dataesr/scientific-paragraphs-categorization huggingface dataset:
PROTECT showed that knowledge engineering techniques can be used to derive novel groupings of adverse event terms dynamically based on the relevant semantic dimensions for a specific topic of interest, although no net improvement in signal detection performance was seen in these studies  [16, 17]. It is interesting to speculate whether it may be possible to go beyond this restricted example and generate alternatives to the standard groupings available in existing adverse event terminologies, such as MedDRA Ò HLTs or SMQs. One of the tangible outcomes of PROTECT work package 3 is the structured database of MedDRA Ò coded ADR information taken from section 4. 8 of the SPC for all products authorised in Europe through the centrally authorised procedure  [25]. This database is publically available on the EMA website and is maintained.
It reduces the need for manual inspection of SPCs when the focus of safety monitoring is detection of new risks. Its use has already been tested and implemented into signal detection processes at the EMA and the UMC. In a broad study across spontaneous report databases, PROTECT showed that the choice of signal detection algorithm (e. g. threshold on the number of reports, the threshold on the disproportionality statistic, and/or statistical significance) was much more important than the choice of disproportionality statistic itself  [35]. Performance of any single algorithm might be very different between one spontaneous report database and another but the relative performance of two algorithms was generally similar across different databases. In a related study across almost the same range of databases, the use of stratification and subgroup analysis in disproportionality analysis was explored  [45].
An earlier study by Caster et al. [37]  showed that the use of subgroup analyses and stratification both out-performed crude disproportionality analysis although the relative contributions of each approach were not determined. In the PROTECT study, it was shown that subgroup analyses tended to be more beneficial over stratified analyses across all datasets studied. Subgroup analyses also provided clear benefits over crude analyses in some datasets whereas stratified analyses did not increase sensitivity or precision beyond random variation. This unexpected finding that has not been reported elsewhere has important implications because a number of organisations routinely use stratification in their analyses, but very few routinely employ subgrouping. Other previous studies  [36, 38]  have observed modest improvements for stratified analyses consistent with the results from the stratified analyses in the PROTECT study and therefore these findings from the other studies may also be artefacts from the stratification process rather than a true effect.
PROTECT explored the impact of masking on statistical signal detection in spontaneous report databases  [49, 50]. Under the conditions of the study, it was rare for masking to affect whether a drug-event pair was considered to be disproportionally reported or not; however, the drug-event pairs that were affected in this way primarily involved rarely reported ADRs. Furthermore, the study only considered removal of single masking drugs from the calculation and in some cases, multiple masking drugs may be present  [51]. For the detection of adverse DDIs, PROTECT showed that statistical interaction measures with additive baseline models outperform those with multiplicative baseline models  [55] , typically available in standard statistical software. This finding was true for both established and emerging DDIs.
Broad studies such as those performed by observational medical outcomes partnership and EU-ADR have explored the merits of different epidemiological designs when applied automatically across broad ranges of drugs and outcomes  [80] [81] [82] [83]  but such studies have been primarily retrospective in nature, and there is also a lack of studies to determine the relative merits of exploratory screening vs. customised confirmatory analyses of longitudinal health data. Some research has proposed signalling showing outputs requiring highlighting in both spontaneous reports and observational data  [84]  but without attempting to assess the relative value of the two data sources. PROTECT performed research on statistical signal detection in the THIN database of longitudinal electronic health records from general practices in the UK.
This shows that comprehensive surveillance for early safety signals requires broad population coverage as well as effective ascertainment of a wide spectrum of newly marketed drugs and adverse events. Concurrent research has found that even networks of longitudinal observational databases can be underpowered for rare adverse reactions, whereas common adverse reactions should be possible to detect for commonly used drugs  [85]. There is an increasing number of observational databases available throughout the world for potential pharmacoepidemiology and signal detection work with each having widely varying characteristics, data structure and data quality concerns. It would be necessary to repeat the PROTECT analyses in these databases to determine to what extent these findings are generalisable.
Before approval of a drug, information on adverse events from clinical trials constitutes the primary basis for safety analysis and signal detection. PROTECT explored two statistical approaches to enhancing signal detection in clinical trials. One study explored the utility of extreme value modelling in early clinical studies as the basis for predicting drug toxicity in the subsequent phases of clinical development and evaluation  [74]. A retrospective analysis showed that extreme value analysis of phase II data would have highlighted the risk for liver toxicity for a compound eventually withdrawn from the market on account of this risk. A second study evaluating different approaches to adjust for multiplicity found that Bayesian Hierarchical Models can improve signal detection performance through borrowing strength from related adverse events in the clinical trial dataset  [75].
A subset of 14 studies  [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45]  found in the published reviews met the inclusion criteria. We extracted the target population of the described interventions -women, health workers, or others, such as information systems -and the communication channels enabled by the described projects. Interventions fell into 1 of 3 possible categories, vertical, horizontal, or hybrid interaction channels, depending on their structural characteristics; 11 studies  [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42]  described interventions that applied vertical communication channels mainly to collect data and to promote maternal and newborn health through mobile phones. The mobile-based services were based on diffusion of information in 1-way streams.
Focus group 1 was conducted in Spanish. Focus groups 2, 3, and 4 were conducted in indigenous languages; the help of a local moderator was needed to translate questions and women's answers into Spanish. Women were initially asked some general demographic questions, and investigators recorded the data on paper. The moderator then initiated group discussions around 2 components: the public health experience and the potential benefits and obstacles of vertical, horizontal, and hybrid mHealth platforms. Investigators took notes during the sessions, which were also recorded. Qualitative techniques were then used to extract common themes. Informed consent was obtained from participating mothers.
Pilot impact evaluation Demographic data and participant flow during the experiment Of 111 indigenous women who were assessed for eligibility, 100 were eligible. Data from 78 individuals was used for analysis, as 7 women did not attend the mobile phone handout meeting and 15 were lost to follow-up (Figure  2 ). Almost all participants (96%, n ¼ 75/78) used public health services as their primary health care for treatment and diagnosis, and over two-thirds (68%, n ¼ 53/78) reported having encountered problems to get basic medical services in the past year. Common complaints were: a shortage of doctors to meet the demand for health services, a lack of punctuality in terms of appointments, a lack of medicine and vaccines, and disrespectful behavior from workers at public health clinics. Participant demographics are presented in Table  1.
Singh et al. (2010)  reviewed multi-satellite sensor and ground observatory data and they have reported anomalous changes in ground, meteorological and atmospheric parameters (air temperature and relative humidity) compared to other days. Electromagnetic precursors have been reviewed by  Zhang and Shen (2011)  and  Zhang et al. (2012). They have shown that electromagnetic anomalies started 2. 5 years earlier and were recorded until three days before the event. A more complete review of many different precursors has been made by  Ma and Wu (2012)  and their paper contains about 140 references related to various precursory phenomena observed before this earthquake. It concerns: -Anomalies in deformation measurement which appear 3 days and 1 hour before, -Anomalous variations in strain/stress measurements 48, 30, 8 hours and 37 minutes before, -Possible structure variation five days before near the Longmenshan fault zone, -Anomalous signals observed in broadband seismic and gravity records starting from about May 9~10, 2008, -Geomagnetic anomalies 2 to 3 months before, -Ionospheric anomalies starting from 13 to 2 days before, -Geothermal and atmospheric anomalies, meteorological condition, temperature variation, largescale satellite Thermal Infrared Anomaly (TIR), infrared radiation anomalies, and anomalies of outgoing long-wave radiation.
The atmospheric chemical potential correction (see Sect. 2) has been evaluated for the Wenchuan earthquake. The epicenter was along the Longmenshan fault where the chemical potential correction distribution shows a minimum during all the period of the earthquake preparation. The temporal evolution of the chemical potential starting from 25 April 2008 is shown in Figure  14. In fact the main activity is observed on both sides of the Longmenshan fault (see Figure  15  where the spatial distribution of the chemical potential is shown on 01 and 11 May). So to observe this activity we have selected a point not exactly at the epicenter, but 1 degree to North and West from this epicenter, i. e. at 32°N, 102°E. An additional analysis of the DEMETER density has been done. The DEMETER data have been checked during one month and half, one month before the shock and fifteen days after.
DEMETER is only two times per day above a given region (once during daytime -10 LT and once during night time -22 LT). Then the data have been studied in a rectangle centred on the epicentre (longitude range between 93° and 113°, latitude range between 22° and 40°). This longitude range has been selected on order to have at least one orbit per day in the seismic region. Each orbit track is therefore more or less close to the epicentre. For each orbit, the electron density data measured by the Langmuir probe have been averaged according to the latitude. Then all daily values which are obtained at 10 LT are displayed in Figure  16. The red line represents the average value of these densities during the complete time interval and the dashed lines correspond to the variance. One can see a decrease of the density 2-3 days before the earthquake during daytime as it was reported in the ionospheric studies mentioned above.
The magnetic activity is quiet during the period of Figure  16  except at the beginning in April where we see a large increase of the density witch cannot be attributed to the seismic activity. 2013 ) have compared data from GPS receivers, the DEMETER satellite, and the Advanced Very High Resolution Radiometer (AVHRR) onboard NOAA satellite. They found that GPS total electron content (TEC) above the epicenter continuously decreased in the afternoon periods from 6 to 10 May but increased in the afternoon of 9 May. The density recorded by DEMETER also decreased from 6 to 10 May, mainly in the south of the epicenter. The brightness temperature from NOAA/AVHRR data is enhanced on the northwest side of the epicenter on 7 May, while ion temperature from DEMETER data increased on 9 May. The flux of energetic particle between 100 and 600 keV is enhanced on 6 May.
OLR anomalies were first observed (13 days before). Next are the abnormal variations of AT, ARH, and AP which occurred almost at the same time (10 days before). It is very interesting to notice that the time of anomaly occurrence of these three parameters also corresponds to the time of the increase of radon. Lastly are the SLHF abnormal variations (one day before). Recently,  Qin et al. (2014)  investigated the occurrence of atmospheric aerosols with MODIS data from both Terra and Aqua satellites. They have clearly shown an enhancement of the atmospheric aerosol optical depth associated with this earthquake by using MODIS data from both Terra and Aqua. It was along the Longmenshan faults 7 days before the quake, i. e. 1 day and 4 days earlier than the reported negative and positive ionospheric disturbances, respectively.
It is also interesting to note that  Gu et al. (2011)   anomalies concomitant with ionospheric perturbations. They have shown variations of 3 displacement components at the LUZH station (28. 87°, 105. 41°) on May 9 and even a vertical displacement of more than 300 mm at PIXI station (30. 91° ,103. 76°), i. e. at 36 km from the epicenter, 1 hour before the earthquake. In this paper we study the OLR in the range of 8-12 microns. A daily mean data footprints covering a significant area (90° N-90° S, 0°E to 357. 5°E) with a spatial resolution of 2. 5°by 2. 5° was used to study the OLR variability in the zone of earthquake activity  (Ouzounov et al. , 2007 (Ouzounov et al. , , 2008;; Xiong at al. , 2010). An increase in radiation and a transient change in OLR were recorded at the top of the atmosphere over seismically active regions and were proposed to be related to thermodynamic processes at the Earth's surface.
The same phenomena were reviled for the ionospheric perturbations of the local electron density measured from ground or by satellite, and of the global TEC measured prior to the different earthquakes. The influence of the global electric circuit between the Earth's surface and the bottom of the ionosphere has been confirmed by  Fan et al. (2015)  who have shown similarities between electric field simultaneously recorded  Nat. Hazards Earth Syst. Sci. Discuss. , doi:10. 5194/nhess-2016 -172, 2016  Manuscript under review for journal Nat. Hazards Earth Syst. Sci. Published: 23 June 2016 c Author(s) 2016. CC-BY 3. 0 License. onboard DEMETER and on ground. These results can be explained by the LAIC concept, which suggests the existence of physical links between the different atmospheric variations and tectonic activity  (Ouyang et al.
Figure16: Day time variation of the electron density in a rectangle around the epicenter (longitude range between 93° and 113°, latitude range between 22° and 40°) as function of days. The arrow indicates the earthquake day. The red line corresponds to the average value over the considered period and the dashed lines are related to the standard deviation. Yan et al. (2013)  have compared data from GPS receivers, the DEMETER satellite, and the Advanced Very High Resolution Radiometer (AVHRR) onboard NOAA satellite. They found that GPS total electron content (TEC) above the epicenter continuously decreased in the afternoon periods from 6 to 10 May but increased in the afternoon of 9 May. The density recorded by DEMETER also decreased from 6 to 10 May, mainly in the south of the epicenter. The brightness temperature from NOAA/AVHRR data is enhanced on the northwest side of the epicenter on 7 May, while ion temperature from DEMETER data increased on 9 May.
First, given that our dataset of informal conversations is unique among studies of future-time expression, it is possible that the futurate present is more frequent in spontaneous, informal conversational data (as opposed to the speech elicited in sociolinguistic interviews reported in most previous studies). We leave this point to future research. Second, as already mentioned, most research has defined the futurate present as the co-occurrence of a presumably present indicative form and a future-time temporal adverbial (e. g. ,  Blondeau, 2006: 74; Le Goffic and Lab, 2001: 78; Moses, 2002: 27). Although such instances are indeed counted among our set of present forms, they are not the majority. If we remove from our dataset all present-for-future forms with no temporal adverbial in the same clause as well as all forms that are clearly or presumably subjunctive, the futurate present would amount to only 13.
Studies that limit their scope to these forms are unable to truly gain access to how a variant functions in a given subsystem of the grammar (see  Aaron, 2010). Moving now to future-time expression in NS French, we note that this study has contributed to the description of French as it is spoken in France. However, much work remains to be done on Hexagonal French. First and foremost, the corpus analyzed in this study is not sufficiently large in order to be representative of Hexagonal French. It thus remains for future research to determine whether the patterns observed in the present data generalize across a larger dataset that includes a wider range of registers and communicative contexts. In addition, although our sampling procedure was based around a set of NNSs located mostly in the Southwest of France, their self-selected NS interlocutors were not all from this region.
Three large-scale events were analysed: the Taiwan Earthquake on December 27, 2006, the Japan Earthquake on  March 11, 2011 , and the USA Blackout on September 8, 2011. For each event, the authors used BGP data collected by the Routing Information Service (RIS) project from RIPE NCC to study the number of routes and geo-localization of IP addresses that became unreachable. The conclusion is that disasters cause catastrophic consequences to the current Internet settings and architecture, in terms of reachability of network prefixes, Internet global connectivity, and recovery time. Moreover, the consequences are not limited to the disaster area, but they also affect farther areas due to the interdependencies between Autonomous Systems and the damages of major transmission links between countries.
However, based on the successful evacuation of the handful of communities that did receive adequate warning through unofficial channels, it is clear that better communications could have saved thousands of lives. Climate change, natural catastrophes and failure of critical infrastructures are ranked at the top of the 2015 Global Risks database prepared by the World Economic Forum  [30]  and for which less progress has been made. The latest UK National Risk Register of Civil Emergencies also considers failure of critical infrastructures to have a high risk level and thus a priority risk  [31]. Irrespective of the success of our mitigation efforts, the impact of climate change will increase in the coming decades. While efforts must continue towards mitigating Fig. 3. Pre-disaster network robustness improvement its effects, there is no other choice but to take adaptation measures to deal with the unavoidable climate impacts that are anticipated and their economic, environmental, and social costs.
This is a rapidly deployable, small-scale cellular network based on the well-tested OpenBTS open-source platform that uses a software-defined radio transceiver to enable GSM transmission/reception. With an additional set of software tools, inbound and outbound VoIP calling and messaging is enabled through Skype for end users with or without Skype clients. Features for automatic reconfiguration of the BSs to maximize the functionality in the face of power, network and/or hardware failures are also provided. Due to the fact that each BS can serve up to 7 concurrent calls, an automatic SMS-based call queueing system is proposed to provide fair access, while avoiding user recalls. EmergeNet can be AC or DC power supplied (e. g. by power grids, portable generators, vehicle batteries). A threecell battery bank can provide 4 days of autonomous operation and solar panels can be used as well to offer 24/7/365 uptime in most locations.
Therefore, in order to provide fast and infrastructurefree service recovery, a great number of techniques engaging mobile user equipment to set-up ad-hoc/mesh networks up to a surviving node have been presented. Among them, the above mentioned STEM-NET  [37] , providing self-configurable SN implementation, allows the set-up of an ad-hoc network on top of existing end-user devices. However, while STEM-NET requires a new software architecture to be pre-installed in the mobile nodes, Minh et al. [51] ,  [52]  propose a tree-based multi-hop WiFi network that involves a downloadable (upon set-up of the network) software-based implementation of network functions. Each mobile acts as a virtual AP (VAP) to provide service to the rest of the mobiles and as a STA (IEEE 802. 11 wireless station) communicating with the VAP to which it belongs.
ACKNOWLEDGMENT We would like to thank the participants of WG1 (Largescale natural disasters) of  COST Action   CA15127  who indirectly collaborated in this task:  Michał Aibin ,  Péter Babarczi ,  Vitoria Bueno Delgado ,  Marco Casazza ,  Anna Fogertun ,  David Hay ,  Bjarne E. Helvik ,  Rita Girão-Silva ,  Róża Goścień ,  Yuming Jiang ,  Peter Kieseberg ,  Ioannis Krikidis ,  Konstantinos Manousakis ,  Maria do Carmo Medeiros ,  Cemalettin Ozturk ,  João Patrício ,  Maria Potop-Butucaru ,  Luis Quesada ,  Sarah Ruepp ,  Dorabella Santos ,  Noor Shirazi ,  Krzysztof Walkowiak , and  Zhongliang Zhao. This article is based upon work from  COST Action   CA15127  ("Resilient communication services protecting enduser applications from disaster-based failures -RECODIS") supported by  COST (European Cooperation in Science and Technology).
e. , service task, user task, script task, etc. ) and duration. Some indicators, like implementation type, input/output and role, are used and don't have impact on the performance in order to define the applicative task. -Non-functional Indicators: related to the run-time aspects of Business Process those are defined from the instance data collected from the environment of execution (BPM, SOA, ERP System). This type of indicators helps to evaluate the evolution of different performance criteria, such as maturity, availability, risk, interoperability over the time. Due to our aggregation model, these functional and non-functional indicators will be calculated at the functional level and the business level using business rules and ponderation. We create at the applicative level a reference analysis framework that will exploit data collected from the business process execution environment (SOA/BPM) given from IT infrastructure.
1) Participant: We obtained data measurements from the left hand of one of the developers of the system. We used his hand because we did not have to expect a subjective bias when evaluating the functioning of the system. 2) Setup and Procedure: We chose the interosseous metacarpal spaces (4, 3, 2) between the knuckles to set the focus locations and sampled data towards the middle of the hand (see Fig. 6-a ). The hand structure in this area allows focusing between the metacarpal bones where more energy can propagate through the tissues. Nine focus and sampling locations were marked on a grid (3 × 3). A thin layer of ultrasound gel was applied on both sides of the hand. The palm was placed on the array, and the upper row of transmitters was aligned to the position under the knuckles. We attached the receiver transducer to the stylus of a Phantom Premium device to ensure a constant pressure of 100 gram during the calibration phase and when logging data (see Fig.