Barbara Lednicka

A radiative transfer—based spectral optical proxy k( λ ) linking inherent optical properties (IOPs) and remote-sensing reflectance (R _rs ) was developed for six representative regions of the Atlantic Ocean. R _rs was modeled at eight wavelengths (412, 440, 488, 510, 532, 555, 650, and 676 nm) using Morel’s proxy algorithm and Monte Carlo radiative-transfer simulations. The analysis used in situ measurements collected during the 33rd Atlantic research cruise of the R/V Akademik Ioffe (March 18–April 28, 2011), comprising 8669 complete optical records acquired along an approximately 15,000 km transect. Absorption (a _pg ) and attenuation (c _pg ) coefficients by dissolved and suspended constituents of seawater were measured with an ac-9 (WET Labs Inc.), particulate scattering (b _p ) was derived from their difference, and chlorophyll a (Chl a) was measured with a 6600V2-M (YSI). A wavelength-dependent parameter k( λ ) was introduced to describe the relationship between IOPs and R _rs and was approximated with a three-parameter quadratic function. Independent validation using the NASA SeaBASS EXPORTS North Atlantic 2021 dataset showed strong agreement between predicted and observed R _rs (r=0.993; R ² =0.986), with systematic underestimation (slope=0.703; MAPE=21.2%). The spectral form of k( λ ) was therefore transferable, whereas its magnitude remained sensitive to model assumptions. The proposed proxy captures bio-optical variability in Case-1 waters and provides a simplified framework for linking measured IOPs with R _rs , with potential applications in semi-analytical inversion, hyperspectral ocean color retrievals, and operational remote-sensing models.

Effective thermal management is essential for the safety and lifetime of lithium-ion batteries. This study presents a proof-of-concept framework combining electrochemical–thermal simulation with machine learning for temperature prediction, thermal-state classification and uncertainty-aware decision support for 26650 cells with phase change material (PCM) cooling. A Newman–Tiedemann–Gu–Kim model, calibrated at 0.5C–1.5C (average voltage and heat-flux accuracies of 94% and 82%), was used to simulate six PCMs and a no-PCM baseline at 2C–4C and three ambient temperatures (53 cases). With hyperparameters selected on training data only, Gaussian Process Regression (GPR) predicted the final temperature with a held-out RMSE of 0.56 K (R² = 0.993) and a nested cross-validated RMSE of 0.76 K, clearly outperforming Random Forest (RF), but did not generalize reliably to materials absent from the training data. Ambient temperature and C-rate were the dominant predictors. An RF classifier of the thermal state (Cold, Phase-Change, Overheating) reached only 58.5% cross-validated accuracy, whereas assigning the state from the GPR predictions reached 92.8%. Monte Carlo input perturbation, predicted temperature and C-rate were combined in a rule-based scheme assigning Normal, Monitor, Pre-Cool and Emergency actions. Larger datasets and experimental validation at high C-rates are required before deployment.

A spectral proxy parameter linking remote sensing reflectance (R_rs) and inherent optical properties (IOPs) was evaluated for six selected areas of the Baltic Sea. Measured IOPs from the individual optical datasets were used to calculate Morel’s unscaled proxy reflectance, whereas area and wavelength specific median IOPs were used as representative inputs to Monte Carlo radiative transfer simulations. Comparison of the individual proxy estimates with the corresponding area-specific Monte Carlo reference reflectance enabled determination of the spectral proxy parameter k. The results demonstrate a clear wavelength dependence and substantial area to area variability of this parameter. The purpose of the study is not to introduce a new fundamental IOPs–R_rs relationship, but to quantify how the effective scaling coefficient of a widely used proxy relationship varies spectrally and regionally in optically complex Case-2 waters. For each investigated Baltic Sea area, the spectral variability of k was approximated using a three-coefficient second-order polynomial. Area-specific parameterizations provide compact descriptions of the regional median k spectra, while a generalized formulation describes only the pooled central tendency. A radiometric evaluation was performed using 24 in situ OCP-100 spectra from the Gulf of Gdańsk (N = 96 wavelength-level pairs). Predicted and measured R_rs yielded r = 0.680, R² = 0.462, a regression slope of 0.258, a mean bias of −0.00475 sr⁻¹, an RMSE of 0.00590 sr⁻¹, and a MAPE of 62.1%. Within-wavelength correlations were weak at 555 and 620 nm (r = 0.140 and 0.067, respectively). Thus, the regional parameterization preserves broad spectral ordering but underestimates absolute R_rs and does not establish strong station-level predictive skill. The results should be interpreted as a regional, configuration-dependent spectral scaling framework.

This study analyses the free vibration and thermal buckling of size-dependent, axially functionally graded Euler–Bernoulli nanobeams using a nonlocal thermo-elastic Fredholm integral model based on Eringen’s nonlocal elasticity. The elastic modulus and mass density vary exponentially along the beam, so the thermal load, modeled as an equivalent axial force, follows the same exponential law. Four successive Fredholm integrations convert the governing equation into a weak-form integral equation that builds in the boundary conditions directly; this equation is then solved with a power series Galerkin method. The critical thermal buckling force is the axial force at which the fundamental frequency vanishes. Simple–simple (S–S), clamped–clamped (C–C), clamped–simple (C–S) and clamped–free beams are examined. Predicted frequencies agree with published solutions to within 0.11%. For a homogeneous beam, increasing the nonlocal parameter from zero to 0.2 lowers the fundamental frequency by 15.3% (S–S) and 18.3% (C–C). Shifting the thermal force from tension (−3) to compression (+3) lowers it by 26.9% (S–S) and 7.1% (C–C). Raising the nonlocal parameter from zero to 0.3 reduces the C–C buckling force by about 78% for a homogeneous beam and 94% for a gradient index of −2. Changing the gradient index from −2 to two lowers the first C–S frequency by 24.7% but the third by only 3.6%.

The development of sustainable adsorbents and reliable predictive tools for dye-contaminated wastewater treatment remains an important challenge in environmental engineering. In this study, the adsorption of malachite green (MG) dye onto sodium dodecyl benzene sulfonate (SDBS)-modified ostrich bone waste was investigated using an integrated modeling framework combining Lindeman–Merenda–Gold (LMG) analysis, Bayesian Regression (BR), and Random Forest (RF) modeling. 52 experimental conditions (each measured in triplicate) were analyzed to evaluate the influence of pH, contact time, initial dye concentration, temperature, adsorbent dosage, and SDBS dosage on dye removal efficiency. The three modeling approaches consistently identified pH and contact time as the two most influential operational variables, whereas the relative importance of initial dye concentration and adsorbent dosage differed among the methods. BR quantified the direction and uncertainty of predictor effects, yielding posterior coefficients of 0.650 (95% CrI: 0.365–0.935) for pH, 0.467 (95% CrI: 0.263–0.672) for contact time, and −0.517 (95% CrI: −0.759 to −0.272) for initial dye concentration. Under internal validation, RF yielded an OOB R² of 0.822, an RMSE of 11.79 pp, and an MAE of 7.85 pp, whereas BR evaluated by LOOCV yielded an R² = 0.417, an RMSE = 21.31 pp, and an MAE = 16.42 pp. Because different validation protocols were used, these internal estimates are not directly comparable. More stringent group-aware validation resulted in under leave-one-series-out validation; neither model achieved positive skill (pooled R² = −0.382 for RF; BR R² was strongly negative, driven mainly by extrapolation when the initial-concentration series was held out), while across the six single-series rotations, R² was negative in five for both models (RF positive only when the C₀ series was held out; BR best when the pH series was held out, R² = 0.410). These results demonstrate that the models capture relationships within the investigated experimental domain but have limited generalization to experimental series or operating regions not represented in model development. The results confirm the potential of SDBS-modified ostrich bone waste (OBW) as an effective and sustainable adsorbent for malachite green removal and demonstrate that the proposed integrated modeling framework therefore enabled complementary assessment of variable importance, probabilistic predictor effects, and predictive behavior within and across the investigated experimental domain.

Waste-derived adsorbents represent a promising strategy for sustainable wastewater treatment; however, adsorption processes are frequently governed by complex nonlinear interactions that remain difficult to interpret using conventional approaches. The present study developed an integrated experimental and explainable machine learning framework to investigate Congo red (CR) removal using cetyltrimethylammonium bromide (CTABr)-modified bio-apatite-based adsorbent. The adsorbent was characterized using SEM–EDX, FTIR, XRD, and thermal analyses, confirming a heterogeneous Ca–P-rich structure associated with hydroxyapatite and CTABr-derived surface functionalities. A dataset comprising batch adsorption experiments was generated by varying solution pH, contact time, initial CR concentration, CTABr concentration, adsorbent dose, and temperature. The experimental results demonstrated that CR removal efficiency ranged from approximately 22.9% to 100%, with the experimental conditions giving the highest capacity (pH ≈ 2, contact time ≈ 120 min, initial CR concentration ≈ 10 mg L⁻¹, CTABr concentration ≈ 0.5 meq g⁻¹, adsorbent dose ≈ 0.2 g L⁻¹, and temperature ≈ 25 °C). Elastic Net, Random Forest (RF), and Gradient Boosting (GB) models were developed and evaluated using nested repeated five-fold cross-validation. The nonlinear ensemble models outperformed the regularized linear approach, indicating that is associated with complex nonlinear relationships among operational variables. RF provided the best predictive performance (R² = 0.675 ± 0.167, RMSE = 14.63 ± 5.81 percentage points) and was subsequently interpreted using SHapley Additive exPlanations (SHAP). Explainable analysis identified initial CR concentration as the dominant predictor within the fitted model and the investigated experimental domain, followed by contact time, adsorbent dose, pH, CTABr concentration, and temperature. SHAP dependence analysis revealed nonlinear behaviors, including concentration-dependent suppression, optimum-like dose effects, and CTABr saturation responses. Integration of characterization and modeling results suggested a multimodal adsorption mechanism involving electrostatic attraction between CTABr-derived quaternary-ammonium groups and CR sulfonate groups, complemented by interactions with the hydroxyapatite matrix, hydrogen bonding, and hydrophobic interactions. The proposed framework demonstrates how explainable machine learning can bridge predictive modeling and adsorption process interpretation while supporting the valorization of waste-derived materials for water treatment.

The study focuses on modeling the remote sensing reflectance (R_rs) for optically complex waters based on the absorption (a), scattering (b), and backscattering (b_b) coefficients measured at selected wavelengths (420 nm, 488 nm, 555 nm, and 620 nm). R_rs was calculated using both Morel’s proxy and Monte Carlo (MC) simulations. A comparison of the R_rs values obtained from the proxy and the MC simulations allowed us to determine the proxy factor (k). The results evidenced that this proxy parameter increases with wavelength. The findings demonstrate that R_rs can be computed from inherent optical properties (IOPs) using radiative transfer modeling, providing light independent reflectance estimates, unlike direct in situ R_rs measurements, which are affected by instantaneous lightening conditions.

This paper is a result of the search for design assumptions for a sensor to detect oil dispersed in the sea waters (oil-in-water emulsions). Our approach is based on analyzing changes in the underwater solar radiance (L) field caused by the presence of oil droplets in the water column. This method would enable the sensor to respond to the presence of oil contaminants dispersed in the surrounding environment, even if they are not located directly at the measurement point. This study draws on both literature sources and the results of current numerical modeling of the spread of solar light in the water column to account for both downward and upward irradiance (Es). The core principle of the analysis involves simulating the paths of a large number of virtual solar photons in a seawater model defined by spatially distributed Inherent Optical Properties (IOPs). The IOPs data were taken from the literature and pertain to the waters of the southern Baltic Sea. The optical properties of the oil used in the model correspond to crude oil extracted from the Baltic shelf. The obtained results were compared with previously published spectral analyses of an analogous polluted sea model, considering vertical downward radiance, vertical upward radiance, and downward and upward irradiance. It was found that the optimal wavelength ratio of 555/412, identified for these quantities, is also applicable to scalar irradiance. The findings indicate that the most effective way to determine this index is by measuring it using a sensor with its window oriented in the direction of upward-traveling light.

The technical condition of underwater port structures is critical to the safety and longevity of maritime infrastructure. Traditional inspection techniques, such as diver-assisted surveys, are constrained by safety risks, limited duration, and reduced efficiency in turbid waters. This study explores the application of a remotely operated vehicle (ROV) equipped with modulated lighting systems to enhance visibility and facilitate high-resolution imaging in optically complex underwater environments. The experiment employed a CHASING M2 ROV, modified with red, green, and white lighting configurations, to inspect the quay wall of a port. The impact of lighting color on image quality was evaluated. Results indicate that modulated lighting tailored to the optical properties of turbid coastal waters can improve image contrast and facilitate defect detection. The findings highlight the potential for advanced ROV systems to augment underwater inspection protocols in challenging optical environments.

The possibility of tracking oil pollution in the sea has been an issue that has been analysed for a long time, and the use of light interactions with the sea polluted with various forms of oil has been the subject of numerous studies. This paper presents the results of the Monte Carlo simulations of the fate of a large number of virtual photons to demonstrate changes in the downwelling vector irradiance and upwelling vector irradiance in oil-free seawater and analogously seawater column polluted with an oil-in-water emulsion. The analyses were carried out for eight wavelengths ranging from 412 to 676 nm, upon the assumption of an oil concentration of 10 ppm, taking into account the data of absorption and scattering properties of the southern Baltic Sea. The most favourable combination of wavelengths for the detection of an oil-in-water emulsion was 555/412 for all tested depths.