Search Results (1,038)

Background and Objectives: Flexible ureteroscopic surgery is a common minimally invasive procedure utilized for the management of various urological conditions. While effective, postoperative complications such as fever can occur, necessitating the identification of reliable biomarkers for early detection and management. In this study, we specifically evaluated the predictive performance of three preoperative hematologic indices: the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune–inflammation index (SII). Materials and Methods: By systematically comparing these biomarkers through receiver operating characteristic (ROC) curve analysis and logistic regression modeling, we aimed to identify the most accurate predictor of postoperative fever development. Our cohort included patients who developed postoperative fever, many of whom exhibited normal WBC counts, allowing us to evaluate the discriminatory power of alternative inflammatory biomarkers. Results: Among the 150 patients, 32 developed postoperative fever. Conventional WBC counts did not predict fever, with 91% of feverish individuals having normal WBC values. In the ROC curve analysis, NLR outperformed SII (AUC 0.847, cutoff 796) and PLR (AUC 0.743, cutoff 106), with an AUC of 0.996 at 2.96. A combined logistic model achieved 100% sensitivity and 91% specificity (AUC = 0.996). Conclusions: This study addresses a critical gap in perioperative monitoring by validating readily available complete blood count-derived ratios as clinically meaningful predictors of postoperative inflammatory responses. Full article

Background and Objectives: Incisional hernia is a common complication following cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC). This study aimed to identify patient and surgical factors associated with its occurrence. Materials and Methods: We conducted a retrospective analysis of 122 patients undergoing CRS and HIPEC. Logistic regression models were applied to identify predictors of incisional hernia development. Results: Incisional hernia occurred in 23.8% of patients. Hypertension was identified as an independent factor associated with increased risk. Peritoneal Cancer Index (PCI), operative time, and abdominal wall closure technique were not found to be significantly associated with hernia development. Conclusions: Preoperative identification of high-risk patients may support the adoption of targeted preventive strategies, including prophylactic mesh placement and enhanced postoperative surveillance. Full article

This study employed multiple machine learning and hyperparameter optimization techniques to analyze and predict the mechanical properties of self-drilling screw connections in thin-walled steel–ply–bamboo shear walls, leveraging the renewable and eco-friendly nature of bamboo to enhance structural sustainability and reduce environmental impact. The dataset, which included 249 sets of measurement data, was derived from 51 disparate connection specimens fabricated with engineered bamboo—a renewable and low-carbon construction material. Utilizing factor analysis, a ranking table recording the comprehensive score of each connection specimen was established to select the optimal connection type. Eight machine learning models were employed to analyze and predict the mechanical performance of these connection specimens. Through comparison, the most efficient model was selected, and five hyperparameter optimization algorithms were implemented to further enhance its prediction accuracy. The analysis results revealed that the Random Forest (RF) model demonstrated superior classification performance, prediction accuracy, and generalization ability, achieving approximately 61% accuracy on the test set (the highest among all models). In hyperparameter optimization, the RF model processed through Bayesian Optimization (BO) further improved its predictive accuracy to about 67%, outperforming both its non-optimized version and models optimized using the other algorithms. Considering the mechanical performance of connections within TWS composite structures, applying the BO algorithm to the RF model significantly improved the predictive accuracy. This approach enables the identification of the most suitable specimen type based on newly provided mechanical performance parameter sets, providing a data-driven pathway for sustainable bamboo–steel composite structure design. Full article

Among the existing challenges in the field of hyperspectral imaging, the need to optimize memory usage and computational capacity in material detection methods stands out, given the vast amount of data associated with the hundreds of reflectance bands. In line with this, this article proposes a comparative study on the effectiveness and efficiency of five computational methods for detecting composite material asbestos cement (AC) in hyperspectral images: correlation, spectral differential similarity (SDS), Fourier phase similarity (FPS), area under the curve (AUC), and decision trees (DT). The novelty lies in the comparison between the first four methods, which represent the spectral proximity method and a machine learning method, such as DT. Furthermore, SDS and FPS are novel methods proposed in the present document. Given the accuracy that detection methods based on supervised learning have demonstrated in material identification, the results obtained from the DT model were compared with the percentage of AC detected in a hyperspectral image of the Manga neighborhood in the city of Cartagena by the other four methods. Similarly, in terms of computational efficiency, a 20 × 20 pixel region with 380 bands was selected for the execution of multiple repetitions of each of the five computational methods considered, in order to obtain the average processing time of each method and the relative efficiency of the methods with respect to the method with the best effectiveness. The decision tree (DT) model achieved the highest classification accuracy at 99.4%, identifying 11.44% of asbestos cement (AC) pixels in the reference image. However, the correlation method, while detecting a lower percentage of AC pixels (9.72%), showed the most accurate visual performance and had no spectral overlap, with a 1.4% separation between AC and non-AC pixels. The SDS method was the most computationally efficient, running 23.85 times faster than the DT model. The proposed methods and results can be applied to other hyperspectral imaging tasks involving material identification in urban environments, especially when balancing accuracy and computational efficiency is essential. Full article

The accurate identification of wood patterns is critical for optimizing the use of sustainable wood building materials, promoting resource efficiency, and reducing waste in construction. This study presents a deep learning-based approach for enhanced wood material recognition, combining EfficientNet architecture with advanced data augmentation techniques to achieve robust classification. The augmentation strategy incorporates geometric transformations (flips, shifts, and rotations) and photometric adjustments (brightness and contrast) to improve dataset diversity while preserving discriminative wood grain features. Validation was performed using a controlled augmentation pipeline to ensure realistic performance assessment. Experimental results demonstrate the model’s effectiveness, achieving 88.9% accuracy (eight out of nine correct predictions), with further improvements from targeted image preprocessing. The approach provides valuable support for preliminary sustainable building material classification, and can be deployed through user-friendly interfaces without requiring specialized AI expertise. The system retains critical wood pattern characteristics while enhancing adaptability to real-world variability, supporting reliable material classification in sustainable construction. This study highlights the potential of integrating optimized neural networks with tailored preprocessing to advance AI-driven sustainability in building material recognition, contributing to circular economy practices and resource-efficient construction. Full article

This study focuses on the influence of prolonged ultraviolet (UV) irradiation on the mechanical properties and surface microstructure of glass fiber-reinforced plastics (GFRPs) and basalt fiber-reinforced plastics (BFRPs), which are widely used in construction and transport infrastructure. The relevance of the research lies in the need to improve the reliability of composite materials under extended exposure to harsh climatic conditions. Experimental tests were conducted in a laboratory UV chamber over 2000 h, simulating accelerated weathering. Mechanical properties were evaluated using three-point bending, while surface conditions were assessed via profilometry and microscopy. It was shown that GFRPs exhibit a significant reduction in flexural strength—down to 59–64% of their original value—accompanied by increased surface roughness and microdefect depth. The degradation mechanism of GFRPs is attributed to the photochemical breakdown of the polymer matrix, involving free radical generation, bond scission, and oxidative processes. To verify these mechanisms, FTIR spectroscopy was employed, which enabled the identification of structural changes in the polymer phase and the detection of mass loss associated with matrix decomposition. In contrast, BFRP retained up to 95% of their initial strength, demonstrating high resistance to UV-induced aging. This is attributed to the shielding effect of basalt fibers and their ability to retain moisture in microcavities, which slows the progress of photo-destructive processes. Comparison with results from natural exposure tests under extreme climatic conditions (Yakutsk) confirmed the reliability of the accelerated aging model used in the laboratory. Full article

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article

Lumped plasticity models available in commercial software offer a limited resolution of damage distribution along structural members. This study presents an open-source workflow that combines force-based fiber elements in OpenSeesPy with automated 3D post-processing for visualizing distributed plasticity in reinforced concrete piles. A 60 cm diameter pile subjected to monotonic lateral loading is analyzed using both SAP2000’s default plastic hinges and OpenSeesPy fiber sections (Concrete02/Steel02). Although the fiber model incurs a runtime approximately 2.5 times greater, it captures the gradual spread of yielding and deterioration with improved fidelity. The presented workflow includes Python routines for interactive stress–strain visualization, facilitating the identification of critical sections and verification of strain limits. This approach offers a computationally feasible alternative for performance-based analysis with enhanced insight into member-level behavior. Because the entire workflow—from model definition through post-processing—is fully scripted in Python, any change to geometry, materials, or loading can be re-run in seconds, dramatically reducing the time taken to execute sensitivity analyses. Full article

This paper assesses experimentally and theoretically the hydrogen-assisted cracking sensitivity of an ultrahigh-strength lath martensitic steel, recently used to manufacture tendon rods for structural engineering. The experimental values of the J-integral were obtained by tensile testing up to failure precracked SENT specimens in air, as an inert environment and in a thiocyanate aqueous solution, as a hydrogen-promoter medium. In parallel, the theoretical resources necessary to apply the Dugdale cohesive model to the SENT specimen were developed from the Green function in order to predict the J-integral dependency on the applied load and the crack size, with the cohesive resistance being the only material constant concerning fracture. The comparison of theoretical and experimental results strongly supports the premise that the cohesive crack accurately models the effect of the mechanisms by which the examined steel opposes crack propagation, both when in hydrogen-free and -embrittled conditions. The identification of experimental and theoretical limit values respectively involving a post-small-scale-yielding regime and unstable extension of the cohesive zone allowed for the value of the cohesive resistance to be determined, its condition as a material constant in hydrogen-free medium confirmed, and its strong decrease with hydrogen exposure revealed. Full article

Accurate identification of active-layer compositions in organic photovoltaic (OPV) devices often relies on invasive techniques such as electrical measurements or material extraction, which risk damaging the device. In this study, we propose a non-invasive classification approach based on simulated full-device absorption spectra. To account for fabrication-related variability, the active-layer thickness varied by over ±15% around the optimal value, creating a realistic and diverse training dataset. A multilayer perceptron (MLP) neural network was applied with various activation functions, optimization algorithms, and data split ratios. The optimized model achieved classification accuracies exceeding 99% on both training and testing sets, with minimal sensitivity to random initialization or data partitioning. These results demonstrate the potential of applying deep learning to spectral data for reliable, non-destructive OPV composition classification, paving the way for integration into automated manufacturing diagnostics and quality control workflows. Full article

The Dangerous Goods Transportation (DGT) presents significant challenges, requiring a strong and systematic risk assessment framework to ensure the safety and efficiency of the supply chain. This study addresses a critical gap by integrating a deterministic and holistic approach to risk assessment and management. Utilizing Geographic Information Systems (GIS), meteorological data, and material-specific information, the research develops a data-driven approach to identify analyze, evaluate, and mitigate risks associated with DGT. The main objectives include monitoring dangerous goods flows to identify critical risk areas, optimizing emergency response using a shared model, and providing targeted training for stakeholders involved in DGT. The study leverages Information and Communication Technologies (ICT) to systematically collect, interpret, and evaluate data, producing detailed risk scenario maps. These maps are instrumental in identifying vulnerable areas, predicting potential accidents, and assessing the effectiveness of risk management strategies. This work introduces an innovative GIS-based risk assessment model that combines static and dynamic data to address various aspects of DGT, including hazard identification, accident prevention, and real-time decision support. The results contribute to enhancing safety protocols and provide actionable insights for policymakers and practitioners aiming to improve the resilience of technological systems for road transport networks handling dangerous goods. Full article

In this study, we aim to analyze the dispersion of ultrasonic waves due to second-gradient contributions and attenuation within the framework of continuum mechanics. To investigate dispersive behavior and attenuation effects, we consider the influence of both higher-order gradient terms (second gradients) and Rayleigh-type viscoelastic contributions. To this end, we employ the extended Rayleigh–Hamilton principle to derive the governing equations of the problem. Using a wave-form solution, we establish the relationship between the phase velocity and the material’s constitutive parameters, including those related to the stiffness of both standard (first-gradient) and second-gradient types, as well as viscosity. To validate the model, we use data available in the literature to identify all the material parameters. Based on this identification, we observe that our model provides a good approximation of the experimentally measured trends of both phase velocity and attenuation versus frequency. In conclusion, this result not only confirms that our model can accurately describe both wave dispersion and attenuation in a material, as observed experimentally, but also highlights the necessity of simultaneously considering both second-gradient and viscosity parameters for a proper mechanical characterization of materials. Full article

The identification of suitable alloy compositions for the formation of bulk metallic glasses (BMGs) is a key challenge in materials science. In this study, we developed machine learning (ML) models to predict the critical casting diameter ($D_{max}$) of Fe-based BMGs, enabling rapid assessment of glass-forming ability (GFA) using composition-based and calculated thermophysical features. Three datasets were constructed: one based on alloy molar fractions, one using thermophysical quantities calculated via the CALPHAD method, and another utilizing Magpie-derived features. The performance of various ML models was evaluated, including support vector machines (SVM), XGBoost, and ensemble methods. Models trained on thermophysical features outperformed those using only molar fractions, with XGBoost and SVM models achieving test $R^2$ scores of up to 0.63 and 0.60, respectively. Magpie features yielded similar results but required a larger feature set. To enhance predictive accuracy, we explored data augmentation using the PADRE method and a modified version (PADRE-2). While PADRE-2 demonstrated slight improvements and reduced data redundancy, the overall performance gains were limited. The best-performing model was an ensemble combining SVM and XGBoost models trained on thermophysical and Magpie features, achieving an $R^2$ score of 0.69 and MAE of 0.69, comparable to published results obtained from larger datasets. However, predictions for high $D_{max}$ values remain challenging, highlighting the need for further refinement. This study underscores the potential of leveraging thermophysical features and advanced ML techniques for GFA prediction and the design of new Fe-based BMGs. Full article

Polygonati Rhizoma, a renowned edible homologous material, encompasses an array of widely distributed species. Despite their morphological and medicinal similarities, their overlapping distribution and evolving varieties present challenges for their classification and identification. This study provides a comprehensive characterization of the physicochemical and antioxidant properties of polysaccharides extracted from three common species: P. sibiricum, P. cyrtonema, and P. kingianum. An analysis of their monosaccharide composition reveals distinct profiles, with P. kingianum polysaccharides (PKPs) demonstrating a significantly higher glucose content compared to P. sibiricum polysaccharides (PSPs) and P. cyrtonema polysaccharides (PCPs). Infrared (IR) spectroscopy and derivative spectral processing affirm both structural similarities and quantitative differences in functional groups among the species. Multivariate analyses, including HCA, PCA, and OPLS-DA, confidently classify the 12 batches of polysaccharides into three distinct groups (PSPs, PCPs, and PKPs), exhibiting strong model robustness (PCA: R²X = 0.951, Q² = 0.673; OPLS-DA: R²Y = 0.953, Q² = 0.922). Importantly, PKPs from number S11 show exceptional in vitro antioxidant activity (DPPH scavenging), which directly correlates with their high monosaccharide content and distinctive spectral features. These findings establish a robust foundation for the quality assessment of Polygonatum polysaccharides as potential natural antioxidants in functional foods, positioning PKPs as leading candidates for dietary supplement development. Full article

This study focuses on optimizing the laser micromachining process to achieve precise geometric features. The main objective is to develop a systematic approach for analyzing the process parameters that influence the repeatability of laser material removal for edge-rounding of 50 and 75 μm in radius. The research employs the Taguchi method for the experimental design, allowing the identification of optimal process parameters that ensure high consistency. Repeatability is assessed through the evaluation of eight consecutive layers in depth, where the laser removes material, and the average depth is calculated by dividing the total removed depth by eight. This statistical approach allows for a comprehensive analysis of process stability. A key aspect of this study is the development of a predictive model based on experimental data, allowing a selection of the most influential parameters to achieve the desired outcome. By implementing a structured optimization process, this research aims to enhance process efficiency, minimize deviations, and improve the overall quality of laser micromachining. These findings contribute to the advancement of precision manufacturing techniques by providing a reliable methodology for process control and repeatability enhancement. Full article