Tejanos in College: How Texas Born Mexican-American Students Navigate Ethnoracial Identity

For Latina\u27s in the United States, navigating the spectrum of racial and ethnic identities can be complicated. This same complication has the potential to affect one of the largest groups of Latina\u27s in the nation: Mexican-Americans living in Texas or Tejanos. Through qualitative analyses of interviews and surveys and the use of an ecological framework on identity development theories for Latina\u27s, Native Americans, Multiracial peoples and those in the Mexican diaspora, this study examines various factors that influences the ethnoracial identity choice of Tejano college students. Findings revealed that there were several common themes across all the participants, even those who did not identify as Tejano. Geographical origin of parents and family and community influence emerged as a noticeable reason as to why students identified as Tejano. A connection to generations of family in the United States and Mexico also impacted how strongly students identified ethnoracially as Tejano. In addition, experiences of not being enough galvanized some students to a stronger Tejano identity. Other themes included the impact of physical appearance, growing up with Spanish in their household, and Tejano representation in media. Recommendations are targeted to staff and faculty who work with Latina students, especially Mexican-Americans in Texas, to provide opportunities to explore and support a more complex ethnoracial identity including connection with their cultural traditions, education on Latina history, an examination on the impact of language on identity, and consideration of ethnoracial affinity group work

Monitoring cell monolayers during electroporation: Electrical impedance spectroscopy measurements

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Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data

In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (GBF), is proposed to estimate crop phenology. Here, phenological scales, which consist of a finite number of discrete stages, represent the one-dimensional state space, and hence GBF provides the optimal phenology estimates. Accordingly, contrarily to literature studies based on EKF and PF, no constraints are imposed on the models and the statistical distributions involved. The prediction model is defined by the transition matrix, while Kernel Density Estimation (KDE) is employed to define the observation model. The approach is applied on dense time series of dual-polarization Sentinel-1 (S1) SAR images, collected in four different years, to estimate the BBCH stages of rice crops. Results show that 0.94 ≤ R2 ≤ 0.98, 5.37 ≤ RMSE ≤ 7.9 and 20 ≤ MAE ≤ 33.This research was funded in part by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22, and in part by the University of Alicante (ref. VIGROB-114)

Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy

In this letter, a new approach for crop phenology estimation with remote sensing is presented. The proposed methodology is aimed to exploit tools from a dynamical system context. From a temporal sequence of images, a geometrical model is derived, which allows us to translate this temporal domain into the estimation problem. The evolution model in state space is obtained through dimensional reduction by a principal component analysis, defining the state variables, of the observations. Then, estimation is achieved by combining the generated model with actual samples in an optimal way using a Kalman filter. As a proof of concept, an example with results obtained with this approach over rice fields by exploiting stacks of TerraSAR-X dual polarization images is shown.This project was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) and in part by EU FEDER under Project TEC2011-28201-C02-02

Dynamical Approach for Real-Time Monitoring of Agricultural Crops

In this paper, a novel approach for exploiting multitemporal remote sensing data focused on real-time monitoring of agricultural crops is presented. The methodology is defined in a dynamical system context using state-space techniques, which enables the possibility of merging past temporal information with an update for each new acquisition. The dynamic system context allows us to exploit classical tools in this domain to perform the estimation of relevant variables. A general methodology is proposed, and a particular instance is defined in this study based on polarimetric radar data to track the phenological stages of a set of crops. A model generation from empirical data through principal component analysis is presented, and an extended Kalman filter is adapted to perform phenological stage estimation. Results employing quad-pol Radarsat-2 data over three different cereals are analyzed. The potential of this methodology to retrieve vegetation variables in real time is shown.This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Project TEC2011-28201-C02-02 and in part by the Generalitat Valenciana under Project ACOMP/2014/136

Modelling of short runout propagation landslides and debris flows

This paper proposes an extension of methods used to predict the propagation of landslides having a long runout to smaller landslides with much shorter propagation distances. The method is based on: (1) a depth-integrated mathematical model including the coupling between the soil skeleton and the pore fluids, (2) suitable rheological models describing the relation between the stress and the rate of deformation tensors for fluidised soils and (3) a meshless numerical method, Smooth Particle Hydrodynamics, which separates the computational mesh (or set of computational nodes) from the mesh describing the terrain topography, which is of structured type – thus accelerating search operations. The proposed model is validated using two examples for which there are analytical solutions, and then it is applied to two short runout landslides which happened in Hong Kong in 1995, for which there is available information

Using wavelet tools to analyse seasonal variations from InSAR time-series data: a case study of the Huangtupo landslide

Synthetic aperture radar interferometry (InSAR) has proven to be a powerful tool for monitoring landslide movements with a wide spatial and temporal coverage. Interpreting landslide displacement time-series derived from InSAR techniques is a major challenge for understanding relationships between triggering factors and slope displacements. In this study, we propose the use of various wavelet tools, namely, continuous wavelet transform (CWT), cross wavelet transform (XWT) and wavelet coherence (WTC) for interpreting InSAR time-series information for a landslide. CWT enables time-series records to be analysed in time-frequency space, with the aim of identifying localized intermittent periodicities. Similarly, XWT and WTC help identify the common power and relative phase between two time-series records in time-frequency space, respectively. Statistically significant coherence and confidence levels against red noise (also known as brown noise or random walk noise) can be calculated. Taking the Huangtupo landslide (China) as an example, we demonstrate the capabilities of these tools for interpreting InSAR time-series information. The results show the Huangtupo slope is affected by an annual displacement periodicity controlled by rainfall and reservoir water level. Reservoir water level, which is completely regulated by the dam activity, is mainly in ‘anti-phase’ with natural rainfall, due to flood control in the Three Gorges Project. The seasonal displacements of the Huangtupo landslide is found to be ‘in-phase’ with respect to reservoir water level and the rainfall towards the front edge of the slope and to rainfall at the higher rear of the slope away from the reservoir.R. Tomás was supported by the Generalitat Valenciana fellowship BEST-2011/225 and by the Ministry of Education, Culture and Sport trough the project PRX14/00100. Part of this work is also supported by the Spanish Ministry of Economy and Competitiveness and EU FEDER funds under project TEC2011-28201-C02-02, by the Natural Environmental Research Council (NERC) through the GAS and LICS projects (ref. NE/H001085/1 and NE/K010794/1, respectively) as well as the ESA-MOST DRAGON-3 projects (ref. 10607 and 10665)

Estimation of Key Dates and Stages in Rice Crops Using Dual-Polarization SAR Time Series and a Particle Filtering Approach

Information of crop phenology is essential for evaluating crop productivity. In a previous work, we determined phenological stages with remote sensing data using a dynamic system framework and an extended Kalman filter (EKF) approach. In this paper, we demonstrate that the particle filter is a more reliable method to infer any phenological stage compared to the EKF. The improvements achieved with this approach are discussed. In addition, this methodology enables the estimation of key cultivation dates, thus providing a practical product for many applications. The dates of some important stages, as the sowing date and the day when the crop reaches the panicle initiation stage, have been chosen to show the potential of this technique.This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Project TEC2011-28201-C02-02, and in part by the Generalitat Valenciana under Project ACOMP/2014/136. All SAR images have been provided by DLR in the framework of projects LAN0021 and LAN0234 of the prelaunch AO of TerraSAR-X

Particle Filter Approach for Real-Time Estimation of Crop Phenological States Using Time Series of NDVI Images

Knowing the current phenological state of an agricultural crop is a powerful tool for precision farming applications. In the past, it has been estimated with remote sensing data by exploiting time series of Normalised Difference Vegetation Index (NDVI), but always at the end of the campaign and only providing results for some key states. In this work, a new dynamical framework is proposed to provide real-time estimates in a continuous range of states, for which NDVI images are combined with a prediction model in an optimal way using a particle filter. The methodology is tested over a set of 8 to 13 rice parcels during 2008–2013, achieving a high determination factor R2=0.93 ( n=379 ) for the complete phenological range. This method is also used to predict the end of season date, obtaining a high accuracy with an anticipation of around 40–60 days. Among the key advantages of this approach, phenology is estimated each time a new observation is available, hence enabling the potential detection of anomalies in real-time during the cultivation. In addition, the estimation procedure is robust in the case of noisy observations, and it is not limited to a few phenological stages.This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Projects TEC2011-28201-C02-02 and TIN2014-55413-C2-2-P

Revealing the mechanisms of hydrogel formation by laccase crosslinking and regeneration of feruloylated arabinoxylan from wheat bran

Feruloylated arabinoxylan (FAX) from cereal brans has large potential to generate multifunctional materials with customized macromolecular and nanostructural architectures and techno-functional properties. Here we investigate the chemical and structural mechanisms of hydrogel formation of wheat bran FAX following enzymatic crosslinking by laccase and a subsequent regeneration procedure involving freeze-drying and resuspension of the crosslinked FAX in different pH buffers, using a battery of biochemical, rheological and physical techniques. The laccase crosslinking induced the conversion of ferulic acid units into a wide diversity of dimeric forms, leading to an increased molecular weight and a closer-packing of the FAX chains. The regeneration step resulted in a remarkable increase in the viscosity and viscoelasticity for all tested pH values. The amount of crystallinity of FAX increased by enzymatic crosslinking, it was however decreased by the regeneration step. The structural characterization revealed that enzymatic crosslinking, in addition to the formation of covalent crosslinks, influences the physical intermolecular interactions between adjacent FAX domains, and the regeneration forms larger clusters with higher dynamic moduli. Our results reveal that both chemical and physical mechanisms influence the network formation and multiscale assembly of wheat bran FAX hydrogels, thus modulating their rheological properties fundamental for their use in food and biomedical applications