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Search Results (241)

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Keywords = multicollinearity analysis

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17 pages, 758 KB  
Article
Impact of ESG Preferences on Investors in China’s A-Share Market
by Yihan Sun, Diyang Jiao, Yiqu Yang, Yumeng Peng and Sang Hu
Int. J. Financial Stud. 2025, 13(4), 191; https://doi.org/10.3390/ijfs13040191 - 15 Oct 2025
Viewed by 361
Abstract
This study explores the time-varying influence of Environmental, Social, and Governance (ESG) factors on asset pricing in China’s A-share market from 2017 to 2023, integrating investor heterogeneity categorized as ESG-unaware (Type-U), ESG-aware (Type-A), and ESG-motivated (Type-M). taxonomy. It adopts a linear regression model [...] Read more.
This study explores the time-varying influence of Environmental, Social, and Governance (ESG) factors on asset pricing in China’s A-share market from 2017 to 2023, integrating investor heterogeneity categorized as ESG-unaware (Type-U), ESG-aware (Type-A), and ESG-motivated (Type-M). taxonomy. It adopts a linear regression model with seven control variables (including firm systematic risk, asset turnover ratio, and ownership concentration) to quantify ESG’s marginal effect on stock returns. Annual regressions (2017–2022) reveal distinct ESG coefficient shifts: insignificant negative coefficients in 2017–2018, significantly positive coefficients in 2019–2020, and significantly negative coefficients in 2021–2022. Heterogeneity analysis across five non-financial industries (Utilities, Properties, Conglomerates, Industrials, Commerce) shows industry-specific ESG effects. Portfolio performance tests using 2023 data (stocks divided into eight ESG groups) indicate that portfolios with medium ESG scores outperform high/low ESG portfolios and the traditional mean-variance model in risk-adjusted returns (Sharpe ratio) and volatility control, avoiding poor governance risks (low ESG) and excessive ESG resource allocation issues (high ESG). Overall, policy shocks and institutional maturation transformed the market from ESG indifference to ESG-motivated pricing within a decade, offering insights for stakeholders in emerging ESG markets. Full article
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13 pages, 2698 KB  
Article
Adopting Biochar as Immobilization Support for Hyper Ammonia-Producing Bacteria Proliferation
by Christiana Bitrus, Ademola Hammed, Tawakalt Ayodele, Kudirat Alarape, Niloy Chandra Sarker, Clairmont Clementson and Ewumbua Monono
Appl. Microbiol. 2025, 5(4), 111; https://doi.org/10.3390/applmicrobiol5040111 - 14 Oct 2025
Viewed by 192
Abstract
The many uses of biochar extend to microbial enhancement in fermentation processes because it acts as a catalyst and a support medium in agricultural industries, particularly for biofertilizer production. This study explores how three key biochar parameters, concentration (0.05–0.25% w/v), [...] Read more.
The many uses of biochar extend to microbial enhancement in fermentation processes because it acts as a catalyst and a support medium in agricultural industries, particularly for biofertilizer production. This study explores how three key biochar parameters, concentration (0.05–0.25% w/v), temperature (30–50 °C), and particle size (250 μm–1.40 mm) affect hyper-ammonia-producing bacteria (HAB) growth during fermentation using commercially sourced pine wood-derived biochar. Fermentation experiments utilized enriched cow rumen fluid under controlled conditions, monitoring bacterial growth via optical density (OD600) over 48 h. Microbial proliferation was strongly influenced by all tested parameters (concentration, temperature, particle size). Highest growth occurred at 0.15% biochar concentration, 45 °C, and 250 μm particle size within the tested parameter ranges. Lower concentrations and smaller particles promoted microbial adhesion and colonization. Higher biochar levels hindered growth due to surface saturation and reduced pore accessibility. SEM imaging supported these findings by revealing structural changes on the biochar surface at different concentrations. Regression analysis demonstrated strong correlation between biochar parameters and microbial activity (R2 = 0.9931), though multicollinearity limited individual variable significance. These findings support biochar optimization for enhanced microbial processing in biotechnological applications. Full article
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21 pages, 3184 KB  
Article
Rethinking Linear Regression: Simulation-Based Insights and Novel Criteria for Modeling
by Igor Mandel and Stan Lipovetsky
AppliedMath 2025, 5(4), 140; https://doi.org/10.3390/appliedmath5040140 - 13 Oct 2025
Viewed by 374
Abstract
Large multiple datasets were simulated through sampling, and regression modeling results were compared with known parameters—an analysis undertaken here for the first time on such a scale. The study demonstrates that the impact of multicollinearity on the quality of parameter estimates is far [...] Read more.
Large multiple datasets were simulated through sampling, and regression modeling results were compared with known parameters—an analysis undertaken here for the first time on such a scale. The study demonstrates that the impact of multicollinearity on the quality of parameter estimates is far stronger than commonly assumed, even at low or moderate correlations between predictors. The standard practice of assessing the significance of regression coefficients using t-statistics is compared with the actual precision of estimates relative to their true values, and the results are critically examined. It is shown that t-statistics for regression parameters can often be misleading. Two novel approaches for selecting the most effective variables are proposed: one based on the so-called reference matrix and the other on efficiency indicators. A combined use of these methods, together with the analysis of each variable’s contribution to determination, is recommended. The practical value of these approaches is confirmed through extensive testing on both simulated homogeneous and heterogeneous datasets, as well as on a real-world example. The results contribute to a more accurate understanding of regression properties, model quality characteristics, and effective strategies for identifying the most reliable predictors. They provide practitioners with better analytical tools. Full article
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11 pages, 517 KB  
Article
Understanding the Will Rogers Phenomenon in Cholangiocarcinoma Research and Beyond
by Ruslan Akhmedullin, Zhandos Burkitbayev, Tair Koishibayev, Zhanat Spatayev, Abylaikhan Sharmenov, Oxana Shatkovskaya, Dinara Zharlyganova, Almira Manatova, Zhuldyz Kuanysh, Sanzhar Shalekenov and Abduzhappar Gaipov
Cancers 2025, 17(19), 3263; https://doi.org/10.3390/cancers17193263 - 8 Oct 2025
Viewed by 246
Abstract
Background. The existing literature highlights a lack of comparative studies between subtypes of cholangiocarcinoma (CC) and the impact of misclassification on the epidemiological parameters. Methods. A retrospective study was conducted to evaluate the surgical outcomes. The authors used Poisson regression with modified errors [...] Read more.
Background. The existing literature highlights a lack of comparative studies between subtypes of cholangiocarcinoma (CC) and the impact of misclassification on the epidemiological parameters. Methods. A retrospective study was conducted to evaluate the surgical outcomes. The authors used Poisson regression with modified errors to calculate the risk ratios (RR) and reported post-estimation marginal effects. Coefficient estimates, variance inflation factors, and Pearson’s goodness-of-fit test statistics were used to check for multicollinearity and model fit, respectively. We also performed a reclassification analysis by modeling Klatskin tumors (PCC) as extrahepatic (ECC), reclassifying them as intrahepatic (ICC), and comparing the corresponding changes in estimates. Results. Regression analysis revealed an increased risk of death in patients with ICC (RR = 2.05, 95% CI 1.11–3.78) and PCC (RR = 2.03, 95% CI 0.97–4.24) compared to those with DCC. When PCC was analyzed as an ECC, the ICC revealed an RR of 1.52 (95% CI 0.84–2.73). Further reclassification of PCC showed an RR of 2.04 for ICC (95% CI: 1.53–3.53). The adjusted marginal effects saw a reduction in the death probability for both ICC and ECC. However, post hoc analyses revealed insufficient evidence for differences between the reclassified models. Conclusions. Patients with DCC had slightly better prognosis compared to ICC and PCC. We found no differences in survival between ICC and ECC (combining DCC and PCC). The decrease in mortality risk due to reclassification in both groups was not confirmed statistically. Future studies should focus on statistical evidence when referring to the Will Rogers phenomenon, instead of inferring from raw comparisons. Full article
(This article belongs to the Section Methods and Technologies Development)
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13 pages, 245 KB  
Article
Resilience as a Predictor of Indirect Trauma Among Korean Adolescents: A Cross-Sectional Correlational Study
by Suyon Baek
Healthcare 2025, 13(19), 2491; https://doi.org/10.3390/healthcare13192491 - 30 Sep 2025
Viewed by 278
Abstract
Background/Objectives: Adolescents aged 13–18 are exposed to traumatic content even without direct experience, owing to the increasing media coverage of disasters. Such indirect exposure can result in post-traumatic stress symptoms, including intrusion, avoidance, and hyperarousal, as well as associated emotions such as sadness, [...] Read more.
Background/Objectives: Adolescents aged 13–18 are exposed to traumatic content even without direct experience, owing to the increasing media coverage of disasters. Such indirect exposure can result in post-traumatic stress symptoms, including intrusion, avoidance, and hyperarousal, as well as associated emotions such as sadness, anger, and guilt. These effects may persist for months, reflecting the vulnerability of adolescents during cognitive and emotional development. This study examined resilience and social support as protective predictors against indirect trauma. Methods: A cross-sectional correlational design was employed, with middle- and high-school students aged 13–18 years in Seoul, South Korea, as participants. Indirect trauma, resilience, and perceived social support were assessed using validated self-report instruments. Correlation analyses were conducted, followed by stepwise regression. Owing to multicollinearity, resilience was retained as the sole predictor in the final model. Results: The average indirect trauma score was 1.20 out of 4, and 59.2% of participants exhibited partial or full post-traumatic stress disorder. The mean resilience and social support scores were 3.47 and 3.82 out of 5, respectively. Resilience was positively correlated with social support (r = 0.60, p = 0.001). The regression analysis indicated that resilience significantly predicted indirect trauma (β = 0.82, p < 0.001), accounting for 66.4% of the variance, whereas social support showed no direct effect. Conclusions: Resilience emerged as a key predictor of indirect trauma, underscoring its importance in mitigating distress. Although social support did not directly predict trauma, its positive correlation with resilience suggests potential indirect effects. These findings highlight the need to strengthen resilience and expand school-based counseling and support systems to help adolescents deal with indirect trauma. Full article
17 pages, 1752 KB  
Article
Methodological Study on Maize Water Stress Diagnosis Based on UAV Multispectral Data and Multi-Model Comparison
by Jiaxin Zhu, Sien Li, Wenyong Wu, Pinyuan Zhao, Xiang Ao and Haochong Chen
Agronomy 2025, 15(10), 2318; https://doi.org/10.3390/agronomy15102318 - 30 Sep 2025
Viewed by 261
Abstract
In response to water scarcity and low agricultural water-use efficiency in arid regions in Northwest China, this study conducted field experiments in Wuwei, Gansu Province, from 2023 to 2024. It aimed to develop a water stress diagnosis model for spring maize to provide [...] Read more.
In response to water scarcity and low agricultural water-use efficiency in arid regions in Northwest China, this study conducted field experiments in Wuwei, Gansu Province, from 2023 to 2024. It aimed to develop a water stress diagnosis model for spring maize to provide a scientific basis for precision irrigation and water management. In this work, two irrigation methods—plastic film-mulched drip irrigation (FD, where drip lines are laid on the soil surface and covered with film) and plastic film-mulched shallow-buried drip irrigation (MD, where drip lines are buried 3–7 cm below the surface under film)—were tested under five irrigation gradients. Multispectral UAV remote sensing data were collected from key growth stages (i.e., the jointing stage, the tasseling stage, and the grain filling stage). Then, vegetation indices were extracted, and the leaf water content (LWC) was retrieved. LWC inversion models were established using Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR). Different irrigation treatments significantly affected LWC in spring maize, with higher LWC under sufficient water supply. In the correlation analysis, plant height (hc) showed the strongest correlation with LWC under both MD and FD treatments, with R2 values of −0.87 and −0.82, respectively. Among the models tested, the RF model under the MD treatment achieved the highest prediction accuracy (training set: R2 = 0.98, RMSE = 0.01; test set: R2 = 0.88, RMSE = 0.02), which can be attributed to its ability to capture complex nonlinear relationships and reduce multicollinearity. This study can provide theoretical support and practical pathways for precision irrigation and integrated water–fertilizer regulation in smart agriculture, boasting significant potential for broader application of such models. Full article
(This article belongs to the Section Water Use and Irrigation)
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22 pages, 24147 KB  
Article
Assessment of Landslide Susceptibility and Risk in Tengchong City, Southwestern China Using Machine Learning and the Analytic Hierarchy Process
by Changwei Linghu, Zhipeng Qian, Weizhe Chen, Jiaren Li, Ke Yang, Shilin Zou, Langlang Yang, Yao Gao, Zhiping Zhu and Qiankai Gao
Land 2025, 14(10), 1966; https://doi.org/10.3390/land14101966 - 29 Sep 2025
Viewed by 440
Abstract
Southwestern China, characterized by highly undulating terrain and mountainous areas, faces frequent landslide disasters. However, previous studies in this region mostly neglected the role of extreme rainfall in landslide susceptibility assessment and the socio-economic risks threatened by landslides. To address these gaps, this [...] Read more.
Southwestern China, characterized by highly undulating terrain and mountainous areas, faces frequent landslide disasters. However, previous studies in this region mostly neglected the role of extreme rainfall in landslide susceptibility assessment and the socio-economic risks threatened by landslides. To address these gaps, this study integrated 688 recorded landslides for Tengchong City in the southwest of China and 10 influencing factors (topography, lithology, climate, vegetation, and human activities), particularly two extreme precipitation indices of maximum consecutive 5 day precipitation (Rx5day) and maximum length of wet spell (CWD). These influencing factors were selected after ensuring variable independence via multicollinearity analysis. Four machine learning models were then built for landslide susceptibility assessment. The Random Forest model performed the best with an Area Under Curve (AUC) of 0.88 and identified elevation, normalized difference vegetation index (NDVI), lithology, and CWD as the four most important influencing factors. Landslides in Tengchong are concentrated in areas with low NDVI (<0.57), indicating increased vegetation cover might reduce landslide frequency. Landslide risk was further quantified via the Analytic Hierarchy Process (AHP) by integrating multiple socio-economic factors. High-risk zones were pinpointed in central-southern Tengchong (e.g., Heshun and Tuantian townships) due to their high social exposure and vulnerability. Overall, this study highlights extreme rainfall and vegetation as key modifiers of landslide susceptibility and identifies the regions with high landslide risk, which provides targeted scientific support for regional early-warning systems and risk management. Full article
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14 pages, 273 KB  
Study Protocol
Protocol for a Prospective Cohort Study on Determinants of Outcomes in Lumbar Radiculopathy Surgery
by Alejandro Aceituno-Rodríguez, Carlos Bustamante, Carmen Rodríguez-Rivera, Miguel Molina-Álvarez, Carlos Rodríguez-Moro, Rafael García-Cañas, Carlos Goicoechea and Luis Matesanz-García
Healthcare 2025, 13(19), 2444; https://doi.org/10.3390/healthcare13192444 - 26 Sep 2025
Viewed by 362
Abstract
Introduction: Lumbar radiculopathies involving the entrapment of nerve roots in the lumbar spine are common neuropathic conditions. These conditions affect 40% to 70% of individuals in their lifetime and lead to significant medical costs. Objective: This study aims to identify clinical, psychological, [...] Read more.
Introduction: Lumbar radiculopathies involving the entrapment of nerve roots in the lumbar spine are common neuropathic conditions. These conditions affect 40% to 70% of individuals in their lifetime and lead to significant medical costs. Objective: This study aims to identify clinical, psychological, and biomarker-based prognostic factors that predict functional outcomes following surgery for lumbar radiculopathy. Materials and Methods: This prospective cohort study, conducted at Hospital Central de la Defensa Gómez Ulla, Madrid (Spain), adheres to the STROBE guidelines. The study includes patients aged 18–75 with lumbar radiculopathy, confirmed by clinical diagnosis, imaging, and electromyography (EMG) findings. Exclusion criteria include previous lumbar spine surgeries and systemic diseases. The primary outcome is the Oswestry Low Back Pain Disability Questionnaire. Sample size calculations, based on a conservative effect size (f2 = 0.20), determined the need for 172 participants, accounting for a 15% dropout rate and 80% power. Procedure: Patients will undergo an initial assessment, including EMG tests, sociodemographic and psychological questionnaires, blood sample tests, and physical questionnaires. This process will be repeated six months post-intervention, except for the blood sample test, expectations questionnaire, and EMG, which will be performed only once. Statistical Analyses: Data will be analyzed using Python 3.12.3, employing a multivariate linear regression analysis. Assumptions of linearity, independence, homoscedasticity, normality, and no multicollinearity will be validated. Corrective measures will be applied if assumptions are violated. Ethics and Dissemination: The study follows the Declaration of Helsinki guidelines and has been approved by the Ethics Committee of Universidad Rey Juan Carlos (070220241052024). Potential risks will be minimized, and adverse events will be recorded and addressed. Findings will be published in high-impact journals and presented at conferences. Full article
12 pages, 1252 KB  
Article
Potential Predictors of Mortality in Adults with Severe Traumatic Brain Injury
by Rachel Marta, Yaroslavska Svitlana, Kreniov Konstiantyn, Mamonowa Maryna, Dobrorodniy Andriy and Oliynyk Oleksandr
Brain Sci. 2025, 15(9), 1014; https://doi.org/10.3390/brainsci15091014 - 19 Sep 2025
Viewed by 465
Abstract
Background: Severe traumatic brain injury (sTBI) in adults remains a leading cause of mortality and disability worldwide. Early identification of reliable predictors of outcome is crucial for risk stratification and ICU management. Disturbances of hemostasis and metabolic factors such as body mass index [...] Read more.
Background: Severe traumatic brain injury (sTBI) in adults remains a leading cause of mortality and disability worldwide. Early identification of reliable predictors of outcome is crucial for risk stratification and ICU management. Disturbances of hemostasis and metabolic factors such as body mass index (BMI) have been proposed as potential prognostic markers, but evidence remains limited. Methods: We conducted a retrospective, multicenter study including 307 adult patients with sTBI (Glasgow Coma Scale ≤ 8) admitted to three tertiary intensive care units in Ukraine between September 2023 and July 2024. All patients underwent surgical evacuation of hematomas and decompressive craniotomy. Laboratory parameters (APTT, INR, fibrinogen, platelets, D-dimer) were collected within 12 h of admission. BMI was calculated from measured height and weight. Predictive modeling was performed using L1-regularized logistic regression and Random Forest algorithms. Class imbalance was addressed with SMOTE. Model performance was assessed by AUC, accuracy, calibration, and feature importance. Results: The 28-day all-cause mortality was 32.9%. Compared with survivors, non-survivors had significantly lower GCS scores and higher INR, D-dimer, and APTT values. Very high VIF values indicated severe multicollinearity between predictors. Classical logistic regression was not estimable due to perfect separation; therefore, regularized logistic regression and Random Forest were applied. Random Forest demonstrated higher performance (AUC 0.95, accuracy ≈ 90%) than logistic regression (AUC 0.77, accuracy 70.1%), although results must be interpreted cautiously given the small sample size and potential overfitting. Feature importance analysis identified increased BMI, prolonged APTT, and elevated D-dimer as leading predictors of mortality. Sensitivity analysis excluding BMI still yielded strong performance (AUC 0.91), confirming the prognostic value of coagulation markers and GCS. Conclusions: Mortality in adult sTBI patients was strongly associated with impaired hemostasis, obesity, and low neurological status at admission. Machine learning-based modeling demonstrated promising predictive accuracy but is exploratory in nature. Findings should be interpreted with caution due to retrospective design, severe multicollinearity, potential overfitting, and absence of external validation. Larger, prospective, multicenter studies are needed to confirm these results and improve early risk stratification in severe TBI. Full article
(This article belongs to the Section Neurosurgery and Neuroanatomy)
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38 pages, 2285 KB  
Article
Short-Term Forecasting of Unplanned Power Outages Using Machine Learning Algorithms: A Robust Feature Engineering Strategy Against Multicollinearity and Nonlinearity
by Khathutshelo Steven Sivhugwana and Edmore Ranganai
Energies 2025, 18(18), 4994; https://doi.org/10.3390/en18184994 - 19 Sep 2025
Viewed by 348
Abstract
Efficient power grid operations and effective business strategies require accurate prediction of power outages. However, predicting outages is a difficult task due to the large amount of heterogeneous, random, intermittent, and non-linear power grid data characterised by highly complex variable relationships. Attempting to [...] Read more.
Efficient power grid operations and effective business strategies require accurate prediction of power outages. However, predicting outages is a difficult task due to the large amount of heterogeneous, random, intermittent, and non-linear power grid data characterised by highly complex variable relationships. Attempting to simultaneously quantify these characteristics using a conventional single (linear or nonlinear) model may lead to inaccurate and costly results. To address this, we propose a hybrid RVM-WT-AdaBoostRT-RF framework using power grid data from the Electricity Supply Commission (Eskom) of South Africa. To achieve model interpretability, the least absolute shrinkage and selection operator (LASSO) is first applied to remedy the adverse effects of multicollinearity through regularisation and variable selection. Secondly, a random forest (RF) is used to select the top 10 most influential variables for each season for further analysis. A relevance vector machine (RVM) captures complex nonlinear relationships separately for each season, while the wavelet transform (WT) decomposes residuals generated from RVM into different frequency subseries (with reduced noise). These subseries are predicted with minimal bias using AdaBoost with regression and threshold (AdaBoostRT). Finally, we stack RVM, AdaBoostRT, RF, and residual individual predictions using RF as a meta-model to produce the final forecast with minimal error accumulation and efficiency. The comparative study, based on point forecast metrics, the Diebold-Mariano test, and prediction interval widths, shows that the proposed model outperforms vector autoregressive (VAR), RF, AdaBoostRT, RVM, and Naïve models. The study results can be utilised for optimising resource allocation, effective power grid management, and customer alerts. Full article
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12 pages, 901 KB  
Proceeding Paper
Multiple Linear Regression-Based Correlation Analysis of Various Critical Weather Factors and Solar Energy Generation in Smart Homes
by Purna Prakash Kasaraneni, Yellapragada Venkata Pavan Kumar and Gogulamudi Pradeep Reddy
Eng. Proc. 2025, 87(1), 106; https://doi.org/10.3390/engproc2025087106 - 11 Sep 2025
Viewed by 441
Abstract
The smart home concept, transforming traditional homes into smart homes thanks to technological advancements, is widespread around the world. In addition, energy consumers are also becoming energy producers by adding renewable energy sources, namely solar, wind, etc., to their homes along with traditional [...] Read more.
The smart home concept, transforming traditional homes into smart homes thanks to technological advancements, is widespread around the world. In addition, energy consumers are also becoming energy producers by adding renewable energy sources, namely solar, wind, etc., to their homes along with traditional energy sources. However, intermittent weather conditions impact the power generation of renewable sources. Hence, there is a need to understand the correlation between several weather parameters and power generation. Traditional statistical methods such as Pearson, and Spearman, Kendall’s Tau, and Phi correlation coefficients are available but are limited to only two variables. Instead, multiple linear regression (MLR) offers multivariate analysis. Thus, this paper employs MLR to analyze the correlation between weather conditions such as temperature, apparent temperature, visibility, humidity, pressure, wind speed, dew point, precipitation, and power generation in kW. All the weather conditions are independent variables, and the generated power is a dependent variable. The key objective is to investigate the significant predictors and their impact on power generation. To implement this, a recent smart home dataset titled “Smart Home Dataset with Weather Information” that provides the required information was downloaded from Kaggle. This dataset contains 32 variables and 503,910 observations. The whole dataset with the considered variables (1 dependent variable and 11 independent variables) is utilized to implement the proposed correlation analysis. A regression model is developed to find the correlation between the parameters mentioned above in the dataset, and the multicollinearity among the independent variables is presented using the variance inflation factor (VIF). If the VIF value is more than 10, it represents high multicollinearity. The results showcase that those variables, such as temperature, humidity, apparent_temperature, and dew_point, produce VIF values of 296.67, 37.35, 126.29, and 152.15, respectively, and are thereby considered critical weather parameters that significantly influence solar energy generation. This aids in better generation and load management planning in smart homes. Full article
(This article belongs to the Proceedings of The 5th International Electronic Conference on Applied Sciences)
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15 pages, 1655 KB  
Article
Quantitative Prediction of Sediment–Water Partition Coefficients for Tetracycline Antibiotics in a Typical Karst Wetland
by Cong Peng, Jianhong Liang, Xiaodong Pan, Jie Zeng, Kun Ren and Jianwen Cao
Water 2025, 17(18), 2670; https://doi.org/10.3390/w17182670 - 9 Sep 2025
Viewed by 573
Abstract
The soil–water partition coefficient (Kd) of antibiotics is a critical indicator for assessing their migration potential in the environment. Currently, research on antibiotic Kd values in specific geological settings such as karst wetlands remains relatively limited. This study uniquely integrates partial least squares [...] Read more.
The soil–water partition coefficient (Kd) of antibiotics is a critical indicator for assessing their migration potential in the environment. Currently, research on antibiotic Kd values in specific geological settings such as karst wetlands remains relatively limited. This study uniquely integrates partial least squares (PLS) regression with redundancy analysis (RDA), a hybrid approach that effectively handles complex environmental datasets prone to multicollinearity. The results identified Fe3+, NO3, and PO43− in water, as well as clay content, organic matter, bulk density, and pH in sediments, as key factors influencing Kd through redundancy analysis. Using PLS, predictive models were developed for the logKd of four antibiotics: tetracycline (TC), doxycycline (DOX), chlortetracycline (CTC), and demeclocycline (DMC). The models demonstrated strong predictability with Q2cum values of 0.96, 0.93, 0.99, and 0.83, respectively, indicating excellent model convergence. These findings provide important insights into how soil and water physicochemical properties influence the distribution of antibiotics, support the prediction of antibiotic transport and fate, and contribute to the exposure and risk assessment of these emerging contaminants in aquatic ecosystems. Full article
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14 pages, 652 KB  
Article
Diagnostic Yield of Fusion-Guided and Randomized Biopsies in Prostate Cancer: Evidence for an Integrated Approach
by Osama Salloum, Iulian-Alexandru Taciuc, Alexandru Dick, Costin Petcu, Costin Gingu, Nicoleta Sanda, Andreea Nicoleta Marinescu, Crenguta Serboiu and Adrian Costache
Healthcare 2025, 13(17), 2214; https://doi.org/10.3390/healthcare13172214 - 4 Sep 2025
Viewed by 547
Abstract
Background/Objectives: Improving prostate cancer (PCa) detection remains a key clinical goal. While multiparametric MRI (mp-MRI) fusion-guided biopsy has shown advantages over systematic randomized biopsy, variability persists across studies. This study aimed to compare detection rates between fusion-guided and randomized biopsy techniques and assess [...] Read more.
Background/Objectives: Improving prostate cancer (PCa) detection remains a key clinical goal. While multiparametric MRI (mp-MRI) fusion-guided biopsy has shown advantages over systematic randomized biopsy, variability persists across studies. This study aimed to compare detection rates between fusion-guided and randomized biopsy techniques and assess the combined predictive value of clinical risk factors. Methods: We retrospectively analyzed 138 male patients aged 50–82 years with PSA (prostate-specific antigen) < 25 ng/mL, undergoing both mp-MRI fusion-guided and systematic randomized biopsies. PI-RADS v2.1 was used for lesion assessment. The patient data included PSA, prostate volume, PI-RADS score, and age. Multicollinearity was evaluated, and a multivariate logistic regression model was developed. ROC analysis assessed predictive performance. Results: Fusion-guided biopsy detected cancer in 68.1% (95% CI: 60.3–75.9%) of cases, randomized biopsy in 76.1% (95% CI: 68.9–83.2%), and the combined approach in 88.4% (95% CI: 83.1–93.7%). McNemar’s test confirmed a significant improvement when combining both methods (p < 0.001). PSA exhibited the strongest individual predictive power (AUC = 0.782, 95% CI: ~0.70–0.86), followed by prostate volume (AUC = 0.631, 95% CI: ~0.53–0.73), PI-RADS score (AUC = 0.619, 95% CI: ~0.51–0.72), and age (AUC = 0.572, 95% CI: ~0.46–0.68). The multivariate model achieved an AUC of 0.751 (95% CI: ~0.66–0.83) and an accuracy of 89.6%. Conclusions: Combining fusion-guided and randomized biopsy techniques enhances prostate cancer detection compared with either method alone. PSA, prostate volume, PI-RADS score, and age contribute independently to risk prediction. Future studies will aim to refine stratification models and explore familial cancer risk factors. Full article
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25 pages, 12887 KB  
Article
Assessment of Soil Quality in Peruvian Andean Smallholdings: A Comparative Study of PCA and Expert Opinion Approaches
by Tomás Samaniego, Beatriz Sales and Richard Solórzano
Sustainability 2025, 17(17), 7610; https://doi.org/10.3390/su17177610 - 23 Aug 2025
Viewed by 1267
Abstract
Soil degradation poses a significant threat to the sustainability of agroecosystems, particularly in mountainous regions where environmental conditions are highly variable and management practices are often suboptimal. In this context, soil quality assessment emerges as a key tool for guiding sustainable land use [...] Read more.
Soil degradation poses a significant threat to the sustainability of agroecosystems, particularly in mountainous regions where environmental conditions are highly variable and management practices are often suboptimal. In this context, soil quality assessment emerges as a key tool for guiding sustainable land use and informing decision-making processes. This study aimed to develop and spatially evaluate a Soil Quality Index (SQI) tailored to the northeast sector of Jangas district, Ancash, Peru. A total of 24 soil indicators were initially considered and reduced using Spearman’s correlations to avoid multicollinearity. Depending on the weighting strategy applied, the final SQI configurations incorporated between 14 and 15 indicators. Two weighting strategies—Principal Component Analysis (PCA) and Expert Opinion (EO)—were combined with linear and non-linear (sigmoidal) scoring functions, resulting in four distinct SQI configurations. The spatial performance of each index was tested using Geographically Weighted Regression Kriging (GWRK), incorporating covariates like NDMI, elevation, slope, and aspect. The SQI constructed using PCA combined with non-linear scoring achieved the highest performance, effectively minimizing skewness and while achieving the highest predictive accuracy under GWRK. By contrast, although the EO-based index with linear scoring demonstrated similar statistical robustness, it failed to achieve comparable effectiveness in terms of spatial predictive accuracy. The SQIs generated offer a practical framework for local institutions to identify and prioritize areas requiring intervention. Through the interpretation of complex soil data into accessible, spatially explicit maps, these indices facilitate the targeted application of inputs—such as organic amendments in low-SQI zones—and support the implementation of improved management practices, including crop rotation and soil conservation, without necessitating advanced technical expertise. Full article
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33 pages, 5773 KB  
Article
Predicting Operating Speeds of Passenger Cars on Single-Carriageway Road Tangents
by Juraj Leonard Vertlberg, Marijan Jakovljević, Borna Abramović and Marko Ševrović
Infrastructures 2025, 10(8), 221; https://doi.org/10.3390/infrastructures10080221 - 20 Aug 2025
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Abstract
This research addresses the challenge of predicting operating vehicles’ speeds (V85) on single-carriageway road tangents. While most previous models rely on preceding segment speeds or focus on curves, this research develops an independent prediction model specifically for road tangents, based on empirical data [...] Read more.
This research addresses the challenge of predicting operating vehicles’ speeds (V85) on single-carriageway road tangents. While most previous models rely on preceding segment speeds or focus on curves, this research develops an independent prediction model specifically for road tangents, based on empirical data collected in Croatia. A total of 46 locations across 23 road cross-sections were analysed, with operating speeds measured using field radar surveys and fixed traffic counters. Following a comprehensive correlation and multicollinearity analysis of 24 geometric, environmental, and traffic-related variables, a multiple linear regression model was developed using a training dataset (36 locations) and validated on a separate test set (10 locations). The model includes nine statistically significant predictors: shoulder type (gravel), edge line quality (excellent and satisfactory), pavement quality (excellent), average summer daily traffic (ASDT), crash ratio, edge lane presence, overtaking allowed, and heavy goods vehicle share. The model demonstrated strong predictive performance (R2 = 0.89, RMSE = 5.24), with validation results showing an average absolute deviation of 2.43%. These results confirm the model’s reliability and practical applicability in road design and traffic safety assessments. Full article
(This article belongs to the Special Issue Sustainable Road Design and Traffic Management)
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