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Decoding digital philanthropy analyzing donors choices in cryptocurrency donations
Get PDFSSRN - pre-printSince Bitcoin’s inception in 2009, cryptocurrencies have found applications in various domains. However, despite the unique potential of cryptocurrencies for the nonprofit sector, especially in fundraising, there is a scarcity of research on the behavior of crypto donors. In this research, we investigate key attributes of cryptocurrencies that correlate with the amount of donations made in that specific digital currency, and explore characteristics of digital wallets that predict the donation behavior of wallet holders. For our regression analysis, we use donation data from the Aid for Ukraine campaign, one of the largest cryptocurrency fundraising initiatives launched in response to the Russian invasion of Ukraine. Our findings indicate that cryptocurrencies with a larger market cap and higher age tend to be favored by donors, while those associated with higher transaction fees are preferred less frequently. Donors also exhibit a propensity to contribute more in cryptocurrencies of higher price volatility. Moreover, everything else equal, stablecoins are used more often for donations. On the other hand, our results highlight that frequent cryptocurrency users tend to make larger crypto donations and are more inclined to continue supporting the campaign through future contributions. Furthermore, donors with identifiable names for their wallets tend to donate larger amounts, although this feature does not predict their inclination for future contributions
Control of meltpool shape in laser welding
Get PDFIn laser welding, the achievement of high productivity and precision is a relatively easy task; however, it is not always obvious how to achieve sound welds without defects. The localised laser energy promotes narrow meltpools with steep thermal gradients, additionally agitated by the vapour plume, which can potentially lead to many instabilities and defects. In the past years, there have been many techniques demonstrated on how to improve the quality and tolerance of laser welding, such as wobble welding or hybrid processes, but to utilise the full potential of lasers, we need to understand how to tailor the laser energy to meet the process and material requirements. Understanding and controlling the melt flow is one of the most important aspects in laser welding. In this work, the outcome of an extensive research programme focused on the understanding of meltpool dynamics and control of bead shape in laser welding is discussed. The results of instrumented experimentation, supported by computational fluid dynamic modelling, give insight into the fundamental aspects of meltpool formation, flow direction, feedstock melting and the likelihood of defect formation in the material upon laser interaction. The work contributes to a better understanding of the existing processes, as well as the development of a new range of process regimes with higher process stability, improved efficiency and higher productivity than standard laser welding. Several examples including ultra-stable keyhole welding and wobble welding and a highly efficient laser wire melting are demonstrated. In addition, the authors present a new welding process, derived from a new concept of the meltpool flow and shape control by dynamic beam shaping. The new process has proven to have many potential advantages in welding, cladding and repair applications.The project was funded by an EPSRC Programme Grant Newam (EP/R027218/1).Welding in the Worl
VSTOL FCS using nonlinear dynamic inversion and scheduled fixed wing controllers
Get PDFThis paper describes the application of input-output Nonlinear Dynamic Inversion (NDI) to flight control of a tilt-rotor actuated Vertical Short Take Off and Landing (VSTOL) platform. The NDI formulation is performed in the inertial reference frame resulting in a decoupled and linearised set of integrator virtual plants controlled using Linear Quadratic Integral (LQI) control. The NDI controller is continuously enabled along with a conventional fixed wing controller with the latter scheduled in and out of control authority as a function of the wing tilt/vector angle control input generated by the NDI controller.2024 UKACC 14th International Conference on Control (CONTROL
Adversarial proximal policy optimisation for robust reinforcement learning
Get PDFRobust reinforcement learning (RL) aims to develop algorithms that can effectively handle uncertainties and disturbances in the environment. Model-free methods play a crucial role in addressing these challenges by directly learning optimal policies without relying on a pre-existing model of the environment. This abstract provides an overview of model-free methods in robust RL, highlighting their key features, advantages, and recent advancements. Firstly, we discuss the fundamental concepts of RL and its challenges in uncertain environments. We then delve into model-free methods, which operate by interacting with the environment and collecting data to learn an optimal policy. These methods typically utilize value-based or policy-based approaches to estimate the optimal action-value function or the policy directly, respectively. To enhance robustness, model-free methods often incorporate techniques such as exploration-exploitation strategies, experience replay, and reward shaping. Exploration-exploitation strategies facilitate the exploration of uncertain regions of the environment, enabling the discovery of more robust policies. Experience replay helps improve sample efficiency by reusing past experiences, allowing the agent to learn from a diverse set of situations. Reward shaping techniques provide additional guidance to the RL agent, enabling it to focus on relevant features of the environment and mitigate potential uncertainties. In this paper, a robust reinforcement learning methodology is adapted utilising a novel Adversarial Proximal Policy Optimisation (A-PPO) method integrating an Adaptive KL penalty PPO. Comparison is made with DQN, DDQN and a conventional PPO algorithm.This work is supported by Thales UK and EPSRC funding, grant number 2454266AIAA SCITECH 2024 Foru
An evaluation of large diameter through-thickness metallic pins in composites: dataset
No full text- Mechanical_Testing_Dataset: Tensile, compression and double canteliever beam experimental data
- mode_I_2mm_pin_job1.dat: Double canteliever beam simulation Marc input file.
- DCB_Simulation_output.xlsx: Double canteliever beam simulation results for load versus crack opening displacement.A “Smart” Self-monitoring composite tool for aerospace composite manufacturing using Silicon photonic multi-sEnsors Embedded using through-thickness Reinforcement technique
Supporting data for the article: Advances in design of high-performance heterostructured scintillators for Time-of-Flight Positron Emission Tomography
No full textData associated with Advances in design of high-performance heterostructured scintillators for Time-of-Flight Positron Emission TomographyHETEROSTRUCTURE RADIATION DETECTOR MATERIALS FOR ADVANCED TIME OF FLIGHT POSITRON EMISSION TOMOGRAPHY (TOF-PET) IMAGIN
Artificial Intelligence in education: let’s ChatGPT about it
No full textRecent advances in Artificial Intelligence (AI), specifically the rapid rise of Natural Language Processing (NLP) platforms such as Open AI’s Chat GPT3.5, are already having a major impact on higher education institutions. There are significant concerns within academic communities about the threats such platforms pose to academic integrity. Many HE institutions have reacted quickly, announcing policies banning the use of AI software in the creation of assignment responses. Some are planning to return to strictly exam-based modes of assessment. In this article we reflect upon these recent events and how it has impacted our own teaching practice in the field of business management. We propose some alternative ways of thinking about these recent developments and focus on the opportunities that these AI platforms have to offer rather than the threats they pose
Energy consumption optimisation for unmanned aerial vehicle based on reinforcement learning framework
Get PDFThe average battery life of drones in use today is around 30 minutes, which poses significant limitations for ensuring long-range operation, such as seamless delivery and security monitoring. Meanwhile, the transportation sector is responsible for 93% of all carbon emissions, making it crucial to control energy usage during the operation of UAVs for future net-zero massive-scale air traffic. In this study, a reinforcement learning (RL)-based model was implemented for the energy consumption optimisation of drones. The RL-based energy optimisation framework dynamically tunes vehicle control systems to maximise energy economy while considering mission objectives, ambient circumstances, and system performance. RL was used to create a dynamically optimised vehicle control system that selects the most energy-efficient route. Based on training times, it is reasonable to conclude that a trained UAV saves between 50.1% and 91.6% more energy than an untrained UAV in this study by using the same map.International Journal of Powertrain
Exploring the use of graphene lubricant and TiO2 nanolubricants in micro deep drawing of stainless steel SUS301
Get PDFThis study investigates the effects of different lubrication conditions on drawing force and microcup formation during micro deep drawing (MDD). Results show that graphene lubricant, in combination with TiO2 nanolubricants, has the potential to reduce friction during MDD. The peak drawing force was reduced by 15.39% when both lubricants were used together, while the use of TiO2 nanolubricant and 10.0 mg/ml graphene lubricant reduced it by 6.03% and 14.52%, respectively. The study also reveals that lubricants reduce wrinkling during the formation of microcups by minimising energy consumption during the primary formation. However, the combination of TiO2 nanolubricant and graphene lubricant can cause inhomogeneous formation on the upper part of the blank, leading to more apparent wrinkling. Overall, the study highlights the potential of TiO2 nanolubricant and graphene lubricant in reducing friction and improving microcup formation during MDD.Open Access funding enabled and organized by CAUL and its Member Institutions. This research received funding from the Australian Research Council (ARC) through the project designated as DP190100738.The International Journal of Advanced Manufacturing Technolog
Interaction of stress corrosion cracks in single crystals Ni-Base superalloys
Get PDFStress corrosion cracking (SCC) can be detrimental to nickel-based superalloy components exposed to harsh environments in aero-gas turbines. During flight, engines consume contaminants deposited on the surface of a blade, often leading to degradation. Cracking can initiate within minutes and rapidly propagate, depending on the temperature, contaminants, and applied stress.
This study investigated the interaction between cracks in single-crystal turbine blades at intermediate temperatures by integrating experimental and computational methods. We performed C-Ring tests to quantify the time required for cracking, along with microscopic characterisation of the damage. In parallel, we developed a finite-element simulation for C-Ring tests using a phase field model calibrated to match the location of the cracks. The results demonstrated that the crack's characteristic spacing and length determine the likelihood of shielding or coalescing mechanisms.The authors are grateful for the support from EPSRC Doctoral Training Partnership UK.Engineering Fracture Mechanic