Search Results (159)

Pharmaceutically active compounds (PhACs), pesticides, and poly- and perfluoroalkyl substances (PFAS) are increasingly detected in surface waters at trace concentrations, raising concerns for both aquatic systems and, consequently, human health. Conventional solutions are insufficient to achieve complete removal at trace compound concentrations, highlighting the need for advanced separation technologies. This study aims to comprehensively analyze rejection and removal mechanisms of selected PhACs, pesticides, and PFAS present in water solutions at reported environmentally relevant concentrations (300 ng L⁻¹), using two nanofiltration (NF) and one reverse osmosis (RO) polyamide membrane. PhACs, pesticides, and PFAS were selected to cover a broad range of physicochemical properties, specifically molecular mass (MM), dissociation constant (pKa), and octanol–water partition coefficient (logK_o/w). Rejection values ranged from 42.1% (acetaminophen) to apparent 100% (for multiple compounds), depending on water pH, solute properties, and membrane characteristics. Size exclusion and electrostatic interactions were identified as the primary removal mechanisms, with hydrophobic interactions having a lower impact, particularly for carbamazepine, bezafibrate, and perfluorooctane sulfonic acid (PFOS). Addition of sodium chloride (3 g L⁻¹) decreased rejection of most negatively charged compounds due to suppression of membrane surface charge, although clarithromycin and ofloxacin exhibited improved rejection. Presented results provide fundamental insight into compound-specific membrane rejection and highlight the importance of membrane–solute interactions under environmentally realistic conditions. The results support further optimization of NF and RO for targeted compound rejection and provide a baseline for data-driven membrane process modeling. Full article

Young’s modulus (E), one of the many material properties, changes in response to thermal actions. The magnitude of these changes also depends on the material used. This is particularly important when the materials used are components of lightweight radiant heating systems (LRHSs) without screeds. Adhesives or adhesive composites take over the role of the screed in LRHSs. The adhesives, which directly connect the thermal insulation layer and the floor, are responsible for the proper functioning of the heated floor. Therefore, changes in their Young’s modulus cause a loss of layer integrity and ultimately delamination of the floor. Thus, research was conducted on the variation of the Young’s modulus of deformable cement adhesive mortars, specifically types C2S1 and C2S2, used in LRHSs under thermal actions. The deformation values of adhesive mortar samples were measured in a thermal chamber, subjected to compressive strength tests, at temperatures from 30 °C to 50 °C. Deformation measurements of heated samples were performed using the extensometer technique. The measurement results were subjected to mathematical analysis using polynomial regression based on the least squares method and the “Madrid parabola” formulas. After analysis, it was assumed that the Young’s modulus E for the deformable C2S1 cement adhesive, depending on the thermal action taken in the study, falls within the range of 4600 MPa to 5800 MPa when the temperature is varied from 30 °C to 50 °C. Simultaneously, the Young’s modulus E remains constant over these temperatures, at 2300 MPa for the C2S2 adhesive. Knowledge of the Young’s modulus and other strength parameters of adhesive mortars connecting layers of lightweight heated floors or other partitions, subjected to temperature can directly impact their durability. This data can be used to analyse the performance of LRHSs and numerical calculation techniques for various building partitions, such as stairs, balconies, and terraces. Full article

The geometrical view of the electron as a spinning bivector leads to the partitioning of the electron’s energy into internal and external. The reduced Compton wavelength, ${\overline{\lambda }}_C$, is taken as the radius of the inertial ring (a disc), while $r_e$ characterizes the EM coupling scale. Within this picture, the fine-structure constant emerges as the structural ratio $\alpha =r_e/{\overline{\lambda }}_C$. We make the partitioning explicit, derive simple ratios among moments of inertia and stored energies, and compare the Bivector Standard Model with the Standard model. Full article

We study rainbow solutions to linear equations modulo a prime p, where the residue classes are partitioned into n color classes. Using the Fourier method, we derive a universal lower bound that depends only on the class densities and a single spectral parameter: the Fourier bias (the largest nontrivial Fourier coefficient) of each class. When the biases are at the square-root cancellation scale $p^{-1/2}$ (for random colorings, up to a $\sqrt{logp}$ factor), the bound recovers the optimal growth $p^{n-1}$ with an explicit leading constant and negligible error. Our results complement recent work: in low-bias regimes (pseudorandom or random) they yield sharper quantitative bounds with transparent constants, and the bound requires no extra hypotheses such as coefficient separability. Full article

A modified Johnson–Mehl–Avrami–Kolmogorov (JMAK) model, including the heating rates, was proposed in this study to improve the accuracy of isothermal austenite formation kinetics prediction. Since the ultrafast heating process affects the behavior of ferrite recrystallization and austenite formation before the isothermal process, which in turn influences the subsequent isothermal austenite formation kinetics, the effects of varying austenitization temperatures and heating rates on isothermal austenite formation in cold-rolled quenching and partitioning (Q&P) steel, which remain insufficiently understood, were systematically investigated. Under a constant heating rate, the austenite formation rate initially increases and subsequently decreases as the austenitization temperature rises from formation start temperature A_c1 to finish temperature A_c3, and complete austenitization is achieved more quickly at elevated temperatures. At a given austenitization temperature, an increased heating rate was found to accelerate the isothermal transformation kinetics and significantly reduce the duration required to achieve complete austenitization. The experimental results revealed that both the transformation activation energy ($Q$) and material constant ($k_0$) decreased with increasing heating rates, while the Avrami exponent ($n$) showed a progressive increase, leading to the development of the heating-rate-dependent modified JMAK model. The model accurately characterizes the effect of varying heating rates on isothermal austenite formation kinetics, enabling kinetic curves prediction under multiple heating rates and austenitization temperatures and overcoming the limitation of single heating rate prediction in existing models, with significantly broadened applicability. Full article

This work demonstrates that the four laws of classical thermodynamics apply to the statistics of symmetric observation distributions, and provides examples of how this can be exploited in uncertainty assessments. First, an expression for the partition function Z is derived. In contrast with general classical thermodynamics, however, this can be performed without the need for variational calculus, while Z also equals the number of observations N directly. Apart from the partition function $Z\equiv N$ as a scaling factor, three state variables m, n, and $ϵ$ fully statistically characterize the observation distribution, corresponding to its expectation value, degrees of freedom, and random error, respectively. Each term in the first law of thermodynamics is then shown to be a variation on $\delta m^2=\delta {(nϵ)}^2$ for both canonical (constant n and $ϵ$) and macro-canonical (constant $ϵ$) observation ensembles, while micro-canonical ensembles correspond to a single observation result bin having $\delta m^2=0$. This view enables the improved fitting and combining of observation distributions, capturing both measurand variability and measurement precision. Full article

The classical definition of mechanical work, W = F × D, assumes that work depends solely on force magnitude and displacement, independent of loading rate. However, biological tissues exhibit inherent rate sensitivity—muscles demonstrate velocity-dependent force generation governed by Hill’s force–velocity relationship, while connective tissues and joints show load-rate-dependent stiffness and injury thresholds. These rate effects profoundly influence mechanical work, energy dissipation, and functional outcomes. In this work, we revisit the work–energy framework within biomechanics and biomaterials contexts, combining theoretical models, simulations, and a proposed rate-matched nano–bio indentation experiment to quantify how loading rate modulates energy partitioning between recoverable elastic storage and irreversible viscous dissipation. Our analyses span muscle contraction, viscoelastic tissue mechanics, and nanoparticle–membrane interactions, revealing that rapid loading markedly increases viscous dissipation and total mechanical work, even when peak force and displacement remain constant. We demonstrate that classical quasi-static formulations underestimate energy costs and tissue stresses by neglecting temporal dynamics and nonlinear material responses. Our multi-physics experimental–simulation platform bridges this gap, enabling controlled investigation of rate-dependent biomechanical phenomena at the nano–bio interface. These insights inform biomaterials design by emphasizing rate-matching viscoelastic properties to native tissues and guide experimental biomechanics toward capturing full dynamic histories. This unified framework advances understanding of rate-dependent mechanical work, improving predictive modeling, optimizing therapeutic delivery, and enhancing design in sports science, orthopedics, rehabilitation, and nanomedicine. Full article

This study presents the design and microstructural investigation of a single-crystal (SX) Re-bearing high-entropy superalloy (HESA-X1) featuring a thermally stable γ–γ′–γ hierarchical microstructure. The alloy exhibits FCC γ nanoparticles embedded within L1₂-ordered γ′ precipitates, themselves distributed in a γ matrix, with the suppression of detrimental topologically close-packed (TCP) phases. To elucidate solidification behavior and phase stability, Scheil–Gulliver and TC-PRISMA simulations were conducted alongside SEM and XRD analyses. Near-atomic scale analysis in 3D using Atom Probe Tomography (APT) revealed pronounced elemental partitioning, with Re strongly segregating to the γ matrix, while Al and Ti were preferentially enriched in the γ′ phase. Notably, Re demonstrated a unique partitioning behavior compared to conventional superalloys, facilitating the formation and stabilization of γ nanoparticles during two-step aging (Ag-2). These γ nanoparticles significantly contribute to improved mechanical properties. Long-term aging (up to 200 h) at 750–850 °C confirmed exceptional phase stability, with minimal coarsening of γ′ and retention of γ nanoparticles. The coarsening rate constant K of γ′ at 750 °C was significantly lower than that of Re-free HESA, confirming the diffusion-suppressing effect of Re. These findings highlight critical roles of Re in enhancing microstructural stability by reducing atomic mobility, enabling the development of next-generation HESAs with superior thermal and mechanical properties for high-temperature applications. Full article

The non-monotonic behavior of amperometric enzyme-based biosensors under uncompetitive and noncompetitive (mixed) substrate inhibition is investigated computationally using a two-compartment model consisting of an enzyme layer and an outer diffusion layer. The model is based on a system of reaction–diffusion equations that includes a nonlinear term associated with non-Michaelis–Menten kinetics of the enzymatic reaction and accounts for the partitioning between layers. In addition to the known effect of substrate inhibition, where the maximum biosensor current differs from the steady-state output, it has been determined that external diffusion limitations can also cause the appearance of a local minimum in the current. At substrate concentrations greater than both the Michaelis–Menten constant and the uncompetitive substrate inhibition constant, and in the presence of external diffusion limitation, the transient response of the biosensor, after immersion in the substrate solution, may follow a five-phase pattern depending on the model parameter values: it starts from zero, reaches a global or local maximum, decreases to a local minimum, increases again, and finally decreases to a steady intermediate value. The biosensor performance is analyzed numerically using the finite difference method. Full article

The occurrence of toxic metal(loid)s in surface freshwater is a global concern due to its impacts on human and ecosystem health. Conceptual and quantitative metal(loid) models are needed to assess the impact of metal(loid)s in watersheds affected by acid rock drainage. Few case studies have focused on arid and semiarid headwaters, with scarce hydrological and hydrochemical information. This work reports the use of WASP8 (US EPA) to model Al, Fe, As, Cu, and SO₄²⁻ concentrations in the Upper Elqui River watershed in north–central Chile. Calibrated model performance for total concentrations was “good” (25.9, RRMSE; 0.7, R²-d) to “very good” (0.8–0.9, R²-d). The dissolved concentrations ranged between “acceptable” (56.3, RRMSE), “good” (28.6, RRMSE; 0.7 d), and “very good” (0.9, R²-d). While the model validation achieved mainly “very good” (0.8–0.9, R²-d) predictions for total concentrations, the predicted dissolved concentrations were less accurate for all indicators. Sensitivity analysis showed that the partition coefficient is a sensitive constant for estimating dissolved concentrations, and that integrating sorption and sediment interaction reduces the model error. This work highlights the need for detailed and site-specific information on the reactive and hydrodynamic properties of suspended solids, which directly impact the partition coefficient, sedimentation, and resuspension velocity calibration. Full article

Among the multiple important properties that characterize strong S-boxes for symmetric cryptography and are used in their designs, this study focuses on two: the non-linearity property, a classical security metric, and the confusion coefficient variance property, a statistical proxy for side channel resistance under the Hamming weight leakage model. Given an S-box, two sets can be created: the set of affine-shifted S-boxes, where S-boxes have the same non-linearity value, and the set of Hamming weight classes, where S-boxes have the same confusion coefficient variance value. The inherent values of these two properties ensure resistance to cryptographic attacks; however, if the value of one property increases, it will imply a decrease in the value of the other property. In view of the aforementioned fact, attaining a trade-off becomes a complex undertaking. The impetus for this research stems from the following hypothesis: if an initial S-box already exhibits a trade-off, it would be advantageous to employ a method that generates new S-boxes while preserving the balance. A thorough review of the extant literature reveals the absence of any methodology that encompasses the aforementioned elements. The present paper proposes a novel methodology for generating an affine-shifted subset of S-boxes, ensuring that the resulting subset possesses the same confusion coefficient variance value. We provide insights on the optimal search strategy to optimize non-linearity and confusion coefficient variance. The proposed methodology guarantees the preservation of constant values on the designated. It is possible to incorporate these properties into a comprehensive design scheme, in which case the remaining S-box properties are to be examined. We also demonstrate that, despite the fact that this subset contains S-boxes with the theoretical resistance to side channel attacks under the Hamming weight model, the S-boxes are in different Hamming weight classes. Full article

The spread of per- and polyfluoroalkyl substances (PFAS) in the environment poses a severe threat to soil organisms, aquatic life, and human health. Many PFAS compounds are mobile and easily transported from soils to groundwater and further to surface waters. Leaching tests are valuable tools for assessing the site-specific leaching behaviour of contaminants. Here, we report the results of an evaluation of two standardized leaching tests for PFAS-contaminated soil materials: the batch test (ISO 21268-2:2019) using either demineralized water or 1 mM CaCl₂ as leachants (liquid-to-solid (L/S) ratio of 10) and the up-flow percolation test (ISO 21268-3:2019) using 1 mM CaCl₂ as leachant. One field-contaminated soil and three spiked (12 PFAS compounds) soils (aged 5 months) were included in the study. Desorption kinetics in the batch test were fast and equilibrium was obtained for all PFAS compounds within 24 h, the prescribed equilibration time. The same solubility was obtained for short-chain PFAS (PFBA, PFHxA, PFHpA, PFBS) in demineralized water and 1 mM CaCl₂, whereas significantly lower solubility was often observed for long-chain PFAS in CaCl₂ than in water, probably due to decreased charge repulsion between soil surfaces and PFAS compounds. In the up-flow percolation test, concentrations of short-chain PFAS in leachates decreased rapidly with increasing L/S, in contrast to long-chain PFAS, where concentrations decreased gradually or remained constant. Solid–solution partitioning coefficients (K_d), calculated from the data of the batch and percolation tests (1 mM CaCl₂), were generally in agreement, although differing by more than three orders of magnitude between different PFAS compounds. Uncertainties and pitfalls when calculating K_d values from leaching test data are also explored. Full article

The practical implementation of transition state theory (TST) commonly assumes equivalence between theoretical and experimentally determined rate constants, represented by Arrhenius parameters—the activation energy and pre-exponential factor. Here, we employed the General Rate Equation (GRE) to analyse solid–gas-phase thermolysis in two paradigms: mass loss (e.g., calcite decomposition) and mass gain (e.g., methane pyrolysis leading to solid carbon formation). By partitioning the Gibbs free energy of activation into forwards and reverse contributions, plus an additional term accounting for concurrent physical phenomena (notably nucleation and diffusion-viscosity effects), we derived an empirical universal expression relating both Arrhenius parameters and $∆G^{+}$ across 500–1500 K. We further demonstrate the utility of the isokinetic temperature for interpreting cases where only Kinetic Compensation or Enthalpy–Entropy Compensation effects are observed. This framework unifies kinetic and thermodynamic descriptions of complex thermolysis processes. Full article

Low-impact development (LID) facilities serve as a fundamental approach in urban stormwater management. However, significant variations in land use among different plots lead to discrepancies in runoff reduction demands, frequently leading to either the over- or under-implementation of LID infrastructure. To address this issue, we propose a cost-effective optimization framework grounded in the concept of “Capacity Trading (CT)”. The study area was partitioned into multi-scale grids (CT-100, CT-200, CT-500, and CT-1000) to systematically investigate runoff redistribution across heterogeneous land parcels. Integrated with the Sequential Least Squares Programming (SLSQP) optimization algorithm, LID facilities are allocated according to demand under two independent constraint conditions: runoff coefficient ($\phi$ ≤ 0.49) and runoff control rate ($\eta$ ≥ 70%). A quantitative analysis was conducted to evaluate the construction cost and reduction effectiveness across different trading scales. The key findings include the following: (1) At a constant return period, increasing the trading scale significantly reduces the demand for LID facility construction. Expanding trading scales from CT-100 to CT-1000 reduces LID area requirements by 28.33–142.86 ha under the $\phi$-constraint and 25.5–197.19 ha under the $\eta$-constraint. (2) Systematic evaluations revealed that CT-500 optimized cost-effectiveness by balancing infrastructure investments and hydrological performance. This scale allows for coordinated construction, avoiding the high costs associated with small-scale trading (CT-100 and CT-200) while mitigating the diminishing returns observed in large-scale trading (CT-1000). This study provides a refined and efficient solution for urban stormwater management, overcoming the limitations of traditional approaches and demonstrating significant practical value. Full article

Line feature matching is a crucial aspect of computer vision and image processing tasks, attracting significant research attention. Most line matching algorithms predominantly rely on local feature descriptors or deep learning modules, which often suffer from low robustness and poor generalization. In response, this paper presents a novel line feature matching approach grounded in point–line invariants through spatial invariant relationships. By leveraging a robust point feature matching algorithm, an initial set of point feature matches is acquired. Subsequently, the line feature supporting area is partitioned, and a constant ratio invariant is formulated based on the distances from point to line features within corresponding neighborhood domains. Additionally, a direction vector invariant is also introduced, jointly constructing a dual invariant for line matching. An initial matching matrix and line feature match pairs are derived using this dual invariant. Subsequent geometric constraints within line feature matches eliminate residual outliers. Comprehensive evaluations under diverse imaging conditions, along with comparisons to several state-of-the-art algorithms, demonstrate that our proposal achieved remarkable performance in terms of both accuracy and robustness. Our implementation code will be publicly released upon the acceptance of this paper. Full article