dataesr/scientific-paragraphs-categorization huggingface dataset:
Information cues on sharing platforms Our research focuses on the case of Airbnb. The platform provides three types of information about service providers that are relatively representative of the practices of some of the largest platforms in other sectors (BlaBlaCar in carpooling, TaskRabbit in personal services, Couchsurfing in P2P lodging). Each type of information provides cues which may be used to form either legitimacy or reputation judgments depending on the evaluation goal, as discussed below Profile: Hosts (those who offer rentals) are expected to provide a set of personal data to inform their profile (e. g. , photo, email address, physical address, Facebook account, LinkedIn account, credit card number, work phone number). The data provided by service providers may vary in length and are visible on their profiles.
H2: The level of stake (low and high) has an influence on the information cues users access first such that users will access legitimacy-related cues first in low-stake trips and reputation-related cues first in high-stake trips 6. 1. Participants and procedure We collected data using the services of a consumer panel company which randomly assigned 202 participants in the United Kingdom (randomly selected in a database of 60 thousand consumers) to a twocondition online experiment (low-vs. high-stake trip). The sample of users was obtained from a market study firm in the UK which has access to a panel of 60,000 consumers in the country. We asked the firm to send the online questionnaire to a sample of 202 participants who had an Airbnb account at the time of the study. A sample size of 202 is considered to be very appropriate for running multiple regressions involving six predictors  [34]  and well-above common recommendations for conducting means comparison tests  [89].
Pre-test and manipulation check We conducted a pre-test to validate the stimuli to be used in the main experiment and used a sample of 30 users who had taken part into the qualitative study to ensure that users perceived the Manchester trip condition as low stake and the Mexico City trip condition as high stake. Recent recommendations in experimental methods suggest that validating the stimulus of an experiment in a pre-test before the main data collection is more appropriate that performing manipulation checks after the main data collection  [25]. In both conditions, we asked the participants to rate the importance of the trip to them "how important is this trip to you?" The result of the manipulation check indicated that the users viewed the two levels of manipulation as sufficiently efficient to create the low-and high-stake conditions (2.
Therefore, we hypothesize that increasing the level of information (by providing all three types of information cues) does not improve users' confidence in the host or the stay: H3: In a low-stake trip, the effect of reviews on a user's level of confidence in a (a) host and (b) stay is diminished 7. 1. Participants and procedure Data were collected using the services of a consumer panel company which randomly assigned 205 UK participants (out of 60 thousand consumers) to a two-condition experiment (low vs. high information) for a low-stake trip. We followed the same rule of thumb for sample size as in Experiment 1. Participants in the low-information condition were presented with the ratings of the rental based on 18 reviews, but without the possibility to read the associated 18 reviews (absence of reviews), while in the high-information condition, participants were presented with both the ratings based on 18 reviews and the actual 18 reviews (presence of reviews).
Each of the platforms has adopted some of the features of their rivals. One notable feature formally adopted by all Big 3 platforms is the co-investment model or what is called private launch by Seedrs and Crowdcube. 2 This is most developed in the case of SyndicateRoom where it is known as the lead investor model but elements of it were later adopted by the other two platforms. The significant difference is that due diligence is led by the professional investor. Thus, one can argue that the UK market has become a predominantly co-investment rather than a pure ECF market in that BA, VC, PE and other early stage investors such as family offices widely invest in ECF campaigns and often pre-commit funds prior to campaigns going public. Apart from the  Zhang et al. (2018)  data referred to above, the British Business Angel Association estimate that approximately one third of all UK and 43% of Londonbased business angels have co-invested on ECF platforms.
Thus, our paper complements the  Stevenson et al. (2021)  study by analysing founder team choice among three competing platform structures employing a large quantitative dataset. The study employs data from 1291 (successful and unsuccessful) initial campaigns that have been conducted on Crowdcube, Seedrs and SyndicateRoom over the 2013-2018 period. It broadly follows  Chemmanur and Paeglis (2005)  in quantifying founder (management) team characteristics and in highlighting team heterogeneity. 3 They proxy management team resources by team size and qualifications (e. g. holding an MBA) and team heterogeneity by average tenure and tenure heterogeneity. In addition, we employ proxies capturing age and nationality heterogeneity. The results suggest that founder team size is negatively associated with the probability of choosing the Seedrs nominee model and with a significantly higher probability of choosing the Syndicate Room co-investment model.
The other literature to which our paper connects is that on ECF shareholder structures. A few studies have examined shareholder structures both within and across platforms. Cumming et al. (2019b)  employ data from the Crowdcube platform and their two stage Heckman results suggest that ownership and control separation from its dual class share structure lowers the likelihood of both short-and long-term success. Coakley et al. (2021a)  investigate both the inter-platform and intra-platform impact of nominee versus direct ownership initial campaigns. They establish that nominee initial campaigns are more likely to succeed, raise more funds, and to attract overfunding relative to direct ownership campaigns. They also find that nominee campaigns enjoy greater long run success in terms of successful seasoned equity crowdfunded offerings.
Our study both complements this study and extends it by analysing the full range of platform shareholder structures available in the UK in a pre-campaign context. Rossi et al. (2019)  employ data from a sample of 185 platforms in Australia, Austria, Canada, France, Germany, Italy, New Zealand, the UK and the US. Their empirical findings establish that the direct ownership approach has a negative effect on campaign success while the nominee account approach has no significant effect in this regard. Walthoff-Borm et al. (2018b)  find that ECF firms which employ the nominee account realize lower losses. There has been less research on the co-investment shareholder structure model possibly because, as  Rossi et al. (2019)  point out, fewer campaigns have been conducted on co-investment or syndicate-like platforms.
Data Our empirical results are based on a sample of successful and unsuccessful ECF campaigns launched on the three major platforms of the UK (Crowdcube, Seedrs and SyndicateRoom) covering the period 2013-2018. The data end in 2018 as SyndicateRoom changed from being a crowdfunding platform to become a fund management firm specialising in startups in 2019. This study obtains ECF data from TAB, which has been used in previous ECF studies  (Ralcheva & Roosenboom, 2019). The unique registration number for UK firms is used to match TAB data with founder data from the UK Companies House. We follow a similar approach as in  Coakley et al. (2021b)  and identify a founding team member to be the one listed as Director on the UK Companies House website. We remove Seasoned Equity crowdfunded offerings.
Conclusions and discussion This study extends the existing ECF literature by examining strategic entrepreneurial choice among competing platform or shareholder structures. It employs firm and campaign data from Crowdcube, Seedrs and SyndicateRoom for the period from January 2012 to December 2018. The multinomial probit results suggest that larger and heterogenous founder teams are more likely to conduct campaigns on a platform that employs the coinvestment model. They lend support to the findings of  Cumming et al. (2019b)  and  Rossi et al. (2019)  in which platform shareholder structure matters in ECF. They are also in line with entrepreneurial studies in which founder team is possibly the most important factor when BA or VC funds choose to invest in a firm  (Van Osnabrugge, 2000; Sudek, 2006; Gompers et al.
Acknowledgements As the guest editors of this special issue, we would like to thank all authors who have submitted papers to the special issue and in particular those whose papers have been accepted for this special issue. Assistance from the editorial staff of the Mobile Networks and Applications is also much appreciated. Besides, the guest editors wish to acknowledge all those who have generously given their time to review the papers submitted for consideration for inclusion in this special issue. Finally, our special thanks go to  Dr. Imrich Chlamtac  (editor-in-chief) for his valuable support throughout the preparation of this special issue.
Methodological procedures The proposition was analyzed under a qualitative descriptive cross-sectional research. Primary data were collected by semi-structured interviews with producers, processors and two key agents in the dairy chain (a researcher in milk and dairy products and a moderator at Livestock Institute, in France). The selection of respondents was made by availability and convenience, considering the regions surveyed and the interviewee's acceptance. Secondary data were used to characterize and understand the production system in the surveyed regions, in both countries. The secondary data sources were: National Interprofessional Center of Dairy Economics (CNIEL), National Establishment of Agricultural and Seafood Products (FRANCEAGRIMER), Agreste figures and data, National Supply Company (CONAB), Paran a Institute of Economic and Social Development (IPARDES), Secretary of State Agriculture and Supply (SEAB/DERAL) and related articles in France and Brazil.
Difficulty of exchanging buyers Producer 8 "Here it's not easy to change buyers. If a producer stops selling to the buyer, no one will want to buy from him to make other producers afraid" Source: Elaborated by the authors from primary data RAUSP 56,4 In the Western region of Paran a, reputation was identified due to path dependence, as discussed by  North (1991)  and it leads to an ongoing relationship between producers and cooperatives (according to P1). Measurement is performed on a recurring basis by a third party, but the information is partially distributed, which possibility processors value appropriation, limiting property rights protection (according to P2). As low financial returns are made, as a result of minimal quality milk transacted, modest investments in production by the producer are identified (validating P3).
Despite reducing transaction costs, it was noted that, in the studied AGS, reputation does not reduce measurement costs. Specific characteristics of milk and its dimensions require measurement in all collections, independent of existing reputation. Source: Elaborated by the authors from primary data RAUSP 56,4 Thus, the present study went on to understand efficiency through measurement, reputation and property rights protection. The study further explores the inter-segment relationship, with the application not only in agro-industrial chains but also in all businesses. The use of TCE and MCE theories together allows a better comprehension of the coordination of systems and the efficiency generation through opportunistic attitudes limited and property right protection. As such, improvements in the inter-segment relationship enhance gains across the entire system.
METHODS This study used data from the 2017 Health Barometer survey, a random survey conducted by telephone on 25319 individuals. It included 6341 respondents who reported that they were daily smokers when the Mois sans tabac campaign was launched in 2016. The association between self-declared exposure to the campaign and making a QA has been studied using multivariate logistic regressions. RESULTS Exposure to the 2016 Mois sans tabac campaign is associated with a QA lasting at least 24 hours in the final quarter of 2016 (AOR=1. 32; 95% CI: 1. 07-1. 63, p<0. 01), with a QA lasting at least 30 days (AOR=1. 95; 95% CI: 1. 31-2. 91, p<0. 001), and being abstinent at the time of the interview in 2017 (AOR=2. 39; 95% CI: 1. 37-4. 15, p<0. 01). A dose-effect relationship is observed between the frequency of exposure to the campaign and QA, which is mostly explained by the number of sources of exposure (television, radio, posters, the press, the internet and social networks).
Data collection Health Barometer is a large annual random phone survey on health behaviors and perceptions of people living in France set up by Santé publique France  19. The 2017 fieldwork was conducted by the survey firm Ipsos between 5 January and 18 July on 25319 people aged 18-75 years. The participation rate was 48. 5%. The present study and analyses are based on data from the 2017 edition, which collected data on multiple topics including substance use (tobacco, alcohol, illicit drugs), mental health, physical activity, and vaccination.
Former daily smokers were asked when exactly they quit smoking. Those who declared they had quit 12 months or less prior to the survey were asked the month and year they quit. These data enabled us to recode ex-smokers who quit smoking after 1 October 2016 as smokers at the time of the launch of the first Mois sans tabac campaign in 2016. In the remainder of this article, the analyzed study sample corresponds to people who smoked daily at the launch of Mois sans tabac in 2016 (n=6341), comprising either self-reported daily smokers in 2017 (n=5968), or ex-daily smokers in 2017 who quit smoking after 1 October 2016 (n=373). For the present study, we hypothesized that the proportion of people who started smoking after Mois sans tabac 2016 among daily smokers at the time of the survey was negligible, given that our study sample included those aged 18-75 years.
Main outcomes The present study's primary dependent variable, was defined a priori as a quit attempt (QA) of at least 24 hours in the final quarter of 2016, among daily smokers at the time of the launch of the Mois sans tabac campaign. To measure this, those classified as daily smokers in 2017 were asked: 'Did you try to quit smoking for at least 24 hours in the last quarter of 2016, i. e. between October and December?'. Former smokers who quit smoking in the final quarter of 2016 were included in the group of those who made a QA. Those who reported a quit date after January 2017, were asked if they had already made a QA in the final quarter of 2016. Data on the duration of QA were collected. These enabled us to study QA of at least 7 days and at least 30 days. Finally, we estimated the proportion of smokers who made an attempt to quit in the final quarter of 2016 and who were abstinent for at least 7 days at the time of the survey in 2017.
Exposure variables The level of exposure to the Mois sans tabac campaign was collected using the questions 'During the final quarter of 2016, the first Mois sans tabac campaign took place. Have you heard about it, whether on TV, on the radio, on the internet, in the press or through another channel?' ('ever exposure'). Those who replied 'yes' were then asked, 'How often did you see or hear about the Mois sans tabac campaign?' (1: Several times a day, 2: Once a day, 3: Several times a week, 4: Once a week, 5: Less than once a week). The frequency of exposure was recoded as daily/weekly/ less than weekly/never. More detailed data on the different sources of exposure were also collected, allowing a number of sources of exposure to be calculated from television, radio, posters, print, internet and social media.
Priority areas for strengthening the evidence base This collection of studies identifies four priority areas for consideration to advance methods for predicting future climate-associated health burdens, with implications for designing adaptation responses. The first area is the generation of a cross-health impact database that allows policy and public health decision makers to prioritise health adaptation efforts from a more comprehensive evidence base using a systems approach. This would be done within a context of prioritising how to spend efforts and budget according to where the largest impact hotspots and Viewpoint adaptation opportunities are identified. For this, one needs to have multi-model health impacts from as many health impact pathways as possible to compare and make decisions from.
Acknowledgments The work was partly funded by the  Swedish Research Council FORMAS project ARBOPREVENT  (grant agreement  2018-05973 ), and the  ISIpedia  project funded through the  ERA4CS  with the  Swedish Research Council FORMAS  (grant  agreement2017-01742 ) and the  Spanish Ministry of Economy, Industry and Competitiveness-State Bureau of Investigation  (grant agreement no  PCIN-2017-046 ).
This means, at any time, stockouts are strictly prohibited in each of the five DSs' warehouses for all five kinds of products. On the other hand, considering the limited storage space and inventory holding costs, the number for all five kinds of products has an upper limit. A brief illustration of the constraints is shown in Figure  3. There are several factors that are needed to be considered while setting the minimum and maximum thresholds. These factors concern both internal data and external data of PFDC. The internal data is always structured, such as retailers' sales reports, stock status, and historical sales. The external data can be both structured and unstructured, such as competing markets launch new products (from newspaper  3 Wireless Communications and Mobile Computing or video), new relevant guidance policies of government (from policy documents or TV).
Wireless Communications and Mobile Computing Concerning the data about the weather, we partially employed "Roussey Catherine's weather ontology"  [6]. Roussey Catherine describes a new meteorological dataset based on the SOSA/SSN ontology. This work is the first to publish meteorological data with the new version of the SOSA/SSN ontology. The network of the ontologies in  [6]  is composed of the following: (i) Ontology to describe the different types of sensors (ii) Ontology to describe the units of measurement (iii) Ontologies to describe the geographical places and their locations (iv) Ontology to describe the temporal entities For example, the rain collector measures the quantity of precipitation that falls during a time period. The property "ssn:phenomenonTime" links the "sosa:Observation"  instance to an instance of the class "time:Interval.
IRPO   IRPO is constructed using Protégé. The structure of IRPO is shown in Figure  5. "owl: Thing" is superclass, which includes five modules "Organization," "DeliveryCost," "Deli-veryModel," "Weather," and "DailySelling. " "Organization" is the organizer who can include other supply modes by means of extending. "Organization" has three instances the "LDC," the "Drugstore," and the "DeliveryCenter". The "LDC" maintains "Product" and "Inventory"; each "Product" has "Inventory" as an attribute. Similarly, "Drugstore" also has "Product" and "Inventory" as attributes. A matter of concern in "Drugstore" is "DailySelling," which is affected by the "Weather. " The "Weather" has five instances "Shineintensity," "DeliveryModel," "Haze," "Humidity," and "Ult_inensity. " "DeliveryModel" has two instances "Express" and "Daily_Truck" as delivery modes.
Furthermore, many tableaux algorithm optimization techniques have been introduced to make efficient reasoning. Second, the reasoning methods are based on rule-based approaches. Ontological reasoning, as a kind of application, can be mapped to the rule reasoning engine for reasoning. Wireless Communications and Mobile Computing There are many ready-made conversion tools to implement OWL as reasoning rules. The ontological reasoning machines currently implemented as rule-based ones are Jess  [22] , Jena, etc. Third, the reasoning methods are based on program editing. Based on the implementation of the deductive database technologies, two typical system projects are F-OWL and KAON2. Fourth, the methods are based on the first-order predicate prover. Because OWL declaration statements can be easily converted into first-order logic, it is easy to use traditional first-order predicate provers to implement ontological reasoning for OWL, such as Hoolet's ontological reasoning machine, which uses Vampire's first-order predicate prover to implement ontological reasoning.
3. 4. 3. The Reasoning Rules Adopted in DKDM4L. We use SWRL  [23]  rule language to define the reasoning rules in DKDM4L. SWRL (Semantic Web Rule Language) is a language that renders rules semantically. Parts of the concepts of SWRL's rules are evolved by RuleML and combined with OWL ontology. SWRL is already a member of the W3C specification. SWRL can be regarded as a combination of rules and ontology. Through the combination of the two, the relationships and vocabulary depicted in ontology can be used directly while writing rules. While the relationships between these categories may otherwise require additional legal descriptions, ontology descriptions can be used directly in SWRL. A total of ten rules with explanations are established as follows. x) (?x has_badWeather ?y) -> (?dhas_delayed_warning ?x)] [(?x possible_has_daily_trunc ?y) (?y equals_to_daily_trunc ?z) -> (?x actual_ has_daily_trunc ?z)] [(?x possible_has_express ?y) (?y equals_to_express ?z) ->(?x actual_has_ express ?z)] [(?x get_drug ?y) (?y equals_to_recent ?z) ->(?x has_recent_delivery ?z)] [(?x has_weather_infuence ?y)(?y equals_to_influence ?z) ->(?x has_ badWeather ?z)] Retail store inventory optimization rule_storage rule_sellingall [(?x reach_maxWarningNum ?y)(?y is_equal_to_notice ?z) ->(?x has_ storage_warning ?z)] [(?x is_less_zero ?y) (?y is_equal_to ?z) -> (?x has_sellall_warning ?z)] Cost optimization rule_lessTranscost rule_fdailytrunc rule_fexpress [(?x has_transcost ?y)(?y is_cheaper_than ?z) ->(?x has_lessTranscost_than ?z)] [(?x possible_take_daily_trunc ?y) (?y equals_to_dtt ?z) -> (?x take_daily_ trunc ?z)] [(?x possible_take_express ?y) (?y equals_to_ext ?z) ->(?x take_express ?z)] 9 Wireless Communications and Mobile Computing data as testing input.
If the delivery weight is more than one hundred kilos, the distribution cost increases by  27. 91 Euros per one hundred kilograms. Normally, this distribution mode covers all five DSs in one route. Together with the transportation costs, the inventory level is another important driving factor in making distribution decisions. As illustrated in Figure  3 , two thresholds are defined to limit the inventory for all five kinds of products in each DS. Table  9  shows all the threshold pairs. Considering the distribution driving factors from the inventory aspect, besides these threshold pairs, there are two other items "current inventory records" and "daily sell outs. " The current inventory records and daily sell outs are real-time data that are automatically generated. PFDC provided the current inventory records (CIR) and daily sell out (DSO) data collected within a period of one month.