Smoking behavior, encompassing both traditional tobacco and electronic cigarette use, is influenced by a range of demographic, familial, and social factors. This study examines the relationship between smoking habits and family dynamics through a cross-sectional survey of 100 participants, using an anonymous questionnaire to collect demographic data, smoking patterns, and familial interactions. Validated instruments, including the Penn State Electronic Cigarette Dependence Index and the Family Relationship Assessment Scale, were employed to assess smoking dependence and family dynamics. The analysis identified key patterns, such as increased smoking frequency among individuals experiencing higher family tension and variations in smoking habits across age and gender groups. Nocturnal smoking was linked to higher cigarette consumption, whereas early-day smokers exhibited a lower desire to quit. Machine learning models were applied to predict and classify smoking behaviors based on socio-demographic and familial variables, with an ensemble learning model achieving the highest accuracy (93.33%), outperforming k-nearest neighbors (90.00%), support vector machines (80.00%), and decision trees (83.33%). These findings underscore the complex interplay between family relationships and smoking behavior, providing insights for public health interventions. Additionally, this study highlights the potential of machine learning in behavioral research, demonstrating its utility in identifying and predicting smoking-related patterns.

Background: Recent research highlights uncertainties surrounding the metabolic effects of nicotine in young adults, particularly among people who use e-cigarettes. While traditional smoking is known to alter body composition, the metabolic impact of using e-cigarettes remains less understood. Methods: In this cross-sectional study, body composition (via bioelectrical impedance analysis) and lifestyle data were collected from 60 university students (mean age: 21.7 ± 1.9 years), who were classified as people who use e-cigarettes exclusively, people who smoke cigarettes exclusively, or people who have never used nicotine products. To address confounding by sex and age, inverse probability of treatment weighting (IPTW) was applied. Results: After adjustment, people who use e-cigarettes had significantly higher body fat percentage compared to people who have never used nicotine (β = 5.45, p = 0.001), while no significant differences were found between people who smoke cigarettes and other groups. Energy drink consumption was also positively associated with body fat percentage and metabolic age. Machine learning models, particularly k-nearest neighbors, achieved moderate classification accuracy (up to 72%) in distinguishing people who use nicotine from people who have never used nicotine based on physiological and lifestyle features. Conclusions: It is important to note that the majority of participants were metabolically healthy, and the observed differences occurred within a clinically normal range. While these findings suggest associations between e-cigarette use and higher adiposity in young adults, no causal inferences can be made due to the observational design. Further longitudinal studies are needed to explore the potential metabolic implications of nicotine use.

The reliability of ocean-going ship engine fuel systems is crucial for the safety and continuous operation of vessels. Failure of this system can lead to serious operational and economic consequences; therefore, effective diagnostics and failure prediction are essential elements of modern fleet management. This paper presents an analysis of the reliability of fuel systems based on operational data from ten bulk carriers operated by Polska Żegluga Morska in Szczecin. The analysis combined classical statistical methods with artificial intelligence algorithms to develop a hybrid diagnostic and forecasting framework. The Weibull lifetime distribution was applied to estimate time-to-failure parameters, revealing mixed failure mechanisms—random failures (k < 1) and aging-related processes (k > 1). Using the k-means algorithm, ships were automatically classified into two reliability groups: high-failure-rate units and stable operational vessels. Individual linear regression models were then developed for each ship to forecast the time to the next failure, achieving satisfactory predictive performance (R² > 0.75 for most vessels). Sensitivity analysis quantified model robustness under different disturbance scenarios, yielding mean Relative Prediction Deviation (RPD) values of approximately 65% for Missing Data, 60% for False Failure, and 26% for Data Noise. These results confirm that the proposed hybrid reliability–AI framework is resistant to random noise but sensitive to incomplete or erroneous historical data. The developed approach provides an interpretable and effective tool for predictive maintenance, supporting reliability management and operational decision-making in marine engine systems. The article presents a hybrid model that has been developed to enable the detailed characterization of emergency processes and the identification of the most important factors that influence damage forecasting. For systems with variable failure risk, it was found that both classical probabilistic models and machine learning methods must be considered to interpret damage patterns correctly. Implementing data filtration and validation procedures before using data in artificial intelligence models has been shown to improve forecast stability and increase the usefulness of forecasts for planning repairs.

Electromechanical systems are inherently hybrid in nature, combining electrical and mechanical processes, and their increasing complexity requires the development of universal and computationally efficient mathematical models. In this study, we propose a macromodeling approach that represents the electromechanical system as a “black box,” in which internal physical processes are disregarded and the system behavior is defined solely by the relationship between input and output signals. The identification of such macromodels is reduced to solving a nonlinear optimization problem. To address this challenge, the directed cone method is applied, which searches for the global minimum of the objective function through stochastic movement across the hyperplane defined by the optimization problem. Several algorithmic improvements of the directed cone method are investigated, including step-size adaptation, simultaneous adaptation of step size and hypercone opening angle, and a tunneling procedure. Their effectiveness is evaluated using the construction of a macromodel of a single-phase asynchronous motor as a case study. Performance was assessed according to computational complexity (measured as the number of objective function evaluations until convergence), relative modeling accuracy, and the dynamics of progression toward the global minimum. The experimental results show that the tunneling-based algorithm provides the highest modeling accuracy with the lowest computational cost, whereas the step-size-only adaptation was found to be the least effective. The proposed approach demonstrates the feasibility of constructing accurate macromodels of electromechanical systems that can be integrated into computer-aided modeling environments such as MATLAB/Simulink R2023b. Future work will focus on extending the approach to a broader class of electromechanical systems and developing hybrid algorithms to enhance robustness with respect to model nonlinearity.

(1) Background: Caffeic acid phenethyl ester (CAPE) exhibits anticancer activity; however, its time-dependent effects on interconnected signalling pathways remain incompletely characterised. (2) Methods: We combined wet-lab experiments (MTT viability assay and ELISA measurements of total $NF-\kappa B$ p65 and p53) with a Bayesian digital twin framework to quantify signalling dynamics in prostate cancer cells following CAPE exposure. p53-deficient PC3 and p53-competent LNCaP cell lines were treated for 24 h and 48 h across multiple CAPE concentrations. Experimental data were integrated into a mechanistic Bayesian model using robust likelihoods, enabling uncertainty-aware parameter inference and posterior predictive validation via leave-one-dose-out analysis. (3) Results: In PC3 cells, CAPE induced dose-dependent inhibition of $NF-\kappa B$ p65 that was consistently associated with reduced cell viability at both time points, consistent with a p53-independent regulatory regime. In contrast, LNCaP cells exhibited a transient $NF-\kappa B$–p53 coupling at 24 h, characterised by delayed $NF-\kappa B$ suppression and pronounced p53 activation, followed by a more stable and weakly coupled signalling state at 48 h. These temporal patterns were supported by posterior parameter estimates and predictive performance under leave-one-dose-out validation. (4) Conclusions: This study demonstrates that Bayesian digital twins enable quantitative, uncertainty-aware analysis of time-dependent drug responses, extending beyond conventional dose–response assessments and supporting mechanistic hypothesis generation in cancer pharmacology.

This study compares the elemental composition of aerosols produced by e-cigarettes and traditional cigarettes using Energy Dispersive Spectroscopy (EDS). Both homemade and store-bought e-liquids were tested under various tank conditions (full, half, and empty), and emissions were collected using nitrocellulose membranes and coil cotton. Additional measurements were performed on unheated e-liquids, cigarette filters, unused cotton, and membranes to assess background contamination. Key elements identified included chromium (Cr), aluminum (Al), sodium (Na), sulfur (S), chlorine (Cl), and copper (Cu). Homemade e-liquids showed significantly higher Cr and Al concentrations, especially under partial tank conditions, suggesting intensified coil degradation. Store-bought e-liquids demonstrated more stable emission profiles. Traditional cigarette smoke contained fewer trace metals but exhibited different elemental patterns. Supplementary viscosity and thermal analyses revealed that homemade liquids degrade more rapidly under heat, which may enhance metal leaching from coils. These findings underscore the influence of liquid composition and device conditions on emission profiles, emphasizing the need for quality control in e-liquid production and further toxicological evaluation of e-cigarette use.