Left ventricular assist devices (LVADs) are emerging innovations that provide a feasible alternative treatment for heart failure (HF) patients to enhance their quality of life. In this work, a novel physiological control system to optimize LVAD pump speed using an H-infinity controller was developed. The controller regulates the calculated target pump flow vs. measured pump flow to meet the changes in metabolic demand. The method proposes the implementation of the Frank–Starling mechanism (FSM) approach to control the speed of an LVAD using the left ventricle end-diastolic volume (V_lved) parameter (preload). An operating point was proposed to move between different control lines within the safe area to achieve the FSM. A proportional–integral (PI) controller was used to control the gradient angle between control lines to obtain the flow target. A lumped parameter model of the cardiovascular system was used to evaluate the proposed method. Exercise and rest scenarios were assessed under multi-physiological conditions of HF patients. Simulation results demonstrated that the control system was stable and feasible under different physiological states of the cardiovascular system (CVS). In addition, the proposed controller was able to keep hemodynamic variables within an acceptable range of the mean pump flow (Qp) (max = 5.2 L/min and min = 3.2 L/min) during test conditions.

Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer’s disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors are leveraged in predicting the transition to the MCI stage at a future time point. The activity dataset from the Oregon Center for Aging and Technology (ORCATECH) includes measures representing various domains such as walk, sleep, etc. Each sensor-captured activity measure is constructed as a time series, and a variety of summary statistics is computed. The similarity between one individual’s activity time series and that of the remaining individuals is also computed as distance measures. The long short-term memory (LSTM) recurrent neural network is trained with time series statistics and distance measures for the prediction modeling, and performance is evaluated by classification accuracy. The model outcomes are explained using the SHapley Additive exPlanations (SHAP) framework. LSTM model trained using the time series statistics and distance measures outperforms other modeling scenarios, including baseline classifiers, with an overall prediction accuracy of 83.84%. SHAP values reveal that sleep-related features contribute the most to the prediction of the cognitive stage at the future time point, and this aligns with the findings in the literature. Findings from this study not only demonstrate that a practical, less expensive, longitudinal monitoring of older adults’ activity routines can benefit immensely in modeling AD progression but also unveil the most contributing features that are medically applicable and meaningful.

Installing and developing a sophisticated control system to optimize left ventricular assist device (LVAD) pump speed to meet changes in metabolic demand is essential for advancing LVAD technology. This paper aims to design and implement a physiological control method for LVAD pumps to provide optimal cardiac output. The method is designed to adjust the pump speed by regulating the pump flow based on a predefined set point (operating point). The Frank–Starling mechanism technique was adopted to control the set point within a safe operating zone (green square), and it mimics the physiological demand of the patient. This zone is predefined by preload control lines, which are known as preload lines. A proportional–integral (PI) controller was utilized to control the operating point within safe limits to prevent suction or overperfusion. In addition, a PI type 1 fuzzy logic controller was designed and implemented to drive the LVAD pump. To evaluate the design method, rest, moderate, and exercise scenarios of heart failure (HF) were simulated by varying the hemodynamic parameters in one cardiac cycle. This evaluation was conducted using a lumped parameter model of the cardiovascular system (CVS). The results demonstrated that the proposed control method efficiently drives an LVAD pump under accepted clinical conditions. In both scenarios, the left ventricle pressure recorded 112 mmHg for rest and 55 mmHg for exercise, and the systematic flow recorded 5.5 L/min for rest and 1.75 L/min for exercise.

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.

In this work, we developed a prototype that adopted sound-based systems for localization of visually impaired individuals. The system was implemented based on a wireless ultrasound network, which helped the blind and visually impaired to navigate and maneuver autonomously. Ultrasonic-based systems use high-frequency sound waves to detect obstacles in the environment and provide location information to the user. Voice recognition and long short-term memory (LSTM) techniques were used to design the algorithms. The Dijkstra algorithm was also used to determine the shortest distance between two places. Assistive hardware tools, which included an ultrasonic sensor network, a global positioning system (GPS), and a digital compass, were utilized to implement this method. For indoor evaluation, three nodes were localized on the doors of different rooms inside the house, including the kitchen, bathroom, and bedroom. The coordinates (interactive latitude and longitude points) of four outdoor areas (mosque, laundry, supermarket, and home) were identified and stored in a microcomputer’s memory to evaluate the outdoor settings. The results showed that the root mean square error for indoor settings after 45 trials is about 0.192. In addition, the Dijkstra algorithm determined that the shortest distance between two places was within an accuracy of 97%.

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.