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Scientists at the University of Massachusetts Amherst have developed a device that uses a natural protein to create electricity from moisture in the air, a new technology they say could have significant implications for the future of renewable energy, climate change and in the future of medicine. By nano-magazine.com.

“We are literally making electricity out of thin air,” says Yao. “The Air-gen generates clean energy ²⁴⁄₇.” Lovely, who has advanced sustainable biology-based electronic materials over three decades, adds, “It’s the most amazing and exciting application of protein nanowires yet.”

The new technology developed in Yao’s lab is non-polluting, renewable and low-cost. It can generate power even in areas with extremely low humidity such as the Sahara Desert.

The Air-gen device requires only a thin film of protein nanowires less than 10 microns thick, the researchers explain. Fascinating read!

An article about using repmgr for Postgres replication failover. repmgr is an open-source toolset from 2ndQuadrant, a leading specialist in PostgreSQL-related technologies and services. The product is used to automate, enhance, and manage PostgreSQL streaming replication. By Sadequl Hussain.

In this two-part series, author will see how repmgr and its daemon repmgrd can automate the high-availability and failover of a three-node PostgreSQL 12 cluster.

The article is split in parts:

• A Walk-through of the environment
• Step 1: Installing PostgreSQL 12 in all nodes
• Step 2: Configuring PostgreSQL in witness and primary node
• Step 3: Installing repmgr in primary and standby nodes
• Step 4: Creating repmgr database and user in the primary node
• Step 5: Adding primary node PostgreSQL config parameter

… and more. You will get a very quick introduction to repmgr which provides an easy-to-use interface over the technology that is already built into PostgreSQL for setting up replication. Good read!

Niko Kosonen published this step by step tutorial how to run Kubernetes on two micro instances on Google Kubernetes Engine (GKE) without external load balancers. Cluster setup from scratch. The code is available here.

My excitement of running Kubernetes on Google Cloud Platform was quickly curbed by the realization that, despite Google’s virtual machines starting at affordable price points, their network ingress is another story.

Author set out to create his GKE cluster – with the requirement of it being as cheap as possible while enjoying a key benefit of the cloud: being free of manual maintenance.

It was composed as a step-by-step tutorial. Based on author’s own experience in setting up a cluster on a fresh GCP account, the tutorial covers every topic from configuring the infrastructure to serving HTTP(S) requests from inside the cluster.

Terraform scripts also included. Super useful!

AWS serverless expert explains what you need to know before launching a serverless pilot. By Joe McKendrick.

The best way to start on a serverless journey is with a serious pilot project that delivers real business benefits and can scale quickly as it shows results. That’s the word from Matt Brayley-Berger, global business development manager for Serverless Compute at Amazon Web Services.

“Digital businesses must innovate as rapidly as possible,” he says. “You need your teams focusing on building applications, and not managing the underlying infrastructure behind them.” Serverless computing is made possible by microservices architectures hosted and managed through the cloud.

The author outlines the best path to take when launching a serverless computing pilot project:

• Set goals
• Recruit team members committed to serverless
• Secure executive buy-in
• Ensure the scope of the pilot is broad enough, but no too broad
• Establish workable metrics
• Include documentation

It’s important for the pilot project to “make sense for the organization and team members, in order to make sure this scales moving forward. Insightful!

While you might have used REST as your service communications layer in the past, more and more projects are moving to an event-driven architecture. Let’s dive into the pros and cons of this popular architecture, some of the key design choices it entails, and common anti-patterns. By Jason Skowronski.

The article delivers on:

• What is event-driven microservice architecture?
• Why use event-driven architecture
• When to use REST
• Your most important design choice – messaging framework

Your basic choice comes down to message processing or stream processing.

Of course, event-driven architectures have drawbacks as well. They are easy to over-engineer by separating concerns that might be simpler when closely coupled; can require a significant upfront investment; and often result in additional complexity in infrastructure, service contracts or schemas, polyglot build systems, and dependency graphs. Good read!

New research has exploded the space of problems that quantum computers can efficiently verify, simultaneously knocking down milestone problems in quantum physics and math. By Mordechai Rorvig.

If today’s computers had dreams and ambitions, there would be some problems that they wouldn’t even dream about solving. Some problems would take far too much time or memory, running on for nearly forever. But in an exciting new result, a team of computer scientists have shown how in theory quantum computers should be able to rapidly verify that a practically infinite problem was solved.

The authors show that quantum computers can rapidly verify the solution to what is known as the halting problem. What allows this remarkable feat is quantum entanglement: each quantum computer contains subatomic particles that are entangled with particles in the other computer.

Entanglement offers a profound communication resource for the two quantum computers. In separate work from 2019, two of the new study’s coauthors, Anand Natarajan and John Wright, showed that entanglement allowed rapid verification of the solution of one class of enormously long problems, called NEEXP problems. To learn more follow the link for truly fascinating reading!

Constantine Yurevich is author of this article in which he argues why you should create a custom attribution tool. With out-of-the-box tools, you’re limited by their functionality, data transformations, models, and heuristics.

With raw data, you can build any attribution model that fits your business and domain area best. While few small companies collect raw data, most large businesses bring it into a data warehouse and visualize it using BI tools such as Google Data Studio, Tableau, Microsoft Power BI, Looker, etc.

• The challenge of attribution
• What machine learning changes
• Four steps to build an attribution model with Google BigQuery ML
  • Step 1: Feature mining
  • Step 2: Training a model
  • Step 3: Model evaluation
  • Step 4: Building an attribution model
• Pros and cons of behavior-based attribution

Quite in depth information about attribution models in general and good steering for anybody interesting in creating a custom attribution tool. Well worth your time!

By adopting agile ways of working, startups as well as established companies have successfully brought customer-centric digital initiatives to market. By Lars Marquardt, Jan-Philipp Martini, Georg Kappen, and Carsten Schaetzberger.

Digital leaders are now being asked to repeat this success with more complex, business-centric, transformative initiatives.

The dilemma is this: Digital teams can use agile ways of working to build customer-centric solutions because they are vertically adjacent to the core processes. Transformative initiatives, however, run horizontally across the value chain and must integrate with the core. Digital teams, therefore, are less free to use agile ways of working with such initiatives because they cannot ignore existing corporate processes and systems.

To meet senior leaders’ high expectations and achieve horizontal digitalization, leaders need to combine the agile ways of working used by startups with traditional, more rigid, corporate ways of working. Excellent read!

Brinnae Bent and Jessilyn Dunn form Duke University wrote an article about wearable technology. Wearable technology has the potential to transform healthcare and research through accessible, continuous, and longitudinal health monitoring.

The accuracy of wearable technologies has been a hotly debated topic both in research and in popular culture. As wearable technologies are increasingly being used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact healthcare decision-making.

Further, the type of physical activity going on during a measurement also plays an important role in the accuracy of the heart rate measurement: we saw an approximate 30% drop in accuracy when a person is walking versus when they are seated at rest

In a study, the authors tested optical heart rate sensors on six of the most popular devices on the market for both consumers (Apple Watch, Fitbit, Garmin, XiaoMi Miband) and for research (Empatica E4, Biovotion Everion) against the clinical reference standard for monitoring heart rate, the electrocardiogram (ECG).

Click the link to the full article to learn more. You will leanr how accurate data actuall wearable devices can provide. Nice one!

Jeff Hawkins has a principle that intuitively makes a lot of sense, yet is something that Deep Learning research has not emphasized enough. This is the notion of embodied learning. That is, biological systems learn from interacting with the environment. By Carlos E. Perez.

The classic Deep Learning training procedure is one of the crudest teaching methods that one can possibly imagine. It is based on repetitively and randomly presenting facts about the world and hoping that the student (i.e. the neural network) is able to disentangle and create sufficient abstractions of the world.

Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment.

Read the article in full to get better idea about Artificial Intelligence, challenges with Deep learning. The links to further reading and white papers are also provided. Nice one!