Satellite-based PMsub.2.5 estimation is an effective means to achieve large-scale and long-term PMsub.2.5 monitoring and investigation. Currently, most of methods retrieve PMsub.2.5 from ...satellite-derived aerosol optical depth (AOD) or top-of-atmosphere reflectance (TOAR) during daytime. A few algorithms are also developed to retrieve nighttime PMsub.2.5 from the satellite day–night band and the accuracy is greatly limited by moonlight and artificial light sources. In this study, we utilize the properties of absorption pollutants in infrared spectrum to estimate PMsub.2.5 concentrations from satellite infrared data, thus achieve the PMsub.2.5 estimation in both day and night. Himawari-8 infrared bands data are used for PMsub.2.5 estimation by a specifically designed neural network and loss function. Quantitative results show the satellite derived PMsub.2.5 concentrations correlates with ground-based data well with Rsup.2 of 0.79 and RMSE of 15.43 μg · msup.−3 for hourly PMsub.2.5 estimation. Spatiotemporal distributions of model-estimated PMsub.2.5 over China are also analyzed, and exhibit a highly consistent with ground-based measurements. Dust storms, heavy air pollution and fire smoke events are examined to further demonstrate the efficacy of our model. Our method not only circumvents the intermediate retrievals of AOD, but also enables consistent estimation of PMsub.2.5 concentrations during daytime and nighttime in real-time monitoring.
In this article, the author argues that playing by ear is the most fundamental of music skills, but that it is often the most neglected in informal circles. He explores Lucy Green's work on how ...children learn by ear and the particular "styles" that they exhibit. Then, he presents a step-by-step approach to learning by ear with a large group of beginners.
Este trabajo forma parte de una investigación más amplia realizada desde un enfoque cualitativo, de carácter etnográfico y a medio camino entre los paradigmas fenomenológico y crítico, cuyo objetivo ...es analizar las concepciones del profesorado de secundaria en torno a la educación literaria, sus estrategias de aprendizaje, sus prácticas de enseñanza y su reflejo en las dinámicas de aula. En este artículo en particular se aborda el estado de una de las cuestiones que han suscitado y suscitan en la actualidad mayor debate en el ámbito de la didáctica de la literatura y entre el profesorado: qué obras constituyen el itinerario apropiado para una educación literaria que desarrolle el gusto por la lectura y lo proyecte más allá de las aulas, es decir, la composición del corpus o canon formativo de secundaria. Para ello, se ha hecho una revisión de algunos de los trabajos que se han ocupado del concepto de canon desde el último tercio del siglo XX, junto con otras investigaciones sobre las creencias del profesorado de secundaria en relación al corpus formativo de la educación literaria. Asimismo, se mencionan las principales líneas que orientan este objeto de estudio en la nueva normativa de enseñanza. Los resultados que se obtienen de esta revisión apuntan a un acuerdo generalizado para ampliar y diversificar el corpus de textos y sus formatos, actualizar los géneros desde una perspectiva multimodal, y normalizar las prácticas sociales de lectura literaria dentro de las aulas.
Who doesn't want to improve teaching and learning? A lot of people continue to ask searching questions like: Will I ever use this in real life? Why waste time learning all this stuff? Such questions ...are never-ending. This book provides answers to these and many other queries. Repeatedly, we hear sayings like, 'No pain, no gain'; 'You'll know it when you feel it'; 'You have to experience it to know about it'; 'Experience teaches!'; and 'Experience is the best teacher!' Such commonly heard adages appear to underscore the importance of experiential learning. Underpinning these aphorisms is the common theme that learning is most effective through experience. This book provides the reader with the tools needed to make better use of experiences to improve teaching and learning. It is divided into several parts to facilitate easy understanding. Operating under the Creative Commons Copyright license, the text is intentionally interspaced with relevant shareware graphics (exhibits) from the public domain. Such exhibits are selected to serve as stimulants for innovation, engagement and personal pleasure.
As no study has systematically theorized and empirically tested an ecological model of students' cooperative behaviors during game-based learning, this study moves toward doing so by modeling ...multiple levels of antecedents of students' prosocial behaviors during game play. Specifically, we propose a theoretical model of how player personality, players’ personality composition, and recent sequences of strategies or game moves affect the likelihood of prosocial behavior in each turn of talk. Then, we empirically tested our model on 8432 turns of talk by 17 adolescents in eight face to face games via statistical discourse analysis.
Players who were agreeable, conscientious, and patient showed prosocial behaviors more often. Meanwhile groups with only one agreeable person, only one extrovert, or only one conformist showed fewer prosocial behaviors. Furthermore, recent strategies such as advise, lend resources, or consent were more likely to precede a prosocial behavior. By contrast, recent aggressive moves reduced the likelihood of an immediate prosocial behavior. For example, a sequence of consecutive attacks sharply reduced the likelihood of a prosocial behavior. Furthermore, interactions among these attributes also affected the likelihood of prosocial behaviors.
These results contribute to and help integrate social identity theory and social learning theory by moving toward an ecological explanatory model with player personalities, player composition, sequences of strategies and game moves, and their interactions. These insights (a) help bridge the gap in our understanding of how students act and react in strategic activities, and (b) inform game design and instructional practices seeking to foster prosocial behaviors and environments.
•Gender, personality, group composition, and prior strategies/game moves are antecedents of players' prosocial actions•Agreeable, conscientious, patient players showed more prosocial actions•Open-minded, resilient players exhibited fewer prosocial actions•Prior strategies (advice, consent-seeking, lend resources) yielded more prosocial actions.•Recent antisocial actions yielded fewer prosocial actions.
Large-scale and multi-annual maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, ...BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, it is still challenging to produce a large-scale BRA due to the rather tiny sizes of individual buildings. From the viewpoint of classification methods, conventional approaches utilize high-resolution aerial images (metric or submetric resolution) to map BRA; unfortunately, high-resolution imagery is both infrequently captured and expensive to purchase, making the BRA mapping costly and inadequate over a consistent spatiotemporal scale. From the viewpoint of learning strategies, there is a nontrivial gap that persists between the limited training references and the applications over geospatial variations. Despite the difficulties, existing large-scale BRA datasets, such as those from Microsoft or Google, do not include China, and hence there are no full-coverage maps of BRA in China yet. In this paper, we first propose a deep-learning method, named the Spatio-Temporal aware Super-Resolution Segmentation framework (STSR-Seg), to achieve robust super-resolution BRA extraction from relatively low-resolution imagery over a large geographic space. Then, we produce the multi-annual China Building Rooftop Area (CBRA) dataset with 2.5 m resolution from 2016-2021 Sentinel-2 images. CBRA is the first full-coverage and multi-annual BRA dataset in China. With the designed training-sample-generation algorithms and the spatiotemporally aware learning strategies, CBRA achieves good performance with a F1 score of 62.55 % (+10.61 % compared with the previous BRA data in China) based on 250 000 testing samples in urban areas and a recall of 78.94 % based on 30 000 testing samples in rural areas. Temporal analysis shows good performance consistency over years and good agreement with other multi-annual impervious surface area datasets. STSR-Seg will enable low-cost, dynamic, and large-scale BRA mapping (
![PM[sub.2.5] Estimation in D...](http://api.summon.serialssolutions.com/2.0.0/image/issn/XR4PS5YY4K/2072-4292/small "PM[sub.2.5] Estimation in Day/Night-Time from Himawari-8 Infrared Bands via a Deep Learning Neural Network")
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