dataesr/scientific-paragraphs-categorization huggingface dataset:
Co-Simulation Approach The building simulation programs such as TRNSYS and EnergyPlus do not accurately include hygrothermal interactions with the building envelope. Therefore, it is essential to integrate a non-isothermal combined heat and mass transfer model into the simulation environment. To overcome this problem and better stimulate the hygrothermal behavior of buildings, several approaches have been proposed in recent years. One of these is co-simulation, which consists in using two existing software packages, one for dynamic building simulation, and the other for precise modeling of hygrothermal behavior at the envelope level. Zhai et al. [88]  classified the co-simulation methods in two groups as either static or dynamic coupling. In static coupling, the interaction between the two simulation tools is one-way, with information flowing from one software program to the other without any feedback or iterative exchange during the simulation.
On the other hand, dynamic coupling involves a two-way interaction between the tools, allowing a continuous exchange of information and feedback during the simulation process. Dynamic coupling enables a more comprehensive and accurate representation of the hygrothermal behavior of buildings, as it accounts for the mutual influence of heat and moisture transfer phenomena, leading to improved predictions and a better understanding of the building's performance. In order to investigate the effects of sorption isotherm hysteresis on indoor climate and energy demand, Kwiatkowski et al. [89]  developed and implemented in the energy performance simulation tool TRNSYS a new module for precise representation of mass transfer in materials in contact with indoor air, which is called Humi-mur. Steeman et al.
Intermodel Validation The intermodel validation method involves comparing the results of the model in question with those obtained from other independently developed models that aim to simulate similar phenomena. By comparing the outcomes, researchers can assess the consistency and accuracy of the model's predictions against other established models. This method can be divided into two categories: comparison with the numerical results of previous research literature and comparison with the simulated results of hygrothermal simulation software, such as COMSOL Multiphysics  [99, 105, 106] , WUFI [107-110], and Delphin  [111, 112]. In addition, the HAMSTAD Benchmark cases #3-5  [98]  are often used for intermodel validation for numerous models.
Analytical/experimental relationships are obtained using thermo-physical characterization techniques such as DSC or the T-history method  [133]. These characterization techniques provide material properties such as melting temperature, enthalpy of phase change, and heat capacities of the liquid and solid phases. These data are used to estimate the heat capacity of PCM using a simple direct relationship by introducing a fictitious phase change range (2 ): C app =      C p,sol i f T < T f -ε C p,sol +C p,liq 2 + L f 2ε i f T f -ε ≤ T ≤ T f + ε C p,sol i f T > T f + ε (86) The choice of phase change range is a very important parameter when modeling with the apparent heat capacity method, as it can lead to convergence problems. If the range is too small or the time step too large, there is a risk of missing the latent heat contribution.
Conclusions The present study reviewed the hygrothermal models of PCMs integrated into building envelopes to evaluate the performance. First, the different models of HAM transfer in buildings are presented. These models have been divided into two groups: the nodal and HAM approaches. Nodal models are suitable for steady-state analyses as well as quick assessments and are currently employed in the most common BES software, such as TRNSYS. They predominantly focus on simulating temperature variations and energy requirements in specific spaces on a larger scale. However, their moisture exchange models at the wall scale often rely on simplified approaches that do not consider the coupling of heat and moisture transfer phenomena through building envelopes. Two simplified models for surface moisture adsorption and desorption, namely the Effective Capacitance Moisture model and the Effective Moisture Penetration Depth model, are distinguished within the nodal framework.
Using Mars Express data from 2007 until 2020 we show how ion outflow from Mars varied over more than a solar cycle, from one solar minimum to another. The data was divided into intervals with a length of one Martian year, starting from 30 April 2007 and ending 13 July 2020. The net escape rate was about 5 × 10 24 𝑠 -1 in the first covered minimum, and 2 -3 × 10 24 𝑠 -1 in the most recent minimum. Ion escape peaked at 1 × 10 25 𝑠 -1 during the intervening solar maximum. The outflow is a clear function of the solar cycle, in agreement with previous studies which found a clear relationship between solar EUV flux and ion escape at Mars. The outflow during solar maximum is 2. 5 to 3 times higher than in the surrounding solar minima. The average solar wind dynamic pressure over a Martian year was investigated, but does not vary much with the solar cycle.
The escape rate at solar maximum is in good agreement with some recent MAVEN studies, and dominated by low energy ions at most sampled locations. A simple linear fit to the data gives a prediction of the escape rate for the much stronger solar maximum during the Phobos mission in 1989 that is consistent with observations. The fit also implies a non-linear response of ion escape for low solar EUV, with a lower initial escape response for lower solar EUV levels than those of the studied data set.
, 2017). Despite the many studies of ion escape from Mars, we can still further refine our understanding by analysis of the ever growing data set from Mars. In this study we make use of more than one solar cycle of heavy ion data from the Mars Express ASPERA-3 IMA sensor, divided into periods with a length of one Martian year. We look at the average flow, and estimate the total escape flux for each period. Finally we look at the average solar EUV and solar wind dynamic pressure values for each of these Martian year long periods. We extend the data by also comparing to the stronger solar maximum during the Phobos mission  (Lundin et al. , 1990; Ramstad et al. , 2013).
Instrument description We use data from the Ion Mass Analyzer (IMA), part of the ASPERA-3 instrument package on Mars Express  (Barabash and the ASPERA-3 team, 2006). Mars Express is a three-axis stabilised spacecraft. The IMA intrinsic field-of-view is 4. 5 • × 360 • in the spacecraft X -Z plane, see  Barabash and the ASPERA-3 team (2006)  or  Nilsson et al. (2006)  for an illustration. A partial three-dimensional field-of-view is obtained through an electrostatic entrance deflection system bringing particles from about ± 45 • out of the viewing plane of the instrument in to the detector. This entrance deflection is stepped through 16 different elevation angles with an angular spacing close to 5. 625 •. Part of the field-of-view is blocked by the spacecraft body and the solar panels. In this study we use only data from viewing directions which are not affected by spacecraft blocking.
The energy resolution of the instrument is 7%, energy is measured in 96 energy levels, stepped through in 12 s. Before May 2007 the instrument did in practice not measure below 30 eV. Between May 2007 and mid November 2009 an energy table going down to 10 eV was used. After that an improved table going down as close as possible to zero is in use. The uncertainty of the energy determination is a few eV judging from the experience of the almost identical instrument on Rosetta  (Nilsson et al. , 2017; Stenberg Wieser et al. , 2017). The spacecraft velocity (ram-effect) needs to be taken into account to estimate the source population distribution. This is straightforward and done in our analysis. The spacecraft potential also affects the measurements  (Bergman et al. , 2020) , but is only known for a subset of the data  (Fränz et al.
Data set The data set covers from 30 April 2007, when the energy table of IMA was first updated, until 12 July 2020. This corresponds to more than one solar cycle, from one minimum to the next, and 7 Martian year long periods. The periods are defined in Table  1. The F10. 7 index, used as proxy for solar EUV levels, is obtained at Earth. Even though Earth and Mars may be on opposite sides of the Sun, this is not a major problem for the average EUV flux on time scales longer than a solar rotation period of 27 days. The average solar wind dynamic pressure used is from the Tao model  (Tao et al. , 2005). The Tao model is driven by OMNI data and uses a simple 1D MHD model for propagation. It has successfully been used with Rosetta data  (Goetz et al. , 2016)  and is available for many targets, including Mars, on the Automated Multi-Dataset Analysis (AMDA) portal, amda.
Conclusions Dividing Mars Express Aspera-3 IMA data into one Martian year long periods we have studied the effect of the solar cycle on ion escape at Mars. The data set starting 30 April 2007 and ending 13 July 2020 covers 7 Martian year long periods. We compared with both the average ionising flux, using the F10. 7 index as a proxy, and the average solar wind dynamic pressure. There was a rather weak negative correlation with the average dynamic pressure. A linear least square fit gave a coefficient estimated to be -4 ± 4 × 10 24 ions∕s∕nPa. As the average solar wind dynamic pressure does not vary strongly with the solar cycle, we can just say that our results are consistent with previous studies finding only a weak negative dependence on solar wind dynamic pressure  (Ramstad et al. , 2018).
The escape flux is clearly modulated with the F10. 7 index and thus over the studied solar cycle, starting at 5 × 10 24 s -1 , reaching 1 × 10 25 s -1 and ending in the most recent minimum at 2 × 10 24 s -1. The final value is a bit less certain due to worse data coverage, but the second last Martian year long period had an escape rate of 3. 4 × 10 24 s -1 , so that the modulation between the peak and the latest minimum was at least a factor 3. The values are in good agreement with a couple of MAVEN studies from the solar maximum. MAVEN data from the current solar minimum will be very important to better estimate the dependence on the escape rate with solar EUV levels. A linear fit to the data gives a reasonable prediction of the escape rates estimated from Phobos mission data from the much stronger solar maximum in 1989.
Data availability The ASPERA-3 IMA data used in this study has been submitted for inclusion in the Planetary Science Archive at https://archives. esac. esa. int/psa. The Tao model data was retrieved through the AMDA service at http://amda. irap. omp. eu. The F10. 7 index was retrieved through the OMNI data service at https://omniweb. gsfc. nasa. gov.
Background There are over 27 million scientific articles listed on the National Health Institute Platform PubMed. Previous investigations have shown that about 2% of scientists admitted to have fabricated, falsified or modified data or results at least once  [1]. The business of scientific publication has surged during the last decade, including a staggering growth in the number of articles submitted and finally accepted for publication  [2]. The peer review process is crucial for assessing the quality of hypotheses, methods, reliability of the data, and identifying any obvious ethical shortcomings. The Covid-19 pandemic was a stress test for the academic publishing system and unveiled several failures in processes evaluating quality of scientific publications  [3] [4] [5] [6] [7]. Neglected or non-existent review procedures  [3, 6] , editorial conflicts of interests combined with expeditive peer review  [3] , inconsistent publications with, e.
These initial reports prompted us to further investigate potential concerns about ethics in the published literature from the institute. We have found and report below about highly worrying data in IHU-MI publications. For post-publication critiques, the Committee on Publication Ethics (COPE) recommends referring to the journal or publisher policy  [23] , depending on the availability of an editorial policy for the reported issue. In case editorial policy does not take post-publication critiques into account, this policy should be amended. We can thus hope that the critiques formulated here might be helpful for journals to improve their peer reviewing policies. This is the whole meaning of our approach.
e. , Professors) at the institute, however given the sheer number of studies, we limited the search parameters to those published in the last 25 years. Screening on PubPeer was conducted by searching author names, and reviewing the resulting comments on papers. The results from PubPeer were then used to complete the data obtained from analysis of "Google Scholar" results. Frequently-repeating IRB-approval numbers were entered into "Google Scholar" to list of all their occurrences.
Contact of editors While papers have been retracted in the past for issues concerning ethical approval  [27] , most of the Retraction Watch database entries seem to contain only Expressions of Concern (EOCs) or retractions relating to "ethical violations by author" or "lack of IRB/IACUC approval. " As such, it is not surprising that COPE did not provide guidelines on reporting and investigating IRB approval duplications such as what we have found. Indeed, current COPE guidelines on issues on ethics approval only focus on handling concerns at the time of submission of a manuscript  [28]  and not after publication or for cases of potentially fraudulent duplication of IRB approval numbers. As a result, we have further analysed the dataset to append the journal Editor-in-Chief 's name and email after screening the journals websites for information.
To our best knowledge, no other public editorial decision has been made from other publishing venues yet, although we hope that the concerns from PLOS Biology will help either obtaining clarifications from the authors on such concerns, or drive other journals to publicly respond too. Editorial practices in verifying ethics and lawfulness of clinical research are still very heterogeneous and all journal are not members of COPE. We wish to initiate a conversation to improve the ethical controls at the editorial level across published academic research, and for changes to facilitate post-hoc investigations in future work, despite not suggesting any mean of enforcing these controls. While some publishers already require the upload of ethics approval, this is not a largely adopted requirement. We thus argue that the practice should become more widely and rigorously adopted, or that, at the very least, ethics approval numbers are provided as metadata along a submission such that post-hoc analysis could be done in a more systematically fashion through mining of submission's metadata.
Farmers vary both within and across archetypes (e. g. , deriving preferences from different distributions). Finally, we note that due to the richness of the farmer and system representation in TELLUS, and the lack of sufficiently comprehensive datasets, side by side comparisons of model outputs with regional case studies are not possible (all parameter choices are detailed and justified in the Supplementary Information). Consequently, we have elected to use the above archetypes and scenarios to both replicate principal findings from the agricultural economics literature, but also provide new insights based on grounded, bottom-up processes (see Supplementary Information for the model validation process).
Acknowledgements Authors T. D. P. and  R. M. discloses support for the research of this work from  Funder Quadrature Climate Foundation  [grant number  OxfordUniversity012021 ]. The authors thank  Dr Caterina Ruggeri Laderchi  and the  Food Systems Economic Commission  for helpful comments and insights during model design, development, and  Pilditch ,  Bailey , and  Merkl  35 validation. Further, we wish to thank  Dr Ernesto Carrella  for model function and code review oversight.
Genomic data analysis We applied an established pipeline "MuLoYDH"  [13]  that is designed for accurately tracking the mutational landscape of yeast diploids. The scripts for read mapping, coverage calculation, CNV calling and annotation, substitutions and INDEL calling and annotation, LOH detection and annotation are all embedded in MuLoYDH, which can be downloaded via https:// bitbucket. org/lt11/muloydhom. For substitutions annotation we used the following criteria: i) All the substitutions must be called by both Freebayes and Samtools; ii) The quality score of each substitution must be higher than 50 and sequencing depth at that locus must be higher than 10X; iii) Each substitution must be supported by at least 6 reads including both forward and reverse reads. Repeat sequences such as telomeric regions were excluded.
Moreover, substitutions existing in the ancestral genomes were filtered out. All substitutions and INDELs that passed these filters were visually checked in the Integrative Genomics Viewer (IGV). The ancestral genomes of WA, WE, SA and NA  [58]  (https://yjx1217. github. io/Yeast_PacBio_ 2016/data/) were processed by the R package BSgenome (version 1. 66. 1, https://bioconductor. org/packages/BSgenome) to forge the corresponding reference genome packages. Then, the substitutions and INDEL mutational signatures were analyzed and fit to the COSMIC (https:// cancer. sanger. ac. uk/cosmic/signatures) signatures using the R/Bioconductor MutationalPatterns package  [59] , in which a strict refitting function "fit_to_signatures_strict" and a cutoff of 0. 004 were applied. In order to systematically estimate the CNV of rDNA, we applied the modules 01.
LOH association analysis LOH was profiled by MuLoYDH  [13]. We used "start-(first-start) ~first" and "last ~end +(end-last)" to define upstream and downstream breakpoints of each LOH event. We used "start ~end" to define the LOH tracts. When analyzing the association between LOH breakpoints and published datasets and genomic features, we excluded the MALs of haploid and tetraploid. We also excluded LOH presented by only one single marker. We lift over the LOH break points as well as the Spo11 hotspots  [60]  and meiotic recombination hotspots  [61]  from their original reference to the SGD reference (S288C_reference_genome_R64-1-1_20110203) by Liftoff  [62]. We performed permutation tests to see if the overlap between our LOH break points dataset and published datasets is higher than expected by chance using overlapPermTest function (ntimes = 1000, and alternative = "greater") in regioneR package  [63].
After AMPure XP Beads clean-up (1:1 ratio), multiple barcoded samples were pooled and sequencing adaptor was ligated to the pooled library (65 ul pooled DNA, 5 ul AMII, 20 ul NEBNext Quick ligation reaction buffer, 10 ul Quick T4 DNA ligase, incubated at 25˚C for 15 minutes). Following AMPure XP Beads cleanup (0. 4X) and L Fragment Buffer wash (ONT, SQK-LSK109), the library was eluted in 15 ul Elution Buffer. The library was loaded into the FLO-MIN106 MinION flow cell according to the manufacturer's guidelines (ONT, SQK-LSK109) to perform the whole-genome long-read sequencing. The raw nanopore reads were processed using the 00. Long_Reads module of the LRSDAY v1. 5. 0 framework  [64]. Briefly, we performed base calling and demultiplexing with Guppy v2. 3. 5. The demultiplexed reads were further processed by Porechop v0.
2. 4 (https://github. com/rrwick/Porechop) for adaptor trimming (option:-discard_middle). The de novo assembly was carried out by the 01. Long-read-based_Genome_Assembly module of LRSDAY. We used "canu" as the assembler (Koren et al, 2017; https://github. com/marbl/canu). SVs were called by MUM&Co  [65] , which uses Whole Genome Alignment information provided by MUMmer (v4) to detect variants. The SVs were further crossvalidated with the results called by the module 11. Long_Read_Mapping of the pipeline "Varathon" (https://github. com/yjx1217/ Varathon) and manually checked in IGV.
Phenotyping We revived the intermediate samples of MALs and phenotyped them by measuring their doubling time and yield. We used a high-resolution large-scale scanning platform, Scan-o-matic, to monitor cell growth in a 1536-colony design on solid agar plate  [66]. The Scan-o-matic program uses data from the time-series images taken by the high-quality desktop scanners to calculate the population size and generate growth curves for the colonies. Phenotyping was run for 3 days, and scans were continuously performed every 20 minutes. The conditions of phenotyping were the same as that in the mutation accumulation experiments. Each sample had at least 6 technical replicates. After quality control filtering, the measurement of doubling time was extracted for downstream analysis in R (R version 3.
gov/bioproject/?term=  PRJNA880975 )Funding: This work is supported by the  National Natural Science Foundation of China  ( 32000395  to JL and  32070592  to J-X Y), the  Natural Science Foundation of Guangdong Province  ( 2022A1515011873  to JL and  2022A1515010717  to J-X Y), the  Guangdong Basic and Applied Basic Research Foundation  ( 2019A1515110762  to J-X Y), the  Guangdong Pearl River Talents Program  ( 2021QN02Y168  to JL,  2019QN01Y183  to J-XY),  Agence Nationale de la Recherche  ( ANR-15-IDEX-01 ),  Fondation pour la Recherche  Medicale ( EQU202003010413 ),  Association pour la Recherche sur le Cancer  ( ARCPJA32020070002320  to GL), and the  Swedish Research Council  ( 2018-03638  to JW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
, 2022)  and ATLID ice cloud retrieval (A-ICE;  Donovan et al. , 2023a). A-TC and C-TC encode the types of targets relevant to each instrument, namely liquid clouds, ice clouds and aerosols by ATLID and liquid clouds, drizzle and rain, ice clouds, and snow by CPR. The single-instrument target classifications A-TC and C-TC are then merged, without the need to reanalyse their measurements, into the synergistic target classification product (AC-TC), which should, in all cases, be consistent with -or superior tothe single-instrument products. AC-TC is a necessary input to EarthCARE's synergistic retrieval of clouds, aerosols and precipitation (ACM-CAP;  Mason et al. , 2022). While EarthCARE's synergistic target classification will make use of the enhanced capabilities of ATLID and CPR to make distinctions in aerosol species and precipitation type that were not possible for CloudSat-CALIPSO, it is expected that AC-TC will facilitate continuity with DARDAR-MASK.
5), making use of simulated data from one of the EarthCARE test scenes derived from high-resolution model data as described in  Qu et al. (2022)  and  Donovan et al. (2023a). In Sect. 6, the performance of the single-instrument and synergistic target classifications will be evaluated by quantifying the extent to which they accurately resolve the amounts of aerosols, clouds and precipitation present in the numerical weather model used to create the simulated test scenes. Finally, we summarise the performance of the target classification products and discuss the contributions of AC-TC to EarthCARE science (Sect. 7).
Coarse layering The coarse layering is determined using the input ATLIDfeaturemask (A-FM). The A-FM product provides a probability mask for the existence of atmospheric features within the lidar profile product  (van Zadelhoff et al. , 2023b)  at the native ATLID resolution. The A-FM is re-gridded to the joint standard grid (JSG;  Eisinger et al. , 2023)  by selecting the maximum featuremask value present within each JSG pixel, except in the case of surface detections. Each data column is then treated in turn, and layer boundaries are assigned whenever 1. a transition between clear-sky featuremask values (as determined by the threshold, e. g. 5) and significant featuremask values exists between two adjacent featuremask values, 2. the absolute difference between two adjacent featuremask values exceeds a defined threshold, 3.