Search Results (12,092)

This paper develops and rigorously analyzes a fully discrete upwind interior penalty discontinuous Galerkin (IPDG) scheme for simulating solute transport in two-dimensional variable-order fractional mobile–immobile media. The temporal variable-order Caputo derivative is discretized via a Grünwald–Letnikov approximation in conjunction with a first-order backward difference, while the spatial discretization employs an IPDG method featuring an upwind numerical flux for the convection term and a penalty formulation for the diffusion operator. Under the physically relevant assumption of a divergence-free velocity field, we establish the unconditional stability of the proposed scheme. A comprehensive error analysis in the $L^2$ norm yields a convergence rate of $O(\Delta t+h^{min(k+1,s)-\chi -1/2})$, explicitly linking the polynomial degree k, solution regularity s, and the penalty variant $\chi$. Numerical experiments in two dimensions are conducted to verify the accuracy and robustness of the proposed scheme in simulating anomalous transport phenomena in subsurface environments. Full article

Objective: To analyze the association between income inequality and age-standardized mortality from diabetes mellitus and hypertensive diseases among adults aged 30–69 across Brazilian Federative Units (2012–2024). Methods: Ecological longitudinal study using balanced panel data from the 27 Federative Units. Data were retrieved from the Mortality Information System and IBGE/IPEA databases. Mortality rates were standardized using the direct method (WHO population). Trends were evaluated via Joinpoint regression, and associations were analyzed using mixed linear models with random intercepts and first-order autoregressive covariance structure. Results: Inequality decreased in 25 Federative Units, accompanied by rising income and declining poverty, whereas national aggregated analysis showed a linear increase in inequality (APC = 0.514%; p < 0.001). Overall mortality trends were stationary for diabetes (AAPC = −1.507%; p = 0.102) and hypertension (AAPC = −0.881%; p = 0.524), despite significant declines from 2021 to 2024. In adjusted mixed models, higher per capita household income was independently associated with lower diabetes mortality (β = −0.012; p = 0.031), while hypertension displayed an upward adjusted linear trend (β = 0.289; p = 0.001). Neither the Gini Index nor poverty rates maintained significant independent associations with mortality. Conclusions: After structural adjustment, only absolute per capita household income maintained an independent protective association with diabetes mortality, highlighting the relevance of material living conditions beyond inequality measures alone. Full article

Background and Objectives: The main neurodegenerative diseases (NDs)—Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS)—represent a growing global health burden with no available disease-modifying therapies. Curcumin, a polyphenol from Curcuma longa, is a promising candidate owing to its pleiotropic antioxidant, anti-inflammatory, and neuroprotective profile observed mainly in preclinical models, but the poor oral bioavailability (<1%) and negligible BBB penetration (<0.1%) have substantially limited curcumin’s clinical translation. The objective of this work was to critically examine the therapeutic potential of curcumin in NDs, focusing on advanced drug delivery systems (DDSs) designed to overcome its pharmacokinetic barriers. Methods: This is a narrative, non-systematic review of PubMed/MEDLINE, Scopus, and Web of Science. The review is organized around five complementary thematic areas selected to span the full translational pipeline of curcumin in neurodegeneration, from mechanistic rationale to clinical applicability: (1) molecular mechanisms, addressing the pleiotropic activities that justify therapeutic interest; (2) pharmacokinetic barriers, the principal obstacle to clinical translation; (3) the evolution of drug delivery systems (DDSs), documenting the technological strategies developed to overcome these barriers; (4) disease-specific applications, evaluating the available evidence across the four main NDs; and (5) translational limitations, identifying the methodological and regulatory gaps that must be closed to enable clinical implementation. Results: Curcumin exhibits neuroprotective activity in preclinical models of the four NDs analysed, acting on six interconnected mechanisms and the gut–brain axis. Four generations of DDSs have been developed, from phytosomes and clinically used lipid dispersions (Meriva^®, BCM-95^®, Longvida^®, and Theracurmin^®) to fourth-generation systems (biomimetic nanoparticles, MOFs, microneedles, 3D scaffolds, hydrogels, and carbon dots) that substantially increase the bioavailability in preclinical studies. Combination strategies, such as curcumin with resveratrol and dutasteride, show preliminary clinical signals in ALS. However, clinical translation remains limited: over 80% of positive animal findings have not been replicated in humans, formulation characterization is frequently incomplete, and most trials lack CNS-exposure biomarkers. Importantly, most of the reported bioavailability claims are based on total curcumin measurements (parent aglycone plus its inactive Phase II conjugates) rather than the active aglycone alone, a methodological limitation that should be considered when interpreting the magnitude of the bioavailability improvements reported for novel formulations. Conclusions: Curcumin exhibits pleiotropic neuroprotective activity in preclinical models of AD, PD, MS, and ALS, mediated by interconnected antioxidant, anti-inflammatory, anti-amyloidogenic, mitochondrial, and gut–brain axis mechanisms. However, its poor systemic bioavailability (<1%), minimal blood–brain barrier penetration, and extensive first-pass metabolism have limited clinical translation. Advanced drug delivery systems (including lipid-based carriers (liposomes, solid lipid nanoparticles, and nanostructured lipid carriers), polymeric nanoparticles (PLGA and chitosan), and bioinspired vesicles (exosomes)) are essential in order to overcome these barriers. Nevertheless, the formulation heterogeneity, limited long-term safety data, and reliance on preclinical models remain major obstacles; a definitive clinical translation will therefore require well-characterized formulations validated in phase II/III trials with cerebrospinal fluid exposure biomarkers, the pharmacokinetic monitoring of active aglycone (rather than total curcumin including inactive conjugates), and adaptive trial designs in neurological populations. Full article

To accurately assess the time-dependent reliability of bridge piers in marine environments, this paper proposes a time-dependent reliability evaluation method for bridge piers based on a Dynamic Bayesian Network (DBN). By establishing a resistance degradation model under the combined effects of reinforcement corrosion and concrete deterioration and embedding it into the DBN framework, dynamic prediction and updating of pier time-dependent reliability are achieved. A case study of a twin-column pier was conducted for verification, showing that the computational results of the proposed DBN model align well with the first-order reliability method (FORM), validating its accuracy and feasibility in time-dependent reliability prediction. Further, using the most severe service condition, that is, the tidal-spray zone as an example, the DBN prediction results were updated with inspection data to achieve dynamic assessment of the actual pier lifespan. Additionally, a comparative analysis of environmental zones revealed that the tidal-spray zone exhibits the fastest reliability degradation, followed by the atmospheric zone, while the submerged zone shows the slowest. Full article

Incomplete station meteorological records constrain sustainable water-demand planning and climate-adaptation decisions in data-limited regions. Open-data reference evapotranspiration (ETo) products can provide continuous hydroclimatic information for atmospheric-demand screening, but they do not directly represent realized water demand and their use as station substitutes may introduce bias because gridded and station-derived ETo represent related but non-identical calculation and spatial domains. This study develops a station-anchored open-data ETo screening workflow for assessing local product compatibility under incomplete meteorological records. The workflow combines four official Turkish State Meteorological Service (MGM) stations in the eastern Marmara region, station-derived Hargreaves–Samani (HS) ETo for 1990–2020, TerraClimate v1.1 reference ETo, calendar-month and anomaly diagnostics, blocked historical gap-transfer tests in which omitted periods are excluded from correction fitting and tuning, and measured-radiation FAO-56 Penman–Monteith (PM) sensitivity subsets. TerraClimate reproduced the first-order annual ETo cycle strongly (r = 0.972–0.978), but mean TerraClimate-minus-HS bias remained station-dependent (+1.78 to +11.55 mm month⁻¹) and strongly calendar-month-dependent. After removing each series’ calendar-month climatology, Pearson correlation decreased to 0.559–0.849, indicating that the common annual cycle explains a substantial part of the raw agreement. Train-only monthly-bias and nested L2 residual corrections reduced pooled blocked out-of-sample MAE, although no correction method was universally superior across stations. Measured-radiation PM subsets at Kocaeli and Yalova showed PM-minus-HS mean offsets of −4.42% and +3.37%, respectively. The blocked tests evaluate transfer to omitted historical periods rather than prospective forecasting. The results support local, season-aware compatibility screening before gridded ETo is used for climate-resilient water-resource planning; they should not be interpreted as validation of actual ET, crop water use, irrigation withdrawals, or realized water demand. Full article

Olopatadine (OLO) is a second-generation antihistamine approved for the treatment of allergic conjunctivitis as ophthalmic and nasal solutions. Due to its intrinsic absorption in the near-UV region (approximately 300 nm), OLO may be susceptible to photodegradation and understanding this behavior is essential for ensuring the quality and safety of OLO-containing pharmaceutical formulations. The forced photodegradation of OLO was investigated under UV/Vis irradiation (300–800 nm) over a wide pH range. Photodegradation kinetics was evaluated using a selective LC-UV method. OLO degradation followed first-order kinetics, with rate constants ranging from 3.45 × 10⁻⁵ to 6.91 × 10⁻⁵ s⁻¹, corresponding to degradation levels in the range 45.54–81.95%. Photodegradation products were characterized using UHPLC-HRMS/MS, leading to the identification of twelve, including seven previously unreported compounds. Seven degradants were isolated by preparative LC-UV and their structures were confirmed by NMR spectroscopy, including three newly reported compounds. The potential toxicity of all identified photodegradants was evaluated using the in silico tools OSIRIS Property Explorer and Toxtree. Five products were predicted to exhibit reproductive toxicity and irritation potential, whereas one compound showed a potential tumorigenic risk. Overall, this study provides comprehensive insight into the photostability of OLO and the formation of its photodegradation products. Full article

This paper proposes a Koopman-based stochastic model predictive control (SMPC) approach for unknown nonlinear systems by exploiting partial probabilistic information. Unlike existing Koopman-based SMPC methods that primarily rely on the first- and second-order moments of stochastic Koopman modeling error, the proposed approach further incorporates available support information into the controller design. By jointly utilizing the mean, covariance, and support information of the resulting uncertainty, the chance-constrained optimization problem is reformulated into a tractable deterministic optimization problem with reduced conservatism. Moreover, the proposed approach is theoretically shown to be no more conservative than the corresponding RMPC in terms of constraint tightening under identical constraint requirements. Furthermore, recursive feasibility and closed-loop quadratic stability of the proposed control scheme are established theoretically. Simulation studies on spacecraft attitude control demonstrate that the proposed method reduces the performance index by 6.7% and 17.0% compared with a Koopman-based SMPC using mean and covariance information and RMPC, respectively, while maintaining a comparable average computation time of approximately 0.010 s. Full article

This study discusses the (2+1)-dimensional Kadomtsev–Petviashvili–Benjamin–Bona– Mahony equation, which emerges in weakly nonlinear dispersive plasma waves and shallow water dynamics in ocean engineering. Logarithmic dependent-variable transformations are applied to reconstruct a one-exponential tau function as a common one-soliton profile and derive its dispersion relation. This is a standard transformed solution listed as three normalized logarithmic maps, but not a new family of solutions. Only a one-exponential soliton is claimed, no two-soliton family and no arbitrary-N soliton family. The explicit rational rogue-wave families of the first, second, and third orders are derived using a modified version of a well-known center-shifted polynomial tau-function method that is applied to the KPBBM bilinear form, with both center parameters $\beta$ and $\gamma$ independent. The novelty is thus limited to the specific model and is not based on a new KPBBM equation or a fundamentally novel symbolic algorithm. The rogue-wave center translates in the longitudinal and transverse directions through $\beta$ and $\gamma$, respectively, for a fixed order N and fixed model parameters. They leave the pattern, localization width, background, and the arrangement of inner patterns unchanged. Lump solutions and lump–soliton interaction structures are also obtained and investigated. The auxiliary Hirota bilinear constraint and its reduced bilinear representation are explicitly given. The higher-degree equations found in the directional logarithmic maps are not new multilinear equations, but rather the denominator-cleared differential polynomial residuals. The validity of each solution family retained is guaranteed by means of analytical substitution or vanishing of symbolically identical-to-zero residual in the original KP–BBM equation. The two- and three-dimensional plots are used only to demonstrate the amplitude profile, localization, and propagation of the solutions, as verified by the analysis. In the weakly nonlinear, long-wave and weakly transverse regime where the KPBBM reduction is valid, these solutions give idealized mathematical representations of localization and interaction mechanisms. They are not predictive of coastal instability or offshore hydrodynamic loading, for which dimensional calibration and experimental/field validation would be necessary. Full article

Cross-event fire-severity mapping requires a model to delineate the burned footprint and grade severity within it across wildfires whose spectral expression varies with vegetation and observation conditions. Nominal multiclass models treat the classes as parallel alternatives and leave the order implicit. We introduce Co-Burn, a bi-temporal Siamese model that adds a pre-to-post dNBR channel to the post-fire branch as an NIR-SWIR change anchor and uses a conditional ordinal head to estimate burn presence before high-severity assignment. Ten methods were compared across 14 New South Wales wildfires against a Sentinel-2 FESM-derived three-class target, with 4 complete fires held out from model development and selection. Co-Burn ranked first under both pixel-pooled and event-mean aggregation, reaching 0.520 ± 0.017 and 0.524 ± 0.028 external burned mIoU. Event-level factorial contrasts showed that burned-mIoU effects varied among fires, while dNBR anchoring reduced false-burn rate on all four external events. At the fixed operating point, Co-Burn assigned 19.9% of reference-unburned pixels to burned classes, against 28.9% for the reflectance-only nominal variant. On the hardest held-out fire, limited false-burn expansion coexisted with a downward shift across the ordered severity classes. Co-Burn supports ordered three-class mapping of previously unseen forest fires before target-fire labels become available. Full article

The minimalist approach for linear stability analysis is applied to fluids and magnetized ideal plasmas in cylindrical geometry. In this approach, the dispersion relation is obtained by integrating a single first-order differential equation—referred to as the principal equation—subject to appropriate boundary conditions. We first derive the principal equation for a general unperturbed state with radially varying density and pressure, axial and azimuthal components of both the velocity and magnetic field, and a radially directed gravitational field. We then use this formulation to analyze rotating flows with axial magnetic fields, addressing both wall-bounded and interface-driven axisymmetric instabilities. In addition to exact results for selected unperturbed states, we obtain approximate dispersion relations using the WKBJ method in the incompressible and compressible limits. The analysis encompasses centrifugal, magnetorotational, and buoyancy-driven instabilities as special cases, and it clarifies how compressibility modifies their stability properties. Full article

Low-frequency normal-mode analysis (NMA) is widely used to predict collective protein motions, but direct Cartesian scaling progressively distorts the alpha-carbon (Cα) backbone as the amplitude increases. We present backbone-angle-compatible as-rigid-as-possible normal-mode analysis (BA-ARAP-NMA), a nonlinear Cα structure-generation method that constrains first-order changes in adjacent and next-nearest Cα distances during anisotropic network model (ANM) calculations and applies a co-rotational as-rigid-as-possible (ARAP) correction during structure generation. Tests on 35 experimentally characterized two-state protein pairs showed that the geometric constraints retained most of the input-to-second-state displacement information. In 34 of 35 pairs, recalculating the constrained modes increased the concentration of the retained transition information in the leading low-frequency modes. Across matched displacements, BA-ARAP-NMA substantially reduced local Cα distance and angle distortions in every pair. At the protein-pair level, BA-ARAP-NMA generally yielded lower root mean square deviation (RMSD) to the paired state and higher transition coverage than linear Cα-ANM while maintaining improved virtual-angle accuracy. BA-ARAP-NMA therefore extends Cα normal-mode structure generation beyond direct linear scaling, retaining transition-related collective information while providing amplitude-ordered structures with frame-level geometric measurements for subsequent rebuilding and refinement. Full article

Multi-source cross-domain hyperspectral image (HSI) classification is challenged by heterogeneous sensor configurations, scene-dependent distribution shifts, and limited labeled target data, which hinder effective knowledge transfer across multiple scenes. Motivated by progressive feature correction, we propose a three-stage Residual Feature Discrepancy Refinement (RFD) framework for collaborative representation learning across heterogeneous HSI domains. RFD formulates this correction as a deterministic, discrepancy-conditioned residual refinement process. First, domain-specific encoders project four source domains and the target domain, which may have unequal spectral dimensions and label spaces, into a common-dimensional feature space. Adaptive severity and domain weighting uses first- and second-order feature discrepancies to estimate source-specific conditioning coordinates and collaborative contribution weights. A shared discrepancy-conditioned residual refiner then performs multi-step feature refinement to reduce domain-dependent statistical deviations. Finally, an exponential-moving-average historical prototype memory stabilizes target adaptation, followed by cosine 1-nearest-neighbor classification. Across ten randomized runs, RFD achieves mean overall accuracies of 94.65%, 94.87%, and 96.95% on NC12, Salinas, and WHU-Hi-LongKou, respectively, and obtains the highest mean overall accuracy, average accuracy, and $\kappa$ among the evaluated unified-protocol methods. Full article

In long-aperture forward-looking radar, nonlinear Doppler mismatch caused by target relative motion can lead to positioning deviation and image defocusing. To address this issue, an adaptive segmented Doppler compensation method based on phase error constraints is proposed. As synthetic aperture time increases, high-order terms in the slant range history broaden the Doppler spectrum and enhance spatially variant phase errors. Conventional global compensation cannot achieve stable focusing, and fixed-length segmentation fails to adapt to varying motion nonlinearity. Accordingly, the high-order nonlinear characteristics of the slant range are first analyzed, and an adaptive sub-aperture partitioning criterion constrained by second-order phase error is derived, ensuring each sub-aperture satisfies the local quasi-linear hypothesis. A cross-segment mapping relationship between different sub-apertures is then established, and the compensation process is formulated as a two-dimensional separable operator. To manage the high computational complexity of solving spatially variant mapping under long apertures, the Alternating Direction Method of Multipliers (ADMM) is introduced to iteratively optimize the operator, achieving phase alignment and coherent reconstruction among sub-apertures. Simulation and experimental results show that the proposed method effectively suppresses nonlinear defocusing under long-aperture conditions. Compared with conventional global methods, it achieves superior energy concentration and focusing resolution in extended target scenarios. Full article

Background: Following industrial energy consumption, freight transportation represents the second-largest source of greenhouse gas emissions nowadays. However, although several metrics for the supply chain’s profitability already exist, indicators for its environmental performance are currently lacking. Methods: Hence, a preliminary review of metrics for green logistics was carried out in order to select a comprehensive indicator for more detailed studies. The effectiveness of the proposed metrics was tested on a real industrial case concerning the Messebo cement factory in Ethiopia to explore potential advances in developing countries, where the availability of renewable energy sources is extremely high but infrastructures for their exploitation are absent. Then, the constrained route optimization problem was solved to investigate the implementation of potential improvements. Results: The outcome of this study shows that delivery route optimization improves transportation environmental performance, on average, by 15%, while the replacement of conventional freight trucks with electric vehicles can abate up to 90% of the overall carbon footprint. In both cases, all constraints were satisfied within the required time window. Conclusions: In conclusion, this work proves the effectiveness of green logistics indicators and represents a first step towards supply chain optimization for carbon-intensive sectors in developing countries. Full article

UAV-based visual inspection has become an effective approach for acquiring surface information from three-dimensional building structures. However, existing coverage path planning methods usually treat viewpoint planning and path sequencing as two separate stages, which may introduce redundant viewpoints, long connection paths, and high computational cost. To address this problem, this paper proposes a skeleton-guided spiral coverage path planning method for UAV inspection of 3D structures. The target building model is first converted into a watertight triangular mesh, from which a one-dimensional skeleton line is extracted to guide both viewpoint generation and path construction. Surface sampling points are generated using rotating radial rays along the skeleton line, and UAV viewpoints are obtained by offsetting these points according to a predefined viewing distance. The ordered viewpoints are then connected to construct spiral coverage paths, while visibility checking, safety-distance constraints, and collision detection are incorporated to ensure path feasibility. Parameter sensitivity analysis shows that the sampling interval has a dominant influence on coverage performance and path cost, while the angular increment mainly affects path compactness and construction efficiency. Comparative experiments on the Christ, Wind Turbine, and Big Ben models demonstrate that the proposed method achieves high coverage rates of 96.62%, 97.72%, and 99.67%, respectively, while generating shorter paths and requiring substantially less computation time than ACO−OPD and Zhao’s method. These simulation results indicate that the proposed method can generate compact coverage paths with substantially lower computation time for UAV coverage inspection of 3D structures. Full article