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AbstractThis study aimed to provide insights into a novel honey screening and authentication approach. Chemometrics associated with a GC/MS instrumentation was applied to verify a total of 13 honey samples having different floral and geographical origin with an aim to detect buckwheat honey and discriminate it from honeys of other floral sources. Non-polar and semi-polar compounds were first extracted and then detected and semi-quantified employing a GC/MS device working in a full scan mode. Fingerprinting signals covering peaks that elute in the first 20 min, comprising 2000 scans of semi-polar compounds, were used as input datasets for unsupervised and supervised chemometric tools. This approach demonstrated excellent classification performances of honey samples according to the declared floral source, regardless of their geographical origin. The obtained results were compared with the outputs of a common melissopalynological procedure. Although exhibiting a high variability in the pollen content, samples declared as buckwheat honey demonstrated similar chemical profiles. The proposed non-targeted and semi-quantitative method showed to be rapid, unbiased, independent of tedious qualitative and quantitative determinations of eluting compounds, thus exhibiting a strong potential to be incorporated into data fusion procedures for honey authentication. Harmonising chemical methodologies with certain indications from pollen analysis to form core-data systems would undoubtedly improve the authentication protocols of honey floral sources.

By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering change (EC) process. Here, failing to adhere to planned implementation dates can have severe impacts on assembly lines, and it is paramount that potential negative cases are identified as early as possible. Current PBPM research, however, has seldomly investigated the predictive performance of machine learning approaches and their applicability at early process steps, let alone for the EC process. In our research, we show that given adequate feature encoding, shallow learners can accurately predict schedule adherence after process initialisation. Based on EC data from an automotive manufacturer, we provide a case sensitive performance overview on algorithm-encoding combinations. For that, three algorithms (XGBoost, Random Forest, LSTM) were combined with four encoding techniques. The encoding techniques used were the two common aggregation-based and index-based last state encoding, and two new combinations of these, which we term advanced aggregation-based and complex aggregation-based encoding. The study indicates that XGBoost-index-encoded approaches outclass regarding average predictive performance, whereas Random-Forest-aggregation-encoded approaches perform better regarding temporal stability due to reduced influence by dynamic features. Our research provides a case-based reasoning approach for deciding on which algorithm-encoding combination and evaluation metrics to apply. In doing so, we provide a blueprint for an early warning and monitoring method within the EC process and other similarly complex processes.

The paper aims to present the importance of project risk assessment within exploration and production (E&P) sector, namely oilfield services. In this paper, some major project management related issues in the field have been highlighted, such as potential risks present within drilling rig preparation projects. The central research of this paper includes systematic risk overview of the several already finalised drilling rig preparation projects. Furthermore, based on the applied risk assessment methodology, the proposals how to successfully identify, analyse and respond to project risks are given. Therefore, presented results of the paper provide oilfield service project managers with an easy-to-use risk assessment guideline. [Received: April 15, 2018; Accepted: August 7, 2020]

Automation has been recognized as a possible solution for increasing air traffic controller workload trends. This paper presents a methodology for creating an air traffic control knowledge graph, which is used as part of a hybrid artificial intelligence system for air traffic control operations. The system combines machine learning and symbolic reasoning with the purpose of achieving artificial situational awareness in a narrow domain of en-route air traffic control operations. This approach allows the use of user-defined knowledge alongside existing knowledge repositories. The novel knowledge graph development methodology is universal for any area of air traffic management which relies on the aeronautical information exchange models. In this paper we also present the open-source tools which were developed to make this approach possible and system performance evaluations. Future work should address achieving real-time operation and additional task automation, accompanied by appropriate ontology and graph expansion.

The current market does not provide commercially available products of modular radios for unmanned aerial vehicles (UAVs). The goal of the project presented in this paper is to improve the usability of technology related to the functions provided by software-defined radios (SDRs) and UAVs, especially for radio frequency spectrum monitoring, migration and border control. The project focuses on integrating SDRs with UAVs, reception sensitivity, directional antennas, the precision of signal localization, and footprint reduction. The conducted activities resulted in a functional prototype of a large UAV, capable of carrying various equipment, and successful integration of SDR with the built UAV. This paper focuses on the technical aspect of integration between the two systems and their interoperability. Further, it provides an overview of the operational possibilities and limitations of the integrated systems.

AISA project introduces human-machine distributed situational awareness (SA) in en-route air traffic control. This paper is a result of the project's preliminary study. As a part of the project, a comparison between artificial and human situational awareness is performed. To investigate if the artificial SA could undistractedly monitor traffic, air traffic controller (ATCO) tasks are defined within Knowledge Graph (KG) system. KG system serves as a database with the ability to define relationships between data points. Human-in-the-loop simulations were performed to acquire needed data. ATCOs conducted different traffic scenarios which were later analysed and assessed regarding SA indicators defined in the paper. Tasks defined within the KG-based system can produce artificial SA that can successfully identify and complement all human SA degradation occurrences, contributing to Team SA. Therefore, defined tasks are sufficient to apply to the traffic data and to produce adequate machine SA.