Green vs fossil-based energy vectors: A comparative techno-economic analysis of green ammonia and LNG value chains

This study conducts a comparative techno-economic assessment on the value chains of ammonia, as a green energy vector, and Liquefied Natural Gas (LNG), representing the benchmark energy vector, for long-distance energy transportation from Middle East to Europe. The value chain involves production from resources, conversion to an energy vector, storage and transport and reconversion of the energy vector to a suitable fuel. For comparison purposes, an electric power output of 400 MW is assumed to be produced by a power plant that utilizes either green or fossil fuels delivered to it. The adopted parameter for this comparison is the Levelized Cost of Energy (LCoE). Greenhouse gas emissions are economically penalized through the Social Cost of Carbon (SCC). Considering a SCC of 0.100 euro/kg, the LCoE of the LNG value chain is 59.19 euro/MWh, while that of ammonia is 231.71 euro/MWh. Since the cost of producing green hydrogen and purified natural gas strongly affects the results, a sensitivity analysis is performed to assess the impact of the assumed values. The SCC required to break even the LCoE of the two value chains is: 0.183 euro/MWh when considering the most favorable scenario for the green energy vector (low green hydrogen and high purified natural gas production costs) and 1.731 euro/kg when considering the most unfavorable one. This study highlights the cost-effectiveness of LNG in the current economic and regulatory landscape. However, the break-even range for the SCC indicates the potential for green ammonia to gain economic viability under higher carbon pricing scenarios

Letters

www.elsevier.com/locate/dsw A branch and bound algorithm for the robust shortest path problem with interval data

Magnetic Reversal Time in Open Long Range Systems

Topological phase space disconnection has been recently found to be a general phenomenon in isolated anisotropic spin systems. It sets a general framework to understand the emergence of ferromagnetism in finite magnetic systems starting from microscopic models without phenomenological on-site barriers. Here we study its relevance for finite systems with long range interacting potential in contact with a thermal bath. We show that, even in this case, the induced magnetic reversal time is exponentially large in the number of spins, thus determining {\it stable} (to any experimental observation time) ferromagnetic behavior. Moreover, the explicit temperature dependence of the magnetic reversal time obtained from the microcanonical results, is found to be in good agreement with numerical simulations. Also, a simple and suggestive expression, indicating the Topological Energy Threshold at which the disconnection occurs, as a real energy barrier for many body systems, is obtained analytically for low temperature

Ant colony optimisation and local search for bin-packing and cutting stock problems

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO

Ambiguous Effects of Autophagy Activation Following Hypoperfusion/Ischemia

Autophagy primarily works to counteract nutrient deprivation that is strongly engaged during starvation and hypoxia, which happens in hypoperfusion. Nonetheless, autophagy is slightly active even in baseline conditions, when it is useful to remove aged proteins and organelles. This is critical when the mitochondria and/or proteins are damaged by toxic stimuli. In the present review, we discuss to that extent the recruitment of autophagy is beneficial in counteracting brain hypoperfusion or, vice-versa, its overactivity may per se be detrimental for cell survival. While analyzing these opposite effects, it turns out that the autophagy activity is likely not to be simply good or bad for cell survival, but its role varies depending on the timing and amount of autophagy activation. This calls for the need for an appropriate autophagy tuning to guarantee a beneficial effect on cell survival. Therefore, the present article draws a theoretical pattern of autophagy activation, which is hypothesized to define the appropriate timing and intensity, which should mirrors the duration and severity of brain hypoperfusion. The need for a fine tuning of the autophagy activation may explain why confounding outcomes occur when autophagy is studied using a rather simplistic approach

Ab initio study of canted magnetism of finite atomic chains at surfaces

By using ab initio methods on different levels we study the magnetic ground state of (finite) atomic wires deposited on metallic surfaces. A phenomenological model based on symmetry arguments suggests that the magnetization of a ferromagnetic wire is aligned either normal to the wire and, generally, tilted with respect to the surface normal or parallel to the wire. From a first principles point of view, this simple model can be best related to the so--called magnetic force theorem calculations being often used to explore magnetic anisotropy energies of bulk and surface systems. The second theoretical approach we use to search for the canted magnetic ground state is first principles adiabatic spin dynamics extended to the case of fully relativistic electron scattering. First, for the case of two adjacent Fe atoms an a Cu(111) surface we demonstrate that the reduction of the surface symmetry can indeed lead to canted magnetism. The anisotropy constants and consequently the ground state magnetization direction are very sensitive to the position of the dimer with respect to the surface. We also performed calculations for a seven--atom Co chain placed along a step edge of a Pt(111) surface. As far as the ground state spin orientation is concerned we obtain excellent agreement with experiment. Moreover, the magnetic ground state turns out to be slightly noncollinear.Comment: 8 pages, 5 figures; presented on the International Conference on Nanospintronics Design and Realizations, Kyoto, Japan, May 2004; to appear in J. Phys.: Cond. Matte

Fully Onboard AI-powered Human-Drone Pose Estimation on Ultra-low Power Autonomous Flying Nano-UAVs

Many emerging applications of nano-sized unmanned aerial vehicles (UAVs), with a few form-factor, revolve around safely interacting with humans in complex scenarios, for example, monitoring their activities or looking after people needing care. Such sophisticated autonomous functionality must be achieved while dealing with severe constraints in payload, battery, and power budget ( 100). In this work, we attack a complex task going from perception to control: to estimate and maintain the nano-UAV’s relative 3D pose with respect to a person while they freely move in the environment – a task that, to the best of our knowledge, has never previously been targeted with fully onboard computation on a nano-sized UAV. Our approach is centered around a novel vision-based deep neural network (DNN), called PULP-Frontnet, designed for deployment on top of a parallel ultra-low-power (PULP) processor aboard a nano-UAV. We present a vertically integrated approach starting from the DNN model design, training, and dataset augmentation down to 8-bit quantization and deployment in-field. PULP-Frontnet can operate in real-time (up to 135frame/), consuming less than 87 for processing at peak throughput and down to 0.43/frame in the most energy-efficient operating point. Field experiments demonstrate a closed-loop top-notch autonomous navigation capability, with a tiny 27-grams Crazyflie 2.1 nano-UAV. Compared against an ideal sensing setup, onboard pose inference yields excellent drone behavior in terms of median absolute errors, such as positional (onboard: 41, ideal: 26) and angular (onboard: 3.7, ideal: 4.1). We publicly release videos and the source code of our work