dataesr/scientific-paragraphs-categorization huggingface dataset:
A consideration of the parameters determining the strength of these materials is beyond the scope of the present article, but influences on the compressive strength of alkali-activated concretes have been discussed extensively in the literature (e. g. , Ref. [9] ). However, there is no correlation apparent between the compressive strength of these materials and their durability-related transport parameters, such as chloride penetration coefficients  [8] ; thus, a classification or preselection of the concretes/mortars based on their compressive strength or strength class was not conducted for the present analysis. The majority of the materials based on blended Portland cements in the database are concretes (68 concretes, six mortars); these will be collectively referred to as 'BCC' below. The SCMs in these materials were GGBS and/or FA; in one case, a natural pozzolan was added to a CEM III/B.
The database contains data for 125 GGBS-based AAC  [6, 8, ; 12 FA-or MK-based AAC  [8, [32] [33] [34] [35] [36] ; 18 other AAC  [36] [37] [38] [39] [40] ; and 74 BCC  [1, 2, 6, 16, 29, [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52]. A plot of the SCM fractions of the binders of the materials (including the compounds dissolved in the activator solution as part of the binder in the calculation) is shown in Fig. 1. ; the plot does not contain the 20 mixes based on CEM III/B with no exact specification of the GGBS content. For most of the concretes/mortars, either data for carbonation approximating natural exposure, or data for accelerated carbonation were available; for a limited number of materials, data for both conditions were reported.
Data evaluation The carbonation resistance of concretes and mortars is commonly described by a single parameter, the carbonation coefficient (k c )  [4]. The definition of k c and its computation from experimental data is based on Fick's first law of diffusion, and it implies that the carbonation rate of cementitious materials can be accurately described by the square-root-of-time law  [53]. As has been discussed previously  [5, 18, 20] , this is likely not true for all alkali-activated materials, since in these materials the carbonation rate is influenced to a great extent by the reactions that take place, and it is, thus, not purely diffusion-controlled. However, to create an internally consistent dataset with values that can be compared between AAC and BCC, k c was calculated in the usual manner for all materials in the present analysis.
e. a negative carbonation depth at t = 0 d, which would be unphysical), the fit was repeated with the linear function forced through the origin of the coordinates, and the value thus obtained was used. It is noted that the assumption of d c = 0 mm at t = 0 d is not necessarily Fig. 1  SCM fractions of the binders of the mixes included in the database. 'Binder' refers to the solid starting materials and the oxides (Na 2 O, SiO 2 etc. ) dissolved in the activator solution combined. Twenty additional blended-cement concretes/mortars (BBC) based on CEM III/B (66-80% GGBS in binder) are not included in the plot. Black lines represent the medians; boxes represent the ranges between the 25% percentile and the 75% percentile; and the whiskers represent the 5% percentiles and the 95% percentiles true, particularly for materials based on alkali-activated binders or blended cements, which can show some surface carbonation during curing and/or conditioning prior to testing  [8, 54].
However, in the absence of carbonation-depth data for the start of the exposure to CO 2 , an assumption about d c at t = 0 d had to be made. The dataset resulting from the round robin testing programme of RILEM TC 247-DTA  [8]  were treated as if they were obtained in a single laboratory, using the average of the carbonation depths at given exposure times, obtained in the participating institutes. However, the d c values obtained in laboratory G in that testing programme were not included in the present analysis, because this laboratory had employed a curing regime that differed from the curing used in the other participating laboratories. The coefficient of variation (COV) of the d c in the reduced dataset was in the range 21. 2-42. 1% for carbonation under conditions approximating indoor/sheltered natural exposure, and 1.
These parameters included the w/CaO ratio, where w is the mass of water in the concrete/mortar, and CaO is the mass of CaO in the binder paste of the mortar/concrete, including the contributions from the solid binder starting materials (e. g. , GGBS) as well as from any compounds added as part of the activator. Analogously, the Na 2 O, K 2 O, and MgO contents refer to the respective masses of these oxides in the solid binder starting materials and in the activator combined. No attempt was made to discriminate ''reactive'' and ''non-reactive'' oxides (cf. Refs. [1, 2, 55] ) in the paste, as the information given in the reports containing the carbonation data was usually not sufficient to calculate these fractions with reasonable accuracy. However, inspection of the mix designs of the concretes/mortars in the database and the information given in the associated reports indicated that the majority of the alkali metals and alkaline-earth metals in the binder starting materials can be considered ''reactive'', since the employed starting materials were generally of a type (GGBS, FA, MK) that typically contains only low fractions of these elements in potentially insoluble, crystalline compounds.
It is noted that a simplified analysis of the database using the uncorrected fractions of Na 2 O, K 2 O, and MgO yields similar conclusions as the analysis using the converted values presented in the following sections. This is because the conversion ratios are in the range 0. 60-1. 39, i. e. in a rather narrow range around unity, and, therefore, the error introduced by the use of uncorrected values is low compared to the general scatter of the data. Several other parameters of the mix designs of the concretes/mortars were also evaluated. For all calculations, the total binder content of the concretes/mortars was defined as including the dissolved activator compounds (e. g. , Na 2 O and SiO 2 in a sodium silicate solution). This definition was followed also for the calculation of the w/b ratios of the concretes/mortars, i.
As GGBS typically contains significant amounts of MgO (usually in the range 5-14 wt. %), it might be conjectured that a correlation could be more evident when the ratio w/(CaO ? MgO eq ) is considered. However, the present dataset does not yield a significant improvement when MgO is considered in addition to CaO in this way (Supplementary Figs. S2  and S3 ). Within the group of BCC, a reasonable correlation between k c-nat and w/CaO was observed (coefficient of determination R 2 = 0. 5484; Fig. 4 ). This finding is in line with previous assessments  [1, 2, 4]  and extends these previous findings to blended cement concretes with particularly high SCM fractions (66-94%) in the binder. It is noted that a correlation between k c-nat and w/CaO can only be expected if CaO is approximately  proportional to CaO reactive , which is assumed to be the case for the present database, as discussed in Sect.
None of these yielded a satisfactory correlation with k c-nat for all concretes/mortars combined (Supplementary Figs. S7-S12 ). It is noted that for the BCC, correlations were found for k c-nat versus w/(CaO ? MgO eq ? Na 2 O eq ? K 2- O eq ) and for k c-nat versus w/b (Supplementary Figs. S5  and S6 ). However, both coefficients of determination were considerably lower than for the relationship between k c-nat and w/CaO (Fig. 4 ). That a correlation between k c-nat and w/b was found can be explained by the fact that GGBS was the major SCM in most of the BCC mixes in the database; thus, for these materials, w/CaO is approximately proportional to w/b. However, since the CaO content of the binders was not identical, the coefficient of determination was lower for w/b than for w/CaO. This result is an additional demonstration of the usefulness of w/CaO to predict the carbonation resistance of concretes based on high-volume SCM blended cements.
9  Carbonation coefficients obtained with A, a CO 2 concentration in the range 3-5% (accelerated conditions), and B, a CO 2 concentration in the range 10-100% (accelerated conditions), versus w/(CaO ? MgO eq ? Na 2 O eq ? K 2 O eq ) ratio from plain Portland cement  [5, 18, 20] ; this merits further investigation. The scope of analysis that is possible for conditions of 10-100% CO 2 (Fig. 9B ) is further restricted by the fact that all available data fall into a very narrow range of w/(CaO ? MgO eq ? Na 2- O eq ? K 2 O eq ) values. However, current trends in materials testing are moving away from these very highly accelerated carbonation tests due to their unrealistic impacts on material chemistry and microstructure compared to natural conditions. Additional parameters were evaluated regarding a correlation with k c-acc , including the w/b ratio of the concretes/mortars, and the silicate modulus of the activator of the AAC.
Though not subject of the present analysis, it appears also necessary to study the effects of relative humidity during carbonation testing in the laboratory and under unsheltered natural conditions, as has been done for conventional (blended) cement concretes  [1]. As regards the present results, two findings appear particularly significant in that they have important implications for the design of alkali-activated concretes and tests of their carbonation resistance: (1) The carbonation coefficient of GGBS-and FAbased AAC obtained under conditions approximating natural exposure and, by implication, under natural carbonation, appears to be related to their w/(CaO ? MgO eq ? Na 2 O eq ? K 2 O eq ) ratio. This correlation included the high-volume SCM concretes (BCC) in the present database; thus, alkali-activated concretes and high-volume SCM concretes appear to behave similarly at least in this respect.
Additional work is required to verify this conclusion and to elucidate the underlying mechanisms. For CO 2 concentrations higher than 1%, the present database shows no correlation with any of the investigated mix design parameters, including w/(CaO ? MgO eq- ? Na 2 O eq ? K 2 O eq ), thus indicating that the establishment of a relationship between these results and the carbonation resistance under natural conditions will be difficult to achieve. In addition, the present data demonstrate that there is a correlation between the carbonation coefficient of high-volume SCM concretes obtained under conditions approximating natural exposure and their w/CaO ratio. This finding is in line with previous studies  [1, 2, 4]  that have shown that the carbonation coefficient of materials based on blended Portland cements obtained under conditions approximating natural exposure is related to their w/CaO reactive ratio, and it indicates that the w/CaO ratio of high-volume SCM concretes can serve as a proxy for their w/ CaO reactive ratio.
g. , refs. [5, 56, 64] ). Thus, differences between these materials may also exist as regards the significance of the carbonation depths obtained with the phenolphthalein-spraying method and the influence of CO 2 concentration on these. To fully understand and describe these differences, additional studies are required, the outcomes of which may ultimately motivate a re-evaluation of the present dataset. Finally, it is noted that the application of data science methods, such as machine learning  [65, 66] , to the present dataset might yield insights into the carbonation resistance of alkali-activated materials in addition to those obtained in the present analysis. To enable such analyses in future studies, the full dataset underlying the present analysis is provided as Electronic Supplementary Material alongside this article.
Material and methods: This is a secondary analysis of a randomized clinical trial (clini caltr ials. gov: NCT02474745) that took place in four French hospitals from 2015 through 2017 (n = 250). Women in labor with a singleton fetus in cephalic presentation at term who had undergone standardized training in both of these types of pushing were randomized after cervical dilation ≥7 cm. The exclusion criteria were a previous cesarean, a cesarean delivery in this pregnancy or a fetal heart rate anomaly. In the intervention group, open-glottis (OG) pushing was defined as a prolonged exhalation contracting the abdominal muscles to help move the fetus down the birth canal. Closed-glottis (CG) pushing was defined as Valsalva pushing. The principal outcome was the stage of pelvic organ prolapse (POP) assessed by the Pelvic Organ Prolapse-Quantification 2 months after delivery.
| Study design This was a planned secondary analysis of the EOLE study, a randomized, controlled, non-blinded multicenter superiority trial with two parallel groups intended to assess the efficacy of directed OG pushing vs directed CG pushing during the active phase of the second stage of labor. 14 The details of the EOLE study have been published previously. 13 The study presented here assessed the effect of the type of pushing used during the second stage of labor (directed OG pushing vs directed CG pushing) on pelvic floor function, that is, on POP and UI. The EOLE study took place in four French centers: two university hospitals and two general hospitals. For this secondary analysis we work with data from only three of these centers.
A recent meta-analysis found that greater parity is associated with UI at 3 months postpartum and throughout the rest of the first year postpartum. 29 In our study, the UI incidence for multiparous women in the OG group was 10. 3%, whereas we found an incidence between vacuum and forceps, 1 and 1 by vacuum and spatulas, and 1 and 0 by spatula; finally 0 and 1 had a maneuver for shoulder dystocia. d There were no fourth-degree perineal lacerations in the study and no secundiparous or multiparous woman had an episiotomy. e One missing data item. 24. 0% and 33. 0% for primiparous women and 44. 4% for multiparous women in the CG group. One strength of our study is that we assessed pelvic floor function with both an anatomic (POP-Q) and a functional (ICIQ-SF) assessment, using two methods widely validated by the international community.
The GIF can transfer the features of the guidance image to the target image. Since SART has good edge retention ability and the TpV minimization can reduce noise, we use the TpV minimization result as the initial guidance image, and the SART result as the filter input. For each round of iteration, the guidance image is updated by the weighted average of the last guidance image and the SART reconstructed result. The experimental results based on simulated and real CBCT data prove the advantages of the combination of the two algorithms and the feasibility of guided image filtering in non-convex optimization problems.
Guided Image Filtering Reconstruction Algorithm Based on Prior Image The emergence of the guiding image filtering theory provides a new direction for sparse angle CT reconstruction. The SART-G algorithm (  47 ) is one of them, which is based on SART and guiding image filtering. The algorithm sets the previously captured CT image as the initial prior guidance image for guided image filtering. Nowadays, the SART-G algorithm is mainly applicable to the situation where a prior image is known, and due to some small parts of the prior image having been changed, the sparse angle projection data is used to reconstruct the changed CT image. The SART-G algorithm is an iterative algorithm. In the first iteration, the initial prior image is used as a guidance image to constrain the CT reconstruction. Later during each iteration, the guidance image is continuously updated.
The updated guidance image combines the information contained in the initial prior guidance image and the image reconstructed by the SART algorithm, and the guided image filter conveys the combined information to the output, so that the changed part of the prior image can be effectively reconstructed. By considering the constantly updated guidance image, the effectiveness of the SART-G algorithm is ensured. The CT scanning process can be discretized into the following linear system: y = Ax (1) Where A∈ R M×N , A i,j = a i,j denotes the length of the ith X-ray through the jth image pixel. y is the projection data collected by the detector, x is the linear attenuation coefficient of the object. In the SART-G algorithm. The iterative formula of SART is as follows: x k+1 j = x k j + lc j , c j = o i ∈ I j mia i,j o i ∈ I j a i,j , mi = b y -y i o N j = 1 a i,j (2) The algorithm steps of the guiding image filtering are shown in Table  1.
Guided Image Filtering Reconstruction Based on TpV Minimization Inspired by the SART-G algorithm, this section proposes the TpV-GIF algorithm, which uses the TpV minimized reconstruction result as the initial guidance image, and dynamically updates the guidance image during iterations. In the initial iteration, the guidance image mainly reflects TpV's output to quickly remove the noise in the SART's result. As the iteration progresses, the noise in the SART reconstruction result is effectively removed, at this time, the proportion of TpV reconstruction result in the guidance image is reduced to avoid over-smoothing, turning to focus on preserving edges and texture. The steps of the TpV-GIF algorithm are as follows: 1. Parameter initialization, projection matrix A, projection data y, the number of iterations N, the initial value of the image f (0) , and the parameters of the guide image filtering step: window radius R, regular term parameters ϵ; 2.
DATASET We first use the digital brain model  (53)  for reconstruction as the data 1. The model is created based on realistic MRI data of the human brain. It is widely used model for cone beam CT reconstruction and very suitable for evaluating reconstruction. Then, we use real clinical projection data for reconstruction as data 2. In the dataset, the distance from the source to the detector is 1040mm, and the distance from the source to the origin is 570mm. We use projection data from 32 angles for reconstruction. The size of reconstructed voxels is 256×256×266, and the voxel resolution is 1. 6272mm×1. 6272mm×1mm.
Simulation Model Reconstruction We use the Siddon line driver to simulate the projection data, generate projection data from 32 angles, and use these projection data for reconstruction. This paper selects 4 widely used methods (SART, ASD-POCS, SART-TV, TpV) as the comparison algorithm, where SART and TpV can be found in section 3, ASD-POCS is an algorithm who combines the ART algorithm and TV algorithm, SART-TV combines the SART algorithm and TV algorithm. For the SART algorithm, l is set to 1, the reduction of l, l red is set to 0. 99. For the ASD-POCS algorithm, l is set to 1, l red is set to 0. 99, TV hyperparameter a is set to 0. 002. For the SART-TV algorithm, l is set to 1, l TV is set to 15 which gives the ratio of importance of the image vs the minimum total variation. and the reconstruction result of the 80th layer of data 1 is shown in Figure  1.
Actual Data Reconstruction Figures 4, 5 are the reconstruction results of CBCT of the place that close to the center plane and bottom plane, respectively, it can be seen from Figure  4B  that with the sparse angle projection, the reconstruction result of SART near the center plane still has serious artifacts. The reconstruction result of the SART-TV algorithm has a significant blocky effect, and many details are blurred. The reconstruction result of ASD-POCS algorithm is better than that of SART-TV reconstruction, but compared with the TpV minimization algorithm, the blocking effect is still more serious. The reconstruction result of the TpV-GIF algorithm not only suppresses artifacts as effectively as TpV, but also has better detail restoration, which can be seen in Figure  6. Observe the details indicated by the black and white arrows in Figure  6 , and compared with the reference image, you can see that the ASD-POCS reconstruction result is relatively fuzzy, and details of the SART reconstruction image are more complete, but they all have serious artifacts.
CONCLUSION Aiming at solving the problem of incomplete projection data under sparse angle projection, this paper proposes and discusses a better reconstruction algorithm based on guided image filtering. Guided image filtering is a kind of edgepreserving filtering that utilizes the guidance image to effect the result of the output image, transfers the characteristics of the guidance image to the output image. We combine the SART with edge preservation characteristics and the TpV      minimization with good smoothing characteristics and capable of suppressing artifacts. The SART reconstruction result is used as the filter input, and the reconstruction results of the two algorithms are combined as the guidance image and are dynamically updated. Verifications and comparisons performed using various datasets illustrate that the proposed method is effective and promising.
⋆ This work is supported by the  French National Research Agency  in the framework of the " Investissemnts d'avenir" program   ANR-15-IDEX-02. This work has also been partially funded by the  Spanish State Research Agency (AEI)  and the  European Regional Development Fund (ERFD)  through the project  SCAV  (ref. MINECO  DPI2017-88403-R ) and by  FPI UPC  grant  2020FPI-UPC-008.
as 'rational' individuals through learning to counterbalance the excessive consumption of low-quality (high-radioactivity) food one day by consuming less radioactive food the next  (Topçu 2013b). Furthermore, the Ethos experts managed to persuade a number of French organizations (expert NGOs, activist groups engaged in humanitarian aid) to help with transferring competencies (on measuring radioactivity for instance) to local organizations and inhabitants. The Ethos approach was progressively diffused beyond Belarus too. In the 2000s, Ethos-type projects were largely funded by the European Commission and supported by the World Bank  (Topçu 2013b). Several European countries, not least France, adopted them as Nuclear Accident or a Radiological Emergency]) were set up to establish, in partnership with expert and activist NGOs, guidelines for coping with a contaminated environment.
CONTEXT Ureteral stents are essential implants that are used on a daily basis. Since their invention, advances in stent design have been directed towards alleviating stent-related symptoms. It remains unclear how the material composition of the stent affects stent-related symptoms. OBJECTIVE To review the literature and define the clinical impact of ureteral stent material on stent-related symptoms. EVIDENCE ACQUISITION A literature search of the Embase, MEDLINE (PubMed), and Web of Science databases was conducted on December 17, 2021 to collect articles comparing stent composition materials regarding stent-related symptoms. Thirteen publications met the inclusion criteria, of which only one met the high-quality requirements of the Cochrane Collaboration tool for assessing the risk of bias in randomized trials.
Evidence acquisition This study was registered in PROSPERO (CRD42022264829). The systematic search was guided by ''A systematic approach to searching''  [15]  in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) checklist  [16]. A literature search was conducted by two authors (M. B. and V. D. C) on December 17, 2021, using the Embase, MEDLINE (PubMed), and Web of Science databases. We determined a clear and focused question using the PICO (Population, Intervention, Compapartor, Outcome) approach: do adult patients receiving a ureteral stent composed of material A, compared to a ureteral stent composed of material B with the same overall stent design, have more or less stent-related symptoms. Then these PICO elements were used to identify appropriate index terms (Emtree and MeSH) and synonyms in the thesaurus of the databases used.