bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unified neutron star EOSs and neutron star structures in RMF models
Authors: Xia Cheng-Jun , Maruyama Toshiki , Li Ang , Yuan Sun Bao , Long Wen-Hui , Zhang Ying-Xun 
Published: May 2022

Abstract
In the framework of the Thomas-Fermi approximation, we systematically study the EOSs and microscopic structures of neutron star matter in a vast density range with n
b ≈ 10−10-2 fm−3, where various covariant density functionals are adopted, i.e., those with nonlinear self couplings (NL3, PK1, TM1, GM1, MTVTC) and density-dependent couplings (DD-LZ1, DDME-X, PKDD, DD-ME2, DD2, TW99). It is found that the EOSs generally coincide with each other at n
b ≲ 10−4 fm−3 and 0.1 fm−3 ≲ n
b ≲ 0.3 fm−3, while in other density regions they are sensitive to the effective interactions between nucleons. By adopting functionals with a larger slope of symmetry energy L, the curvature parameter K
sym and neutron drip density generally increases, while the droplet size, proton number of nucleus, core-crust transition density, and onset density of non-spherical nuclei, decrease. All functionals predict neutron stars with maximum masses exceeding the two-solar-mass limit, while those of DD2, DD-LZ1, DD-ME2, and DDME-X predict optimum neutron star radii according to the observational constraints. Nevertheless, the corresponding skewness coefficients J are much larger than expected, while only the functionals MTVTC and TW99 meet the start-of-art constraints on J. More accurate measurements on the radius of PSR J0740 + 6620 and the maximum mass of neutron stars are thus essential to identify the functional that satisfies all constraints from nuclear physics and astrophysical observations. Approximate linear correlations between neutron stars’ radii at M = 1.4M
⊙ and 2M
⊙, the slope L and curvature parameter K
sym of symmetry energy are observed as well, which are mainly attributed to the curvature-slope correlations in the functionals adopted here. The results presented here are applicable for investigations of the structures and evolutions of compact stars in a unified manner.
A deep learning method for solving high-order nonlinear soliton equations
Authors: Cui Shikun , Wang Zhen , Han Jiaqi , Cui Xinyu , Meng Qicheng 
Published: May 2022

Abstract
We propose an effective scheme of the deep learning method for high-order nonlinear soliton equations and explore the influence of activation functions on the calculation results for higher-order nonlinear soliton equations. The physics-informed neural networks approximate the solution of the equation under the conditions of differential operator, initial condition and boundary condition. We apply this method to high-order nonlinear soliton equations, and verify its efficiency by solving the fourth-order Boussinesq equation and the fifth-order Korteweg–de Vries equation. The results show that the deep learning method can be used to solve high-order nonlinear soliton equations and reveal the interaction between solitons.
Variational Bayesian dropout with a Gaussian prior for recurrent neural networks application in rainfall–runoff modeling
Authors: Sadeghi Tabas S , Samadi S 
Published: May 2022

Abstract
Recurrent neural networks (RNNs) are a class of artificial neural networks capable of learning complicated nonlinear relationships and functions from a set of data. Catchment scale daily rainfall–runoff relationship is a nonlinear and sequential process that can potentially benefit from these intelligent algorithms. However, RNNs are perceived as being difficult to parameterize, thus translating into significant epistemic (lack of knowledge about a physical system) and aleatory (inherent randomness in a physical system) uncertainties in modeling. The current study investigates a variational Bayesian dropout (or Monte Carlo dropout (MC-dropout)) as a diagnostic approach to the RNNs evaluation that is able to learn a mapping function and account for data and model uncertainty. MC-dropout uncertainty technique is coupled with three different RNN networks, i.e. vanilla RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) to approximate Bayesian inference in a deep Gaussian noise process and quantify both epistemic and aleatory uncertainties in daily rainfall–runoff simulation across a mixed urban and rural coastal catchment in North Carolina, USA. The variational Bayesian outcomes were then compared with the observed data as well as with a well-known Sacramento soil moisture accounting (SAC-SMA) model simulation results. Analysis suggested a considerable improvement in predictive log-likelihood using the MC-dropout technique with an inherent input data Gaussian noise term applied to the RNN layers to implicitly mitigate overfitting and simulate daily streamflow records. Our experiments on the three different RNN models across a broad range of simulation strategies demonstrated the superiority of LSTM and GRU approaches relative to the SAC-SMA conceptual hydrologic model.
Non-equilibrium thermodynamics of diffusion in fluctuating potentials
Authors: Alston Henry , Cocconi Luca , Bertrand Thibault 
Published: May 2022

Abstract
A positive rate of entropy production at steady-state is a distinctive feature of truly non-equilibrium processes. Exact results, while being often limited to simple models, offer a unique opportunity to explore the thermodynamic features of these processes in full detail. Here we derive analytical results for the steady-state rate of entropy production in single particle systems driven away from equilibrium by the fluctuations of an external potential of arbitrary shapes. Subsequently, we provide exact results for a diffusive particle in a harmonic trap whose potential stiffness varies in time according to both discrete and continuous Markov processes. In particular, studying the case of a fully intermittent potential allows us to introduce an effective model of stochastic resetting for which it is possible to obtain finite non-negative entropy production. Altogether, this work lays the foundation for a non-equilibrium thermodynamic theory of fluctuating potentials, with immediate applications to stochastic resetting processes, fluctuations in optical traps and fluctuating interactions in living systems.
Computational properties of multi-compartment LIF neurons with passive dendrites
Authors: Stöckel Andreas , Eliasmith Chris 
Published: May 2022

Abstract
Mixed-signal neuromorphic computers often emulate some variant of the LIF neuron model. While, in theory, two-layer networks of these neurons are universal function approximators, single-layer networks consisting of slightly more complex neurons can, at the cost of universality, be more efficient. In this paper, we discuss a family of LIF neurons with passive dendrites. We provide rules that describe how input channels targeting different dendritic compartments interact, and test in how far these interactions can be harnessed in a spiking neural network context. We find that a single layer of two-compartment neurons approximates some functions at smaller errors than similarly sized hidden-layer networks. Single-layer networks with with three compartment neurons can approximate functions such as XOR and four-quadrant multiplication well; adding more compartments only offers small improvements in accuracy. From the perspective of mixed-signal neuromorphic systems, our results suggest that only small modifications to the neuron circuit are necessary to construct more computationally powerful and energy efficient systems that move more computation into the dendritic, analogue domain.
Developing metrics to assess the effectiveness of cybersecurity awareness program
Authors: Chaudhary Sunil , Gkioulos Vasileios , Katsikas Sokratis 
Published: May 2022

Abstract
Cybersecurity awareness (CSA) is not just about knowing, but also transforming things learned into practice. It is a continuous process that needs to be adjusted in subsequent iterations to improve its usability as well as sustainability. This is possible only if a CSA program is reviewed and evaluated timely. Review and evaluation of an awareness program offer an insight into the program's effectiveness on the audience and organization, an invaluable piece of information for the continuous improvement of the program. Further, it provides the information required by the management and sponsor to decide on whether to invest in the program or not. Despite these advantages, there does not exist a common understanding of what factors to measure and how to measure them during the evaluation process. As a result, we have proposed evaluation metrics for the purpose. In order to do so, we performed a literature review of 32 papers mainly to extract the following data: (i) what factors did the paper measure, and (ii) how did it measure the factors? Next, we adapted the European Literacy Policy Network's four indicators (i.e. impact, sustainability, accessibility, and monitoring) for awareness evaluation to make it appropriate for evaluating a CSA program. We believe that measuring all four indicators will contribute to making the evaluation process systematic, complete, and replicable. More importantly, it will help to produce more inclusive, accurate, and usable results for the future enhancement of the program.
The Effect of Thermally Robust Ballistic Mechanisms on Climatic Niche in Salamanders
Authors: Friedman Sarah T , Muñoz Martha M 
Published: May 2022

Synopsis
Many organismal functions are temperature-dependent due to the contractile properties of muscle. Spring-based mechanisms offer a thermally robust alternative to temperature-sensitive muscular movements and may correspondingly expand a species’ climatic niche by partially decoupling the relationship between temperature and performance. Using the ballistic tongues of salamanders as a case study, we explore whether the thermal robustness of elastic feeding mechanisms increases climatic niche breadth, expands geographic range size, and alters the dynamics of niche evolution. Combining phylogenetic comparative methods with global climate data, we find that the feeding mechanism imparts no discernable signal on either climatic niche properties or the evolutionary dynamics of most climatic niche parameters. Although biomechanical innovation in feeding influences many features of whole-organism performance, it does not appear to drive macro-climatic niche evolution in salamanders. We recommend that future work incorporate micro-scale environmental data to better capture the conditions that salamanders experience, and we discuss a few outstanding questions in this regard. Overall, this study lays the groundwork for an investigation into the evolutionary relationships between climatic niche and biomechanical traits in ectotherms.
The Persistent Poverty of Diversity in International Relations and the Emergence of a Critical Canon
Authors: Andrews Nathan 
Published: May 2022

Abstract
Discussions about diversifying the discipline of international relations (IR) are often met with limited evidence in practice. Employing the concepts of epistemic oppression and academic dependency, this article contributes to filling the existing knowledge gap by examining what the pedagogical practices of IR professors, particularly in terms of syllabi design and content, tell us about the state of disciplinary diversity. The article examines results from a preliminary study that analyzes different graduate-level IR syllabi from leading universities in the Global North (represented by United States and United Kingdom) and Global South (Africa in particular) in order to determine how their design, including required readings and other pedagogical choices in the classroom, contributes to the explicit diversity needed to push IR beyond its usual canon. The findings suggest that although more perspectives have become accepted or recognized, what is considered essential for graduate students to study and further propagate is still primarily mainstream. Another point is that what has become known as “critical IR” cannot automatically be equated with diversity. This means there is the need to further interrogate and open up more avenues that go beyond what can be characterized as a “critical canon” of IR.
Application and Management of Cultured Epidermal Autografts on Posterior Burns—A 5-Year, Multicenter, Retrospective Review of Outcomes
Authors: Homsombath Bounthavy , Mullins Robert F , Brandigi Claus , Hassan Zaheed , Fagan Shawn , Craft-Coffman Beretta , Olaveson Tait , Fidler Philip , Cramer Caryn , Hershman John 
Published: May 2022

Abstract
Severe burns on the posterior trunk present a treatment challenge in that these surfaces bear the major portion of body weight, with shearing forces exerted when changing the patient from supine to prone position. In their high-volume center at Burn and Reconstructive Centers of America, the authors developed protocols for use of cultured epidermal autografts (CEAs) for coverage of large burns, including those specific to posterior burns. This paper describes techniques and approaches, including milestone timelines, to treat and manage these patients. Key factors for successful treatment begin with early development of a detailed surgical plan. Members of the trained team participate in the plan and understand standard procedures and any deviation. Patients are identified early for treatment with CEA so that a full thickness skin biopsy can be sent to the manufacturer for processing. Patients with &gt;30% total body surface area (TBSA) burns are considered for CEA burn wound coverage due to the potential for conversion of superficial partial thickness to deep partial thickness or full thickness burns over hospitalization time. We also present the outcomes in patients with posterior trunk burns treated with CEA from 2016 to 2019 in three participating centers within our network. Data in 40 patients with mean TBSA of 56% demonstrated a high rate of successful CEA engraftment (83%), and overall survival rate (90%) following one or two applications with CEA and/or CEA + split thickness skin graft (STSG). Development of standard treatment protocols and surgical plans has enabled positive outcomes with CEA in severe burns including posterior burns.
Rehabilitation of Burn Victims: Improving Quality of Life in Victims With Face and Neck Burn Through an Augmented Reality Coupled Pamphlet
Authors: Zal Masoumeh , Deldar Kolsoum , Froutan Razieh , Ahmadabadi Ali , Mazlom Seyed Reza 
Published: May 2022

AbstractThe present study aimed to investigate the effect of implementing rehabilitation programs using an augmented reality (AR) coupled pamphlet on the quality of life (QOL) of patients with face/neck burns. This randomized clinical trial was conducted on 60 patients (intervention = 30, control = 30) admitted to the burn center of Imam Reza Hospital, Mashhad, Iran. Patients in the intervention group performed their rehabilitation program using AR coupled pamphlet during 6 weeks, while control patients used simple pamphlet. The Burn-Specific Health Scale for Face and Neck (BSHS-FN) was completed at the beginning, and 2 and 6 weeks after the intervention. The results for 2 weeks after intervention measures in model 1 ANCOVA showed significant raise of measures only for Hand function (P = .035). However, for the total QOL score and other domains the differences were not significant (All P &gt; .05). Adjusting for confounders, model 2 ANCOVA showed similar results for 2 weeks after intervention. However, in both models, the results indicated significant intervention effect for 6 weeks after intervention in QOL score and all domains (All P &lt; .05), so that the amount of increase in measures were significantly higher in the intervention group. According to the findings, implementing rehabilitation program for patients with face and neck burns using AR coupled pamphlet can improve their QOL.
Real-World Use of Tedizolid Phosphate for 28 Days or More: A Case Series Describing Tolerability and Clinical Success
Authors: Morrisette Taylor , Molina Kyle C , Da Silva Beatriz , Mueller Scott W , Damioli Laura , Krsak Martin , Miller Matthew A , Fish Douglas N 
Published: May 2022

Abstract
Tedizolid has activity against Gram-positive pathogens as well as Mycobacterium spp and Nocardia spp. Real-world evidence supporting long-term tolerability and clinical success of tedizolid is lacking. Prolonged tedizolid therapy (median, 188 days; interquartile range, 62–493 days) appeared to be well tolerated in 37 patients (8.1% experienced adverse effect leading to discontinuation). Clinical success was 81.3% in those evaluated.
A molecular framework of ethylene-mediated fruit growth and ripening processes in tomato
Authors: Huang Wei , Hu Nan , Xiao Zhina , Qiu Yuping , Yang Yan , Yang Jie , Mao Xin , Wang Yichuan , Li Zhengguo , Guo Hongwei 
Published: May 2022

Abstract
Although the role of ethylene in tomato (Solanum lycopersicum) fruit ripening has been intensively studied, its role in tomato fruit growth remains poorly understood. In addition, the relationship between ethylene and the developmental factors NON-RIPENING (NOR) and RIPENING INHIBITOR (RIN) during ripening is under debate. Here, we carried out comprehensive genetic analyses of genome-edited mutants of tomato ETHYLENE INSENSITIVE 2 (SlEIN2), four EIN3-like genes (SlEIL1–4), and three EIN3 BINDING F-box protein genes (SlEBF1–3). Both slein2-1 and the high-order sleil mutant (sleil1 sleil2 sleil3/SlEIL3 sleil4) showed reduced fruit size, mainly due to decreased auxin biosynthesis. During fruit maturation, slein2 mutants displayed the complete cessation of ripening, which was partially rescued by slebf1 but not slebf2 or slebf3. We also discovered that ethylene directly activates the expression of the developmental genes NOR, RIN, and FRUITFULL1 (FUL1) via SlEIL proteins. Indeed, overexpressing these genes partially rescued the ripening defects of slein2-1. Finally, the signal intensity of the ethylene burst during fruit maturation was intimately connected with the progression of full ripeness. Collectively, our work uncovers a critical role of ethylene in fruit growth and supports a molecular framework of ripening control in which the developmental factors NOR, RIN, and FUL1 act downstream of ethylene signaling.
Stochastic block models reveal a robust nested pattern in healthy human gut microbiomes
Authors: Cobo-López Sergio , Gupta Vinod K , Sung Jaeyun , Guimerà Roger , Sales-Pardo Marta 
Published: May 2022

Abstract
A key question in human gut microbiome research is what are the robust structural patterns underlying its taxonomic composition. Herein, we use whole metagenomic datasets from healthy human guts to show that such robust patterns do exist, albeit not in the conventional enterotype sense. We first introduce the concept of mixed-membership enterotypes using a network inference approach based on stochastic block models. We find that gut microbiomes across a group of people (hosts) display a nested structure, which has been observed in a number of ecological systems. This finding led us to designate distinct ecological roles to both microbes and hosts: generalists and specialists. Specifically, generalist hosts have microbiomes with most microbial species, while specialist hosts only have generalist microbes. Moreover, specialist microbes are only present in generalist hosts. From the nested structure of microbial taxonomies, we show that these ecological roles of microbes are generally conserved across datasets. Our results show that the taxonomic composition of healthy human gut microbiomes is associated with robustly structured combinations of generalist and specialist species.
Tick Size Wars: The Market Quality Effects of Pricing Grid Competition
Authors: Foley Sean , Meling Tom G , Ødegaard Bernt Arne 
Published: May 2022

AbstractWe explore the effects of a “tick size war” in which European trading venues directly competed on the minimum pricing increment in the limit order book, the tick size. We find that venues that reduced their tick size immediately captured market shares of both quoted and executed volume from the exchanges that kept their ticks large. We find that tick size competition improves market quality, reducing trading costs, and increasing market-wide depth and volume. These market quality improvements are strongest in stocks where the bid–ask spread was constrained to one tick, where liquidity providers use the finer pricing grid to engage in price competition.
Using Artificial Neural Networks to Predict Intra-Abdominal Abscess Risk Post-Appendectomy
Authors: Alramadhan Morouge M. , Al Khatib Hassan S. , Murphy James R. , Tsao KuoJen , Chang Michael L. 
Published: May 2022

Objective:
To determine if artificial neural networks (ANN) could predict the risk of intra-abdominal abscess (IAA) development post-appendectomy.

Background:
IAA formation occurs in 13.6% to 14.6% of appendicitis cases with “complicated” appendicitis as the most common cause of IAA. There remains inconsistency in describing the severity of appendicitis with variation in treatment with respect to perforated appendicitis.

Methods:
Two “reproducible” ANN with different architectures were developed on demographic, clinical, and surgical information from a retrospective surgical dataset of 1574 patients less than 19 years old classified as either negative (n = 1,328) or positive (n = 246) for IAA post-appendectomy for appendicitis. Of 34 independent variables initially, 12 variables with the highest influence on the outcome selected for the final dataset for ANN model training and testing.

Results:
A total of 1574 patients were used for training and test sets (80%/20% split). Model 1 achieved accuracy of 89.84%, sensitivity of 70%, and specificity of 93.61% on the test set. Model 2 achieved accuracy of 84.13%, sensitivity of 81.63%, and specificity of 84.6%.

Conclusions:
ANN applied to selected variables can accurately predict patients who will have IAA post-appendectomy. Our reproducible and explainable ANNs potentially represent a state-of-the-art method for optimizing post-appendectomy care.