In this article, the utility of graph neural network (GNN) surrogates for Monte Carlo (MC) sampling-based risk quantification in daily operations of power grid is investigated. The MC simulation process necessitates solving a large number of optimal power flow (OPF) problems corresponding to the sample values of stochastic grid variables (power demand and renewable generation), which is computationally prohibitive. Computationally inexpensive surrogates of the OPF problem provide an attractive alternative for expedited MC simulation. GNN surrogates are especially suitable due to their superior ability to handle graph-structured data. Therefore, GNN surrogates of OPF problem are trained using supervised learning. They are then used to obtain Monte Carlo (MC) samples of the quantities of interest (operating reserve, transmission line flow) given the (hours-ahead) probabilistic wind generation and load forecast. The utility of GNN surrogates is evaluated by comparing OPF-based and GNN-based grid reliability and risk for IEEE Case118 synthetic grid. It is shown that the GNN surrogates are sufficiently accurate for predicting the (bus-level, branch-level and system-level) grid state and enable fast as well as accurate operational risk quantification for power grids. The article thus develops various tools for fast reliability and risk quantification for real-world power grids using GNNs.

In the pursuit of accurate experimental and computational data while minimizing effort, there is a constant need for high-fidelity results. However, achieving such results often requires significant computational resources. To address this challenge, this paper proposes a deep operator learning-based framework that requires a limited high-fidelity dataset for training. We introduce a novel physics-guided, bi-fidelity, Fourier-featured Deep Operator Network (DeepONet) framework that effectively combines low and high-fidelity datasets, leveraging the strengths of each. In our methodology, we began by designing a physics-guided Fourier-featured DeepONet, drawing inspiration from the intrinsic physical behavior of the target solution. Subsequently, we train this network to primarily learn the low-fidelity solution, utilizing an extensive dataset. This process ensures a comprehensive grasp of the foundational solution patterns. Following this foundational learning, the low-fidelity deep operator network's output is enhanced using a physics-guided Fourier-featured residual deep operator network. This network refines the initial low-fidelity output, achieving the high-fidelity solution by employing a small high-fidelity dataset for training. Notably, in our framework, we employ the Fourier feature network as the Trunk network for the DeepONets, given its proficiency in capturing and learning the oscillatory nature of the target solution with high precision. We validate our approach using a well-known 2D benchmark cylinder problem, which aims to predict the time trajectories of lift and drag coefficients. The results highlight that the physics-guided Fourier-featured deep operator network, serving as a foundational building block of our framework, possesses superior predictive capability for the lift and drag coefficients compared to its data-driven counterparts.

Aerial mapping systems are important for many surveying applications (e.g., industrial inspection or agricultural monitoring). Semi-autonomous mapping with GPS-guided aerial platforms that fly preplanned missions is already widely available but fully autonomous systems can significantly improve efficiency. Autonomously mapping complex 3D structures requires a system that performs online mapping and mission planning. This paper presents Osprey, an autonomous aerial mapping system with state-of-the-art multi-session mapping capabilities. It enables a non-expert operator to specify a bounded target area that the aerial platform can then map autonomously, over multiple flights if necessary. Field experiments with Osprey demonstrate that this system can achieve greater map coverage of large industrial sites than manual surveys with a pilot-flown aerial platform or a terrestrial laser scanner (TLS). Three sites, with a total ground coverage of $7085$ m$^2$ and a maximum height of $27$ m, were mapped in separate missions using $112$ minutes of autonomous flight time. True colour maps were created from images captured by Osprey using pointcloud and NeRF reconstruction methods. These maps provide useful data for structural inspection tasks.

Over the last decade there has been an increasing frequency and intensity of wildfires across the globe, posing significant threats to human and animal lives, ecosystems, and socio-economic stability. Therefore urgent action is required to mitigate their devastating impact and safeguard Earth's natural resources. Robust Machine Learning methods combined with the abundance of high-resolution satellite imagery can provide accurate and timely mappings of the affected area in order to assess the scale of the event, identify the impacted assets and prioritize and allocate resources effectively for the proper restoration of the damaged region. In this work, we create and introduce a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations for the Greek Area). This dataset is unique as it comprises of satellite imagery acquired before and after a wildfire event, it contains information from Sentinel-2 and MODIS modalities with variable spatial and spectral resolution, and contains a large number of events where the corresponding burnt area ground truth has been annotated by domain experts. FLOGA covers the wider region of Greece, which is characterized by a Mediterranean landscape and climatic conditions. We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas, approached as a change detection task. We also compare the results to those obtained using standard specialized spectral indices for burnt area mapping. Finally, we propose a novel Deep Learning model, namely BAM-CD. Our benchmark results demonstrate the efficacy of the proposed technique in the automatic extraction of burnt areas, outperforming all other methods in terms of accuracy and robustness. Our dataset and code are publicly available at: https://github.com/Orion-AI-Lab/FLOGA.

When juxtaposed with conventional soft and compliant robotic systems, HCMs exhibit pronounced rigidity, augmented mobility, reproducible repeatability, and an effective design and fabrication paradigm. In this research, we investigate the feasibility of utilizing carbon fiber-reinforced plastic (CFRP) as the foundational material for an HCM-based fish robot, herein referred to as "CarbonFish." Our objective centers on realizing high-frequency undulatory motion, thereby laying the groundwork for accelerated aquatic locomotion in subsequent models. We proffer an exhaustive design and fabrication schema underpinned by mathematical principles. Preliminary evaluations of our single-actuated CarbonFish have evidenced an undulation frequency approaching 10 Hz, suggesting its potential to outperform other biologically inspired aquatic entities as well as real fish. Keywords: soft fish robot, compliant mechanism, bistability, undulation swimming Main Text Introduction Soft and compliant robotics represents an advancing domain in robotics research, emphasizing the design and development of robots utilizing soft and deformable materials.

The Hair clip mechanism (HCM) is an in-plane prestressed bistable mechanism proposed in our previous research [1]~[5] to enhance the functionality of soft robotics. HCMs have several advantages, such as high rigidity, high mobility, good repeatability, and design and fabrication simplicity, compared to existing soft and compliant robotics. Using our experience with fish robots, this work delves into designing a novel HCM robotic propulsion system made from PETG plastic, carbon fiber-reinforced plastic (CFRP), and steel. Detailed derivation and verification of the HCM theory are given, and the influence of key parameters like dimensions, material types, and servo motor specifications are summarized. The designing algorithm offers insight into HCM robotics. It enables us to search for suitable components, operate robots at a desired frequency, and achieve high-frequency and high-speed undulatory swimming for fish robots.

For the particular case of thermal transport, while these approaches are gaining popularity, they are still limited. Thermal conductivity (κ) is an important material For instance, Pal et al. [25] employed a scale-invariant property critical in determining the performance and efficiency ML model to accelerate the search of quaternary chalcogenides of devices in various technological applications with low κ, Hu et al. [26] employed ML to minimize such as thermoelectric energy generation, thermal insulation, coherent heat conduction across aperiodic superlattices, and memory storage [1-4]. For many of these applications, Rodiguez et al. [27] trained neural network based low thermal conductivity semiconducting solids interatomic forcefield to do bottom-up prediction of κ are desired, while for others (such as heat dissipation based on intermediate phonon properties such as mean and microprocessors), materials with high κ are desired square displacements and bonding/anti-bonding characters, [2, 5, 6]. For materials used in most of these applications, and Visaria and Jain [28] employed neural network the thermal transport is dominated by atomic vibrations, based auto-encoders to do space transformation to search i.e., phonons, with room temperature κ in the range of for material configurations with low-and high-κ from the 0.1-3000 W/m-K [7]. The traditional search for novel low exponentially-large search space of considered superlattices.

This level of autonomy, in Space exploration, particularly the long-term habitation of conjunction with collaboration between astronauts and robots, planetary surfaces, requires significant technological advances, is pivotal for the successful construction of structures and the with a strong focus on collaboration between robots and astronauts accomplishment of mission-specific tasks. This paper presents where the modularity and autonomy of space robots will the development of the CISRU suite, the preparation of field stand out, allowing them to perform different tasks [2], [3].

The European Moon Rover System (EMRS) Pre-Phase A activity is part of the European Exploration Envelope Programme (E3P) that seeks to develop a versatile surface mobility solution for future lunar missions. These missions include: the Polar Explorer (PE), In-Situ Resource Utilization (ISRU), and Astrophysics Lunar Observatory (ALO) and Lunar Geological Exploration Mission (LGEM). Therefore, designing a multipurpose rover that can serve these missions is crucial. The rover needs to be compatible with three different mission scenarios, each with an independent payload, making flexibility the key driver. This study focuses on modularity in the rover's locomotion solution and autonomous on-board system. Moreover, the proposed EMRS solution has been tested at an analogue facility to prove the modular mobility concept. The tests involved the rover's mobility in a lunar soil simulant testbed and different locomotion modes in a rocky and uneven terrain, as well as robustness against obstacles and excavation of lunar regolith. As a result, the EMRS project has developed a multipurpose modular rover concept, with power, thermal control, insulation, and dust protection systems designed for further phases. This paper highlights the potential of the EMRS system for lunar exploration and the importance of modularity in rover design.

Given rapid progress toward advanced AI and risks from frontier AI systems (advanced AI systems pushing the boundaries of the AI capabilities frontier), the creation and implementation of AI governance and regulatory schemes deserves prioritization and substantial investment. However, the status quo is untenable and, frankly, dangerous. A regulatory gap has permitted AI labs to conduct research, development, and deployment activities with minimal oversight. In response, frontier AI system evaluations have been proposed as a way of assessing risks from the development and deployment of frontier AI systems. Yet, the budding AI risk evaluation ecosystem faces significant coordination challenges, such as a limited diversity of evaluators, suboptimal allocation of effort, and perverse incentives. This paper proposes a solution in the form of an international consortium for AI risk evaluations, comprising both AI developers and third-party AI risk evaluators. Such a consortium could play a critical role in international efforts to mitigate societal-scale risks from advanced AI, including in managing responsible scaling policies and coordinated evaluation-based risk response. In this paper, we discuss the current evaluation ecosystem and its shortcomings, propose an international consortium for advanced AI risk evaluations, discuss issues regarding its implementation, discuss lessons that can be learnt from previous international institutions and existing proposals for international AI governance institutions, and, finally, we recommend concrete steps to advance the establishment of the proposed consortium: (i) solicit feedback from stakeholders, (ii) conduct additional research, (iii) conduct a workshop(s) for stakeholders, (iv) analyze feedback and create final proposal, (v) solicit funding, and (vi) create a consortium.