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Ruben Juarez
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Ruben Juarez

Bilingual Journalist

AS SEEN ON
Nature Portfolio, KGBT-TV (Harlingen, TX)

Preston's Summary

Ruben Juarez is a Bilingual Journalist at CBS 4 RGV and KVEOTV 23. He specializes in covering a wide range of sports topics, including both team and individual sports disciplines, while also sharing his passion for the Premier League and playing basketball and soccer. Ruben's work has been featured in KGBT-TV (Harlingen, TX) and Nature Portfolio.

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Geo Focus

Edinburg, United States (Local)

Coverage Attributes:

Beta
Informative: 50 %
Events: 17 %
Interviews Q&as: 13 %
Promotional: 4 %
Listicles: 4 %

Themes Covered:

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Most Recent Topics:

  • Team Sports
  • Individual Sports
  • Sports Disciplines

Pitching Insights

Ruben Juarez's focus is primarily on local sports events, particularly high school-level basketball, wrestling, and football. He seems to prioritize event coverage and private sector announcements related to sports.

If you're looking to reach out to Ruben, consider offering access or insights into local sporting events in the Texas area, particularly those involving high school athletes. Providing exclusive interviews with promising athletes or coaches involved in these events could also be a strong pitch for his coverage interests.

This information evolves through artificial intelligence and human feedback. Improve this profile .

Articles

Nature

Structural optimization principles for edge AI in motorsport telemetry

Structural optimization principles for edge AI in motorsport telemetry

By: Fernando, Rubén Juárez

In motorcycle motorsport, engineering decisions are constrained by mass, stiffness, fatigue life, and thermal margins. In a comparable way, deploying large language models (LLMs) for edge or trackside assistance is limited by memory capacity, bandwidth, latency, and power. This paper presents a systems-engineering framework that relates structural lightweighting and post-training quantization (PTQ) through second-order sensitivity. In mechanics, safe material removal is governed by the stiffness matrix $$\textbf{K}$$; in neural networks, safe precision reduction is governed by curvature information encoded in the loss Hessian $$\textbf{H}$$. To ground this analogy, we combine a structural case study based on topology optimization of a Formula Student upright with an edge-oriented evaluation of low-precision LLM deployment using established PTQ pipelines (GPTQ and AWQ). The structural example illustrates how sensitivity-guided material removal preserves load paths and mechanical integrity, while the LLM study examines how sensitivity-aware quantization can reduce computational footprint while maintaining operational usefulness in telemetry-related inference. To assess deployment feasibility, we define two practical metrics: a Digital Factor of Safety ($$\textrm{FoS}_{\textrm{dig}}$$), based on a perplexity-derived integrity threshold, and Intelligence-per-Watt (IPW), which captures energy-aware inference efficiency. For a 32B-class operating point, INT4 weight-only deployment reduces memory footprint from 61 GB to 18 GB, improves throughput from 26.0 to 69.9 tok/s, and lowers measured power from 295 W to 165 W while remaining within the proposed integrity envelope. The contribution is methodological rather than algorithmic: we do not introduce a new quantization method, but a reproducible framework for analyzing when low-precision LLM deployment becomes feasible under motorsport-inspired resource constraints. The results support the practical relevance of stiffness-informed digital lightweighting, while also highlighting that the evidence is limited to the structural and computational case studies considered here.

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