Search Results (67)

Background/Objectives: Birth preparedness and complication readiness (BPCR) is a cornerstone of maternal health strategies designed to minimize the “three delays” in seeking, reaching, and receiving skilled care. In Ethiopia, uptake of BPCR remains insufficient, and little evidence exists on how individual- and community-level factors interact to shape preparedness. This study assessed the determinants of BPCR among women of reproductive age in Hawela Lida district, Sidama Region. Methods: A community-based cross-sectional study was conducted among 3540 women using a multistage sampling technique. Data were analyzed with multilevel mixed-effect negative binomial regression to account for clustering at the community level. Adjusted prevalence ratios (APRs) with 95% confidence intervals (CIs) were reported to identify determinants of BPCR. Model fitness was assessed using Akaike’s Information Criterion (AIC), the Bayesian Information Criterion (BIC), and log-likelihood statistics. Results: At the individual level, women employed in government positions had over three times higher expected BPCR scores compared with farmers (AIRR = 3.11; 95% CI: 1.89–5.77). Women with planned pregnancies demonstrated higher BPCR preparedness (AIRR = 1.66; 95% CI: 1.15–3.22), as did those who participated in model family training (AIRR = 2.53; 95% CI: 1.76–4.99) and women exercising decision-making autonomy (AIRR = 2.34; 95% CI: 1.97–5.93). At the community level, residing in urban areas (AIRR = 2.78; 95% CI: 1.81–4.77) and in communities with higher women’s literacy (AIRR = 4.92; 95% CI: 2.32–8.48) was associated with higher expected BPCR scores. These findings indicate that both personal empowerment and supportive community contexts play pivotal roles in enhancing maternal birth preparedness and readiness for potential complications. Random-effects analysis showed that 19.4% of the variance in BPCR was attributable to kebele-level clustering (ICC = 0.194). The final multilevel model demonstrated superior fit (AIC = 2915.15, BIC = 3003.33, log-likelihood = −1402.44). Conclusions: Both individual- and community-level factors strongly influence BPCR practice in southern Ethiopia. Interventions should prioritize women’s empowerment and pregnancy planning, scale-up of model family training, and address structural barriers such as rural access and community literacy gaps. Targeted, multilevel strategies are essential to accelerate progress toward improving maternal preparedness and reducing maternal morbidity and mortality. Full article

Melasma management is challenged by heterogeneity in patient presentation, particularly among individuals with darker skin tones. This study applied k-means clustering, an unsupervised machine learning algorithm that partitions data into k distinct clusters based on feature similarity, to identify patient subgroups that could provide a hypothesis-generating framework for future precision strategies. We analysed clinical and demographic data from 150 South African women with melasma using k-means clustering. The optimal number of clusters was determined using the Elbow Method and Bayesian Information Criterion (BIC), with t-distributed stochastic neighbour embedding (t-SNE) visualization for assessment. The k-Means algorithm identified seven exploratory patient clusters explaining 52.6% of the data variability (R² = 0.526), with model evaluation metrics including BIC = 951.630 indicating optimal model fit and a Silhouette Score of 0.200 suggesting limited separation between clusters consistent with overlapping clinical phenotypes, while the Calinski-Harabasz index of 26.422 confirmed relatively well-defined clusters that were characterized by distinct profiles including “The Moderately Sun Exposed Young Women”, “Elderly Women with Long-Term Melasma”, and “Younger Women with Severe Melasma”, with key differentiators being age distribution and menopausal status, melasma severity and duration patterns, sun exposure behaviours, and quality of life impact profiles that collectively define the unique clinical characteristics of each subgroup. This study demonstrates how machine learning can identify clinically relevant patient subgroups in melasma. Aligning interventions with the characteristics of specific clusters can potentially improve treatment efficacy. Full article

This work presents a practical approach to improve risk quantification for heavy-tailed insurance claims through model averaging and grid map visualization, addressing the drawbacks of traditional single “best” model selection commonly used in actuarial and model-fitting literature. This is a data-driven study with a focus on Danish fire loss data, where the following are fitted: (i) 16 standard single distributions, (ii) 256 composite distributions, and (iii) 256 mixture distributions; wherein, for the composite and mixture distributions, we focus on the top 20 leading models in terms of the information criterion (i.e., Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)). Model selection uncertainty is explicitly addressed by AIC and BIC weighted averaging within the Occam’s window (relying on weighted point estimates), while grid maps simultaneously plot information criteria against risk measures, specifically the Value-at-Risk (VaR) and Tail Value-at-Risk (TVaR) at 95% and 99% thresholds, to highlight critical-fit versus tail-risk trade-offs. It is observed that the model-averaged risk measures from composite models align more closely with the empirical values. That is, model-averaged estimates across all categories align closely with empirical VaR_0.95 but conservatively elevate TVaR_0.99, promoting safer capital reserves. Grid maps and model averaging confirm that mixture and composite models better capture the heavy-tailed nature of Danish fire claims data as compared to fitting a single distribution. Full article

This paper introduces a new hybrid model, HAR-RV-CARMA, which combines the Heterogeneous Autoregressive model for Realized Volatility (HAR-RV) with the Continuous Autoregressive Moving Average (CARMA) model. The key innovation of this study lies in the use of a Kalman filter-based dynamic state weighting mechanism to optimally combine the predictive capabilities of both models while mitigating overfitting. The proposed model is applied to five major Covered Call Exchange-Traded Funds (ETFs), QYLD, XYLD, RYLD, JEPI, and JEPQ, utilizing daily realized volatility data from 2019 to 2024. Model performance is evaluated against standalone HAR-RV and CARMA models using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Quasi-Likelihood (QLIKE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Additionally, the study assesses directional accuracy and conducts a Diebold-Mariano test to compare forecast performance against the standalone models statistically. Empirical results suggest that the HAR-RV-CARMA hybrid model significantly outperforms both HAR-RV and CARMA in volatility forecasting across all evaluation criteria. It achieves lower forecast errors, superior goodness-of-fit, and higher directional accuracy, with Diebold-Mariano test outcomes rejecting the null hypothesis of equal predictive ability at significant levels. These findings highlight the effectiveness of dynamic model weighting in improving predictive accuracy and offer a strong framework for volatility modeling in financial markets. Full article

To mitigate the impact of photovoltaic (PV) power generation uncertainty on power systems and accurately depict the PV output range, this paper proposes a quantile regression probabilistic prediction model (TCN-QRBiLSTM) integrating a Temporal Convolutional Network (TCN) and Bidirectional Long Short-Term Memory (BiLSTM). First, the historical dataset is divided into three weather scenarios (sunny, cloudy, and rainy) to generate training and test samples under the same weather conditions. Second, a TCN is used to extract local temporal features, and BiLSTM captures the bidirectional temporal dependencies between power and meteorological data. To address the non-differentiable issue of traditional interval prediction quantile loss functions, the Huber norm is introduced as an approximate replacement for the original loss function by constructing a differentiable improved Quantile Regression (QR) model to generate confidence intervals. Finally, Kernel Density Estimation (KDE) is integrated to output probability density prediction results. Taking a distributed PV power station in East China as the research object, using data from July to September 2022 (15 min resolution, 4128 samples), comparative verification with TCN-QRLSTM and QRBiLSTM models shows that under a 90% confidence level, the Prediction Interval Coverage Probability (PICP) of the proposed model under sunny/cloudy/rainy weather reaches 0.9901, 0.9553, 0.9674, respectively, which is 0.56–3.85% higher than that of comparative models; the Percentage Interval Normalized Average Width (PINAW) is 0.1432, 0.1364, 0.1246, respectively, which is 1.35–6.49% lower than that of comparative models; the comprehensive interval evaluation index (I) is the smallest; and the Bayesian Information Criterion (BIC) is the lowest under all three weather conditions. The results demonstrate that the model can effectively quantify and mitigate PV power generation uncertainty, verifying its reliability and superiority in short-term PV power probabilistic prediction, and it has practical significance for ensuring the safe and economical operation of power grids with high PV penetration. Full article

Background: Electrochemical impedance spectroscopy (EIS) is indispensable for disentangling charge-transfer, capacitive, and diffusive phenomena, yet reproducible parameter estimation and objective model selection remain unsettled. Methods: We derive closed-form impedances and analytical Jacobians for seven equivalent-circuit models (Randles, constant-phase element (CPE), and Warburg impedance (ZW) variants), enforce physical bounds, and fit synthetic spectra with 2.5% and 5.0% Gaussian noise using hybrid optimization (Differential Evolution (DE) → Levenberg–Marquardt (LM)). Uncertainty is quantified via non-parametric bootstrap; parsimony is assessed with root-mean-square error (RMSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC); physical consistency is checked by Kramers–Kronig (KK) diagnostics. Results: Solution resistance ($R_s$) and charge-transfer resistance ($R_{ct}$) are consistently identifiable across noise levels. CPE parameters $(Q,n)$ and diffusion amplitude $\left(\sigma \right)$ exhibit expected collinearity unless the frequency window excites both processes. Randles suffices for ideal interfaces; Randles+CPE lowers AIC when non-ideality and/or higher noise dominate; adding Warburg reproduces the ${45}^{\circ }$ tail and improves likelihood when diffusion is present. The $(R_{ct}+Z_W)\Vert \mathrm{CPE}$ architecture offers the best trade-off when heterogeneity and diffusion coexist. Conclusions: The framework unifies analytical derivations, hybrid optimization, and rigorous statistics to deliver traceable, reproducible EIS analysis and clear applicability domains, reducing subjective model choice. All code, data, and settings are released to enable exact reproduction. Full article

This paper proposes a hybrid predictive maintenance framework that combines the discriminative power of XGBoost with the interpretability of a Bayesian Network automatically learned from sensor data. Targeted at textile manufacturing equipment operating under Industry 4.0 conditions, the system addresses the trade-off between early fault detection and decision transparency. Sensor data, including vibration, temperature, and electric current, were collected from a multi-needle quilting machine using a custom IoT-based platform. A degradation-aware labeling scheme was implemented using historical maintenance logs to assign semantic labels to sensor readings. A Bayesian Network structure was learned from this data via a Hill Climbing algorithm optimized with the Bayesian Information Criterion, capturing interpretable causal dependencies. In parallel, an XGBoost model was trained to improve classification accuracy for incipient faults. Experimental results demonstrate that XGBoost achieved an F1-score of 0.967 on the high-degradation class, outperforming the Bayesian model in raw accuracy. However, the Bayesian Network provided transparent probabilistic reasoning and root cause explanation capabilities—essential for operator trust and human-in-the-loop diagnostics. The integration of both models yields a robust and interpretable solution for predictive maintenance, enabling early alerts, visual diagnostics, and scalable deployment. The proposed architecture is validated in a real production line and demonstrates the practical value of hybrid AI systems in bridging performance and interpretability for predictive maintenance in Industry 4.0 environments. Full article

Background/Objectives: To assess the impact of the 2023 FIGO staging revision on stage distribution, survival outcomes, and prognostic performance in endometrial cancer compared to the 2009 system. Methods: This retrospective cohort study analyzed 2969 patients with FIGO 2009 stage I–III endometrial cancer diagnosed at Samsung Medical Center (1994–2023). Patients were reclassified per the 2023 FIGO system. Stage migration, progression-free survival (PFS), and overall survival (OS) were evaluated. Prognostic performance was compared using the Akaike information criterion (AIC), Bayesian information criterion (BIC), concordance index (C-index), and area under the receiver operating characteristic curve (AUC). Results: Stage migration occurred in 20.2% of patients, with 98.3% involving upstaging from FIGO 2009 stage I, largely due to the inclusion of aggressive histology, p53 abnormality, and substantial lymphovascular space invasion (LVSI). The proportion of stage I tumors decreased from 81.5% to 65.2%, while stage II increased to 21.9%, including 14.8% newly classified as stage IIC. Patients remaining in stage I showed favorable outcomes (5-year PFS: 95.3%, OS: 98.5%), whereas those upstaged—especially to stage IIC—had significantly worse outcomes (5-year PFS: 76.5%, OS: 83.1%). Tumors with p53 abnormalities had poorer survival (PFS: 70.8%, OS: 76.6%). The 2023 FIGO system outperformed the 2009 system in prognostic discrimination across all metrics. Conclusions: The FIGO 2023 staging revision improves prognostic accuracy in endometrial cancer by integrating histopathologic and molecular risk factors. These refinements enhance risk stratification and may support more individualized treatment strategies. Full article

Hip dysplasia (HD) is an orthopaedic condition of the hip joints with a complex mode of inheritance that has proven difficult to address through traditional breeding practices in dogs, particularly the most common method, which is the selection by individual phenotype. Employing estimated breeding values (EBVs) into the selection would be a more effective method to reduce the prevalence of HD and would also enable the genetic trends to be monitored. The Golden Retriever is a popular large breed in Brazil, with a reported HD prevalence of up to 19.6%. This study aimed to estimate the breeding values (EBVs) of a sample of Golden Retrievers from Brazilian kennels using Bayesian analysis on a pedigree sample of 1686 dogs, 951 of them with known phenotypes. The posterior means of heritability estimated for hip score through the lowest deviance information criterion value model was 0.15 (posterior standard deviation of 0.08). The EBVs ranged from −0.299 to 0.368, with the average accuracy of 39% with values up to 67%. As expected by simple phenotypic selection, the genetic trend concerning hip scores has been stable since 1975. The study provides breeders with a valuable tool to make informed decisions about selecting sires and dams and contribute to long-term genetic improvement in reducing the prevalence of HD in Golden Retrievers. Full article

Bragg gratings are fundamental components in a wide range of sensing applications due to their high sensitivity and tunability. In this work, we present an augmented Bayesian approach for efficiently acquiring limited but highly informative training data for machine learning models in the design and simulation of Bragg grating sensors. Our method integrates a distance-based diversity criterion with Bayesian optimization to identify and prioritize the most informative design points. Specifically, when multiple candidates exhibit similar acquisition values, the algorithm selects the point that is farthest from the existing dataset to enhance diversity and coverage. We apply this strategy to the Bragg grating design space, where various analytical functions are fitted to the optical response. To assess the influence of output complexity on model performance, we compare different fit functions, including polynomial models of varying orders and Gaussian functions. Results demonstrate that emphasizing output diversity during the initial stages of data acquisition significantly improves performance, especially for complex optical responses. This approach offers a scalable and efficient framework for generating high-quality simulation data in data-scarce scenarios, with direct implications for the design and optimization of next-generation Bragg grating-based sensors. Full article

Growth is the most influential aspect in demographic species analysis. Collecting data on ages and sizes (such as length and weight) is a fundamental step in growth modeling, particularly in fishery science. Residual analysis plays a crucial role in parameterizing the mathematical models chosen to describe the growth patterns of the species under investigation. Using optimal residual criteria is essential to improving model performance and accuracy. In the present study, the length-at-age data of the Gulf corvina (Cynoscion othonopterus) were evaluated with the Schnute model to obtain the best error type and to establish the most accurate growth pattern. Later, the observed, constant, depensatory, and compensatory variance approaches were tested using the logistic model. The Bayesian information criterion (BIC) was used as the goodness-of-fit test to obtain the best variance approach parametrizing the growth model. The BIC values selected the observed variance as the best approach to parametrize the logistic growth model. The conclusion is that the observed variance approach produces robust results—that is, the observed variance produced the most plausible fits. It is suggested that the observed error structure should be used to estimate individual growth. Full article

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 ($\alpha =14.033$ 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

The research focuses on probabilistic modeling of maximum rainfall in Zielona Góra, Poland, to improve urban drainage system design. The study utilizes archived pluviographic data from 1951 to 2020, collected at the IMWM-NRI meteorological station. These data include 10 min rainfall records and aggregated hourly and daily totals. The study employs various statistical distributions, including Fréchet, gamma, generalized exponential (GED), Gumbel, log-normal, and Weibull, to model rainfall intensity–duration–frequency (IDF) relationships. After testing the goodness of fit using the Anderson–Darling test, Bayesian Information Criterion (BIC), and relative residual mean square Error (rRMSE), the GED distribution was found to best describe rainfall patterns. A key outcome is the development of a new rainfall model based on the GED distribution, allowing for the estimation of precipitation amounts for different durations and exceedance probabilities. However, the study highlights limitations, such as the need for more accurate local models and a standardized rainfall atlas for Poland. Full article

Does semantic communication require a semantic information theory parallel to Shannon’s information theory, or can Shannon’s work be generalized for semantic communication? This paper advocates for the latter and introduces a semantic generalization of Shannon’s information theory (G theory for short). The core idea is to replace the distortion constraint with the semantic constraint, achieved by utilizing a set of truth functions as a semantic channel. These truth functions enable the expressions of semantic distortion, semantic information measures, and semantic information loss. Notably, the maximum semantic information criterion is equivalent to the maximum likelihood criterion and similar to the Regularized Least Squares criterion. This paper shows G theory’s applications to daily and electronic semantic communication, machine learning, constraint control, Bayesian confirmation, portfolio theory, and information value. The improvements in machine learning methods involve multi-label learning and classification, maximum mutual information classification, mixture models, and solving latent variables. Furthermore, insights from statistical physics are discussed: Shannon information is similar to free energy; semantic information to free energy in local equilibrium systems; and information efficiency to the efficiency of free energy in performing work. The paper also proposes refining Friston’s minimum free energy principle into the maximum information efficiency principle. Lastly, it compares G theory with other semantic information theories and discusses its limitation in representing the semantics of complex data. Full article

This study explores the application of time series, machine learning (ML), and deep learning (DL) models to predict the prices and performance of covered call ETFs. Utilizing historical data from major covered call ETFs like QYLD, XYLD, JEPI, JEPQ, and RYLD, the research assesses the predictive accuracy and reliability of different forecasting approaches. It compares traditional time series methods, including ARIMA and Heterogeneous Autoregressive Model (HAR), with advanced ML techniques such as Random Forests (RF) and Support Vector Regression (SVR), as well as DL models like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). Model performance is evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Results indicate that the DL models are effective at identifying the nonlinear patterns and temporal dependencies in the price movements of covered call ETFs, outperforming both traditional time series and ML techniques. These findings enhance the existing financial forecasting literature and offer valuable insights for investors and portfolio managers aiming to improve their strategies using covered call ETFs. Full article