This study considers the usage of multilinear regression and artificial neural network modelling to forecast ozone concentrations with regard to weather-related indicators (wind speed, wind direction, relative humidity and temperature). Initial data were obtained by measuring the meteorological parameters using the PC Radio Weather Station. Ozone concentrations near high-voltage lines were measured using RS1003 and at a 220 m distance using ML9811. Neural network models such as the multilayer perceptron and radial basis function neural networks were constructed. The prognostic capacities of the designed models were assessed by comparing the result data by way of the square of the coefficient of multiple correlations (R²) and mean square error (MSE) values. The number of hidden neurons was optimised by decreasing an error function that recorded the number of units in the hidden layers to the precision of the expanded networks. The neural software IBM SPSS 26v was used for artificial neural network (ANN) modelling. The study demonstrated that the linear regression modelling approach was lacking in its capacity to predict the investigated ozone concentrations by used parameters, whereas the use of an ANN offered more precise outcomes. The conducted tests’ results established the strength of the designed artificial neural network models with irrelevant differences between detected and forecasted data.

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

This survey is focussed on distance learning studies, where there can be met a lot of technical obstacles, which creates complications in decision making. To get an ideal solution for these kinds of problems, the Fuzzy TOPSIS (Technique for Order Preference by Similarities to Ideal Solution) is one of the best solutions. Therefore, this paper presents the distance learning quality assessment surveys when the Fuzzy AHP (Analytic Hierarchy Process) and TOPSIS methods are used. Research results describe the application of the Fuzzy AHP—TOPSIS hybrid method. MCDM (Multi-Criteria Decision Making) programs with MATLAB (R2020b) mathematical package were written to calculate the evaluation results for three distance learning courses. In the practical implementation of the proposed distance learning module evaluation methodology, the experts’ evaluation method was applied. Thirty-four judges were chosen with specific knowledge and skills and with very different competencies to assess three alternatives by fourteen criteria. Following the experts’ evaluation, a statistical analysis method was used to process the data. After applying the complex evaluation, the comparative analysis method was used to summarize the obtained results. This work further provides useful guidelines for the development of an easily understandable hierarchy of criteria model that reflects the main goal of study quality assessment.

The article analyzes the generational (age cohorts) effect on subjective wellbeing at work. The main aim is to reveal generational differences that lead to subjective wellbeing at work and possible discrepancies between the generations. The article contributes to the literature by creating and testing a theoretical model of subjective wellbeing at work as a composition of several dimensions that vary among generations, including physical experience, cognitive job performance, and appraisal, as well as social relationships and socio-emotional enablement at work. Using the military as an organization where daily routine creates similar conditions for different generations, we surveyed 890 army professionals representing three different generations: Gen Y, Gen X, and Gen Z. A structural equation modelling (SEM) technique is used to test the research model. The results support the hypothesis that generation has a strong moderating impact on the components of subjective wellbeing at work. Social relationship, as a socio-emotional experience, is the most important component of wellbeing at work for Gen Z, while socio-emotional enablement plays a central role in subjective wellbeing at work for Gen X and Gen Y.

Background: The current operational military environment is changing, complex, unpredictable, and ambiguous. Due to such situations, soldiers are constantly forced to think about their values, norms, and roles that should be part of their profession. Consequently, they must first be educated and trained on how to behave in a particular operational military environment. Pursuing an officer’s education at military academies is very difficult not only physically but also psychologically. Cadets are required to be prepared to lead in extreme environments upon graduation. Despite the fact that military tasks are technically complex, the individual operational activities of soldiers are gaining more and more strategic meaning. Therefore, the importance of selecting the process and military education programs of soldiers is increasingly stressed. Cognitive abilities and skills individually predict performance in academic and professional settings, but it is less clear how personality can influence performance. Therefore, this study focused on the explanation of the individual factors that affect the achievements of the cadets. Specifically, the objective of this study was to examine direct and mediated relationships between personality traits and the military and academic performance of cadets.Methods: This study followed a quantitative method analysis. The research models were assessed using the structural equation modeling technique. Bootstrap was applied to evaluate a 95% level confidence interval on estimates with 5,000 bootstrap samples, and to evaluate direct and indirect effects. The analysis was based on a sample of 120 cadets from the Lithuanian Military Academy. The effects on military and academic performance were evaluated using the Self-Efficacy scale, the Big Five personality trait scale, academic performance was evaluated through academic grades and military performance was evaluated using instructor ratings.Results: To support our hypotheses, it was found that self-efficacy has a mediating effect on the performance of cadets. Additionally, the traits of conscientiousness, openness to experience and extraversion were related to both military and academic performance. Furthermore, self-efficacy appeared as a partial mediator of the relationship between personality traits and cadet performance.Conclusion: The findings of this study help clarify the relationship between the personality traits of the cadets and the military and academic performance. In addition, these results may be useful for the further development of military education and training, for the development of testing, and selection of military personnel.

This research aims to explore an analytical model, gauge the interplay between psychological resilience and achieved military competencies, and examine an intermediary role of effort, proactivity, and self-efficacy. In this study, 337 reserve soldiers from Lithuania were investigated with the Lithuanian Military Training Competences Assessment Scale, Resilience Scale, Self-Efficacy Scale, Short Grit Scale (Grit-S), and Proactivity Scale. Our findings highlighted the importance of the psychological resilience of reserve soldiers due to its positive relationship with the personality traits. Implicit interconnectedness between psychological resilience and psychological skills together with military competences was researched, and it became evident that self-efficacy determined a statistically important mediating role (indirect effect = 0.264 **, p < 0.05) between reservists’ psychological resilience and perceived military training course results. However, the other components included in the study did not have a mediating effect (effort indirect effect = 0.03, p > 0.1; proactivity indirect effect = 0.094, p > 0.1). The research findings are important because of greater prominence of the role of reserve troops in military operations around the world and achievements in the domain of security and defense. This study has great theoretical and empirical value in making decisions concerning the psychological resilience of reserve soldiers’ promotion and sustainable improvement of military preparedness strategies.