Search Results (289)

Due to the limited number of deep sound speed profile (SSP) samples, the existing wide-area SSP inversion methods cannot estimate the full-depth SSP. In this paper, the full-depth SSP inversion is achieved by adding physical mechanism constraints to the neural network inversion algorithm. A dimensionality reduction approach for SSP perturbation, based on the hydrodynamic mechanism of seawater, is proposed. Constrained by the characteristics of ocean stratification, a self-organizing map is employed to invert the depth of the sound channel axis and reconstruct the SSP from the sea surface to the sound channel axis. The SSP from the sound channel axis to the seabed is reconstructed by integrating the characteristics of the sound channel axis and the sound speed gradient characteristics of the deep sea isothermal layer. The efficacy of the method was validated by the Argo data from the South China Sea. The average root mean square error of the reconstructed full-depth SSP is 2.85 m/s. Additionally, the average error of transmission loss prediction within 50 km is 2.50 dB. The proposed method is capable of furnishing effective full-depth SSP information without the necessity of any in situ measurements, thereby meeting the requirements of certain underwater acoustic applications. Full article

In this work, we propose a fully decentralized, self-organizing control framework for a swarm of autonomous ground mobile robots. The system integrates potential field-based mechanisms for simultaneous trajectory tracking, formation control, and obstacle avoidance, all based on local sensing and neighbor interactions without centralized coordination. Each robot autonomously computes attractive, repulsive, and formation forces to navigate toward target positions while maintaining inter-robot spacing and avoiding both static and dynamic obstacles. Inspired by biological swarm behavior, the controller emphasizes robustness, scalability, and flexibility. The proposed method has been successfully validated in the ARGoS simulator, which provides realistic physics, sensor modeling, and a robust environment that closely approximates real-world conditions. The system was tested with up to 15 robots and is designed to scale to larger swarms (e.g., 100 robots), demonstrating stable performance across a range of scenarios. Results obtained using ARGoS confirm the swarm’s ability to maintain formation, avoid collisions, and reach a predefined goal area within a configurable 1 m radius. This zone serves as a spatial convergence region suitable for multi-robot formation, even in the presence of unknown fixed obstacles and movable agents. The framework can seamlessly handle the addition or removal of swarm members without reconfiguration. Full article

Microalgae have been characterized as an effective raw material for obtaining bioproducts from a biorefinery approach. However, production costs limit the large-scale production of microalgae, which makes these processes uncompetitive in the market. Therefore, in the present work, different agricultural fertilizers were evaluated as low-cost culture media for microalgae growth and the use of the biomass for biocrude production. The tests were carried out in three phases: phase I, Laboratory scale 1 L Erlenmeyer (Boeco, Hamburg, Germany) and phase II–III Pilot scale with cylindrical photobioreactors (PBRs) (Atb services S.A.S, Medellin, Colombia) with a capacity of 20 L. In phase I, four commercial fertilizers Crecilizer^® (C), Florilizer^® (F) (Fertilizer, Bogota, Colombia), AcuaLeaf Macros^® (Ma), and AcuaLeaf Micros^® (Mi) (Deacua, Medellin, Colombia) were tested separately and in combination (C + Ma, F + M, and Ma + Mi). The most effective treatments (C and F) in phase I were chosen for scale-up during phase II. In phase III, the concentration of the best treatment from phase II was increased. The biomass obtained from the best phase III treatment showed a cultivation medium cost 50% lower than the biomass obtained using Bold’s Basal Medium (BBM). Following each treatment, the harvested biomass was processed via hydrothermal liquefaction (HTL) to yield biocrude. The reduction in culture medium cost contributed to an estimated 40% decrease in the relative biocrude yield cost. Full article

Satellite tracking has dramatically improved research on wide-ranging large marine vertebrates such as sea turtles. Traditionally, sea turtle tracking has relied on Argos-based satellite telemetry tags, which estimate location via Doppler shift and can also transmit sensor data. GPS-equipped Argos satellite tags represented a significant evolution, offering higher location accuracy. More recently, GPS-equipped satellite tags transmitting via the Iridium satellite network have become available for sea turtle tracking, and this study aims to assess whether they offer additional advantages. The performance of three satellite tag types—Argos-only, Argos-GPS, and Iridium-GPS (Iridium)—was assessed using data on 116,074 positions from 48 sea turtles representing five species and multiple ocean basins. Performance was evaluated using three indicators: the proportion of days with location data, the duration of gaps between locations, and the number of positions per day. Bayesian generalized linear mixed models assessed the effect of satellite tag type, technical settings, species, and activity (migration, foraging, internesting). Results indicate that Iridium satellite tags generally perform similarly to both Argos-based satellite tags, but performance improves significantly when programmed with high-frequency GPS acquisition (>24 positions/day), a result made possible by their tenfold higher transmission capacity compared to Argos. This capacity also enables transmission of more sensor data. Performance, however, varied by species and activity. These findings highlight the potential of Iridium tags to enhance fine-scale movement studies by improving the spatial and temporal resolution of sea turtle tracking, with important implications for ecological research and conservation planning. Full article

We assess precipitation and key atmospheric water-cycle terms over South America (SA) in three modern reanalyses—MERRA-2, ERA5, and CFSR/CFSv2—during 1980–2021. Two observation-based datasets (CPC Unified Gauge and MSWEP-V2) serve as references to bracket observational uncertainty. Diagnostics include regional means for the Tropical and Subtropical South Atlantic Convergence Zone (TSACZ, SSACZ) and southeastern South America (SESA), Taylor-diagram skill metrics, and a vertically integrated moisture-budget residual as a proxy for closure. All products reproduce the large-scale spatial and seasonal patterns, but disagreements persist over the Andes and parts of the central/northern Amazon. Relative to CPC/MSWEP-V2, MERRA-2 exhibits the smallest precipitation biases and the highest correlations, followed by ERA5; CFSR/CFSv2 shows a warm-season wet bias. Moisture-budget residuals are smallest in MERRA-2, moderate in ERA5, and largest in CFSR/CFSv2, with clear regional and seasonal dependence. These results document improvements in the new generation of reanalyses while highlighting persistent challenges in gauge-sparse and complex-orography regions. For hydroclimate applications that depend on internally consistent P, E, moisture-flux convergence, and runoff, MERRA-2 provides the most coherent depiction among the three, whereas ERA5 is a strong alternative when higher spatial/temporal resolution or dynamical fields are needed and CFSR/CFSv2 should be applied with caution for warm-season precipitation and closure-sensitive analyses. Full article

Microwave Radiation Imagers (MWRIs) onboard the FY-3B, FY-3C, and FY-3D satellites are the primary sensors for sea surface temperature (SST) observation. Benefiting from the resolution of several key calibration issues in brightness temperature products, MWRI SST records spanning more than a decade have been reprocessed. In this study, these reprocessed SST products are evaluated using direct comparison and the extended triple collocation (ETC) method, along with additional error analyses. Compared with iQuam SST, the reprocessed MWRI SST products from the three satellites show total root mean square errors (RMSEs) of 0.80–0.82 °C and total biases of −0.12 °C to 0.00 °C. ETC analyses based on MWRI, ERA5, and Argo SSTs indicate random errors of 0.76–0.78 °C. Furthermore, the reprocessed MWRI SST products demonstrate temporal stability and exhibit minimal crosstalk effects from sea surface wind speed, columnar water vapor, and columnar cloud liquid water in SST retrievals. Compared with previous versions, the reprocessed products show significant improvements, with consistent performance across FY-3B, FY-3C, and FY-3D. However, differences in SST observations due to the varying local times of the ascending nodes among the three satellites should be corrected in practical applications. Full article

Matched-field processing localizes underwater acoustic targets by measuring the degree of correlation between the acoustic field and replica fields. The intrusion of mesoscale eddies can induce sound speed mismatch in the matched-field process. Therefore, it is essential to investigate the impact of mesoscale eddies on matched-field localization errors. In this study, the typical vertical structure of mesoscale eddies in a certain region of the Northwestern Pacific was synthesized using the mesoscale eddy dataset META 2.0 and Argo float data. Furthermore, by employing both an idealized eddy model and composite-analysis structure of eddy, the performance of the localization algorithm was evaluated under the influence of mesoscale eddies with different structures and in different regions. The results show that under specific conditions, the distribution of localization errors exhibits certain patterns, which is beneficial for inverting eddy parameters via matched-field processing. Finally, the mechanism behind the systematic distribution of localization errors is discussed and analyzed. In the simulations, the source frequency was swept from 50 to 75 Hz with a 1 Hz step, and a circular array was employed as the receiving aperture. These findings indicate that, in the absence of small-scale interference and within a certain range of sound speed mismatch, the localization error of underwater acoustic targets increases with the strengthening of mesoscale eddy disturbances. Full article

Collaborative mapping has emerged in recent decades as a key practice for producing open geospatial knowledge and fostering critical citizenship. However, several studies have shown that these platforms may reproduce existing gender inequalities, both in terms of participation and representation. This article examines the potential of collaborative feminist cartography as a strategy for making inequalities visible and promoting gender equality in public space. Methodologically, the study focuses on the project Las Calles de las Mujeres, developed by Geochicas OSM, combining quantitative analysis of street naming in urban development with qualitative implementation in educational contexts. A global overview of 32 cities in 11 countries is provided, with a detailed case study of 11 Spanish cities. Results confirm the persistence of a significant gender gap in urban toponymy: streets named after men not only outnumber those dedicated to women but are also on average longer, more central, and symbolically more prominent. Educational experiences in Spain provide learning outcomes and demonstrate that collaborative mapping strengthens spatial thinking, digital competence, and critical awareness, linking geography education to the Sustainable Development Goals (SDG 5 and SDG 11). The article concludes that feminist mapping initiatives are simultaneously pedagogical, social, and political tools, capable of fostering more inclusive and sustainable cities. Full article

Additive manufacturing is driving a significant change in industry, extending beyond prototyping to the inclusion of printed parts in final designs. Stereolithography (SLA) is a polymerization technique valued for producing highly detailed parts with smooth surface finishes. This study presents a hybrid intelligent framework for modeling and optimizing the SLA 3D printer process’s parameters for Acrylonitrile Butadiene Styrene (ABS) photopolymer parts. The nonlinear relationships between the process’s parameters (Orientation, Lifting Speed, Lifting Distance, Exposure Time) and multiple performance characteristics (ultimate tensile strength, yield strength, modulus of elasticity, Shore D hardness, and surface roughness), which represent complex relationships, were investigated. A Taguchi design of the experiment with an L18 orthogonal array was employed as an efficient experimental design. A novel hybrid fuzzy logic–Particle Swarm Optimization (PSO) algorithm, ARGOS (Adaptive Rule Generation with Optimized Structure), was developed to automatically generate high-accuracy Mamdani-type fuzzy inference systems (FISs) from experimental data. The algorithm starts by customizing Modified Learn From Example (MLFE) to create an initial FIS. Subsequently, the generated FIS is tuned using PSO to develop and enhance predictive accuracy. The ARGOS models provided excellent performances, achieving correlation coefficients (R²) exceeding 0.9999 for all five output responses. Once the FISs were tuned, a multi-objective optimization was carried out based on the weighted sum method. This step helped to identify a well-balanced set of parameters that optimizes the key qualities of the printed parts, ensuring that the results are not just mathematically ideal, but also genuinely helpful for real-world manufacturing. The results showed that the proposed hybrid approach is a robust and highly accurate method for the modeling and multi-objective optimization of the SLA 3D process. Full article

Mountain protected areas in the tropical Andes experience localized yet severe soil erosion that threatens erosion-regulating services and downstream water–energy security. We mapped soil loss at 30 m using the Revised Universal Soil Loss Equation (RUSLE) and quantified the erosion-control service in El Cajas National Park, Ecuador (28,544 ha) using an NDVI-based index. Replacing categorical land cover C factors with a continuous NDVI surface increased the park-wide soil loss estimate by ∼58%, yielding an area-weighted mean of 5.3 t ha⁻¹ yr⁻¹ and local maxima of 120 t ha⁻¹ yr⁻¹ on steep and sparsely vegetated escarpments. Relative to a bare soil scenario, existing páramo grasslands, shrub mosaics, and scattered Polylepis woodlots avert 95% of potential erosion, quantifying the service supplied by vegetation. Between 2023 and 2024, a ∼60% rise in mean NDVI more than doubled the area delivering moderate-to-high erosion control. A hot-spot analysis further identified ∼30 km² (≈5% of the park) where high modeled soil loss coincides with low protection; these clusters generate ∼80% of predicted sediment and constitute priority targets for restoration or visitor use regulation. The integrated RUSLE–NDVI–EC approach provides a concise and transferable screening tool for aligning conservation investments with Ecuador’s restoration pledges and for safeguarding critical hydrological services in Andean protected areas. Full article

Panama, with nearly 3000 km of coastline and half its population living in coastal zones, faces high vulnerability to sea level rise, flooding, and extreme events. The most vulnerable areas include low-lying coastal provinces such as Panama, Colón, and Chiriquí. This review explores the use of sustainable concrete to address the effects of climate change in Panama towards coastal resilience. The methodology combined a bibliometric analysis using VOSviewer, a systematic literature review (2015–2025) of 99 sources including regulations and technical standards, and a socioeconomic SWOT analysis to assess adoption drivers and barriers. A 2050 permanent inundation map was examined to identify vulnerable areas, and an inventory of concrete-based protection structures was developed. The results highlight that concrete is already used in Panama for coastal resilience through structures such as breakwaters, dolos, and Xbloc units. However, as the country still needs to expand its coastal protection infrastructure, there is a crucial opportunity to implement lower-impact, sustainable concrete alternatives that minimize environmental burdens while ensuring long-term durability and performance. Sustainable options, including supplementary cementitious materials (SCMs), recycled aggregates, and CO₂ injection technologies, demonstrate strong mitigation potential, with national initiatives such as Vertua, Greentec, and Argos pozzolan offering early pathways. The conclusions emphasize the need to expand sustainable concrete applications, integrate nature-based solutions, and strengthen Panama’s regulatory and technical capacity to achieve resilient, low-carbon coastal infrastructure. Full article

Three-dimensional (3D) ocean physical fields are essential for understanding ocean dynamics, but reconstructing them solely from sea-surface remote sensing remains challenging. We present 3DV-Unet, an end-to-end deep learning framework that reconstructs eddy-resolving three-dimensional essential ocean variables (temperature, salinity, and currents) from multi-source satellite data. The model employs a 3D Vision Transformer bottleneck to capture cross-depth and cross-variable dependencies, ensuring physically consistent reconstruction. Trained on 2011–2019 reanalysis and satellite data, 3DV-Unet achieves RMSEs of ~0.30 °C for temperature, 0.11 psu for salinity, and 0.05 m/s for currents, with all R² values above 0.93. Error analyses further indicate higher reconstruction errors in dynamically complex regions such as the Kuroshio Extension, while spectral analysis indicates good agreement at 100 km+ but systematic deviation in the 20–100 km band. Independent validation against 6113 Argo profiles confirms its ability to reproduce realistic vertical thermohaline structures. Moreover, the reconstructed 3D fields capture mesoscale eddy structures and their life cycle, offering a valuable basis for investigating ocean circulation, energy transport, and regional variability. These results demonstrate the potential of end-to-end volumetric deep learning for advancing high-resolution 3D ocean reconstruction and supporting physical oceanography and climate studies. Full article

The Auxin-Regulated Gene Involved in Organ Size (ARGOS) proteins have crucial regulatory effects on organ size and responses to environmental stresses. Despite their importance, Brassica oleracea ARGOS gene members and their functions in response to abiotic stresses have not been thoroughly investigated. In this study, we identified 40 ARGOS genes via a genome wide analysis of cauliflower and two other B. oleracea morphotypes as well as Brassica rapa, Brassica nigra, and Raphanus sativus. Expression pattern analyses indicated that these genes are responsive to multiple abiotic stresses, including salinity, heat, cold, and diverse hormones. Notably, the expression of an ARGOS-like gene (BobARL2) was upregulated in cauliflower treated with 1-aminocyclopropane-1-carboxylic acid (ACC). Moreover, the overexpression of BobARL2 decreased ethylene sensitivity, resulting in less inhibition of root elongation compared to the wild-type. Additionally, the overexpression lines exhibited enhanced salt tolerance. A yeast two-hybrid assay and luciferase complementation imaging (LCI) assay confirmed that BobARL2 can interact with Reversion-to-ethylene sensitivity Like4 (BobRTL4), which negatively regulates ethylene signal transduction. These findings advance our understanding of the evolution and functional roles of ARGOS genes in cauliflower and other Brassicaceae species, particularly in relation to abiotic stress responses, while also offering valuable insights relevant to the genetic improvement and breeding of novel varieties. Full article

The subsurface chlorophyll maximum (SCM) is widely observed in the ocean and is often associated with phytoplankton biomass, where aggregated phytoplankton leads to increased chlorophyll concentrations in the water column. Pycnocline facilitates biomass accumulation by trapping nutrients and providing favorable physical conditions. However, comprehensive studies remain lacking regarding the coupling mechanism between pycnocline and SCM and the extent to which this relationship affects SCM dynamics through biomass accumulation. To investigate the seasonal coupling between the pycnocline and SCM, we established a linear regression model and quantified their relationship using a coupling coefficient, which describes the seasonal transition of SCM in terms of biomass accumulation. The results were validated using BGC-Argo data. Our findings reveal that SCM and the pycnocline consistently exhibit periodic coupling patterns within seasonal cycles, and in the Indian Ocean and the northwestern Pacific, SCM is predominantly biomass-driven during seasons with strong pycnocline coupling (the coupling coefficient ranges between 0.5 and 0.7). In contrast, this coupling weakens significantly in oligotrophic regions (the coupling coefficient remained below 0.3 in more than half of the months studied), where SCM no longer exhibits a clear overlap with peaks in particulate backscattering (BBP). Full article

Purpose: To compare the performance of two swept-source optical coherence tomography-based biometers in the measurement of ocular biometry and the prediction of postoperative refractive errors in eyes with short axial length (AL). Methods: A total of 48 eyes from 29 patients with AL ≤ 22 mm were included. AL, anterior chamber depth (ACD), keratometry (K), and lens thickness (LT) measured using the IOLMaster^® 700 and ARGOS^® before cataract surgery were compared. The refractive error prediction accuracy of the two devices was also compared. Results: This study included four men (7 eyes) and 25 women (41 eyes), with an average age of 70.7 ± 8.1 years (mean ± SD; range, 47–82 years). The two devices demonstrated good agreement in measurements of ocular biometry with high intraclass correlation coefficients (AL = 0.975; ACD = 0.957; K = 0.988; LT = 0.994). However, AL and ACD were significantly shorter when measured with the IOLMaster^® 700 compared to the ARGOS^® (p < 0.001 for both). There was no significant difference in mean absolute prediction errors between the two devices (p = 0.423). The IOLMaster^® 700 showed a significantly lower mean prediction error than the ARGOS^® (+0.12 ± 0.39 diopters vs. +0.20 ± 0.39 diopters, p = 0.006), although the difference was of limited clinical relevance. There were no significant differences in the percentages of eyes within ± 0.50 D (77.1% vs. 75.0%, p = 0.811) and ± 1.00 D (100% vs. 97.9%, p = 0.315) of the predicted refractive error. Conclusions: Although IOLMaster^® 700 and ARGOS^® showed good agreements in eyes with short AL, significant differences were observed in the measurements of AL and ACD. Both devices demonstrated good efficacy and comparable performance in predicting postoperative refractive errors. Full article