Low-energy electronic excitations and band-gap renormalization in CuO

Combining nonresonant inelastic x-ray scattering experiments with state-of-the-art ab initio many-body calculations, we investigate the electronic screening mechanisms in strongly correlated CuO in a large range of energy and momentum transfers. The excellent agreement between theory and experiment, including the low-energy charge excitations, allows us to use the calculated dynamical screening as a safe building block for many-body perturbation theory and to elucidate the crucial role played by d-d excitations in renormalizing the band gap of CuO. In this way we can dissect the contributions of different excitations to the electronic self-energy which is illuminating concerning both the general theory and this prototypical material.Combining nonresonant inelastic x-ray scattering experiments with state-of-the-art ab initio many-body calculations, we investigate the electronic screening mechanisms in strongly correlated CuO in a large range of energy and momentum transfers. The excellent agreement between theory and experiment, including the low-energy charge excitations, allows us to use the calculated dynamical screening as a safe building block for many-body perturbation theory and to elucidate the crucial role played by d-d excitations in renormalizing the band gap of CuO. In this way we can dissect the contributions of different excitations to the electronic self-energy which is illuminating concerning both the general theory and this prototypical material.Combining nonresonant inelastic x-ray scattering experiments with state-of-the-art ab initio many-body calculations, we investigate the electronic screening mechanisms in strongly correlated CuO in a large range of energy and momentum transfers. The excellent agreement between theory and experiment, including the low-energy charge excitations, allows us to use the calculated dynamical screening as a safe building block for many-body perturbation theory and to elucidate the crucial role played by d-d excitations in renormalizing the band gap of CuO. In this way we can dissect the contributions of different excitations to the electronic self-energy which is illuminating concerning both the general theory and this prototypical material.Peer reviewe

Deep Learning-based Fingerprinting for Outdoor UE Positioning Utilising Spatially Correlated RSSs of 5G Networks

Outdoor user equipment (UE) localisation has attracted a significant amount of attention due to its importance in many location-based services. Typically, in rural and open areas, global navigation satellite systems (GNSS) can provide an accurate and reliable localisation performance. However, in urban areas GNSS localisation accuracy is significantly reduced due to shadowing, scattering and signal blockages. In this work, the UE positioning assisted by deep learning in 5G and beyond networks is investigated in an urban area environment. We study the impact of utilising the spatial correlation in the received signal strengths (RSSs) on the UE positioning accuracy and how to utilise such correlation with deep learning algorithms to improve the localisation accuracy. Numerical results showed the importance of utilising the spatial correlation in the RSS to improve the prediction accuracy for all of the considered models. In addition, the impact of varying the number of access points (APs) transmitters on the localisation accuracy is also investigated. Numerical results showed that a lower number of APs may be sufficient when not considering uncertainties in RSS measurements. Moreover, we study how much the degrading effect of RSS uncertainty can be compensated for by increasing the number of APs.Peer reviewe

Multi-Objective Deep Reinforcement Learning for 5G Base Station Placement to Support Localisation for Future Sustainable Traffic

Millimeter-wave (mmWave) is a key enabler for next-generation transportation systems. However, in an urban city scenario, mmWave is highly susceptible to blockages and shadowing. Therefore, base station (BS) placement is a crucial task in the infrastructure design where coverage requirements need to be met while simultaneously supporting localisation. This work assumes a pre-deployed BS and another BS is required to be added to support both localisation accuracy and coverage rate in an urban city scenario. To solve this complex multi-objective optimisation problem, we utilise deep reinforcement learning (DRL). Concretely, this work proposes: 1) a three-layered grid for state representation as the input of the DRL, which enables it to adapt to the changes in the wireless environment represented by changing the position of the pre-deployed BS, and 2) the design of a suitable reward function for the DRL agent to solve the multi-objective problem. Numerical analysis shows that the proposed deep Q-network (DQN) model can learn/adapt from the complex radio environment represented by the terrain map and provides the same/similar solution to the exhaustive search, which is used as a benchmark. In addition, we show that an exclusive optimisation of coverage rate does not result in improved localisation accuracy, and thus there is a trade-off between the two solutions.Comment: Accepted EuCNC 202

siRNA-Mediated Reduction of Inhibitor of Nuclear Factor-κB Kinase Prevents Tumor Necrosis Factor-α–Induced Insulin Resistance in Human Skeletal Muscle

OBJECTIVE—Proinflammatory cytokines contribute to systemic low-grade inflammation and insulin resistance. Tumor necrosis factor (TNF)-α impedes insulin signaling in insulin target tissues. We determined the role of inhibitor of nuclear factor-κB kinase (IKK)β in TNF-α–induced impairments in insulin signaling and glucose metabolism in skeletal muscle

High-precision measurements of low-lying isomeric states in 120−124^{120-124}In with JYFLTRAP double Penning trap

Neutron-rich $^{120-124}$In isotopes have been studied utilizing the double Penning trap mass spectrometer JYFLTRAP at the IGISOL facility. Using the phase-imaging ion-cyclotron-resonance technique, the isomeric states were resolved from ground states and their excitation energies measured with high precision in $^{121,123,124}$In. In $^{120,122}$In, the $1^+$ states were separated and their masses were measured while the energy difference between the unresolved $5^+$ and $8^-$ states, whose presence was confirmed by post-trap decay spectroscopy was determined to be $\leq15$ keV. In addition, the half-life of $^{122}$Cd, $T_{1/2} = 5.98(10)$ s, was extracted. Experimental results were compared with energy density functionals, density functional theory and shell-model calculations.Comment: 11 pages, 7 figure

Binding energies of ground and isomeric states in neutron-rich ruthenium isotopes: measurements at JYFLTRAP and comparison to theory

We report on precision mass measurements of $^{113,115,117}$Ru performed with the JYFLTRAP double Penning trap mass spectrometer at the Accelerator Laboratory of University of Jyv\"askyl\"a. The phase-imaging ion-cyclotron-resonance technique was used to resolve the ground and isomeric states in $^{113,115}$Ru and enabled for the first time a measurement of the isomer excitation energies, $E_x(^{113}$Ru$^{m})=100.5(8)$ keV and $E_x(^{115}$Ru$^{m})=129(5)$ keV. The ground state of $^{117}$Ru was measured using the time-of-flight ion-cyclotron-resonance technique. The new mass-excess value for $^{117}$Ru is around 36 keV lower and 7 times more precise than the previous literature value. With the more precise ground-state mass values, the evolution of the two-neutron separation energies is further constrained and a similar trend as predicted by the BSkG1 model is obtained up to the neutron number $N=71$.Comment: 12 pages, 9 figures, submitted to Physical Review