dataesr/scientific-paragraphs-categorization huggingface dataset:
It is noted that our BPAT-UNet does not simply insert Transformers or self-attention like the Transformer network mentioned above. We focus on both global and local feature information, which may be the fundamental reason for obtaining superior DSC metrics. Considering the correlation between HD95 indicators and the shape and boundary of thyroid nodules, we compare them through HD95, which is the focus of this article. Compared with the existing segmentation methods, the proposed BPAT-UNet performs more prominently on HD95. This is due to the boundary point monitoring module we designed and the introduction of deformable computing operations. These features assist the model to remain comfortable in the face of thyroid with large shape differences and fuzzy boundaries, which is what other models lack.
Results on the private dataset We also conduct experiments on a private thyroid dataset to further illustrate the effectiveness of our proposed method. Given the small number of private datasets, overfitting may occur when training the network, so we use the pre-trained weights of the TN3k dataset for transfer learning. Table  3  and Fig. 9  show that our proposed BPAT-UNet achieves the best segmentation performance on DSC compared with other schemes. Similar to the TN3k dataset, we also focus on analyzing DSC and HD95. For DSC aspect, the proposed BPAT-UNet reaches the score of 85. 63 % which is an improvement of 4. 91 % compared with the score obtained by UNet (80. 72 % ), which is an improvement of 3. 07 % compared with FCN (82. 56 % ), which is an improvement of 9. 63 % compared with Deeplabv3+Resnet50 (76.
By observing the segmentation performance of the aforementioned models on the two datasets, we find that the results are basically consistent, that is, models that perform well on TN3k also have ideal effects on our own thyroid datasets, and vice versa. The reason is that the image distribution of the two datasets is nearly similar so the segmentation results will not differ. As with the previous results based on public datasets, models with attention mechanisms on private datasets and models with a mixture of Transformer and CNN outperform convolutional models in metrics. Notably, we also find that the experimental results on our thyroid dataset were generally better than those on the TN3k dataset because transfer learning can help the model converge better, and   the quality of our dataset is relatively high.
Ablation study To validate the role of each module in the proposed network, we use UNet as the baseline network and perform ablation studies on the TN3k dataset and our own thyroid dataset. Table  4  and Table  5  show the different network structures by adding the proposed BPSM, AMFFM, and ATM to UNet, respectively. The segmentation performance on the TN3k dataset is significantly improved, such that DSC increases by 1. 87 % , 1. 02 % , and 0. 82 % , whereas HD95 decreases by 6. 06, 5. 25, and 4. 96. Compare with UNet, our strategy improves DSC with 2. 85 % and HD95 with 7. 45. The segmentation performance on our private dataset also improves such that DSC increases by 1. 96 % , 1. 84 % , and 2. 43 % , whereas HD95 decreases by 8. 35, 8. 73, and 8. 7. Compare with UNet, our strategy improves DSC with 4.
Effect of boundary points supervision module To validate the role of BPSM, we place BPSM in different layers for the ablation experiment. Table  6  and Fig. 10  show the various results on the TN3k dataset. We used three strategies for comparison. We place the BPSM module in the 2nd layer, 5th layer of the network, and key points selection without the DP algorithm. The performance is significantly improved, such that DSC increases by From the results, we investigate the effect of the location and number of BPSMs on the TN3k dataset. When we place BPSM in the 2nd layer of the encoder stage, the improvement effect is minimal, but the calculation amount increases considerably because the low-level features are difficult to accurately predict key points. The performance is significantly improved when boundary point features are merged at multiple scales.
Effect of adapted multi-scale feature fusion module Table  4  and Table  5  also show that, on the TN3k dataset, when AMFFM is introduced, DSC and HD95 improve by 1. 02 % and 5. 25, respectively. On the private thyroid dataset, the experimental results also show significant improvement, which DSC and HD95 improved by 1. 84 % and 8. 73, respectively. Fig. 11  shows the experimental results of these models. The first row of Fig. 12  shows that small objects are discarded using UNet directly. The third row shows that the proposed BPAT-UNet overcomes this issue and can segment relatively small nodules, which can confirm that our AMFFM can compensate for the problem of smalle feature loss, which can bring about serious problems such as missing targets and treatment difficulty during the medical diagnosis.
Effect of assembled transformer module Furthermore, we conducted experiments to compare different structures of ATM. In addition, we also conducted experiments to compare different structures of ATM. We separated ATM into three blocks the local features extraction block including WDatt and Dcn, the global feature extraction block, and EA block. Then, we combine these blocks for experiments. The segmentation results are listed in Table  7  and Fig. 13. Compared with the UNet baseline, the results display significant improvement in both inserting local branches and global branches. The improved DSC and HD95 reveal the ability of deformable computing and global attention. It can also be seen that after parallel fusion of global and local branches, all indicators have significantly improved. Thus, a complementary feature relationship exists between the two branches.
Discussion This work builds a novel boundary-preserving assembly Transformer UNet (BPAT-UNet) for thyroid ultrasound image segmentation. BPAT-UNet focuses on handling the segmentation of the nodules with irregular shapes and with small-size. For irregular shape issues, the proposed BPAT-UNet using BPSM module improves thyroid nodules boundary segmentation. In addition, the DP algorithm is used to select edge points that adversely affect segmentation and can handle segmenting thyroid nodules with large shape variance. For small nodules, the proposed BPAT-UNet pays more attention to small features, using AMFFM with channel attention to enhance small-scale thyroid nodules features and using ATM to integrate high-frequency local and low-frequency global feature information. Both AMFFM and ATM help improve the detection of objects with large shape differences as they can characterize the correlation between features and deformable feature calculation.
Acknowledgement This work was supported in part by the  National Natural Science Foundation of China  under Grants  62171125  and  62141401  , in part by the 67th National Postdoctoral Program of China under Grants  2020M671277  , in part by the  General Program of Jiangsu Provincial Health Commission  under Grants  M2020 0 06 , in part by the  Jiangsu Provincial Key Research and Development Program Social Development Project  under Grants  BE2022720 , in part by the  Key Laboratory of Computer Network and Information Integration (Southeast University)  of the  Ministry of Education  under Grants  K93-9-2021-08 , in part by the  Science and Technology Project of Changzhou City  under Grants under  CE20215045  and  CJ20220136.
As part of the contribution of this work, the results are obtained by specifically modifying the open-source simulator WiLabV2Xsim  [15] , thus providing a platform for the development and testing of new ideas from any researcher interested in this topic. (The simulator is available at https://github. com/V2Xgithub/WiLabV2Xsim (accessed on 25 April 2023) and will include the mitigation methods in its next release). Results provide a detailed analysis of the performance of both IEEE 802. 11p and NR-V2X under several metrics, in scenarios with variable density and variable proportions of vehicles equipped with either technology, and for both periodic and non-periodic traffic. As it will be shown, Methods A and C appear to be the only two able to show an overall positive impact on the performance of the two technologies, although they also imply modifications to the standards and drawbacks that are further clarified in the following.
Conclusions In this paper, we focused on co-channel coexistence between IEEE 802. 11p and 5G NR-V2X to investigate the reciprocal interference and the impact of possible mitigation methods. In particular, we evaluated the performance of a number of mitigation methods, here called eM A -time-split, M B -E-signals, dM C -preamble, M C -preamble-no-SF, and M F -CTS-to-Self, which have been recently proposed by the ETSI with a focus on ITS-G5 and LTE-V2X. Simulations have been carried out through the open-source simulator WiLabV2Xsim. In addition to a summary of the methods and a discussion of their applicability to NR-V2X, results were provided in highway scenarios in terms of various metrics, considering variable vehicle density, technology distribution, and data generation patterns. The main conclusions from the perspective of both NR-V2X and IEEE 802.
To solve the problem, a multi-level heuristic framework using a genetic algorithm is proposed. The individuals of the genetic algorithm only assign client orders to vehicles. A two-phase procedure is used to construct a solution from an individual. In the first phase, the orders are assigned to the machines, vehicle by vehicle, using a first fit decreasing heuristic and the longest processing time first rule. In the second phase, the routes for the vehicles are determined as well as their departure and delivery times. Whenever there are only 5 orders or less, routes are determined by an enumeration method. For larger instances, a tabu search and the 2-opt operator are used. The authors generate random instances according to the method proposed by  Ullrich (2013). They first compare their results to optimal solutions found with the GUROBI solver on small instances and afterwards to results found by the genetic algorithm presented in Ullrich (2013) on large instances.
The biogeochemical model runs for the period from 15 August 2011 to 2 May 2021, but outputs are analyzed from December 2013. The time step is 20 min for advection and diffusion of biogeochemical variables and 2 h for biogeochemical reactions. We initialized the biogeochemical model with observation profiles during the stratified period averaged over 10 regions of the Mediterranean Sea (indicated in Fig. S2 ). For inorganic nutrient profiles, we used the CARIMED (CARbon, tracer and ancillary data In the MEDsea) database  (Àlvarez et al. , 2019;  see Sect. 2. 2. 2), considering only summer data over the period 2011-2012, when data were available. Due to the lack of summer observations for the Levantine region, we used spring observations over the same period. Regarding the dissolved oxygen and chlorophyll concentrations, we initialized using summer in situ observations from Biogeochemical-Argo floats (BGC-Argo, Argo, 2022) (see Sect.
2. 2. 3) and the CARIMED database. Solar radiation and wind forcing for the biogeochemical simulation are those used for the hydrodynamic simulation. Atmospheric depositions of inorganic nutrients were taken into account. Nitrate and ammonium atmospheric depositions were applied as constant values based on the results of  Kanakidou et al. (2012 ), Ribera d'Alcala et al. (2003) ,  Powley et al. (2017), and Richon et al. (2018) , and silicate deposition was prescribed as constant values for western (west of the Sicily Strait) and eastern basins based on estimates given by Ribera d'  Alcalà et al. (2003). We deduced phosphate deposition from monthly Saharan dust deposition modeled with the regional model Aladdin Climate  (Nabat et al. , 2015)  and averaged over the period 1979-2016. We hypothesized that phosphorus represents 0.
Satellite data To evaluate the modeled surface chlorophyll concentration, we used daily level 4 reprocessed data obtained from the European Copernicus Marine Environment Monitoring Service (CMEMS) from the website (http://resources. marine. copernicus. eu/, last access: 16 June 2022, products: OCEAN-COLOUR_MED_CHL_L4_REP_OBSERVATIONS_009_ 078, last access: 16 June 2022), with a spatial resolution of 1 km. The latter is a regional product with daily interpolated chlorophyll concentrations from multi sensors (MODIS-Aqua, NOAA-20-VIIRS, NPP-VIIRS, MERIS sensors). To compare model results with those data, we interpolated the data on the model grid.
Cruise observations We used the CARIMED (CARbon, tracer and ancillary data In the MEDsea) database with 862 profiles covering cruise observations available in the Mediterranean Sea for the period  2011-2018 (Àlvarez et al. , 2019)  to complete the assessment of the spatial distribution of the simulated variables. The data passed two quality controls following the GLODAP (Global Ocean Data Analysis Project) procedure https://doi. org/10. 20, 2023  (  Key et al. , 2004)   Fourrier, 2020). Sea water was collected using Niskin bottles from the surface to 4600 m of depth. An SBE 43 oxygen sensor was used during the cruises and adjusted with Winkler measurements. The inorganic nutrients were determined following the colorimetric methods of  Grasshoff et al. (1999). In addition, we also used the PERLE cruise data set: PERLE-1 (25 profiles) covering the Levantine Basin in October 2018 and PERLE-2 (29 profiles) in  February-March 2019 (D'Ortenzio et al.
BGC-Argo floats To evaluate the temporal and the spatial variability of the modeled dissolved oxygen and chlorophyll, we used observations from BGC-Argo floats providing O 2 (1566 profiles) and Chl a (1171 profiles) measurements (Argo, 2022) deployed in the eastern Mediterranean, downloaded from the Argo Global Data Assembly Center web portal, accessible through the Coriolis database (http://www. coriolis. eu. org, last access: 21 June 2022). Regarding O 2 measurements made by Argo floats, air measurements and optode calibration protocols have led to a significant improvement in the quality and accuracy of these data  (Bittig et al. , 2018; Bittig and Körtzinger, 2015). These can now reach accuracies of 1-1. 5 µmol kg -1 close to those achieved in situ by the Winkler method. Mignot et al. (2019)  conducted a similar study to try to quantify the observational errors for dissolved oxygen and chlorophyll concentrations.
Model skill assessment The comparisons of model results with the available data sets, presented in Sect. 3. 1, show an overall good agreement in the seasonal dynamics and vertical distribution of chlorophyll, nutrients, and dissolved oxygen in the Levantine Sea. We notice, however, an underestimation in the magnitude of the modeled subsurface maximum of chlorophyll and dissolved oxygen concentration when comparing with both the BGC-Argo float and cruise data. The model also produces chlorophyll maxima that are too deep in summer and profiles that are too mixed in winter. Concerning the overestimation of the maximum depths, further studies will be necessary to improve the model parameterizations (optical model, POC degradation processes, particle sinking). Concerning the too-strong homogenization of the winter profiles, we notice that in winter, mixed chlorophyll and DCM-like profiles alternate, indicating small-scale (few kilometers) or temporal variability related to meteorological conditions.
Acknowledgements. This study is a contribution to the  MerMex (Marine Ecosystem Response in the Mediterranean Experiment)  project of the  MISTRALS international program. The numerical simulations were performed using the SYMPHONIE model, developed by the SIROCCO group (https://sirocco. obs-mip. fr/, last access: 17 October 2022), and computed on the cluster of Laboratoire d'Aérologie and HPC resources from CALMIP grants ( P1325 ,  P09115 , and P1331). We acknowledge the scientists and crews of the Flotte océanographique française (https://www. flotteoceanographique. fr/, last access: November 2022), who contributed to the cruises carried out in the framework of the PERLE project. The authors would like to acknowledge the  National Council for Scientific Research of Lebanon (CNRS-L), Campus France , the  University of Toulouse , and  LEGOS  for granting a doctoral fellowship to  Joëlle Habib.
Code availability. The SYMPHONIE model and the MATLAB codes used to process the model outputs are available from the authors on request. Data availability. Data used to validate the model are available on different websites specified in the main text of the paper. These data and the model outputs are also available from the authors on request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-20-3203-2023-supplement.
Statistics The data in this study is presented as an average ± standard deviation. One-way analysis of variance (ANOVA) was used using Microsoft Excel software to determine significant differences which were declared at a probability level P < 0. 05. Principal component analysis (PCA) was applied to evaluate effects of pretreating the acid whey with RO and applied current. Principal component analysis (PCA) is a statistical unsupervised machine learning algorithm used to reduce data based on variance across the datasets (while eliminating noise). It was applied to spectral data (FTIR and 2D fluorescence spectra independently) to compare and evaluate effects of pretreating the acid whey with RO and of applied current, as well as the efficacy of membrane cleaning procedures applied. The PCA analysis was performed in GNU Octave version 6.
As expected, calcium and magnesium were primarily found in the CEM, while phosphorous, present in the form of negatively-charged phosphate species was found in the AEM, which is related to the transport of the respective counterions through these membranes. Lactic acid and lactose were found on both the AEM and CEM. The analytical data obtained in the present study confirm the suitability and complementarity of 2D fluorescence spectroscopy and FTIR spectroscopy to monitor fouling in IEMs as well the feasibility of the applied cleaning procedures, thus opening the possibility to design dedicated IEM cleaning procedures for saving chemicals, since the cleaning procedure for the concentrate and diluate compartments do not need to be necessarily the same. Thus, the results obtained provide useful information to improve the efficiency of the ED acid whey treatment process, depending on the physicochemical properties of the diluate and process operating conditions employed.
Acknowledgement The authors would like to thank the staff at  Hobro dairy  for collection of acid whey samples. Also, special thanks to MemBrain s. r. o. and MEGA a. s. for providing us with the electrodialysis unit and the ion-exchange membranes. This present study is a part of the Platform for Novel Gentle Processing supported by the  Dairy Rationalisation Fund (DDRF) ,  Copenhagen University  and  Arla Foods. This work received support from PT national funds ( FCT/MCTES ,  Fundação para a Ciência e Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior)  through the projects  UIDB/50006/2020  and  UIDP/50006/2020.
Potential implications for the rheology of the subducted slab To understand the potential effect of the omphacitic Cpx→cubic Na,Si-rich Maj transformation on the rheology of the subducted slab, we used our results to model the density, longitudinal (VP) and shear (VS) wave velocities of mid-ocean ridge basalt (MORB) at the pressures of the mantle transition region (MTR), using elastic parameters of major minerals available in the literature (Table  2 ). The elastic properties of the In the absence of direct measurements, the pressure and temperature dependences of the adiabatic bulk (K S ) and shear (G) moduli of Na 2 MgSi 3 O 12 Maj were derived from the elasticity dataset given by  41. Comparison of our models for MORB and pyrolite Maj with experimental data allows to test the effect of non-ideal mixing on the elasticity of Maj Grt solid solutions.
Slices were double polished to a thickness of 600 µm, which is close to the optimum thickness based on sample composition 87 and corresponds to a dimensionless effective thickness of 2 (5 mg Fe/cm 2 ). A region of recovered products containing majorite with dimensions of 800 µm × 600 µm was exposed on each side of sample slices, and the area outside this region was covered with 25 µm thick Ta foil, which absorbs 99% of 14. 4 keV gamma rays. Mössbauer spectra were recorded at room temperature in transmission mode on a constant acceleration spectrometer using a nominal 370 MBq  57  Co point source at Bayerisches Geoinstitut, Bayreuth. The collecting time for each spectrum was 2 weeks. The velocity scale, set at ~ 5 mm/s, was calibrated relative to an α-Fe foil reference standard. Once folded, spectra were tted to Lorentzian line-shapes using the tting program MossA 88.
Acknowledgments V. S. gratefully acknowledges nancial support from  PRIUS program  (GRC,  Ehime University)  and " Fondi di Ateneo 2021 " by  Sapienza University (Rome). LB was supported by the " Progetto di Ateneo 2015 " issued by the  Università di Firenze, Italy. SA was supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  -Project number  AU356/12. Nano-Infrared Microscopy measurements were performed at  Elettra Synchrotron of Trieste.
Statistical Analyses Patient demographics and disease characteristics at baseline were summarized using descriptive statistics as mean ± SD for continuous variables and n (%) for categorical variables. Longitudinal data for observed outcomes (as absolute values and change from baseline) at baseline and each year of followup were also summarized. Factors with significant predictive value for affecting height outcomes over time were analyzed using a repeated-measures regression analysis for ΔHSDS separately for each patient cohort, with adjustments for the covariates of age at treatment start, HSDS at baseline, and average GH dose over the previous year. Longitudinal growth responses over follow-up years 1 to 5 were evaluated to provide indicator variables allowing for differences in improvement from baseline across the 5 years.
The dose in the previous year was used to account for the delay between treatment administration and physiological response. This same approach was used to analyze covariates that significantly affected ΔBMI SDS in patients with PWS only. These analyses were performed using a fixed-effects linear regression model that employed a variance-covariance model matrix incorporating correlations for all of the observations arising from the same patient, using the compound symmetry structure that assigned equal intrapatient correlations. The data were assumed to be Gaussian, and their likelihood was maximized to estimate the model parameters. All patient annual records were used, irrespective of loss to follow-up or potential gaps in annual records, with no imputation for missing values. The contribution of interactions of covariates with indicators for each follow-up year over and above the main effects were tested.
Acknowledgments The authors acknowledge the medical writing assistance of  Amy Ross, PhD , of  PRECISIONscientia, Yardley, PA , which was supported financially by  Novo Nordisk Inc. , Plainsboro, NJ, in compliance with international Good Publication Practice guidelines. A detailed outline and reference list were prepared in 2 online meetings with all authors; discussion of subsequent manuscript drafts took place by email. Final version was approved by all authors.