Małgorzata Grzelak

Urban heat islands (UHI) intensify as cities expand, exposing residents to elevated thermal stress and complicating urban climate adaptation planning. Existing satellite-based approaches to detecting surface urban heat islands (SUHI) typically rely on a single class of data and narrow temporal windows, limiting their ability to capture the full range of processes driving surface overheating. This study develops and evaluates a random forest model for SUHI detection in Warsaw, Poland, integrating two classical spectral indices (NDVI, NDBI) derived from Landsat 8/9 Collection 2 imagery with three land-cover probability layers (built-up, tree, water) from the Dynamic World deep-learning product, processed in Google Earth Engine. Both a multi-year summer median composite (2020–2025) and individual annual summer composites were used, the latter enabling a leave-one-year-out temporal validation. Heat island pixels were defined as those whose land surface temperature anomaly exceeded +3 °C relative to the study area mean, a local criterion rather than a city-versus-rural contrast. The model achieved high and stable performance (accuracy = 0.831, AUC = 0.910 on the test set; AUC = 0.907 ± 0.004 in five-fold cross-validation and 0.905 ± 0.018 in leave-one-year-out validation). An ablation analysis showed that combining the probability layers with the spectral indices clearly outperformed the indices alone (AUC = 0.852 vs. 0.905), whereas the additional gain over the Dynamic World layers alone remained within uncertainty. Vegetation-related predictors (NDVI and tree probability) contributed more to classification than built-up indicators. These results indicate that vegetation deficit, rather than built-up presence alone, is the primary driver of surface overheating in Warsaw and that the proposed open-data workflow offers municipalities a low-cost screening tool for identifying priority areas for climate adaptation and, thanks to its reliance solely on open data, can be adapted to other cities, subject to further validation.

Background: Predictive maintenance (PdM) is increasingly recognized as essential for reducing fleet downtime and maintenance costs, yet the existing literature on transport and logistics fleets relies predominantly on traditional or single-model approaches, with ensemble methods such as random forest and gradient boosting remaining comparatively underexplored. Methods: This study develops and compares three classification algorithms—logistic regression, random forest and XGBoost—for predicting vehicle maintenance needs. Each classifier was first fitted once on a 70:30 train–test split (35,000/15,000 observations) to support interpretation of coefficients, predictor importance, and ROC curves, using precision, recall, and ROC-AUC. Performance and model ranking were then confirmed for robustness using 5-fold stratified cross-validation with formal significance testing. Results: Reported issues and service history emerged as the dominant predictors of maintenance needs. Random forest and XGBoost achieved comparable predictive performance under 5-fold cross-validation (mean AUC-ROC of 0.8546 and 0.8528, respectively), with the difference between them not statistically significant; both clearly outperformed logistic regression (AUC-ROC 0.8215). Conclusions: By integrating operational and technical data within a validated, comparative machine learning framework, the study provides a practical basis for decision-support systems enabling the reduction of unnecessary interventions, minimizing downtime, and improving fleet cost efficiency and reliability.

W artykule dokonano charakterystyki roli bezpieczeństwa operacyjnego w przedsiębiorstwach produkcyjnych, definiowanego jako zdolność do prowadzenia procesów w sposób niezakłócony i akceptowalny pod względem ryzyka. Szczególną uwagę zwrócono na potrzebę wczesnej identyfikacji i eliminacji przyczyn błędów w warunkach rosnącej złożoności systemów technicznych i zaostrzających się wymagań jakościowych. Celem artykułu jest przedstawienie możliwości zastosowania metody FMEA do identyfikacji potencjalnych niezgodności w procesach wytwórczych oraz oceny ich wpływu na bezpieczeństwo operacyjne przedsiębiorstwa. Omówiono podstawy teoretyczne metody, sposób wyznaczania wskaźnika priorytetu ryzyka RPN oraz jego wykorzystanie do priorytetyzacji działań korygujących i zapobiegawczych. W studium przypadku, na przykładzie producenta rowerów, zidentyfikowano kluczowe niezgodności w procesach produkcji ram, kół i kierownic oraz określono ich skutki oraz przyczyny. Na tej podstawie wskazano czynności krytyczne oraz zaproponowano działania doskonalące ukierunkowane na redukcję ryzyka, wzrost wykrywalności błędów i ograniczenie skutków zakłóceń, podkreślając znaczenie FMEA jako narzędzia wspierającego bezpieczeństwo operacyjne.

The dynamic development of the food delivery sector and the resulting increase in last-mile distribution operations generate the need to simultaneously improve the efficiency of delivery processes and reduce the environmental impacts of urban logistics. In this context, shortening delivery time contributes not only to higher service quality and competitiveness but also to lower energy consumption and carbon dioxide emissions, which are key elements of sustainable urban mobility and logistics. Therefore, the aim of this study is to develop a delivery time optimization algorithm for the food delivery sector using selected machine learning methods, supporting the implementation of sustainable development principles in the operations of transport enterprises. This study presents an integrated approach to modelling delivery time in food distribution as a tool for building the competitive advantage of logistics enterprises under the conditions of implementing sustainable development principles. The study combines a literature review on sustainable last-mile logistics and data-driven optimization with an empirical analysis using traditional methods such as multiple regression and selected machine learning methods: decision trees, the Gradient Boosting Machine (GBM) method, and the XGBoost algorithm. The operational data include parameters related to delivery execution, such as supplier characteristics, vehicle type, order execution date, weather conditions and traffic situation. The developed mathematical models enable high-accuracy prediction of delivery time and the identification of the most important factors affecting both timeliness and potential energy consumption in the delivery process. The comparative assessment of the applied methods makes it possible to indicate the algorithms that provide the best forecast quality and practical usefulness in logistics decision-making. The proposed delivery time optimization algorithm supports data-driven decision-making that leads to shorter delivery times and lower energy intensity and thus to a reduction in the carbon footprint of last-mile operations, simultaneously strengthening the competitiveness and environmental responsibility of logistics enterprises. The results contribute to the development of sustainable urban logistics by linking predictive modelling with the economic, environmental and operational dimensions of efficiency in last-mile transport processes. Overall, this study offers an original, high-quality contribution to sustainable last-mile food delivery by integrating large-scale operational data with advanced machine learning models to deliver practically relevant, highly accurate delivery time predictions for logistics enterprises.

In view of increasingly stringent environmental requirements and global efforts to reduce greenhouse gas emissions, it is becoming increasingly important to identify the design factors of powertrains that directly affect fuel efficiency and carbon dioxide (CO₂) emissions. This study analyses the impact of selected technical parameters of passenger vehicles powertrains – in particular, engind isplacement, number of cylinders, type of transmission and type of fuel – on fuel consumption in urban, highway and combined conditions, as well as on CO₂ emissions. The study used a set of operational data covering the design parameters of selected passenger vehicle models on the market. The statistical analysis allowed for the identification of significant relationships between the design of the drive system and its impact on the environment. The results of the study can make a significant contribution to the development of forecasting tools to support the design of new-generation engines, as well as support the decision-making process and improve the formulation of strategies for the sustainable development of low-emission transport technologies

The article presents issues related to fossil fuel energy consumption and CO₂ emissions from motor vehicles. It identifies the main areas of research in this field in the context of motor vehicles, namely driver behavior, fuel consumption, and OBD systems. The research sample consisted of experimental data containing records of a series of test drives conducted with a passenger vehicle equipped with a gasoline-powered internal combustion engine, collected via an OBD diagnostic interface. Three subsets related to engine operation and energy demand patterns were distinguished for the study: during vehicle start-up and low-speed driving (vehicle start-up mode), during urban driving, and during extra-urban driving. Multiple regression models were constructed for the analyzed subsets to predict CO₂ emissions based on engine energy output parameters (power, load) and vehicle kinematic parameters. The developed models were subjected to detailed evaluation and mutual comparison, taking into account their predictive performance and the interpretability of the results. The analysis made it possible to identify the variables with the most substantial impact on CO₂ emissions and fuel energy consumption. The models allow individual drivers to monitor and optimize vehicle energy efficiency in real-time. The extra-urban driving model achieved the highest predictive accuracy, with a mean absolute error (MAE) of 19.62 g/km, which makes it suitable for real-time emission monitoring during highway driving.

This article discusses key aspects of deposit–refund system design in Poland, highlighting the importance of energy-sustainable collection logistics. The main role in this system is played by the operator responsible for collecting and transporting packaging to metering centers and recycling plants. The research focused on the optimal placement of logistics facilities to minimize energy expenditures, using the balanced center of gravity method. It took into account the distribution of collection points and the intensity of material flows to develop an efficient and environmentally friendly reverse logistics model. The most important results of the research are the development of a model for the layout of logistics facilities that minimizes energy consumption, the identification of key factors such as the location of collection points and material flows, the development of a methodology for green logistics, and practical recommendations for system designers. The proposed solutions, although innovative in Poland, are universal in nature and can be applied in other countries. The article makes an important contribution to the development of sustainable bail logistics and promotes a balanced energy transition.

Prawidłowa realizacja procesu zabezpieczenia żołnierzy w przedmioty umundurowania i wyekwipowania (PUiW) jest kluczowym determinantem właściwego wykonywania zadań przez wojska, zarówno w czasie pokoju, jak i kryzysu i wojny. Obecnie gospodarka magazynowa służby mundurowej, jak również sam proces dostarczania PUiW oceniany jest negatywnie przez użytkowników końcowych. W związku z powyższym, niezbędnym jest opracowanie nowego modelu dystrybucji tych środków. Przedstawienie koncepcji nowego modelu dystrybucji przedmiotów umundurowania i wyekwipowania w kontekście potrzeb Sił Zbrojnych RP (SZ RP) wymaga szczegółowego zrozumienia mechanizmów generowania oraz przepływu potrzeb między poszczególnymi komórkami instytucjonalnymi. W niniejszym artykule uwagę poświęcono dwóm zasadniczym podmiotom: Regionalnym Bazom Logistycznym (RBLog) oraz Wojskowym Oddziałom Gospodarczym (WOG), analizując ich wzajemne relacje oraz sposób funkcjonowania w omawianym obszarze. Rozważono możliwość wykorzystania potencjału cywilnego podczas dystrybucji PUiW, koncentrując się na roli pośredników zewnętrznych, mogących wspierać procesy logistyczne, świadcząc usługi z zakresu transportu oraz magazynowania. Celem artykułu jest przedstawienie scentralizowanego modelu dystrybucji PUiW w oparciu o cywilne ogniwa logistyczne. Problem badawczy sformułowano w formie pytania: Jaki jest optymalny model dystrybucji PUiW w SZ RP? Wykorzystano metody badawcze, takie jak: analiza, synteza, modelowanie matematyczne oraz wnioskowanie. Na tej podstawie przedstawiono charakterystykę stanu obecnego procesu dystrybucji PUiW w SZ RP, omówiono możliwość wykorzystania pośredników zewnętrznych w procesie oraz zaproponowano nowy, scentralizowany model dystrybucji PUiW wraz z przeprowadzeniem kalkulacji optymalnej lokalizacji obiektów logistycznych, niezbędnych do realizacji procesu, które dostosowane będą do obecnej lokalizacji jednostek wojskowych.

The article analyzes the impact of selected operational parameters of internal combustion engine vehicles on CO₂ emissions. The study was preceded by a detailed analysis of the issues related to CO₂ emissions in the EU, with a focus on Poland, where the tests were conducted. The key scientific assumption is that individual vehicle users’ behaviors significantly impact global CO₂ emissions. Daily use of private vehicles, driving style, and attention to fuel efficiency contribute to cumulative effects that can drive the transformation toward more sustainable transport. Therefore, the study was conducted using real-time empirical data obtained from the vehicles’ OBD (On-Board Diagnostics) diagnostic systems. This approach enabled the creation of a diagnostic tool allowing each vehicle user to assess CO₂ emissions and ultimately manage its levels, which is the biggest innovation of the work. Two levels of CO₂ emissions were identified as categorical variables in the model, considered either ecological or non-ecological from the perspective of sustainable transport. The CO₂ emission threshold of 200 g/km was adopted based on the average age of vehicles in Poland (14.5 years) and Regulation (EC) No 443/2009 of the European Parliament and of the Council. Three models of logistic regression dedicated to different driving cycle phases—starting, urban driving, and highway driving—were proposed and compared. This study demonstrated that during vehicle starting, the most significant factors influencing the probability of ecological driving are vehicle velocity, relative engine load, and relative throttle position, while for the other two types of movement, engine power and torque should also be considered. The logistic regression model for vehicle start-up obtained a value of sensitivity at about 82% and precision at about 85%. In the case of urban driving, the values of the discussed parameters reach significantly higher levels, with sensitivity at around 96% and precision at about 92%. In turn, the model related to highway driving achieved the highest values among the created models, with sensitivity at around 97% and precision at about 93%.

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.