Today e-learning is a term which is commonly used, but does not have a universally accepted definition, but it can be considered as technology-enhanced learning, where all types of digital technologies are used to support the learning process. Over the years some new functionality has appeared as mobile and wireless technologies (m-learning) and digital television provided possibilities for interactive study materials management (tv-learning). Latest options for training process acceleration are offered by virtual and augmented reality (VR/AR) technologies. The aim of this paper is to find solution for appropriate hardware selection before constructing VR/AR system for training needs focusing on platforms used for operators preparation to work with industrial equipment. In the beginning of the paper all hardware devices for such systems are summarized and explained. Then by using set theory and combinatorics all possible sets of input/output devices are described and calculated. Next dynamic modelling is used to create deterministic, static simulation model with an aim to ease the process of hardware selection for VR/AR training systems development. Simulation model is used for development of two pilot projects.

Intrusion Detection Systems is defined as a component that analyses system and user operations in computer and network system to protect it from possible intrusions. Current intrusion detection technologies have several shortcomings. Applying mobile agents to intrusion detection design is step forward on better intrusion detection. Mobile-agent based distributed intrusion detection systems are very promising for the following reasons: reduction of data movement, load-balance, flexibility, fault-tolerance, detection of distributed attacks. Hybrid intrusion detection is defined by both the method used to detect attacks and the placement of the system on the network. Intrusion detection system may perform either misuse detection or anomaly detection and may be deployed as network-based or host-based system. This paper proposes to distribute classical intrusion detection model with mobile agents making an agent-based hybrid intrusion detection system. The proposed model can help detect simple intrusions in early stage and also distributed intrusions by monitoring several subjects installed on network. Main benefit from mobile agents in such system is ability to generate separate services for specific tasks and analyze unknown user patterns with several methods of artificial intelligence.

Computer models of bioprocesses have become an essential tool in biotechnology. This paper describes various bioprocess scaleup releated problems. The problems at different scales are explained. In this paper the use of computer model in E.coli fermentation scale-up from shake flask to laboratory scale bioreactor is discussed. A black box modelling approach was used. Fermentation results have been visualized and discussed. The computer model created in Matlab environment was used for bioprocess behaviour prediction. Possible bioprocess scale-up software improvements and bioprocess optimization are discussed.

Competitiveness is the key factor in order to reach the economic transformation, smart growth, governance and legal framework for the state and society for sustainable development that are stated as the major goals in the current planning period in Latvia. High-technology industries are the main drivers of economic activity in developed economies. To ensure data comparability, the official definition of the high-technology industries by Eurostat is applied in this research including two industries: manufacture of basic pharmaceutical products and pharmaceutical preparations and manufacture of computer, electronic and optical products. The research aims to analyse high-technology industries in Latvia taking into account the competitiveness and the regional allocation using a set of several indicators. According to NUTS 3, Latvia is subdivided into 6 regions; however, the high-technology industries are regionally allocated mainly in two regions, Riga and Pieriga regions. Manufacture of basic pharmaceutical products etc. employs 99% of employees in these two regions, manufacture of computer etc. products – 81% despite the fact that only 54% of employees reside in these two regions. The research confirms that the high-technology regional allocation depends on available labour resources and also education and professional training facilities in the region. We conclude that the high-technology industries are competitive in the global market as the share of the high-tech products in total exports is several times higher than the share of these industries in the manufacturing sector or in total economy. The global competitiveness of Latvia’s high-tech industries is stable and gradually rising.

Cyberattacks have become a regular part of network activity. To mitigate the risks from possible threats, organisations have implemented firewalls and intrusion detection systems, which can help stop network attacks. The problem is that often the accuracy of these systems is not effective enough. Another part of network security is security information and management platforms. These systems are more advanced versions of Threat Intelligence Platforms, because it is possible to make in-depth analyses of real-time events in a network. This research paper proposes improving intrusion detection system functionality using Open-Source Intelligence. Anomaly-based intrusion detection systems often generate alerts, but these alerts require deeper analysis to understand whether it is a real attack or just a false alarm. By making Open-Source Intelligence requests and evaluating extra information, it is possible to make more precise rules to stop attacks against network infrastructure. Open-Source Intelligence requests are generated directly from the intrusion detection system or with Python scripts based on the organisation’s infrastructure profile. The proposed architecture was experimentally tested by automating Open-Source Intelligence requests and intrusion detection rule generation by Python scripts.