Miracle-dz/UniFAR-FTUs huggingface dataset:
The delaying effect of financiing constraints on investment

We develop a simple model in which a firm considers a number of investment projects. Because of limited financial resources, the firm can undertake at most one project. In line with the literature on real options we stress features like irreversibility, uncertainty and the possibility of postponing the investment decision and show under which conditions limited availability of funds tends to increase the value of waiting.
Determinant Factors of Customers Switching Behavior to Customer Satisfaction and Loyalty in Online Transportation Users in Bandung

This study was aimed to find out which factors that become the most influence on customers switching behavior for online transportation and how the impact on their satisfaction and loyalty for future consumption. Transportation service is one of the service industry sectors that play a strategic role in human life. The intense competition in the online transportation industry and the various choice of brands in the market make the consumers easy to switch from their current product to other brand products. The research method used in this study was a quantitative method, with Structural Equation Modeling (SEM) analysis technique using SMART PLS 2.0 software. The sampling method used was accidental sampling with 400 respondents. The results of the study showed that the contribution of price, promotion and e-service quality simultaneously influenced on which directly affected customer satisfaction was 64.9%. Whereas, the results of the study also showed the contribution of price, promotion, e-service quality and customer satisfaction simultaneously influenced on which directly affected customer loyalty was 48.3%. E-service quality has the biggest impact on customer satisfaction by 30.69%; meanwhile, promotion has the biggest impact on loyalty by 3.17%.
Envisioning a Shared Checklist Display to Support Teamwork During Emergency Medical Care

Information technology and systems in healthcare are becoming increasingly complex and dynamic, requiring design approaches that consider not only providers’ information needs and work practices, but also their perceptions and expectations about technology. In this paper, we describe an exploratory study conducted to assess the feasibility and impact of a shared checklist display for supporting teamwork during trauma resuscitations. Using the concept of technological frames, we explore how members of multidisciplinary emergency medical teams perceive current technology and what they expect from a future shared checklist display. Our results showed discrepancies in team members’ perceptions of technology and its roles. Some care providers envisioned a shared checklist display as a tool for achieving a global view of the process, improving communication and maintaining situation awareness. Others perceived it as a point of distraction, where team members may be checking each other’s work to ensure accuracy Copyright 2020 held by Authors, DOI: 10.18420/ecscw2020_ep02 Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, contact the Authors.
Challenges and Opportunities in Collecting and Modeling Ambulatory Electrodermal Activity Data

Background: Ambulatory assessment of electrodermal activity (EDA) is an emerging technique for capturing individuals’ autonomic responses to real-life events. There is currently little guidance available for processing and analyzing such data in an ambulatory setting. Objective: This study aimed to describe and implement several methods for preprocessing and constructing features for use in modeling ambulatory EDA data, particularly for measuring stress. Methods: We used data from a study examining the effects of stressful tasks on EDA of adolescent mothers (AMs). A biosensor band recorded EDA 4 times per second and was worn during an approximately 2-hour assessment that included a 10-min mother-child videotaped interaction. The initial processing included filtering noise and motion artifacts. Results: We constructed the features of the EDA data, including the number of peaks and their amplitude as well as EDA reactivity, quantified as the rate at which AMs returned to baseline EDA following an EDA peak. Although the pattern of EDA varied substantially across individuals, various features of EDA may be computed for all individuals enabling within- and between-individual analyses and comparisons. Conclusions: The algorithms we developed can be used to construct features for dry-electrode ambulatory EDA, which can be used by other researchers to study stress and anxiety.
I Know Why the Caged Bird Sings

I Know Why the Caged Bird Sings is a 1969 autobiography about the early years of African-American writer and poet Maya Angelou. The first in a sevenvolume series, it is a coming-of-age story that illustrates how strength of character and a love of literature can help overcome racism and trauma. The book begins when three-year-old Maya and her older brother are sent to Stamps, Arkansas, to live with their grandmother and ends when Maya becomes a mother at the age of 16. In the course of Caged Bird, Maya transforms from a victim of racism with an inferiority complex into a selfpossessed, dignified young woman capable of responding to prejudice.
Empirical heterogeneity in the institutions-economic growth literature: A critical review

The literature has arrived at a consensus regarding the positive effects of institutions on growth. Nevertheless, many economists argue that a unified analytical framework of institutions-growth studies is still missing and more research needs to be done to fully operationalise the institutional effects in the empirical growth analysis. This paper critically reviews the literature on institutions-economic growth nexus and carefully outlines the important dimensions of empirical heterogeneity the growing number of institutional studies have given rise to. Via a careful assessment and thorough evaluation of selected institutions-growth studies, the dimensions of heterogeneity are identified, namely modelling the institutions-growth link, measures of institutions, channel and size of effects of institutions on growth, reverse causality issue, estimation techniques and sources of institutional data. Arguably, a critical review of a similar stature is rarely done, and the findings of this review are expected to better-inform future empirical works in this field towards strengthening the empirical evidence to support the proposition that “institutions matter to growth. ”
Launch strategy and alliance formation

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
www.econstor.eu Natural Resources, Demand for External Finance and Financial Development

The paper contributes to the ongoing debate on the natural resource curse, which postulates a negative link between natural resource abundance and economic growth. It shows empirically that resource-rich countries appear to have a less developed financial system and investigates a potential mechanism behind this connection by applying insights from the finance and trade literature. It tests whether the resource sectors’ lower demand for short-term external credit negatively affects financial development. This is done with cross-sectional and panel analysis, using an instrument for credit demand based on exogenous geographic determinants. The results, however, suggest that poor economic diversity rather than firms’ credit demand drives the detrimental effect of resources on finance. JEL classifications: F10, G10, O13, O16
Application of Dimensionless Sediment Rating Curves to Predict Suspended-Sediment Concentrations and Bedload for Rivers in Minnesota

The U.S. Geological Survey, in cooperation with the Minnesota Pollution Control Agency and the Minnesota Department of Natural Resources, completed a study to evaluate the use of dimensionless sediment rating curves (DSRCs) to predict suspended-sediment concentrations (SSCs) and bedload for selected rivers and streams in Minnesota based on data collected during 2007 through 2013. This study included the application of DSRC models developed for a small group of streams located in the San Juan River Basin near Pagosa Springs in southwestern Colorado to rivers in Minnesota. Regionally based DSRC models for Minnesota also were developed and compared to DSRC models from Pagosa Springs, Colorado, to evaluate which model provided more accurate predictions of SSCs and bedload in Minnesota. More than 600 dimensionless ratio values of SSC, bedload, and streamflow were evaluated and delineated according to Pfankuch stream stability categories of “good/fair” and “poor” to develop four Minnesota-based DSRC models. The basis for Pagosa Springs and Minnesota DSRC model effectiveness was founded on measures of goodness-of-fit that included proximity of the model(s) fitted line to the 95-percent confidence intervals of the site-specific model and Nash-Sutcliffe Efficiency values. Composite plots comparing Pagosa Springs DSRCs, Minnesota DSRCs, site-specific regression models, and measured data indicated that regionally developed DSRCs (Minnesota DSRC models) more closely approximated measured data for nearly every site. Pagosa Springs DSRC models had markedly larger exponents (slopes) when compared to the Minnesota DSRC models and over-represented SSC and bedload at streamflows exceeding bankfull. The Nash-Sutcliffe Efficiency values for the Minnesota DSRC model for suspended-sediment concentrations closely matched Nash-Sutcliffe Efficiency values of the site-specific regression models for 12 of 16 sites. Pagosa Springs DSRC models were less accurate than the mean of the measured data at predicting SSC values for one-half of the good/fair stability sites and one-half of the poor stability sites. Results of data analyses indicate that DSRC models developed using data collected in Minnesota were more effective at compensating for differences in individual stream characteristics across a variety of basin sizes and flow regimes than DSRC models developed using data collected for Pagosa Springs, Colorado. Minnesota DSRC models retained a substantial portion of the unique sediment signatures for most rivers, although deviations were observed for streams with limited sediment supply and for rivers in southeastern Minnesota, which had markedly larger regression exponents. The results from this study indicated that regionally based DSRCs can be used to estimate reasonably accurate values of SSC and bedload.
INCOME INEQUALITY AND MIDDLE-INCOME TRAP: A PANEL DATA ANALYSIS ON THE EFFECTS OF ECONOMIC FREEDOM AND DEMOCRACY

This paper investigates the distributive effect of economic freedom and democracy in a panel of countries up to 117 over 1970-2014. With a specific focus on the middle-income countries (MICs) which are shown to have been trapped at that level long after their transition from low-income status, this paper hypothesizes that income inequality could be the underlying factor behind the countries’ stagnated income. Using the latest Standardized World Income Inequality dataset, and via panel fixed effects and system GMM estimation methods, the interrelationship between income inequality, economic freedom, and democracy are empirically examined. The findings yield robust evidence that freedom to trade internationally, unpredictable inflation and money supply, and small government size have significant relationship with inequality. Nevertheless, the inequality-effects of these liberalization policies depends on the types of political regime, as the results show that these policies only yield the intended positive distributive effect in a democratic regime. The paper concludes with several policy implications.
Political and Social Correlates of Covid-19 Mortality

Do political and social features of states help explain the evolving distribution of reported Covid-19 deaths? We identify national-level political and social characteristics that past research suggests may help explain variation in a society's ability to respond to adverse shocks. We highlight four sets of arguments---focusing on (1) state capacity, (2) political institutions, (3) political priorities, and (4) social structures---and report on their evolving association with cumulative Covid-19 deaths. After accounting for a simple set of Lasso-chosen controls, we find that measures of government effectiveness, interpersonal and institutional trust, bureaucratic corruption and ethnic fragmentation are currently associated in theory-consistent directions. We do not, however, find associations between deaths and many other political and social variables that have received attention in public discussions, such as populist governments or women-led governments. Currently, the results suggest that state capacity is more important for explaining Covid-19 mortality than government accountability to citizens, with potential implications for how the disease progresses in high-income versus low-income countries. These patterns may change over time with the evolution of the pandemic, however. A dashboard with daily updates, extensions, and code is provided at https://wzb-ipi.github. io/corona/.
High temperature has no impact on the reproduction number and new cases of COVID-19 in Bushehr, Iran

Background: COVID-19 was first reported in Iran on February 19, 2020. Bushehr, one of the warmest provinces of Iran, was the last province confirmed to be infected on March 5, 2020. At the beginning of April, Bushehr was announced as a white, coronavirus-free, province. However, increasing the temperature in the next months did not affect the spread of coronavirus and the number of confirmed cases increased during the next months, so that Bushehr was announced as a Red province on June 13, 2020. Methods: This paper aims 1) to estimate the reproduction number of COVID-19 in Bushehr considering COVID-19 reported cases of Bushehr from April to June 12, 2020, using exponential function and SIR epidemic model, and 2) to investigate the impact of temperature on reproduction number and the spread of coronavirus in Bushehr the temperature data. Result: The reproduction number is estimated to be between 2.564 to 2.641. Regarding the increase of the temperature during April to June, the results show that not only was the spread of COVID-19 not reduced but it also increased. Conclusions: Data analysis on this study shows that high temperature has no impact on the reproduction number and does not slow down the spread of coronavirus in Bushehr.
Predictive accuracy of a hierarchical logistic model of cumulative SARS-CoV-2 case growth

Background: Infectious disease predictions models, including virtually all epidemiological models describing the spread of the SARS-CoV-2 pandemic up to June 2020, are rarely evaluated. The aim of the present study was to investigate the predictive accuracy of a prognostic model for forecasting the development of the cumulative number of reported SARS-CoV-2 cases in countries and administrative regions worldwide. Methods: The cumulative number of reported SARS-CoV-2 cases was forecasted in 251 regions with a horizon of two weeks, one month, and two months using a previously described hierarchical logistic model at the end of March 2020. Forecasts were compared to actual observations by using a series of evaluation metrics. Results: On average, predictive accuracy was very high in nearly all regions at the two weeks forecast, high in most regions at the one month forecast, and notable in the majority of the regions at the two months forecast. Higher accuracy was associated with the availability of more data for estimation and with a more pronounced cumulative case growth from the first case to the date of estimation. In some strongly affected regions, cumulative case counts were considerably underestimated. Conclusions: With keeping its limitations in mind, the investigated model can be used for the preparation and distribution of resources during the SARS-CoV-2 pandemic. Future research should primarily address the model's assumptions and its scope of applicability. In addition, establishing a relationship with known mechanisms and traditional epidemiological models of disease transmission would be desirable.
Assessment of impact of COVID-19 outbreak & lockdown on mental health status & its associated risk and protective factors in adult Indian population

Nationwide lockdown to tackle the COVID-19 outbreak in India is anticipated to have psychological impact on the population. Our study aims to study the effect of the outbreak & lockdown on mental health status of adult Indian population along with identifying high risk groups. An online survey was conducted during 3rd phase of lockdown gathering details about sociodemographic variables, precautionary measures, awareness and concerns regarding COVID-19 and mental health status through DASS21 questionnaire from 873 adults. The prevalence of depression, anxiety and stress were 18.56%, 25.66%, and 21.99% respectively with majority being mild depression (15.1%) and stress (14.5%) and moderate anxiety (16.3%). Female gender, age <25 years, unemployment, self-business, employed in private sector, lack of formal education, larger household size, parenthood (>2 kids) were associated with increased likelihood of negative mental health. Confidence in physician's ability to diagnose COVID-19 infection, decreased self-perceived likelihood of contracting COVID-19, lesser frequency of checking for information on COVID-19 and satisfaction of information received were protective against negative mental health. This landmark study identified the protective and risk factors of mental health during COVID-19 pandemic, to help authorities and mental health workers to strategize and deliver interventional methods to maintain psychosocial wellbeing of the population.
A Systematic Review Comparing Neostigmine Versus Sugammadex

Postoperative residual muscle paralysis can lead to very serious complications, such as hypoxia, airway obstruction, and generalized muscle weakness after surgical procedures. Traditional anesthesia practice has utilized a class of medications called cholinesterase inhibitors to mitigate these complications. Cholinesterase inhibiting agents have the potential to create adverse effects of their own, such as bradycardia, due to the drug class’s indirect mechanism of action. An ideal reversal agent would reverse quickly, and reliably, with minimal adverse effects. Recently, sugammadex, a selective relaxant- binding agent, has been introduced as an alternative reversal agent. The purpose of this systemic review was to compare the administration of sugammadex versus neostigmine and time to re-establish normal muscular function, as evidenced by a train-of-four (TOF) ratio of 0.9. Databases were searched for pertinent randomized control trials and literature regarding the topic of this review. Inclusion and exclusion criteria were utilized to finalize the five studies that were included in this systematic review. The PRISMA checklist and CASP tool was utilized to extract and critically appraise each study. Additionally, a cross study analysis was performed. Overall, sugammadex was found to be a faster and more reliable reversal agent, with mild-to-moderate adverse effects reported, when compared to the anticholinesterase, neostigmine. Consistently and reliably reversing neuromuscular blocking agents and educating other health care professionals about the negative consequences of postoperative residual muscle paralysis, are initiatives that the advance practice nurse, particularly the CRNA, can lead.
Impact of temperature and sunshine duration on daily new cases and death due to COVID-19

Background: The coronavirus pandemic (COVID-19) control has now become a critical issue for public health. Many ecological factors are proven to influence the transmission and survival of the virus. In this study, we aim to determine the association of different climate factors with the spread and mortality due to COVID-19. Methods: The climate indicators included in the study were duration of sunshine, average minimum temperature and average maximum temperature, with cumulative confirmed cases, deceased and recovered cases. The data was performed for 138 different countries of the world, between January 2020 to May 2020. Both univariate and multivariate was performed for cumulative and month-wise analysis using SPSS software. Results: The average maximum temperature, and sunshine duration was significantly associated with COVID-19 confirmed cases, deceased and recovered. For every one degree increase in mean average temperature, the confirmed, deceased and recovered cases decreased by 2047(p=0.03), 157(p=0.016), 743 (p=0.005) individuals. The association remained significant even after adjusting for environmental such as sunshine duration as well as non-environmental variables. Average sunshine duration was inversely correlated with increase in daily new cases ({rho}= -2261) and deaths ({rho}= -0.2985). Conclusion: Higher average temperature and longer sunshine duration was strongly associated with COVID-19 cases and deaths in 138 countries. Hence the temperature is an important factor in SARS CoV-2 survival and this study will help in formulating better preventive measures to combat COVID-19 based on their climatic conditions.
Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app

Longitudinal clustering of symptoms can predict the need for respiratory support in severe COVID-19. As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between 1 and 28 May 2020. Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic – area under the curve) of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.