In this work, a novel model for hourly PM2.5 time series imputation is proposed for the estimation of missing values in different gap sizes, including 1, 3, 6, 12, and 24 h. The proposed model is based on statistical techniques such as moving averages, linear interpolation smoothing, and linear interpolation. For the experimentation stage, two datasets were selected in Ilo City in southern Peru. Also, five benchmark models were implemented to compare the proposed model results; the benchmark models include exponential weighted moving average (EWMA), autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). The results show that, in terms of average MAPEs, the proposed model outperforms the best deep learning model (GRU) between 26.61% and 90.69%, and the best statistical model (ARIMA) between 2.33% and 6.67%. So, the proposed model is a good alternative for the estimation of missing values in PM2.5 time series.

Spilocaea oleagina is a common and dangerous fungal disease in olive trees that significantly reduces olive production. The early and accurate detection of this disease is essential for implementing effective control measures. In this study, we propose the creation of a new meta-architecture for identifying peacock spots on olive leaves. This new meta-architecture integrates Xception and VGG16 as the basis for the methodology employed in this study. Furthermore, a machine learning approach was used to pretrain the model in an unsupervised manner, thereby improving its generalization capacity. Metrics such as Kappa (K), True Skill Statistic (TSS), Proportion of Expected Success (Pe), Threat Index (Ts), and the Heidke Skill Score (HSS) were used to validate the model results. Based on these indicators, we evaluated the robustness, accuracy, and ability of the model to identify peacock spots. The testing results showed that the suggested meta-architecture, named SSL-XceVNet, substantially outperformed the baseline XceVNet model (88.24%) in detecting peacock spots, with an accuracy of 95.22%. The validation measurement results (K, 73.57%; TSS, 72.66%; Pe, 55.06%; Ts, 90.71%; HSS, 90.44%) attested to the efficacy and resilience of the suggested model, underscoring its ability to generalize and produce accurate predictions under a variety of circumstances. In conclusion, the combination of Xception and VGG16 into a new meta-architecture added the strengths of both models, and self-supervised learning helped learn meaningful representations without the need for explicit labels.

Obtaining viable Argopecten purpuratus seeds faces challenges, especiallyci the unpredictability of the marine environment and high production costs in hatcheries. However, improving the method of "Broodstock Conditioning In Hatcheries" is key to ensure permanent seed supplies by minimizing the dependence on marine conditions and by maximizing economic viability in hatcheries. In an effort to overcome these barriers, broodstock were conditioned into two different environments: (a) Natural Environment: Natural marine conditions located in Bahía Inglesa, Atacama Region, Chile. (b) Hatchery: Laboratory conditions to achieve gonadal maturation, spawning induction, fertilization and larval development. The purpose of this research was to evaluate how the type of reproductive conditioning affects the reproductive potential and nutritional quality of the progeny. Both methods were successful at inducing the necessary maturity for reproduction, obtaining viable gametes and larvae. On the other hand, it was observed that in the natural environment, the oocytes and D larvae reached a greater size and nutritional value, being the most significant differences with (p < 0.05): the size of the D larvae reached figures of 95.8 ± 3.1 $\mathsf{\mu }\mathrm{m}$ and 91.2 ± 2.7 $\mathsf{\mu }\mathrm{m}$ in the environment and hatchery, respectively; the lipid content in dry mass was 25.2 ± 3.1 mg g${}^{-1}$ and 13.5 ± 1.9 mg g${}^{-1}$ for the natural environment and hatchery, respectively. Although quality indicators in hatcheries were slightly lower compared to the natural environment, the possibility of conditioning A. purpuratus broodstock independently of environmental variability highlights the importance of further optimizing broodstock conditioning aspects in hatcheries that would allow more predictable and sustainable production.

This article describes a novel method for the multi-step forecasting of PM2.5 time series based on weighted averages and polynomial interpolation. Multi-step prediction models enable decision makers to build an understanding of longer future terms than the one-step-ahead prediction models, allowing for more timely decision-making. As the cases for this study, hourly data from three environmental monitoring stations from Ilo City in Southern Peru were selected. The results show average RMSEs of between 1.60 and 9.40 ug/m³ and average MAPEs of between 17.69% and 28.91%. Comparing the results with those derived using the presently implemented benchmark models (such as LSTM, BiLSTM, GRU, BiGRU, and LSTM-ATT) in different prediction horizons, in the majority of environmental monitoring stations, the proposed model outperformed them by between 2.40% and 17.49% in terms of the average MAPE derived. It is concluded that the proposed model constitutes a good alternative for multi-step PM2.5 time series forecasting, presenting similar and superior results to the benchmark models. Aside from the good results, one of the main advantages of the proposed model is that it requires fewer data in comparison with the benchmark models.

The agro-industrial sector faces significant challenges in product classification, which directly affect product quality, production efficiency and food safety. This paper proposes a machine learning model that correctly identifies the different attributes of Persea americana. For this, an automatic agro-industrial plant was implemented following industrial standards where advanced image processing techniques were used on a dataset of 346 images for training and 146 images for testing, with three deep convolutional neural networks with improved training strategies and advanced validation techniques including True Skill Statistic (TSS), Cohen’s Kappa (K), Threat Score (TS), Heidke Skill Score (HSS) and Probability of Error (Pe). The results showed that the DenseNet model outperforms other state-of-the-art models in accuracy, reaching an F1 score of 99.27%, ResNet 50 reached 99.26% and EfficientNet B4 reached 99.19%, also in the validation phase TSS, K, TS and HSS for all models were higher than 0.98 while the Pe index was higher than 0.55. It is concluded that the DenseNet model is shown to be the effective and reliable technique for the classification of Persea Americana. These promising results open new possibilities for the implementation of machine learning in the agri-food industry. For future research, the possibility of expanding the data set and extending the application of this model to other varieties of fruits is proposed.

This study proposes the SS-XceVNet meta-architecture, which integrates Xception and VGG16 to detect structural peeling in rigid pavements in an automated manner, thereby reducing the reliance on large, labeled datasets. With increasing traffic and exposure to environmental factors, failures in rigid pavement joints and corners are becoming increasingly frequent, making manual inspections time consuming, dangerous, and subjective. To address this problem, a method was developed in four phases. First, photogrammetry and data augmentation techniques are used to collect and increase the variety of images. Self-supervised learning was then applied to unlabeled data to reduce reliance on manual annotations. SS-XceVNet was then trained on labeled data, combining the robustness of Xception and VGG16 to refine the accuracy. Finally, the results were compared with those of the XceVNet-based model. Evaluations showed that SS-XceVNet achieved 80.43% accuracy, surpassing 47.83% of the base model. This underscores the usefulness of self-supervised learning and integration of advanced architectures in pavement failure detection. In conclusion, this approach improves efficiency in the functional evaluation of road infrastructures while reducing costs and occupational risks, laying the foundation for future research that contributes to the implementation of proactive maintenance systems. Automated detection thus strengthens the quality control of pavements, favoring road safety, and prolonging the useful life of roads.