As in any industry exposed to risk, human and organizational factors are the main stakes of maritime safety. Understanding the causes and risks of maritime accidents is integral to the sustainability of shipping. The investigation of marine accidents is a crucial tool for their identification in areas related to operations and ships, including social and situational systems, their design, and technical systems. The authors conducted a cause–effect analysis of marine incidents. For this purpose, case-by-case analysis and an Ishikawa diagram were used, which is a tool that helps identify actual or potential causes of accidents. The study showed that by far the most significant cross-section of causes of accidents were elements of social and situational systems that affect the safety of the ship, crew, and environment. The least significant contribution came from the machinery area. Through the detailed descriptions, a picture emerges not so much of a lack of knowledge of the regulations as of a failure to comply with existing procedures or best practices. In the authors’ opinion, more emphasis is needed on preventive measures, including safety culture, training, competence assessment, and increased awareness of the need for sustainability.

The operation of injection apparatus in self-ignition engines results from the design, manufacturing technology and wear and tear during operation. The technical state of the injector apparatus significantly affects the engine performance, fuel consumption, toxicity and smoke opacity of outlet gases. The most unreliable element of the injection apparatus is the injector nozzle, the quality of which depends on the quality of construction and production, operating conditions and the of the fuels used, etc. One of the design parameters of the injector nozzles, determining the technical state is the geometry of the nozzle holes. An attempt was made to optimize the selection of the dimensions and surface condition of the spray holes to significantly affect the flow properties of the injector nozzles and, consequently, to decide on the size and form of fuel dosed streams to individual cylinders of a self-ignition engine and the quality of fuel atomization. In work, a simulation model was developed, and the pressure losses and the mass fluid of the injected fuel were minimized for selected significant geometric features, taking into account the influence of operating conditions. With the use of Mathematica software, simulation optimization methods and methods based on evolutionary algorithms were elaborated.

The article focuses on the transport preferences of the inhabitants of a medium-sized city in the context of changing urban mobility and sustainable development. It fills the gap in the literature researching the changing communication behavior of the inhabitants of a given city. The main purpose of this article is to identify and analyze the preferred means of movement for inhabitants of a medium-sized city in Central and Eastern Europe. The research area was the city of Szczecin, Poland. The research was carried out using the following research methods: literature review, analysis of legal acts and documents, case study. The research material was collected using an anonymous questionnaire based on a proprietary survey made available to the inhabitants of the largest city of north-western Poland, Szczecin, in the GOOGLE browser in the period 17 May 2019–1 April 2021. The multiple-response contingency tables, chi-square test of independence and non-parametric U Mann–Whitney and Kruskal–Wallis tests were used for study development. The conducted scientific research showed that, when determining the relationship between the method and the destination, the respondents most often chose a car or on-foot commuting in all of the surveyed destinations. The research results increase the level of knowledge regarding residents’ needs in terms of urban mobility and the development of Szczecin’s transport system. They can be used in the rational actions of local authorities aiming to improve residents’ the quality of life in the context of the sustainable development of city transport.

The need for urban societies to move continues to grow with the intensity of their various activities. One of the challenges in micromobility in cities based on bike, scooter, or public scooter systems is determining the potential yet realistic demand for such services. This article aims to present the level of demand for eco-friendly non-motorized transport systems and identify the motivations driving users of public transport to choose these eco-friendly Personal Mobility Devices (PMD). The authors propose a methodology based on the integration of four components: a case study, an analysis of participatory budget projects, a metadata analysis of bikesharing (BSS) in Szczecin, and a meta plan. They utilized a case study method and conducted a survey based on a custom questionnaire. An analysis of participatory budget projects and data from the operation of bikesharing in Szczecin was performed using statistical methods. Applying the heuristic tool meta plan, they outlined the directions for the development and support of bikesharing as an example of micromobility in Szczecin. The research identified connections between the socio-demographic structure of respondents and attributes of bike communication. This study highlighted periods of intensive system use and locations with the highest exploitation. It showed that the demand for BSS depends on weather conditions, traveler convenience, the density and coherence of bike infrastructure with important locations for residents, transfer connections, and safety. The presented research results can assist planners and authorities in Szczecin and other cities in expanding bikesharing.

The article presents the possibility of using machine learning (ML) in artificial intelligence to classify the technical state of marine engine injectors. The technical condition of the internal combustion engine and injection apparatus significantly determines the composition of the outlet gases. For this purpose, an analytical package using modern technology assigns experimental test scores to appropriate classes. The graded changes in the value of diagnostic parameters were measured on the injection subsystem bench outside the engine. The influence of the operating conditions of the fuel injection subsystem and injector condition features on the injector needle vibration displacement waveforms was subjected to a neural network (NN) ML process and then tested. Diagnostic parameters analyzed in the amplitude, frequency, and time–frequency domains were subjected after a learning process to recognize simulated various regulatory and technical states of suitability and unfitness with single and complex damage of new and worn injector nozzles. Classification results were satisfactory in testing single damage and multiple changes in technical state characteristics for unfitness states with random wear injectors. Testing quality reached above 90% using selected NNs of Statistica 13.3 and MATLAB R2022a environments.

This paper presents the challenges encountered in the dimensional control of ships, platforms, and offshore units. This novel approach utilizes machine learning (MLP—Multilayer Perceptron Neural Network) for three-dimensional (3D) spatial coordinate transformations when only three common points are known. The proposed method was verified based on laboratory and field data. The main issue was to provide a sufficient number of valid training points. The oversampling method was used to meet this criterion. The achieved results indicate equal or better accuracy when the points were located inside the adjustment points array. In the case where the points lay outside this array, no improvement in the accuracy of the transformation was observed. The neural approach restores the transformation symmetry, and in some cases, such as the study of deformation of engineering objects, breaks the symmetry rather than restoring it.