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Innovations in industrial automation, information and communication technology (ICT), renewable energy as well as monitoring and sensing fields have been paving the way for smart devices, which can acquire and convey information to the Internet. Since there is an ever-increasing demand for large yet affordable production volumes for such devices, printed electronics has been attracting attention of both industry and academia. In order to understand the potential and future prospects of the printed electronics, the present paper summarizes the basic principles and conventional approaches while providing the recent progresses in the fabrication and material technologies, applications and environmental impacts.

This article analyses the impacts of the COVID-19 pandemic on Brazil's populist radical right (PRR), as well as the responses of PRR actors to the pandemic, during the period from March 2020 to October 2021. Despite high death rates and declining popularity in the final months of that period, the Brazilian president consistently maintained a denialist narrative that incorporated key aspects of populist ideology. Based on the analysis of opinion surveys, documents, online messages and secondary sources, we argue that explaining this denialism requires understanding Brazil's radical-right populism as more than an ideology: it is a social movement. The impacts of the pandemic on Bolsonaro's PRR government and its responses can only be understood by simultaneously analysing the top-down actions of the leader and the bottom-up role of bolsonarismo – that is, the broad coalition of actors who actively support the radical-right project. The case of bolsonarismo suggests that literature on populism in general would profit from taking right-wing movements more seriously as co-producers of populist rhetoric and practices.

The goal of clinical and translational science (CTS) is to fill gaps in medical knowledge toward improving human health. However, one of our most pressing challenges does not reside within the biological map we navigate to find sustainable cures but rather the moral compass to recognize and overcome racial and ethnic injustices that continue to influence our society and hinder diverse research rigor. The Georgetown-Howard Universities Center for Clinical and Translational Science includes an inter-institutional TL1-funded training program for predoctoral/postdoctoral trainees in Translational Biomedical Science (TBS). In the fall of 2020, the TBS program responded to the national social justice crisis by incorporating a curriculum focused on structural racism in biomedical research. Educational platforms, including movie reviews, Journal Clubs, and other workshops, were threaded throughout the curriculum by ensuring safe spaces to discuss racial and ethnic injustices and providing trainees with practical steps to recognize, approach, and respond to these harmful biases in the CTS. Workshops also focused on why individuals underrepresented in science are vital for addressing and closing gaps in CTS. Paring analysis using REDCap software de-identified participants after invitations were sent and collected in the system to maintain anonymity for pre- and post-analysis. The Likert scale evaluated respondents' understanding of diverse scientific circumstances. The pre/Fall and post/Spring surveys suggested this curriculum was successful at raising institutional awareness of racial and ethnic biases. Evaluating the effectiveness of our program with other training Clinical and Translational Science Awards (CTSA) consortiums will strengthen both the academic and professional TBS programs.

Background The advancements in unmanned aerial vehicle (UAV) technology have recently emerged as an effective, cost-efficient, and versatile solution for monitoring crop growth with high spatial and temporal precision. This monitoring is usually achieved through the computation of vegetation indices (VIs) from agricultural lands. The VIs are based on the incoming radiance to the camera, which is affected when there is a change in the scene illumination. Such a change will cause a change in the VIs and subsequent measures, e.g., the VI-based chlorophyll-content estimation. In an ideal situation, the results from VIs should be free from the impact of scene illumination and should reflect the true state of the crop’s condition. In this paper, we evaluate the performance of various VIs computed on images taken under sunny, overcast and partially cloudy days. To improve the invariance to the scene illumination, we furthermore evaluated the use of the empirical line method (ELM), which calibrates the drone images using reference panels, and the multi-scale Retinex algorithm, which performs an online calibration based on color constancy. For the assessment, we used the VIs to predict leaf chlorophyll content, which we then compared to field measurements. Results The results show that the ELM worked well when the imaging conditions during the flight were stable but its performance degraded under variable illumination on a partially cloudy day. For leaf chlorophyll content estimation, The r 2 of the multivariant linear model built by VIs were 0.6 and 0.56 for sunny and overcast illumination conditions, respectively. The performance of the ELM-corrected model maintained stability and increased repeatability compared to non-corrected data. The Retinex algorithm effectively dealt with the variable illumination, outperforming the other methods in the estimation of chlorophyll content. The r 2 of the multivariable linear model based on illumination-corrected consistent VIs was 0.61 under the variable illumination condition. Conclusions Our work indicated the significance of illumination correction in improving the performance of VIs and VI-based estimation of chlorophyll content, particularly in the presence of fluctuating illumination conditions.

With the development of big data technology, traditional monitoring methods for the scope of marine pollution can no longer meet the current needs of accuracy and timeliness. In light of the outstanding topic, this study proposed to use big data technology to monitor the scope of marine pollution. The intelligent digital remote sensing technology was used for multi-dimensional monitoring of ocean water quality and completed the calculation of data collected by water quality sensors through the improved big data comparative analysis method. Finally, the scope of pollution monitoring was realized. The results verified that the proposed monitoring method could achieve high-precision and time-sensitive monitoring of the range of marine pollutants, and could identify the basic information of pollutants.

Background Leaf water content (LWC) significantly affects rice growth and development. Real-time monitoring of rice leaf water status is essential to obtain high yield and water use efficiency of rice plants with precise irrigation regimes in rice fields. Hyperspectral remote sensing technology is widely used in monitoring crop water status because of its rapid, nondestructive, and real-time characteristics. Recently, multi-source data have been attempted to integrate into a monitored model of crop water status based on spectral indices. However, there are fewer studies using spectral index model coupled with multi-source data for monitoring LWC in rice plants. Therefore, 2-year field experiments were conducted with three irrigation regimes using four rice cultivars in this study. The multi-source data, including canopy ecological factors and physiological parameters, were incorporated into the vegetation index to accurately predict LWC in rice plants. Results The results presented that the model accuracy of rice LWC estimation after combining data from multiple sources improved by 6–44% compared to the accuracy of a single spectral index normalized difference index (ND). Additionally, the optimal prediction accuracy of rice LWC was produced using a machine algorithm of gradient boosted decision tree (GBDT) based on the combination of ND (1287,1673) and crop water stress index (CWSI) (R 2  = 0.86, RMSE = 0.01). Conclusions The machine learning estimation model constructed based on multi-source data fully utilizes the spectral information and considers the environmental changes in the crop canopy after introducing multi-source data parameters, thus improving the performance of spectral technology for monitoring rice LWC. The findings may be helpful to the water status diagnosis and accurate irrigation management of rice plants.

Crop pests reduce productivity, so managing them through early detection and prevention is essential. Data from various modalities are being used to predict crop diseases by applying machine learning methodology. In particular, because growth environment data is relatively easy to obtain, many attempts are made to predict pests and diseases using it. In this paper, we propose a model that predicts diseases through previous growth environment information of crops, including air temperature, relative humidity, dew point, and CO 2 concentration, using deep learning techniques. Using large-scale public data on crops of strawberry, pepper, grape, tomato, and paprika, we showed the model can predict the risk score of crop pests and diseases. It showed high predictive performance with an average AUROC of 0.917, and based on the predicted results, it can help prevent pests or post-processing. This environmental data-based crop disease prediction model and learning framework are expected to be universally applicable to various facilities and crops for disease/pest prevention.

Nitrogen (N), phosphorus (P), and potassium (K) contents are crucial quality indicators for forage in alpine natural grasslands and are closely related to plant growth and reproduction. One of the greatest challenges for the sustainable utilization of grassland resources and the development of high-quality animal husbandry is to efficiently and accurately obtain information about the distribution and dynamic changes in N, P, and K contents in alpine grasslands. A new generation of multispectral sensors, the Sentinel-2 multispectral instrument (MSI) and Tiangong-2 moderate-resolution wide-wavelength imager (MWI), is equipped with several spectral bands suitable for specific applications, showing great potential for mapping forage nutrients at the regional scale. This study aims to achieve high-accuracy spatial mapping of the N, P, and K contents in alpine grasslands at the regional scale on the eastern Qinghai-Tibet Plateau. The Sentinel-2 MSI and Tiangong-2 MWI data, coupled with multiple feature selection algorithms and machine learning models, are applied to develop forage N, P, and K estimation models from data collected at 92 sample sites ranging from the vigorous growth stage to the senescent stage. The results show that the spectral bands of both the Sentinel-2 MSI and Tiangong-2 MWI have an excellent performance in estimating the forage N, P, and K contents (the R 2 values are 0.68–0.76, 0.54–0.73, and 0.74–0.82 for forage N, P, and K estimations, respectively). Moreover, the model integrating the spectral bands of these two sensors explains 78%, 74%, and 84% of the variations in the forage N, P, and K contents, respectively. These results indicate that the estimation ability of forage nutrients can be further improved by integrating Tiangong-2 MWI and Sentinel-2 MSI data. In conclusion, integration of the spectral bands of multiple sensors is a promising approach to map the forage N, P, and K contents in alpine grasslands with high accuracy at the regional scale. This study offers valuable information for growth monitoring and real-time determination of forage quality in alpine grasslands.

Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution (“hyperspectral”) spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation—information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata : wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400–2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.

Background Detection and counting of wheat heads are of crucial importance in the field of plant science, as they can be used for crop field management, yield prediction, and phenotype analysis. With the widespread application of computer vision technology in plant science, monitoring of automated high-throughput plant phenotyping platforms has become possible. Currently, many innovative methods and new technologies have been proposed that have made significant progress in the accuracy and robustness of wheat head recognition. Nevertheless, these methods are often built on high-performance computing devices and lack practicality. In resource-limited situations, these methods may not be effectively applied and deployed, thereby failing to meet the needs of practical applications. Results In our recent research on maize tassels, we proposed TasselLFANet, the most advanced neural network for detecting and counting maize tassels. Building on this work, we have now developed a high-real-time lightweight neural network called WheatLFANet for wheat head detection. WheatLFANet features a more compact encoder-decoder structure and an effective multi-dimensional information mapping fusion strategy, allowing it to run efficiently on low-end devices while maintaining high accuracy and practicality. According to the evaluation report on the global wheat head detection dataset, WheatLFANet outperforms other state-of-the-art methods with an average precision AP of 0.900 and an R 2 value of 0.949 between predicted values and ground truth values. Moreover, it runs significantly faster than all other methods by an order of magnitude (TasselLFANet: FPS: 61). Conclusions Extensive experiments have shown that WheatLFANet exhibits better generalization ability than other state-of-the-art methods, and achieved a speed increase of an order of magnitude while maintaining accuracy. The success of this study demonstrates the feasibility of achieving real-time, lightweight detection of wheat heads on low-end devices, and also indicates the usefulness of simple yet powerful neural network designs.