dataesr/scientific-paragraphs-categorization huggingface dataset:
FSM incorporate elements of positivity, i. e. | Methodology | Philosophy, 10(3) : 521-553  representing what is, in their bottom-up, technical representation of many processes. This, as we will see in subsequent sections, partly relates to their development as applied tools for forest policy planning. However, this is often limited to processes that are well defined or central to the question studied  (Buongiorno, 1996). For other facts, such as spatial equilibria or consumption behaviours, technical representations are harder to construct, either because there is a lack of data or a lack of understanding of the process. In these cases, theory-based optimisation algorithms or stylized equations may be used: because of this, FSM also incorporate normative elements, i. e. representing what ought to be.
, 1972) -where emphasis is on change and the system's evolution is described by sets of rules and differential equations. A synthesis was proposed in the PELPS software  (Gilless and Buongiorno, 1985) , a modelling environment for equilibrium models where static market equilibria are described with econometric equations and activity analysis, solved with mathematical programming, following which a "dynamic phase" akin to SD recursively updates conditions  (Buongiorno, 1996).
Acknowledgements This work was enabled by the support of a wide range of people. Particular thanks to  Herbert Dawid  and  Philipp Harting  for practical help and fruitful discussions. Thanks to  Mattias Endres ,  Robert Wilms  and two anonymous reviewers for helpful comments. Further thanks to  Wolfgang Britz ,  Sander van 1050 der Hoog ,  Antoine Mandel ,  Angelo Secchi  and many others who have facilitated this project. Particular gratitude is owed to the  Studienstiftung des Deutschen Volkes  who have granted me the (not only) financial independence to pursue this project. This work uses a modified version of the  Eurace@Unibi model , developed by  Herbert Dawid ,  Simon Gemkow ,  Philipp Harting , Sander van der  1055 Hoog  and  Michael Neugart , as an extension of the research within the  EU 6th Framework Project Eurace.
Data We study J = 10 agricultural (corn, wheat, soybean, sugar, cotton, coffee), energy (crude oil, gasoline, natural gas) and biofuel (ethanol) commodities. We use daily information about the opening, highest and lowest prices of the nearest to maturity contracts traded in the corresponding future markets. Data are available on Thomson Reuters Eikon. The time span ranges from January 3 rd 2012 to October 28 th 2016, thus T = 1070 daily observations. Table  2  reports the label and the description of each variable. We obtain univariate volatility measures using the realized daily high-low range proposed by  Parkinson (1980) , see Appendix B for more details. We check for stationarity of the estimated volatilities with univariate unit root tests and the pooled unit root test of  Levin et al.
Data Collection and Measurements at Baseline A multidisciplinary team comprising a geriatrician, pharmacists, nutritionists, and nurses collected the data. A hospital pharmacist first conducted a systematic review of all the medications each patient was taking in order to detect drugs that can cause interactions with oncologic systemic treatment. Drugs informations were collected from the patient and/or caregiver, the most recent medical prescription, and the patient's medical record. The CMR included indications and doses of the prescribed drugs, as well as an evaluation of self-administration of nonprescribed medication and consumption of medicinal products (phytotherapy, herbs and spices). All the medications were classified according to the Anatomical Therapeutic Chemical (ATC) classification, as recommended by the World Health Organization.
Follow-Up Data and Measurements Data on cancer progression and treatment were obtained from medical records: number of systemic treatment cycles, toxicities, oncologic treatment adaptation (ie, dose reduction) and premature discontinuation, as well as causes for treatment adaptation and discontinuation. A total number of frailties (from 0 to 6) was computed by giving 1 point to each of the following frailties: CIRS-G severe comorbidities, malnutrition, polypharmacy, cognitive disorders, impaired mobility and/or handgrip strength decrease, impaired autonomy for the instrumental activities of daily living. These particular frailties were selected by the geriatrician as the main domains used to determine systemic treatment dose adaptation. The hospital pharmacist conducted follow-up calls with the patients at 30, 60, and 90 days after enrollment to collect information about unplanned hospitalizations and overall survival (OS) (collected up to January 2019).
Statistical Analysis A descriptive analysis was performed to describe the main demographic, oncological, geriatric, and treatment characteristics of our population using headcounts and percentages for discrete data as well as mean values plus or minus the standard error and the interval between the minimum and maximum values for continuous data. Comparative analysis were performed to compare patients according to initial doses of systemic therapy (reduced doses vs standard doses), premature systemic treatment discontinuation (yes vs no), and presence of toxicities grade 3-5 (yes vs no). The Chi-squared test was used to analyze discrete data whereas Student's t-test was used to analyze continuous variables. A Kaplan-Meier analysis was used to compare the overall survival rates in two groups of patients: those who received reduced doses at systemic therapy initiation, and those who received standard doses.
Acknowledgments This work is supported by  French Ministry of Health: Appel à projet pharmacie Clinique  ( DGOS/PF2/2016-49 ). The funding body had no role in design of the study, the collection and analysis of data or in the writing of the manuscript. The authors would like to thank all patients and GPs who agreed to participate in this work. The authors would like to thank  Jude Sweeney  for his proofreading.
Inequality is measured with the Gini coefficient and data are drawn from the United Nations World Income Inequality Database (WIID) (UNU-WIDER, 2005). 5 We use several definitions of corruption: international country risk guide (ICRG) corruption index, the corruption perception indeed (CPI), and the IMF's governance indicator. Data on GDP and openness are obtained from Penn World Tables  (PWT7). 6 The polity data base is used for data on political stability. Data on i i i 2 i 2 i 1 0 i u Z I I S + + + + = a b b b i S the remaining explanatory variables are obtained from World Development Indicators (2010). Table  A1  in the Appendix reports variable definitions. Summary statistics are shown in Table  1. One of the main problems in estimating equation (  1 ) is the potential endogeneity of the explanatory variables.
Developing a Content Analysis Tool Based on Implementation Theories For analysing the input from patients and HCPs, a content analysis tool was developed prior to the public hearing and dedicated meeting, and independent from the sets of PRAC questions used at the hearing/meeting (for the PRAC questions, see Appendix 1 of the ESM). Content analyses are qualitative research and can be used to describe phenomena not yet well understood and to be explored The responses consisted of real-world feedback and data, including from surveys that targeted patients and HCP organisations had conducted among their members. At the same time, information was collected by EMA from the regulatory authorities in EU member states. Almost all member states confirmed the dissemination of materials to healthcare settings, but data on the effectiveness of the RMM were limited.
Acknowledgements The authors thank  Thomas Goedecke  and  Ivana Silva  at  EMA  for supporting the note taking of the dedicated stakeholder meeting for the purpose of this case study. The authors also thank  Nathalie Bere ,  Juan Garcia ,  Thomas Goedecke ,  Xavier Kurz  and  Viola Macolic Sarinic  at  EMA  for their comments on the draft case study report, and  Patrick Brown, University of Amsterdam  and  Brian J. Taylor ,  Ulster University  for advice on the manuscript. This study is registered in the EU PAS Register under reference  EUPAS35947.
Data collection The data collection methods are detailed in table  1. The data are collected in a CRF and then in an e-CRF. Data will be collected based on the patient's computerised record, the patient's paper record and if, appropriate, an interview with the patient or the trusted person and/or family member of the patient. If the department in which the patient is hospitalised normally has a pharmaceutical team, the study pharmacist will contact them to inform them that the patient has been enrolled and to gather information about what has already been done (medication reconciliations on admission, previous pharmaceutical interventions). In addition, if necessary, the investigator will contact the community pharmacist to find out which treatments are usually taken at home by the patient.
For the callback at the time of discharge, the data will be collected by the pharmacist of the mobile team or the clinical research associate by telephone, and/or by a visit to the department in question, and/or from data in the patient's computerised file. For the 3-month callback (±15 days) after discharge from the hospital, the data are collected by the pharmacist of the MGT or the clinical research associate by a telephone call to the patient (and/ or the patient's trusted person and/or family member, if necessary) and from the community pharmacist. The following data will be collected: drugs prescribed, falls, hospitalisations, emergency department visits and institutionalisation. A fall is defined as 'an event which results in a person coming to rest inadvertently on the ground or floor or other lower level' by WHO.
Data analysis Concerning the statistics regarding the primary and secondary end points: Open access ► The quantitative variables are described in terms of the mean and SD or as a range and compared using Student's t-test for paired data if the distributions are normal and a Wilcoxon signed-rank test if they are not. ► The qualitative variables will be described as numbers and percentages and compared using a McNemar test if the validity conditions are met and a Fisher exact test if they are not. ► For economic data, the results will be presented in the form of incremental costs per patient and 95% CIs from a bootstrap. The economic analysis will be done from a national health insurance point of view using a time horizon of 1 month. The significance threshold will be set at 0. 05, and all the tests will be two-tailed.
A mixed model will be used to explain the acceptance rate of the PharMoG team's proposed treatment optimisations and the changes in PIDPs before and after the intervention of the PharMoG team by the following: ► The patients' characteristics: age, gender, weight, number of medicines before the intervention, comorbidity index (Charlson)  36  and adverse drug event risk score in geriatrics (Trivalle score  37  ). ► The type of department (surgical vs medicine vs emergency, and the presence and level of pharmaceutical analysis according to the French Society of Clinical Pharmacology). SAS software V. 9. 3 (SAS Institute) will be used to produce the statistical outcomes.
Open access French Data Protection Act. This change was ratified in a decision dated 5 January 2006 and updated on 21 July 2016. Toulouse University Hospital has signed an agreement to comply with this 'Reference Methodology'. The results will be presented at national and international conferences and published in peer-reviewed journals. This study is registered in the European Union Clinical Trials Register (IDRCB 2018-A00180-55) and at clinicaltrials. gov.
They conclude that the pace of the RMB appreciation is mostly based on domestic policy concerns. They document some evidence that the Sino-US meetings also make the NDF more volatile. Bacchetta, Benhima, and Kalantzis (2014)  reflect on an alternative perspective to China's exchange rate policy. Their paper analyzes the optimal exchange rate policy by modelling the central bank as a Ramsey planner. The paper shows that the optimal exchange rate path is close to the one that would result in an economy with full capital mobility and no central bank intervention. Using a pre-central parity regime dataset between January 1, 2000 and September 30, 2013,  Kawai and Pontines (2016)  demonstrate that the RMB has taken on some importance in the implicit currency baskets of several regional economies in recent years, but not to the extent of supplanting the role of the US dollar as the major anchor currency in the region.
Data source We use quarterly and seasonally adjusted data for eight advanced economies: CA, FR, DE, IT, JP, ES, the UK and the US. We took the longest publicly available series for each country. Start dates range from 1968q4 to 1985q1 depending on the country, while the end date is always 2016q4. 8  Gross domestic product, consumer price index and the rate of capacity utilisation are obtained from the OECD Main Indicators database. The unemployment rate comes from the OECD Economic Outlook database. Total credit to the non-financial sector comes from the BIS total credit statistics database. Real house prices are calculated using house prices from national sources, BIS Residential Property Price database and the Consumer Price Index from the OECD. 9 The credit and house prices series were seasonally adjusted.
Real time performance To assess the real-time performance of the models, we compute the average revision of the output gap as T t=t 0 1 T -t 0 +1 |ŷ t it -ŷ t it | σ i , with ŷt it and ŷt it being the output gaps of country i at date t for a given model estimated with data respectively up to date t or t ∈ {t + 1 year, t + 3 years}, σ i the standard deviation of the baseline extended HP filter, t 0 the first date used for the exercise (2000 was chosen so that the models are stabilised for all countries) and T is the last usable date (2015 for t = t + 1 year or 2013 for t = t + 3). 13 o meaningfully compare different models (with possibly very different output gap volatility), the revisions are normalised using the standard deviation of the most simple model, the extended HP filter in its baseline specification.
Usual business cycles are captured in these models by the unemployment rate, the capacity utilisation rate and (in the semi-structural model) CPI inflation. As a second step, the implications of adding data on credit and on house prices to capture financial cycles are tested. The three models have different properties along several important dimensions. The comparison of the two UCMs (models A and B) assesses the practical advantages and the potential pitfalls of treating the cycle indicator variables as deterministic (as in model A) compared to modelling them as stochastic processes (as in model B). On the one hand, the reduced form extended HP filter is particularly appealing for its simplicity and real-time stability. Indeed, there are only a limited number of parameters to estimate, the identification of the model is quite straightforward and, as a consequence, the model exhibits relatively stable output gap estimates in terms of real-time revisions.
The study also builds upon the recently proposed evidence of quantile-dependence between GPR and tourism stocks  [23]. Supplementing the regional evidence of tourism stock-US GPR interaction  [23] , we provide country-level evidence by considering the predictive potential of local and global GPR for the returns of tourism sector equities of emerging economies. The balance of the study unfolds as follows. Section 2 provides a brief review of the literature related to the topic. Section 3 introduces the methodology. Section 4 details the dataset and preliminary tests. Section 5 includes empirical findings. Section 6 concludes.
Data and Preliminary Tests As mentioned above, the purpose of this paper is to ascertain whether GPR carries any predictive potential for tourism stocks. To this end, we collected monthly tourism indices of 13 emerging economies from Thomson Reuters Datastream. The Datastream Global Equity Indices were utilized in this study. Both the selection of countries and the sample period were based on the availability of tourism stock indices, whereas the sample period varied for each country. Our sample countries included Brazil, China, Colombia, India, Israel, Malaysia, Mexico, Philippines, Russia, South Africa, South Korea, Thailand, and Turkey. Caldara and Iacoviello  [1]  developed GPR indices for 18 emerging economies, of which only those included in our sample have their tourism sector indices available, thus constituting our sample.
Data and methodology The sample for this study consists of all common stocks (share code 10 or 11) listed on the NYSE, AMEX and NASDAQ from January 1980 to December 2015. Stock information such as returns, prices, trading volumes, shares outstanding and industry codes are from the Center for Research in Security Prices (CRSP). Financial statement data are obtained from Compustat. To alleviate concerns about market microstructure induced biases, we exclude stocks with prices less than $5 at the end of the portfolio formation period. Empirically, we evaluate the performance of our proposed investment strategies with three risk-factor models: (a) the Fama and French five-factor model  (Fama and French, 2015) , (b) a seven-factor model that augments the Fama and French five-factor model with a momentum factor and a short-term reversal factor, and (c) the qfactor model of  Hou et al.
The 5 9 5 portfolios report EW raw returns. The data include all common stocks listed on the NYSE, AMEX and NASDAQ exchanges. The sample period is from 1980 to 2015. Stocks with price less than $5 at the end of formation periods are excluded. Newey and West (1987)  heteroscedasticity and autocorrelation consistent t-statistics are reported in parentheses. This table reports the monthly equal-weighted (EW) and value-weighted (VW) raw and factor-adjusted returns for short-term reversal and earnings momentum double-sorted portfolios defined in Table  2  for two subsamples: large stocks vs. small stock. The 5 9 5 portfolios report EW raw returns. Each month, sample stocks are divided into two groups based on their market capitalisations. The large (small) stock group includes stocks with market capitalisations above (below) the sample median.
Specifically, the earnings momentum-reversal joint strategy has an average return of 2. 46 percent with a highly significant t-statistic of 12. 32. In comparison with the results from Table  2 , we find that our results are not affected by the industry effect. This table reports the monthly equal-weighted (EW) and value-weighted (VW) raw and factor-adjusted returns for short-term reversal and earnings momentum double-sorted portfolios as defined in Table  2  for two subsamples: RDQ and non-RDQ. The 5 9 5 portfolios report EW raw returns. The RDQ sample includes observations in which stocks' quarterly earnings announcements occur in the formation month; the non-RDQ sample includes the remaining stock observations. Our data include common stocks listed on the NYSE, AMEX and NASDAQ exchanges.
08) (8. 57) (9. 46) (12. 89) (9. 00) (9. 73) R1M5-R5M1 EW VW EW VW R1M5-R5M1 EW VW EW VW RAW 2. 75 1. 25 1. 67 0. 69 RAW 3. 52 2. 04 2. 12 0. 93 (12. 10) (3. 72) (5. 39) (1. 98) (6. 46) (3. 65) (9. 92) (3. 52) FF5 2. 45 0. 90 1. 42 0. 70 FF5 3. 68 2. 31 1. 71 0. 75 (7. 56) (2. 22) (4. 37) (1. 78) (6. 97) (3. 38) (5. 92) (2. 33) FF5MR 2. 18 0. 62 1. 25 0. 50 FF5MR 2. 77 1. 08 1. 58 0. 51 (10. 46) (2. 13) (5. 16) (1. 46) (5. 27) (1. 82) (10. 21) (2. 04) q 2. 32 0. 97 1. 40 0. 41 q 3. 31 2. 26 1. 46 0. 45 (6. 00) (2. 07) (3. 63) (1. 03) (5. 84) (2. 75) (4. 40) (1. 25) 9 5 portfolios report EW raw returns. The data include all common stocks listed on the NYSE, AMEX and NASDAQ exchanges. Stocks with price are less than $5 at the end of formation periods are excluded. Newey and West (1987) heteroscedasticity and autocorrelation consistent t-statistics reported in parentheses.
Underweight, overweight, or obesity are categories of body mass index which can show a health risk. Deficiency or excess of adipose tissue have detrimental consequences for human metabolism  (Masanovic, Bavcevic, & Prskalo, 2019b). In children, underweight gives a higher risk for infectious diseases, in youth underweight can also endanger reproductive ability  (Han, Mulla, Beiene, Liao, & McDonald, 2010 , Masanovic, Milosevic, & Corluka, 2018; Masanovic, Corluka, & Milosevic, 2018). Overweight and obesity can cause a variety of cardiovascular and chronic diseases  (Singh, Mulder, Twisk, Van Mechelen, & Chinapav, 2008). Searching index databases, the authors of this study did not find enough researches on trends in body mass index of the population in Montenegro. Considering the very specific body height and appearance of Montenegrins, it was very interesting to observe the trend of body height, body weight, and body mass index in this population in previous decades.
Explanation: To date, studies on qBRm applied to vaccines have used a variety of non-harmonised terms to describe their quantitative benefit-risk approach (e. g. benefit risk, benefit harm, risk benefit). Knowing that electronic databases use words in the title and abstract to yield search results, vague or ambiguous titles and abstracts might be inappropriately indexed  [28]. A clear and precise title mentioning the type of analysis conducted and its scope will increase the likelihood of the publication being appropriately indexed. Currently, no recognised terminology exists for publications focusing on qBRm. In this context, we propose systematically using in the publication's title the 'quantitative benefit-risk model' terminology derived from the IMI-PROTECT glossary  [29]. First, the terminology 'quantitative' ensures a clear differentiation from the 'qualitative' approach, based on descriptive templates or guidelines and relying on expert judgement only.
Example: We conducted a risk-benefit analysis using published rotavirus and intussusception epidemiologic data to model the impact of a vaccination programme with either 20 or 90% coverage scenarios compared with no vaccination programme. Based on the methodology of Patel et al. 2011, we used a birth cohort in 2005 to estimate the number of hospitalisations attributable to rotavirus that could potentially be prevented and the number of excess intussusception hospitalisations that could be caused by the vaccination. We also calculated the number of infants who would need to be vaccinated to prevent one rotavirus hospitalisation or cause one excess intussusception hospitalisation. Because rotavirus and intussusception mortality is negligible in Singapore, we analysed hospitalisation as our outcome of interest.
Example: We simulated a closed static cohort of 4. 3 million adopters of influenza vaccination using aggregated data from the PRISM system database. We did not take into consideration the induced herd protection, and a sigmoid (reverse S) shape curve was fitted to year 1 and year 2 efficacy to account for waning of protection with time post-vaccination. Credible intervals around the benefit-risk ratios and differences were calculated based on probabilistic uncertainty analyses and Monte Carlo simulations. (Hypothetical example).