bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gravity waves and primordial black holes in scalar warm little inflation
Authors: Bastero-Gil Mar , Subías Díaz-Blanco Marta 
Published: December 2021

Abstract
In warm inflation, dissipation due to the interactions of the inflaton field to other light degrees of freedom leads naturally to the enhancement of the primordial spectrum during the last 10-20 efolds of inflation. We study this effect in a variant of the Warm Little Inflaton model, where the inflaton couples to light scalars, with a quartic chaotic potential. These large fluctuations on re-entry will form light, evaporating Primordial Black Holes, with masses lighter than 106 g. But at the same time they will act as a source for the tensors at second order. The enhancement is maximal near the end of inflation, which result in a spectral density of Gravitational Waves (GW) peaked at frequencies f ∼ O(105-106) Hz today, and with an amplitude ΩGW ∼ 10-9. Although the frequency range is outside the reach of present and planned GW detectors, it might be reached in future high-frequency gravitational waves detectors, designed to search for cosmological stochastic GW backgrounds above MHz frequencies.
Peculiarities of interaction of a quantum dot with non-classical light in the self-phase modulation regime
Authors: Balybin S N , Zakharov R V , Tikhonova O V 
Published: December 2021

Abstract
Influence of the self-phase modulation of quantum light on the induced resonant excitation of a semiconductor quantum dot is studied analytically in the case of the Kerr-nonlinearity of the medium. The phase nonlinearity is found to result actually in a resonance detuning specific for each field photon number state. This effect is shown to provide significant decrease of the excitation efficiency accompanied at the same time by more regular excitation dynamics obtained even for initial squeezed vacuum field state. The enhancement of entanglement between semiconductor and field subsystems with growing non-linearity is demonstrated. As a result, the formation of different types of non-Gaussian field states is found with features being analyzed in details.
Stability of large complex systems with heterogeneous relaxation dynamics
Authors: Mergny Pierre , Majumdar Satya N 
Published: December 2021

Abstract
We study the probability of stability of a large complex system of size N within the framework of a generalized May model, which assumes a linear dynamics of each population size n

i
(with respect to its equilibrium value):

d

n

i

d
t

=
−

a

i

n

i

−

T

∑

j

J

i
j

n

j

. The a

i
> 0’s are the intrinsic decay rates, J

ij
is a real symmetric (N × N) Gaussian random matrix and

T

measures the strength of pairwise interaction between different species. Our goal is to study how inhomogeneities in the intrinsic damping rates a

i
affect the stability of this dynamical system. As the interaction strength T increases, the system undergoes a phase transition from a stable phase to an unstable phase at a critical value T = T
c. We reinterpret the probability of stability in terms of the hitting time of the level b = 0 of an associated Dyson Brownian motion (DBM), starting at the initial position a

i
and evolving in ‘time’ T. In the large N → ∞ limit, using this DBM picture, we are able to completely characterize T
c for arbitrary density μ(a) of the a

i
’s. For a specific flat configuration

a

i

=
1
+
σ

i
−
1

N

, we obtain an explicit parametric solution for the limiting (as N → ∞) spectral density for arbitrary T and σ. For finite but large N, we also compute the large deviation properties of the probability of stability on the stable side T < T
c using a Coulomb gas representation.
Uncertainty relations for mesoscopic coherent light
Authors: Soret Ariane , Shpielberg Ohad , Akkermans Eric 
Published: December 2021

AbstractThermodynamic uncertainty relations unveil useful connections between fluctuations in thermal systems and entropy production. This work extends these ideas to the disparate field ofzero temperaturequantum mesoscopic physics where fluctuations are due to coherent effects and entropy production is replaced by a cost function. The cost function arises naturally as a bound on fluctuations, induced by coherent effects—a critical resource in quantum mesoscopic physics. Identifying the cost function as an important quantity demonstrates the potential of importing powerful methods from non-equilibrium statistical physics to quantum mesoscopics.
The low-frequency limiting behavior of ambipolar diffusive models of impedance spectroscopy
Authors: Barbero G , Evangelista L R , Tilli P 
Published: December 2021

Abstract
The Poisson–Nernst–Planck (PNP) diffusional model is a successful theoretical framework to investigate the electrochemical impedance response of insulators containing ionic impurities to an external ac stimulus. Apparent deviations of the experimental spectra from the predictions of the PNP model in the low frequency region are usually interpreted as an interfacial property. Here, we provide a rigorous mathematical analysis of the low-frequency limiting behavior of the model, analyzing the possible origin of these deviation related to bulk properties. The analysis points toward the necessity to consider a bulk effect connected with the difference in the diffusion coefficients of cations and anions (ambipolar diffusion). The ambipolar model does not continuously reach the behavior of the one mobile ion diffusion model when the difference in the mobility of the species vanishes, for a fixed frequency, in the cases of ohmic and adsorption–desorption boundary conditions. The analysis is devoted to the low frequency region, where the electrodes play a fundamental role in the response of the cell; thus, different boundary conditions, charged to mimic the non-blocking character of the electrodes, are considered. The new version of the boundary conditions in the limit in which one of the mobility is tending to zero is deduced. According to the analysis in the dc limit, the phenomenological parameters related to the electrodes are frequency dependent, indicating that the exchange of electric charge from the bulk to the external circuit, in the ohmic model, is related to a surface impedance, and not simply to an electric resistance.
Stresses Analysis of Roller Gear Drive
Authors: Dai Zih-Chun 
Published: December 2021

Abstract
The roller worm gear drives have been widely adopted in numerous industrial applications such as robot joint reducer, heavy-duty production line. This study is to improve the performance of a roller gear drive by utilizing an iterative optimization scheme to improve the tooth profile of the hourglass worm gear in the roller gear drive. Dedicated design of the variable-pitch slot on the hourglass worm gear can remedy the power efficiency of the roller gear drive by enhancing the contact ratio dramatically. This research showed that the roller gear drive is a better mechanism for the high reduction ratio reducers. The CAD design and performance analysis of a roller gear drive by SolidWorks have provided the engineers an optimizing methodology.
Mode classification for vortex shedding from an oscillating wind turbine using machine learning
Authors: Cann Matthew , McConkey Ryley , Lien Fue-Sang , Melek William , Yee Eugene 
Published: December 2021

Abstract
This study presents an effective strategy that applies machine learning methods to classify vortex shedding modes produced by the oscillating cylinder of a bladeless wind turbine. A 2-dimensional computational fluid dynamic (CFD) simulation using OpenFOAMv2006 was developed to simulate a bladeless wind turbines vortex shedding behavior. The simulations were conducted at two wake modes (2S, 2P) and a transition mode (2PO). The local flow measurements were recorded using four sensors: vorticity, flow speed, stream-wise and transverse stream-wise velocity components. The time-series data was transformed into the frequency domain to generate a reduced feature vector. A variety of supervised machine learning models were quantitatively compared based on classification accuracy. The best performing models were then reevaluated based on the effects of artificial noisy experimental data on the models’ performance. The velocity sensors orientated transverse to the pre-dominant flow (u

y
) achieved improved testing accuracy of 15% compared to the next best sensor. The random forest and k-nearest neighbor models, using u

y
, achieved 99.3% and 99.8% classification accuracy, respectively. The feature noise analysis conducted reduced classification accuracy by 11.7% and 21.2% at the highest noise level for the respective models. The random forest algorithm trained using the transverse stream-wise component of the velocity vector provided the best balance of testing accuracy and robustness to data corruption. The results highlight the proposed methods’ ability to accurately identify vortex structures in the wake of an oscillating cylinder using feature extraction.
α′-corrections and their double formulation
Authors: Lescano Eric 
Published: December 2021

Abstract
The present notes are based on three lectures, each ninety minutes long, prepared for the school “Integrability, Dualities and Deformations”, that ran from 23 to 27 August 2021 in Santiago de Compostela and virtually. These lectures, aimed at graduate students, require only a basic knowledge of string theory. The main goal is to introduce α′-corrections to the gravitational sector of diﬀerent formulations of closed string theory and to reformulate them using novel techniques based on double ﬁeld theory.
Preparing exact eigenstates of the open XXZ chain on a quantum computer
Authors: Van Dyke John S , Barnes Edwin , Economou Sophia E , Nepomechie Rafael I 
Published: December 2021

Abstract
The open spin-1/2 XXZ spin chain with diagonal boundary magnetic fields is the paradigmatic example of a quantum integrable model with open boundary conditions. We formulate a quantum algorithm for preparing Bethe states of this model, corresponding to real solutions of the Bethe equations. The algorithm is probabilistic, with a success probability that decreases with the number of down spins. For a Bethe state of L spins with M down spins, which contains a total of

L

M

2

M

M
!

terms, the algorithm requires L + M
2 + 2M qubits.
Quantum optical analysis of squeezed state of light through dispersive non-Hermitian optical bilayers
Authors: Pilehvar Elnaz , Amooghorban Ehsan , Moravvej-Farshi Mohammad Kazem 
Published: December 2021

AbstractWe investigate the propagation of a normally incident squeezed coherent state of light through dispersive non-Hermitian optical bilayers, particularly at a frequency that the bilayers hold parity-time (𝒫𝒯) symmetry. To check the realization of 𝒫𝒯-symmetry in quantum optics, we reveal how dispersion and loss/gain-induced noises and thermal effects in such bilayers can affect quantum features of the incident light, such as squeezing and sub-Poissonian statistics. The numerical results show thermally induced noise at room temperature has an insignificant effect on the propagation properties in these non-Hermitian bilayers. Moreover, tuning the bilayers’ loss/gain strength, we show that the transmitted squeezed coherent states through the structure can retain to some extent their nonclassical characteristics, specifically for the frequencies far from the emission frequency of the gain layer. Furthermore, we demonstrate, only below a critical value of gain, quantum optical effective medium theory can correctly predict the propagation of quantized waves in non-Hermitian and 𝒫𝒯-symmetric bilayers.
BML: a versatile web server for bipartite motif discovery
Authors: Vahed Mohammad , Vahed Majid , Garmire Lana X 
Published: December 2021

Abstract
Motif discovery and characterization are important for gene regulation analysis. The lack of intuitive and integrative web servers impedes the effective use of motifs. Most motif discovery web tools are either not designed for non-expert users or lacking optimization steps when using default settings. Here we describe bipartite motifs learning (BML), a parameter-free web server that provides a user-friendly portal for online discovery and analysis of sequence motifs, using high-throughput sequencing data as the input. BML utilizes both position weight matrix and dinucleotide weight matrix, the latter of which enables the expression of the interdependencies of neighboring bases. With input parameters concerning the motifs are given, the BML achieves significantly higher accuracy than other available tools for motif finding. When no parameters are given by non-expert users, unlike other tools, BML employs a learning method to identify motifs automatically and achieve accuracy comparable to the scenario where the parameters are set. The BML web server is freely available at http://motif.t-ridership.com/ (https://github.com/Mohammad-Vahed/BML).
PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network
Authors: Wang Xiaowen , Zhu Hongming , Jiang Yizhi , Li Yulong , Tang Chen , Chen Xiaohan , Li Yunjie , Liu Qi , Liu Qin 
Published: December 2021

AbstractAlthough drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein–protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision–recall curve of 0.63 and a Cohen’s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network’s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.
scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation
Authors: Wang Jing , Xia Junfeng , Tan Dayu , Lin Rongxin , Su Yansen , Zheng Chun-Hou 
Published: December 2021

Abstract
Rapid development of single-cell RNA sequencing (scRNA-seq) technology has allowed researchers to explore biological phenomena at the cellular scale. Clustering is a crucial and helpful step for researchers to study the heterogeneity of cell. Although many clustering methods have been proposed, massive dropout events and the curse of dimensionality in scRNA-seq data make it still difficult to analysis because they reduce the accuracy of clustering methods, leading to misidentification of cell types. In this work, we propose the scHFC, which is a hybrid fuzzy clustering method optimized by natural computation based on Fuzzy C Mean (FCM) and Gath-Geva (GG) algorithms. Specifically, principal component analysis algorithm is utilized to reduce the dimensions of scRNA-seq data after it is preprocessed. Then, FCM algorithm optimized by simulated annealing algorithm and genetic algorithm is applied to cluster the data to output a membership matrix, which represents the initial clustering result and is taken as the input for GG algorithm to get the final clustering results. We also develop a cluster number estimation method called multi-index comprehensive estimation, which can estimate the cluster numbers well by combining four clustering effectiveness indexes. The performance of the scHFC method is evaluated on 17 scRNA-seq datasets, and compared with six state-of-the-art methods. Experimental results validate the better performance of our scHFC method in terms of clustering accuracy and stability of algorithm. In short, scHFC is an effective method to cluster cells for scRNA-seq data, and it presents great potential for downstream analysis of scRNA-seq data. The source code is available at https://github.com/WJ319/scHFC.
MetaLogo: a heterogeneity-aware sequence logo generator and aligner
Authors: Chen Yaowen , He Zhen , Men Yahui , Dong Guohua , Hu Shuofeng , Ying Xiaomin 
Published: December 2021

Abstract
Sequence logos are used to visually display conservations and variations in short sequences. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators are based on the assumption that all the input sequences are from the same homologous group, which will lead to an overlook of the heterogeneity among the sequences during the sequence logo making process. Heterogeneous groups of sequences may represent clades of different evolutionary origins, or genes families with different functions. Therefore, it is essential to divide the sequences into different phylogenetic or functional groups to reveal their specific sequence motifs and conservation patterns. To solve these problems, we developed MetaLogo, which can automatically cluster the input sequences after multiple sequence alignment and phylogenetic tree construction, and then output sequence logos for multiple groups and aligned them in one figure. User-defined grouping is also supported by MetaLogo to allow users to investigate functional motifs in a more delicate and dynamic perspective. MetaLogo can highlight both the homologous and nonhomologous sites among sequences. MetaLogo can also be used to annotate the evolutionary positions and gene functions of unknown sequences, together with their local sequence characteristics. We provide users a public MetaLogo web server (http://metalogo.omicsnet.org), a standalone Python package (https://github.com/labomics/MetaLogo), and also a built-in web server available for local deployment. Using MetaLogo, users can draw informative, customized and publishable sequence logos without any programming experience to present and investigate new knowledge on specific sequence sets.
The global prevalence of burnout among general practitioners: a systematic review and meta-analysis
Authors: Shen Xin , Xu Hongbin , Feng Jing , Ye Jun , Lu Zuxun , Gan Yong 
Published: December 2021

Abstract

Background
Burnout among general practitioners (GPs) has attracted the attention of more and more researchers. An adequate understanding the prevalence and related factors of burnout to prevent and reduce burnout is necessary. This study systematically measured the global prevalence of burnout among GPs.

Methods
Eligible original studies were identified from the PubMed, Ovid Embase, Ovid Medline (R), and Web of science databases. We searched the full-time period available for each database, up to 30 September 2021. The adjusted prevalence rate was estimated using a random-effects meta-analysis. The heterogeneity was evaluated using I2 statistic. Differences by study-level characteristics were estimated via subgroup analyses and meta-regression.

Results
A total of 16 cross-sectional studies with 7,595 participants were included. The pooled burnout rate showed 37%, 28%, and 26% of general GPs suffer from high emotional exhaustion (EE), high depersonalization (DP), and low personal exhaustion (PA), respectively. Groups comparisons found that high EE, high DP, and low PA rate data obtained from 2001 to 2009, high DP rate data obtained from Europe, low PA rate data obtained from high-quality studies had much higher rates.

Conclusions
This study demonstrated the prevalence of burnout in the GPs and alert health managers to tailor their strategies to retain this community. Targeted initiatives are needed to provide adequate GPs’ well-being and maintain primary health care.
Using system dynamics modelling to estimate the costs of relaxing health system constraints: a case study of tuberculosis prevention and control interventions in South Africa
Authors: Bozzani Fiammetta M , Diaconu Karin , Gomez Gabriela B , Karat Aaron S , Kielmann Karina , Grant Alison D , Vassall Anna 
Published: December 2021

Abstract
Health system constraints are increasingly recognized as an important addition to model-based analyses of disease control interventions, as they affect achievable impact and scale. Enabling activities implemented alongside interventions to relax constraints and reach the intended coverage may incur additional costs, which should be considered in priority setting decisions. We explore the use of group model building, a participatory system dynamics modelling technique, for eliciting information from key stakeholders on the constraints that apply to tuberculosis infection prevention and control processes within primary healthcare clinics in South Africa. This information was used to design feasible interventions, including the necessary enablers to relax existing constraints. Intervention and enabler costs were then calculated at two clinics in KwaZulu-Natal using input prices and quantities from the published literature and local suppliers. Among the proposed interventions, the most inexpensive was retrofitting buildings to improve ventilation (US$1644 per year), followed by maximizing the use of community sites for medication collection among stable patients on antiretroviral therapy (ART; US$3753) and introducing appointments systems to reduce crowding (US$9302). Enablers identified included enhanced staff training, supervision and patient engagement activities to support behaviour change and local ownership. Several of the enablers identified by the stakeholders, such as obtaining building permissions or improving information flow between levels of the health systems, were not amenable to costing. Despite this limitation, an approach to costing rooted in system dynamics modelling can be successfully applied in economic evaluations to more accurately estimate the ‘real world’ opportunity cost of intervention options. Further empirical research applying this approach to different intervention types (e.g. new preventive technologies or diagnostics) may identify interventions that are not cost-effective in specific contexts based on the size of the required investment in enablers.