dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Performance modeling in tourism research received a great attention from scholars this last decade  (Assaf and Tsionas, 2019). Several techniques are available in the literature in order to model tourism performance. Among others, Data Envelopment Analysis (DEA) and its derivatives is very popular. As identified by  Assaf and Tsionas (2019, p. 274) , a key point in tourism performance analysis is the question about the determinants of efficiency. In a first stage, efficiency scores are calculated by using non-parametric approaches based on linear programming in the DEA framework. In a second stage, the efficiency scores are regressed by using determinants which are termed contextual or environmental variables in the literature. The goal is to explain their possible impacts on the efficiency level of the units analyzed and these two-stage approaches have been used extensively by tourism researchers  (Assaf and Josiassen, 2016).
This is of particular importance for efficiency analysis as the literature clearly shows that, in order to be discriminatory in terms of efficiency, some rules are required between the number of units in the sample and the number of inputs and outputs  (Sarkis, 2007). In general, but without real consensus, the number of units is expected to be greater or equal to three times the sum of inputs and outputs  (Cooper et al. , 2006)  or larger than two times the product of the number of inputs and outputs  (Dyson et al. , 2001). Then, a production technology with two inputs and two outputs is considered. The variables are selected by following the literature and according to the data availability. The two inputs are accommodation capacity in the hospitality sector (number of rooms) and number of employees in the tourism sector (Sellers-Rubio and Casado-Díaz, 2018).
The efficiency scores of other cases like the region Bourgogne-Franche-Comté can be explained by the merger of regions with the territorial reform. The lowest region is Auvergne-Rhône-Alpes with a relative efficiency level of 76. 66% characterized by a shortfall of 30. 45% in the outputs production, confirming that this region is too big in size  (Corne, 2018). For the second stage with fsQCA, all the calculations are made by using the fs/QCA 2. 5 software  (Ragin and Davey, 2014). The calibration of the variables is the first step. Concerning the efficiency score and the explanatory variables monuments and museums, the thresholds are selected by using a cluster analysis  (Cronqvist, 2004)  and the researcher knowledge  (Ragin, 2008). The calculations are done with the Tosmana software  (Cronqvist, 2016).
By using the same data sources, we consider the year 2016 in order to test the predictive validity of the configurations we obtained in Table  6  for 2017. First, DEA efficiency scores are estimated and the same four regions are again technically efficient. Second, the fsQCA procedure is done and the three configurations identified in Table  6  obtain a high consistency index. More precisely, configurations (1) and (3) obtain respectively 0. 981 and 0. 901 for the consistency index and 0. 235 and 0. 315 for the coverage index, suggesting that these models have a good predictive validity. For a comparative purpose, Table  7  shows the findings of the bootstrapped truncated regression proposed by  Simar and Wilson (2007). Calculations are implemented with the R software by using the rDEA package  (Simm and Besstremyannaya, 2020).
Acknowledgements  Dr. Corrado Altomare  acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Grant  Agreement No. 792370. A. Souto-Iglesias acknowledges the funding by the  Spanish Ministry for Science, Innovation and Universities (MCIU)  under Grant  RTI2018-096791-B-C21  " Hidrodinámica de elementos de amortiguamiento del movimiento de aerogeneradores flotantes ".
Our two research questions are: • (RQ1) Can we extract daily changes of PA behaviour from accelerometer data, at an appropriate granularity to meaningfully assess the impact of health interventions with regards to the daily recommended targets? • (RQ2) What technique can be used to further the analysis of PA behaviour change in relation to the part of the day it occurred? This paper builds on our earlier work  [19]  to understand the impact of a learning intervention by analysing physical activity behaviour changes at a daily level. We propose a more comprehensive approach that enables a deeper analysis of both daily and hourly behaviours, and we apply it to a larger dataset. The paper is structured as follows. Section 2 presents existing work in mining sensors data for extracting health related behaviours.
II. RELATED WORK A. NON DATA MINING APPROACHES Evaluating PA changes after health interventions is traditionally done through statistical techniques, on data aggregated over the test period. For example,  [20]  reports the daily average amount of MVPA (min/day), Light PA (min/day), accelerometer counts (counts/min) and step counts (steps/day). [21]  use accelerometers, questionnaires and a series of fitness tests (Eurofit) to evaluate PA and fitness. The authors compare pre-and post-intervention data by computing the percentage of time spent at various intensities per day and their mean values, standard deviations and main effects (Fvalues). Whilst such analysis is useful to provide a big picture of changes, these measures are highly aggregated and do not provide information about the patterns of PA, and whether/how these may have evolved.
C. UNSUPERVISED TECHNIQUES Unsupervised data mining has been used to detect changes and analyse physical activity. For the detection of change in PA behaviours, time window-based techniques have been developed. For example  [11]  created a Physical Activity Change Detection algorithm (PACD), an adaptation of Permutation-based Change Detection in Activity Routine algorithm (PCAR)  [30] , which uses the distance between PA time series windows to detect a significant change in PA. They found that the PACD algorithm detected more changes than other window-based change detection algorithms (RuLSIF, texture-based, PCAR and a virtual classifier) using synthetic and PA data (Fitbit data). While this work aims to detect PA changes in accelerometer data, it focuses on detecting changes in daily activity-based routines to identify and quantify changes from those routines, rather than comparing two different sets of data.
Two clusters were found, one active and one much less active. Through these clusters, the authors analysed demographic, lifestyle, PA level and health characteristics. [32]  used three accelerometers and a heart rate monitor, and clustered the data using Hierarchical Clustering Analysis (HCA) and k-means algorithms with time and frequency domain as features to provide patients and caregivers with a more accurate overview of their physical activities. Whilst  [31]  and  [32]  are the most related works to ours, their focus was to highlight general temporal PA patterns in their population without addressing the needs of an intervention context, and in particular to evaluate pre-and post-intervention behaviour changes, assessing both changes in PA and sedentary time. Recently,  [5]  analysed the data from wrist activity trackers that children wore during a school-based health education intervention (iEngage) by creating daily behaviour clusters.
A. IENGAGE STUDY The accelerometry data was collected before and after a 5-week school-based digital health education intervention (iEngage)  [33]. The experimental group of children (N=61) followed the iEngage learning sessions over 5 weeks, whilst the control group (N=26) did not. The iEngage program aims to develop health knowledge and skills to empower 9-11 year old school children achieve healthy behaviours, with a focus on reaching recommended levels of daily PA (at least 60 mins per day of MVPA  [34] ) and decreasing sedentary time.
B. ACCELEROMETER DATASETS Pre-and post-tests were carried out, measuring unobtrusively and continuously the children's PA with research grade activity trackers (GENEActiv  [35] ) for five consecutive school days, hence generating two five-day long datasets per child. The GENEActiv accelerometers were placed on the wrist of their non-skilled hand and captured acceleration in three axes (x,y,z) with a sample frequency of 60 Hz. To ensure that all daily records had the same length, we excluded records with missing data. The result of this filtering is summarised in Table  1.
IV. METHODOLOGY FOR EXTRACTING PHYSICAL ACTIVITY BEHAVIOURS The methodology is summarised in Figure  1. Both datasets (pre-and post-intervention) were pre-processed and transformed into sequences of PA intensity levels vectors. Then we compressed these into bouts of PA per intensity level and used characteristics of these bouts as features to cluster the data and identify types of PA behaviours during the relevant period (day or hour). The next sub-sections will detail these steps. Data Pre-Processing.
C. BOUTS OF PA As mentioned earlier, we analysed the data through the notion of bouts with a focus on MVPA and sedentary time (SED) bouts during daytime. Let us first introduce some definitions. • A bout is a continuous episode of PA at a specific range of intensity level. • The length of a bout is the number of seconds spent during that bout. • The bout frequency is the number of occurrences of all bouts of a certain length during a day. As the daily recommendations are expressed in terms of MVPA (combining moderate and vigorous PA), we merged M and V into one category "MVPA". For instance, a sequence of 11 seconds spent in M, 8 seconds in V, and 12 seconds in M preceded and followed by L's would generate one bout of MVPA that would be 31 seconds long. This is pictured in Figure  3. Before using these bouts as features for the clustering, we conducted a quick analysis of their distribution, by computing the total time spent in MVPA done in bouts of at least x seconds.
B. PA HOURLY BEHAVIOURS CLUSTERING For the hourly PA behaviours analysis, the entire cleaned dataset was used. This included nights and the days where the bracelets were installed and removed, resulting in 10496 hours of data for the 104 PA records. The detail for each group can be seen in Table  7. We computed hourly behaviour vectors using again the 12 daily features from Table  3. The resulting 10496 vectors (up to 121 hours x 104 participants pre-and post-intervention) would thus characterise a child's PA over that specific hour. After feature normalisation, the first three Principal Components were retained (see Figure  6 ). We ran k-means unsupervised algorithm with k=4. The number of clusters was again determined using the total within-cluster sum of square method. Table  8  shows the centroids of the hourly clusters.
B. WHAT TECHNIQUE CAN BE USED TO FURTHER THE ANALYSIS OF PA BEHAVIOUR CHANGE IN RELATION TO THE PART OF THE DAY IT OCCURRED? For RQ2, the hourly behaviour clusters provide more specific insights into the period of the day where such potential behaviour changes occurred. We examined the distribution of time spent on each hourly cluster throughout their school day. Figure  7  displays the proportion of time spent in each of the four clusters during a typical day pre-intervention. It is very informative to see that most children (90%) spend time in active clusters (H3 and H4) between 8am and 3pm. After 3pm, only about 25% of children remain active. The data also clearly show that the time before class start (8-9am) morning and afternoon recess times (10am and 2pm) are characterised by the highest proportion of children in the more active cluster (H4).
VII. CONCLUSION We presented a methodology to extract salient aspects of children's PA behaviour and how these changed before and after a Health education intervention. We model the PA into bouts of PA intensities, using their length and frequency, as features to cluster the daily and hourly PA behaviour in relation to international recommendations. These clusters are then used to examine the differences between before and after the intervention. This approach is complementary to using descriptive statistics to measure overall behaviour changes between pre and post-intervention data (via ANOVA or similar): It provides an aggregated analysis (via clusters), yet capturing important and essential aspects of the activity (the length and frequency of bouts of PA). It also enables a smaller grained analysis to understand the impact of the intervention at a general and individual level, starting from a high level and zooming into more specific parts of the day, as we illustrated with the After School time example.
The observations are not always made at a regular time lag (every hour, every day etc. ) and this latter may differ from one place to another  [1, 3]. Moreover, the use of functional data also allows the estimation of the velocity and acceleration of the time series. As a result, a considerable amount of research has been dedicated to the development of statistical methods and tools for analysis of functional data  [1, 2, [4] [5] [6]. The works by Ramsay et al. have made these approaches popular, and R and MATLAB programs (The R Foundation for Statistical Computing, Vienna, Austria) have made the methods available to a wider group of researchers  [5]. Applications in public health and biomedical sciences have been reviewed by  Ullah and Finch (2013)   [7]. In areas with low malaria transmission, because of the spatial heterogeneity of malaria incidence, World Health Organization (WHO) recommends the development of targeted control strategies adapted to the local epidemiological context  [8].
Thus, temporal variations of velocity and acceleration together provide information about the malaria dynamic. Moreover, key features of the malaria dynamic derived from velocity and acceleration functions as onsets, peaks, ends, and their lags between patterns are useful to refine targeted intervention schedules. In this paper, we introduced an approach based on functional data analysis to identify patterns of malaria incidence over a five-year period at village scale, in west-central Senegal. In addition, with the epidemiological indicators determined from the velocity and the acceleration of the resulting patterns, we investigated the spatiotemporal variation and features of malaria incidence in local context, in order to guide the targeted malaria control measures in a low transmission area and local context.
Study Area and Dataset The data used for this study were collected between January 2008 and December 2012 during a field trial of Seasonal Malaria Chemoprevention (SMC) among children from 575 villages in west-central Senegal  [23, 24]. This area is a part of the two national rural health districts, Bambey and Fatick, where the national malaria control program estimated the incidence under 5 cases/1000 person-years in 2018  [25]. The protocols for the field studies were approved by Senegal's Conseil National pour la Recherche en Santé and the ethics committee of the London School of Hygiene and Tropical Medicine. The SMC trial  [23]  was registered number NCT00712374. The datasets analyzed during the current study are available from the corresponding author on reasonable request. Malaria surveillance was maintained in 38 health facilities serving a population of about 500,000 living in 575 villages (single villages or groups of adjacent hamlets).
Malaria cases were patients examined at health facilities with fever or history of fever, in the absence of evident alternative causes of the fever, who had a positive rapid diagnostic test (RDT). Date of diagnosis, village of residence, and other details were obtained for each case from facility registers. The surveillance system is described by Cissé et al. [23]. The population was counted through a census in 2008 and updated through visits to each household at approximately 10 months intervals from 2008 to 2012. The coordinates of each village center were obtained by Global Positioning System (GPS). For this analysis only aggregated data by villages were used.
Estimating the Smooth Function (Functional Data) for Each Time Series The time series of the observed weekly malaria incidence (i. e. , the number of confirmed cases per week divided by the total population of the village at this week) was determined for each of the 575 villages. A square root transformation was applied to these incidence rates to stabilize the variance  [4]. The functional data method  [4, 5]  states that the square root of observed malaria incidence rate for a village i at a week j is the sum of a function on the time continuous of week j and an error term  (1). y ij = Inc ij = x i t j + ε ij i = 1,. , 575, j = 1,. , 261 (1) where x i is a regular (smooth) function which describes the temporal pattern of malaria incidence in village i, t j is the continuous time of week j, and ε ij is an error term representing the difference between the function value and the observed data for village i at week j.
At this stage only the Euclidean distance and the dynamic time warping distance were implemented in R package with this dissimilarity measure. We have written a R program to estimate the functional Euclidean distance and the discrete wavelet transformation dissimilarity based on valued and behavior with the same formula above. Thus, hierarchical ascending clustering (HAC) was performed on smooth functions using each dissimilarity measure with Ward aggregation method  [32]. This was to find the most able dissimilarity measure to assess the difference between curves for reaching a better quality of classification. Thus, we obtained 20 HAC results. To assess the HAC results according to potential numbers of patterns (chosen after examination of dendrograms), four validity indices were used in a multidimensional space for functional data  [33] [34] [35] : connectivity, Dunn, silhouette width, and the percentage of inertia explained by the number of patterns R 2.
Velocity and Acceleration To further describe the malaria incidence patterns, the first (velocity) and second (acceleration) derivative were determined for each functional data of a pattern. Their variations over time indicated the growth and decline phase periods in each pattern, and the degree of speed: quickly or slowly. Thus, with mathematical properties of univariate function optimization [21] and one-dimensional kinematics in physics  [22] , seven epidemiological indicators based on velocity and acceleration were defined (Figure  1 , Table  2 ). These epidemiological indicators were as follows: the beginning of seasonal outbreaks and the start acceleration of the growth phase (A); the beginning of the pre-slowdown of the growth phase (B); the deceleration's beginning of growth phase (C); the peak (D) also corresponding after to the beginning of the acceleration of the decrease phase; the beginning of the deceleration of the decrease phase (E); the beginning of the tail (F); the end of the seasonal outbreaks (G).
Finally, a PCA was performed on the durations: AB, AD, CE, DG, FG, BF, and AG to look for those that characterized patterns of seasonal outbreaks. Let Q be the number of patterns identified by the HAC, we defined the functional data of each pattern by  (), q = 1, …, Q being the cumulative weekly incidence of the villages belonging to the pattern q. Then, their 95% point-wise confidence intervals were computed by adding and subtracting two of the standard errors, that is, the square root of the sampling variances, to the actual fit  [4].
Velocity and Acceleration To further describe the malaria incidence patterns, the first (velocity) and second (acceleration) derivative were determined for each functional data of a pattern. Their variations over time indicated the growth and decline phase periods in each pattern, and the degree of speed: quickly or slowly. Thus, with mathematical properties of univariate function optimization [21] and one-dimensional kinematics in physics  [22] , seven epidemiological indicators based on velocity and acceleration were defined (Figure  1 , Table  2 ). These epidemiological indicators were as follows: the beginning of seasonal outbreaks and the start acceleration of the growth phase (A); the beginning of the preslowdown of the growth phase (B); the deceleration's beginning of growth phase (C); the peak (D) also corresponding after to the beginning of the acceleration of the decrease phase; the beginning of the deceleration of the decrease phase (E); the beginning of the tail (F); the end of the seasonal outbreaks (G).
Circadian systems provide a fitness advantage to organisms by allowing them to adapt to daily changes of environmental cues, such as light/dark cycles. The molecular mechanism underlying the circadian clock has been well characterized. However, how internal circadian clocks are entrained with regular daily light/dark cycles remains unclear. By collecting and analyzing indirect calorimetry (IC) data from more than 2000 wild-type mice available from the International Mouse Phenotyping Consortium (IMPC), we show that the onset time and peak phase of activity and food intake rhythms are reliable parameters for screening defects of circadian misalignment. We developed a machine learning algorithm to quantify these two parameters in our misalignment screen (SyncScreener) with existing datasets and used it to screen 750 mutant mouse lines from five IMPC phenotyping centres.
These are all zeitgebers of the circadian clock and will impart phase information on their target tissues  [22]. Circadian misalignment is a consequence of conflicting signals of these zeitgebers. The International Mouse Phenotyping Consortium (IMPC) project is generating a genome-wide annotation of gene functions by systematically generating and phenotyping a collection of standardized single-gene knockout mice  [23] [24] [25]. Indirect calorimetry (IC) datasets are collected by the IMPC pipelines with standardized protocols (https://www. mousephenotype. org/impress/protocol/86). Activity parameters are monitored using a metabolic chamber equipped with infrared beam instead of using a running wheel to avoid artificially enhanced or weakened activity. A food intake monitoring system is also integrated to investigate diurnal patterns of feeding rhythms and behaviours.
The availability of the IC datasets from mice generated and phenotyped by the IMPC offered a unique opportunity to perform a large-scale screen for mutants with defective circadian misalignment. By analyzing the IC data from more than 2,000 wild-type mice available from the IMPC, we identified two reliable parameters of circadian misalignment. We developed a machine learning algorithm for circadian parameter recognition (SyncScreener). Using this algorithm, we screened mice from 750 mutant lines from five IMPC centres and identified five novel genes involved in circadian misalignment. Among these genes, the function of Slc7a11 in circadian entrainment was confirmed by creating a knockout mouse, demonstrating that our approach is effective in uncovering mechanistic insights into circadian entrainment.