We have built a novel system for the surveillance of drinking water reservoirs using underwater sensor networks. We implement an innovative AI-based approach to detect, classify and localize underwater events. In this paper, we describe the technology and cognitive AI architecture of the system based on one of the sensor networks, the hydrophone network. We discuss the challenges of installing and using the hydrophone network in a water reservoir where traffic, visitors, and variable water conditions create a complex, varying environment. Our AI solution uses an autoencoder for unsupervised learning of latent encodings for classification and anomaly detection, and time delay estimates for sound localization. Finally, we present the results of experiments carried out in a laboratory pool and the water reservoir and discuss the system's potential.

Google search, Facebook news feed, Amazon product recommendations are obvious examples of digital services used by billions of consumers everyday that successfully leverage Machine Learning (ML)¹. In fact you could say that the stellar growth these companies have experienced over the last decade or more just would not be possible without it. The internet giants have each conquered specific segments of consumers' daily digital lives and are now an ever-present habit for billions of people around the world. Google enables people to discover knowledge and information about products, places and things. Facebook enables people to engage with friends who have similar interests and stories.

Marine research organization ProMare has performed repairs and improvements on the Mayflower Autonomous Ship (MAS400) that was forced to stop its transatlantic voyage due to "a small mechanical problem." The IBM-sponsored autonomous vessel Mayflower started its journey on 15 June 2021 from Turnchapel Wharf, Plymouth, UK. Three days after the initial launching, the unmanned vessel had to interrupt its voyage due to a mechanical issue and sail back to England. Once back in the base, ProMare determined that the issue was caused by a fracture in the flexible metal coupling between the ship's generator and exhaust system. MAS400 uses solar panels to draw as much energy as possible from the sun.

Climate change has become one of the biggest global problems increasingly compromising the Earth's habitability. Recent developments such as the extraordinary heat waves in California & Canada, and the devastating floods in Germany point to the role of climate change in the ever-increasing frequency of extreme weather. Numerical modelling of the weather and climate have seen tremendous improvements in the last five decades, yet stringent limitations remain to be overcome. Spatially and temporally localized forecasting is the need of the hour for effective adaptation measures towards minimizing the loss of life and property. Artificial Intelligence-based methods are demonstrating promising results in improving predictions, but are still limited by the availability of requisite hardware and software required to process the vast deluge of data at a scale of the planet Earth. Quantum computing is an emerging paradigm that has found potential applicability in several fields. In this opinion piece, we argue that new developments in Artificial Intelligence algorithms designed for quantum computers - also known as Quantum Artificial Intelligence (QAI) - may provide the key breakthroughs necessary to furthering the science of climate change. The resultant improvements in weather and climate forecasts are expected to cascade to numerous societal benefits.

Embeddings of high-dimensional data are widely used to explore data, to verify analysis results, and to communicate information. Their explanation, in particular with respect to the input attributes, is often difficult. With linear projects like PCA the axes can still be annotated meaningfully. With non-linear projections this is no longer possible and alternative strategies such as attribute-based color coding are required. In this paper, we review existing augmentation techniques and discuss their limitations. We present the Non-Linear Embeddings Surveyor (NoLiES) that combines a novel augmentation strategy for projected data (rangesets) with interactive analysis in a small multiples setting. Rangesets use a set-based visualization approach for binned attribute values that enable the user to quickly observe structure and detect outliers. We detail the link between algebraic topology and rangesets and demonstrate the utility of NoLiES in case studies with various challenges (complex attribute value distribution, many attributes, many data points) and a real-world application to understand latent features of matrix completion in thermodynamics.

They can be treated as deterministic, as the noise of the manufacturing Monte Carlo Tree Search (MCTS) has shown its outcomes influence the processing result only to a minor strength for a lot of deterministic and stochastic extent. In this paper, we deal with the less common case examples, but literature lacks reports of applications of high precision manufacturing: here, the manufacturing to real world industrial processes. Common tolerances of the different processing steps are in the range reasons for this are that there is no efficient simulator of the product tolerance. As the manufacturing outcomes of the process available or there exist problems vary, the chain of manufacturing steps has to be adapted.

The purpose of this paper is to examine the opportunities and barriers of Integrated Human-Machine Intelligence (IHMI) in civil engineering. Integrating artificial intelligence's high efficiency and repeatability with humans' adaptability in various contexts can advance timely and reliable decision-making during civil engineering projects and emergencies. Successful cases in other domains, such as biomedical science, healthcare, and transportation, showed the potential of IHMI in data-driven, knowledge-based decision-making in numerous civil engineering applications. However, whether the industry and academia are ready to embrace the era of IHMI and maximize its benefit to the industry is still questionable due to several knowledge gaps. This paper thus calls for future studies in exploring the value, method, and challenges of applying IHMI in civil engineering. Our systematic review of the literature and motivating cases has identified four knowledge gaps in achieving effective IHMI in civil engineering. First, it is unknown what types of tasks in the civil engineering domain can be assisted by AI and to what extent. Second, the interface between human and AI in civil engineering-related tasks need more precise and formal definition. Third, the barriers that impede collecting detailed behavioral data from humans and contextual environments deserve systematic classification and prototyping. Lastly, it is unknown what expected and unexpected impacts will IHMI have on the AEC industry and entrepreneurship. Analyzing these knowledge gaps led to a list of identified research questions. This paper will lay the foundation for identifying relevant studies to form a research roadmap to address the four knowledge gaps identified.

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario. For this, we propose a new mathematical modelling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of \$412,500 per month in a projection analysis carried out.

The abundance of clouds, located both spatially and temporally, often makes remote sensing (RS) applications with optical images difficult or even impossible to perform. Traditional cloud removing techniques have been studied for years, and recently, Machine Learning (ML)-based approaches have also been considered. In this manuscript, a novel method for the restoration of clouds-corrupted optical images is presented, able to generate the whole optical scene of interest, not only the cloudy pixels, and based on a Joint Data Fusion paradigm, where three deep neural networks are hierarchically combined. Spatio-temporal features are separately extracted by a conditional Generative Adversarial Network (cGAN) and by a Convolutional Long Short-Term Memory (ConvLSTM), from Synthetic Aperture Radar (SAR) data and optical time-series of data respectively, and then combined with a U-shaped network. The use of time-series of data has been rarely explored in the state of the art for this peculiar objective, and moreover existing models do not combine both spatio-temporal domains and SAR-optical imagery. Quantitative and qualitative results have shown a good ability of the proposed method in producing cloud-free images, by also preserving the details and outperforming the cGAN and the ConvLSTM when individually used. Both the code and the dataset have been implemented from scratch and made available to interested researchers for further analysis and investigation.

Volcanologists can predict when volcanos are going to erupt if they have a full detail of its eruptions. But for potentially apocalyptic supervolcanoes, such as the one bubbling under Yellowstone National Park, it's nearly impossible, given how varied their known eruptions have been, according to a new study. Researchers at Cardiff University noted there is not a'single model' that can help scientists understand how eruptions from supervolcanoes happen, making it difficult to understand when they might occur in the future. The researchers looked at geochemical and petrological evidence of 13 supereruptions that have happened over the past 2 million years, including the most recent one, Taupō volcano in New Zealand, which happened more than 24,000 years ago. Experts said there is not a'single model' that can help them understand how eruptions from supervolcanoes happen There was no'single, unified mode' that showed how each of the 13 played out, with some starting gradually over a period of weeks to months, while others exploded suddenly and violently. The researchers also found that the eruptions lasted for varying times, some as short as a period of days or weeks, while others lasted decades.