The Effect of the Short-Range Correlations on the Generalized Momentum Distribution in Finite Nuclei

The effect of dynamical short-range correlations on the generalized momentum distribution $n(\vec{p},\vec{Q})$ in the case of $Z=N$, $\ell$-closed shell nuclei is investigated by introducing Jastrow-type correlations in the harmonic-oscillator model. First, a low order approximation is considered and applied to the nucleus $^4$He. Compact analytical expressions are derived and numerical results are presented and the effect of center-of-mass corrections is estimated. Next, an approximation is proposed for $n(\vec{p}, \vec{Q})$ of heavier nuclei, that uses the above correlated $n(\vec{p},\vec{Q})$ of $^4$He. Results are presented for the nucleus $^{16}$O. It is found that the effect of short-range correlations is significant for rather large values of the momenta $p$ and/or $Q$ and should be included, along with center of mass corrections for light nuclei, in a reliable evaluation of $n(\vec{p},\vec{Q})$ in the whole domain of $p$ and $Q$.Comment: 29 pages, 8 figures. Further results, figures and discussion for the CM corrections are added. Accepted by Journal of Physics

Impact of curvature on the optimal configuration of flexible luminescent solar concentrators

Flexible luminescent solar concentrators (LSCs) could deliver integrated photovoltaics in all aspects of our lives, from architecture to wearable electronics. We present and experimentally verify a model for the optimization of the external optical efficiency of LSCs under varying degrees of curvature. We demonstrate differences between the optimization of flat and bent LSCs, showing that optimal fluorophore concentrations can differ by a factor of two

Losses in luminescent solar concentrators unveiled

A novel experimental method is presented to determine the optical efficiency and the loss channels of a luminescent solar concentrator (LSC). Despite strong promise, LSCs have not yet reached their full potential due to various mechanisms affecting the device's optical efficiency. Among those loss channels, escape cone and non-unity quantum yield losses are generally the most dominant. To further advance the field of LSCs, it is vital to understand the impact of each independently. So far, researchers have only characterized the total loss in LSCs. Here, an experimental method is proposed to separate the contribution from each individual loss channel. The experimental apparatus is the same as used for quantum yield measurements of fluorophores in solid samples. Therefore, the setup is commonly available to research groups already involved in LSC research. The accuracy of this method is demonstrated by comparing the experimental results with Monte-Carlo ray tracing. Our experimental method can have a strong impact on LSC research as it offers a means to unveil the loss channels of LSCs in addition to the optical efficiency

To what extent are we confident that tapentadol induces less constipation and other side effects than the other opioids in chronic pain patients? : A confidence evaluation in network meta-analysis

Open Access offered under Sage Agreement-waiting for decisionPeer reviewedPostprin

Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management

We present a multi-agent Deep Reinforcement Learning (DRL) framework for managing large transportation infrastructure systems over their life-cycle. Life-cycle management of such engineering systems is a computationally intensive task, requiring appropriate sequential inspection and maintenance decisions able to reduce long-term risks and costs, while dealing with different uncertainties and constraints that lie in high-dimensional spaces. To date, static age- or condition-based maintenance methods and risk-based or periodic inspection plans have mostly addressed this class of optimization problems. However, optimality, scalability, and uncertainty limitations are often manifested under such approaches. The optimization problem in this work is cast in the framework of constrained Partially Observable Markov Decision Processes (POMDPs), which provides a comprehensive mathematical basis for stochastic sequential decision settings with observation uncertainties, risk considerations, and limited resources. To address significantly large state and action spaces, a Deep Decentralized Multi-agent Actor-Critic (DDMAC) DRL method with Centralized Training and Decentralized Execution (CTDE), termed as DDMAC-CTDE is developed. The performance strengths of the DDMAC-CTDE method are demonstrated in a generally representative and realistic example application of an existing transportation network in Virginia, USA. The network includes several bridge and pavement components with nonstationary degradation, agency-imposed constraints, and traffic delay and risk considerations. Compared to traditional management policies for transportation networks, the proposed DDMAC-CTDE method vastly outperforms its counterparts. Overall, the proposed algorithmic framework provides near optimal solutions for transportation infrastructure management under real-world constraints and complexities