dataesr/scientific-paragraphs-categorization huggingface dataset:
Step 1: Bid Coordination Detection The extent to which coordination can be detected hinges on the type of data available. With one auction only, the UC-RAE and EOS equilibria are indistinguishable as they both satisfy inequalities (2). By construction, under UC-RAE, the conditions in (2) must hold with equality for all coalition bidders except the one with the highest valuation. This, however, suggests that when multiple auctions are observed for the same keyword, the UC-RAE and EOS equilibria might be distinguished. In particular, since quality scores are changed nearly in real-time by the search engine, while valuations and CTRs are likely more persistent, let us suppose that we observe a dataset with T auctions that are identical in terms of the number of bidders and their valuations and the number of slots and their CTRs, but differ for the quality scores.
Under competitive bidding, b t3 must be high enough that J t ≥ 0, under UC-RAE it is as low as to make J t = 0, and under the Eff-RAE it is even lower so that J t < 0. Clearly, observing the distribution of J t in the data reveals the type of equilibrium being played, and this is exactly what we see in panel (a) of Figure  1. The three curves report the distribution of J t across the 100,000 auctions in the example above: under the UC-RAE the distribution is degenerate with a mass point at zero, while it is a non-degenerate distribution with positive support (in the case of EOS) or with negative support (in the case of Eff-RAE). As discussed above, however, a useful empirical model of the search auctions must entail perturbations in the quality scores. This will clearly impact the detectability of bid coordination.
Indeed, while there is overlap in the support of all the three distributions, it is still the case that most of the mass lies in the positive realm in case of competition, in the negative realm in the case of Eff-RAE, and it is approximately centered around zero in the case of the UC-RAE. Although the accuracy of this approach worsens as the magnitude of belief errors grows -as shown by the comparison of panels (b) and (c) -detecting coordination should still be feasible under the moderate size of belief errors measured by the earlier literature on EOS bidding. 15   Empirical Results -We now turn to the proprietary data to apply the detection method. Separately for each one of the keywords, k = 1,. , 71, we use all the available auctions to calculate the value of J kt for the lowest ranked member of the 2-bidder coalitions in these data.
To further clarify the approach, note that even if there might exist other perturbations of qualities (d i ) i∈I that would satisfy the set of equilibrium inequalities and equalities, it is not the case that we can rationalize the data with any collection of d i that are larger than those that are currently found, due to the interdependencies that the program generates across different i's. 18   Provided with the estimated ( di ) i∈I , we can proceed as in the example above and point identify, for each bid placed by an independent, the corresponding valuation. From that, we then obtain bounds on the coalition bidders' values. Finally, assigning to each independent bidder its estimated value and to the coalition bidders their estimated upper bound valuations, we can solve for the competitive EOS equilibrium and compare its revenues to the ones under coordinated bidding.
The J t distribution: a separate test of validity In this subsection, we explore a further check of the validity of our approach (that is, to detect coordination based on the patterns in the distribution of J t ). One may wonder how the distributions for the keywords for which we detected coordination compare with a possible reference distribution of J t , in auctions which are known to involve no bid coordination. 20  The most obvious way of doing this would be to consider a dataset of "comparable keywords" where no coalition is present and then compare the J t distributions for keywords with and without coalitions. Unfortunately, such a direct solution is not viable within our dataset, since the provider had specifically constructed it in order to have exactly two bidders under a common intermediary for all keywords.
We thus developed an alternative solution to explore this idea, which we believe to be the second-best. Specifically, we used publicly available data from Yahoo!, easily accessible from the website https://webscope. sandbox. yahoo. com/catalog. php?datatype=a&did=21, which contain detailed information on the positions, bids, impressions, and clicks for a large number of advertiserkeywords pairs over 123 days, from January 2008 to April 2008. 21  For a better comparison with the data used in the previous sections of this paper, we focused on the auctions in this dataset with exactly nine bidders. Unfortunately, the same dataset does not also contain information about agencies but based on our general knowledge and given what is observed in the SEMRush data, agency bidding was not a common phenomenon at the time of these data.
These observations, as well as the fact (which will be shown below) that the J t distribution for all of the bidders in these auctions looks a lot like the EOS distribution, makes us confident that the distributions of J t in these auctions can be safely taken as a meaningful reference against which to compare the ones that we obtained from our original dataset. 22 To calculate all J t for this dataset, we first need to estimate the quality scores. We follow a similar method as in  Agarwal and Mukhopadhyay (2016) , and infer quality scores from the available ad positions and bids for every keyword. Finally, we calculate the J t across all keywords and create the reference distribution (a description is included in the Appendix, where we also provide a "one-click" code that creates the reference distribution).
) This kind of coordinated strategies would entail even stronger downward pressures on the cost-per-click. Hence, if agencies engage in this kind of coordination, the actual revenue losses might be even larger than those identified by our methodology. It should be pointed out, however, that market splitting is not as profitable in multi-item auctions as they normally are in standard single-item auctions, since they may come at the cost of forgoing the surplus that the agencies' clients may still make by obtaining a different slot, and crowding out non-agency bidders. In fact, coalition bidding in the same auctions is indeed frequent in the data, as confirmed in both our proprietary data and the SEMrush data discussed in Section 2. Hence, at least for some keywords, agencies' strategy does not entail a complete market split.
In 2000, the ID team of the Grenoble University Hospital set up a telephone hotline to respond to non-infectious disease physicians. This hotline was used more by GPs than by hospital physicians  [17] [18] [19]. In this context, GPs expressed a need for support and expertise in infectiology  [20]. GPs were generally satisfied with the system (97. 9%), as were infectious disease specialists (94. 7%)  [20]. ID hotlines for GPs were gradually deployed in other French hospitals and regions. Deployment was based on the hypothesis that ID hotlines respond to a need of GPs and could contribute to improved antibiotic use. In order to estimate the utility of ID hotlines for GPs, a French study group on antimicrobial stewardship programs proposed to ID teams to use a single dedicated computer database and to record the advice requested on the hotlines from 2019 to 2022.
Data collection All requests to ID specialists made by GPs were recorded on a paper form, then entered into the AIRBUS database by a clinical research assistant. The AIRBUS database was a computer application developed on the Voozanoo platform (Epiconcept) accessible via the Internet and authorized to host medical data. It was also available on tablets or smartphones. Data recorded were: characteristics of the caller; object of the call; date and time of the call; characteristics of the patient; characteristics of the advice given; recommendations proposed (hospitalization, consultation, advice on antibiotic therapy, diagnostic assistance, etc. ); time spent giving the advice. At the end of the registration period, a telephone interview was conducted by an investigator using a questionnaire with all the participating ID teams to collect information on the organization of the hotline, the difficulties encountered, and their perception of the usefulness of the hotline.
Statistical analysis The statistical unit was the advice given by the ID specialist. Qualitative variables were described by proportions with 95% confidence intervals; quantitative variables by median and 25th -75th percentiles. Correlations between the usage rate and hotline characteristics were analyzed by Spearman's test. The threshold of statistical significance was set at 5%, in a two-sided situation. Analyses were performed with Stata SE software (version 15. 0, StataCorp, College Station, TX, USA).
Acknowledgements For their contribution to the data collection:  Etienne Canouï ,  Equipe mobile d'Infectiologie, APHP, Hôpital Cochin, F-75014, Paris, France. Sophie Alviset, Equipe mobile d'Infectiologie, APHP, Hôpital Cochin, F-75014, Paris, France. Florence Suy, Médecine Interne Infectieuse et Tropicale, MiiT médical selarl, Lyon-Villeurbanne, France. Emmanuel Forestier, Service des maladies infectieuses et tropicales, Centre Hospitalier Métropole Savoie, Chambéry, France. Mylène Maillet, Internal Medicine, Annecy Hospital, Annecy, France. Adrien Contejean, Equipe mobile d'Infectiologie, APHP, Hôpital Cochin, F-75014, Paris, France. Marie France Lutz ,  Service de maladies infectieuses, Centre Hospitalier Universitaire de Saint-Etienne, Saint-Etienne, France.
Preparing data collection A sample of 75 research participants was identified from a pool of 909 women entrepreneurs. Two methods were utilised to identify the sample, and these included (i) their records in a register containing details of women entrepreneurs. The registers we used were provided by business associations in Lagos State, Nigeria, and (ii) snowball sampling was used to reach other potential participants. The registers of the aforementioned associations were the main sources used to identify suitable participants to contact for the purposes of this study. However, not all of the initial contacts identified through the registers responded to the request for interviews. To mitigate this, we utilised snowball techniques  (Saunders et al. , 2003) , i. e. , those who had given consent to participate in the interviews led us to other participants.
Interviews Interviews were mainly carried out face-to-face. We employed an interview guide comprising semi-structured questions. To allow the participants to express their thoughts, interviews took place in their familiar surroundings  (Creswell, 2014; Gray, 2014) , and on average, an interview lasted 65 min. The interviewing process was terminated after the 75th interview. At this cut-off point, it was felt that a point of data saturation was reached  (Saunders et al. , 2018). That is, conversations with selected respondents were no longer, yielding any new insights into the financial scheme(s) women entrepreneurs were using in Lagos State, Nigeria. All the interviews were audio recorded and transcribed. For any information gaps that emerged as we were transcribing data from the interviews, we engaged a research assistant to carry out additional interviews.
,  Corley & Gioia, 2011). These first-order codes were deduced from the initial analysis, which we terminated when we could not identify additional codes  (Gioia et al, 2013). Following a line-by-line coding process, we recorded a total of 197 first-order codes (e. g. , community relations, cooperatives, stringent loan procedures, local and outside connections, etc. ) (Fig. 1 ). Fig. 1. Category and analytical themes from qualitative data analysis. Through a series of iterations, we reviewed and aggregated similar first-order codes (i. e. , primary codes) under second-order codes. Second-order codes were derived from both collected data and theory. In the second round of coding, a total of 55 secondary codes, including social ties and investment in connections, community resources, gender biases, peer-to-peer support, etc.
, emerged from the axial coding process (Fig. 1 ) (cf. ,  Corley & Gioia, 2011). Afterwards, we categorised all the second-order codes under aggregated dimensions (e. g. , community financing, collectivism and community financing, trust and community resources, reciprocity and solidarity, socialisation and community financing) using both data-driven and a priori themes. Fig. 1  summarises our analysis illustrating how we progressed from primary codes to secondary concepts and from secondary concepts to aggregate dimensions. The concepts at different levels were analysed further to reveal the connections and interrelationships between them. We vetted the interconnections that emerged against the qualitative data to ensure that the connections worked for the primary source material-the actual words of the participants.
The coverage of death registration in Brazil is between 90-99%  (12). We selected all cancer deaths (except non-melanoma skin cancer) among those aged 30-69 between 2001 and 2015. The specific underlying cause of death was coded according to the International Classification of Diseases, 10th Revision (ICD-10). Ill-defined causes of death were redistributed as recommended  (13)  as were unspecified codes for deaths from uterine and digestive tract cancers  (14, 15). Population data by region and age-group were obtained from demographic census and inter-census projections, from the Brazilian Institute of Geography and Statistics  (11).
Predictions Predictions were performed using Nordpred software package for R (version 3. 2. 0) developed by the Cancer Registry of Norway  (17). NORDPRED is widely used to make long-term predictions of cancer incidence and mortality. The model requires at least fifteen years of consecutive data (three fiveyear periods). For each combination of region, cancer topography, sex, and observed periods, predictions were calculated. Then, changes in the number of cases in the last projected period, 2026-2030, compared to the last observed period, 2011-2015, were calculated. This global change in the    (18, 19). These two components can be different from zero and present a positive or negative direction. The calculation of these components follows the equation: Dtot = Drisk + Dpop = Nfff -Noff ð Þ+ Noff -Nooo ð Þ Where Dtot is the total change, Drisk is the change as a function of risk, Dpop is the change as a function of population, Nooo is the number of observed cases, Nfff is the number of projected cases, and Noff is the number of cases expected when mortality rates increase during the observed period.
, 2022)  through the application of the OIPT theory  (Galbraith, 1973). This way, organizations can use digital technologies to design their infrastructure and architecture to drive value and maximize impact to address the competitive landscape. This research design is presented in three critical phases -conceptualization, mechanism, is developed (see Fig. 2 ), representing the selforganizing vision of an IIoT-driven information system. Finally, the system architecture is developed, integrating an operational technology system into the IT system presented in Fig. 3  to achieve digital transformation in the first phase in the DI pipe plant. Further study identified and developed a pioneering knowledge system based on TML's customer centricity, an integral part of the company. This information and store data to system platform servers and that further transfer it to the system application and database server.
, 2018). Sensor-generated data is collected and analysed on heat level, number of units produced, operational qualification and warning signs for maintenance. With an IIOT-driven information system, it is possible to track the real-time condition of a machine as compared to its average lifespan, identify the performance and schedule maintenance to ensure uninterrupted operations. IIoT-driven IS also enable the organisation's growth by reducing maintenance cost and helping in optimising the capital investment among machines and equipment. development, and execution of an IIoT-driven information system further mapped to OVT and OIPT theory elements that facilitate self-organizing vision as a knowledge contribution, indicated in Fig. 4. A four-step mechanism is conceptualized to integrate the OT and IT systems for unique decision support through IIoT-driven information systems.
, is maintained by the process control system. In contrast, the lab system is connected to the server through LAN (Local Area Network). OPC workstation works with OT systems and operates on PLC to produce the Pig Iron through a structured process. The OPC workstation, through PLCs, helps monitor, communicate, control, and optimize the operations of a blast furnace. OPC workstation gathers data from multiple sensors and equipment, along with other devices of the blast furnace system. OPC workstations enable the graphical representation, including charts and dashboards, facilitating better decision-making through real-time monitoring. The IIoT-based information system in the blast furnace department of the PI plant helps monitor irregular conditions and variations from the operating specification and triggers an alarm when required.
0 at TML  (Huber et al. , 2022; Li et al. , 2022; Oberländer et al. , 2018; Zhang et al. , 2021). One of the significant challenges in creating value from the data is the availability of data and its structure (both real-time and historical). Further, whether data is in a central location or can be extracted to develop descriptive, predictive, prescriptive, and cognitive models at different stages of business activities is the critical concern. This is even more challenging in a legacy business like a manufacturing plant, where multiple data collection engines work separately and on different platforms and reports are generated manually (see Fig. 1 : Data Sourcing). For example, at TML, all machine-related data are primarily available at the PLC level. These data are scattered and collected locally, whereas plant operations-related data is collected in a physical logbook or, at maximum, in Excel sheets on computers.
Earlier, dispatch-related data was collected through an ERP system (SAP S4 Hana). The architecture and implementation of IIoT-driven IS offered a platform where different data sources connected seamlessly through an OPC, FTP, and IIoT ignition system. The OT and IT system integration presented a robust information system architecture  (Huang et al. , 2014; Puschmann & Alt, 2005)  with the latest, upgraded hardware, software, and cyber security measures  (Oks et al. , 2022). The data generated through OT systems way, operators are alerted to any potential issue to avoid disruption and hazards while working in the blast furnace plant. Further, the OPC workstation is integrated with the SCADA system, which provides a detailed view of the entire process. The IIoT-based system in the PI plant allows operators to make adjustments to process air parameters, fuel supply feed rate, Etc.
Further, DI pipes have a separate IIoT ignition server, whereas, in the case of the blast furnace of the PI plant, there is an IIoT central server, which is coupled to the OPC workstation, offering quick, reliable and actionable decision support. However, both the ISs are developed on the same principle of how IIoT-based IS can best integrate IT and OT systems. For example, an OPC server is required in both cases, where the server translates the hardware communication through PLC into OPC protocol, and it acts as a standard interface among different types of data sources ranging from lab testing equipment to databases, facilitating superior decision-making.
However, the contribution of advertising has increased significantly in recent years, and hyper-casual games are primarily monetized by advertising  (Frid 2019). Motivating Evidence of Cross Promotion: To motivate our investigation, we present model-free evidence from an established publisher of multiple mobile games, several of which have reached the Top 100 in the major app stores. We obtained data on the adoption, retention, and usage of nine consecutive games, where each game was introduced one week after the launch of the previous one. This example represents a case of internal cross-promotion, where a customer is transferred to another product within the publisher's portfolio. Game publishers create a portfolio of products to ensure that the satiated customers of one game will become the new customers of another of their games  (Popescu 2020).
6 % for Games 5 to 10. Therefore, more than a quarter of each game's user base stems from cross-promotion, which shows the overall considerable size of the phenomenon. Retention, Satiation, and Cross Promotion: Table  2  shows a typical dataset for one of the games (Game 3), including the total number of play sessions and average session length per week for each game. This enables us to determine the total activity (number of sessions times average session length). By dividing this measure by the number of active users, we compute the average activity per active user in the last column of the table. This last column clearly shows a decline in activity per user over time that reflects satiation. To show the relationship between satiation and cross-promotion, we ran a log-linear regression for each game where the activity per user at time t is given by.
". Customer swap occurs in practice in two ways: direct, or via an app network exchange  (Dogtiev 2020b ; Rankmyapp 2019). In direct cross-promotion, two apps connect directly and agree to send each other traffic. Large advertising networks may enable brands that work with them to use their database to search for potential partners for free as part of their service. This activity will be associated with some transaction costs of finding the right partner of mutual interest and drafting the agreement. An app network exchange is a platform such as Tappx or Tapdaq that mediates between the sides  (Salz 2015 , Banis 2018 ). The platform can use its data science market knowledge to offer informed matches to potential game publishers and save on negotiation costs. The platform provides credit to a focal app for allowing other apps to advertise in them, and in exchange, the focal app can use the credit to advertise itself in other apps.
First, from a theoretical point of view, note that if Facebook determines both quality and price, there is no causality implied by the relationship of Equation (  17 ), and the relationship might very well be negative. 6 Second, from an empirical point of view, observe Table  7 , which shows the relationship between the average Google display advertising cost per conversion and the conversion rates in various industries  Bond (2022). The correlation between average Google ad cost per conversion and the conversion rates is negative (-0. 86). Moreover, if we take the same tables for Facebook ads and Google Search ads (also in  Bond (2022) ) and run a pooled regression of the three datasets using Equation (  17 ), we find that the regression fits the data relatively well (R-Squared of 70 %) and that c ¼ À0:52, and is significant at the 99 % level.
To do so, a novel approach based on environmental priority strategy (EPS) as an LCSA method for impacts monetization coupling with Big Data Analytics (BDA) techniques and tools is developed to evaluate and analyze the manufacturing enterprises' impacts on the environment and society. Moreover, the developed approach evaluates manufacturing enterprises' progress toward sustainable development goals (SDGs). Finally, to demonstrate the applicability of the developed approach, a case study from the corporate environmental impact database is used, and the obtained numerical results are analyzed showing its efficiency and added value.