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The main objective of this work was to retrieve surface soil moisture (SSM) by using scattering models and a support vector machine (SVM) technique driven by backscattering coefficients obtained from Sentinel-1 satellite images acquired over bare agricultural soil in the Tensfit basin of Morocco. Two backscattering models were selected in this study due to their wide use in inversion procedures: the theoretical integral equation model (IEM) and the semi-empirical model (Oh). To this end, the sensitivity of the SAR backscattering coefficients at V V ( σ v v ∘ ) and V H ( σ v h ∘ ) polarizations to in situ soil moisture data were analyzed first. As expected, the results showed that over bare soil the σ v v ∘ was well correlated with SSM compared to the σ v h ∘ , which showed more dispersion with correlation coefficients values (r) of about 0.84 and 0.61 for the V V and V H polarizations, respectively. Afterwards, these values of σ v v ∘ were compared to those simulated by the backscatter models. It was found that IEM driven by the measured length correlation L slightly underestimated SAR backscatter coefficients compared to the Oh model with a bias of about − 0.7 dB and − 1.2 dB and a root mean square (RMSE) of about 1.1 dB and 1.5 dB for Oh and IEM models, respectively. However, the use of an optimal value of L significantly improved the bias of IEM, which became near to zero, and the RMSE decreased to 0.9 dB. Then, a classical inversion approach of σ v v ∘ observations based on backscattering model is compared to a data driven retrieval technic (SVM). By comparing the retrieved soil moisture against ground truth measurements, it was found that results of SVM were very encouraging and were close to those obtained by IEM model. The bias and RMSE were about 0.28 vol.% and 2.77 vol.% and − 0.13 vol.% and 2.71 vol.% for SVM and IEM, respectively. However, by taking into account the difficultly of obtaining roughness parameter at large scale, it was concluded that SVM is still a useful tool to retrieve soil moisture, and therefore, can be fairly used to generate maps at such scales.

The multiscaling quantum mechanics/molecular mechanics (QM/MM) approach was introduced in 1976, while the extensive acceptance of this methodology started in the 1990s. The combination of QM/MM approach with molecular dynamics (MD) simulation, otherwise known as the QM/MM/MD approach, is a powerful and promising tool for the investigation of chemical reactions’ mechanism of complex molecular systems, drug delivery, properties of molecular devices, organic electronics, etc. In the present review, the main methodologies in the multiscaling approaches, i.e., density functional theory (DFT), semiempirical methodologies (SE), MD simulations, MM, and their new advances are discussed in short. Then, a review on calculations and reactions on metalloproteins is presented, where particular attention is given to nitrogenase that catalyzes the conversion of atmospheric nitrogen molecules N₂ into NH₃ through the process known as nitrogen fixation and the FeMo-cofactor.

This paper reviews the basic principles of the density-functional tight-binding (DFTB) method, which is based on density-functional theory as formulated by Hohenberg, Kohn and Sham (KS-DFT). DFTB consists of a series of models that are derived from a Taylor series expansion of the KS-DFT total energy. In the lowest order (DFTB1), densities and potentials are written as superpositions of atomic densities and potentials. The KohnSham orbitals are then expanded to a set of localized atom-centred functions, which are obtained for spherical symmetric spin-unpolarized neutral atoms self-consistently. The whole Hamilton and overlap matrices contain one- and two-centre contributions only. Therefore, they can be calculated and tabulated in advance as functions of the distance between atomic pairs. The second contributions to DFTB1, the DFT double counting terms, are summarized together with nuclear repulsion energy terms and can be rewritten as the sum of pairwise repulsive terms. The second-order (DFTB2) and third-order (DFTB3) terms in the energy expansion correspond to a self-consistent representation, where the deviation of the ground-state density from the reference density is represented by charge monopoles only. This leads to a computationally efficient representation in terms of atomic charges (Mulliken), chemical hardness (Hubbard) parameters and scaled Coulomb laws. Therefore, no additional adjustable parameters enter the DFTB2 and DFTB3 formalism. The handling of parameters, the efficiency, the performance and extensions of DFTB are briefly discussed.

Vegetation canopy height is a relevant proxy for aboveground biomass, carbon stock, and biodiversity. Wall‐to‐wall information of canopy height with high spatial resolution and accuracy is not yet available on large scales. For the globally consistent TanDEM‐X data, simplifications are necessary to estimate canopy height with semi‐empirical models based on polarimetric synthetic aperture radar interferometry (PolInSAR). We trained the semi‐empirical models with sampled GEDI data, because the assumptions behind the application of such simplifications are not always valid for TanDEM‐X. General linear as well as sinc models and empirical parameterizations of these models were applied to estimate the canopy height in tropical landscapes of Sumatra, Indonesia. Airborne laser scanning (ALS) data were consistently used as an independent reference. The general simplified models were compared with the trained empirical versions to assess the potential improvement of the empirical parameterization of the models. The residuals of the different canopy height models were further evaluated in relation to land use and structural information of the vegetation. Our results indicated that the empirical parameters substantially improved the estimation from a root‐mean‐square‐error (RMSE) of 10.3 m (55.8%) to 8.8 m (47.7%), when using the linear model. In contrast, the improvement of the sinc model with empirical parameters was not substantial compared to the general sinc model (7.4 m [40.4%] vs. 6.9 m [37.5%]). A consistent improvement was observed in the linear model, whereas the improvement of the sinc model was dependent on the land‐use type. Structural attributes like the canopy height itself and vegetation cover had a significant effect on the accuracies, with higher and denser vegetation generally resulting in higher residuals. We demonstrate the potential of the combined exploitation of the TanDEM‐X and GEDI missions for a wall‐to‐wall canopy height estimation in a tropical region. This study provides relevant findings for a consistent mapping of vegetation canopy height in tropical landscapes and on large scales with spaceborne laser and SAR data.

This study describes a semi-empirical model developed to estimate volumetric soil moisture ( v ϑ) in bare soils during the dry season (March–May) using C-band (5.42 GHz) synthetic aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 m spatial resolution. The semi-empirical model was developed using backscatter coefficient (σ° dB) and in situ soil moisture collected from Siruguppa taluk (sub-district) in the Karnataka state of India. The backscatter coefficients 0 VV σ and 0 VH σ were extracted from SAR images at 62 geo-referenced locations where ground sampling and volumetric soil moisture were measured at a 10 cm (0–10 cm) depth using a soil core sampler and a standard gravimetric method during the dry months (March–May) of 2017 and 2018. A linear equation was proposed by combining 0 VV σ and 0 VH σ to estimate soil moisture. Both localized and generalized linear models were derived. Thirty-nine localized linear models were obtained using the 13 Sentinel-1 images used in this study, considering each polarimetric channel Co-Polarization (VV) and Cross-Polarization(VH) separately, and also their linear combination of VV + VH. Furthermore, nine generalized linear models were derived using all the Sentinel-1 images acquired in 2017 and 2018; three generalized models were derived by combining the two years (2017 and 2018) for each polarimetric channel; and three more models were derived for the linear combination of 0 VV σ and 0 VH σ . The above set of equations were validated and the Root Mean Square Error (RMSE) was 0.030 and 0.030 for 2017 and 2018, respectively, and 0.02 for the combined years of 2017 and 2018. Both localized and generalized models were compared with in situ data. Both kind of models revealed that the linear combination of 0 VV σ + 0 VH σ showed a significantly higher R2 than the individual polarimetric channels.

Hansen Solubility Parameters (HSPs) are widely used as a tool in solubility studies. Given the variety of existent approaches to predict these parameters, this investigation focused on estimating the HSPs of a set of Natural Deep Eutectic Systems (NADES), using empirical (EM) and semi-empirical models (SEM), and then understanding their differences/similarities. Although these theoretical models are designed and recommended mostly for simple molecules or simple solutions, they are still being used in eutectic systems studies, mainly empirical ones. Thus, a preliminary test was conducted with a set of conventional solvents, in which their experimental values of HSPs are known. Besides the confirmation of the EM as the most suitable for these kinds of regular solvents, the results found also showed a very similar behaviour to what was observed in NADES, i.e., in terms of suggesting the EM and SEM with the highest/lowest similarity. Furthermore, it was concluded that although there is a large discrepancy between the estimated values of the hydrogen bond parameter, especially for systems with a higher polar character, there is still a good similarity for the other parameters. In fact, it was observed that, when combining the semi-empirical models, it was possible to obtain a value of the hydrogen bond parameter more similar to the empirical ones.

The coupling of various modes in centrifugal pumps renders the process of modal parameter identification through frequency response functions both time-consuming and labour-intensive. To accurately identify modal parameters, current modal testing typically requires the deployment of numerous measurements or excitation points. However, certain modal parameters may suffer from low SNR, leading to distorted modal shapes that deviate from reality. This study employs a semi-empirical approach to ingeniously design the excitation vector by leveraging the relationships among the centre of mass, inertial axes, and excitation points. During modal testing experiments, specific modes of the centrifugal pump are decoupled, significantly improving the SNR for the targeted modes. Experimental validation demonstrates that the proposed method markedly enhances the accuracy of modal parameters identification, thereby facilitating the analysis of its vibration characteristics. The findings enable rapid identification of modal parameters related to vibration issues in engineering applications, contributing to the swift localization and resolution of vibration problems.

Accurate solute solubility measuring and modeling in supercritical carbon dioxide (ScCO 2 ) would address the best working conditions and thermodynamic boundaries for material processing with this type of fluid. Theory- and data-driven methods are two general modeling approaches. Using theory-driven methods, the solubility is estimated based on the principles of thermodynamics, while data-driven methods are developed by training the algorithms. Despite acceptance of each of these methods, more experimental solubility data are still needed to promote modeling performances. In this study, for the first time, solubility of morphine sulfate is determined and modeled by a set of 13 semi-empirical (theory-driven) and random forest (data-driven) models. Using a laboratory system with an ultraviolet-visible (UV-Vis) spectroscopy, the experimental solubilities including 48 data points were obtained at different temperatures (308–338 K) and pressures (12–27 MPa). The minimum (0.806 × 10 −5 ) and maximum (5.902 × 10 −5 ) equilibrium mole fractions were observed at working pressures of 12 and 27 MPa, respectively, both at the same temperature of 338 K. It was indicated that random forest model (with AARD% of 1.29%) had an excellent predictive performance against semi-empirical models (with AARD% from 9.33 to 19.76%). The results showed that solute molecular weight had the highest effect on random forest modeling. Using modeling results from Chrastil and Bartle models, total and vaporization enthalpies of dissolution of morphine sulfate in ScCO 2 were found to be 35.12 and 59.04 kJ/mole, respectively.

Sound policies for protecting coastal communities and assets require good information about vulnerability to flooding. Here, we investigate the influence of sea level rise on expected storm surge-driven water levels and their frequencies along the contiguous United States. We use model output for global temperature changes, a semi-empirical model of global sea level rise, and long-term records from 55 nationally distributed tidal gauges to develop sea level rise projections at each gauge location. We employ more detailed records over the period 1979-2008 from the same gauges to elicit historic patterns of extreme high water events, and combine these statistics with anticipated relative sea level rise to project changing local extremes through 2050. We find that substantial changes in the frequency of what are now considered extreme water levels may occur even at locations with relatively slow local sea level rise, when the difference in height between presently common and rare water levels is small. We estimate that, by mid-century, some locations may experience high water levels annually that would qualify today as 'century' (i.e., having a chance of occurrence of 1% annually) extremes. Today's century levels become 'decade' (having a chance of 10% annually) or more frequent events at about a third of the study gauges, and the majority of locations see substantially higher frequency of previously rare storm-driven water heights in the future. These results add support to the need for policy approaches that consider the non-stationarity of extreme events when evaluating risks of adverse climate impacts.

This paper examines the climate change impact on the spatiotemporal soil salinity dynamics during the last 30 years (1987–2017) in the arid landscape. The state of Kuwait, located at the northwest Arabian Peninsula, was selected as a pilot study area. To achieve this, a Landsat- Operational Land Imager (OLI) image acquired thereabouts simultaneously to a field survey was preprocessed and processed to derive a soil salinity map using a previously developed semi-empirical predictive model (SEPM). During the field survey, 100 geo-referenced soil samples were collected representing different soil salinity classes (non-saline, low, moderate, high, very high and extreme salinity). The laboratory analysis of soil samples was accomplished to measure the electrical conductivity (EC-Lab) to validate the selected and used SEPM. The results are statistically analyzed (p ˂ 0.05) to determine whether the differences are significant between the predicted salinity (EC-Predicted) and the measured ground truth (EC-Lab). Subsequently, the Landsat serial time’s datasets acquired over the study area with the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and OLI sensors during the last three decades over the intervals (1987, 1992, 1998, 2000, 2002, 2006, 2009, 2013, 2016 and 2017) were radiometrically calibrated. Likewise, the datasets were atmospherically and spectrally normalized by applying a semi-empirical line approach (SELA) based on the pseudo-invariant targets. Afterwards, a series of soil salinity maps were derived through the application of the SEPM on the images sequence. The trend of salinity changes was statistically tested according to climatic variables (temperatures and precipitations). The results revealed that the EC-Predicted validation display a best fits in comparison to the EC-Lab by indicating a good index of agreement (D = 0.84), an excellent correlation coefficient (R2 = 0.97) and low overall root mean square error (RMSE) (13%). This also demonstrates the validity of SEPM to be applicable to the other images acquired multi-temporally. For cross-calibration among the Landsat serial time’s datasets, the SELA performed significantly with an RMSE ≤ ± 5% between all homologous spectral reflectances bands of the considered sensors. This accuracy is considered suitable and fits well the calibration standards of TM, ETM+ and OLI sensors for multi-temporal studies. Moreover, remarkable changes of soil salinity were observed in response to changes in climate that have warmed by more than 1.1 °C with a drastic decrease in precipitations during the last 30 years over the study area. Thus, salinized soils have expanded continuously in space and time and significantly correlated to precipitation rates (R2 = 0.73 and D = 0.85).