dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018). Modern supply chains have evolved around the intervention of 3 PLs to optimise the freight logistics, yet horizontal collaborations are increasingly considered as an alternative strategy. Horizontal collaborations can benefit from the application of Physical Internet and the p-container transportation models, yet until now, most studies have been conceptual in nature based on simulations. This study utilised actual transportation micro-data from the European Road Freight Transport. The analysis of different scenarios has shown that pooling together cargoes can produce substantial benefits and reduce CO 2 emissions, without the need to invest heavily in new technologies or fuels. This finding may have significant repercussion for freight transportation: the container constituted a major innovation in logistics and transportation, when it was first introduced several decades ago; the pcontainer and the Physical Internet would also constitute a major innovation that would allow the horizontal collaboration and the pooling of similar cargos together.
Another possible area of research would be the use of Blockchain Technology  (Casey and Wong, 2017)  to improve the transparency of the Physical Internet  (Atlam and Wills, 2018). Blockchain was originally created for Bitcoin  (Hughes et al. , 2019) , however, it can be potentially used in the Physical Internet to track each container at every stage of the Supply Chain  (Wang et al. , 2019; Min,   2019). Since the Blockchain database is publicly accessible it would provide transparency and based on cryptography, will allow stakeholders to carry out verification  (Lu, 2019).
The results of these optimisations were impressive with a 12% reduction in emissions and 23% increase in the overall efficiency, when applied on the dataset collected. The performance of these algorithms were greatly restricted by the level of detail in the data provided and there is potential for improvement in real-time applications. Findings from this study will serve as a foundation for future development and research in the field considering the potential for optimisation. There may be alternative solutions for further development, to improve this solution like the approximation algorithm for the Knapsack Problem. Some other drawbacks of the approach taken include the inability to pool empty journeys and lack of support for reverse logistics, which can also be developed as part of future work.
Stage 1: Document and verify This stage involved gathering evidence on the extent of government action to implement food environment policies across 13 policy and infrastructure support domains and 43 related subareas (indicators) of good practice (Fig  2 ). We searched government policies, work plans, and national strategies, as well as evidence of formal/informal activity across policy processes (from agenda-setting to implementation, monitoring and evaluation). We systematically searched government websites, websites of other institutions (e. g. FAO, WHO, UNICEF) and academic databases (for peer-reviewed articles) for evidence of action in addition to requests for information from relevant government authorities. Physical searches and reviews of documents not available online were also conducted.
For the level of implementation of policies and government infrastructure support in comparison to international best practice, ratings were also grouped into four categories: "very little, if any" (<25%), "low" (26-50%), "medium" (51-75%) and "high"' (>75%). Inter-rater reliability (Gwet AC2 coefficient) was calculated using Agreestat 2013. 1, an Advanced Analytics, Gaithersburg, USA. In order to examine the differences in the ratings by professional characteristics of experts, we tested the distribution of the data using the Shapiro-Wilk test. As data were found to be non-normally distributed, we used Mann-Whitney U test to compare ratings based on experts' professional background (governmentemployed versus NGO-employed), gender, and years of experience. Statistical significance was set as p value threshold of <0�05 for all analyses.
Limitations This study only examined formal national policies and published articles from academic databases but not sub-national policies or community based bye-laws. Kenya has a decentralized system of governance through county governments; some counties could have developed and implemented policies at the county level that were not captured in this study. However, in the Kenyan context the role of policy-making is reserved for central government, with the county taking the role of implementation. This study did not aim to identify how and why policies have or have not been successfully implemented as planned. Such information is needed to design interventions. The process of rating could be judged as subjective, since some participants did not read the evidence pack until the day of the rating workshop, however we addressed this challenge by presenting the evidence pack to the participants for each indicator before rating thus minimizing inter-rater variability.
Acknowledgments This is an output of the Dietary transitions in African cities project: leveraging evidence for interventions and policy to prevent diet-related non-communicable diseases ( TACLED  project), led by  Professor Michelle Holdsworth  of the  University of Sheffield, UK. We received technical support from INFORMAS through  Dr. Stefanie Vandevijvere. We would like to extend our sincerest gratitude to the government representatives who verified the information in evidence and thankfully acknowledge the  Kenya Expert Panel  selected from both government and non-government sectors who participated in the rating workshop, identified actions, and subsequent prioritization.
Sample To test our hypotheses, we use a sample of European firms. We use Bureau Van Dijk's Amadeus database to collect our data. This database provides access to financial and accounting information for private and public European firms. We include all German, French and Italian firms for which ten years of data are available which considerably reduces the number of firms for which we have information 4. The observation period covers the years 2009 to 2018. The choice of these countries is motivated by the fact that they are all bank-oriented economies which is important to capture the impact of the financing costs of WCM. Moreover, these countries have different practices about payment delays 5  so we alleviate concerns that our results might be driven by a country's specific trade credit practices.
In a second round of coding, we identified the alliance motivations in the coded texts of our first round. In this second round, we used theory-driven markers on alliance motivations to code the text, as described in Table  4. These markers of the RBV, RDV, IT and TCE are examples and descriptions of alliance motivations given by the studies in the last column of Table  4. This second round of coding enabled us to identify specific alliance motivations and populate the four categories of generating and internalizing environmental and knowledge value with these motivations. Thus, we were able to identify when firms were motivated to form environmental alliances to generate or internalize environmental or knowledge value. We used Nvivo (11. 4. 1) software to code the text and the theory-driven markers.
Because of the exploratory nature of this research, the selection of respondents (35 participants) and configurators (six configurators for different products) was expected to provide a broad range of situations and cases through maximum variation sampling  [19]. Next, we collected in-depth data about the process that clients adopted to select, display, and use information to understand what they perceived, understood, and felt, and how they justified their navigation behaviors. Finally, we used the data collected to put forward a theoretical model, making a few adjustments to the TAM itself and assuming that learning processes are antecedents of the TAM variables. The results suggest that a configurator's efficiency is improved when it is supported by a design that facilitates the users' learning processes.
We then met to compare our codes, with a comparison made at the end of each analysis. When convergence was reached before or after discussion, the reasons were recorded. When some divergence remained after discussion, it was decided that the reason would not be reported in the paper. In the end, the analyses provided three categories linked to the experiential dimension of the offer and the configurator: learning from the offer, perceptions of satisfaction/dissatisfaction, and the emotions perceived. Appendix B indicates the thematic coding with definitions and provides a few examples of the verbatim statements. In addition to the data analysis, we observed that some variables, linked to the way configurators were designed, had a determinant effect on learning, such as inducing an end to the configuration process or provoking a high level of positive or negative emotions.
It was not an easy task to find interesting data for case study. Since the data coming from our project cannot be exploited here, we used the event log provided by the process mining website. It is used as an illustrative example to illustrate the method mentioned in Section 3. 2. 2, but both methods have been tested in the study. Our initial purpose was to investigate the value creation of multiple actors across enterprises in the service process. Although the actors in the process shown in the example log are all from the repair company, we can treat the departments of the actors in different activities as independent entities or enterprises. This is because in an actual telephone repair service, multiple enterprises are required to cooperate to complete the process. The customer has to apply for repair from the product manufacturing enterprise, and the repair is completed by the outsourcing of the production enterprise to the professional repair company, and the telephone also needs to be transported by the logistics company.
Implementation and Evaluation of Results The proposed models were implemented in the optimization software MPL® 5. 0. 6. 114 and solved with the solver Gurobi TM 8. 1. 1. Microsoft Access Databases were used to import input data and to collect the obtained values for decision variables. The computer used to solve the model had an Intel® Xeon® CPU E5-2640 v2 with two 2. 00GHz processors, with an installed memory RAM of 32. 0 GB and a 64-bits operating system. Data used to solve the models was extracted from  [3]  in which a 120 daily periods horizon (4 months) was considered with some modifications. Demand data is modified by multiplying it by a random value between 80 and 120% to create an imbalance between supply and demand. In addition, it is considered that 80% of demand is for 1 st quality vegetables while the rest represents the demand for 2 nd quality vegetables.
We suggest the criteria used by individuals to make judgments about the behavioural effectiveness of managers are inevitably influenced by their unconscious, personally held, implicit leadership theories (ILTs). These ILTs consist of beliefs, convictions, and assumptions about the managerial attributes and behaviours that distinguish effective from ineffective managers  (Eden and Leviatan, 1975). They can best be understood as 'cognitive prototypes'  (Phillips and Lord, 1981). The better the fit between an individual's cognitive prototype and what he or she observes, the more likely it is the observed manager will be perceived and judged effective, or conversely ineffective  (Foti and Luch, 1992). The researchers of the two empirical source studies from which we obtained our empirical data adopted a multiple-constituency approach for exploring the issue of perceived managerial and leadership effectiveness.
Empirical source data used for the present study The empirical source data was comprised of the respective sets of positive (effective) and negative (ineffective) managerial behaviour findings that had resulted from the two Type 3 (emic-as-emic) studies cited above. The researchers of these Indian and South Korean replication studies used  Flanagan's (1954)  critical incident technique (CIT) to collect concrete examples (critical incidents-CIs) of effective and ineffective managerial behaviour from samples of research participants comprised of a mix of managers and non-managerial employees. In the case of the Indian study the collaborating organization was a U. S. owned subsidiary that manufactured property management software for its American parent company. The CIT data were collected from 35 research participants of whom 2 were directors, 7 senior managers, 23 middle or first-line managers, and 3 were non managerial employees.
To ensure trustworthiness of the findings, the respective researchers engaged in 'member-checking' during and immediately following the CIT interviewing. In the Indian study the CIT data were collected in the English language because all the Indian research participants were bi-lingual. In the case of South Korean study, all the CIT data were collected in the Korean language by the four of five coresearchers who were native South Koreans. The data were then translated into English involving an iterative 'back-and-forth' translation process with bilingual native English speakers. At the data analysis stage of both studies, a form of investigator triangulation (Easterby-Smith, Thorpe and Lowe, 1991) was used to ensure internal validity (credibility) and reliability (dependability). The translated CIs were first analysed independently by the researchers of each study, and then jointly to arrive at a consensus regarding their respective deduced BS data sets.
Implementation of Newton's method Using the MATLAB software, we implemented the Newton's method for solving a quadratic problem under linear constraints. We adapted the general form of the quadratic problem to coincide with our MFOP and then conducted simulations by solving some problems using different predefined demand processes. The pseudocode of the Newton's algorithm is shown as follows: Newton's algorithm: Input of the problem data , 7, , ¼ and Z 3 for 6 ∈ 1, … , + 7" Input of the algorithm parameters: 6 óôÍ (maximal iterations number) and ü (maximal accepted error) Entry of the initial estimates: _ = 0, " = { $ " : 0 " | and Á " = { Á $ " : Á É " | While þ¿ À [ Á [ Âþ ≥ ü or þ [ $ -[ Á [ $ -Á [ þ ≥ ü or _ < 6 óôÍ Compute ¿ À [ Á [ Â and ∇¿ À [ Á [ Â Solve ∇¿ À [ Á [ Â. ∆» [ = -¿ À [ Á [ Â using the Pivot-Gauss method and deduce À [ $ Á [ $ Â = À [ Á [ Â + ∆» [ = À [ Á [ Â + À ∆ [ ∆Á [ Â _ = _ + 1.
Case study In what follows, we illustrate the model previously described by a numerical example. The model is solved on a one week horizon with a temporal mesh of one hour (168 periods). The daily demand has been converted to hourly demand by assuming it is evenly distributed during the day. The program has been solved in few minutes using Xpress solver. The model has calculated an integrated plan of all components of the overall production system. These plans have enabled a demand satisfaction rate of 100%. For each period, product and site, the model proposes: (i) A production plan defined by the quantity to be produced per period and per product in each site where the fact that product P2 can be manufactured in two different sites has been considered. (ii) An inventory plan for the whole stock of the productive system.
Final considerations With the objective of improving the productivity in the supply and separation of a logistics distribution center, the present work was able to deliver to the scientific community and area professionals, a tool able to prioritize products by number of accesses, to classify them according to the ABC curve and perform a line balancing in large warehouses with a great quantity of products. In addition, the technique proved to be simple to execute in a widely used software, Microsoft Excel®, with fast and expressive gains. The study was limited to a specific sector and could be extended to other areas. In this way, it is possible, for example, to perform line balancing at the products storage area, where they are placed in shelves as soon as they are received and checked. The classification of the products with the greatest number of accesses and the correct storage in the most accessible places would probably provide benefits, such as the use independence of forklifts or platforms to reach them, reduction in supply time, reduction in the number of failures, among others.
Background The availability of administrative data generated from different sources is increasing. The possibility to link these data sources with other databases offers unique opportunities to answer those research questions, which require a large sample size or detailed data on hard-toreach populations. This methodology link available information from different sources and can generate evidence at population level with a high level of external validity and relevance for policy making  [1]. Over an extensive period, data linkage ensures a high statistical power, thereby reducing methodological issues relating to attrition, recall bias and lost-to-follow up  [2]. This technique also allows performing more detailed stratified analyses of subgroups according to age, or specific geographical regions, and providing rapid access to data collected in a standardized format  [3] [4] [5].
The value of any surveillance system ultimately depends on timely and reliable information  [6]. There are several data sources, which are used for public health surveillance, for example, health interviews and examination surveys, diseases-specific registries, epidemiological cohort studies, hospital discharge data, health insurance claims, mortality database, etc. Traditional data sources (e. g. , health interview and examination surveys, diseasespecific registries, etc. ) and administrative data sources (e. g. , hospital discharge, health insurance claims, causes of mortality data, etc. ) complement each other and can increase the completeness and comprehensiveness of health information by taking into account various dimensions of health and risk factors influencing health status directly and indirectly.
Definition of innovative use of data sources We developed the definition of "innovative use of data sources" in the context of public health and health information system activities and defined as: The linkage of different data sources (health surveys and/or disease-specific or population-based registries and/or national cohort and/or clinical research datasets and/or administrative data and/or electronic health records and/or X-data sources i. e. , information on determinants of health and can include data on various exposures [Additional file 2]) with each other using linkage technology and/or The use of AI either applied to linked data or to an individual data set, allowing a better understanding of what determines population health or to promote the efficiency of the health system and guide decision making at different geographical levels, or at another categorization parameter level.
General characteristics of linked dataset Our results showed that among 24 European countries who perform data linkage in routine, 17 do linkage at national level (Table  2 ). France, Portugal and Scotland do data linkage both at national and sub-national levels. Denmark, Germany, Norway and Sweden do data linkage at all levels. 23 countries either perform the deterministic type of linkage (12 countries) or a combination of deterministic and probabilistic linkage (11 countries). In 16 out of 24 countries, linked data is available and is Fig. 1  Flow diagram of studies using linked data and artificial intelligence for health status monitoring to develop a survey on identifying various data linkage practices across European countries in 2019. *To be more specific for AI techniques, we looked for studies using machine learning techniques (i.
Furthermore, this technique also permits exploring underlying unobserved trends and patterns in large datasets without priori hypothesis. Our results highlighted that a few member countries have achieved the most advanced levels of data linkage by linking health information (i. e. , clinical, biobanks/laboratory tests, genetics) with non-health population data sources and registers of education, occupation, housing quality, air pollution, criminal statistics and transport/road accidents, etc. These innovative linkages offer the exceptional opportunities to enrich information, investigate non-health determinants and to perform high quality epidemiological research, health surveillance, to guide health policies to improve population health. These innovative linkages are especially relevant and of high value when considering health-in-all policies approaches.
Acknowledgements We acknowledge the kind support of  Jessica Gane  ( Data Department, Santé Publique France ) for developing the web-based survey. We specially acknowledge  Jean-Claude Desenclos  and  Yann Le Strat  for their kind support in developing the survey questionnaire. We acknowledge the following partners who contributed to complete this survey: Bulgaria:  Raina Nikolova  ( Public Health Institute ), Croatia:  Ivan Pristaš  (CIPH, Cyprus:  Vasos Scoutellas  (Public health institute), Czech Republic:  Šárka Daňková ,  Jiří Jarkovský  ( Institute of health information and statistics of Czech Republic ),  Denmark: Mette Bjerrum Koch  ( Danish Health data authority ),  Maja Baeksgaard Jørgensen  ( Statens Institute for Folkesundhed ), Estonia:  Eleri Lapp (MoSH) ,  Finland  ( Mike Gissler  (THL), Germany:  Angelika Schaffrath Rosario  (RKI), Greece:  Spyridon Goulas  ( Governmental Health Insurance Organization ), France:  Jennifer Zeitlin  ( INSERM U1153 ),  Sandrine Fosse  ( Santé Publique France [SPF]), Laure Carcaillon-Bentata, (SPF ),  Valérie Olie  (SPF),  Louis-Marie Paget (SPF) ,  Viet Nguyen-Thanh  (SPF), Ireland:  Sheona Gilsenan  (DoH), Italy:  Brigid Unim (ISS) ,  Latvia: Janis Misins  (CDPC), Luxembourg:  Anne-Charlotte Lorcy  ( Health Directorate ), Malta:  Neville Calleja  (MFH), Norway:  Heidi Lyshol  ( Norwegian Institute of Public Health ),  Inger Johanne Bakken  ( Norwegian Directorate of Health, Poland: Jakub Adamski (MoH ),  Piotr Nowosielski  (MoH), Portugal:  Paulo Nogueira  (DGS),  Martin José  ( Public Health Institute), Carlos Dias (Public health Institute), Romania: Silviu Radulescu (Public Health Institute ), Serbia:  Maja Krstic  (Batut), Slovakia:  Jan Cap  ( National Health Information Centre), Sweden: Hanne Lobosco (MoH ),  Rosita Claesson Wigand  ( Public Health Agency of Sweden ),  Jenny Börlin  ( Public Health Agency of Sweden ), England:  Robert Aldridge  ( Institute of Health Informatics, University of London ), Scotland:  Ian Grant  ( National Statistics Department ) and Wales:  Ronan Lyons  ( University of Swansea ).
MethODOlOgy Data Three residential neighbourhoods were selected: Jamestown in Accra (Accra population ~1. 5 million), Ho Dome in Ho (Ho population ~100 000) both in Ghana, and Makadara in Nairobi, Kenya (Nairobi population ~3. 5 million). Online supplementary appendix A presents details and maps of the three study sites. They were each identified by randomly selecting one deprived neighbourhood or community in each city that was not a slum based on the following sources: in Accra, we were informed by the findings of the Accra Poverty Mapping exercise  30  ; in Ho, we used data from the United Nations Human Settlements Programme 31 ; finally, in Nairobi we used ward-level deprivation estimates from the Kenya National Bureau of Statistics. We conducted a full audit of each neighbourhood. Community audit approaches are common in studies of the food environment and allow the construction of detailed and focused datasets.