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Search Results (121)

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Keywords = akaike information criteria

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17 pages, 545 KiB  
Article
Concordance Index-Based Comparison of Inflammatory and Classical Prognostic Markers in Untreated Hepatocellular Carcinoma
by Natalia Afonso-Luis, Inés Monescillo-Martín, Joaquín Marchena-Gómez, Pau Plá-Sánchez, Francisco Cruz-Benavides and Carmen Rosa Hernández-Socorro
J. Clin. Med. 2025, 14(15), 5514; https://doi.org/10.3390/jcm14155514 - 5 Aug 2025
Abstract
Background/Objectives: Inflammation-based markers have emerged as potential prognostic tools in hepatocellular carcinoma (HCC), but comparative data with classical prognostic factors in untreated HCC are limited. This study aimed to evaluate and compare the prognostic performance of inflammatory and conventional markers using Harrell’s [...] Read more.
Background/Objectives: Inflammation-based markers have emerged as potential prognostic tools in hepatocellular carcinoma (HCC), but comparative data with classical prognostic factors in untreated HCC are limited. This study aimed to evaluate and compare the prognostic performance of inflammatory and conventional markers using Harrell’s concordance index (C-index). Methods: This retrospective study included 250 patients with untreated HCC. Prognostic variables included age, BCLC stage, Child–Pugh classification, Milan criteria, MELD score, AFP, albumin, Charlson comorbidity index, and the inflammation-based markers neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), Systemic Inflammation Response Index (SIRI), and Systemic Immune-inflammation Index (SIII). Survival was analyzed using Cox regression. Predictive performance was assessed using the C-index, Akaike Information Criterion (AIC), and likelihood ratio tests. Results: Among the classical markers, BCLC showed the highest predictive performance (C-index: 0.717), while NLR ranked highest among the inflammatory markers (C-index: 0.640), above the MELD score and Milan criteria. In multivariate analysis, NLR ≥ 2.3 remained an independent predictor of overall survival (HR: 1.787; 95% CI: 1.264–2.527; p < 0.001), along with BCLC stage, albumin, Charlson index, and Milan criteria. Including NLR in the model modestly improved the C-index (from 0.781 to 0.794) but significantly improved model fit (Δ–2LL = 10.75; p = 0.001; lower AIC). Conclusions: NLR is an accessible, cost-effective, and independent prognostic marker for overall survival in untreated HCC. It shows discriminative power comparable to or greater than most conventional predictors and may complement classical stratification tools for HCC. Full article
(This article belongs to the Section General Surgery)
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14 pages, 414 KiB  
Article
A New Statistical Modelling Approach to Explain Willingness-to-Try Seafood Byproducts Using Elicited Emotions
by Silvia Murillo, Ryan Ardoin, Bin Li and Witoon Prinyawiwatkul
Foods 2025, 14(15), 2676; https://doi.org/10.3390/foods14152676 - 30 Jul 2025
Viewed by 237
Abstract
Seafood processing byproducts (SB) such as bones and skin can be safely used as food ingredients to increase profitability for the seafood sector and provide nutritional value. An online survey of 716 US adult seafood consumers was conducted to explore SB trial intent, [...] Read more.
Seafood processing byproducts (SB) such as bones and skin can be safely used as food ingredients to increase profitability for the seafood sector and provide nutritional value. An online survey of 716 US adult seafood consumers was conducted to explore SB trial intent, responsiveness to health and safety information, and associated elicited emotions (nine-point Likert scale). Consumers’ SB-elicited emotions were defined as those changing in reported intensity (from a baseline condition) after the delivery of SB-related information (dependent t-tests). As criteria for practical significance, a raw mean difference of >0.2 units was used, and Cohen’s d values were used to classify effect sizes as small, medium, or large. Differences in willingness-to-try, responsiveness to safety and health information, and SB-elicited emotions were found based on self-reported gender and race, with males and Hispanics expressing more openness to SB consumption. SB-elicited emotions were then used to model consumers’ willingness-to-try foods containing SB via logistic regression modeling. Traditional stepwise variable selection was compared to variable selection using raw mean difference > 0.2 units and Cohen’s d > 0.50 constraints for SB-elicited emotions. Resulting models indicated that extrinsic information considered at the point of decision-making determined which emotions were relevant to the response. These new approaches yielded models with increased Akaike Information Criterion (AIC) values (lower values indicate better model fit) but could provide simpler and more practically meaningful models for understanding which emotions drive consumption decisions. Full article
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22 pages, 2323 KiB  
Article
Finite Mixture Model-Based Analysis of Yarn Quality Parameters
by Esra Karakaş, Melik Koyuncu and Mülayim Öngün Ükelge
Appl. Sci. 2025, 15(12), 6407; https://doi.org/10.3390/app15126407 - 6 Jun 2025
Viewed by 342
Abstract
This study investigates the applicability of finite mixture models (FMMs) for accurately modeling yarn quality parameters in 28/1 Ne ring-spun polyester/viscose yarns, focusing on both yarn imperfections and mechanical properties. The research addresses the need for advanced statistical modeling techniques to better capture [...] Read more.
This study investigates the applicability of finite mixture models (FMMs) for accurately modeling yarn quality parameters in 28/1 Ne ring-spun polyester/viscose yarns, focusing on both yarn imperfections and mechanical properties. The research addresses the need for advanced statistical modeling techniques to better capture the inherent heterogeneity in textile production data. To this end, the Poisson mixture model is employed to represent count-based defects, such as thin places, thick places, and neps, while the gamma mixture model is used to model continuous variables, such as tenacity and elongation. Model parameters are estimated using the expectation–maximization (EM) algorithm, and model selection is guided by the Akaike and Bayesian information criteria (AIC and BIC). The results reveal that thin places are optimally modeled using a two-component Poisson mixture distribution, whereas thick places and neps require three components to reflect their variability. Similarly, a two-component gamma mixture distribution best describes the distributions of tenacity and elongation. These findings highlight the robustness of FMMs in capturing complex distributional patterns in yarn data, demonstrating their potential in enhancing quality assessment and control processes in the textile industry. Full article
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23 pages, 2098 KiB  
Article
Modeling Time Series with SARIMAX and Skew-Normal and Zero-Inflated Skew-Normal Errors
by M. Alejandro Dinamarca, Fernando Rojas, Claudia Ibacache-Quiroga and Karoll González-Pizarro
Mathematics 2025, 13(11), 1892; https://doi.org/10.3390/math13111892 - 5 Jun 2025
Viewed by 650
Abstract
This study proposes an extension of Seasonal Autoregressive Integrated Moving Average models with exogenous regressors (SARIMAX) by incorporating skew-normal and zero-inflated skew-normal error structures to better accommodate asymmetry and excess zeros in time series data. The proposed framework demonstrates improved flexibility and robustness [...] Read more.
This study proposes an extension of Seasonal Autoregressive Integrated Moving Average models with exogenous regressors (SARIMAX) by incorporating skew-normal and zero-inflated skew-normal error structures to better accommodate asymmetry and excess zeros in time series data. The proposed framework demonstrates improved flexibility and robustness compared to traditional Gaussian-based models. Simulation experiments reveal that the skewness parameter significantly affect forecasting accuracy, with reductions in mean absolute error (MAE) and root mean square error (RMSE) observed across both positively and negatively skewed scenarios. Notably, in negative-skew contexts, the model achieved an MAE of 0.40 and RMSE of 0.49, outperforming its symmetric-error counterparts. The inclusion of zero-inflation probabilities further enhances model performance in sparse datasets, yielding superior values in goodness-of-fit criteria such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). To illustrate the practical value of the methodology, a real-world case study is presented involving the modeling of optical density (OD600) data from Escherichia coli during stationary-phase growth. A SARIMAX(1,1,1) model with skew-normal errors was fitted to 200 time-stamped absorbance measurements, revealing significant positive skewness in the residuals. Bootstrap-derived confidence intervals confirmed the significance of the estimated skewness parameter (α=14.033 with 95% CI [12.07, 15.99]). The model outperformed the classical ARIMA benchmark in capturing the asymmetry of the stochastic structure, underscoring its relevance for biological, environmental, and industrial applications in which non-Gaussian features are prevalent. Full article
(This article belongs to the Special Issue Applied Statistics in Management Sciences)
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29 pages, 510 KiB  
Article
Statistical Inference and Goodness-of-Fit Assessment Using the AAP-X Probability Framework with Symmetric and Asymmetric Properties: Applications to Medical and Reliability Data
by Aadil Ahmad Mir, A. A. Bhat, S. P. Ahmad, Badr S. Alnssyan, Abdelaziz Alsubie and Yashpal Singh Raghav
Symmetry 2025, 17(6), 863; https://doi.org/10.3390/sym17060863 - 1 Jun 2025
Viewed by 468
Abstract
Probability models are instrumental in a wide range of applications by being able to accurately model real-world data. Over time, numerous probability models have been developed and applied in practical scenarios. This study introduces the AAP-X family of distributions—a novel, flexible framework for [...] Read more.
Probability models are instrumental in a wide range of applications by being able to accurately model real-world data. Over time, numerous probability models have been developed and applied in practical scenarios. This study introduces the AAP-X family of distributions—a novel, flexible framework for continuous data analysis named after authors Aadil Ajaz and Parvaiz. The proposed family effectively accommodates both symmetric and asymmetric characteristics through its shape-controlling parameter, an essential feature for capturing diverse data patterns. A specific subclass of this family, termed the “AAP Exponential” (AAPEx) model is designed to address the inflexibility of classical exponential distributions by accommodating versatile hazard rate patterns, including increasing, decreasing and bathtub-shaped patterns. Several fundamental mathematical characteristics of the introduced family are derived. The model parameters are estimated using six frequentist estimation approaches, including maximum likelihood, Cramer–von Mises, maximum product of spacing, ordinary least squares, weighted least squares and Anderson–Darling estimation. Monte Carlo simulations demonstrate the finite-sample performance of these estimators, revealing that maximum likelihood estimation and maximum product of spacing estimation exhibit superior accuracy, with bias and mean squared error decreasing systematically as the sample sizes increases. The practical utility and symmetric–asymmetric adaptability of the AAPEx model are validated through five real-world applications, with special emphasis on cancer survival times, COVID-19 mortality rates and reliability data. The findings indicate that the AAPEx model outperforms established competitors based on goodness-of-fit metrics such as the Akaike Information Criteria (AIC), Schwartz Information Criteria (SIC), Akaike Information Criteria Corrected (AICC), Hannan–Quinn Information Criteria (HQIC), Anderson–Darling (A*) test statistic, Cramer–von Mises (W*) test statistic and the Kolmogorov–Smirnov (KS) test statistic and its associated p-value. These results highlight the relevance of symmetry in real-life data modeling and establish the AAPEx family as a powerful tool for analyzing complex data structures in public health, engineering and epidemiology. Full article
(This article belongs to the Section Mathematics)
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14 pages, 2334 KiB  
Article
Balance or Strength? Reconsidering Muscle Metrics in Sagittal Malalignment in Adult Sagittal Deformity Patients
by Donghua Huang, Zhan Wang, Mihir Dekhne, Atahan Durbas, Tejas Subramanian, Gabrielle Dykhouse, Robert N. Uzzo, Luis Felipe Colón, Stephane Owusu-Sarpong, Han Jo Kim and Francis Lovecchio
J. Clin. Med. 2025, 14(10), 3293; https://doi.org/10.3390/jcm14103293 - 9 May 2025
Viewed by 533
Abstract
Background/Objectives: Atrophy of the paraspinal and psoas major muscles is closely linked to sagittal malalignment in adult spinal deformity (ASD). However, most studies overlook the balance between these muscle groups. This study investigates the relationship between trunk muscle balance and sagittal alignment [...] Read more.
Background/Objectives: Atrophy of the paraspinal and psoas major muscles is closely linked to sagittal malalignment in adult spinal deformity (ASD). However, most studies overlook the balance between these muscle groups. This study investigates the relationship between trunk muscle balance and sagittal alignment in ASD patients. Methods: A single-institution database was reviewed for patients with sagittal malalignment (PT > 20° and PI–LL > 10°). Standard sagittal parameters were measured based on standing X-rays. The cross-section area (CSA) of trunk posterior muscles (CSAP: erector spinae and multifidus) and anterior muscles (CSAA: psoas) at L4 were measured based on a T2-weighted MRI. Patients with prior lateral fusions were excluded. Muscle balance was evaluated by the CSA ratio of trunk posterior to anterior muscles (CSAP/A). The relationship between sagittal alignment parameters and CSAP, CSAA, as well as CSAP/A were analyzed using linear and quadratic regressions. Akaike information criteria (AIC) compared model fit. Subgroup analyses examined the relationship between sagittal alignment changes and different CSAP/A levels. Results: A total of 112 patients met inclusion and exclusion criteria. CSAP correlated linearly with SS (r2 = 0.057, p = 0.011), PT (r2 = 0.043, p = 0.028), and T4–L1PA mismatch (r2 = 0.044, p = 0.027). CSAA showed no significant linear or quadratic relationships with sagittal spinal alignment parameters. In contrast, CSAP/A was quadratically associated with LL (r2 = 0.056, p = 0.044), SS (r2 = 0.134, p < 0.001), PI (r2 = 0.096, p = 0.004), L1PA (r2 = 0.114, p = 0.001), and T4–L1PA mismatch (r2 = 0.094, p = 0.005). Quadratic models of CSAP/A consistently had higher r2 and lower AIC values compared to the linear models of CSAP for most sagittal alignment parameters, especially in SS, PI, L1PA, and T4–L1PA mismatch (AIC difference ≥4). Higher CSAP/A is correlated to larger PI (and consequently, larger LL, SS, and L1PA). Conclusions: Trunk posterior–anterior muscle balance (CSAP/A) demonstrates a stronger relationship with sagittal alignment than individual muscle metrics. Quantitative MRI-based definitions of sarcopenia may need to be adjusted for PI. Full article
(This article belongs to the Special Issue Optimizing Outcomes in Scoliosis and Complex Spinal Surgery)
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19 pages, 4399 KiB  
Article
Spike Stall Precursor Detection in a Single-Stage Axial Compressor: A Data-Driven Dynamic Modeling Approach
by Anish Thapa, Jichao Li and Marco P. Schoen
Machines 2025, 13(4), 338; https://doi.org/10.3390/machines13040338 - 21 Apr 2025
Viewed by 428
Abstract
Operational safety and fuel efficiency are critical, yet often conflicting, objectives in modern civil get engine designs. Optimal efficiency operating conditions are typically close to unsafe regions, such as compressor stalls, which can cause severe engine damage. Consequently, engines are generally operated below [...] Read more.
Operational safety and fuel efficiency are critical, yet often conflicting, objectives in modern civil get engine designs. Optimal efficiency operating conditions are typically close to unsafe regions, such as compressor stalls, which can cause severe engine damage. Consequently, engines are generally operated below peak efficiency to maintain a sufficient stall margin. Reducing this margin through active control requires stall precursor detection and mitigation mechanisms. While several algorithms have shown promising results in predicting modal stalls, predicting spike stalls remains a challenge due to their rapid onset, leaving little time for corrective actions. This study addresses this gap by proposing a method to identify spike stall precursors based on the changing dynamics within a compressor blade passage. An autoregressive time series model is utilized to capture these dynamics and its changes are related to the flow condition within the blade passage. The autoregressive model is adaptively extracted from measured pressure data from a one-stage axial compressor test stand. The corresponding eigenvalues of the model are monitored by utilizing an outlier detection mechanism that uses pressure reading statistics. Outliers are proposed to be associated with spike stall precursors. The model order, which defines the number of relevant eigenvalues, is determined using three information criteria: the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the Conditional Model Estimator (CME). For prediction, an outlier detection algorithm based on the Generalized Extreme Studentized Deviate (GESD) Test is introduced. The proposed method is experimentally validated on a single-stage low-speed axial compressor. Results demonstrate consistent stall precursor detection, with future application for timely control interventions to prevent spike stall inception. Full article
(This article belongs to the Section Turbomachinery)
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33 pages, 1233 KiB  
Article
Volatility Modelling of the Johannesburg Stock Exchange All Share Index Using the Family GARCH Model
by Israel Maingo, Thakhani Ravele and Caston Sigauke
Forecasting 2025, 7(2), 16; https://doi.org/10.3390/forecast7020016 - 3 Apr 2025
Viewed by 2476
Abstract
In numerous domains of finance and economics, modelling and predicting stock market volatility is essential. Predicting stock market volatility is widely used in the management of portfolios, analysis of risk, and determination of option prices. This study is about volatility modelling of the [...] Read more.
In numerous domains of finance and economics, modelling and predicting stock market volatility is essential. Predicting stock market volatility is widely used in the management of portfolios, analysis of risk, and determination of option prices. This study is about volatility modelling of the daily Johannesburg Stock Exchange All Share Index (JSE ALSI) stock price data between 1 January 2014 and 29 December 2023. The modelling process incorporated daily log returns derived from the JSE ALSI. The following volatility models were presented for the period: sGARCH(1, 1) and fGARCH(1, 1). The models for volatility were fitted using five unique error distribution assumptions, including Student’s t, its skewed version, the generalized error and skewed generalized error distributions, and the generalized hyperbolic distribution. Based on information criteria such as Akaike, Bayesian, and Hannan–Quinn, the ARMA(0, 0)-fGARCH(1, 1) model with a skewed generalized error distribution emerged as the best fit. The chosen model revealed that the JSE ALSI prices are highly persistent with the leverage effect. JSE ALSI price volatility was notably influenced during the COVID-19 pandemic. The forecast over the next 10 days shows a rise in volatility. A comparative study was then carried out with the JSE Top 40 and the S&P500 indices. Comparison of the FTSE/JSE Top 40, S&P 500, and JSE ALLSI return indices over the COVID-19 pandemic indicated higher initial volatility in the FTSE/JSE Top 40 and S&P 500, with the JSE ALLSI following a similar trend later. The S&P 500 showed long-term reliability and high rolling returns in spite of short-run volatility, the FTSE/JSE Top 40 showed more pre-pandemic risk and volatility but reduced levels of rolling volatility after the pandemic, similar in magnitude for each index with low correlations among them. These results provide important insights for risk managers and investors navigating the South African equity market. Full article
(This article belongs to the Section Forecasting in Economics and Management)
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27 pages, 306 KiB  
Article
Development of Equations to Predict Percentage Empty Body and Carcass Chemical Composition Adjusted for Breed Type and Sex in Growing/Finishing Cattle
by Phillip A. Lancaster
Ruminants 2025, 5(2), 14; https://doi.org/10.3390/ruminants5020014 - 2 Apr 2025
Viewed by 391
Abstract
The estimation of body chemical composition is necessary to determine the nutrient requirements of growing/finishing cattle, but recent analyses indicate that published equations provide erroneous results when applied to diverse breed types and sexes. The objective of this analysis was to develop equations [...] Read more.
The estimation of body chemical composition is necessary to determine the nutrient requirements of growing/finishing cattle, but recent analyses indicate that published equations provide erroneous results when applied to diverse breed types and sexes. The objective of this analysis was to develop equations to estimate empty body and carcass chemical composition for different breed types and sexes. A dataset was developed from the published literature that contained 359 treatment means from 46 studies published between 1971 and 2021. Stepwise regression was used to develop prediction equations using Akaike’s Information Criteria to estimate empty body and carcass fat, protein, and ash concentrations (%). Empty body fat, protein, and ash could be predicted from combinations of empty body water, empty body fat, and empty body protein (RMSE = 1.53, 1.85, and 0.67; R2 = 0.99, 0.98, and 0.95). Breed type and sex affected the intercept and (or) slope coefficients to predict empty body fat, protein, and ash. Carcass fat, protein, and ash could be predicted from combinations of carcass water, carcass fat, and carcass protein (RMSE = 1.77, 1.62, and 0.82; R2 = 0.97, 0.98, and 0.93). Breed type and sex affected the intercept and (or) slope coefficients to predict protein and ash, but not fat. Equations adjusted for breed type and sex may be more robust than previously published equations based on a single breed or sex. Full article
15 pages, 2168 KiB  
Article
The Prediction of Intrapartum Fetal Compromise According to the Expected Fetal Weight
by José Morales-Roselló, Alicia Martínez-Varea, Blanca Novillo-Del Álamo, Carmen Sánchez-Arco and Asma Khalil
J. Pers. Med. 2025, 15(4), 140; https://doi.org/10.3390/jpm15040140 - 1 Apr 2025
Cited by 1 | Viewed by 495
Abstract
Objectives: To assess the predictive accuracy of the expected fetal weight in the third trimester (ExFW3t), based on the estimated fetal weight (EFW) at mid-trimester ultrasound scan, for the prediction of intrapartum fetal compromise (IFC) (an abnormal intrapartum fetal heart rate or intrapartum [...] Read more.
Objectives: To assess the predictive accuracy of the expected fetal weight in the third trimester (ExFW3t), based on the estimated fetal weight (EFW) at mid-trimester ultrasound scan, for the prediction of intrapartum fetal compromise (IFC) (an abnormal intrapartum fetal heart rate or intrapartum fetal scalp pH requiring urgent cesarean section). Methods: This retrospective study included 777 singleton pregnancies that underwent a 20-week study and a 3t scan. The extrapolated EFW at 20 weeks to the 3t or ExFW3t was considered a proxy of the potential growth. The percentage difference with the actual 3t EFW (%ExFW3t) was compared with other ultrasonographic and clinical parameters—EFW centile (EFWc), middle cerebral artery pulsatility index (MCA PI) in multiples of the median (MoM), umbilical artery (UA) PI MoM, cerebroplacental ratio (CPR) MoM, and maternal height—for the prediction of IFC by means of the area under the curve (AUC) and Akaike Information Criteria (AIC). Results: Pregnancies with IFC presented higher values of UA PI MoM (1.19 vs. 1.09, p = 0.0460) and lower values of population and Intergrowth EFWc (45.9 vs. 28.9, p < 0.0001, 48.4 vs. 33.6, p = 0.0004), MCA PI MoM (0.97 vs. 0.81, p < 0.0001), CPR MoM (1.01 vs. 0.79, p < 0.0001), %ExFW3t (89.9% vs. 97.5%, p = 0.0003), and maternal height (160.2 vs. 162.9, p = 0.0083). Univariable analysis selected maternal height, EFWc, %ExFW3t, and UA PI MoM as significant parameters. However, %ExFW3t did not surpass the prediction ability of cerebral Doppler. Finally, multivariable analysis showed that the best models for the prediction of IFC resulted from the combination of cerebral Doppler (MCA PI MoM or CPR MoM), fetal weight (%ExFW3t or EFWc), and maternal height (AUC 0.75/0.76, AIC 345, p < 0.0001). Conclusions: Fetal weight-related parameters, including %ExFW3t, a proxy of the proportion of potential growth achieved in the 3t, were less effective than fetal cerebral Doppler for the prediction of IFC. The best performance was achieved by combining hemodynamic, ponderal, and clinical data. Full article
(This article belongs to the Section Clinical Medicine, Cell, and Organism Physiology)
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19 pages, 4542 KiB  
Article
Forecasting Volatility of the Nordic Electricity Market an Application of the MSGARCH
by Muhammad Naeem, Hothefa Shaker Jassim, Kashif Saleem and Maham Fatima
Risks 2025, 13(3), 58; https://doi.org/10.3390/risks13030058 - 19 Mar 2025
Viewed by 743
Abstract
This paper studies the volatility of electricity spot prices in the Nordic market (Sweden, Finland, Denmark, and Norway) under regime switching. Utilizing Markov-switching GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, we provide strong evidence of nonlinear regime shifts in the volatility dynamics of these [...] Read more.
This paper studies the volatility of electricity spot prices in the Nordic market (Sweden, Finland, Denmark, and Norway) under regime switching. Utilizing Markov-switching GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, we provide strong evidence of nonlinear regime shifts in the volatility dynamics of these prices. Using in-sample criteria, we find that regime-switching models have lower AIC (Akaike information criterion) than single-regime GARCH models. In addition, out-of-sample forecasts indicate that regime-switching GARCH models have superior Value-at-Risk (VaR) prediction ability relative to single-regime models, which is directly pertinent to risk management. These findings highlight the importance of incorporating regime shifts into volatility models for accurately assessing and mitigating risks associated with electricity price fluctuations in deregulated markets. Full article
(This article belongs to the Special Issue Modern Statistical and Machine Learning Techniques for Financial Data)
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20 pages, 2739 KiB  
Article
Analysis of Molecular Aspects of Periodontitis as a Risk Factor for Neurodegenerative Diseases: A Single-Center 10-Year Retrospective Cohort Study
by Amr Sayed Ghanem, Marianna Móré and Attila Csaba Nagy
Int. J. Mol. Sci. 2025, 26(6), 2382; https://doi.org/10.3390/ijms26062382 - 7 Mar 2025
Cited by 2 | Viewed by 949
Abstract
Neurodegenerative diseases (NDDs) represent a considerable global health burden with no definitive treatments. Emerging evidence suggests that periodontitis may contribute to NDD through shared inflammatory, microbial, and genetic pathways. A retrospective cohort design was applied to analyze hospital records from 2012–2022 and to [...] Read more.
Neurodegenerative diseases (NDDs) represent a considerable global health burden with no definitive treatments. Emerging evidence suggests that periodontitis may contribute to NDD through shared inflammatory, microbial, and genetic pathways. A retrospective cohort design was applied to analyze hospital records from 2012–2022 and to determine whether periodontitis independently increases NDD risk when accounting for major cardiovascular, cerebrovascular, metabolic, and inflammatory confounders. Likelihood ratio-based Cox regression tests and Weibull survival models were applied to assess the association between periodontitis and NDD risk. Model selection was guided by Akaike and Bayesian information criteria, while Harrell’s C-index and receiver operating characteristic curves evaluated predictive performance. Periodontitis demonstrated an independent association with neurodegenerative disease risk (HR 1.43, 95% CI 1.02–1.99). Cerebral infarction conferred the highest hazard (HR 4.81, 95% CI 2.90–7.96), while pneumonia (HR 1.96, 95% CI 1.05–3.64) and gastroesophageal reflux disease (HR 2.82, 95% CI 1.77–4.51) also showed significant increases in risk. Older individuals with periodontitis are at heightened risk of neurodegenerative disease, an effect further intensified by cerebrovascular, cardiometabolic, and gastroesophageal conditions. Pneumonia also emerged as an independent pathophysiological factor that may accelerate disease onset or progression. Attention to oral and systemic factors through coordinated clinical management may mitigate the onset and severity of neurodegeneration. Full article
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19 pages, 5256 KiB  
Article
Comparison of Machine Learning Models for Real-Time Flow Forecasting in the Semi-Arid Bouregreg Basin
by Fatima Zehrae Elhallaoui Oueldkaddour, Fatima Wariaghli, Hassane Brirhet, Ahmed Yahyaoui and Hassane Jaziri
Limnol. Rev. 2025, 25(1), 6; https://doi.org/10.3390/limnolrev25010006 - 5 Mar 2025
Cited by 1 | Viewed by 736
Abstract
Morocco is geographically located between two distinct climatic zones: temperate in the north and tropical in the south. This situation is the reason for the temporal and spatial variability of the Moroccan climate. In recent years, the increasing scarcity of water resources, exacerbated [...] Read more.
Morocco is geographically located between two distinct climatic zones: temperate in the north and tropical in the south. This situation is the reason for the temporal and spatial variability of the Moroccan climate. In recent years, the increasing scarcity of water resources, exacerbated by climate change, has underscored the critical role of dams as essential water reservoirs. These dams serve multiple purposes, including flood management, hydropower generation, irrigation, and drinking water supply. Accurate estimation of reservoir flow rates is vital for effective water resource management, particularly in the context of climate variability. The prediction of monthly runoff time series is a key component of water resources planning and development projects. In this study, we employ Machine Learning (ML) techniques—specifically, Random Forest (RF), Support Vector Regression (SVR), and XGBoost—to predict monthly river flows in the Bouregreg basin, using data collected from the Sidi Mohamed Ben Abdellah (SMBA) Dam between 2010 and 2020. The primary objective of this paper is to comparatively evaluate the applicability of these three ML models for flow forecasting in the Bouregreg River. The models’ performance was assessed using three key criteria: the correlation coefficient (R2), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results demonstrate that the SVR model outperformed the RF and XGBoost models, achieving high accuracy in flow prediction. These findings are highly encouraging and highlight the potential of machine learning approaches for hydrological forecasting in semi-arid regions. Notably, the models used in this study are less data-intensive compared to traditional methods, addressing a significant challenge in hydrological modeling. This research opens new avenues for the application of ML techniques in water resource management and suggests that these methods could be generalized to other basins in Morocco, promoting efficient, effective, and integrated water resource management strategies. Full article
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11 pages, 1053 KiB  
Article
Dyslipidemia and Development of Chronic Kidney Disease in Non-Diabetic Japanese Adults: A 26-Year, Community-Based, Longitudinal Study
by Yukari Okawa and Toshiharu Mitsuhashi
Kidney Dial. 2024, 4(4), 246-256; https://doi.org/10.3390/kidneydial4040020 - 23 Dec 2024
Cited by 1 | Viewed by 1044
Abstract
Follow-up studies evaluating the relationship between dyslipidemia and chronic kidney disease (CKD) in non-diabetic populations are limited. This longitudinal study (1998–2024) examined whether the prevalence of dyslipidemia is associated with the subsequent development of CKD in non-diabetic Japanese adult citizens of Zentsuji, Kagawa [...] Read more.
Follow-up studies evaluating the relationship between dyslipidemia and chronic kidney disease (CKD) in non-diabetic populations are limited. This longitudinal study (1998–2024) examined whether the prevalence of dyslipidemia is associated with the subsequent development of CKD in non-diabetic Japanese adult citizens of Zentsuji, Kagawa Prefecture, Japan. Dyslipidemia was defined as low-density lipoprotein cholesterol concentrations ≥ 140 mg/dL, high-density lipoprotein cholesterol concentrations < 40 mg/dL, and/or triglyceride concentrations ≥ 150 mg/dL. Participants were considered to have developed CKD if their estimated glomerular filtration rate was <60 mL/min/1.73 m2. The proportional hazards assumption was violated. Therefore, the Weibull accelerated failure-time model was selected using the Akaike and Bayesian information criteria. The final cohort included 5970 participants, 41.6% of whom were men. The mean follow-up was 7.09 years. After the follow-up, 1890 (31.7%) participants developed CKD. Participants with dyslipidemia had a 5% shorter survival time (95% confidence interval: 3–7%) to incident CKD compared with those without dyslipidemia in the full model. High-density lipoprotein cholesterol concentrations < 40 mg/dL and triglyceride concentrations ≥ 150 mg/dL also reduced the survival time to CKD onset by 5–6%. Our results indicate that controlling the lipid profile to an appropriate range may contribute to reducing the risk of future onset of CKD. Full article
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15 pages, 1159 KiB  
Article
From Tension to Triumph: Design and Implementation of an Innovative Algorithmic Metric for Quantifying Individual Performance in Women Volleyball’s Critical Moments
by Carlos López-Serrano, María Zakynthinaki, Daniel Mon-López and Juan José Molina Martín
Appl. Sci. 2024, 14(24), 11906; https://doi.org/10.3390/app142411906 - 19 Dec 2024
Viewed by 1009
Abstract
This study introduces the critical individual contribution coefficient (CR-ICC), a novel metric that evaluates player effectiveness in critical moments of the game. We analyzed 16,631 technical actions from the top eight teams across 77 sets of the 2019 FIVB Women’s Club World Championship, [...] Read more.
This study introduces the critical individual contribution coefficient (CR-ICC), a novel metric that evaluates player effectiveness in critical moments of the game. We analyzed 16,631 technical actions from the top eight teams across 77 sets of the 2019 FIVB Women’s Club World Championship, ensuring data quality through inter- and intra-observer reliability. Traditional variables such as points scored, attack and reception efficiency, and balance were examined. Python programming was utilized to calculate the values of CR-ICC, which consider the contextual variables of set period, score difference, competitive load, and opponent’s level. Akaike’s and Bayesian information criteria, along with Nagelkerke’s coefficient of determination, were employed. Binomial logistic regression and receiver operating characteristic curves estimated the probability of victory associated with each variable. Interactive dashboards were developed, enabling dynamic analysis and data visualization. Statistically significant differences were observed in all variables (p < 0.05), except for reception efficiency (p < 0.05), at both the team and individual player levels. At the team level, points scored, attack efficiency, and balance exhibited the highest predictive abilities, with CR-ICC also demonstrating a strong predicting ability. The proposed CR-ICC has remarkable potential as a strategic asset for coaches, enabling the identification of players who excel in critical moments of the game. Full article
(This article belongs to the Special Issue Human Performance in Sports and Training)
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