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The coastal region of northwest Africa experiences a persistent and variable upwelling phenomenon throughout most of the year, contributing to the presence of one of the world's largest and most productive fishing ports. In this study, we introduce Deep ${\rm{Coas}}{{\rm{t}}_{up}}$ Coas t up -Net a convolutional neural network architecture designed for identifying and extracting upwelling regions from satellite sea surface temperature (SST) images. Our model is trained on a dataset of SST images captured along the Moroccan Atlantic coast, utilizing essential input parameters, including SST, latitudinal position ($LA{T_{pos}}$ LA T pos ), and distance from the coastline (${D_{coast}}$ D coast ). By incorporating these physical parameters as input features and training the model with corresponding masks for the Atlantic coast of Morocco, the Deep ${\rm{Coas}}{{\rm{t}}_{up}}$ Coas t up -Net network learns to accurately segment upwelling regions in major coastal zones and demonstrates its generalization capability. We validate our methodology using a quantitative index. The results of this validation showcase the effectiveness of our approach. Subsequently, we explore seasonal and interannual upwelling trends within a 21-year time series, spanning from 2000 to 2020. [ABSTRACT FROM AUTHOR]

Using parodies to mock politicians' personal flaws is a prevalent practice and represents an important threat to their brand image and reputation. However, the ability of parodies based on personal attacks to create positive social media engagement from the parodist's point of view is still unexplored. This study contributes to the attribution–emotion–action theory by exposing the unintended effects of parodies on social media engagement. We began with a content analysis of 331 parodies of political figures posted on YouTube from 2012 to 2019. Then, by using both a machine‐learning‐based analysis of 31,300 comments on those parodies and an experiment, we compared the effectiveness of two types of parodies based on personal attacks, depending on the type of stigma depicted: controllable versus uncontrollable. Our findings are paradoxical. Compared to parodies based on controllable personal stigmas, parodies of uncontrollable stigmas foster less supportive social media engagement toward the parodist and his/her parody (i.e., likes, shares), especially among individuals who were originally detractors and opponents of the parodied politician. Those effects are mediated by the role of moral‐condemning emotions expressing the inappropriateness of the topic selected by the parodist. Henceforth, it is advisable for parodists to refrain from incorporating representations of an uncontrollable stigma into their spoof creations, as such actions may imperil the levels of engagement exhibited by their audience, encompassing both their reception of the parodies and their perception of the parodists themselves. [ABSTRACT FROM AUTHOR]