dataesr/scientific-paragraphs-categorization huggingface dataset:
Twenty of these 49 stated that there was an increase in visitation in urban green spaces, which is approximately 40%. Additionally, the 13 municipalities (16%) present in both databases reported that they made investments to adapt these areas to resume activities taking into account the COVID protocols, even though they did not answer about an increase of demands for green spaces, or they answered they have no conclusive data on this issue. Thus, out of this sample of municipalities, 33 of them (67%) highlight the relevance of these spaces for local managers as urban facilities for collective uses. From a formal point of view, the management of urban green spaces in Brazil is the responsibility of the municipalities, since the Brazilian Federal Constitution of 1988 determined that topics of local interest are the exclusive competence of the municipalities.
Acknowledgements: The authors are grateful to the  INCT-ODISSEIA Project, "Observatory of Socio-environmental Dynamics" at Brasília University (UnB)  for their technical support. We also thank  Coordination for the Improvement of Higher Education Personnel (Capes) , the  National Council for Scientific and Technological Development (CNPq) , and the  Research Support Foundation of the Federal District (FAP-DF)  for their financial support.
Participants and procedure The data used in this study were collected in 2019 from 539 undergraduate students organized into 94 teams. Students were distributed in four classes (i. e. UB1, UB2, UB3 and UB4) in the last year of the Bachelor of International Business Administration (BIBA) program at a top-ten-ranked French business school. Students spent three months working in teams on a group assignment, and the grade on this assignment was used in this research as an indicator of group performance. The assignment consisted of three interrelated activities: one in-class activity on income statements and balance sheets, one home activity of the same kind and one final report with an oral presentation of a business proposal for exporting a product. The in-class and home activities required students to work together to analyze and solve different case studies on income statements and balance sheets.
Hence, future studies should include these variables and other potential moderators (e. g. attitudes toward diversity;  Traavik and Adavikolanu, 2016)  to deepen the understanding of how and when gender diversity may affect group performance. Fifth, the data for this study were collected in a pre-COVID-19 period. The forced transition to online learning during the pandemic, which moved collaborative learning groups to virtual settings and cut down important aspects of in-person communication (J€ arvel€ a and Ros e, 2020), complicated group members' collaborative learning experiences  (Seymour-Walsh et al. , 2022). Due to the technological progress following the COVID-19 CCSM 30,4 pandemic, universities and organizations have adopted more flexible modalities for training program delivery. Accordingly, additional research is needed to shed light on the cultural conditions upon which gender-diverse collaborative learning groups exhibit higher or lower performance within hybrid and/or remote learning settings.
T a g g e d A P T A R A E n d T a g g e d A P T A R A H 2 Body composition assessmentT a g g e d A P T A R A E n d T a g g e d A P T A R A P Data regarding body composition were retrospectively analyzed based on CT scans performed for initial cancer staging within 30 days before neoadjuvant treatment, and if available for those performed after neoadjuvant treatment and before surgery. The image software used for analysis was Synapse Ò 3D (Fujifilm, Tokyo, Japan). T a g g e d A P T A R A E n d T a g g e d A P T A R A P Skeletal muscle index (SMI) was calculated as cross-sectional area of muscle (cm 2 ) at the L3 level and divided by the square of the height (m 2 ). The muscles evaluated were psoas, erector spinae, quadratus lumborum, transversus abdominis, abdominal external and internal oblique muscles, and rectus abdominis muscle.
Allocation and Blinding Patients are allocated 1:1 to NB or CT-TTNB. Randomization tables were computer-generated by the trial statistician then locked into the trial database preventing access by other personnel. Permuted blocks of 2 and 4 were utilized within the following strata: 1) nodule location: middle vs. outer third, 2) pre-test probability of malignancy per validated risk calculator: ≤50% vs. >50%, and 3) study site. Allocation is performed via a randomization module embedded in the research database. NB and CT-TTNB are performed in different departments using different layperson-identifiable technique preventing patient or proceduralist blinding. However, the central panel adjudicating diagnostic yield and accuracy (defined below), including pathologists performing central pathology review, will be blinded.
Sample size A Frequentist approach was used to conduct an initial power calculation. Diagnostic accuracy of CT-TTNB was assessed at 90%, with noninferiority margin of 10%, one-sided type I error rate of 5%, and power of 80%, yielding a sample of n=112 per group, increased 15% to account for attrition to total n=258. The noninferiority margin was chosen in accordance with contemporary NB data utilizing digital tomosynthesis for CT-body divergence correction with diagnostic accuracy reports exceeding 80%. A diminution in diagnostic yield by less than 10% additionally seems clinically relevant as it is balanced by the assumed greater than 20% increase in pneumothorax risk in CT-TTNB versus NB. A simulation study confirmed the above operating characterizes remained accurate within a Bayesian paradigm in which there is no type I error rate (Statistical Analysis Plan, supplement).
Missing data and protocol nonadherence The primary per-protocol analysis will include only those patients with complete follow-up data, which is conservative for the non-inferiority objective. We have planned adequate additional accrual to account for attrition with respect to the primary outcome and will monitor attrition rates as the trial progresses to determine if target accrual requires adjustment. The Bayesian design will allow for sample size change without inflation of the probability of incorrect conclusions. Sensitivity analyses will be performed for the primary per-protocol analysis in which all non-malignant diagnostic pathology results in patients lost to follow-up are considered true-negative and another in which they are all considered false-negative to determine the range of potential variation due to missing data.
Data Collection and Monitoring The central coordinating center for this trial is composed of the Principal Investigator (FM), central investigators (RJL, OBR), central research coordinators, interventional pulmonary and interventional radiology adjudicating panels, and study statistician (TK), with support from the Vanderbilt-Ingram Cancer Center Clinical Trials Office for independent logistical, monitoring, auditing, and regulatory oversight. No data monitoring committee was utilized as both procedures are current usual care. All data are entered and maintained in a secured web-based Research Electronic Data Capture (REDCap) database by central and local investigators and research coordinators. 17, 18  Study personnel only have access to data entry forms pertinent to them. Source data verification is performed during standard audits.
ACKNOWLEDGEMENTS Guarantor statement: The study sponsor-investigator,  Fabien Maldonado , has full authority over study design; data collection, management, analysis and interpretation; writing of the report; and the decision to submit the report for publication. Study oversight was performed by the  Vanderbilt Ingram Cancer Center, Clinical Trials Office, Coordinating Center. The funder had no role in study design, data collection/management/analysis/interpretation, oversight, writing of the report, or the decision to submit the report for publication. Summary COI statement: FM reports consulting fees from Intuitive. OBR reports consulting fees from  Medtronic. LY reports educational grants from  Medtronic  and consulting fees, educational and research grants from  Olympus ,  Intuitive , and  Johnson & Johnson.
MCA reports speaking fees from  Medtronic  and consulting fees from Noah Medical. PFD is the  CEO of Upstream Vision LLC , proprietor of LungHive software used in this work. No other authors report any financial or non-financial conflicts of interest pertinent to this work. Funding information: This work is supported by an unrestricted research grant from  Medtronic (Fridley, Minnesota, USA)  and the  Pierre Massion Directorship in Pulmonary Medicine  (F. M. ). E. G reports support from  U01 CA152662. Notification of prior publication/presentation: No aspect of this work has been previously published nor is under consideration by any other journals. All authors participated in critical revision of the manuscript for important intellectual content and provided final approval to submit this version of the manuscript and have agreed to be accountable for all aspects of the work.
(0. 2-1. 0) x 10 -9 yr -1 , depending on the area, data quality, and network geometry. Considering the dimensions of the Western Alps, their icecap during the LGM, and the lithosphere elastic thickness (cf. Section 3), 100-200 km is a reasonable estimation of the expected wavelength of the GIA signal. Thus, in the flowing, we apply the same 90-km-half-width Gaussian filter on GNSS velocities and GIA model velocities for strain rate comparisons. Details of the method and additional strain rate maps at different filtering half-widths can be found in the Supplementary Material 1 for reference. This overall deformation pattern (slow horizontal extension rates coupled with relatively fast uplift rates) is at the core of the current debate on the processes responsible for the ongoing geodynamics of the Western Alps.
The fault geometries are from neotectonic databases  (Grellet et al. , 1993; Jomard et al. , 2017)  or assuming Andersonian geometries for the unknown structures. Due to the fault dip uncertainties, we test a large range of dip angles (25-90˚). Seismicity associated with these faults concentrates in the upper crust ca. 5-10 km depth  (Mathey et al. , 2021; Thouvenot et al. , 2003a) , associated with bending stresses in the upper half of the elastic plate (cf. Section 4. 1). Because of the present-day GIA stress perturbations correspond to horizontal compressive stresses, faults with nearvertical dip angles (80-90˚) and standard effective friction coefficient (µ' = 0. 6) tend to be clamped and are associated with negative DCFS values (i. e. , slip inhibition). In contrast, faults with low effective friction (µ' = 0.
Acknowledgments This work was supported by. Figures were made with the Generic Mapping Tool (Wessel et al. , 2013) and Python 3 software. We thank  T. Camelbeeck ,  Y. Klinger , and  B. Lünd  for their reviews and suggestions on early versions of this manuscript,  R. Steffen  for an early discussion about this work,  J. Mey  for providing his numerical Alpine icecap model, and  M. Gamelin  for her 1 st year M. Sc. internship during the initiation of this work.
Study Design The study will use data from 2 academic medical centers in North Florida. Leveraging data from 2 medical centers will allow us to use data from 1 center for training and data from the other center for testing. Specifically, 1 medical center has about 1095 beds and 54,757 admissions per year, and the other medical center has about 603 beds and 23,685 admissions per year. We will include older adult (≥65 years) admissions to a medical-surgical unit between January 2012 and May 2021. We will obtain the data from the Integrated Data Repository (IDR). The IDR was created to serve as a common source of information for clinicians, executives, researchers, and educators. The IDR enables new research discoveries as well as patient care quality and safety improvements through a continuous flow of information between the clinical enterprise and research community.
Outcome We aim to develop a computable phenotype that has greater sensitivity to identify patients with hospital-induced delirium than ICD codes alone. Along with using ICD code and medication data, we will incorporate nursing assessment documentation from structured data fields as part of our phenotype. Examples of the different types of data we will use include the ICD-10 code F05, the medication haloperidol, and the nursing assessment "observed/expressed feelings: agitated. " Expert clinicians will validate the phenotype; after evaluating performance, we will iteratively refine the phenotype and use it as our outcome measure.
Text Mining Supervised and unsupervised text mining approaches will be used to identify concepts related to risk factors, signs, and symptoms of delirium. For risk factors, we will be reviewing and adapting NLP pipelines developed to extract concepts related to risk factors for hospital-acquired falls. To identify and extract signs and symptoms of delirium, we will start by creating a reference thesaurus of keywords and phrases (ie, n-grams and sequence of consecutive keywords) thought to indicate signs and symptoms of delirium based on domain expertise and review of the literature. New themes that emerge from the data will be added to the reference thesaurus, which will be used to develop a guide for the manual annotation process. In total, 2 practicing bedside nurses will be trained as annotators to review progress notes and label phrases related to signs and symptoms of delirium.
These tasks might include identifying patients at high risk for delirium or patients with signs or symptoms of delirium. Tasks could focus on pulling data from structured EHR data fields, structured data derived from the NLP, or both. To assess functionality, we will measure perceived ease of use, processing speed, and errors reported. To determine scalability, we will assess memory usage and processing time of all "black-box" testing to identify bottlenecks or problem areas that could increase system load at scale-up. We will use an iterative testing approach to refine the integration of the text-mining pipelines until processing speed, memory use, and error reporting have reached acceptable levels.
Results Work on this project will take place through March 2024. For this study, we will use data from approximately 332,230 encounters of 170,868 patients, including 6,980,470 narrative notes; these encounters took place from January 2012 to May 2021. We will create and validate a computable phenotype for hospital-induced delirium, develop prediction models for hospital-induced delirium using structured data and unstructured text data, and integrate text pipelines into the IDR. In addition, we will identify important nursing structured EHR data, curate data, and bring these data elements into the IDR to make them available for other future research projects. Research findings will be disseminated through scientific conferences and peer-reviewed journals. Dissemination of the results and future studies (informed by this project) may expand practice-based evidence of hospital-induced delirium, which will improve clinical decision-making by clinicians and improve patient care in hospitals.
3. Access to the ICSBEP Handbook is available to OECD NEA member countries and recent handbook contributors from non-member countries; requests can be made using the following website: https://oe. cd/ICSBEP. The Database for the International Handbook of Evaluated Criticality Safety Benchmark Experiments (DICE) tool was developed to facilitate use and searching of the extensive ICSBEP database  [13] ; it can also be accessed online: https://oe. cd/nea-dice.
Latest IRPhEP Contributions The 2021 edition of the IRPhEP Handbook now contains data for 57 unique nuclear facilities with evaluations containing benchmark specifications for 169 experimental series. Four of the 169 evaluations are draft benchmarks specifications yet to be formally adopted into the handbook. Draft evaluations typically represent incompletely evaluated experiments, where the data has been preserved and made available for public use. A summary of the handbook contents is provided in Table  2  with a breakdown of contributions by country shown in Fig. 4. Access to the IRPhEP Handbook is available to OECD NEA member countries and recent handbook contributors from non-member countries; requests can be made using the following website: https://oe. cd/IRPHE. Similarly, the IRPhEP Database and Analysis Tool (IDAT) was prepared for use with the IRPhEP Handbook  [14]  and is also available online: https://oe.
BEAVRS-PWR-POWER-001 BEAVRS Additional opportunities for involvement with similar NEA databases exist through the Shielding Integral Benchmark Archive and Database (SINBAD)  [15]  and the Spent Fuel Composition (SFCOMPO) database  [16]. There are no shortages of international opportunities for involvement via the OECD NEA. Contributions include provision of additional experimental data to evaluate, evaluation of experimental data to prepare new benchmark reports, service as technical reviewers, and programmatic or financial support. Additionally, as users of these handbooks have questions or identify possible errors in the benchmark reports, they are encouraged to inform the leadership of these projects so that improvements can be made in future revisions of the handbooks.
Indeed, a comprehensive dataset of river flow discharge was generated using a numerical model, encompassing a wide range of potential future floods. This significantly improved the ML training process to generalize the accuracy of results. Utilizing this dataset, a novel ML model called the Expanded Framework of Group Method of Data Handling (EFGMDH) has been developed. Its purpose is to provide decision-makers with explicit equations for estimating three crucial hydrodynamic characteristics of the Ottawa River: floodplain width, flow velocity, and river flow depth. These predictions rely on various inputs, including the location of the desired cross-section, river slope, Manning roughness coefficient at different river sections (right, left, and middle), and river flow discharge. To establish practical models for each of the aforementioned hydrodynamic characteristics of the Ottawa River, different input combinations were tested to identify the most optimal ones.
It has established itself as a well-recognized and extensively embraced software in the realm of hydraulic modeling, boasting a vast user community  [23] [24] [25]. This software offers an array of capabilities for assessing and simulating river hydraulics, encompassing aspects such as floodplain deluge scenarios, sediment conveyance, water surface profiles, and flow rates  [26] [27] [28] [29]. It demonstrates its competence in accommodating diverse hydraulic conditions essential for the cartography of floodplains and handling intricate river systems, utilizing Geographic Information System data  [30, 31]. Nevertheless, it is worth noting that the computational demands of running simulations in HEC-RAS can vary based on the scale and intricacy of the model, sometimes necessitating substantial computational resources  [32].
These advantages make GMDH a valuable tool in various domains, particularly when dealing with complex datasets. Nevertheless, the GMDH has limitations, including the exclusion of non-adjacent layers, restriction to second-order polynomials, and a limitation to two neurons per layer. To overcome these drawbacks, the Expanded Framework of GMDH (EFGMDH) is introduced in the current study for forecasting the hydrodynamics characteristics of the river. Although previous studies have demonstrated the high predictive capabilities of various ML algorithms in flood prediction, their accuracy heavily relies on the historical data used during the training process  [48]. Acknowledging that none of these algorithms can anticipate "non-experienced" or "unseen" floods is essential. Given the impacts of climate change, significant changes in flood patterns have been observed in Canada.
For instance, the 2019 flood surpassed the discharge levels expected once every 100 years, which caught decision-makers by surprise as such an event was unprecedented in historical records dating back to the 1900s. To address these challenges, this study aims to combine numerical modeling with the ML approaches. A comprehensive dataset of river flow discharge will be generated using the numerical model (i. e. , HEC-RAS), encompassing a wide range of potential future floods. This extensive dataset will serve as training input for ML algorithms, facilitating the development of user-friendly explicit equations for decision-makers. These equations will enable the direct calculation of three important hydrodynamic characteristics of the Ottawa River, including floodplain width, flow velocity, and river flow depth.
The model efficiency characterization based on R 2 , NSE, and NRMSE intervals is provided in Table 2  [62]. Table  2. Descriptive performance of different indices. Index R 2 NSE NRMSE Unsatisfactory R 2 < 0. 5 NSE < 0. 4 30% < NRMSE Acceptable - 0. 4 < NSE < 0. 5 - Satisfactory 0. 5 < R 2 < 0. 6 0. 5 < NSE < 0. 65 20% < NRMSE< 30% Good 0. 6 < R 2 < 0. 7 0. 65 < NSE < 0. 75 10% < NRMSE < 20% Very Good 0. 7 < R 2 < 1 0. 75 < NSE < 1 NRMSE < 10% The coefficient of determination, denoted as R 2 gauges the combined dispersion in comparison to the individual dispersion of both the observed and predicted datasets. The main advantage of the coefficient of determination is that it provides a simple and interpretable measure of how well the independent variable(s) predict the dependent variable. Its range lies between 0 and 1, where 0 signifies no correlation, implying that the prediction does not explain the observed variation.
The MAPE is widely employed as a metric for assessing the precision of a forecasting model. It quantifies the average absolute percentage deviation between predicted and actual values. The MAPE range spans from 0% to positive infinity. A lower MAPE value signifies higher accuracy, with 0% indicating a flawless prediction in which the predicted values precisely match the actual values. Conversely, higher MAPE values indicate a more significant percentage of error in the predictions when compared to the actual values. The MAPE has several notable advantages: simplicity, scale independence, and interpretability. Its straightforward calculation makes it easy to understand and apply. Furthermore, the MAPE is not influenced by the scale of the data, allowing for direct comparisons across different datasets or forecasting models.
The software calculates this slope, which is a crucial parameter for accurately representing the hydraulic behavior of the river downstream. Generating a comprehensive dataset of river flow discharge using a numerical model is a crucial step in enhancing the accuracy and effectiveness of the ML training process. A diverse set of scenarios and conditions can be captured by simulating a wide range of potential future floods via the numerical model. Therefore, a data bank is generated by employing the calibrated HEC-RAS. This extensive dataset enables the ML model to learn from a broader spectrum of situations, including various flood magnitudes, flow patterns, and hydraulic characteristics. As a result, the ML model becomes more robust and capable of generalizing its predictions, even for "non-experienced" or "unseen" flood events that were not part of the historical training data.