In this study, machine learning algorithms were applied to predict the main injection quantity in a high-pressure common rail system on a diesel engine test bench. The input parameters included engine load, fuel pressure, injection speed, and pulse time. Two models were selected for comparison: Support Vector Regression (SVR) and Random Forest (RF). The results showed that on the training dataset, the RF model outperformed SVR, with RMSE and MAE values of 0.027362 and 0.017628 respectively, significantly lower than those of SVR (RMSE = 0.051563, MAE = 0.027733). Additionally, RF achieved a higher coefficient of determination R² (0.995759 vs. 0.984939), indicating better learning of the relationships among variables. However, on the test dataset, SVR demonstrated superior predictive accuracy, achieving RMSE = 0.050097, MAE = 0.027673, and R² = 0.983550, while RF showed higher RMSE (0.060355), greater MAE (0.040485), and lower R² (0.976123). These results indicate that SVR has better generalization capability and is less prone to overfitting than RF. To assess the contribution of each input parameter, SHAP (SHapley Additive exPlanations) analysis was employed. The results revealed that injection speed, pulse duration, and fuel pressure had the most significant impact on the injection quantity. Meanwhile, engine load had a relatively lower influence but still played an important role under certain operating conditions. These analyses not only provide an intuitive understanding of model sensitivity but also help identify key factors to prioritize in control strategies. This study lays a foundation for the development of optimized control systems aimed at accurately and effectively reducing engine emissions in the future.

This review synthesizes research on AI and IoT in urban mobility, focusing on traffic management, public transportation systems, and autonomous vehicles to address escalating urban congestion, environmental impact, and mobility demands. This review aimed to evaluate AI and IoT applications in traffic flow optimization, benchmark integration in public transit, identify autonomous vehicle frameworks, compare predictive models and sensor networks, and analyze adoption challenges. A systematic analysis of global empirical, simulation, and theoretical studies was conducted, emphasizing technological convergence, performance outcomes, data utilization, and barriers. The findings reveal that AI-driven predictive models combined with IoT sensor networks significantly improve traffic efficiency and reduce emissions, whereas AI-IoT integration enhances public transit reliability through predictive maintenance and dynamic scheduling. Autonomous vehicles, supported by IoT-enabled communication and AI decision-making, demonstrate the potential for safety and sustainability gains but face regulatory, infrastructural, and acceptance challenges. Advanced machine learning techniques optimize real-time data analytics but encounter scalability and explainability limitations. Collectively, these findings underscore the transformative potential of AI-IoT in urban mobility, contingent on addressing privacy, infrastructure, and social factors. The synthesis highlights the need for interdisciplinary approaches to advance scalable, secure, and user-centered AI-IoT urban mobility solutions that inform future research and practical implementations.

The aim of the study is to analyze the financial sustainability of airline companies. In this study, ESG (Environmental, Social, and Governance) scores, financial failure, and financial rating scores were analyzed using Multi-Criteria Decision Making (MCDM) methods to analyze the financial sustainability performance of airlines. The study measures financial sustainability performance using data obtained from 30 airlines in 2023. The Altman Z model used in the study has been adopted as an optimal method for measuring the risk of financial failure in the airline industry. Additionally, the TAA financial rating method was used to evaluate the financial efficiency and risk levels of airlines, presenting a unique approach as the first application in the literature in this field. The TAA financial rating method and MCDM methods used in the study enable the evaluation of not only the current financial status of companies but also their future financial risks and opportunities. The study provides strategic guidance to the industry on integrating ESG scores with financial failure analysis and financial rating methods. These findings will serve as an important reference point for both academic research and airline policies and practices.

The text focuses on road toll collection in the Kingdom of Poland in the early 1830S. Indicating that the law in force was initially from the pre-insurrectional period and new premises were introduced only in late 1835, the article emphasizes the endeavors of the authorities to monitor and prevent any possible extortions in the above-mentioned collection. The typical elements of the tender and lease process included paying a deposit, conducting tenders according to the “in plus” formula, and submitting bids by competitors in sealed envelopes. The numerous examples of the government proposals of tender conditions in the span of years 1832-1836 have been presented. The conclusion states that the entire bidding system seems to have proved inadequate for financing the maintenance of the road network of the Kingdom of Poland.

Maintaining saturation flow at signalized intersections is crucial for both intersection capacity and sustainable traffic management. Efficient signal systems reduce congestion, lower emissions, and improve urban air quality. Factors such as signal timing, traffic demand, vehicle types, and intersection design significantly impact traffic flow efficiency. This study investigates the signal system and traffic flow parameters affecting lane inefficiency using Response Surface Methodology (RSM). Key factors included green time (G), the ratio of unused green time to total green time (ϴ/G), and discharge flow rate (β), while lane inefficiency (ẟ) served as the response variable. The full quadratic model was identified as the best model for explaining lane inefficiency due to its high adjusted R-squared value and low error values. The study recommends a green time of 30 seconds and a discharge flow rate of 0.540 vehicles per second per lane to obtain minimum lane inefficiency. These findings support decision-makers in creating smarter, more efficient signal-controlled intersections, ultimately contributing to sustainable urban transport infrastructure by improving traffic flow, reducing emissions, and lowering fuel consumption.

This paper presents a data-driven trend analysis of sustainable, shared, and zero-crash mobility within the Italian context, serving as a starting point for research aimed at assessing the current level of knowledge regarding novel mobility concepts and challenges. A pilot sample of 30 respondents over the age of 60 years old was selected for the prototype survey interview conducted to evaluate their knowledge and perceptions concerning the transition towards sustainable and smart mobility. Key findings from the interviews provided valuable insights into older adults' understanding of the topic and their expectations, offering a foundation for future policies and inclusive initiatives to contextualize Italian experiences within global trends in sustainable mobility for urban planners and policymakers.

This article presents and describes the results of research on determining the accuracy of a Digital Terrain Model (DTM) developed based on image data obtained from an Unmanned Aerial Vehicle (UAV). The Digital Terrain Model was created using image data acquired by an Unmanned Aerial Vehicle, specifically the fixed-wing with electric propulsion, flying at an altitude of 300 meters. The image data were collected during a photogrammetric survey conducted over a mountainous area in 2021. The final elevation values of the Digital Terrain Model were recorded in a GRID format with a spatial resolution of 5 meters. The article also includes a comparison of the DTM elevations with results obtained from the satellite GPS RTK technique. Based on this, an accuracy of elevation determination for different vertical profiles ranged from 0.19 m to 0.24 m was obtained. Moreover, the study also involves the development of a DTM from data acquired by the Unmanned Aerial Vehicle at an altitude of 150 meters. In this case, the accuracy of determining the elevations of the DTM for different vertical profiles ranged from 0.10 m to 0.16 m. The results of the research are very interesting for the application of UAV technology in aerial photogrammetry, particularly in inaccessible areas, especially mountainous regions.

Titanium alloys have an excellent strength-to-weight ratio. Tit alloys are almost as strong as steel but are much lighter. This translates into reduced mass in means of transportation (e.g., aircraft, F1 cars, electric vehicles, and motorcycles), which translates into better fuel efficiency, speed. Titanium can withstand very high temperatures without losing its mechanical properties. As a result, it has found applications in exhaust systems, hydraulic lines, fuel systems, and structural elements exposed to extreme conditions. In the construction of means of transport, two types of titanium with different structures are mainly used (alpha titanium and alpha + beta titanium). There will certainly soon be a need to weld these two dissimilar materials together. This is a research gap. An absolute novelty is the attempt to weld dissimilar titanium alloys without using a protective vacuum chamber. The purpose of this article is to establish the correct parameters for this process.

The paper presents control algorithms in the path following task. An important step in planning the motion of an autonomous vehicle is the initial definition and description of the route. In this study, the route has been approximated using the spline function B3. The paper presents a comparison of the effectiveness of the control algorithms that are applied to determine steering angle. Our own algorithm B3M is formulated, and its effectiveness is compared with classical ones. The proposed algorithm has been developed on the basis of a model with 3 degrees of freedom (3DoF) and it can be used in combination with more complex vehicle dynamic models, such as those with 5, 7 and 10 degrees of freedom. After implementing computer models of vehicle dynamics with 3, 5, 7 and 10 DoF, they were verified and validated. The computer simulation results presented in this paper confirmed the correctness of the models and the proposed B3M steering algorithm. This algorithm does not require the declaration of constants and is as effective as other geometrical algorithms such as Pure Pursuit.

This article explores the formal and legal framework governing the air transport of oversized cargo, emphasizing applicable regulations and available aircraft types. Drawing on insights from a semi-structured in-depth interview (IDI) with an industry expert, the study outlines the key stages of the transport process and identifies potential hazards that may arise at each step. Particular attention is given to operational challenges such as regulatory compliance, appropriate aircraft selection, and coordination of logistics. In response, the paper proposes targeted preventive and corrective measures aimed at minimizing disruptions and enhancing the safety and efficiency of operations. The results not only offer practical guidance for industry professionals but also serve as a foundation for future research focused on developing tailored risk assessment tools and methodologies suited to the specific demands for non-standard air cargo transport.