Search Results (3,130)

Change point models are widely used in medical and epidemiological studies to capture the threshold effects of continuous covariates on health outcomes. These threshold effects represent critical points at which the relationship between biomarkers or risk factors and disease risk shifts, often reflecting underlying biological mechanisms or clinically relevant intervention points. While most existing methods focus on right-censored data, interval censoring is common in large-scale clinical trials and follow-up studies, where the exact event times are not observed but are known to fall within time intervals. In this paper, we propose a semiparametric transformation model with an unknown change point for interval-censored data. The model allows flexible transformation functions, including the proportional hazards and proportional odds models, and it accommodates both main effects and their interactions with the threshold variable. Model parameters are estimated via the EM algorithm, with the change point identified through a profile likelihood approach using grid search. We establish the asymptotic properties of the proposed estimators and evaluate their finite-sample performance through extensive simulations, showing good accuracy and coverage properties. The method is further illustrated through an application to the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial data. Full article

Basalt-fiber-reinforced polymer (BFRP) composites, utilizing a natural high-performance inorganic fiber, exhibit excellent weathering resistance, including tolerance to high and low temperatures, salt fog, and acid/alkali corrosion. They also possess superior mechanical properties such as high strength and modulus, making them widely applicable in aerospace and shipbuilding. This study experimentally investigated the mechanical properties of BFRP plates under various strain rates (10⁻⁴ s⁻¹ to 10³ s⁻¹) and directions using an electronic universal testing machine and a split Hopkinson pressure bar (SHPB).The results demonstrate significant strain rate dependency and pronounced anisotropy. Based on experimental data, relationships linking the strength of BFRP composites in different directions to strain rate were established. These relationships effectively predict mechanical properties within the tested strain rate range, providing reliable data for numerical simulations and valuable support for structural design and engineering applications. The developed strain rate relationships were successfully validated through finite element simulations of low-velocity impact. Full article

This study synthesizes and comparatively investigates two squaric acid-based phthalocyanine-like dyes, SNF and its long-chain alkylated derivative LNF, to systematically elucidate the influence of peripheral hydrophobic groups on their third-order nonlinear optical (NLO) properties. The NLO characteristics were comprehensively characterized using femtosecond Z-scan and I-scan techniques at both 800 nm and 900 nm. Both dyes exhibited strong saturable absorption (SA), confirming their potential as saturable absorbers. Critically, the comparative analysis revealed that SNF exhibits a significantly greater nonlinear absorption coefficient (β) compared to LNF under identical conditions. For instance, at 800 nm, the β of SNF was approximately 3–5 times larger than that of LNF. This result conclusively demonstrates that the introduction of long hydrophobic alkyl chains attenuates the NLO response. Furthermore, I-scan measurements revealed excellent SA performance, with high modulation depths (e.g., LNF: 43.0% at 900 nm) and low saturation intensities. This work not only clarifies the structure–property relationship in these D-A-D dyes but also presents a clear strategy for modulating the NLO properties of organic chromophores for applications in near-infrared pulsed lasers. Full article

The double wishbone suspension (DWS) system is widely used in automotive engineering because of its favorable kinematic properties, which affect vehicle dynamics, handling, and ride comfort; hence, it is important to have an accurate 3D model, simulation, and analysis of the system in order to optimize its design. This requires efficient computational tools for parametric study. The development of effective computational tools that support parametric exploration stands as an essential requirement. Our research demonstrates a complete Wolfram Mathematica system that creates parametric 3D kinematic models and conducts simulations, performs analyses, and generates interactive visualizations of DWS systems. The system uses homogeneous transformation matrices to establish the spatial relationships between components relative to a global coordinate system. The symbolic geometric parameters allow designers to perform flexible design exploration and the kinematic constraints create an algebraic equation system. The numerical solution function NSolve computes linkage positions from input data, which enables fast evaluation of different design parameters. The integrated 3D visualization module based on Mathematica’s manipulate function enables users to see immediate results of geometric configurations and parameter effects while calculating exact 3D coordinates. The resulting robust, systematic, and flexible computational environment integrates parametric 3D design, kinematic simulation, analysis, and dynamic visualization for DWS, serving as a valuable and efficient tool for engineers during the design, development, assessment, and optimization phases of these complex automotive systems. Full article

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

A modular strategy for the molecular design of silicone-based antifoaming agents was developed by precisely controlling the architecture of poly (methylhydrosiloxane) (PMHS). Sixteen PMHS variants were synthesized by systematically varying the siloxane chain length (L1–L4), backbone composition (D₃T₁ vs. D₃₀T₁), and terminal group chemistry (H- vs. M-type). These structural modifications resulted in a broad range of Si-H functionalities, which were quantitatively analyzed and correlated with defoaming performance. The PMHS matrices were integrated with high-viscosity PDMS, a nonionic surfactant, and covalently grafted fumed silica—which was chemically matched to each PMHS backbone—to construct formulation-specific defoaming systems with enhanced interfacial compatibility and colloidal stability. Comprehensive physicochemical characterization via FT-IR, ¹H NMR, GPC, TGA, and surface tension analysis revealed a nonmonotonic relationship between Si-H content and defoaming efficiency. Formulations containing 0.1–0.3 wt% Si-H achieved peak performance, with suppression efficiencies up to 96.6% and surface tensions as low as 18.9 mN/m. Deviations from this optimal range impaired performance due to interfacial over-reactivity or reduced mobility. Furthermore, thermal stability and molecular weight distribution were found to be governed by repeat unit architecture and terminal group selection. Compared with conventional EO/PO-modified commercial defoamers, the PMHS-based systems exhibited markedly improved suppression durability and formulation stability in high-viscosity environments. These results establish a predictive structure–property framework for tailoring antifoaming agents and highlight PMHS-based formulations as advanced foam suppressors with improved functionality. This study provides actionable design criteria for high-performance silicone materials with strong potential for application in thermally and mechanically demanding environments such as coating, bioprocessing, and polymer manufacturing. Full article

Accurate prediction of the coal abrasive index (AI) is critical for optimizing coal processing efficiency and minimizing equipment wear in industrial applications. This study explores tree-based machine learning models; Random Forest (RF), Gradient Boosting Trees (GBT), and Extreme Gradient Boosting (XGBoost) to predict AI using selected coal properties. A database of 112 coal samples from the KwaZulu-Natal Coalfield in South Africa was used. Initial predictions using all eight input properties revealed suboptimal testing performance (R²: 0.63–0.72), attributed to outliers and noisy data. Feature importance analysis identified calorific value, quartz, ash, and Pyrite as dominant predictors, aligning with their physicochemical roles in abrasiveness. After data cleaning and feature selection, XGBoost achieved superior accuracy (R² = 0.92), outperforming RF (R² = 0.85) and GBT (R² = 0.81). The results highlight XGBoost’s robustness in modeling non-linear relationships between coal properties and AI. This approach offers a cost-effective alternative to traditional laboratory methods, enabling industries to optimize coal selection, reduce maintenance costs, and enhance operational sustainability through data-driven decision-making. Additionally, quartz and Ash content were identified as the most influential parameters on AI using the Cosine Amplitude technique, while calorific value had the least impact among the selected features. Full article

Based on molecular dynamics simulation, we conducted a comprehensive study on the tensile behaviors and properties of the $\gamma$(Ni)/${\gamma }^{\prime }$(Ni₃Al) superalloy with varying ${\gamma }^{\prime }$(Ni₃Al) phase volume fractions ($V\left({\gamma }^{\prime }\right))$ under high-temperature, high-strain-rate service environments. Our investigation revealed that the tensile behavior of the superalloy depends critically on the $V\left({\gamma }^{\prime }\right)$. When the $V\left({\gamma }^{\prime }\right)$ increased from 13.5 to 67%, the system’s tensile strength exhibited a non-monotonic response, peaking at $\mathrm{V}\left({\gamma }^{\prime }\right)$ = 40.3% before progressively decreasing. Conversely, the maximum uniform plastic strain decreased linearly and significantly when $V\left({\gamma }^{\prime }\right)$ increased. These results establish an atomistically informed framework that elucidates the composition–microstructure–property relationships in $\gamma$(Ni)/${\gamma }^{\prime }$(Ni₃Al) superalloys, specifically addressing how $V\left({\gamma }^{\prime }\right)$ governs variations in deformation mechanisms and mechanical performance. Furthermore, this work provides quantitative design paradigm for optimizing ${\gamma }^{\prime }$(Ni₃Al) precipitate architecture and compositional tuning in the Ni-based $\gamma$(Ni)/${\gamma }^{\prime }$(Ni₃Al) superalloy. Full article

The polytetrafluoroethylene/aluminum (PTFE/Al) granular composite, a common formulation in impact-initiated energetic materials, undergoes mechanochemical coupling reactions under sufficiently strong dynamic loading. This investigation discusses the dynamic properties and the constitutive relationship of the PTFE/Al granular composite to provide a preliminary guide for the research on mechanical properties of a series of composite materials based on PTFE/Al as the matrix. Firstly, the 26.5Al-73.5PTFE (wt.%) composite specimens are prepared by preprocessing, mixing, molding, high-temperature sintering, and cooling. Then, the quasi-static compression and Hopkinson bar tests are performed to explore the mechanical properties of the PTFE/Al composite. Influences of the strain rate of loading on the yield stress, the ultimate strength, and the limited strain are also analyzed. Lastly, based on the experimental results, the material parameters in the Johnson–Cook constitutive model are obtained by the method of piecewise fitting to describe the stress–strain relation of the PTFE/Al composite. Combining the experimental details and the obtained material parameters, the numerical simulation of the dynamic compression of the PTFE/Al composite specimen is carried out by using the ANSYS/LS-DYNA platform. The results show that the computed stress–strain curves present a reasonable agreement with the experimental data. It should be declared that this research does not involve the energy release behavior of the 26.5Al-73.5PTFE (wt.%) reactive material because the material is not initiated within the strain rate range of the dynamic test in this paper. Full article

Heavy metal contamination in natural waters and soils poses a significant environmental challenge, necessitating efficient and sustainable water treatment solutions. This study presents the computational design of low-density polyethylene (LDPE) films functionalized with iron oxide (Fe₃O₄) nanoparticles (NPs) for enhanced water purification applications. Composite materials containing 5%, 10%, and 15% were synthesized and characterized in terms of adsorption efficiency, surface morphology, and reusability. Advanced molecular modeling using BIOVIA Pipeline was employed to investigate charge distribution, functional group behaviour, and atomic-scale interactions between polymer chains and metal ions. The computational results revealed structure–property relationships crucial for optimizing adsorption performance and understanding geochemically driven interaction mechanisms. The LDPE/Fe₃O₄ composites demonstrated significant removal efficiency of Cu²⁺ and Ni²⁺ ions, along with favourable mechanical properties and regeneration potential. These findings highlight the synergistic role of mineral–polymer interfaces in water remediation, presenting a scalable approach to designing multifunctional polymeric materials for environmental applications. This study contributes to the growing field of polymer-based adsorbents, reinforcing their value in sustainable water treatment technologies and environmental protection efforts. Full article

The olive oil industry, a key component of Southern Europe’s agricultural sector, generates large amounts of by-products during processing, including olive leaves, branches, stones, and seeds. In the context of growing environmental concerns and limited natural resources—particularly in the Mediterranean regions—there is increasing interest in circular economy approaches that promote the valorization of agricultural residues. These by-products are rich in bioactive compounds, particularly phenolics such as oleuropein and hydroxytyrosol, which are well known for their antioxidant and anti-inflammatory activities. This study aimed to evaluate the phenolic content and antioxidant capacity of by-products from three olive cultivars using high-performance liquid chromatography with photodiode array detection (HPLC–PDA) and mass spectrometry (MS). The leaves and seeds, particularly from the “Cobrança” and a non-identified variety, presented the highest antioxidant activity, as well as the highest concentration of phenolic compounds, demonstrating once again the direct relationship between these two parameters. The identification of the compounds present demonstrated that the leaves and branches have a high diversity of phenolic compounds, particularly secoiridoids, flavonoids, phenylpropanoids, phenylethanoids, and lignans. An inverse relationship was observed between the chlorophyll and carotenoid content and the antioxidant activity, suggesting that phenolic compounds, rather than pigments, are the major contributors to antioxidant properties. Therefore, the by-products of the olive oil industry are a valuable source of sustainable bioactive compounds for distinct industrial sectors, such as the food, nutraceutical, and pharmaceutical industries, aligning with the European strategies for resource efficiency and waste reduction in the agri-food industries. Full article

Background: Peptides are a class of molecules that can be presented as good antimicrobials and with mechanisms that avoid resistance, and the design of peptides with good activity can be complex and laborious. The study of their quantitative structure–activity relationships through machine learning algorithms can shed light on a rational and effective design. Methods: Information on the antimicrobial activity of peptides was collected, and their structures were characterized by molecular descriptors generation to design regression and classification models based on machine learning algorithms. The contribution of each descriptor in the generated models was evaluated by determining its relative importance and, finally, the antimicrobial activity of new peptides was estimated. Results: A structured database of antimicrobial peptides and their descriptors was obtained, with which 56 machine learning models were generated. Random Forest-based models showed better performance, and of these, regression models showed variable performance (R² = 0.339–0.574), while classification models showed good performance (MCC = 0.662–0.755 and ACC = 0.831–0.877). Those models based on bacterial groups showed better performance than those based on the entire dataset. The properties of the new peptides generated are related to important descriptors that encode physicochemical properties such as lower molecular weight, higher charge, propensity to form alpha-helical structures, lower hydrophobicity, and higher frequency of amino acids such as lysine and serine. Conclusions: Machine learning models allowed to establish the structure–activity relationships of antimicrobial peptides. Classification models performed better than regression models. These models allowed us to make predictions and new peptides with high antimicrobial potential were proposed. Full article

This work presents a comprehensive study of the structural, luminescent, and photoconversion properties of epitaxial composite phosphor converters based on single crystalline films of Ce³⁺-activated Ca_2−xY_1+xMg_1+xSc_1−xSi₃O₁₂:Ce (x = 0–0.25) (CYMSSG:Ce) garnet, grown using the liquid phase epitaxy (LPE) method on single-crystal Y₃Al₅O₁₂ (YAG) and YAG:Ce substrates. The main goal of this study is to elucidate the structure–composition–property relationships that influence the photoluminescence and photoconversion efficiency of these film–substrate composite converters, aiming to optimize their performance in high-power white light-emitting diode (WLED) applications. Systematic variation in the Y³⁺/Sc³⁺/Mg²⁺ cationic ratios within the garnet structure, combined with the controlled tuning of film thickness (ranging from 19 to 67 µm for CYMSSG:Ce/YAG and 10–22 µm for CYMSSG:Ce/YAG:Ce structures), enabled the precise modulation of their photoconversion properties. Prototypes of phosphor-converted WLEDs (pc-WLEDs) were developed based on these epitaxial structures to assess their performance and investigate how the content and thickness of SCFs affect the colorimetric properties of SCFs and composite converters. Clear trends were observed in the Ce³⁺ emission peak position, intensity, and color rendering, induced by the Y³⁺/Sc³⁺/Mg²⁺ cation substitution in the film converter, film thickness, and activator concentrations in the substrate and film. These results may be useful for the design of epitaxial phosphor converters with tunable emission spectra based on the epitaxially grown structures of garnet compounds. Full article

Biochar serves as an effective adsorbent for the heavy metal cadmium, with its performance significantly influenced by its physicochemical properties and various environmental features. Traditional machine learning models, though adept at managing complex multi-feature relationships, rely heavily on expertise in feature engineering and hyperparameter optimization. To address these issues, this study employs an automated machine learning (AutoML) approach, automating feature selection and model optimization, coupled with an intuitive online graphical user interface, enhancing accessibility and generalizability. Comparative analysis of four AutoML frameworks (TPOT, FLAML, AutoGluon, H₂O AutoML) demonstrated that H₂O AutoML achieved the highest prediction accuracy (R² = 0.918). Key features influencing adsorption performance were identified as initial cadmium concentration (23%), stirring rate (14.7%), and the biochar H/C ratio (9.7%). Additionally, the maximum adsorption capacity of the biochar was determined to be 105 mg/g. Optimal production conditions for biochar were determined to be a pyrolysis temperature of 570–800 °C, a residence time of ≥2 h, and a heating rate of 3–10 °C/min to achieve an H/C ratio of <0.2. An online graphical user interface was developed to facilitate user interaction with the model. This study not only provides practical guidelines for optimizing biochar but also introduces a novel approach to modeling using AutoML. Full article

The utilization of machine learning (ML) has brought more opportunities for the discovery of high-performance materials with specific properties to replace traditional engineering materials. The glass transition temperature (T_g) is a crucial characteristic of polyimide (PI). But small datasets can only partially reveal structural information and decrease the ability of the models to learn from the observed data. In this investigation, a dataset comprising 1261 PIs was assembled. A quantitative structure–property relationship targeting T_g was constructed using nine regression algorithms, with the Categorical Boosting demonstrating the highest accuracy, achieving a coefficient of determination of 0.895 for the test set. SHapley Additive exPlanations analysis identified the NumRotatableBonds descriptor had a significantly negative impact on T_g. Finally, all-atom molecular dynamics (MD) simulations calculated eight PI structures to verify the accuracy of the prediction model. The ML prediction was consistent with the MD simulation, with the lowest prediction deviation of approximately 6.75%, but the time and resource consumption were tremendously reduced. These findings emphasize the significance of utilizing extensive datasets for model training. This available and interpretable ML framework provides impressive acceleration over the MD simulation and serves as a reference for the structural design of PI with the desired T_g in the future. Full article