The design of efficient delivery systems for urease inhibitors is a critical challenge in mitigating nitrogen loss in agriculture. In this work, we investigated the molecular recognition and inclusion of phosphoramidate (PHOS) derivatives within the cavities of α-, β-, and γ-cyclodextrins (CDs). A hierarchical computational strategy was employed using the ORCA 5.0 package, starting with geometry optimizations at the semiempirical GFN2-xTB level, followed by refined electronic energy calculations utilizing Density Functional Theory (DFT) with the range-separated hybrid functional ωB97X-D3 and the 6-31G(d,p) basis set. Solvation effects in water were accounted for using the Solvation Model based on Density (SMD), and the nature of host-guest stabilization was characterized via Non-Covalent Interaction (NCI) index analysis. We demonstrate that host cavity dimensions are the primary determinant of complex stability. Our results reveal that β-CD provides the optimal hydrophobic environment, exhibiting superior host-guest complementarity for PHOS compared to its α- and γ-congeners. This “optimal steric complementarity” fit maximizes stabilizing non-covalent interactions, yielding a denser network of hydrogen bonds and enhanced London dispersion forces within the β-CD framework. These findings provide a definitive molecular basis for the preference for β-CD in both gas and aqueous phases, offering a predictive blueprint for the rational design of advanced, controlled-release nitrogenous fertilizer formulations.
Precise modeling of induction motors is essential for high-performance industrial applications. Traditionally, equivalent circuit with constant parameters is derived from standardized locked-rotor and no-load tests or alternative methods. However, these models exhibit limitations when calculating the electrical and mechanical quantities developed by the motor at low speeds, leading to significant inaccuracies. This work presents an innovative methodology for estimating the equivalent circuit parameters of induction machines, considering the variation of rotor reactance and resistance with speed. Rather than aiming to identify the exact physical parameters, the method seeks to obtain parameter values that accurately reproduce the motor input and output variables over the entire speed range. The proposed approach is non-intrusive, relying exclusively on the manufacturer’s catalog data. The methodology consists of three steps: (i) application of an established method from the literature for traditional constant-parameter estimation; (ii) formulation of an optimization problem to address the full speed range; and (iii) a nonlinear combination of affine functions using switching functions, following the logic of Takagi-Sugeno fuzzy inference, to produce accessible mathematical expressions for the equivalent circuit under any speed/slip condition. To evaluate the effectiveness of the methodology, the torque and current curves obtained from the model are compared to data provided by manufacturers and established literature. Parameter estimation results for nine induction motors, obtained from different manufacturers, are reported. A mean error of 10^-2
Genotype by environment interaction (GxE) remains a central challenge for tree breeding, as it is difficult to extrapolate trial results to untested sites and complicates confident genotype deployment. Enviromics, by integrating environmental covariates into predictive models, offers a way to overcome these limitations and guide clonal deployment. This study evaluated an enviromic framework applied to 15 Eucalyptus spp. clones across 5189 inventory plots located in southern Brazil. 10,000 Engineered Enviromic Markers were built from 3869 soil, climate, and remote sensing covariates, using random forest models and integrated into a mixed-model ensemble to predict mean annual increment standardized at seven years across the Target Population of Environments. Predictive accuracy was assessed through a Leave-One-Region-Out cross-validation procedure. The enviromic model achieved a higher performance than a baseline GxE model, with Pearson and Spearman correlations above 0.90 and a root mean squared error (RMSE) of 3.09, compared to 0.45-0.39 correlations and RMSE of 8.73 for the baseline. Spatial predictions enabled the delineation of breeding zones that minimized GxE, while also revealing regions with high discriminant power for testing new genotypes. We also applied a two-step clonal deployment procedure combining enviromic predictions with a frost-risk penalization map, refining recommendations for frost-prone areas. When comparing recommended versus planted clones in inventory plots, the framework indicated an average expected productivity gain of approximately 13 %. These results demonstrate the potential of enviromics as a decision-support tool for clonal deployment, enhancing productivity while accounting for environmental risks, and paving the way for future multi-omics integration.
Background: Remimazolam, a short-acting benzodiazepine, has emerged as a potential safer alternative for sedation in Flexible Bronchoscopy (FB). This meta-analysis compares its efficacy and safety with Propofol, Dexmedetomidine, and Midazolam in adult patients undergoing FB. Methods: PubMed, Embase, and Cochrane databases were searched on July 17, 2025, for trials comparing Remimazolam with other sedatives. Primary outcomes included hypotension, bradycardia, and intraprocedural opioid consumption; secondary outcomes were hypoxia, respiratory depression, patient satisfaction, induction time, and recovery time. Pooled Risk Ratios (RR), Mean Differences (MD), and Standardized Mean Differences (SMD) were calculated using a random-effects model in R (4.4.0). Risk of bias was assessed using the RoB2 tool, and subgroup analyses were conducted for each comparator. Results: Eleven trials (1,884 patients) were included. Remimazolam reduced respiratory depression (RR = 0.44 [95% CI 0.29; 0.67]; p = 0.0002; I² = 0%), hypoxia incidence (RR = 0.60 [95% CI 0.39; 0.93]; p = 0.0227; I² = 64.7%), bradycardia (RR = 0.39 [95% CI 0.20; 0.77]; p = 0.0069; I² = 52.3%), and hypotension (RR = 0.61 [95% CI 0.40; 0.95]; p = 0.0289; I² = 74.0%) compared to all sedatives. Compared to Propofol, Remimazolam reduced the incidence of hypotension (RR = 0.42 [95% CI 0.31; 0.58]; p < 0.0001; I² = 0%), respiratory depression (RR = 0.41 [95% CI 0.25; 0.68]; p = 0.0005; I² = 12.3%), but increased induction time (MD = 0.61 min [95% CI 0.23; 0.99]; p = 0.002; I² = 90.9%). Compared to Dexmedetomidine, it improved satisfaction (SMD = 0.23 [95% CI 0.07; 0.39]; p = 0.004; I² = 0%) and reduced recovery time (MD = -1.79 min [95% CI -2.66; -0.92]; p < 0.001; I² = 90.7%), hypoxia incidence (RR = 0.49 [95% CI 0.28; 0.88]; p = 0.0162; I² = 60.3%), and induction time (MD = -2.21 min [95% CI -2.41; -2.00]; p < 0.001; I² = 0%). Compared to Midazolam, Remimazolam increased sedation success (RR = 2.03 [95% CI 1.40; 2.95]; p = 0.0002; I² = 50%), shortened induction time (MD = -0.69 min [95% CI -1.37; -0.01]; p = 0.047; I² = 81.5%), and recovery time (MD = -4.49 min [95% CI -7.06; -1.92]; p < 0.001; I² = 40.9%). Conclusions: Remimazolam reduced respiratory depression overall and demonstrated improved safety, faster recovery, and greater efficacy compared to Propofol, Dexmedetomidine, and Midazolam, respectively, supporting its potential as an effective alternative for sedation in FB. Nonetheless, substantial heterogeneity in certain outcomes and the relatively small sample size in some comparisons limit the generalizability of our findings. Systematic review protocol: PROSPERO (CRD 42024568148).
The Elovich, pseudo-first-order (PFO), and pseudo-second-order (PSO) equations are widely used in adsorption kinetics, although the connection among them is often treated empirically. Here, we analyze a generalized adsorption rate equation in which the apparent adsorption coefficient decreases exponentially with surface coverage. The model recovers Elovich behavior at low coverage and yields effectively PFO- or PSO-like relaxation near equilibrium, depending on the magnitude of a composite coverage parameter. It also predicts an intermediate regime in which neither pseudo-order approximation is fully adequate. Application to representative literature datasets shows that this approach provides a practical tool for interpreting why different empirical fits succeed in different coverage domains and why some kinetic curves require direct numerical treatment. The framework is therefore best viewed as an effective tool for kinetic interpretation and regime classification rather than as a microscopic theory of surface heterogeneity.
