Data Points Research Articles

Pipeline thickness estimation using the dispersion of higher-order SH guided waves

Abstract Thickness measurement plays an important role in the monitoring of pipeline corrosion damage. However, the requirement for prior knowledge of the shear wave velocity in the pipeline material for popular ultrasonic thickness measurement limits its widespread application. This paper proposes a method that utilizes cylindrical shear horizontal (SH) guided waves to estimate pipeline thickness without prior knowledge of shear wave velocity. The inversion formulas are derived from the dispersion of higher-order modes with the high-frequency approximation. The waveform of the example problems is simulated using the real-axis integral method. The data points on the dispersion curves are processed in the frequency domain using the wave-number method. These extracted data are then substituted into the derived formulas. The results verify that employing higher-order SH guided waves for the evaluation of thickness and shear wave velocity yields less than 1% error. This method can be applied to both metallic and non-metallic pipelines, thus opening new possibilities for health monitoring of pipeline structures.

Robot joint space grid error compensation based on three-dimensional discrete point space circular fitting

The poor absolute positioning accuracy of industrial robots has limited their application in fields such as aerospace manufacturing. To address this issue, the spatial grid compensation method has been proposed as an effective solution. In this paper, we propose a sampling method based on three-dimensional discrete point space circular fitting for grid points to significantly reduce the sampling workload and improve compensation accuracy compared to traditional joint space grid compensation methods. Additionally, we use the Kriging interpolation algorithm instead of the inverse distance weighting (IDW) algorithm for spatial interpolation prediction of pose error. Based on this, the proposed sampling and interpolation prediction method in this paper was verified on a Comau NJ500–2.7 manipulator equipped with a fiber-laying end effector. The experimental results demonstrate that using our proposed sampling method yields pose data of grid points that have only a small deviation from directly sampled results and are only slightly higher than the robot's repeat positioning accuracy. Moreover, our proposed method can significantly reduce the sampling workload by 60% under the experimental conditions of this study (sampling 10 groups of data within 90 degrees range), with increasing sampling range leading to more obvious efficiency improvements. Finally, we show that compared to the IDW algorithm, the Kriging interpolation algorithm yields better results and improves the mean absolute positioning accuracy of robots after compensation by 30%.

Refereeing the Sport of Squash with a Machine Learning System

Squash is a sport where referee decisions are essential to the game. However, these decisions are very subjective in nature. Disputes, both from the players and the audience, regularly occur because the referee made a controversial call. In this study, we propose automating the referee decision process through machine learning. We trained neural networks to predict such decisions using data from 400 referee decisions acquired through extensive video footage reviewing and labeling. Six positional values were extracted, including the attacking player’s position, the retreating player’s position, the ball’s position in the frame, the ball’s projected first bounce, the ball’s projected second bounce, and the attacking player’s racket head position. We calculated nine additional distance values, such as the distance between players and the distance from the attacking player’s racket head to the ball’s path. Models were trained on Wolfram Mathematica and Python using these values. The best Wolfram Mathematica model and the best Python model achieved accuracies of 86% ± 3.03% and 85.2% ± 5.1%, respectively. These accuracies surpass 85%, demonstrating near-human performance. Our model has great potential for improvement as it is currently trained with limited, unbalanced data (400 decisions) and lacks crucial data points such as time and speed. The performance of our model is almost surely going to improve significantly with a larger training dataset. Unlike human referees, machine learning models follow a consistent standard, have unlimited attention spans, and make decisions instantly. If the accuracy is improved in the future, the model can potentially serve as an extra refereeing official for both professional and amateur squash matches. Both the analysis of referee decisions in squash and the proposal to automate the process using machine learning is unique to this study.

The macrocycle inhibitor landscape of SLC-transporter.

In the past years the interest in Solute Carrier Transporters (SLC) has increased due to their potential as drug targets. At the same time, macrocycles demonstrated promising activities as therapeutic agents. However, the overall macrocycle/SLC-transporter interaction landscape has not been fully revealed yet. In this study, we present a statistical analysis of macrocycles with measured activity against SLC-transporter. Using a data mining pipeline based on KNIME retrieved in total 825 bioactivity data points of macrocycles interacting with SLC-transporter. For further analysis of the SLC inhibitor profiles we developed an interactive KNIME workflow as well as an interactive map of the chemical space coverage utilizing parametric t-SNE models. The parametric t-SNE models provide a good discrimination ability among several corresponding SLC subfamilies' targets. The KNIME workflow, the dataset, and the visualization tool are freely available to the community.

Durability of Lead-Free Solder Interconnections for Printed Circuit Board Applications: Comparing Energy-Based Thermo-Mechanical Fatigue Models

Fatigue models for predicting the cycles to failure of solder interconnections under temperature cycling situations have been discussed and developed for decades. However, most models were developed for different solder materials, components, and printed circuit boards. No previous work has systematically compared these models. Therefore, the variability of their durability predictions is unknown. This study compared nine existing low-cycle energy-based fatigue models for different solder materials and components and then analyzed the differences among them. Each fatigue model had a specific combination of the factors that affect the strain energy density accumulation. Therefore, we adjusted the strain energy density (input) and the predicted cycles to failure (output) in a consistent way to compare the selected fatigue models on the same basis. The differences among the predictions on cycles to failure from the fatigue models was significantly reduced after applying the adjustments, and they exhibited excellent consistency around 1 mJ/mm 3 strain energy density. In the end, factors that can affect the prediction consistency of fatigue mode were provided, including the number of data points while building the fatigue model, the range of strain energy density while selecting the electronic components, and the application of volume-weighted averaging technique on the critical solder joint.

An interval-valued estimation method of aircraft route carbon emission: A function of aircraft seat capacity and route flight time

In this paper, we introduce a novel approach that utilizes aircraft seat capacity and its route flight time to predict carbon emissions for aircraft manufacturers during the aircraft design stage. To address data fluctuations arising from the human-aircraft-environment-management system throughout the production and operation of the future-designed aircraft, we employ the interval-valued data type, replacing the traditional point data type, and construct an interval-valued regression model. Within the parameterized method framework, our programming model incorporates additional constraints to ensure the intersection of each interval-valued sample. This addresses the challenges significant data fluctuations pose, leading to improved fitting performance. Furthermore, the model is demonstrated to satisfy Kuhn-Tucker conditions for solvability, and the obtained regressed parameters exhibit favorable small-sample properties. An empirical study using airline operating data from China validates the prediction effectiveness of the programming model. Finally, based on these predictions, we provide applications and suggestions for decision-makers aimed at reducing CO2 emissions.

A Grain Size Profile Prediction Method Based on Combined Model of Extreme Gradient Boosting and Artificial Neural Network and Its Application in Sand Control Design

Summary The grain size distribution along the well depth is of great significance for the prediction of the physical properties and the staged sand control design of the unconsolidated or weakly consolidated sandstone reservoir. In this paper, a new method for predicting the formation median grain size profile based on the combination model of extreme gradient boosting (XGBoost) and artificial neural network (ANN) is proposed. The machine learning algorithm and weighted combination model are applied to the prediction and analysis of reservoir grain size. The prediction model is improved from two aspects: First, the feature engineering of the XGBoost-ANN model is constructed by using the data of multiple sampling points on the logging curve. Second, the prediction accuracy is improved by increasing the dimension of the prediction model, that is, the XGBoost and ANN single-prediction models are weighted by the error reciprocal method and a combined prediction model containing multidimensional information is established. The research results show that compared with the single-point mapping model, the prediction accuracy of the multipoint mapping model considering the vertical geological continuity of the reservoir is higher than that of the single-point prediction and the coefficient of determination in the testing set can be improved up to 14.5%. The influence of different weighting methods on prediction performance is studied, and the prediction performance of original XGBoost, ANN, and XGBoost-ANN combined models is compared. The combined prediction model has a higher prediction accuracy than the single XGBoost and ANN models with the same number of sampling points and the coefficient of determination can be improved by up to 16.5%. The prediction accuracy and generalization ability of the XGBoost-ANN combined model are evaluated comprehensively. The combined model is used to design layered sand control of a well in an adjacent block, and good results have been achieved in production practice. This study provides a new method with high accuracy and efficiency for the prediction of unconsolidated sand median grain size profile.

Determination of Thermal Conductivity Constant for Spray-Dried Mung Bean Protein Isolate using Series-Parallel “Block” Method and Central Composite Design

The efficient production of mung bean protein isolate (MBPI) is essential for sustainable food processing, yet it is energy-intensive due to drying phase required to convert raw mung beans into a fine powder. This work was carried out to calculate the thermal conductivity constant (k) of protein (A), moisture (B) and residual carbohydrate (C) of spray-dried MBPI. The protein slurry was prepared by dissolving MBPI in water at the ratio of 1:3, 1:4, and 1:5 prior to spray drying. The protein slurry was then spray dried at three different inflow temperatures of 140, 160, and 180 °C. A Series and Parallel Combination "Block" Model in tandem with a Central Composite Design (CCD) were selected to calculate the thermal conductivity constant of A, B and C. Eight arrangement components on A, B, and C component, measurements were derived from the initial three data points by utilizing the CCD method, in the preliminary study. By utilizing the series-parallel approach and statistical combination, there are a total of forty study arrangements. The thermal conductivity of MBPI with arrangements of A, B, and C (pABC); and a series of combinations of A and C in parallel to B (sAC pB) ranged from 0.1964-0.224 Wm-1 ℃ -1, with R2 = 98.73%-99.42%. Therefore, the energy requirement of spray drying process for MBPI could be predicted by the thermal properties of each component selected in the protein isolate.

Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping

Land subsidence (LS) due to natural and human-driven forces (e.g., earthquakes and overexploitation of groundwater) has detrimental and irreversible impacts on the environmental, economic, and social aspects of human life. Thus, LS hazard mapping, monitoring, and prediction are important for scientists and decision-makers. This study evaluated the performance of seven machine learning approaches (MLAs), comprising six classification approaches and one regression approach, namely (1) classification and regression trees (CARTs), (2) boosted regression tree (BRT), (3) Bayesian linear regression (BLR), (4) support vector machine (SVM), (5) random forest (RF), (6) logistic regression (LogR), and (7) multiple linear regression (MLR), in generating LS susceptibility maps and predicting LS in two case studies (Semnan Plain and Kashmar Plain in Iran) with varying intrinsic characteristics and available data points. Multiple input variables (slope, aspect, groundwater drawdown, distance from the river, distance from the fault, lithology, land use, topographic wetness index (TWI), and normalized difference vegetation index (NDVI)), were used as predictors. BRT outperformed the other classification approaches in both case studies, with accuracy rates of 75% and 74% for Semnan and Kashmar plains, respectively. The MLR approach yielded a Mean Square Error (MSE) of 0.25 for Semnan plain and 0.32 for Kashmar plain. According to the BRT approach, the variables playing the most significant role in LS in Semnan Plain were groundwater drawdown (20.31%), distance from the river (17.11%), land use (14.98%), NDVI (12.75%), and lithology (11.93%). Moreover, the three most important factors in LS in Kashmar Plain were groundwater drawdown (35.31%), distance from the river (23.1%), and land use (12.98%). The results suggest that the BRT method is not significantly affected by data set size, but increasing the number of training set data points in MLR results in a decreased error rate.

Earthquake forecasting from paleoseismic records

Forecasting large earthquakes along active faults is of critical importance for seismic hazard assessment. Statistical models of recurrence intervals based on compilations of paleoseismic data provide a forecasting tool. Here we compare five models and use Bayesian model-averaging to produce time-dependent, probabilistic forecasts of large earthquakes along 93 fault segments worldwide. This approach allows better use of the measurement errors associated with paleoseismic records and accounts for the uncertainty around model choice. Our results indicate that although the majority of fault segments (65/93) in the catalogue favour a single best model, 28 benefit from a model-averaging approach. We provide earthquake rupture probabilities for the next 50 years and forecast the occurrence times of the next rupture for all the fault segments. Our findings suggest that there is no universal model for large earthquake recurrence, and an ensemble forecasting approach is desirable when dealing with paleoseismic records with few data points and large measurement errors.

Acid rain reduced soil carbon emissions and increased the temperature sensitivity of soil respiration: A comprehensive meta-analysis

Soil respiration the second-largest carbon flux in terrestrial ecosystems, has been extensively studied across a wide range of biomes. Surprisingly, no consensus exist on how acid rain (AR) impacts the spatiotemporal pattern of soil respiration. Therefore, we conducted a meta-analysis using 318 soil respiration and 263 soil respiration temperature sensitivity (Q10) data points obtained from 48 studies to assess the impact of AR on soil respiration components and their Q10. The results showed that AR reduced soil total respiration (Rt) and soil autotrophic respiration (Ra) by 7.41 % and 20.75 %, respectively. As the H+ input increased, the response rates of Ra to AR (RR-Ra) and soil heterotrophic respiration (Rh) to AR (RR-Rh) decreased and increased, respectively. With increased AR duration, the RR-Ra increased, whereas the RR-Rh did not change. AR increased the Q10 of Rt (Rt-Q10) and Rh (Rh-Q10) by 1.92 % and 9.47 %, respectively, and decreased the Q10 of Ra (Ra-Q10) by 2.77 %. Increased mean annual temperature, mean annual precipitation, and initial soil organic carbon increased the response rate of Ra-Q10 to AR (RR-Ra-Q10) and decreased the response rate of Rh-Q10 to AR (RR-Rh-Q10). However, as the AR frequency and initial soil pH increased, both RR-Ra-Q10 and RR-Rh-Q10 also increased. In summary, AR decreased Rt but increased Q10, likely due to soil acidification (soil pH decreased by 7.84 %), reducing plant root biomass (decreased by 5.67 %) and soil microbial biomass (decreased by 5.67 %), changing microbial communities (increased fungi to bacteria ratio of 15.91 %), and regulated by climate, vegetation, soil and AR regimes. To the best of our knowledge, this is the first study to reveal the large-scale, varied response patterns of soil respiration components and their Q10 to AR. It highlights the importance of applying the reductionism theory in soil respiration research to enhance our understanding of soil carbon cycling processes with in the context of global climate change.

Technology Focus: Artificial Lift (March 2024)

The industry is now in its second year beyond the COVID-19 pandemic, and the workforce has changed in many ways, but issues facing production performance remain. Major conferences saw 116 submissions from 28 countries, with 64% regarding electrical submersible pumps (ESPs), and 18% and 8% devoted to gas lift and rod lift, respectively. Many of the papers dealt with case history successes. That is all well and good, but it doesn’t necessarily drive reader interest if it can’t be applied to their asset. The top three papers were selected on the guidelines of clarity of the abstract in addressing the scope, methods/procedures/process, results/observations/conclusions, and novel or additive information. The papers described here focus on novel unconventional horizontal downhole pump card signatures, carbon reduction through alternative energy to pump wells, and predicting downhole-discharge pressure of ESPs. Paper SPE 214723 presents a discussion of the design, evaluation, and field testing of a new type of mechanical gas separator with what the authors term a “hydrohelical” design. The separator, in Permian Basin tests, almost doubled the flow capacity of conventional gas separators with a high level of efficiency. The authors draw particular attention to the improvement of ESP operations under multiphase conditions. Carbon reduction is a hot topic, and paper OTC 32671 does not disappoint as it looks at the use of a 10-MW floating solar photovoltaic power system to meet the energy needed for 10 ESP wells on an oil platform in the Brazil equatorial region. Collaboration with the National Renewable Energy Laboratory and its System Advisor Model was used with extensive meteorological and wind data to examine the sustainability of green energy use for the petroleum industry and offer valuable guidance for future renewable energy projects in regions with similar characteristics. Finally, paper SPE 216598 comes from Sudan and Saudia Arabia and focuses on digital solutions for the challenge of knowing the downhole-discharge pressure of an ESP system. Most ESP systems do not have downhole gauges, so an artificial neural network (ANN) approach is used to predict the ESP discharge pressure for performance optimization. Forty wells and more than 12,000 data points were reduced to 16 inputs plus one hidden layer of seven neurons to provide a high degree of accuracy in prediction of discharge pressure. The authors state that the ability to accurately predict discharge pressure can lead to the early detection of possible anomalies, which can prevent costly failures and reduce downtime. Artificial intelligence technology may improve the accuracy of ANNs, but, for now, ANNs are worth a look to obtain much-needed ESP discharge pressure. Other notable papers dealt with gas separation, ESP reliability, and the increasing challenges of low rates, low pressures, and high gas fractions. Recommended additional reading at OnePetro: www.onepetro.org. SPE 214913 Solved: ESP Shutdowns and Restart Issues—Novel Pump Protection and Active 3D Dispersion System Improve ESP Performance in Multiple Wells by A. Bhattacharjee, SLB, et al. SPE 215474 A New Artificial Lift Approach in Telisa Low-Productivity Reservoir: Utilized Very Low-Rate ESP by Agus Aryanto, Medco E&P Indonesia, et al. SPE 215170 A Comprehensive Study of a Novel Submersible Pump by Hattan Banjar, Saudi Aramco, et al. SPE 214722 Case Study: Predicting Electrical Submersible Pump Failures Using Artificial Intelligence and Physics-Based Hybrid Models by Shejuti Silvia, Baker Hughes, et al. SPE 214733 Gas-Flow-Management Technology Designed To Decrease Downtime and Improve ESP Efficiency by S. Fulwider, ConocoPhillips, et al.

Numerical simulation and experimental study of dip angle spray film thickness under elliptical double Gaussian sum model

To address the problem of modeling the growth rate of coating film thickness when spraying at inclination angle, based on Gaussian sum model, it is proposed to use the elliptic double Gaussian sum model to establish the cumulative model of coating film thickness when spraying at static inclination angle of the spray gun. The differential geometry amplification theorem is used to establish the coating growth rate model with the spraying inclination angle as the variable; after that, the static inclination spraying experiments are carried out, and the coating thickness data of the sampling points are recorded through the spraying experiments, and the Levenberg-Maquart algorithm is used for the least-squares fitting of the model, which results in the static spraying film thickness distribution model. Finally, compared with the elliptic double β model, the fitting accuracy of the elliptic double Gaussian sum model is 6.3% higher than that of the elliptic double β model when spraying at inclination angle by comparing the R-square values, and the elliptic double Gaussian sum model is more capable of obtaining a better fitting accuracy, which further confirms the validity and practicability of the model.

Synthesis, Characterization and Antioxidant Evaluation of Tranexamic Acid-derived Benzimidazole, Azomethines, and Oxazolidine

This work includes the synthesis of heterocyclic compounds and azomethines starting from tranexamic acid. The benzimidazole C2 was prepared by mixing equal molar amounts of tranexamic acid with 4-methyl-o-phenylenediamine). Compounds C13-C18 were prepared by the reaction of tranexamic acid with the aromatic aldehydes. The oxazolidine (C19) was synthesized from the reaction of the compound C13 with hydroxyacetic acid. The synthesized compounds were identified by physical properties such as melting point, color change, and spectral data (Fourier transform infrared and mass). The effectiveness of the compounds C2, C13, C15, C17, and C19 was estimated as antioxidants outside the body (in vitro) and by three methods, namely, reducing power assay, scavenging of hydrogen peroxide, and phosphomolybdenum method.. KEYWORDS :Antioxidant, Azomethine, Benzimidazole, Oxazolidine, Tranexamic acid.

Explainable ensemble learning predictive model for thermal conductivity of cement-based foam

Cement-based foam has emerged as a strong contender in sustainable construction owing to its superior thermal and sound insulation properties, fire resistance, and cost-effectiveness. To effectively use cement-based foam as a thermal insulation material, it is important to accurately predict its thermal conductivity. The current study aims at coining an accurate methodology for predicting the thermal conductivity of cement-based foam using state-of-the-art machine learning techniques. A comprehensive experimental dataset of 504 data points was developed and used for training ensemble learning models including XGBoost, CatBoost, LightGBM and Random Forest. The independent variables of this dataset affecting the thermal conductivity are the cast density, percentage of pozzolan, porosity, percentage of moisture, and duration of hydration in days. Using the Isolation Forest algorithm proved effective in detecting and eliminating outliers in the dataset. All the ensemble learning techniques explored in this study achieved superior predictive accuracy with a coefficient of determination greater than 0.98 on the test dataset. The influence of the input features on the thermal conductivity was visualized using the SHapley Additive exPlanations (SHAP) approach and individual conditional expectation (ICE) plots. The cast density had the greatest effect on thermal conductivity. The explainable machine learning models demonstrated superior accuracy, efficiency, and reliability in estimating the thermal insulation of cement-based foam, opening the door for wider acceptance of this material in sustainable energy efficient construction.

Effect of Tax Revenue and Foreign Grants on Government Activities: A Study in Nepalese Perspective

This study aims to examine the impact of previous years’ government expenditure, tax revenue, and foreign grants on determining Nepal's government activities or expenditures. The secondary data from used in the analysis. It includes 48 data points from 1974/75 to 2021/22, collected from the publication of Nepal Rastra Bank and economic surveys of Nepal. The descriptive and exploratory research designs are used in this study. Some statistical and econometric tools like descriptive statistics, Kolmogorov-Smirnov test, multiple regression analysis, analysis of variance (ANOVA), and residual analysis are used in this study. The previous expenditure determines the present government activities. Tax revenue, foreign grants, and previous years’ government expenditures are individually and jointly significant in determining the government expenditure. One unit increase in tax revenue and foreign grants resulted in a 0.177 and 0.046 unit increase in government expenditure in Nepal, respectively. 86.5 percent variation in government activities is determined by the previous year's government expenditure, tax revenue, and foreign grants. The positive impact of tax revenue is more than foreign grants on spending government activities. So, the policymakers must prioritize measures that increase domestic tax revenue.

Does Addiction Cause Addictive Behavior?

Abstract: Is addiction a behavioral pattern, or the underlying cause of a behavioral pattern? Both views are found in prominent accounts of addiction, but theorists generally do not notice that they are taking a controversial position, let alone justify it. A third possibility is that addiction consists in both addictive behavior and its causes, though this view is less obviously present in the literature. I argue that two important considerations favor the "cause view" over the "behavior" and "hybrid" views. The first is that we want to be able to explain addictive behavior with reference to addiction. The second is that addiction seems to persist long after addictive behavior has ceased. Only the cause view can easily accommodate both data points.

A new method of measuring Forbush decreases

Context. Forbush decreases (FDs) are short-term depressions in the galactic cosmic ray (GCR) flux and one of the common signatures of coronal mass ejections (CMEs) in the heliosphere. They often show a two-step profile, the second one associated with the CME’s magnetic structure. This second step can be described by the recently developed analytical FD model for flux ropes (FRs) – ForbMod. Aims. The aim of this study is to utilise ForbMod to develop a best-fit procedure to be applied on FR-related FDs as a convenient measurement tool. Our motivation is to develop a best-fit procedure that can be applied to a data series from an arbitrary detector. Thus, the basic procedure would facilitate measurement estimation of the magnitude of the FR-related FD, with the possibility of being adapted for the energy response of a specific detector for a more advanced analysis. Methods. The non-linear fitting was performed by calculating all possible ForbMod curves constrained within the FR borders to the designated dataset and minimising the mean square error (MSE). In order to evaluate the performance of the ForbMod best-fit procedure, we used synthetic measurements produced by calculating the theoretical ForbMod curve for a specific example CME and then applying various effects to the data to mimic the imperfection of the real measurements. We also tested the ForbMod best-fit function on the real data, measured by detector F of the SOHO/EPHIN instrument on a sample containing 30 events, all of which have a distinct FD corresponding to the magnetic obstacle. The extraction of FD profiles (from the onset to the end) was performed manually by an observer, whereby we applied two different versions of border selection and assigned a quality index to each event. Results. We do not find notable differences between events marked by a different quality index. For events with a selection of two different borders, we find that the best fit applied on extended interplanetary coronal mass ejection (ICME) structure borders results in a slightly larger MSE and differences compared to the traditional method due to a larger scatter of the data points. We find that the best-fit results can visually be categorised into six different FD profile types. Although some profiles do not show a visually pleasing FD, the ForbMod best-fit function still manages to find a solution with a relatively small MSE. Conclusions. Overall, we find that the ForbMod best-fit procedure performs similar to the traditional algorithm-based observational method, but with slightly smaller values for the FD amplitude, as it’s taking into account the noise in the data. Furthermore, we find that the best-fit procedure has an advantage compared to the traditional method as it can estimate the FD amplitude even when there is a data gap at the onset of the FD.

A machine learning and experimental-based model for prediction of soil sorption capacity toward phenanthrene

Sorption by soil is the fundamental basis for environment fate of hydrophobic organic contaminants (HOCs), which varies significantly depending on diverse properties of soils. Therefore, a generalized approach to predict HOC sorption by soils is required. In this study, 488 data points were extracted from references and adopted to develop models for estimating the sorption capacities of phenanthrene in soils using six different machine learning (ML) approaches. The extreme gradient boosting (XGBT) model demonstrated the most favorable performance, achieving a coefficient of determination of 0.91 and root-mean-square errors of 0.24 for the testing dataset. The XGBT model's performance was further demonstrated by comparing with experimental data from batch sorption tests conducted on 20 soil samples collected from 17 provinces of China. The differences between the predicted values and the experimental values were statistically equal to zero (p = 0.14). Leveraging the XBGT model together with soil properties from the Harmonized World Soil Database, the distribution of sorption capacities in Chinese soils was successfully depicted on a national scale. This research is expected to contribute to a deeper understanding of the migration of persistent organic pollutants in terrestrial system. Furthermore, the established model holds implications for more precise and scientific soil environmental management.

Absent Multiview Semisupervised Classification.

With the advent of vast data collection ways, data are often with multiple modalities or coming from multiple sources. Traditional multiview learning often assumes that each example of data appears in all views. However, this assumption is too strict in some real applications such as multisensor surveillance system, where every view suffers from some data absent. In this article, we focus on how to classify such incomplete multiview data in semisupervised scenario and a method called absent multiview semisupervised classification (AMSC) has been proposed. Specifically, partial graph matrices are constructed independently by anchor strategy to measure the relationships among between each pair of present samples on each view. And to obtain unambiguous classification results for all unlabeled data points, AMSC learns view-specific label matrices and a common label matrix simultaneously. AMSC measures the similarity between pair of view-specific label vectors on each view by partial graph matrices, and consider the similarity between view-specific label vectors and class indicator vectors based on the common label matrix. To characterize the contributions of different views, the p th root integration strategy is adopted to incorporate the losses of different views. By further analyzing the relation between the p th root integration strategy and exponential decay integration strategy, we develop an efficient algorithm with proved convergence to solve the proposed nonconvex problem. To validate the effectiveness of AMSC, comparisons are made with some benchmark methods on real-world datasets and in the document classification scenario as well. The experimental results demonstrate the advantages of our proposed approach.