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.

In this study, the effects of drying medium temperature and velocity were surveyed on the image texture features of shrimp (Penaeus spp.) batches in a dryer equipped with a perpendicular dual-view computer vision system (CVS). This was carried out by applying an innovative rotation- and scale-invariant image texture processing approach with the capability of eliminating the effects of sample shrinkage on the visual textural features. Moreover, the variations in image texture parameters were investigated with moisture ratio, color, and geometrical characteristics of the shrimp samples. Drying experiments were conducted at hot air drying (HAD) temperatures of 50–90°C and superheated steam drying (SSD) temperatures of 110–120°C with drying medium velocities of 1–2 m/s. Several configurations of a multilayer perceptron artificial neural network (MLP-ANN) were also used to predict the moisture ratio and the geometrical characteristics of the shrimp batch using the image texture parameters. Generally, the image texture features were significantly affected by drying medium temperatures (p < 0.01), and the effects of drying medium velocities on the textural properties were nonsignificant (p > 0.05). Additionally, the higher drying temperatures generated products with uniform and regular texture patterns. The SSD produced samples with somewhat nonuniform and irregular texture patterns compared with HAD at 90°C. Finally, selected MLP-ANN topologies successfully predicted the moisture ratio and the geometrical characteristics of the shrimp batch using the textural properties with correlation coefficients higher than 0.99.

A systematic approach to the design of an adaptive fuzzy logic controller (AFLC) for intelligent drying with a computer vision system (CVS) in a feedback loop is proposed. Developed AFLC is based on an artificial neural network (ANN), geno-fuzzy algorithm, and multi-objective fuzzy cost function. Fuzzy sets for the moisture content and product quality are automatically generated by using principal component analysis (PCA) and fuzzy clustering. In addition, the concept of fuzzy time is introduced to optimize the duration of each control step. The fuzzy rule base for the controller was constructed through a two-stage process of (i) warming-up based on simulation and optimization (offline) and (ii) fine-tuning during real-time drying (online). The application of AFLC for shrimp drying showed advantages of the unsupervised fuzzy logic control, such as decreased drying time, less quality degradation, and smaller energy consumption.

Microrobotics, particularly in the field of biomedicine, has garnered considerable attention due to its potential for noninvasive medical interventions enabled by the small size of microrobots. However, controlling and imaging them present unique challenges compared to their macroscale counterparts, primarily due to the intricate anatomical spaces and dynamic environments within the human body. Existing imaging modalities also face limitations, hindering real‐time visualization and control of microrobots in deep tissue. Machine learning (ML) algorithms offer promising solutions to these challenges by enabling adaptive motion control and enhancing image resolution through robust data analysis and decision‐making capabilities. In this review, a comprehensive overview of recent advancements in ML‐based techniques for microrobotic research is provided, emphasizing their applications in imaging and control in biomedical contexts. Additionally, current obstacles and potential future directions for ML algorithms in microrobotics, particularly regarding their translation to clinical settings, are discussed.

In this study, a novel low power, high frequency piezoelectric-based ultrasonic reactor was developed and evaluated for intensifying the transesterification process. The reactor was equipped with an automatic temperature control system, a heating element, a precise temperature sensor, and a piezoelectric-based ultrasonic module. The conversion efficiency and specific energy consumption of the reactor were examined under different operational conditions, i.e., reactor temperature (40‒60 °C), ultrasonication time (6‒10 min), and alcohol/oil molar ratio (4:1‒8:1). Transesterification of waste cooking oil (WCO) was performed in the presence of a base-catalyst (potassium hydroxide) using methanol. According to the obtained results, alcohol/oil molar ratio of 6:1, ultrasonication time of 10 min, and reactor temperature of 60 °C were found as the best operational conditions. Under these conditions, the reactor converted WCO to biodiesel with a conversion efficiency of 97.12%, meeting the ASTM standard satisfactorily, while the lowest specific energy consumption of 378 kJ/kg was also recorded. It should be noted that the highest conversion efficiency of 99.3 %, achieved at reactor temperature of 60 °C, ultrasonication time of 10 min, and alcohol/oil molar ratio of 8:1, was not favorable as the associated specific energy consumption was higher at 395 kJ/kg. Overall, the low power, high frequency piezoelectric-based ultrasonic module could be regarded as an efficient and reliable technology for intensifying the transesterification process in terms of energy consumption, conversion efficiency, and processing time, in comparison with high power, low frequency ultrasonic system reported previously. Finally, this technology could also be considered for designing, developing, and retrofitting chemical reactors being employed for non-biofuel applications as well.

This study aimed to evaluate the power of various vegetation indices for sugarcane yield modelling in Shoeibeyeh area in Khuzestan province of Iran. Seven indices were extracted from satellite images and were then converted to seven days' time-series via interpolation. To eliminate noise from the time-series data, all of them were reconstructed using the Savitzky-Golay algorithm. Thus seven different time-series of vegetation indices were obtained. The growth profile was drawn via averaging of NDVI time-series data and was divided into three growth intervals. Then the accumulative values of vegetation indices related to first and second periods of growth (from 2004 to 2016 extracted from time-series data) were evaluated by simple linear regression models against the average observed yields efficiency. The result showed the accumulative IAVI (γ = 1.4) vegetation index relative to first period of growth with R² = 0.66 and RMSE = 3.78 ton/ha and the accumulative NDI vegetation index relative to second period of growth with R² = 0.66 and RMSE = 3.79 ton/ha and the accumulative NDI vegetation index relative to sum of the first and the second growth periods with R² = 0.78 and RMSE = 3.09 ton/ha had good agreement with sugarcane stem yield efficiency at the middle of growth and before harvesting season.