bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simplified photogalvanic cell design with promise for the enhanced solar electricity generation and storage
Authors: Koli Pooran , Dayma Yashodhara , Pareek Ramesh Kumar , Kumar Rajendra , Jonwal Meenakshi 
Published: September 2021

AbstractPhotogalvanicists have conventionally used complicated, multichambered, sophisticated, and very costly cell designs for solar electricity and storage. We authors have simplified cell design with encouraging electrical output as well. We authors have used a simple, cheap, and one‐chambered cell design based on cylindrical glass tubes instead of the costly, conventional, and complex H‐shaped glass tube‐based cell fabrication design. The study has been done under similar electrolytic and illuminating conditions for different cell designs based on various blackened H‐shaped glass tubes of the different diffusion lengths, nonblackened simple glass boiling tube, blackened simple glass boiling tube, and simple noncoated glass beakers. Nonblackened glass tube‐based cell design has shown very good electrical output, that is, power 439 μW, current 2100 μA, potential 1045 mV, efficiency 6.1%, and storage capacity 130 minutes. Under similar chemical and illuminating conditions, the electrical output of cells fabricated of the simple glass tube is as good as that of the conventional cells made from the complex H‐shaped glass tube. Furthermore, the cell design based on a simple cylindrical glass tube is three‐edged more advantageous (in terms of the cost, electrical output, and ease of fabrication) over all other cell designs reported so far.
Contribution analysis: A promising method for assessing advocacy's impact
Authors: Kane Robin , Levine Carlisle , Orians Carlyn , Reinelt Claire 
Published: September 2021

AbstractThe dynamic nature of advocacy presents a challenge for evaluators to rigorously assess cause‐and‐effect relationships linking advocacy efforts to policy change. Contribution analysis, developed by Dr. John Mayne, offers a nonexperimental impact evaluation method for testing and validating contribution claims in a policy context with multiple actors and influences. Using two case examples, the authors share how they applied the steps of contribution analysis to determine with greater confidence the extent to which an initiative or campaign contributed to a policy outcome. The authors provide observations and tips for advocacy and policy evaluators on how to create a succinct, evidence‐based story about the contribution claim and how coalitions can use these stories to inform strategic decision‐making.
Evaluating power building: Concepts and considerations for advocacy evaluators
Authors: Fox Katie , Post Margaret 
Published: September 2021

AbstractPower is a fundamental dimension of social change that evaluators regularly overlook. To evaluate power effectively, the evaluation field needs a necessary reorientation in understandings of social change goals, outcomes, strategies, and actors. A focus on power is a missing component in advocacy evaluation and represents a paradigm shift for advocacy evaluators. This article explores the theoretical contributions and methodological considerations of evaluating power, drawing from our experience evaluating nine community organizations' power‐building work focused on economic justice policy reforms. We conclude that four considerations can reorient evaluations to the role of power: (1) grounding power evaluations in equitable evaluation, (2) expanding the scope of evaluations beyond a focus on policy wins to examine individual and collective liberation, (3) incorporating frameworks that acknowledge and assess the iterative and cyclical nature of power building, and (4) clarifying the unit of analysis to consider how a wide array of actors build and wield power.
Pushing boundaries: Advocacy evaluation and real‐time learning in an HIV prevention research advocacy coalition in sub‐Saharan Africa
Authors: Dasmariñas Jules , Schlangen Rhonda 
Published: September 2021

AbstractEvaluation processes that facilitate learning among advocates must be nimble, creative, and meaningful while transcending putative performance and accountability management. This article describes the experience, lessons, and trajectory of one such approach, Simple, Participatory Assessment of Real Change (SPARC), that a transnational HIV prevention research advocacy coalition pilot‐tested in sub‐Saharan Africa. Inspired by the pioneering work of the outcome harvesting (OH) and participatory evaluation community, we recuperate advocates' centrality as storytellers, sense‐makers, and strategists in advocacy evaluation and describe how we recalibrated SPARC to meet their evaluation and learning needs. This article highlights the normative value of deliberative discourse in evaluation as it contributes to the interpretation of OH and the enrichment of the theory and practice of advocacy evaluation.
Who puts the value in Evaluation? The need for self‐reflection and transparency in advocacy and policy change evaluation
Authors: Taylor‐Schiro ‐ Biidabinikwe Elizabeth , Cram Aneta 
Published: September 2021

AbstractAdvocacy and policy change evaluators navigate uniquely complex environments, made up of layers with many moving parts, and are actors typically in highly political spaces. Evaluators bring their unique components to the evaluation in the form of values, biases, and belief systems that are continually constructing the identities they take on within these environments. In turn, those identities influence which roles evaluators will take, such as collaborator or advocate, and bring biases that have the potential to impact their behavior and decision‐making as they evaluate advocacy and policy change endeavors. The authors of this article discuss how evaluator identity, role, and bias can impact advocacy and policy change evaluation; as well as describe how power and privilege may show up in advocacy and policy change evaluation praxis. The authors then challenge the reader to engage with teachings from Indigenous Evaluation practices that are naturally grounded in advocacy principles and employ reflexivity as a means of ensuring that their work is culturally responsive and supports the minimization of unintended harm. Finally, the authors present a reflexivity framework with the hope that this will support advocacy and policy change evaluation practitioners to be more intentional in how they critically reflect on their work, the layers of complexity in advocacy and policy change, and how their values and biases might be impacting their evaluation practice. The reflexivity framework serves to support the practice and incite further discussion on the use of reflexivity as a normal part of advocacy and policy change evaluation.
Recession forecasting with high‐dimensional data
Authors: Nevasalmi Lauri 
Published: September 2021

AbstractIn this paper, a large amount of different financial and macroeconomic variables are used to predict the U.S. recession periods. We propose a new cost‐sensitive extension to the gradient boosting model, which can take into account the class imbalance problem of the binary response variable. The class imbalance, caused by the scarcity of recession periods in our application, is a problem that is emphasized with high‐dimensional datasets. Our empirical results show that the introduced cost‐sensitive extension outperforms the traditional gradient boosting model in both in‐sample and out‐of‐sample forecasting. Among the large set of candidate predictors, different types of interest rate spreads turn out to be the most important predictors when forecasting U.S. recession periods.
Firm dynamics and bankruptcy processes: A new theoretical model
Authors: Çelik Şaban , Aktan Bora , Burton Bruce 
Published: September 2021

AbstractThis paper introduces a theoretical model that relates firm dynamics to bankruptcy processes on both univariate and multivariate bases. The distinctiveness of the study lies in the estimation of default using a framework where firm dynamics are linked to the bankruptcy process itself. The structure builds on theoretical relations among the constructs viewed from a perspective sufficiently wide to demonstrate value addition and dilution. The univariate‐based estimation results indicate that the variables determined via the model successfully differentiate distressed firms from non‐distressed and other firms, although the evidence from the multivariate analysis facilitate a 90%+ correct distressed/non‐distressed classification rate for both 1‐ and 2‐year periods. The findings provide a road map for selecting the most appropriate default prediction variables and identifying the role of each in the prediction process. The prediction accuracy of the model does not depend solely upon statistical techniques but also reflects each variable's theoretical underpinning. The model is shown to be useful for firm rating in general and credit approval decisions in particular.
A novel deep learning model based on convolutional neural networks for employee churn prediction
Authors: Pekel Ozmen Ebru , Ozcan Tuncay 
Published: September 2021

AbstractEmployees are one of the most important resources of a company. The churn of valuable employees significantly affects a company's performance. The design of systems that predict employee churn is critical importance for companies. At this point, machine learning algorithms offer important opportunities for the diagnosis of employee churn. Nowadays, traditional classification algorithms have been replaced by deep learning models. In this study, firstly, a convolutional neural network (CNN) model was applied on a numerical data set for employee churn prediction in retailing. Later, because the data loss is too much in data transformations, a new hybrid extended convolutional decision tree model (ECDT) was proposed by improving the CNN algorithm. Finally, a novel model (ECDT‐GRID) was developed by applying grid search optimization to improve the classification accuracy of ECDT. Numerical results showed that the developed ECDT‐GRID model outperformed the CNN and ECDT models and basic classification algorithms in terms of classification accuracy, and this model provided an efficient methodology for prediction of employee churn.
Mixed data sampling regression: Parameter selection of smoothed least squares estimator
Authors: Toker Selma , Özbay Nimet , Månsson Kristofer 
Published: September 2021

AbstractWe investigate mixed data sampling (MIDAS) regression models, which include time series data sampled at different frequencies. MIDAS framework introduced by Ghysels et al. (2004, 2005, 2006, 2007) comprises diverse lag structures that are employed for parameterizing the regression model. Since some specific parametric functions are utilized, MIDAS is a parametric approach and thus nonlinear least squares (NLS) technique is used to estimate the parameters. In this parametric approach, the shape of the lag distribution is controlled by an arbitrary class of parametric functions, but unfortunately, inaccurate approximations of the lag distribution may arise under such a circumstance. Breitung and Roling (2015) suggested a nonparametric approach as a solution to this complication. The product of this approach is smoothed least squares (SLS) estimator which depends on a smoothing parameter. The main objective of this paper is to solve the complexity of selecting this parameter. We offer various selection methods for the smoothing parameter to enhance the performance of the SLS estimator. A Monte Carlo simulation is conducted to test the effect of different choices of the smoothing parameter on the performance of the SLS estimator by means of both in‐sample and out‐of‐sample forecasting. Additionally, we carry out a data analysis where future values of gross domestic product are forecasted by unemployment rate.
Fundamental index aligned and excess market return predictability
Authors: Ze‐To Samuel YM 
Published: September 2021

AbstractWe document the significant predictive power of an aligned fundamental index for aggregate excess stock market returns. The index incorporates the major financial indicators used by Piotroski (2000) and eliminates the idiosyncratic error components of individual indicators using the partial least squares approach modified by Kelly and Pruitt (2015). Our proposed fundamental index outperforms the aggregate financial score index of Piotroski (2000) and the equally weighted financial index in predicting returns for both in‐sample and out‐of‐sample tests. The aligned financial index also provides significant forecasting power for future market returns after controlling for the major economic variables. The proposed index persistently exhibits strong return forecasting power for longer time horizons and generates higher certainty equivalent gain for risk‐averse investors. The positive relations between the proposed index and future returns of portfolios sorted by momentum, book‐to‐market ratio, and company size are all significant.
Connectivity gradients on tractography data: Pipeline and example applications
Authors: Blazquez Freches Guilherme , Haak Koen V. , Beckmann Christian F. , Mars Rogier B. 
Published: September 2021

AbstractGray matter connectivity can be described in terms of its topographical organization, but the differential role of white matter connections underlying that organization is often unknown. In this study, we propose a method for unveiling principles of organization of both gray and white matter based on white matter connectivity as assessed using diffusion magnetic ressonance imaging (MRI) tractography with spectral embedding gradient mapping. A key feature of the proposed approach is its capacity to project the individual connectivity gradients it reveals back onto its input data in the form of projection images, allowing one to assess the contributions of specific white matter tracts to the observed gradients. We demonstrate the ability of our proposed pipeline to identify connectivity gradients in prefrontal and occipital gray matter. Finally, leveraging the use of tractography, we demonstrate that it is possible to observe gradients within the white matter bundles themselves. Together, the proposed framework presents a generalized way to assess both the topographical organization of structural brain connectivity and the anatomical features driving it.
Flexible Bayesian longitudinal models for cost‐effectiveness analyses with informative missing data
Authors: Mason Alexina J. , Gomes Manuel , Carpenter James , Grieve Richard 
Published: September 2021

AbstractCost‐effectiveness analyses (CEA) are recommended to include sensitivity analyses which make a range of contextually plausible assumptions about missing data. However, with longitudinal data on, for example, patients' health‐related quality of life (HRQoL), the missingness patterns can be complicated because data are often missing both at specific timepoints (interim missingness) and following loss to follow‐up. Methods to handle these complex missing data patterns have not been developed for CEA, and must recognize that data may be missing not at random, while accommodating both the correlation between costs and health outcomes and the non‐normal distribution of these endpoints. We develop flexible Bayesian longitudinal models that allow the impact of interim missingness and loss to follow‐up to be disentangled. This modeling framework enables studies to undertake sensitivity analyses according to various contextually plausible missing data mechanisms, jointly model costs and outcomes using appropriate distributions, and recognize the correlation among these endpoints over time. We exemplify these models in the REFLUX study in which 52% of participants had HRQoL data missing for at least one timepoint over the 5‐year follow‐up period. We provide guidance for sensitivity analyses and accompanying code to help future studies handle these complex forms of missing data.
Pharmaceutical opioid marketing and physician prescribing behavior
Authors: Beilfuss Svetlana , Linde Sebastian 
Published: September 2021

AbstractPhysicians' relationships with the pharmaceutical industry have recently come under public scrutiny, particularly in the context of opioid drug prescribing. This study examines the effect of doctor‐industry marketing interactions on subsequent prescribing patterns of opioids using linked Medicare Part D and Open Payments data for the years 2014–2017. Results indicate that both the number and the dollar‐value of marketing visits increase physicians' patented opioid claims. Furthermore, direct‐to‐physician marketing of safer abuse‐deterrent formulations of opioids is the primary driver of positive and persistent spillovers on the prescribing of less safe generic opioids ‐ a result that we show appears to be driven by insurance coverage policies. These findings suggest that pharmaceutical marketing efforts may have unintended public health implications.
Generation of neutrophil extracellular traps in patients with acute liver failure is associated with poor outcome
Authors: von Meijenfeldt Fien A. , Stravitz R. Todd , Zhang Jingwen , Adelmeijer Jelle , Zen Yoh , Durkalski Valerie , Lee William M. , Lisman Ton 
Published: September 2021

Abstract

Background and Aims
Acute liver failure (ALF) is characterized by significant changes in the hemostatic system and by systemic inflammation. The formation of neutrophil extracellular traps (NETs), in which an activated neutrophil expels its DNA, histones, and granular enzymes, such as myeloperoxidase (MPO), has been associated with immune‐mediated and thrombotic diseases. We hypothesized that formation of NETs in patients with ALF contributes to progression of disease.

Approach and Results
A total of 676 patients with ALF (international normalized ratio [INR], ≥1.5) or severe acute liver injury (ALI; INR, ≥2.0) were recruited from the U.S. ALF Study Group Registry between 2011 and 2018, of whom 308 patients (45.6%) had acetaminophen‐induced ALF. Up to 21 days after admission, 483 patients (71.5%) survived without liver transplantation (LT). Levels of cell‐free DNA (cfDNA) and the specific NET marker MPO‐DNA complexes were measured in plasma samples obtained on admission and compared to levels in healthy controls. In addition, liver tissue obtained at transplantation of 20 ALF patients was stained for NETs. Levels of cfDNA were 7.1‐fold, and MPO‐DNA complexes 2.5‐fold, higher in patients with ALF compared to healthy controls. cfDNA levels were not associated with 21‐day transplant‐free survival, but were higher in those patients with more‐severe disease on admission, as reflected by various laboratory and clinical parameters. MPO‐DNA levels were 30% higher in patients with ALF who died or required urgent LT. Liver tissue of ALF patients stained positive for NETs in 12 of 18 evaluable patients.

Conclusions
Here, we provide evidence for NET formation in patients with ALF. Elevated plasma levels of MPO‐DNA complexes in patients with ALF were associated with poor outcome, which suggests that NET formation contributes to disease progression.
Multiple slip effects on nanofluid dissipative flow in a converging/diverging channel: A numerical study
Authors: Bég O. Anwar , Bég Tasveer , Khan W. A. , Uddin M. J. 
Published: September 2021

AbstractA mathematical model is developed for viscous slip flow and heat transfer in water/Ethylene glycol‐based nanofluids containing metallic oxide nanoparticles, through a converging/diverging channel. We adopt the single‐phase Tiwari–Das model. The governing equations are transformed to a set of similarity differential equations with the help of similarity transformation, before being solved numerically using Maple 20. Validation of the velocity gradient and temperature solutions is achieved with the second‐order implicit finite difference Keller Box method. Further validation is included for the special case of no‐slip nanofluid flow in the absence of viscous heating. The effects of the parameters, namely velocity slip, thermal jump, channel apex angle, Eckert number, Prandtl number, Reynolds number, and nano‐particle volume fraction on velocity, temperature, skin friction, and heat transfer rate are investigated in detail. It is found that with increasing velocity slip, for water‐TiO2 and ethylene glycol‐TiO2 nanofluids, the channel bulk flow is decelerated whilst with greater solid (nanoparticle) volume and in the presence of momentum slip, the flow is also retarded. With the increasing semivertex angle, the channel flow is generally accelerated. An increase in divergent semiangle leads to decelerate the flow from the centerline for the core flow region, whereas near and at the channel wall, it results in a weak acceleration. Higher temperatures are achieved with greater thermal slip values, for both water‐TiO2 and ethylene glycol‐TiO2 nanofluids, whereas for greater nanoparticle volume fraction, temperatures are weakly decreased for water‐TiO2 whereas a more significant decrease is observed for ethylene glycol‐TiO2 nanofluid. With a greater diverging channel angle, a substantial decrease in temperatures is caused by greater Reynolds numbers, and the reverse effect is computed for the converging channel. The novelty of the current work is that it extends previous studies to include multiple slip effects and viscous heating (Eckert number effects), which are shown to exert a significant influence on heat and momentum transfer characteristics. The study is relevant to certain pharmaco‐dynamics devices (drug delivery), next‐generation 3D nanotechnological printers, and also nano‐cooling systems in energy engineering where laminar flows in diverging/converging channels arise
Revised runoff curve number for runoff prediction in the Loess Plateau of China
Authors: Shi Wenhai , Wang Ni , Wang Miaomiao , Li Donghao 
Published: September 2021

AbstractThe soil conservation service (now Natural Resources Conservation Service) Curve Number (SCS‐CN), one of the most commonly used methods for surface runoff prediction. The runoff calculated by this method was very sensitive to CN values. In this study, CN values were calculated by both arithmetic mean (CN_C) and least square fit method (CN_F) using observed rainfall‐runoff data from 43 sites in the Loess Plateau region, which are considerably different from the CN2 values obtained from the USDA‐SCS handbook table (CN_T). The results showed that using CN_C instead of CN_T for each watershed produce little improvement, while replacing CN_T with CN_F improves the performance of the original SCS‐CN method, but still performs poorly in most study sites. This is mainly due to the SCS‐CN method using a constant CN value and discounting of the temporal variation in rainfall‐runoff process. Therefore, three factors—soil moisture, rainfall depth and intensity—affecting the surface runoff variability are considered to reflect the variation of CN in each watershed, and a new CN value was developed. The reliability of the proposed method was tested with data from 38 watersheds, and then applied to the remaining five typical watersheds using the optimized parameters. The results indicated that the proposed method, which boosted the model efficiencies to 81.83% and 74.23% during calibration and validation cases, respectively, performed better than the original SCS‐CN and the Shi and Wang (2020b) method, a modified SCS‐CN method based on tabulated CN value. Thus, the proposed method incorporating the influence of the temporal variability of soil moisture, rainfall depth, and intensity factors suggests an accurate runoff prediction for general applications under different hydrological and climatic conditions on the Loess Plateau region.