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In integrated power and gas energy system optimization models (ESOMs), pipeline gas transmission with linepack is a particularly complex problem due to its non-linear and non-convex character. For ESOMs based on mixed-integer linear programing, piecewise linearization is a well-established convexification approach for this problem, which, however, requires binary variables to model feasible combinations of linear gas flow and pressure segments and thus can quickly become computationally challenging. In order to improve computational performance, this paper proposes a piecewise linearization method specifically designed to be tight, resulting in a reduced problem space a solver can explore faster. We provide numerical results comparing the proposed formulation against two piecewise linearizations from the literature, both from a theoretical point of view and in terms of practical computational performance, with results showing an average speed-up of 2.83 times for our case study. Test cases are carried out on a modified 24-bus IEEE Reliability Test System and a 12-node gas system, considering discrete unit commitment decisions.

We address the issue of which broad set of initial conditions for the planet Jupiter best matches the current presence of a ``fuzzy core" of heavy elements, while at the same time comporting with measured parameters such as its effective temperature, atmospheric helium abundance, radius, and atmospheric metallicity. Our focus is on the class of fuzzy cores that can survive convective mixing to the present day and on the unique challenges of an inhomogeneous Jupiter with stably-stratified regions now demanded by the \textit{Juno} gravity data. Hence, using the new code \texttt{APPLE}, we attempt to put a non-adiabatic Jupiter into an evolutionary context. This requires not only a mass density model, the major relevant byproduct of the \textit{Juno} data, but a thermal model that is subject to interior heat transport, a realistic atmospheric flux boundary, a helium rain algorithm, and the latest equation of state. The result is a good fit to most major thermal, compositional, and structural constraints that still preserve a fuzzy core and that should inform future more detailed models of the current Jupiter in the context of its evolution from birth., Comment: 19 pages, 10 figures, 3 tables, submitted to ApJ

orbitize! is a package for Bayesian modeling of the orbital parameters of resolved binary objects from time series measurements. It was developed with the needs of the high-contrast imaging community in mind, and has since also become widely used in the binary star community. A generic orbitize! use case involves translating relative astrometric time series, optionally combined with radial velocity or astrometric time series, into a set of derived orbital posteriors. This paper is published alongside the release of orbitize! version 3.0, which has seen significant enhancements in functionality and accessibility since the release of version 1.0 (Blunt et al., 2020)., Comment: Published in JOSS

A strong increase of the reflection loss (from 25 up to 35 dBs) and an extension of the absorption bandwidth up to 20% under far-field real radar conditions are experimentally reported in this work by creating a functional bilayer system. Each layer consists of a composite material, typically a dielectric matrix filled with magnetic entities (powder or wires). Combining the two types of materials into a bilayer structure has shown unprecedented improvements in microwave absorption capacities compared to the former absorption of each layer. The potential of random magnets to create thin broadband absorbers is also demonstrated. The capacity to improve the shielding behavior is strongly related to each layer's permittivity, permeability, and thickness. This provides strong control over the design of novel materials for stealth applications.

The electromagnetic properties and microwave absorption capabilities are studied in an anechoic chamber under real radar conditions for mono and bilayer composite samples consisting of a polymeric matrix and a magnetic powder filler, either metallic or ceramic. The effect of the filler type and the filling factor is investigated. The results demonstrate exceptional broadband microwave absorption, making these materials highly suitable for stealth technology applications. The experimentally measured absorptions reach -40 dB, while model-based predictions suggest that these systems could overpass the barrier of -50 dB. The experimental results are supported by models, both for single and bilayer systems.

Energy system models are crucial for planning, supporting, and understanding energy transition pathways. Flexible energy modelling tools have emerged to provide practitioners, planners, and decision-makers with various alternatives to represent diverse energy systems, including green hydrogen or exclusively renewable-powered storage assets. The increased interaction between energy sectors, temporal resolution, and extensive geographical scopes have led to large-scale problems posing significant computational challenges. Despite improvements in computing power and linear programming (LP) solvers, large-scale LP models are often simplified, sacrificing fidelity to speed up solutions. This paper aims to debunk the misconception that an LP model's simplicity cannot be improved without sacrificing fidelity. We propose exploiting the graph nature of energy systems using a single building block, the Energy Asset, to reduce computational complexity. By using only one building block, the Energy Asset, we avoid intermediary assets and connections, thus reducing the number of variables by 26% and constraints by 35%. This approach naturally speeds up solving times by 1.27 times without sacrificing model fidelity. Our illustrative case study demonstrates these improvements compared to traditional two-building-block approaches. This paper raises awareness in the energy modelling community about the quality of LP models and shows that not all LPs are created equal. Our proposed method speeds up energy system models regardless of anticipated advances in software and hardware, allowing for the solution of larger and more detailed models with existing technology., Comment: Preprint submitted to Energy (Elsevier)

Many state-of-the-art methods for safety assessment and motion planning for automated driving require estimation of the probability of collision (POC). To estimate the POC, a shape approximation of the colliding actors and probability density functions of the associated uncertain kinematic variables are required. Even with such information available, the derivation of the POC is in general, i.e., for any shape and density, only possible with Monte Carlo sampling (MCS). Random sampling of the POC, however, is challenging as computational resources are limited in real-world applications. We present expressions for the POC in the presence of Gaussian uncertainties, based on multi-circular shape approximations. In addition, we show that the proposed approach is computationally more efficient than MCS. Lastly, we provide a method for upper and lower bounding the estimation error for the POC induced by the used shape approximations., Comment: Accepted for the 2024 Intelligent Vehicles Symposium, 8 pages

We introduce APPLE, a novel planetary evolution code designed specifically for the study of giant exoplanet and Jovian planet evolution in the era of Galileo, Juno, and Cassini. With APPLE, state-of-the-art equations of state for hydrogen, helium, ice, and rock are integrated with advanced features to treat ice/rock cores and metals in the gaseous envelope; models for helium rain and hydrogen/helium immiscibility; detailed atmosphere boundary tables that also provide self-consistent albedos and spectra; and options to address envelope metal gradients and stably-stratified regions. Our hope is that these purpose-built features of APPLE will help catalyze the development of the next generation of giant exoplanet and Jovian planet evolutionary models., Comment: Accepted for publication in ApJ

This work reports experimental evidence of random magnetic behavior observed in modified barium hexagonal ferrites. We observe a significant transition in the magnetic properties of this system when divalent cations (Ni2+, Cu2+, Mn2+) are incorporated into the structure and give rise to a magnetic nanocomposite. Such introduction randomly occurs throughout each sample and creates conditions for such materials to behave as random anisotropy magnets. We verify the occurrence of such behavior in our samples by fitting the magnetization in approaching saturation to the corresponding theoretical model. We therefore analyze the microwave absorption capacities of random anisotropy magnets in the GHz range and predict large and broad absorption signals under certain conditions. The findings presented here postulate, for the first time, ceramic materials as promising random anisotropy magnets and underline their potential as microwave absorbers, in good agreement with recent theoretical models.

The equation of state of hydrogen-helium (H-He) mixtures plays a vital role in the evolution and structure of gas giant planets and exoplanets. Recent equations of state that account for hydrogen-helium interactions, coupled with hydrogen-helium immiscibility curves, can now produce more physical evolutionary models, such as accounting for helium rain with greater fidelity than in the past. In this work, we present a set of tools for planetary evolution\footnote{All tables of thermodynamic quantities and derivatives are available at \url{https://github.com/Rob685/hhe_eos_misc}, along with a unified Python interface. Tutorials demonstrating the interface are also available in the repository.} that provides a Python interface for existing tables of useful thermodynamic quantities, state-of-the-art H-He equations of state, and pressure-dependent H-He immiscibility curves. In particular, for a collection of independent variable choices, we provide scripts to calculate the variety of thermodynamic derivatives used to model convection and energy transport. These include the chemical potential derived from the internal energy, which is a modeling necessity in the presence of composition gradients when entropy is the other primary variable. Finally, an entropy-based convection formalism is presented and fully described that highlights the physical differences between adiabatic and isentropic interior models. This centralized resource is meant to facilitate both giant planet structural and evolutionary modeling and the entry of new research groups into the field of giant planet modeling., Comment: 22 pages, 9 figures, 2 tables, updated after final revisions

This paper introduces the TulipaProfileFitting.jl package, a tool developed in Julia for generating renewable energy profiles that fit a specified capacity factor. It addresses the limitations of naive methods in adjusting existing profiles to match improved technology efficiency, particularly in scenarios lacking detailed weather data or technology specifications. By formulating the problem mathematically, the package provides a computationally efficient solution for creating realistic renewable energy profiles based on existing data. It ensures that the adjusted profiles realistically reflect the improvements in technology efficiency, making it an essential tool for energy modellers in analyzing $CO_2$-neutral energy systems.

Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells. When combined with large-scale perturbation screens, through which specific biological mechanisms can be targeted, these technologies allow for measuring the effect of targeted perturbations on the whole transcriptome. These advances provide an opportunity to better understand the causative role of genes in complex biological processes such as gene regulation, disease progression or cellular development. However, the high-dimensional nature of the data, coupled with the intricate complexity of biological systems renders this task nontrivial. Within the machine learning community, there has been a recent increase of interest in causality, with a focus on adapting established causal techniques and algorithms to handle high-dimensional data. In this perspective, we delineate the application of these methodologies within the realm of single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology and discuss and challenge the assumptions it entails from the biological point of view. We then identify open problems in the application of causal approaches to single-cell data: generalising to unseen environments, learning interpretable models, and learning causal models of dynamics. For each problem, we discuss how various research directions - including the development of computational approaches and the adaptation of experimental protocols - may offer ways forward, or on the contrary pose some difficulties. With the advent of single cell atlases and increasing perturbation data, we expect causal models to become a crucial tool for informed experimental design., Comment: 35 pages, 7 figures, 3 tables, 1 box

Many car-following models like the Intelligent Driver Model (IDM) incorporate important aspects of safety in their definitions, such as collision-free driving and keeping safe distances, implying that drivers are safety conscious when driving. Despite their safety-oriented nature, when calibrating and evaluating these models, the main objective of most studies is to find model parameters that minimize the error in observed measurements like spacing and speed while studies specifically focused on calibrating and evaluating unobserved safe behavior captured by the parameters of the model are scarce. Most studies on calibration and evaluation of the IDM do not check if the observed driving behavior (i.e. spacing) are within the model estimated unobserved safety thresholds (i.e. desired safety spacing) or what parameters are important for safety. This limits their application for safety driven traffic simulations. To fill this gap, this paper first proposes a simple metric to evaluate driver compliance with the safety thresholds of the IDM model. Specifically, we evaluate driver compliance to their desired safety spacing, speed and safe time gap. Next, a method to enforce compliance to the safety threshold during model calibration is proposed. The proposed compliance metric and the calibration approach is tested using Dutch highway trajectory data obtained from a driving simulator experiment and two drones. The results show that compliance to the IDM safety threshold greatly depends on braking capability with a median compliance between 38% and 90% of driving time, indicating that drivers can only partially follow the IDM safety threshold in reality., Comment: Submitted for presentation at TRB2025 in Washington DC . Also for consideration for publication in Transportation Research Records Journal

In this paper, we aim to analyse the impact of hydrogen production decarbonisation and electrification scenarios on the infrastructure development, generation mix, $CO_{2}$ emissions, and system costs of the European power system, considering the retrofit of the natural gas infrastructure. We define a reference scenario for the European power system in 2050 and use scenario variants to obtain additional insights by breaking down the effects of different assumptions. The scenarios were analysed using the European electricity market model COMPETES, including a proposed formulation to consider retrofitting existing natural gas networks to transport hydrogen instead of methane. According to the results, 60% of the EU's hydrogen demand is electrified, and approximately 30% of the total electricity demand will be to cover that hydrogen demand. The primary source of this electricity would be non-polluting technologies. Moreover, hydrogen flexibility significantly increases variable renewable energy investment and production, and reduces $CO_{2}$ emissions. In contrast, relying on only electricity transmission increases costs and $CO_{2}$ emissions, emphasising the importance of investing in an $H_{2}$ network through retrofitting or new pipelines. In conclusion, this paper shows that electrifying hydrogen is necessary and cost-effective to achieve the EU's objective of reducing long-term emissions., Comment: Preprint submitted to International Journal of Electrical Power \& Energy Systems

Tulipa Energy Model aims to optimise the investment and operation of the electricity market, considering its coupling with other sectors, such as hydrogen and heat, that can also be electrified. The problem is analysed from the perspective of a central planner who determines the expansion plan that is most beneficial for the system as a whole, either by maximising social welfare or by minimising total costs. The formulation provides a general description of the objective function and constraints in the optimisation model based on the concept of energy assets representing any element in the model. The model uses subsets and specific methods to determine the constraints that apply to a particular technology or network, allowing more flexibility in the code to consider new technologies and constraints with different levels of detail in the future.

We present updated atmospheric tables suitable for calculating the post-formation evolution and cooling of Jupiter and Jupiter-like exoplanets. These tables are generated using a 1D radiative transfer modeling code that incorporates the latest opacities and realistic prescriptions for stellar irradiation and ammonia clouds. To ensure the accuracy of our model parameters, we calibrate them against the measured temperature structure and geometric albedo spectrum of Jupiter, its effective temperature, and its inferred internal temperature. As a test case, we calculate the cooling history of Jupiter using an adiabatic and homogeneous interior and compare with extant models now used to evolve Jupiter and the giant planets. We find that our model reasonably matches Jupiter after evolving a hot-start initial condition to the present age of the solar system, with a discrepancy in brightness temperature/radius within two per cent. Our algorithm allows us to customize for different cloud, irradiation, and metallicity parameters. This class of boundary conditions can be used to study the evolution of solar-system giant planets and exoplanets with more complicated interior structures and non-adiabatic, inhomogeneous internal profiles., Comment: 11 pages, 5 figures. Accepted to ApJ

This paper summarises the main multi-year investment modelling approaches in energy planning models. Therefore, here we will go from a simple (basic) formulation to a more complex (general) one to understand different levels of detail, including examples to make more accessible the understanding of the concepts., Comment: Working paper

Microwave power absorption by a two-layer system deposited on a metallic surface has been studied in the experimental setup emulating the response to a radar signal. Layers containing hexaferrite and iron powder in a dried paint of thickness under 1mm have been used. The data is analyzed within a theoretical model derived for a bilayer system from the transmission line theory. A good agreement between experimental and theoretical results is found. The advantage of using a bilayer system over a single-layer system has been demonstrated. How the maximum microwave absorption (minimum reflection loss) can be achieved through the optimization of the filling factors and thicknesses of the two layers is shown.

One of the challenges of excitonic materials is the accurate determination of the exciton binding energy and bandgap. The difficulty arises from the overlap of the discrete and continuous excitonic absorption at the band edge. Many researches have modeled the shape of the absorption edge of such materials on the Elliott model and its several modifications such as non-parabolic bands, magnetic potentials and electro-hole-polaron interactions. However, exciton binding energies obtained from measured data often vary strongly depending on the chosen model. Here, we propose an alternative and rather simple approach, which has previously been successful in the determination of the optical bandgap of amorphous, direct and indirect semiconductors, based on the bands-fluctuations (BF) model. In this model, the fluctuations due to disorder, temperature or lattice vibrations give rise to the well known exponential distribution of band tail states (Urbach tails). This analysis results in an analytic equation with 5 parameters only. The binding energies and optical bandgaps of GaAs and the family of tri-halide perovskites ($\textrm{MAPbX}_{3}$), $\textrm{X=Br,I,Cl}$, over a wide range of temperatures, are obtained with this model. The results for the bandgap, linewidth and exciton binding energy are in good agreement with previous reports. Moreover, due to the polar nature of perovskites, the obtained binding energies can be compared with the ones computed with a theoretical model for polar materials via a model proposed by Kane et al. In this model, the exciton is surrounded by a cloud of virtual phonons interacting via the Fr$\ddot{\textrm{o}}$lich interaction. As a consequence, the upper bound for the binding energy of the exciton-polaron system is calculated. Coincidentally, these results are in good agreement with the optical constants obtained with the EBF model., Comment: Preprint submitted to: J. Phys. D: Appl. Phys