We assessed the recovery of 2 face transplantation patients with measures of complexity during neuromuscular rehabilitation. Cognitive rehabilitation methods and functional electrical stimulation were used to improve facial emotional expressions of full-face transplantation patients for 5 months. Rehabilitation and analyses were conducted at approximately 3 years after full facial transplantation in the patient group. We report complexity analysis of surface electromyography signals of these two patients in comparison to the results of 10 healthy individuals. Facial surface electromyography data were collected during 6 basic emotional expressions and 4 primary facial movements from 2 full-face transplantation patients and 10 healthy individuals to determine a strategy of functional electrical stimulation and understand the mechanisms of rehabilitation. A new personalized rehabilitation technique was developed using the wavelet packet method. Rehabilitation sessions were applied twice a month for 5 months. Subsequently, motor and functional progress was assessed by comparing the fuzzy entropy of surface electromyography data against the results obtained from patients before rehabilitation and the mean results obtained from 10 healthy subjects. At the end of personalized rehabilitation, the patient group showed improvements in their facial symmetry and their ability to perform basic facial expressions and primary facial movements. Similarity in the pattern of fuzzy entropy for facial expressions between the patient group and healthy individuals increased. Synkinesis was detected during primary facial movements in the patient group, and one patient showed synkinesis during the happiness expression. Synkinesis in the lower face region of one of the patients was eliminated for the lid tightening movement. The recovery of emotional expressions after personalized rehabilitation was satisfactory to the patients. The assessment with complexity analysis of sEMG data can be used for developing new neurorehabilitation techniques and detecting synkinesis after full-face transplantation.

Automated seed sorting is widely used in the agricultural industry. Deep learning is a new field of study in agricultural seed sorting applications. In this study, a classification of buckwheat seeds and foreign materials, such as sticks, chaff, stones was performed using deep learning. The main purpose of the study was to show the effect of scaling the images on the classification results, while creating a dataset. An industrial experimental setup was used to generate the datasets of buckwheat seeds and foreign materials to be sorted by deep learning. The images in the created dataset were rescaled with two different techniques, precision scaling and direct scaling, which were labelled as Type1 dataset and Type2 dataset, respectively. To classify buckwheat seeds and foreign materials, AlexNet architecture was used. The classification accuracy was calculated as 98.57% for Type1 Dataset and 97.34% for Type2 Dataset. As a result, it was concluded that the Type1 dataset had a higher accuracy and the use of precision scaling can be used to improve the classification results in industrial applications.

Multi-motor drive systems are widely used in modern applications that require precise synchronization of speed or torque, particularly when motors are mechanically coupled to a common load. A lack of proper coordination leads to unbalanced torques, mechanical stress, and vibrations, which ultimately reduce system efficiency. Maintaining reliable synchronization remains challenging due to system asymmetries and the limitations of conventional centralized or master–follower approaches. This study introduces a control strategy for three Permanent Magnet Synchronous Motors (PMSMs) rigidly coupled to a single shaft. In the experimental setup, three synchronization methods were implemented: Parallel, Torque-Follower, and the proposed Multilateral control. Each motor is driven by an independent controller and driver unit. Depending on the selected topology; these units perform either torque control or integrated velocity and torque control. Comparative results show that the proposed Multilateral control improves torque and velocity synchronization. It reduces mean squared errors compared to Torque-Follower method and provides more balanced torque distribution than Parallel under both no-load and loaded conditions. These findings show its potential as a scalable solution for advanced multi-motor applications.