In this study, daily historical records from Mexican weather stations across the country were classified according to corresponding Moon phases at the time of rainfall occurrence: New Moon, Waxing Crescent, First Quarter, Waxing Gibbous, Full Moon, Waning Gibbous, Last Quarter, and Waning Crescent. Out of the 5839 Mexican weather stations analyzed, 2412 met the specified data quality standards, which included a historical daily record period ranging from 30 to 51 years (1960–2011) and a maximum tolerance of 20% missing data. Correlation behavior between Moon phases and historical cumulative rainfall in Mexico was identified at two levels: general and particular. At the general level, the total historical cumulative rainfall by Moon phase was quantified. At the particular level, the correlation patterns between the Moon phases and the highest and lowest historical cumulative rainfall were identified. The results showed that the historical cumulative rainfall was highest at 17.24% during the New Moon and lowest at about 10.01% on average during the Waxing Crescent, First Quarter, and Waning Crescent phases (with 9.64% as the lowest value). During the Waxing Gibbous, Full Moon, and Waning Gibbous phases, rainfall remained at average values of approximately 13.18%. At 89.09% of the weather stations, the rainiest Moon phase was New Moon, and at 56.05%, the least rainy was Waning Crescent. In a few geographical areas, there are clearly defined patterns, which is atypical, given that in other geographical areas, the patterns are typically not so evident. This work demonstrates remarkable and strong correlation behavior between Moon phases and historical cumulative rainfall in Mexico.

Agriculture is an area that currently benefits from the use of new technologies and techniques, such as artificial intelligence, to improve production in crop fields. Zacatecas is one of the states producing the most onions in the northeast region of Mexico. Identifying and determining vegetation, soil, and humidity zones could help solve problems such as irrigation demands or excesses, identify spaces with different levels of soil homogeneity, and estimate the yield or health of the crop. This study examines the application of artificial intelligence through the use of deep learning, specifically convolutional neural networks, to identify the patterns that can be found in a crop field, in this case, vegetation, soil, and humidity zones. To extract the mentioned patterns, the K-nearest neighbor algorithm was used to pre-process images taken using unmanned aerial vehicles and form a dataset composed of 3672 images of vegetation, soil, and humidity (1224 for each class). A total of six convolutional neural network models were used to identify and classify the patterns, namely Alexnet, DenseNet, VGG16, SqueezeNet, MobileNetV2, and Res-Net18. Each model was evaluated with the following validation metrics: accuracy, F1-score, precision, and recall. The results showed a variation in performance between 90% and almost 100%. Alexnet obtained the highest metrics with an accuracy of 99.92%, while MobileNetV2 had the lowest accuracy of 90.85%. Other models, such as DenseNet, VGG16, SqueezeNet, and ResNet18, showed an accuracy of between 92.02% and 98.78%. Furthermore, our study highlights the importance of adopting artificial intelligence in agriculture, particularly in the management of onion fields in Zacatecas, Mexico. The findings can help farmers and agronomists make more informed and efficient decisions, which can lead to greater production and sustainability in local agriculture.

Effective communication is crucial for mutual understanding and remains a significant challenge for Deaf and Hard-of-Hearing individuals. This study aims to establish the basis for creating a portable communication system that can translate Mexican Sign Language (MLS) to facilitate two-way communication for Deaf and Hard-of-Hearing individuals. The device achieves a high accuracy of 98% in recognizing individual signs and features real-time audio-to-text translation capabilities. The Random Forest algorithm demonstrated a recognition accuracy of 98.97% for each letter of the Mexican alphabet. The device maintained a confidence level of 98% under optimal lighting conditions but decreased to 83% in low-light environments. It successfully recognized signs at distances up to 100 cm, with notable performance in distinguishing visually similar signs. This study has established a foundational framework for creating a portable device that allows Deaf and Hard-of-Hearing individuals to communicate effectively. It proves the device's potential as a reliable tool for bidirectional communication and instills optimism about its potential impact.

This work explores the prediction of daily Global Horizontal Irradiance (GHI) patterns in the region of Zacatecas, Mexico, using a diverse range of predictive models, encompassing traditional regressors and advanced neural networks like Evolutionary Neural Architecture Search (ENAS), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Meta’s Prophet. This work addressing a notable gap in regional research, and aims to democratize access to accurate solar radiation forecasting methodologies. The evaluations carried out using the time series data obtained by Comisión Nacional del Agua (Conagua) covering the period from 2015 to 2018 reveal different performances of the model in different sky conditions, showcasing strengths in forecasting clear and partially cloudy days while encountering challenges with cloudy conditions. Overall, correlation coefficients (r) ranged between 0.55 and 0.72, with Root Mean Square Error % (RMSE %) values spanning from 20.05% to 20.54%, indicating moderate to good predictive accuracy. This study underscores the need for longer datasets to bolster future predictive capabilities. By democratizing access to these predictive tools, this research facilitates informed decision-making in renewable energy planning and sustainable development strategies tailored to the unique environmental dynamics of the region of Zacatecas and comparable regions.

The present work investigates the quality and the chemical effects of dehydration, using a novel dehydration system based on an electromagnetic induction and low pressures technique, comparing it with the thermo-solar drying system. High oleic sunflower seeds, which are an important oil seed crop, were used due to the fact that they have a special place in the food industry. The seed samples were exposed to electromagnetic induction and low pressures by 0.5 and 1 h, then several chemical characterizations were carried out, in the electrophoresis study, it was found that most proteins in the hull were degraded or denatured, some of them were lost during the time in the thermosolar dryer while in kernel keeps 94.9% of the concentration in control proteins. Otherwise, the electromagnetic induction dryer did not lose the most of proteins in the kernel keeping 99.1% in 0.5 h and 98.4% in 1 h, just degrading its concentration. Germination viability results did not show changes after 0.5 h in the electromagnetic fields, but they decreased in 1 h from 66 to 40% until the thermosolar method fell to 24% in 4 h, both analysis results change proportionally with the treatment time and moisture content and the amount of the oxygen.

One of the main concerns in precision agriculture (PA) is the growth of weeds within a crop field. Currently, to prevent the spread of weeds, automatic techniques and computational tools are used to help to identify, classify, and detect the different types of weeds found in agricultural fields. One of the technologies that can help us to process digital information gathered from the agricultural fields is high-performance computing (HPC); this technology has been adopted to carry out projects requiring extra processing and storage in order to execute tasks with a large computational cost. This paper shows the implementation of an HPC cluster (HPCC), in which image processing (IP) and analysis are executed using deep learning (DL) techniques, specifically, convolutional neural networks (CNNs) with the VGG16 and InceptionV3 models, to classify different weed species. The results show the great benefits of using high-performance computing clusters in PA, specifically for classifying images. To apply distributed computing within the HPCC, the Keras and Horovod frameworks were used to train the CNN models, obtaining the best time with the InceptionV3 model with a value of 37 min 55.193 s using six HPCC cores, obtaining an accuracy of 0.65 as a result.

One of the main challenges agricultural greenhouses face is accurately predicting environmental conditions to ensure optimal crop growth. However, the current prediction methods have limitations in handling large volumes of dynamic and nonlinear temporal data, which makes it difficult to make accurate early predictions. This paper aims to forecast a greenhouse’s internal temperature up to one hour in advance using supervised learning tools like Extreme Gradient Boosting (XGBoost) and Recurrent Neural Networks combined with Long-Short Term Memory (LSTM-RNN). The study uses the many-to-one configuration, with a sequence of three input elements and one output element. Significant improvements in the R2, RMSE, MAE, and MAPE metrics are observed by considering various combinations. In addition, Bayesian optimization is employed to find the best hyperparameters for each algorithm. The research uses a database of internal data such as temperature, humidity, and dew point and external data such as temperature, humidity, and solar radiation, splitting the data into the year’s four seasons and performing eight experiments according to the two algorithms and each season. The LSTM-RNN model produces the best results for the metrics in summer, achieving an R2 = 0.9994, RMSE = 0.2698, MAE = 0.1449, and MAPE = 0.0041, meeting the acceptability criterion of ±2 °C hysteresis.

The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on onion crops and the challenges presented throughout its phenological cycle. Unmanned aerial vehicles (UAVs) and digital image processing were used to monitor the crop and identify patterns such as humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 tons/ha. However, only 25.00 tons/ha were reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being medium-sized. Approximately 20% of the production was lost due to blight and unfavorable weather conditions.

Boosting by the world's context, several efforts have been maiden toward the digital transformation of Education. Technology-based active learning has become pivotal in pursuing a more flexible education system. This work presents the development of an Educational Mechatronics (EM) training system based on computer vision that performs as a positioning system for mobile robots in the 2D plane. The results show that the obtained precision, exactitude, and resolution of the EM training system are suitable for robotics applications comprising position, velocity, and acceleration variables. Moreover, an instructional design aligned with the EM conceptual framework, using the EM training system and a LEGO mobile robot, is presented to construct the mechatronic concept: line segment.

This article presents two procedures involving a maximal hyperconnected function and a hyperconnected lower leveling to segment the brain in a magnetic resonance imaging T1 weighted using new openings on a max-tree structure. The openings are hyperconnected and are viscous transformations. The first procedure considers finding the higher hyperconnected maximum by using an increasing criterion that plays a central role during segmentation. The second procedure utilizes hyperconnected lower leveling, which acts as a marker, controlling the reconstruction process into the mask. As a result, the proposal allows an efficient segmentation of the brain to be obtained. In total, 38 magnetic resonance T1-weighted images obtained from the Internet Brain Segmentation Repository are segmented. The Jaccard and Dice indices are computed, compared, and validated with the efficiency of the Brain Extraction Tool software and other algorithms provided in the literature.

One of the main problems in crops is the presence of pests. Traditionally, sticky yellow traps are used to detect pest insects, and they are then analyzed by a specialist to identify the pest insects present in the crop. To facilitate the identification, classification, and counting of these insects, it is possible to use digital image processing (DIP). This study aims to demonstrate that DIP is useful for extracting invariant characteristics of psyllids (Bactericera cockerelli), thrips (Thrips tabaci), whiteflies (Bemisia tabaci), potato flea beetles (Epitrix cucumeris), pepper weevils (Anthonomus eugenii), and aphids (Myzus persicae). The characteristics (e.g., area, eccentricity, and solidity) help classify insects. DIP includes a first stage that consists of improving the image by changing the levels of color intensity, applying morphological filters, and detecting objects of interest, and a second stage that consists of applying a transformation of invariant scales to extract characteristics of insects, independently of size or orientation. The results were compared with the data obtained from an entomologist, reaching up to 90% precision for the classification of these insects.

Breast cancer is one of the diseases of most profound concern, with the most prevalence worldwide, where early detections and diagnoses play the leading role against this disease achieved through imaging techniques such as mammography. Radiologists tend to have a high false positive rate for mammography diagnoses and an accuracy of around 82%. Currently, deep learning (DL) techniques have shown promising results in the early detection of breast cancer by generating computer-aided diagnosis (CAD) systems implementing convolutional neural networks (CNNs). This work focuses on applying, evaluating, and comparing the architectures: AlexNet, GoogLeNet, Resnet50, and Vgg19 to classify breast lesions after using transfer learning with fine-tuning and training the CNN with regions extracted from the MIAS and INbreast databases. We analyzed 14 classifiers, involving 4 classes as several researches have done it before, corresponding to benign and malignant microcalcifications and masses, and as our main contribution, we also added a 5th class for the normal tissue of the mammary parenchyma increasing the correct detection; in order to evaluate the architectures with a statistical analysis based on the received operational characteristics (ROC), the area under the curve (AUC), F1 Score, accuracy, precision, sensitivity, and specificity. We generate the best results with the CNN GoogLeNet trained with five classes on a balanced database with an AUC of 99.29%, F1 Score of 91.92%, the accuracy of 91.92%, precision of 92.15%, sensitivity of 91.70%, and specificity of 97.66%, concluding that GoogLeNet is optimal as a classifier in a CAD system to deal with breast cancer.

Convolutional neural networks (CNNs) and digital holographic interferometry (DHI) can be combined to improve the calculation efficiency and to simplify the procedures of many DHI applications. In DHI, for the measurements of concentration differences between liquid samples, two or more holograms are compared to find the difference phases among them, and then to estimate the concentration values. However, liquid samples with high concentration difference values are difficult to calculate using common phase unwrapping methods as they have high spatial frequencies. In this research, a new method to skip the phase unwrapping process in DHI, based on CNNs, is proposed. For this, images acquired by Guerrero-Mendez et al. (Metrology and Measurement Systems 24, 19–26, 2017) were used to train the CNN, and a multiple linear regression algorithm was fitted to estimate the concentration values for liquid samples. In addition, new images were recorded to evaluate the performance of the proposed method. The proposed method reached an accuracy of 0.0731%, and a precision of ±0.0645. The data demonstrated a high repeatability of 0.9986, with an operational range from 0.25 gL−1 to 1.5 gL−1. The proposed method was performed with liquid samples in a cylindrical glass.

Tomato plants are highly affected by diverse diseases. A timely and accurate diagnosis plays an important role to prevent the quality of crops. Recently, deep learning (DL), specifically convolutional neural networks (CNNs), have achieved extraordinary results in many applications, including the classification of plant diseases. This work focused on fine-tuning based on the comparison of the state-of-the-art architectures: AlexNet, GoogleNet, Inception V3, Residual Network (ResNet) 18, and ResNet 50. An evaluation of the comparison was finally performed. The dataset used for the experiments is contained by nine different classes of tomato diseases and a healthy class from PlantVillage. The models were evaluated through a multiclass statistical analysis based on accuracy, precision, sensitivity, specificity, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the GoogleNet technique, with 99.72% of AUC and 99.12% of sensitivity. It is possible to conclude that this significantly success rate makes the GoogleNet model a useful tool for farmers in helping to identify and protect tomatoes from the diseases mentioned.

Light stimulation and biofloc technology can be combined to improve the efficiency and sustainability of tilapia production. A 73-day pilot experiment was conducted to investigate the effect of colored light on growth rates and nutritional composition of the Nile tilapia fingerlings (Oreochromis niloticus) in biofloc systems. The effect of colored light on the nutritional composition of bioflocs as a food source for fish was measured. Three groups were illuminated in addition to natural sunlight with colored light using RGB light emitting diodes (LEDs) with peak wavelengths (λ) of 627.27 nm for red (R), 513.33 nm for green (G), and 451.67 nm for blue (B) light. LED light intensity was constant (0.832 mW / cm 2 ), and had an 18-h photoperiod of light per day throughout the study. The control group was illuminated only with natural sunlight (natural). Tilapia had an average initial weight of 0.242 g. There was a significant effect of colored light on tilapia growth and composition. The R group showed the best growth rate, highest survival, and highest lipid content. The B group showed homogeneous growth with the lowest growth rate and lipid content, but the highest protein level. On the other hand, the biofloc composition was influenced by the green light in the highest content of lipids, protein, and nitrogen-free extract.

Modeling and simulation of internal variables such as temperature and relative humidity are relevant for designing future climate control systems. In this paper, a mathematical model is proposed to predict the internal variables temperature and relative humidity (RH) of a growth chamber (GCH). Both variables are incorporated in a set of first-order differential equations, considering an energy-mass balance. The results of the model are compared and assessed in terms of the coefficients of determination (R2) and the root mean squared error (RMSE). The R2 and RMSE computed were R2 = 0.96, R2 = 0.94, RMSE = 0.98 °C, and RMSE = 1.08 °C, respectively, for the temperature during two consecutive weeks; and R2 = 0.83, R2 = 0.81, RMSE = 5.45%RH, and RMSE = 5.48%RH, respectively, for the relative humidity during the same period. Thanks to the passive systems used to control internal conditions, the growth chamber gives average differences between inside and outside of +0.34 °C for temperature, and +15.7%RH for humidity without any climate control system. Operating, the GCH proposed in this paper produces 3.5 kg of wet hydroponic green forage (HGF) for each kilogram of seed (corn or barley) harvested on average.

We present an interactive simulation software to help to teach and learn the holography concept. The education interface was developed in the Java platform. The Holographic Interface is a computer assisted learning that can be used in classrooms or for distance education. The binary holograms are numerically generated and reconstructed in the virtual optical laboratory. Several procedures are shown. The interface has been implemented with different options such as addition, subtraction, multiplexing and some properties of holography. Moreover, the software was designed to simultaneously visualize the 2D object, the generated hologram and the recovered image. We have taken into account the students' suggestions in this version in Java.

We present a novel method based on Digital Holographic Interferometry to detect slight physical variations of refractive index with high sensitivity in liquid substances. The technique is grounded in the measurement of a phase difference between two reconstructed wavefields. The optical system was tested using a series of sodium chloride (NaCl) solutions to detect a variation in its physical property such as concentration. A first hologram records a wavefront coming from the light scattered by a common cylindrical glass container filled with certain NaCl solution. Later, a second hologram is recorded when the solution mentioned above slightly changes its concentration. The difference between the phase maps obtained from the correlation from the two holograms will provide information about a refractive index variation, which is directly related to a concentration change. The achieved results have proven to be more accurate and faster to get than with other techniques. The process requires just a few special optical elements and is able to measure the three-dimensional distribution of the refractive index of a sample. This method can be extended to identify adulteration in liquids, measure the variation in refractive index in gaseous flames, apart from analyzing and visualizing the mechanical properties of a liquid sample.

We present an alternative method to detect and measure the concentration changes in liquid solutions. The method uses Digital Holographic Interferometry (DHI) and is based on measuring refractive index variations. The first hologram is recorded when a wavefront from light comes across an ordinary cylindrical glass container filled with a liquid solution. The second hologram is recorded after slight changing the liquid’s concentration. Differences in phase obtained from the correlation of the first hologram with the second one provide information about the refractive index variation, which is directly related to the changes in physical properties related to the concentration. The method can be used − with high sensitivity, accuracy, and speed − either to detect adulterations or to measure a slight change of concentration in the order of 0.001 moles which is equivalent to a difference of 0.003 g of sodium chloride in solutions. The method also enables to measure and calculate the phase difference among each pixel of two samples. This makes it possible to generate a global measurement of the phase difference of the entire sensed region.