bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phrenic palsy after pediatric cardiac surgery: what is the best modality of management?
Authors: Ramadan Ahmed Talaat , Albatrek Hani Mahmoud , Abou Shokka Rania D. Eldin , Algebaly Ahmed Azmy 
Published: January 2023

Background: Pediatric cardiac surgery is one of the most common causes of diaphragmatic palsy (DP) in infants and young children. The main target in managing a patient with DP is to preserve the respiratory function. Surgical diaphragmatic plication is widely used surgical treatment of DP nowadays especially in infants than in young children. Tracheostomy was suggested also to facilitate the suction of the chest and decrease the pulmonary complications. Thus, the aim of this study was to detect the effect of early intervention to correct the phrenic palsy either by diaphragmatic plication, Tracheostomy or both in pediatric cardiac surgery patients.
Methods: This is a retrospective study on pediatric patients who had cardiac surgical procedures between June 2008 and Dec 2018. There were 3706 patients had been operated at different age groups of whom 42 patients developed DP. The patients were divided into four groups according to type of treatment: Group A for conservative treatment, Group B for diaphragmatic plication only, Group C for tracheostomy only, and Group D for tracheostomy and plication.
Results: The mean age of the studied group was 19.7 ±23 . There was male predominance (78.6 %). Timing of tracheostomy in days was as 30 ±7.1, and 31.8 ±5.4 in group C, and D respectively. Timing of plication was as 10 ±8.3, and 11.1 ±7.6 in group B, and D consequence
Conclusion: The least sepsis recorded for patients with both tracheostomy and DP, delayed surgical intervention led to higher mortality and more sepsis recorded as in conservative technique.
UTransBPNet: A General Deep Learning Model for Cuffless Blood Pressure Estimation under Activities
Authors: Zheng Yali , Liu Qing , Hong Jingyuan , Wu Shenghao , Zhang Yuanting , zheng Yali 
Published: January 2023

<p>Cuffless blood pressure (BP) monitoring has gained great attention in the past twenty years considering its significant benefits in cardiovascular healthcare. However, the main challenge of this technology is the inaccurate BP modeling under activities, i.e., existing work have either been inappropriately validated with sufficient intra-individual BP variations, or did not show promising estimation accuracy under activities. In this study, a novel deep learning model <em>UTransBPNet</em>, featured in short- and long-range feature representation, is proposed aiming to improve the estimation accuracy and tracking capability of intra-individual BP changes under activities. The model performance was comprehensively evaluated in three different datasets, i.e., one public dataset (Dataset_MIMIC) and two datasets under daily activities (Dataset_Drink and Dataset_Exercise). Under subject-independent validation, the model achieved state-of-the-art performance in the Dataset_MIMIC, with the mean absolute differences (MADs) for systolic BP (SBP) and diastolic BP (DBP) of 4.38 and 2.25 mmHg, respectively. In addition, the model achieved strong tracking capability of intra-individual BP variations under activities, with the individual Pearson’ correlation coefficients for SBP and DBP of 0.61±0.17 and 0.62±0.13 (Dataset_Drink), 0.82±0.11 and 0.72±0.18 (Dataset_Exercise), respectively. Moreover, this study for the first time tested the generalization capability across different activities, and showed that with small-sized scenario-specific data for finetuning, our model showed good cross-scenario generalization capability which however degraded significantly when there are differences in the BP distribution and variation patterns between the datasets. In conclusion, <em>UTransBPNet</em> is generally a very promising deep learning model for accurate cuffless BP estimation and tracking BP changes. Future work should further investigate the influence of BP distribution and variation patterns on the generalization capability for building effective training dataset. </p>
Semi-supervised deep architecture for classification in streaming data with emerging new classes: application in condition monitoring
Authors: Wang Yu , Wang Qingbo , Vinogradov Alexey 
Published: January 2023

<p>Classification in streaming data with emerging new classes (SENC) is challenging because the surrogate models trained on the closed datasets are open to new information never seen before. The fundamental sub-tasks include: (1) Pattern recognition of these already known classes with high accuracy; (2) Timely identification of emerging new classes; (3) Model update to adapt to new classes and (4) distinguish different new classes. Existing SENC frameworks utilize predefined threshold to set the ambiguous decision boundaries between the known and emerging new classes, they have poor performance in distinguishing different emerging new classes. To this end, a novel SENC framework leveraging Generative Adversarial Network (GAN) and history-state ensemble method is proposed in this paper, which is referred to as ensemble multi-classification GAN (EMC-GAN). Compared with traditional SENC frameworks, the proposed method is designed based on deep learning, thus, it can automatically explore implicit features from the raw data without human intervention. Besides, it doed not require pre-set threshold for novelty detection. The proposed method is evaluated on the condition monitoring of rotating machinery - a typical SENC case in real-world industry. Experimental results show that the proposed SENC framework can not only effectively detect emerging new classes from known ones, but also exhibit excellent performance in distinguishing different new classes. </p>
Cgp Index, Umur Perusahaan dan Pengungkapan Lingkungan pada Bumn Pertambangan 2016-2020
Authors: Dani Usmar Dani , Andi Usmar , Yusep. M Usmar 
Published: January 2023

Di negara Indonesia penilaian kinerja lingkungan telah bertransformasi dari penilaian berbasis pengendalian pencemaran kepada penilaian berbasis perbaikan berkelanjutan dan daya tanggap kebencanaan. tujuan yang utama adalah menyediaan informasi yang relevan dan bermanfaat kepada para pengguna laporan. Penelitian berfokus pada hubungan CGP-Index dan pengaruhnya terhadap pengungkapan lingkungan dengan menghadirkan umur perusahaan sebagai variabel moderasi. Metode Deskriptif dengan analisis verifikatif Moderated Regression Analysis (MRA) data panel. Umur perusahaan tidak mampu memoderasi pengaruh CGP-Index terhadapt Pengungkapan lingkungan. CGP-Index berpengaruh positif dan signifikan terhadap pengungkapan lingkungan (PEL). Vaiabel umur perusahaan tidak dapat memoderasi hubungan pengaruh CGP-Index terhadap pengungkapan lingkungan (PEL), karena variabel umur perusahaan tidak layak untuk menjadi variabel moderasi (bukan variabel).
Di negara Indonesia penilaian kinerja lingkungan telah bertransformasi dari penilaian berbasis pengendalian pencemaran kepada penilaian berbasis perbaikan berkelanjutan dan daya tanggap kebencanaan. tujuan yang utama adalah menyediaan informasi yang relevan dan bermanfaat kepada para pengguna laporan. Penelitian berfokus pada hubungan CGP-Index dan pengaruhnya terhadap pengungkapan lingkungan dengan menghadirkan umur perusahaan sebagai variabel moderasi. Metode Deskriptif dengan analisis verifikatif Moderated Regression Analysis (MRA) data panel. Umur perusahaan tidak mampu memoderasi pengaruh CGP-Index terhadapt Pengungkapan lingkungan. CGP-Index berpengaruh positif dan signifikan terhadap pengungkapan lingkungan (PEL). Vaiabel umur perusahaan tidak dapat memoderasi hubungan pengaruh CGP-Index terhadap pengungkapan lingkungan (PEL), karena variabel umur perusahaan tidak layak untuk menjadi variabel moderasi (bukan variabel).
PREDICTION OF SUBSOILING DRAFT REQUIREMENTS OF SILT LOAM SOILS FROM EMPIRICAL DATA: ARTIFICIAL NEURAL NETWORK VERSUS MULTIPLE LINEAR REGRESSION
Authors: S.O. Abich , A.N. Gitau , D. M. Nyaanga 
Published: January 2023

Modern agriculture is intensive resulting in increased passes of machinery on soils leading to soil compaction. Wheel traffic makes the soil harder resulting in higher tillage draft requirement and fuel consumption. There is little documentation on prediction of subsoiling draft requirements for silt loam soils of Njoro Sub County, Nakuru County in Kenya. This study aimed to evaluate the performance of multiple linear regression (MLR) and Artificial Neural Networks (ANN) in predicting subsoiling draft requirement. Experimental data were collected from plots with wheel traffic treatments of 1, 2, 3, 4 and 5 passes to determine the effect of traffic intensity at three depths of 0 – 20 cm, 20 - 30 cm and 20 - 40 cm. The subsoiling draft was determined for each experiment and modeled using MLR and ANN. The results showed that ANN gave a more accurate prediction of the draft requirement of a subsoiler than an MLR. The RMSE for the models were 0.313 and 0.03 kN for the MLR and ANN model respectively. The MAPE for the MLR model was 5.41% while for ANN was 1.27%. The coefficient of determination for the two models were 0.984 and 0/999 respectively; ANN was therefore found to be suitable for predicting the subsoiling draft.
Persuasive Strategies in Donald Trump's Political Speeches
Authors: Prafitri Wilma , Nasir Muhammad Alim Akbar 
Published: January 2023

This study analyses persuasive strategies on Donald Trump’s political speeches. This study uses qualitative research as the design and Content Analysis as the approach. The researcher explores persuasive strategies used by Donald Trump using Aristotle’s Theory to see what persuasive strategies are used and how Trump’s political speech could affect audiences to vote for him. The researcher selected three speeches such as campaign speech (2015),victory speech (2016) and inauguration speech (2017) as the object of study. These speeches are selected based on some considerations, such as lexical density, political concepts described by Trump, academic scrutiny and also to make this research more manageable. Furthermore, the researcher found that Trump has used persuasive strategies in order to convince Americans to vote him as President. According to Aristotle , there are three types of Persuasive Strategies such as Logos, Ethos and Pathos. The researcher found thirty one statements indicated of containing pathos, thirteen statements of Logos and fifteen statements as Ethos. Pathos encompasses the emotional influence on the audience. Based on the analysis, the researcher concludes that persuasive strategies played crucial part to evoke audience’s emotions and feelings.
Duration discrimination: A diffusion decision modeling approach
Authors: Schumacher Lukas , Voss Andreas 
Published: January 2023

AbstractThe human ability to discriminate the duration of two subsequently presented stimuli is often studied with tasks that involve a comparison between a standard stimulus (with fixed duration) and comparison stimuli (with varying durations). The performance in such tasks is influenced by the presentation order of these successively presented stimuli. The so-called Type A effect refers to the impact of presentation order on the point of subjective equality. The Type B effect describes effects of presentation order on the just-noticeable-difference. Cognitive models that account for these context effects assume that participants’ duration estimation is influenced by the history of previously encountered stimuli. For example, the internal reference model assumes that the magnitude of a “typical” stimulus is represented by an internal reference. This internal reference evolves throughout an experiment and is updated on every trial. Different recent models have in common that they describe how the internal reference is computed but are agnostic to the decision process itself. In this study, we develop a new model that incorporates the mechanisms of perceptual discrimination models into a diffusion model. The diffusion model focuses on the dynamics of the decision process itself and accounts for choice and response times based on a set of latent cognitive variables. We show that our model accurately predicts the accuracy and response time distribution in a classical duration discrimination task. Further, model parameters were sensitive to the Type A and B effect. The proposed model opens up new opportunities for studying human discrimination performance (e.g., individual differences).
On the role of exploitation and exploration strategies in the maintenance of cognitive biases: Beyond the pursuit of instrumental rewards
Authors: Kasper Jakob , Fiedler Klaus , Kutzner Florian , Harris Chris 
Published: January 2023

AbstractWhy can initial biases persist in repeated choice tasks? Previous research has shown that frequent rewards can lure the decision maker into premature exploitation of a supposedly best option, which can result in the persistence of initial biases. Here, we demonstrate that even in the absence of rewards, initial biases can be perpetuated through a positive testing strategy. After eliciting a biased preference for one of two equally rewarding options, participants (N = 203) could sample freely from both options without the lure of any financial rewards. When participants were told to rule out alternatives in this phase, they explored the supposedly worse option and thereby managed to overcome their initial bias. When told to optimize their strategy, however, they exhibited a positive testing strategy resulting in the continued exploitation of the supposedly better option, a bias they maintained in an incentivized choice phase and later judgments. Across all participants, individual tendencies to exploit one option in earlier phases predicted biased behavior in subsequent phases. The findings highlight that not only the pursuit of instrumental rewards can lead to exploitation and the maintenance of initial biases. We discuss potential consequences for interventions.
Factores de éxito para las Pymes metalmecánicas de Córdoba, Argentina
Authors: Marcelo Tavella  , Ariel Miropolsky  , Roxana Manera  
Published: January 2023

El presente trabajo intenta desarrollar un análisis de diferentes aspectos en los que las pymes del sector metalmecánico de la provincia de Córdoba (Argentina) articulan su ventaja competitiva, teniendo en cuenta las necesidades y limitantes que caracterizan estructuralmente al sector. Los resultados obtenidos dejan en evidencia aquellos factores que se consideraron como críticos para el logro de dicha ventaja competitiva, destacándose las inversiones en bienes de capital, en mejora de los procesos y en capacitación.
Abstract
The present work tries to develop an analysis of different aspects in which the SMEs of the metalworking sector of the province of Córdoba (Argentina) articulate their competitive advantage, taking into account the needs and limitations that structurally characterize the sector. The results obtained reveal those factors that were considered critical for achieving said competitive advantage, highlighting investments in capital goods, process improvement and training.
Factores emocionales en el aprendizaje y rendimiento académico del alumnado de primaria
Authors: Delgadillo Sustaita Araceli , Villagrán Rueda Sonia , Jasso Velázquez David , Rodríguez Ortiz Mónica 
Published: January 2023

El objetivo del presente artículo es demostrar cómo influyen los factores emocionales en el aprendizaje y rendimiento académico del alumnado de primaria específicamente en un grupo de tercero A de una escuela ubicada en el estado de Zacatecas. La metodología llevada a cabo es cualitativa con base al diseño fenomenológico con una población objeto de estudio de 62 alumnas y alumnos turno matutino. Es un caso no probabilístico ya que permite observar, explorar y describir los resultados de 8 alumnos y 10 alumnas con un muestreo por cuotas debido a un objetivo en particular de indagar eligiendo solo a un grupo. El instrumento que se utilizó fue un cuestionario de opción múltiple de elaboración propia. Los hallazgos encontrados comprueban que la motivación, inseguridad, miedo, actitud y escucha activa son factores que se presentan durante la clase en actividades muy concretas como: lectura frente al grupo, convivencia escolar, evaluaciones escolares y por supuesto la reacción del alumnado ante diversas situaciones, se concluyen que prevalecen más las emociones negativas en las niñas, sin embargo, después de realizar un examen sobresalen las positivas y en los niños se identifica un mayor porcentaje en experimentar la inseguridad.
Korg: A Modern 1D LTE Spectral Synthesis Package
Authors: Wheeler Adam J. , Abruzzo Matthew W. , Casey Andrew R. , Ness Melissa K. 
Published: January 2023

Abstract
We present Korg, a new package for 1D LTE spectral synthesis of FGK stars, which computes theoretical spectra from the near-ultraviolet to the near-infrared, and implements both plane-parallel and spherical radiative transfer. We outline the inputs and internals of Korg, and compare synthetic spectra from Korg, Moog, Turbospectrum, and SME. The disagreements between Korg and the other codes are no larger than those between the other codes, although disagreement between codes is substantial. We examine the case of a C2 band in detail, finding that uncertainties on physical inputs to spectral synthesis account for a significant fraction of the disagreement. Korg is 1–100 times faster than other codes in typical use, compatible with automatic differentiation libraries, and easily extensible, making it ideal for statistical inference and parameter estimation applied to large data sets. Documentation and installation instructions are available at https://ajwheeler.github.io/Korg.jl/stable/.
DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning
Authors: Morgan R. , Nord B. , Bechtol K. , Möller A. , Hartley W. G. , Birrer S. , González S. J. , Martinez M. , Gruendl R. A. , Buckley-Geer E. J. , Shajib A. J. , Rosell A. Carnero , Lidman C. , Collett T. , Abbott T. M. C. , Aguena M. , Andrade-Oliveira F. , Annis J. , Bacon D. , Bocquet S. , Brooks D. , Burke D. L. , Kind M. Carrasco , Carretero J. , Castander F. J. , Conselice C. , Costa L. N. da , Costanzi M. , De Vicente J. , Desai S. , Doel P. , Everett S. , Ferrero I. , Flaugher B. , Friedel D. , Frieman J. , García-Bellido J. , Gaztanaga E. , Gruen D. , Gutierrez G. , Hinton S. R. , Hollowood D. L. , Honscheid K. , Kuehn K. , Kuropatkin N. , Lahav O. , Lima M. , Menanteau F. , Miquel R. , Palmese A. , Paz-Chinchón F. , Pereira M. E. S. , Pieres A. , Malagón A. A. Plazas , Prat J. , Rodriguez-Monroy M. , Romer A. K. , Roodman A. , Sanchez E. , Scarpine V. , Sevilla-Noarbe I. , Smith M. , Suchyta E. , Swanson M. E. C. , Tarle G. , Thomas D. , Varga T. N. 
Published: January 2023

Abstract
Gravitationally lensed supernovae (LSNe) are important probes of cosmic expansion, but they remain rare and difficult to find. Current cosmic surveys likely contain 5–10 LSNe in total while next-generation experiments are expected to contain several hundred to a few thousand of these systems. We search for these systems in observed Dark Energy Survey (DES) five year SN fields—10 3 sq. deg. regions of sky imaged in the griz bands approximately every six nights over five years. To perform the search, we utilize the DeepZipper approach: a multi-branch deep learning architecture trained on image-level simulations of LSNe that simultaneously learns spatial and temporal relationships from time series of images. We find that our method obtains an LSN recall of 61.13% and a false-positive rate of 0.02% on the DES SN field data. DeepZipper selected 2245 candidates from a magnitude-limited (m

i
< 22.5) catalog of 3,459,186 systems. We employ human visual inspection to review systems selected by the network and find three candidate LSNe in the DES SN fields.
The Hydrogen-poor Superluminous Supernovae from the Zwicky Transient Facility Phase I Survey. II. Light-curve Modeling and Characterization of Undulations
Authors: Chen Z. H. , Yan Lin , Kangas T. , Lunnan R. , Sollerman J. , Schulze S. , Perley D. A. , Chen T.-W. , Taggart K. , Hinds K. R. , Gal-Yam A. , Wang X. F. , De K. , Bellm E. , Bloom J. S. , Dekany R. , Graham M. , Kasliwal M. , Kulkarni S. , Laher R. , Neill D. , Rusholme B. 
Published: January 2023

Abstract
We present analysis of the light curves (LCs) of 77 hydrogen-poor superluminous supernovae (SLSNe I) discovered during the Zwicky Transient Facility Phase I operation. We find that the majority (67%) of the sample can be fit equally well by both magnetar and ejecta–circumstellar medium (CSM) interaction plus 56Ni decay models. This implies that LCs alone cannot unambiguously constrain the physical power sources for an SLSN I. However, 23% of the sample show inverted V-shape, steep-declining LCs or features of long rise and fast post-peak decay, which are better described by the CSM+Ni model. The remaining 10% of the sample favors the magnetar model. Moreover, our analysis shows that the LC undulations are quite common, with a fraction of 18%–44% in our gold sample. Among those strongly undulating events, about 62% of them are found to be CSM-favored, implying that the undulations tend to occur in the CSM-favored events. Undulations show a wide range in energy and duration, with median values (and 1σ errors) being as

1.7

%

−
0.7
%

+
1.5
%

E

rad
,
total

and

28.8

−
9.1

+
14.4

days, respectively. Our analysis of the undulation timescales suggests that intrinsic temporal variations of the central engine can explain half of the undulating events, while CSM interaction (CSI) can account for the majority of the sample. Finally, all of the well-observed He-rich SLSNe Ib either have strongly undulating LCs or the LCs are much better fit by the CSM+Ni model. These observations imply that their progenitor stars have not had enough time to lose all of the He-envelopes before supernova explosions, and H-poor CSM are likely to present in these events.
Statistics of Galactic-scale Quasar Pairs at Cosmic Noon
Authors: Shen Yue , Hwang Hsiang-Chih , Oguri Masamune , Chen Nianyi , Matteo Tiziana Di , Ni Yueying , Bird Simeon , Zakamska Nadia , Liu Xin , Chen Yu-Ching , Kratter Kaitlin M. 
Published: January 2023

Abstract
The statistics of galactic-scale quasar pairs can elucidate our understanding of the dynamical evolution of supermassive black hole (SMBH) pairs, the duty cycles of quasar activity in mergers, or even the nature of dark matter, but they have been challenging to measure at cosmic noon, the prime epoch of massive galaxy and SMBH formation. Here we measure a double quasar fraction of ∼6.2 ± 0.5 × 10−4 integrated over ∼0.″3–3″ separations (projected physical separations of ∼3–30 kpc at z ∼ 2) in luminous (L
bol > 1045.8 erg s−1) unobscured quasars at 1.5 < z < 3.5 using Gaia EDR3-resolved pairs around SDSS DR16 quasars. The measurement was based on a sample of 60 Gaia-resolved double quasars (out of 487 Gaia pairs dominated by quasar+star superpositions) at these separations, corrected for pair completeness in Gaia, which we quantify as functions of pair separation, magnitude of the primary, and magnitude contrast. The double quasar fraction increases toward smaller separations by a factor of ∼5 over these scales. The division between physical quasar pairs and lensed quasars in our sample is currently unknown, requiring dedicated follow-up observations (in particular, deep, subarcsecond-resolution IR imaging for the closest pairs). Intriguingly, at this point, the observed pair statistics are in rough agreement with theoretical predictions both for the lensed quasar population in mock catalogs and for dual quasars in cosmological hydrodynamic simulations. Upcoming wide-field imaging/spectroscopic space missions such as Euclid, CSST, and Roman, combined with targeted follow-up observations, will conclusively measure the abundances and host galaxy properties of galactic-scale quasar pairs, offset AGNs, and subarcsecond lensed quasars across cosmic time.