dataesr/scientific-paragraphs-categorization huggingface dataset:
Discriminating features The final selected set of MOmics features used for classifier training comprised different omics features for each disease combination. The PmiRNA and PSmallMB features represent 88% of the whole MOmics dataset (Figure  3a ), a similar share was observed within the final selected set of features (Figure  3b ). For example, PSmallMB forms a considerable part of all the disease combinations except CS-PHT where a high number of PmiRNAs were found to be highly discriminating (»58% of total selected features). In contrast, for PPGL-PHT, very few PmiRNAs (»5. 5% of total features) were selected. The commonality of selected MOmics features amongst different disease combinations was also investigated (Figure  3c ). Two PSmallMB features (O5_PC ae C38:1 and O5_C9) and one PmiRNA (O1_hsa-miR-15a-5p) were present in all five disease combinations (Table  3 ).
On the contrary, for PSmallMB classifier only 9 features were selected in CS-PHT amongst all disease combinations. The contribution of each omic in the final selected MOmics features (with regard to the count in the whole dataset) was also analysed. For example, 3 out of 4 PMetas features (75%) were selected in ALLÀALL and PPGL-PHT disease combinations. None of the PSteroids features was selected in PPGL-PHT. No PMetas were selected in PA-PHT and CS-PHT combinations (Figure  3d ). The close examination of features amongst MOmics and mono-omics classifiers highlighted that all the features selected in MOmics were part of the individual omics classifiers, except PSmallMB and PmiRNA (Figure  3d ). In case of PSmallMB, some features were exclusively selected in MOmics classifiers (For example, O5_PC aa C34:3 and O5_PC ae C40:2 in PA-PHT).
In-depth analysis of primary and endocrine hypertension The training set of the MOmics data was studied for a different set of scenarios (Table  2 ). These scenarios include the use of age and sex as features and understanding the effect of age and sex segregated subsets on  feature selection in different disease combinations (See stage 2 in Figure  1 ). Scenario 1: including (Set A) vs excluding (Set B) age and sex as features For Scenario 1, MOmics data provided better performance for all disease combinations (Appendix Figure  6 ). In intra-set comparison, MOmics achieved similar performance in Sets A and B across all disease combinations. Hence, excluding age and sex (in Set B) as features did not materially alter the performance of the classification. However, for PMetas, balanced accuracy dropped when age and sex were excluded (Set B) from the feature set (except for PPGL-PHT).
Scenario 2: males (Set C) vs females (Set D) The classification performance of Sets C and D was not comparable since they used different numbers of samples for training and testing. However, it is noticeable that the female subset provided better accuracy for EHT-PHT and PA-PHT in comparison to male subset for MOmics data (Appendix Figure  6 ). The intra-set comparison highlighted the superior performance of MOmics dataset for ALL-ALL and EHT-PHT disease combinations in comparison to mono-omics irrespective of classifier selection. For PPGL-PHT, PMetas outperformed the MOmics classification for both the Sets. However, in case of CS-PHT, Set C could not be run due to an insufficient number of male CS samples for classifier training (Appendix Table  4 ). From the perspective of feature selection, in the MOmics dataset, different features were selected for Sets C and D (Appendix Figure  10 ).
For example, in ALL-ALL, O1_hsa-miR-15a-5p, O5_Spermidine and O5_Spermidine/Putrescine were only selected for male dataset. On the contrary, various other features such as O5_PC ae C38:1, O3_18oxo-Cortisol, and O4_18-OHF were only selected for female dataset. On close examination, it was evident that the union of Set C and Set D features approximately intersect with both Sets A and B. Similar trends were also observed across most of the disease combinations in MOmics and mono-omics datasets (Appendix Figures  7 À 9 ). Scenario 3: older (Set E) vs younger (Set F) Overall, MOmics data provided better classification performance in comparison to mono-omics (except PPGL-PHT), irrespective of the cohort age (Appendix Figure  6 ). When considering the inter-set comparison, Set E (age >= 50) provided better results than Set F (age < 50) for almost all disease combinations.
Discussion This study investigates MOmics ML integration for stratification of arterial hypertension. Our results show that the MOmics approach provided improved discriminatory power in comparison to single omics (monoomics) data analysis and was able to correctly identify different forms of endocrine hypertension with high sensitivity and specificity, providing potential diagnostic biomarker combinations for diagnosing hypertension subtypes. In particular, it was able to classify subtypes of hypertension with 96% specificity and 0. 95 AUC in the ALL vs ALL disease combination, achieving »11% improvement in balanced accuracy on an unseen test set in comparison to the best performing mono-omics classifier using a reduced set of 57 features. The feature set had distinct characteristics when compared with normotensive volunteers and hence can be potentially utilised as biomarker in clinical application.
Finally, all the samples could not be used for MOmics integration because of limitations in sample volume or specific quality measures, which is a common problem for a study with multisite biosamples and multi-centre omics measurement. However, a major strength of this study was to rely on unambiguous diagnosis of the major subtypes of EHT according to guidelines by expert centres. In addition, our analysis only explored the MOmics data using a ML based data-driven approach. The discovered top discriminating features need further investigation in terms of biological significance and pathway network analysis. This study provided a valuable insight into a complex multi-class problem of endocrine hypertension stratification. It uses multi-omics data and evaluated its superior performance in comparison to mono-omics.
Although internal validation was conducted using an unseen test set, further validation on external cohorts before any translation in clinical setting will be valuable. The ENS@T-HT study is currently capturing data from a wider population in a prospective manner to measure the most discriminating features of the new samples and perform independent validation (ClinicalTrials. gov Identifier: NCT02772315). This would allow the classifier to become more robust and well-trained for a formal clinical deployment. The refined algorithm could be deployed as a webserver-based classification tool and utilised to screen patients at primary care to refer patients identified as being at risk of endocrine hypertension to centres with appropriate expertise for subsequent evaluation if required. The developed ML pipeline is fully customisable and can be deployed for other mono/ MOmics data-based biomarker discovery and analysis studies.
S. R. , S. Reel, K. Lang, A. E. T. , M. R. , W. A. , F. B. , G. E. , M-C. Z. and E. J. are listed as co-inventors on patents filed related to Biomarkers for Diagnosis and Treatment of Endocrine Hypertension, and Methods of Identification thereof. P. S. R. , S. R. , J. C. v. K. , K. Langton, K. Lang, Z. E. , C. K. L. , L. A. , P. M. , A. B. , M. Kabat, S. Robertson, S. M. M. , A. E. T. , M. P. , F. C. , M. Kroiss, M. C. D. , S. M. , J. D. , G. P. R. , L. L. , J. D. M. , A. R. , A. S. , I. S. , J. A. , A-P. G-R. , G. A. , W. A. , F. B. , G. E. , E. D. , M-C. Z. and E. J. reports grants from European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, during the conduct of the study K. Langton reports grants from CRC/TRR 205 Project B 15 and Patient Cohorts and Biobanks Fonds 041_526513, outside the submitted work.
Acknowledgements This project was funded by the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 633983. Clinical Research Priority Program  of the  University of Zurich  for the CRPP HYRENE (to  Zoran Erlic  and  Felix Beuschlein ) and grants from the  Deutsche Forschungsgemeinschaft  ( CRC/Transregio 205/1  "The Adrenal: Central relay of health and disease"). We thank all members of the  Genetics Department, Biological Resources Center and Tumor Bank Platform, Hopital europ een Georges Pompidou  ( BB-0033-00063 ) for technical support.
P. S. R. , S. Reel, K. Lang, A. E. T. , M. R. , W. A. , F. B. , G. E. , M-C. Z. and E. J. are listed as co-inventors on patents filed related to Biomarkers for Diagnosis and Treatment of Endocrine Hypertension, and Methods of Identification thereof. P. S. R. , S. R. , J. C. v. K. , K. Langton, K. Lang, Z. E. , C. K. L. , L. A. , P. M. , A. B. , M. Kabat, S. Robertson, S. M. M. , A. E. T. , M. P. , F. C. , M. Kroiss, M. C. D. , S. M. , J. D. , G. P. R. , L. L. , J. D. M. , A. R. , A. S. , I. S. , J. A. , A-P. G-R. , G. A. , W. A. , F. B. , G. E. , E. D. , M-C. Z. and E. J. reports grants from European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, during the conduct of the study K. Langton reports grants from CRC/TRR 205 Project B 15 and Patient Cohorts and Biobanks Fonds 041_526513, outside the submitted work.
Model predictive control (MPC) has been widely employed to control a large variety of water systems, such as dams, irrigation canals, inland waterways, drinking water networks and wastewater treatment plants. Its predictive capabilities and the possibility to incorporate constraints make MPC well suited to address several, and sometimes opposite, management objectives linked to water systems. The design of MPC for water systems is usually performed via dedicated software (e. g. , Matlab) and tested in simulation using dedicated hydraulic software. However, the implementation of MPC strategies in real systems requires additional development to allow for its embedding within the information systems that are used by system managers. A possible solution is to create a tool based on Python that can be easily integrated with the information systems of managers, and within which existing Matlab solutions can be incorporated.
Hence, an optimal control strategy should minimize the use of pumps by playing with the tidal range of the canal, i. e. , accumulate water in the canal during high tide and release it during the next low tide period using the gates. At the same time, the control strategy must incorporate the main management objective, i. e. , avoid flooding by keeping the level of the canal below a safety limit. The MPC strategy has been designed and validated using a simulation architecture between Matlab 1  and SIC ∧ 2 2  software. Two hydraulic softwares have been investigated: on the one hand, HEC-RAS 3  , which allows to use a dynamical model of canals using an accurate topographical and technical survey  (Deshays et al. , 2021) ; on the other hand, SIC ∧ 2, for which digital twins of real canals have been created  (Ranjbar et al.
, 2020). The Matlab-SIC ∧ 2 architecture has been found to be more appropriate in terms of computational burden, and has been used for the canal of Calais located in the north of France. The control strategy is designed to be used in real time to assist the Calais canal managers. Given the fact that the MPC developed in Matlab cannot be directly used in real time without the right license, a solution consisting in coding the MPC strategy in Python 4  is proposed. To this end, it is necessary to (i) install all the required libraries, (ii) transcode the Matlab files into Python, (iii) synchronize the execution of the code with the information systems used by managers, and (iv) provide optimal control values to the managers. There are some papers in the literature dealing with similar problems.
Hence, an optimal control strategy should minimize the use of pumps by playing with the tidal range of the canal, i. e. , accumulate water in the canal during high tide and release it during the next low tide period using the gates. At the same time, the control strategy must incorporate the main management objective, i. e. , avoid flooding by keeping the level of the canal below a safety limit. The MPC strategy has been designed and validated using a simulation architecture between Matlab 1 and SIC ∧ 2 2 software. Two hydraulic softwares have been investigated: on the one hand, HEC-RAS 3 , which allows to use a dynamical model of canals using an accurate topographical and technical survey  (Deshays et al. , 2021) ; on the other hand, SIC ∧ 2, for which digital twins of real canals have been created  (Ranjbar et al.
, 2020). The Matlab-SIC ∧ 2 architecture has been found to be more appropriate in terms of computational burden, and has been used for the canal of Calais located in the north of France. The control strategy is designed to be used in real time to assist the Calais canal managers. Given the fact that the MPC developed in Matlab cannot be directly used in real time without the right license, a solution consisting in coding the MPC strategy in Python 4 is proposed. To this end, it is necessary to (i) install all the required libraries, (ii) transcode the Matlab files into Python, (iii) synchronize the execution of the code with the information systems used by managers, and (iv) provide optimal control values to the managers. There are some papers in the literature dealing with similar problems.
⋆ This work has been supported by ARMINES through the contract n • 1908V/1700661 and the IIW (Institution Intercommunale des Wateringues). Hence, an optimal control strategy should minimize the use of pumps by playing with the tidal range of the canal, i. e. , accumulate water in the canal during high tide and release it during the next low tide period using the gates. At the same time, the control strategy must incorporate the main management objective, i. e. , avoid flooding by keeping the level of the canal below a safety limit. The MPC strategy has been designed and validated using a simulation architecture between Matlab 1 and SIC ∧ 2 2 software. Two hydraulic softwares have been investigated: on the one hand, HEC-RAS 3 , which allows to use a dynamical model of canals using an accurate topographical and technical survey  (Deshays et al.
, 2021) ; on the other hand, SIC ∧ 2, for which digital twins of real canals have been created  (Ranjbar et al. , 2020). The Matlab-SIC ∧ 2 architecture has been found to be more appropriate in terms of computational burden, and has been used for the canal of Calais located in the north of France. The control strategy is designed to be used in real time to assist the Calais canal managers. Given the fact that the MPC developed in Matlab cannot be directly used in real time without the right license, a solution consisting in coding the MPC strategy in Python 4 is proposed. To this end, it is necessary to (i) install all the required libraries, (ii) transcode the Matlab files into Python, (iii) synchronize the execution of the code with the information systems used by managers, and (iv) provide optimal control values to the managers.
There are some papers in the literature dealing with similar problems. Takács et al. The main contribution of this paper is the transcoding of a hierarchical MPC approach (originally developed in Matlab) into Python for its embedding within the information system of the Calais canal managers. The main objective is to allow for a real-time deployment of the the hierarchical MPC, which allows to determine control actions for gates and pumps taking the time-varying tidal periods into account, and reject unknown disturbances. The rest of the paper is organized as follows: the management of the Calais canal is described in Section 2. The design of the hierarchical MPC is detailed in Section 3. Transcoding of the hierarchical MPC into Python and its embedding in the information system are presented in Section 4.
, k + 1} (3f) x(l|k) = xMHE (l|k), l ∈ {k -N e -τ + 1,. , k -N e }, (3g) u(m|k) = u db (m|k), m ∈ {k -N e -τ + 1,. , k}, (3h) where R -1 and Q -1 are the weighting inverse matrices of appropriate dimensions specifying the confidence in the measurements and quality of the model, respectively, and W (k -N e + 1|k) in (3b) is the error between x(k -N e + 1), i. e. , the most probable initial state, and x(k -N e + 1|k), i. e. , the first value of the optimal state sequence, estimated by the MHE at time instant k. This error is weighted by matrix P -1 , which indicates the confidence into the initial state, and its tuning allows to satisfy estimation boundedness  (Copp and Hespanha, 2017). Moreover, y(i) are the measured water levels and u db are the controls applied to the system, which are recorded in the database of managers.
, k -1}, ( 4g ) where k ∈ Z ≥0 is the current time instant, i ∈ Z ≥0 is the time instant during the prediction horizon, (k + i|k) indicates the predicted value of the variable at instant k + i using information available at instant k, and j ∈ Z ≥0 and l ∈ Z ≥0 indicate the use of past actions applied to the system and stored in the manager's database (u db ) and information computed by the MHE ( xMHE ), respectively. The different operational objectives included in (4a) are defined as follows: • Keep water levels close to the navigation objective NNL: ℓ y (k) ≜ (y(k) -y re f ) ⊤ W y (y(k) -y re f ), (5) where y re f is the vector of NNL values and W y is a diagonal positive definite matrix. • Minimize cost of operating equipment: ℓ e (k) ≜ u ⊤ (k)W e u(k), ( 6 ) where W e is a diagonal positive definite matrix, whose entries linked to pumping actions are much larger than those linked to the gates.
Hierarchical control architecture The hierarchical MPC proposed by  Segovia et al. (2020)  consists of three layers. • The upper layer specifies the available hydraulic components according to the discrete state of the system. In low tide, pumps and gates are available; in high tide, the gates cannot be utilized. The manipulated inputs u k are modified according to the tide. Finally, the control setpoints for the gate and pumps are provided to the managers every T c time units. The corresponding values are sent to the database using the following tags: -Setpoint for Gate: CA G1 SP, -Setpoint for Pump: CA PUMP SP. An interface has been created to provide the optimal control setpoints u c (k)) to the managers and tune the MHE and MPC parameters, and is depicted in Figure  5. Default values for weights in low/high tides are given.
However, the managers can modify these values if the performance of the control strategy is not as expected. The horizon time can also be modified. The complete Python code cannot be shown in this paper due to lack of space and relevance. However, the MHE code is given below to illustrate the approach: i f i == n : obj MHE +=( quad form ( ( x e s t [ : , i + 1 ] -\ ( Af @ x e s t [ : , i ] + Bf @ w in [ : , i ] + \ Bdf @ d i s t f [ : , i + n + T Sim -Ne ] + \ s e l 1 @ ( Bf n @ w f i n [ : , i ] + \ Bdf n @ d i s t f [ : , i + T Sim -Ne ] ) + \ s e l 2 @ ( Bf n @ w in [ : , 1 ] + \ Bdf n @ d i s t f [ : , i + T Sim -Ne + 1 ] ) ) ) , \ np. l i n a l g. i n v (Q ) ) ) + \ ( quad form ( ( y f i n [ : , 5 ] -\ ( Cf @ x e s t [ : , i ] + Df @ w in [ : , i ] + \ Ddf @ d i s t f [ : , i + n + T Sim -Ne ] + \ s e l 1 @ ( Df n @ w f i n [ : , i ] + \ Ddf n @ d i s t f [ : , i + T Sim -Ne ] ) + \ s e l 2 @ ( Df n @ w in [ : , 1 ] + \ Ddf n @ d i s t f [ : , i + T Sim -Ne + 1 ] ) ) ) , \ np.
CONCLUSION In this paper, the transcoding of a hierarchical MPC strategy for the Calais canal into Python is proposed. The original control strategy is based on MHE and MPC, and was designed using Matlab. Transcoding is carried out with the objective to implement this solution within the information system of the managers, which allows to test the designed predictive control strategy on the real system. Next steps regard its implementation on the information system of the managers to obtain feedback (which will be used to improve the solution) and gain insight on its performance. In particular, performance obtained during extreme weather situations, e. g. , flooding periods, is of great relevance, and will lead to future research on strategies to mitigate their effects.
Required measurements from the database of French managersFrom the measurements y(k), the levels of the canal in Calais and of the sea are provided to the upper layer. The sea level is compared to the canal level in Calais at each step time k. If the sea level is inferior to the canal level during three consecutive step times, the state of the system, denoted with S(k), is low tide; otherwise it is high tide. The measurements y(k) and the control actions sent to the hydraulic devices u db (k) are used by the MHE at the intermediate layer to estimate the state of the system xMHE (k) every T s time units, with N e = 12 hours. The MPC function is executed with a control period T c = 2 hours by considering N p = 12 hours (covering 2 types of tide), but its value can be easily modified. Then, and based on the system state S(k) and the estimated state xMHE (k), the MPC is solved, providing control setpoint u MPC (k) every T c time units.
Using this tool and libraries, all layers and problem have been coded as functions, following the ar- chitecture depicted in Figure 4. The database query collects the measurements from the database using a sampling time T s = 2 minutes. The data are summarized in Table 1, with binary variables at PS, and measurement values for levels. tide : spring tide for C tide ≥ 90, neap tide for C tide ≤ 45, and mean tide for 45 < C tide < 90.
The application of noise reduction technologies depends strongly on the aircraft configurations itself which requires the detailed consideration of potential future configurations. This was the reason to implement a specific work package within ARTEM dealing with the assessment of technologies on novel aircraft configurations, starting with a detailed definition of these configurations. The noise signature of the anticipated configurations will be strongly influenced by the interaction of several aircraft components: the interaction of airframe, high-lift-system, and propulsive jet of the engine(s), the interaction of airframe and engine inlet, the interaction of the landing gear with the airframe. These effects -which directly involve the noise generation -are investigated in the ARTEM framework by dedicated experiments and high-fidelity numerical calculations.
For the aircraft fuselage, a clear trend towards lift-providing structures is visiblewhich is consistent with the airframes of NOVA, BOLT, and REBEL presented here in the framework of the ARTEM project. TU Delft has pursued its "Flying V" -a variation of the blended wing concept  [35]. AGILE, CENTRELINE, IMOTHEP and NACOR  [36, 37]  are recent or on-going EC funded projects which also deal with future aircraft configurations -mainly from performance and aerodynamic point of view. For the introduction of disruptive configurations like the blended wing body, rather drastic changes are likely being required for current airport facilities, maintenance procedures, but also for design and manufacturing routines. So far, there is a certain lack of data and therefore in reliability of all predictions with respect to performance, aerodynamics, but especially also noise emissions of these configurations.
Acknowledgements The main authors wish to thank all contributors to this chapter -most coauthors are mentioned directly in conjunction with their respective contributions. The funding provided by the  EC  for the project ARTEM under grant number  769350  is gratefully acknowledged. Also, many other projects receive funding by the  EC  (grant numbers are given above). Finally, we would like to thank the  ANIMA team  for initiating the current book project and giving us the opportunity to share our views and findings on the future of air transport.