bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stochastic equations and cities
Authors: Barthelemy Marc 
Published: July 2023

Abstract
Stochastic equations constitute a major ingredient in many branches of science, from physics to biology and engineering. Not surprisingly, they appear in many quantitative studies of complex systems. In particular, this type of equation is useful for understanding the dynamics of urban population. Empirically, the population of cities follows a seemingly universal law—called Zipf’s law—which was discovered about a century ago and states that when sorted in decreasing order, the population of a city varies as the inverse of its rank. Recent data however showed that this law is only approximate and in some cases not even verified. In addition, the ranks of cities follow a turbulent dynamics: some cities rise while other fall and disappear. Both these aspects—Zipf’s law (and deviations around it), and the turbulent dynamics of ranks—need to be explained by the same theoretical framework and it is natural to look for the equation that governs the evolution of urban populations. We will review here the main theoretical attempts based on stochastic equations to describe these empirical facts. We start with the simple Gibrat model that introduces random growth rates, and we will then discuss the Gabaix model that adds friction for allowing the existence of a stationary distribution. Concerning the dynamics of ranks, we will discuss a phenomenological stochastic equation that describes rank variations in many systems—including cities—and displays a noise-induced transition. We then illustrate the importance of exchanges between the constituents of the system with the diffusion with noise equation. We will explicit this in the case of cities where a stochastic equation for populations can be derived from first principles and confirms the crucial importance of inter-urban migrations shocks for explaining the statistics and the dynamics of the population of cities.
The influence of porosity on the superconducting properties of Y–Ba–Cu–O single grains
Authors: Baumann Josef , Shi Yunhua , Dennis Anthony R , Durrell John H , Cardwell David A 
Published: July 2023

Abstract
The trapped field in Y–Ba–Cu–O (YBCO) bulk single grain superconductors correlates directly with the critical current density J
c, which, in turn, is dependent on the microstructure of the bulk material. It has been shown recently and indirectly that porosity influences J
c of these technologically important materials, in addition to the presence of well-researched Y2BaCuO5 (Y-211) particles in the bulk sample. In this work, we report the direct impact of porosity on the critical current density of a single grain YBCO bulk superconductor using 3D x-ray computer tomography scans and superconducting quantum interference device measurements. It is concluded that porosity has a considerably more substantial impact on the measured critical current density than Y-211 on the micrometre scale with, predictably, a decrease in porosity leading to an increase in J
c. J
c is directly proportional to the trapped field, so any method that can reduce porosity, therefore, improves J
c, and, subsequently, the trapped field of these technologically important bulk superconductors.
CMB signature of non-thermal Dark Matter produced from self-interacting dark sector
Authors: Ghosh Dilip Kumar , Ghosh Purusottam , Jeesun Sk 
Published: July 2023

Abstract
The basic idea of this work is to achieve the observed relic density of a non-thermal dark matter(DM) and its connection with Cosmic Microwave Background (CMB) via
additional relativistic degrees of freedom which are simultaneously generated during
the period T
BBN to TCMB
from a long-lived dark sector particle.
To realize this phenomena we minimally extend the type-I seesaw scenario with a Dirac
fermion singlet(χ) and a complex scalar singlet (φ) which transform
non-trivially under an unbroken symmetry Z̶
3. χ being the lightest
particle in the dark sector acts as a stable dark matter candidate while the next to
lightest state φ operates like a long lived dark scalar particle. The initial
density of φ can be thermally produced through either self-interacting number
changing processes (3φ ⟶ 2φ) within dark sector or the standard
annihilation to SM particles (2φ ⟶ 2 SM).
The late time (after neutrino decoupling) non-thermal decay of φ can produce
dark matter in association with active neutrinos. The presence of extra
relativistic neutrino degrees of freedom at the time of CMB can have a
significant impact on ΔNeff. Thus the precise measurement of
ΔNeff by current PLANCK 2018 collaboration and future experiments
like SPT-3G and CMB-S4 can indirectly probe this non-thermal dark matter scenario
which is otherwise completely secluded due to its tiny coupling with the standard
model.
Regularized Maximum Likelihood Image Synthesis and Validation for ALMA Continuum Observations of Protoplanetary Disks
Authors: Zawadzki Brianna , Czekala Ian , Loomis Ryan A. , Quinn Tyler , Grzybowski Hannah , Frazier Robert C. , Jennings Jeff , Nizam Kadri M. , Jian Yina 
Published: July 2023

Abstract
Regularized Maximum Likelihood (RML) techniques are a class of image synthesis methods that achieve better angular resolution and image fidelity than traditional methods like CLEAN for sub-mm interferometric observations. To identify best practices for RML imaging, we used the GPU-accelerated open source Python package MPoL, a machine learning-based RML approach, to explore the influence of common RML regularizers (maximum entropy, sparsity, total variation, and total squared variation) on images reconstructed from real and synthetic Atacama Large millimeter/submillimeter Array (ALMA) continuum observations of protoplanetary disks. We tested two different cross-validation (CV) procedures to characterize their performance and determine optimal prior strengths, and found that CV over a coarse grid of regularization strengths easily identifies a range of models with comparably strong predictive power. To evaluate the performance of RML techniques against a ground truth image, we used MPoL on a synthetic protoplanetary disk data set and found that RML methods successfully resolve structures at fine spatial scales present in the original simulation. We used ALMA DSHARP observations of the protoplanetary disk around HD 143006 to compare the performance of MPoL and CLEAN, finding that RML imaging improved the spatial resolution of the image by up to a factor of 3 without sacrificing sensitivity. We provide general recommendations for building an RML workflow for image synthesis of ALMA protoplanetary disk observations, including effective use of CV. Using these techniques to improve the imaging resolution of protoplanetary disk observations will enable new science, including the detection of protoplanets embedded in disks.
Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR
Authors: Kim Minwoo , Shin G. , Lee J. , Ko W.H. , Han H. , Hahn S.-H. , Kim S.K. , Yang S.M. , Shousha R. , Kim H.S. , Juhn J.-W. , Park G.Y. , Kolemen E. 
Published: July 2023

Abstract
The integrated Resonant Magnetic Perturbation (RMP)-based Edge-Localized Mode (ELM)-crash-control process aims to enhance the plasma performance during the RMP-driven ELM crash suppression, where the RMP induces an unwanted confinement degradation. In this study, the normalized beta (

β

N

) is introduced as a metric for plasma performance. The integrated process incorporates the latest achievements in the RMP technique to enhance

β

N

efficiently. The integrated process triggers the n = 1 Edge-localized RMP (ERMP) at the L–H transition timing using the real-time Machine Learning (ML) classifier. The pre-emptive RMP onset can reduce the required external heating power for achieving the same

β

N

by over 10% compared to the conventional onset. During the RMP phase, the adaptive feedback RMP ELM controller, demonstrating its performance in previous experiments, plays a crucial role in maximizing

β

N

during the suppression phase and sustaining the

β

N

-enhanced suppression state by optimizing the RMP strength. The integrated process achieves

β

N

up to ∼2.65 during the suppression phase, which is ∼10% higher than the previous KSTAR record but ∼6% lower than the target of the K-DEMO first phase (

β

N

= 2.8), and maintains the suppression phase above the lower limit of target

β

N

(= 2.4) for ∼4 s (∼60

τ

E

). In addition to

β

N

enhancement, the integrated process demonstrates quicker restoration of the suppression phase and recovery of

β

N

compared to the adaptive control with the n = 1 Conventional RMP (CRMP). The post-analysis of the experiment shows the localized effect of the ERMP spectrum in radial and the close relationship between the evolution of

β

N

and the electron temperature.
Global gyrokinetic simulations of the impact of magnetic island on ion temperature gradient driven turbulence
Authors: Li Jingchun , Xu J.Q. , Qu Y.R. , Lin Z. , Dong J.Q. , Peng X.D. , Li J.Q. 
Published: July 2023

Abstract
The effect of island width on the multi-scale interactions between magnetic island (MI) and ion temperature gradient (ITG) turbulence has been investigated based on the global gyrokinetic approach. It is found that the coupling between the island and turbulence is enhanced when the MI width (w) becomes larger. A vortex flow that is highly sensitive to the width of the MI can be triggered, ultimately resulting in a potent

E
×
B

shear flow and a consequent reduction in turbulent transport. The shearing rate induced by the vortex flow is minimum at the O-point while it is maximum at both of the two reconnection points of the island, i.e. the X-points, regardless of the island width. There exists a nonmonotonic relationship between zonal flow (ZF) amplitude and island width, showing that the ZF is partially suppressed by medium-sized MIs whereas enhanced in the case of large island. A larger MI can tremendously damage the ITG mode structure, resulting in higher turbulent transport at the X-point whereas a lower one at the O-point, respectively. Such phenomenon will be less distinct at very small island widths below w/a ∼ 8% ≈ 12

ρ
i

(a is the minor radius and

ρ
i

the ion gyroradius), where it shows that turbulence near the X-point is hardly affected although it is still suppressed inside the island. Furthermore, the influence of different island sizes on turbulence transport level is also discussed.
Comparison of a novel machine learning approach with dynamical downscaling for Australian precipitation
Authors: Nishant Nidhi , Hobeichi Sanaa , Sherwood Steven , Abramowitz Gab , Shao Yawen , Bishop Craig , Pitman Andy 
Published: July 2023

Abstract
Dynamical downscaling (DD), and machine learning (ML) based techniques have been widely applied to downscale global climate models and reanalyses to a finer spatiotemporal scale, but the relative performance of these two methods remains unclear. We implement an ML regression approach using a multi-layer perceptron (MLP) with a novel loss function to downscale coarse-resolution precipitation from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia from grids of 12–48 km to 5 km, using the Australia Gridded Climate Data observations as the target. A separate MLP is developed for each coarse grid to predict the fine grid values within it, by combining coarse-scale time-varying meteorological variables with fine-scale static surface properties as predictors. The resulting predictions (on out-of-sample test periods) are more accurate than DD in capturing the rainfall climatology, as well as the frequency distribution and spatiotemporal variability of daily precipitation, reducing biases in daily extremes by 15%–85% with 12 km prediction fields. When prediction fields are coarsened, the skill of the MLP decreases—at 24 km relative bias increases by ∼10%, and at 48 km it increases by another ∼4%—but skill remains comparable to or, for some metrics, much better than DD. These results show that ML-based downscaling benefits from higher-resolution driving data but can still improve on DD (and at far less computational cost) when downscaling from a global climate model grid of ∼50 km.
Shared mobility and lifestyles as mechanisms to reduce environmental impacts from passenger transportation
Authors: Arbeláez Vélez Ana María , Ivanova Diana , Stadler Konstantin 
Published: July 2023

Abstract
Despite the deployment of low- or zero-emission technologies, achieving emissions reductions in the passenger transportation sector remains challenging. Demand-side mechanisms can be instrumental in reducing environmental impacts of transportation and reconfiguring transportation systems in a way that shifts users away from private car ownership. In this article we look at the Netherlands, Sweden, and the United States to quantify the environmental benefits from such shifts in passenger transportation, considering socio-technological drivers of transportation including well-being, digitalization, shared mobility, and electrification. We establish pathways for each of these countries considering their context. We frame these pathways using the avoid-shift-improve framework which shapes the scenarios that we quantify in our analysis. We use a travel demand model as an input to calculate carbon, energy, and air pollution footprints. We quantify direct emissions considering the characteristics of the private fleet and indirect using multiregional input-output analysis. The results show that target thresholds can be reached under the proposed supply and demand initiatives. For the United States, these actions are more dramatic than for the Netherlands and Sweden due to that country’s stronger car dependence. A deep social transformation is needed to make these scenarios possible and enable a shift towards public, active and shared transportation in urban areas.
Screening for volatile biomarkers of colorectal cancer by analyzing breath and fecal samples using thermal desorption combined with GC-MS (TD-GC-MS)
Authors: Śmiełowska Monika , Ligor Tomasz , Kupczyk Wojciech , Szeliga Jacek , Jackowski Marek , Buszewski Bogusław 
Published: July 2023

Abstract
Breath and fecal VOCs, among others, represent a new and encouraging clinical practice for the differential diagnosis of CRC. The purpose of our research was to identify VOCs present in exhaled air and feces of 20 HVs and 15 CRC patients. For collection of gas phase released from feces, emission microchambers were applied. Sorption tubes were used to enrich analytes for both breath and fecal samples. TD technique combined with GC-MS was used at the separation and identification step. The combination of statistical methods was used to evaluate the ability of VOCs to classify control group and CRC patients. Heptanoic acid, acetone, 2,6,10-trimethyldodecane, n-hexane, skatole, and dimethyl trisulfide are observed in elevated amounts in the patients group. The performance of diagnostic models on the tested data set was above 90%. This study is the first attempt to document the using of TD-GC-MS to analyze both breath and fecal samples to search for volatile biomarkers of CRC. A full evaluation of the results described herein requires further studies involving a larger number of samples. Moreover, it is particularly important to understand the metabolic pathways of substances postulated as tumor biomarkers.
Establishing a cell-based screening workflow for determining the efficiency of CYP2C9 metabolism: moving towards the use of breath volatiles in personalised medicine
Authors: Lochmann Franziska , Nikolajevic Aleksandar , Stock Valentina , Kammerer Sarah , Fernández-Quintero Monica L , Loeffler Johannes R , Liedl Klaus R , Troppmair Jakob , Mayhew Chris A , Ruzsanyi Veronika 
Published: July 2023

Abstract
The use of volatile biomarkers in exhaled breath as predictors to individual drug response would advance the field of personalised medicine by providing direct information on enzyme activity. This would result in enormous benefits, both for patients and for the healthcare sector. Non-invasive breath tests would also gain a high acceptance by patients. Towards this goal, differences in metabolism resulting from extensive polymorphisms in a major group of drug-metabolizing enzymes, the cytochrome P450 (CYP) family, need to be determined and quantified. CYP2C9 is responsible for metabolising many crucial drugs (e.g., diclofenac) and food ingredients (e.g., limonene). In this paper, we provide a proof-of-concept study that illustrates the in vitro bioconversion of diclofenac in recombinant HEK293T cells overexpressing CYP2C9 to 4ʹ-hydroxydiclofenac. This in vitro approach is a necessary and important first step in the development of breath tests to determine and monitor metabolic processes in the human body. By focusing on the metabolic conversion of diclofenac, we have been able to establish a workflow using a cell-based system for CYP2C9 activity. Furthermore, we illustrate how the bioconversion of diclofenac is limited in the presence of limonene, which is another CYP2C9 metabolising substrate. We show that increasing limonene levels continuously reduce the production of 4ʹ-hydroxydiclofenac. Michaelis-Menten kinetics were performed for the diclofenac 4ʹ-hydroxylation with and without limonene, giving a kinetic constant of the reaction, K
M, of 103 µM and 94.1 µM, respectively, and a maximum reaction rate, V
max, of 46.8 pmol min−1 106 cells−1 and 56.0 pmol min−1 106 cells−1 with and without the inhibitor, respectively, suggesting a non-competitive or mixed inhibition type. The half-maximal inhibitory concentration value (IC50) for the inhibition of the formation of 4ʹ-hydroxydiclofenace by limonene is determined to be 1413 µM.
Laser-guided programmable construction of cell-laden hydrogel microstructures for in vitro drug evaluation
Authors: Dong Jianpei , Zhou Jianhua , Tang Hao , Chen Baiqi , Huang Lu 
Published: July 2023

Abstract
Cell-laden hydrogel microstructures have been used in broad applications in tissue engineering, translational medicine, and cell-based assays for pharmaceutical research. However, the construction of cell-laden hydrogel microstructures in vitro remains challenging. The technologies permitting generation of multicellular structures with different cellular compositions and spatial distributions are needed. Herein, we propose a laser-guided programmable hydrogel-microstructures-construction platform, allowing controllable and heterogeneous assembly of multiple cellular spheroids into spatially organized multicellular structures with good bioactivity. And the cell-laden hydrogel microstructures could be further leveraged for in vitro drug evaluation. We demonstrate that cells within hydrogels exhibit significantly higher half-maximal inhibitory concentration values against doxorubicin compared with traditional 2D plate culture. Moreover, we reveal the differences in drug responses between heterogeneous and homogeneous cell-laden hydrogel microstructures, providing valuable insight into in vitro drug evaluation.
Synthesis of partially coherent Bessel-mode vortex-beams with radial coherence
Authors: Olvera-Santamaría M Á , Ostrovsky A S 
Published: July 2023

Abstract
Partially coherent Bessel-mode vortex-beams with radial coherence are introduced. The generated beams are fully coherent at pair of points along the same radial coordinate. The field is completely incoherent for pairs of points belonging to different angular positions. By using the coherent-mode structure of propagation invariant fields, the analytical expression of the propagated cross-spectral density, representing fields with radial coherence, is derived. It is shown that beams of this type can be generated in a Fourier transforming optical system. An important feature of the synthesized beams is their ability of being invariant under propagation. The behaviour of the degree of coherence is analysed in terms of the eigenvalues of the modal structure. A numerical ensemble of realizations, at both planes of the considered system, was generated. From this ensemble, the spectral intensity of the proposed beams was obtained. The numerical results show a well-defined principal thin ring of maximum intensity followed by secondary concentric rings, in complete agreement with theoretical predictions. We believe that presented scheme can trigger new research routes in the synthesis of fields with structured coherence.
Effects of quenching temperature on threshold, driving voltage and morphology of reverse mode liquid crystal gel films
Authors: Chen Yi-Xuan , Hsu Jy-Shan 
Published: July 2023

Abstract
This study demonstrated that the electro-optical properties and morphology of reverse mode liquid crystal (LC) gel films are strongly influenced by the quenching temperature (TQ) during the manufacturing processes. Composite films were self-assembled using LC and gelator molecules by using different TQ values (70, 80, 90, 100, 110 and 120 °C) and a cold brine (−15 °C). As TQ increased, the morphology of the gel in the polarised light microscope images are changed from neatly arranged fibre-like patterns to random disordered networks. Furthermore, the threshold and driving voltages of the films decreased. For further investigating the gelation processes, Steady cooling rates of 3, 10 and 30 °C min−1 were adopted during the manufacturing process of the LC films. The corresponding morphologies of the films were carefully inspected and compared with those of the quenched films.
Dynamic & norm-based weights to normalize imbalance in back-propagated gradients of physics-informed neural networks
Authors: Deguchi Shota , Asai Mitsuteru 
Published: July 2023

Abstract
Physics-Informed Neural Networks (PINNs) have been a promising machine learning model for evaluating various physical problems. Despite their success in solving many types of partial differential equations (PDEs), some problems have been found to be difficult to learn, implying that the baseline PINNs is biased towards learning the governing PDEs while relatively neglecting given initial or boundary conditions. In this work, we propose Dynamically Normalized Physics-Informed Neural Networks (DN-PINNs), a method to train PINNs while evenly distributing multiple back-propagated gradient components. DN-PINNs determine the relative weights assigned to initial or boundary condition losses based on gradient norms, and the weights are updated dynamically during training. Through several numerical experiments, we demonstrate that DN-PINNs effectively avoids the imbalance in multiple gradients and improves the inference accuracy while keeping the additional computational cost within a reasonable range. Furthermore, we compare DN-PINNs with other PINNs variants and empirically show that DN-PINNs is competitive with or outperforms them. In addition, since DN-PINN uses exponential decay to update the relative weight, the weights obtained are biased toward the initial values. We study this initialization bias and show that a simple bias correction technique can alleviate this problem.
Evidence that tissue recoil in the early Drosophila embryo is a passive not active process
Authors: Goldner Amanda Nicole , Fessehaye Salena M. , Rodriguez Nataly , Mapes Kelly Ann , Osterfield Miriam , Doubrovinski Konstantin 
Published: July 2023

Techniques to examine the contribution of specific proteins to tissue mechanical properties are extremely limited. Here, Goldner et al. develop two complementary techniques for rapid protein depletion combined with mechanical measurements and show that myosin activity is dispensable for tissue elasticity.