Search Results (4,645)

This study aims to examine the role of emotional maturity and social support in predicting the level of quarter-life crisis in emerging adulthood. The employed research method was multiple linear regression analysis. The participants were individuals aged 18 to 29 years. Further, 122 participants were selected using convenience sampling. The data were collected using a questionnaire survey based on the Multidimensional Scale of Perceived Social Support to assess social support and the quarter-life crisis scale based on the theory by Robbins and Wilner. To assess emotional maturity, we used the emotional maturity scale based on the theory by Walgito. Emotional maturity and social support were important in predicting the level of quarter-life crisis in emerging adulthood. Higher levels of emotional maturity and social support were associated with lower levels of quarter-life crisis experiences in emerging adulthood. Full article

As urban expansion continues, the intensifying land surface temperature (LST) underscores the critical need for accurate predictions of future thermal environments. However, no study has investigated which method can most effectively and consistently predict the future LST. To address these gaps, our study employed four methods—the multiple linear regression (MLR), geographically weighted regression (GWR), random forest (RF), and artificial neural network (ANN) approach—to establish relationships between land use/cover and LST. Subsequently, we utilized these relationships established in 2006 to predict the LST for the years 2012 and 2018, validating these predictions against the observed data. Our results indicate that, in terms of fitting performance (R² and RMSE), the methods rank as follows: RF > GWR > ANN > MLR. However, in terms of temporal stability, we observed a significant variation in predictive accuracy, with MLR > GWR > RF > ANN for the years 2012 and 2018. The predictions using MLR indicate that the future LST in 2050, under the SSP2 and SSP5 scenarios, is expected to increase by 1.8 ± 1.4 K and 2.1 ± 1.6 K, respectively, compared to 2018. This study emphasizes the importance of the MLR method in predicting the future LST and provides potential instructions for future heat mitigation. Full article

This comprehensive study looks at how operational conditions affect the performance of a novel seven-channel titania ceramic ultrafiltration membrane for the treatment of produced water. A full factorial design experiment (2³) was conducted to study the effect of the cross-flow operating factors on the membrane permeate flux decline and the overall permeate volume. Eleven experimental runs were performed for three important process operating variables: transmembrane pressure (TMP), crossflow velocity (CFV), and filtration time (FT). Steady final membrane fluxes and permeate volumes were recorded for each experimental run. Under the optimized conditions (1.5 bar, 1 m/s, and 2 h), the membrane performance index demonstrated an oil rejection rate of 99%, a flux of 297 L/m²·h (LMH), a 38% overall initial flux decline, and a total permeate volume of 8.14 L. The regression models used for the steady-state membrane permeate flux decline and overall permeate volume led to the highest goodness of fit to the experimental data with a correlation coefficient of 0.999. A Multiple Linear Regression method and an Artificial Neural Network approach were also employed to model the experimental membrane permeate flux decline and analyze the impact of the operating conditions on membrane performance. The predictions of the Gaussian regression and the Levenberg–Marquardt backpropagation method were validated with a determination coefficient of 99% and a Mean Square Error of 0.07. Full article

Gait scores are widely used in the genetic evaluation of horses. However, the nature of such measurement may limit genetic progress since there is subjectivity in phenotypic information. This study aimed to assess the application of machine learning techniques in the prediction of breeding values for five visual gait scores in Campolina horses: dissociation, comfort, style, regularity, and development. The dataset contained over 5000 phenotypic records with 107,951 horses (14 generations) in the pedigree. A fixed model was used to estimate least-square solutions for fixed effects and adjusted phenotypes. Variance components and breeding values (EBV) were obtained via a multiple-trait model (MTM). Adjusted phenotypes and fixed effects solutions were used to train machine learning models (using the EBV from MTM as target variable): artificial neural network (ANN), random forest regression (RFR) and support vector regression (SVR). To validate the models, the linear regression method was used. Accuracy was comparable across all models (but it was slightly higher for ANN). The highest bias was observed for ANN, followed by MTM. Dispersion varied according to the trait; it was higher for ANN and the lowest for MTM. Machine learning is a feasible alternative to EBV prediction; however, this method will be slightly biased and over-dispersed for young animals. Full article

Isothermal constant-strain-rate compression experiments of S280 ultrahigh-strength stainless steel were conducted at 800–1150 °C, 0.001–10 s⁻¹, and 70% height reduction. The flow stress behaviors were analyzed based on the compression data. The strain compensation Arrhenius constitutive relation, multiple linear regression constitutive relation, and back-propagation (BP) neural network constitutive relation of this alloy were established for the first time. The S280 ultrahigh-strength stainless steel is characterized by a positive strain rate and negative temperature sensitivity. Its flow stress at high temperature (1000–1150 °C) and low temperature (800–950 °C) is generally at the steady state and the softening state, respectively. The three new flow stress constitutive relations all meet the requirements for engineering applications in terms of predictive precision. The BP neural network constitutive relation shows the highest predictive precision, with correlation coefficient R of 0.999 and average absolute relative error AARE of 1.04%. The strain compensation Arrhenius constitutive relation shows the lowest predictive precision, with R of 0.994 and AARE of 14.748%. The multiple linear regression constitutive relation shows the modest predictive precision, with R of 0.994 and AARE of 6.24%. Full article

Professional football clubs invest vast amounts of money in the recruitment of players. This article presents the latest advances in statistical modeling of the factors that market actors take into consideration to determine the transfer prices of professional football players. It extends to a global scale the econometric approach previously developed by the authors to evaluate the transfer prices of players under contract with clubs from the five major European leagues. The statistical technique used to build the model is multiple linear regression (MLR), with fees paid by clubs as an independent variable. The sample comprises over 8000 transactions of players transferred for money from clubs worldwide during the period stretching from July 2014 to March 2024. This paper shows that a statistical model can explain up to 85% of the differences in the transfer fees paid for players. Despite the specific cases and other possible distortions mentioned in the discussion, the use of a statistical model to determine player transfer prices is thus highly relevant on a global scale. Full article

Background: Out-of-hospital cardiac arrest (OHCA) is associated with high mortality and cerebral disability in survivors. Current models of risk prediction and survival are mainly based on resuscitation duration. We examined the prognostic value of circulating biomarkers in predicting mortality and severe cerebral disability for OHCA survivors, alongside traditional clinical risk indicators. Methods: Biomarkers including BNP, troponin I, and galectin-3 were measured at hospital admission in resuscitated OHCA patients. Prognostic significance for mortality and cerebral disability involving circulating biomarkers, resuscitation duration, demographics, and laboratory and clinical characteristics was examined via univariate and multivariate Cox proportional hazards regression models. The incremental prognostic value of the index covariates was examined through model diagnostics, focusing on the Akaike information criterion (AIC) and Harrell’s concordance statistic (c-statistic). Results: In a combinatorial analysis of 144 OHCA survivors (median follow-up 5.7 years (IQR 2.9–6.6)), BNP, galectin-3, arterial pH, and resuscitation time were significant predictors of all-cause death and severe cerebral disability, whereas troponin I levels were not. Multivariate regression, adjusting for BNP, arterial pH, and resuscitation time, identified galectin-3 as an independent predictor of long-term mortality. Multiple linear regression models also confirmed galectin-3 as the strongest predictor of cerebral disability. The incorporation of galectin-3 into models for predicting mortality and cerebral disability enhanced fit and discrimination, demonstrating the incremental value of galectin-3 beyond traditional risk predictors. Conclusions: Galectin-3 is a significant, independent long-term risk predictor of cerebral disability and mortality in OHCA survivors. Incorporating galectin-3 into current risk stratification models may enhance early prognostication and guide targeted clinical interventions. Full article

Objectives: Controlling the lifestyle associated with dementia risk can delay the process of cognitive decline. Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are early states in the development of dementia and are also the window period for early intervention in dementia. The purpose of this study was to explore the association between multi-domain lifestyle and objective cognitive impairment in elderly people with SCD and MCI in Chinese communities and to provide reference for effective implementation of precise health management measures to reduce the risk of dementia. Methods: A total of 265 middle-aged and elderly volunteers recruited from the community were divided into SCD group (107 cases), MCI group (80 cases), and healthy control (HC) group (78 cases). All participants received clinical interview, examination, and cognitive assessments. Results: The total Dementia Risk Reduction Lifestyle Scale (DRRLS) scores in the HC, SCD, and MCI groups [110.00 (11.25) vs. 101.00 (10.00) vs. 79.50 (20.75)] exhibited statistically significant differences among them. The total score of the DRRLS showed a significant negative correlation with the Trail-Making Test (TMT), and significant positive correlations with both the Verbal Fluency Test (VFT) and Auditory Verbal Learning Test (AVLT) scores (p < 0.05). After adjusting for confounding factors, such as age and years of education, multiple linear regression analysis revealed several points. In the SCD group, brain-strengthening exercise and interpersonal relationship scores were negatively correlated with TMT scores (β = −11.257, −15.077; all p < 0.05), while health responsibility, smoking control behavior, and interpersonal relationship scores were positively correlated with AVLT scores (β = 0.485, 0.344, and 0.406; all p < 0.05). In the MCI Group, brain-strengthening exercise, brain-healthy diet, and interpersonal relationship were negatively correlated with TMT (β = −22.011, −16.206, −11.696; all p < 0.01), whereas health responsibility, mental activity, smoking control behavior, interpersonal relationship, and stress management were positively correlated with AVLT (β = 0.450, 0.435, 0.308, 0.256, 0.607; all p < 0.05). Conclusions: In Chinese communities, the unhealthy lifestyle of elderly individuals with SCD and MCI is significantly associated with cognitive function impairment. The greater their unhealthy lifestyle habits, the more pronounced the scope and severity of cognitive function impairment becomes. Furthermore, different dimensions of lifestyle have varying impacts on cognitive domains. Full article

Background: Numerous studies in the literature have aimed to evaluate the difficulty level of removing third molars. However, most of these studies have focused on the lower third molars, which can lead to complications. There is a lack of a method to determine the complexity of upper third molar extraction. Therefore, this study’s objective was to develop an equation using multiple linear regression to estimate the extraction time of an upper third molar based on its complexity. Methods: This study involved patients enrolled in the Master of Oral Surgery program at the University of Seville. To determine their relationship with surgical time, the researchers analyzed various factors, such as depth, root morphology, and the need for odontosection. They then validated their findings by studying patients treated at Palmaplanas Hospital in Mallorca. Results: The cohort analysis from the University of Seville revealed significant associations between surgical time and the identified factors. A regression equation design was performed to predict the total duration of surgical intervention for wisdom teeth extraction. This equation incorporates several independent variables, represented by Xi, together with a constant term, C, and the corresponding coefficients, Bi, which weight the impact of each variable on the intervention time. The results are as follows: −0.312 (spatial relationship), 0.651 (depth), −0.443 (bone and mucosa integrity), 0.214 (roots), −0.745 (ostectomy), 0.713 (odontosection), and −0.426 (suture). Upon application of the statistical methodology to the Palmaplanas Hospital cohort, a regression coefficient of 0.770 was determined. This indicates a strong correlation between the input data and the estimated surgical time. Conclusions: In conclusion, the proposed formula demonstrates notable validity in predicting the surgical time required to extract upper third molars. Full article

Large-scale gridded population product datasets have become crucial sources of information for sustainable development initiatives. However, mainstream modeling approaches (e.g., dasymetric mapping based on Multiple Linear Regression or Random Forest Regression) do not consider the heterogeneity and multiscale characteristics of the spatial relationships between influencing factors and populations, which may seriously degrade the accuracy of the prediction results in some areas. This issue may be even more severe in large-scale gridded population products. Furthermore, the lack of detailed 3D human settlement data likewise poses a significant challenge to the accuracy of these data products. The emergence of the unprecedented Global Human Settlement Layer (GHSL) data package offers a possible solution to this long-standing challenge. Therefore, this study proposes a new Gridded Population Mapping (GPM) method that utilizes the Multiscale Geographically Weighted Regression (MGWR) model in conjunction with GHSL-3D Building, POI, nighttime light, and land use/cover datasets to disaggregate population data for third-level administrative units (districts and counties) in mainland China into 100 m grid cells. Compared to the WorldPop product, the new population map reduces the mean absolute error at the fourth-level administrative units (townships and streets) by 35%, 51%, and 13% in three test regions. The proposed mapping approach is poised to become a crucial reference for generating next-generation global demographic maps. Full article

Wheat breeding programs are currently focusing on using non-destructive and cost-effective hyperspectral sensing tools to expeditiously and accurately phenotype large collections of genotypes. This approach is expected to accelerate the development of the abiotic stress tolerance of genotypes in breeding programs. This study aimed to assess salt tolerance in wheat genotypes using non-destructive canopy spectral reflectance measurements as an alternative to direct laborious and time-consuming phenological selection criteria. Eight wheat genotypes and sixteen F₈ RILs were tested under 150 mM NaCl in real field conditions for two years. Fourteen spectral reflectance indices (SRIs) were calculated from the spectral data, including vegetation SRIs and water SRIs. The effectiveness of these indices in assessing salt tolerance was compared with four morpho-physiological traits using genetic parameters, SSR markers, the Mantel test, hierarchical clustering heatmaps, stepwise multiple linear regression, and principal component analysis (PCA). The results showed significant differences (p ≤ 0.001) among RILs/cultivars for both traits and SRIs. The heritability, genetic gain, and genotypic and phenotypic coefficients of variability for most SRIs were comparable to those of measured traits. The SRIs effectively differentiated between salt-tolerant and sensitive genotypes and exhibited strong correlations with SSR markers (R² = 0.56–0.89), similar to the measured traits and allelic data of 34 SSRs. A strong correlation (r = 0.27, p < 0.0001) was found between the similarity coefficients of SRIs and SSR data, which was higher than that between measured traits and SSR data (r = 0.20, p < 0.0003) based on the Mantel test. The PCA indicated that all vegetation SRIs and most water SRIs were grouped with measured traits in a positive direction and effectively identified the salt-tolerant RILs/cultivars. The PLSR models, which were based on all SRIs, accurately and robustly estimated the various morpho-physiological traits compared to using individual SRIs. The study suggests that various SRIs can be integrated with PLSR in wheat breeding programs as a cost-effective and non-destructive tool for phenotyping and screening large wheat populations for salt tolerance in a short time frame. This approach can replace the need for traditional morpho-physiological traits and accelerate the development of salt-tolerant wheat genotypes. Full article

The rapid increase in population and economic activities has greatly influenced land use and spatial development. In urban agglomerations where socioeconomic activities are densely concentrated, the clash between ecological protection and economic growth is becoming more evident. Therefore, a thorough quantitative assessment of spatial changes driven by land use dynamics, alongside an examination of temporal and spatial driving factors, is crucial in offering scientific backing for the long-term and sustainable growth of urban agglomerations. This paper focuses on the major urban agglomerations in China’s Yangtze River Delta region, examining the spatiotemporal evolution of land use and landscape patterns from 2000 to 2020. By employing the standard deviation ellipse technique, coupled with multiple linear regression and the geographical detector model, we conduct a quantitative assessment of the directional trends in urban construction land expansion as well as the diverse impacts of temporal and spatial factors on this expansion across various periods and regions. The findings indicate that over the past 20 years, construction land in the Yangtze River Delta Urban Agglomeration expanded in concentrated patches, showing significant scale effects with relatively intact farmland and forest land being increasingly encroached upon. Landscape-type transitions predominantly occurred in cities around Taihu Lake and Hangzhou Bay, with the most significant transition being farmland converted to construction land, resulting in a greater number of patches and more pronounced land fragmentation. Throughout the 20 years, the standard deviation ellipse of construction land in the Yangtze River Delta Urban Agglomeration expanded and shifted, with the predominant expansion trending from the northwest toward the southeast, and the EN orientation being the most intense expansion area, covering 1641.24 km². The influence of temporal and spatial driving factors on the expansion of urban construction land differed across various periods and regions. This study thoroughly examines the driving factors that affect the evolution of urban construction land in the region, offering valuable scientific evidence and references for future planning and development of the Yangtze River Delta Urban Agglomeration, aiding in the formulation of more precise and efficient urban management and land use strategies. Full article

An accurate assessment of frost damage in coffee plantations can help develop effective agronomic practices to cope with extreme weather events. Remotely piloted aircrafts (RPA) have emerged as promising tools to evaluate the impacts caused by frost on coffee production. The objective was to evaluate the impact of frost on coffee plants, using vegetation indices, in plantations of different ages and areas of climatic risks. We evaluated two coffee plantations located in Brazil, aged one and two years on the date of frost occurrence. Multispectral images were collected by a remotely piloted aircraft, three days after the occurrence of frost in July 2021. The relationship between frost damage and these vegetation indices was estimated by Pearson’s correlation using simple and multiple linear regression. The results showed that variations in frost damage were observed based on planting age and topography conditions. The use of PRA was efficient in evaluating frost damage in both young and adult plants, indicating its potential and application in different situations. The vegetation index MSR and MCARI2 indices were effective in assessing damage in one-year-old coffee plantations, whereas the SAVI, MCARI1, and MCARI2 indices were more suitable for visualizing frost damage in two-year-old coffee plantations. Full article

Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems. These methods are often used arbitrarily, despite their profound impact on prediction due to changes caused to the data structure. We hypothesize that their use affects the performance of ML models fitted to automated milking systems (AMSs) data for mastitis prediction. We compare three imputations—simple imputer (SI), multiple imputer (MICE) and linear interpolation (LI)—and three resampling techniques: Synthetic Minority Oversampling Technique (SMOTE), Support Vector Machine SMOTE (SVMSMOTE) and SMOTE with Edited Nearest Neighbors (SMOTEEN). The classifiers were logistic regression (LR), multilayer perceptron (MLP), decision tree (DT) and random forest (RF). We evaluated them with various metrics and compared models with the kappa score. A complete case analysis fitted the RF (0.78) better than other models, for which SI performed best. The DT, RF, and MLP performed better with SVMSMOTE. The RF, DT and MLP had the overall best performance, contributed by imputation or resampling (SMOTE and SVMSMOTE). We recommend carefully selecting resampling and imputation techniques and comparing them with complete cases before deciding on the preprocessing approach used to test AMS data with ML models. Full article

Background: The purpose of this study is to evaluate the effectiveness of Defocus Incorporated Multiple Segments (DIMSs) in slowing myopia progression in pediatric patients as a function of age. Methods: This was a non-randomized experimenter-masked retrospective controlled observational study of European individuals aged 6–16 years with progressive myopia but no ocular pathology. We retrospectively reviewed the charts of the participants allocated to receive DIMS spectacles (Hoya^® MiyoSmart^®) or single-vision spectacle lenses (control group). Cycloplegic spherical equivalent (SE) and axial length (AL) were measured at baseline and at 12-, 24-, and 36-month follow-ups. The results were stratified by age into four groups: patients wearing DIMS spectacles older or younger than 10 years of age (group A, 20 patients mean age 13.6 ± 2.2, and group C, 20 patients mean age 9.0 ± 1.2) and age-matched control groups (group B, 18 patients mean age 13.2 ± 2.5, and group D, 22 patients mean age 8.5 ± 0.9). Results: At 36 months, SE and AL increase were significantly reduced in groups A and C, respectively, compared to groups B and D (p < 0.05). Linear regression analysis showed a significant correlation (p < 0.05) between patient age and myopia progression for SE in groups A and C, but only in group A for AL. Groups B and D did not show any significant correlation (p > 0.05). Conclusions: DIMS spectacles seem to slow myopia progression in pediatric patients; however, their effectiveness shows the greatest results in children older than 10 years of age. Moreover, our findings suggest that AL may be the more reliable parameter for evaluating myopia progression. Full article