A sewer robot that monitors pipework and raises blockage alerts before flooding occurs is set for its first mission. Pipebot Patrol is a 1.8m project led by Northumbrian Water and funded by the Ofwat Water Breakthrough Challenge. The robot can inspect miles of pipes over a 30-day period and automatically report back issues from underground. A spokesman for the water company said the robot would be a "game-changer" and would help cut down the number of emergency repairs. Northumbria Water said 10 organisations had played a part in the robot's development, including councils in Sunderland, Gateshead and Newcastle.

Infrastructure systems play a critical role in providing essential products and services for the functioning of modern society; however, they are vulnerable to disasters and their service disruptions can cause severe societal impacts. To protect infrastructure from disasters and reduce potential impacts, great achievements have been made in modeling interdependent infrastructure systems in past decades. In recent years, scholars have gradually shifted their research focus to understanding and modeling societal impacts of disruptions considering the fact that infrastructure systems are critical because of their role in societal functioning, especially under situations of modern societies. Exploring how infrastructure disruptions impair society to enhance resilient city has become a key field of study. By comprehensively reviewing relevant studies, this paper demonstrated the definition and types of societal impact of infrastructure disruptions, and summarized the modeling approaches into four types: extended infrastructure modeling approaches, empirical approaches, agent-based approaches, and big data-driven approaches. For each approach, this paper organized relevant literature in terms of modeling ideas, advantages, and disadvantages. Furthermore, the four approaches were compared according to several criteria, including the input data, types of societal impact, and application scope. Finally, this paper illustrated the challenges and future research directions in the field.

A multi-period planning framework is proposed that exploits multi-step ahead traffic predictions to address service overprovisioning and improve adaptability to traffic changes, while ensuring the necessary quality-of-service (QoS) levels. An encoder-decoder deep learning model is initially leveraged for multi-step ahead prediction by analyzing real-traffic traces. This information is then exploited by multi-period planning heuristics to efficiently utilize available network resources while minimizing undesired service disruptions (caused due to lightpath re-allocations), with these heuristics outperforming a single-step ahead prediction approach. Network capacity demand is rapidly increasing, due to the emergence of new services and applications. To cope with this growing demand, the use of machine learning (ML) techniques for traffic-driven service provisioning has emerged as a promising solution to effectively model real-world traffic traces [1] and deal with overprovisioning that is present in staticallyprovisioned elastic optical networks (EONs) [2].

Your teams have spent years building your organization's networked systems and connected applications. With so many employees working remotely, your business depends on reliable IT applications and services to keep their operations going, employees productive, and customers happy. A single major incident can jeopardize business operations when we depend on our technology more than ever. But with IT analytics, organizations can recognize possible threats before they have a chance to cause major service disruptions. Using Machine Learning (ML), an IT analytics solution can identify signals that act as warning signs for an impending major incident.

Moving people and cargo around the globe, safely and on time, is a logistical challenge that draws on vast amounts of data. This data is a powerful but under-leveraged resource that can be put to greater use with artificial intelligence (AI). Think more efficient fleets, better route and capacity planning, and smoother passenger bookings and deliveries when faced with potential service disruptions. You may have heard the terms analytics, advanced analytics, machine learning and AI. AI is often built from machine-learning algorithms, which owe their effectiveness to training data. The more high-quality data available for training, the smarter the machine will be.

Change management as we know it is outdated and ineffective, with nearly 70 percent of all change projects failing to achieve their goals. That's why today's IT teams are no longer just solving change management issues – they're predicting them. The most common IT service management (ITSM) issue during a large change is an application outage, when a system or platform shuts down and is no longer operational. Something as simple as email migration can wreak havoc on IT teams and stakeholders without proper change management protocol. When large technology changes like email migrations are not properly planned, servers can overload and ill-equipped service desks are unable to handle the influx of requests.

Bottom line, a typo crashed the AWS powered Internet! AWS outages already have a long history and the more AWS customers running their web infrastructure on the cloud giant, the more issues end customers will experience in the future. According to SimilarTech only Amazon S3 is already used by 152,123 websites and 124,577 unique domains. However, following the philosophy of "Everything fails all the time (Werner Vogels, CTO Amazon.com)"

Wireless carrier competition in the U.S. is white hot -- analysts increasingly see signs of wireless market saturation, meaning that growth is most likely going to come from competitors. Of the four major U.S. carriers, T-Mobile USA has led the way, slashing prices, killing the two-year contract and daring its competitors to follow suit. As competition intensifies, Finnish mobile technology provider Nokia believes customer service will emerge as an even more important key differentiator, and analytics and machine learning will take customer service to the next level in the U.S. and around the globe. In 2011, when AT&T announced its intention to acquire T-Mobile USA from Deutsche Telekom, it looked like the wireless market in the U.S. was well on its way to becoming a duopoly. Verizon Wireless and AT&T already held the lion's share of customers between them, and they both held licenses to the majority of wireless spectrum too.