Sleep is a period of rest that is essential for functional learning ability, mental health, and even the performance of normal activities. Insomnia, sleep apnea, and restless legs are all examples of sleep-related issues that are growing more widespread. When appropriately analyzed, the recording of bio-electric signals, such as the Electroencephalogram, can tell how well we sleep. Improved analyses are possible due to recent improvements in machine learning and feature extraction, and they are commonly referred to as automatic sleep analysis to distinguish them from sleep data analysis by a human sleep expert. This study outlines a Systematic Literature Review and the results it provided to assess the present state-of-the-art in automatic analysis of sleep data. A search string was organized according to the PICO (Population, Intervention, Comparison, and Outcome) strategy in order to determine what machine learning and feature extraction approaches are used to generate an Automatic Sleep Scoring System. The American Academy of Sleep Medicine and Rechtschaffen & Kales are the two main scoring standards used in contemporary research, according to the report. Other types of sensors, such as Electrooculography, are employed in addition to Electroencephalography to automatically score sleep. Furthermore, the existing research on parameter tuning for machine learning models that was examined proved to be incomplete. Based on our findings, different sleep scoring standards, as well as numerous feature extraction and machine learning algorithms with parameter tuning, have a high potential for developing a reliable and robust automatic sleep scoring system for supporting physicians. In the context of the sleep scoring problem, there are evident gaps that need to be investigated in terms of automatic feature engineering techniques and parameter tuning in machine learning algorithms.

One of the key challenges related to the threat posed by the COVID-19 pandemic is preservation of employment and protecting staff who are working in port operations and struggling to keep ports operating for ship calls. These activities performed by port labour are deemed to be crucial for the EU and European ports, since 75% of the EU external trade and 30% of intra-EU transport goods are moved by waterborne transport. As a response to the global lockdown and the vulnerability of global supply chains, the majority of international organisations and maritime ports networks have shortlisted measures necessary to keep the severe effects of the lockdown to a minimum. One of the key measures identified is how to limit physical interaction. As an effect, millions of people and organisations across the globe have had to use and/or increase their deployment of digital technologies, such as digital documentation, tracing information systems and digital group-working platforms. Hence, blockchain and data-enabling systems have become to be recognised as a core element maintaining the uninterrupted flow of goods and services at ports. In pursuing uninterrupted trade and keeping ports open and running, this research paper addresses how the current situation afflicts the small and medium-sized ports located on the Baltic Sea which are argued to be critical actors of the port-centric logistics’ ecosystem. Given the topicality of this research and addressing the research gap, the authors suggest a conceptual capacity-building framework for port employees. This suggested framework is based on empirical insights: primary and secondary data collected from the project Connect2SmallPorts, part-financed by the Interreg South Baltic Programme 2014–2020 from the European Regional Development Fund (ERDF). The conceptual framework aims towards a practical training programme dedicated to fill in the missing skills or expand the limited competence of human resources and ports’ capacity when adapting or advancing digitalisation in the ports’ ecosystems. In particular, specific areas of capacity building are addressed and individual solutions suggested to foster a digital transformation of ports. The conceptual training framework is designed as a training tool indicating opportunities to help ports upgrade their competences with the blockchain technology, and to advance their transportation, environmental and economic performance with improved digitalisation. For this purpose, the conducted research employed mixed methods and applied concepts and approaches based on the field of management. For example, the construct of absorptive capacity, organisational learning, transformation, resource-based view and the concept of dynamic capabilities are included in the ecosystem discourse and are linked with open innovation and service design. The research presented in this article provides both theoretical and practical contributions, in which the affected stakeholders can test and utilise the developed tool as well as transfer it to other regions.

Optimization transportation cargo and passengers between ports and regions are very important, because industrial regions are located some distance from ports. The demand for energy request for the movement of transport is a necessity in the modern world. Transport and activity called transportation are used daily, everywhere, and a lot of energy is needed to power the various transport modes. Today different transport modes are being used to transport passengers and cargo. It is quite common to use road transport, which can transport passengers and cargo from door to door. Considering alternative possibilities (road, railway and/or inland waterway transport), it is important, based on theoretical and experimentation, to identify optimal solutions. In finding transport modes that are either most technically or economically effective, we could unearth possible solutions which would require minimal energy use. Unfortunately, with increased transportation, this often leads to traffic congestion on the roads, which requires additional energy (fuel). This situation generates requirements from many stakeholders in terms of finding ways to decrease the transportation time and energy (fuel) consumed by transport modes. A theoretical method evaluation is conducted on the optimal transportation possibility that minimizes transportation time and energy (fuel) use by employing graph theory, which is presented in this paper. The scientific contribution is the development of a transport modes comparative index, which is then used for evaluations. This paper presents possible alternative transportation conditions based on a multi-criteria evaluation system, proposes a theoretical basis for the optimal solutions from an eco-economic perspective that considers energy, and provides for experimental testing during a specific case study. The final results from the case study provide recommendations and conclusions.