# QC Published articles for QC. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## From Raw Sensor Signals to Regulatory-Grade Data: How Air Quality Monitoring Networks Are Engineered DevFeed: [From Raw Sensor Signals to Regulatory-Grade Data: How Air Quality Monitoring Networks Are Engineered]() Original publisher: [Read original article]() Author: Kevin John Published: 2026-11-09T09:52:38Z Content type: article Language: en Sources: [HackerNoon]() Topics: [Data Quality](), [Data pipelines]() Tags: [air-quality](), [air-quality-data-apis](), [air-quality-monitoring](), [air-sensor-co-location](), [alerts](), [api](), [cents-17660](), [cities](), [data-pipelines](), [kunak-air-cloud](), [kunak-air-pro](), [networks](), [pm2-5-monitoring](), [qa](), [qc](), [regulatory](), [sensor](), [sensor-drift-correction](), [standards]() ### AI overview Raw air-sensor readings are affected by drift, cross-sensitivity, temperature and humidity, so reliable monitoring depends on managing the whole system. The article describes factory calibration, co-location with certified reference analysers to validate performance in real conditions, ongoing baseline and span adjustments, and automated QA/QC that flags or corrects suspect data. Reference stations provide an accuracy anchor, while denser, lower-cost networks add local coverage and faster alerts. A typical pipeline buffers readings during outages, centrally compensates and validates them, supports alerts, and integrates data through APIs or protocols such as Modbus. Performance should be assessed against the intended use and relevant evidence, including CEN/TS 17660, MCERTS, EPA targets, and independent evaluations. Deployment planning should specify pollutants and averaging periods, calibration upkeep, traceability, data integration, siting, and operational ownership. The excerpt gives Kunak AIR Pro and AIR Cloud as product examples; it presents sensor networks as complementary to reference stations. ### Source excerpt Learn how air quality networks use sensor calibration, co-location, QA/QC, and data pipelines to deliver reliable measurements under recognised standards. ## Recurrent Bipartite Graph Neural Networks for QC-MDPC Error Decoding in McEliece Cryptosystems DevFeed: [Recurrent Bipartite Graph Neural Networks for QC-MDPC Error Decoding in McEliece Cryptosystems]() Original publisher: [Read original article]() Author: Parth Rajeshkumar Tandel, Elisabeth Fink Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security](), [arXiv: Machine Learning]() Topics: [Post-quantum cryptography](), [Cryptography](), [Neural Network](), [Decoding](), [graph theory]() Tags: [algorithms](), [cryptography](), [cs-cr](), [cs-lg](), [cycles](), [decoder](), [decoding](), [encryption](), [error](), [failure](), [graph](), [graph-neural-networks](), [key](), [leak](), [network](), [neural](), [noise](), [post-quantum](), [post-quantum-cryptography](), [post-quantum-cryptography-pqc](), [qc](), [recurrent]() ### AI overview The paper proposes a recurrent bipartite graph neural network (R-BGNN) for decoding QC-MDPC codes in McEliece cryptosystems. It incorporates Gated Recurrent Units into message passing and uses multi-topology curriculum training to learn to break limit-cycle oscillations that can stall decoding. On 20 unseen matrix topologies, the abstract reports that it outperforms Bit-Flipping and Min-Sum baselines, with a 63.7% lower decoding failure rate at moderate noise (t=10) and convergence in fewer iterations. The supplied abstract describes this as a proof of concept. ### Source excerpt arXiv:2610.38479v1 Announce Type: cross Abstract: As the advent of large-scale quantum computing threatens classical encryption, Quasi-Cyclic Moderate Density Parity-Check (QC-MDPC) codes have emerged as a foundational element of Post-Quantum Cryptography (PQC). However, decoding these relatively dense codes using classical Belief Propagation algorithms often leads to failures caused by short graph cycles that stall the decoder. In cryptographic applications, these failures are severe security vulnerabilities that leak secret key information. To address this, this paper proposes a Recurrent Bipartite Graph Neural Network (R-BGNN) as proof of concept. By integrating Gated Recurrent Units (GRUs) into the message-passing framework and employing a multi-topology curriculum training strategy, the R-BGNN actively learns to break limit-cycle oscillations. Evaluated across 20 unseen matrix topologies, the proposed architecture significantly outperforms classical Bit-Flipping and Min-Sum baselines, reducing the Decoding Failure Rate (DFR) by 63.7\% at moderate noise levels ($t=10$) while converging in fewer iterations. ## Dogfooding vs. QA vs. User Research DevFeed: [Dogfooding vs. QA vs. User Research]() Original publisher: [Read original article]() Author: Therese Fessenden Published: 2026-08-07T17:00:00Z Content type: article Language: en Sources: [NN/g latest articles and announcements]() Topics: [dogfooding](), [FIRST]() Tags: [article](), [bugs](), [development](), [dogfooding](), [qa](), [qc](), [quality-assurance](), [quality-control](), [user-research](), [user-testing]() ### AI overview Dogfooding--using a company's own products internally--can reveal bugs, broken flows, crashes, and missing features while building empathy and shared ownership. The article explains that it complements but cannot replace user research, because insiders understand the product too well to represent real users. It also distinguishes genuine dogfooding from performative displays of loyalty. ### Source excerpt Dogfooding, or using your own products internally, helps catch bugs, but it can't replace user research: your team knows too much to represent real users.