Rice (Oryza sativa L.) is a globally consumed staple food, commonly in its white rice form. However, milling processes such as dehusking and polishing remove nutrient-dense outer layers, causing significant losses of key compounds like vitamin E, minerals, and other bioactives (i.e. γ-oryzanol and polyphenols). This study evaluated the impact of rice milling on nutrient retention in 11 rice samples from subspecies Indica, Aromatic, and Japonica grown in Costa Rica, and for selected samples examined the in vitro bioaccessibility and Caco-2 cellular absorption of major bioactive compounds in rice bran. Samples were processed into white rice and rice bran and analyzed for vitamin E (tocopherols and tocotrienols) and mineral content. White rice contained 66
The ever-growing use of wind energy makes necessary the optimization of turbine operations through pitch angle controllers and their maintenance with early fault detection. It is crucial to have accurate and robust models imitating the behavior of wind turbines, especially to predict the generated power as a function of the wind speed. Existing empirical and physics-based models have limitations in capturing the complex relations between the input variables and the power, aggravated by wind variability. Data-driven methods offer new opportunities to enhance wind turbine modeling of large datasets by improving accuracy and efficiency. In this study, we used physics-informed neural networks to reproduce historical data coming from 4 turbines in a wind farm, while imposing certain physical constraints to the model. The developed models for regression of the power, torque, and power coefficient as output variables showed great accuracy for both real data and physical equations governing the system. Lastly, introducing an efficient evidential layer provided uncertainty estimations of the predictions, proved to be consistent with the absolute error, and made possible the definition of a confidence interval in the power curve.
Electromobility is increasingly recognized as a cornerstone of sustainable transport, yet its adoption remains uneven across regions. This study develops an integrated framework that combines geospatial analysis, multi-criteria decision-making (MCDM), and power system evaluation to identify and prioritize fast-charging sites at the national scale. Applied to Costa Rica’s national road network (NRN), encompassing both urban centers and peripheral regions, the framework integrates spatial suitability, socioeconomic priorities, and grid readiness across projected electric vehicle (EV) penetration scenarios. Critically, power system simulations reveal voltage instability at distribution nodes (as low as 89.88% p.u.) under 3% EV penetration despite 99% renewable generation, demonstrating that grid capacity, not planning methodology, constitutes the primary barrier to electric mobility adoption. This finding, derived from the first national-scale analysis that integrates equity-driven spatial prioritization with comprehensive grid validation using real fleet projections, challenges conventional assumptions in transport-focused infrastructure planning. The framework provides a transferable tool for countries seeking to align EV infrastructure planning with sustainability and decarbonization objectives, while highlighting that grid reinforcement must precede, not follow, the deployment of fast-charging infrastructure.
Modular multilevel converters (MMCs) have been proposed as a suitable power electronics topology for the integration of second-life batteries (SLBs) sourced from electric vehicles (EVs). The inherent parameter variations among the SLBs located in different clusters of the MMC require the implementation of control strategies to regulate battery discharge rates and prevent battery damage from overcharging or overdischarging. In this article, a decoupled modeling approach based on the Sigma Delta alpha beta 0 transform is proposed for the SLB-MMC, and the resulting model is used to develop a decoupled nonlinear control strategy for state of charge (SoC) equalization of SLBs integrated in the MMC. The benefits of using the proposed controller are demonstrated using simulation work, moreover, the proposed control strategy is experimentally validated using an experimental rig where 18 SLBs retired from electrical scooters with capacities between 9 and 13 Ah are integrated in a three-phase MMC composed of 18 sub-modules (SMs). The experimental results demonstrate good SoC balancing performance between phases and clusters of the MMC in three scenarios comprising a discharge test, a cycling test and a charge test where reactive power is provided to the grid.
Thermochromic smart windows are a promising technology to reduce energy consumption in buildings, particularly in tropical regions where cooling demands are high. Vanadium dioxide (VO2) is the most studied thermochromic material due to its reversible semiconductor-to-metal transition near 68 °C. Conventional synthesis routes require long reaction times and post-annealing steps. In this work, we report a rapid hydrothermal synthesis of monoclinic VO2(M) and tungsten-doped VO2(M) powders obtained within only 6 h at 270 °C, using vanadyl sulfate as precursor and controlled precipitation at pH ≈ 8.5. Differential scanning calorimetry confirmed the reversible transition at 59 °C for the undoped VO2, with a hysteresis of 18 °C, while tungsten doping reduced the transition temperature by ~17 °C per wt.% of W. X-ray diffraction verified the monoclinic phase with minor traces of VO2(B), a non-thermochromic polymorph of VO2, and microstructural analysis revealed crystallite sizes below 35 nm. Electron microscopy and dynamic light scattering confirmed particle sizes suitable for dispersion in polymeric matrices. This approach significantly reduces synthesis time compared to typical hydrothermal methods requiring 20-48 h and avoids further annealing. The resulting powders provide a low-cost and scalable route for fabricating thermochromic coatings with transition temperatures closer to ambient conditions, making them relevant for smart-window applications in tropical climates, where lower transition temperatures are generally regarded as beneficial.
