21 years ago NEC introduced a laptop powered by aN internal fuel cell. The fuel cell technology behind the PC was first shown at Nano Tech 2003 in February. NEC’s …

The post The Laptop Powered By A Fuel Cell appeared first on Electronics Weekly .

Just received the BeagleBoard Foundation Newsletter – the August “Easy AI Edition” – and a case study on building automation caught my eye.

The post Case Study: BeagleBone Green-based building automation appeared first on Electronics Weekly .

Distributor Rutronik has added three surface-mount RTK-enhanced GNSS modules from Unicore to its portfolio. RTK – ‘real time kinematics’ – is a service that increases the accuracy of satellite navigation …

The post All-constellation RTK GNSS modules appeared first on Electronics Weekly .

Anders Storm is the new CEO at Swedish digital audio company Dirac. “We are very excited to have Anders join Dirac at this critical moment in the company’s 20-year history,” …

The post Anders Storm joins Dirac as CEO appeared first on Electronics Weekly .

AWS Config conformance packs and organization-level management capabilities for conformance packs and individual AWS Config rules are now available in additional AWS Regions. Conformance packs allow you to bundle AWS Config rules and their associated remediation actions into a single package, simplifying deployment at scale. You can deploy and manage these conformance packs throughout your AWS environment. With this launch, AWS Config conformance packs are now available in 10 additional Regions, and organization-level management capabilities for conformance packs and individual AWS Config rules are now available in 12 additional Regions.

Conformance packs provide a general-purpose compliance framework designed to enable you to create security, operational, or cost-optimization governance checks using managed or custom AWS Config rules and AWS Config remediation actions. This allows you to monitor compliance based on your own groupings and also apply remediation automatically. With this launch, you can also manage the AWS Config conformance packs and individual AWS Config rules at the organization level which simplifies the compliance management across your AWS Organization.

To get started, you can either use the provided sample conformance pack templates or craft a custom YAML file from scratch based on a custom conformance pack . Conformance pack deployment can be done through the AWS Config console, AWS CLI, or via AWS CloudFormation.

You will be charged per conformance pack evaluation in your AWS account per AWS Region. Visit the AWS Config pricing page for more details. To learn more about AWS Config conformance packs, see our documentation .

Today, we are announcing the availability of AWS Backup support for cross-Region backup of Amazon Neptune backups in Asia Pacific (Hong Kong), Israel (Tel Aviv), and Middle East (Bahrain, UAE). Cross-Region backup enables customers to copy backups from one AWS Region to a different AWS Region, helping increase data resiliency. AWS Backup is a policy-based, fully managed and cost-effective solution that enables you to centralize and automate data protection of Amazon Neptune along with other AWS services (spanning compute, storage, and databases) and third-party applications.

With Cross-Region backup, customers can copy data from a source backup vault to a destination backup vault in another AWS Region, either on-demand or as part of a scheduled backup plan. Cross-Region backup helps customers meet their compliance requirements and disaster recovery needs by storing copies of backup data in a separate Region to their production data. Customers can also recover from backups in the new Region, reducing the risk of downtime and ensuring business continuity requirements are met.

AWS Backup for Amazon Neptune is available in all Regions where Amazon Neptune is available except for Africa (Cape Town) and Asia Pacific (Osaka). With today’s launch, cross-Region backup is available for Amazon Neptune backups in all Regions where AWS Backup supports Amazon Neptune. For more information on regional availability, feature availability, and pricing, see the AWS Backup pricing page and the AWS Backup Feature Availability page .

To learn more about AWS Backup support for Amazon Neptune, visit AWS Backup’s technical documentation . To get started, visit the AWS Backup console .

Today, we are excited to announce a new enhancement in EMR Managed Scaling that improves application resiliency and scales the cluster based on executor and ApplicationMasters demand by adding support for Yarn Node Labels. Amazon EMR by default ensures that the processes that controls running jobs and needs to stay alive for the life of the job (ApplicationMasters) can run on both core and task nodes. However, many customers specially who use Spot Instances to run task nodes choose to run ApplicationMasters only on On-Demand core nodes to ensure running jobs do not fail if application masters running on Spot Instances are interrupted. With today’s launch, EMR Managed Scaling will now scale the clusters based on the demand for the individual AM’s or executors requests as defined by YARN node labels. Intelligently scaling the cluster based on AM’s or executors demand leads to better performance, utilization and lower cost.

As part of today’s launch, with EMR release 7.2 and later, Amazon EMR will also let you specify application level YARN node labels expressions by market type i.e. On-Demand vs Spot. Previously, customers were only able to specify YARN node labels expressions by node type level i.e. Core vs Task. Now, with this new enhancement customers will have the additional flexibility to better suit the needs of your cluster workloads.

This feature is available with Amazon EMR release 7.2 and above in all the AWS Regions where Amazon EMR Managed Scaling is available . Review our Managed Scaling documentation to learn more.

Amazon Personalize is excited to introduce the ability for developers to modify automatic training configurations after a Personalize solution has been created. With this launch, developers gain greater flexibility over the automatic training process for both new and existing solutions. Previously, changing configurations like training frequency required re-creating the solution entirely. Now, you can easily modify automatic training settings of any solution via API or console. When updating a solution’s configuration, you can choose to enable or disable automatic retraining, as well as adjust the training frequency as needed.

Automatic training mitigates model drift and makes sure recommendations align with users’ evolving behaviors and preferences. Modifying the configuration of solution training allows you to adapt model retraining to your evolving business needs and data volumes. For example, you can increase training cadence during peak seasons to further optimize the relevance of recommendations. This saves time and resources by making incremental adjustments as needed, rather than re-creating the entire solution. The automatic training will continue at the new cadence until you make another update or disable automatic training entirely. Existing solutions will remain unchanged unless you modify the training configuration.

Amazon Personalize helps companies elevate the customer experience with AI-powered personalization and deliver hyper- personalized user experiences in real-time with precision and scale to improve user engagement, customer loyalty, and business results. Modifications for automatic solution training is supported in all service regions . To learn more, visit our documentation .
 

AWS WAF now supports setting lower rate limit thresholds for rate-based rules. Customers can now configure rate-based rules with rate limits as low as 10 requests per evaluation window, compared to the previous minimum of 100 requests.

With AWS WAF rate-based rules, customers can count incoming requests and limit traffic that exceeds a defined request rate. Now, in addition to existing threshold options, customers can set rate-based rule thresholds as low as 10 requests per the evaluation time window. This granular control allows customers to more effectively detect and respond to traffic spikes targeting sensitive applications and APIs, enabling quicker mitigation of sudden usage increases or malicious activity.

To use lower rate thresholds, simply set the ‘Rate limit’ to any value between 10 and 100 when configuring rate-based rules. Existing rules will remain unchanged. To customize, edit your rule to select a lower threshold then save. To learn more, see the AWS WAF developer guide . There is no additional cost for using this feature, however standard AWS WAF charges still apply. For details, visit the AWS WAF Pricing page .

This feature is available in all AWS Commercial Regions, except Asia Pacific (Hyderabad), Europe (Spain), Australia (Melbourne), Europe (Zurich), Israel (Tel Aviv), US-GovCloud and China Regions.
 

AWS Amplify now offers two new features for its Functions capability: Scheduled Cron Jobs and Streaming Logs. Cron Jobs allow developers to configure serverless functions to run at specific intervals, while Streaming Logs enable developers to quickly iterate and test function execution by streaming logs directly to their terminal.

The scheduling feature allows developers to use natural language or cron expressions to configure their serverless functions to run automatically at specified intervals or times. This is particularly useful for tasks such as data processing, batch operations, or scheduled maintenance. Additionally, streaming logs provides real-time visibility into function execution logs, enabling developers to monitor and debug their functions more effectively.

To learn more about Scheduling and Streaming Logs, visit the Amplify documentation . Explore the comprehensive guides, code samples, and best practices to get started with these new features.