Search Results (381)

Accurate time series forecasting of sea surface temperature (SST) is essential for understanding the ocean climate system and large-scale ocean circulation, yet it remains challenging due to regime-dependent variability and correlated errors across heterogeneous prediction models. This study addresses these challenges by formulating SST ensemble time series forecasting aggregation as a stochastic, sample-adaptive weighting problem. We propose a diffusion-conditioned ensemble framework in which heterogeneous base forecasters generate out-of-sample SST predictions that are combined through a noise-conditioned weighting network. The proposed framework produces convex, sample-specific mixture weights without requiring iterative reverse-time sampling. The approach is evaluated on short-horizon global SST forecasting using the Global Ocean Data Assimilation System (GODAS) reanalysis as a representative multivariate dataset. Under a controlled experimental protocol with fixed input windows and one-step-ahead prediction, the proposed method is compared against individual deep learning forecasters and conventional global pooling strategies, including uniform averaging and validation-optimized convex weighting. The results show that adaptive, diffusion-weighted aggregation yields consistent improvements in error metrics over the best single-model baseline and static pooling rules, with more pronounced gains in several mid- to high-latitude regimes. These findings indicate that stochastic, condition-dependent weighting provides an effective and computationally practical framework for enhancing the robustness of multivariate time series forecasting, with direct applicability to global SST prediction from large-scale geophysical reanalysis data. Full article

Lignin, an abundant and renewable biopolymer, holds significant potential for asphalt modification owing to its unique aromatic structure and reactive functional groups. This review summarizes the main lignin preparation routes and key physicochemical attributes and assesses its applicability for enhancing asphalt performance. The physical incorporation of lignin strengthens the asphalt matrix, improving its viscoelastic properties and resistance to oxidative degradation. These enhancements are mainly attributed to the cross-linking effect of lignin’s polymer chains and the antioxidant capacity of its phenolic hydroxyl groups, which act as free-radical scavengers. At the mixture level, lignin-modified asphalt (LMA) exhibits improved aggregate bonding, leading to enhanced dynamic stability, fatigue resistance, and moisture resilience. Nevertheless, excessive lignin content can have a negative impact on low-temperature ductility and fatigue resistance at intermediate temperatures. This necessitates careful dosage optimization or composite modification with softeners or flexible fibers. Mechanistically, lignin disperses within the asphalt, where its polar groups adsorb onto lighter components to boost high-temperature performance, while its strong interaction with asphaltenes alleviates water-induced damage. Furthermore, life cycle assessment (LCA) studies indicate that lignin integration can substantially reduce or even offset greenhouse gas emissions through bio-based carbon storage. However, the magnitude of the benefit is highly sensitive to lignin production routes, allocation rules, and recycling scenarios. Although the laboratory research results are encouraging, there is a lack of large-scale road tests on LMA. There is also a lack of systematic research on the specific mechanism of how it interacts with asphalt components and changes the asphalt structure at the molecular level. In the future, long-term service-road engineering tests can be designed and implemented to verify the comprehensive performance of LMA under different climates and traffic grades. By using molecular dynamics simulation technology, a complex molecular model containing the four major components of asphalt and lignin can be constructed to study their interaction mechanism at the microscopic level. Full article

The growing demand for sustainable alternatives to synthetic composites has increased the interest in natural-fibre-reinforced composites (NFRCs), due to their reduced environmental impact. This study presents a comparative investigation of the mechanical properties of mono-fibre and intraply sisal/flax hybrid composites as cost-effective bio-based solutions. Flax offers high tensile performance but is constrained by higher cost and geographical availability. Sisal, on the other hand, is widely available at lower cost, but exhibits a coarser morphology and reduced processing versatility. Mechanical testing demonstrated that intraply hybrids achieved well-balanced performance, with reduced flax content still delivering competitive tensile strength and stiffness when compared to the higher performing mono-fibre flax composites. However, sisal-rich and hybrid laminates outperformed mono-fibre flax composites in transverse and shear behaviour, with the 67% sisal/33% flax hybrid composite exhibiting the highest transverse properties and the 33% sisal/67% flax hybrid achieving the highest shear properties. Rule-of-mixtures models predicted longitudinal tensile behaviour effectively, while Halpin–Tsai models successfully estimated shear but not transverse and compressive properties. Compressive strength showed limited variation across configurations. Failure analysis identified intra-yarn fracture in flax, limited resin infiltration in sisal, and compressive failure modes such as brooming and microbuckling. Overall, intraply sisal/flax hybrid mats provide a structurally efficient, sustainable, and economically viable alternative to mono-fibre natural composites. Full article

This paper investigates the tensile behavior of cotton ring-spun yarn through experimental testing, numerical simulation, and theoretical calculation. Firstly, scanning electron microscope testing of the microscopic geometric morphologies of yarns was performed for the development of basic finite element (FE) models. Then, the influences of tensile speed and yarn length on the tensile properties of yarn were studied using tensile experiments. Numerical simulations were further performed to investigate the effects of yarn diameter, twist angle, and friction between fibers on the tensile modulus of yarn. Finally, a modified ‘rule-of-mixtures’ equation was proposed to effectively calculate the tensile modulus of yarn through incorporating the friction correction factor. The experimental results show that the tensile modulus and strength of tested yarn are significantly affected by the yarn structure and are not sensitive to the yarn length and tensile speed. Furthermore, the tensile moduli of yarns obtained from the numerical simulations show a good fitting accuracy with those obtained from experimental tests when the friction coefficient is set to 0.5 in the FE models. The simulation results show that the twist angle and friction coefficient are two key factors affecting the tensile modulus of yarn. The modified ‘rule-of-mixtures’ equation presents better accuracy for the calculation of the tensile modulus of yarn compared with the traditional ‘rule-of-mixtures’ equation, which can be used to replace the FE modeling and simulation and reduce the computational cost. This work will provide a deeper understanding of the mechanical properties of cotton ring-spun yarns and enhance their application in the textile industry. Full article

This study proposes a robust chaotic encryption framework based on a Fuzzy Rule-Based Sugeno Inference (FRBSI) system, integrated with high-level security analyses. The algorithm employs a dynamic mixture of Lorenz chaotic state variables, which are numerically modeled using the Euler-Forward method to ensure computational accuracy. Unlike conventional methods, the carrier signal’s characteristics are not static; instead, its amplitude and dynamic behavior are continuously adapted through the FRBSI mechanism, driven by the instantaneous thresholds of the information signal. The security of the proposed system was rigorously evaluated through Histogram analysis, Number of Pixels Change Rate (NPCR), and Unified Average Changing Intensity (UACI) metrics, which confirmed the algorithm’s high sensitivity to plaintext variations and resistance against differential attacks. Furthermore, Key Sensitivity tests demonstrated that even a single-bit discrepancy in the receiver-side Sugeno rule base leads to a total failure in signal reconstruction, providing a formidable defense against brute-force attempts. The system’s performance was validated in the MATLAB/Simulink of R2021a version environment, where frequency and time-domain analyses were performed via oscilloscope and Fourier transforms. The results indicate that the proposed multi-layered fuzzy-chaotic structure significantly outperforms traditional encryption techniques in terms of unpredictability, structural security, and robustness. Full article

Aside from languages having no form of written expression, it is usually the case with every language on this planet that texts are written in a single character. But every rule has its exceptions. A very rare exception is Japanese, the texts of which are written in the three kinds of characters. In European languages, no one can find a text written in a mixture of the Latin, Cyrillic, and Greek alphabets. For several Japanese texts currently available, we conduct a quantitative analysis of how the three characters are mixed using a methodology based on a binary pattern approach to the sequence that has been generated by a procedure. Specifically, we consider two different texts in the former and present constitutions as well as a famous American story that has been translated at least 13 times into Japanese. For the latter, a comparison is made among the human translations and four machine translations by DeepL and Google Translate. As metrics of divergence and diversity, the Hellinger distance, chi-square value, normalized Shannon entropy, and Simpson’s diversity index are employed. Numerical results suggest that in terms of the entropy, the 17 translations consist of three clusters, and that overall, the machine-translated texts exhibit entropy higher than the human translations. The finding suggests that the present method can provide a tool useful for stylometry and author attribution. Finally, through comparison with the diversity index, capabilities of the entropic measure are confirmed. Lastly, in addition to the abovementioned texts, applicability to the Japanese version of the periodic table of elements is investigated. Full article

This paper presents an experimental determination of the long-term mechanical properties of lightweight concrete with sintered aggregate under cyclic loading and the corresponding analytical standard models. The research was designed around two concrete mixtures. Multiple tests were conducted at the Building Structures, Geotechnics and Concrete Laboratory of the Building Research Institute (ITB), using various equipment including creep-testing machines and tensometric measurements of sample deformations. As a result of these tests, in addition to strength properties, the following time-dependent parameters were determined: the secant modulus of elasticity, shrinkage strains, and creep-recovery strains under cyclic loading. For the parameterization and modeling of constitutive equations, an analysis of creep strains under cyclic loads was carried out, taking into account the integral hereditary law according to the Boltzmann superposition principle and the long-term models formulated according to the following standards and pre-standards: Eurocode 2 (2004), Model Code 2010, Model Code 2020, and Eurocode 2 (2023). The results from the individual models were compared with the test results using the rules for evaluating correction factors for models determined according to Eurocode 2 (2023). It was concluded that the development of creep strain is correctly modeled by the aforementioned standard methods, albeit with the aforementioned correction factors. One of the research objectives was to determine whether the ratchetting phenomenon could be observed during creep of the tested concrete under cyclic loading; however, due to the very low level of plastic deformation, this phenomenon was not detected. The research confirmed the suitability of lightweight concrete with sintered aggregate for use in cyclically loaded concrete structures. Full article

This paper asks whether debt among affluent consumers reflects rational leverage, comparable to firms, or the influence of cognitive biases. Using survey data on Brazilian bank clients, we combine logistic regressions with a finite-mixture-inspired, rule-based classification and a test based on a ten-business-day overdraft grace period to identify heterogeneity in borrowing behavior. In the high-income subsample, Cognitive Reflection Test scores are unrelated to debt incidence, diverging from prior evidence in mixed-income populations. Among indebted affluent respondents, most borrowing is cost-sensitive and consistent with deliberate leverage (about 80 percent), while a minority displays patterns consistent with optimism bias and overconfidence (about 20 percent). The institutional feature of a temporary grace period lowers the effective cost of short-term credit and is associated with a marked reduction in overdraft use, reinforcing the leverage interpretation. Overall, consumer debt is heterogeneous; for the affluent, it largely aligns with leverage, though behavioral biases persist at the margins. Policy for high-income borrowers should prioritize targeted measures that address optimism bias and overconfidence while preserving deliberate leverage management through clear disclosures and monitoring of sensitivity to short-term credit costs. Full article

In this study, we investigate the catalytic degradation of polystyrene (PS) in water at low temperature (90–110 °C, 1 atm) using a multiphase carbide–alloy catalyst obtained by mechanosynthesis. X-ray diffraction and scanning electron microscopy confirm a mixture of Mo–W carbides and Fe/Ni alloys, consistent with multiple types of active sites. High-resolution mass spectrometry (MS) is used to assign products by oligomer-series spacing (styrene repeat mass, 104.15 Da) and the residual mass $\Delta m$ for end-group identification. At 90 °C without catalyst, the spectrum shows PS fragments between $m/z=888$–4618, consistent with thermal depolymerization. With catalyst at 90 °C, new lower-$m/z$ peaks emerge and long-chain signals diminish, indicating enhanced chain scission under mild conditions. Increasing the temperature to 100 and 110 °C yields even lighter ions (e.g., $m/z=307.59$ and 247.88), confirming stronger cracking and a larger number of distinct products. End groups inferred from $\Delta m$ include alkenes (C₃–C₇), alkanes (C₄, C₇), cyclic C₆–C₇ fragments, and alcohols, which are consistent with protolytic C–C bond cleavage (Haag–Dessau), oxidative dehydrogenation, and subsequent hydrogenation/hydration on metal/carbide sites. Overall, the results show that water-activated carbide–alloy catalysts can drive PS deconstruction at low temperature, shifting products toward shorter chains with useful functional groups, while a simple MS-based rule set provides a transparent and reproducible approach to product assignment. Full article

The aim of this study was to investigate the nucleation, growth, and surface deposition of poly(2,2,2-trifluoroethyl methacrylate) [poly(TFEMA)] from the one-phase, cloud point, and two-phase regions of a supercritical CO₂–toluene solvent. A ternary mixture of 20 wt% toluene + 79 wt% scCO₂ + 1 wt% poly(TFEMA) at 40.0 °C was exposed to a fluorine-doped tin oxide (FTO) surface for 30 min at pressures placing the solution in (i) a one-phase region (15.86 MPa), (ii) the cloud point (12.37 MPa), and (iii) a two-phase region (8.96 MPa). Using the Altunin–Gadetskii–Haar–Gallagher–Kell (AG–HGK) equation of state (EOS), the corresponding CO₂ densities are 793.9, 729.2, and 477.8 kg m⁻³. Scanning electron microscopy (SEM) and particle-size analysis (sample sizes N = 852–1177) show particle-size distributions (PSDs) that are well described by the following lognormal form: the mean diameter increases monotonically with a decrease in pressure (1.767 μm → 2.605 μm → 2.863 μm), while dispersion tightens slightly near the cloud point (coefficient of variation, CV: ≈0.47 → 0.44) and then broadens strongly in the two-phase region (CV ≈ 1.02). Morphologies transition from sparse, compact islands (one-phase) to agglomerated, necked spheres (cloud point) and finally hierarchical populations containing hollow/pitted large particles (two-phase). These outcomes are consistent with a phase-state-controlled shift in nucleation pathways, as follows: from heterogeneous surface nucleation in the one-phase regime to homogeneous nucleation with agglomeration at the cloud point, and to homogeneous nucleation with coalescence and solvent capture in the two-phase regime. The results provide a mechanistic basis and practical design rules for pressure-programmable control of fluoropolymer coatings prepared from scCO₂/aromatic-cosolvent systems. Full article

The expansion of native invasive plants severely impacts alpine meadow ecosystems and regional development on the Qinghai-Xizang Plateau by reducing vegetation productivity and hindering livestock production. However, the rules underlying their effects on forage grass establishment and effective mitigation strategies remain poorly understood. Here, using three main allelochemicals—benzoic acid (BA), caffeic acid (CA), and p-hydroxybenzoic acid (HA)—from typical native invasive plants, we investigated concentration-dependent effects (0, 100, 300, and 500 mg/L) on the seed germination and seedling growth of four common forage species: Festuca elata Keng ex E. B. Alexeev (FE), Lolium perenne L. (LP), Medicago sativa L. (MS), and Trifolium repens L. (TR). Our findings revealed a concentration-dependent hormesis effect: low concentrations stimulated germination and growth, while inhibition intensified with increasing concentrations. Roots exhibited significantly higher sensitivity than stems (p < 0.01). The phytotoxic intensity of allelochemicals on forage grass growth follows the order BA > CA > HA. For germination (germination rate/potential), sensitivity orders were FE > LP > TR > MS and LP > FE > TR > MS, respectively. For seedling growth, toxicity orders were TR > MS > FE > LP (root length), TR > FE > MS > LP (root weight), TR > MS > FE > LP (stem length), and TR > FE > LP > MS (stem weight). In summary, different allelochemicals exerted significantly varied effects on the germination and growth of distinct forage grass species. Therefore, forage species selection should consider local allelochemical profiles, or alternatively, grass-legume mixtures could be employed to enhance biomass yield. Our findings provide valuable insights for developing effective grassland restoration strategies. Full article

This study investigates the elastic properties of bio-epoxy composites reinforced with natural fibres (flax, hemp) and synthetic fibres (S-glass), with particular focus on the effect of the fibre volume fraction (VF) ranging from 10% to 70%. Three-dimensional representative volume element (RVE) models were developed for single-fibre, hybrid, and multi-fibre systems. The mean-field homogenisation (MF) approach, based on the Mori–Tanaka scheme, and finite element analysis (FEA) with periodic boundary conditions were employed to predict the effective elastic properties, including longitudinal, transverse, and shear moduli, as well as Poisson’s ratio. These numerical predictions were validated against analytical models, including the rule of mixtures, Chamis, and composite cylinder assemblage (CCA) methods. The results demonstrate that increasing the VF enhances longitudinal, transverse, and shear moduli while reducing Poisson’s ratio in natural fibre composites. The good agreement between numerical, semi-analytical, and analytical methods validates the 3D RVE models as useful tools for predicting the properties of multi-hybrid natural fibre composites, supporting their design for lightweight structural applications. Full article

Natural fiber-reinforced unidirectional composites are increasingly adopted in modern industries due to their superior mechanical performance and desirable properties from both material and engineering perspectives. Among various approaches, representative volume element (RVE) generation and analysis is considered one of the most suitable and convenient methods for predicting the elastic moduli of composites. The main aim of this study is to investigate and compare the elastic moduli of natural fiber–reinforced unidirectional composite RVEs using theoretical, numerical, and machine learning models. The numerical predictions in this study were generated using the ANSYS Material Designer tool (version ANSYS 19). A comparison was made between experimental results reported in the literature and different theoretical models, showing high accuracy in validating these numerical outcomes. A dataset comprising 1600 samples was generated from numerical models in combination with the well-known theory of RVE, namely rule of mixture (ROM), to train and test two machine learning algorithms: Random Forest and Linear Regression, with the goal of predicting three major elastic moduli—longitudinal Young’s modulus (E₁₁), in-plane shear modulus (G₁₂), and major Poisson’s ratio (V₁₂). To evaluate model performance, mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R²) were calculated and compared against datasets with and without the theoretical values as input variables. The performance metrics revealed that with the theoretical values, both Linear Regression and Random Forest predict E₁₁, G₁₂, and V₁₂ well, with a maximum MSE of 0.033 for G₁₂ and an R² score of 0.99 for all cases, suggesting they can predict the mechanical properties with excellent accuracy. However, the Linear Regression model performs poorly when theoretical values are not included in the dataset, while Random Forest is consistent in accuracy with and without theoretical values. Full article

Ceramic Matrix Composites (CMCs) have found numerous applications in aerospace, automotive and space vehicles due to their light weight and ability to withstand extreme temperatures. To develop a design criterion for CMCs, elastic properties at different scales need to be evaluated. In this research, elastic properties of CMCs are evaluated at the micro- and meso-level using representative volume element (RVE) in the Ansys Material Designer module. These properties are then validated using various analytical models including Rule of Mixture (ROM), the Chamis Model and the Mori–Tanaka Model. In-plane elastic properties (E11 and G12) of numerical models are in close agreement with the analytical models at both micro- and mesoscales. However, for out of plane properties (E22, G23), Mori–Tanaka Model provides the highest and the Chamis Model provides the lowest. Full article

Dy@C₈₂ is one of the metallofullerenes studied as dopants for improvements of stability and performance of solar cells. Calculations should help in formulating rules for selections of fullerene endohedrals for such new applications in photovoltaics. Structure, energetics, and relative equilibrium populations of two potential-energy-lowest IPR (isolated pentagon rule) isomers of Dy@C₈₂ under high synthetic temperatures are calculated using the Gibbs energy based on molecular characteristics at the B3LYP/6-31G*∼SDD level. Dy@$C_{2v}\left(9\right)$-C₈₂ and Dy@$C_s\left(6\right)$-C₈₂ are calculated as 58 and 42%, respectively, of their equilibrium mixture at a synthetic temperature of 1000 K, in agreement with observations. The Dy@$C_{2v}\left(9\right)$-C₈₂ species is found as lower in the potential energy by 1.77 kcal/mol compared to the Dy@$C_s\left(6\right)$-C₈₂ isomer. Full article