In this study, the effects of tillage depth, forward speed and soil moisture content during the cultivator operation on the draft force, energy requirement, and soil disturbance were investigated using five types of cultivators. The experiments were performed in the factorial design based on the randomized complete block design (RCBD) with three replications in loamy sand soil. Different soil moisture contents (factor A) from 5 to 16% for dry soils and 17 to 35% for wet soils, forward speed of tractor (factor B) at four levels of 1.16, 1.61, 1.97, and 3.82 km/hand working depth (factor C) at two levels of 10 and 20 cm were selected. The analysis of variance results showed that the main effects on the draft force, energy requirement, and soil disturbance were significant. With increasing the forward speed, working depth, and blade width, the draft force, energy requirement, and soil disturbance significantly increased. As the soil moisture content increased, the amount of draft force decreased. The average maximum draft force and energy requirement are related to the crescent cultivator and the lowest ones to the cultivator with a sweep blade. The maximum amounts of draft force and energy requirement at the speed of 3.82 km/h were 296.702 N and 0.03708 MJ in the dry conditions, respectively. The average maximum draft force and energy requirement are related to the crescent cultivator and the lowest ones to the cultivator with a sweep blade. The average maximum draft force and energy requirement in dry soil at 10–20 cm depth were 313.534 N and 0.039204 MJ, respectively, and the lowest values were 189 N and 0.019512 MJ in wet soil at the depth of 0–10 cm, respectively. The highest mean value of the area obtained from the profiles was 254.62 cm² related to the dry conditions and forward speed of 3.82 km/h, and the lowest mean value of the area obtained in the wet conditions was 199.6 cm² at the forward speed of 1.16 km/h. The highest average area obtained from the profiles was observed in the dry conditions for C4 as 434.813 cm² and the lowest one was 57.94 cm² in the wet conditions for the cultivator with a chisel blade and L-shaped shank. The highest average area created by cultivators at the 10–20 cm depth in the dry conditions was 332.875 cm² and the lowest one at the 0–10 cm depth in the wet conditions was 123.55 cm². The results of this study can help the designers and manufacturers of agricultural machinery to effectively design and manufacture the machinery with optimum draft and energy requirements.

This study aims to investigate the effect of different drying methods on the thermal, qualitative, and antioxidant properties and pH of terebinth. To perform the experiments in this study, the hot air (HA), infrared (IR), microwave (MW), hot air–infrared (IR–HA), and hot air–microwave drying (MW–HA) methods were considered. The results showed that the minimum drying time was obtained by the hot air–microwave (MW–HA) method. However, the lowest specific energy consumption (SEC) and the highest energy efficiency (η_e) were obtained by the MW method. Considering the color criteria, the best method was obtained by the MW–HA method. The highest amount of rehydration ratio (RR) and the lowest shrinkage (S_b) of the dried terebinth samples were obtained using the MW dryer compared with other drying methods. The MW and MW–HA methods resulted in higher contents of total phenol content (TPC), total flavonoid content (TFC), and antioxidant capacity (AntiOX) than other methods. According to the results of this study, the most effective drying method for terebinth was determined to be the MW and MW-HA methods.

The concept of Quality of Aquaculture Services (QoAS) is inspired by the Quality of Service (QoS) principle, originally developed in the field of networks and telecommunications, where it refers to the ability to guarantee the quality, availability, and priority of service in a communications system. Adapted to the aquaculture context, QoAS is fundamental to maximising the benefits of Integrated Multi-Trophic Aquaculture (IMTA). IMTA has emerged as a sustainable approach to meet the growing global demand for aquatic food products by combining species from different trophic levels in a single system, optimising resource use, improving environmental performance, and diversifying production. However, ensuring QoAS in these complex systems requires the implementation of advanced technologies to monitor, manage, and optimise every aspect of the aquaculture process. This article presents a comprehensive review of technologies applied at IMTA, focusing on IoT-based monitoring systems, resource management algorithms, water recirculation technologies, intelligent automation, biosecurity, and data management platforms. Our review finds that IoT and automation-based solutions significantly enhance real-time monitoring, increasing operational efficiency and environmental sustainability. Key challenges identified include integration complexity, high costs, and technical expertise requirements, but the ongoing development of modular, user-friendly solutions indicates a promising trajectory. This review highlights the transformative role of technological innovation in IMTA, providing a foundation for future research and advancements in QoAS management in aquaculture.

Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network–imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network–harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the K-nearest-neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network–biogeography-based optimization (ANN-BBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a t-test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen.

La Inteligencia Artificial (IA) ha emergido como una herramienta poderosa en campos científicos y sociales, mejorando la educación y la investigación al ofrecer nuevas formas de enseñanza y acceso al conocimiento. Sin embargo, su adopción plantea desafíos éticos y morales significativos, como es la necesidad de abordar la transparencia, los sesgos y la discriminación, así como la privacidad de los datos, la rendición de cuentas y el impacto socioeconómico. Y, no sólo ello, sino también, aspectos como la responsabilidad de las empresas desarrolladoras, los sesgos algorítmicos, la toma de decisiones automatizada, la ética en la investigación, la regulación y la gobernanza, el desempleo tecnológico, la seguridad cibernética así como la integración social y ética en las aplicaciones. En este sentido reviste una especial importancia contribuir con la discusión sobre el uso académico y de investigación del contenido que, como respuesta, la IA produce, crea o simula para que, debido a ello, discernir en qué medida o bajo qué parámetros existe la noción de plagio cuando se emplean estos productos.

The transformation of traditional energy systems into smart energy systems has ushered in an era of efficiency, sustainability and technological growth. In this paper, we propose a new definition for “Quality of Energy Service” that focuses on ensuring optimal power-supply quality, encompassing factors such as availability, speed (i.e., the time to restore or adjust supply following interruptions or load changes) and reliability of supply. We explore the integration of advanced algorithms specifically tailored to enhance the Quality of Energy Services. By concentrating on key aspects—reliability, availability and operational efficiency—the study reviews how various algorithmic approaches, from machine learning models to classical optimisation techniques, can significantly improve power grid management. These algorithms are evaluated for their potential to optimise load distribution, predict system failures and manage real-time adjustments in power supply, thereby ensuring higher service quality and grid stability. The findings aim to provide actionable insights for policymakers, engineers and industry stakeholders seeking to advance smart grid technologies and meet global energy standards. Furthermore, we present a case study to demonstrate how these models can be integrated to optimise grid management, forecast energy demand and enhance operational efficiency. We employ multiple machine learning models—including Random Forest, XGBoost version 1.6.1 and Long Short-Term Memory (LSTM) networks—to predict future energy demand. These models are then combined within an ensemble learning framework to improve both the accuracy and robustness of the forecasts. Our ensemble framework not only predicts energy consumption but also optimises battery storage utilisation, ensuring continuous energy availability and reducing reliance on external energy sources. The proposed stacking ensemble achieved a forecasting accuracy of 99.06%, with a Mean Absolute Percentage Error (MAPE) of 0.9364% and a Coefficient of Determination (R²) of 0.998345, highlighting its superior performance compared to each individual base model.