dataesr/scientific-paragraphs-categorization huggingface dataset:
Such approaches should also be encouraged for spectroscopic methods allowing for better comparison of results from different studies. As an example of harmonisation, among all FT-IR studies (n=21), only 52% provided a threshold beyond which the identification of the polymer's nature is certain, and this threshold varied from 60% to 85%. None of the six studies performing Raman identification provided such information. Working with the same parameters would help to harmonise identification methods and ultimately enable sharing of research databases, increasing the diversity of spectra available to everyone, and thereby the power of identification of the different tools  [102]. Finally, it could be suggested to add positive identification controls (PIC), i. e. a single polymer or multiple known polymers, and negative identification controls (NIC), i.
eu/) developed on Galaxy is mostly dedicated to infrared spectrometry. Next steps involve the development of ad-hoc analysis pipelines  [100] , made available to the research community, able to process problematic spectra and improve identification levels. In parallel to the chemometric approach, as proposed by different authors  [102, 109] , some works must also be undertaken with weathering and analytical specialists to obtain Raman/FT-IR/Py-GC/MS spectra related to weathered polymers. Indeed, most of the particles found in seafood are secondary sourced MP that have been damaged by the action of UV, oxidisation and swell. This results in spectra that could be different from pristine polymers conventionally used for databases. Moreover, this has been demonstrated for PVC after analysis by µ-Raman  [105]  and Karami et al.
We visually compare the proposed algorithm with two state-of-the-art algorithms, namely the output of the block nuclear norm based algorithm  [11] , which is claimed to outperform variational methods such as  [10] , and the directional non-linear filter proposed in  [14] , which certainly achieves the best trade-off between the quality of the cartoon / texture separation and computation time. We use the software implementation kindly provided by the authors of  [11]  and  [15]. Note that the implementation of  [11]  only gives gray-level outputs. The default parameters of  [11]  are used, and the scale-factor parameter σ CT in  [15]  is adjusted to obtain the best visual results. Our algorithm is implemented as a publicly available Matlab code. To begin with, we illustrate the proposed algorithm with a famous image containing several different textures.
The assessment of internal exposure for workers is usually based on ambient activity measured at the workplace or individual activity from bioassay interpreted with dose coefficients and time-dependent retained and excreted fractions of intake established by the International Commission on Radiological Protection (ICRP) from reference biokinetic and dosimetric models. To gain flexibility in the implemented models and enable consideration of non-standard situations of exposure such as wound contamination, clinical trials of radiopharmaceuticals, exposure to radon decay products with specific aerosol parameters or intake followed by decorporation treatment, a new software module, called "Internal dose CAlculation for Radionuclide Exposure (ICARE)", was developed for expertise and research. It provides the dose coefficients and retained/excreted fractions used by OPSCI software  [1]  (IRSN-ORANO) which performs occupational dose calculation from bioassay measurement, evaluates the associated uncertainty and supports design of monitoring programs of internal exposure.
ICARE will be presented as software developed with the Interactive Data Language (IDL, Harris) to implement biokinetic models of the ICRP 60  [2]  series of publications by the method described by Leggett et al. [3]. It provides retained fractions of intake in body regions, daily urinary and fecal excretions after occupational exposure by inhalation, ingestion or wound  [4]. S coefficient values are calculated from emission spectra of ICRP  [5]  convoluted with specific absorbed fractions published by Cristy and Eckerman  [6]  for photons and by ICRP  [7, 8]  for electrons and alpha particles. S coefficients are combined with numbers of nuclear transformations to provide absorbed, equivalent and effective doses per unit intake. For validation, calculation results were extensively compared with ICRP equivalent and effective dose coefficients [9] and with bioassay quantities predicted by DCAL  [10]  and IMBA  [11]  software.
, 2009; Berrang-Ford et al. , 2011; Huq and Reid, 2004; Smit and Wandel, 2006; Kristjanson et al. , 2012). Farmers' adaptation to climate opportunity results from the combination of necessary conditions, ancestral know-how, and economic and bioecological utilities. We aimed at establishing the causal link between a climatic factor (the increase in rainfall) and the recurrence of a variety of longcycle millet (adaptation strategy), describing the spread of longcycle millet cropping and understanding the determinants of this strategy. To this end, we analysed meteorological, agronomic and socioeconomic databases, collected at different multidecadal timeframes  (1950-2015; 1997-2015)  in 665 villages and 1061 farms in West Africa across scales (across individuals, farms and villages). We focused on the old groundnut basin area in Senegal that is characterised by a recent increase in annual rainfall and the reappearance of a long-cycle millet.
The study zone (yellow in Fig. 1a ) is situated 135 km east of Dakar, lying mainly in the department of Fatick (the Fatick region) and extending into the regions of Thies in the West and Diourbel in the North. It also includes the Niakhar Health and Demographic Surveillance System (HDSS-Niakhar) (green in Fig. 1a ), a long-running health and demography surveillance system, which is made up of 30 villages in large majority sereer across 203 square kilometres within the continental Sahel-Sudan climate zone. The objective of this study is to determine whether a climate opportunity can help farmers to adapt and pave the way for agriculture transformation. We use four databases-meteorological, agronomic, geographic and socio-behavioural-for the various analyses described in the Method section (spatial and temporal correlation models, event coincidence analysis, agronomic simulation and logistic regression).
• Declarative and retrospective agricultural survey data (data on agricultural practices and on the spatial distribution of villages cultivating the sanio). These databases are sometimes exhaustive, sometimes representative (random sample). To understand the dynamics of the reappearance and distribution of sanio in the study zone we questioned rural populations in all of the villages listed in the territory (usually including the village chief) using a questionnaire with approximately fifteen questions. The questions covered: (i) the use of sanio in the 2015 rainy season; (ii) the year this grain reappeared in the village; (iii) the advantages and drawbacks of growing sanio and (iv) the presence and number of farmers who had never stopped growing this variety. The geographic coordinates of the village centre were taken using GPS.
Crop model calibration and validation. Trials were used to calibrate the model to farmers' varieties (souna 3 and sanio) by adjusting model parameters such as phenological stages, sums of temperature, photoperiod sensitivity coefficients, biomass partitioning coefficients and potential grain yields. These trials were carried out at the Bambey research station of ISRA (Senegal Agricultural Research Institute) in 2008  (Kouakou et al. , 2013). Independent data from trials conducted in the same site (1996-1999 for souna 3, 2012 for sanio; see  Kouakou et al. , 2013)  were used to validate the model. These experiments included different plots under varying levels of water supply, but under constantly high nutrient supply and no significant biotic stresses. The performances of the model were assessed using the two following indicators applied to yield estimates at harvest as follows: Relative Root Mean Square Error : RRMSE ¼ P n i¼1 Pi À Oi ð Þ 2 =n À Á 1=2 =O ð1Þ Index of Agreement : IA ¼ 1 À P n i¼n Pi À Oi ð Þ 2 = P n i¼n Pi À O þ Oi À O À Á 2 ð2Þ where Pi = predicted value, Oi = actual value, O = average of the actual values, n = number of cases considered  (Willmott, 1982).
As the performance of a model increases, RRMSE and IA tend respectively towards 0% and 1. After calibration of souna 3 variety, the values of RRMSE (Eq. 1) and IA (Eq. 2) were 36% and 0. 76, respectively. RRMSE and IA reach 19% and 0. 63, respectively, after the calibration of sanio. More details on these calibration and validation steps can be found in  Kouakou et al. (2013). In this study, we used SARRA-H to simulate crop yields of both souna 3 and sanio over the 1950-2015 period. The simulation was done at the Bambey location, which was the only site in our surveyed area with all the necessary daily weather data to force the crop model (rainfall, global radiation, surface wind speed, humidity, and minimum, maximum and mean temperature). Statistical analysis. Rainfall trends were evaluated using data from rainfall measurement stations and from three meteorological stations set up by ANACIM (National Meterology Agency), ISRA (Senegal Institute for Agronomic Research) and IRD (French Research Institute for Development).
Using rasterformat files generated by spatial interpolation of the observed data (standard kriging), the average annual rainfall was estimated for every year from 1998 to 2015 and in the three vector-format polygonal areas that represent the degree of sanio cultivation. Once the three rainfall measurement series were obtained, we looked for the best fractional polynomial of annual rain accumulation to define the polynomial function that fits the incidence level of villages adopting sanio with annual rainfall. This function is: f x ð Þ ¼ X k i¼1 γx Pi þ ε ð3Þ The degrees of the polynomial function (Eq. 3) were tested at all strengths using the group P = {-2,-1,-0. 5,0,1,2,3,4}, where p 0 becomes ln(p). More generally, third degree equations were adequate to obtain a good fit. The model that best fit the data was selected using the AIC criterion.
Sigmund  (Sigmund et al. , 2017). The trigger coincidence rate is: r t ΔT; τ ð Þ¼ 1 N A X N A i¼1 θ X N B j¼1 1 0;Δt ½ t A i À τ À Á À t B j h i ð4Þ where r t (ΔT,τ) measures the fraction of B type events that are followed by at least one A-type event  (Donges et al. , 2016). The statistical significance of trigger coincidence rate is based on the calculation of event coincidence analysis for a large number of randomly shuffled time series having the same number of events like the original time series ("shuffle. surrogate"). The ECA approach is appropriate for a naive conception of causality, in the sense given to it by physics: the cause precedes the effect, but is not appropriate to causality considered predictive (in the sense of C. Granger). The explanatory model of sanio cultivation was constructed using data from the questionnaire survey together with information collected on a routine basis at the HDSS-Niakhar.
This model was tested using binary logistic regression on the STATA® 13. 1 software. The sample created for the ESCAPE survey was designed to be representative for all of the 30 villages of the HDSS-Niakhar. To increase the sensitivity of the prediction model, we cleared the model (analysis of residuals), eliminating values that differed significantly from others (outliers) and those that excessively affected the model (leverage points and influencers). The overall adjusted and cleared model was shown to be quite robust (Hosmer-Lemeshow test) and had greater sensitivity, which increased to 77% after the sample was adjusted.
Analyses of various databases show that the cultivation of sanio, a long-cycle millet, which had nearly disappeared from the Fatick region for nearly a quarter of a century, has once again spread throughout the study zone (in the centre of the Fatick department). The resumption of sanio cultivation is clearly associated with increased rainfall, including the crossing of the 550-600 mm/year threshold starting from 2008. These Fig. 3  Correlation between rainfall and sanio-a for the entire study zone and (b) by area. Figure  3  shows non-linear fitting between the incidence rate of villages adopting sanio millet and the average of past year total rainfall. The incidence rate indicates the number of new villages adopting sanio relative to the number of villages that have not adopted sanio millet (including the new villages).
This rain shortage led to a 34% reduction of the number of sanio producers. However, the great majority of growers continued to produce sanio, because this crop is better suited to late rainfall in the season, and that it was able to Fig. 4  Percentage of years over the annual rainfall threshold that triggers the adoption of millet by area for the 1997-2015 period. The indicator in the graph was calculated using the event coincidence analysis (Package CoinCalc) under R. The smoothed curve was calculated by local regression (lowess). This indicator represents the trigger coincidence rate for the adoption of millet by the villages, according to an annual rainfall threshold. For each area, a rainy year is considered a stimulus that triggers the adoption of millet, if the proportion of new villages adopting the cereal, in one of the following two years, exceeds the 65th percentile of the distribution of millet-growing villages.
Acknowledgements We thank  G. Panthou  and  T. Vischel  for providing us SPI time series. This work has been funded by the  French National Research Agency (ANR)  through the  ESCAPE  project ( ANR-10-CEPL-005 ) and the  NERC/DFID   Future Climate For Africa programme  under the  AMMA-2050  project, grant number  NE/M020126/1.
Patients Twenty-nine MSA and 26 PD patients matched for age and sex were prospectively recruited at the outpatient clinic of the Toulouse PD Expert Center and the Toulouse site of the French Reference Center for MSA. Inclusion criteria were: (1) diagnosis of PD or MSA according to established international diagnostic criteria  (Gilman et al. , 2008; Hughes et al. 1992)   5 ) no treatment with deep brain stimulation; and (6) no evidence of movement artefacts, vascular brain lesions, brain tumor, and/or marked cortical and/or subcortical atrophy on MRI scan (2 expert radiologists examined all MRIs to exclude potential brain abnormalities as apparent on conventional FLAIR, T2-weighted, and T1-weighted images). At the time of MRI data acquisition, 22 MSA patients were classified as "probable", while 7 MSA patients were classified as "possible".
R2* images The six T2*-weighted volumes were averaged to generate a mean T2*-weighted volume. A full affine 3D alignment was calculated between each of the six T2*-weighted volumes and the mean T2*weighted volume. For each subject, a voxel-by-voxel nonlinear leastsquares fitting of the data was acquired at the six TEs to obtain a monoexponential signal decay curve (S = S0e -t/T2*). This method combining data acquisition and data processing of T2* images demonstrated good reproducibility  (Péran et al. , 2007). To facilitate the analysis of the relaxation results, we considered the inverse of the relaxation times (i. e. , relaxation rates R2* = 1/T2*) as previously described  (Cherubini et al. , 2009; Péran et al. , 2007 Péran et al. , , 2009)). The mean T2*-weighted volume was registered to the T1-weighted volume using a full affine alignment, T1-weighted volume was than non-linearly registered into the MNI space and the calculated deformation field applied to the R2* images.
Relieff based features selection Relieff  (Kira and Rendell, 1992; Kononenko et al. , 1997 ) is a feature selection algorithm that is widely used in the machine learning literature. It estimates a weight for each feature by comparing, for each case, the distance of the closest intra and inter-class cases in that feature space and increasing the weight if the distance is greater for the interclass than for the intra-class case. For each modality, we submitted the features surviving the variance threshold to the Relieff algorithm (as implemented in the CORElearn package for R (Robnik-Sikonja and Savicky 2017)). In order to select the most relevant features we used a screen test approach  (Mori et al. , 2000)  as implemented in  (Meng et al. , 2017). We calculated the selection threshold as the first minimum of the second derivative of the sorted (in decreasing order) and smoothed (via a loess regression  (Cleveland et al.
After the third step all clusters smaller than k and isolated voxels were discarded (this step was performed using the spatstat package in R  (Baddeley et al. 2015) ). The rationale for this step was that voxels with supra-threshold Relieff weight but "isolated" could add noise in the model (i. e. due to the spatial dependency of voxels in the brain, isolated voxels have higher chance of not being physiologically relevant). Moreover, too many such voxels would increase the space of possible features subsets to evaluate (see below). We tried several k (i. e. 30, 50, 100, 200) in order to observe the effect that this parameter could have on the discrimination performance. Finally, we extract the average signal for each cluster, thus effectively reducing the number of features for each modality from hundreds to tens.
, 2010). Using an inner 10-fold cross-validation scheme we tested all the possible combinations of modalities: for each combination we merged the modalities in one matrix (having dimensions  [subjects]  x [N of clusters from all modalities in the combination at hand]) and performed subset selection using the select. cfs function  (Wang et al. , 2005)  of the Biocomb package  (Novoselova et al. 2017). We chose a 10-folds cross validation to be consistent with the outer cross-validation loop. In the end, we obtained a cross-validated performance score for each combination (i. e. balanced accuracy). We selected the subset of cluster/modalities that maximized the performance score and used this subset to fit the discriminant model.
Fitting of the model Finally, the model is fitted using the sequential minimal optimization (SMO) algorithm  (Platt 1998; Schölkopf and Smola 2002)  with polynomial kernel. The model was fitted using the RWeka package  (Hornik et al. , 2009) , a wrapper of the java-based software Weka  (Witten et al. , 2011). The free parameters of the SMO algorithm are the order of the polynomial and the lambda (i. e. allowed error); we left these parameters to the default value of respectively 5 and 10.
The choice of this cross-validation scheme naturally leads to a reduction in performance, as the training set gets smaller, but gives on the same time a less biased estimate of the performance. Moreover, it should be noted that this is a quite unique dataset; we have well characterized PD and MSA patients who underwent what is, to our knowledge, the most complete multimodal MRI protocol in the literature. We hope that in the future more centres specialized in movement disorders will acquire the same sequences we did, allowing to test our pipeline on a much bigger sample. Another limitation is related to the difficulty in the differential diagnosis between MSA-p and PD. However, the diagnosis for all patients have been confirmed at a 2-years follow up. A similar problem is present for the differential diagnosis between MSA (especially MSA-p) and progressive supranuclear palsy  (O'Sullivan et al.
For this, attaining crop canopy at different periods and air temperature are needed for the calculation of CWSI. A wireless sensors based monitoring system where all the field sensors are connected to collect the mentioned measurements, further transmit to processing center where corresponding intelligent software applications are used to analyze the farm data. Not only this but information from other sources including weather data and satellite imaging is applied to CWSI models for water need assessment, and finally specific irrigation index value is produced for each site. A prominent example is VRI (Variable Rate Irrigation) optimization by CropMetrics  [37] , which works according to topography or soil variability, ultimately improves the water use efficiency.
2) ZIGBEE Zigbee is primarily designed for a wide range of applications especially to replace existing non-standard technologies. Depending on the application requirements, the devices based on this protocol can be one of three types including Coordinator, Router and End User. Further, three different topologies are supported by Zigbee networks named, Start, Cluster Tree and Mesh  [34]. Based on these characteristics, and further considering the agriculture application requirements, Zigbee can play vital role especially targeting the greenhouse environment where usually short range communications are required. During monitoring the various parameters, the real time data from the sensor node is transferred through Zigbee to end server. For the applications like, irrigation and fertilization, Zigbee modules are networked for communication, e.
In most cases, all this comes along with expert advice from a wide range of sources (for example, on farming and the processing of agricultural products). To make it more effective, the scenario can be extended further to include access to consumer databases, supply chains, and billing systems. Surely, moving towards Cloud-based services offer opportunities to explore advancements, but it comes with new challenges, as well. First, a vast range of sensors are being developed and used in precision agriculture, each of which has its own data format and semantics. Secondly, most of the decision-support systems are application-specific while, on the other hand, a farmer can be in the need of accessing various systems for a specific application, e. g. , soil monitoring. Considering both of these cases, the Cloud-based decisionsupport system not only needs to handle the diversity of data and their formats but also must be able to configure these formats for different applications.
The technologies include the HANA, a Cloud database which supports speedy data capture, retrieval, and analytics; the Leonardo IoT suite to connect and exchange information over any protocol; and the Connected Agriculture suite to provide a GUI-based, graphical-use interface to the farmers. While it depends on the situation, in most cases, few hours delays doesn't leave serious impact in most of the agriculture applications but flight time is more important as need to cover larger areas due field vastness. Not only vastness, but in some applications of pesticide and fertilizer, UAVs need to carry heavy payloads. In such situations, optimum battery utilization becomes crucial to extend the flight time. For this purpose, many factors can be considered to increase the drone efficiency. Firstly, when flying, choose right conditions e.
Based on the facts, one can conclude that increasing food production is not sufficient to achieve food security, but, rather, some practical actions are required to find skillful ways for efficient distribution of the already available food. There are different ways to monitor and control food temperature. The manual method of checking a thermometer and recording the temperature has many drawbacks, where someone must actually do it and, most importantly, take the readings correctly. On other side, implementing an automatic method that uses wireless sensors to electronically measure and record temperatures can substantially improve food safety. This method allows for a continuous data of temperatures simply-24 hours a day, 7 days a week. By doing so, temperatures can be recorded consistently and on time, leaving little room for interpretation; in short, the entire process is based on facts and nothing more.