La conquista Hitita de Alašiya

La influencia del Imperio hitita durante el Bronce Final en el Oriente Próximo era indudable; sin embargo, el declive al que se vio abocado el Imperio durante sus últimas décadas obligó a sus últimos reyes a fijar sus metas, por vez primera, más allá del mar. Las sucesivas conquistas del reino de Alašiya, en la isla de Chipre, intentan paliar una grave situación para el reino que era ya irreversible. En el presente artículo se lleva a cabo un análisis de las relaciones entre el reino de Ḫatti y Alašiya, antes y después de su conquistaThe influence of the Hittite Empire through the Late Bronze Age in the Near East was certain; nevertheless, the decline of the Empire during its last decades, forced its kings to set their goals, for the first time, beyond the sea. The successive conquests of the kingdom of Alašiya, on the island of Cyprus, sought to alleviate a difficult condition for the Hittite Kingdom, which was already irreversible. This paper presents an analysis of the relations between the kingdom of Ḫatti and Alašiya, before and after its conques

An accurate machine learning model to study the impact of realistic metal grain granularity on Nanosheet FETs

In this work, we present a machine learning neural network model to predict the impact of realistic metal grain granularity (MGG) variability on the threshold voltage V Th and on the ID -VG characteristics of a silicon-based 12 nm gate length nanosheet FET. This model is based on the multi-layer perceptron (MLP) machine learning architecture. As realistic MGG maps consist of the distribution of grains on the gate with different work-function values, it is relevant to apply algorithms such as the principal component analysis to reduce these features to the most representative ones. Once the realistic MGG features are correctly reduced without losing information, we train two different neural networks with the neurons in the output layer as the only difference, to predict the VTh and the ID - VG characteristics, respectively. The comparison between TCAD results and the model, shows excellent agreement for the mean and standard deviation of VTh distributions for different average grain sizes values (from 3 nm to 10 nm) demonstrating the accuracy of the machine learning model. Also, we study the amount of data needed to accurately train the MLPs, leading to results that allow us to drastically reduce the computational time required to perform variability studies for state-of-art nano FET devicesS

Millimeter-scale genetic gradients and community-level molecular convergence in a hypersaline microbial mat

To investigate the extent of genetic stratification in structured microbial communities, we compared the metagenomes of 10 successive layers of a phylogenetically complex hypersaline mat from Guerrero Negro, Mexico. We found pronounced millimeter-scale genetic gradients that were consistent with the physicochemical profile of the mat. Despite these gradients, all layers displayed near-identical and acid-shifted isoelectric point profiles due to a molecular convergence of amino-acid usage, indicating that hypersalinity enforces an overriding selective pressure on the mat community