Search Results (2,720)

This study applies the Log-Periodic Power-Law (LPPL) framework to three major equity markets—Mexico (IPC), Brazil (IBOVESPA), and the United States (NYSE Composite)—using daily closes from 8 November 1991–30 January 2025 for IPC and NYSE, and 3 May 1993–30 January 2025 for IBOVESPA. Multi-window calibrations (L$ϵ$ 180, 240, 300, 360, 420) are estimated in raw and log space to evaluate bubble signatures and the stability of the critical time $t_c$. Across all indices, log-space fits consistently outperform raw fits in terms of RMSE and $R^2$, and longer windows reduce parameter variability, yielding coherent clusters of $t_c$. Under full-sample conditions, the LPPL structure points to March–April 2025 for NYSE, mid-October 2025 for IBOVESPA, and October–December 2025 for IPC, while shorter windows pull $t_c$ forward. A rolling early-warning ensemble translates these estimates into lead-based risk bands, with numerical reporting used when median leads fall just outside the 60-trading-day decision horizon. The early-2025 weakening in the U.S. market is consistent with the NYSE cluster, whereas Brazil and Mexico remain within their projected windows as of September 2025. The analysis highlights the strengths of LPPL—behavioral interpretability and hazard-based framing—while noting limitations such as window sensitivity and parameter sloppiness, reinforcing the need for conservative communication and the use of longer-window weighting in practical applications. Full article

Fruit softening in Carica papaya L. is a significant postharvest limitation, primarily driven by the dynamic remodeling of cell wall polysaccharides. In this study, we conducted a genome-wide identification and in silico characterization of gene families involved in cell wall assembly and disassembly in papaya. A total of 181 genes were identified and classified into metabolic pathways: hemicellulose (58), pectin (69), extensin (24), expansin (13), and cellulose (17). These genes encode 176 predicted proteins, ranging in size from 100 to 1093 amino acids, featuring family-specific catalytic domains, including glycosyl hydrolases, transferases, and serine/threonine kinases. Phylogenetic analyses revealed strong conservation within the expansin-A and pectin polygalacturonase subfamilies, while hemicellulose-related XTH genes exhibited significant diversification. Experimental validation of nine XTH members confirmed this diversification, with amplicons ranging from 322 to 1370 bp, consistent with computational predictions. Notably, CpXTH1 and CpXTH32 produced bands of approximately 1200 and 1400 bp, respectively. These findings underscore the complexity of papaya cell wall gene families and provide a molecular framework for understanding fruit softening. Given that postharvest losses of papaya in Mexico exceed 34.7% of production (approximately 150,000 tons annually), our results offer valuable genomic resources for biotechnological strategies aimed at extending shelf life and reducing economic losses. Full article

Based on a large-signal chip model, this paper designs and implements an L-band broadband continuous-wave 400 W high-efficiency power amplifier fabricated using 0.5 μm GaN High Electron Mobility Transistor (HEMT) technology. The input-matching circuit employs a hybrid structure combining a lumped-element pre-matching network and a multi-section microstrip capacitor network to achieve impedance matching with a 50 Ω port. The output-matching circuit uses a multi-segment microstrip structure to meet the impedance requirements of the continuous mode, thereby achieving broadband impedance matching. In addition, in the circuit implementation, by optimizing the placement of the blocking capacitor, the current flowing through it is minimized to a low level, enhancing the circuit’s high-power handling capability under continuous-wave operation. Additionally, the power amplifier’s reliability lifetime was calculated based on simulation results of the operating temperature of the GaN amplifier chip. Measurement results demonstrate that across a wide operating bandwidth within the L-band, the output power exceeds 400 W with a drain efficiency greater than 70%. The estimated reliability lifetime (MTTF) of the power amplifier is 8.1 × 10⁷ h. Full article

The caldén (Neltuma caldenia) forest, a xerophytic low-stature ecosystem in central Argentina, faces increasing threats from land use change and desertification. This study assesses the capability of full-polarimetric L-band SAR data from the Argentine SAOCOM-1A satellite to characterise forest attributes in this ecosystem. We computed the Generalised Radar Vegetation Index (GRVI) and compared it with aboveground biomass and tree canopy cover data from the Second National Forest Inventory, under fire and non-fire conditions. We also assessed other SAR indices and polarimetric decompositions. GRVI values exhibited limited variability relative to the broad range of field-estimated biomass, and most regression models were not statistically significant. Nevertheless, GRVI effectively distinguished woody from non-woody vegetation and showed a weak correlation with canopy cover. Statistically significant, albeit weak, correlations were also observed between biomass and specific polarimetric components, such as the helix term of the Yamaguchi decomposition and the Pauli volume component. Key challenges included limited spatial and temporal coverage of SAOCOM-1A data and the distribution of field plots. Despite these limitations, our results support the use of GRVI for land cover monitoring in semiarid regions, emphasising the importance of multitemporal data, integration with C-band SAR, and enhanced field sampling to improve forest attribute modelling. Full article

Liquid sloshing in partially filled tanks is commonly studied because of its influence on vehicle stability, structural loading, and control performance. In experimental investigations, sloshing measurements can be contaminated by mechanically induced fluid–structure interactions originating from the actuation system itself. This study presents a bench-scale experimental investigation of the interaction between static magnetic fields and the dynamic response of a mechanically excited water-tank system, with particular emphasis on distinguishing sloshing-related motion from higher-frequency mechanical effects. A rectangular acrylic tank was subjected to near-resonant horizontal excitation at a fixed fill height. A ferromagnetic steel plate was mounted externally beneath the tank and kept identical in all experiments, while either permanent magnets or mass-matched nonmagnetic dummies were attached externally to one sidewall. Two configurations were examined: a symmetric split-wall layout (15 + 15) magnets and a single-wall high-field arrangement with 30 magnets (Mag–30@L) together with its dummy control (Dummy–30@L). The center-of-gravity motion ${\mathrm{CG}}_y\left(t\right)$ was reconstructed from four load cells and analyzed in the time and frequency domains. Band-limited analysis of the primary sloshing mode near 0.55 Hz revealed no statistically significant influence of the magnetic field, indicating that static magnets do not measurably affect the fundamental sloshing dynamics under the present conditions. In contrast, a higher-frequency response component in the 10–20 Hz range, attributed to mechanically induced fluid–structure interaction associated with actuator reversal dynamics, was consistently attenuated when magnets were present; this component is absent in corresponding CFD simulations and is, therefore, not associated with sloshing motion. Given the extremely small magnetic Reynolds and Stuart numbers for water, the observations do not support any volumetric magnetohydrodynamic mechanism; instead, they demonstrate a modest magnetic influence on a mechanically excited, high-frequency coupled mode specific to the present experimental system. The study is intentionally limited to bench scale and provides a reproducible dataset that may inform future investigations of magnetically influenced fluid–structure interactions in experimental sloshing rigs. Full article

This study proposes GSM, an interpretable and operational GAM-SHAP-MCDA framework for stroke risk stratification by integrating generalized additive models (GAMs), a point-based clinical scoring system, SHAP-based explainability, and multi-criteria decision analysis (MCDA). Using a publicly available dataset of $n=5110$ individuals ($4.87\%$ stroke prevalence), a GAM was fitted to capture nonlinear effects of key physiological predictors, including age, average blood glucose level, and body mass index (BMI), together with linear effects for hypertension, heart disease, and categorical covariates. The estimated smooth functions revealed strong age-related risk acceleration beyond 60 years, threshold behavior for glucose levels above approximately $180\mathrm{mg}/\mathrm{dL}$, and a non-monotonic BMI association with peak risk at moderate BMI ranges. In a comparative evaluation, the GAM achieved superior discrimination and calibration relative to classical logistic regression, with a mean AUC of $0.846$ versus $0.812$ and a lower Brier score ($0.045$ vs. $0.051$). A calibration analysis yielded an intercept of $-0.04$ and a slope of $1.03$, indicating near-ideal agreement between the predicted and observed risks. While high-capacity ensemble models such as XGBoost achieved slightly higher AUC values ($0.862$), the GAM attained near-upper-bound performance while retaining full interpretability. To enhance clinical usability, the GAM smooth effects were discretized into clinically interpretable bands and converted into an additive point-based risk score ranging from 0 to 42, which was subsequently calibrated to absolute stroke probability. The calibrated probabilities were incorporated into the TOPSIS and VIKOR MCDA frameworks, producing transparent and robust patient prioritization rankings. A SHAP analysis confirmed age, glucose, and cardiometabolic factors as dominant global contributors, aligning with the learned GAM structure. Overall, the proposed GAM–SHAP–MCDA framework demonstrates that near-state-of-the-art predictive performance can be achieved alongside transparency, calibration, and decision-oriented interpretability, supporting ethical and practical deployment of medical artificial intelligence for stroke risk assessment. Full article

In this study, the authors wish to introduce an unsupervised procedure designed for real-time generation of maps depicting advected aerosols, specifically focusing on desert dust and smoke originating from biomass combustion. This innovative approach leverages the high-resolution capabilities provided by Sentinel-2 imagery, operating at a 10 m scale, which is particularly advantageous for urban settings. Concurrently, it takes advantage of the near-daily revisit frequency afforded by Sentinel-3. The methodology involves generating aerosol maps at a 10 m resolution using bands 2, 3, 4, and 5 of Sentinel-2, available in L1C and L2A formats, conducted every five days, contingent upon the absence of cloud cover. Subsequently, this map is enhanced every two days through spatial modulation, utilizing a similar map derived from the visible and near-infrared observations (VNIR) captured by the OLCI instrument aboard Sentinel-3, which is accessible at a 300 m scale. Data from the two satellites undergo independent processing, with integration at the feature level. This process combines Sentinel-3 and Sentinel-2 maps to update aerosol concentrations in each 300 m × 300 m grid every two days or more frequently. For the dates when Sentinel-2 data is unavailable, the spatial texture or the aerosol distribution within these grid cells is extrapolated. This spatial index represents an advancement over prior studies that focused on differentiating between dust and smoke based on their scattering and absorption characteristics. The entire process is rigorously validated by comparing it with point measurements of fine- and coarse-mode Aerosol Optical Depth (AOD) obtained from AERONET stations situated at the test sites, ensuring the reliability and accuracy of the generated maps. Full article

The main emphasis of the current study is to develop an eco-friendly method for producing silver nanoparticles (AgNPs) using an aqueous flower extract from Talipariti tiliaceum L., and to evaluate the photocatalytic degradation of azo dyes. The synthesized AgNPs were characterized using various spectroscopical and microscopical methods. The photocatalytic capacity of AgNPs was assessed through the degradation of methylene blue (MB) and methyl orange (MO) dye under solar irradiation. The results revealed that the AgNPs were spherical in morphology and 4–15 nm in size. The phytochemical analysis showed that the bioactive compounds from the flower extract aided in the reduction of silver ions to nanoparticles. Both visual observations and spectroscopic methods confirmed the photocatalytic degradation of MB and MO dyes. The degradation processes adhered to a pseudo-first-order kinetic model, demonstrating that photocatalytic activity is time-dependent. In addition, the AgNPs demonstrated stability and reusability through four consecutive cycles with little decline in efficiency. This research contributes significantly to sustainable nanotechnology, offering a practical solution for mitigating water pollution caused by industrial dye discharges. Full article

The quality of rice depends on physical, nutritional, and sensory attributes. However, in industrial practice, quality is predominantly based on physical characteristics evaluated by the conventional method for categorizing commercial atches. In this context, the present study aimed to characterize the physical quality and proximate composition and to classify commercial batches of polished white rice using machine learning (ML) algorithms based on spectral data. Individual samples (healthy grains and physical defects) and samples from commercial batches (Type 1 to Type 5 and Off-Type) were analyzed and prepared in accordance with current legislation. Spectral data were obtained using NIR and hyperspectral measurements covering the VIS/NIR/SWIR regions, and proximate composition was determined for moisture (MOI), starch (ST), protein (PRO), lipids (LIP), fiber (FIB), and ash (ASH). Multivariate analyses and ML classification models were applied to evaluate differences among grain types and commercial categories and to assess the discriminatory capacity of spectral information. The results showed that including physicochemical attributes to evaluate the quality of commercial batches simplifies the commercial categories currently used. For spectral behavior, batches classified as Type 1 and Type 2 showed low reflectance in the NIR and SWIR regions, suggesting greater interaction of radiant energy with compounds associated with nutritional and sensory quality. The MLP, LGBM, CAT, XGB and RF models performed best for the classification of commercial white polished rice batches, with metrics above 95%. The SWIR region, especially the 2173 nm spectral point, demonstrated high discriminatory power. In conclusion, the application of machine learning models based on VIS/NIR/SWIR spectroscopy proved highly efficient for classifying commercial batches of polished white rice, integrating physical and physicochemical attributes of the grains. Full article

Non-invasive glucose monitoring remains a significant challenge in diabetes management, with existing approaches often limited by poor accuracy, high cost, or patient discomfort. Microwave-based biosensors offer a promising label-free alternative by exploiting the dielectric contrast between glucose and water. This paper presents a compact, dual-band concentric square-shaped split-ring resonator (SRR-type) biosensor fabricated on a low-cost FR-4 substrate for aqueous glucose detection. The sensor leverages electric field confinement in inter-ring gaps to transduce glucose-induced permittivity changes into measurable shifts in resonance frequency and reflection coefficient. Experimental results demonstrate a linear, monotonic response across the clinical range up to 250 mg/dL, with a frequency-domain sensitivity of 1.964 MHz/(mg/dL) and amplitude-domain sensitivity of 0.0332 dB/(mg/dL), achieving high coefficients of determination (R² = 0.9956 and 0.9927, respectively). The design achieves a normalized size of 0.137 λ_g², combining high sensitivity and compact size within a scalable platform. Operating in the UWB-adjacent band (2.76–3.25 GHz), the proposed biosensor provides a practical, reproducible, and PCB-compatible solution for next-generation label-free glucose monitoring. Full article

Understanding life history parameters is key to assessing demography, biological productivity, and extinction risk of fishes. Age and growth analyses in chondrichthyan fishes (sharks, rays, and ghost sharks) is primarily undertaken through counting vertebral band pairs. For rare, threatened, and protected species such as river sharks (Carcharhinidae; Glyphis), obtaining sufficient vertebrae samples may not be possible. Here we use a very small sample size, selective size-class sampling, back-calculation techniques, and a Bayesian hierarchical model that accounts for repeated measures to provide age and growth information for the Speartooth Shark Glyphis glyphis from which comprehensive sampling is not possible. Ten individuals were selectively sampled from the Adelaide River, Northern Territory, Australia. Bayesian length-at-age models using a combination of informative and uninformative priors in a multi-model framework were applied to the observed and back-calculated data with the sexes combined. Band pair counts produced age estimates of 0–11 years and suggest that age at maturity is possibly >12 years. Most model parameter estimates for length-at-birth (L₀) and asymptotic length (L_∞) were biologically plausible. The Gompertz growth function, applied through a Bayesian hierarchical approach to back-calculated data, provided the best fitting and most biologically appropriate length-at-age parameters: L_∞ = 229.5 cm TL ± (14.6 SE), g_Gom = 0.16 yr⁻¹ ± (0.01 SE), and L₀ = 58.2 cm TL ± (1.4 SE). The results presented here are the first study to apply Bayesian methods to back-calculated length-at-age data while accounting for repeated measures. Full article

In this study, we present a new, facile, and eco-friendly approach to the synthesis of silver nanoparticles using an aqueous extract obtained from wasted goat bone, which acted as a reducing and stabilizing agent. Hydroxyapatite (GHAP) derived from the same biogenic source was then added to the Ag-NPs solution, resulting in the formation of a nanocomposite (Ag@GHAP). Biogenic GHAP and Ag@GHAP have been characterized using Fourier transform infrared spectroscopy (FTIR), dynamic light scattering (DLS), zeta potential, scanning electron microscopy (SEM), atomic force microscopy (AFM), and powder X-ray diffraction (XRD), confirming the formation of crystalline GHAP with well-dispersed silver nanoparticles. According to AFM studies, the Ag@GHAP composite exhibits a higher surface roughness alteration than GHAP. XRD revealed that the crystalline sizes of GHAP and Ag@GHAP are 10.2 and 15.6 nm, respectively. Zeta potential showed that GHAP and Ag@GHAP possessed values of −12.4 and −11.7 mV, respectively. Ag@GHAP showed a promising performance in photocatalysis and antioxidant applications as compared to GHAP. The energy band gap (Eg) values are 5.1 eV and 4.5 eV for GHAP and Ag@GHAP, respectively. Ag@GHAP showed photocatalytic activity during the degradation of methylene blue dye (5 ppm) under solar irradiation with a removal efficiency of 99.15% in 100 min at the optimum conditions. The antioxidant activity of GHAP and Ag@GHAP was determined using the DPPH method. The results showed enhanced antioxidant activity of a silver decorated sample with IC50 values of 36.83 and 2.95 mg/mL, respectively. As a result, the Ag@GHAP composite is a promising candidate in environmental treatment and scavenging of free radicals. Full article

Radiometric calibration of multispectral imagery plays a critical role in the determination of vegetation-related features. This radiometric calibration strongly depends on a proper sensor configuration when acquiring images, the shutter speed being a critical parameter. The objective of the present study was to appraise the influence of shutter speed on the reflectance in the visible and near-infrared (NIR) spectral regions registered by a low-cost multispectral sensor (MAPIR Survey3) on a homogeneous field of turfgrass (Cynodon dactylon L.—Poaceae) and on the vegetation index (VI) values calculated from them, under different solar radiation conditions. For this purpose, 10 shutter speed configurations were tested in field campaigns with variable solar radiation values. The main results demonstrated that the reflectance in the green spectral region was more sensitive to shutter speed than that of the red and NIR spectral regions, particularly under high solar radiation conditions. Moreover, VIs calculated using the green band were more sensitive to slow shutter speeds, thus presenting a higher probability of providing meaningless artifact values. In conclusion, this study provides shutter speed recommendations under different illumination conditions to optimize the reflectance and the VI sensitivity within the image, which can be applied as a simple method to optimize image acquisition from unmanned aerial vehicles under varying solar radiation conditions. Full article

Background/Objectives: Recurrent oral ulceration (ROU) is the most prevalent disorder of the oral mucosa, affecting approximately 20% of the global population. Current therapies are limited by adverse effects and high recurrence rates. Ai-Fen, enriched in the anti-inflammatory monoterpenoid L-borneol (54.3% w/w), exhibits therapeutic potential but suffers from poor aqueous solubility and low bioavailability. This study aimed to improve the physicochemical properties and in vivo efficacy of Ai-Fen through the preparation of solid dispersions. Methods: Ai-Fen solid dispersions (AF-SD) were prepared by a melt-fusion method using polyethylene glycol 6000 (PEG 6000) as the carrier. An L₉(3³) orthogonal design was employed to optimize three critical parameters: drug-to-carrier ratio, melting temperature, and melting duration. The resulting dispersions were systematically characterized by differential scanning calorimetry (DSC), powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FTIR). A chemically induced ROU model in rats (n = 8 per group) was established to evaluate the effects of AF-SD on ulcer area, serum inflammatory cytokines (TNF-α, IL-6), vascular endothelial growth factor (VEGF) levels, and histopathological outcomes. Results: The optimal formulation was obtained at a drug-to-carrier ratio of 1:2, a melting temperature of 70 °C, and a melting time of 5 min. Under these conditions, L-borneol release increased 2.5-fold. DSC and PXRD confirmed complete conversion of Ai-Fen to an amorphous state, while FTIR revealed a 13 cm⁻¹ red shift in the O-H stretching band, indicating hydrogen-bond formation. In vivo, AF-SD reduced ulcer area by 60.7% (p < 0.001) and achieved a healing rate of 74.16%. Serum TNF-α and IL-6 decreased by 55.5% and 49.6%, respectively (both p < 0.001), whereas VEGF increased by 89.6% (p < 0.001). Histological analysis confirmed marked reduction in inflammatory infiltration, accelerated re-epithelialization (score 2.50), and a 5.9-fold increase in neovascularization. Conclusions: AF-SD markedly enhanced the bioavailability of Ai-Fen through amorphization and accelerated ROU healing, likely via dual mechanisms involving suppression of nuclear factor kappa-B (NF-κB)-mediated inflammation and promotion of angiogenesis. This formulation strategy provides a promising approach for modernizing traditional herbal medicines. Full article

This work extends the steady flow Lenoir cycle within finite-time thermodynamics (FTT) by incorporating heat transfer irreversibilities through the $\epsilon -NTU$ formalism and a non-isentropic expansion modeled via the expander isentropic efficiency ${\eta }_E$. The total conductance $U_T$ (sum for the two heat exchangers) is partitioned between hot and cold units using $u_L=U_L/U_T$, with $U_T=U_H+U_L$. For each triplet ($\tau =T_H/T_L$, $U_L$, $U_T$), we closed the cycle by determining $T_1$, the working fluid temperature at the cooler outlet and heater inlet, $T_2$, the heater outlet and expander inlet, and $T_3$, the expander outlet and cooler inlet. Using these states, we compute the heat rates ${\dot{Q}}_{12}$, ${\dot{Q}}_{31}$ and the net power $P$. In addition to the thermal efficiency $\eta$, the following extended objective functions are evaluated: the efficient power $E_F$, the ecological efficiency $\varphi$, and the second law efficiency ${\eta }_{II}$. Parametric sweeps on $u_L$ for $\tau ϵ \left\{3.25,3.75\right\}$ and $U_{T }ϵ \left\{\mathrm{2.5,5.0},\mathrm{7.5,10}\right\} \mathrm{k}\mathrm{W}$ show unimodal curves for P($u_L$) and maxima. A robust result places the optima of $P$, $\eta$, $E_F$, $\varphi$, and ${\eta }_{II}$ in a distribution band at $u_L~0.6$. This guideline offers clear design guidance for allocating exchange area in heat recovery and microgeneration, maximizing power, high η, and exergetic utilization with contained entropic penalty. Full article