This study presents a comprehensive two-dimensional numerical analysis of natural laminar convection within a square cavity containing two circular heat sources, which simulate electric cables generating heat due to Joule heating. This scenario is particularly relevant in aeronautics, where excessive heating of electrical installations can lead to significant material and human safety risks. The primary objective of this research is to identify the optimal spacing between the two heat sources to mitigate the risk of overheating and ensure the safe operation of the electrical installation. To achieve this, various configurations were analyzed by adjusting the distance between the heat sources while also varying the Rayleigh number across a range from 10³ to 10⁶. The governing equations for the fluid flow and heat transfer were solved using a FORTRAN-based numerical code employing the finite volume method. The results indicate that the heat transfer characteristics within the cavity are significantly influenced by both the distance between the heat sources and the Rayleigh number. The analysis revealed that the average Nusselt number (Nu_avg) peaked at a value of 14.69 when the distance between the heat sources was set at 0.7 units and the Rayleigh number was at 10⁶. This finding suggests that maintaining this specific spacing between the electrical cables can optimize heat dissipation and enhance the safety of the installation. In conclusion, the study recommends adopting a spacing of 0.7 units between the electrical cables to ensure optimal thermal performance and minimize the risk of overheating, thereby safeguarding both the materials and personnel involved in aeronautical operations.

The discourse on advocacy planning involving actors has not explicitly addressed the question of who the actor advocate planner is and how an actor can become an advocate planner. This paper attempts to exploring the actor advocate planner in the context of Regional Splits as, employing social network analysis as a research tool. This research employs an exploratory, mixed-methods approach, predominantly qualitative in nature. The initial phase entailed the investigation and examination of qualitative data through the acquisition of information from interviews with key stakeholders involved in Regional Splits, including communities, non-governmental organizations (NGOs), governmental entities, and political parties. The subsequent phase utilized quantitative techniques derived from the findings of the qualitative analysis, which were then analysis into the Gephi application. The findings indicate that the Regional Splits the Presidium Community represents civil society and political parties serve as crucial advocate planners, facilitating connections between disparate actors and promoting Regional Splits through political parties.

The study examines the impact of various theories on the reflection and transmission phenomena caused by obliquely incident longitudinal and transverse waves at the interface between a continuously elastic solid half-space and a thermoelastic half-space, using multiple thermoelastic models. Numerical calculations reveal that the thermoelastic medium supports one transmitted transverse wave and two transmitted longitudinal waves. The modulus of amplitude proportions is analyzed as a function of the angle of incidence, showing distinct variations across the studied models. Energy ratios, derived from wave amplitudes under consistent surface boundary conditions for copper, are computed and compared across angles of incidence. The results demonstrate that the total energy ratio consistently sums to one, validating energy conservation principles. Graphical comparisons of amplitude proportions and energy ratios for SV and P waves across different models illustrate significant differences in wave behavior, emphasizing the influence of thermoelastic properties on wave transmission and reflection.

Optimizing Storage Location Assignment (SLA) is essential for improving warehouse operations, reducing operational costs, travel distances and picking times. The effectiveness of the optimization process should be evaluated. This study introduces a novel, generalized objective function tailored to optimize SLA through integration with a Genetic Algorithm. The method incorporates key parameters such as item order frequency, storage grouping, and proximity of items frequently ordered together. Using simulation tools, this research models a picker-to-part system in a warehouse environment characterized by complex storage constraints, varying item demands and family-grouping criteria. The study explores four scenarios with distinct parameter weightings to analyze their impact on SLA. Contrary to other research that focuses on frequency-based assignment, this article presents a novel framework for designing SLA using key parameters. The study proves that it is advantageous to deviate from a frequency-based assignment, as considering other key parameters to determine the layout can lead to more favorable operations. The findings reveal that adjusting the parameter weightings enables effective SLA customization based on warehouse operational characteristics. Scenario-based analyses demonstrated significant reductions in travel distances during order picking tasks, particularly in scenarios prioritizing ordered-together proximity and group storage. Visual layouts and picking route evaluations highlighted the benefits of balancing frequency-based arrangements with grouping strategies. The study validates the utility of a tailored generalized objective function for SLA optimization. Scenario-based evaluations underscore the importance of fine-tuning SLA strategies to align with specific operational demands, paving the way for more efficient order picking and overall warehouse management.

The application of optimization algorithms is crucial for analyzing oil and gas company portfolio and supporting decision-making. The paper investigates the process of optimizing a portfolio of oil and gas projects under economic uncertainty. The literature review explores the advantages of applying various optimizers to models that consider the mean and semi-standard deviations of stochastic multi-year cash flows and revenues. The methods and results of three different optimization algorithms are discussed: ranking and cutting algorithms, linear (Simplex) and evolutionary (genetic) algorithms. Functions of several key performance indicators were used to test these algorithms. The results confirmed that multi-objective optimization algorithms that examine various key performance indicators are used for efficient optimization in oil and gas companies. This paper proposes a multi-criteria optimization model for investment portfolios of oil and gas projects. The model considers the specific features of these projects and is based on the Markowitz portfolio theory and methodological recommendations for project assessment. An example of its practical application to oil and gas projects is also provided.