Angiogenin, a potent inducer of neovascularization, is the only angiogenic molecule known to exhibit ribonucleolytic activity. Its overall structure, as determined at 2.4 A, is similar to that of pancreatic ribonuclease A, but it differs markedly in several distinct areas, particularly the ribonucleolytic active center and the putative receptor binding site, both of which are critically involved in biological function. Most strikingly, the site that is spatially analogous to that for pyrimidine binding in ribonuclease A differs significantly in conformation and is "obstructed" by glutamine-117. Movement of this and adjacent residues may be required for substrate binding to angiogenin and, hence, constitute a key part of its mechanism of action.

Services that exploit Unmanned Aircraft Systems (UAS) are poised to revolutionise the service industry with a projected value of 71 BUSD by the end of the decade. A key enabler of this technology is the unlocking of autonomous Beyond Visual Line of Sight (BVLOS) operations. BVLOS operations will depend on a robust Detect and Avoid (D&A) capability. Yet, currently in the UK and EU, BVLOS operations are only allowed in specific cases and scenarios. As a result, the technological landscape for the development of robust D&A faces limitations, and there is little market incentive for development. Furthermore, while automated BVLOS is a future technology, a strong D&A capability is of importance now for all types of UAS operations. As the remote pilot has to deal with information overload from the controller device and the environment. These high-risk UAS operations are becoming more common. In this paper, we discuss the current legal framework in the UK making comparisons to EU countries. We make the case that even when an operation abides by the current framework the remote pilot is exposed to several legal liabilities. We review the roadmaps for UAS adoption (including certification processes for UAS-based products) and highlight that for software-intensive systems, key steps are missing to assure the quality of the product. Finally, we build on these findings to set forwards a path to complement future certification processes to enable autonomous based UAS operations to share the airspace with remotely piloted operations.

Unmanned aerial vehicle (UAV) state estimation is fundamental across applications like robot navigation, autonomous driving, virtual reality (VR), and augmented reality (AR). This research highlights the critical role of robust state estimation in ensuring safe and efficient autonomous UAV navigation, particularly in challenging environments. We propose a deep learning-based adaptive sensor fusion framework for UAV state estimation, integrating multi-sensor data from stereo cameras, an IMU, two 3D LiDAR’s, and GPS. The framework dynamically adjusts fusion weights in real time using a long short-term memory (LSTM) model, enhancing robustness under diverse conditions such as illumination changes, structureless environments, degraded GPS signals, or complete signal loss where traditional single-sensor SLAM methods often fail. Validated on an in-house integrated UAV platform and evaluated against high-precision RTK ground truth, the algorithm incorporates deep learning-predicted fusion weights into an optimization-based odometry pipeline. The system delivers robust, consistent, and accurate state estimation, outperforming state-of-the-art techniques. Experimental results demonstrate its adaptability and effectiveness across challenging scenarios, showcasing significant advancements in UAV autonomy and reliability through the synergistic integration of deep learning and sensor fusion.

In recent years, we have seen significant interest in the use of permanently deployed resident robotic vehicles for commercial inspection, maintenance and repair (IMR) activities. This paper presents a concept and demonstration, through offshore trials, of a low-cost, low-maintenance, navigational marker that can eliminate drift in vehicle INS solution when the vehicle is close to the IMR target. The subsea localisation marker system is fixed on location on the resident field asset and is used in on-vehicle machine vision algorithms for pose estimation and facilitation of a high-resolution world coordinate frame registration with a high refresh rate. This paper presents evaluation of the system during trials in the North Atlantic Ocean during January 2019. System performances and propagation of position error is inspected and estimated, and the effect of intermittent visual based position update to Kalman filter and onboard INS solution is discussed. The paper presents experimental results of the commercial state-of-the-art inertial navigation system operating in the pure inertial mode for comparison.

Many current and future applications of underwater robotics require real-time sensing and interpretation of the environment. As the vast majority of robots are equipped with cameras, computer vision is playing an increasingly important role it this field. This paper presents the implementation and experimental results of underwater StereoFusion, an algorithm for real-time 3D dense reconstruction and camera tracking. Unlike KinectFusion on which it is based, StereoFusion relies on a stereo camera as its main sensor. The algorithm uses the depth map obtained from the stereo camera to incrementally build a volumetric 3D model of the environment, while simultaneously using the model for camera tracking. It has been successfully tested both in a lake and in the ocean, using two different state-of-the-art underwater Remotely Operated Vehicles (ROVs). Ongoing work focuses on applying the same algorithm to acoustic sensors, and on the implementation of a vision based monocular system with the same capabilities.

This paper describes the functional theory and design of a modular simulator to generate, in real-time, physically representative spatiotemporal sidescan sonar echo data. The seafloor topography is generated using fractal theory and the resulting terrain is tesselated into triangular facets. Propagation of the acoustic fan beam is determined by a ray theory solution of the governing wave equation while the contribution of each facet to the recorded intensity at the sonar transducer is resolved by applying Jackson's seafloor scattering model. The computational bottleneck inherent in the discovery of illuminated facets at each ping is significantly reduced by the implementation of an optimised mesh refinement scheme intended for interactive rendering of large-scale complex surfaces described by polygonal meshes. The resulting performance increases enabled the successful integration of the sonar simulator with an existing AUV simulator. Synthetic images and the performance measures of the sidescan simulator image generation are presented.