In line with the optimal management to water consumption, the usage of irrigation systems equipped with water cleanup systems increased the acceptance of these systems. In this research, a smart filter equipped with automatic self-cleaning technology was constructed. The experimental of this research in the form of a factorial test based on a completely randomized design with three factors working pressure, water quality and the diameter of filter openings with three repetitions in controlled laboratory conditions. The amount of water consumed will be higher for samples containing large amounts of impurities and filters with larger aperture diameters, and using filters with smaller aperture diameters and considering low operating pressure can reduce the amount of water consumed for washing. The results showed that the best quality of purified water (96.3%) and the lowest amount of water used for washing occurred (21.2 L) at a working pressure of 100 kPa, although this has increased the number of filter washing times. Setting the washing process at the beginning of the first point of pressure deviation causes a high amount of water to be purified in each cycle. Setting up the working pressure unit of the smart filter control unit within 100 kPa and using a net with an opening diameter of 0.1 mm reduces the amount of water used to wash (79%) and increases the quality of the treated water (90%), which will allow the use of waters containing large amounts of suspended soluble solid materials.

In determining the mass flow rate of bulk materials such as grains that pass through closed channels such as pipes, the online measurement of the volume ratio of solid materials and their distribution in the pipe cross-section is of particular importance. Considering the features of conventional methods such as be creating obstacles in the passage of materials (permeability) and low accuracy, in this research the non-contact tomography method of electrical capacitance was investigated for monitoring the flow of solid materials. In this method, to measure the amount of material and its density, it uses the measurement of the dielectric properties of the material inside the pipe. The developed electro-capacitance tomography system has 8 main electrodes and 16 secondary electrodes, anti-noise guards and a transmitter and receiver circuit, which was installed on a non-conductive pipe with a diameter of 20 cm. The main problem in the performance of the electro-capacitance tomography is the noise and lack of optimal image reconstruction with the conventional LBP algorithm. In this study, the performance of Tikhonov algorithm was compared with the conventional LBP algorithm. In this research, by using different guards, the noise level of the system was reduced so that the signal-to-noise ratio reached 56.09 dB. The results of the comparison of two algorithms showed that the Tikhonov algorithm has a good behavior in reconstruction of a tomogram of the wheat mass next to the pipe walls compared to the LBP algorithm, and except for the condition that the pipe is completely full, in other filling patterns of the pipe, has a better performance.

Fruit leathers are nutritious products that are made by dehydrating a thin layer of fruit puree or juice under specific conditions. The aim of this study was to investigate the effect of various plant components (pistachio, walnut, sesame, hemp and moringa leaves) on the physicochemical, sensory and antioxidant properties of plum leather. The amounts of 5% of these components were added to the plum leather formulation and the pH, total acidity, vitamin C content, color indices, mechanical property (rupture force), phenolic compounds, antioxidant activity, and sensorial properties of these treatments were compared with control. The results showed that the lowest pH (3.0) and the highest acidity (4.2% w/w) and vitamin C content (210 mg/100 g) were related to the control. Addition of different plant components increased the pH and decreased total acidity. Adding these components to plum leather increased the phenolic compounds and antioxidant activity of samples and the highest amount of phenolic compounds (1024 mg/100 g) and anti‌radical activity (52%) were observed in treatments with sesame powder. The highest amount of rupture force was related to the control (25.4 N). Other treatments did not show a significant difference in terms of rupture force (P> 0.05). In terms of sensorial characteristics, the highest overall acceptance was obtained by control and the samples containing pistachio and moringa leaves. Addition of these components decreased the brightness (L *) and yellowing (b *) indices and increased a * index compared to the control. In general, the results of this study showed that the addition of plant components, in addition to creating new leather formulations, increases their antioxidant properties and nutritional values.

Chicken nugget is a popular processed form of chicken meat. Nugget’s special flavor and color are interesting for consumers. Healthy meat is an important quality factor of nuggets. In the production process, the nugget is deeply fried for 1 minute at 185 °C. So, oil quality has an important effect on the final quality and healthy aspects. In this research, for the first time, the meat and oil quality used in chicken nuggets was checked using automatic and non-destructive techniques including an electronic nose (E-nose) and machine vision system. Meat health (healthy and spoiled) and consumed oil (healthy and burnt) were considered quality indicators. After frying, the excess oil was removed, and then the samples were subjected to machine vision and electronic nose systems for quality evaluations. In this research, the optimum of important parameters in the E-nose and the required times for sensing and removing the odor were determined. Also, the sensors' response was investigated. Principal component analysis (PCA), linear discriminant analysis (LDA) and artificial neural network (ANN) were used to analyze and classify the data. The accuracy results of both machine vision and electronic nose systems were similar and healthy meat and healthy oil classes were detected with up to 90 % accuracy. The evaluation results of the E-nose were similar to the machine vision system. Therefore, the combination of the two methods can be used in the measurement of color and smell characteristics.

A chicken meat processing systems are a primary source of valuable materials because of the considerable amount of waste they generate. Among the most effective methods for managing and converting waste into higher-value materials is the adoption of a circular bioeconomy strategy within biorefineries. this study examines two biorefineries that utilize chicken waste. In the initial pathway, chicken meat and sound insulation from chicken feathers are manufactured while the second pathway also involves the production of chicken meat, sound insulation, as well as biodiesel and glycerol from chicken fat waste. In this research, Simapro software and ReCiPe method were used to evaluate the life cycle assessment. The findings from this research indicated that the second pathway offers superior environmental performance when compared to the first pathway. Based on the findings, the second pathway presents a decrease of 21.92% in the overall environmental impact caused by producing chicken meat when compared to the first pathway. Following the first pathway, the production of one ton of chicken meat can leads to damages of 6.83E-03 DALY to human health, 6.81E-05 species.yr to ecosystems, and $101 to resources. Following the second pathway, production leads to a reduction of 23.26% in human health damage, 24.48% in ecosystem damage, and 17.76% in resource depletion. As such, moving toward production along the second pathway, i.e. diverse materials production, represents a promising strategy for managing the existing waste and reducing the environmental damage caused by chicken meat production.

Proper management of post-harvest systems is one of the most crucial steps in reducing agricultural losses and waste. This study was aimed to find the effect of forced air pre-cooling of dill using a window air conditioner on its shelf life. For this purpose, a metal frame with dimensions of 2×1×1 m was covered with a polyethylene foam sheet and a window air conditioner (after removing its thermostat) equipped with a controller was connected to one end of this chamber. In the next step, bunches of dill without packaging and the cardboard packaging with polyethylene liner were placed in three different conditions, including without pre-cooling and keeping at ambient temperature, with pre-cooling and keeping at ambient temperature, with pre-cooling and keeping under refrigeration. The results showed that during 4 days of storage, bunches of dill kept at ambient temperature had more total loss (50-60%) but after that, packaging caused severe loss (100%) due to moisture accumulation. If cold storage and packaging was ignored after pre-cooling, total loss would increase swiftly. For this reason pre-cooled dill with proper packaging and refrigeration showed the least amount of total loss (8.95±1.03%), physiological water loss (5.16±1.78%), pH (6.16±0.16), a*(-20.29±1.45) and the highest amount of moisture content (87.60±3.76), hardness (764±50 N), ascorbic acid (2.74±0.05 mg. g-1 of dill dry weight basis), total acceptance (4.1±0.65), L* (43.44±6.39), and b* (20.09±4.31).

Maintaining the well-being of individuals is greatly influenced by a healthy lifestyle and balanced diet. The identification and segmentation of food items can be improved by utilizing a mobile-based system in this era of rapid lifestyle changes and technology. This article introduces a novel system that, upon receiving input images, detects and segmentation the food items within the images. The system utilizes deep learning techniques and models, employing the YOLO algorithm. By incorporating regression-based simple methods, the system achieves the capability to detect and categorize food items in a single pass through the network, aiming to enhance accuracy and speed in the detection process. YOLOv7 was employed for food detection and YOLOv5, YOLOv7, and YOLOv8 was utilized for image segmentation. Based on the results, the accuracy, recall, and average precision values for YOLOv7 were 0.844, 0.924, and 0.932, respectively. Furthermore, the instance segmentation performance of YOLOv7 outperformed YOLOv5 and YOLOv8, with precision, recall, and mean average precision values of 0.959, 0.943, and 0.906, respectively. These findings underscore the high accuracy in detecting Iranian foods and the remarkable speed and precision in food image segmentation attainable through advanced deep-learning algorithms. Consequently, this study establishes that accurate detection of Iranian foods can be accomplished through the utilization of sophisticated deep-learning techniques. This research focuses on promoting a healthy lifestyle through intelligent technology and novel deep learning algorithms in Iran.

One of the indicators of sustainable development in rural and agricultural fields is the animal husbandry industry. This research presents the method for scientific and appropriate planning for improving and developing livestock production to increase the productivity of livestock production and related industries in order to use the existing potentials, abilities, and capacities optimally. The statistical population consisted of three groups of experts from the Deputy of Livestock Affairs of the Agricultural Jihad Organization, experts from the Animal Science Research Department of the Agricultural Research Center, and representatives of active private sector organizations in Lorestan province. The collection tool was a closed questionnaire consisting of 70 questions prepared in 7 indexes. The results of the ranking of factors and indicators using the TOPSIS technique showed that the hands of the process of supplying inputs and sales and upgrading the activity capacity are the priority and hands of upstream documents, sustainable development plans, mechanisms, perspectives and strategies, and considerations and social requirements and institutions were placed in the following preferences, respectively. By investigating all the sustainable development indicators of the province, Borujerd city, with a ratio of closeness to the ideal option (A*) of 0.79, has won the first rank among the cities of the area. Rumeshgan, Poldokhtar, Azna, Selseleh, Delfan, Chegani, Khorramabad, Aligodarz cities and Durood with the ratio of closeness to the ideal option of 0.76, 0.55, 0.54, 0.53, 0.51, 0.5, 0.46, 0.33 and 0.32 were placed in the following ranks, respectively.

Determining the best extraction method to obtain high yield of essential oils from medicinal plants has been identified as one of the challenges in scientific research. The sequential ultrasound-microwave method is a new extraction method that in this research has been applied to extraction of essential oil from basil. In order to investigate the effect of this approach on enhancing extraction yield, the microwave method was examined as a base method. To optimize the extraction conditions of the sequential method, ultrasonic power, microwave power, and extraction time were determined as independent variables and essential oil yield as response. Experiments were modeled by central composite design (CCD) of response surface methodology (RSM). The highest yield (2.3% w/w) was obtained at 300 W of ultrasonic power, 600 W of microwave power, and 40 min of extraction time. While the base method yield was 0.74% w/w. The chemical composition analysis of the active compounds showed that the number of compounds in the sequential approach is higher than those in the microwave method. Furthermore, by comparing the antioxidant properties obtained by DPPH and FRAP methods, the sequential method was better in preserving the antioxidant properties of basil essential oil. Scanning electron microscopy images also showed more damage to the glands containing the active ingredients by the sequential process than the base method. Generally, the sequential ultrasound-microwave method can be introduced as an efficient method for the extraction of essential oils from Ocimum basilicum.

Abstract: Electrical impedance tomography (EIT) is a non-invasive imaging technique that measures properties of multiphase fluids such as particles distribution and volume concentration by injecting a current into a set of electrodes and reading voltages from the electrodes. A strategy of injection and signal measurement has an important role in the image reconstruction quality and measurement accuracy. In large phantoms with high-conductivity, conventional strategies such as adjacent are not able to measure the signal with suitable quality. Therefore, the purpose of this study is to construct and evaluate the EIT system under an innovative strategy for online determination of particles distribution and concentration of solid-liquid fluid in large phantoms. The sensors of this instrument consist of 16 circular electrodes. The liquid phase was water with known conductivity and solid phase was the bottle in different sizes and in three different situations. The results showed that the innovative strategy has the ability to recognize and differentiate the target in different dimensions and different positions. The signal-to-noise rate was 1.05 dB and the dynamic range of boundary potentials was 1600 mV. The sensitivity to the sides and near the electrodes was more than the sensitivity to the middle. In positions close to the electrodes, size error decreases in medium and large target. In the three sizes of the target, ringing has no negative effect on the reconstructed image quality. Therefore, it can be concluded that the innovative strategy has a desirable performance for determining the distribution of materials in the large phantom.