搜索结果：offline-first

System-level telemetry is fundamental to modern remote monitoring, predictive maintenance, and AI-driven infrastructure optimisation. Existing telemetry encodings such as JSON, JSON Lines, CBOR, and Protocol Buffers were designed for high-bandwidth, always-online environments. They impose significant overhead when deployed in bandwidth-constrained networks common across Sub-Saharan Africa, rural enterprise deployments, and unstable LAN environments. This paper introduces MTS-1 (Magenta Telemetry Standard v1), a novel delta-encoded binary telemetry format designed for offline-first system monitoring, LAN-assisted proxy delivery, and energy-efficient IoT-to-server transmission. We compare MTS-1 against JSON, JSON Lines, CBOR, MessagePack, and Protocol Buffers across payload size, encoding cost, network efficiency, and cost-latency performance. Synthetic benchmarking demonstrates preliminary compression improvements of up to 74.7% versus JSON and 5.4% versus MessagePack, with linear scaling characteristics across dataset sizes.

Access to specialist clinical expertise remains severely limited across sub-Saharan Africa, where physician-to-patient ratios can fall below 1:25,000 in rural settings. Existing AI-assisted diagnostic tools predominantly require reliable internet connectivity and high-specification hardware, rendering them impractical for frontline healthcare workers in district hospitals and health centres. This paper presents Aletheia, an offline-first clinical decision support system designed for low-resource healthcare contexts across sub-Saharan Africa. Aletheia is built upon Qwen2.5-3B-Instruct, fine-tuned using Quantised Low- Rank Adaptation (QLoRA) on a curated dataset of 27,000 clinical reasoning samples spanning 50 disease conditions with elevated prevalence in East Africa. Evaluation demonstrates a Top-1 diagnostic accuracy of 80.0%, Top-3 accuracy of 100.0%, BERTScore-F1 of 0.909, and METEOR of 0.467 across ten representative clinical case categories. The system achieves an Expected Calibration Error (ECE) of 0.275 and passes the Africa Deep Tech Challenge 2026 (ADTC 2026) memory budget constraint of 7 168 MB, achieving a peak inference RAM of approximately 3 630 MB on the standardised

Artificial intelligence and large language models (LLMs) are transforming educational technology by enabling conversational tutoring, personalised explanations, and inquiry-driven learning. However, most AI-based learning systems rely on continuous internet connectivity and cloud-based computation, limiting their use in bandwidth-constrained environments. This paper presents Arapai, an offline-first large language model architecture designed for AI-assisted learning in low-connectivity settings. The system performs all inference locally using quantized language models and incorporates hardware-aware model selection to enable deployment on low-specification, CPU-only devices. By removing dependence on cloud infrastructure, the system provides curriculum-aligned explanations and structured academic support through natural-language interaction. To support learners at different educational stages, the system includes adaptive response levels that generate explanations at varying levels of complexity: Simple English, Lower Secondary, Upper Secondary, and Technical. The system was evaluated with 120 students and 9 instructors from secondary and tertiary institutions under limited-connect