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The aim of this work is to analyse greenhouse gases (GHGs), their emissions in the agricultural sector, and the possibility of monitoring them through remote sensing (RS), and data-driven solutions. In this paper using GIS software analysed the GHG emission reports of 43 member countries that are regularly submitted to the Secretariat of the United Nations Framework Convention on Climate Change. The analysis highlighted Sweden's leadership in the energy sector, where up to 66% of its electricity comes from renewable sources. New Zealand also stood out in the context of all countries, with very high methane (CH4) emissions due to the country's large livestock population and poor emission controls in the agricultural sector. The article also provided an overview of the satellites currently available on the market for monitoring GHG emissions and a partial analysis of their characteristics. Future work is planned to further investigate the applicability of satellites for monitoring GHG emissions, to provide a detailed analysis of the characteristics of public, commercial, and hybrid satellites, to carry out practical applications of satellite data for the determination of agricultural emissions, and to develop a methodology for continuous emissions monitoring.

The paper discusses the design of functional observers with unknown inputs for nonlinear systems described by Takagi-Sugeno fuzzy models and time-varying bounded delays. The considered system extends previous works by considering a T-S model with time bounded delays and unknown inputs. The main innovative idea behind the design of the functional observer is that the unknown inputs are present in both the state and output equations, and bounds on the delays maybe used to reduce conservatism. The observer design and analysis are based on the Lyapunov-Krasovskii stability theory, and the resulting observer convergence conditions are formulated in terms of linear matrix inequalities (LMIs), which can be numerically and symbolically solved using YALMIP/MATLAB toolbox. In the final section of the paper, some examples are provided to demonstrate the application of this approach.

In modern economy information and communication technologies (ICT) play the essential role. The improvement of ICT infrastructure, developing ICT knowledge and usage can lead to economic growth due to higher productivity. However, this impact can depend on countries’ ICT investment or expenditure amount, reached ICT development and productivity levels. Moreover, ICT impact on productivity growth can occur after a certain period. Consequently, forming the ICT development strategies it is important to identify ICT impact period and impact differences between relatively high and low productivity countries. Authors examined the impact of ICT investments on productivity in EU countries covering the period of 1995–2015. Research results have revealed that ICT development positively and directly influences productivity, but this effect manifests with a lag in time. Moreover, it was found that the impact of ICT development on productivity is about twice bigger in countries with relatively high productivity level compared with countries that have relatively low productivity level.

The EUREF Permanent Network Densifcation is a collaborative efort of 26 European GNSS analysis centers providing series of daily or weekly station position estimates of dense national and regional GNSS networks, in order to combine them into one homogenized set of station positions and velocities. During the combination, the station meta-data, including station names, DOMES numbers, and position ofset defnitions were carefully homogenized, position outliers were efciently eliminated, and the results were cross-checked for any remaining inconsistencies. The results cover the period from March 1999 to January 2017 (GPS week 1000-1933) and include 31 networks with positions and velocities for 3192 stations, well covering Europe. The positions and velocities are expressed in ITRF2014 and ETRF2014 reference frames based on the Minimum Constraint approach using a selected set of ITRF2014 reference stations. The position alignment with the ITRF2014 is at the level of 1.5, 1.2, and 3.2 mm RMS for the East, North, Up components, respectively, while the velocity RMS values are 0.17, 0.14, and 0.38 mm/year for the East, North, and Up components, respectively. The high quality of the combined solution is also refected by the 1.1, 1.1, and 3.5 mm weighted RMS values for the East, North, and Up components, respectively.

The increasing amount of malware and cyberattacks on a host level increases the need for a reliable anomaly-based host IDS (HIDS) that would be able to deal with zero-day attacks and would ensure low false alarm rate (FAR), which is critical for the detection of such activity. Deep learning methods such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are considered to be highly suitable for solving data-driven security solutions. Therefore, it is necessary to perform the comparative analysis of such methods in order to evaluate their efficiency in attack classification as well as their ability to distinguish malicious and benign activity. In this article, we present the results achieved with the AWSCTD (attack-caused Windows OS system calls traces dataset), which can be considered as the most exhaustive set of host-level anomalies at the moment, including 112.56 million system calls from 12110 executable malware samples and 3145 benign software samples with 16.3 million system calls. The best results were obtained with CNNs with up to 90.0% accuracy for family classification and 95.0% accuracy for malicious/benign determination. RNNs demonstrated slightly inferior results. Furthermore, CNN tuning via an increase in the number of layers should make them practically applicable for host-level anomaly detection.

Communication needs of consumers change in the context of transformations and companies not only have to find out changing needs of consumers but also find proper communication measures that are necessary to satisfy such needs in order to create as bigger value as possible during a process of communication with consumers. The web opportunities not only provide new tools but also generate challenges related to the satisfaction of consumer needs. When communication needs of consumers change in the context of transformation, then consumer becomes more demanding in cyberspace. Therefore, organizations must not only continually analyse consumer needs, changes of behaviour, but also select effective measures of communication. In order to deal with the issues of such nature the research focuses on the purpose to identify ever-changing needs of different consumer types in the cyberspace and to provide industrial organizations with recommendations how communication measures could help meet these needs more effectively.

BACKGROUND: Transapical off-pump mitral valve (MV) repair is a novel minimally-invasive surgical technique, allowing to correct mitral regurgitation (MR) caused by chordae tendineae rupture. While numerical simulation of the MV structure has proven to be useful to evaluate the effects of the MV surgical repair techniques, no numerical simulation studies on the outcomes of transapical MV repair have been done up to now. OBJECTIVE: The purpose of this study is to evaluate the transapical MV repair using finite element modeling and to determine the effect of the neochordal length on the function of the prolapsing MV. METHODS: The reconstruction of the MV geometry based on the patient-specific data was performed. In order to simulate prolapse, chordae inserted into the middle segment of the posterior leaflet (P2) were ruptured. A total of four virtual transapical repairs using neochordae of different length were performed. The function of the MV before and after virtual repairs was simulated. RESULTS: The evaluation of the effect of the neochordal length on post-repair MV function showed that the length of the implanted neochordae has a significant impact on the correction of MR caused by chordae tendineae rupture. CONCLUSIONS: The presented results can improve the understanding of the effects of transapical MV repair.

In this article, the authors suggest a methodology to adapt learning units to the needs and talents of individual students using an intelligent learning system. Learning personalisation is done based on several factors. Felder and Silverman Learning Styles model is used to create student's profile with conjunction of data mining technologies and previously recorded behaviour of the student. Firstly, the authors perform systematic review of application of intelligent software agents in teaching throughout Clarivate Analytics Web of Science database. Secondly, they present methodologies to personalise learning by means of intelligent technologies. They analyse preferences of students according to Soloman-Felder Learning Styles questionnaire. The resulting model of a student is used in the creation of a personalised learning unit. The model of an adaptive intelligent teaching system based on application of aforementioned technologies is presented in more detail.

The paper presents semantic clustering and artificial neural network (ANN) based learning analytics software agent for personalised adaptive multi-agent learning system. First of all, systematic literature review on application of ANN to personalise learning in Web of Science database was performed. After that, methodology of application of ANN and semantic clustering of learning material in personalised adaptive multi-agent learning system is presented. In the paper, personalisation in multi-agent learning system is based on learning styles model that requires the use of psychological questionnaire to determine student’s learning styles. The results of filling in the questionnaire could be incorrect since some students may answer the questionnaire dishonestly, irresponsibly, or make mistakes in self-diagnosis. This results in creation of an incorrect student’s model. This causes the system to provide suboptimal learning objects and scenarios to the student. The authors present a model of ANN based learning analytics agent to be used in the system. Proposed agent uses ANN to associate students’ learning styles with their real behaviour within the learning environment. The key factor describing the behaviour of students within the system is the learning content they seek out independently. Clustering the visited documents based on semantic content categorizes students into groups. Belonging to a semantic cluster is one of the inputs that can be used to train ANN agent. After training, the agent could identify potentially faulty student models by looking for anomalous behaviour for those learning styles. Such problems can be resolved by providing alternative learning objects or scenarios to the students and observing their choices. 1.

In this paper, we conduct a systematic survey of 50 Civic Tech platforms in Lithuania to evaluate their potential to co-create collective intelligence or “civic intelligence” applying Collective Intelligence Potential Index (CIPI) methodology that includes different socio-technological indicators. Civic intelligence is a form of collective intelligence that refers to the group's capacity to perceive societal problems and to address them effectively. The research focuses on evaluation of diverse organizational designs that increase efficient collective performance. By employing an interdisciplinary perspective and post-soviet countries context, the paper contributes to the increased.