THE STRATIGRAPHY OF THE UPPER BATHONIAN TO MIDDLE OXFORDIAN SUCESSION OF THE ARAGONESE BRANCH OF THE CORDILLERA IBÉRICA (SPAIN) AND ITS EUROPEAN CONTEXT

The Upper Bathonian-Middle Oxfordian succession of the Aragonese branch of the Cordillera Ibérica is one of the most completely developed in Europe and includes localities of international importance for Jurassic bio- and chronostratigraphy. Of particular importance are a potential stratotype for the Upper Bathonian of North West Europe and reference sections for a number of Submediterranean Province Middle Oxfordian biostratigraphic units. The intervening Callovian sequence, albeit locally strongly condensed, also includes faunas of key stratigraphical importance. The sequence of stratigraphically important ammonite faunas for this interval is here reviewed and placed in its European context

Improved Methods for Automatic Facial Expression Recognition

Facial expressions constitute one of the most effective and instinctive methods that allow people to communicate their emotions and intentions. In this context, the both Machine Learning (ML) and Convolutional Neural Networks (CNNs) have been used for emotion recognition. Efficient recognition systems are required for good human-computer interaction. However, facial expression recognition is related to several methods that impact the performance of facial recognition systems.  In this paper, we demonstrate a state-of-the-art of 65% accuracy on the FER2013 dataset by leveraging numerous techniques from recent research and we also proposed some new methods for improving accuracy by combining CNN architectures such as VGG-16 and Resnet-50 with auxiliary datasets such as JAFFE and CK.  To predict emotions, we used a second approach based on geometric features and facial landmarks to calculate and transmit the feature vector to the SVM model. The results show that the ResNet50 model outperforms all other emotion prediction models in real time by maximizing

Bismuth Silver Oxysulfide for Photoconversion Applications: Structural and Optoelectronic Properties

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