Littelfuse’s NANO² SMD 708 fuses are small, square, surface mount fuses aimed at compact high-power applications.

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A report produced for the UK’s Space Partnership by the consultants know.space examines the impact of academia on the UK space sector. Space research For context, the report – Role …

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A report on UK digital services by the Science, Innovation and Technology Committee has recommended that the government trigger a break clause in the £330 million NHS contract awarded to …

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Amazon CloudWatch now allows you to query metrics data up to two weeks in the past using the Metrics Insights query source. CloudWatch Metrics Insights offers fast, flexible, SQL-based queries. This new capability allows you to display, aggregate, or slice and dice metrics data older than 3 hours, for enhanced visualization and investigation.

Previously, when creating dashboards and alarms to monitor dynamic groups of metrics over your resources and applications, you could visualize up to 3 hours of data when using Metrics Insights SQL queries. This enhancement helps you identify trends and investigate impact for a longer period of time, even days after an event. This extended query time range helps improve the operational health of teams and ensures impacts are never missed.

Querying metrics data up to two weeks old with Metrics Insights is now available in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions.

The ability to query metrics data up to 2 weeks old is automatically available at no additional cost. Standard pricing applies for alarms, dashboards or API usage on Metrics Insights, see CloudWatch pricing for details. To learn more about metrics queries with Metrics Insights, visit the CloudWatch documentation .

AWS HealthLake now provides native support for healthcare payers to comply with the CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F). This rule requires Medicare Advantage organizations, Medicaid managed care plans, CHIP managed care entities, and Qualified Health Plan (QHP) issuers to implement four standardized FHIR-based APIs by January 1, 2027.

New capabilities

Patient Access API

• CARIN IG for Blue Button® 2.1.0 — enables patients to access their claims, encounter data, and prior authorization information through third-party applications 

• SMART App Launch 2.0.0 — provides secure, standards-based authorization for patient-facing applications

• DaVinci PDex Drug Formulary 2.1.0 — allows patients to query drug coverage information

• API use metrics collection — supports the annual reporting requirement for aggregated, de-identified Patient Access API usage data

Provider Access API 

• CARIN IG for Blue Button® 2.1.0 — enables sharing of patient claims and encounter data with in-network providers

• DaVinci PDex 2.1.0 — supports payer-to-provider clinical data exchange

• DaVinci PDex Drug Formulary 2.1.0 — provides drug formulary information to treating providers

• Consent management integration — supports patient opt-out workflows and provider attribution through integration with AWS services 

Payer-to-Payer API

• CARIN IG for Blue Button® 2.1.0 — facilitates claims and encounter data exchange between payers

• DaVinci PDex 2.1.0 — supports clinical data exchange across payer boundaries

• $bulk-member-match operation — enables payers to identify shared members at scale for data exchange, supporting the requirement to request patient data from previous payers within one week of coverage start 

• $bulk-member-match status tracking — provides asynchronous status polling for large-scale member matching operations

• DaVinci Data Export — supports bulk FHIR data export aligned with the DaVinci implementation guide for efficient payer-to-payer data transfer

• Group Discovery APIs — enables auto-discovery of resource types available for export, streamlining payer-to-payer integration

• Consent management integration — supports patient opt-in workflows required for payer-to-payer data exchange 

Prior Authorization API

• DaVinci Coverage Requirements Discovery (CRD) 2.1.0 — allows providers to query whether prior authorization is required and discover documentation requirements for items and services

• DaVinci Documentation Templates and Rules (DTR) 2.1.0 — supports compilation of necessary documentation to accompany prior authorization requests

• DaVinci Prior Authorization Support (PAS) 2.1.0 — enables end-to-end electronic prior authorization including submission, status tracking, and decision communication via FHIR APIs 

• SMART App Launch 2.0.0 — provides secure authorization for prior authorization workflow applications

• Prior authorization metrics support — enables payers to collect and expose required metrics including approval rates, denial rates, appeal outcomes, and average decision timeframes

 

AWS HealthLake is a HIPAA-eligible service. Customers are responsible for determining their own compliance obligations under CMS-0057-F and should consult with legal and compliance counsel regarding their specific requirements.

AWS HealthLake is available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Europe West (London), Europe (Ireland), and Asia Pacific SouthEast (Sydney) Regions. Visit the AWS Region Table  to see all the regions.

 

To learn more, see the AWS HealthLake product page and AWS HealthLake Developer Guide .

Today, AWS Parallel Computing Service (AWS PCS) launches PCS-ready DLAMI, an AWS-maintained Amazon Machine Image built on the Deep Learning Base GPU AMI (Ubuntu 24.04). It provides a production-quality foundation for AI/ML training and high performance computing (HPC), with core infrastructure components pre-installed and tested for compatibility.

AWS PCS is a managed service that makes it easier for you to run and scale your HPC workloads and build scientific and engineering models on AWS using Slurm. You can use AWS PCS to build complete, elastic environments that integrate compute, storage, networking, and visualization tools. AWS PCS simplifies cluster operations with managed updates and built-in observability features, helping to remove the burden of maintenance. You can work in a familiar environment, focusing on your research and innovation instead of worrying about infrastructure.

The AMI inherits the operating system, NVIDIA GPU drivers, CUDA toolkit, EFA drivers, and Lustre client from the source Deep Learning Base GPU AMI, and adds PCS Agent, Slurm for PCS, and EFS utilities. Multiple supported Slurm versions are included, and the correct version activates automatically based on your cluster configuration. You can add frameworks, libraries, and application software on top to complete your environment. AWS releases updated AMIs regularly when the source DLAMI or PCS components are updated, providing ongoing security patches and driver updates.

AWS PCS-ready DLAMI is available for x86_64 and arm64 architectures at no additional cost in all AWS Regions where AWS PCS is available. To get started, specify a PCS-ready AMI when configuring your compute node groups. For more information, see Using PCS-ready DLAMI in the AWS PCS User Guide. For a reference cluster architecture that builds on PCS-ready DLAMI, see the awsome-distributed-ai repository on GitHub.

Amazon Connect Customer now supports up to 5,000 agents per schedule, making it easier for you to schedule larger business units or multiple business units that share agents (multi-skilled agents) within a single schedule. Additional scale limit updates include up to 350 agents per staffing group and up to 300 staffing groups per forecast group (for a total of up to 5,000 agents per forecast group). This launch eliminates the need to split scheduling across multiple runs or maintain separate schedules for shared agent pools, thus reducing operational complexity and enabling more accurate schedule optimization across the entire workforce.

This feature is available in all AWS Regions where Amazon Connect Customer agent scheduling is available. To learn more about Amazon Connect Customer agent scheduling, click here .

Amazon Connect Customer now provides schedule update notifications, making it easier for you to automatically notify agents when their schedules change. You can define rules to send email or text notifications (via EventBridge) to supervisors and agents when new schedules are published, when existing schedules are updated, as well as when an agent’s leave request has a change in status. For example, you can automatically email all agents when their schedules for next month become available. These automated notifications eliminate manual effort for schedulers to notify workforce when schedules are updated and for agents to continuously monitor their leave request status, improving both scheduler and agent productivity.

This feature is available in all AWS Regions where Amazon Connect Customer agent scheduling is available. To learn more about Amazon Connect Customer agent scheduling, click here .

Amazon Connect Customer now optimizes placement of ad-hoc activities in agent schedules, making it easier for you to schedule non-productive events while automatically minimizing impact to service level goals. For example, automatically find the optimal time within the next 2 weeks to schedule compliance training for 500 agents. When adding activities such as training or meetings, you can choose one of the following placement methods: (a) anytime within the shift; (b) within a specific time window, for example, 12:00pm–4:00pm; (c) relative to the shift, for example, 1 hour after shift start and 2 hours before shift end. Based on your selected placement method, system automatically identifies optimal time within your specified constraints to schedule the activity while minimizing impact to service levels. This launch eliminates the need for supervisors to manually look for optimal times to schedule these events, thus improving supervisor productivity and ensuring consistent service levels for end customers.

This feature is available in all AWS Regions where Amazon Connect Customer agent scheduling is available. To learn more about Amazon Connect Customer agent scheduling, click here .

Amazon SageMaker Studio quick setup now completes in under twenty seconds, reduced from over two minutes. Whether you are building ML pipelines, exploring data, developing with notebooks, or fine-tuning foundation models, you can go from sign-in to a fully configured Studio environment almost instantly.

As part of this streamlined setup, newly created Studio environments now come with serverless model customization permissions automatically configured. A new managed policy, AmazonSageMakerModelCustomizationCoreAccess, is created and attached for you, providing permissions for serverless model customization jobs including fine-tuning with custom reward functions for reinforcement learning, model evaluation, and deployment to SageMaker or Bedrock endpoints. This eliminates the need to manually create and configure IAM roles and policies before you can start experimenting. For existing Studio environments, actionable messages with direct links to documentation guide you through adding these permissions.

This feature is available in all AWS Commercial Regions where Amazon SageMaker Studio is supported. To get started, create a new Studio environment using quick setup in the SageMaker AI Console. To learn more, see Quick setup and Model Customization permissions setup in the Amazon SageMaker documentation.