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Java/GWT client-server on MySQL. Evaluation measured throughput and latency, NOT model accuracy: the pipeline sustained ~500 samples/s against tri-axial vibration sensors delivering 72.9 kSPS, so the thesis concluded data reduction, filtering or batch scheduling is required for real-time use.", "thesis_limits": "PSO was SURVEYED ONLY and NOT used in the implementation -- never claim it as an applied method. No real operational data: training data came from a cited thesis, test data was modified plus a random generator. No model-accuracy figures exist. Back-propagation retraining was listed as future work, not implemented. Frame as a methods prototype/framework, never as a validated production predictive-maintenance system.", "transcripts_language": "English-language originals available for B.Eng. and M.Eng." }, { "id": "EDU-BENG", "degree": "B.Eng. 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Data types worked on (user-confirmed 2026-08-02): semiconductor fab sensor and process data -- defect management records, wafer inspection images, and electrical parameters from Process Control Monitoring (PCM). 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Practitioner use only — do not claim administration, rollout or ownership. Satisfies 'or similar metadata/catalogue platform' phrasing; is NOT a substitute claim for Purview, Collibra or Alation, which remain unevidenced."}, {"name": "Kubernetes", "evidence": "production-current-and-historical", "output": "allowed"}, {"name": "Docker", "evidence": "production-current-and-historical", "output": "allowed"}, {"name": "Java", "evidence": "production-historical", "output": "allowed-with-context"}, {"name": "C#", "evidence": "production-historical", "output": "allowed-with-context"}, {"name": "C++", "evidence": "production-historical-limited", "output": "allowed-with-context"}, {"name": "JavaScript", "evidence": "production-historical-limited", "output": "allowed-with-context"}, {"name": "LiteLLM", "evidence": "hands-on-current", "output": "allowed-with-context"}, {"name": "custom GPTs", "evidence": "hands-on-current", "output": "allowed-with-context"}, {"name": "Kiro", "evidence": "hands-on-current", "output": "allowed-with-context"}, {"name": "Copilot", "evidence": "hands-on-current", "output": "allowed-with-context"}, {"name": "TensorFlow/Keras", "evidence": "certification", "output": "certification-context-only"}, {"name": "PyTorch", "evidence": "coursework-or-personal-unverified", "output": "certification-context-only"}, {"name": "TypeScript", "evidence": "unverified", "output": "forbidden"}, {"name": "FastAPI", "evidence": "unverified", "output": "forbidden"}, {"name": "Flask", "evidence": "unverified", "output": "forbidden"}, {"name": "Django", "evidence": "unverified", "output": "forbidden"}, {"name": "LangChain", "evidence": "never-used", "output": "forbidden"}, {"name": "LangGraph", "evidence": "never-used", "output": "forbidden"}, {"name": "LlamaIndex", "evidence": "never-used", "output": "forbidden"}, {"name": "formal model evaluation", "evidence": "unverified", "output": "forbidden"}, {"name": "LLM fine-tuning", "evidence": "unverified", "output": "forbidden"}, {"name": "Azure", "evidence": "unverified", "output": "forbidden"}, {"name": "GCP", "evidence": "unverified", "output": "forbidden"}, {"name": "Terraform", "evidence": "unverified", "output": "forbidden"} ], "global_forbidden_output_patterns": [ "petabyte scale", "petabyte-scale", "own the AWS data platform", "LangChain-based", "customer-embedded delivery", "formal model evaluation", "3 consecutive years" ] }