dataesr/scientific-paragraphs-categorization huggingface dataset:
This method takes advantage of the exponential decrease in the concentration of CO after the injection to determine the catalytic response over a large range of concentrations in a single experiment. [12] Figure  1  shows typical signals obtained with CODH-II and -IV. The current instantly increases after the injection (at t = 0), it remains constant while the enzyme is still saturated with CO, and, eventually, decreases exponentially when the concentration of CO drops below the value of Km. This final decrease in current occurs much later for CODH-IV than for CODH-II, which shows that the Km value for CODH-IV is smaller than that of CODH-II. Fitting the model in ref  [13]  to the data (dotted lines in figure  1 ) yielded Km values of 8 ± 1 μM (CODH-II) and 47 ± 3 nM (CODH-IV) (at 25°C). IV after injecting 50 µM CO in the electrochemical cell, together with the fit of equation 9 in ref  [13]  (dashed lines).
) As a consequence, any change in kcat must be compensated by a similar change in Km. In contrast, the activity of CODH-II is not limited by diffusion in the temperature range that we could investigate. We used transient exposures to CO and O2 to compare the reactivity of CODH-II and -IV with O2. Using the six-injection method that we explained before  [6]  , we exposed films of CODH-IV at 25 °C to O2 spanning a large range of concentrations (from 30 nM to 20 µM) and measured the fraction of activity remaining after the departure of O2 and following a reductive poise (an example experiment is shown in supplementary figure  S1 ). The results are shown in figure  3 , together with the CODH-II and Dv-CODH data reproduced from ref  [6]. All things being equal, CODH-IV reactivates more after the departure of O2 than does CODH-II (open symbols); however, unlike CODH-II and Dv-CODH, CODH-IV does not reactivate upon reduction.
The dashed line in the bottom panel is an Arrhenius law with an activation energy of 24 kJ/mol deduced from the dependence of the CO diffusion coefficient in water in ref  [14]. The lines are just guide for the eyes. CODH-IV crystals diffracted to a resolution of 2. 52 Å and the complete structure of CODH-IV, containing residues 3-633 was located in the electron density (Table  S1  in the Supporting Information). The structures of CODH-II (pdb Id: 3B53) and CODH-IV are very similar and can be superimposed with an rmsd-value for Cα-atoms in the core region of 1. 1 Å (Figure  S2 ). Channels in CODHs were calculated using HOLLOW 1. 1  [16]  and are shown as surfaces. Upper panels represent van der Waals-radii (spheres) of residues around S312 in CODH-II and F312 in CODH-IV. As reactivity and O2-sensitivity of CODH depends on the state of the metal clusters, we compared the direct environment of the Fe/S-clusters in CODH-II and CODH-IV.
Acknowledgements H. D. acknowledges the cluster of excellence " Unifying concepts of catalysis -UniCat " and  DFG DO 785/6-1  for funding of the project. M. M, C. L, S. D, and V. F acknownledge financial support from  CNRS  ,  Aix Marseille Université ,  Agence Nationale de la Recherche  ( ANR-12-BS08-0014 ,  ANR-14-CE05-0010 ,  ANR-15-CE05-0020 ) and the  A * MIDEX  grant ( ANR-11-IDEX-0001-02 ) funded by the  French Government "Investissements d'Avenir" program. M. M, C. L, S. D, and V. F are members of the FrenchBIC (http://frenchbic. cnrs. fr).
Acknowledgements This research was supported by  MAXNET Energy ,b yA NR under contract  2011 CHEX 004 01 , and by the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIP)  ( NRF-2017R1C1B2002918 ). The authors deeply appreciate financial assistance from the  U. S. Department of Energy , E ERE  ( DE-EE-0000459 ), and  Alexander von Humboldt Foundation.
These scenarios have been published in the following studies  (Bukowski, 2013; Criqui et al. , 2015; Hillebrandt et al. , 2015; Paroussos et al. , 2016; Pye et al. , 2015; Virdis et al. , 2015). Finally, an extensive database was gathered for the historical performance of the EU28 and all of its Member States on each of the sectoral Kaya identity parameters. It should be noted that while the database of historical performance contains each EU Member States, for reasons of concision the following section and figures present results only for the EU28, UK, France, Germany, Poland, and Italy, as well as the 'best' and 'worst' performing Member States on each indicator. The main databases used to build this database were  (Enerdata, 2017 (Enerdata, , 2016)). This performance data was analysed in the light of the sectoral benchmark ranges identified from the deep decarbonisation scenarios.
Acknowledgements The authors wish to acknowledge SOLEIL for provision of synchrotron radiation facilities under proposals 20100532 and 20120822 on beamline SMIS. We would also like to acknowledge the mechanical workshops in both the  Department of Physics at York  and at SOLEIL for constructing bespoke experimental equipment for use in these experiments. This work was funded in part under  EPSRC  grants  EP/K013114/1  and  EP/K03278X/1.
We believe that the current focus on improving the speciation of ultra-dilute elements in environmental media through the ongoing optimization of synchrotron technologies (e. g. , brighter light sources, improved monochromators, more efficient detectors) will help to significantly push back the frontiers of environmental biogeochemistry research. As many of the relevant techniques produce extremely large datasets, we also identify ongoing improvements in data processing and analysis (e. g. , software improvements and harmonization of analytical methods) as a significant requirement for environmental biogeochemists to maximize the information that can be gained using these powerful tools.
Fall detection systems are designed in view to reduce the serious consequences of falls thanks to the early automatic detection that enables a timely medical intervention. The majority of the state-of-the-art fall detection systems are based on machine learning (ML). For training and performance evaluation, they use some datasets that are collected following predefined simulation protocols i. e. subjects are asked to perform different types of activities and to repeat them several times. Apart from the quality of simulating the activities, protocol-based data collection results in big differences between the distribution of the activities of daily living (ADLs) in these datasets in comparison with the actual distribution in real life. In this work, we first show the effects of this problem on the sensitivity of the ML algorithms and on the interpretability of the reported specificity.
fall related injuries of older adults are substantial. Fall detection systems are designed in view to reduce the serious consequences of falls thanks to the early automatic detection that enables a timely medical intervention. These systems are based on either wearable or ambient sensors. In this paper, we focus on wearable fall detectors that can be used continuously accompanying the user all the time. Fall detection is still an open research topic to date  [2] ,  [3] ,  [4]. Most of fall detection algorithms are based on machine learning (ML) like  [5] ,  [6] ,  [7] ,  [8] ,  [9] ,  [10] ,  [11]. MLbased solutions require relatively large datasets for training and performance evaluation. The majority of the state-of-theart works use datasets of simulated falls and activities of daily living (ADLs) that are collected following some predefined protocols  [12].
Some examples of the datasets used to develop ML-based fall detection systems are: FallAllD  [8] , SisFall  [13] , UMA-Fall  [14]  and UniMiB  [15]. To show the drawbacks of the state-of-the-art works that are considerably dependent on such kind of datasets, we first discuss the considered performance criteria. ML-based fall detection is generally formulated as a binary classification problem that aims at classifying human activities into two classes: falls and ADLs. ML models are iteratively optimized, on a given training set, to maximize the classification accuracy. In other words, they implicitly aim at finding the best trade-off between sensitivity and specificity i. e. they aim at detecting as much falls as possible and avoiding false positives (FPs). In all state-of-the-art datasets that we explored  [8] ,  [13] ,  [14] ,  [15] ,  [16] , the ADLs were collected in a protocol-based way i.
e. subjects were asked to do a set of activities for a while and to repeat them several times. Hence, the distribution of ADL types in these datasets is considerably different from the actual distribution in real life. For instance, in the FallAllD dataset  [8] , the number of samples representing jumping is almost equal to the number of samples representing walking. This balance between the number of samples that belong to different ADL types in a given dataset is beneficial to conduct some statistical analysis. However, the aforementioned difference in the ADLs distribution between the simulated datasets and real life leads to two major issues. The first is that maximizing the accuracy with the presence of a large percentage of highly ambiguous ADLs (e. g. jumping, stumbling without falling,.
For example, assume we want to embed a given fall detection system that has been evaluated on a simulated dataset and reported a specificity of 99. 99% in a wearable fall detector that is designed to make one decision per second (86 400 decision/day). Theoretically, the specificity of 99. 99% translates to 8 false positives per day which is relatively high. Besides the drawbacks inherited from the aforementioned gap in the ADLs' distribution, simulations in most of the stateof-the-art datasets have been executed by young subjects. On the one hand, simulating falls by young people is justified because collecting a large number of real falls is a tough task that requires long time and extensive resources. On the other hand, simulating ADLs by young subjects is not well justified since it is possible to collect ADLs from older adults.
In fact, the SisFall dataset  [13]  that was collected using waist-worn devices tackled this problem where the ADLs were collected from 15 subjects aged over 60 years. However, like in most of the state-of-the-art datasets, ADLs were collected following a predefined protocol. Therefore, developing ML-based systems using these datasets suffer from the aforementioned problems related to the distribution of ADL types. It is worth mentioning that collecting fall events data in real-world conditions has received little attention in the literature. Besides the FARSEE-ING real-world fall repository  [17] , where data have been recorded using inertial sensors, the majority of datasets on the public domain are lab-based as previously discussed. Our current work tackles all the above-mentioned problems.
The first step in the proposed strategy is collecting a large amount of data that represents the activities of older adults in their daily life. Then, these real-world data are analyzed and a preprocessing system is proposed to extract the ambiguous ADLs (ADLs that are suspected to be falls) from the recorded data. A hybrid dataset that consists of the aforementioned ambiguous ADLs and a wide variety of simulated falls is constructed. A reliable ML-based fall detection system is then proposed where, contrary to the classical works, the objective is to discriminate falls from the real-life ambiguous ADLs. The objectives of the proposed fall detection system are summarized as follows: • fall detection sensitivity has the highest priority. Nonetheless, the specificity is to be maximized (but not at the expense of sensitivity), since frequent false alarms may cause additional burdens on users; • the same fall detection system could be used in neck-or wrist-worn devices; • the fall detection system is rotation-invariant, i.
e. rotating the device does not affect the system behavior; • the ML model is to be trained on real-world ADLs of older adults collected for a long time period. The falls used for training represent a wide variety of fall types simulated by healthy young subjects; • the fall detection algorithm is to be evaluated for a long period in order to give an accurate estimate of the number of false positives in real-world conditions. The paper is organized as follows: we start by exploring the data acquisition system in Section II. Then, 400 days of real-life activities of older adults are analyzed in Section III in order to develop an efficient preprocessing system for fall detectors. The proposed ML-based fall detection algorithms are then explained in Section IV. The performance of the proposed algorithms is evaluated in Section V.
Then, the following formula is applied  [18] : h = T 0. 0065 × (1 -( P P 0 ) 1/5. 257 ) ( 1 ) where h is the height in meters, T represents the measured temperature in kelvin, P represents the measured pressure and P 0 represents the pressure at sea level in normal conditions. This real world dataset will be named RealAct throughout this paper. In the remainder of the paper, the lab-based dataset FallAllD  [8]  and the real world dataset RealAct are both considered to develop and to evaluate the performance of the proposed fall detection approach. The falls of FallAllD were simulated by relatively young subjects who wore body protection during these simulations. Although a comprehensive protocol has been set to represent all possible falls, the human reflex will prevent the subjects unconsciously from falling strongly, which leads to an attenuated impact compared to real falls.
Furthermore, younger subjects are more dynamic during the pre-impact phase, and the ADLs are simulated with higher intensities compared to the movements of elderly. Therefore, RealAct is better suited for performance evaluation in freeliving conditions. Nonetheless, seeing the lack of falls in this dataset, FallAllD will be used as a complementary dataset to train the machine learning models and to evaluate their sensitivity. The ADLs as well as the falls in the RealAct dataset are analyzed in the next section.
DETECTORS In this section, the RealAct dataset described in Section II is analyzed where the objective is to propose a pre-processing system for fall detectors with minimal assumptions about the mechanism of falling. This pre-processing system is aimed T inac ≥ 3200 ms The user remained inactive for 3200 ms at least C 3 ∆H ≤ -25cm The user moved downwards 25 cm at least to achieve 100% of sensitivity with an initial reasonable specificity. The idea is inspired by the generic definition of a fall given by WHO [1]: "A fall is defined as an event which results in a person coming to rest inadvertently on the ground or floor or other lower level". Following this definition, we define three conditions {C 1 , C 2 , C 3 } that are described in Table  I. The condition C 1 reflects the inadvertent movement of a person coming to rest which could be characterized by the presence of an impact shock in the acceleration signal.
IV. THE PROPOSED FALL DETECTION ALGORITHMS In this section, we propose several machine learning algorithms for fall detection. We first describe the datasets used for training and evaluating the performance of the proposed algorithms. we explain how an activity window is dynamically segmented into several phases from which the features are extracted. Feature engineering is then explored where the objective is to derive a set of rotation-invariant features that could be used for representing acceleration signals in both wrist-and neck-worn fall detectors. The extracted features are then used by a multitude of machine learningbased classifiers that are introduced at the end of this section.
A. Datasets In order to train the models, two data sources are employed: 1) the dataset that has introduced in Section II, and 2) the public dataset, FallAllD  [8] , that has been recently published by our team as an open access dataset. Particularly, RealAct is used as a source of real world ADLs (+ two real falls) and FallAllD is used as a source of simulated falls. While both acceleration and barometric pressure signals are used in the preprocessing system, only acceleration signals are considered in the machine learning models. The preprocessing system keeps only the acceleration signals that are suspected to represent falls and introduces them to the machine learning model. To this end, we extract from the RealAct dataset only the acceleration signals where the conditions C 1 , C 2 and C 3 are satisfied.
Clearly, only such kind of signals will arrive to the machine learning model and it is not appropriate to train the model on conditions that could never be seen. In other words, we train the proposed models to discriminate between falls and the ambiguous ADLs. The total number of the considered data samples is 3665. These data are divided into a training set of 1259 samples and a test set of 2406 samples. Particularly, a balanced training set has been built such that the following samples are used for training: 1) ambiguous ADLs from 97 days (621 samples) extracted from the RealAct dataset, and 2) around 75% of falls from the FallAllD dataset (638 samples). On the other hand, the test set contains: ambiguous ADLs from 303 days (2170 samples) of the RealAct dataset, 2 real falls from the same dataset and 25% of falls from the FallAllD dataset.
D. Machine learning-based classifiers Falls and ADLs, represented by the proposed features, are to be classified using nine machine learning-based classifiers. We use the scikit-learn Python library in which all the considered classifiers are implemented. To avoid listing all the tested configurations, only the configurations that have shown an interesting change in the results are reported. Unless explicitly mentioned otherwise, all the configurations are set to the defaults of the scikit-learn. The first family of the considered classifiers covers three ensemble methods: gradient boosting (GB)  [21] ,  [22] , random forest (RF)  [23] ,  [22]  and AdaBoost  [24]  classifiers. The GB classifier is configured such that the learning rate is 0. 4, the maximum tree depth is 2, and the number of estimators is n est ∈ {50, 100}.
To experimentally show the effect of using simulated ADLs on the system sensitivity, the same best model, GB {nest=100} , has been trained on the same falls but on simulated ADLs from the FallAllD dataset instead of the RealAct dataset. The sensitivity decreased to 95. 43% and 80. 12% for neck-and wrist-worn devices, respectively. This emphasizes the importance of the proposed strategy of considering real-life ADLs. The results also confirm our claims concerning the interpretability of the specificity results. We note that the classifier GB {nest=100} shows a specificity of 98. 52% and 96. 73% on the neck-and wrist-worn devices, respectively. If we have no access to the results of the 4 th criterion, the average number of FPs per day, it will be quite difficult to understand the impact of the small difference (1.
VII. CONCLUSION The majority of the state-of-the-art ML-based fall detection systems are trained on simulated datasets. Typically, the distribution of the ADL types in these datasets is considerably different from the actual distribution in real life. In this work, we have shown that this phenomenon causes two major problems. First, it leads to improving the specificity at the expense of sensitivity. Second, it makes the specificity measure uninterpretable. These problems have been tackled in this work where a reliable design of a machine learning-based fall detection system has been proposed. It is based on analyzing the activities of older adults experiencing their daily life. The proposed system can be used either with neck-or wrist worn detectors and it is rotation invariant. The system has shown a sensitivity of 100% on real falls as well as on simulated falls.
This work was supported in part by the  European Union  through the  European Regional Development Fund (ERDF) , in part by the  Ministry of Higher Education and Research , in part by the  French Region of Brittany  and  Rennes M étropole , and in part by the  French National Research Agency (ANR)  in the context of the  ACCORDS Project  under Grant  ANR-17-CE19-0024-01.
Data source Data on hospitalized patients with COVID-19 were collected from the national SI-VIC online health emergency registry. This database is designed to count victims and obtain real-time information about bed availability during large-scale health emergencies such as natural disasters, terrorist attacks or other exceptional healthcare situations, like the COVID-19 pandemic. Its data are exhaustive. Since the start of the outbreak, all hospitals in France had recorded COVID-19 cases in the registry daily. COVID-19 cases were defined and standardized at the national level and had to be biologically confirmed with a positive RT-PCR test or highly suspected case based on a chest CT scan. A user's guide to the SI-VIC registry was distributed to all hospitals to improve the quality of data collection.
The registry included all hospital stays of patients with COVID-19 in acute units or ICU, in realtime, as well as with transfer data and hospital outcomes. All patients were identified with a unique anonymous national identifier, enabling all hospital stays to be identified and extracted for statistical analysis. All patients registered in SI-VIC and admitted to a French ICU for COVID-19 from 1 March to 21 June 2020 were included in the study. Follow-up was until July 14, 2020. The following variables were extracted from the SI-VIC registry for each patient: (i) age; (ii) sex; (iii) origin (admitted from home, or from another hospital unit); iv) dates of admission to and discharged from the ICU or acute care unit; (v) destination after discharge from ICU or acute care unit (home, acute care unit, ICU, rehabilitation, death, still in ICU or acute care at the end of followup); (vi) length of acute care hospital stay (LOS) before ICU admission (days), LOS in ICU before transfer (days), LOS in ICU (days), total LOS (in acute unit and ICU, days); (vii) identification of the hospital; (viii) hospital type (university teaching hospital (CHU), general (nonacademic) public hospital (CH), private not-for-profit hospital or private for-profit hospital (private)), and (ix) second admission to the ICU (binary: yes or no).
Patients were classified in one of three transfer categories: no transfer, intra-regional transfer, or interregional/international transfer. An additional binary variable (yes or no) was created to distinguish hospital stays of patients coming from regions considered to have "high incidence" at the time of hospitalization. In these four regions (from among a total of 13 regions in Metropolitan France), incidence and hospital admission rates rose sharply during the first semester of 2020, and ICU capacity was saturated > 100%, with daily incidence of new patients admitted to the ICU greater than five per million inhabitants. The Euclidian distance (km) was also calculated and recorded in the database to describe the distance between the sending and receiving hospitals, regardless of the means of transfer.
Due to the unavailability of numerous clinical variables (including severity scores), transferred group and nontransferred patient groups were not comparable. This, however, was not an objective of the study since our main goal was to challenge the safety of the nationwide transfer strategy. All available variables were included in the model. Adjusted risk ratios were computed with data of non-transferred patients present at Day 5 in ICU. Reported results were generated using SAS/STAT 14. 3 (SAS Institute Inc. , Cary, NC, USA). A mapping of transfer distances according to time was conducted to visually describe the chronological and spatial distribution of transfer types. Mapping was conducted with the PROC GMAP function of SAS.