Scientific Journal of Gdynia Maritime University

The current issue of the Scientific Journal of Gdynia Maritime University contains articles on a wide variety of topics, falling within the disciplines of Management and Quality Sciences, Mechanical Engineering and Electrical Engineering and Electronics.

The first article presents the results of efforts to improve the company's production process by implementing tools and organizational practices related to the Lean Management concept. The authors focused on analysing the effects of using 5S analysis, but other tools were also included: Kaizen, root cause analysis, value stream mapping (VSM), standardization and the determination of OEE based on manual labour analysis. The study responds to the problems of modern organizations, with the use of the analysed methods helping to eliminate waste, increase labour productivity and improve employee safety and morale, which is important for improving the organization's processes and production management.

The second article is devoted to product labelling. It focuses on the Nutri-Score labelling system, which is intended to promote healthy dietary choices by consumers and increase their awareness of the nutritional value of food. Nonetheless, the system is subject to growing political, institutional and industry criticism, which calls into question the legitimacy of its use. This article examines the benefits and limitations of the Nutri-Score system, taking into account the current scientific literature related to its algorithmic modifications and recent regulatory developments.

The third article analyzes the impact of malfunctions in compression-ignition engine systems on exhaust composition and cycle parameters. The research work covers both laboratory experiments and computer simulations, using DIESEL-RK as a tool for optimizing the engine processess and thermodynamic cycles. The laboratory and simulation results were cross-validated, ensuring reliability and providing a comprehensive analysis of the engine's conditions.

The fourth article is devoted to the analysis of the problem of dimensionality reduction of large datasets. The principal component analysis is implemented by using one of the two high-accuracy algorithms, the sigular value decomposition (SVD) and eigenvalue decomposition (EVD). The article concludes that EVD is efficient if the dataset consists of no fever than several hundred observations (objects) having  at least three double-precision features.

The subect of the last article is the determination of the optimal path for a mobile agent to take in an environment with static obstacles, using reinforcement learning (RL). The authors have analyzed such alghorithms as Q -learning and Sarsa in the classical version as well as in the version extended with Adam's Optimizer. The results of the work may prove to be particularly actionable in practical applications of movement path plotting, such as with mobile robotics.

Agnieszka Rybowska