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In recent years, national and international health systems and scientific research centers have been spending great financial and economic efforts on strategies to prevent the disease and manage risk factors in cases where diabetes, cholesterol, and hypertension coexist in individuals with cardiovascular disease. Multiple studies have shown that treating hypertension and dyslipidemia together with antihypertensive drugs and statins leads to a significant and synergistic reduction in cardiovascular risk, compared to managing these conditions separately (Multiple Risk Factor Intervention Trial Research Group,1982; Sever et al., 2003; Schwalm et al.,2016). The combined use of AML and ROS drugs not only reduces blood pressure and lipid levels but also contributes to the reduction of cardiovascular events and mortality by treating patients in their complexity (Chapman, Yeaw % Roberts, 2010; Kim et al., 2020). [...]this combination, offered in a single pill formulation, reduces blood pressure and lipid values more effectively than two separate drugs (Kim et al., 2020; Sarzani et al., 2022). RESULTS AND DISCUSSION In traditional spectral analysis methods, the most common handicap is the overlapping of spectral bands in the solution of two or more components.

A laser stripe sensor has two kinds of calibration methods. One is based on the homography model between the laser stripe plane and the image plane, which is called the one-step calibration method. The other is based on the simple triangular method, which is named as the two-step calibration method. However, the geometrical meaning of each element in the one-step calibration method is not clear as that in the two-step calibration method. A novel mathematical derivation is presented to reveal the geometrical meaning of each parameter in the one-step calibration method, and then the comparative study of the one-step calibration method and the two-step calibration method is completed and the intrinsic relationship is derived. What is more, a one-step calibration method is proposed with 7 independent parameters rather than 11 independent parameters. Experiments are conducted to verify the accuracy and robust of the proposed calibration method.

The paper deals with the development and application of a new adaptive calibration method that extends the geometrical calibration of mechanisms from calibration of only dimensions and kinematical joint positions into the calibration of kinematic joint shape imperfections. Originally unknown nonlinear properties of the kinematical pairs are adaptively included into the kinematical models that are used during the calibration calculation. The method uses description by local linear models and validity functions in order to identify and describe nonlinear properties of the kinematical pairs. During each calibration step, various models with growing complexity are considered before the best model variant is selected to improve calibration results. The method is mainly devoted to the structures with many loops like parallel and fiber-driven parallel mechanisms. The method is applied to parallel mechanism Sliding Star and parallel fiber-driven mechanism Quadrosphere.

Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R2) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R2 (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods.

A convenient and effective binocular vision system is set up. Gesture information can be accurately extract from the complex environment with the system. The template calibration method is used to calibrate the binocular camera and the parameters of the camera are accurately obtained. In the phase of stereo matching, the BM algorithm is used to quickly and accurately match the images of the left and right cameras to get the parallax of the measured gesture. Combined with triangulation principle, resulting in a more dense depth map. Finally, the depth information is remapped to the original color image to realize three-dimensional reconstruction and three-dimensional cloud image generation. According to the cloud image information, it can be judged that the binocular vision system can effectively segment the gesture from the complex background.

In the industrial field, the anthropomorphism of grasping robots is the trend of future development, however, the basic vision technology adopted by the grasping robot at this stage has problems such as inaccurate positioning and low recognition efficiency. Based on this practical problem, in order to achieve more accurate positioning and recognition of objects, an object detection method for grasping robot based on improved YOLOv5 was proposed in this paper. Firstly, the robot object detection platform was designed, and the wooden block image data set is being proposed. Secondly, the Eye-In-Hand calibration method was used to obtain the relative three-dimensional pose of the object. Then the network pruning method was used to optimize the YOLOv5 model from the two dimensions of network depth and network width. Finally, the hyper parameter optimization was carried out. The simulation results show that the improved YOLOv5 network proposed in this paper has better object detection performance. The specific performance is that the recognition precision, recall, mAP value and F1 score are 99.35%, 99.38%, 99.43% and 99.41% respectively. Compared with the original YOLOv5s, YOLOv5m and YOLOv5l models, the mAP of the YOLOv5_ours model has increased by 1.12%, 1.2% and 1.27%, respectively, and the scale of the model has been reduced by 10.71%, 70.93% and 86.84%, respectively. The object detection experiment has verified the feasibility of the method proposed in this paper.

The dynamic behavior of quartz flexure accelerometers remains a subject of ongoing investigation, particularly in areas such as theoretical modeling, standardization, calibration methodology, and performance evaluation. To address the limitation of conventional static calibration models in accurately representing accelerometer responses under dynamic acceleration excitation, a dynamic calibration model is proposed. A mathematical model is first developed based on the physical mechanism of the accelerometer, characterizing its intrinsic dynamic response. Simulation-based analysis demonstrates that the proposed dynamic model offers significantly improved accuracy compared to traditional static approaches. Furthermore, a dynamic calibration method leveraging a dual-axis precision centrifuge is designed and validated. The results confirm that the proposed approach enables the precise calibration of quartz flexure accelerometers in accordance with the dynamic model. The calibration of the dynamic parameter yields a relative standard deviation of −0.048%.

Microdialysis is a sampling technique first introduced in the late 1950s. Although this technique was originally designed to study endogenous compounds in animal brain, it is later modified to be used in other organs. Additionally, microdialysis is not only able to collect unbound concentration of compounds from tissue sites; this technique can also be used to deliver exogenous compounds to a designated area. Due to its versatility, microdialysis technique is widely employed in a number of areas, including biomedical research. However, for most in vivo studies, the concentration of substance obtained directly from the microdialysis technique does not accurately describe the concentration of the substance on-site. In order to relate the results collected from microdialysis to the actual in vivo condition, a calibration method is required. To date, various microdialysis calibration methods have been reported, with each method being capable to provide valuable insights of the technique itself and its applications. This paper aims to provide a critical review on various calibration methods used in microdialysis applications, inclusive of a detailed description of the microdialysis technique itself to start with. It is expected that this article shall review in detail, the various calibration methods employed, present examples of work related to each calibration method including clinical efforts, plus the advantages and disadvantages of each of the methods.

Camera calibration is a core task in computer vision. Traditional calibration methods usually achieve approximate solutions due to the complexity of solving nonlinear equations, resulting in reprojection errors. This article proposes a camera calibration optimization algorithm based on the Starling-Inspired Strategy optimization algorithm, which improves calibration accuracy and stability by combining chaotic mapping and sine cosine optimization strategies. First, we constructed a real camera calibration image dataset that includes various calibration scenarios and calculated the initial values of camera calibration parameters based on Zhang’s calibration method. Then, we established a reprojection error model to evaluate the accuracy of the calibration parameters. Finally, we reduced the reprojection error through a hybrid optimization method based on the Steller Jay optimization algorithm. Experimental results show that our algorithm significantly reduces the reprojection error and improves the camera calibration accuracy.