摩托罗拉

This methodology article presents a novel framework for conducting a political economy analysis (PEA) of insulin access in the Comoros. It addresses a critical gap in understanding the systemic barriers to diabetes care in small island developing states. The research problem centres on the urgent need to elucidate the complex interplay between supply chain actors, pricing policies, and patient outcomes that perpetuate insulin insecurity. The proposed multi-method approach, implemented between 2021 and 2026, integrates three components: (1) supply chain mapping via stakeholder interviews and document review to trace insulin flows from import to point-of-care; (2) a policy analysis of national pricing regulations, tariffs, and procurement mechanisms; and (3) a longitudinal assessment of patient-level clinical and financial outcomes. The framework is designed to generate granular evidence on how power dynamics, institutional interests, and market structures shape affordability and availability. Its significance lies in providing a replicable tool for health systems researchers and policymakers to diagnose systemic failures in medicine access, moving beyond the mere quantification of stockouts. By foregrounding political and economic determinants, this methodology offers a pathway to design more equitable, context-sensitive interventions that strengthen pharmaceutical sovereignty and improve chronic disease management.

This survey research investigates the role of African diaspora health professionals in strengthening health systems in the Comoros via digital platforms from 2021 to 2026. It addresses critical human resource shortages and limited specialist expertise within the Comorian healthcare sector. Using a structured online survey, data were collected from 150 diaspora health professionals of Comorian origin and 85 in-country healthcare workers to evaluate the prevalence, nature, and perceived efficacy of digital contributions. Quantitative and qualitative analyses reveal a significant increase in virtual mentorship and telemedicine consultations since 2021, particularly in paediatrics, cardiology, and public health. Respondents reported that these interventions enhanced clinical knowledge, influenced patient management protocols, and strengthened professional networks. However, the study identifies persistent barriers, including inconsistent internet connectivity, variable digital literacy, and a lack of formalised integration within national health systems. The research concludes that the diaspora represents a vital, underutilised asset for health system strengthening. It advocates for structured, institutionally led digital health partnerships to harness this expertise sustainably. This work underscores the potential of south-south digital collaboration in advancing health equity and building resilient health systems across Africa.

Abstract This offers a design framework to guide more effective and ethical design of AI systems that model human likeness. Drawing on ethnographic research with StoryFile, a company pioneering conversational video AI, it traces how the promise of “authentic interactions” and lasting legacies emerge through the assemblage of human storytelling, performance, design choices, technical affordances, corporate narratives, and cultural values. For practitioners and organizations building similar products, this paper offers the myth/mess framework as a design tool: to locate where corporate narratives meet messy realities, to surface how values are embedded or eroded, and to guide the development of AI systems, such as grief tech or heritage projects, that model human likeness and mediate embodied practices through which we form identity, memory, and relationships. Using anthropologists Paul Dourish and Genevieve Bell's myth/mess framework, the paper analyzes two key tensions that illuminate the distributed, relational nature of ‘intelligence’ in such systems.

Sign language is a natural language widely used by Deaf and hard of hearing (DHH) individuals. Advanced wearables are developed to recognize sign language automatically. However, they are limited by the lack of labeled data, which leads to a small vocabulary and unsatisfactory performance even though laborious efforts are put into data collection. Here we propose SignRing, an IMU-based system that breaks through the traditional data augmentation method, makes use of online videos to generate the virtual IMU (v-IMU) data, and pushes the boundary of wearable-based systems by reaching the vocabulary size of 934 with sentences up to 16 glosses. The v-IMU data is generated by reconstructing 3D hand movements from two-view videos and calculating 3-axis acceleration data, by which we are able to achieve a word error rate (WER) of 6.3% with a mix of half v-IMU and half IMU training data (2339 samples for each), and a WER of 14.7% with 100% v-IMU training data (6048 samples), compared with the baseline performance of the 8.3% WER (trained with 2339 samples of IMU data). We have conducted comparisons between v-IMU and IMU data to demonstrate the reliability and generalizability of the v-IMU data. This interdisciplinary work covers various areas such as wearable sensor development, computer vision techniques, deep learning, and linguistics, which can provide valuable insights to researchers with similar research objectives.