Rahul Rathod | Searcharxiv

arXiv2026

Insights from Farmer-Managed Decentralized Solar Irrigation Systems

Solar irrigation systems are increasingly deployed in rural regions, yet their distributed and remote deployment makes maintenance challenging for farmers. While formal monitoring processes and applications exist, they often fall short in practice. We present insights from grid-connected solar irrigation schemes that incentivize farmers to feed energy to the grid, focusing on how farmers maintain their systems. We found that farmers face multiple challenges but are also devising strategies, including the appropriation of WhatsApp to share daily generation data with peers and compare performance across installations to identify potential system anomalies. Our findings highlight how messaging platforms function as informal digital infrastructures enabling collective sensemaking around distributed energy systems. We discuss implications for designing agricultural energy technologies that support peer comparison, contextual interpretation, and community-driven maintenance, framing these as a socio-technical platform. Finally, we outline directions for future work integrating such practices with formal monitoring tools and explore their potential to support citizen science initiatives in environmental sensing.

cs.CY↗

arXiv2023

Deep Learning Pipeline for Preprocessing and Segmenting Cardiac Magnetic Resonance of Single Ventricle Patients from an Image Registry

Purpose: To develop and evaluate an end-to-end deep learning pipeline for segmentation and analysis of cardiac magnetic resonance images to provide core-lab processing for a multi-centre registry of Fontan patients. Materials and Methods: This retrospective study used training (n = 175), validation (n = 25) and testing (n = 50) cardiac magnetic resonance image exams collected from 13 institutions in the UK, US and Canada. The data was used to train and evaluate a pipeline containing three deep-learning models. The pipeline's performance was assessed on the Dice and IoU score between the automated and reference standard manual segmentation. Cardiac function values were calculated from both the automated and manual segmentation and evaluated using Bland-Altman analysis and paired t-tests. The overall pipeline was further evaluated qualitatively on 475 unseen patient exams. Results: For the 50 testing dataset, the pipeline achieved a median Dice score of 0.91 (0.89-0.94) for end-diastolic volume, 0.86 (0.82-0.89) for end-systolic volume, and 0.74 (0.70-0.77) for myocardial mass. The deep learning-derived end-diastolic volume, end-systolic volume, myocardial mass, stroke volume and ejection fraction had no statistical difference compared to the same values derived from manual segmentation with p values all greater than 0.05. For the 475 unseen patient exams, the pipeline achieved 68% adequate segmentation in both systole and diastole, 26% needed minor adjustments in either systole or diastole, 5% needed major adjustments, and the cropping model only failed in 0.4%. Conclusion: Deep learning pipeline can provide standardised 'core-lab' segmentation for Fontan patients. This pipeline can now be applied to the >4500 cardiac magnetic resonance exams currently in the FORCE registry as well as any new patients that are recruited.

cs.CV↗