A study of 250 commercial drugs to act as corrosion inhibitors on steel has been developed by applying the quantitative structure-activity relationship (QSAR) paradigm. Hard-soft acid-base (HSAB) descriptors were used to establish a mathematical model to predict the corrosion inhibition efficiency (IE%) of several commercial drugs on steel surfaces. These descriptors were calculated through third-order density-functional tight binding (DFTB) methods. The mathematical modeling was carried out through autoregressive with exogenous inputs (ARX) framework and tested by fivefold cross-validation. Another set of drugs was used as an external validation, obtaining SD, RMSE, and MSE, obtaining 6.76%, 3.89%, 7.03%, and 49.47%, respectively. With a predicted value of IE% = 87.51%, lidocaine was selected to perform a final comparison with experimental results. By the first time, this drug obtained a maximum IE%, determined experimentally by electrochemical impedance spectroscopy measurements at 100 ppm concentration, of about 92.5%, which stands within limits of 1 SD from the predicted ARX model value. From the qualitative perspective, several potential trends have emerged from the estimated values. Among them, macrolides, alkaloids from Rauwolfia species, cephalosporin, and rifamycin antibiotics are expected to exhibit high IE% on steel surfaces. Additionally, IE% increases as the energy of HOMO decreases. The highest efficiency is obtained in case of the molecules with the highest ω and ΔN values. The most efficient drugs are found with pK_a ranging from 1.70 to 9.46. The drugs recurrently exhibit aromatic rings, carbonyl, and hydroxyl groups with the highest IE% values.

This paper addresses a forest harvesting problem with adjacency constraints, including additional environmental constraints to protect wildlife habitats and minimize infrastructure deployment costs. To this end, we propose an integer programming model to include those considerations during the optimization of the harvest regime of a Mexican forest. The model considered was based on the Unit Restriction Model, a benchmark approach that merges the management units before the optimization process. The resulting model, namely the Green Unit Restriction Model (GURM) and the benchmark model (URM) from the literature were tested with the forest Las Bayas, using information obtained from the SiPlaFor project from Universidad Juárez. The proposed model was solvable in all tested instances. Furthermore, a sensitivity analysis study over a core data set of test instances was carried out on the different parameters of the GURM model to determine optimal configurations for the specific case study. Several environmental measures were assessed in our experimental work. The parameters evaluated were the distance value between pairs of units harvested in the same period, the distance value between those considered natural reserve units, the timber volume to be harvested, the green-up period, and the minimum forest reserve area. An interesting observation from the experiments was that the maximum area inversely affected the URM and GURM models; larger regions resulted in a reduced number of management units in the URM model, thus reducing the computational time to solve the instance of the problem, but in this case, at the expense of a reduced profit. One of the interesting findings was that, in all experiments under all different factors, harvesting every 5 or 6 years yields better profits than harvesting every 10 or 12 years. The current standard in the Mexican system is to harvest every 10 years.