Search Results (886)

Background: Polycystic ovary syndrome (PCOS) is characterized by substantial inter-individual variability in ovarian sensitivity to recombinant follicle-stimulating hormone (rFSH) during controlled ovarian stimulation (COS). Clinically applicable tools for personalized dosing in this population remain limited. Methods: This retrospective single-center study (2013–2024) analyzed 369 PCOS patients undergoing GnRH antagonist protocols who achieved optimal ovarian responses (10–20 oocytes with at least 40% of follicles ≥ 16 mm in diameter on trigger day). The final retrospective dataset was randomly split into modeling (n = 258) and validation (n = 111) groups. A multivariate linear regression model incorporating age, BMI, basal FSH, basal LH, AMH, and AFC was developed to estimate the average daily rFSH dose. Model performance was evaluated using correlation analysis, prediction error metrics, and calibration assessment. Results: Age, BMI, and basal FSH were positively associated with average daily rFSH dose, whereas basal LH, AMH, and AFC were negatively associated. The model explained 40.4% of the variability in average daily rFSH dose. In the modeling cohort, 77.9% of estimated doses fell within ±20% of the observed values, with a moderate correlation between predicted and observed doses (ρ = 0.646). In the validation cohort, 67.6% of estimates met the predefined accuracy threshold (ρ = 0.676). Calibration analyses demonstrated robust agreement between predicted and observed doses. Conclusions: By integrating endocrine markers, ovarian reserve indicators, and clinical characteristics, this study provides a practical example of personalized medicine in COS in women with PCOS. The internally validated approach may support individualized rFSH dosing during COS and serve as a basis for future development of decision support tools in this specific population. Full article

Objectives: To systematically evaluate the efficacy and safety of intraovarian platelet-rich plasma (PRP) administration in women with diminished ovarian reserve (DOR) and related conditions, given the growing clinical interest and the conflicting evidence from uncontrolled and controlled studies. Methods: This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Comprehensive searches were performed in PubMed, Web of Science, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), and Scopus up to January 2026. Eligible studies included randomized controlled trials (RCTs), prospective cohort studies, and before–after studies investigating PRP-based interventions in women diagnosed with DOR, premature ovarian insufficiency (POI), or poor ovarian response (POR). Given the limited availability of controlled data, these populations were analyzed together with cautious interpretation. Study quality was assessed using the Joanna Briggs Institute (JBI) checklists and the Critical Appraisal Skills Programme (CASP) tool for RCTs. Pooled estimates were calculated using random- or fixed-effects models depending on heterogeneity (I²). Results: Nineteen studies involving 1794 women were included, of which two were randomized controlled trials. In single-arm and before–after analyses, PRP administration was associated with increases in serum anti-Müllerian hormone (AMH) levels and antral follicle count (AFC), as well as a reduction in serum follicle-stimulating hormone (FSH). In addition, the number of metaphase II (MII) oocytes retrieved and transferable embryos increased following PRP treatment. However, pooled analyses of controlled studies, including RCTs, did not demonstrate consistent improvements in mature oocyte yield compared with control groups. In single-arm analyses, the pooled clinical pregnancy rate and live birth rate following PRP treatment were 15.5% (95% CI: 11.1–21.2%) and 10.7% (95% CI: 6.7–16.6%), respectively. No major procedure-related adverse events were reported across included studies. Conclusions: In conclusion, intraovarian PRP is associated with improvements in ovarian reserve markers such as AMH and AFC in uncontrolled studies. However, evidence from randomized controlled trials does not demonstrate a consistent benefit in pregnancy and live birth. Well-designed RCTs with standardized protocols are needed before clinical recommendation. Full article

Virtual power plants (VPPs) aggregate flexible resources, such as distributed photovoltaics (PV), energy storage, and flexible loads, to provide substantial reserve capacity for grid operation. However, the combined effects of renewable energy output uncertainty, load forecast errors, and user-bounded rationality responses lead to significant errors in traditional deterministic VPP reserve assessment methods, severely affecting the balance between system supply and demand. To address this challenge, this paper proposes a credible reserve assessment method that accounts for user-bounded rationality. First, thermodynamic models with on–off constraints for air conditioning loads, energy feasible region, and power constraint models for electric vehicles (EVs) and energy storage systems (ESSs), as well as PV forecast error models are established to characterize physical reserve boundaries. Second, prospect theory is introduced to describe user-bounded rationality and a logit-based response probability model is developed. Monte Carlo sampling and kernel density estimation are employed to derive credible reserve sets under different confidence levels, achieving a probabilistic quantification of VPP reserve capacity distribution. Case studies demonstrate that the proposed method accurately characterizes the probabilistic distribution characteristics of VPP reserve provision under multiple uncertainties, providing comprehensive and reliable assessment information for power dispatching agencies. Full article

Forests constitute a fundamental component of terrestrial carbon stocks and play a pivotal role in mitigating climate change through carbon sequestration. Accurate estimation of aboveground biomass (AGB) is essential for quantifying carbon budgets and informing ecosystem models. This study takes Wolong Nature Reserve in Sichuan Province, China, a mountainous area with high vegetation coverage and diverse forest types dominated by coniferous and mixed forests, as the study area, and constructs and evaluates AGB estimation models by integrating canopy height, leaf area index (LAI), vegetation indices (VIs), and topographic variables. Initially, univariate parametric models (linear, exponential, logarithmic, power, and polynomial) were established to relate canopy height to field-measured AGB. Subsequently, multivariate regression models incorporating VIs, LAI, and topographic metrics were developed. Finally, a decision tree-based machine learning framework was implemented to exploit the combined predictor set. Comparative analysis revealed that both canopy height-based and conventional multivariate regression models tended to overestimate AGB, limiting their applicability for large-scale assessments. In contrast, the optimized decision tree model, following parameter tuning and cross-validation, achieved superior predictive accuracy. Full article

Understanding the long-term production trends of MDFLAM panels, which are widely used in panel furniture manufacturing, is important for evaluating the sector’s competitiveness and environmental performance. In this study, MDF/HDF production data for Türkiye covering the period 1995–2024 were analyzed. The observations for 1995–2019 were used for model estimation, while the period 2020–2024 was reserved for out-of-sample validation. Production projections for 2025–2030 were generated using the ARIMA time series model. The relationships between fiberboard production and selected socio-economic variables (population, GDP per capita, forest area, and number of enterprises) were evaluated through correlation analysis. While strong correlations were observed in the level data, additional analysis using first-differenced (growth rate) series indicated that these relationships are weak and statistically insignificant in the short term, suggesting that the observed associations are largely influenced by common time trends. Assuming that approximately 60% of total fiberboard production consists of MDFLAM, future GWP values were estimated using verified EPD data. The results indicate that production is expected to continue increasing in the coming years. Although negative GWP values are observed due to biogenic carbon storage during the production stage, this reflects temporary carbon sequestration rather than a permanent reduction in atmospheric emissions. Emissions are expected to increase during end-of-life stages as the stored carbon is released. Overall, the study provides a forward-looking framework by integrating time-series forecasting with EPD-based environmental indicators, offering a useful basis for sustainability assessment and policy-oriented decision-making in the wood-based panel sector. Full article

Background: Oral squamous cell carcinoma (OSCC) remains a major global health burden due to delayed diagnosis. Although salivary biomarkers have been explored in previous meta-analyses, these studies were limited to specific biomarker types. Methods: This study followed PRISMA guidelines and was registered in PROSPERO (CRD 420261296936). PubMed, Scopus, MEDLINE, and CINAHL were searched for diagnostic accuracy studies of salivary biomarkers for OSCC. Studies providing sufficient data to construct 2 × 2 tables were included. Pooled sensitivity, specificity, DOR, and HSROC curves were estimated using a bivariate random-effects model, and study quality was assessed using QUADAS-2. Results: Eighteen studies comprising 1647 participants yielded 45 diagnostic datasets across multiple biomarker classes. The pooled sensitivity and specificity were 0.64 (95% CI: 0.59–0.69) and 0.71 (95% CI: 0.66–0.76), respectively. The pooled DOR was 4.53 (95% CI: 3.18–6.47), indicating moderate discriminatory ability, with an AUC of 0.75 (95% CI: 0.71–0.79). Fagan’s nomogram analysis demonstrated that these biomarkers are not suitable for screening the general population and should be reserved for enriched populations (pre-test probability > 10%). Conclusions: Salivary biomarkers demonstrate moderate but highly heterogeneous diagnostic accuracy. Clinical utility is context-dependent and limited to enriched populations with a baseline probability of OSCC >10%. In screening the general population (prevalence < 0.01%), these tests offer no significant clinical utility. They should be considered complementary triage tools rather than definitive diagnostic modalities. Full article

Fatigue failure constitutes an issue that cannot be ignored when designing welded steel structures due to the initiation of cracks at weld toes and defects under cyclic loading conditions. Traditional methods, such as the notch stress approach, estimate fatigue life by modeling local stress distributions using idealized weld geometries. While these methods are widely accepted in design codes, they can be limited by complexity and reduced accuracy in real-world applications. This study explores the use of artificial neural networks (ANNs) to enhance fatigue life prediction through data-driven modeling. The proposed method involves training an ANN using synthetic data generated through finite element simulations of S355 steel weldments under various loading histories, rates, and frequencies. The objective is to capture the influence of local geometric and stress features without relying solely on assumptions used in conventional approaches. The FEM-based training data incorporate both classical experimental findings and validated modeling practices. While performance evaluation of the ANN model is reserved for future work, this study lays the groundwork for replacing or supplementing the notch stress approach with a more adaptable and efficient predictive tool. The integration of machine learning into fatigue assessment has the potential to improve reliability, reduce computational burden, and support more informed maintenance and design decisions. Full article

Leopards (Panthera pardus) are considered solitary carnivores, but recent research reveals a more complex lifestyle that incorporates kin selection, hidden social structures, and behavioral innovation and plasticity. This paper combines theoretical advancements in kin selection with empirical findings from the peri-urban Jhalana Forest Reserve in Jaipur, India. Our research demonstrates that kin-tolerant spatial organization, maternal investment, temporal avoidance strategies, and adaptive responses to human-induced pressures form the foundation of leopard ecology in Jhalana. Female philopatry leads to the formation of matrilineal clusters, and maternal strategies play a crucial role in cub survival, with a cumulative two-year survival rate estimated at 61.8%. Emotional behaviors, such as grief-like responses to the loss of cubs, further challenge the notion that leopards are purely solitary animals. These findings have significant implications for evolutionary theory, conservation management, and human–wildlife coexistence. Jhalana serves as a model system where solitude and social behavior intersect, redefining our understanding of leopard ecology and guiding conservation efforts in human-dominated landscapes. Full article

Objectives: To assess the diagnostic accuracy of a deep learning (DL)-based algorithm for non-invasive computation of fractional flow reserve (FFR-CT) from coronary computed tomography angiography (CCTA) and to evaluate the model’s ability to automatically assign cardiovascular risk categories according to the Coronary Artery Disease–Reporting and Data System (CAD-RADS). Materials and Methods: Sixty patients with suspected coronary artery disease who underwent both CCTA and invasive coronary angiography (ICA) were retrospectively included in this multicenter study. Curved multiplanar reconstructions derived from CCTA were analyzed by the deep learning-based model to estimate FFR-CT values and to automatically assign CAD-RADS risk categories. The diagnostic performance of the software for the identification of hemodynamically significant coronary stenoses was evaluated using ICA as the reference standard. Receiver operating characteristic (ROC) curve analysis was performed to determine the area under the curve (AUC), sensitivity, and specificity on both a per-patient and per-vessel basis. Finally, agreement between CAD-RADS risk categories assigned by the DL algorithm and those determined by an expert radiologist was assessed. Results: FFR-CT demonstrated high diagnostic accuracy, with AUC of 0.935, sensitivity of 93.2%, specificity of 93.7%, and excellent agreement with reference standard (k = 0.836) on a per-patient level. Per-vessel diagnostic performance was consistently high across all major coronary arteries, with the left anterior descending artery (LAD) showing the highest accuracy (AUC = 0.932). Automated CAD-RADS classifications generated by the software showed good agreement with those assigned by human (k = 0.765). Conclusions: The DL-based model demonstrated high diagnostic accuracy and represents a promising noninvasive approach for ischemia assessment and cardiovascular risk stratification. Full article

This study examines the determinants of gold returns over the period 2000–2025, a period marked by recurrent financial crises, geopolitical tensions, and major shifts in global monetary conditions. As gold represents both a strategic commodity and a key reserve asset, understanding the channels driving its price dynamics is central to debates in commodity finance and macro-finance. Using Lasso variable selection combined with post-Lasso estimation, block bootstrap inference, and rolling and subsample analyses, the paper investigates the role of major macro-financial factors in shaping gold returns. The results indicate that U.S. Dollar Index (DXY) movements have strong incremental explanatory power for gold returns, consistent with a reduced-form dollar-channel interpretation. At the same time, the marginal contribution of inflation, volatility, and the tariff episode becomes limited once the DXY is included. Overall, the findings contribute to the commodity-finance literature by offering a parsimonious reduced-form interpretation of gold return dynamics and by highlighting implications for commodity price risk, hedging strategies, portfolio allocation, and reserve management in an increasingly interconnected global economy. Full article

Background: Concerns that human papillomavirus (HPV) vaccination may adversely affect ovarian reserve contribute to vaccine hesitancy, yet longitudinal data with paired anti-Müllerian hormone (AMH) measurements are limited. We evaluated whether HPV vaccination was associated with short-term changes in AMH compared with an unvaccinated control group. Methods: In this retrospective cohort, women aged 18–45 years who completed a three-dose 9-valent HPV vaccination (Gardasil 9®, Merck Sharp & Dohme LLC, West Point/Pennsylvania/USA) schedule and had AMH measured before dose 1 and after dose 3 were compared with unvaccinated controls who had two AMH measurements during routine gynecologic evaluation. AMH change was summarized as absolute change (ΔAMH), percent change, and log change. To compare rates of AMH change while accounting for heterogeneous follow-up and confounding, AMH was analyzed on the natural log scale using a linear mixed-effects model with a random intercept for participant and fixed effects for time (years), group, and a time × group interaction, adjusted for age, current smoking, gravidity, and parity. Annual percent change was derived from model coefficients. Prespecified sensitivity analyses repeated the primary model under follow-up restrictions and after restricting baseline AMH to 1.0–5.0 ng/mL. Results: The cohort included 158 vaccinated and 106 control women. Baseline AMH was similar between groups (median 1.88 vs. 1.94 ng/mL), while the follow-up interval was shorter in vaccinated women (6.7 vs. 8.9 months). Unadjusted AMH decline was smaller in vaccinated women (median ΔAMH −0.13 vs. −0.27 ng/mL; p = 0.015; median percent change −10.9% vs. −20.6%; p = 0.006). In the adjusted mixed-effects model, controls showed an estimated AMH decline of −27.6% per year (95% CI −35.5% to −18.7%; p < 0.001). The time × group interaction was positive (β = 0.170, 95% CI 0.027 to 0.312; p = 0.020), corresponding to a slope ratio of 1.185 (95% CI 1.02–1.366) and an implied annual change of −14.2% per year (95% CI −21.0% to −6.7%) in vaccinated women. Results were broadly consistent in follow-up-restricted sensitivity analyses; however, in the baseline AMH 1.0–5.0 ng/mL restricted cohort (vaccinated n = 82, control n = 67), the interaction was attenuated and not statistically significant (β = 0.082, p = 0.237). Conclusions: In this retrospective cohort with paired AMH measurements, HPV vaccination was not associated with evidence of clinically meaningful short-term impairment in ovarian reserve as assessed by AMH. Observed differences in AMH alterations were modest and should be interpreted cautiously, given residual confounding, measurement variability, and reduced precision in restricted-cohort analyses. Full article

Customer Lifetime Value (CLV) estimation over the observed transactional horizon is a fundamental challenge in hospitality analytics, supporting revenue management, personalization, and long-term customer relationship strategies. However, existing models predominantly rely on structured behavioral data while overlooking the emotional intelligence embedded in guest narratives. This study proposes an interpretable multimodal deep learning (DL) framework that bridges behavioral and emotional intelligence for CLV estimation by integrating structured booking records with unstructured hotel review text. Model interpretability is ensured through SHAP analysis for structured attributes, LIME for local textual explanations, and attention visualization for modality interaction analysis. Experimental evaluation on large-scale hospitality datasets demonstrates that the proposed multimodal framework outperforms traditional machine learning models, unimodal deep learning baselines, and classical ensemble learners, yielding consistent improvements across multiple error metrics and a notable increase in goodness of fit. The results confirm that emotional intelligence extracted from guest reviews significantly enhances CLV estimation and provides actionable insights for hospitality decision-making, supporting the deployment of transparent and explainable artificial intelligence (XAI) systems for strategic customer value management. Full article

Transect surveys and distance sampling are widely used to estimate wildlife population densities, but these methods can be biased when animals aggregate near features such as waterholes or other resources that occur along survey routes. Using empirical data from the NamibRand Nature Reserve in Namibia, we developed spatial simulations to examine how clumping of oryx (Oryx gazella) near water sources affects density and population estimates. We simulated surveys along a 50 km transect and varied the proportion of the population concentrated at waterholes (5–20%). Our analyses from the simulated surveys show that such aggregation can cause substantial positive bias, as population estimates were inflated by 67% to 967% relative to the known population size. We evaluated two correction approaches: censoring observations and transect segments near waterholes and redistributing animals from waterholes across the landscape. Both methods reduced bias when applied to our simulated survey data, but censoring was simpler and consistently produced more accurate estimates. These findings demonstrate that nonrandom animal distributions near linear survey features can severely compromise distance sampling assumptions. Accounting for such biases is essential for producing reliable population estimates, particularly in arid and semi-arid systems where wildlife strongly congregates around limited water sources. Full article

Optical and passive remote sensing-based estimation of aboveground biomass (AGB) using forest structural stratification has shown improvements over global models. This study investigated whether stratification by human-mediated disturbances improves prediction accuracy. Disturbance variables included proximity to mines, roads, and settlements, evaluated across three regimes: the full Atewa landscape (“FSR”), the Atewa Range Forest Reserve (“FR”), and the surrounding disturbed area (“SR”). Predictor selection for regimes was performed using recursive feature elimination with cross-validation, applied to random forest (RF) and support vector machine (SVM) algorithms. AGB was then estimated using local, global, and retuned global models, and the results were compared using the coefficient of determination (r²) and root mean square error (RMSE). The global RF model achieved the best performance (r² = 0.54; RMSE = 57.71 Mg/ha), likely due to structured heterogeneity captured across combined regimes. The “SR” models, however, performed poorly, indicating that excessive unstructured heterogeneity introduces noise and redundancy that weaken predictions. The low performance of the “FR” regime was attributed to spectral saturation and limited variance in observed AGB. Although disturbance factors added minimal bias, heteroscedasticity was evident in the “SR” and “FSR” regimes. Overall, this study indicates that disturbance-based stratification may not necessarily improve AGB estimation accurately compared to global models. However, it highlights the value of disturbance information for AGB modeling in heterogeneous forest landscapes. Full article

Shale pore volume is a critical parameter for reservoir evaluation. Accurate and rapid determination of this parameter is essential for identifying sweet spots and performing reliable reserve estimations. Currently, laboratory experiments remain the standard for determining pore volume; however, these methods are typically time-consuming, costly, and labor-intensive. To complement traditional experimental approaches, we developed a Gaussian Process Regression (GPR) model to estimate shale pore volume based on mineralogical compositions. The model is specifically tailored for the Longmaxi shale, utilizing six input features: the contents of Total Organic Carbon (TOC), clay, quartz, feldspar, carbonate, and pyrite. The GPR model achieved a mean absolute percentage error (MAPE) of 9.97% on the testing dataset, while it yielded an MAPE of 17.66% when applied to an additional independent validation set. Finally, a sensitivity analysis using the Shapley additive explanations was conducted to elucidate the influence of mineralogical constituents on shale pore volume. Full article