This study evaluates, from a cost and entrepreneurial opportunity perspective, the viability of maintaining a university cluster composed of ten HP ProDesk 600 G1 nodes (40 cores, 60 GB RAM) versus contracting equivalent capacity on Amazon EC2 (C6i.2xlarge instances). A detailed total cost of ownership (TCO) model was developed, incorporating capital investment, maintenance, electricity (USD 0.22/kWh), internet connectivity (200 Mbps), and cloud tariffs under both On-Demand and three-year Savings Plan modalities. At 30% utilization, the local cluster achieves a unit cost of USD 0.025/core-hour, while EC2 ranges from USD 0.048 to 0.105/core-hour. Sensitivity curves indicate that the cloud only becomes cost-effective when utilization exceeds 65% or electricity surpasses USD 0.45/kWh. The analysis further explores implications for deep-tech startups, university spin-offs, and incubator programs: while the cloud provides elasticity and access to advanced hardware, it may erode early-stage margins. The study proposes a reproducible decision-making framework tailored to emerging innovation ecosystems in Latin America, highlighting the strategic value of hybrid infrastructures for balancing cost, flexibility, and regional value retention.

Cardiovascular diseases pose significant health challenges in low-and middle-income countries (LMICs), making early detection of heart disease vital to improve outcomes, but access to conventional diagnostic tools. Mobile electrocardiography (MECG) offers a promising solution for remote and resourcelimited settings. To evaluate the use of MECG technology to detect cardiac disease in underserved areas of Honduras. It focuses on mobile health technologies' efficacy, challenges, and potential to expand access to cardiovascular diagnosis. MECG devices were deployed in the El Paraíso, Honduras, using Prince 180D devices and a cloud-based mobile application to record and transmit ECG data. A total of 1044 ECGs were collected between November 2023 and January 2024, and the data were analyzed remotely by cardiologists. Among the 1044 ECGs recorded, 262 had abnormalities, such as ischemic heart disease, atrial fibrillation, atrial flutter, and third-degree atrioventricular block. MECG technology demonstrated sensitivity to various cardiac pathologies, and abnormalities were predominantly identified in women and patients aged 41 years or older. MECG technology demonstrated high accuracy and reliability in detecting multiple cardiac pathologies, especially in geographically isolated regions. This system enabled early diagnosis and facilitated timely referrals, offering a scalable solution to health disparities in LMICs.