Miracle-dz/UniFAR-FTUs huggingface dataset:
Private Equity and Debt Contract Enforcement: Evidence from Covenant Violations

We document the importance of a financial sponsor when a borrower violates a covenant, providing creditors the opportunity to enforce debt contracts. We identify private-equity (PE) sponsored borrowers in the Shared National Credit Program (SNC) data and find PE-sponsored borrowers violate covenants more often than comparable non-PE borrowers. Yet, compared to non-PE, PE-backed borrowers experience smaller reductions in credit commitment upon violation, suggesting lenders are lenient with PE sponsors. Moreover, this leniency is stronger among financially healthier lenders. We show that our results are consistent with a repeated-deals mechanism, as lenders frequently interact with financial sponsors and choose to preserve relationship rent. Consistent with this mechanism, we find little evidence that PE-sponsored loans eventually underperform relative to non-PE-sponsored ones following covenant violations. Our findings have important implications for understanding heterogeneity in debt contract enforcement and credit constraints faced by distressed borrowers with financial sponsors.
Compliance with Ministry of Health Regulations among Israeli Nurses during the COVID-19 Pandemic: The Mediating Role of Risk Perception

The COVID-19 pandemic has created a sustained state of emergency, causing uncertainty and risk taking. Israeli nurses were required to follow new regulations and safety measures issued by the Israeli Ministry of Health (MOH). This study aimed to examine nurses’ compliance with MOH regulations and its association with their risk and threat perceptions and their positive and negative emotions. A cross-sectional online survey was conducted among 346 Israeli nurses. The study model was examined with path analysis. Most nurses reported complying with MOH regulations either fully (49%) or very often (30%). Negative emotions were positively associated with perceptions of both threat and risk, yet only risk perception was positively associated with nurses’ compliance. A significant mediated relationship was found between negative emotions and nurses’ compliance, with the possible mediator being risk perception. Hence, higher negative emotions were associated with a greater risk perception, which was associated with higher compliance. Health systems leaders must strategize to deal with the wave-like character of the pandemic. Solutions to nursing teams’ negative emotions must be provided to keep the balance between feelings of complacency and a situation of high-level, intense negative feelings, which might lead to abstention, burnout, or emotional injury.
Fluid Flow with Dynamic Contact Angle: Numerical Simulation

A liquid at rest in a container will show a contact angle at the wall depending on material properties. If the liquid, or the boundary walls, are moving with constant speed, this angle will change with velocity. We perform numerical experiments for a two-dimensional model free boundary value problem that has been proved to be well-posed and show that the dependence of the contact angle on the velocity is qualitatively correct.
Robust dissipative control for linear systems with dissipative uncertainty

This paper focuses on the problem of quadratic dissipative control for linear systems with or without uncertainty. We consider the design of feedback controllers to achieve (robust) asymptotic stability and strict quadratic dissipativeness. Both linear static state feedback and dynamic output feedback controllers are considered. First, the equivalence between strict quadratic dissipativeness of linear systems and a H performance is established. Necessary and sufficient conditions for the solution of the quadratic dissipative control problem are then obtained using a linear matrix inequality (LMI) approach. As for uncertain systems, we consider structured uncertainty characterized by a dissipative system. This uncertainty description is quite general and contains commonly used types of uncertainty, such as norm-bounded and positive real uncertainties, as special cases. It is shown that the robust dissipative control problem can be solved in terms of a scaled quadratic dissipative control problem without un.
A (2/3)n3 Fast-Pivoting Algorithm for the Gittins Index and Optimal Stopping of a Markov Chain

This paper presents a new fast-pivoting algorithm that computes the n Gittins index values of an n-state bandit---in the discounted and undiscounted cases---by performing (2/3)n3 + O(n2) arithmetic operations, thus attaining better complexity than previous algorithms and matching that of solving a corresponding linear-equation system by Gaussian elimination. The algorithm further applies to the problem of optimal stopping of a Markov chain, for which a novel Gittins-index solution approach is introduced. The algorithm draws on Gittins and Jones' (1974) index definition via calibration, on Kallenberg's (1986) proposal of using parametric linear programming, on Dantzig's simplex method, on the Varaiya et al. (1985) algorithm, and on the author's earlier work. This paper elucidates the structure of parametric simplex tableaux. Special structure is exploited to reduce the computational effort of pivot steps, decreasing the operation count by a factor of three relative to conventional pivoting, and by a factor of 3/2 relative to recent state-elimination algorithms. A computational study demonstrates significant time savings against alternative algorithms.
Risks and outcome of fatal respiratory events after lung cancer surgery: cohort study in South Korea

Background Severe complications such as acute respiratory distress syndrome or respiratory failure can occur after lung cancer surgery. However, the prevalence and risk factors have not been well identified. The aim of this study was to investigate the prevalence of and risk factors for fatal respiratory events after lung cancer surgery in South Korea. Methods The National Health Insurance Service database in South Korea was used to extract data of all adult patients who were diagnosed with lung cancer and underwent lung cancer surgery from January 1, 2011, to December 31, 2018, for a population-based cohort study. Diagnosis of acute respiratory distress syndrome or respiratory failure after surgery was defined as postoperative fatal respiratory event. Results A total of 60,031 adult patients who received lung cancer surgery were included in the analysis. Among them, 0.5% (285/60,031) experienced fatal respiratory events after the lung cancer surgery. In multivariable logistic regression, some risk factors (older age, male sex, higher Charlson comorbidity index score, underlying severe disability, bilobectomy, pneumonectomy, redo-case, lower case volume, and open thoracotomy) for developing postoperative fatal respiratory events were identified. Moreover, the development of postoperative fatal respiratory events was associated with high in-hospital mortality, 1-year mortality, longer length of hospital stays, and higher total hospitalization costs. Conclusions Postoperative fatal respiratory events might worsen the clinical outcomes of lung cancer surgery. The knowledge of potential risk factors related to postoperative fatal respiratory events could enable earlier intervention to reduce the occurrence of these events and improve the postoperative clinical outcome.
Fe II Emission in the NLS1 galaxy I Zw 1: Results from NIR Spectroscopy

Fe ii comprises up to one third of the line emission in AGNs. For that reason it is an important coolant that needs to be taken into account to fully understand the energetics of the broad line region (BLR). As such, Fe+ is a potential diagnostic of gas density, column density, turbulence, temperature and continuum shape. However, understanding Fe ii is complicated because of the energy level configuration and different mechanisms that may contribute to the line emission.
Stochastic Process Methods with an Application to Budgetary Data

Political scientists have increasingly focused on causal processes that operate not solely on mean differences but on other stochastic characteristics of the distribution of a dependent variable. This paper surveys important statistical tools used to assess data in situations where the entire distribution of values is of interest. We first outline three broad conditions under which stochastic process methods are applicable and show that these conditions cover many domains of social inquiry. We discuss a variety of visual and analytical techniques, including distributional analysis, direct parameter estimates of probability density functions, and quantile regression. We illustrate the utility of these statistical tools with an application to budgetary data because strong theoretical expectations at the micro- and macrolevel exist about the distributional characteristics for such data. The expository analysis concentrates on three budget series (total, domestic, and defense outlays) of the U.S. government for 1800–2004.
Context-specific adaptation of an eHealth-facilitated, integrated care model and tailoring its implementation strategies—A mixed-methods study as a part of the SMILe implementation science project

Background Contextually adapting complex interventions and tailoring their implementation strategies is key to a successful and sustainable implementation. While reporting guidelines for adaptations and tailoring exist, less is known about how to conduct context-specific adaptations of complex health care interventions. Aims To describe in methodological terms how the merging of contextual analysis results (step 1) with stakeholder involvement, and considering overarching regulations (step 2) informed our adaptation of an Integrated Care Model (ICM) for SteM cell transplantatIon faciLitated by eHealth (SMILe) and the tailoring of its implementation strategies (step 3). Methods Step 1: We used a mixed-methods design at University Hospital Basel, guided by the Basel Approach for coNtextual ANAlysis (BANANA). Step 2: Adaptations of the SMILe-ICM and tailoring of implementation strategies were discussed with an interdisciplinary team (n = 28) by considering setting specific and higher-level regulatory scenarios. Usability tests were conducted with patients (n = 5) and clinicians (n = 4). Step 3: Adaptations were conducted by merging our results from steps 1 and 2 using the Framework for Reporting Adaptations and Modifications–Enhanced (FRAME). We tailored implementation strategies according to the Expert Recommendations for Implementing Change (ERIC) compilation. Results Step 1: Current clinical practice was mostly acute-care-driven. Patients and clinicians valued eHealth-facilitated ICMs to support trustful patient-clinician relationships and the fitting of eHealth components to context-specific needs. Step 2: Based on information from project group meetings, adaptations were necessary on the organizational level (e.g., delivery of self-management information). Regulations informed the tailoring of SMILe-ICM`s visit timepoints and content; data protection management was adapted following Swiss regulations; and steering group meetings supported infrastructure access. The usability tests informed further adaptation of technology components. Step 3: Following FRAME and ERIC, SMILe-ICM and its implementation strategies were contextually adapted and tailored to setting-specific needs. Discussion This study provides a context-driven methodological approach on how to conduct intervention adaptation including the tailoring of its implementation strategies. The revealed meso-, and macro-level differences of the contextual analysis suggest a more targeted approach to enable an in-depth adaptation process. A theory-guided adaptation phase is an important first step and should be sufficiently incorporated and budgeted in implementation science projects.
On volatile growth: Simple fitting of exponential functions taking into account values of every observation with any signs, applied to readily calculate a novel covariance-invariant CAGR

Abstract The commonly used compound annual growth rate does not consider volatility, and its calculation fails for time series beginning or terminating with a zero or negative value, which may be the case for a company’s earnings history. Thus, a modification of the standard definition is proposed, derived from a covariance-invariant mapping of observations to a two-parameter exponential model. The novel growth rate is called “covariance-invariant “, which becomes for the special case of steady growth at a constant rate. It can be obtained using different options such as a chart, look-up table or formula. Further, the extension of the model by an additive constant may be used if negative values dominate. The approach is viewed as easy to apply as the log-linear model but with a superior performance. Compared to nonlinear least-squares regression, unique solutions can be obtained that allow a rather quick calculation.
Integrating UAV-Derived Information and WorldView-3 Imagery for Mapping Wetland Plants in the Old Woman Creek Estuary, USA

The classification of wetland plants using unmanned aerial vehicle (UAV) and satellite synergies has received increasing attention in recent years. In this study, UAV-derived training and validation data and WorldView-3 satellite imagery are integrated in the classification of five dominant wetland plants in the Old Woman Creek (OWC) estuary, USA. Several classifiers are explored: (1) pixel-based methods: maximum likelihood (ML), support vector machine (SVM), and neural network (NN), and (2) object-based methods: Naïve Bayes (NB), support vector machine (SVM), and k-nearest neighbors (k-NN). The study evaluates the performance of the classifiers for different image feature combinations such as single bands, vegetation indices, principal components (PCs), and texture information. The results showed that all classifiers reached high overall accuracy (>85%). Pixel-based SVM and object-based NB exhibited the best performance with overall accuracies of 93.76% and 93.30%, respectively. Insignificantly lower overall accuracy was achieved with ML (92.29), followed by NN (90.95) and object-oriented SVM (90.61). The k-NN method showed the lowest (but still high) accuracy of 86.74%. All classifiers except for the pixel-based SVM required additional input features. The pixel-based SVM achieved low errors of commission and omission, and unlike the other classifiers, exhibited low variability and low sensitivity to additional image features. Our study shows the efficacy of combining very high spatial resolution UAV-derived information and the super spectral observation capabilities of WorldView-3 in machine learning for mapping wetland vegetation.
Least circular distance regression for directional data

Least-squares regression is not appropriate when the response variable is circular, and can lead to erroneous results. The reason for this is that the squared difference is not an appropriate measure of distance on the circle. In this paper, a circular analog to least-squares regression is presented for predicting a circular response variable by another circular variable and a set of linear covariates. An alternative maximum-likelihood formulation yields the same regression parameter estimates. Under the maximum-likelihood model, asymptotic standard errors of the parameter estimates are obtained. As an example, the regression model is used to model data from a marine biology study.
What role do students’ beliefs play in a successful transition from school to university mathematics?

The transition from school to university is a challenging process for many students which is reflected in high dropout and low examination success rates during the first year at university. Qualitative studies have found hints that students’ beliefs play an important role during the transition. Due to their dialectic nature with cognitive as well as affective valences, it is sensible to assume that beliefs influence rather cognitive and objective criteria of a successful transition such as exam achievement as well as subjective criteria like satisfaction that draw on affective sources. However, quantitative studies have been mostly focusing on the role of students’ beliefs for study achievement, neglecting possible effects on students’ satisfaction and actual dropout. In this contribution, I draw a more holistic picture of the effects of beliefs during the transition by analysing effects on students’ achievement, students’ satisfaction and students’ actual dropout behaviour. Data from over 600 first-year students in a longitudinal quantitative study show that dynamic beliefs (that emphasize the process character of mathematics and the usability of mathematics in everyday life and other disciplines) have a positive impact during the transition, while static beliefs (focusing on schematic aspects of mathematics) are problematic.
Bayesian analysis of binary and polychotomous response data

Abstract A vast literature in statistics, biometrics, and econometrics is concerned with the analysis of binary and polychotomous response data. The classical approach fits a categorical response regression model using maximum likelihood, and inferences about the model are based on the associated asymptotic theory. The accuracy of classical confidence statements is questionable for small sample sizes. In this article, exact Bayesian methods for modeling categorical response data are developed using the idea of data augmentation. The general approach can be summarized as follows. The probit regression model for binary outcomes is seen to have an underlying normal regression structure on latent continuous data. Values of the latent data can be simulated from suitable truncated normal distributions. If the latent data are known, then the posterior distribution of the parameters can be computed using standard results for normal linear models. Draws from this posterior are used to sample new latent data, and t.
Application of EEG in migraine

Migraine is a common disease of the nervous system that seriously affects the quality of life of patients and constitutes a growing global health crisis. However, many limitations and challenges exist in migraine research, including the unclear etiology and the lack of specific biomarkers for diagnosis and treatment. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity. With the updating of data processing and analysis methods in recent years, EEG offers the possibility to explore altered brain functional patterns and brain network characteristics of migraines in depth. In this paper, we provide an overview of the methodology that can be applied to EEG data processing and analysis and a narrative review of EEG-based migraine-related research. To better understand the neural changes of migraine or to provide a new idea for the clinical diagnosis and treatment of migraine in the future, we discussed the study of EEG and evoked potential in migraine, compared the relevant research methods, and put forwards suggestions for future migraine EEG studies.
Measurement report: Emission factors of NH3 and NHx for wildfires and agricultural fires in the United States

Abstract. During the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) study, the NASA DC-8 carried out in situ chemical measurements in smoke plumes emitted from wildfires and agricultural fires in the contiguous United States. The DC-8 payload included a modified proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) for the fast measurement of gaseous ammonia (NH3) and a high-resolution time-of-flight aerosol mass spectrometer (AMS) for the fast measurement of submicron particulate ammonium (NH4+). We herein report data collected in smoke plumes emitted from 6 wildfires in the Western United States, 2 prescribed grassland fires in the Central United States, 1 prescribed forest fire in the Southern United States, and 66 small agricultural fires in the Southeastern United States. Smoke plumes contained double to triple digit ppb levels of NH3. In the wildfire plumes, a significant fraction of NH3 had already been converted to NH4+ at the time of sampling (≥2 h after emission). Substantial amounts of NH4+ were also detected in freshly emitted smoke from corn and rice field fires. We herein present a comprehensive set of emission factors of NH3 and NHx, with NHx=NH3+NH4+. Average NH3 and NHx emission factors for wildfires in the Western United States were 1.86±0.75 g kg−1 and 2.47±0.80 g kg−1 of fuel burned, respectively. Average NH3 and NHx emission factors for agricultural fires in the Southeastern United States were 0.89±0.58 and 1.74±0.92 g kg−1, respectively. Our data show no clear inverse correlation between modified combustion efficiency (MCE) and NH3 emissions. The observed NH3 emissions were significantly higher than measured in previous laboratory experiments in the FIREX FireLab 2016 study.
Densimeters for very accurate density measurements of fluids over large ranges of temperature, pressure, and density

This paper comprehensively describes those types of densimeters that allow very accurate density measurements of fluid substances over large ranges of temperature and for high pressures and that also cover the entire density range from low gas densities up to high densities of compressed liquids. Densimeters meeting these requirements are based on special applications of hydrostatic balances in combination with magnetic suspension couplings. These densimeters can be subdivided into two-sinker densimeters, and single-sinker densimeters which allow pρT measurements with total uncertainties in density of about 0.01% to 0.02% (level of confidence 95%) depending on their individual design. Besides a discussion of the design of such densimeters, it is also shown which substances in which ranges of the fluid state have been investigated with these instruments. Using nitrogen as an example, the experimental results obtained with these densimeters are discussed and compared with the pρT data obtained with other types of densimeters. A description of the features of other types of densimeters is outside the scope of this paper, but they are briefly summarized at the beginning of this paper.
Seasonality of human sleep: Polysomnographic data of a neuropsychiatric sleep clinic

While short-term effects of artificial light on human sleep are increasingly being studied, reports on long-term effects induced by season are scarce. Assessments of subjective sleep length over the year suggest a substantially longer sleep period during winter. Our retrospective study aimed to investigate seasonal variation in objective sleep measures in a cohort of patients living in an urban environment. In 2019, three-night polysomnography was performed on 292 patients with neuropsychiatric sleep disturbances. Measures of the diagnostic second nights were averaged per month and analyzed over the year. Patients were advised to sleep “as usual” including timing, except alarm clocks were not allowed. Exclusion criteria: administration of psychotropic agents known to influence sleep (N = 96), REM-sleep latency > 120 min (N = 5), technical failure (N = 3). Included were 188 patients: [46.6 ± 15.9 years (mean ± SD); range 17–81 years; 52% female]; most common sleep-related diagnoses: insomnia (N = 108), depression (N = 59) and sleep-related breathing disorders (N = 52). Analyses showed: 1. total sleep time (TST) longer during winter than summer (up to 60 min; not significant); 2. REM-sleep latency shorter during autumn than spring (about 25 min, p = 0.010); 3. REM-sleep longer during winter than spring (about 30 min, p = 0.009, 5% of TST, p = 0.011); 4. slow-wave-sleep stable winter to summer (about 60–70 min) with 30–50 min shorter during autumn (only significant as % of TST, 10% decrease, p = 0.017). Data suggest seasonal variation in sleep architecture even when living in an urban environment in patients with disturbed sleep. If replicated in a healthy population, this would provide first evidence for a need to adjust sleep habits to season.