Mars Avatar Project: Control Without Presence is a conceptual research project proposing a next-generation architecture for autonomous robotic avatars operating on Mars under human strategic supervision. Instead of continuous teleoperation, human operators define mission goals while local artificial intelligence coordinates robotic execution, overcoming the 4–22 minute communication delay between Earth and Mars. The project presents a scalable framework integrating Mission AI, autonomous robotic systems, scientific payloads, and future human–machine interfaces for planetary exploration and infrastructure development. This work is intended as a conceptual foundation for future research in space robotics, AI-assisted exploration, and interplanetary mission architecture.<br>
<b>Abstract:</b>Modern real estate platforms manage heterogeneous buyer populations ranging from first-time residential home buyers to institutional corporate entities and international high-net-worth investors. Traditional marketing strategies relying on broad demographic genera…
Background & Objective: Chronic sleep deprivation among adolescents has accelerated over the past three decades, aligning with widespread digital media saturation and reported cognitive focus issues. Traditional public health tracking often evaluates short‑term trends, overlo…
Leaf Area Index (LAI) serves as a key biophysical parameter for characterizing vegetation canopy structure and ecosystem functions. To address the absence of LAI products for the Fengyun-3B (FY-3B) satellite and the limitations of current satellite LAI products, this study propos…
Record of the development of the deep learning pipeline CADENCE, capable of generating novel competitive kinase inhibitors from amino acid sequence. CADENCE was trained on the Davis dataset of kinase inhibitors and binding scores, creating a binding affinity prediction model. Thi…
Precise and interpretable classification of autism-related behaviors is importantfor initial diagnosis, personalized intervention, and support arrangements. This studyproposes an interpretable machine learning (ML) model using Light GradientBoosting Machine (LightGBM) and Categor…
Repository storing all data used for the paper: "Taking a Swing at Uncertainty: A Neural Network Analysis of Major League Baseball Strikeout Rates"These data were derived from the following resources available in the public domain: FanGraphs