dataesr/scientific-paragraphs-categorization huggingface dataset:
By performing the dynamic panel Generalized Method of Moment method (GMM), results indicate that non-interest activities of Asian banks reduce risk, but do not increase profitability. More recently,  Sun et al. (2017)  have used a dataset of 16 listed Chinese commercial banks, over the period of 2007 to 2013 to test the effect of noninterest income on the bank performance. By performing panel threshold model, empirical findings indicate that that there is a nonlinear relationship between noninterest income and bank performance. Findings also indicate that there is a negative association between the noninterest income ratio and performance of commercial banks. The authors reported that it should raise the ratio of noninterest income to a certain level to make banks more profitable. Ismail et al.
Data and methodology The empirical section is done upon three steps: the first one is to study the determinants of non-interest income in Tunisia. We follow the previous studies initially developed by De Young and Rice (2004) and  Stiroh (2004a, b)  on the determinants of non-interest income by estimating the following model (1): NII i;t ¼ α 0 þα 1 NII i;t-1 þ α 2 RROA i;t RROE i;t À Á þ α 3 Size i;t þ α 4 LR i;t þ α 5 LS i;t þα 6 CAPR i;t þ α 7 CD i;t þ α 8 HHI i;t þ α 9 ATM i;t þ α 10 CARDS i;t þα 11 GDP i;t þ α 12 Inf i;t þ ε i;t ð1Þ The dependent variable is the total non-interest income (NII) as a share of total assets. This variable includes revenues from commissions plus other net noninterest income. RROA and RROE are the relative return on assets and relative return on equity that capture the bank performance.
To this end, the dependent variable becomes ROA and then ROE. Unlike eq. (  1 ), Non-interest income becomes an explanatory variable and bank performance indicator is the dependent variable. The model (  2 ) is expressed as follows: ROA ROE ð Þ i;t ¼ 0 þα 1 ROA ROE ð Þ ð Þ i;t-1 þ α 2 Size i;t þ α 3 LR i;t þ α 4 LS i;t þ α 5 CAPR i;t þα 6 CD i;t þ α 7 HHI i;t þ α 8 NII i;t þ α 9 GDP i;t þ α 10 Inf i;t þα 11 CRISIS i;t þ ε i;t ð2Þ In eq. 2, we added a dummy variable CRISIS equal to 0 before 2008 and 1 after that date to see the impact of the global financial crisis on the performance of Tunisian banks. Finally we analyze whether non-interest income has become a source of risk or not. To do this, we calculated the standard deviation of ROA and ROE simultaneously and we conducted the following model (3): SHROA SHROE ð Þ i;t ¼ α 0 þ α1 SHROA SHROE ð Þ ð Þ i;t-1 þ α 2 Size i;t þ α 3 LR i;t þ α 4 LS i;t þ α 5 CAP i;t þ α 6 CD i;t þ α 7 HHI i;t þ α 8 NII i;t þ α 9 GDP i;t þ α10 Inf i;t þ α 11 CRISIS þ ε i;t ð3Þ The data used in this study is related to 20 Tunisian banks observed during the period 2005-2015 and were extracted from their financial statements.
Conclusion The objective of this paper is to investigate the determinants of non-interest income in Tunisian banks and to test the effect of non-interest income on the risk-performance. To this end, we have used annual data related to 20 Tunisian banks during the period 2005-2015. The econometric method served in this paper is the dynamic panel data analysis; precisely we performed the GMM in system method. Empirical results indicate that main determinants of non-interest income are bank specifics such as bank size, liquidity risk, capital adequacy ratio and loan specialization. As for technological innovations proxied by ATM and credit cards, the results show that they are positively associated with NII and bank performance as well (ROA/ROE) but their coefficients are small. Here, it is obvious that technology has an important role in promoting banks 'revenues but we recommend Tunisia' bankers to integrate more technological innovations in their business activities and to introduce new financial service channels and products to their customers.
8. 6 Percentage taken from the World Development Indicators database. 7 Annual Report of Professional Association of Tunisian Banks (2015), p. 37. 8 Annual Report of Professional Association of Tunisian Banks (2015), p. 37. 9 Annual Report of Professional Association of Tunisian Banks (2015), p. 12. 10 This coefficient is equal to operating expenses divided by net banking income. 11 Annual Report of Professional Association of Tunisian Banks (2015), p. 13. 12 Annual Report of Professional Association of Tunisian Banks (2015), p. 12. 13 Annual Report of Professional Association of Tunisian Banks (2015), p. 37. 14 Annual Report of Professional Association of Tunisian Banks (2015), p. 37. 15 Annual Report of Professional Association of Tunisian Banks (2015), p. 14. 16 For more details, see appendix 2 relative to the correlation matrix.
Materials and methods In a first step, a search was conducted for existing incentives for developing new indications as well as pricing rules for EPs in Europe, with a special attention on the five largest European markets, i. e. France, Germany, the UK, Italy and Spain. A targeted literature search in Medline (via PubMed) and of internet resources was performed, including the European Medicines Agency (EMA) website (http://www. ema. europa. eu) and relevant government and healthcare authorities' websites (medicine agencies, HTA agencies) for, respectively, European-level and country-level information. In addition, grey literature and available proprietary resources (internal database and reports) were searched. In a second step, a targeted search was performed aiming to analyse the consequences of the current regulations.
Four products with a decrease in price associated with generic entry and/or approval of new indication were selected. Pricing databases were also searched, i. e. AMELI (for prices in France) and IHS (for prices in Italy and Spain) in order to analyse the price evolution of the selected products. As a final step two expert panel meetings were organized aiming to review and complete the findings as well as further discuss the consequences of existing regulations and propose possible policy improvements. Seven experts in the field of drug market access participated in the meetings: one from France, one from Germany, one from UK, two from Italy and two from Spain (see Table  S1  in supplementary files for the key opinion leaders profiles). The first meeting was held in Paris, in July 2015 and the second in Milan, in November 2015.
2 Everolimus is also marketed by Novartis under the brand name Afinitor® (date of marketing approval: 3 August 2009) and Votubia® (date of marketing approval: 2 September 2011); each brand is approved in different set of indications. [25]ure 3. Price evolution: Ramipril (Tritace®/Triatec®, Sanofi-Aventis)1price per 10 mg tablets; 28 or 30 units. 1 -Only generic forms available in Spain. 2 -Initial marketing approval in the treatment of hypertension and secondary prevention after acute myocardial infarction. 3 -Extension of indication to the treatment of renal disease and symptomatic heart failure. Source of prices: AMELI (France) and IHS (Italy, Spain) database. evaluation,oftenresulting in price cuts. As a consequence, manufacturers appear rarely to develop new indications for EPs, instead being prone to undertake R&D efforts either for new chemical entities or at early to mid-stage of a product's life cycle rather than with EPs.
[25]Due to a lack of incentives for developing new indications for EPs, valuable treatment options may therefore never be reaching the market for value assessment by HTA bodies with the potential to bring additional benefits to patients and society. 1 -Initial marketing approval in herpes simplex virus (HSV) infections. 2 -Extension of indication to varicella zoster virus (VZV) infections. 3 -Extension of indication to cytomegalovirus (CMV) infections. 4 -Extension of indication to VZV infections in immunocompromised patients. S urce of prices: AMELI (France) and IHS (Italy, Spain) database.
Data extraction To be included in the quantitative synthesis, studies had to provide data on odds or risk estimates (or data allowing us to compute these ourselves) for any-type CVD or for specific CVD (i. e. , odds ratios [ORs] and hazard ratios [HRs] for cross-sectional and longitudinal studies, respectively), together with precision estimates (95% confidence interval [95%CI]) comparing frailty status conditions (taking robustness as reference group). Two authors (NV, EC) independently recorded data extracted from the selected studies into a standardized Microsoft Excel spreadsheet. Any disagreement was resolved by consensus with a third author (CC). The following information was extracted: i) study characteristics (e. g. sample size, demographics, country in which the study was performed); ii) study setting; iii) diagnostic criteria for frailty; iv) demographic characteristics (percentage of women and age) and risk factors for CVD prevalence (obesity, type 2 diabetes and hypertension) by frailty status; v) type and number of adjustments in the multivariate analyses; vi) follow-up period (only for longitudinal studies); vii) method of ascertainment of CVD.
When information on CVD was missing, study authors were contacted to obtain unpublished data. When two or more studies represented a cohort, all the articles were included contacting the first/corresponding author in order to have the most complete analyses available. These unpublished data were validated by the first or last authors of the studies from which these data were obtained. When raw data were shared with the main investigators of this project, these were independently analyzed by two authors of this meta-analysis (NV and EC). In addition to number of CVD by frailty status, we requested OR/HR estimates adjusted for the maximum number of the following covariates: age (as continuous variable), gender, obesity, type 2 diabetes, and hypertension. All the data were finally confirmed by the main investigator of each article.
and were implemented in Ovid Medline 1946-, Embase via Emabse. com 1947-, Cumulative Index of Nursing and Allied Health Literature 1937-, Cochrane Database of Systematic Reviews via Wiley, Cochrane Central Register of Controlled Trials via Wiley, and Database of Abstracts of Review via Wiley. To increase the chance of generating generalizable data, we conservatively excluded intervention studies, as randomized controlled trials generally have a whole host of exclusion criteria. Thus, articles indexed by the publication type of randomized or controlled trials were eliminated with the "NOT" Boolean operator. All searches were conducted from their inception until 12/31/2015 and without language restriction. All results were exported to EndNote for the removal of duplicates. The search strategy used in Pubmed is reported in eTable 1.
ACKNOWLEDGMENTS We thank prof. Robert McLean  and  Margaret Bryan ,  Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA and Department of Medicine, Beth Israel Deaconess Medical Center ,  Harvard Medical School, Boston, MA, USA  for giving us the data regarding the  MOBILIZE Boston Study ;  Natalia Aquaroni Ricci ,  Master's and Doctoral Programs in Physical Therapy, Universidade Cidade de São Paulo (UNICID) ,  São Paulo, Brazil , for her support regarding the elaborations of the data from FIBRA study;  Susan Fowler ,  Washington University in Saint Louis, Saint Louis, MO, USA  for her expertize regarding search strategy preparation.
Crop and livestock model outputs are aggregated as inputs to regional and global economic models to determine regional and global vulnerabilities, changes in comparative advantage, price effects, and potential adaptation strategies in the agricultural sector. A critical area for improving crop and livestock models involves the simulation of pests and diseases under changing climate conditions. The improvement and application of pest and disease models (PDMs) for predicting yield losses due to climate change is still a challenge. Reference datasets for the development of empirical models are no longer viable since the climatic patterns to which the models are calibrated are changing. Simulation models based on quantitatively known processes represent an important method for estimating the important effects of pests and diseases on agricultural production, and groups of experts within AgMIP are tackling these challenges for a range of pests (van Bruggen et al 2015,  Donatelli et al 2016).
In an iterative consensus process that builds on input from the stakeholder and scientific communities, the Strategy Group decides on the guiding questions and the simulation protocols needed to answer them for each round (see figure  3  and ISIMIP Mission and Implementation document, https://isimip. org/about/ #mission). An economic integration unit has also been established within ISIMIP as a forum for mutual exchange between biophysical and economic modelers, with the goal of improving explicit linkages among the disciplinary models. For example, translation of runoff projections into flood risks, flooded areas, and direct damages will make them more usable in economic assessments. The aim is to enable better understanding of the mutual requirements within the different modeling settings and more effective data exchange.
Sources I collected the data from four main sources. The first source is the NSE that provides quantitative data regarding the market capitalization of companies and the distribution of theirs shareholders by broad categories. This information has been supplemented by the annual activity reports of the companies, which constitute our second source of information. These reports give personal details regarding members of the Board of Directors, the shareholding patterns according to the size of the shares held (information not given by the NSE), the distribution of the revenues by geographical segments (unfortunately the business segments are too heterogeneous to be codified) and, lastly, the technical certification of the firm (if any). When a firm has subsidiaries, the data coded relate to the group, that is the standalone firm and its subsidiaries.
I ascertained the NASSCOM membership of the companies from the register provided by NASSCOM. Finally, I have supplemented these three main sources of information by variety of available sources (books, magazines, economic websites, like those of Dataquest, Bloomberg or Forbes) in order to complete the data collected either on the firms or regarding the biography of their managers. Although I conducted a thorough investigation of a variety of the available or easily accessible sources, the data collected always appeared unsatisfactory when compared to the time and effort invested in their collection.
Data and methods The preliminary step of this research was to select the first hundred plus IT companies listed on the NSE. This selection was reached on the basis of the market capitalization of the companies over a period of ten years before the year of reference, which is the end of the 2011-2012 fiscal year. For each company I collected two sets of data: on the one hand, biographical information for one top manager, whether Chief Executive Officer (CEO), Chief Manager (CM) or Managing Director (MD), according to the structure of the Board of Directors (BoD); and, on the other hand, data related to the company itself, its economic structure and position on the IT market. Finally the database contains 95 software companies with their executive managers, which we call statistical individuals (see the list in Appendix Table  9 ).
ABSTRACTS The aim of this article is to study Indian software capital considered as the nexus of IT companies and their top managers. For this purpose, we have conducted a Multi Correspondence Analysis of a set of data collected for the first hundred IT firms listed on the National Stock Exchange in Mumbai in 2012. We address three main research questions. Firstly, what is the sociodemographic profile of these entrepreneurs, secondly, what is the shareholding pattern of these companies and their position on the IT market and, lastly, what are the differences between these IT companies and traditional business groups? Although the managers are highly educated and share the same technical culture, the results show a clear division of software capital in 3 clusters. First, the Multinational companies, which are the oldest firms and the biggest in terms of manpower and global revenue, are opposed to a second group of IT companies founded or managed by executives who are mainly graduates, and whose activities are oriented towards the domestic market; lastly, a third group of companies run by highly-qualified managers (with MBAs) combined characters of clusters 1 and 2, but are smaller companies more dependent on the stock-market.
These methodological problems stem from the fact that there is to date no comprehensive database covering all CCSs on a worldwide scale and providing quantitative data on their users 2. Without proper econometric studies -which are impracticable -it is difficult to account properly for individual behaviour, for quantitative aspects and for the logics involved. Fieldwork should be undertaken to obtain data through impact analyses. For example,  Krohn and Snyder (2008) , in an econometric study of local currencies in the USA, have shown that the local multiplying effect was too weak to be detected in growth per capita income during the 1990s. However, these results should be relativized: the authors failed to compare the volume of local currency in circulation (and the value added created in CCS) with that of the overall volume of money in circulation.
As Lietaer et al. , (2010)rency Systems Difficult to Grasp within Conventional. As Lietaer et al. , (2010)we use the term of "conventional economic thought" which assumes that the monopoly of fiat national currency is an unquestionable given and therefore which ignores monetary plurality. In this article we exclude cryptocurrencies. Contrarily to the Bitcoin, CCSs are associated to social projects based on solidarity and environmental values. Bitcoins and cryptocurrencies have been characterized as anonymous and deterritorialized currencies in opposition to CCS (Ould NOTES 1. 2. This does not preclude the existence of published databases and supplemented by practitioners, see for example : http://www. complementarycurrency. org or http://community- currency. info/ 3. Revue Interventions économiques, 59 | 2018.
Acknowledgements This study has been first presented as a poster at the  PALLONC 2017 Symposium in San Diego USA , and further published as an abstract in the conference proceedings in: Barth C, Colombet I, Vinant P. First referral to an integrated onco-palliative care program: A retrospective analysis of its timing. Journal of Clinical Oncology 2017; 35:31_suppl,167.
g. Germany, UK, France) to support conditional reimbursement decisions for OMPs with different data variables collected  [64]. Where possible, the collection and analysis of real-world data should be co-ordinated at a European level and should be integrated in disease level registries and databases to obtain more consistency in the continuous assessment and appraisal of OMPs. This requires common definitions of outcomes to be collected as well as uniform sets of standards to guarantee data quality and completeness. It is essential to ensure that the duties and rights of every party involved in real-world data collection are respected. Therefore, financial responsibilities for collecting registry data should be shared proportionally between interested stakeholders as per the EUCERD recommendation on rare disease patient registration  [85].
However, coherent systems are necessary to facilitate this collaboration between Member States and to allow for the co-ordination of postlaunch data collection. In advance of product launch, manufacturers, payers, HTA agencies, centres of expertise and European reference networks should collaborate to collect data on the true prevalence of a given rare disease in order to minimise financial uncertainty for payers. Such early collaboration will also ensure development of adequate capabilities for large scale post-launch data collection. The heterogeneous nature of rare diseases and small patient populations make it difficult to collect extensive data  [57]. For ethical reasons, it may not be possible to generate data from certain subgroups of populations (e. g. children) who are often affected by rare diseases, but may not be allowed to Where adaptive processes are required, all parties (payers, HTA agencies, involved HCPs, patients and industry) need to agree on this iterative process and clearly document the following: • the evidence required and milestones for each step of the assessment • the implications of not meeting the requirements and expectations initially agreed • each stakeholder's shared responsibility to collect and evaluate the data e) Where possible, the collection and analysis of real-world data should be co-ordinated at a European or international level and should be integrated in disease level registries and databases: • to obtain more European consistency in the continuous assessment and appraisal of OMPs • to collect data on the true prevalence of a given rare disease in order to minimise financial uncertainty for payers participate in clinical trials  [87].
Acknowledgements ORPH-VAL wishes to thank  EURORDIS  for facilitating the public consultation exercise and providing the opportunity to present the principles at the 2016 Multi-Stakeholder meeting. It would also like to thank  EUCOPE  and  Celgene  for providing support to the group, and to  Judith Balvanyos  and  Zara Babakordi  for assistance in preparation of meetings and materials.
• β 2 : Here we use the parameter restrictions 1 ≤ β 1 ≤ 1. 02. The upper boundary is chosen to make sure that we cover all empirically relevant parameters of β 2. Then the prior distribution we use is β 2 ∼ U[1, 1. 02]. In the SMC procedure we use M = 2, 048 parameter particles with N = 128 state particles in each particle filter. The resample-move step is triggered when the efficient sample size drops below N/2. In the move-steps we use an independent mixture normal proposal. The routine has been coded in MATLAB with the particle filtering operation programmed efficiently in CUDA and run on a Kepler K20 GPU.
The main results are summarized in Table  2. Results have been obtained with R code with a Processor Intel(r) Core(tm) i7-2600 CPU @ 3. 40 GHz and 16 GB of installed RAM. So, we see that the speed up in computing time is over 620 times. Moreover, we have directly the surface of hedging sensitivities using the AAD method (with no other simulations required), whereas simulations would be needed to value hedging sensitivities in the standard MC-MC framework. Disclaimer. Views expressed by authors are the authors' own and do not necessarily reflect the view of the companies they work at.
1. Survey development and data collection Seven implantable cardiovascular devices/procedures were selected for the analysis: Implantable Cardioverter Defibrillator (ICD), Cardiac Resynchronization Therapy (CRT), Drug-eluting Stent (DES), Transcatheter Aortic Valve Implantation (TAVI), Renal Denervation (RD), Left Atrial Appendage Closure Device (LAA), and MitraClip (MC). These devices were chosen after consulting medical experts based on their relevance and different profiles of medical evidence and stages in the diffusion process. CE mark approval dates were used to proxy for the latter (see Table  II ). We referred to all devices with CE mark approval dates before 2007 as 'old' devices. All devices with CE mark approval dates since 2007 were termed 'new' devices. According to our experts, medical evidence for RD, MC, and LAA was weak or unclear.