Maintenance is one of the critical areas in operations in which a careful balance between preventive costs and the effect of failures is required. Thanks to the increasing data availability, decision-makers can now use models to better estimate, evaluate, and achieve this balance. This work presents a maintenance scheduling model which considers prognostic information provided by a predictive system. In particular, we developed a prescriptive maintenance system based on run-to-failure signal segmentation and a Long Short Term Memory (LSTM) neural network. The LSTM network returns the prediction of the remaining useful life when a fault is present in a component. We incorporate such predictions and their inherent errors in a decision support system based on a stochastic optimization model, incorporating them via chance constraints. These constraints control the number of failed components and consider the physical distance between them to reduce sparsity and minimize the total maintenance cost. We show that this approach can compute solutions for relatively large instances in reasonable computational time through experimental results. Furthermore, the decision-maker can identify the correct operating point depending on the balance between costs and failure probability.

The prognostics and health management disciplines provide an efficient solution to improve a system’s durability, taking advantage of its lifespan in functionality before a failure appears. Prognostics are performed to estimate the system or subsystem’s remaining useful life (RUL). This estimation can be used as a supply in decision-making within maintenance plans and procedures. This work focuses on prognostics by developing a recurrent neural network and a forecasting method called Prophet to measure the performance quality in RUL estimation. We apply this approach to degradation signals, which do not need to be monotonical. Finally, we test our system using data from new generation telescopes in real-world applications.

Natural disasters have the potential to pose a significant threat to property, critical infrastructure, human health and safety, and some others. One of the most relevant natural disasters is earthquakes, which are high on the list of natural phenomena that most affect infrastructures and, at the same time, are the most unpredictable. This study uses the probabilistic seismic risk analysis method to estimate the condition of an industrial-scale telescope after the effect of an earthquake. The approach considers a seismic source model and ground motion prediction equations to evaluate the intensity measure of each telescope as a function of its location. The implemented simulation uses reliability via Monte Carlo method to discover the probability of failure of each telescope taking into consideration the fragility curves described by its natural structure. Their method incorporates terrain characteristics and component robustness into the analysis of telescope performance at an affordable computational cost. The proposed method was applied to the observatories using industrial-scale telescopes established in Chile, and it was confirmed that reliability is highly dependent on the robustness or fragility of the infrastructure.

In this paper, we present a comprehensive approach for depth map completion using a variable pipeline model. The pipeline is designed to be flexible, and its structure is dynamically determined during the training phase by minimizing a fitness function. To achieve this objective, we used the Particle Swarm Optimization (PSO) technique, which efficiently ex-plores the solution space to optimize key parameters. The pipeline itself consists of three main stages: a convolutional stage (Lab Convolution), an Interpolation Model (IM), and a post-Convolutional Stage (SC2). During training, our model automatically adjusts several aspects, including the selection of filter parameters, interpolator settings, and the number of filters used at each stage. Moreover, the pipeline is capable of interchanging the execution order between the stages, either processing in the sequence of Lab Convolution-IM-SC2 or SC2-IM-Lab Convolution. This flexible structure allows for the discovery of better configurations that significantly im-prove the performance of depth map completion tasks. Our approach demonstrates superior results by adapting to the specific characteristics of the input data.

In this study, we present a quantitative characterization of skin pigmentation through the analysis of 1005 diffuse reflectance spectra associated with Fitzpatrick skin phototypes (I–VI) of Hispanic individuals. We employed complementary metrics, including the individual typology angle (ITA), the melanosome volume fraction ( f me ), the structural similarity index measure (SSIM), and the percentage of spectral overlap. Our findings revealed that the average ITA values decreased progressively from ( 51.76 ± 6.40 ) ∘ in phototype I to ( − 33.63 ± 15.91 ) ∘ in phototype VI, confirming the inverse correlation between the typological angle and the degree of skin pigmentation. The volumetric fraction of melanosomes ( f me ) – derived from a quadratic relationship with the chromaticity coordinate ( L ∗ ) and exhibiting a RMSE of 0.0016 – ranged from ( 1.85 ± 0.46 ) % in phototype I to ( 10.83 ± 2.19 ) % phototype VI. These results provide a quantitative estimate of intra-phototype variability, which becomes more pronounced in darker skin tones. Furthermore, SSIM values (0.42 to 0.94) and spectral overlap (0.00% to 52.05%) confirmed high structural similarity between adjacent groups (especially between phototypes IV and V) and total divergence between extreme phototypes (I vs VI). Notably, intermediate SSIM values (0.74–0.86) were observed between second-adjacent phototypes (e.g. III vs V), suggesting a gradual spectral continuity across the scale. Finally, the total melanin content ( µ g mg ⁻¹ ) was determined from f me , achieving a correlation coefficient of ρ = 0.996, highlighting the chromatic heterogeneity and morphological stability essential for clinical and technological applications in diverse populations.