dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods A general methodology framework of a garment block pattern generating for pregnant women was proposed in the present study which is depicted in Figure  1. The pregnancy period is divided into three trimesters which each one includes three months of pregnancy period. The data for all three trimesters are listed in Table  1. Therefore, the body morphology of a pregnant woman (subject 1) was investigated week by week to have a comprehensive understanding of body evolution throughout pregnancy. The body has been scanned between the 16th and 26th weeks of pregnancy length. The pregnant woman was scanned using 3D Scan Tecmath supplied by Human Solution Company while she was dressed only in underwear. The subject has been asked to stand in a predefined position in the area of the accessibility of the lasers for scanning.
After that, scanner data were exported to ScanworX TM software supplied by Human Solution and digitized by this software. Then, the changes in body morphology were analyzed for the second trimester. It is noted that the body scan data of this pregnant woman was accessible for this specific duration. It should be considered that this work concentrated on torso area as the electromagnetic shield will be applied around this area to protect both mother and fetus against the detrimental effects of electromagnetic waves  [33] [34] [35]. After that, the data acquired through the body scanner had to be transformed in order to create a surface of the body that can be used in most CAD systems. For this purpose, the scanned body data of each week was first imported into Geomagic Design X software to improve the body surface and reconstruct the modified parts meanwhile scanning.
Analysis and Modeling of Body Evolution All body surfaces had to be imported into the 3D Design Concept software environment to be analyzed. Hence, the surfaces of the second trimester (16th to 26th weeks of pregnancy period) were separately transformed into 3D Design Concept and the surfaces were cleaned. Then, all the shapes were placed in a joint position so as to investigate body evolution. The bodies should be in the joint position to gain a correct understanding of body evolution. So, the body of 16th weeks of pregnancy was taken as a reference body and the legs of the scanned body for all the weeks were placed in the same position. The initial position and new position of the second scanned body on the reference body are depicted in Figure  2a, b. Although the position of legs seemed fine for the second body as given in Figure  2b , the spine line position was not adequate as shown in Figure  2c, d.
The increasing volume is generally linear while the rising rate for the belly and waist was the most among all the cuts. Then, the growth of curves for all the cuts was measured at different angles according to the spine line as a reference. All data were sorted into an Excel file and the total rise was calculated between 16th and 26th weeks of maternity period for all the curves (between crotch and armpit). The collected data was applied to make an adaptive surface of torso using the linear upward from 16th to 26th weeks of pregnancy period. The evolution of each curve was measured at different angles with constant intervals (15°) to have a comprehensive understanding of body evolution. The volume values from 16th to 26th weeks of pregnancy period are presented in Figure  5. The increasing volume is generally linear while the rising rate for the belly and waist was the most among all the cuts.
Virtual Mannequin Estimation for Whole Maternity Length based on Weight Gain It should be noted that the virtual mannequin was verified by real data obtained by the body scanner in the previous section. However, it is required to develop the virtual mannequin for the whole maternity period. The idea was to make a virtual mannequin for weeks before 16th and after 26th weeks based on weight gain during pregnancy. Thus, the relationship between weight gain and body evolution had to be described. For this purpose, the graph of weight for the pregnant woman was characterized throughout pregnancy. The weight of this woman was 58 kg and 73 kg before pregnancy and on delivery day, respectively. It is noted that the maximum changes of body transpire in torso during pregnancy  [36]. Thus, applying the correlation between weight gain and torso evolution instead of whole-body evolution remained reasonable.
Validation of the Proposed Model Scanned data of another pregnant woman (subject 2) was collected and analyzed for the third trimester of pregnancy. The objective was to verify the proposed model of the 3D virtual mannequin for the pregnancy period based on weight. Figure  7  (dashed line) shows the weight gain graphs (2) for subjects 2. It should be noted that body scanning data has been collected during the interval of 30-35th week pregnancy i. e. , the third trimester. So, a virtual mannequin for the whole pregnancy period was made regarding weight gain and body evolution of the third trimester of pregnancy for subject 2. Consequently, body position adjustment according to legs and spine lines were done for all the bodies the same process explained for subject 1. However, the body of the 30th week of pregnancy was considered as a reference body for subject 2.
Finally, the garment block pattern was modeled by triangulating and assembling different parts illustrated in Figure  11a. It should be mentioned that there was no specific requirement for the number and size of triangular meshes as the fabric properties would be given in the following section (virtual try-on). Then, the flattering operation was applied to obtain the corresponding 2D pattern for the garment block pattern. The garment block pattern consists of eight different pieces in which four are for the front and four are for the back. The flattened 2D pattern was exported into Modaris software for adjustment in order to fulfill the desired properties used in the apparel industry. All the pieces were sewn together in the Modaris environment. It should be taken into consideration that the body of a pregnant woman experiences enormous changes only in the torso and as a result, the other parts of the scanned body were merged to develop a complete mannequin for the pregnant woman.
The flattened 2D pattern was exported into Modaris software for adjustment in order to fulfill the desired properties used in the apparel industry. All the pieces were sewn together in the Modaris environment. It should be taken into consideration that the body of a pregnant woman experiences enormous changes only in the torso and as a result, the other parts of the scanned body were merged to develop a complete mannequin for the pregnant woman. Finally, the garment was tried on the virtual mannequin of the pregnant woman while a number of darts were tested for different pieces of the block pattern. As a result, two armpit darts were placed for the two upper front pieces of the pattern due to the better garment fitting on the body. In addition, an elastic seam was employed to improve the fitting of the garment on the body.
It should be taken into consideration that the pieces were modified in a 2D environment (Modaris software) to obtain a well-fitted pregnancy block pattern and the modified pattern was simulated in a 3D virtual environment. In a few words, the sequence of design-display-evaluation correction was performed repeatedly until a satisfying design was attained. The final garment block pattern and the corresponding virtual try-on result are represented in Figure  11b-d. As can be seen, the block pattern with armpit darts suggested the appropriate fit on the pregnant virtual mannequin (subject 2). Characteristics of the formerly manufactured fabric were assigned to the design pieces to virtually try on the developed garment block pattern on the body. It should be noted that the thickness of the produced fabric for electromagnetic shielding clothing in our previous work was 3.
The body modification of the torso (between the crotch and underbust) was analyzed. The collected data was applied to generate a 3D adaptive virtual mannequin using the linear upward week-to-week for the pregnant woman (subject 1). This created surface for torso was fitted on the virtual scanned body for all the weeks of the second trimester. Even though the virtual mannequin was created for the second trimester of the pregnancy period, there is still a desperate need for the entire nine months of pregnancy period to apply in the garment design sector. It seems impossible to scan a pregnant body every week without any interruption or gap in between. Several problems might cause gaps since each pregnant woman individually experiences changes in her physical and mental states during pregnancy.
The mannequin that was created was well-matched with the real body evolution during the pregnancy period. It means the developed mannequin by weight gain was in good agreement with real body shape during pregnancy. error method was applied to determine the appropriate position of the seam for the developed block pattern in the present study. Therefore, the patterns were simulated having an elastic seam for the block pattern. It should be taken into consideration that the pieces were modified in a 2D environment (Modaris software) to obtain a well-fitted pregnancy block pattern and the modified pattern was simulated in a 3D virtual environment. In a few words, the sequence of design-display-evaluation correction was performed repeatedly until a satisfying design was attained. The final garment block pattern and the corresponding virtual try-on result are represented in Figure  11b-d.
Validation of the Proposed Model Scanned data of another pregnant woman (subject 2) was collected and analyzed for a different period of pregnancy to verify the proposed model of the 3D virtual mannequin based on weight gain. Referring to the previous argument in this study, the objective was to create a suitable mannequin for the whole pregnancy period. The expected weight gain of a woman throughout pregnancy along with her initial weight before pregnancy was the main data based on which mannequin was developed. Therefore, the proposed model for mannequin creation based on weight gain had to be confirmed for further investigations on garment design. The body scan data of another pregnant woman was applied to estimate the 3D virtual mannequin according to her weight gain. The idea was to prove that the created mannequin was appropriate.
The scan data collected from the third trimester of pregnancy for the second pregnant (subject 2) although it was collected for the second trimester for the first one (subject 1). Finally, the mannequin for the whole pregnancy length was made for the second woman based on her weight gain during pregnancy and the result was promising. It was suggested that the proposed model to make a 3D virtual mannequin based on weight gain was valid for two different pregnant women. Therefore, 3D virtual mannequins can be generated based on weight gain expectancy for each pregnant woman. Moreover, this model can be applied in the ready-to-wear sector.
Conclusions This work concentrated on developing a virtual well-fitted block pattern garment for a pregnant woman with the potential of applying in protective garment manufacturing. Therefore, the body morphology evolution was analyzed and the adaptive mannequin surface was generated for the specified period of pregnancy. Then, the virtual surface was verified by available scan bodies. After that, a virtual mannequin was generated for the whole pregnancy period based on the weight gain graph of pregnancy for the woman (subject 1). The reason is correlated to mannequin making for pregnancy period using the available data of the women e. g. , the weight and size before pregnancy and the weight gain during pregnancy. Next, a similar process was applied to make a virtual surface for another scanned pregnant body (subject 2) in order to validate the proposed model.
Behavioral recording and data analyses Any locations and durations of behavior (see Section 3. 1. ) that did not correspond to the expected (correct) responses (i. e. errors) were removed from the videos by one of the judges (FG, see S2 Text), as in  [31, 32]. We computed five variables (percentages of dogs that successfully completed the tasks, number of errors, types of errors, types of initiatives, and durations of the memorized and evaluated paths) for each task and for all the dogs taken together. Descriptive statistics are provided for each task (see Section 3. 1. ). We also calculated the relationships between the tasks in terms of errors and the percentages of dogs that succeeded (see Section 3. 2. ), and between the dogs that succeeded and their individual characteristics (see Section 3.
Data were compatible with the use of an egocentric encoding strategy that is reliable for short paths but not for longer ones after a single journey of incidental learning, suggesting segmented integration of the path's characteristics. It should be pointed out that these behaviors were obtained with verbal encouragements and food rewards, matching the usual conditions in which guide dogs are taught new paths. We further found that the durations of the memorized and evaluated paths were positively correlated for each task, suggesting similar processes across tasks. Moreover, the fact that the duration of the evaluation phase exceeded that of the memorization phase for the reproduction and return tasks suggests that the dogs engaged in different activities in these two phases: whereas they mainly followed instructions during the first phase, with a walk facilitated by the sighted handler, they required time for spatial processing during the evaluation phase.
Acknowledgements This paper is dedicated to the memory of  Dorothy L Cheney  who loved baboons. We thank the funders of the  Frontiers in Baboon Research Symposium , namely the  Deutsche Forschungsgemeinschaft  ( FI 707/21-1 ), the  Leibniz Science-Campus Primate Cognition  and the  University of Go ¨ttingen. We thank  PJ Perry  and the editors and reviewers, for valuable comments and suggestions.
Participants Twenty-two younger (M age = 19. 71, SD = 2. 19, 11 females) and 22 older (M age = 74. 55, SD = 7. 82, 10 females) adults participated in our study. Younger adults were undergraduate students at the University of Waterloo who signed up for the study and received partial course credit for their participation. Older adults were recruited from the Waterloo Research in Aging Participant (WRAP) database, and received $10. 00 remuneration for their participation. WRAP is a database of healthy seniors residing in the Kitchener-Waterloo area recruited by means of newspaper ads, flyers, and local television segments to participate in research studies taking place on campus at the University of Waterloo. All older adults completed the Montreal Cognitive Assessment (MoCA) to assess cognitive decline  [44].
Materials VR equipment. The VR program was run on an Asus© Zenfone 3 Laser ™ (see specificities on www. asus. com, Asus, Taipei, Taiwan) and a lightweight hand-held cardboard VR headset for viewing. The VR application run on the Android phone was designed using the 3D engine Unity© (ver. 2017. 2. 0. f3, Unity Technologies ApS, San Francisco, CA, USA). Twelve virtual environments (VE) were built according to three styles of: city streets, mall hallways, and park trails (see Figure  1  for samples). The virtual environments were topographically similar, with an average area of 234 square meters. Additionally, each environment had six intersections, which is a point where 3+ roads converge. Brain Sci. 2019, 9, x FOR PEER REVIEW 4 of 13.
Materials VR equipment. The VR program was run on an Asus© Zenfone 3 Laser ™ (see specificities on www. asus. com, Asus, Taipei, Taiwan) and a lightweight hand-held cardboard VR headset for viewing. The VR application run on the Android phone was designed using the 3D engine Unity© (ver. 2017. 2. 0. f3, Unity Technologies ApS, San Francisco, CA, USA). Twelve virtual environments (VE) were built according to three styles of: city streets, mall hallways, and park trails. (See Figure  1  for samples). The virtual environments were topographically similar, with an average area of 234 square meters. Additionally, each environment had six intersections, which is a point where 3+ roads converge. Neuropsychological assessments. Spatial visualization ability was evaluated with the Hooper Visual Orientation test  [45] , which had good internal reliability, as indicated by a coefficient α of 0.
See Figure  2  for a visual representation of the normalized paths used in the calculation of the Route Overlap values. This process resulted in 12 values for percent overlap scores per person, 6 of which corresponded to active trials, and six to passive trials. For each participant, the averages were calculated for the active and passive trials separately, resulting in one measure representing performance following active encoding, and another following passive encoding. See the example in Figure  2. Brain Sci. 2019, 9, x FOR PEER REVIEW 6 of 13 normalize the routes travelled (during both encoding and retrieval) against the established paths in the environments, and then to compare those normalized routes. The process of normalizing the participant's paths involved removing positional data points that corresponded to the subject travelling between specified points on the pathways in the environments.
The details of the experimental set up can be found in  Behrel et al. (2016). Runs with kite in static flight were carried out. Logged data were used to compute the lift to drag ratio and the lift coefficient of the kite. The presented method is specifically adapted to the collected data. Indeed, due to a lack of information on the kite orientation during flight and on wind along the altitude, strong assumptions and estimations have to be done, leading to results which must be interpreted with caution. However, the obtained lift to drag ratio and lift coefficient make sense with other experimental works like  Dadd (2012). 2 Post-processing of kite flight data The aim of the following part is to process data to retrieve the lift to drag ratio of the system {kite þ tethers}. However, to compute the lift to drag ratio, the wind direction at the altitude of the kite needs to be known as it directly affects the orientation of the wind window.
Recent evidence shows that melanopsin and/ or rhodopsin are the main drivers for melatonin suppression in humans  [26, 27]. Furthermore, based on a metaanalysis on a large data set from  [37] , Prayag et al. [38]  concluded that suppression of melatonin by monochromatic lights is predominantly driven by melanopsin, and that it can be initiated at extremely low "melanopic lux" levels in experimental conditions. Here we did not calculate dose response relationships for other alpha-opic irradiances than melanopic irradiance. Study volunteers needed to participate in at least two light conditions, while one was always the control DL condition. Out of the 26 young participants: 18 (12 m and 6 f) participated in all three lighting conditions (i. e. , DL, WL, and BL): 6 with the following order: DL, BL, WL; 4 with WL, BL, DL; 3 with DL, WL, BL; 2 with WL, DL, BL; 1 with WL, DL, BL; 1 with BL, DL, WL, and 1 with BL, WL, DL.
Summary and Conclusions Our data show that the light environment impacts on human homeostatic sleep regulation. This adds to the growing insight that besides its impact on circadian physiology, light is an important environmental factor in shaping sleep-wake behavior. Although, there is evidence for a link between light exposure and subsequent sleep in the field  [23] , the results from this laboratory study need further validation under less controlled situations. However, they may have important ramifications when it comes to designing light solutions that support alertness or sleep promotion, while minimizing effects on the circadian timing system. Appendix A Table  A1. Sleep variables during the baseline and recovery night in minutes for TST (total sleep time), S1 (sleep stage 1) latency, S2 (sleep stage 2) latency, and REMS (rapid eye movement sleep) latency; and in percentages of TST for Stages 1 to 4 sleep, NREMS (non-rapid eye movement sleep), REMS, wake and MT (movement time).
Data collection Semi-structured interviews based on medical and lifestyle data were conducted by the examiners (A. P. and A. K. ) at chairside. Age, medications, and smoking were recorded. Selfreported oral health behaviours included dental visit frequency (≥ twice/year, once/year, more than once every three years, or once every three years or less), and toothbrushing frequency (once a day, twice a day, or > twice a day). Height and weight were assessed the day of the examination by nurses in the Psychiatry and Addiction department (Paul Brousse Hospital) for ED patients, and by the examiners in the Odontology department (Rothschild hospital) for controls. Body weight was measured to the nearest 0. 1 kg with subjects in indoor clothing and no shoes. Height was measured to the nearest 0. 5 cm with a wall-mounted stadiometer in the same conditions.
B. ). Examiner reliability for the periodontal parameters was acceptable if the ICC value was ≥0. 9. The sample size calculation was based on the prevalence of sites with attachment loss >2mm in French women previously established at 19. 62±2. 09, for the age group 35-39  [21]. To detect a 75% difference of prevalence between the eating disorders group and the control group, with a two-sided test, an alpha error of 5%, and a beta error of 20%, 64 participants were needed per group (software epiR package 0. 9-96). To secure the matching (no more than 2 years difference between each case and matched-control), it was decided to include 70 participants per group.
Changes in self-perception and changes in the disease or treatment effect reappraisal over time may have an impact on self-assessment of MDD. The most widely used self-report scale to measure depression and assess treatment efficacy is the BDI (Beck Depression Inventory). Its psychometric properties allow accurate discrimination between depressed and non-depressed populations and a good estimation of depression severity  (7). Longitudinal data are often collected to assess changes in the BDI scores, and it is usually assumed that the scale measures MDD in the same way over time (longitudinal measurement invariance. However, the assumption of longitudinal measurement invariance may sometimes be compromised and impact conclusions  (8). Our belief is that treatment has a direct effect on outcomes but also an indirect impact via treatment-induced changes in appraisal in the interpretation of the items of the scale, or even the concept of being measured (depression).
The knowledge of these technical reasons would help to evaluate the quality of end product. This would also enable to fulfill the need of additional performance tests in the durability indicator, underlined by De Saxcé et al. 6  In this study, a methodology is proposed to address the lack in scoring the quality of textile products from their technical performances and the deterioration perception. Deterioration perception, called here consumer sensitivity, is evaluated from survey data and an MCDM method. An FAHP tool is proposed in this study and enables the subjectivity of respondents to be taken into account. As illustrated in Figure  1 , the factors of product replacement or disposal are highlighted and integrated into the calculation of a consumer-based quality score. The remainder of this article is structured as follows.