Abstract: Systems, methods, and computer-readable media are provided for automatically and intelligently creating journal entries, such as journal entries to solve mismatches identified via the intercompany elimination reporting process. In an example, a machine learning model is trained to generate journal entries such as journal entries based on certain characteristics of historical journal entries associated with historical discrepancies. The trained machine learning model may be used, for example, to automatically generate a journal entry by recording a ledger acknowledging a discrepancy between a first data entry of a first entity and a second data entry of a second entity. The journal entry automatically generated using the machine learning model may reconcile the discrepancy, for example, by recording the ledger in alignment with the first data entry of the first entity or in alignment with the second data entry of the second entity.

Abstract: The present invention relates to parenteral pharmaceutical compositions of suitable Hypoxia-Inducible Factor Prolyl Hydroxylase Inhibitor, particularly to Desidustat or suitable pharmaceutically acceptable salts thereof. The invention also relates to processes for preparing such compositions. The present invention also relates to the development of therapeutic compound for the treatment of radiation induced toxicity. Specifically, present invention relates to use of Desidustat or its pharmaceutically acceptable salt or oral and parenteral pharmaceutical composition thereof for the treatment of radiation induced toxicity.

Abstract: Systems, methods, and computer-readable media are provided for automatically and intelligently creating journal entries, such as journal entries to solve mismatches identified via the intercompany elimination reporting process. In an example, a machine learning model is trained to generate journal entries such as journal entries based on certain characteristics of historical journal entries associated with historical discrepancies. The trained machine learning model may be used, for example, to automatically generate a journal entry by recording a ledger acknowledging a discrepancy between a first data entry of a first entity and a second data entry of a second entity. The journal entry automatically generated using the machine learning model may reconcile the discrepancy, for example, by recording the ledger in alignment with the first data entry of the first entity or in alignment with the second data entry of the second entity.