The application of industrial robots has greatly promoted the development of industry in the past decades. Now with the proposal and prevalence of Industry 4.0, industrial robots are required to be more independent and intelligent to accomplish more complex and flexible tasks. The advancement of industrial robots relies on the development and progress of multiple technologies, among which sensors are the indispensable part. They can acquire abundant information to help industrial robots implement their functions. This paper reviews the recent literatures and gives a summary and introduction of the commonly used sensors in industrial robots. Additionally, the applications of these sensors in diverse functions of industrial robots are also presented. Finally, the developing direction and challenges of industrial robots in the future are discussed in the last part of this article.
A new queue length information fused iterative learning control approach (QLIF-ILC) is presented for freeway traffic ramp metering to achieve a better performance by utilizing the error information of the on-ramp queue length. The QLIF-ILC consists of two parts, where the iterative feedforward part updates the control input signal by learning from the past control data in previous trials, and the current feedback part utilizes the tracking error of the current learning iteration to stabilize the controlled plant. These two parts are combined in a complementary manner to enhance the robustness of the proposed QLIF-ILC. A systematic approach is developed to analyze the convergence and robustness of the proposed learning scheme. The simulation results are further given to demonstrate the effectiveness of the proposed QLIF-ILC.
Aims/hypothesis
Acetyl coenzyme A acetyltransferase (ACAT), also known as acetoacetyl-CoA thiolase, catalyses the formation of acetoacetyl-CoA from acetyl-CoA and forms part of the isoprenoid biosynthesis pathway. Thus, ACAT plays a central role in cholesterol metabolism in a variety of cells. Here, we aimed to assess the effect of hepatic Acat2 overexpression on cholesterol metabolism and systemic energy metabolism.
Methods
We generated liver-targeted adeno-associated virus 9 (AAV9) to achieve hepatic Acat2 overexpression in mice. Mice were injected with AAV9 through the tail vein and subjected to morphological, physiological (body composition, indirect calorimetry, treadmill, GTT, blood biochemistry, cardiac ultrasonography and ECG), histochemical, gene expression and metabolomic analysis under normal diet or feeding with high-fat diet to investigate the role of ACAT2 in the liver.
Results
Hepatic Acat2 overexpression reduced body weight and total fat mass, elevated the metabolic rate, improved glucose tolerance and lowered the serum cholesterol level of mice. In addition, the overexpression of Acat2 inhibited fatty acid, glucose and ketone metabolic pathways but promoted cholesterol metabolism and changed the bile acid pool and composition of the liver. Hepatic Acat2 overexpression also decreased the size of white adipocytes and promoted lipid metabolism in white adipose tissue. Furthermore, hepatic Acat2 overexpression protected mice from high-fat-diet-induced weight gain and metabolic defects
Conclusions/interpretation
Our study identifies an essential role for ACAT2 in cholesterol metabolism and systemic energy expenditure and provides key insights into the metabolic benefits of hepatic Acat2 overexpression. Thus, adenoviral Acat2 overexpression in the liver may be a potential therapeutic tool in the treatment of obesity and hypercholesterolaemia.
Graphical abstract
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