthetic products, and final yield. The measurement and analysis of chlorophyll content in plants can provide valuable insights into their nutritional status and overall health. The non-destructive and efficient estimation of relevant plant physiological and biochemical indicators using hyperspectral technology can provide a reliable method for collecting data on nutrient levels and health status during plant growth and development. Fifty-three Carya illinoensis plants of Jiande and Changlin series known for their exceptional qualities and significant economic benefits were used as the research object for collecting their leaf and canopy hyperspectral data. Firstly, fractional order derivative (FOD) was used for spectral preprocessing. Secondly, the spectral response relationship between spectrum and relative chlorophyll content (soil and plant analyzer development, SPAD) was explored by combining single-band and two-band spectral index (normalized difference spectral index, NDSI). The correlation coefficient of Pearson correlation analysis was used to estimate the linear correlation between variables. Finally, the correlation between the spectral feature variables and SPAD was analyzed and calculated. Top 10 absolute values of the correlation coefficients were screened out as modeling variables. eXtreme gradient boosting (XGBoost) machine learning algorithm was used to construct the optimal estimation model of SPAD of Carya illinoensis leaves. Results showed that the correlation between leaf and canopy spectrum after FOD pretreatment and SPAD was substantially improved, compared with raw spectrum. FOD combined with leaf and canopy NDSI was more effective than single band in improving the correlation between spectral characteristics and target components, which was increased by 0.166 and 0.338, respectively. The leaf spectrum could estimate SPAD more accurately than that of canopy spectrum. The optimal SPAD model was the 0.5th-order derivative transformation combined with two-band leaf spectral index (NDSI) model. The R2 P was 0.788, and the RMSEP was 0.842 in prediction set. On one hand, this study confirms the feasibility of rapid and non-destructive estimation of SPAD of Carya illinoensis leaves using hyperspectral technology. On the other hand, FOD combined with two-band spectral indices can significantly improve the response relationship between spectral characteristics and target variables, enrich hyperspectral data processing methods, and propose a novel approach for the detection of plant nutrient level and health.

Novel Genetic and Phenotypic Expansion in Ameliorated PUF60-Related Disorders.

Heterozygous variants in the Poly(U) Binding Splicing Factor 60kDa gene (PUF60) have been associated with Verheij syndrome, which has the key features of coloboma, short stature, skeletal abnormalities, developmental delay, palatal abnormalities, and congenital heart and kidney defects. Here, we report five novel patients from unrelated families with PUF60-related disorders exhibiting novel genetic and clinical findings with three truncating variants, one splice-site variant with likely reduced protein expression, and one missense variant. Protein modeling of the patient's missense variant in the PUF60 AlphaFold structure revealed a loss of polar bonds to the surrounding residues. Neurodevelopmental disorders were present in all patients, with variability in speech, motor, cognitive, social-emotional and behavioral features. Novel phenotypic expansions included movement disorders as well as immunological findings with recurrent respiratory, urinary and ear infections, atopic diseases, and skin abnormalities. We discuss the role of PUF60 in immunity with and without infection based on recent organismic and cellular studies. As our five patients showed less-severe phenotypes than classical Verheij syndrome, particularly with the absence of key features such as coloboma or palatal abnormalities, we propose a reclassification as PUF60-related neurodevelopmental disorders with multi-system involvement. These findings will aid in the genetic counseling of patients and families.

Serum Olink Proteomics-Based Identification of Protein Biomarkers Associated with the Immune Response in Ischemic Stroke.

The immune response is considered essential for pathology of ischemic stroke (IS), but it remains unclear which immune response-related proteins exhibit altered expression in IS patients. Here, we used Olink proteomics to examine the expression levels of 92 immune response-related proteins in the sera of IS patients (n = 88) and controls (n = 88), and we found that 59 of these proteins were differentially expressed. Feature variables were screened from the differentially expressed proteins by the least absolute shrinkage and selection operator (LASSO) and the random forest and by determining whether their proteins had an area under the curve (AUC) greater than 0.8. Ultimately, we identified six potential protein biomarkers of IS, namely, MASP1, STC1, HCLS1, CLEC4D, PTH1R, and PIK3AP1, and established a logistic regression model that used these proteins to diagnose IS. The AUCs of the models in the internal validation and the test set were 0.962 (95% confidence interval (CI): 0.895-1.000) and 0.954 (95% CI: 0.884-1.000), respectively, and the same protein detection method was performed in an external independent validation set (AUC: 0.857 (95% CI: 0.801-0.913)). These proteins may play a role in immune regulation via the C-type lectin receptor signaling pathway, the PI3K-AKT signaling pathway, and the B-cell receptor signaling pathway.

Multi-year mapping of cropping systems in regions with smallholder farms from Sentinel-2 images in Google Earth engine

ABSTRACT Accurate acquisition of spatial and temporal distribution information for cropping systems is important for agricultural production and food security. The challenges of extracting information about cropping systems in regions with smallholder farms are considerable, given the varied crops, complex cropping patterns, and the fragmentation of cropland with frequent reclamation and abandonment. This study presents a specialized workflow to solve this problem for regions with smallholder farms, which utilizes field samples and Sentinel-2 data to extract cropping system information over multiple years. The workflow involves four steps: 1) processing Sentinel-2 data to simulate crop growth curves with the Savitzky‒Golay filter and computing feature variables for classification, including phenology indices, spectral bands, and time series of vegetation indices; 2) mapping annual croplands with one-class support vector machine; 3) mapping various cropping patterns, including single cropping, intercropping, double cropping, multiple harvest, and fallow by decision tree and K-means clustering; and 4) mapping crops with random forest where Jeffries-Matusita distance was used to select appropriate vegetation indices. The workflow was applied in the Hetao irrigation district in Inner Mongolia Autonomous Region, China from 2018 to 2021. The overall accuracies were 0.98, 0.96, and 0.97 for cropland, cropping patterns, and crop type mapping, respectively. The mapping results indicated that the study area has low cropping continuity and is dominated by single cropping patterns. Furthermore, the area of wheat cultivation has decreased, and vegetable cultivation has expanded. Overall, the proposed workflow facilitated the accurate acquisition of cropping system information in regions with smallholder farms and demonstrated the effectiveness of available Sentinel-2 imagery in classifying complex cropping patterns. The workflow is available on Google Earth Engine.

Substrate-Assisted Laser-Induced Breakdown Spectroscopy Combined with Variable Selection and Extreme Learning Machine for Quantitative Determination of Fenthion in Soybean Oil

The quality and safety of edible vegetable oils are closely related to human life and health, meaning it is of great significance to explore the rapid detection methods of pesticide residues in edible vegetable oils. This study explored the applicability potential of substrate-assisted laser-induced breakdown spectroscopy (LIBS) for quantitatively determining fenthion in soybean oils. First, we explored the impact of laser energy, delay time, and average oil film thickness on the spectral signals to identify the best experimental parameters. Afterward, we quantitatively analyzed soybean oil samples using these optimized conditions and developed a full-spectrum extreme learning machine (ELM) model. The model achieved a prediction correlation coefficient (RP2) of 0.8417, a root mean square error of prediction (RMSEP) of 167.2986, and a mean absolute percentage error of prediction (MAPEP) of 26.46%. In order to enhance the prediction performance of the model, a modeling method using the Boruta algorithm combined with the ELM was proposed. The Boruta algorithm was employed to identify the feature variables that exhibit a strong correlation with the fenthion content. These selected variables were utilized as inputs for the ELM model, with the RP2, RMSEP, and MAPEP of Boruta-ELM being 0.9631, 71.4423, and 10.06%, respectively. Then, the genetic algorithm (GA) was used to optimize the parameters of the Boruta-ELM model, with the RP2, RMSEP, and MAPEP of GA-Boruta-ELM being 0.9962, 11.005, and 1.66%, respectively. The findings demonstrate that the GA-Boruta-ELM model exhibits excellent prediction capability and effectively predicts the fenthion contents in soybean oil samples. It will be valuable for the LIBS quantitative detection and analysis of pesticide residues in edible vegetable oils.

Deep gold prospectivity modeling in the Jiaojia gold belt, Jiaodong Peninsula, eastern China using machine learning of geometric and geodynamic variables

Gold mineralization in the Jiaojia gold belt was formed in a structurally-dominant hydrothermal mineral system showing a close spatial association with the Jiaojia detachment fault. This study delves into the Jiaojia gold belt from the perspective of coupled spatial association and ore-forming processes by employing spatial analysis of three-dimensional (3D) models, 3D ore-forming numerical modeling, and 3D prospectivity modeling using machine learning techniques (random forest (RF) and multilayer perceptron (MLP)). The overarching goal is to gain insight into the structural-hydrothermal gold system and pinpoint potential areas of deep-seated gold deposits for future exploration endeavors. The spatial analysis of ore-controlling faults uncovers a close correlation between gold enrichment and specific fault geometrical attributes, including a dip angle ranging from 20° to 40°, minimal variations in dip angle (less than 5°), and convex topographical features. These attributes likely stem from the influence of fault morphology on the flow and pooling of fluids. In conjunction with this, 3D ore-forming numerical modeling of structural deformation and fluid flow reveals that gold mineralization is intertwined with moderate volumetric strain and shear strain of rock and fluid divergence. This interaction seems particularly pronounced in areas characterized by channel-like or gentle features. Consequently, it is plausible that gold distribution in the Jiaojia region is the outcome of a comprehensive coupling process involving strain localization, rock deformation, fluid flow, heat transfer and/or interaction. The deep gold prospectivity models of RF and MLP for the Jiaojia district jointly using the predictive variables of fault geometry features and ore-forming simulation data (volume strain, shear strain, temperature variation, and fluid flux) exhibit higher AUC (area under the curve) values compared to models employing individual predictor variable datasets. This improvement underscores their enhanced predictive capability. The prospectivity results thus were used for identifying gold potential within the Jiaojia region, where five promising gold targets at depth were ultimately determined.

Ensemble learning based approach for traffic incident detection and multi-category classification

Traffic incident is one of the important causes of road congestion. Traffic incident detection plays a crucial role in the safety application of intelligent transportation systems, which provides timely information for traffic management departments and reducing losses. Despite many researches on incident detection approach, the identification of different incident categories is not enough. In addition, traffic incident detection is still a challenging task due to the problem of data imbalance and feature selection. In this study, we propose a two-stage traffic incident detection framework based on ensemble learning. In the first stage, a binary classification algorithm based on XGBoost (eXtreme Gradient Boosting) is established to detect whether there is a traffic incident, and 24 feature variables are determined by model feature selection. In the second stage, three resampling algorithms are utilized to reconstruct and balance the dataset. Through comparative analysis, SMOTE (Synthetic Minority Over-sampling Technique)-XGBoost is the best method for incident multi-category classification with precision of 87.27%, 78.52% and 92.54%, respectively. Moreover, the baseline comparison experiments are conducted to evaluate our model performance with real-word datasets. The proposed model achieves the highest average accuracy of 93.45% in the first stage and the macro-precision of 86.11% in the second stage. The results indicate that the proposed method outperforms baselines and the two-stage framework can accurately realize the incident detection and multi-category classification.

MEP and TEP features variability: is it just the brain-state?

Objective. The literature investigating the effects of alpha oscillations on corticospinal excitability is divergent. We believe inconsistency in the findings may arise, among others, from the electroencephalography (EEG) processing for brain-state determination. Here, we provide further insights in the effects of the brain-state on cortical and corticospinal excitability and quantify the impact of different EEG processing. Approach. Corticospinal excitability was measured using motor evoked potential (MEP) peak-to-peak amplitudes elicited with transcranial magnetic stimulation (TMS); cortical responses were studied through TMS-evoked potentials’ TEPs features. A TMS-EEG-electromyography (EMG) dataset of 18 young healthy subjects who received 180 single-pulse (SP) and 180 paired pulses (PP) to determine short-intracortical inhibition (SICI) was investigated. To study the effect of different EEG processing, we compared the brain-state estimation deriving from three published methods. The influence of presence of neural oscillations was also investigated. To evaluate the effect of the brain-state on MEP and TEP features variability, we defined the brain-state based on specific EEG phase and power combinations, only in trials where neural oscillations were present. The relationship between TEPs and MEPs was further evaluated. Main results. The presence of neural oscillations resulted in more consistent results regardless of the EEG processing approach. Nonetheless, the latter still critically affected the outcomes, making conclusive claims complex. With our approach, the MEP amplitude was positively modulated by the alpha power and phase, with stronger responses during the trough phase and high power. Power and phase also affected TEP features. Importantly, similar effects were observed in both TMS conditions. Significance. These findings support the view that the brain state of alpha oscillations is associated with the variability observed in cortical and corticospinal responses to TMS, with a tight correlation between the two. The results further highlight the importance of closed-loop stimulation approaches while underlining that care is needed in designing experiments and choosing the analytical approaches, which should be based on knowledge from offline studies to control for the heterogeneity originating from different EEG processing strategies.

Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables

Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static positional information and dynamic variables. Additionally, most existing deep learning models have not integrated the global system characteristics of the Earth’s features and teleconnection during the learning phase. Here, we propose a static location-aware ConvLSTM (SLA-ConvLSTM) model that is aware of static positional elements and interconnected with global information and teleconnection. The model we propose can discern the influence of dynamic variables across various geographical locations on predictive outcomes. Compared with other deep learning models, our SLA-ConvLSTM model has achieved commendable performance. The outcomes indicate that the collaborative interplay of spatiotemporal features and the extraction of static positional information present a promising technique for wildfire prediction. Moreover, the incorporation of climate indices and global feature variables enhances the predictive capability of the model in wildfire prediction.

Bayesian optimization algorithm‐based Gaussian process regression for in situ state of health prediction of minorly deformed lithium‐ion battery

AbstractAccurate on‐board state‐of‐health (SOH) prediction is crucial for lithium‐ion battery applications. This study presents an in situ prediction technique for minorly deformed battery SOH, utilizing a Gaussian process regression (GPR) model tuned by a Bayesian optimization algorithm. Unlike previous methods that interpret voltage–time data as incremental capacitance curves, our approach directly operates on raw voltage–time data. We apply gray relational analysis to select feature variables as inputs and train the Bayesian Gaussian process regression (BGPR) model using experimental data from batteries under different working conditions. To demonstrate the performance of the BGPR model, we compare it with stepwise linear regression, neural network, and Bayesian support vector machine (BSVM) models. The performance of these four models is evaluated using different performance indicators: mean absolute percentage error (MAPE), root‐mean‐squared percentage error (RMSPE), and coefficient of determination (R²). The results demonstrate that the BGPR model exhibits superior prediction performance with the lowest MAPE (0.11%), RMSPE (0.12%), and the highest R² (0.9915) for minorly deformed batteries. Furthermore, the BGPR model exhibits excellent robustness for SOH prediction of normal batteries under different conditions. This study provides an effective and robust method for accurate on‐board SOH prediction in lithium‐ion battery applications.

Predicting systemic financial risk with interpretable machine learning

Predicting systemic financial risk is essential for understanding the financial system's stability and early warning of financial crises. In this research, we use the financial stress index to measure systemic financial risk. We construct the stress index for five financial submarkets and composite stress index, employ the Markov regime switching model to identify the systemic financial risk stress state. On this basis, we use interpretable machine learning models to forecast systemic financial risk, analyze and compare the results of the intrinsic interpretable machine learning models and the post-hoc explainable methods. The results indicate that systemic financial risk can be effectively predicted using both the submarket stress index and the feature variables, with the submarket stress index as the independent variable providing relatively higher accuracy. There is a linearly positive relationship between the stress index of each submarket and systemic financial risk, with financial stress in the stock and money markets having the greatest impact on systemic financial risk. For each feature variable, stock–bond correlation coefficient, stock valuation risk, the maximum cumulative loss of the SSE Composite Index (SSE CMAX), and loan-deposit ratio have strong predictive power. Our research can provide reference for government to construct prediction model and indicator monitoring platform of systemic financial crisis.

Variability in the Distinctive Features of Silica Sands in Central Europe

Quality quartz sand is globally utilized in construction due to its availability and economic factors, especially in the production of composite cements. Despite its positive properties, quartz sand also has several disadvantages. The dilation of quartz sand can be technologically significant for certain high-temperature applications. This dilation has a non-continuous character with sharp volume change caused by the phase transformation from β to α SiO2 at temperatures around 573 °C. The extent of dilation depends on various factors such as compaction, grain size, the quantity of sand, as well as the shape and character of the grain and chemical purity, particularly the SiO2 content. In this study, six types of quartz sand from different locations in Central Europe were examined, and the influence of chemical composition and grain shape was correlated with the final dilation of these samples. Evaluation methods included X-ray fluorescence spectroscopy (XRFS), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), differential thermal analysis (DTA), and linear thermal expansion analysis. It was found that angular grains, despite their chemical purity, may exhibit minimal dilation. Conversely, the least suitable combination in terms of dilation appears to be a high SiO2 content and high roundness of grains with a smooth surface.

Game-Based Assessment of Students’ Digital Literacy Using Evidence-Centered Game Design

This study measured secondary students’ digital literacy using a digital game-based assessment system that was designed and developed based on the Evidence-Centered Game Design (ECGD) approach. A total of 188 secondary students constituted the valid cases in this study. Fine-grained behavioral data generated from students’ gameplay processes were collected and recorded with the assessment system. The Delphi method was used to extract feature variables related to digital literacy from the process data, and the Analytic Hierarchy Process (AHP) method was used to construct the measurement model. The assessment results of the ECGD-based assessment had a high correlation with standardized test scores, which have been shown to be reliable and valid in prior large-scale assessment studies.

Extracting Shrubland in Deserts from Medium-Resolution Remote-Sensing Data at Large Scale

Shrubs are important ecological barriers in desert regions and an important component of global carbon estimation. However, the shrubland in deserts has been hardly presented, although many high-quality land cover datasets with a 10 m scale based on remote-sensing data have been publicly released products. Therefore, the underestimation of carbon storage is inevitable with the absence of desert shrublands. The existing land-cover datasets have been analyzed and compared, and it has been found that the reason for missing the shrubland in deserts is mainly indued by the absence of shrubland samples, which are easy to neglect and difficult to retrieve. In this study, we developed a semi-automatic method to extract shrubland samples in deserts as the updated input for the machine-learning method. Firstly, the initial samples of desert shrublands were identified from the very high spatial-resolution (0.3~0.5 m) imagery on GEE, and the maximum NDVI from Sentinel-2 was used for double-checking. Secondly, a feature-based method was used to learn the feature from the initial samples and a similarity-based searching method was employed to automatically expand the samples. Finally, the expanded samples and their corresponding time-series satellite images were inputted into different machine-learning methods at a large region (1.63 × 106 km2) for extracting the shrubland in the desert. It was found that different combinations of feature variables and time-series combinations have different impacts on the overall accuracy (OA) of the classification results, as well as the performance of identifying and classifying the different land-cover types. Compared to the existing global-scale land-cover products, the proposed method can better identify the shrubland in deserts and show better overall accuracy.