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.

Sagittal plane deformity can be defined as a deviation from the normal curvature of the spine in the sagittal plane. This deformity can distort the natural shape of the spine and cause posture problems. In recent years, effects such as reduced activity in daily life, increased time spent in front of computers and mobile phones, and inactivity during the recent pandemic have also led to a significant increase in sagittal plane deformity. In this study, 16 healthy and 16 healthy adolescents with sagittal plane deformity participated. Surface EMG (sEMG) recordings were obtained from thoracal kyphosis and lumbar lordosis subjects, which are frequently seen in sagittal plane deformity (SPD) subjects, and healthy subjects. After filtering the raw sEMG data, wavelet packet transform analysis was performed. The energy values of the wavelet packets corresponding to the low and high frequency components have been calculated. Statistical analysis of the analysed data identified channels with significant differences between the two groups. The results showed that SPD subjects showed more muscle activity than healthy subjects and spent more energy to increase the quality of movements and to perform them with the correct muscle dynamics. It has been observed that SPD subjects develop compensations from different muscle regions in order to perform movements correctly due to postural changes. In healthy subjects, it was observed that movements were completed in accordance with the kinematics of the movement and that maximum movement quality was observed with less energy.

In the global economy, traditional accounting-based ratios alone are often insufficient to fully explain firm performance, increasing the importance of complementary information sources such as sustainability and governance disclosures. In this context, environmental, social, and governance (ESG) indicators, together with corporate governance signals, have increasingly been recognized as important drivers of firm performance. However, the literature does not provide a clear and generalizable view on the impact of ESG indicators on profitability. This study aims to examine whether sustainability and corporate governance signals provide additional information value beyond traditional financial ratios in predicting ROE. To this end, two models were compared using a sample of 428 non-financial publicly traded companies operating in Turkey. The firm-level dataset was constructed using financial statements and independent audit disclosures obtained from the Turkish Public Disclosure Platform (KAP). Tree-based machine learning models were employed to capture potential nonlinear relationships and complex interactions between financial and non-financial indicators. Model performance was evaluated within a Bootstrapped Grouped Cross-Validation framework that considered firm-level dependency; the statistical reliability of performance differences was tested using bootstrap-based confidence intervals and matched tests. Among the evaluated models, Random Forest achieved the strongest overall predictive performance. In conclusion, this study demonstrates that sustainability and corporate governance disclosures provide statistically significant additional information value to ROE prediction. Due to the use of multiple algorithms, it contributes to the literature in a generalizable manner.