Search Results (160)

The Solar Agro Savior (SAS) is an innovative solution that is assisted by drones for the sustainable utilization of water and plant disease observation in the agriculture sector. This system integrates an alerting mechanism for humidity, moisture, and temperature variations, which affect the plants’ health and optimization in water utilization, which enhances plant yield productivity. A significant feature of the system is the efficient monitoring system in a larger region through drones’ high-resolution cameras, which enables real-time, efficient response and alerting for environmental fluctuations to the authorities. The machine learning algorithm, particularly recurrent neural networks, which is a pioneer with agriculture and pest control, is incorporated for intelligent monitoring systems. The proposed system incorporates a specialized form of a recurrent neural network, Long Short-Term Memory (LSTM), which effectively addresses the vanishing gradient problem. It also utilizes an attention-based mechanism that enables the model to assign meaningful weights to the most important parts of the data sequence. This algorithm not only enhances water utilization efficiency but also boosts plant yield and strengthens pest control mechanisms. This system also provides sustainability through the re-utilization of water and the elimination of electric energy through solar panel systems for powering the inbuilt irrigation system. A comparative analysis of variant algorithms in the agriculture sector with a machine learning approach was also illustrated, and the proposed system yielded 99% yield accuracy, a 97.8% precision value, 98.4% recall, and a 98.4% F1 score value. By encompassing solar irrigation and artificial intelligence-driven analysis, the proposed algorithm, Solar Argo Savior, established a sustainable framework in the latest agricultural sectors and promoted sustainability to protect our environment and community. Full article

Extreme surface winds and wave heights of tropical cyclones (TCs)—pose serious threats to coastal community, infrastructure and environments. In recent decades, progress in numerical wave modeling has significantly enhanced the ability to reconstruct and predict wave behavior. This review offers an in-depth overview of TC-related wave modeling utilizing different computational schemes, with a special attention to WAVEWATCH III (WW3) and Simulating Waves Nearshore (SWAN). Due to the complex air–sea interactions during TCs, it is challenging to obtain accurate wind input data and optimize the parameterizations. Substantial spatial and temporal variations in water levels and current patterns occurs when coastal circulation is modulated by varying underwater topography. To explore their influence on waves, this study employs a coupled SWAN and Finite-Volume Community Ocean Model (FVCOM) modeling approach. Additionally, the interplay between wave and sea surface temperature (SST) is investigated by incorporating four key wave-induced forcing through breaking and non-breaking waves, radiation stress, and Stokes drift from WW3 into the Stony Brook Parallel Ocean Model (sbPOM). 20 TC events were analyzed to evaluate the performance of the selected parameterizations of external forcings in WW3 and SWAN. Among different nonlinear wave interaction schemes, Generalized Multiple Discrete Interaction Approximation (GMD) Discrete Interaction Approximation (DIA) and the computationally expensive Wave-Ray Tracing (WRT) A refined drag coefficient (C_d) equation, applied within an upgraded ST6 configuration, reduce significant wave height (SWH) prediction errors and the root mean square error (RMSE) for both SWAN and WW3 wave models. Surface currents and sea level variations notably altered the wave energy and wave height distributions, especially in the area with strong TC-induced oceanic current. Finally, coupling four wave-induced forcings into sbPOM enhanced SST simulation by refining heat flux estimates and promoting vertical mixing. Validation against Argo data showed that the updated sbPOM model achieved an RMSE as low as 1.39 m, with correlation coefficients nearing 0.9881. Full article

Objectives: We devised a simple and practical method for optimizing intraocular lens (IOL) constants using linear interpolation, based on the IOL power calculation study protocol proposed by Hoffer et al., and evaluated its effectiveness. Methods: This retrospective study included 188 eyes from 188 Japanese patients who underwent cataract surgery with the implantation of CNA0T0 (Alcon) between June 2022 and September 2024. Preoperative biometric data were obtained using ARGOS (Alcon) and OA-2000 (Tomey). Predicted refractions were calculated using the European Society of Cataract and Refractive Surgeons’ (ESCRS) IOL Web Calculator with the EVO, Hill-RBF 3.0 (Hill), and Kane formulas, using both A-constants of 119.1 and 119.33. The mean prediction error (MPE) was calculated as the difference between the predicted and postoperative spherical equivalent at 3 months. Linear interpolation was applied to the paired results to derive optimized A-constants yielding MPE = 0 and to correct each case’s predicted refraction values (“corrected values”). Additionally, predicted refractions were recalculated using the optimized A-constants with the ESCRS IOL Web Calculator to obtain “actual values”. Both corrected and actual values achieved an MPE of 0 and were compared using the Friedman test and Cochran’s Q test. Results: The optimized A-constants for ARGOS were 119.540 (EVO), 119.733 (Hill), and 119.563 (Kane); for OA-2000, they were 119.388, 119.532, and 119.417, respectively. No significant differences were found between corrected and actual values under any condition. Conclusions: This method is simple, accurate, and applicable to new IOLs, devices, and formulas, with potential to improve the precision of clinical IOL power calculations. Full article

Ocean temperature and salinity are core elements influencing ocean dynamics and biogeochemical cycles, critical to climate change and ocean process studies. In recent years, Argo floats and satellite remote sensing data have provided key support for observing and reconstructing three-dimensional (3D) ocean temperature and salinity. However, due to the challenges and high costs of in situ observations and the limitation of satellite measurements to surface data, effectively combining multi-source data to enhance the reconstruction accuracy of 3D temperature and salinity remains a significant challenge. In this study, we propose a VI-UNet model that incorporates a Vision Transformer module into UNet model and apply it to reconstruct 3D temperature and salinity in the equatorial oceans (20°S–20°N, 20°E–60°W) at depths from 1 to 6000 m using sea surface data acquired by satellites. In addition, we also investigate the impact of incorporating significant wave height (SWH) on the reconstruction of temperature and salinity. The results demonstrate that the VI-UNet model performs remarkably well in reconstructing temperature and salinity, achieving maximum reductions in root mean square error (RMSE) of up to 40% and 100%, respectively. Additionally, incorporating SWH enhances model accuracy, particularly in the upper 1000 m. Full article

Background/Objectives: Swept-Source Optical Coherence Tomography (SS-OCT) is a novel optical biometry technology with limited published data on its reliability compared to the gold standard, partial coherence interferometry (PCI). This study aims to assess the agreement between an SS-OCT biometer (Argos) and a PCI device (IOLMaster 500) in terms of biometry values, intraocular lens (IOL) power calculation and mean prediction error (ME). Methods: In this prospective comparative study, axial length (AL), anterior chamber depth (ACD), flat (K1), steep (K2) and mean (Km) keratometry values, astigmatism power, J0, and J45 vector components, white-to-white distance (WTW), and IOL power calculations for nine IOL models using four formulas were compared in cataract patients. Refractive outcomes were assessed in eyes implanted with SN60WF and Panoptix IOLs, with ME calculated for each module and formula for both IOLs postoperatively. Results: This study included 133 eyes (mean age: 66.0 ± 10.95 years). Argos measured significantly higher ACD and steeper keratometry values than IOLMaster, albeit without significant differences in AL, astigmatism power, WTW, J0, and J45. Mean IOL power differences were within the clinically acceptable threshold (0.50 D), except for SN6ATx with Hoffer Q and Haigis, and Clareon with Haigis. For Panoptix and SN60WF, IOLMaster demonstrated a more hyperopic ME than Argos with SRK/T, Holladay 1, and Hoffer Q; however, this was without clinically significant differences. Conclusions: Argos and IOLMaster 500 presented differences in ACD, keratometry values, and IOL power calculation. However, both devices showed non-clinically significant differences in IOL power calculation and ME in the majority of formulas. Full article

Accurately and timely estimating three-dimensional ocean states is crucial for improving operational ocean forecasting capabilities. Although satellite observations provide valuable evolutionary information, they are confined to surface-level variables. While in situ observations can offer subsurface information, their spatiotemporal distribution is highly uneven, making it difficult to obtain complete three-dimensional ocean structures. This study developed an operational-oriented lightweight framework for three-dimensional ocean state reconstruction by integrating multi-source observations through a computationally efficient multivariate empirical orthogonal function (MEOF) method. The MEOF method can extract physically consistent multivariate ocean evolution modes from high-resolution reanalysis data. We utilized these modes to further integrate satellite remote sensing and buoy observation data, thereby establishing physical connections between the sea surface and subsurface. The framework was tested in the South China Sea, with optimal data integration schemes determined for different reconstruction variables. The experimental results demonstrate that the sea surface height (SSH) and sea surface temperature (SST) are the key factors determining the subsurface temperature reconstruction, while the sea surface salinity (SSS) plays a primary role in enhancing salinity estimation. Meanwhile, current fields are most effectively reconstructed using SSH alone. The evaluations show that the reconstruction results exhibited high consistency with independent Argo observations, outperforming traditional baseline methods and effectively capturing the vertical structure of ocean eddies. Additionally, the framework can easily integrate sparse in situ observations to further improve the reconstruction performance. The high computational efficiency and reasonable reconstruction results confirm the feasibility and reliability of this framework for operational applications. Full article

Background: The most widely used contemporary intraocular lens power calculation formulas are the Kane formula, Barrett Universal II formula, Hill–Radial Basis Function, and Ladas Super Formula, each of which was developed to improve postoperative refractive accuracy. This study aims to conduct a comprehensive comparative analysis of these formulas to evaluate their predictive accuracy across diverse biometric profiles. Methods: A total of 210 eyes that met the inclusion criteria were analyzed in this study. This study was designed as a retrospective observational investigation. The biometric parameters of the intraocular lens were evaluated using the ARGOS optical biometer. Refractive intraocular lens power calculations were performed using the formulas, and the resulting values were systematically compared to assess predictive accuracy. In our research, a parametric approach was adopted by applying ANOVA repeated measures analysis. Multiple measurements were evaluated through homogeneity of covariances. Pairwise comparisons between formula-derived values were conducted using the Bonferroni test to identify significant differences. A paired-sample t-test was used to compare the spherical equivalent levels calculated at the first and last controls. Potential correlations were examined using Pearson correlation analysis. Results: A statistically significant difference was observed between formulas. The differences among the formulas were caused by the values obtained from the Ladas Super Formula being significantly higher than the others. There was a statistically significant positive correlation between the data obtained from the formulas. The spheric equivalent values were similar, with no statistically significant difference. Conclusions: This study reinforces the notion that modern intraocular lens power calculation formulas exhibit a high degree of accuracy and correlation in predicting postoperative refractive outcomes. Full article

The main goal of this study is to develop a method for finding the joint probability distribution of the state of the characteristics of the NEMO (Nucleus for European Modeling of the Ocean) ocean dynamics model with data assimilation using the Generalized Kalman filter (GKF) method developed earlier by the authors. The method for finding the joint distribution is based on the Karhunen–Loeve decomposition of the covariance function of the joint characteristics of the ocean. Numerical calculations of the dynamics of ocean currents, surface and subsurface ocean temperatures, and water salinity were carried out, both with and without assimilation of observational data from the Argo project drifters. The joint probability distributions of temperature and salinity at individual points in the world ocean at different depths were obtained and analyzed. The Atlantic Meridional Overturning Circulation (AMOC) system was also simulated using the NEMO model with and without data assimilation, and these results were compared to each other and analyzed. Full article

The sound speed in the ocean has a considerable impact on the characteristics of underwater acoustic propagation. The swift gathering of the underwater three-dimensional (3D) sound speed field is essential for target detection, underwater acoustic communication, and navigation. Currently, the reconstruction of the underwater sound speed utilizing satellite remote sensing data of the sea surface has emerged as a significant area of research. However, dynamic activities within the ocean result in varying degrees of perturbation in the sound speed structure. Relying solely on sea surface information will restrict the accuracy of sound speed reconstruction. In response to this issue, by utilizing multi-source satellite remote sensing data alongside Argo profiles, we first implemented the random forest (RF) algorithm to establish the statistical mapping relationship from the sea surface temperature (SST), sea level anomaly (SLA), and absolute dynamic topography (ADT) to the density, and thus, reconstructed a 3D density field. Subsequently, based on the sea surface environmental information, we introduced the underwater vertical density as a novel input for sound speed calculations and proposed a new model for 3D sound speed field reconstruction (RF-SDR). The experimental results indicate that utilizing both the sea surface environmental variables and underwater density as inputs yielded an average root-mean-square error (RMSE) of 1.51 m/s for the reconstructed sound speed, along with an average mean absolute error (MAE) of 0.85 m/s. Following the incorporation of density into the reconstruction inputs, the two error metrics exhibited reductions of 31% and 35%, respectively. And the proposed RF-SDR model demonstrated a reduction in the RMSE by 36% and in the MAE by 43% when compared with the commonly utilized single Empirical Orthogonal Function regression (sEOF-r) method. Furthermore, simulations of the sound propagation with both the reconstructed sound speed and Argo sound speed demonstrated a high degree of consistency in the computed acoustic propagation losses. The correlation coefficients consistently exceeded 0.7, thereby reinforcing the validity of the reconstructed sound speed. Full article

CTD profilers are used as reference instruments to qualify temperature and salinity data. Their metrological specifications can be controlled in a calibration bath, and calibration coefficients can be applied to correct the linearity of sensors and the trueness of measured data with a given uncertainty. However, in ocean areas with thermal gradients, the uncertainty of the measured data is questionable due to the thermal inertia of sensors and the movements of the CTD in relation to the roll or pitch of the boat. In order to evaluate these measurement uncertainties and in order to be able to use the upcast profiles, a double C–T sensor SBE 9 profiler was fixed under a carousel water sampler, the second C–T couple being at the top of the carousel frame. This configuration allows the evaluation of the temperature measurement deviations of recorded profiles. In order to quantify the different sources of instrumental uncertainties, the temperature signal has been modelled accounting for the movements induced by the boat. The result allows one to quantify what can be called the representativeness of CTD’s temperature measurements. This notion is very useful in the data assimilation process. A table quantifying the various sources of uncertainty has been created from profiles obtained during four offshore campaigns. In the future, it could be used to find the representativeness of similar profiles obtained with a single pair of sensors. Ship-based CTD profiles are generally considered as perfect or without uncertainty in data assimilation and in the qualification per comparison of other instruments (XBT, Argo profiles, etc.). Our findings imply that this hypothesis will have to be reconsidered. Full article

The evolution of the three-dimensional thermohaline structure of mesoscale eddies is crucial for assessing energy and mass transfer during their long-distance propagation in the ocean. However, the understanding and quantitative evaluation of the role that mesoscale eddies play in driving variations of thermohaline in the deep sea remains constrained due to the scarcity of in situ observations, particularly in marginal seas such as the South China Sea (SCS). In this study, we propose an artificial intelligence (AI)–physics-based deep learning model that integrates satellite measurements and Argo data from 2003 to 2021 to reconstruct the three-dimensional thermohaline structure of mesoscale eddies in the SCS. Besides utilizing basic sea surface hydrodynamic parameters obtained from satellite data for model training, an additional branch incorporating eddy physical parameters was introduced to optimize the model. The results demonstrate that the model effectively reconstructs thermohaline properties within mesoscale eddies in the SCS. Compared to Argo observations, the average root mean square error (RMSE) for temperature (salinity) within anticyclonic eddies was 0.34 °C (0.036 PSU), while it was 0.36 °C (0.032 PSU) within cyclonic eddies in the upper 1500 m. Further validation using high-resolution glider observations tracking an anticyclonic eddy originating in the SCS confirms the model’s efficiency, achieving an RMSE of 0.2962 °C (0.0138 PSU) for temperature (salinity). The accuracy of our proposed model significantly outperforms that of HYCOM and GLORYS simulations, with the RMSE reduced by 40% to 60%. The distinctive capabilities provide valuable insights into understanding the fine-scale structures of mesoscale eddies, especially in regions with limited in situ data. Full article

The East Sea is a jointly managed maritime area of Korea, Russia, and Japan, where the frequency of strong typhoons is anticipated to increase with climate change, affecting its marine ecosystem and regional climate regulation. This study investigated the environmental and ecological impacts of summer typhoons entering the East Sea by analyzing satellite-derived chlorophyll-a (Chl-a) data, Argo float measurements, and ERA5 wind data. Our findings revealed that summer typhoons generally increased surface Chl-a concentrations by 65.4%, with typhoon intensity substantially influencing this process. Weak typhoons caused marginal Chl-a increases attributed to redistribution rather than nutrient supply, whereas normal and strong typhoons increased Chl-a through enhanced vertical mixing and nutrient upwelling in the East Sea. Stronger typhoons notably impacted the mixed layer depth and isothermal layer depth, leading to greater Chl-a concentrations within the strong wind radius. However, the increased Chl-a magnitude was lower than that of other strong typhoons in other regions. The East Sea uniquely responds to typhoons with fewer upper environment changes, possibly due to a stable barrier layer limiting vertical mixing. These findings underscore the importance of continuous monitoring and integrated observational methods in order to better understand the ecological effects of typhoons, particularly as their intensity increases with climate change. Full article

This study focuses on typical regions of strong ageostrophic processes in the Kuroshio using high-resolution remote sensing satellite reanalysis data and Argo float data. By analyzing the relationship between the Rossby number and chlorophyll concentration from June to August in the summer of 2020, the spatial characteristics of ageostrophic processes and their impact on the phytoplankton community distribution are explored. The results indicate that ageostrophic processes, driven by coastal topography, are stably generated in the regions of the Bashi Channel, northeastern Taiwan waters, southwestern Kyushu Island, and southern Shikoku Island. Furthermore, the intensity of these ageostrophic processes shows an overall positive correlation with chlorophyll concentration. The local mixing and subfront circulations induced by ageostrophic processes pump deep nutrients into the euphotic zone, supporting the growth and reproduction of phytoplankton, which leads to the formation of significant chlorophyll hotspots in regions controlled by ageostrophic processes. Full article

Satellite-derived observations of ocean colour provide continuous data on chlorophyll-a concentration (Chl-a) at global scales but are limited to the ocean’s surface. So far, biogeochemical models have been the only means of generating continuous vertically resolved Chl-a profiles on a regular grid. MULTIOBS is a multi-observations oceanographic dataset that provides depth-resolved biological data based on merged satellite- and Argo-derived in situ hydrological data. This product is distributed by the European Union’s Copernicus Marine Service and offers global multiyear, gridded Chl-a profiles within the ocean’s productive zone at a weekly temporal resolution. MULTIOBS addresses the scarcity of observation-based vertically resolved Chl-a datasets, particularly in less sampled regions like the Eastern Mediterranean Sea (EMS). Here, we conduct an independent evaluation of the MULTIOBS dataset in the oligotrophic waters of the EMS using in situ Chl-a profiles. Our analysis shows that this product accurately and precisely retrieves Chl-a across depths, with a slight 1% overestimation and an observed 1.5-fold average deviation between in situ data and MULTIOBS estimates. The deep chlorophyll maximum (DCM) is adequately estimated by MULTIOBS both in terms of positioning (root mean square error, RMSE = 13 m) and in terms of Chl-a (RMSE = 0.09 mg m⁻³). The product accurately reproduces the seasonal variability of Chl-a and it performs reasonably well in reflecting its interannual variability across various depths within the productive layer (0–120 m) of the EMS. We conclude that MULTIOBS is a valuable dataset providing vertically resolved Chl-a data, enabling a holistic understanding of euphotic zone-integrated Chl-a with an unprecedented spatiotemporal resolution spanning 25 years, which is essential for elucidating long-term trends and variability in oceanic primary productivity. Full article

This study addresses the issues of the limited data storage capacity of Argo buoys and satellite communication charges on the basis of data volume by proposing a block lossless data compression method that combines bidirectional long short-term memory networks and multi-head self-attention with a multilayer perceptron (BiLSTM-MHSA-MLP). We constructed an Argo buoy data compression system using the main buoy control board, Jetson nano development board, and the BeiDou-3 satellite transparent transmission module. By processing input sequences bidirectionally, BiLSTM enhances the understanding of the temporal relationships within profile data, whereas the MHSA processes the outputs of the BiLSTM layer in parallel to obtain richer representations. Building on this preliminary probability prediction model, a multilayer perceptron (MLP) and a block length parameter (block_len) are introduced to achieve block compression during training, dynamically updating the model and optimizing symbol probability distributions for more accurate predictions. Experiments conducted on multiple 4000 m single-batch profile datasets from both the PC and Jetson nano platforms demonstrate that this method achieves a lower compression ratio, shorter compression time, and greater specificity. This approach significantly reduces the communication time between Argo buoys and satellites, laying a foundation for the future integration of Jetson Nano into Argo buoys for real-time data compression. Full article