Search Results (24,474)

While Forgetting Factor Recursive Least Square (FFRLS) algorithms with evaluation mechanisms have been developed to address SOC-dependent parameter mapping shifts and their efficacy has been proven in Li-ion batteries, their applicability to lithium–sulfur (Li-S) batteries remains uncertain due to different electrochemical characteristics. This study critically evaluates the applicability of a Fisher information matrix-constrained FFRLS framework for online parameter identification in Li-S battery equivalent circuit network (ECN) models. Experimental validation using distinct drive cycles showed that the identification results of polarization-related parameters are significantly biased between different current excitations, and root mean square error (RMSE) variations diverge by 100%, with terminal voltage estimation errors more than 0.05 V. The parametric uncertainty under variable excitation profiles and voltage plateau estimation deficiencies confirms the inadequacy of such approaches, constraining model-based online identification viability for Li-S automotive applications. Future research should therefore prioritize hybrid estimation architectures integrating electrochemical knowledge with data-driven observers, alongside excitation capturing specifically optimized for Li-S online parameter observability requirements and cell nonuniformity and aging condition consideration. Full article

Micro-pulse photon-counting LiDAR has difficulty accurately extracting geophysical information in strong-noise environments, with solar noise interference being a key limiting factor. This study proposes a hierarchical coarse-to-fine denoising framework, combining grid-based pre-filtering with an optimized horizontal and vertical recursive division method using Otsu’s method to achieve high time efficiency and denoising accuracy. First, an adaptive meshing strategy is employed to remove most of the noise in the data while retaining more than 99.1% of the signal. Subsequently, an alternating horizontal and vertical recursive division algorithm with automatically selected parameters is applied for denoising; the method was validated on ICESat-2 ATL03 data, GlobeLand30 V2020 data, and USGS 3DEP airborne radar data, where the method achieved a classification accuracy of more than 91.2%, with a several-fold reduction in runtime compared to traditional clustering methods. The framework demonstrates high efficiency, robustness, and computational scalability across diverse terrains, including polar, forest, and plains. It can contribute to geographic mapping, environmental protection, and ecological monitoring. Full article

There has been considerable scientific interest in third-order nonlinear optical materials for photonic applications. In particular, materials exhibiting a strong electronic optical Kerr effect serve as essential components in the ultrafast nonlinear photonic devices and are instrumental in the development of all-optical signal processing technologies. Therefore, the accurate prediction of material-relevant properties, such as second hyperpolarizabilities, remains a key topic in the search for efficient photonic materials. However, the field standards in quantum chemical computation are still inconsistent, as studies often lack a firm statistical foundation. This work presents a comprehensive in silico investigation based on multiple full-factorial experiments, aiming to clarify the strengths and limitations of various computational approaches. Our results indicate that the coupled-cluster approach at the CCSD level in its current response-equation implementations is not yet able to outperform the range-separated hybrid density functionals, such as LC-BLYP(0.33). The exceptional performance of the specifically tailored basis set Sadlej-pVTZ is also described. Not only was the presence of diffuse functions found to be mandatory, but also adding ample polarization functions is shown to be inefficient resource-wise. HF/Sadlej-pVTZ is proven to be reliable enough to use in molecular screening. Meta functionals are confirmed to produce poorly consistent results, and specific guidelines for constructing range-separated functionals for polarizability calculations are drawn out. Additionally, it was shown that many of the contemporary solvation models exhibit significant limitations in accurately capturing nonlinear optical properties. Therefore, further refinement in the current methods is pending. This extends to the statistical description as well: the mean absolute deviation descriptor is found to be deficient in rating various computational methods and should rather be replaced with the parameters of the linear correlation (the slope, the intercept, and the R²). Full article

Deep eutectic solvents (DESs) have been studied to obtain extracts from medicinal plants, aiming for a more environmentally friendly process. Aligned with this initiative, the use of predictive thermodynamic models for screening the best solvent represents a theoretical action to reduce experimental time and cost. Therefore, this study aimed to perform and validate a relative solubility screening of 5-methoxy-6,7-methylenedioxycoumarin and prenyletin-methyl-ether at 313 K in choline chloride, menthol, and betaine-based DES, using the COSMO-RS model in COSMOThermX software. The density of DES was also predicted with a maximum error of 7.31% for this property. Ultrasound-assisted extraction (UAE) with DES at 313 K, 30 min, and a solid/liquid ratio of 1:20 (w/w) was performed to confirm the theoretical solubility results experimentally, as the extracts were analyzed through ultrafast liquid chromatography (UFLC) for coumarin content. For the results, the coumarin molecules presented intense peaks in the nonpolar region of their σ-profile, and the relative solubility screening indicated the DES Men/Lau (2:1), known for its hydrophobic nature and low polarity, as the best DES to solubilize these coumarins. Nevertheless, the UFLC results, and the complementary solubility screening of pigments, showed an interaction preference of this DES with chlorophylls instead of coumarins. This result was corroborated by spectrophotometric analysis of the extracts in UV-Vis, demonstrating that experimental validation is still mandatory in extraction processes and that predictive methodologies such as COSMO-RS should be used as guiding tools and analyzed in a greater context, considering the complexity of plant matrices in the beginning of simulations. Full article

Niosomes are nanocarriers with a bilayer structure, consisting of a polar region and a non-polar region. This unique structure allows them to encapsulate compounds with varying polarities, addressing solubility challenges in the transport and delivery of bioactive molecules. The formation of niosomes involves key structural, geometric, and thermodynamic factors influenced by the choice of surfactants and preparation methods. These factors, including the critical packing factor and the hydrophilic–lipophilic balance (HLB), play a crucial role in determining the properties of the final niosomes. Additionally, the use of Tandford’s equations allows for the calculation of geometric parameters. These factors determine the structural integrity and functional properties of niosomes, making it possible to design functional niosomes with characteristics tailored for specific applications. This ability to design niosomes with desired properties is especially valuable in biomedical fields, where precise control over drug delivery and targeting is essential. This review highlights the importance of niosome formulation and presents examples of niosomes that have been functionalized for specific applications, including anticancer treatments, immunological treatments, and their action in the central nervous system. Full article

The Kolmogorov–Arnold network (KAN) is a regression model that is based on a representation of an arbitrary continuous multivariate function by a composition of functions of a single variable. Experimentally obtained datasets for regression models typically include uncertainties, which in some cases, cannot be neglected. The conventional way to account for the latter is to model confidence intervals of the systems’ outputs in addition to the expected values of the outputs. However, such information may be insufficient, and in some cases, researchers aim to obtain probability distributions of the outputs. The present paper proposes a method for estimating probability distributions of the outputs by constructing an ensemble of models. The suggested approach covers input-dependent probability distributions of the outputs and is capable of capturing the multi-modality, as well as the variation of the distribution type with the inputs. Although the method is applicable to any regression model, the present paper combines it with KANs, since their specific structure leads to the construction of computationally efficient models. The source codes are available online. Full article

Background: This study aimed to investigate the impact of the diabetic environment on the development of experimental autoimmune uveoretinitis (EAU) and the activation status of microglia in the eye. Methods: EAU was induced in wild-type (WT) and streptozotocin (STZ)-induced diabetic mice (STZ-EAU mice). Disease severity was assessed using funduscopy, optical coherence tomography (OCT), and histopathological analysis. The proportions of Th1, Th17, and regulatory T cells in the spleen were analyzed by flow cytometry. Retinal microglia were quantified using immunohistochemistry. To further characterize retinal cell populations and gene expression profiles, single-cell RNA sequencing (scRNA-seq) was performed. Results: STZ-EAU mice exhibited significant reductions in both the incidence and severity of EAU compared with WT-EAU mice. These were accompanied by a decreased proportion of Th1 cells, which are crucial for EAU pathogenesis, in the spleens of STZ-EAU mice. Retinal microglial accumulation was markedly reduced in STZ-EAU mice compared with WT-EAU mice. scRNA-seq analysis revealed a significant change in the microglial phenotype in STZ-EAU mice, characterized by decreased expression of MHC class I/II and the suppression of antigen presentation signaling pathways. Activated microglia in STZ-EAU mice showed reduced gene expression of M1 markers (CD68, CD74, and IL1B) and increased gene expression of M2 markers (MSR1, CD163, and MRC1), suggesting a shift toward an anti-inflammatory M2 phenotype. Conclusions: EAU is suppressed in STZ-induced diabetic mice, likely due to alterations in microglial polarization toward an M2 phenotype. These results suggest a decrease in T cell responses to pathogens in a diabetic environment, which could be one of the underlying factors for the increased susceptibility to infection in diabetic patients. Inhibiting the M2 polarization of microglia may reduce the susceptibility to infection in patients with diabetes. Full article

Increased Atlantic water transport and river discharge are more pronounced effects of global warming at high latitudes. Both phenomena may lead to changes in the species composition of small-celled algae populations in marine ecosystems, as well as to the emergence of new species. This study investigated the spatial distribution of picocyanobacterial (PC) abundance and the phylogenetic diversity of PC Synechococcus in the Kara Sea. PC abundance varied from 2 to 88 cells mL⁻¹ and increased with warming temperatures and decreasing salinity caused by river water influence. The contribution of Synechococcus to the total picophytoplankton biomass was low (<16%). The Synechococcus community was characterized at deep taxonomic level using amplicon sequencing targeting the petB gene. Diversity was low, revealing only Synechococcus subcluster 5.1 polar lineages I and IV, and euryhaline subcluster 5.2. Synechococcus subcluster 5.1.I represented on average 97% of the total reads assigned to cyanobacteria. For the first time, the presence of estuarine Synechococcus subcluster 5.2 was documented as far north as 82° N. Modified Atlantic water was the main source of cyanobacteria in the Kara Sea, followed by river discharge. Our study contributes to the understanding of PC sources in the Kara Sea and allows for the further monitoring of PC distribution and evolution. Full article

Reinforced concrete structures in harsh environments are highly vulnerable to structural damage caused by rebar corrosion. However, there remains a critical shortage of high-performance, environmentally friendly repair materials that integrate both structural restoration and long-term corrosion protection functionalities to address this issue. To meet this demand, this study innovatively developed an eco-friendly, high-performance repair material using lotus leaf extract (LLE)-modified mortar and systematically evaluated its corrosion protection performance and mechanisms under chloride attack conditions. The primary chemical constituents of LLE include alkaloids and flavonoids, rich in polar functional groups such as O–H, N–H, and C–O. The LLE modifier increased the fluidity of fresh cement paste, thereby improving its construction workability. A low dosage of LLE modifier promoted cement hydration. When the LLE dosage was 0.2 wt%, the 7-day and 28-day flexural strengths of the LLE-modified mortar increased by 16.8% and 7.48%, respectively, compared to those of unmodified mortar, while the compressive strengths increased by 30.6% and 14.5%, respectively. The LLE-modified mortar demonstrated significant protection against chloride corrosion, effectively inhibiting rebar corrosion. Electrochemical corrosion results indicated that compared to unmodified mortar, the modified mortar containing 0.5 wt% LLE exhibited an 80% improvement in protection efficiency against chloride corrosion. These results demonstrate that an appropriate dosage of LLE modifier can simultaneously optimize the fundamental properties of mortar and provide excellent chloride corrosion protection. Therefore, LLE-modified mortar shows promising application potential in integrated repair and corrosion protection engineering for reinforced concrete structures. Full article

Background: Magnolia alejandrae is a tree endemic to Tamaulipas, Mexico, distributed in the forests of the Sierra Madre Oriental. Objective: Our objective was to analyze the secondary metabolite profile of different parts of M. alejandrae and evaluate their antiproliferative activity in vitro. Methods: Different extracts of leaf, bark, and fruit were obtained using conventional and unconventional extraction methods with solvents of different polarity. The extracts were analyzed by Ultra-Performance Liquid Chromatography-Mass Spectra (UPLC-MS), and their antiproliferative activity against cancer cell lines was determined. Results: The primary yields of the extracts obtained from M. alejandrae ranged from 8.32% to 36.19%. Three hundred and twelve secondary metabolites previously reported from the Magnolia genus were detected. The most frequent were magnone A, pinoresinol, and yangambin. Honokiol and magnolol were not detected. Two of the extracts (FSW and BSW) had antiproliferative activity (IC₅₀ < 140 µg/mL) against HeLa, MCF-7, A549, U373, and PC3 cancer cell lines. The higher activity was against the A549 cell line. Conclusions: M. alejandre extracts showed secondary metabolites previously reported and unreported in other species. Interestingly, some extracts had antiproliferative activity against cancer cell lines. Therefore, M. alejandrae is a source of molecules that could be explored to develop new drugs. Full article

In industrial production, flue gas heat exchangers are often affected by the low-temperature condensation of industrial flue gas due to the influence of the working environment, resulting in serious ash deposition and corrosion. In order to solve this problem, in this study, we developed an ash deposition and corrosion monitoring system to compare the ash deposition prevention performance and corrosion resistance of different materials, as well as its influence on the heat transfer performance of different materials in the same environment. The following coatings were selected for the experiment (values in parentheses are the concentrations of the added compounds): ND, Q235, 316L, Ni-Cu (0.4 g/L)-P, Ni-P-SiO₂ (40 g/L), Ni-Cu (0.4 g/L)-P-SiO₂ (20 g/L), Ni-Cu (0.4 g/L)-P-SiO₂ (40 g/L), and Ni-Cu (0.4 g/L)-P-SiO₂ (60 g/L). The results show that the Ni-Cu (0.4 g/L)-P-SiO₂ (40 g/L) coating has excellent corrosion resistance, while the Ni-Cu (0.4 g/L)-P-SiO₂ (60 g/L) coating shows excellent antifouling performance. Through the comparative analysis of polarization curves, impedance spectra, and coupled corrosion experiments, the test materials were ranked as follows based on their corrosion resistance: 316L > Ni-Cu-P-SiO₂ (40 g/L) > Ni-Cu-P-SiO₂ (20 g/L) > Ni-P-SiO₂ > Ni-Cu-P-SiO₂ (60 g/L) > Ni-Cu-P > ND > Q235. It was also demonstrated that the new coated pipes were able to reduce the exhaust temperature below the dew point and maximize the recovery of energy from the exhaust gas. The acid–ash coupling mechanism of the coating in the flue gas environment was further analyzed, and an acid–ash coupling model based on Cu and SiO₂ is proposed. This model analyzes the effect of the coating under the acid–ash coupling mechanism. Using coated tubes in heat exchangers helps to recover waste heat from coal-fired boilers, enhance heat exchange efficiency, extend the service life of heat exchangers, and reduce costs. Full article

Amphidinols (AM) are a diverse group of bioactive polyketides produced by dinoflagellates of the genus Amphidinium, known for their hemolytic, antifungal, and cytotoxic activities. This work presents the assessment of AM profiles in a comprehensive number of strains, whose species boundaries were previously established through detailed taxonomic analysis. Using UHPLC-MS/MS, we characterized the spectrum of AM analogs in 54 Amphidinium strains isolated from diverse geographical locations. In addition, toxicity was assessed using brine shrimp assays, which revealed significant inter- and intraspecific variability. Despite the broad diversity in AM content, no clear correlation was observed between total AM levels and toxicity across all strains. Multivariate analysis grouped the strains into clusters distinguished by distinct AM profiles and toxicity levels, suggesting that AM production alone does not predict toxicity. Our findings highlight the complexity of Amphidinium bioactivity, emphasizing the influence of strain-specific factors and other bioactive compounds. This work highlights the importance of integrating chemical, genetic, and biological assessments to understand better the factors that govern toxicity in this genus, with implications for ecological studies and the monitoring of harmful dinoflagellates. Full article

Vortex electromagnetic (EM) waves exhibit spiral wavefront phase distributions, owing to their orbital angular momentum (OAM). Thus, the scattered waves from targets contain OAM characteristics, demonstrating novel scattering properties. Although researchers have carried out both theoretical and experimental studies on the target scattering characteristics of vortex EM waves, a comprehensive and standardized characterization framework is still lacking. This paper proposes and defines an EM vortex scattering matrix (EVSM), which can be used as a characterization method for the target scattering characteristics in the OAM dimension of vortex EM waves. Since vortex EM waves carry both OAM and spin angular momentum (SAM), the EM vortex-polarization joint scattering matrix (EVPJSM) is defined by extending EVSM. This joint matrix simultaneously describes the target scattering characteristics in both OAM and SAM dimensions of vortex EM waves. And it can offer a thorough framework of target scattering characteristics for arbitrary OAM–SAM combinations in new-dimensional EM waves. Numerical simulations are performed to compute each element in EVPJSM for two typical targets under twelve different pairs of OAM modes and two SAM polarization combinations. The numerical results can be used as an example of the characterization method in new dimensions for the targets’ scattering characteristics. Full article

Photocatalysis is an emerging technology that harnesses light energy to facilitate chemical reactions. It has garnered considerable attention in the field of catalysis due to its promising applications in environmental remediation and sustainable energy generation. Recently, researchers have been exploring innovative techniques to improve the surface reactivity of ferroelectric materials for catalytic purposes, leveraging their distinct properties to enhance photocatalytic efficiency. With their switchable polarization and improved charge transport capabilities, ferroelectric materials show promise as effective photocatalysts for various reactions, including carbon dioxide (CO₂) reduction. Through a blend of experimental studies and theoretical modeling, researchers have shown that these materials can effectively convert CO₂ into valuable products, contributing to efforts to reduce greenhouse gas emissions and promote a cleaner environment. An artificial neural network (ANN) was employed to analyze parameter relationships and their impacts in this study, demonstrating its ability to manage training data errors and its applications in fields like speech and image recognition. This research also examined changes in charge separation, light absorption, and surface area related to variations in band gap and polarization, confirming prediction accuracy through linear regression analysis. Full article

This study demonstrates the feasibility of exciting NV centers using ITO-anode OLED devices, followed by the fabrication of GO/PEDOT:PSS hybrid anodes via spin-coating. Through interfacial modification, the OLED devices exhibit significantly enhanced luminescence intensity, leading to improved NV center excitation efficiency. Experimental results show that the optimized GO/PEDOT:PSS (40%) hybrid anode device achieves a lower turn-on voltage, with the NV center fluorescence peak intensity reaching 3.7 times that of the ITO-anode device, confirming the enhanced excitation effect through interfacial engineering of the light source. By integrating NV centers with OLED technology, this work establishes a new approach for efficient excitation. This integration approach provides a new pathway for applications such as quantum sensing. Full article