In learning, one of the fundamental motivating factors is self-efficacy. Therefore, it is crucial to understand the level of students’ self-efficacy in learning programming. This article presents a quantitative study on undergraduate students’ perceived programming self-efficacy. 110 undergraduate computing students took part in this survey to assess programming self-efficacy. Before being given to the respondents, the survey instrument, which included a 28-item self-efficacy assessment and 30 multiple-choice programming questions, was pilot-tested. The survey instrument had a reliability of 0.755. The study results show that the students’ self-efficacy was low when they solved complex programming tasks independently. However, they felt confident when there was an assistant to guide them through the tasks. From this study, it could be concluded that self-efficacy is an essential achievement component in programming courses and can avoid education dropouts.

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.

Given the large amount of railway maintenance work in China, whereas the maintenance time window is continuously compressed, this paper proposes a novel network model-based maintenance planning and optimization method, transforming maintenance planning and optimization into an integer linear programming problem. Based on the dynamic inspection data of track geometry, the evaluation index of maintenance benefit and the model of the decay and recovery of the track geometry are constructed. The optimization objective is to maximize the railway network’s overall performance index, considering budget constraint, maximum length constraint, maximum number of maintenance activities within one single period constraint, and continuity constraint. Using this method, the track units are divided into several maintenance activities at one time. The combination of surrounding track units can be considered for each maintenance activity, and the specific location, measure, time, cost, and benefit can be determined. Finally, a 100 km high-speed railway network case study is conducted to verify the model’s effectiveness in complex optimization scenarios. The results show that this method can output an objective maintenance plan; the combination of unit track sections can be considered to expand the scope of maintenance, share the maintenance cost and improve efficiency; the spatial-temporal integrated maintenance planning and optimization can be achieved to obtain the optimal global solution.

We analyze Thailand’s projected 2023–2030 energy needs for power generation using a constructed linear programming model and scenario analysis in an attempt to find a formulation for sustainable electricity management. The objective function is modeled to minimize management costs; model constraints include the electricity production capacity of each energy source, imports of electricity and energy sources, storage choices, and customer demand. Future electricity demands are projected based on the trend most closely related to historical data. CO₂ emissions from electricity generation are also investigated. Results show that to keep up with future electricity demands and ensure the country’s energy security, energy from all sources, excluding the use of storage systems, will be necessary under all scenario constraints.