This study aimed at measuring the level of job burnout among King Khalid University staff. The descriptive-analytical approach was employed to describe job burnout, determine its prevalence, identify its causes, and propose ways to address it. This method was used for comparison, interpretation, and generating information to assist in understanding the phenomena of job burnout and to devise recommendations for mitigating its prevalence. The results showed that the overall mean estimation of the dimensions of the level of occupational burnout from the perspective of university staff was (2.28), with a standard deviation of (0.81), indicating a low degree. The arithmetic means of the study sample responses to the dimensions ranged from (1.98–2.66). This provides a good indicator of the prevalence of occupational burnout. The findings showed that individuals in higher ranks experience higher levels of job burnout compared to the rest of the ranks classified in the study.

Effective harvesting strategies are crucial for maximizing annual catch and ensuring the sustainability of lobster (Homarus americanus) farming. This paper presents a nonlinear objective programming model to optimize harvesting intensity based on lobster life cycle dynamics and harvesting characteristics. We model the population dynamics of 1-4 year-old lobsters using differential equations to account for natural mortality, spawning, and harvesting effects. Solving the model with LINGO 12.0, we determine that the optimal harvesting intensity coefficient is 17.36, which maximizes annual catch to 3.88 × 10¹⁰ grams. Results indicate that maintaining harvesting intensity around this optimal value balances economic benefits and population stability, ensuring sustainable farm operations.

One of the most important ways to achieve the goals stipulated by the Paris (2015) Agree-ment on climate change is to solve a two-fold task: 1) the adsorption of CO2 by the forest communities fcom the atmosphere during global warming and 2) their adaptation to these climate changes, which should ensure the effectiveness of adsorption itself. Report presents the regional experience of the numerical solution of this task. Calculations of the carbon balance of forests in the Oka-Volga River basin were carried out for global forecasts of moderate and extreme warming. The proposed index of labile elastic-plastic stability of forest ecosystems, which characterizes their succession-restorative po-tential, was used as an indicator of adaptation. A numerical experiment was conducted to assess the effect of the elastic-plastic stability of forest formations and the predicted climatic conditions on the carbon balance. In the upcoming 100-year forecast period, the overall stability of forest formations should increase, and to the greatest extent with extreme warming. Accordingly, one should expect a significant increase in the ability of boreal forests to ab-sorb greenhouse gases. It is determined unambiguous picture of a significant increase in the adsorption capacity of boreal forests with a rise in their regenerative potential.

This research introduces a novel framework integrating stochastic finite element analysis (FEA) with advanced circular statistical methods to optimize heat pump efficiency under material uncertainties. By modeling directional variability in thermal conductivity using both uniform and Von Mises distributions, the study highlights the superiority of the Von Mises distribution in providing consistent and efficient thermal performance. The Von Mises distribution, known for its concentration around a mean direction, demonstrates a significant advantage over the uniform distribution, resulting in higher mean efficiency and lower variability. The findings underscore the importance of considering both stochastic effects and directional consistency in thermal systems, paving the way for more robust and reliable design strategies.