Search Results (780)

Background/Objectives: Neoadjuvant cisplatin-based chemotherapy (NAC) followed by radical cystectomy (RC) is a standard treatment for cisplatin-eligible patients with muscle-invasive bladder cancer (MIBC), yet baseline tools to refine prognostic stratification remain limited. We aimed to develop and internally validate a clinicopathological nomogram integrating the neutrophil-to-lymphocyte ratio (NLR) to estimate event-free survival (EFS) in patients with MIBC treated with NAC. Methods: We retrospectively analyzed 210 patients with cT2–T4aN0–1M0 MIBC treated with cisplatin-based NAC at two Spanish institutions between 2010 and 2021. Candidate predictors included demographic, clinicopathological, and routine laboratory variables. A multivariable Cox model with backward selection based on the Akaike information criterion (AIC) was used to derive the final model, and internal validation was performed using 1000 bootstrap resamples. Results: Sex, age, prior non–muscle-invasive bladder cancer (NMIBC), and NLR were retained in the final nomogram. The model showed moderate discrimination, with a Harrell’s c-index of 0.60 and an optimism-corrected c-index of 0.58. The nomogram stratified patients into low-, intermediate-, and high-risk groups, with median EFS not reached, 47.5 months, and 18.0 months, respectively. High-risk patients also showed lower pathological complete response (pCR) rates. Conclusions: This exploratory nomogram integrates an accessible systemic inflammatory marker with baseline clinical variables to identify patients with poorer outcomes despite NAC. External validation in contemporary cohorts is warranted before clinical implementation. Full article

Background/Objectives: Rehospitalization after traumatic spinal cord injury (SCI) is common, but binary or count summaries may obscure heterogeneity in timing, recurrence, frequency, and duration. We aimed to identify clinically interpretable rehospitalization burden profiles in the SCIMS 2021ARPublic dataset and examine descriptive associations with clinical correlates and participation outcomes. Methods: We analyzed Form I, Form II, and Record Status public-use files. Among 29,310 individuals with at least one non-lost follow-up interview, 28,745 with at least one non-missing rehospitalization indicator entered latent class analysis. Four prespecified indicators captured early, recurrent, frequent, and prolonged rehospitalization. Candidate two- through six-class models were compared using AIC, BIC, entropy, class size, posterior probabilities, and interpretability. Pairwise adjusted logistic models examined candidate clinical correlates in 10,407 participants with complete 2016+ follow-up data. Adjusted linear models examined CHART participation domains in 20,766–20,949 participants. Results: A four-profile solution was retained: low rehospitalization burden (59.8%), early/prolonged rehospitalization (18.9%), frequent/prolonged rehospitalization (7.7%), and high recurrent/frequent/prolonged burden (13.6%). UTI and pressure ulcer history showed the most consistent associations with burdened profiles. Severe pain and frequent sleep problems were associated with selected heavier-burden profiles, while depressive symptoms showed smaller and less precise associations. Sensitivity analyses supported structural stability while highlighting observation-time bias and classification uncertainty inherent to wave-based public-use data. Compared with the low-burden profile, burden profiles showed lower CHART scores, especially for mobility and occupation. Conclusions: Rehospitalization after traumatic SCI is heterogeneous. These utilization burden profiles summarize distinct observed patterns but require prospective validation before use in risk stratification or follow-up planning. Full article

For hydrological parameterization in high-Andean catchments, it is necessary to understand whether near-surface hydro-structural soil properties can provide a surrogate signal of particle-size composition when direct texture information is sparse. This study evaluated the extent to which sand, silt, and clay fractions can be approximated from organic matter ($\mathrm{OM}$), bulk density (${\rho }_b$), and saturated hydraulic conductivity ($K_{\mathrm{sat}}$) in the Zamora Huayco (ZH) and Irquis catchments, southern Ecuador. A harmonized dataset ($n=44$) was analyzed through exploratory statistics, compositional assessment, correlation analysis, PCA, fraction-wise regression, $\mathrm{ILR}$-based modeling, AIC/BIC term reduction, sensitivity analysis excluding $\mathrm{OM}$, nested $\mathrm{LOOCV}$, and bootstrap-based uncertainty intervals. Among LULC classes, samples classified as paramo occupied a distinct high-Andean hydro-edaphic domain, characterized by a differentiated relationship between soil physical properties and hydrological behavior. PCA showed that the dominant covariance structure involved $\mathrm{OM}$, ${\rho }_b$, $K_{\mathrm{sat}}$, and the redistribution between sand and silt. The BIC-reduced $\mathrm{ILR}$ model provided the most balanced formulation, with positive nested $\mathrm{LOOCV}$ performance for sand, silt, and clay ($R_{\mathrm{LOOCV}}^2=0.147$, $0.704$, and $0.124$, respectively) and exact $100\%$ compositional closure after inverse transformation. Silt was the most stable predicted fraction, whereas sand and clay retained larger residual uncertainty, stronger tail departures, and partial compression of the observed variability. The proposed equations provide local hydro-pedotransfer support, although their predictive signal remains dependent on further refinement, uncertainty assessment, and external validation before regional application. Full article

This study presents the growth data of indigenous Bargur cattle calves maintained at the Bargur Cattle Research Station, Tamil Nadu, India. Bargur cattle are an endangered breed known for their adaptability to hilly environments and production potential. The dataset included 1803 weight–age records collected from 174 calves, covering measurements from birth (age code 1) to approximately 16 months of age (age code 17). In the research station database, birth weight was recorded as age code 1, with subsequent age codes representing approximately monthly weight records. To describe the growth pattern, five non-linear models, Brody, Logistic, Von Bertalanffy, Gompertz, and Generalized Weibull, were fitted to the data. Key growth parameters, such as asymptotic weight, initial weight, and growth rate were estimated, along with indicators like age and weight at inflection. Because the available records covered growth from birth (age code 1) to approximately 16 months of age (age code 17), asymptotic weight estimates should be interpreted as model-derived projections rather than observed mature body weight. Among the models evaluated, the Von Bertalanffy model showed the best overall statistical fit based on AIC, BIC, and RMSE criteria, followed by the Gompertz model. The Logistic model, although not the best-fitting model statistically, retained biological interpretability in describing early growth patterns in calves. The dataset, along with graphical outputs of growth curves and residuals, provides useful insights into the early growth trajectory of Bargur cattle and may support conservation, management, and future breeding programs. Full article

Objectives: An integrated methodological framework was developed for modeling and multiobjective optimization of sustained-release drug delivery systems. Methods: The cumulative release percentage was fitted as a function curve, and functional principal component analysis was subsequently used to transform the function curves into functional principal component scores (FPCs). FPCs were then treated as dependent variables, while the proportions of the formulation factors were used as independent variables to construct Scheffé polynomial regression models. Finally, Non-dominated Sorting Genetic Algorithm III (NSGA-III) was applied to perform multi-objective optimization. Results: FPC1, FPC2, and FPC3 captured 95.18%, 4.39%, and 0.32% of the total variation, respectively. Corresponding Scheffé polynomial regression models were established, including quadratic models for FPC1 (adjusted R² = 0.751, AIC = 168.557) and FPC2 (adjusted R² = 0.592, AIC = 119.302), and a special cubic model for FPC3 (adjusted R² = 0.597, AIC = 64.574). The NSGA-III algorithm generated a Pareto optimal set, yielding stable formulation compositions with mean (SD) values of X₁ = 0.123 (0.015), X₂ = 0.821 (0.032), X₃ = 0.012 (0.017), and X₄ = 0.045 (0.015). The corresponding FPCs were −41.787 (2.544), 10.009 (0.168), and 8.264 (0.010) for FPCs1–FPCs3, respectively. The reconstructed cumulative release percentages were 42.471 (1.661), 52.623 (2.868), 69.942 (1.200), 84.275 (1.010), and 93.330 (0.832), demonstrating good agreement with the target release profiles. Conclusions: The integrated FDA–Scheffé–NSGA-III framework provides a robust and effective approach for accurately modeling release behavior and optimizing sustained-release formulations. Full article

Background and Objectives: Patients undergoing incisional hernia repair constitute a clinically heterogeneous population with variable postoperative outcomes. Conventional risk models based on isolated risk factors may inadequately capture this complexity. This study aimed to identify data-driven clinical phenotypes and evaluate their association with surgical outcomes. Methods and Materials: A retrospective cohort of 1262 patients undergoing retromuscular incisional hernia repair (Rives–Stoppa technique) was analyzed. Unsupervised clinical phenotyping was performed using latent class analysis based on seven preoperative variables. Model selection was guided by Akaike information criterion (AIC), Bayesian information criterion (BIC), entropy, and clinical interpretability. Postoperative outcomes were compared across phenotypes. Results: Three distinct phenotypes were identified: metabolic (34.6%), structural (33.9%), and frailty (31.5%). The structural phenotype showed the highest complication (22.7%) and recurrence rates (8.6%), while the frailty phenotype had the lowest complication burden (14.6%). The metabolic phenotype was characterized by obesity and diabetes, consistent with increased wound-related morbidity. Cluster robustness was supported by internal validation metrics and sensitivity analyses. Conclusions: In this retrospective single-center cohort, distinct clinical phenotypes with different outcome profiles were identified among patients undergoing incisional hernia repair, supporting the concept that this population comprises clinically heterogeneous subgroups with distinct patterns of vulnerability. These findings should be considered preliminary and hypothesis-generating. Further external validation and prospective studies are required to determine the clinical utility of phenotype-based risk stratification. Full article

Accurate arrival picking of acoustic emission (AE) data is essential for AE event localization and hydraulic fracture characterization in true triaxial hydraulic fracturing experiments. However, conventional arrival picking methods are highly sensitive to manually defined thresholds, whereas existing deep learning models are constrained by low signal-to-noise ratios (SNRs) and limited AE dataset sizes. To address these challenges, this study proposes an attention-based deep learning method for AE arrival picking. The proposed method introduces an attention mechanism into the PhaseNet framework to suppress noise feature transmission in the skip connections. In addition, a kernel density estimation (KDE)-based label smoothing strategy was adopted to alleviate label imbalance and account for arrival-time uncertainty. The results demonstrate that the proposed method reduced the mean absolute error (MAE) by 10.58%, 92.92%, and 98.25% compared with PhaseNet, STA/LTA, and AR-AIC, respectively. The proposed method exhibited superior picking accuracy, robustness, and computational efficiency relative to the other methods, providing a reliable foundation for AE event localization and high-precision AE monitoring in hydraulic fracturing experiments. Full article

Landslides constitute one of the most lethal and costly hydrometeorological hazards at the global scale. There is a growing trend associated with the increase in extreme precipitation and the expansion of urban development on unstable slopes. In the tropical Andes, this problem is intensified under climate change scenarios. The objective of this study is to develop a logistic regression model to analyze socioeconomic risk due to landslides in the Bogotá Savannah (Colombia). An integrated risk model was developed using binary logistic regression and a socioeconomic vulnerability index. A total of 12 physical–biotic variables and SSP climate projections (2021–2040) were used. A GIS-based environment was implemented to generate prospective spatial risk scenarios. The model demonstrated high robustness and predictive capability, with an improvement in statistical goodness-of-fit of 8.2% (AIC: 2574–2367), adequate probabilistic calibration (Pseudo-R²: 0.675; Brier Score: 0.084), and excellent predictive performance (AUC: 0.935; sensitivity: 84.7%; specificity: 90.0%). Simulations estimated maximum risk probabilities close to 0.600 (scale between 0 and 1), concentrated in geomorphologically critical sectors. Simulations under SSP scenarios showed a progressive increase in risk toward 2040 (up to 0.673), associated with precipitation increases between 10 and 30%. Integrated modeling constitutes a reliable technical tool for land-use planning, climate adaptation, and prospective landslide risk management in urbanized Andean regions. Full article

Small scymnine coccinellids, including many Scymnus species, are common aphid predators, but stage-specific feeding data are still limited for many species. We investigated feeding behavior, first-instar prey sharing, stage-specific consumption, and functional responses of Scymnus (Scymnus) folchinii (Canepari) on third-instar Myzus persicae nymphs on chili pepper seedlings. Larvae showed feeding behavior consistent with extra-oral digestion, whereas adults consumed aphids by direct mastication. In the single-prey first-instar assay, prey sharing occurred in all 15 arenas; the period during which two or more larvae fed on the same aphid lasted a median of 31.0 min and accounted for 84.8% of the observed feeding period. Aphid consumption varied with predator stage, exposure time, and initial aphid density. At the highest tested density of 16 aphids, third- and fourth-instar larvae and adults left few aphids alive after 24 h in the seedling micro-arena. Logistic regression diagnosed Type II responses only for adults at 1 h, whereas AIC-based model comparison selected Rogers’ Type II in 15 of 18 stage-by-duration combinations. Short exposures indicated higher attack rates and shorter handling times in fourth instars and adults, whereas longer exposures were affected by prey depletion and satiation. These results show clear stage dependence in aphid consumption by S. folchinii and justify further testing on larger plants and under field conditions. Full article

Understanding the structural characteristics of Pinus taiwanensis plantations in climatically transitional regions is essential for developing science-based management strategies under global change. This study investigated 23 plots in Huangbai Mountain Forest Farm, Henan Province, China, classified into low-, medium-, and high-density stands (n = 9, 9, and 5, respectively). Diameter distributions were fitted using six probability functions, and four spatial structure parameters—mixing degree (Mc), size ratio (U), uniform angle index (W), and forest layer index (S)—were quantified. In addition, five comprehensive spatial structure indices—average superiority coefficient index (SPV), spatial structure comprehensive index (Q), stand spatial structure distance index (FSI), Comprehensive Distance Evaluation (CDEV), and Comprehensive Assessment of Proximity Vector (CAPV)—were constructed using a combined analytic hierarchy process and entropy weight method. Given the unbalanced sample sizes, non-parametric Kruskal–Wallis tests were employed for comparisons, and bootstrap resampling (1000 iterations) was performed to assess the reliability of mean estimates. The results showed that both the Gamma and Weibull distributions were equally suitable for describing diameter distribution under different stand densities, as their AIC differences were below 2 for all density classes. Correlation analysis indicated that the relative importance of spatial parameters followed the order S > U > Mc > W. Medium-density stands exhibited the most optimal spatial structure, whereas low-density stands showed the poorest performance. These findings suggest that both overly dense and sparse stands negatively affect spatial organization. Appropriate management practices, such as thinning or enrichment planting, are recommended to optimize stand structure and enhance ecological resilience. Full article

The COVID-19 pandemic exposed critical limitations in conventional screening protocols, particularly in high-traffic environments where rapid, accurate, and contactless health assessment became essential to mitigate transmission risks. In response, this study presents the development of an Artificial Intelligence-Enhanced Contactless Screening Kiosk (AICS-K) that integrates multimodal sensing, embedded systems engineering, and machine learning into a unified workflow. Utilizing a Raspberry Pi platform with computer vision, thermal sensing, QR-based contact tracing, and intelligent control logic, the system enables efficient real-time screening while minimizing human intervention. The proposed architecture demonstrates the potential of extensible, affordable AI-driven solutions for early signs detection and institutional health resilience. Full article

Background: Dengue fever has emerged as a major public health concern in Bangladesh, with increasing incidence and geographic spread of outbreaks in recent years. This study aimed to investigate the lagged and non-linear associations between climatic factors and dengue incidence across all eight administrative divisions of Bangladesh from 2014 to 2025. Materials and Methods: An ecological time-series design was employed using monthly dengue case data (n = 741,338) and meteorological variables. A generalized additive model (GAM) with a negative binomial distribution was applied to account for overdispersion and capture complex relationships. Descriptive analysis was conducted to assess spatial heterogeneity, and choropleth maps were constructed to visualize the spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis was performed to identify significant lagged associations between climatic variables and dengue incidence. Results: Descriptive analysis showed substantial spatial heterogeneity, with the highest incidence observed in Dhaka (6.53 per 100,000) and the lowest in Sylhet (0.21 per 100,000). Choropleth maps illustrated distinct spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis identified significant lagged associations for temperature and rainfall (lag 1–3 months), humidity (lag 1–2 months), and wind speed (lag 2–3 months). The final GAM explained 88.6% of the deviance in dengue incidence (AIC = 7404.15; dispersion = 0.767). The approximate significance of smooth terms revealed that temperature at a lag of 1 month (p < 0.001, edf = 12.28), rainfall at a lag of 3 months (p < 0.001, edf = 2.85), and wind speed at a lag of 2 months (p < 0.001, edf = 2.25) were highly significant non-linear predictors of dengue transmission. Relative humidity was not significantly associated with dengue incidence. Non-linear effects revealed peak dengue risk at temperatures between 25 and 30 °C and moderate rainfall (~10 mm), particularly during monsoon months (June–October). A strong autoregressive effect indicated that prior dengue incidence significantly influenced current transmission. Conclusions: Overall, dengue transmission in Bangladesh is driven by complex, lagged, and non-linear interactions between climatic variables, seasonality, and regional factors. These findings provide critical evidence for climate-based early warning systems, enhance outbreak prediction, and inform evidence-based vector control strategies. Full article

As software systems increasingly combine machine learning, deep learning, and generative AI components with classical deterministic logic, the systematic detection of AI-based algorithmic elements in application code is becoming essential for software audit, compliance with the EU AI Act (Regulation (EU) 2024/1689), and quality assurance. This paper introduces Quality-Informed Neural Networks (QINN), an architecture in which the structured knowledge encoded in the Quality Function Deployment (QFD) House of Quality is embedded into the network topology and weight initialisation through QFD-derived binary structural masks and knowledge-calibrated initialisation—in direct analogy with Physics-Informed Neural Networks (PINNs). The QFD relationship matrices act as structural priors that constrain the hypothesis space toward quality-consistent solutions by enforcing domain-expert-validated sparsity on network connectivity, while an optional QFD-regularised loss term provides an additional soft constraint on the learned weight structure. As a proof of concept, QINN is instantiated in its masked-architecture configuration for the binary classification of software repositories as AI-enabled or classical. On the AIC-199 proof-of-concept dataset, the proposed QINN attains a cross-validated AUC of 99.47% (±1.18%), recall of 100.00% (±0.00%), and F1-score of 99.02% (±1.34%) under QFD-informed structural masking, outperforming a non-learned QFD scoring baseline by 37.37 percentage points in recall and exceeding a cross-validated Random Forest ensemble on AUC by 2.47 percentage points (W = 0, p < 0.05), while producing explanations at three QFD-grounded levels—feature salience, named Technical-Evidence activations, and per-criterion quality requirement scores—that align directly with the EU AI Act documentation obligations. Validation on larger, independently curated datasets and sensitivity analysis of the QFD elicitation process are identified as priorities for future work. A domain-general seven-phase application protocol is provided. Full article

Taxi drivers are a group with high driving exposure and are involved in a significant number of urban traffic casualties. Using two modelling approaches, this study examines whether the intention to speed, as measured by the Theory of Planned Behaviour (TPB), can better fit a crash frequency model than errors or lapses as measured by the Driving Behaviour Questionnaire (DBQ). Data from 1000 drivers in Tehran was collected through questionnaires. The crash prediction model included a cross-sectional model using negative binomial (NB) regression methods and a tree regression model from a previous study. In the last three years, the drivers had been involved in 544 road crashes, and of those, 42 resulted in serious injuries. Due to the rare and random nature of crashes, the empirical Bayesian (EB) method was used for model testing. Comparing AIC and BIC showed that zero-inflated NB (ZINB) models performed better. The final selected model was the intention-based ZINB model without the age variable. The coefficients for intention, exposure, and driver experience were 0.205, 0.103, and −0.443, respectively. The high EB coefficients indicated strong reliance on predicted crash values. The conclusion is that road crashes are closely related to taxi drivers’ intention to speed rather than errors and lapses. This indicates that it can be described as a traffic violation, rather than a mistake. Therefore, significant efforts are required to increase compliance with speed limits and reduce road crashes. Further education and high-quality campaigns are essential elements to achieve this goal. Full article

This paper designs a novel aperiodic intermittent control (AIC) strategy using discrete-time observation information. It can stabilize unstable hybrid McKean–Vlasov stochastic differential equations and reduce control consumption effectively. Key contributions include the following: (1) Lévy noise is introduced into the hybrid McKean–Vlasov framework to describe discontinuous disturbances. We further derive the existence, uniqueness and generalized Itô formula for the above system. (2) A new distribution-dependent Lyapunov functional to prove moment finiteness, mean square, and asymptotic exponential stability is constructed. (3) We derive explicit ranges for the AIC time rate and observation intervals. By tightening the state error bound via an innovative technique, the control design constraints are effectively relaxed. (4) We prove the equivalence of exponential stability between the controlled system and its particle approximation. This approach avoids the computational intractability of the exact probability distribution. Finally, the efficacy of our method is demonstrated through a numerical example. Full article