dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis The intent-to-treat population, all randomised patients regardless of treatment compliance, were analysed for efficacy. For the primary efficacy analysis, only data from visits in which patients were treated with the recommended IXE dosing regimen were included. Data collected at visits with IXE Q2W dose adjustments were excluded prior to any summary or applying imputations. Three methods were used to summarise efficacy data through 204 weeks: as-observed, multiple imputation (MI) and a modified non-responder imputation (mNRI; combined MI and non-responder imputation (NRI)  5  ). In addition, the data from patients on Q2W/Q4W dosing regimen through 204 weeks, including data from visits where dose was adjusted to IXE Q2W, were also analysed. The as-observed method did not require any imputation for missing values; only data from completers at a visit were relevant.
At week 204, about 88% (n = 65) of patients were retained on the IXE treatment, which was similar to that observed previously in patients with moderate-tosevere psoriasis, suggesting a clinical benefit with IXE longterm treatment. During the LTE period, the overall safety profile of IXE treatment was similar to that reported previously during the induction period,  3  week 108 5 and week 156. 6 Overall, 88. 6% Grade 4 0 2 (0. 5) 0 0 2 (0. 2) †Adverse events include events that first occurred or worsened during the LTE treatment period. Adverse events reported here are cumulative and include events from the LTE population that occurred during the treatment period before the 204-week database lock. ‡For patients with >1 occurrence of the same event, severity was categorised based on the event of highest severity.
In 1993, G. St Onge formed the classification of IC, which became a classic, dividing IC into human, client, and structural capital  [2]. K. Swabey included customer relations in the IC classification  [3]. L. Edwinsson, M. Malone have developed a classification of IC, consisting of human and structural capital  [4]. They combined internal and external company structures into structural capital, namely, relationship capital (reputation, trademark, regular customers, distribution channels) and organizational capital, which in turn was divided into innovation capital (intellectual property (hereinafter  IP) and other intangible assets (hereinafter referred to as IA) and process capital (organizational culture and structure, databases, information networks). And Brooking expanded the components of IC by adding to its human-centered and infrastructure assets also market assets and assets related to IP  [5, p.
Next, let's look at the features of incorporating the most common elements of scientists into human resources: human, structural, social, technological, client capital, as well as information and databases. V. Schultz, Nobel Prize winner, one of the founders of the theory of human capital, stated that the decisive factors in economic development and improvement of the well-being of the population are not energy, space or agriculture, but rather the improvement of the level of knowledge and education  [11]. Human capital is crucial to the enterprise. It is due to human capital that the enterprise can ensure the reproduction of both activity and individual resources, as well as innovative development. Human capital is the main source of creation of other types of IC and represents the knowledge and skills of the workforce of the enterprise.
As D. Roos, G. Roos, N. Dragonetti and L. Edwinson have aptly defined, structural capital can be represented as what remains at the enterprise after the workers go home, as what glues all the enterprise resources into a single organism. [12]. Structural capital is a critical link that makes it possible to identify all other elements of an enterprise's IC; without a structural capital, as without a spine, the enterprise would not be able to function. The structure of the structural capital includes attribution of knowledge carriers of the enterprise (with the exception of personnel): databases, processes, organizational structure, policy and organization of performance of functions by personnel (all types of internal provisions, regulations and internal guidelines for the performance of functions).
However, in our opinion, combining structural organizational and technological components within the structural capital only complicates the process of IC classification. Particular attention should be paid to databases as a possible element of IC, which is a technical means of accumulating information that aims to replace the natural composition of information  human beings. Man has learned to store and transmit information through various forms of his record, which ensured its accumulation and use it on a growing scale, but the invention of electronic drives and their evolution have turned this process into an explosive one. However, it is not possible to distinguish databases as a classification group of objects of an enterprise IC, since information, knowledge comprises all elements of an IC, and databases must be used to store, process and transmit information.
That is, a database is a technical means of forming all components of IC, not its kind. In general, information is not a separate kind of IC, but a means of transforming, storing and transmitting knowledge of all kinds. K. Corrado et al. thinks that a software and computer information within an enterprise IC plays the key role  [13]. Of course, software plays an important role in enterprise activity, but it is only one of the tools of enterprises activity. Some authors quite rightly allocate technological capital separately -knowledge that already takes the form of material objects and tools implemented in the material form. E. Bueno et al. attributes to technological capital those technological processes that are based on innovation  [14]. One of the important elements of an enterprise's IC is its social capital, or capital of relations with society, which is formed through the marketing activity of the enterprise.
44 ]. It identifies eleven classification features: scope; usage in production and commercial activity; ownership; features of functioning in the cycle of capital; directions of use; duration of use period; role in the production and economic process; conditions of formation; the possibility of alienation; the level of risk of usage; functional content  [17,   The main feature in the classification of IC by A. Kendyukhov is the allocation of IC by functional content: personified -those that are accepted by other authors to refer to human capital; infrastructure  information technology, databases, organizational structure, philosophy, management culture and business cooperation; technical and technological  those with engineering embodiment; client -relations and contracts with buyers, license agreements; branded IC  trade marks and marks, reputation  [17, p.
Sample of countries and fundamental variables We consider yearly data for a panel of 42 commodity-exporting countries spanning from 1980 to 2016. The list of countries is given in Table  A. 1 (in the Appendix), together with the main type of commodity exported by each one, which are -except for energy-exporting countries -derived from Cashin et al. (  2004 ) and used for the construction of our panels (see Table  A. 2 in the Appendix). Among the different panels, the energy panel is the largest, with 13 energy-exporting countries, of which 7 countries belong to OPEC (Indonesia, Iran, Kuwait, Kingdom of Saudi Arabia, Nigeria, United Arab Emirates and Venezuela), 2 are other Gulf countries (Bahrain and Oman), 1 is a leading oil exporter (Mexico), 1 is a coal exporter (Australia) and another 2 are gas-and oil-exporting countries (Algeria and Norway).
This is followed by the food and beverages panel, in which 7 are food-exporting countries (Argentina, India, Malaysia, Paraguay, the Philippines, Thailand and Uruguay) and 5 are beverage-exporting countries (Colombia, Ivory Coast, Honduras, Kenya and Uganda). The two other panels are raw materials and metals, which are respectively composed of 9 and 8 exporting countries. We decided to discard and remove some countries derived from the initial list of 52 countries of  Cashin et al. (2004)  based on the lack of available data or extensive periods of war, which would have distorted the results (Syria, Libya, Central African Republic, etc. ). The dependent variable is the variation of the real effective exchange rate expressed in logarithm (∆ hereafter) and is provided by the Bank of International Settlements and Bruegel databases.
10 10 We used the real effective exchange rates provided by the Bank of International Settlements (BIS) and Bruegel databases because, with those databases, we have a balanced panel. Indeed, real effective exchange rates from the IMF database (International Financial Statistics), for some countries, are either not available or incomplete. For countries with data availability, we calculated a correlation coefficient between our initial series (from BIS and Bruegel databases) and those of The exogenous variables are derived from the previous empirical studies on the REER and its main determinants. Following the previous literature, for example,  Clark and MacDonald (1998) ,  Chinn (2005)  or  Ricci et al. (2008) , we retain three main fundamentals variables as determinants of the real effective exchange rate: (i) the relative productivity in the traded-goods sectors (relative to the non-traded goods one) as a proxy for the Balassa-Samuelson effect; (ii) the country's net foreign asset position in percentage of GDP; and (iii) the country's commodity terms of trade.
increase its trade surpluses, which can be done by depreciating its exchange rate. Another reason that could justify the impact of the net foreign asset position is that the currencies of countries with a significant negative NFA position and growing current account deficits may appear to be vulnerable to currency speculators, who may seek to drive it lower. The NFA series are taken from Lane and Milesi-Ferretti's database for the period 1980-2011. 12 For all the subsequent years (2012 to 2016), we compute the variable by adding the previous NFA position to the contemporaneous current account: ! a , = ! a ,. + ba , (7) and consider the variable as a percentage of GDP. The current account and GDP (in US dollars) data is taken from the IMF World Economic Outlook (WEO). The commodity terms of trade are calculated in the same way as in  Cashin et al.
(2004). Consequently, the country's commodity terms of trade are a weighted average price of the three main commodities exported by the country, deflated by the manufactured unit value (MUV). 13 Therefore, the commodity terms of trade are expressed as: c , SC+ = ∑ deTfg @ h Y h[X ×U A h jkl A (8) where mℎop% ^ is the share of commodity q among the three main commodities exported by country i (they are displayed in Table  A. 2 in the Appendix), ' ^ is the price of commodity k extracted from the World Bank's GEM database, and 8rs is the manufactured unit value extracted from the IMF World Economic Outlook (WEO) database. The list of exogenous variables can also include the country's degree of financial integration. Following  Levine et al. (2000) , the degree of financial integration is measured by a standard indicator, that is, the ratio of M2 to GDP, and taken from Datastream.
We also use two other indicators as robustness checks of the financial integration degree, such as the ratio of private credit to GDP and the ratio of foreign direct 12 http://www. philiplane. org/EWN. html. See  Lane and Milesi-Ferretti (2007). 13 For oil-exporting countries, the terms of trade are calculated as the oil price deflated by the manufactured unit value (MUV). This is done for the 11 oil-exporting countries where the oil price stands for 100% of the weight. investment to GDP. These two ratios are taken from the World Bank database (World Development Indicators). Table  A. 3 (in the Appendix) provides some descriptive statistics on our data.
The commodity price index In order to investigate the non-linear relationship between real commodity price volatilities and real exchange rates, we select the real commodity price index in accordance with the main type of commodity exported by the countries present in our panels. All price indices are extracted from the IMF database (Primary Commodity Prices). For the energy panel, we use the "energy price index", which includes the crude oil (petroleum), natural gas and coal price indices. 14 For the food and beverages panel, we have opted for the "food and beverage price index". For the metals group, we select the "metals price index", which incorporates the copper, aluminium, iron, ore, tin, nickel, zinc, lead and uranium price indices. Finally, for the raw materials panels, we choose the "agricultural raw materials index", which incorporates the timber, cotton, wool, rubber and hides price indices.
The Balassa-Samuelson effect, or productivity differential, states that a country experiencing high productivity growth in tradable goods, relative to non-tradable goods, will experience a growth in wagesThe net foreign asset position (NFA) refers to the value of the sum of foreign assets held by monetary authorities and deposit money banks, less the value of domestic assets owned by foreigners. It is expressed in percentage points of GDP. The net foreign asset position is a measure of indebtedness, indicating whether or not the country is a net creditor or debtor to the rest of the world. The NFA position can drive changes in exchange rates, since a country that faces a growing current account deficit needs to database. 11 P = ∏ DDD QRD STU T @,A DDD QRD STU T V,A $W V -V[X,V\@ XYZ (6) where O * = 9:; * / ∑ _, 9:; ^ and ∑ _, O * = 1.
(in both sectors). It will inexorably give rise to higher prices in non-tradable goods. Consequently, the real exchange rate appreciates. The Balassa-Samuelson effect is approximated by the GDP per capita measured in purchasing power parity (PPP) relative to the trading partners. The weights O * correspond to the shares in the world PPP GDP per capita, calculated on average over the period 1980-2016. Both variables, PPP GDP and GDP data, are extracted from the IMF International Financial Statistics (IFS) the IMF. Correlation is very strong, except for Cameroon, Iran, Nigeria and Togo. Detailed results are available upon request from the authors. 11For robustness check, we also use Balassa-Samuelson effect data from the EQCHANGE database (Cepii). Results, available upon request from the authors, are very similar.
In a second step, medication prescription could be also improved out of the STOPP and START criteria using the expert clinical knowledge of the team. Once a year, the team meets 1 hour each week and examines, on average, six prescriptions. The medication changes are decided collegially during the review and proposed to the residents by the geriatrician in charge of them. Palliative care residents are excluded from the medication review. In 2015, the medication reviews were prospectively conducted from 14 January 2015 to 18 March 2015. A pilot study was conducted from 14 January 2015 to 17 June 2015 to evaluate the impact of the medication review 3 months afterward. Opposition of the resident (or of his legal representative) to the collection of his personal data was recorded. START criteria refer to PPOs in patients aged 65 years and older.
Each criterion is named by the letter of the section followed by its number. 16 seline data collection Data were collected anonymously from the medical record by the pharmacist (WT) after the medication review in a   A1  in the Appendix) per prescription, the number and type of STOPP and START criteria and the associated therapeutic changes (stop, withdrawal and start of drugs). Others drug changes following medication review but not related to STOPP and START criteria were also collected in order to present all therapeutic changes resulting from medication review. The geriatrician (MLGD) checked the data collection for accuracy or any data entry error.
The cost of each drug is entered in the software PHARMA ® (Computer Engineering version 5. 4. 40526, Paris, France) per dose. For each resident and for each stopped or introduced drug, the pharmacist has researched its cost per dose and its cost per day; then she calculated the sum of the stopped drugs over 3 months by taking into account its cost per day and its possible date of resumption; she calculated the sum of the introduced drugs over 3 months by taking into account its cost per day and its possible date of stop. The cost saving was the difference between the sum of the stopped drugs and the sum of the introduced drugs for 3 months.
For the ensemble, several pieces of research  [9] ,  [17] ,  [19] ,  [26]  have shown that the addition operation can achieve satisfactory results for time series forecasting. The proposed model in this study enhances forecasting accuracy of the decomposition-ensemble models by reconstructing their modes. Generally, the proposed methodology involved four steps including data decomposition, reconstruction of modes based on AMI into two components (stochastic and deterministic), prediction of each individual component and assembling for the final prediction of the original series. Next, at the stage of individual mode prediction, the hybrid ARIMA Kalman Filter (KF) model was employed to all stochastic modes and one deterministic mode. The model ARIMA-KF model recently used by  [27]   To further improve the forecasting efficiency and effectiveness, in this paper, we aim at proposing a novel method of the use of decomposed modes obtained from EEMD.
5) ARIMA KALMAN FILTER MODEL To be more accurate in time series forecasting, applications of Kalman Filter model  [40]  was used to handle the stochastic uncertainty. Nowadays in many applications, the KF model is used such as in the metrological applications for forecasting the wind speed data  [41]  and crude oil prices  [27]. The AR-KF model is used to maintain simplicity instead of the hybrid model of ARIMA-KF when the parameters equal zero of the MA and integration terms  [41] -  [43]. In this study, a hybrid model of ARIMA-KF was initiated through KF and ARIMA (p, d, q). ARIMA model can be re-written in the state-space required for KF works in two equations. One is called the state equation (SE) and another observation equation (OE) and presented as follows: SE : S t = HS t-1 + Vw t (6) OE : Y t = ZS t + ε t (7) where S t is the r-dimensional state vectors.
6) EEMD-ARIMA-KF MODEL For the nonlinear time series, ARIMA models are inappropriate because of their linear nature. This paper proposed an EEMD-ARIMA-KF hybrid model for improving the forecasting accuracy of crude oil prices. After separating the stochastic and deterministic IMFs, all the deterministic IMFs are combined to one component and all the stochastic IMFs are treated as a single component. To apply the KF model on each of the IMF, the SE and OE are initialized through the ARIMA model as discussed in the previous section. The output of each of the Kalman Filter model for all stochastic IMFs and one deterministic component represents the fitted values of each IMF for training data and the forecasted values for the testing data. For the original series, the output of the fitted series is simply added.
B. EEMD MODEL EEMD depends on the two parameters which directly affect the performance of this procedure and should be fixed before using EEMD. The two parameters are the number of ensembles and the magnitude of the white noise. The statistical rule which is already established among them are the ensemble size m, added white noise µ and the error term e m and presented in equation (  14 )  [28]. EEMD procedure was employed in this work with the added white noise amplitude equals 0. 20 times of the sample standard deviation and the ensemble number was equal to 100. e m = µ √ m ( 14 ) EEMD technique is employed in this work by using the above description and decomposed the two crude oil series in different IMFs accordingly. The R package ''Rlibeemd'' is used for data decomposition in this work  [46].
C. SINGLE MODELS In this section, the single models will be computed. As a benchmark, the best ARIMA models for Brent and WTI crude oil prices are computed. The best-chosen model for Brent series was ARIMA (5,2,0) and for WTI ARIMA (3,1,2),  the R package forecast is used for ARIMA model selection and computations  [48]. Next is the selection of the GARCH model based on AIC values for both data sets. For Brent crude oil price, the best model was GARCH (1,1) and for WTI GARCH (0,1). For GARCH modelling the data converted to the log returns. The formula for the log-returns is Z t = ln Y t Y t-1 and for the final forecasting the data converted again to the original units. Next is the NAÏVE model in which the current value is used as forecast for the next observation. i. e. ŷt+1 = y t. The p-values of Table  1  justified that all parameters of the ARIMA and GARCH model are statistically significant at 1% level of significance for both Brent and WTI crude oil prices series.
E. ARIMA-KF MODEL ARIMA-KF model can be created once the ARIMA modeling performed for the time series under study. Box-Jenkins Methodology was applied to time series when the stationarity of the data is achieved. To handle the stochastic variability and to get more accurate forecasts for the crude oil price KF model was used. Initially, KF needs the starting values for recursions purpose so the estimated values of ARIMA parameters shown in Table  1  are used. The KF model equations which are SE in  (6)  & OE in (7) can be expressed in appendix B for the Brent crude oil price using the formulations discussed in the previous subsection. For KF the R software package ''dlm'' is used  [51]. Once the SE and OE of the Brent and WTI crude oil prices are initialized, KF recursion is used for crude oil price forecasting as mentioned by  [44].
01. The seventh is the ARIMA-KF model which underperformed over the NAÏVE and GARCH model for Brent while for ARIMA model the results were not statistically different. For WTI all the three models NAÏVE, GARCH and ARIMA models results were statistically insignificant. The eighth is the NAÏVE model which outperformed the ARIMA model for Brent data while the results were statistically insignificant for GARCH and ARIMA models for WTI. The last is the GARCH model which outperformed the ARIMA model for Brent data while for WTI the result was not significant. Finally, the model EEMD(SD)-ARIMA-KF performed better than the other models included in this paper. The proposed model EEMD(SD)-ARIMA-KF forecasted values of the testing set were plotted against the original values of Brent and WTI crude oil prices.
Its economy has a tertiary role, which has been reinforced in the latest years. This diversity and strength of the local economy situates Lyon between the most dynamic European metropolitan cities of this size like Cologne, Turin, Dublin, Helsinki, etc. In total, the urban area had more than 850,000 jobs (142,500 establishments-self-employed excluded) in 2011 (INSEE, 1 databases of SIRENE 2 and general population census). More than 43% were concentrated in the area's central municipalities (Lyon-Villeurbanne -Centre in Figure  1 ) which covers less than 2% of the surface of the total analysis area. During the period 2005-2011, the net employment increase was almost 6% and the number of firms increased by almost 17%. Despite the economic crisis of the period, the urban area maintained its economic attractiveness thanks to its diversified economy and the growth of the tertiary sector and liberal professions.