dataesr/scientific-paragraphs-categorization huggingface dataset:
Enhancing Competencies The third and final phase of the approach ensures that the design supports enhancing existing skills and developing new competencies. This is achieved by considering the context of use of a design from the onset and ensuring that the design incorporates skills that can be nurtured into competencies. For example, the playlist feature which was not part of participants YouTube participation, yet was still incorporated into the design of SkillsTube as participants showed potential (by exhibiting the skills to follow easy step-by-step guides/prompts) in using the feature with some support during the co-design sessions. Thus, including the playlist feature in the app provided an opportunity for participants to develop competence in creating and archiving content. Similarly, though the QR code scanning feature was neither a YouTube nor Facebook feature, participants through the design process got acquainted with the feature and quickly developed competence in using it to help them log in and add friends.
Competency-Based Design upholds other Ability-based Design Principles Besides extending the abilities stance and 'system-centric' adaptation context of ability-based design, the Competency-Based design approach upholds its other principles. For example, we adapted the accountability, transparency, an d adaptation principles of ability-based framework to design most of the features of the SkillsTube app. The iterative design of the features of the app such as adapting the QR code scanning for logging in and adding friends, to respond to users' poor performance leveraged the accountability principle of ability-based design. Further, all use activities on the app, that require a choice action, such as responding to a new friend request, creating a new playlist, or deleting a playlist or friend, leveraged the transparency principle of ability-based design.
The novelty of our study is in the use of advanced modelling and data visualisation techniques (including the 'xdPlot' dataviz framework recently proposed by the authors), especially in application to CSE data. We conduct a thorough exploratory data analysis (EDA) aiming to spot data anomalies and initiate appropriate data transformations. Given the results of EDA and the nature of the data available, we select the Arellano-Bond estimator as the most adequate method for regression analysis. Market Price per share (MPS) termed as the dependent variable, whereas Dividend per Share (DPS) is viewed as the independent variable. The results validated theoretical literature such as signaling effect and bird in hand theory, but questioned some previous empirical studies. The study validated cash dividends as stimulus to investors given the positive relationship between DPS and MPS.
Gaps in Empirical Studies In order to test the above theories it is required to conduct statistical modelling based on real data. This task is, however, not straightforward due to the complex features of relevant datasets. Below we provide an overview of existing empirical research, identify gaps and limitations we found in existing literature, and emphasize the need for additional research in order to make empirical results more reliable and avoid erroneous conclusions. In particular, we identify potential methodological issues, such as spurious correlation, endogeneity assumption violations, unaccounted non-stationarity, and over-fitting. Generally, our analysis of empirical literature shows that there have been different conclusions arrived at using different methodologies. Moreover, different studies relate to different countries, company types, and time intervals so it's hard to generalize the results.
In our brief overview below we will look at a number of studies with a particular regard to signaling theory, as it better relates to the context of this research. In order to study the effects described above, empirical studies usually are based on so-called 'panel data' instead of looking at individual companies. For datasets having a panel design we assume that for a set of objects (in our case, companies) quantities of interest are measured over time. This approach gives "more informative data, more variability, less collinearity among the variables, more degrees of freedom and more efficiency. " (Baltagi, 2001, p. 6 ). However, panel data features individual heterogeneity and time effect. These features require very careful testing for modelling assumptions. For example, stationarity is a prerequisite for applying statistical tests for dependency in order to avoid spurious correlation.
In our study we employed a framework for visual analysis of panel data proposed in  (Davydenko & Charith, 2020a). The visual analysis aimed to improve overall transparency of our research. We believe the application of the data visualisation framework may present interest to researchers who wish to conduct similar analysis. Finally, we'd like to mention that for most studies the datasets used were not readily available. In order to ensure reproducibility and transparency of our results and to allow other researchers to try alternative methods, we made the dataset used and its detailed description publically available via the 'Figshare' platform, see  (Charith & Davydenko, 2020a).
Research Dataset We conducted a panel study using data obtained through annual reports from 22 companies from 2011 to 2019 listed on the Colombo Stock Exchange under the manufacturing sector. The dataset contains 198 observations and relates to companies that were paying dividends on a regular basis only. Following  Farrukh et al. (2017) , we excluded companies not paying dividends for more than three years. Several companies were excluded due to lack of data in order to make the panel balanced for further analysis. Table  1  shows the variables used for the analysis. The currency used is Rs, Sri Lankan rupee. The dataset is available online via the 'Figshare' platform (see  Charith & Davydenko, 2020a). Charith, K. , &  Davydenko, A. (2021). Informational value of dividend initiations: Impact of cash dividends on share prices of manufacturing companies in Sri Lanka.
Summary Statistics Table  2  shows summary statistics for the dataset described in Section 4. Table  2  shows that there are zero DPS cases, but shows no obvious data anomalies. While the table gives some  Charith, K. , & Davydenko, A. (2021). Informational value of dividend initiations: Impact of cash dividends on share prices of manufacturing companies in Sri Lanka. International Journal of Economics and Finance, 13(3), 13-30. https://doi. org/10. 5539/ijef. v13n3p13 overview of the data, it does not allow to fully explore data features and detect potential data flaws. In order to obtain a better understanding of the data, we used a set of graphical tools (additional EDA methods) as described below.
Correlation Plots and Suitable Data Transformations To describe the distribution of MPS cases and explore autoregressive dependencies in MPS series we constructed a scatterplot shown on Figure  1. Each company is coded using individual color and mark. We used standard marks and colors implemented in 'ggplot2' R-package  (Wickham, 2016)  to code panels. Figure  1  shows that the variance of MPS is not constant and depends on MPS_lag. One well-known approach here is log-transformation. For example, Olaniyi (2019) emphasized that logarithmic transformation helps mitigate differences in variables across firms and reduce heteroskedasticity with reference to studies by  Uusitalo (2012)  and  Zhu and Tian (2009). This transformation was also used by de  Wet and Mpinda (2013). One additional advantage of log-transformations is that coefficients can be interpreted as elasticities  (de Wet & Mpinda, 2013).
The Sargan tests indicated that we cannot reject the null hypotheses (p-val=0. 199, which are more than 0. 05). Resultantly, our instrumental variables do not correlate with the error terms. Also, we confirmed the model does not suffer from serial correlation. The presence of 1st degree and 2nd degree autocorrelation was tested. The AR(2) term appeared to be negative and insignificant (p-val=0. 821). According to  Bostanci et al. (2018)  for the Generalized Moments Estimator to be effective, it is sufficient that there be no second degree autocorrelation with reference to  (Tatoğlu, 2013). Estimates of coefficients are given in Table  6. We used 'plm' R-package  (Croissant & Millo, 2008)  to produce the output. The coefficients of the model can be interpreted in terms of elasticity given the use of log variables (de Wet & Mpinda, 2013).
Conclusions In this paper we explored the association between MPS and DPS using a set of statistical tools. This task is important as it investigates the short and long term impact of dividends on shares helping investors make more informed decisions. A number of studies have previously focused on the same issues. However, different countries were studied using different methods, which make it difficult to obtain a clear picture, especially with regard to the context of developing markets. Moreover, some methods used previously could pose a risk of spurious correlation, thus we focused on improved modeling techniques to obtain reliable results. To conduct the study we used empirical data relating to Sri Lankan companies. This study is conducted with a particular regard to data properties and addressing issues like spurious correlation, endogeneity and non-stationarity, thereby selecting appropriate modeling techniques.
We also conducted a thorough visual analysis of the available data in order to gain insights and detect potential data issues. We demonstrated how the EDA framework proposed by  Davydenko and Charith, 2020a  can be used to conduct the visual analysis of a real-world dataset. In particular, we demonstrated how data transformations can be effectively applied to financial panel data, such as MPS and DPS. Apart from the visual analysis, we used formal panel unit root tests in order to detect the presence of non-stationarity in time series. As a result of the prerequisite data analysis, we selected the Arellano-Bond (AB) approach to perform regression modelling. We found that MPS and DPS form time series having integration order less than 2. According to our knowledge, the use of the AB estimator has been explored in the Sri Lankan context, which this study one of the first studies where this particular method is applied to a Sri Lankan dataset.
The validation of the VESTA 2. 2. 0 Monte Carlo depletion code has been initiated using the Spent Fuel Isotopic Composition Database (SFCOMPO). The work presented in this paper is limited to one fuel sample, the GU3 PWR-UOX sample from the ARIANE program, which has a reported burn up of 52. 5 MWd. kgHM -1. The chemical analyses of the studied fuel sample were performed by 2 independent laboratories at the end of irradiation and cooling time. US and European evaluated nuclear data libraries, namely ENDF/B-VII. 1 and JEFF-3. 2, but also the more recent ENDF/B-VIII. 0 and JEFF-3. 3 are used for the VESTA 2. 2. 0 calculations. The isotopic concentration results are compared to experimental data and the C/E agreement is analyzed in the light of the previous VESTA 2. 1. 5 validation results obtained using ENDF/B-VII.
INTRODUCTION VESTA is a Monte Carlo depletion interface code developed by IRSN. From its inception, VESTA is intended to be a "generic" interface code so that it will ultimately be capable of using any Monte Carlo code or depletion module and that can be tailored to the user's needs. VESTA 2. 1. 5 [1]  allowed for the use of any version of MCNP(X)  [2]  with ORIGEN 2. 2  [3]  or PHOENIX  [1]  depletion module, developed by IRSN. A new version of the code, VESTA 2. 2. 0, has been recently finalized. This new version introduces several new features and improvements, like the output of the secondary particles spectra and the integration of the MORET 5  [4]  Monte-Carlo code. Depletion codes such as VESTA are widely used to calculate the evolution of a material subjected to radiation (be it neutrons or another type of particle) for a wide variety of applications in the fields of nuclear safety, radiation protection and environmental health safety.
Other important applications for this type of codes include fuel cycle studies, criticality safety, nuclear safeguards, waste characterization, etc. For application in these fields, experimental validation is paramount. VESTA 2. 1. 5 has been validated for an isotopic inventory (of 65 isotopes) from 41 samples of radiochemical measurements and 52 measurements of residual power, using the JEFF-3. 1 and ENDF/B-VII. 0 nuclear data libraries  [5, 6, 7]. It contains a wide variety of reactors (PWR, BWR, VVER) and fuel (UOX, MOX) types with and without burnable absorbers. It is validated using MCNPX-2. 6. 0 as the transport module and PHOENIX as the depletion module. For the experimental validation of VESTA 2. 2. 0, new samples with radiochemical analysis are being added, namely JAERI BWR UO2-Gd2O3 fuel samples and REGAL PWR UO2-Gd2O3 fuel samples measured at low burn up.
VESTA 2. 2. 0 experimental validation, using the JEFF-3. 2 and ENDF/B-VII. 1 nuclear data libraries, is ongoing. It will be based basically on several fuel samples used for VESTA 2. 1. 5 validation, in particular the GU3 sample of the ARIANE program  [8]  from the SFCOMPO database. For this paper, we will limit ourselves to this GU3 sample extracted from a PWR UO2 assembly.
VESTA allows the depletion of materials for different geometrical scales, and the calculation of the material isotopic compositions during irradiation at the fuel rod level. VESTA 2. 2. 0 allows for the use of either MCNP(X/6) or MORET 5 transport codes, and both ORIGEN 2. 2, PHOENIX, or FISPACT  [9]  depletion modules. MCNP6 is used by default when choosing MCNP transport module with VESTA 2. 2. 0 version. We will limit ourselves to the use of MCNP6 and PHOENIX for the validation case presented in this paper.
Description of the simulation The Actinide Research In A Nuclear Element (ARIANE) program  [8]  examined irradiated MOX (Mixed OXide) and LEU (Low Enriched Uranium) fuel samples in both commercial PWR (Pressurized Water Reactor) and BWR (Boiling Water Reactors) power reactors. The ARIANE. GU3 sample is a PWR UO2 sample with an estimated burn up of 52. 5 MWd. kgHM -1. It has been irradiated in the Gösgen PWR in Switzerland between 1994 and 1997 during three cycles. An overview of the sample characteristics is given in Table  I. The fuel rod containing the GU3 sample was extracted from the original assembly after two cycles and inserted into another assembly for the third and final cycle. After ~2 years of cooling time, the 10 cm sample was cut from the middle of the rod, between 122. 42 and 132.
Monte Carlo depletion code has been initiated using the ARIANE-GU3 chemical assay data from the OECD/NEA SFCOMPO database. The chemical analyses of the studied fuel sample were performed by 2 independent laboratories at the end of irradiation and cooling time. US and European evaluated nuclear data libraries, namely ENDF/B-VII. 1 and JEFF-3. 2, but also the more recent ENDF/B-VIII. 0 and JEFF-3. 3 were used for the VESTA 2. 2. 0 calculations. The C/E results were compared to the previous VESTA 2. 1. 5 validation results obtained using ENDF/B-VII. 0 and JEFF-3. 1 nuclear data libraries. The U and Pu contents are globally correctly estimated within 5 %, both for ENDF/B-VII. 1 and JEFF-3. 2, as it was the case for ENDF/B-VII. 0 and JEFF-3. 1. The 238 Pu results, which show an underestimation with ENDF/B-VII.
2. 0 20 Monte Carlo depletion code has been initiated using the ARIANE-GU3 chemical assay data from the OECD/NEA SFCOMPO database. The chemical analyses of the studied fuel sample were performed by 2 independent laboratories at the end of irradiation and cooling time. US and European evaluated nuclear data libraries, namely ENDF/B-VII. 1 and JEFF-3. 2, but also the more recent ENDF/B-VIII. 0 and JEFF-3. 3 were used for the VESTA 2. 2. 0 calculations. The C/E results were compared to the previous VESTA 2. 1. 5 validation results obtained using ENDF/B-VII. 0 and JEFF-3. 1 nuclear data libraries. The U and Pu contents are globally correctly estimated within 5 %, both for ENDF/B-VII. 1 and JEFF-3. 2, as it was the case for ENDF/B-VII. 0 and JEFF-3. 1. The 238 Pu results, which show an underestimation with ENDF/B-VII.
Objectives: Tyrosine kinase inhibitors (TKIs) account for the vast majority of healthcare expenditure on patients with chronic myeloid leukemia (CML), and it has been demonstrated that TKI discontinuation in patients in long-term deep molecular remission (DMR) is safe and improves quality of life. Our objective was to estimate the budget impact of TKI discontinuation in CML patients in long-term DMR from the perspective of the French healthcare system. Methods: This analysis was conducted over a 5-year time horizon using a Markov model with cycles of 6 months. Transition probabilities were estimated through systematic reviews and meta-analyses. Costs were estimated from the French National Claims Database. Monte Carlo simulations were performed to take into account the uncertainty surrounding model parameters.
Estimating Costs Costs were estimated from the perspective of the French healthcare system. TKIs and all CML related healthcare costs are reimbursed at 100% by the French healthcare system. Estimated costs were based on the French National Claims Database (SNDS). The SNDS covers around 99% of the French population from birth to death, even if a subject changes occupation or retires. 25 It contains pseudonymized information on all reimbursed healthcare consumptions, including medical and paramedical encounters, drugs claims, hospital admissions and procedures, and their corresponding costs. It also provides information on registration for long term disease (LTD), ensuring full coverage for all medical expenses related to some chronic diseases and cause and date of death. Drugs are coded according to the Anatomic Therapeutic Chemical classification and hospital or LTD diagnoses according to the International Classification of Disease, 10th edition (C92.
1). We developed a claim-based algorithm using healthcare consumption data from the SNDS including outpatient dispensing, LTD registration or hospitalization diagnosis, or medical procedures. TKIs included imatinib, dasatinib, nilotinib, bosutinib, and ponatinib. Using this algorithm, the included patients were all adults ($18 years, as recommended for TKI discontinuation) presenting with CML and a first discontinuation of TKI treatment (ie, no TKI dispensing for $61 days) in 2010 to 2014 after a 3-year period of TKI regular treatment (ie, $10 TKI dispensing per year over 3 years). The excluded patients were all patients who died ,1 year after TKI discontinuation or had an allogeneic or autogenic hematopoietic stem-cell transplant, a diagnosis of HIV/AIDS or chronic hepatitis C or B during the 3-year period before TKI discontinuation, or had a recent or ongoing pregnancy, a concomitant cancer, a TKI-related adverse effect leading to hospitalization, or psychiatric disorders ongoing at the time of TKI discontinuation.
Estimating Transition Probabilities All the transition probabilities in the Markov models were estimated through systematic reviews and meta-analyses. This part of the work has been published elsewhere. 13 Because there were no data available, the probability of TKI withdrawal due to toxicity was estimated from a local cohort of CML patients (Table  1 ). Because the overall survival of CP-CML patients has reached that of the normal population,  28  the probability of death was derived from French National Institute of Demographic Studies data for the general population aged from 60 to 64 years in 2014. 29.
1 016/j. jval. 2020. 11. 010). 31 The resulting distribution was used to derive the 95% confidence interval (CI) and to estimate the probability that the TKI discontinuation strategy is more expensive than the TKI continuation strategy. Sensitivity analyses were carried out by increasing the number of eligible patients up to 200 per year (assuming that 40% of incident cases of CML patients would reach sustained deep molecular response each year) and by taking into account the reduction in the price of TKI due to generics (V987. 21 per box of TKI). 32 he Markov model was analyzed using Microsoft Excel (2016) software (Microsoft Corp, Redmond, WA). Statistical analysis of SNDS data was performed with SAS software (SAS Institute, Version 9. 4, Cary, NC). This study was authorized by the French national data protection committee on July 20, 2017, and registered under number 18568 in the European Union electronic Register of Post-Authorisation Studies.
39 ut the economic results presented in those 3 studies were secondary objectives based on estimation of the costs of care in potentially unrepresentative samples, not population-wide budget impact analyses as recommended by ISPOR. 23 To our knowledge, only 1 budget impact analysis of TKI discontinuation in CML with DMR has been published so far. In this study from Japan, the mean savings from the TKI discontinuation strategy was U7 625 174,640 approximately V63 288 850) in a target population of 901 CML patients within a 3-year time horizon. 40 We strictly followed the ISPOR recommendations, and our results are therefore interpretable by policy makers, transparent, and reproducible. Another strength of our study is that we used real-life healthcare costs retrieved from the French national claims database (SNDS), which covers 98% of the French population.
Among the 355 CML patients who stopped TKI identified in this database, 188 (53%) did not resume TKIs. This is consistent with results from the literature about the probability of molecular relapse after TKI discontinuation. Because the SNDS strictly captures all the diversity in healthcare management and reimbursement, the use of these data reinforces the relevance and the reliability of our results and their usefulness for health authorities. We have also taken into account the uncertainty regarding the model parameters estimates. Our probabilistic analysis enables us to produce 95% confidence intervals and to estimate the probability that TKI discontinuation might be more expensive than TKI continuation. We have considered the emergence of generic drugs in sensitivity analyses. As expected, savings were lower with generics but still significant in a context of constrained budget and more than offset the extra cost due to more frequent PCR monitoring after TKI discontinuation.
Acknowledgment: The authors sincerely thank the other members of the  Bordeaux PharmacoEpi team  who worked on the study:  Marie-Agnès Bernard ,  Cécile Droz-Perroteau ,  Jérôme Dupont ,  Clélia Favary , and  Edwige Yon. They also thank  Richard Iggo  for having proofread the English of this article.
Photometric Redshifts from CAMIRA The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) is a deep optical grizy survey reaching to depths fainter than 26th magnitude in the r-band  (Miyazaki et al. 2018; Aihara et al. 2018). The HSC-SSP full-depth full-color (FDFC) footprint corresponding to observations up to the 2019A semester has 469 deg 2 of overlap with the ACT DR5 cluster search area, as shown in Fig. 1. An optical cluster finding algorithm named CAMIRA (Cluster finding Algorithm based on Multi-band Identification of Red-sequence gAlaxies; Oguri 2014), which is similar to redMaPPer but was developed independently, has been run on the HSC data. Here we use the CAMIRA cluster catalog based on HSC-SSP S19A photometry; note that the CAMIRA cluster search uses a less conservative mask and covers slightly more area than the FDFC mask.