Search Results (8,148)

Microplastic (MP) contamination has become a widespread environmental concern in coastal and freshwater wetlands, ecosystems that play a crucial role in hydrological regulation, nutrient cycling, and biodiversity conservation. Despite their ecological importance, research on MPs in wetlands remains fragmented and comparatively underexplored. This study presents a comprehensive bibliometric and visualization analysis of global research on MPs in coastal wetlands. A total of 17,523 publications were retrieved from the Web of Science Core Collection (2002–2025) using predefined search strings and screening criteria. Analytical tools, including VOSviewer version 1.6.20, were employed to examine co-authorship networks, country contributions, and keyword co-occurrence patterns. The results indicate a significant increase in MP-related publications after 2016, with China, the United States, and India emerging as leading contributors. However, wetland-specific studies constitute only a small fraction compared to marine-focused MP research, highlighting a substantial research gap. Key research themes identified include MP sources, transport pathways, sediment–water interactions, and ecotoxicological impacts. Additionally, there is growing attention to remediation approaches, particularly those involving TiO₂, ZnO, Fe₃O₄, and graphene derivatives, employing photocatalytic, magnetic, and adsorptive mechanisms. Overall, the findings underscore the limited focus on wetland ecosystems in MP research and emphasize the urgent need for integrated research efforts and management strategies to address MP contamination in these vulnerable ecosystems. Full article

The weak-field, quasi-static regime of gravity is commonly described by the Newton–Poisson equation as an effective response law. We construct this response within a cost-first discrete variational framework. The Recognition Composition Law (RCL) uniquely selects a reciprocal closure cost within the restricted quadratic symmetric composition class; together with the discrete ledger axioms AX1–AX5 (including conservation) and standard DEC refinement, the Newton–Poisson baseline is then recovered in the instantaneous-closure limit. Conditional on Assumption AS1 (scale-free latency) and Assumption AS2 (causal frequency–wavenumber ansatz), allowing finite equilibration introduces fractional memory into the response, yielding a scale-free modification of the source–potential relation characterized by a power-law kernel $w_{\ker }\left(k\right)=1+C{(k_0/k)}^{\alpha }$ in Fourier space. The kernel exponent $\alpha =\frac{1}{2}(1-{\phi }^{-1})\approx 0.191$, where $\phi =(1+\sqrt{5})/2$, is derived from self-similarity of the discrete ledger closure; the amplitude $C={\phi }^{-2}\approx 0.382$ is identified as a hypothesis from a three-channel factorization argument. We evaluate this quasi-static kernel-motivated response against SPARC galaxy rotation curves under a strict global-only protocol (fixed $M/L=1$, no per-galaxy tuning, conservative ${\sigma }_{\mathrm{tot}}$), using a controlled multiplicative surrogate for the full nonlocal disk operator implied by the kernel. In this deliberately over-constrained setting, the surrogate interface achieves $\mathrm{median}({\chi }^2/N)=3.06$ over 147 galaxies (2933 points), outperforming a strict global-only NFW benchmark and remaining less efficient than MOND under identical constraints. The analysis is restricted to the non-relativistic, quasi-static sector and should be read as a falsifier-oriented galactic-regime consistency check of the scaling window, not as a relativistic completion or a claim of Solar System viability without additional UV regularization/screening. Full article

This paper presents a closed-form tracking and collision avoidance framework for multi-agent differential drive robots. Existing reactive methods often rely on purely geometric proximity, leading to conservative detours and local minima. A state-dependent adaptive avoidance strategy is developed to dynamically modulate repulsive forces using the time-derivative of fractional barrier risk functions, alleviating unnecessary evasive maneuvers. Within a convergence vector field (CVF) architecture, an active safety-corrected tracking mechanism orthogonally strips hazardous velocity projections from the spatial error. This mitigates the inherent conflict between target tracking and obstacle repulsion. A matrix projection-based Lyapunov approach demonstrates the finite-time convergence of the vehicle orientation, bounded tracking errors, and collision-free properties of the closed-loop system, with effectiveness further validated through simulations. Full article

The rotational evolution of pulsars is governed by torque mechanisms whose mathematical structure encodes fundamental symmetries of the underlying physics. We demonstrate that the standard spin-down equation $\dot{f}=-sf-r{f}^3-g{f}^5$ derives from a discrete antisymmetry requirement, namely invariance of the torque under reversal of rotation sense, which restricts the frequency dependence to odd integer powers. We show that physically motivated plasma processes systematically break this symmetry, introducing fractional frequency exponents: viscous Ekman pumping at the crust–superfluid boundary layer ($f^{3/2}$), magnetohydrodynamic turbulent dissipation via Kolmogorov and Sweet–Parker cascades ($f^{10/3}$, $f^{11/3}$), non-linear superfluid vortex dynamics ($f^{5/2}$), and saturated r-mode oscillations ($f^{7-2\beta }$). The central result is an exact analytical resolution of the long-standing Crab pulsar braking index puzzle: the observed $n=2.51\pm 0.01$, which has defied explanation for nearly four decades, emerges naturally from the superposition of magnetic dipole radiation ($\dot{f}\propto f^3$) and boundary layer Ekman pumping ($\dot{f}\propto f^{3/2}$), with analytically derived coefficients yielding a dipole-component surface field $B_p=6.2\times {10}^{12}$ G—higher than the standard $\sqrt{P\dot{P}}$ estimate of $3.8\times {10}^{12}$ G, because that formula conflates dipole and non-dipole torques, but lower than applying the Larmor formula to the full spin-down rate ($7.6\times {10}^{12}$ G), since $32.7\%$ of the total torque is non-radiative boundary-layer dissipation. We develop the Riemann–Liouville fractional calculus formalism for these equations, showing that fractional derivatives break time-translation symmetry through intrinsic memory effects, with solutions expressed in terms of Mittag-Leffler and Fox H-functions that interpolate continuously between exponential (fully symmetric) and power-law (scale-free symmetric) relaxation. Lambert–Tsallis $W_q$ functions with non-extensive parameter q encoding broken statistical symmetry enable equation-of-state-independent inference of neutron star compactness and tidal deformability. Our framework establishes a unified symmetry-based classification of pulsar spin-down mechanisms and predicts frequency-dependent braking indices evolving at rate $\mathrm{d}n/\mathrm{d}t\sim 2\times {10}^{-4}$ yr⁻¹, yielding $\mathrm{\Delta }n\approx 0.01$ over 50 years—testable with current pulsar timing programmes. The formalism provides a coherent theoretical foundation connecting plasma microphysics at the neutron star interior to macroscopic observables in electromagnetic and gravitational wave channels. Full article

Soot and black carbon (BC) are typically regarded as troublesome products of incomplete combustion; however, growing interest in circular economy strategies and sustainable manufacturing highlights their potential as secondary functional carbon materials, including additive manufacturing (AM). This review synthesises the recovery, upgrading, and valorization pathways for soot/BC and recovered carbon black (rCB), with a particular focus on streams captured by mandatory emission-control systems (e.g., diesel/gasoline particulate filters, electrostatic precipitators, baghouse filters, and chimney soot) and the requirements for transforming these heterogeneous residues into reproducible AM feedstocks. A two-stage approach was applied, combining (i) an analysis of the European Union regulatory context (waste classification, end-of-waste routes, and chemical safety obligations, including REACH) with (ii) a structured literature review of studies published in 2017–2026 indexed in the Web of Science and Scopus, culminating in a qualitative synthesis of 152 papers. Evidence indicates that scale-up is primarily constrained by strong compositional variability and contaminant burdens (ash, metals, and PAHs), which affect dispersion, rheology, and property reproducibility, necessitating robust standardisation and risk assessment. This review maps key preparation and upgrading strategies (e.g., classification, ash/metal reduction, and control of organic fractions) and discusses their relevance across AM routes such as FDM/FFF, SLS, DLP, and DIW. Overall, realising credible waste-to-value pathways requires aligning technical performance targets with regulatory compliance and developing consistent characterisation protocols to enable the safe and predictable use of soot/rCB-derived fillers in AM. Full article

The development of More Electric Aircraft (MEA) necessitates that Electro-Hydrostatic Actuator (EHA) controllers achieve exceptional power density within rigorously constrained volumes. However, the compact layout design of these controllers constitutes a challenging NP-hard problem, characterized by strong multi-physics coupling—such as electromagnetic, thermal, and structural fields—and complex nonlinear constraints. Traditional meta-heuristic algorithms frequently suffer from premature convergence and struggle to balance global exploration with local exploitation. To address these challenges, the core contribution of this paper is the proposal of a novel Fractional-Order Anteater Foraging Optimization Algorithm (AFO), which is successfully applied to an established EHA controller layout optimization model. At the algorithmic level, by incorporating the Grünwald–Letnikov fractional derivative, the algorithm exploits the inherent memory property of fractional calculus to dynamically adjust the search step size and direction based on historical evolutionary information, thereby preventing stagnation in local optima. At the engineering application level, a high-fidelity mathematical model of the EHA controller is established, comprising 11 design variables and 10 critical physical constraints, including parasitic inductance minimization, thermal radiation efficiency, and electromagnetic interference (EMI) isolation. Extensive validation against the CEC2005 and CEC2022 benchmark functions demonstrates the superior convergence accuracy and stability of the AFO algorithm. In a specific EHA case study, the proposed method reduced the controller volume by 33.9% while strictly satisfying all multi-physics constraints, compared to traditional methods. Furthermore, a physical prototype was fabricated based on the optimized layout, and experimental tests confirmed its stable operation and excellent thermal performance. The results validate the efficacy of incorporating fractional calculus into bio-inspired algorithms to solve complex, high-dimensional engineering optimization problems. Full article

Therapeutics derived from donated blood or its constituents are classifiable into blood components and plasma derivatives. The latter are defined as medicines/drugs/pharmaceuticals produced from the industrial fractionation of thousands of pooled plasma donations and characterised with relative precision to a pre-defined specification through sampling of a homogenous pharmaceutical batch. The former are defined as components/biologicals produced using relatively simple (but increasingly complex) technologies in blood centres from single or small pools of isolated components from whole blood and are pre-specified through regulatory standards with relatively wide limits because of the inherent biologic variability of individual donors. This review discusses the evolution of technology to reduce the risk of pathogen transmission by blood-derived therapeutics, assess the state of the approved technologies for pathogen-reduced blood components, and examine the features of the blood-provider and regulatory framework globally that have shaped, and in some instances impeded, the implementation of component pathogen reduction to an extent equivalent to that achieved for plasma derivatives. The ensuing risks to the public’s confidence in the blood supply are discussed, and remedial actions are proposed. The features of a new paradigm for blood safety are outlined. Full article

The performance of remote sensing-based mineral alteration extraction is significantly restricted in the vegetation area. Spectral unmixing is one of the effective methods to address the vegetation problem during mineral alteration extraction. However, the spectral curves of different tree species vary a lot; if multiple tree species are regarded as a whole during the spectral unmixing stage, the proportions of vegetation would be estimated with more errors. The purpose of this study was to verify the effects of dominant tree species classification on spectral unmixing and reconstruction, and to apply the proposed method to the mineral alteration extraction practice. To accomplish this, the Shabaosi gold deposit region in Mohe City, China, with an area of 650 km², was selected as the study area. Firstly, reference spectral curves, GaoFen-1/6 (GF-1/6) satellite imageries, ZiYuan-1F (ZY-1F) satellite imageries, Sentinel-1B satellite synthetic aperture radar (SAR) data, the ALOS digital elevation model (DEM), and sub-compartment dominant tree species data were collected; subsequently, simulated mixed-pixel reflectance images of ZY-1F, reflectance images of GF-1/6, ZY-1F, backscattering data of Sentinel-1B, slope, aspect, and 5484 tree species samples were derived from the collected data. Secondly, to verify the effect of dominant tree species classification on mineral alteration extraction, the reference spectra of pine, oak, goethite, and kaolinite were used to construct a simulated ZY-1F mixed-pixel image, and spectral unmixing and reconstruction experiments were conducted. Thirdly, fourteen independent variables were selected from the derived data, five dominant tree species classification models were trained and tested using tree species samples via the ResNet50 algorithm, and the pine- and birch-dominated parts were segmented from the ZY-1F images. Fourthly, minimum noise fraction (MNF), pixel purity index (PPI), n-dimensional visualizer auto-clustering, and spectral angle mapper (SAM) methods were separately applied to the pine- and birch-dominated parts of ZY-1F images to extract and identify endmembers; subsequently, the fully constrained least squares (FCLS) and linear spectral unmixing (LSU) methods were separately applied to the pine- and birch-dominated parts to estimate endmember proportions and generate spectrally reconstructed ZY-1F images. Fifthly, the pine- and birch-dominated parts of spectrally reconstructed ZY-1F images were mosaiced, and the SAM was utilized to extract mineral alteration in the study area. The result showed that in the spectral unmixing and reconstruction experiment, the spectral reconstruction error declined from 0.0594 (simulated ZY-1F image without segmentation) to 0.0292 and 0.0388 (simulated ZY-1F image that was segmented by pine- and oak-dominated parts), suggesting that dominant tree species classification could improve the accuracy of spectral unmixing and reconstruction and help obtain a more reliable mineral alteration extraction result. In the study area, the tested overall accuracies (OA) and Kappa coefficients of the five dominant tree species classification models were 0.75 ± 0.03 and 0.50 ± 0.05, respectively, suggesting that conducting dominant tree species classification was feasible in dense vegetation areas and could facilitate mineral alteration extraction. After segmenting the ZY-1F image by pine- and birch-dominated parts and spectral reconstruction, eight main types of alteration, including kaolinite, vesuvianite, montmorillonite, rutile, limonite, mica, sphalerite, and quartz, were identified, and nine mineral alteration areas (MA) were delineated accordingly. Full article

The species Psychotria densicostata Müll.Arg. is a shrub belonging to the Rubiaceae family, endemic to Brazil. So far, there are reports neither of phytochemical work on nor of biological evaluation of it. This study investigated its alkaloid profile and evaluated the inhibitory effects of extracts, alkaloid-enriched fractions and one of its major constituents on human neutrophil elastase (HNE). The monoterpene indole alkaloids (MIAs) strictosidine (1), (3α,5α)-5-carboxystrictosidine (2), strictosidine lactam (3), lyaloside (4), lyalosidic acid (5), 5-carboxystrictosamide (6), 3,4-dehydrostrictosidinic acid (7), and N-glucopyranosyl vincosamide (8) were characterized in mixture, in its leaves, and/or stems by using an integrated approach combining nuclear magnetic resonance (NMR) techniques, high performance liquid chromatography coupled to a tandem mass spectrometer with an electrospray ionization source (HPLC-ESI-MS/MS), and molecular networks. The crude leaf extract and an alkaloid-enriched fraction derived from it showed inhibitory activity against HNE. These results contribute to the chemical knowledge of the species and suggest its potential biological property. Full article

Nonobstructive coronary artery disease (NOCAD) is increasingly recognized as a heterogeneous condition characterized by diverse pathophysiological mechanisms despite the absence of flow-limiting stenosis. We sought to establish a rule-based dominant imaging phenotype framework integrating functional, structural, and inflammatory dimensions derived from multiparametric coronary computed tomography angiography (CCTA). In this retrospective cohort of 485 patients with NOCAD, CT-derived fractional flow reserve (CT-FFR), quantitative plaque burden and high-risk plaque features, and perivascular fat attenuation index (FAI) were assessed. Using predefined percentile thresholds and hierarchical rules, patients were categorized into function-, structure-, inflammation-dominant, or low-risk phenotypes. During a median follow-up of 36 months, 56 patients (11.5%) experienced major adverse cardiovascular events (MACE). After multivariable adjustment, function dominance was associated with the highest risk (hazard ratio [HR] 4.054, 95% confidence interval [CI] 1.984–8.281; p < 0.001), followed by structure dominance (HR 3.129, 95% CI 1.410–6.944; p = 0.005), whereas isolated inflammation dominance did not show a statistically significant independent association with events, with wide confidence intervals indicating limited precision. These findings suggest a graded pattern of prognostic associations across functional and structural abnormalities in NOCAD and support a phenotype-oriented interpretation of CCTA metrics reflecting distinct biological axes of coronary pathology. Full article

This research paper demonstrates how to manage Caputo fractional neutral integro-differential equations which include both integral and nonlinear elements through a unified framework that models dynamic systems with memory-based dynamics. The research establishes sufficient conditions for controllability through fixed point theory in a Banach space framework which requires particular assumptions while the study focuses on the ${\mathfrak{K}}_1<1$ condition which leads to the existence of a controllable solution. The proposed criteria are demonstrated through a numerical example which tests the theoretical results. The real-world case study uses artificial neural network (ANN) technology to predict Litecoin prices through the application of the fractional controllability model which analyzes historical financial data. The hybrid framework enables precise forecasting of nonlinear time series because it combines fractional calculus mathematical principles with ANN learning abilities. The proposed method demonstrates its predictive efficiency. The method shows robust performance through experimental results using cross-validation and performance metrics. The proposed model demonstrates competitive performance while providing additional advantages such as incorporation of memory effects and theoretical controllability. The research establishes a novel connection between fractional dynamical systems and machine learning which serves as an essential tool for studying complicated systems in theoretical research and practical applications. Full article

This study evaluates the potential of marc, a by-product of clitoria (Clitoria ternatea L.) and borage (Borago officinalis L.) infusions, as a preliminary step toward their subsequent conversion into functional food ingredients. After infusion, the marc was collected and processed by carrier-assisted crushing, aqueous maceration, and subsequent separation into extract and residue fractions. The impact of flower pretreatment by milling and marc matrix modification by inulin and maltodextrin was studied on the physical (dry matter (DM), water activity, color), chemical (total phenolic content (TPC), sum of individual phenolic compounds, and antioxidant capacity), and solubility of the microencapsulated fractions. Inulin-formulated powders derived from intact flowers’ marc were characterized by higher dry matter, decreased water activity, and improved chemical profiles. Under these conditions, clitoria by-products exhibited mean dry matter 94.17 ± 0.20%, water activity 0.301 ± 0.003, TPC 3.285 ± 0.052 mg GAE/g DM, sum of individual phenolic compounds 6.267 ± 0.103 mg/g DM, and ABTS-determined antioxidant capacity 0.100 ± 0.001 mmol Trolox/g DM. For borage by-products under identical conditions, dry matter content (−1.60%), water activity (−12.62%), TPC (−39.82%), sum of individual phenolic compounds (−67.55%), and antioxidant capacity (−65.00%) were lower compared with clitoria by-products. An efficient extraction and stabilization approach can open opportunities for upcycling post-extraction herbal residues into high-value food ingredients. Full article

This paper develops novel similarity and dissimilarity measures for $(p,q,r)$-fractional fuzzy sets to enhance information discrimination and decision-making under complex uncertainty. We first introduce axiomatic dissimilarity measures and establish their fundamental mathematical properties, including boundedness, symmetry, monotonicity, and identity conditions. Based on these, we derive corresponding similarity measures that improve discrimination capability. We further propose a multi-criteria group decision-making framework to facilitate robust, accurate ranking of alternatives by integrating the developed measures into a $(p,q,r)$-fractional fuzzy inferior ratio method. The approach evaluates alternatives using relative inferiority relationships and provides stable, reliable rankings in uncertain environments. Illustrative examples demonstrate the proposed method’s effectiveness and applicability, and sensitivity analysis examines decision robustness. Comparative analysis with existing methods confirms the superiority of the proposed framework, showing that it offers stronger discrimination ability and serves as a flexible, reliable tool for complex multi-criteria group decision problems under $(p,q,r)$-fractional fuzzy environments. Full article

The global burden of hypertension continues to rise, highlighting an urgent need for effective therapeutic strategies. Angiotensin-converting enzyme (ACE) is central to blood pressure regulation, but commonly used synthetic ACE inhibitors often have adverse side effects, spurring the search for safer natural alternatives. The aim of this study was to investigate Yanbian cattle hemoglobin as a novel precursor for ACE inhibitory peptides. The <1 kDa fraction was identified as exhibiting the highest inhibitory activity through the systematic screening of hydrolysates across multiple molecular weight ranges. LC-MS/MS analysis identified 1980 peptides, of which four were selected for further experiments. Solid-phase synthesis confirmed that NFGYDL exhibited the strongest ACE inhibition (IC₅₀ = 54.95 μM). Inhibition kinetics showed FHDYL acted as a mixed-type inhibitor, DLGHF and NFGYDL as competitive inhibitors and GFHLD as a non-competitive inhibitor. Molecular dynamics simulations validated the stable binding of these bovine blood-derived peptides to the ACE complex. HUVEC functional assays demonstrated that four peptides significantly increased angiotensin II-induced nitric oxide production and endothelin-1 levels, suggesting their potential antihypertensive activity. These findings suggested that bovine blood is a promising natural source of ACE-inhibitory peptides and holds potential for application as a functional component in functional foods targeting hypertension management. Full article

Baicalin, a natural plant-derived compound, holds promise in addressing clinical bacterial resistance when combined with antibiotics. This study evaluated the antibacterial activity of the combination of baicalin and cefotaxime and explored its mechanism of action on the cell wall and biofilm of multidrug-resistant Pseudomonas aeruginosa (MRPA). The results showed that the combination of baicalin and cefotaxime exerted a synergistic inhibitory effect on the growth of MRPA, with a fractional inhibitory concentration index (FICI) of 0.28. Mechanistically, compared with cefotaxime alone, the combination of baicalin and cefotaxime enhanced the permeability of the cell membrane and cell wall of MRPA, thereby increasing cell damage. It also exhibited stronger antibiofilm activity by inhibiting numerous virulence factors (pyocyanin, elastase, lectin), reducing cellular metabolic activity, and downregulating the expression of biofilm genes (pslA, pelA, algD) and quorum-sensing genes (lasl, lasR, rhll, rhlR, pqsA, pqsR). The molecular docking results revealed that baicalin could stably bind to wbpE, LasR, and RhlR. Therefore, this interaction may indirectly influence the processes related to antibiotic resistance and biofilm formation in bacterial cells. Metabolomic analysis revealed that the combination of baicalin and cefotaxime upregulated 863 metabolites and downregulated 587 metabolites. These metabolites mainly included amino acids, lipids, nucleotides, carbohydrates, and secondary metabolites. The combination primarily enriched key pathways such as amino acid metabolism, lipid metabolism (sphingolipid metabolism) and secondary metabolite biosynthesis. Through these pathways, it triggers significant metabolic reprogramming, thereby interfering with the supply of cell wall synthesis precursors, membrane structural stability, and the generation of biomembrane matrix. Ultimately, it synergistically enhances the effects of cell wall damage and biomembrane inhibition. In conclusion, this study confirms that the combination of baicalin and cefotaxime exerts significant synergistic antibacterial activity against MRPA. It also reveals the mechanism of action of the combination on the cell wall and biofilm of MRPA at the metabolic level, providing theoretical support for the development of novel strategies to combat MRPA. Full article