bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors associated with severe COVID-19 in people with idiopathic inflammatory myopathy: results from the COVID-19 Global Rheumatology Alliance physician-reported registry
Authors: Yeoh Su-Ann , Gianfrancesco Milena , Lawson-Tovey Saskia , Hyrich Kimme L , Strangfeld Anja , Gossec Laure , Carmona Loreto , Mateus Elsa F , Schäfer Martin , Richez Christophe , Hachulla Eric , Holmqvist Marie , Scirè Carlo Alberto , Lorenz Hanns-Martin , Voll Reinhard E , Hasseli Rebecca , Jayatilleke Arundathi , Hsu Tiffany Y-T , D’Silva Kristin M , Pimentel-Quiroz Victor R , Vasquez del Mercado Monica , Shinjo Samuel Katsuyuki , Neto Edgard Torres dos Reis , Junior Laurindo Ferreira da Rocha , de Oliveira e Silva Montandon Ana Carolina , Pons-Estel Guillermo J , Ornella Sofía , D'Angelo Exeni Maria Eugenia , Velozo Edson , Jordan Paula , Sirotich Emily , Hausmann Jonathan S , Liew Jean W , Jacobsohn Lindsay , Gore-Massy Monique , Sufka Paul , Grainger Rebecca , Bhana Suleman , Wallace Zachary , Robinson Philip C , Yazdany Jinoos , Machado Pedro M 
Published: September 2022

ObjectivesTo investigate factors associated with severe COVID-19 in people with idiopathic inflammatory myopathy (IIM).MethodsDemographic data, clinical characteristics and COVID-19 outcome severity of adults with IIM were obtained from the COVID-19 Global Rheumatology Alliance physician-reported registry. A 3-point ordinal COVID-19 severity scale was defined: (1) no hospitalisation, (2) hospitalisation (and no death) and (3) death. ORs were estimated using multivariable ordinal logistic regression. Sensitivity analyses were performed using a 4-point ordinal scale: (1) no hospitalisation, (2) hospitalisation with no oxygen (and no death), (3) hospitalisation with oxygen/ventilation (and no death) and 4) death.ResultsOf 348 patients, 48% were not hospitalised, 39% were hospitalised (and did not die) and 13% died. Older age (OR=1.59/decade, 95% CI 1.31 to 1.91), high disease activity (OR=3.50, 95% CI 1.25 to 9.83; vs remission), ≥2 comorbidities (OR=2.63, 95% CI 1.39 to 4.98; vs none), prednisolone-equivalent dose >7.5 mg/day (OR=2.40, 95% CI 1.09 to 5.28; vs no intake) and exposure to rituximab (OR=2.71, 95% CI 1.28 to 5.72; vs conventional synthetic disease-modifying antirheumatic drugs only) were independently associated with severe COVID-19. In addition to these variables, in the sensitivity analyses, male sex (OR range: 1.65–1.83; vs female) was also significantly associated with severe outcomes, while COVID-19 diagnosis after 1 October 2020 (OR range: 0.51–0.59; vs on/before 15 June 2020) was significantly associated with less severe outcomes, but these associations were not significant in the main model (OR=1.57, 95% CI 0.95 to 2.59; and OR=0.61, 95% CI 0.37 to 1.00; respectively).ConclusionsThis is the first large registry data on outcomes of COVID-19 in people with IIM. Older age, male sex, higher comorbidity burden, high disease activity, prednisolone-equivalent dose >7.5 mg/day and rituximab exposure were associated with severe COVID-19. These findings will enable risk stratification and inform management decisions for patients with IIM.
Developing and validating a scoring system for measuring frailty in patients with hip fracture: a novel model for predicting short-term postoperative mortality
Authors: Forssten Maximilian Peter , Cao Yang , Trivedi Dhanisha Jayesh , Ekestubbe Lovisa , Borg Tomas , Bass Gary Alan , Mohammad Ismail Ahmad , Mohseni Shahin 
Published: September 2022

ObjectivesFrailty is common among patients with hip fracture and may, in part, contribute to the increased risk of mortality and morbidity after hip fracture surgery. This study aimed to develop a novel frailty score for patients with traumatic hip fracture that could be used to predict postoperative mortality as well as facilitate further research into the role of frailty in patients with hip fracture.MethodsThe Orthopedic Hip Frailty Score (OFS) was developed using a national dataset, retrieved from the Swedish National Quality Registry for Hip Fractures, that contained all adult patients who underwent surgery for a traumatic hip fracture in Sweden between January 1, 2008 and December 31, 2017. Candidate variables were selected from the Nottingham Hip Fracture Score, Sernbo Score, Charlson Comorbidity Index, 5-factor modified Frailty Index, as well as the Revised Cardiac Risk Index and ranked based on their permutation importance, with the top 5 variables being selected for the score. The OFS was then validated on a local dataset that only included patients from Orebro County, Sweden.ResultsThe national dataset consisted of 126,065 patients. 2365 patients were present in the local dataset. The most important variables for predicting 30-day mortality were congestive heart failure, institutionalization, non-independent functional status, an age ≥85, and a history of malignancy. In the local dataset, the OFS achieved an area under the receiver-operating characteristic curve (95% CI) of 0.77 (0.74 to 0.80) and 0.76 (0.74 to 0.78) when predicting 30-day and 90-day postoperative mortality, respectively.ConclusionsThe OFS is a significant predictor of short-term postoperative mortality in patients with hip fracture that outperforms, or performs on par with, all other investigated indices.Level of evidenceLevel III, Prognostic and Epidemiological.
Progressive thermal onset of modified hybrid nanoparticles for oscillating flow via modified fractional approach
Authors: Raza Ali , Al-Khaled Kamel , Khan Sami Ullah , Elboughdiri Noureddine , Farah Anouar , Gasmi Hatem , Helali Abdelhamid 
Published: September 2022

The aim of this research is to develop a fractional supported thermal model for studying the features of modified hybrid nanofluid endorsed by uniformly accelerating plate. The novel impact of this work is observing the comparative thermal enhancement of water base fluid by utilizing four types of nanoparticles. The silver, copper, aluminum oxide and titanium oxide nanomaterials are utilized to present the comparative thermal aspect of modified hybrid nanofluid model. Moreover, the inclined direction of magnetic impact is treated. The second-grade nonlinear model is used to explore the base fluid properties. The fractional model is first attained into dimensionless form. The fractional computations with employing the Prabhakar fractional mathematical definitions are reported. The motivations for suggesting the Prabhakar algorithm are justified as this fractional algorithm contains modern definitions without any restriction of singularities. The verification of model is accomplished after simulating the comparison task with already performed studies. The physical dynamic and thermal enhancement of transportation phenomenon is performed for specific range of flow parameters like [Formula: see text] [Formula: see text] and [Formula: see text] Based on the computational model, it is concluded that the thermal transportation phenomenon is more impressive for water-based titanium oxide nanoparticles. The temperature profile rises due to factional parameter for both copper–water- and sliver — water-based hybrid nanofluid suspension.
FD-CNN: A Frequency-Domain FPGA Acceleration Scheme for CNN-Based Image-Processing Applications
Authors: Wang Xiaoyang , Zhou Zhe , Yuan Zhihang , Zhu Jingchen , Cao Yulong , Zhang Yao , Sun Kangrui , Sun Guangyu 
Published: September 2022

In the emerging edge-computing scenarios, FPGAs have been widely adopted to accelerate convolutional neural network (CNN)–based image-processing applications, such as image classification, object detection, and image segmentation, and so on. A standard image-processing pipeline first decodes the collected compressed images from Internet of Things (IoTs) to RGB data, then feeds them into CNN engines to compute the results. Previous works mainly focus on optimizing the CNN inference parts. However, we notice that on the popular ZYNQ FPGA platforms, image decoding can also become the bottleneck due to the poor performance of embedded ARM CPUs. Even with a hardware accelerator, the decoding operations still incur considerable latency. Moreover, conventional RGB-based CNNs have too few input channels at the first layer, which can hardly utilize the high parallelism of CNN engines and greatly slows down the network inference. To overcome these problems, in this article, we propose FD-CNN, a novel CNN accelerator leveraging the partial-decoding technique to accelerate CNNs directly in the frequency domain. Specifically, we omit the most time-consuming IDCT (Inverse Discrete Cosine Transform) operations of image decoding and directly feed the DCT coefficients (i.e., the frequency data) into CNNs. By this means, the image decoder can be greatly simplified. Moreover, compared to the RGB data, frequency data has a narrower input resolution but has 64× more channels. Such an input shape is more hardware friendly than RGB data and can substantially reduce the CNN inference time. We then systematically discuss the algorithm, architecture, and command set design of FD-CNN. To deal with the irregularity of different CNN applications, we propose an image-decoding-aware design-space exploration (DSE) workflow to optimize the pipeline. We further propose an early stopping strategy to tackle the time-consuming progressive JPEG decoding. Comprehensive experiments demonstrate that FD-CNN achieves, on average, 3.24×, 4.29× throughput improvement, 2.55×, 2.54× energy reduction and 2.38×, 2.58× lower latency on ZC-706 and ZCU-102 platforms, respectively, compared to the baseline image-processing pipelines.
A Novel Biometric Identification Scheme Based on Zero-Knowledge Succinct Noninteractive Argument of Knowledge
Authors: Guo Chunjie , You Lin , Hu Gengran 
Published: September 2022

Biometric identification is a convenient and reliable method in identity authentication. The widespread adoption of biometric identification requires strong privacy protection against possible theft or loss of biometric data. Existing techniques for privacy-preserving biometric identification mainly rely on traditional cryptographic technology such as oblivious transfer and homomorphic encryption, which will incur huge expenses to the system and cannot be applied to large-scale practical applications. For these issues, we propose a biometric identification scheme by constructing zero-knowledge succinct noninteractive argument of knowledge (zk-SNARK). Our scheme not only reduces the communication overhead, which only needs to send 8 constants to the verifier but also can protect the fingerprint template from disclosure. The time complexity of proof generation and proof verification are about O(C) and

O

x

+
log

C

, respectively, and the size of the proof is only 8 constants, where C and x represent the size of the circuit and the public input, respectively. We have implemented the proposed authentication solution on a public data set of fingerprint images and evaluated the performance and security.
Cracking Behaviour of Alkali-Activated Aluminosilicate Beams Reinforced with Glass and Basalt Fibre-Reinforced Polymer Bars under Cyclic Load
Authors: Nagajothi S. , Elavenil S. , Angalaeswari S. , Natrayan L. , Paramasivam Prabhu 
Published: September 2022

Cement is an essential material for concrete, which is mostly used worldwide second to the consumption of water. Due to the emission of CO2 into the atmosphere, the alternative material of geopolymer concrete was used. In this research work, silica and alumina content such as ground granulated blast furnace slag (GGBS), fly ash, and triggered by alkali activator solutions were used in geopolymer concrete. Due to the dwindling of river sand, alternative material of manufactured sand (M-Sand) was considered. To avoid corrosion problems in reinforced concrete structures, glass fibre reinforced polymer (GFRP) and basalt fibre-reinforced polymer (BFRP) bars were used as an alternative material for steel reinforcement in this work. As per the code, IS: 10262, the concrete mix design of M30 grade has arrived for the control mix and the same proportion was adopted for geopolymer concrete. Six beams of geopolymer and a concrete control beam of

100
×
160
×
1700

mm were cast and examined under a four-point cyclic load. Cyclic load results were compared with static load under ambient curing. Residual deflection, moment capacity, energy dissipation, and stress–strain behaviour results were compared and discussed. A sudden shear and premature failure were observed in FRP beams under static and cyclic bending tests.
Improved Supply Chain Crisis Response: A Study of Modular Design
Authors: Skiver Ryan L. 
Published: September 2022

Abstract
In 2020 COVID-19 caused global disruptions in the supply chain that are still showing ripple effects in mid-2022. Pandemics are not the only form of crisis that causes supply chain disruptions. With increasing instances of natural disasters, organizational misconduct, economic instability, and political unrest, crisis management has become a growing issue within businesses and the supply chain field. This article examines the previous literature on crisis management by identifying the types of crises that will affect a focal firm’s suppliers in the chain, and how those types of crises will affect the supply chain. We continue by adding response measures through creating modular design and modular innovation capability, which will not prevent the crisis but will drastically decrease supply disruption and thus decrease response time for the focal firm. Lastly, a model is created for future empirical research based off of the previous literature that shows how modular design and modular innovation capability can alleviate the damage of crises in the supply chain through greater agility, flexibility, and adaptability.
Peace Negotiations as Sites of Gendered Power Hierarchies
Authors: McAuliff Alexandra K. 
Published: September 2022

Abstract
Since the passage of UNSCR 1325, women’s formal inclusion in peace negotiations has been advocated as a means to pursue gender equality and improve peace outcomes. A narrow focus on inclusion and the embodied presence of women, however, does not address the gendered hierarchies embedded within negotiations. This article highlights how gender functions as a power structure that normalizes masculinity as the operating standard within the practice of peace negotiations. By focusing on the centrality of militarization and masculinity to liberal peacebuilding, I suggest three ways negotiations function as patriarchal institutions: the issues centered as essential peace components; the types of violence that “count” as conflict-related; and the actors deemed legitimate for inclusion. While inclusion is a critical aspect of improving gendered peace outcomes, attention to gendered bodies must include recognition of gender as an analytical category that shapes not just who is included but how the process is built.
A new Neotropical sharpshooter genus Spinagonalia (Insecta, Hemiptera, Cicadellidae, Cicadellinae) with description of a new species from Citrus orchards and grapevines
Authors: CAVICHIOLI RODNEY RAMIRO 
Published: September 2022

The leafhoppers included in the Cicadellini are distributed all over the world, with approximately 1,100 species and 164 genera (Mejdalani, 1998) recorded from the New World. Since the generic revision of Cicadellini by Young (1977), where he redefined the known genera and erected many new ones (91), only eleven new genera (and approximately 70 new species) have been described or revalidated from the New World (Cavichioli 1996, 1998, 2000a, 2000b and 2003; Hamilton 1985; Mejdalani 1994; Nielson and Godoy 1995; Takiya et al. 2001,2003; Takiya and Cavichioli, 2005). In the present paper, a new genus and new species are proposed from Santa Catarina and Rio Grande do Sul states of Southern Brazil. This new species was collected in Citrus orchards from Santa Catarina and grapevines from Rio Grande do Sul. This species is a xylem sap specialist, like all other species of the subfamily, and a potential vector of Xylella fastidiosa. However, this species is not a proven efficient vector of this bacterium. Specimens of the new species will not key beyond couplet 145 in Young`s (1977) key to the New World genera, because only the median anteapical cell of the forewing is open basally. The combination of characters of the male and female genitalia warrants its formal description and placement in a new genus.
Upscale versus “Up-Amplitude” Growth of Forecast-Error Spectra
Authors: Rotunno Richard , Snyder Chris , Judt Falko 
Published: September 2022

Abstract
Atmospheric predictability is measured by the average difference (or “error”) within an ensemble of forecasts starting from slightly different initial conditions. The spatial scale of the error field is a fundamental quantity; for meteorological applications, the error field typically varies with latitude and longitude and so requires a two-dimensional (2D) spectral analysis. Statistical predictability theory is based on the theory of homogeneous, isotropic turbulence, in which spectra are circularly symmetric in 2D wavenumber space. One takes advantage of this circular symmetry to reduce 2D spectra to one-dimensional (1D) spectra by integrating around a circle in wavenumber polar coordinates. In recent studies it has become common to reduce 2D error spectra to 1D by computing spectra in the zonal direction and then averaging the results over latitude. It is shown here that such 1D error spectra are generically fairly constant across the low wavenumbers as the amplitude of an error spectrum grows with time and therefore the error spectrum is said grow “up-amplitude.” In contrast computing 1D error spectra in a manner consistent with statistical predictability theory gives spectra that are peaked at intermediate wavenumbers. In certain cases, this peak wavenumber is decreasing with time as the error at that wavenumber increases and therefore the error spectrum is said to grow “upscale.” We show through theory, simple examples, and global predictability experiments that comparisons of model error spectra with the predictions of statistical predictability theory are only justified when using a theory-consistent method to transform a 2D error field to a 1D spectrum.
A Bayesian Attribution Analysis of Extreme Temperature Changes at Global and Regional Scales
Authors: Seong Min-Gyu , Min Seung-Ki , Zhang Xuebin 
Published: September 2022

Abstract
Recent studies showed that anthropogenic greenhouse gas (GHG) increase is a major driver of the observed increases in extreme temperatures at global and regional scales using an optimal fingerprint (OF) method, which is a frequentist approach based on linear regression. Here, a Bayesian decision method is employed, which finds the most probable cause of the observed changes by comparing likelihoods of different forcings in view of observations. To quantify individual forcing contributions, a new modified attribution procedure based on Bayesian decision is proposed, i.e., computing the likelihood ratio [Bayes factor (BF)] between different forcings. First, the contribution of anthropogenic forcing (ANT) is measured by BF between anthropogenic-plus-natural forcing (ALL) and natural forcing (NAT) using a threshold for “substantial” evidence (lnBF ≥ 1). Similarly, the NAT contribution is assessed by BF between ALL and ANT. Further, the GHG contribution to the detected ANT is quantified by BF between ANT and anthropogenic aerosols (AA), and the AA contribution is evaluated by BF between ANT and GHG. The devised Bayesian approach is applied to HadEX3 observations and CMIP6 multimodel simulations for extreme temperature intensities (warmest day/night and coldest day/night) for global, continental, and regional domains following previous studies. Bayesian attribution results indicate that the ANT signal is detected in many continental and subregions for all extremes indices. This is generally consistent with OF-based results but with less frequent detection, indicating that the Bayesian method is slightly stricter than the OF method. However, GHG contributions to the detected ANT are identified over more subregions in the Bayesian attribution, suggesting its potential advantage over conventional methods in case of low signal-to-noise ratio and high collinearity.
Impact of Tropical Cyclone Wind Forcing on the Global Climate in a Fully Coupled Climate Model
Authors: Li Hui , Hu Aixue , Meehl Gerald A. , Rosenbloom Nan , Strand Warren G. 
Published: September 2022

Abstract
Tropical cyclones (TCs) alter upper-ocean temperature and influence ocean heat content via enhanced turbulent mixing. A better understanding of the role of TCs within the climate system requires a fully coupled modeling framework, where TC-induced ocean responses feed back to the atmosphere and subsequently to the climate mean state and variability. Here, we investigate the impacts of TC wind forcing on the global ocean and the associated feedbacks within the climate system using the fully coupled Community Earth System Model version 1.3 (CESM1.3). Using the low-resolution version of CESM1.3 (1° atmosphere and ocean grid spacing) with no intrinsic TCs, we conduct a suite of sensitivity experiments by inserting TC winds extracted from a high-resolution (0.25° atmosphere grid spacing) TC-permitting simulation into the low-resolution model. Results from the low-resolution TC experiment are compared to a low-resolution control simulation to diagnose TCs’ impact. We found that the added TC winds can increase ocean heat content by affecting ocean vertical mixing, air–sea enthalpy fluxes, and cloud amount. The added TCs can influence mean SST, precipitation, ocean subsurface temperature, and ocean mixed layer depth. We found a strengthening of the wind-driven subtropical cells and a weakening of the Atlantic meridional overturning circulation due to the changes of surface buoyancy fluxes. TCs in the model cause anomalous equatorward ocean heat convergence in the deep tropics and an increase of poleward ocean heat transport out of the subtropics. Our modeling results provide new insights into the multiscale interactions between TCs and the coupled climate system.
Evaluation of Model Summertime Boundary Layer Cloud Development over Complex Terrain in New York State
Authors: Min Lanxi , Min Qilong , Du Yuyi 
Published: September 2022

Abstract
Weather forecasting over complex terrain with diverse land cover is challenging. Utilizing the high-resolution observations from New York State Mesonet (NYSM), we are able to evaluate the surface processes of the Weather Research Forecast (WRF) Model in a detailed, scale-dependent manner. In the study, possible impacts of land–atmosphere interaction on surface meteorology and boundary layer cloud development are investigated with different model resolutions, land surface models (LSMs), and planetary boundary layer (PBL) physical parameterizations. The High-Resolution Rapid Refresh, version 3 (HRRR), forecasting model is used as a reference for the sensitivity evaluation. Results show that over complex terrain, the high-resolution simulations (1 km × 60 vertical levels) generally perform better compared to low-resolution (3 km × 50 levels) in both surface meteorology and cloud fields. LSMs play a more important role in surface meteorology compared to PBL schemes. The NoahMP land surface model exhibits daytime warmer and drier biases compared to the Rapid Update Cycle (RUC) due to better prediction of the Bowen ratio in RUC. The PBL schemes would affect the convective strength in the boundary layer. The Shin–Hong (SH) scale-aware scheme tends to produce the strongest convective strength in the PBL, while the ACM2 PBL scheme rarely resolved convection even at 1-km resolution. By considering the radiation effect of subgrid-scale (SGS) clouds, the Mellor–Yamada–Nakanishi–Niino eddy diffusivity mass flux (MYNN-EDMF) predicted the highest cloud coverage and lowest surface solar radiation bias. The configuration of SGS clouds in MYNN-EDMF would not only significantly reduce shortwave radiation bias, but also affect the convection behaviors through land surface–cloud–radiation interaction.