Random Regression Research Articles

Rapid Determination of Crude Protein Content in Alfalfa Based on Fourier Transform Infrared Spectroscopy.

The crude protein (CP) content is an important determining factor for the quality of alfalfa, and its accurate and rapid evaluation is a challenge for the industry. A model was developed by combining Fourier transform infrared spectroscopy (FTIS) and chemometric analysis. Fourier spectra were collected in the range of 4000~400 cm-1. Adaptive iteratively reweighted penalized least squares (airPLS) and Savitzky-Golay (SG) were used for preprocessing the spectral data; competitive adaptive reweighted sampling (CARS) and the characteristic peaks of CP functional groups and moieties were used for feature selection; partial least squares regression (PLSR) and random forest regression (RFR) were used for quantitative prediction modelling. By comparing the combined prediction results of CP content, the predictive performance of airPLST-cars-PLSR-CV was the best, with an RP2 of 0.99 and an RMSEP of 0.053, which is suitable for establishing a small-sample prediction model. The research results show that the combination of the PLSR model can achieve an accurate prediction of the crude protein content of alfalfa forage, which can provide a reliable and effective new detection method for the crude protein content of alfalfa forage.

Integrating key ecosystem services to study the spatio-temporal dynamics and determinants of ecosystem health in Wuhan’s central urban area

The central areas of many Chinese cities have experienced large-scale urbanization in recent decades, a trend that is affecting the quality of life of residents and posing challenges to the sustainable development of urban ecosystems. Hence, it is vital to conduct assessments of urban ecosystems’ health conditions to ensure their sustainability. In this study, the spatial and temporal dynamics of ecosystem health in the central city of Wuhan were analyzed using the Vigor-Organization-Resilience-Service (VORS) model. The sensitivity of ecosystem health at different scales was also investigated using hierarchical health assessment delineation and random forest regression methods. The results showed that the composite index of ecosystem health has been declining since 2000, highlighting the negative impacts of shrinking ecological space, declining service capacity and urban expansion. Health zoning divides areas into five categories. Meanwhile, the health zones are located in areas of dense shrubs, cropland and water bodies on the edge of the central city, while the weak zones are mainly in the core of the city, which is 99.33 % impervious surface. The main factors influencing ecosystem health, including impervious surface area, water bodies and topography, vary from region to region. The study proposes targeted ecological management strategies for different health zones in Wuhan, emphasizing ecosystem protection and providing guidance for sustainable urban development.

Multimodal Image Fusion Workflow Incorporating MALDI Imaging Mass Spectrometry and Microscopy for the Study of Small Pharmaceutical Compounds.

Multimodal imaging analyses of dosed tissue samples can provide more comprehensive insights into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multimodal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity coregistration with other higher-resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high-spatial resolution microscopy image. As a proof of concept, our multimodal workflow was applied to brain tissue extracted from a Sprague-Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models, including linear regression, partial least-squares regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then coregistered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess colocalization of corynantheidine with brain cells. Our study also provides insights into the different evaluation parameters to consider when utilizing image fusion for biological applications.

Machine learning enhanced modeling of steel‐concrete bond strength under elevated temperature exposure

AbstractThis study uses machine learning techniques to investigate the bond strength between steel and concrete under various elevated temperature scenarios. Five distinct machine learning algorithms, including Random Forest (RF), XGBoost, AdaBoost, Decision Tree, Linear Regression, and hyperparameteric optimisations, were used to predict changes in bond strength. The models underwent rigorous optimisation using GridSearchCV to achieve optimal performance. In this study, we evaluated several metrics such as Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2) Score to compare and assess the models' prediction capabilities. After optimisation, results indicate that the RF model exhibited exceptional performance in estimating bond strength across different temperature conditions, demonstrating minimal errors and a high R2 Score. Visual comparisons of actual and predicted values further confirmed the efficacy of the RF model in capturing complex fluctuations in bond strength. The findings of this study underscore the potential of machine learning models, particularly the optimized RF method, in accurately predicting bond strength under varying thermal conditions, with promising implications for engineering and construction practices.

Ownership Structure and Firm Performance. A Panel Regression Study from Poland

This paper explores the relationship between ownership structure and firm performance, using the framework of agency theory. Given a sample of 126 Polish non-financial firms listed on the Warsaw Stock Exchange between 2016-2021, we applied fixed and random effects panel regressions with robust standard errors, tested for specification, endogeneity, cross-sectional heteroskedasticity, and serial autocorrelation. Our models identify several significant associations of adopted ownership structures with firm performance measured by accounting and market-based measures. We find that ownership concentration by the largest shareholder is negatively related to enterprise market-measured performance. Additionally, our results indicate the significance of shareholder heterogeneity.

Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models

Landslide susceptibility assessment is crucial in predicting landslide occurrence and potential risks. However, traditional methods usually emphasize on larger regions of landsliding and rely on relatively static environmental conditions, which exposes the hysteresis of landslide susceptibility assessment in refined-scale and temporal dynamic changes. This study presents an improved landslide susceptibility assessment approach by integrating machine learning models based on random forest (RF), logical regression (LR), and gradient boosting decision tree (GBDT) with interferometric synthetic aperture radar (InSAR) technology and comparing them to their respective original models. The results demonstrated that the combined approach improves prediction accuracy and reduces the false negative and false positive errors. The LR-InSAR model showed the best performance in dynamic landslide susceptibility assessment at both regional and smaller scale, particularly when identifying areas of high and very high susceptibility. Modeling results were verified using data from field investigations including unmanned aerial vehicle (UAV) flights. This study is of great significance to accurately assess dynamic landslide susceptibility and to help reduce and prevent landslide risk.

A machine learning strategy for enhancing the strength and toughness in metal matrix composites

Particle-reinforced metal matrix composites (MMCs) are highly sought after for various applications due to their robust mechanical properties containing high strength and high elastic modulus. However, the introduction of highly rigid particles inevitably causes drastic degradation of toughness, resulting in the trade-off between strength and toughness in MMCs. This work proposed a multi-structure, multi-objective collaborative enhancement cyclic strategy that seamlessly integrates machine learning (ML) and genetic algorithms for realizing synergistic improvement of strength and toughness with the forward prediction and inverse exploration. By leveraging diverse configurations in MMCs such as laminate, network, and micro/nano hybrid structures, robust datasets were initially constructed. Random forest regression (RFR) was employed as surrogate model for forward prediction, accurately estimating the elastic modulus, yield strength, ultimate tensile strength, and toughness. Inverse exploration was also conducted using the combination of RFR and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify optimized structures with favorable strength-toughness matching. Utilizing this strategy, this work explores the microstructures with synergistic enhancement of strength and toughness, being confirmed by finite element analysis (FEA). The proposed optimization strategy, demonstrated in particle-reinforced MMCs, offers a promising solution to the trade-off between strength and toughness and is highly transferrable to other material systems.

Using Artificial Intelligence to Predict the Aerodynamic Properties of Wind Turbine Profiles

This study describes the use of artificial intelligence to predict the aerodynamic properties of wind turbine profiles. The goal was to determine the lift coefficient for an airfoil using its geometry as input. Calculations based on XFoil were taken as a target for the predictions. The lift coefficient for a single case scenario was set as a value to find by training an algorithm. Airfoil geometry data were collected from the UIUC Airfoil Data Site. Geometries in the coordinate format were converted to PARSEC parameters, which became a direct feature for the random forest regression algorithm. The training dataset included 60% of the base dataset records. The rest of the dataset was used to test the model. Five different datasets were tested. The results calculated for the test part of the base dataset were compared with the actual values of the lift coefficients. The developed prediction model obtained a coefficient of determination ranging from 0.83 to 0.87, which is a good prognosis for further research.

Optimal spatial distribution of seismic stations to detect magma migration using the seismic amplitude ratio analysis

Magma migrations frequently trigger seismic swarms, resulting in seismic events that overlap in time and hinder real-time phase picking commonly used for hypocenter location. Addressing this challenge, seismic amplitude ratio analysis (SARA) allows identification of seismic migrations in real-time by simply tracking the relative seismic amplitude between a pair of seismic stations.This paper aims to identify key statistical features of the seismic network array locations that improve their ability to detect seismic migrations using SARA. We evaluated the capability to detect the most frequently oriented magma migrations in over 100 volcanoes, using a criterion previously proposed to study vertical magma migrations in Piton de la Fournaise. Additionally, we investigate the influence of vent-station proximity on magma conduit coverage and identify the distance ratio that yields improved detection.Furthermore, we estimate the seismic network efficiency by calculating the detection capability volume per station. We then use the random forest regression algorithm to identify which statistical features of the seismic network location contribute more to the efficiency disparity among different volcanoes. Notably, our findings reveal that optimizing seismic network coverage entails maximizing the standard deviation of relative pair station distances, while maintaining a prescribed minimum separation distance between station pairs. Our results reveal important criteria that can be used to optimize seismic network location design.

Overexpression of RpKTI2 from Robinia pseudoacacia Affects the Photosynthetic Physiology and Endogenous Hormones of Tobacco.

Kunitz trypsin inhibitor genes play important roles in stress resistance. In this study, we investigated RpKTI2 cloned from Robinia pseudoacacia and its effect on tobacco. RpKTI2 was introduced into the tobacco cultivar NC89 using Agrobacterium-mediated transformation. Six RpKTI2-overexpressing lines were obtained. Transgenic and wild-type tobacco plants were then compared for photosynthetic characteristics and endogenous hormone levels. Transgenic tobacco showed minor changes in chlorophyll content, fluorescence, and photosynthetic functions. However, the maximum photochemical efficiency (Fv/Fm) increased significantly while intercellular CO2 concentration (Ci) decreased significantly. Stomatal size and hormone content (indole-3-acetic acid, zeatin riboside, gibberellin, and indole-3-propionic acid) were reduced, while brassinosteroid content increased. Random forest regression revealed that RpKTI2 overexpression had the biggest impact on carotenoid content, initial fluorescence, Ci, stomatal area, and indole-3-acetic acid. Overall, RpKTI2 overexpression minimally affected chlorophyll synthesis and photosynthetic system characteristics but influenced stomatal development and likely enhanced the antioxidant capacity of tobacco. These findings provide a basis for future in-depth research on RpKTI2.

High-resolution long-term average groundwater recharge in Africa estimated using random forest regression and residual interpolation

High-resolution long-term average groundwater recharge in Africa estimated using random forest regression and residual interpolation

Unlocking the relationship between board of directors’ attributes and international diversification in Indian firms

PurposeThe study aims to investigate the relationship between certain key attributes of the board of directors as their international experience, tenure, age, independence and size and internationalization strategies of firms in an emerging economy.Design/methodology/approachThe study uses random effects panel Tobit regression on a data set of 418 Indian companies spanning over a time frame of nine years from fiscal year (F.Y.) 2009–2010 to F.Y. 2017–2018.FindingsBoard members substantially associate with internationalization choices of emerging economy firms. Specifically, board members with extensive international experience, shorter tenure, higher age, greater independence and larger board size are associated with high level of internationalization in these firms.Research limitations/implicationsThe study advocates that Indian companies should focus on recruiting board members with international experience, periodically refreshing their boards, valuing wisdom and experience, adhering to regulatory requirements of independent directors and increase the number of board members to expand internationally. By following this, Indian companies can successfully expand into international markets.Originality/valueThe study’s originality lies in its focus on the role of board members in the internationalization strategy of emerging economy firms, which remains an under-researched area.

Effects of process parameters on the surface characteristics of laser powder bed fusion printed parts: machine learning predictions with random forest and support vector regression

Laser powder bed fusion (L-PBF) fuses metallic powder using a high-energy laser beam, forming parts layer by layer. This technique offers flexibility and design freedom in metal additive manufacturing (MAM). However, achieving the desired surface quality remains challenging and impacts functionality and reliability. L-PBF process parameters significantly influence surface roughness. Identifying the most critical factors among numerous parameters is essential for improving quality. This study examines the effects of key process parameters on the surface roughness of AlSi10Mg, a widely used aluminum alloy in high-tech industries, fabricated by L-PBF. Part orientation, laser power, scanning speed, and layer thickness were identified as crucial parameters via cause-and-effect analysis. To systematically examine their effects, the Taguchi method was employed within the framework of the design of experiment (DoE). Experimental results and statistical analysis revealed that laser power, scanning speed, and layer thickness significantly influence surface roughness parameters: arithmetic mean (Ra) and root mean square (Rq). Main effect plots and energy density analyses confirmed their impact on surface quality. Microscopic investigations identified surface flaws such as spattering, balling, and porosity contributing to poor quality. Given the complex interplay between parameters and surface quality, accurately predicting their effects is challenging. To address this, machine learning models, specifically random forest regression (RFR) and support vector regression (SVR), were used to predict the effects on surface roughness. The RFR model’s R2 values for predicting Ra and Rq are 97% and 85%, while the SVR model’s predictions are 85% and 66%, respectively. Evaluation metrics demonstrated that the RFR model outperformed SVR in predicting surface roughness.

Maternal victimization and neglected offspring: Child marriage, IPV and depression symptoms among Salar Muslim women

BackgroundChild neglect is prevalent in western rural China, yet there is limited research among ethnic minority communities. The Salar, a Turkic-Muslim ethnic minority residing primarily in western China, also face this specific problem. The group is deeply influenced by ethnicity, Islam and Chinese Confucianism, which in turn makes women vulnerable to child marriage and IPV. These victimizations, coupled with various life stressors, further complicate the challenges of providing adequate care for their children. ObjectiveThis study hypothesizes a relationship between child neglect and maternal child marriage, IPV victimization, and depression symptoms. Participants and setting201 married Salar women from five villages in Xunhua Salar Autonomous County, China, were randomly selected to participate in the study. MethodA probability proportional to size (PPS) sampling approach was used to collect a random representative multi-stage cluster sample in 2022. Random effects Poisson regression models were used to test the hypotheses. ResultsThe participants reported a 65.6 % rate of child neglect and a 30.8 % rate of IPV in the past year. 37.6 % experienced child marriage. Results revealed significant associations between child neglect and child marriage, IPV, and depression symptoms. A two-way interaction between IPV and depression symptoms was strongly positively associated with child neglect. ConclusionsThis research indicates that Salar Muslim mothers who have experienced child marriage, adulthood victimization, and depression are at a higher risk of neglecting their children. The findings represent a valuable initial step toward researching and addressing the protection needs of women and children from Muslim ethnic minorities in China.

Impact of urban and rural residents medical insurance on self-rated health of residents in China: a panel study from the China family panel studies national baseline survey.

This study aimed to investigate the health performance of the Urban and Rural Residents Medical Insurance (URRMI) scheme in China and to make practical recommendations and scientific references for its full implementation in China. This is a panel study that uses data from the China Family Panel Studies from 2018 to 2020, which is separated into treated and control groups each year, utilizing the key approach of propensity score matching and difference-in-difference (PSM-DID). Using 1-to-1 k-nearest neighbor matching, we proportionate the baseline data. Using difference-in-difference model, we examine the mean treatment impact of the outcome variables. Using a 500-time random sample regression model, we validate the robustness of the model estimation. The result was credible after matching, minimizing discrepancies. Good overall performance of self-rated health with an average Hukou status of, respectively, 0.8 and 0.4 in the treated and control group, primarily in rural and urban regions separately. The participation of URRMI significantly impacted self-rated health of residents, with a 0.456-unit improvement probabilities observed (p < 0.1). Additionally, the individuals are categorized into urban and rural, and those with urban hukou had a 0.311 expansion in the probability of having better health status compared to rural hukou (p < 0.05). Other factors, such as age, highest education, annual income, medical expenditure, hospital scale, clinic satisfaction, and napping, also impacted self-rated health. Moreover, elder individuals, higher education levels, and higher medical expenditure having a higher probability of improvement. The study utilized a placebo test to verify the robustness of the URRMI regression. The estimated coefficients showed that basic medical insurance did not significantly improve the health of insured residents under the URRMI scheme. The study demonstrates the crucial role of PSM-DID in determining the influence of URRMI on self-rated health status. It indicates that purchasing in URRMI has a favorable influence on the health of residents, advancing enhanced self-rated health effectiveness. It does, however, reveal geographical disparities in health, with urban dwellers faring far better than those who live in the suburb. Study suggests expanding URRMI coverage, narrowing urban-rural divide, increasing insurance subsidies, reforming laws, and developing effective advertising strategies.

O-155 Limited utility of mosaicism reporting in PGT-A: independent confirmation from a European multisite, double-blinded analysis including 4293 embryo transfers following the first US experience

Abstract Study question Are results showing limited clinical utility of mosaicism reporting in PGT from a blinded mosaic embryo transfer American study, confirmed in a European setting? Summary answer Even between different patient populations and clinical settings, putative mosaicism reporting has limited clinical utility for embryo selection when co-evaluated with other clinical/embryological factors. What is known already Intermediate chromosomal copy number (ICN) results in NGS-based PGT-A are commonly interpreted as embryo mosaicism. A recent prospective, non-selection study in America showed that reporting mosaicism would not significantly improve embryo selection. At odds with this, results suggesting that embryo selection based on putative mosaicism can improve clinical outcomes continue to be reported, possibly reflecting limitations in accurate mosaicism identification or retrospective analysis biases. This study replicates the design of previously reported American study, using the same PGT-A assay, examining the predictive value of whole-chromosome ICN (wICN) and segmental ICN (sICN) following concurrent copy-number and genotyping-PGT in a European setting. Study design, size, duration A multisite double-blinded study involving 14 European IVF clinics was conducted from April 2022 to July 2023, including 3365 patients and 4293 single embryo transfers. Embryos, reported as negative for uniform aneuploidies, including those with wICN or sICN, were chosen for transfer based solely on standard developmental and morphological evaluation. Primary outcome measure was live birth rate defined as any pregnancy reaching the 24th week of gestation (LBR). Participants/materials, setting, methods Trophectoderm biopsies were analysed using a validated targeted-NGS assay with approximately 5000 loci across the genome, providing both quantitative and genotyping information to support aneuploidy classification. Putative mosaicism was inferred from ICN deviations from the expected two copies for autosomes and partly supported by corresponding SNP B-allele frequency patterns. Related clinical and embryological variables were analysed in a multivariate analysis and a Random Forrest regression used to model the clinical utility of putative mosaicism. Main results and the role of chance Of the embryos transferred, 6.8% (293/4293) were wICN only; 6.2% (269/4293) were sICN only and 0.5% (23/4293) were a combination of wICN and sICN. The detected ICN ranged from 23%-84% with no overrepresentation of any individual chromosomes. The LBR of embryos in the control group (without ICN), sICN and wICN were 46.8% (1736/3708; 95%CI:45.2-48.4%), 36.4% (98/269; 95%CI:30.9-42.3%) and 39.2% (115/293; 95%CI:33.8-44.9%) respectively. The miscarriage rate for control, sICN and wICN were 12.9% (257/1993; 95%CI:11.5-14-4%), 19.7% (24/122; 95%CI:13.6-27-6%) and 16.7% (23/138; 95%CI:11.4-23.8%), respectively. Logistic regression (P = 0.05) showed that the presence of sICN (OR: 0.7; 95%CI: 0.54-0.9) and wICN (OR: 0.62; 95%CI: 0.36-1.07) were modestly but significantly associated with LBR along with other embryological and clinical factors including embryo morphology (OR: 0.89; 95%CI: 0.84-0.96), day of biopsy (OR: 0.65;95%CI: 0.56-0.75), previous ET failures (OR: 0.79; 95%CI: 0.71-0.88) that were more strongly associated. No significant effect was observed of any specific intermediate CN cut-off on the association with LBR. Random Forrest regression, considering all correlating variables, showed a LBR prediction (AUC) equal to 0.578 without putative mosaicism and AUC of 0.583 with putative mosaicism included, a negligible increase considering the low incidence of mosaicism in our clinical setting along with its modest predictive value. Limitations, reasons for caution Follow-up prenatal and post-natal data at this time was not available, hence conclusions about these outcomes wasn’t possible. Despite the overall large sample size, the analysis of specific effects at different ICN values lacked sufficient power. Furthermore, this study’s data is platform-specific and cannot be translated to other PGT-A assays. Wider implications of the findings Although putative mosaicism was associated with modestly impaired pregnancy rate, two independent studies now confirm that reporting mosaicism has limited impact when prospectively selecting embryos for transfer based on standard embryological parameters. Considering the established downsides, including potential embryo disposal, the practice of mosaicism reporting in PGT is questionable. Trial registration number Not applicable

Estimating forest aboveground biomass in tropical forests with Landsat time-series data and recurrent neural network

ABSTRACT The insensitivity and saturation of sensor signals significantly limit the application of Landsat data on estimating biomass in tropical forests with a high level of biomass. The previous studies with single-date Landsat data have lower accuracies in tropical forests compared to boreal and temperate forests. Landsat time-series data provide a promising opportunity to improve the accuracy by enhancing the relationship between Landsat spectral reflectance and forest aboveground biomass with disturbance and recovery dynamics. Compared to the single-date image, Landsat time-series data can capture abrupt spectral changes (e.g. harvesting and fire) and show the regrowth process in forested pixels. However, very limited studies take advantage of Landsat time-series data to estimate aboveground biomass in tropical forests. Recurrent neural networks (RNNs) are powerful deep learning techniques to capture time dependencies in sequence data. However, the application of RNNs in estimating forest biomass has not been explored yet. Therefore, we integrate the long short-term memory network (LSTM) and the fully connected neuron network (FNN) to establish an RNN-FNN model for estimating forest biomass with Landsat time-series imagery and airborne LiDAR data. We compared the proposed model with the commonly used Random Forest model and linear regression model which are implemented with single-date data. The results show that the RNN-FNN model can deal with Landsat time-series sequence data to enhance the relationship between Landsat spectral reflectance and forest aboveground biomass. The proposed model achieves the R2 of 0.63 and RMSE of 25.5 Mg/ha, which significantly outperformed the Random Forest model and linear regression model with Landsat single-date data. This study demonstrates the value of RNN and Landsat time-series imagery in estimating forest biomass for tropical forests.

Estimating LoD-s Based on the Ionization Efficiency Values for the Reporting and Harmonization of Amenable Chemical Space in Nontargeted Screening LC/ESI/HRMS.

Nontargeted LC/ESI/HRMS aims to detect and identify organic compounds present in the environment without prior knowledge; however, in practice no LC/ESI/HRMS method is capable of detecting all chemicals, and the scope depends on the instrumental conditions. Different experimental conditions, instruments, and methods used for sample preparation and nontargeted LC/ESI/HRMS as well as different workflows for data processing may lead to challenges in communicating the results and sharing data between laboratories as well as reduced reproducibility. One of the reasons is that only a fraction of method performance characteristics can be determined for a nontargeted analysis method due to the lack of prior information and analytical standards of the chemicals present in the sample. The limit of detection (LoD) is one of the most important performance characteristics in target analysis and directly describes the detectability of a chemical. Recently, the identification and quantification in nontargeted LC/ESI/HRMS (e.g., via predicting ionization efficiency, risk scores, and retention times) have significantly improved due to employing machine learning. In this work, we hypothesize that the predicted ionization efficiency could be used to estimate LoD and thereby enable evaluating the suitability of the LC/ESI/HRMS nontargeted method for the detection of suspected chemicals even if analytical standards are lacking. For this, 221 representative compounds were selected from the NORMAN SusDat list (S0), and LoD values were determined by using 4 complementary approaches. The LoD values were correlated to ionization efficiency values predicted with previously trained random forest regression. A robust regression was then used to estimate LoD values of unknown features detected in the nontargeted screening of wastewater samples. These estimated LoD values were used for prioritization of the unknown features. Furthermore, we present LoD values for the NORMAN SusDat list with a reversed-phase C18 LC method.

Internet use and frailty in middle-aged and older adults: findings from developed and developing countries

BackgroundWith increasing trend of internet use in all age groups, whether internet use can prevent frailty in middle-aged and older adults remains unclear.MethodsFive cohorts, including Health and Retirement Study (HRS), China Health and Retirement Longitudinal Study (CHARLS), the Survey of Health, Ageing and Retirement in Europe (SHARE), English Longitudinal Study of Aging (ELSA), and Mexican Health and Aging Study (MHAS), were used in this study. Internet use, social isolation, and frailty status was assessed using similar questions. The Generalized estimating equations models, random effects meta-analysis, COX regression, and mediation analysis were utilized.ResultsIn the multicohort study, a total of 155,695 participants were included in main analysis. The proportion of internet use was varied across countries, ranging from 5.56% in China (CHARLS) to 83.46% in Denmark (SHARE). According to the generalized estimating equations models and meta-analysis, internet use was inversely associated with frailty, with the pooled ORs (95%CIs) of 0.72 (0.67,0.79). The COX regression also showed that participants with internet use had a lower risk of frailty incidence. Additionally, the association was partially mediated by social isolation and slightly pronounced in participants aged 65 and over, male, not working for payment, not married or partnered, not smoking, drinking, and not co-residence with children.ConclusionsOur findings highlight the important role of internet use in preventing frailty and recommend more engagements in social communication and activities to avoid social isolation among middle-aged and older adults.

Analysis and Prediction of Influencing Factors of American Real Estate Price Based on Machine Learning

The American real estate market has always been a subject of widespread interest, with its price fluctuations impacting not only the lives of millions of American households but also exerting profound effects on the nation's overall economic health. This paper focuses on analyzing which factors are more significant in influencing the American real estate market in order to gain a better understanding of the factors influencing this market and to offer insightful information for stakeholders, policymakers, and investors navigating this dynamic market. We apply linear regression model, lasso regression model and random forest regression model to United States real estate data to explore variables that influence the U.S. housing price and find that PPI, M3, and population are the critical variables impacting the housing price most. By comparing the performance of three regression models, we identify the random forest regression model as the optimal model for predicting real estate prices.