When self-adaptive systems encounter changes within their surrounding environments, they enact tactics to perform necessary adaptations. For example, a self-adaptive cloud-based system may have a tactic that initiates additional computing resources when response time thresholds are surpassed, or there may be a tactic to activate a specific security measure when an intrusion is detected. In real-world environments, these tactics frequently experience tactic volatility which is variable behavior during the execution of the tactic. Unfortunately, current self-adaptive approaches do not account for tactic volatility in their decision-making processes, and merely assume that tactics do not experience volatility. This limitation creates uncertainty in the decision-making process and may adversely impact the system's ability to effectively and efficiently adapt. Additionally, many processes do not properly account for volatility that may effect the system's Service Level Agreement (SLA). This can limit the system's ability to act proactively, especially when utilizing tactics that contain latency. To address the challenge of sufficiently accounting for tactic volatility, we propose a Tactic Volatility Aware (TVA) solution. Using Multiple Regression Analysis (MRA), TVA enables self-adaptive systems to accurately estimate the cost and time required to execute tactics. TVA also utilizes Autoregressive Integrated Moving Average (ARIMA) for time series forecasting, allowing the system to proactively maintain specifications.

Metal–air batteries, consisting of a metal anode and an air cathode, have been attracted significant interest by the research community as energy storage devices, because of their high energy density (in particular, compared to lithium ion batteries -LIBs-). A wide diversity of active metals can be used as anode material such as Li, Ca, Mg, Al, Fe, and Zn. However, they have so far found their use only in very particular markets requiring high energy density such as hearing aids. Indeed, despite very significant experimental research efforts, recharging them electrochemically constitute a significant challenge, that if unlocked, will pave the way to a wider diversity of ZAB applications such as Electric Vehicles. Reversing this process to recharge electrochemically a ZAB would imply a heterogeneous deposition of Zn in the anode and the formation of dendrites that can short-circuit the cell, similarly to what can happen in lithium metal batteries.

With 2019 emerging as the warmest on record for the world's oceans, the call to climate action continues as the theme for the 50-year anniversary of Earth Day 2020, described as the world's largest environmental movement to drive transformative change for people and planet. Alongside the pandemic, the climate crisis presents an opportunity to use data and AI in ways never before considered. IBM itself began focusing on environmental sustainability before the first Earth Day was ever celebrated -- but its track record on greening its supply chain and driving innovative uses of tech has put it among the world's top eco-friendly Fortune 500 companies. Where IBM leads, customers reap benefits. Digital transformation efforts across industries has given the company a unique vantage point on critical challenges facing the world -- putting AI the work on a number of different issues, from drastically reducing energy consumption to lower C02 to optimizing large scale food production in the wake of climate chaos.

Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-term health monitoring of a large number of engineering structures encompassing buildings, bridges, towers, and offshore platforms. In this context, this paper proposes the application of a relatively new time series representation named "Shapelet transform", which is based on local similarity in the shape of the time series subsequences. In consideration of the individual attributes distinctive to time series signals in earthquake, wind and ocean engineering, the application of this transform yields a new shape-based feature representation. Combining this shape-based representation with a standard machine learning algorithm, a truly "white-box" machine learning model is proposed with understandable features and a transparent algorithm. This model automates event detection without the intervention of domain practitioners, yielding a practical event detection procedure. The efficacy of this proposed shapelet transform-based autonomous detection procedure is demonstrated by examples, to identify known and unknown earthquake events from continuously recorded ground-motion measurements, to detect pulses in the velocity time history of ground motions to distinguish between near-field and far-field ground motions, to identify thunderstorms from continuous wind speed measurements, to detect large-amplitude wind-induced vibrations from the bridge monitoring data, and to identify plunging breaking waves that have a significant impact on offshore structures.

This work presents a Long Short-Term Memory (LSTM) network for forecasting a monthly electricity demand time series with a one-year horizon. The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition. Pattern representation simplifies the complex nonlinear and nonstationary time series, filtering out the trend and equalizing variance. Two types of patterns are defined: x-pattern and y-pattern. The former requires additional forecasting for the coding variables. The latter determines the coding variables from the process history. A hybrid approach based on x-patterns turned out to be more accurate than the standard LSTM approach based on a raw time series. In this combined approach an x-pattern is forecasted using a sequence-to-sequence LSTM network and the coding variables are forecasted using exponential smoothing. A simulation study performed on the monthly electricity demand time series for 35 European countries confirmed the high performance of the proposed model and its competitiveness to classical models such as ARIMA and exponential smoothing as well as the MLP neural network model.

This thesis contributes to the mathematical foundation of domain adaptation as emerging field in machine learning. In contrast to classical statistical learning, the framework of domain adaptation takes into account deviations between probability distributions in the training and application setting. Domain adaptation applies for a wider range of applications as future samples often follow a distribution that differs from the ones of the training samples. A decisive point is the generality of the assumptions about the similarity of the distributions. Therefore, in this thesis we study domain adaptation problems under as weak similarity assumptions as can be modelled by finitely many moments.

As AI usage grows, its energy consumption and carbon emissions are becoming an environmental concern. Here's why -- and how we can find solutions. When Mohammad Haft-Javaherian, a student at the Massachusetts Institute of Technology, attended MIT's Green AI Hackathon in January, it was out of curiosity to learn about the capabilities of a new supercomputer cluster being showcased at the event. But what he had planned as a one-hour exploration of a cool new server drew him into a three-day competition to create energy-efficient artificial-intelligence programs. The experience resulted in a revelation for Haft-Javaherian, who researches the use of AI in healthcare: "The clusters I use every day to build models with the goal of improving healthcare have carbon footprints," Haft-Javaherian says.

While most consumers may find Internet of Things (IoT) devices like Google's Nest or Ring's doorbells new and exciting technology, the manufacturing world has embraced the IoT to optimize discrete and process manufacturing operations for decades. The industrial IoT (IIoT), which started as remote sensing of things like temperature and pressure, has today matured into a way of linking operational systems that control production with the wider world of applications outside of the control room like ERP platforms and supply chain management systems. "The major benefits of the industrial IoT is to bring more visibility to existing processes," said report author Jaques Durand, director of Standards and Engineering at Fujitsu North America and a member of the Industrial Internet Consortium Steering Committee. People want to understand what's going on." Getting to an advanced state of IIoT usage can be difficult without understanding the mistakes to avoid along the way. That's why the Industrial Internet Consortium (IIC), has spent the last six years developing and deploying testbeds for manufacturers to use when evaluating different IIoT technologies, platforms, designs, products, architectures, and use cases. Based on the results of these testbed proofs-of-concept (POC), today the IIC released a white paper, A Compilation of Testbed Results: Toward Best Practices for Developing and Deploying IIoT Solutions, detailing the best practices companies should adopt to ensure successful IIoT deployments. "The IoT problem that each company is facing or each organization is facing is different," Durand said. "Even if they use the same technologies, which is not granted, they are facing very different conditions and priorities in real-world conditions.

Simulation of fluid flow in porous media has many applications, from the micro-scale (cell membranes, filters, rocks) to macro-scale (groundwater, hydrocarbon reservoirs, and geothermal) and beyond. Direct simulation of flow in porous media requires significant computational resources to solve within reasonable timeframes. An integrated method combining predictions of fluid flow (fast, limited accuracy) with direct flow simulation (slow, high accuracy) is outlined. In the tortuous flow paths of porous media, Deep Learning techniques based on Convolutional Neural Networks (CNNs) are shown to give an accurate estimate of the steady state velocity fields (in all axes), and by extension, the macro-scale permeability. This estimate can be used as-is, or as initial conditions in direct simulation to reach a fully accurate result in a fraction of the compute time. A Gated U-Net Convolutional Neural Network is trained on a datasets of 2D and 3D porous media generated by correlated fields, with their steady state velocity fields calculated from direct LBM simulation. Sensitivity analysis indicates that network accuracy is dependent on (1) the tortuosity of the domain, (2) the size of convolution filters, (3) the use of distance maps as input, (4) the use of mass conservation loss functions. Permeability estimation from these predicted fields reaches over 90\% accuracy for 80\% of cases. It is further shown that these velocity fields are error prone when used for solute transport simulation. Using the predicted velocity fields as initial conditions is shown to accelerate direct flow simulation to physically true steady state conditions an order of magnitude less compute time. Using Deep Learning predictions (or potentially any other approximation method) to accelerate flow simulation to steady state in complex pore structures shows promise as a technique push the boundaries fluid flow modelling.

Microsoft Real Estate and Security (RE&S) is responsible for heating and cooling 115 buildings in the Puget Sound area. Microsoft Core Services and Engineering (CSEO) partnered with RE&S to improve the effectiveness of the schedules for their heating, ventilation, and air conditioning (HVAC) system to reduce costs and increase employee comfort. CSEO implemented machine learning to predict when employees will arrive into Microsoft buildings each morning and how long it will take for a building to reach its optimal comfort temperature. As a result, we were able to generate a dynamic HVAC schedule that resulted in significant cost savings and increased employee comfort for RE&S. We're continuing to implement machine learning in our buildings throughout the Puget Sound region and we're encouraging the rest of Microsoft to use machine learning to optimize operations and drive digital transformation.