Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends.

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.

Functionality Analysis of Electric Actuators in Renewable Energy Systems-A Review.

Various mechanical, hydraulic, pneumatic, electrical, and hybrid actuators can alter motion per the requirements of particular applications. However, except for electrical ones, all actuators are restricted due to their size, complex auxiliary equipment, frequent need for maintenance, and sluggish environment in renewable applications. This brief review paper highlights some unique and significant research works on applying electrical actuators to renewable applications. Four renewable energy resources, i.e., solar, wind, bio-energy, and geothermal energy, are considered to review electric actuators applicable to renewable energy systems. This review analyses the types of actuators associated with the mentioned renewable application, their functioning, their motion type, present use, advantages, disadvantages, and operational problems. The information gathered in this paper may open up new ways of optimization opportunities and control challenges in electrical actuators, thereby making more efficient systems. Furthermore, some energy-efficient and cost-effective replacements of convectional actuators with new innovative ones are suggested. This work aims to benefit scientists and new entrants working on actuators in renewable energy systems.

Association between Serum Lipid Profile and Renal Dysfunction in Patients with Heart Failure.

Renal dysfunction often accompanies heart failure which leads to an increase in hospitalization and mortality. The pathophysiological features have demonstrated that heart failure may cause reduction in cardiac output and decrease in renal perfusion, which leads to development of chronic renal disease. Thus, more attention should be paid to the associated risk factors with the aim to reduce the prevalence, hospitalization and mortality. According to recent studies, dyslipidemia has become one of the key risk factors which leads to progression of renal dysfunction in heart failure patients. MATERIAL: 105 hospitalized heart failure patients with left ventricular ejection fraction (LVEF) ≤ 45%, and New York Heart Association (NYHA) class II -IV were enrolled for a study period of 6 months, that is from March 2021 to August 2021.The estimated glomerular filtration rate (eGFR) <90 mL/min/1.73m2 was defined as renal dysfunction. Heart failure was confirmed clinically and with the echocardiography report. Significant cognitive impairment and life threatening comorbidity were the main exclusion criteria. OBSERVATION: Among the 105 patients with heart failure, a total of 59 (56.2%) had renal dysfunction, and 46 (43.8%) did not have renal dysfunction. The high density lipoprotein cholesterol (HDL-C) and the left ventricular ejection fraction (LVEF) were found to be positively correlated with estimated glomerular filtration rate (eGFR) (p<0.05), that is, were significantly lower in those patients with renal dysfunction than those patients without renal dysfunction. The total cholesterol (TC), triglyceride, low density lipoprotein cholesterol (LDL-C) showed no significant difference between the patients with renal dysfunction and those without renal dysfunction. CONCLUSION: Among the lipid profile of TC, triglyceride, LDL-C, HDL-C, the HDL-C is the only lipid factor significantly associated with renal dysfunction in hospitalized heart failure patients.

Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis.

The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold-Mariano and Durbin-Watson tests are carried out to establish the robustness of the tested models.

Blood glucose regulation in type 1 diabetic patients: an adaptive parametric compensation control-based approach.

Here, a direct adaptive control strategy with parametric compensation is adopted for an uncertain non-linear model representing blood glucose regulation in type 1 diabetes mellitus patients. The uncertain parameters of the model are updated by appropriate design of adaptation laws using the Lyapunov method. The closed-loop response of the plasma glucose concentration as well as external insulin infusion rate is analysed for a wide range of variation of the model parameters through extensive simulation studies. The result indicates that the proposed adaptive control scheme avoids severe hypoglycaemia and gives satisfactory performance under parametric uncertainty highlighting its ability to address the issue of inter-patient variability.