transformer

Well logs are interpreted/processed to estimate the in-situ reservoir properties (petrophysical, geomechanical, and geochemical), which is essential for reservoir modeling, reserve estimation, and production forecasting. The modeling is often based on multi-mineral physics or empirical formulae. When sufficient amount of training data is available, machine learning solution provides an alternative approach to estimate those reservoir properties based on well log data and is usually with less turn-around time and human involvements.

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 Development and Evaluation of a Novel GPT-like Conditional Molecule Generator

This study presents the development and evaluation of a novel GPT-like conditional molecule generator designed to optimize the synthesis of chemical compounds with desirable properties. The model incorporates six pivotal physicochemical properties as conditions:

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We downloaded the dataset of Hindi Poems from the Website, contains around 2500 poems the downloaded dataset link is: link In the initial phase of our data preprocessing pipeline, we collected text data from a diverse set of HTML files, totaling 2500 documents. These files, constituting a substantial corpus, were meticulously curated for subsequent analysis. To facilitate further investigation, we amalgamated all the extracted text into a consolidated text file, a crucial step in preparing the data for subsequent processing.

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Many of the publicly available electrocardiogram (ECG) databases either have a low number of people in the database, each with longer recordings, or have more people, each with shorter recordings. As a result, attempting to split a single database into training, testing, and, optionally, validation datasets is challenging. Some models seem to do well with larger training sets, but that leaves only a small set of data for testing. Moreover, if the ECG is segmented by heartbeat, the data are further limited by the number of heartbeats in the recording.

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Analysis method of Dissolved gas in transformer oil based on EW Pentagon

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