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Microrobotics has opened new horizons for various applications, especially in medicine. However, it also witnessed challenges in achieving maximum optimal performance. One key challenge is the intelligent, autonomous, and precise navigation control of microrobots in fluid environments. The intelligence and autonomy in microrobot control, without the need for prior knowledge of the entire system, can offer significant opportunities in scenarios where their models are unavailable. In this study, two control systems based on model-free deep reinforcement learning were implemented to control the movement of a disk-shaped magnetic microrobot in a real-world environment. The training and results of an off-policy SAC algorithm and an on-policy TRPO algorithm revealed that the microrobot successfully learned the optimal path to reach random target positions. During training, the TRPO exhibited a higher sample efficiency and greater stability. The TRPO and SAC showed 100% and 97.5% success rates in reaching the targets in the evaluation phase, respectively. These findings offer basic insights into achieving intelligent and autonomous navigation control for microrobots to advance their capabilities for various applications.

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Potato chips are popular high-consuming ready-to-eat meals in all of the world which specially attract a lot of attention from youth and children. Reducing oil absorption and improving the quality of chips are major undertakings within the industry. This research aimed to find the best ultrasonic bath-based method by investigating the optimal ultrasonic pre-treatment and developing an ultrasound (US) assisted frying system (UAFS) to reduce the oil absorption of potato chips while maintaining an acceptable quality. Through this technique, the potato chips get sonicated during deep frying in hot oil. US-pretreatment at temperatures of 25 °C and 73 °C, along with US-assisted frying, resulted in the minimal amount of oil which may be due to the US creating potential pores during the pre-treatment phase, which then expand further during the subsequent sonication stage. UAFS in combination with US-pretreatment produced more crispy chips due to the fact that the texture of potato slices becomes more porous. UAFS resulted in a decrease in the moisture content of the fried chips attributed to an increase in the effective diffusion coefficient and mass. Pretreating the chips at 73 °C significantly reduce the color change producing brighter product by inactivation of enzymes such as polyphenol oxidase. Finally, the result of TOPSIS optimization based on potato chips properties confirms that US-pretreatment in 73 °C brine followed by frying using UAFS is the best approach. Scanning electron microscope (SEM) images of potato chips also support this issue.

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The study proposes a novel ultrasound-assisted freezing method for button mushrooms, which combines probe-type ultrasonication and immersion freezing. The effects of power ultrasound in both continuous and pulse modes (at five levels of 50, 100, 200, 300, and 400 W) on the freezing process and quality attributes of frozen mushrooms were investigated. Results showed that ultrasound-assisted freezing significantly reduced freezing time compared to immersion freezing, potentially due to the formation of abundant cavitation bubbles that enhanced heat and mass transfer rates. The lowest weight loss was achieved by the continuous mode of 200 W ultrasound (UC200), which may be attributed to the formation of smaller ice crystals during ultrasonication. Ultrasound also prevented acidification and resulted in the retention of the samples' pH and higher total solid solution than immersion freezing. The optimal ultrasound power and duty cycle for different freezing outcomes were determined using the TOPSIS method. UC200 was found to be the best treatment for total freezing time and minimizing weight loss, while a combination of UC200 and UC400 was optimal for gumminess and chewiness. The proposed ultrasound-assisted freezing method shows promise as an environmentally friendly and safe technique for commercial use in the future.

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High intensity ultrasound (HIUS) has a wide range of applications in different sectors of food processing. It is a promising and emerging technology demonstrating the potential to promote food processes without or at least damage to the quality of products. Among the processes of the meat industry, freezing, thawing, cooking and fermentation are very sensitive and important, because they have significant effects on product quality and are also very energy and time consuming. This review paper provides an interpretation of high intensity ultrasound (HIUS) applications, a summary of recent outstanding published research and an overview of the freezing/thawing, cooking/frying and fermentation processes in meat and its products assisted by HIUS. The effects, benefits and drawbacks as well as the challenges ahead in the commercialization of this technology in the meat industry are studied. The research results confirmed that the use of HIUS in the meat freezing/thawing, cooking/frying and fermentation in combination with the corresponding processing methods demonstrates a great potential to promote the process, improve the general quality of the final product and reduce the time and energy required. However, many issues remain that require further research to address these challenges. These challenges and subsequent research that is useful for developing and increasing the efficiency of this technology have been reviewed. After the literature review, it is concluded that HIUS may be a useful technology for meat processing because of its significant effects on the quality factors and related process variables that leads to the preservation of the initial nutritional and sensory properties of meat and its products. Of course, research must be continued to eliminate the disadvantages or minimize the undesirable effects of this technology on the final product and to remove barriers to commercialization and optimization of this method.

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Homogeneity of appearance attributes of bell peppers is essential for consumers and food industries. This research aimed to develop an in-line sorting system using a deep convolutional neural network (DCNN) which is considered the state-of-the-art in the field of machine vision-based classifications, for grading bell peppers into five classes. According to export standards, the crop should be graded based on maturity stage and size. For that, the fully connected layer in the ResNet50 architecture of DCNN was replaced with a developed classifier block, including a global average-pooling layer, dense layers, batch normalization, and dropout layer. The developed model was trained and evaluated through the five-fold cross-validation method. The required processing time to classify each sample in the proposed model was estimated as 4 ms which is fast enough for real-time applications. Accordingly, the DCNN model was integrated with a machine vision-based designed sorting machine. Then, the developed system was evaluated in the in-line phase. The performance parameters in the in-line phase include accuracy, precision, sensitivity, specificity, F1-score, and overall accuracies were 98.7%, 97%, 96.9%, 99%, 96.9%, and 96.9%, respectively. The total rate of sorting the bell pepper was also measured as approximately 3000 sample/h with one sorting line. The proposed sorting system demonstrates a very good capability that allows it to be used in industrial applications. PRACTICAL APPLICATION: A developed intelligent model was integrated with a machine vision-based designed sorting machine for bell peppers. The developed system can sort the crop according to export criteria with an accuracy of 96.9%. The proposed sorting system demonstrated a very good capability that allows it to be used in industrial applications.

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The emergence of many new food products on the market with need of consumers to constantly monitor their quality until consuming, in addition to the necessity for reducing food corruption during preservation time, have led to the development of some modern packaging technologies such as intelligent packaging (IP) and active packaging (AP). The benefits of IP are detecting defects, quality monitoring and tracking the packaged food products to control the storage conditions from the production stage to the consumption stage by using various sensors and indicators such as time-temperature indicators (TTIs), gas indicators, humidity sensors, optical, calorimetric and electrochemical biosensors. While, AP helps to increase the shelf-life of products by using absorbing and diffusion systems for various materials like carbon dioxide, oxygen, and ethanol. However, there are some important issues over these emerging technologies including cost, marketability, consumer acceptance, safety and organoleptic quality of the food and emphatically environmental safety concerns. Therefore, future researches should be conducted to solve these problems and to prompt applications of IP and AP in the food industry. This paper reviews the latest innovations in these advanced packaging technologies and their applications in food industry. The IP systems namely indicators, barcoding techniques, radio frequency identification systems, sensors and biosensor are reviewed and then the latest innovations in AP methods including scavengers, diffusion systems and antimicrobial packaging are reviewed in detail.

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Recently, ultrasound has been widely used in the researches in the food science and technology. Among the food materials, fruits, juices and dairy products are strongly sensitive to ultrasound-based techniques. In this review paper, applications of ultrasound in terms of high and low-power modes in processing, instrumentation and control of the industrial operations are studied in the fruit, juice and dairy sectors of the food science and technology by emphasizing the principles of these techniques and their physicochemical effects on the products. Also, a comprehensive analysis of these methods and important factors influencing their performance are presented, along with the advantages and the drawbacks of each ultrasound-assisted techniques. The solutions for better productivity of this technique would be presented and future trend of this technology would be outlined. By studying the latest researches and advances in the field of ultrasound applications, it has been confirmed that this technique can be helpful in accelerating processes, reducing energy requirements, increasing productivity, and producing better quality food materials in the fields of fruits, juices and dairy products. Notwithstanding, in order to solve the challenges ahead, and for potential applications of ultrasound technology, further researches need to be expanded in the areas mentioned. Also, with the advancement of technology, more advanced equipment, ultrasound transducers and instrumentations would be introduced that needs to be applied in this regard, to solve the current challenges.

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