Search Results (338)

Solubility studies are an essential requirement for the development of more efficient industrial processes. In this context, the use of cosolvents is a relevant strategy in pharmaceutical sciences, especially when dealing with green solvents such as water (W (2)) and Polyethylene glycol 400 (PEG 400 (1)). The objective of this study is to thermodynamically analyze the solubility of isoniazid in {PEG 400 (1) + W (2)} cosolvent mixtures at seven temperatures (288.15 to 318.15 K). The study was conducted by calculating thermodynamic functions from experimental solubility data determined using the flask shaking method, employing UV spectrophotometry as the quantification technique. The dissolution process was shown to be endothermic and entropy-driven. Although maximum solubility would be expected to be achieved in a cosolvent mixture, given that the solubility parameter of isoniazid (30.54 MPa^1/2) has an intermediate value between the two pure solvents (PEG 400 ≈ 22.5 MPa^1/2; Water $\approx 47.8$ MPa^1/2), maximum solubility is achieved in pure PEG 400 and the lowest solubility is achieved in pure water. Full article

To improve the accuracy and robustness of lithium-ion battery state of charge (SOC) estimation, this paper proposes a generalized mixture maximum correlation-entropy criterion-based adaptive extended Kalman filter (GMMCC-AEKF) algorithm, addressing the performance degradation of the traditional extended Kalman filter (EKF) under non-Gaussian noise and inaccurate initial conditions. Based on the GMMCC theory, the proposed algorithm introduces an adaptive mechanism and employs two generalized Gaussian kernels to construct a mixed kernel function, thereby formulating the generalized mixture correlation-entropy criterion. This enhances the algorithm’s adaptability to complex non-Gaussian noise. Simultaneously, by incorporating adaptive filtering concepts, the state and measurement covariance matrices are dynamically adjusted to improve stability under varying noise intensities and environmental conditions. Furthermore, the use of statistical linearization and fixed-point iteration techniques effectively improves both the convergence behavior and the accuracy of nonlinear system estimation. To investigate the effectiveness of the suggested method, experiments for SOC estimation were implemented using two lithium-ion cells featuring distinct rated capacities. These tests employed both dynamic stress test (DST) and federal test procedure (FTP) profiles under three representative temperature settings: 40 °C, 25 °C, and 10 °C. The experimental findings prove that when exposed to non-Gaussian noise, the GMMCC-AEKF algorithm consistently outperforms both the traditional EKF and the generalized mixture maximum correlation-entropy-based extended Kalman filter (GMMCC-EKF) under various test conditions. Specifically, under the 25 °C DST profile, GMMCC-AEKF improves estimation accuracy by 86.54% and 10.47% over EKF and GMMCC-EKF, respectively, for the No. 1 battery. Under the FTP profile for the No. 2 battery, it achieves improvements of 55.89% and 28.61%, respectively. Even under extreme temperatures (10 °C, 40 °C), GMMCC-AEKF maintains high accuracy and stable convergence, and the algorithm demonstrates rapid convergence to the true SOC value. In summary, the GMMCC-AEKF confirms excellent estimation accuracy under various temperatures and non-Gaussian noise conditions, contributing a practical approach for accurate SOC estimation in power battery systems. Full article

In this paper, we propose FedENLC, an end-to-end noisy label correction model that performs model training and label correction simultaneously to fundamentally mitigate the label noise problem of federated learning (FL). FedENLC consists of two stages. In the first stage, the proposed model employs Symmetric Cross Entropy (SCE), a robust loss function for noisy labels, and label smoothing to prevent the model from being biased by incorrect information in noisy environments. Subsequently, a Bayesian Gaussian Mixture Model (BGMM) is utilized to detect noisy clients. BGMM mitigates extreme parameter bias through its prior distribution, enabling stable and reliable detection in FL environments where data heterogeneity and noisy labels coexist. In the second stage, only the top noisy clients with high noise ratios are selectively included in the label correction process. The selection of top noisy clients is determined dynamically by considering the number of classes, posterior probabilities, and the degree of data heterogeneity. Through this approach, the proposed model prevents performance degradation caused by incorrect detection, while improving both computational efficiency and training stability. Experimental results show that FedENLC achieves significantly improved performance over existing models on the CIFAR-10 and CIFAR-100 datasets under data heterogeneity settings along with four noise settings. Full article

The use of waste rubbers and polyurethane has a significant impact on the abrasion resistance of the porous elastic road surface (PERS) mixture. The purpose of this work is to study the anti-abrasion performance of the PERS mixture under different contents of waste rubbers. First, features of the surface of the PERS mixture were collected by image processing technology. Then, the abrasion performance of the mixture was studied by image processing and wear tests. The correlation between the surface texture parameters and the anti-abrasion performance of the mixture was analyzed by the gray entropy correlation method. It is found that the change of convex particle area in the equivalent diameter range of 2–5 mm had the greatest correlation with the abrasion resistance of the PERS mixture. The effect of the waste rubber content of the mixture on the anti-abrasion performance was investigated, and a waste rubber content of 10% showed the best anti-abrasion performance. It is expected that this work can provide a new method for analyzing the anti-abrasion performance of functional pavement. 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

Active restoration structures such as microtopographic water-harvesting designs are widely implemented in dryland ecosystems to improve soil moisture, reduce erosion, and promote vegetation recovery. We assessed the combined effects of planted species identity, planting diversity (mono-, bi- and multi-species mixtures), and micro-catchment (half-moon) structures on seedling performance and spontaneous natural regeneration in a hyper-arid restoration pilot site in Sharaan National Park, northwest Saudi Arabia. Thirteen native plant species, of which four—Ochradenus baccatus, Haloxylon persicum, Haloxylon salicornicum, and Acacia gerrardii—formed the dominant planted treatments, were established in 18 half-moons and monitored for survival, growth, and natural recruitment. Seedling survival after 20 months differed significantly among planting treatments, increasing from 58% in mono-plantings to 69% in bi-plantings and 82% in multi-plantings (binomial GLMM, p < 0.001), indicating a positive effect of planting diversity on establishment. Growth traits (height, collar diameter, and crown dimensions) were synthesized into an Overall Growth Index (OGI) and an entropy-weighted OGI (EW-OGI). Mixed-effects models revealed strong species effects on both indices (F_12,369 ≈ 7.2, p < 0.001), with O. baccatus and H. persicum outperforming other taxa and cluster analysis separating “fast expanders”, “moderate growers”, and “decliners”. Trait-based modeling showed that lateral crown expansion was the main driver of overall performance, whereas stem thickening and fruit production contributed little. Between 2022 and 2024, half-moon soils exhibited reduced electrical conductivity and exchangeable Na, higher organic carbon, and doubled available P, consistent with emerging positive soil–plant feedbacks. Spontaneous recruits were dominated by perennials (≈67% of richness), with perennial dominance increasing from mono- to multi-plantings, although Shannon diversity differences among treatments were small and non-significant. The correlation between OGI and spontaneous richness was positive but weak (r = 0.29, p = 0.25), yet plots dominated by O. baccatus hosted nearly two additional spontaneous species relative to other plantings, highlighting its strong facilitative role. Overall, our results show that half-moon micro-catchments, especially when combined with functionally diverse native plantings, can simultaneously improve soil properties and promote biotic facilitation, fostering a transition from active intervention to passive, self-sustaining restoration in hyper-arid environments. Full article

Attention-Deficit/Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition that is typically identified through behavioral assessments and subjective clinical reports. However, electroencephalography (EEG) offers a cost-effective and non-invasive alternative for capturing neural activity patterns closely associated with this disorder. Despite this potential, EEG-based ADHD classification remains challenged by overfitting, dependence on extensive preprocessing, and limited interpretability. Here, we propose a novel neural architecture that integrates transformer-based temporal attention with Gaussian mixture functional connectivity modeling and a cross-entropy loss regularized through $\alpha$-Rényi mutual information, termed T-GARNet. The multi-scale Gaussian kernel functional connectivity leverages parallel Gaussian kernels to identify complex spatial dependencies, which are further stabilized and regularized by the $\alpha$-Rényi term. This design enables direct modeling of long-range temporal dependencies from raw EEG while enhancing spatial interpretability and reducing feature redundancy. We evaluate T-GARNet on a publicly available ADHD EEG dataset using both leave-one-subject-out (LOSO) and stratified group k-fold cross-validation (SGKF-CV), where groups correspond to control and ADHD, and compare its performance against classical and modern state-of-the-art methods. Results show that T-GARNet achieves competitive or superior performance (82.10% accuracy), particularly under the more challenging SGKF-CV setting, while producing interpretable spatial attention patterns consistent with ADHD-related neurophysiological findings. These results underscore T-GARNet’s potential as a robust and explainable framework for objective EEG-based ADHD detection. Full article

Early risk prediction is essential for hospitalized prostate cancer (PCa) patients, who face acute events, such as mortality, ICU transfer, AKI (acute kidney injury), ED30 (unplanned 30-day Emergency Department revisit), and prolonged LOS (length of stay). We developed an MMoE (Multitask Mixture-of-Experts) model that jointly predicts these outcomes from the features of the multimodal EHR (Electronic Health Records) in MIMIC-IV (3956 admissions; 2497 patients). A configuration with six experts delivered consistent gains over strong single-task baselines. On the held-out test set, the MMoE improved rare-event detection (mortality AUPRC (Area Under the Precision-Recall Curve) of $0.163$ vs. $0.091$, $+79\%$) and modestly boosted ED30 discrimination (AUROC (Area Under the Receiver Operating Characteristic Curve) $\approx 0.66$ with leakage-safe ClinicalBERT fusion) while maintaining competitive ICU and AKI performance. Expert-routing diagnostics (top-1 shares, entropy, and task-dead counts) revealed clinically coherent specialization (e.g., renal signals for AKI), supporting interpretability. An efficiency log showed that the model is compact and deployable (∼85 k parameters, $0.34$ MB; $0.027$ s/sample); it replaced five single-task predictors with a single forward pass. Overall, the MMoE offered a practical balance of accuracy, calibrated probabilities, and readable routing for the prognostic layer of digital-twin pipelines in oncology. Full article

The aim of this study is to generate density and viscosity data for carbon capture utilization and storage (CCUS) mixtures using equilibrium molecular dynamics (EMD) simulations. Binary CO₂ mixtures with SO₂ and H₂S impurities at mole fractions of 0.05, 0.10, and 0.20 were constructed. Simulations were performed across a temperature range of 223–323.15 K and at pressures up to 27.5 MPa using ms2 software. The simulation results were compared with predictions from established models. These included the Multi-Fluid Helmholtz Energy Approximation (MFHEA) for density, and the Lennard-Jones (LJ), Residual Entropy Scaling (ES-NIST), and Extended Corresponding States (SUPERTRAPP) models for viscosity. Available experimental data from the literature were also used for validation. Density predictions showed excellent agreement with MFHEA, especially for CO₂ + SO₂ mixtures, with %AARD values below 1% for 0.05 and 0.10, and 1.60% for 0.20 mole fraction SO₂. For CO₂ + H₂S mixtures, deviations also increased with impurity concentration, reaching a maximum %AARD of 4.72% at 0.20 mole fraction. Viscosity data were validated against experimental values from the literature for a CO₂ + CH₄ (xCH₄ = 0.25) mixture, showing strong agreement with both models and experiments. This confirms the reliability of the MD approach and the thermodynamic models, even for systems lacking experimental data. However, viscosity estimates showed higher uncertainty at lower temperatures and higher densities, a known limitation of the Green–Kubo method. This highlights the importance of selecting an appropriate correlation time to ensure the pressure correlation functions reach a plateau, avoiding inaccurate or uncertain viscosity values. Full article

Objectives: This study proposes a systematic strategy for optimizing sustained-release formulations using mixture experiments. Methods: Model variables were identified and screened via LASSO regression, Smoothly Clipped Absolute Deviation (SCAD), and Minimax Concave Penalty (MCP), leading to the construction of a quadratic inference function-based objective model. Using this model, three multi-objective optimization algorithms—NSGA-III, MOGWO, and NSWOA—were employed to generate a Pareto-optimal solution set. Solutions were further evaluated through the entropy weight method combined with TOPSIS to reduce subjective bias. Results: The MCP-screened model demonstrated strong fit (AIC = 19.8028, BIC = 45.2951) and suitability for optimization. Among the Pareto-optimal formulations, formulation 45, comprising HPMC K4M (38.42%), HPMC K100LV (13.51%), MgO (6.28%), lactose (17.07%), and anhydrous CaHPO₄ (7.52%), exhibited superior performance, achieving cumulative release rates of 22.75%, 64.98%, and 100.23% at 2, 8, and 24 h, respectively. Compared with the original formulation, drug release was significantly improved across all time points. Conclusions: This integrated workflow effectively accounted for component interactions and repeated measurements, providing a robust and scientifically grounded approach for optimizing multi-component sustained-release formulations. Full article

Compost amendments are widely recognized as an effective strategy for improving soil quality, modulating enzyme activities, and enhancing nitrogen cycling. Urease, a key enzyme in nitrogen transformation, is characterized by kinetic parameters such as the maximum reaction rate (V_max) and Michaelis constant (K_m), as well as thermodynamic attributes including temperature sensitivity (Q₁₀), activation energy (E_a), enthalpy change (ΔH), Gibbs free energy change (ΔG), and entropy change (ΔS). However, how different compost sources regulate urease kinetics, thermodynamics, and nitrogen availability remains poorly understood. In this study, we evaluated the effects of three compost amendments—mushroom residue (MR), mushroom residue–straw mixture (MSM), and leaf litter (LL)—on urease kinetics and thermodynamics in a temperate agroecosystem. The MSM treatment significantly enhanced urea hydrolysis capacity and catalytic efficiency. In contrast, LL treatment resulted in the highest Km value, indicating a substantially lower enzyme-substrate affinity. Furthermore, MSM reduced the E_a and increased the thermal stability of urease, thereby supporting enzymatic performance under fluctuating temperatures. Collectively, our findings highlight that compost composition is a critical determinant of urease function and nitrogen turnover. By elucidating the coupled kinetic and thermodynamic responses of urease to compost inputs, this study provides mechanistic insights to guide optimized soil management and sustainable nitrogen utilization in temperate agricultural systems. Full article

This study presents a dual-phase lanthanum manganite ceramic composite based on a mixture of equal weight ratios of La_0.7Ca_0.2Sr_0.1MnO₃ and La_0.7Ca_0.25Sr_0.05MnO₃ designed to enhance the magnetocaloric effect (MCE) of individual compounds, under a low magnetic field (≤18 kOe). X-ray diffraction (XRD) analysis revealed the coexistence of two orthorhombic manganite phases corresponding to the individual compounds, with no secondary phases detected. Temperature-dependent magnetization measurements in the composite evidenced two Curie temperatures at 286.8 K and 307.6 K, reflecting the effect of Ca²⁺ and Sr²⁺ concentrations. Arrott plots and β parameters confirmed that the phase transition is of second order. Although the maximum magnetic entropy change (ΔS_M) of the composite is slightly lower than that of the individual manganite phases, its relative cooling power (RCP) reaches 188.82 J·kg⁻¹, with an extended operational temperature window (OTW) of approximately 85 K, spanning from around 243 K to 328 K. This broad OTW enables efficient operation over a wider temperature range compared to similar materials, such as the individual La_0.7Ca_0.2Sr_0.1MnO₃ and La_0.7Ca_0.25Sr_0.05MnO₃ compounds, which exhibit an RCP of 55.24 and 65.12 J·kg⁻¹, respectively, under a comparable magnetic field (~18 kOe). The improved magnetocaloric performance is attributed to interfacial exchange coupling and strain-mediated effects that broaden the ΔS_M response and generate a non-additive RCP. These results demonstrate that interphase coupling and microstructural tuning effectively broaden the operating temperature range for magnetic refrigeration under moderate fields, making this composite a strong candidate for practical cooling applications. Full article

The synthesis, reaction process, and mechanical properties of medium-entropy (TiVNb)₂AlC MAX phase materials were investigated. The Ti, V, Nb, Al, and C powders were mixed and sintered by the powder metallurgy method. The experimental results showed that the highest purity M₂AlC phase with a mass fraction of 95.8% was obtained when the raw material ratio was M(Ti:V:Nb):Al:C = 2:1.2:0.7 and the sintering temperature was 1450 °C. In order to explore the sintering process reactions and optimize the purity of sintered products, sintering was carried out under different temperatures and various molar ratios of raw materials. During the sintering process, the metal elements firstly reacted with aluminum to generate intermetallic compounds (IMCs), and with the increase in temperature, the IMCs gradually reacted with carbon to generate M₂AlC. Mechanical property tests revealed that the Vickers hardness of the medium-entropy (TiVNb)₂AlC material was 6.52 GPa, significantly higher than both the theoretical prediction based on the rule of mixtures and the hardness of traditional MAX phases. The severe lattice distortions in the polymeric solid solution structure contributed to this significant increase in hardness. In addition, the medium-entropy (TiVNb)₂AlC exhibited temperature-dependent friction behavior within the temperature range of room temperature to 400 °C, with the lowest friction coefficient observed at 200 °C when the sample was in contact with the bearing steel. This study provided an important theoretical and experimental basis for the synthesis and future application of medium-entropy MAX phase materials. Full article

This work evaluates a hydrogen-fueled planar micro-combustor featuring three triply periodic minimal surface (TPMS) structures, namely, gyroid, lidinoid, and Neovius matrix lattices, aiming to advance heat transfer processes and enhance system efficiency in micro-thermophotovoltaic (MTPV) applications. Through three-dimensional numerical investigations, a series of simulations are conducted under varying TPMS lengths, inlet volume flow rate, and inlet equivalence ratios to optimize the design and operating conditions. The outcomes reveal that increasing the length of the TPMS structures is an effective means of improving heat transfer from the combustion zone to the walls, as indicated by significant increases in both mean wall temperature and radiation efficiency. However, longer internal structures reduce the uniformity of wall temperature and slightly increase entropy generation. Of the three topologies, the Neovius lattice demonstrates superior performance in all length scales, exhibiting a marginal improvement over the gyroid and a substantially greater advantage over the lidinoid structure. Increasing the inlet volume flow rate enhances wall temperature and its uniformity; however, the performance parameters decrease for all structures, indicating a limitation of the micro-combustor in benefiting from higher input power. Notably, the gyroid structure shows a lower rate of performance degradation at higher velocities, making it a potentially ideal design under such conditions. Finally, varying the equivalence ratio identifies the stoichiometric condition as optimal, yielding superior performance metrics compared to both lean and rich mixtures. Full article

To facilitate the sustainable recycling of retired wind turbine blades (RWTBs) and promote the green development of the wind energy sector in China, this study investigates the reuse of crushed RWTBs as composite fiber additives in asphalt mixtures. A systematic optimization of the incorporation process was conducted, and the effects of RWTB fibers on pavement performance were comprehensively evaluated. Using the entropy weight method, the optimal fiber content and particle size were identified as 0.15 wt% and 0.3–1.18 mm, respectively. The experimental results demonstrated that, under optimal conditions, the dynamic stability, low-temperature flexural tensile strain, Marshall stability after water immersion, and freeze-thaw splitting strength of the base asphalt mixture increased by 27.1%, 23.8%, 9.9%, and 8.1%, respectively. Microstructural analyses using SEM and EDS revealed that the reinforcing mechanism of RWTB fibers involves adsorption, bridging, and network formation, which collectively enhance the toughness and elasticity of the asphalt matrix. In addition, a comparative evaluation was performed using the Analytic Hierarchy Process (AHP), incorporating both performance and cost considerations. The comprehensive performance ranking of fiber-modified asphalt mixtures was consistent for both base and SBS-modified asphalt: BF AC-13 > RWTB AC-13 > GF AC-13 > PF AC-13 > unmodified AC-13. Overall, this study confirms the feasibility of high-value reuse of RWTB waste in road engineering and provides practical insights for advancing resource recycling and promoting sustainability within the wind power industry. Full article