Magnetic polymer composites have recently attracted considerable interest, primarily because of their promising applications, especially in the biomedical industry. The aim of this study is to investigate the impact of ultraviolet C (UVC) irradiation as a disinfection method on the mechanical characteristics of composite polymer magnets. Tensile and compression tests were conducted following the standards set by ASTM D3039 and ASTM D3410, respectively. In addition, energy dispersive spectroscopy (EDS) was used to determine the effect of the disinfection method on the amount of carbon, oxygen, and iron within the surface of the composite polymer magnet material. The UVC’s irradiation impact was statistically assessed by a t-test. The results of the tensile tests demonstrated a significant increase in the transition force, measuring 0.41 kN and 0.58 kN before and after UVC exposure, respectively. Similarly, the outcomes of the compression tests showed a notable increase in yield force, registering 4.9 kN and 6 kN before and after UVC treatment. This suggests that the composite magnetic material has gained a higher capacity to withstand compressive loads than tensile loads. Finally, the EDS analysis revealed the carbon mass percentage was 71.69% prior to UVC radiation exposure, with it increasing to 78.56%, following exposure. This suggests that the composite material exhibited improved hardness. These findings highlight that UVC irradiation has a beneficial impact on both the mechanical and chemical properties of the composite magnet material, which support its use as a disinfection method in clinical settings.

This study explores the impact of disinfection techniques on the mechanical properties of poly(lactic acid) (PLA), a crucial material in the production of medical implants, tissue engineering, orthopedic devices and drug delivery systems, owing to its biocompatibility and ease of manufacturing. The focus is on evaluating the effectiveness of ultraviolet (UV) type C (254 nm wavelength) and the combined use of type C and B (310 nm wavelength) disinfection methods. Fifteen tensile test specimens (ASTM D638) and fifteen compression test specimens (ASTM D695) were utilized to assess PLA’s mechanical properties, including yield strength, ultimate strength, and fracture strength. The investigation involved subjecting the specimens to the specified disinfection methods and evaluating these properties both before and after the disinfection process. In the tensile test, a statistically significant difference (p = 0) in yield displacement was observed among the three groups. Additionally, a notable difference (p = 0.047) in fracture displacement was identified between the untreated group and the UVC and UVB combination group. No discernible impact on yield or fracture forces was noted. In the compression test, there was a significant difference (p = 0.04) in yield displacement and a clear difference (p = 0.05) in fracture force between the untreated group and the UVC and UVB combination group. The hybrid combination of UVC and UVB disinfection techniques did not affect yield force in both tensile and compression tests. However, it demonstrated a clear impact on displacement, suggesting its potential as a promising disinfection technique in the medical field.

Some people may experience accidents, strokes, or diseases that lead to both motor and speech disabilities, making it difficult to communicate with others. Those with paralysis face daily challenges in meeting their basic needs, particularly if they have difficulty speaking. Individuals with dysarthria, amyotrophic lateral sclerosis, and similar conditions may find it challenging to understand speech.The proposed system for automatic recognition of daily basic needs aims to improve the quality of life for individuals suffering from dysarthria and quadriplegic paralysis. The system achieves this by recognizing and analyzing brain signals and converting them to either audible voice commands or texts that can be sent to a healthcare provider's mobile phone based on the system settings.The proposed system uses a convolutional neural network (CNN) model to detect event-related potentials (ERPs) within the EEG signal to select one of six basic daily needs while displaying their images randomly. Ten volunteers participated in this study, contributing to the creation of the dataset used for training, testing, and validation. The proposed approach achieved an accuracy of 78.41%.

The need to identify people is as old as human kind. Biometric devices automate the personal recognition process. Each of us is unique from every other human being. We have unique physical characteristics, such as fingerprints, blood vessel patterns and hand shape. Biometric devices measure and record these characteristics for automated comparison and verification. The aim of this study is accurately identifying people using their retinal image. A new algorithm is proposed for the extraction of the retinal characteristic features based on image analysis and image statistics. These features are extracted from the plane of the fundus image captured by a fundus camera. These features can be used for either the verification or identification process. The algorithm was tested with sixty fundus images. Success rate of 94.5% is reached.