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researching, developing, and implementing models based on artificial intelligence to optimize energy consumption and product quality in industrial production units. The plan is divided into the following phases
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Interior, under the following conditions: Research Field: Multiview technology, Radiance Fields and Plenoptics. Objectives: Develop models for Processing, Coding, Quality and Representation of radiance
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creating and training a large language model tailored to the OMS context, integrating AI agents, and implementing a vehicle mobility interface. The platform will be tested, validated, and continually refined
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predict behavior in real scenarios. This validation will ensure that AI models can effectively manage key crop variables such as temperature, moisture, and nutrients while generating profiles based on real
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, rearranged layouts, or altered routes. Traditional navigation models struggle to generalize to these evolving scenarios without retraining from scratch. This work explores lifelong incremental learning
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of the DNXT RWA MARKETPLACE solution; Development of the DNXT RWA MARKETPLACE Platform; Evaluation and validation of the conceptual model, with the introduction of improvements; Integration of components
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for the prevention of cardiovascular effects and assess their stability; and characterize the bioactive phytochemical compounds (phenolics) by HPLC-DAD; conduct studies with the extract formulations in disease models