dataesr/scientific-paragraphs-categorization huggingface dataset:
In the same context, research continues to defin the basic functions of the knowledge management process. In 2017, Olson and Wu dealt with the KM processes in the service of the DSS and distinguished the following 4 core functions: the identification of the most important information and measures to support the decision-making, the acquisition of the data corresponding to these measurements and the storage in specifi databases for allowing the retrieval  (Olson 2018). Using KM is very important in several field and especially in production and manufacturing. Berawi and Woodhead (2005)  confi m that a good KM strategy helps to improve the overall production management system. Several research works deal with these themes in all the stages of the production process: purchasing  (Lau et al. 2005; Zsidisin, Melnyk, and Ragatz 2005) , planning  (Gernhardt et al.
The analysis phase A clear distinction between the different types of heterogeneous data and knowledge circulating in the industry is necessary for optimal capitalisation. In order to build this classific tion of knowledge, the first step adopted in the proposed methodology was to develop process models to analyse all information flows in the manufacturing shop floor of mechanical parts. They were built based on literature survey and consolidated with the industrial partners of this research project in order to validate the adequacy of these formalised manufacturing processes  (Meski et al. 2019b ). In addition to the output data flows generated from the various phases of product manufacturing, monitoring data are also traced using a specifi device composed of hardware components (such as sensors and acquisition cards) and software components that include monitoring database and processing algorithms.
These reporting operations are executed within a specifi mode (periodic, on-demand, after a phenomenon occurs) and are customised for particular business roles or departments in the company (quality control, maintenance, process planning, programmer, etc. ). After reporting and studying the characteristics of detected problems and phenomena,adiagnosisprocessmustbecarriedouttodetectthe cause of their appearance. Finally, once the situation has been studied, solutions can be proposed to facilitate the decision-making process to improve the product quality or to prevent any future occurrences. The separation between data and knowledge types conducts to two repositories. The first one includes raw and smart data (separated in three databases) while the second gathers all the available industry knowledge, the business rules, the set of KPIs and reports generated through the reporting processes.
As shown in the figure above, the three layers constituting the ontology are strongly interlinked. In general, the execution of a decision support scenario should begin by analyzing and identifying the context elements, characterising the situation or case to be treated. They are instantiated based on dictionaries and sometimes-standard layers. Data and knowledge bases are in continuous collaboration: the execution of a scenario starts at the knowledge base level to retrieve the necessary structure then the reasoning system queries the databases to retrieve the corresponding data. The processing is then executed and it instantiates specifi elements in the knowledge base. For an event diagnosis scenario, these elements, also knownasindividuals,arerepresentedbycasesandthose  are structured with the objects 'cause, effect, decision, context', which will be presented in the following section.
Proposed approach to diagnosis process At the conceptual level, the proposed approach is based on the well-known FMEA (Failure Modes and Effects Analysis) method in order to set up the main criteria and parameters representing failures, causes and eff cts. F o l l o w i n gt h i sm e t h od ,n ewk n o wl e d g e( ru l e sa n dp r ocedures) has been integrated into the ontology in order to support the diagnosis process at the decision aid level. This knowledge is structured as a 'case' which is composed by the quadric 'Context-Cause-Effect-Decision'. • The CONTEXT calls, from the other layers of the ontology, all the elements necessary to characterise the manufacturing environment, namely: the name and reference of the tool, the name of the program and subprogram, the date, etc. Additional characteristics of the machining resource can be reached from the resource library.
More formally: « Effect1 → Cause1 = Effect2 → Cause2 » In this case, the context may be the same for both scenarios (referred as 'ContextGlob') or may be specifi to both  (referred as 'Context1' and 'Context2'). More formally: Knowing that {Cause1, Effect1}∈Context1 {Cause2, Effect2}∈Context2 More technically, in order to structure diagnosis operations at the ontology level, these four objects have been defin d as classes. In addition, a list of relationships linking these classes and attributes has been specified. Progressively, as the ontology is implemented and real scenarios are run, these classes will be instantiated by creating the corresponding group of individuals. This instantiation is based on previously structured industrial databases (raw, smart and traceability); this highlight the utility of the knowledge management strategy, promoting the interaction with data to generate new knowledge.
On each line, parameters necessary to detect a given eff ct and the rules applied to determine its cause of appearance are detailed. Following this mapping phase, an algorithm of the diagnosis agent is defin d. It starts by an interaction with t h eu s e rt od e fi n et h es p e c i fi cc o n t e x ta n dt h ed i a g n osis operation to perform. The scenario category to be executed may depend on the user's profile, or on the importance and seriousness of the phenomena studied. Hence, the importance of defining a criterion or a criticalscore for each anomaly. This rating is measured based on the following table  (Table 2 ). The diagnosis agent is in continuous communication with the knowledge base and databases. Accessing the knowledge base, it retrieves a list of criteria, conditions, thresholds, etc.
Technical aspects and user interface (UI) The Global Framework is developed using the Java language. The technological issue was to find the most suitable API to communicate with the ontology developed using the Web Ontology Language (OWL). The required solution is to use the Jena API. Apache Jena,a ss t a t e d o nt h eJ e n aw e b s i t e ,i saJ a v af r a m e w o r kt oc o n s t r u c t Semantic Web Applications. It provides a programmatic environment for: • Resource Description Framework (RDF), a graph model for describing web resources and their metadata,sothatsuchdescriptionscanbeprocessedautomatically. Developed by the W3C, RDF is the basic language of the Semantic Web. • The Jena inference subsystem, designed to allow a range of inference engines or reasoners to be plugged into Jena.
Diagnosis process details In the specific scenario of a tool failure, the first step is the verification of the possible exceedance of the threshold defin d for the detection criterion 'U b '. To do this, a threshold, calculated using machine learning algorithms, has been instantiated beforehand at the knowledge base level. This is the first manifestation of interaction between data and knowledge. Thequeryingoftheontologyfordataretrievalisdone using the SPARQL query language. The figure below shows an example of a query used in this demonstrator (Figure  7 ). Once the threshold value is recovered, the monitoring databases must be analysed by comparing the U b variations with this threshold. An exceeding value means the appearance of a tool failure and thus initiates the diagnosis process. Currently, the smart database is in form of a CSV fil.
Among the proposed tools, this work enhanced the eBPMN language  [7]. Bocciarelli et al. (2011)   [20]  introduced PyBPMN, a lightweight extension of BPMN that includes performance and reliability measurements using text annotations. Moreover, they proposed an approach merging the modeling and simulation phases at the time of design by introducing their eBPMN language. eBPMN is a language based on a «model-driven» method (model-to-model and model-to-text transformations) that automatically builds executable simulation code from a BPMN model  [7]. The «model-driven» business process simulation method starts with a BPMN model that includes both functional and non-functional measurements using text annotation. The model-to-model (M2M) transformation takes as input the annotated BPMN model and yields as output the corresponding PyBPMN model.
Then, the model to text (M2T) transformation, takes as input the PyBPMN model serialized into XMI and yields as output the corresponding eBPMN code. The M2M transformation is implemented by the use of OMG's language; QVT-O and M2T transformation is implemented by the use of Acceleo/OCL. More details are described in Reference  [7]. The advantages of this approach are to: • Merge modeling and simulation in the same step using a «model-driven» method to automatically build executable simulation code from BPMN; • Guarantee the compatibility of the model by not modifying the BPMN metamodel and implementing eBPMN according to the BPMN execution semantics using token concepts; • Allow non-functional simulation through process resources by using text annotation; • Take into consideration composite resources;.
Extension of the PyBPMN Metamodel As mentioned in Section 2. 3, this work aimed to improve the eBPMN language, as it already implements simulation components for performance analysis. This section briefly described a part of the PyBPMN metamodel used in the eBPMN language. More details are mentioned in References  [20, 22]. PyBPMN addresses three components to represent a resource in its model: In this work, new classes were added to the PyBPMN metamodel to distinguish resource types, as defined in MDSEA. Three specialization classes were added to PyPerformer: PyHuman, PyPhysicalMeans, and PyIT (Figure  4 ).
Thus, the business analysts and stakeholders could concentrate their efforts on finding solutions for the most likely process cases that can be executed (eliminating unnecessary steps, increasing resources, decreasing failure activities, etc. ). To do so, the eBPMN language uses the token concept for BPMN simulation. The token demonstrates the underlying behavior of each BPMN element. It traverses a sequential flow from the start point to the end point of a process  [15]. Thus, a token could be considered a reference of a process instance, and it is used to display the different elements it went through. In the eBPMN implementation, simulation entities are categorized into two types: • eBPMN objects corresponding to the original BPMN metamodel, • eBPMN objects derived from the PyBPMN metamodel.
Conclusions This paper presents a data process automation methodology based on the MDSEA approach, using BPMN and eBPMN languages for M&S. Visual modeling allows a more detailed and accurate setting of resource involvement by separating and taking into consideration the resource structure of human, IT, and physical means. Visual modeling interfaces and annotation components developed in this work allow the definition of new composite resources including user (human resource) and task forms (IT resource) in order to obtain finer-granularity simulations. The simulation proposed in this work will help analysts and stakeholders to analyze and predict the behavior of the process following several alternative actions before making decisions on their implementation. It will help to quantify the time needed to call and use different resources involved within a process case.
The business model of the company is based on the reuse of household appliances by multiple users in different use cycles where they track the product with an IoT-kit. Philips CityTouch model provides intelligent public lighting services to extend the use phase of streetlights, to remanufacture specific lighting components at the end of the first use cycle and to increase efficiency in public energy consumption for cities all around the world. They provide an IoT platform called lighting management platform and connect each lighting fixture to this platform with RFID (Radio Frequency Identification) network. The platform is used via web applications to manage streetlights and to analyse lighting data stored in the cloud. ZenRobotics is a leader from Helsinki in waste sorting robots and proposes intelligent solutions for recycling activities by combining AI and Robotics.
These intelligent assets are enablers for Philips to offer remanufactured products and not to lose the value embedded in the used city lightings. In this case, they benefit from the virtualization for communication because Philips, city authorities and the intelligent lightings can communicate via lighting management software. As all the lightings are connected to the platform, Philips can maintain the system, authorities can control the system and lightings are acting such as an intelligent object that can communicate with them. Thus, with this control mechanism, the stakeholders can save energy and material by optimizing energy consumption of the lightings and generate more revenue from the retrieved value of remanufactured resources. ZenRobotics case: In this intelligent solution, AI algorithms act as an enabler of the main activity of recycling: waste sorting and reprocessing of materials.
In order to achieve a high rate of pure secondary materials, the first step to do is to separate the waste accurately for the right recycling process. In addition, as the waste robots can be trained and the interface for the AI software is very user-friendly, this technology suits to different industries and to different waste types. Therefore, it can be a suitable solution for enterprises seeking to create partnerships with recycling firms using this kind of intelligent solution. Furthermore, with the use of AI, it becomes easier to use retrieved resources to offer recycled products and to create positive impact for the environment. ZenRobotics defends that this is the least expensive and the most efficient solution for separating waste that is why additional costs for sorting and reprocessing can be balanced with material savings and value of retrieved materials.
The relation of DT functionalities with the Circular Business Model Canvas is not a research done before. Based on the findings, the enabler role is more dominant than the trigger role. The trigger role only exists in the value proposition by offering a use-oriented PSS, exists in key resources by creating intelligent products and in revenue streams by making the pay per use logic possible. It means that this role is about the reuse option and it initiates some elements of this business model type. The functionalities behind this role are data collection, exchange, and storage and data analysis. The detailed functionalities about data analysis are monitoring product and creating intelligent product according to case findings and they are linked to Internet of Things. However, the enabler role facilitates eight building blocks of BMC such as partners, activities, key resources; value proposition; customer relations, channels, and cost structure and revenue streams.
According to  Hayes (2018) , this technique helps testing "the phenomenon in which the product of X and a moderator of X's effect (W) on Y carries its effect on Y through M" (p. 467), with a rule of thumb that the effect carried should be different from zero. CPA was performed using a SPSS macro process (model 14 in the present research, see  Hayes, 2018, p. 591) , which automatically creates the interaction variable and provides the low satisfaction level (1 standard deviation below the mean), and the high satisfaction level (1 standard deviation above the mean). Additionally, the index of moderated mediation was computed (see Table  7 ), which is akin to an inferential statistical test, to assess "whether the proposed moderator variable has a nonzero weight in the function linking the indirect effect of X on Y through M to the moderator"  (Hayes, 2015, p.
Although Lendosphere is the youngest of the three platforms, it is the best performing, with more than 25 million euros raised, 84 projects financed and 263 investors on average per project. Our dataset comprises all campaigns posted on the three website platforms since their inception. We gather the most campaign information from the three crowdlending platforms where they are publicly available. Some variables of our study relating to the company, such as its net income, its age and the number of its employees are not always mentioned and we used the Diane database to complete missing financial variables. Other variables relate to human capital of the entrepreneur: age, gender, and professional background. In general, we find this information in the description of the project on the platform website.
However, for more accurate data, we also include information provided on the entrepreneur's LinkedIn profile, when available. In addition to quantitative data that we collected on the characteristics and success of crowdlending campaigns, we also recorded information on the region of each campaign. As we can observe from Fig. 2 , all the projects posted on the three platforms originate in France. The highest percentage of renewable energy projects are in the Nouvelle Aquitaine region (25. 7 percent of all projects supported by Enerfip, Lumo and Lendosphere), followed by Occitanie and the Hauts de France region (20. 3 percent and 18. 5 percent of all projects respectively). We notice that for some French regions, notably Centre Val de Loire and Bourgogne France Comté, there are no renewable energy crowdlending projects yet.
With an open market share buyback program, firms announce the total number of shares authorized for potential buybacks, but offer no commitments about price, timing or even implementation of shares buyback  (Grullon & Ikenberry, 2000). Open market share buybacks have been a big pay-back strategy for various US companies in recent years. According to Compustat database, firms expended about 26% of their annual profit on share buybacks between 1984 and 2000, more than 90% are open market repurchases  (Grullon & Michaely, 2004). Second, a fixed price tender offer is off market share buyback method  (Zhang, 2008). Firms begin making announcement to invite shareholders for the tender of shares buyback over a certain period of time at a specified price reflecting some premium usually around 15 to 20 percent above the prevailing price in the market  (Grullon & Ikenberry, 2000; G.
Methodology We employed the descriptive approach to provide insights on the engagement of share buyback activities among Malaysian listed firms. We collected the data from Thomson Financial DataStream and the annual reports of Malaysian firms listed on Bursa Malaysia over the period from 2010 to 2015. We focus on these specific years to avoid the impact of the global financial crisis from 2008 to 2009. First, the treasury shares are collected from DataStream to identify the the firms with shares buyback activities. Subsequently, the data related to the value RM and numbers of actual shares buybacks are collected manually from the firm's annual report that is available on the website of Bursa Malaysia (http://www. bursamalaysia. com/ market/). The final sample is 836 firms, which are financial and nonfinancial listed firms that were involved in share buyback activities during the sample period.
In Malaysia, data is not available of the actual share buybacks by companies through external data providers. However we were able to extract the treasury stock accounts from Datastream. Therefore, we first highlighted which companies repurchased their shares via the treasury stock accounts and then manually collected the data of the actual shares and value of each company. Table  1  show the number of firms engaged in shares buyback during the years 2010 to 2015, as well as the Ringgit value spent on buyback programs. As it appears in We then have manually collected the data of actual share buybacks from the annual financial reports of each firms for the period from 2010 until 2015. Table  1  Panel A presents the sample distribution by year, while Panel B presents the sample distribution by industry which is based on the Bursa Malaysia classification.
Conclusion This paper aims to provide an overview on the phenomena of share buyback practices in Malaysian context. We explore whether the Malaysian listed firms use share buyback activities to manage their earnings. Based on the data by Thompson Reuters DataStream, 1385 Malaysian public firms have treasury share accounts. Based on those firms, we went through their annual financial reports and manually collected the actual shares buybacks, in RM value and shares. We found that 836 firms engaging in actual share buybacks during the period from 2010 to 2015. We utilised the criteria of  Hribar et al. (2006)  to distinguish accretive share buybacks and whether share buybacks significantly affect earnings per share. We found that more than 75% of firms were engaging in accretive share buybacks at least one time during the period.
This study examined the impact of collaboration in agri-food supply chains on firm performance and the moderating role of scale constraints and firm strategy. Collaboration was measured by three constructs: horizontal collaboration, vertical collaboration, and customer engagement and SME performance with three variables: growth, value for money and innovativeness. Two strategies were evaluated: price strategy and quality strategy. Three types of scale constraints were assessed: financial, efficiency, and innovation. A survey of 504 agri-SMEs was conducted to test the research hypotheses developed according to the resource based view. Data analysis included a moderated hierarchical analysis. The results show that supply chain collaboration impacts positively on agri-SME performance, whilst it is partially accepted that scale constraints moderate the supply chain collaboration-SME performance relationship.
Verification stage Depending on a BP complexity, software testing and/or manual checks could turn out insufficient and hence, time constraints violations could still arise. To address this concern, we propose model checking as an accurate and exhaustive verification alternative  [2]. The BP engineer checks the correctness of the time-constrained, TPN-based process model using a model checker like TIme petri Net Analyzer (TINA)  [3]. If the check reports any temporal violation, a list of violations (could be based on counter-examples) is reported and the designer is referred back to the placement stage. At this stage, a threshold could be put to limit the number of times the designer has to initiate this stage. Otherwise, the designer proceeds with executing the BP. Violations at this stage could refer to deadlocks due to temporal inconsistencies (e.
, 2017; Hong, Thong, Chasalow, & Dhillon, 2011; Maruping et al. , 2009a). Our sampling frame consisted of 3989 software developers working in development projects at our target software vendor in 2016. We approached the developers via the company's management team who invited them to participate in our two-stage survey. We discarded responses from developers who did not complete both surveys. Moreover, we restricted our sample to projects that had no overlap in terms of developers in order to prevent confounding effects of multiple project/team membership. These exclusions resulted in a final sample of individual-level data from 1894 developers who worked in 217 software development projects. The average age in our sample was 29. 4 years (SD = 4. 75) and average tenure at the company was 2.