When planning a solar energy conversion system, having sufficiently reliable values of the monthly average daily solar radiation (MADSR) on a horizontal surface is essential. Traditionally, estimates based on other climatological variables for which more information is available have been relied upon to compensate for the lack of direct solar radiation measurements. Solar radiation varies widely, which requires the creation of site-specific forecast models. By using artificial neural network (ANN) models or similar methods using historical datasets, the monthly average daily solar radiation can be easily assessed. To verify the validity of the established ANN model, a series of analyses was performed using the mean squared error, the coefficient of determination (R²), and the mean absolute error. The study used a dataset collected from nine weather stations in Saudi Arabia from 1985 to 2000. The input parameters for the ANN model were the maximum air relative humidity, latitude, the maximum ambient air temperature, longitude, the minimum ambient air temperature, the minimum air relative humidity, sunshine duration, location altitude, and the corresponding month. The R² for the whole test dataset was 0.8449. Furthermore, a sensitivity analysis using the established ANN model showed that site elevation (location altitude) had the most significant effect on MADSR on a horizontal surface, with a contribution value of 14.66%. The analysis results show that the ANN model accurately estimates MADSR on horizontal surfaces regardless of seasonal variations in weather conditions. Furthermore, this work is important not only for its contribution to the shape of information in solar radiation forecasting but also for establishing the practical application of ANNs in renewable energy management. The results of this work will help improve the utilization of solar energy and support sustainable energy efforts. Furthermore, the proposed ANN model is believed to be useful for predicting MADSR on horizontal surfaces in other locations in Saudi Arabia with similar climatic conditions to the study sites. Furthermore, the ANN approach may be functional to the basic strategy of a solar arrangement and is suitable for forecasting other meteorological data.

Mangoes are highly valued for their flavor and nutritional content, but their shelf life is limited due to quick ripening and susceptibility to microbial deterioration. The use of salicylic acid and melatonin as postharvest dipping treatments for mango fruits has the potential to significantly improve shelf life while retaining fruit quality. Salicylic acid modulates ethylene synthesis and stress reactions, whereas melatonin provides a strong antioxidant defense. These treatments might be used in postharvest management methods to minimize losses and improve mango marketability. The goal of this study is to look at the effects of salicylic acid and melatonin postharvest treatments on Keitt mango fruit in terms of physiochemical quality, enzyme activity, and ascorbic acid content during cold storage. Fruits were chilled at 5 °C ± 2 and 85 ± 2 percent humidity throughout the 2022–2023 seasons. The treatments were as follows: control (untreated fruits), salicylic acid (1 and 2 mM), and melatonin (200 and 400 mM). Data showed that dipping mango fruits in salicylic acid and melatonin under cold conditions decreased weight loss and fruit decay while improving physiochemical properties such as fruit firmness, total soluble solids (TSS%), total acidity, total sugars, enzyme activities, and ascorbic acid levels over time. In both seasons, dipping mango fruits in a melatonin solution at 200 mM followed by a salicylic acid solution at 1 mM produced the highest values for all examined parameters when compared to the control and other treatments. These findings indicate that postharvest administration of salicylic acid and melatonin can successfully increase the storability and quality of Keitt mangoes under refrigerated circumstances, providing a feasible technique for reducing postharvest losses and increasing marketability.

One of the major challenges for crop breeding scientists is climate change. Their task is to develop new crop varieties that can withstand this phenomenon. For this study, a new Egyptian paddy variety called Giza 183, which is designed to adapt to mitigate the effects of climate change, was chosen. We focused on examining the physical and engineering properties of this variety in order to design strategies for storage, handling, transportation, drying, parboiling, and processing equipment in rice mills. The goal was to minimize post-harvest losses during the milling process, thereby maximizing high-quality yields while reducing losses. The physical properties of the rice grains, such as the length, width, and thickness, were measured at an average moisture content of 13.7% ± 0.25% (wet basis). The results reveal that the mean values of length, width, and thickness averaged 7.50 mm, 3.18 mm, and 2.19 mm, respectively. Additionally, the geometric mean diameter, the equivalent mean diameter, surface area, arithmetic mean diameter, and volume were approximately 3.74 mm, 2.38 mm, 37.37 mm², 4.29 mm, and 28.23 mm³, respectively. The mean of sphericity was 49.9%, and the grain shape (length/width) was 2.19. The true density was measured at 1218.28 kgm⁻³, while the bulk density was 572.17 kgm⁻³. The porosity was found to be 53.03%. Furthermore, the milling production rates for brown rice, hull, white rice, and broken rice were determined to be 76.83%, 23.15%, 67.97%, and 17.36%, respectively. The average weight of one thousand grains was 25.49 g. A linear regression model for describing the mass of rough rice grain was investigated. The mass was estimated with the single variable of the grain aspect ratio (width/length) with a determination coefficient of 0.9908. Information gained from the current study will be useful in designing post-harvest processing and storage structures in rice processing industries.

The evaporative cooling system (ECS) is an energy-efficient and eco-friendly air-cooling technology that is very effective in dry climates, and the conventional method with cellulosic pads is widely used. However, because of the accumulation of dust and salts, these pads have a tendency to degrade quickly. This study aimed to examine the viability of using volcanic stone (Scoria) as an innovative material for evaporative cooling pads. The experiments were conducted in a wind tunnel (0.4 m × 0.6 m) with different pad thicknesses (d = 10 cm and 15 cm), water addition rates (m_w =1.6, 2.4, and 4 kg.min⁻¹.m⁻¹), and air speeds (v = 0.75, 1.25, and 1.75 m·s⁻¹). The results showed that the 10 cm thick pad consistently performed better than the 15 cm thick pad across all air speeds and water addition rates. The 10 cm thick pad achieved the highest cooling efficiency of 82% at a water addition rate of 2.4 L.min⁻¹.m⁻¹ and an air speed of 1.75 m·s⁻¹. In contrast, the cooling efficiency for the 15cm-t hick pad was 64% under the same conditions. The 10 cm thick pad consumed more water (1.8 to 2.8 kg·h⁻¹ compared to 1.0 and 2.4 kg·h⁻¹ for the 15 cm pad), as the ECS performance was directly associated with the amount of water used. Higher air speed led to a drop in pressure, which impacted fan performance. The pressure drops across the pads were between 10 and 13 Pa for an air speed of 1.75 m·s⁻¹. These results suggest that volcanic stone (Scoria) pads can provide effective cooling performance similar to that of commercial cellulosic pads but have added benefits of durability, less maintenance, and biological degradation resistance. The non-evaporative medium, especially the 10 cm thick Scoria pad, could be a more viable medium for evaporative cooling applications in arid areas.

Ozone treatment in the food and horticulture product capitalization sectors is widely acknowledged as completely safe for human use, in accordance with the most recent rules of the relevant authorities. Ozone-generating devices for research and education are known to allow the introduction of ozone gas for many uses, especially in food and agricultural applications. Despite their usefulness, their high cost prevents them from being widely available in research and educational institutions in underdeveloped nations, limiting practical training and the development of local applications to support the capacities of the food and agriculture sectors. In this study a device was constructed to generate ozone (O₃) using the high-voltage principal circuit. An Arduino board was used to accomplish the control operation. An MQ-131 ozone sensor was utilized to measure the ozone concentration with a measuring unit of (%); however, the detecting range of the MQ-131 sensor is 10~1000 ppb, so in the present study, a formula to covert the measuring units between ppm and (%) for the concentration of the generated ozone (Y) as (Y, %) = 0.0333 × (Y, ppm) was presented. Different ozone concentrations are generated by varying the high voltage level from 20 to 35 kV with an increment of 5 kV and flow rate variations of 1.5, 2, 2.5, and 3 L/min. It was found that ozone concentration increases with increasing applied high voltage and decreases with increasing oxygen flow rate at a fixed applied high voltage. This study uses experimental data in a multiple linear regression analysis to predict ozone concentration based on levels of high voltage and oxygen flow rate, with a coefficient of determination of 0.7686 using a testing dataset. The findings provide evidence of the viability of constructing an inexpensive ozone generator with inexpensive parts, thereby promoting sustainable technological advancement. Drawing from our research, we can highlight the educational value and cost-effective benefits of employing an ozone-generating device, which can be used to produce ozone for a variety of purposes.

For every tractor test carried out on a concrete road under defined conditions, the Nebraska Tractor Test Laboratory (NTTL) provides values of the specific volumetric fuel efficiency (SVFE) in unit of kWh/L). Because soil tillage is a highly energy-intensive process and the energy consumption of tillage operations is a significant component of a farm budget, there is a growing amount of attention being given to the examination of the SVFE for tillage operations. Nonetheless, the study of the tillage process and a scientific approach to the tillage process are becoming more and more dependent on scientific modeling. Therefore, in this study based on real-tillage field operation, an artificial neural network (ANN) model was built to predict SVFE. This study aimed to confirm that the ANN model could incorporate 10 inputs for prediction: initial soil moisture content, draft force, initial soil bulk density, sand, silt, and clay proportions in the soil tractor power, plow width, tillage depth, and tillage speed. The Qnet v2000, as an ANN simulation software, was employed for the simulation of the SVFE. In this regard, 20,000 runs of Qnet v2000 were completed for the training and testing stages. The anticipated results displayed that the determination coefficient (R²) was larger than 0.96; using the training dataset, R² was 0.982 and using the testing dataset, R² was 0.9741, indicating that the recognition of a full ANN model makes it likely to reply to essential enquiries that were previously unanswerable regarding the impact of working and soil conditions on the SVFE of a tractor–tillage implement system. Additionally, sensitivity analyses were completed to specify which modeled parameters were more sensitive to the factors using the obtained ANN model. According to the sensitivity analysis, SVFE was more affected by changes in the tillage speed (21.07%), silt content in the soil (15.56%), draft force (11.01%), and clay content in the soil (10.86%). Predicting SVFE can lead to more appropriate decisions on tractor–chisel plow combination management. Therefore, it is highly advisable to use the newly created ANN model to appropriately manage SVFE to reduce tractor–tillage implement energy dissipation. Additionally, suitable management of some variables, for example, tillage depth, tillage speed, and soil moisture content, can help enhance fuel consumption in the tractor–tillage implementation system.