dataesr/scientific-paragraphs-categorization huggingface dataset:
To PFDC, the customer satisfaction always comes first. (i) Optimization of the product delivery. Input the oneday sales data of each product in each drug store, and DKDM4L gives the total amount of required products for the next day. DKDM4L can generate distribution alerts before (and after) the   To sum up, DKDM4L has the following two advantages: ensure the continuous supply of products and bring customers (drug store) a better being served experience. One point to be emphasized, PFDC is a pharmaceutical and cosmetic company, and the selling of these kinds of products is  12 Wireless Communications and Mobile Computing sensitive to the weather. DKDM4L is aimed at serving this kind of companies to optimize their logistics. This is also a limitation on the usage of DKDM4L.
The original trigger of this research work is the C2Net Project. At first, we focused only on proposing a new VMI optimization algorithm (a pure data-driven one). As one partner of the C2Net Project, PFDC provided a practical business scenario with real data as the test case. During the project, we found that a pure data-driven optimization method had encountered many limitations. Therefore, we extended our work to the current status. Again, the practical scenario (a simplified version with four hypotheses) from PFDC is the foundation of proposing DKDM4L. The performance of DKDM4L has also been tested and evaluated with the collected real data. The testing results approve that DKDM4L is a potential solution to IRP of modern logistics. We will extend DKDM4L in three aspects in the future. First, enrich the domain ontology (knowledge model) to improve the generality.
Acknowledgments This work was supported by the  National Key R&D Program of China  (Grant No. 2018YFB1003900 ), the  Natural Science Foundation of China  ( 61872182 ), and the  CCF-Huawei Database System Innovation Research Plan  under Grant  CCF-HUAWEI DBIR2020001A. The authors also would like to acknowledge the financial support given by the  C2Net  Project as a trigger of this work.
Case Study: Insights from Visualizing a Mobility Business Ecosystem In order to characterize this facet of urban experimentation, we report insights achieved as part of a smart city initiative addressing the Munich metropolitan region  [59, [87] [88] [89]. As part of this initiative, our research engaged in a community-based, agile approach to model and visualize the urban ecosystem related to the mobility of people in the metropolitan region, or mobility ecosystem. In addition, this research comprised our own software engineering design work and a field-test of the developed system prototype of a visual analytic system (VAS) in two case studies we conducted in close collaboration with industry partners  [88]. The city of Munich in Germany has a population of more than 2. 5 million in its urban area and more than 5.
These innovations took the form of Internet-of-Things (IoT) systems, Artificial Intelligence (AI) techniques, smartphone applications, and digital platform interfaces, etc. The principles at the foundation of this project related to (a) co-creation involving quadruple-helix stakeholders, seen as important for inclusive design, and for adoption and scalable innovation, (b) real-world development and testing of innovations that support their real-world use value, (c) multiple-city experimentation to support innovations that can be developed and tested across numerous European cities, and (d) a 'toolkit' encouraging good practice and diminished barriers to the innovation, within the context of urban innovation ecosystems. At the core of this toolkit was the OC-EaaS digital platform supporting the availability, interoperability, and visualization of urban data arising from the different experimenters involved, thus supporting thinking and practice oriented towards urban innovation ecosystems.
The UDO was positioned as a service for both experimenters and citizens to view and contextualize data generated via assets available. In summary, the OC digital platform was configured to encourage the visibility and sharing of data for all involved in the experimentation and innovation process. For example, experimenters could share data amongst each other through the 'Assets Directory', whilst the UDO online interface enabled citizens to rate and comment on the quality of data being generated. Finally, (d) OC facility co-creation was seen as important to organically improve OrganiCity's offerings over time. Throughout the OC project, there was an ongoing evaluation of the OrganiCity project accomplishments and outputs. Including periodic surveys and interviews with experimenters, city stakeholders, ongoing technical feedback loops with experimenters on digital capabilities, and documentation of OC staff decisions and operations, the OC facility undertook a significant refresh between the two funding rounds.
OrganiCity: Raising Mindfulness for Open Urban Data and Scalability of Experiments OrganiCity illustrated ways to make experimenters recognize the value of urban data. This was achieved by integrating urban data sources within the OC-EaaS platform, making this open urban data available to experimenters, and by supporting experiments that demonstrated the impact resulting from data use. As of today, many cities are starting to create "open datasets" to share publicly available data such as e. g. , the London Data Store (LDS) and the Open Data DK (ODD) initiatives. In OrganiCity, in dialogue with experimenters and city stakeholders, e. g. , members of the Smart London Board, voices raised the need for mechanisms to better exploit such open urban data. Thus, enabling the exploitation of open urban data by providing adequate mechanisms along with digital platforms surfaced as a significant aspect in achieving urban sustainability.
OC also supported public awareness and acceptance of urban data-driven initiatives whilst adhering to ethical and responsible use and protection of data. This was accomplished by educating experimenters and urging them to adhere to ethical principles in the collection and exploitation of urban data. Measures included 'liability and intellectual property rights' passages as well as an 'ethics and privacy checklist' to experimenter funding contracts. In qualifying local innovation, OrganiCity also supported one-off experiments where data added value in supporting future action and innovation. For example, the grassrootsled experimenters of 'Tranquil City' collected data to identify the areas combining low noise and air pollution in London in order to provide alternative routes for travel and leisure commutes.
This resulted in an open dataset of over 180 category points, potentially benefitting (1) city planners, (2) a business's commuting app offering (3) citizen housing renting/buying choices. Similarly, 'Colour in City' took a social innovation approach to ascertaining new data-driven insights related to well-being issues of parents living in overcrowded housing in parts of London. This resulted in policy recommendations for the London Economic Action Partnership (LEAP). In relation to the above, a key insight from experimenter use cases concerned the need for additional sources of data that enable or provide added value to experimenter solutions being realized. In this respect, by enabling and supporting one-off experiments to release new forms of urban data, in addition to supporting experiments that lead to specific innovations, a richer data ecosystem with which to stimulate the development of urban ecosystems can be created.
Funding: We acknowledge that the projects preceding this research have received funding from the following sources: The  German Federal Ministry of Education and Research (BMBF)  grant  BEEx+ 01IS17049 , the  TUM Living Lab Connected Mobility (TUM LLCM)  project, and the  Bavarian Ministry of Economic Affairs, Energy and Technology (StMWi)  through the  Center Digitisation, Bavaria , an initiative of the  Bavarian State Government. The OrganiCity project was co-funded by the  European Commission  under contract  H2020-645198  and the Making Sense project under contract  H2020-688620. This work was supported with funding from  Science Foundation Ireland  grant  13/RC/2094  and  Intel Labs Europe.
It investigates the major effects of organization, technology and environmental factors, along with innovation and benefits helpful in making investment decisions in favour of RFID technology. In order to evaluate the model of research, data were collected from 297 employees working in a range of construction organizations in Australia. The key factors that may pose a significant impact on investment in RFID technology in the construction industry are investigated using this sample of participants. In the following section of the study, the theoretical groundwork will be introduced, related to determinants for adopting RFID technology. The third part of the study will describe the methodology of the research, while the fourth part will focus on data analysis along with the discussion of the findings.
A. HYPOTHESES ANALYSIS Thirteen hypotheses were generated and tested in order to analyse the theoretical research model expounded in the previous section. Survey data was collected from companies in the construction industry; and this study was limited to the construction industry. Relative advantage is the degree to which adopting/using the IT innovation is perceived as being better than using the practice it supersedes  [15]. It also refers to the perceived benefits of innovation in comparison to existing technology  [15] ,  [69] ,  [98] ,  [109]. These benefits include efficiency, economy, and popularity, as well as the cost of adopting the innovation  [15] ,  [100]. When the user believes that the innovation provides a greater relative advantage than the existing technology, the innovation is usually adopted  [100] ,  [103].
A. RESEARCH DATA The survey distributed to Australian construction companies returned 297 individual responses from IT-related staff. Table  3  describes the demographics of respondents. The key demographic information collected comprised the role of the respondent within IT, their knowledge of RFID, and their total number of years' experience in IT-related areas. Table  3  shows that 46. 5% of respondents worked on system analytics and development, and 39. 4% had 'little knowledge' of RFID. The highest proportion of respondents had between 2-5 years' experience in construction, with 1-2 years and 5-10 years being the next most common response categories. For IT, the highest proportion had 10-15 years' experience, while equal numbers had 2-5 years' and 10-15 years' experience.
Inclusion criteria • Practical initiatives to implement a card-based system; • At least one reference to a difficult find during the implementation process or on an existing system related to human aspects. • Simulation or mathematical analysis of the systems; Source: Authors It was necessary to include conference papers, like  Jungherr (2016)  and  Liao et al. (2017) , as many empirical studies reporting card-based systems implementation are presented in this type of document. Subsequently, two filters were applied to this database. In the first one, authors read the title and the abstract of the 514 documents to assess if they met the inclusion and exclusion criteria of the research protocol (Figure  2 ). The remaining 112 studies had their content fully and inclusion and exclusion criteria were applied again, resulting in 8 studies (Chart 2).
Longitudinal case study An in-depth inductive case study was conducted to identify more problems and create a larger database for further analysis. The authors selected a Brazilian unit of an organization that produces a high diversity of consumer goods first because they have unrestricted access to the company and to its information databases, what make it possible to conduct a longitudinal study collecting data in multiple phases. Moreover, the unit was starting to implement kanban in the final 3 mini-factories at the beginning of data collection for this study, what was very convenient to the authors. Furthermore, as the implementation was not well succeeded, the case become more interesting, as literature usually reports only success cases. However, a failure case is more adequate to understand the difficulties face during an empirical kanban implementation.
The case study carried out in this paper is longitudinal as data was collected in three different stages. In the first stage, we have followed for 8 months (from February to September 2012) the entire implementation process of kanban. However, at that time it was not clear for company managers and employees the main reasons for the unsuccessful implementation. Between 2012 and 2018 (stage 2), we visited the focus company on a weekly basis. In these visits, we have collected additional information and conducted informal conversation with company's employees. In 2018 (stage 3), six years after the company decided to interrupt the Kanban implementation, interviews with key leaders were undertaken. At this time, it was much clearer which problems were faced during the implementation process, as well as what could have been done differently.
Content analysis In step (iii), a content analysis in 8 articles selected and the case study information was conduct in order to identify soft factors critical to a successful implementation of card-based systems. An exploratory approach was used, as codes were not clear before the analysis of the material. It is worth mentioning that a computer-assisted text analysis software was used, named QDA Miner 5. To code the material, we defined a procedure based on  Haapanen and Tapio (2016)  and  Friel and Pinot de Villechenon (2018). We evaluated if sentences presented problems to implement a card-based system that were related to soft factors influence. If so, we tried to summarize the content without losing meaning (meaning units). Second, we grouped terms into 14 groups, associating each one of them to a factor.
Data and Empirical Results Data, published by International Organization of Vine and Wine (IOV), are annual per capita consumption of wine averaged over the period 2010-2013 to avoid single-yearrelated problems. Production data for each country are also available in OIV statistics. Our sample of 52 countries represents 90% of the world consumption of wine. The human development index (HDI) developed by the United Nations Development Program (UNDP) is a computed value based on the education level. It is available in annual reports of the organization. The urban variable shows the percentage of the urban population and religion shows the percentage of each religion in a country. These variables are available from the World Factbook. (www. cia. gov/the-world-factbook/countries/, accessed on 6 September 2021).
Background: The COVID 19 epidemic submerged many health systems in the Amazon. The objective of the present study was to focus on the epidemic curves of the COVID 19 epidemic in different centers, and to look at testing and mortality data. Methods: Publicly available datasets were used. The log 10 of the daily cumulated number of cases starting from the day the territory reached 100 cumulated cases was plotted to compare the magnitude, shape and slope of the different curves. The maximum daily testing efforts were plotted for each territory in relation to the maximum daily number of diagnoses. The case fatality rate was computed by dividing the number of COVID 19 deaths by the number of confirmed cases. Results: In the Amazonian regions in general the speed of growth was generally lower than in Europe or the USA, or Southern Brazil.
Data Analysis Data was analyzed with STATA 15. In order to track the epidemic dynamics, we computed the effective reproduction number, which is the average number of new infections caused by a single infected individual at time t in the partially susceptible population. Indeed varying proportions of the population are immune to any given disease at any given time and social distancing may vary over time. The R t values were calculated using the R EpiEstim package which computes R from daily new cases of COVID 19. Median values for each territory were plotted on a single graph.
Computable general equilibrium (CGE) models are generally used to conduct trade policy analysis; however, given the complexity in data collection and modeling, tariff rate quotas (TRQs) are often simplified in these models. However, TRQs are crucial for trade negotiations because they are rarely completely liberalized and often the obstacles to negotiations. We propose an approach to model TRQs explicitly and at the product level within CGE models and compare with previous approaches that considered an explicit (or implicit) representation at the tariff line or sector level. Using the Canada-EU trade agreement as an example, we find significant aggregation bias if TRQ shocks are implemented at the aggregate sectoral level. This bias is only partially eliminated if TRQs are implicitly represented by ad-valorem equivalents at the tariff line.
Sectoral-versus product-level representation of TRQs Another aspect related to the implementation of TRQs is the product detail considered in the analysis. TRQs often include some product levels ranging from HS4 to HS6 or even narrower definitions for which international trade statistics are not generally easily available. Databases dedicated to global CGE analysis offer fewer details such that either the model structure is expanded to reflect the variables related to bilateral trade (and potentially further transactions, e. g. , domestic sales) at higher detail, such as at tariff line level (see, Chepeliev et al. , 2019; Golub et al. ,  2020; Jafari et al. , 2021), or TRQs defined at the product level are aggregated to the sectoral level of the CGE. Lips and Rieder (2002) suggested two approaches to implement TRQs for individual tariff lines at a more aggregate level.
5% of Canadian imports from the EU. Bilateral import shares of agri-food products are slightly lower, with 0. 7% and 12. 1%, respectively. However, the share of EU imports from Canada is substantially higher in some cases, such as for wheat (10. 6%), oilseeds (3. 0%), and other cereals (2. 8%). For beverages and tobacco (43. 1%), other animal products (35. 9%), dairy products (29. 6%), and wool products (28. 3%), the EU's share in Canadian imports is quite important. Table  1  also shows the initial AVEs of tariffs between the EU and Canada and their percentage reductions because of CETA (calculated with MAcMap) at the agri-food sectoral level of GTAP database for agrifood. Manufacturing and extraction sectors are summarized because they are fully liberalized, whereas no trade in services and related protection is currently covered by the data.
Methodology This study uses the modular platform for CGE modeling "CGEBox" (Britz and van der Mensbrugghe, 2018). It takes the standard GTAP model (Corong et al. , 2017; van der Mensbrugghe, 2018) as its core, expanding it optionally by variants dealing with factor supply; production; demand; and a range of options to depict trade (Jafari and Britz,  2018), including a disaggregation of bilateral trade to a more fine-grained resolution, as in Jafari et al. (2021). CGEBox, accompanied by an aggregation and disaggregation facility (Britz, 2021), is used to transform the GTAP Data Base into the desired dataset for simulation purposes. It comprises an algorithm to filter out small data entries from the global CGE database and later rebalance it to improve numerical stability when solving the CGE model (see Britz and van der Mensbrugghe, 2016).
Representation of TRQs in the tariff line module based on MCP Jafari et al. (  2021 ) have further categorized commodities relating to selected bilateral trade, see extensions in Figs. 2  and 3  [for further detail see Jafari et al. (2021)], thereby leaving domestic supply and demand at the sectoral detail of the CGE database. We extend this module for an explicit MCP representation of bilateral TRQs, thereby yielding a fully consistent aggregation from the tariff line to the product level and to the sectoral level. The bilateral import tax defined at the tariff line level becomes endogenous under TRQs. Therefore, we use the MCP approach to allow for tariff regime shifts based on complementary slackness conditions similar to that found in the LINKAGE model (van der Mensbrugghe, 2005), the GLOBE model (Burrell et al.
Equation (2) defines bounds for the shadow tariff that should be equal to the difference of IQTRs and OQTRs (t iq and t oq , respectively) if out-of-quota imports I oq occur. Equation (3) defines the endogenously determined applied tariff rates τ m based on the inquota and shadow rates, and finally equation (  4 ) is the import balance defining total imports I This system of equations is defined for all tariff lines that are subject to bilateral TRQs. 17 The definition in the CETA text for trade that falls under each TRQ exceeds the HS8 detail for which trade data are available. For example, for the MEAT sector, no trade information is readily accessible to distinguish the trade of fresh and frozen beef, bison, and HQB beef. At the 8-digit tariff line, the Fresh-beef-TRQ and Frozen-beef-TRQs are mutually exclusive across tariff lines; however, trade under some tariff lines could be placed under each of three TRQs: Bison-TRQ, HQB-TRQ, and Fresh/ Frozen-TRQ (see Table  A5 , for the complexity of TRQs in the meat sector).
In the TRQ_TL scenario, the parts of meat and beef sector unaffected by TRQs are modeled as that in AVE_TL. The implementation of TRQs for all other commodity sectors other than Cheese and Beef is consistent with AVE_Sec. 19 We also introduce a framework (see Appendix B) that endogenously allocates quotas across tariff lines, where a virtual export distributor allocates quotas endogenously to different tariff lines to maximize its profit. This approach, however, requires the marginal willingness to pay at the tariff line level by the export partner that is unfortunately not easily obtainable. Future studies might use the introduced approach depending on data availability. 20 The aggregate Canadian tariff on cheese imported from the EU as reported in the GTAP 10 database is 1. 7% (Table  1 ), far below the tariffs obtained from the MacMap database.
In our database, this aggregated 1. 7% tariff is split across different tariff lines, based on the tariff rates reported in Table  4. Therefore, small trade impacts are observed. The large differences between the tariffs found in the GTAP and MacMap databases mostly reflect that different fill rates are assumed at the benchmark. The GTAP database assumes an underfill such that the resulting tariff is an average of IQTRs, whereas the MacMap approach data assumes filled quotas and, thus, reports the QQTRs as initial tariffs. 21 A zero elasticity (lower bound for our sensitivity analysis) corresponds to the standard (fix) trade weighted tariff aggregator, which is implicitly used when the aggregation is performed pre-model. trade subject to protection and the trade not subject to protection. Moreover, the implicit and explicit treatments of TRQs at the aggregate sector estimate different trade impacts primarily because the two approaches project different percentage reductions in tariffs.
Third, as found in our empirical example, even products at a fine-grain tariff line level may fall  under different TRQs. With our 8-digit-level detail trade data, we were not always able to associate each TRQ to a single tariff line. Here, further data might help. Fourth, although our approach captures bilateral trade changes at the detailed commodity level, production changes are not simulated at this detail as, for example, in the GTAP-HS-TRQ model. Finally, for benchmarking under a binding quota when TRQs are implemented explicitly, an assumption on the per unit quota rent must be made. In this study, we took the average of the IQTR and the OQTR, following the MAcMap approach. Source. Authors' compilation based on the definition of tariff lines by the EU available from TARIC database, and by Canada available from Canada border Services Agency.