As a result of ever-growing energy demands, motor vehicles are among the largest contributors to overall energy consumption. This has led researchers to focus on fuel consumption, which has important implications for the environment, the economy, and geopolitical stability. This article presents a comprehensive analysis of various fuel consumption modeling methods, with the aim of identifying parameters that significantly influence fuel consumption. The scientific novelty of this article lies in its use of low-cost technology, i.e., an OBD-II interface paired with a mobile phone, combined with modern mathematical modeling methods to create an accurate model of the fuel consumption of a vehicle. A vehicle test drive was performed, during which variations in selected parameters were recorded. Based on the obtained data, a model of the vehicle’s fuel consumption was built using three forecasting methods: a multivariate regression model, decision trees, and neural networks. The results show that the multivariate regression model obtained the lowest MSE, MAR, and MRSE coefficients, indicating that this was the best forecasting method among those tested. Sufficient forecast error results were obtained using neural networks, with increases of approximately 73%, 10%, and 131% in MSE, MAE, and MRAE, respectively, compared to regression results. The worst results were obtained with the decision tree model, with increases of approximately 163%, 21%, and 92% in MSE, MAE, and MRAE compared to the regression results.

Military heavy vehicle drivers experience low-frequency vibrations that are associated with fatigue, drowsiness, and other adverse health effects. The existing research papers focus on performing different types of analysis, but few use advance signal processing tools based on recurrence plot representation; therefore, the main goal of this paper is to assess the whole-body vibration (WBV) and hand-arm vibration (HAV) exposure of a driver, comparing armoured personnel carriers and cargo destined vehicles. For this purpose, the power of a signal distributed over its frequency was analysed using power spectral density (PSD) and diagonal line quantification (DLQ) analysis. According to the results, in the case of the cargo vehicle, the driver experienced vibration dose values of frequency weighted acceleration above the limits during all three experimental tests, with a maximum value of 26.802 m/s², whereas the results in the case of the armoured personnel carrier are below the 5 m/s² limit imposed by the ISO 5349-2 standard. From the developed tests it was observed that, to protect the driver against the fatigue induced by the vibrations of the vehicle body, it is necessary to provide an elastic and also damping linkage between the vehicle and the driver’s seat. This is the only way to ensure the needed protection and it is, by far, the least expensive.

Background: A measurable feature of the efficiency of vehicle use in transportation companies is the revenue from transport orders, which has a significant impact on their profitability. Therefore, it is important to skillfully analyze the parameters related to the operation of vehicles and their impact on the bottom line. Transportation companies, when managing their operations, take steps to reduce operating costs. The above makes a large number of studies available in the literature on the analysis of vehicle damage or wear of system components, as well as ways to predict them. However, there is a lack of studies treating the impact of the parameters of specific orders on economic efficiency, which is a research niche undertaken in the following study. Methods: The purpose of this article was to analyze the economic efficiency of vehicle operation in terms of the financial security of enterprises. The main research problem was formulated in the form of the question of how the various parameters of a transport order affect its profitability. During our study, critical analysis of the literature, mathematical modeling and inference were used. A detailed analysis of transport orders executed by SMEs (small and medium-sized enterprises), which are characterized by a fleet of light commercial vehicles with a capacity of up to 3.5 t, was carried out in the FMCG (Fast-Moving Consumer Good) industry in Poland in 2021–2022. Due to the binary variable form, a logistic regression model was elaborated. The estimated parameters of the model and the calculated odds ratios made it possible to assess the influence of the selected factors on the profitability of orders. Results: Among other things, it was shown that in the case of daily vehicle mileage, the odds quotient indicates that with each additional kilometer driven, the probability of profitability of an order increases by 1%. Taking into account the speed of travel, it is estimated that with an increase in its value by 1 km/h, the probability of profitability of an order decreases by 3%. On the other hand, an increase in cargo weight by 1 kg makes the probability of a profitable order increase by 9%. Conclusion: Through this study, the limited availability of low-cost analytical tools that can be applied during transportation fleet management in SME companies was confirmed, as was the use of simple and non-expansive mathematical models. At the same time, they are not “black boxes” and therefore enable drawing and implementing model conclusions into operations. The results obtained can help shape the overall strategy of companies in the area of vehicle operation and can support the decision-making process related to the management of subsequent orders, indicating those that will bring the highest profit. The above is very important for SME companies, which often operate on the verge of profitability.

This study addresses the condition monitoring of rolling bearings by applying robust linear regression to statistically derived features from vibration data. Four datasets of acceleration signals were collected under varying operating conditions: aligned and misaligned bearings at rotational speeds of 1000 rpm and 1500 rpm. From each signal, key statistical indicators were extracted, including root mean square (RMS), skewness, kurtosis and crest factor, to capture signal characteristics that were relevant to fault detection. To follow-up, we applied the Kolmogorov–Smirnov test to assess data normality and the results confirmed significant deviations from a Gaussian distribution, motivating the use of robust regression techniques for further investigations. The regression model created incorporated rotational speed and alignment conditions as predictors of acceleration and the results indicated that while the coefficient associated with misalignment suggested a possible increase in acceleration (~1.115 units), statistical testing (p = 0.5233) indicated that neither speed nor alignment had a significant influence on the measured vibration levels within the dataset. The findings suggest that under the tested conditions, misalignment does not manifest as a strong linear change in acceleration magnitude, and the study underscores the importance of robust modeling techniques and feature selection in the condition monitoring of rotating machinery.

This article focuses on the investigation and analysis of vibrations transmitted to cargo during off-road transportation, with particular emphasis on the impact of vehicle and road surface interactions. The primary objective of the research is to quantify and characterize the amplitudes of...

Effective prediction of customer migration is only possible through knowledge of the customer life cycle, which is characterized by the length of the relationship between buyer and provider, i.e. customer retention. A concept of opposite importance is customer migration, defined as the partial or total abandonment of the products or services offered by a company. Its knowledge and ability to predict it is crucial in terms of ensuring the continued financial security of target companies. The primary objective of this article was to present a method for assessing the risk of telecom industry customer migration using machine learning methods. The objective was achieved through the use of research methods, theoretical deductions such as and induction, system analysis and synthesis, and mathematical modelling, which additionally allowed for a practical analysis of the migration of customers of the telecommunications industry. Predictors with the greatest impact on the phenomenon under study were selected. It should be noted that the gain chart indicates that, in the case of contacting the 20% of customers selected by the models, the target coverage would be at the following levels, respectively: 70% for the boosted tree model and the decision tree based on the CART algorithm, and 75% for the random forest model. The research niche addressed in the article is the development of methods for assessing migration risk using machine learning techniques. The tool developed in the article can support decision-making in the creation of marketing campaigns aimed at retaining the largest number of customers.