dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioinformatics analysis The raw FASTQ les were quality checked using FASTQC and MultiQC to generate a single quality report for all samples. To avoid introducing errors, vcf les were generated by ltering with read depth greater than 7 and mapping quality greater than 10 using bcftools, and only SNPs of high quality and with depth of high site coverage were considered in the downstream analysis. Our sample sequences and SARS-CoV-2 FASTA sequences are shared in the GISAID database. The reads from FASTQ samples were assembled using the Flye tool, the resulting assembled contigs were joined using contigMerger to generate a single scaffold per sample. The per-sample scaffold was combined into a single multifasta le that was used in the phylogenetic analysis. Multiple sequence alignment was performed using MAFFT version 7.
to be possible, although not necessarily probable. Following an interdisciplinary approach, we integrated perspectives from the disciplines of Positive Psychology, Risk Management, and the Philosophy of Hope to elucidate the difference between trust and confidence. We further differentiate between perceived hope, on the one hand, and dispositional hope and optimism, on the other, backed in this interdisciplinary approach. Using data from the Hope Barometer in 2018 and 2019 we investigated the role of internal and external sources and activities of hope in two studies. Study 1 comprised 12 countries (N = 10, 193)  and aimed to analyze several personal and external hope sources with a specific focus on social support, religiosity and the feeling of luck in relation to hope. Study 2 was performed with 8 samples from 7 countries (N = 6245), centering on the assessment of several hope activities and their effects on perceived hope.
Main Aim Conceptualizing hope as a wish or desire for a valuable good which is considered to be possible (however not necessarily probable), together with trust in the availability of individual, social or spiritual resources to overcome difficulties and obstacles, the aim of our empirical studies reported in this chapter was to explore the sources and activities of hope relevant for maintaining hope and making one's hopes happen across several countries. Specifically, we focused on trust and social support. In the following sections we present two studies based on data collected through the Hope Barometer in 2018 and 2019. Study 1 examined the role of several sources of hope, with a specific focus on social support and further external hope resources such as religiosity and luck. We also explored their relationship with perceived and dispositional hope.
Procedure and Participant Samples Data was collected as part of the Hope Barometer in November 2019 through announcements in online newspapers, social media and e-mails. No incentives were offered. For this study we selected 12 countries each displaying a robust database of at least 200 participants. People younger than 18 were excluded from the analysis. A total of 10,287 participants completed the questionnaire, of 94 were removed due to a high number of missing values and obvious erroneous answers (e. g. , always 0 or 1). Participants were recruited in Australia (N = 474), Colombia (N = 311), the Czech Republic (N = 469), India (N = 1092), Israel (N = 884), Italy (N = 272), Nigeria (N = 665), Poland (N = 481), Portugal (N = 507), South Africa (N = 574), Spain (N = 529) and Switzerland (N = 3935).
Data Analysis All statistical analyses were performed with IBM SPSS and AMOS version 27. 0. We first determined mean values of the sources of hope and compared them between countries. Thereafter, we calculated partial bivariate Pearson correlations controlling for demographic variables between the sources of hope and both constructs to assess perceived and dispositional hope. Further analyses established levels of receiving and giving emotional and instrumental support as well as of religiosity and luck in all countries. By means of hierarchical regression analyses we then estimated the predictive power of different constructs representing several external factors of hope. After starting with demographic variables in step 1, we entered the two variables which measured receiving emotional and instrumental support in step 2, after which we included the two factors for giving emotional and instrumental support in step 3.
Procedure and Participant Samples Data was collected with the Hope Barometer survey in November 2018, announcing it in online newspapers, social media and e-mails. No incentives were offered. For this study we selected eight samples from German (N = 3049) and French (N = 1109) speaking Switzerland, France (N = 135), Spain (N = 528), Portugal (N = 808), Czechia (N = 338), Poland (N = 169) and South Africa (N = 109). People younger than 18 were excluded from the analysis. A total of 6548 people completed the questionnaire, from which 303 were removed due to a high number of missing values, obvious erroneous answers (e. g. , always 0 or 1) and multivariate outliers. The demographic structure of the samples is exhibited in Appendix 6. 2. Gender distribution was quite balanced in the two Swiss samples (around 40% male and 60% female).
Data Analysis Statistical analyses were performed with IBM SPSS and AMOS version 27. 0. The first step was to calculate the mean values of the hope activities, analyze the rank order within the samples and compare the scores of the single activities across samples. The next step was dedicated to determining partial bivariate correlation coefficients between the 13 hope activities and the general level of perceived hope (controlling for demographic variables). In order to demonstrate measurement invariance of the Perceived Hope Scale across the eight investigated samples a multi-group Confirmatory Factor Analysis (MGCFA) was performed. The fit of the general model by means of maximum likelihood estimation was evaluated using the following indices: Comparative fit index (CFI) and Tucker-Lewis index (TLI) (study criterion ≥0.
They revealed that social influence is the most impactful factor that drives the intention to use cryptocurrencies. Although these extended TAM models consistently determine user intention, responses are often biased. In other words, the answers may lack frankness and honesty for different reasons, including respect for socially acceptable and concern for protecting their Privacy. For these reasons, we propose using the search volume query data from Google Trends as the central database. In fact, these data were employed by  Yelowitz and Wilson (2015)  to identify the use of cryptocurrencies such as speculative matters, criminal acts by libertarians, or computer programming. Other studies have focused on the influence of religiosity and culture on the intention to use innovative technologies (Abou-Youssef, Kortam, Abou-Aish, & El-Bassiouny, 2015;  Dey, Balmer, Pandit, & Saren, 2018).
, 2021). To quantify the constructs of the proposed model, we employ the Google Trends data, and the cryptocurrency prices (Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) and Litecoin (LTC)) retrieved from the www. coinmarketcap. com website in weekly frequencies during the period post-pandemic outbreak (after 31/12/2019). The sample size of the data is about 3646 observations of each variable. The data are extracted from the "Google Trends" database in daily frequencies from 01/01/2018 to 31/ 12/2022. Google Labs provide the frequency with which a term has been typed in the Google search engine, with the possibility of visualizing this data by region and by language. In other words, Google Trends shows search trends for a keyword specific to an area or language. It acts as a sort of research frequency calculator.
There are some potential advantages of using Google Trends over interviews when exploring the relationship between religiosity and intention to use cryptocurrency. First, Google Trends provides a large and diverse data set covering various locations and periods which can help identify patterns and trends that may be difficult to detect through interviews, especially when dealing with large or dispersed populations. Second, Google Trends provides real-time data, which can help track changes in attitudes and behaviors over time, especially when studying a topic as dynamic as cryptocurrencies, where attitudes and perceptions can change rapidly in response to new developments and news events. Third, Google Trends data are anonymous, which can help reduce bias and social desirability effects that may be present in interviews.
Measurement consistency The empirical part of this research employs the PLS-SEM, a variance-based SEM, and the two-step approach to validate the proposed hypothesis as recommended by  Marcoulides, Saunders, and Marcoulides (2016). This multivariate statistical approach is used in this research by applying SmartPLS 3. 3 software. PLS enables the simultaneous evaluation of both the outer and inner models. Furthermore, PLS may comply with normality using the sophisticated predictive research model and provide a more flexible randomness requirement  (Chen & Hung, 2016). Besides, we choose to use PLS-SEM because the statistical goal is prediction rather than theory confirmation  (Hair, Risher, Sarstedt, & Ringle, 2019). Based on the steps indicated by  Hair et al. (2019) , we perform our analysis by checking each item's factor loading, which should be greater than 0.
Theoretical, managerial and practical implications In this paper, several theoretical and managerial implications are observed. From a theoretical perspective, this work has proposed and tested a conceptual model for identifying the impact of religion, PU and PEOU on users' perception of cryptocurrency technologies. This is a unique contribution because previous studies investigating the relationships between the technology acceptance constructs rarely extend their model with religiosity. Even though the influence of religiosity is employed indirectly or through a moderating effect  (Usman et al. , 2020) , this work investigates this effect by treating religiosity as an independent variable. Besides, our results complement the literature by combining data from the Google Trends and cryptocurrency prices to identify four primary constructs related to an extended TAM model: PU, PEOU, intention and religion.
In the current study, the behavioral and NIRS versions of the experiments were matched as closely as possible, placing similar demands on the infants and providing the same amount of exposure to the same stimuli. Specifically, we compared the extraction of an AxB dependency in a flat, i. e. , no pitch, and a pitch experiments behaviorally and using NIRS. We expected the behavioral data to uncover whether convergent prosodic cues to the nonadjacent dependency improve learning as early as 9 months of age. In addition, we used NIRS to reveal whether the brain is sensitive to the presence of dependencies even in the absence of overt discrimination in behavior and to identify the brain network responsible for improved sensitivity to nonadjacent dependencies in the pitch experiment. Experiments 1 and 2 (Fig.
Comparison between NIRS experiments To directly assess the effect of pitch cues using NIRS, we compared oxyHb concentrations in response to the structural regularities in the absence versus presence of pitch cues in a mixed ANOVA with the between-subjects factor pitch (flat experiment 2/pitch experiment 4) and the within-subjects factors rule (rule/no rule) and hemisphere [left hemisphere (LH)/right hemisphere (RH)]. For this analysis, we extracted data from the significant clusters obtained in the permutation test for each experiment, therefore defining regions of interest (ROIs) in a data-driven, functional way. When a significant cluster was not present (flat experiment, RH), the analogous channels on the contralateral side were used (Fig. 5A ). Specifically, for the flat experiment, the ROI in the LH included channels 1 and 4, and the ROI in the RH included channels 14 and 16 (time window, 6 to 30 s).
Assuming a target power of at least 0. 8, a low correlation of 0. 2 between repeated measures and a small-to-moderate effect size of 0. 30 derived from the previous NIRS study testing rule-learning in infants  (31) , the necessary sample size was calculated to be a total of 22 infants across the two groups. We thus included a total of 40 infants in the final sample, achieving a 0. 98 power. The software G*Power was used for all calculations. We ran the behavioral (experiment 1 and 3) experiments in parallel, and we assigned infants to conditions randomly. We ran the NIRS experiment flat and the NIRS experiment with pitch at different periods. We thus assigned infants to each experiment upon recruitment. All parents signed informed consent for the participation of the infant before the enrollment in the experimental procedure.
Experiment 2 (flat) Twenty healthy full-term French-hearing monolingual 9-monthold infants (M age = 8. 21; SD = 0. 15; 6 boys and 14 girls) were included in the analysis. Thirty-two additional infants were tested but excluded from the final analysis due to fussiness or crying (n = 12), parental interference (n = 1), insufficient data (i. e. , less than two trials per condition), or bad data quality (n = 19).
Experiment 4 (pitch) Twenty healthy full-term French-hearing monolingual 9-monthold infants (M age = 9. 03; SD = 0. 21; 9 boys and 11 girls) were included in the analysis. Forty-five additional infants were tested but excluded from the final analysis due to fussiness or crying (n = 23), parental interference (n = 1), insufficient data (i. e. , less than two trials per condition), or bad data quality (n = 21).
Stimuli All auditory stimuli in familiarization and test were synthesized with MBROLA (36) using the fr4 French-speaking female voice of the MBROLA diphone database  (31). The duration of consonants was set to 120 ms and vowels to 150 ms  (31). In experiment 1 and 2, pitch was uniformly set to 200 Hz. In experiment 3 and 4, pitch was not monotonous; on the intervening syllables, pitch was set to 200 Hz, while for the first and last syllables, pitch was raising (from 200 to 220 Hz)  (5).
Behavioral experiments (experiment 1 and 3) Each infant was tested individually in a quiet, dimmed room. Infants were seated on a caregiver's lap in the center of the experimental booth. Each infant was tested individually with a central fixation procedure. The stimuli were presented using Habit 2. 0 (34) software, which automatically recorded the orientation times to a screen placed at approximately 80 cm in front of the infant. The auditory stimuli were played from speakers placed on the left and right sides of the screen covered by black curtains. The caregiver was instructed to not interact with or talk to the child. During the experiment, caregivers listened to masking music over tight-fitting headphones to prevent them from hearing the stimuli. An observer operated the experiment from outside the test booth, controlling stimulus presentation and observing infants' behavior through an online video stream.
NIRS experiments (experiments 2 and 4) Each infant was tested individually in a quiet, dimmed room. Infants were seated on a caregiver's lap in the center of the experimental booth. The stimuli were presented using E-Prime software on a screen placed at approximately 80 cm in front of the infant. The auditory stimuli were played from speakers placed on the left and right sides of the screen. The caregiver was instructed to not interact with or talk to the child. During the experiment, caregivers listened to masking music over tight-fitting headphones to prevent them from hearing the stimuli. A NIRx NIRScout NIRS machine with eight sources and eight detectors (source-detector separation, 3 cm; two wavelengths of 760 and 850 nm; sampling rate, 15. 625 Hz) combined into 20 channels in a stretchy cap (Fig.
NIRS data preprocessing NIRS analysis was performed on all infants in batch using MATLAB scripts requiring no user input once basic analysis parameters were defined. The authors were thus blinded to the experimental conditions during data analysis. Changes of oxyHb and deoxyHb were calculated by means of the modified Beer-Lambert law. A bandpass filter between 0. 01 and 0. 7 Hz eliminated noise and systemic blood flow variations (e. g. , infants' heartbeat) and overall trends. Block-channel pairs were rejected when movement artifacts (defined by intensity changes above 0. 1 mmol × mm over two samples) occurred. For the remaining nonrejected blocks, a baseline was linearly fitted between the means of the 5 s preceding the onset of the block and the 5 s after the 15 s following the offset of the block.
Acknowledgments: We are thankful to  A. Gaule ,  O. Jiang , and  V. Huet  for help with infant scheduling and to  I. de la Cruz Pavía  for help with piloting. Funding: This work was supported by  European Commission-ERC StG , grant  313841 ,  TuningLang  (R. d. D. -B. );  Ministerio de Ciencia, Innovación y Universidades , which is part of  Agencia Estatal de Investigación (AEI) , through the.
The bunch size was crucial for measuring the collision rate. Its calculation depends on: σ radial was measured optically, giving σ radial = 0. 5 mm, taking a picture of the transverse dimensions of the bunches, σ longitudinal was inferred from the measured lifetime due to quantum fluctuations σ l ≈ 7 cm (at E beam = 195 MeV and V RF = 5. 5 kV), σ vertical was the only big unknown. From the Touschek effect σ vertical ≈ 20μ, while σ vertical expected from synchrotron radiation recoil effects was only 2μ. When all this was understood, the team had a realistic estimate of which process could prove that collisions had taken place, namely e + e -→ e + e -+ γ , whose theoretical calculation was being done in Rome by two students of Raoul Gatto, Guido Altarelli and Franco Buccella  [8]. During fall 1963 and spring 1964 the team focused on gathering data and submitted an article about the first observation ever of electron-positron collisions in a storage ring  [4].
The data extracted for this study included daily estimates of the population staying at home, the population not staying at home, and, when they leave home, the average number of trips by distance in miles (< 1, 1-3, 3-5, 5-10, 10-25, 25-50, 50-100, 100-250, 250-500, and 500+). Any movement involving a stay of longer than 10 min at an anonymized location away from home (which was imputed at the weekly level) was considered a trip; multiple stops of longer than 10 min before returning home were estimated as multiple trips. All modes of transportation are captured as trips including driving, rail, and air travel. This dataset began in January 2019 and data used for this study went from August 31st, 2020 through June 15th, 2021, when the tier system stopped, and California was fully reopened.
Data from 2019 were additionally utilized for this analysis. Data on the California tier system was downloaded from the California Department of Public Health (CDPH)  [45]. Each county in California was classified into a tier  [1] [2] [3] [4]  based on indicators of COVID-19 transmission and risk using test positivity and adjusted case rates every week from August 31st, 2020, to June 15th, 2021. All metrics were based on county population size, and adjustments were made for small counties (defined as having less than 106,000 residents), including an exemption from adjusted rates based on testing volume and meeting health equity metrics that specify that test positivity rates in disadvantaged neighborhoods are not different from overall county estimates. Starting April 20th, 2021, absolute case number and vaccine coverage were also considered for moving counties to a less restrictive tier if the prior assessment prevented it from changing tiers.
Such robust differences target the mobility pattern that would have been observed in the absence of the California Tier system restrictions for each week in 2020 (using weekly estimates from the previous year). A difference in average mobility at the weekly level was used as the outcome of interest in our models. It should be noted that the 1st of January was considered the first day of the first week of each year, and weekly measures were computed accordingly. With this, weeks started on a different weekday each year (Tuesday in 2019, Wednesday in 2020, and Friday in 2021). The week following the tier system evaluation was used for analysis; for example, the first week where the tier system was evaluated on the 30th of August 2020 corresponded to mobility data from September 2nd to 8th, 2020.
3 ). It is possible that this reverse effect may be explained by residents traveling to neighboring counties when moving to a more restrictive tier to avoid restrictions; however, this would have to be further explored with a dataset that includes information on traveling behaviors between counties. This differs from some of the existing research showing that lockdowns have a stronger reduction in long-range mobility than short-range trips  [17, 37]. However, previous work on this topic has been limited and varied; Pullano and colleagues identified that lockdown was associated with decreases in shorter trips in France  [37] , whereas Schlosser and colleagues found that in Germany, long distance trips decreased more strongly than short distance trips following the COVID-19 lockdown  [58]. This may be due to differences between the tier system and full lockdown measures evaluated in prior studies.
First, as the mobility dataset started in 2019, we only had one year to draw comparisons. Ideally, we would have had more years to use as a baseline mobility measure, but we feel contrasting 2020 and 2021 to 2019 remains useful, as 2019 was not affected by the pandemic or related policies. Second, the mobility data used is experimental, and data quality standards may be lacking. By comparing mobility within each county using the same data source, we feel the data are sufficiently reliable for the purpose of this study. Also, any residents that do not own or go out with a mobile phone will be excluded from the sample; this could produce a bias as, for example, older persons may be less likely to own or travel with mobile phones; phone behaviors may have also changed because of the pandemic.
It would also be interesting to explore mobility for those moving between counties as well as those moving across states; this would have to be explored with a dataset which provides this information. Lastly, the effects of the pandemic coincided with wildfires and heat waves that affected the state during the summer and fall of 2020, which could also impact mobility; future work could disentangle the specific effect of these events. In conclusion, we found strong evidence that the California tier system and associated restrictions were effective in decreasing population mobility. However, results also showed unintended effects of increased travel for longer trips when moving to a more restrictive tier, which is important to consider when developing and implementing future policies. It may indicate that greater coordination is required between neighboring counties.
The paper by Brottrager, Stahl, and Arslan, proposed a formalization of literary historical reception. They analyzed and compared the relationship between the concept of canonicity based on extrinsic data (i. e. the contexts of the works) and intrinsic features (i. e. their textual content). The results showed a clear lack of correlation between the two methods. As an extension of this research,  Brottrager, Stahl, Arslan, et al. evaluated  how literary reception as a social process can be linked to textual qualities. They obtained a 78% accuracy in predicting if a text was reviewed in literary periodicals in the English context. Empirical research on literary prestige is scarce in France, and few experiments have been carried out on French corpora. An exception is a study on the successive selection of works for the Prix Goncourt 2020  (Bernard).