Community resilience in the face of natural hazards relies on a community's potential to bounce back. A failure to integrate equity into resilience considerations results in unequal recovery and disproportionate impacts on vulnerable populations, which has long been a concern in the United States. This research investigated aspects of equity related to community resilience in the aftermath of Winter Storm Uri in Texas which led to extended power outages for more than 4 million households. County level outage and recovery data was analyzed to explore potential significant links between various county attributes and their share of the outages during the recovery and restoration phases. Next, satellite imagery was used to examine data at a much higher geographical resolution focusing on census tracts in the city of Houston. The goal was to use computer vision to extract the extent of outages within census tracts and investigate their linkages to census tracts attributes. Results from various statistical procedures revealed statistically significant negative associations between counties' percentage of non-Hispanic whites and median household income with the ratio of outages. Additionally, at census tract level, variables including percentages of linguistically isolated population and public transport users exhibited positive associations with the group of census tracts that were affected by the outage as detected by computer vision analysis. Informed by these results, engineering solutions such as the applicability of grid modernization technologies, together with distributed and renewable energy resources, when controlled for the region's topographical characteristics, are proposed to enhance equitable power grid resiliency in the face of natural hazards.

More than 100 companies from Partner Country Portugal have already registered for HANNOVER MESSE 2022. Thanks to its excellent economic relations with Germany and its position as an attractive location for German business and investment, Portugal is Partner Country of HANNOVER MESSE 2022. Under the motto «Portugal Makes Sense», companies from Portugal will display products and solutions for digital transformation, energy transition and reliable supply chains and demonstrate to German companies why it makes sense to minimize risk by sourcing from, outsourcing to, innovating with, and investing in Portugal. The Portuguese contingent features a strong on-site presence, with a central pavilion and three thematic pavilions dedicated to the Engineered Parts & Solutions, Energy Solutions and Digital Ecosystems sectors. Each area features dozens of companies from Portugal with the most advanced technologies and processes.

The metaverse as described to us by science fiction is a world of infinite possibilities. The easiest way to conceptualise it is by looking at Hollywood blockbusters such as Avatar and Ready, Player One. In the movies, the metaverse is a three-dimensional digital universe where players can escape physical reality, engage with each other as an avatar of their creation and experience anything they want, only limited by the human imagination and technology, says Selina Yuan, general manager of international business unit, Alibaba Cloud Intelligence. Apart from being a wondrous twin digital reality of our physical world, the metaverse's true potential lies in its ability to make better use of the digital intelligence we are already gaining and visualising it in a way that uncovers new insights that might have otherwise remain hidden. This could be the key to helping us solve real-world problems and building a greener, more inclusive, and technically advanced world.

Visualized are two-dimensional pressure fluctuations within a larger three-dimensional magnetically confined fusion plasma simulation. With recent advances in machine-learning techniques, these types of partial observations provide new ways to test reduced turbulence models in both theory and experiment. MIT researchers are testing a simplified turbulence theory's ability to model complex plasma phenomena using a novel machine-learning technique. To make fusion energy a viable resource for the world's energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel.

Specialized accelerators have recently garnered attention as a method to reduce the power consumption of neural network inference. A promising category of accelerators utilizes nonvolatile memory arrays to both store weights and perform $\textit{in situ}$ analog computation inside the array. While prior work has explored the design space of analog accelerators to optimize performance and energy efficiency, there is seldom a rigorous evaluation of the accuracy of these accelerators. This work shows how architectural design decisions, particularly in mapping neural network parameters to analog memory cells, influence inference accuracy. When evaluated using ResNet50 on ImageNet, the resilience of the system to analog non-idealities - cell programming errors, analog-to-digital converter resolution, and array parasitic resistances - all improve when analog quantities in the hardware are made proportional to the weights in the network. Moreover, contrary to the assumptions of prior work, nearly equivalent resilience to cell imprecision can be achieved by fully storing weights as analog quantities, rather than spreading weight bits across multiple devices, often referred to as bit slicing. By exploiting proportionality, analog system designers have the freedom to match the precision of the hardware to the needs of the algorithm, rather than attempting to guarantee the same level of precision in the intermediate results as an equivalent digital accelerator. This ultimately results in an analog accelerator that is more accurate, more robust to analog errors, and more energy-efficient.

Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting.

Virtual network embedding (VNE) is an crucial part of network virtualization (NV), which aims to map the virtual networks (VNs) to a shared substrate network (SN). With the emergence of various delay-sensitive applications, how to improve the delay performance of the system has become a hot topic in academic circles. Based on extensive research, we proposed a multi-domain virtual network embedding algorithm based on delay prediction (DP-VNE). Firstly, the candidate physical nodes are selected by estimating the delay of virtual requests, then particle swarm optimization (PSO) algorithm is used to optimize the mapping process, so as to reduce the delay of the system. The simulation results show that compared with the other three advanced algorithms, the proposed algorithm can significantly reduce the system delay while keeping other indicators unaffected.

Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization problems. Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC. Furthermore, its evolved versions, such as deep RL (DRL), can achieve higher convergence speed efficiency and learning accuracy based on the parametric approximation for the large-scale state-action space. This paper provides a comprehensive research review on RL-enabled MEC and offers insight for development in this area. More importantly, associated with free mobility, dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they can be solved by RL solutions in diverse mobile applications. Finally, the open challenges are discussed to provide helpful guidance for future research in RL training and learning MEC.

Houston, Texas – August 10, 2020 – American Infrastructure Group, Inc. ("AIGI") ("Company") a proprietary technology company utilizing deployment of artificial intelligence, machine learning and predictive analytics for deployment into critical and civil infrastructure announces today the launch of InfraSix technology platform. Senior Advisor, Peter Quinn was excited to say, "The launch of this platform we believe is going to be distributive to the infrastructure sector as it fully embraces technology. The technology was part of our previously announced acquisition of InfraSix LLC, our first of several planned deals that are currently in the pipeline as we advance to become a total infrastructure solution with integrated technology, services and supply chain." InfraSix LLC was the first in a series of acquisitions that AIGI intends to make to further integrate our portfolio to give the Company a competitive advantage and improve the value proposition to the industry. Management is pleased to announce that the next acquisition is nearly under agreement as well which will bring a large operating infrastructure services business with a specific focus on underground utilities and water.

Fortunately, between dialogue-heavy scenes, the plot falls away far enough to make more room for what "Stay Human" is actually good at: creating a massive urban jungle gym for players to swing, climb, paraglide and sprint across. The game's movement controls are simple enough to learn quickly, allowing for creative, nearly instinctive decision-making about ad hoc paths to take across the heights of skyscraper roofs, the shutters of houses and the power lines crisscrossing between buildings. Just as importantly, the design of the city itself is beautifully constructed, absent of much of the repeatable, modular design used to bulk out many open-world games and filled instead with bespoke spaces that each provide their own self-enclosed acrobatic challenges both during and between missions. For instance, a series of windmills that supply power to city districts are spread across Villedor, each one functioning as a test of the player's reflexes and ability to spot ways to climb ever higher. The layout of each new neighborhood provides opportunities to flex Aiden's expanding moveset, too, as the opening hours' two- or three-story apartment buildings and shops eventually transition to towering office and condo buildings best traversed with death-defying leaps or grappling hook-aided swings.