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.

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.

Definitive diagnosis of Craniosynostosis (CS) with computed tomography (CT) is readily available, however, exposure to ionizing radiation is often a hard stop for parents and practitioners. Lowering head CT radiation exposure helps mitigate risks and improves diagnostic utilization. The purpose of the study is to quantify radiation exposure from head CT in patients with CS using a ‘new’ (ultra-low dose) protocol; compare prior standard CT protocol; summarize published reports on cumulative radiation doses from pediatric head CT scans utilizing other low-dose protocols. A retrospective study was conducted on patients undergoing surgical correction of CS, aged less than 2 years, between August 2014 and February 2022. Cumulative effective dose (CED) in mSv was calculated, descriptive statistics were performed, and mean ± SD was reported. A literature search was conducted describing cumulative radiation exposure from head CT in pediatric patients and analyzed for ionizing radiation measurements. Forty-four patients met inclusion criteria: 17 females and 27 males. Patients who obtained head CT using the ‘New’ protocol resulted in lower CED exposure of 0.32 mSv ± 0.07 compared to the prior standard protocol at 5.25 mSv ± 2.79 (p < 0.0001). Five studies specifically investigated the reduction of ionizing radiation from CT scans in patients with CS via the utilization of low-dose CT protocols. These studies displayed overall CED values ranging from 0.015 mSv to 0.77 mSv. Our new CT protocol resulted in 94% reduction of ionizing radiation. Ultra-low dose CT protocols provide similar diagnostic data without loss of bone differentiation in CS and can be easily incorporated into the workflow of a children’s hospital.