Currently, destructive methods are often used to measure the quality parameters of agricultural products. These methods are often complex, time consuming and costly. Recently, studying to find a solution to the disadvantages of destructive methods has become a major challenge for researchers. Non-destructive methods can be useful for the rapid detection of the quality parameters of agricultural products. In this study, hyperspectral imaging was used to evaluate the non-destructive quality parameters of Red Delicious (Red Delicious) and Golden Delicious (Golden Delicious) apples, including pH, soluble solids content (SSC), titratable acid (TA) and total phenol (TP). In order to predict the quality characteristics of apples, the partial least squares (PLS) method with different pre-processing was used. The developed models were evaluated using the root mean square parameters of RMSECV validation error, correlation coefficient (R_cv) and standard deviation ratio (SDR). The results showed that in Red Delicious, for pH, TA, SSC and TP the best forecasting methods were SNV, SNV, MSC and normalized pre-processing with the regression coefficient values of 0.9919, 0.9939, 0.9909 and 0.9899, respectively. In Golden Delicious (Golden Delicious), for pH, TA, SSC and TP, the first derivative, (smoothing and second derivative), normalize (and SNV and normalize) preprocessors were selected as the best prediction models, with values of 0.9989, 0.9989, 0.9999 and 0.9989, respectively. The results related to an artificial neural network also showed that in hyperspectral imaging, the best state of the feed-forward network structure with the LM training algorithm was R = 0.93, Performance = 0.005 and RMSE = 0.03 in 325 inputs, 5 outputs and 2 hidden layers. The results showed that hyperspectral imaging has different predictive capabilities for the qualitative characteristics studied in this study with high accuracy.

Accurately determining the nutritional status of plants can prevent many diseases caused by fertilizer disorders. Leaf analysis is one of the most used methods for this purpose. However, in order to get a more accurate result, disorders must be identified before symptoms appear. Therefore, this study aims to identify leaves with excessive nitrogen using one-dimensional convolutional neural networks (1D-CNN) on a dataset of spectral data using the Keras library. Seeds of cucumber were planted in several pots and, after growing the plants, they were divided into different classes of control (without excess nitrogen), N_30% (excess application of nitrogen fertilizer by 30%), N_60% (60% overdose), and N_90% (90% overdose). Hyperspectral data of the samples in the 400–1100 nm range were captured using a hyperspectral camera. The actual amount of nitrogen for each leaf was measured using the Kjeldahl method. Since there were statistically significant differences between the classes, an individual prediction model was designed for each class based on the 1D-CNN algorithm. The main innovation of the present research resides in the application of separate prediction models for each class, and the design of the proposed 1D-CNN regression model. The results showed that the coefficient of determination and the mean squared error for the classes N_30%, N_60% and N_90% were 0.962, 0.0005; 0.968, 0.0003; and 0.967, 0.0007, respectively. Therefore, the proposed method can be effectively used to detect over-application of nitrogen fertilizers in plants.

Today, as fossil fuels are depleted, renewable energy must be used to meet the needs of human beings. One of the renewable energy sources is undoubtedly the solar–geothermal power plant. In this paper, the conventional and advanced, exergo-environmental and exergo-economic analysis of a geothermal–solar hybrid power plant (SGHPP) based on an organic Rankin cycle (ORC) cycle is investigated. In this regard, at first, a conventional analysis was conducted on a standalone geothermal cycle (first mode), as well as a hybrid solar–geothermal cycle (second mode). The results of exergy destruction for simulating the standalone geothermal cycle showed that the ORC turbine with 1050 kW had the highest exergy destruction that was 38% of the total share of destruction. Then, the ORC condenser with 26% of the total share of exergy destruction was in second place. In the hybrid geothermal–solar cycle, the solar panel had the highest environmental impact and about 56% of the total share of exergy destruction. The ORC turbine had about 9% of all exergy destruction. The results of the advanced analysis of exergy in the standalone geothermal cycle showed that the avoidable exergy destruction of the condenser was the highest. In the hybrid geothermal–solar cycle, the solar panel, steam economizer and steam evaporator were ranked first to third from an avoidable exergy destruction perspective. The avoidable exergo-economic destruction of the evaporator and pump were higher than the other components. The hybrid geothermal–solar cycle, steam economizer, solar pane and steam evaporator were ranked first to third, respectively, and they could be modified. The avoidable exergo-environmental destruction of the ORC turbine and the ORC pump were the highest, respectively. In the hybrid geothermal–solar cycle, steam economizers, solar panel and steam evaporators had the highest avoidable exergy destruction, respectively. For the standalone geothermal cycle, the total endogenous exergy destruction and exogenous exergy destruction was 83.61% and 16.39%. Moreover, from an exergo-economic perspective, 89% of the total destruction rate was endogenous and 11% was exogenous. From an exergo-environmental perspective, 88.73% of the destruction rate was endogenous and 11.27% was exogenous. For the hybrid geothermal–solar cycle, the total endogenous and exogenous exergy destruction was 75.08% and 24.92%, respectively. Moreover, 81.82% of the exergo-economic destruction rate was endogenous and 18.82% was exogenous. From an exergo-environmental perspective, 81.19% of the exergy destruction was endogenous and 18.81% was exogenous.

In this study, artificial neural networks (ANNs) were used to predict the draft force of a rigid tine chisel cultivator. The factorial experiment based on the randomized complete block design (RCBD) was used to obtain the required data and to determine the factors affecting the draft force. The draft force of the chisel cultivator was measured using a three-point hitch dynamometer and data were collected using a DT800 datalogger. A recurrent back-propagation multilayer network was selected to predict the draft force of the cultivator. The gradient descent algorithm with momentum, Levenberg–Marquardt algorithm, and scaled conjugate gradient descent algorithm were used for network training. The tangent sigmoid transfer function was the activation functions in the layers. The draft force was predicted based on the tillage depth, soil moisture content, soil cone index, and forward speed. The results showed that the developed ANNs with two hidden layers (24 and 26 neurons in the first and second layers, respectively) with the use of the scaled conjugate gradient descent algorithm outperformed the networks developed with other algorithms. The average simulation accuracy and the correlation coefficient for the prediction of draft force of a chisel cultivator were 99.83% and 0.9445, respectively. The linear regression model had a much lower accuracy and correlation coefficient for predicting the draft force compared to the ANNs.

Non-destructive assessment of the physicochemical properties of food products, especially fruits, makes it possible to examine the internal quality without any damage. This is applicable at different stages of fruit growth, harvesting stage, and storage as well as at the market stage. In this regard, the present study aimed to estimate the total chlorophyll content using three types of data: color data, spectral data, and spectral data related to the most effective wavelengths. The most important steps of the proposed algorithms include extracting spectral and color data from each sample of Fuji cultivar apple, selecting the most effective wavelengths at the range of 660–720 nm using hybrid artificial neural network–particle swarm optimization (ANN-PSO), non-destructive assessment of the chemical property of total chlorophyll content based on color data, and spectral data using hybrid artificial neural network-Imperialist competitive algorithm (ANN-ICA). In order to assess the reliability of the hybrid ANN-ICA, 1000 iterations were performed after selecting the optimal structure of the artificial neural network. According to the results, in the best training mode and using spectral data and the most effective wavelength, total chlorophyll content was predicted with the R2 and RMSE of 0.991 and 0.0035, 0.997 and 0.001, 0.997 and 0.0006, respectively.

La discalculia es un trastorno del aprendizaje que afecta la habilidad de una persona para comprender y utilizar conceptos matemáticos. En las últimas décadas, las neurociencias y las tecnologías de la información han desempeñado un papel fundamental en el estudio y comprensión de la discalculia, permitiendo avances significativos en la identificación y tratamiento de este trastorno. Las neurociencias han proporcionado una visión profunda de cómo funciona el cerebro de las personas con discalculia. Mediante técnicas de imagen cerebral, como la resonancia magnética funcional, los investigadores pueden observar diferencias en la activación de áreas cerebrales clave durante tareas matemáticas. Esto ha revelado que las personas con discalculia pueden tener alteraciones en áreas relacionadas con el procesamiento numérico, la memoria de trabajo y la representación simbólica de los números. Por otro lado, las tecnologías de la información, han permitido el desarrollo de herramientas y programas de entrenamiento específicos para abordar la discalculia. Aplicaciones, juegos y software diseñados para adaptarse a las necesidades individuales de estudiantes con discalculia pueden mejorar sus habilidades matemáticas. Además, las tecnologías computarizadas de asistencia, como lectores de pantalla y programas de voz a texto, hacen que el contenido matemático sea más accesible para quienes tienen dificultades con la lectura y escritura.