Efecto del extracto de cáscara de granada tunecina sobre la estabilidad oxidativa del aceite de maíz en condiciones de calentamiento

The effect of pomegranate peel extract (PPE) on the oxidative stability of corn oil during heating was studied. Oxidation was followed by determining peroxide value (PV), p-anisidine value (p-AV), free fatty acid value (FFA), conjugated dienes (CD), conjugated trienes hydroperoxides (CT) and the calculated total oxidation value (TOTOX). Polyphenol (TPC) and ortho-diphenol (TOPC) contents as well as the antioxidant activity of each oil sample were evaluated before and after heating. PPE showed a significant inhibitory effect on lipid oxidation. Heating samples for 8 hours supplemented by PPE to a level of 1000 ppm resulted in the highest significant decreases in investigated indices compared to the control and BHT values. It was concluded that the antioxidant activity of PPE delayed oxidation and can be used in the food industry to prevent and reduce lipid deterioration in oil.Se estudió el efecto del extracto de cáscara de granada (ECG) sobre la estabilidad oxidativa del aceite de maíz durante condiciones de calentamiento. La oxidación se siguió mediante la determinación del índice de peróxido (IP), el índice de p-anisidina (p-AV), el índice de acidez (IA), los dienos conjugados (DC), los hidroperóxidos de trienos conjugados (TC) y el valor calculado de la oxidación total (TOTOX). Se evaluó el contenido de polifenoles totales (PT) y de orto-difenoles (o-DF), así como la actividad antioxidante de cada muestra de aceite, antes y después del calentamiento. El ECG mostró un efecto inhibidor significativo sobre la oxidación de lípidos. El calentamiento de las muestras, durante 8 horas suplementadas con ECG a un nivel de 1000 ppm, dio como resultado una significativa disminución de los índices investigados en relación con los valores de control y con BHT. Se concluyó que la actividad antioxidante de los ECG retrasó la oxidación y que se puede utilizar en la industria alimentaria para prevenir y reducir el deterioro de los lípidos del aceite

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society

On the Relations Between 2D and 3D Fractal Dimensions: Theoretical Approach and Clinical Application in Bone Imaging

The inner knowledge of volumes from images is an ancient problem. This question becomes complicated when it concerns quantization, as the case of any measurement and in particular the calculation of fractal dimensions. Trabecular bone tissues have, like many natural elements, an architecture which shows a fractal aspect. Many studies have already been developed according to this approach. The question which arises however is to know to which extent it is possible to get an exact determination of the fractal dimension of a given volume only from the fractal measurement made on the projections or slice images given by medical imaging. This paper gives general results about the Minkowski dimensions and contents of projections and sections of a set. We also show with examples that they depend essentially on the directions of the planes and so there is - in general case - no relation between 3D and 2D fractal dimensions. This consideration is then illustrated with examples from synthetic models and from CT scan images of wrists. In conclusion, this study reveals that the quantitative characterization of an organic volume (in particular osseous) requires taking into account the whole volume, and not only some of its slices or projections

Effect of nitrogen concentration on the electronic and vibrational properties of zinc-blende InN xP 1-x (x > 0.01)

Taking into account the recent advances in the epitaxial growth of single-crystal InN leading to a drastic re-evaluation of its fundamental energy band gap, we have studied the electronic properties of InN xP 1-x (x > 0.01) ternary alloy. Using the empirical pseudopotential method under the virtual crystal approximation, combined with the Harrison bond orbital model, the band gap at Γ, X and L points, the effective masses of the Γ valley and the electronic charge densities are calculated as a function of nitrogen composition. The fitted expressions of the energy band gaps indicate that the bowing parameter at Γ reached a broad value for very low nitrogen incorporation ( $x\le 1\%$ ). Furthermore, the band gap at Γ point decreases drastically with increasing nitrogen composition up to 1%. The elastic constants and the optical phonon frequencies are also reported. Our theoretical results provide a good agreement with the available data. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 200671.20.Nr Semiconductor compounds, 71.15.-m Methods of electronic structure calculations, 71.15.Dx Computational methodology,

Algorithm for queue estimation with loop detector of time occupancy in off-ramps on signalized motorways

Long traffic queues on off-ramps significantly compromise the safety and throughput of motorways. Obtaining accurate queue information is crucial for countermeasure strategies. However, it is challenging to estimate traffic queues with locally installed inductive loop detectors. This paper deals with the problem of queue estimation with the interpretation of queuing dynamics and the corresponding time-occupancy distribution over motorway off-ramps. A novel algorithm for real-time queue estimation with two detectors is presented and discussed. Results derived from microscopic traffic simulation validated the effectiveness of the algorithm and revealed some of its useful features: (a) long and intermediate traffic queues could be accurately measured, (b) relatively simple detector input (i.e., time occupancy) was required, and (c) the estimation philosophy was independent with signal timing changes and provided the potential to cooperate with advanced strategies for signal control. Some issues concerning field implementation are also discussed

Public transport demand estimation by frequency adjustments

This article addresses the problem of estimating the demand for public transport from two approaches. First, we propose a bilevel optimization problem that allows estimating the demand using historical data and the observed bus frequencies. This model has been applied to small theoretical networks and the transit network of Tandil (a medium-sized city in Buenos Aires, Argentina), showing good results. However, from a practical point of view, the computation time of the algorithm used to solve the bilevel problem is long, reducing its applicability by traffic authorities. To solve this, we propose to use an artificial neural network module that allows to quickly detect if the change in demand is significant enough (for example, beyond a predefined threshold). If it is substantial, the operator can decide to run the algorithm to estimate the demand and take action to adapt the system to the new reality, for example, adapting vehicle frequencies or incorporating more vehicles into the system so that the current demand can be served. The machine learning approach allows it to be used as a fast change detection tool, avoiding running the expensive algorithm for false positives

A bilevel model for public transport demand estimation

For the case of public transport, we consider the problem of demand estimation. Given an origin-destination matrix representing the public transport demand, the distribution of flow among different lines can be obtained assuming that it corresponds to a certain equilibrium characterized by an optimization problem. The knowledge of that matrix is expensive and sometimes unaffordable in practice. In this work, we explore its estimation through the numerical solution of a bilevel optimization problem