Search Results (142)

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

Structural adhesives in aeronautical applications are routinely exposed to complex loading histories that generate time-dependent deformation, making accurate prediction of their viscoelastic response essential for reliable assessment of joint integrity. This work presents an integrated experimental and modeling study of the aerospace-grade epoxy adhesive 3M Scotch-Weld EC-2216 using multi-step creep and stress-relaxation tests performed at room temperature and controlled loading rates, combined with fractional viscoelastic modeling. Unlike traditional single-step characterizations, the multi-step protocol employed here captures the cumulative loading effects and fading-memory dynamics that govern the adhesive’s mechanical response. The experimental data were analyzed using fractional Maxwell, Voigt–Kelvin, and Zener formulations. Statistical evaluation based on the Bayesian Information Criterion (BIC) consistently identified the Fractional Zener Model (FZM) as the most robust representation of the stress-relaxation behavior, effectively capturing both the unrelaxed and relaxed modulus. The results demonstrate that EC-2216 exhibits hierarchical relaxation mechanisms and history-dependent viscoelasticity that cannot be accurately described by classical integer-order models. Overall, the study validates the use of fractional operators to represent the broad and hierarchical relaxation spectra typical of toughened aerospace epoxies and provides a rigorous framework for durability assessment and predictive modeling of adhesively bonded structures. 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

Increasing consumption of plant-based alternatives is promoted to reduce the environmental impact of food systems, yet adoption remains limited. The aim of this study was to identify distinct consumer segments and examine differences in their perceptions, consumption habits, and trial intentions concerning plant-based dairy alternatives (PBDAs). Conceptually, it advances PBDAs segmentation by jointly incorporating pro-dairy justifications, avoidance of animal-origin considerations, and self-reported PBDAs familiarity, capturing psychological defence mechanisms alongside knowledge-related influences on adoption. Data were collected in a nationwide cross-sectional CAWI survey of 1220 Polish adults responsible for household food purchasing, stratified and quota-matched by gender, age, region, and settlement size. Factor analysis of the segmenting variables was conducted using principal component analysis with varimax rotation, followed by two-step cluster analysis. Alternative cluster solutions were compared using the Bayesian Information Criterion based on the log-likelihood (BIC-LL). The selected five-cluster solution showed acceptable to good clustering quality, as indicated by silhouette-based measures of cohesion and separation. Given the cross-sectional CAWI design and reliance on self-reported measures, the findings do not allow causal inference and should be interpreted as context-specific to the Polish, dairy-centric food culture. Cluster analysis identified five segments that differed in PBDA-related beliefs, product image evaluations, consumption patterns, and trial intentions. PBDA-oriented segments, comprising a dairy-critical segment and a dual-consumption segment, exhibited higher perceived familiarity and stronger ethical and environmental concerns and showed greater PBDA use and willingness to try new products. The dual-consumption segment reported the highest use and trial readiness. In contrast, resistant segments showed stronger dairy attachment, lower perceived familiarity, and more sceptical evaluations of PBDAs’ healthfulness, naturalness, and sensory appeal, and rarely consumed plant-based alternatives. The findings highlight substantial heterogeneity in how Polish dairy consumers perceive PBDAs, emphasising the importance of segment-specific approaches for communication and product development. Tailored strategies can help address the diverse motivations and barriers of consumers, supporting a dietary shift toward more plant-based options. Full article

Risk management is vital for financial institutions to evaluate and mitigate potential losses. Thunderstorm count modeling with risk analysis is used by various sectors, such as insurance and utility companies, to forecast storm recurrence, analyze risk, and estimate financial losses based on factors like wind speed, hail size, and tornado potential. This paper introduces a novel discrete distribution, the Beta-Gamma Discrete (BGD) distribution, designed for modeling count data that inherently excludes zero values. Developed through the compounding of a discrete gamma distribution with a beta distribution, the BGD offers significant flexibility in handling overdispersion and complex data characteristics. The study derives key statistical properties of the BGD, including its probability mass function, moments, hazard rate function, moment generating function, and mean residual life. A comprehensive characterization theorem is also established. The model’s practical utility is demonstrated through an application to thunderstorm event data from the Kennedy Space Center (KSC), where the frequency of thunderstorms per event is a critical operational concern. The performance of the BGD is thoroughly assessed against established zero-truncated models—namely, the Zero-Truncated Generalized Poisson (ZTGP), Size-Biased Negative Binomial (SBNB), and Zero-Truncated Generalized Negative Binomial (ZTGNB)—using evaluation criteria such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Chi-square goodness-of-fit, and the Vuong test. The results consistently show that the BGD provides a superior and more accurate fit for the thunderstorm data, thus help NASA and other space agencies for establishing it as a robust and effective tool for modeling positive count data in meteorological and other applied contexts with risk analysis. Full article

This study aimed to assess the impact and vulnerability of climate change by classifying 26 clusters of meteorologically homogeneous regions. To determine the optimal clustering method, both K-means and Gaussian Mixture Model (GMM) clustering were analyzed using the effective storage capacity to watershed area ratio. The optimal number of clusters was derived based on several evaluation metrics, including the Silhouette Score, Calinski-Harabasz Index, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Ultimately, GMM clustering was identified as the optimal method, with the best clustering results obtained at k = 4 for an effective storage capacity of 100,000 to 400,000 tons and k = 5 for an effective storage capacity of 400,000 to 10,000,000 tons. Additionally, standard reservoirs applicable to agricultural production infrastructure design standards were identified based on homogeneous weather region clusters, the optimal clustering method, and centroid results. The findings of this study can serve as fundamental data for the development and revision of design standards, contributing to more climate-resilient agricultural infrastructure. Full article

Hidden Markov Model (HMM) is a well-known probabilistic framework for representing sequential phenomena governed by doubly stochastic processes. Specifically, it features a Markov chain with hidden (unobserved) states, where each state emits observable values through a state-conditioned emission distribution at every time step. In this framework, selecting an appropriate emission distribution is essential because an unsuitable choice may prevent the HMM from accurately representing the observed phenomenon. To accommodate emission phenomena with situational symmetry, we propose an HMM framework with an adaptive emission distribution, named MSNBurr-HMM. This method is based on the MSNBurr distribution, which can effectively represent symmetric, right-skewed, and left-skewed emission patterns. We also provide its parameter estimation algorithm using the Baum–Welch algorithm. For model validation, we conduct fitting simulations across diverse scenarios and compare the findings against Gaussian-HMM and Fernández–Steel Skew Normal-HMM using log-likelihood, the Akaike Information Criterion (AIC), the corrected AIC (AICc), and the Bayesian Information Criterion (BIC). The results demonstrate that the algorithm can effectively estimate the target parameters accurately in all tested scenarios. In terms of performance, MSNBurr-HMM generally outperforms other models with strong dominance in various aspects across all evaluation metrics, confirming the promising and excellent results of this proposed method. 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

The difficulty in precisely extracting single-fault signatures from hydraulic pump composite faults, which stems from structural complexity and coupled multi-source vibrations, is tackled herein via a new diagnostic technique based on underdetermined blind source separation (UBSS). Utilizing sparse component analysis (SCA), the proposed method achieves blind source separation without relying on prior knowledge or multiple sensors. However, conventional SCA-based approaches are limited by their reliance on a predefined number of sources and their high sensitivity to noise. To overcome these limitations, an adaptive source number estimation strategy is proposed by integrating information–theoretic criteria into density peak clustering (DPC), enabling automatic source number determination with negligible additional computation. To facilitate this process, the short-time Fourier transform (STFT) is first employed to convert the vibration signals into the frequency domain. The resulting time–frequency points are then clustered using the integrated DPC–Bayesian Information Criterion (BIC) scheme, which jointly estimates both the number of sources and the mixing matrix. Finally, the original source signals are reconstructed through the minimum L1-norm optimization method. Simulation and experimental studies, including hydraulic pump composite fault experiments, verify that the proposed method can accurately separate mixed vibration signals and identify distinct fault components even under low signal-to-noise ratio (SNR) conditions. The results demonstrate the method’s superior separation accuracy, noise robustness, and adaptability compared with existing algorithms. 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

Background/Objectives: Poor sleep quality is a prevalent and burdensome concern among breast cancer survivors. However, its assessment relies heavily on the Pittsburgh Sleep Quality Index (PSQI), whose latent structure has shown inconsistent support across populations. This study aimed to examine the underlying factor structure and reliability of the PSQI among Korean breast cancer survivors using confirmatory factor analysis. Methods: A cross-sectional survey was conducted with 386 non-metastatic breast cancer survivors recruited from a university cancer center in South Korea. Ten competing one-, two-, and three-factor models were identified in previous studies and tested using confirmatory factor analysis with maximum likelihood estimation. Model fit was assessed with χ²/df, Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR), and model parsimony was compared using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Results: The mean global PSQI score was 7.46 (SD = 3.95), and 72.8% of participants were classified as poor sleepers. Among the tested model, a three-factor solution provided the best fit (χ²/df = 0.795, CFI ≈ 1.000, TLI ≈ 1.000, RMSEA ≈ 0.000, SRMR = 0.017) and achieved the lowest AIC and BIC values. This finding indicates the most favorable balance between fit and parsimony. This three-factor model delineates three distinct but related domains: perceived sleep quality, sleep efficiency, and daily disturbances. The global PSQI demonstrates acceptable reliability. Conclusions: These findings support the three-factor structure of the PSQI as the most valid representation of sleep quality among Korean breast cancer survivors. These results underscore the importance of population-specific validation of sleep measures and confirm the clinical utility of this measure as a multidimensional tool for assessing sleep in survivorship care. Full article

Accurate modelling of near surface wind speeds is essential for robust resource assessment, turbine design, and grid integration. This study presents a unified framework comparing four candidate marginal distributions—Weibull, Gamma, Lognormal, and Generalised Extreme Value (GEV)—across 21 years of daily observations from 11 sites in New South Wales and southern Queensland, Australia. Parameters are estimated by maximum likelihood, with L-moments used when numerical fitting fails. Univariate goodness-of-fit is evaluated via information criteria (Akaike Information Criterion, AIC; Bayesian Information Criterion, BIC) and distributional tests (Anderson–Darling, Cramér–von Mises, Kolmogorov–Smirnov). To capture spatial dependence, we fit an 11-dimensional regular vine (“R-vine”) copula to the probability-integral-transformed data, selecting pair-copula families by AIC and estimating parameters by sequential likelihood. A composite score (70% univariate, 30% copula) ranks distributions per location. Results demonstrate that Lognormal best matches central behaviour at most sites, Weibull remains competitive for bulk modelling, Gamma often excels in moderate tails, and GEV best represents extremes. All turbine yield results presented are illustrative, showing how statistical choices impact energy estimates; they should not be interpreted as operational forecasts. In a case study, 5000 joint simulations from the top-two models drive IEC V90 and E82 power curves, revealing up to 10% variability in annual energy yield due solely to marginal choice. This workflow provides a replicable template for comprehensive wind resource and load hazard analysis in complex terrains. Full article

This study evaluates the performance of count regression models in the presence of zero inflation, outliers, and overdispersion using both simulated and real-world maternal mortality dataset. Traditional Poisson and negative binomial regression models often struggle to account for the complexities introduced by excess zeros and outliers. To address these limitations, this study compares the performance of robust zero-inflated (RZI) and robust hurdle (RH) models against conventional models using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to determine the best-fitting model. Results indicate that the robust zero-inflated Poisson (RZIP) model performs best overall. The simulation study considers various scenarios, including different levels of zero inflation (50%, 70%, and 80%), outlier proportions (0%, 5%, 10%, and 15%), dispersion values (1, 3, and 5), and sample sizes (50, 200, and 500). Based on AIC comparisons, the robust zero-inflated Poisson (RZIP) and robust hurdle Poisson (RHP) models demonstrate superior performance when outliers are absent or limited to 5%, particularly when dispersion is low (5). However, as outlier levels and dispersion increase, the robust zero-inflated negative binomial (RZINB) and robust hurdle negative binomial (RHNB) models outperform robust zero-inflated Poisson (RZIP) and robust hurdle Poisson (RHP) across all levels of zero inflation and sample sizes considered in the study. Full article