Search Results (560)

While steady-state visual evoked potentials (SSVEPs) are widely used in brain–computer interfaces (BCIs) due to their robustness to rhythmic visual stimuli, their generation mechanisms remain poorly understood. Challenges such as experimental complexity, inter-subject variability, and limited physiological interpretability hinder the development of efficient BCI systems. This study employed a single-channel neural mass model (NMM) of V1 cortical dynamics to investigate the biophysical underpinnings of SSVEP generation. By systematically varying synaptic gain, time constants, and external input parameters, we simulated δ/α/γ band oscillations and analyzed their generation principles. The model demonstrates that synaptic gain controls oscillation amplitude and harmonic content, and time constants determine signal decay kinetics and frequency precision, while input variance modulates harmonic stability. Our results reveal how V1 circuitry generates frequency-locked SSVEP responses through excitatory–inhibitory interactions and dynamic filtering mechanisms. This computational framework successfully reproduces fundamental SSVEP characteristics without requiring multi-subject experimental data, offering new insights into the physiological basis of SSVEP-based brain–computer interfaces. Full article

Compared with atmospheric urban heat islands, surface urban heat islands (SUHIs) are easily monitored by the thermal sensors on satellites and have a more stable spatial pattern resembling the urban and built-up lands across single cities, large metropolitans, and urban agglomerations; hence, they are gaining more attention from scholars and urban planners worldwide in the search for reasonable urban spatial patterns and scales to guide future urban development. Traditional urban–rural dichotomies, being sensitive to the representative urban and rural areas and the diurnal and seasonal variations in the land surface temperature (LST), obtain inflated and varying SUHI spatial footprints of approximately 1.0–6.5 times the urban size from different satellite-retrieved LST datasets in many cities and metropolitan areas, which are not conducive to urban planners in developing reasonable strategies to mitigate SUHIs. Taking the Yangtze River Delta urban agglomeration of China as an example, we proposed an improved structural similarity index to quantify more reasonable spatial patterns and footprints of SUHIs from multiple LST datasets at an annual interval. We identified gridded LST anomalies (LSTAs) related to urbanization by adopting random forest models with climate, urbanization, geographical, biophysical, and topographical parameters. Using a structural similarity index of the LSTA annual cycle at a grid point relative to the urban reference LSTA annual cycle in terms of average values, variances, and shapes to characterize the SUHIs, cross-validated SUHI footprints ~1.06–2.45 × 10⁴ km² smaller than the urban size and clear transition zones between urban areas and the SUHI zone were obtained from multiple LST datasets for 2000–2022. Hence, urban planners can balance urbanization’s benefits with the adverse effects of SUHIs by enhancing the transition zone between urban areas and the SUHI zone in future urban design. Considering that urban areas rapidly transformed into SUHIs, with the ratio of the SUHI extent to the urban size increasing from 0.43 to 0.62 during 2000–2022, urban planners should also take measures to prevent the rapid expansion of high-density urban areas with an ISA density above 65% in future urban development. Full article

Coral reefs experience numerous natural and anthropogenic environmental gradients that alter biophysical conditions and affect biodiversity. While many studies have focused on drivers of reef biodiversity using traditional diversity metrics (e.g., species richness, diversity, evenness), less is known about how environmental variability may influence functional diversity. In this study, we tested the impact of submarine groundwater discharge (SGD) on taxonomic and functional diversity metrics in Mo‘orea, French Polynesia. SGD is the expulsion of terrestrial fresh or recirculated seawater into marine environments and is associated with reduced temperatures, pH, and salinity and elevated nutrient levels. Using a regression approach along the SGD gradient, we found that taxon and functional-entity richness displayed unimodal relationships to SGD parameters, primarily nitrate + nitrite and phosphate variability, with peak richness at moderate SGD for stony coral and the full benthic community. Macroalgae showed this unimodal pattern for functional-entity but not taxonomic richness. Functional community composition (presence and abundance of functional entities) increased along the gradient, while taxonomic composition showed a nonlinear relationship to SGD-related parameters. SGD is a common feature of many coastal ecosystems globally and therefore may be more important to structuring benthic functional diversity than previously thought. Further, studying community shifts through a functional-trait lens may provide important insights into the roles of community functions on ecosystem processes and stability, leading to improved management strategies. Full article

Microfluidics has significantly advanced the field of single-cell analysis, particularly in studies related to cell growth, division, and heterogeneity. Electrical impedance spectroscopy (EIS), a label-free and non-invasive biosensing technique, has been integrated into microfluidic devices for high-throughput and long-term monitoring of single budding yeast cells. Accurate interpretation of EIS measurements of cell growth dynamics necessitates the establishment of theoretical equivalent circuit models for the single-cell sensing system. Here, we report on the development of equivalent circuit models of an in situ EIS sensing system to elucidate cell growth. Firstly, finite element modeling and simulation of an EIS measurement of cell growth in the EIS sensing unit were performed, guiding the fittings of electrical components for an established equivalent circuit model (ECM). From the ECM, we extracted an equivalent volume fraction applicable to various cell and sensing unit geometries to describe the geometry-dependent sensing characteristics corresponding to the electrical response in the model. Then, EIS measurements of an immobilized cell in a microfluidic device were conducted via peripheral circuits. A lumped parameter model for the entire EIS measurement system was established, with electrical components determined by fitting to experimental data. The rationality of the proposed theoretical model was validated through the long-term impedance variation induced by cell growth in experiments, demonstrating its feasibility in linking EIS data with the bio-physics underlying the experimental phenomenon. Full article

This study presents a mathematical analysis of the collective effect of chemical reactions, variable fluid properties, and thermal stability of a hydromagnetic couple-stress fluid flowing through a microchannel driven by electro-osmosis and a pressure gradient. The viscosity of the biofluid is assumed to depend on the temperature, while the electrical conductivity is assumed to be a linear function of the drift velocity. The governing equations are derived non-dimensionalized, and numerical solutions are obtained using the spectral Chebyshev collocation method. The numerical solution is validated using the shooting Runge–Kutta method. The effects of varying the parameters on the thermal stability, temperature, velocity, and entropy profiles are discussed with adequate interpretations using tables and graphs. The results reveal that the chemical reactions and viscosity parameter increase the fluid temperature, while the Hartmann number decreases the temperature and increases the flow velocity and entropy generation. It was also observed that the chemical reactions and viscosity parameter increased the entropy at the channel walls, while the Hartmann number decreased the entropy at the core center of the channel. This study has tremendous empirical significance, including but not limited to biophysical applications of devices, engineering applications such as control systems, and thermo-fluidic transport. Full article

The Albufera of Valencia (Spain) is a representative case of pressure on water quality, which caused the hypertrophic state of the lake to completely change the ecosystem that once featured crystal clear waters. PerúSAT-1 is the first Peruvian remote sensing satellite developed for natural disaster monitoring. Its high spatial resolution makes it an ideal sensor for capturing highly detailed products, which are useful for a variety of applications. The ability to change its acquisition geometry allows for an increase in revisit time. The main objective of this study is to assess the potential of PerúSAT-1′s multispectral images to develop multi-parameter algorithms to evaluate the ecological state of the Albufera lagoon. During five field campaigns, samples were taken, and measurements of ecological indicators (chlorophyll-a, Secchi disk depth, total suspended matter, and its organic-inorganic fraction) were made. All possible combinations of two bands were obtained and subsequently correlated with the biophysical variables by fitting a linear regression between the field data and the band combinations. The equations for estimating all the water variables result in the following R² values: 0.76 for chlorophyll-a (NRMSE: 16%), 0.75 for Secchi disk depth (NRMSE: 15%), 0.84 for total suspended matter (NRMSE: 11%), 0.76 for the inorganic fraction (NRMSE: 15%), and 0.87 for the organic fraction (NRMSE: 9%). Finally, the equations were applied to the Albufera lagoon images to obtain thematic maps for all variables. Full article

Background: Oxaliplatin-based hyperthermic intraperitoneal chemotherapy (HIPEC) using the original 30 min protocol has shown limited benefits in patients with peritoneal metastasis of colorectal cancer (PMCRC), likely due to the short duration, which limits drug penetration into tumor nodules. Bevacizumab, an antiangiogenic antibody that modifies the tumor microenvironment, may improve drug delivery during HIPEC. This in silico study evaluates the availability of oxaliplatin within tumor nodules when HIPEC is performed after bevacizumab treatment. Methods: Using a computational fluid dynamics (CFD) model of HIPEC, the temperature and oxaliplatin distribution within the rat abdomen were calculated, followed by a model of drug transport within tumor nodules located at various sites in the peritoneum. The vascular normalization effect of the bevacizumab treatment was incorporated by adjusting the biophysical parameters of the tumor nodules. The effective penetration depth values, including the thermal enhancement ratio of cytotoxicity, were then compared between HIPEC alone and HIPEC combined with the bevacizumab treatment. Results: After bevacizumab treatments at doses of 0.5 mg/kg and 5 mg/kg, the oxaliplatin availability increased by up to 20% and 45% when HIPEC was performed during the vascular normalization phase, with the penetration depth increasing by 1.5-fold and 2.3-fold, respectively. Tumors with lower collagen densities and larger vascular pore sizes showed higher oxaliplatin enhancement after the combined treatment. Bevacizumab also enabled a reduction in the oxaliplatin dose (up to half at 5 mg/kg bevacizumab) while maintaining effective drug levels in the tumor nodules, potentially reducing systemic toxicity. Conclusions: These findings suggest that administering oxaliplatin-based HIPEC during bevacizumab-induced vascular normalization could significantly improve drug penetration and enhance treatment efficacy. Full article

Background: Zanthoxylum piperitum (L.) DC., commonly known as Japanese pepper, is a deciduous shrub native to East Asia. Its berries are widely used as a spice, known for imparting a distinctive, tingly numbing sensation. Biologically, Z. piperitum has antimicrobial, antioxidant, and anti-inflammatory properties, and it is studied for its potential benefits in pain relief and digestive health. This study proposed a novel biotechnological Z. piperitum phytocomplex (ZPP) obtained by plant cell culture for skin health, specifically targeting collagen synthesis, extracellular matrix stability, and resilience against cellular stress. Given the bioactivity of Z. piperitum, we aimed to analyze its efficacy as a sustainable alternative for skin-supportive applications in cosmetics and supplements. Methods: ZPP was produced through stable plant cell cultures, yielding a lignan-rich (3.02% w/w) phytocomplex. Human fibroblasts (HFFs) were treated with varying ZPP concentrations to assess cellular viability, collagen metabolism, and ECM-related enzyme activities, both under normal and cell stress conditions. The in vivo assessment was performed by measuring biophysical skin parameters such as hydration, elasticity, and roughness in female volunteers for a period of six weeks. Results: In vitro, ZPP exhibited non-cytotoxicity at all concentrations tested. Under hyperosmotic stress, ZPP reduced cellular damage, suggesting enhanced resilience. ZPP upregulated lysyl oxidase (LOX) protein levels, critical for collagen cross-linking and ECM stability, with protective effects observed under oxidative/inflammatory conditions. Additionally, ZPP selectively inhibited collagenase, attenuating collagen breakdown, though antioxidant activity was modest. In vivo evaluation highlighted improved skin hydration, elasticity, and roughness. Conclusions: ZPP shows promise as a biotechnological agent for skin health, particularly in supporting collagen integrity, ECM stabilization, and cellular resilience under stress. While further studies are needed to explore its full efficacy, especially for aging and environmentally stressed skin, these findings highlight ZPP’s potential as a new ingredient for cosmetic formulations aimed at skin care and the treatment of alterations caused by aging or environmental conditions. Full article

Forest canopy height (FCH) is a vital indicator for assessing forest health and ecosystem service capacity. Over the past two decades, full-waveform (FW) LiDAR has been widely employed for estimating forest biophysical variables due to its high precision in measuring vertical forest structures. However, the impact of terrain undulations on forest parameter estimation remains challenging. To address this issue, this study proposes a bisection approximation decomposition (BAD) method for processing GEDI L1B data and FCH estimation. The BAD method analyzes the energy composition of simplified echo signals and determines the fitting parameters by integrating overall signal energy, the differences in unresolved signals, and the similarity of inter-forest signal characteristics. FCH is subsequently estimated based on waveform peak positions. By dynamically adjusting segmentation points and Gaussian fitting parameters, the BAD method achieved precise separation of mixed canopy and ground signals, substantially enhancing the physical realism and applicability of decomposition results. The effectiveness and robustness of the BAD method for FCH estimation were evaluated using 2049 footprints across varying slope conditions in the Harvard Forest region of Petersham, Massachusetts. The results demonstrated that digital terrain models (DTMs) extracted using the GEDI data and the BAD method exhibited high consistency with the DTMs derived using airborne laser scanning (ALS) data (coefficient of determination R² > 0.99). Compared with traditional Gaussian decomposition (GD), wavelet decomposition (WD), and deconvolution decomposition (DD) methods, the BAD method showed significant advantages in FCH estimation, achieved the smallest relative root mean square error (rRMSE) of 17.19% and greatest mean estimation accuracy of 84.57%, and reduced the rRMSE by 10.74%, 21.49%, and 28.93% compared to GD, WD, and DD methods, respectively. Moreover, the BAD method exhibited a significantly stronger correlation with ALS-derived canopy height mode data than the relative height metrics from GEDI L2A products (r = 0.84, p < 0.01). The robustness and adaptability of the BAD method to complex terrain conditions provide great potential for forest parameters using GEDI data. Full article

The clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. However, accurate estimations of the seasonal CI have substantial challenges, e.g., from the need for accurate hot spot measurements, i.e., the typical feature of the bidirectional reflectance distribution function (BRDF) shape in the current CI algorithm framework. Therefore, deriving a phenologically simplified stable CI product from a high-frequency CI product (e.g., 8 days) to reduce the uncertainty of CI seasonality and simplify CI applications remains important. In this study, we applied the discrete Fourier transform and an improved dynamic threshold method to estimate the start of season (SOS) and end of season (EOS) from the CI time series and indicated that the CI exhibits significant seasonal variation characteristics that are generally consistent with the MODIS land surface phenology (LSP) product (MCD12Q2), although seasonal differences between them probably exist. Second, we divided the vegetation cycle into two phenological stages based on the MODIS LSP product, ignoring the differences mentioned above, i.e., the leaf-on season (LOS, from greenup to dormancy) and the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), and developed the phenologically simplified two-stage CI product for the years 2001–2020 using the MODIS 8-day CI product suite. Finally, we assessed the accuracy of this CI product (RMSE = 0.06, bias = 0.01) via 95 datasets from 14 field-measured sites globally. This study revealed that the CI exhibited an approximately inverse trend in terms of phenological variation compared with the NDVI. Globally, based on the phenologically simplified two-stage CI product, the CI_LOS is smaller than the CI_LFS across all land cover types. Compared with the LFS stage, the quality for this CI product is better in the LOS stage, where the QA is basically identified as 0 and 1, accounting for more than ~90% of the total quality flag, which is significantly higher than that in the LFS stage (~60%). This study provides relatively reliable CI datasets that capture the general trend of seasonal CI variations and simplify potential applications in modeling ecological, meteorological, and other surface processes at both global and regional scales. Therefore, this study provides both new perspectives and datasets for future research in relation to CI and other biophysical parameters, e.g., the LAI. Full article

One of the main challenges faced by many rural areas is the presence of natural constraints, such as climatic conditions, steep slopes, and poor soil quality, which make agricultural production and activities more difficult and costly. In these areas, there is a significant risk of agricultural land abandonment, leading to potential losses in biodiversity, degradation of rural landscapes, desertification, and increased forest fire risk. The Common Agricultural Policy (CAP) aims to mitigate these risks through specific payment schemes provided to areas facing natural and other specific constraints. In this context, mapping and measuring territorial differentiation is essential for informing policy responses. At the end of the previous CAP programming period, the EU updated its classification of Less Favored Areas (LFAs), experimenting with a flexible approach based on common biophysical criteria (definitions and thresholds) and methodological guidelines to delineate territorial differentiations that are both locally relevant and comparable across member states. This contribution presents a review of the current state of data and spatial inference systems used in Italy to delineate biophysical limitations and assess the presence of factors that may help offset the impact of natural constraints. This process has supported the analysis of territorial differentiation and highlighted the related implications for agricultural entrepreneurs operating in diverse contexts. Full article

Accurate crop yield estimation is critical to successful agricultural operations. Current crop growth models often overlook the spatial and geographic components of the lands, leading to suboptimal yield estimates. To address this issue, assimilation of satellite vegetation products into these models can account for spatial variations in the land and improve estimation accuracy. In this paper, the AquaCrop model, a water-driven crop growth model, was selected for recalibration and assimilation of satellite-derived biophysical products due to its simplicity and lack of computational complexity. To this end, field samples of soil (sampled before cultivation) and crop features were collected during the growing season of silage maize. Digital hemisphere photography (DHP) and destructive sampling methods were used for measuring fraction vegetation cover (fCover) and biomass in Qaleh-Now County, southern Tehran, in 2019. Based on our proposed workflow in previous studies, a Gaussian process regression–particle swarm optimization (GPR-PSO) algorithm and global sensitivity analysis were applied to retrieve the fCover and biomass from Sentinel-2 satellite data and to identify the most sensitive parameters in the AquaCrop model, respectively. Here, we propose the use of an optimization water cycle algorithm (WCA) instead of a PSO algorithm as an assimilation method for the parameter calibration of AquaCrop. This study also focused on using both fCover and biomass state variables simultaneously in the model, as opposed to only the fCover, and found that using both variables led to significantly higher calibration accuracy. The WCA method outperformed the PSO method in AquaCrop’s calibration, leading to more accurate results on maize yield estimates. It has enhanced results, decreasing RMSE values by 3.8 and 4.7 ton/ha, RRMSE by 6.4% and 10%, and increasing R² by 0.17 and 0.35 for model calibration and validation, respectively. These results suggest that assimilating satellite-derived data and optimizing the calibration process through WCA can significantly improve the accuracy of crop yield estimations in water-driven crop growth models, highlighting the potential of this approach for precision agriculture. Full article

Plant height is a critical biophysical trait indicative of plant growth and developmental conditions and is valuable for biomass estimation and crop yield prediction. This study examined the effects of flight altitude and camera angle in quantifying cotton plant height using unmanned aerial system (UAS) imagery. This study was conducted in a field with a sub-surface irrigation system in Lubbock, Texas, between 2022 and 2023. Images using the DJI Phantom 4 RTKs were collected at two altitudes (40 m and 80 m) and three sensor angles (45°, 60°, and 90°) at different growth stages. The resulting images depicted six scenarios of UAS altitudes and camera angles. The derived plant height was subsequently calculated as the vertical difference between the apical region of the plant and the ground elevation. Linear regression compared UAS-derived heights to manual measurements from 96 plots. Lower altitudes (40 m) outperformed higher altitudes (80 m) across all dates. For the early season (4 July 2023), the 40 m altitude had r² = 0.82–0.86 and RMSE = 2.02–2.16 cm compared to 80 m (r² = 0.66–0.68, RMSE = 7.52–8.76 cm). Oblique angles (45°) yielded higher accuracy than nadir (90°) images, especially in the late season (24 October 2022) results (r² = 0.96, RMSE = 2.95 cm vs. r² = 0.92, RMSE = 3.54 cm). These findings guide optimal UAS parameters for plant height measurement. Full article

Grasslands are one of the most abundant and biodiverse ecosystems in the world. However, in southern European countries, the abandonment of traditional management activities, such as extensive grazing, has caused many semi-natural grasslands to be invaded by shrubs. Therefore, there is a need to characterize semi-natural grasslands to determine their aboveground primary production and livestock-carrying capacity. Nevertheless, current methods lack a realistic identification of vegetation assemblages where grassland biophysical parameters can be accurately retrieved by the inversion of turbid-medium radiative transfer models (RTMs) in fine-grained landscapes. To this end, in this study we proposed a novel framework in which multiple endmember spectral mixture analysis (MESMA) was implemented to realistically identify grassland-dominated pixels from Sentinel-2 imagery in heterogeneous mountain landscapes. Then, the inversion of PROSAIL RTM (coupled PROSPECT and SAIL leaf and canopy models) was implemented separately for retrieving grassland biophysical parameters, including the leaf area index (LAI), fractional vegetation cover (FCOVER), and aboveground biomass (AGB), from grassland-dominated Sentinel-2 pixels while accounting for non-vegetated areas at the subpixel level. The study region was the southern slope of the Cantabrian Mountains (Spain), with a high spatial variability of fine-grained land covers. The MESMA grassland fraction image had a high accuracy based on validation results using centimetric resolution aerial orthophotographs (R² = 0.74, and RMSE = 0.18). The validation with field reference data from several mountain passes of the southern slope of the Cantabrian Mountains featured a high accuracy for LAI (R² = 0.74, and RMSE = 0.56 m²·m⁻²), FCOVER (R² = 0.78 and RMSE = 0.07), and AGB (R² = 0.67, and RMSE = 43.44 g·m⁻²). This study provides a reliable method to accurately identify and estimate grassland biophysical variables in highly diverse landscapes at a regional scale, with important implications for the management and conservation of threatened semi-natural grasslands. Future studies should investigate the PROSAIL inversion over the endmember signatures and subpixel fractions depicted by MESMA to adequately address the parametrization of the underlying background reflectance by using prior information and should also explore the scalability of this approach to other heterogeneous landscapes. Full article

The objective of this study was to correlate the biophysical parameters of forage cactus with visible vegetation indices obtained by unmanned aerial vehicles (UAVs) and predict them with machine learning in different agricultural systems. Four experimental units were conducted. Units I and II had different plant spacings (0.10, 0.20, 0.30, 0.40, and 0.50 m) with East–West and North–South planting directions, respectively. Unit III had row spacings (1.00, 1.25, 1.50, and 1.75 m), and IV had cutting frequencies (6, 9, 12 + 6, and 18 months) with the clones “Orelha de Elefante Mexicana”, “Miúda”, and “IPA Sertânia”. Plant height and width, cladode area index, fresh and dry matter yield (FM and DM), dry matter content, and fifteen vegetation indices of the visible range were analyzed. The RGBVI and ExGR indices stood out for presenting greater correlations with FM and DM. The prediction analysis using the Random Forest algorithm, highlighting DM, which presented a mean absolute error of 1.39, 0.99, and 1.72 Mg ha⁻¹ in experimental units I and II, III, and IV, respectively. The results showed potential in the application of machine learning with RGB images for predictive analysis of the biophysical parameters of forage cactus. Full article