Juan Cruz-Castro

Accurate pharmaceutical demand forecasting is essential to align production with actual market needs, reduce overproduction, prevent stockouts, and improve resource efficiency. This study develops a hybrid bidirectional long short-term memory (BiLSTM)–Transformer model to support sustainable pharmaceutical production through data-driven demand forecasting. We trained the model on real monthly sales and inventory data from a nationwide pharmaceutical company in Mexico, along with epidemiological, climatic, demographic, and economic variables. The study analyzed thirteen sodium naproxen and oseltamivir phosphate pharmaceutical products over forecasting horizons of 3, 6, and 12 months. We evaluated robust scaling and logarithmic transformation during preprocessing and compared the Adam, RMSprop, Nadam, and SGD optimizers. Nadam generally produced the lowest forecasting errors, especially at short- and medium-term horizons. Six of the thirteen evaluated pharmaceutical products achieved cumulative percentage errors below 10% at the three-month forecasting horizon. Compared with the Seasonal Naïve baseline, the proposed model achieved lower cumulative forecasting errors for 11 of 13 products at the 3-month horizon and 9 of 13 products at both the 6- and 12-month horizons, although the baseline performed better for certain products. Performance became more heterogeneous over longer horizons and for products with irregular or highly volatile demand, revealing important limitations for operational use. The findings suggest that hybrid AI-based forecasting may support production planning and inventory management decisions, provided that model selection accounts for product-specific characteristics and forecasting horizons. However, potential benefits related to reduced overproduction, excess inventory, product expiration, and environmental impacts remain prospective applications rather than outcomes directly demonstrated in this study.

Path planning and formation control in leader–follower robotic systems are active areas of research, as both are highly relevant to the proper execution of the assigned task. In this work, a dual approach to the A* algorithm is applied to generate consistent trajectories for a multi-agent robotic system with a leader–follower scheme. The conventional A* algorithm aims to minimize the cost of finding the best path by minimizing distances. In this case, a modified A* algorithm is used because, although decision-making also involves choosing among eight options or cells, the goal is not to minimize distance; instead, the focus is on analyzing the direction of acceleration. The proposed algorithm is robust regarding the initial and relative pose of the leader with respect to the followers. The leader is tracked using a digital accelerometer. The algorithm is tested by simulating various patterns and implemented in two experimental test scenarios: the first with differential mobile robots, and the second with an Ackerman-type mobile robot. In both scenarios, the trajectories were achieved with deviations in x and y between the follower’s path and the leader’s path of less than 0.03, and the leader’s pose independence was maintained.

Self-healing materials have garnered significant interest in the industry due to their potential for various applications, particularly in the automotive sector, including exterior parts and interior components

Flow-induced vibrations occur in some of the internal components of a nuclear reactor. When specific conditions are present, these vibrations may result in excessive deformations or fatigue that can generate mechanical damage. Several boiling water reactor (BWR) of nuclear power plants (NPP) have experienced failures in the jet pump assembly due to flow-induced vibration (FIV) which could be caused by acoustic pulsations derived from recirculation pumps, vibration induced by turbulence and vibration induced by leakage at the slip joint. The purpose of this paper is to establish a viable numerical methodology to evaluate the fluid-structural interaction at the slip joint of a jet pump. In this analysis, the fluid-structural interaction was evaluated with the finite element method and finite volume method with ANSYS^® code in the case of two steel plates with a divergent gap. Results show that a critical velocity could cause fluidelastic instability, if only one flow in a two-way fluid-structural interaction was considered. This is one of the phenomena that could take place at the slip joint of a jet pump assembly.