Results for “medical-devices”

Captures a read-only health snapshot from a Jetson device, reporting identity, memory, GPU, thermal, power, storage, services, and top processes.

Guides monitoring and observability setup for Qdrant vector search deployments, including Prometheus scraping, health checks, and metric-based debugging of production issues.

Bootstraps a clinical ASR evaluation environment by verifying NVIDIA_API_KEY, installing Python dependencies, and running a smoke test against hosted TTS/ASR services.

CAPA system management for medical device QMS. Covers root cause analysis, corrective action planning, effectiveness verification, and CAPA metrics. Use for CAPA investigations, 5-Why analysis, fishbone diagrams, root cause determination, corrective action tracking, effectiveness verification, or CAPA program optimization.

Analyze product health across acquisition, activation, engagement, retention, quality, and monetization.

Integrates endpoint health signals from CrowdStrike ZTA, Microsoft Intune, and Jamf into conditional access policies to enforce device compliance before granting resource access.

Restricts unauthorized removable media access on endpoints by implementing USB device control policies via Group Policy, Intune, or EDR platforms to prevent data exfiltration and malware introduction.

Addresses HIPAA compliance for mobile health (mHealth) applications, wearable devices, and remote patient monitoring. Covers OCR guidance on mobile device PHI, FDA-regulated mobile medical applications, FTC Health Breach Notification Rule for non-HIPAA apps, BYOD policies, and encryption requirements for ePHI on mobile platforms. Keywords: mHealth, mobile health, HIPAA mobile, wearable, remote monitoring, BYOD, mobile device management, app privacy.

Save the current diagnosis state to disk for cross-session recall, including conclusions, rejected directions, and next steps.

Acquire and analyze mobile device data using Cellebrite UFED and open-source tools to extract communications, location data, and application artifacts.

Develop firmware for microcontrollers, implement RTOS applications, and optimize power consumption for resource-constrained devices.

Restores the most recent diagnosis snapshot saved by dbs-save, allowing users to continue a previous diagnostic session.

Recommends that all diabetes insipidus patients wear an emergency bracelet or necklace to alert clinicians to their condition if incapacitated. Use when establishing care for a diabetes insipidus patient; triggers include diabetes insipidus diagnosis.

CAPA system management for medical device QMS. Covers root cause analysis, corrective action planning, effectiveness verification, and CAPA metrics. Use for CAPA investigations, 5-Why analysis, fishbone diagrams, root cause determination, corrective action tracking, effectiveness verification, or CAPA program optimization.

Filtros de química medicinal. Aplique regras de similaridade a fármacos (Lipinski, Veber), filtros PAINS, alertas estruturais, métricas de complexidade, para priorização de compostos e filtragem de bibliotecas.

Discovers connected MATLAB-supported hardware devices, searches by capability, and checks required support packages and add-ons. Use when the user asks what hardware is connected, finds a device that supports a capability (e.g., CAN, analog input), or checks if required support packages or add-ons are installed.

ADC with Mbed OS. analog input.

Design, select, and analyze waveforms for radar, sonar, and active sensing using the Phased Array System Toolbox. Covers LFM, NLFM, FMCW, phase-coded, CW, stepped FM, custom IQ, ambiguity functions, sidelobe reduction, and Doppler tolerance. Key objects: phased.LinearFMWaveform, phased.NonlinearFMWaveform, phased.CustomFMWaveform, phased.PhaseCodedWaveform, phased.FMCWWaveform, phased.SteppedFMWaveform, phased.MFSKWaveform, phased.RectangularWaveform, nlfmspec2freq, shapespectrum, ambgfun, pambgfun, sidelobelevel, legendreseq, mlseq, radarWaveformGenerator.

Applies medicinal-chemistry filters with the medchem library — drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, and molecular complexity metrics for compound prioritization and library cleanup. Use when filtering or triaging a compound library, flagging PAINS or reactive groups, or assessing drug-likeness of candidate molecules. Part of the AlterLab Academic Skills suite.

Utilize Shodan, the search engine for Internet-connected devices, to discover exposed assets, vulnerable ports, default credentials, and specific infrastructure configurations using advanced search queries (dorks).

Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.