Coaxial 3D Bioprinting Process Research and Performance Tests on Vascular Scaffolds.

Three-dimensionally printed vascularized tissue, which is suitable for treating human cardiovascular diseases, should possess excellent biocompatibility, mechanical performance, and the structure of complex vascular networks. In this paper, we propose a method for fabricating vascularized tissue based on coaxial 3D bioprinting technology combined with the mold method. Sodium alginate (SA) solution was chosen as the bioink material, while the cross-linking agent was a calcium chloride (CaCl2) solution. To obtain the optimal parameters for the fabrication of vascular scaffolds, we first formulated theoretical models of a coaxial jet and a vascular network. Subsequently, we conducted a simulation analysis to obtain preliminary process parameters. Based on the aforementioned research, experiments of vascular scaffold fabrication based on the coaxial jet model and experiments of vascular network fabrication were carried out. Finally, we optimized various parameters, such as the flow rate of internal and external solutions, bioink concentration, and cross-linking agent concentration. The performance tests showed that the fabricated vascular scaffolds had levels of satisfactory degradability, water absorption, and mechanical properties that meet the requirements for practical applications. Cellular experiments with stained samples demonstrated satisfactory proliferation of human umbilical vein endothelial cells (HUVECs) within the vascular scaffold over a seven-day period, observed under a fluorescent inverted microscope. The cells showed good biocompatibility with the vascular scaffold. The above results indicate that the fabricated vascular structure initially meet the requirements of vascular scaffolds.

Design and Implementation of a Linear Active Disturbance Rejection Control-Based Position Servo Control System of an Electromotive Valve for Exhaust Gas Recirculation.

An exhaust gas recirculation (EGR) valve is used to quickly and dynamically adjust the amount of recirculated exhaust gas, which is critical for improving engine fuel economy and reducing emissions. To address problems relating to the precise positioning of an electromotive (EM) valve under slowly varying plant dynamics and uncertain disturbances, we propose a servo control system design based on linear active disturbance rejection control (LADRC) for the EGR EM valve driven by a limited angle torque motor (LATM). By analyzing the structure of the LATM and the transmission, the dynamic model of the system is derived. In addition, to solve the problems caused by slowly varying plant dynamics and uncertain disturbances, we combine the effects of uncertain model parameters and external disturbances as the total disturbance, which is estimated in real time by an extended state observer (ESO) and then compensated. In addition, accurate angular information is obtained using a non-contact magnetic angle measurement method, and a high-speed digital communication channel is established to help implement a closed-loop position control system with improved responsiveness and accuracy. Simulation and experimental results show that the proposed servo system design can effectively ensure the precision and real-time performance of the EM valve under slowly changing plant dynamics and uncertain disturbances. The proposed servo system design achieves a full-stroke valve control accuracy of better than 0.05 mm and a full-stroke response time of less than 100 ms. The controlled valve also has good robustness under shock-type external disturbances and excellent airflow control capability. The repeatability of the airflow control is generally within 5%, and the standard deviation is less than 0.2 m3/h.

Analysis and Compensation of Phase Current Measuring Error Caused by Sensing Resistor in PMSM Application.

Field Oriented Control (FOC) effectively realizes independent control of flux linkage and torque, and is widely used in application of Permanent Magnet Synchronous Motor (PMSM). However, it is necessary to detect the phase current information of the motor to realize the current closed-loop control. The phase current detection method based on a sampling resistor will cause a measurement error due to the influence of parasitic parameters of the sampling resistor, which will lead to the decrease in PMSM control performance. This paper reveals the formation mechanism of the current sampling error caused by parasitic inductance and capacitance of the sampling resistor, and further confirms that the above error will lead to the fluctuation of the electromagnetic torque output by simulation. Moreover, we propose an approach for online observation and compensation of the current sampling error based on PI-type observer to suppresses the torque pulsation of PMSM. The phase current sampling error is estimated by the proportional and integral (PI) observer, and the deviation value of current sampling is obtained by low-pass filter (LPF). The above deviation value is further injected into the phase current close-loop for error compensation. The PI observer continues to work to keep the current sampling error close to zero. The simulation platform of Matlab/Simulink (Version: R2021b) is established to verify the effectiveness of online error observation and compensation. Further experiments also prove that the proposed method can effectively improve the torque fluctuation of the PMSM and enhance its control accuracy performance of rotation speed.

Vision SLAM algorithm for wheeled robots integrating multiple sensors.

Wheeled robots play a crucial role in driving the autonomy and intelligence of robotics. However, they often encounter challenges such as tracking loss and poor real-time performance in low-texture environments. In response to these issues, this research proposes a real-time localization and mapping algorithm based on the fusion of multiple features, utilizing point, line, surface, and matrix decomposition characteristics. Building upon this foundation, the algorithm integrates multiple sensors to design a vision-based real-time localization and mapping algorithm for wheeled robots. The study concludes with experimental validation on a two-wheeled robot platform. The results indicated that the multi-feature fusion algorithm achieved the highest average accuracy in both conventional indoor datasets (84.57%) and sparse-feature indoor datasets (82.37%). In indoor scenarios, the vision-based algorithm integrating multiple sensors achieved an average accuracy of 85.4% with a processing time of 64.4 ms. In outdoor scenarios, the proposed algorithm exhibited a 14.51% accuracy improvement over a vision-based algorithm without closed-loop detection. In summary, the proposed method demonstrated outstanding accuracy and real-time performance, exhibiting favorable application effects across various practical scenarios.

A novel reconstruction method combining multi-detector SPECT with an elliptical orbit and computer tomography for cardiac imaging.

The myocardial single photon emission computed tomography (SPECT) is a good study due to its clinical significance in the diagnosis of myocardial disease and the requirement for improving image quality. However, SPECT imaging faces challenges related to low spatial resolution and significant statistical noise, which concerns patient radiation safety. In this paper, a novel reconstruction system combining multi-detector elliptical SPECT (ME-SPECT) and computer tomography (CT) is proposed to enhance spatial resolution and sensitivity. The hybrid imaging system utilizes a slit-slat collimator and elliptical orbit to improve sensitivity and signal-to-noise ratio (SNR), obtains accurate attenuation mapping matrices, and requires prior information from integrated CT. Collimator parameters are corrected based on CT reconstruction results. The SPECT imaging system employs an iterative reconstruction algorithm that utilizes prior knowledge. An iterative reconstruction algorithm based on prior knowledge is applied to the SPECT imaging system, and a method for prioritizing the reconstruction of regions of interest (ROI) is introduced to deal with severely truncated data from ME-SPECT. Simulation results show that the proposed method can significantly improve the system's spatial resolution, SNR, and image fidelity. The proposed method can effectively suppress distortion and artifacts with the higher spatial resolution ordered subsets expectation maximization (OSEM); slit-slat collimation.

Research on Cartilage 3D Printing Technology Based on SA-GA-HA.

Cartilage damage is difficult to heal and poses a serious problem to human health as it can lead to osteoarthritis. In this work, we explore the application of biological 3D printing to manufacture new cartilage scaffolds to promote cartilage regeneration. The hydrogel made by mixing sodium alginate (SA) and gelatin (GA) has high biocompatibility, but its mechanical properties are poor. The addition of hydroxyapatite (HA) can enhance its mechanical properties. In this paper, the preparation scheme of the SA-GA-HA composite hydrogel cartilage scaffold was explored, the scaffolds prepared with different concentrations were compared, and better formulations were obtained for printing and testing. Mathematical modeling of the printing process of the bracket, simulation analysis of the printing process based on the mathematical model, and adjustment of actual printing parameters based on the results of the simulation were performed. The cartilage scaffold, which was printed using Bioplotter 3D printer, exhibited useful mechanical properties suitable for practical needs. In addition, ATDC-5 cells were seeded on the cartilage scaffolds and the cell survival rate was found to be higher after one week. The findings demonstrated that the fabricated chondrocyte scaffolds had better mechanical properties and biocompatibility, providing a new scaffold strategy for cartilage tissue regeneration.

Influence of Circular through Hole in Pt-Rh Bushing on Temperature Propagation at High Temperature.

In the fiberglass industry, Pt-Rh bushings made of platinum and rhodium have very good characteristics, such as high temperature resistance, corrosion resistance, oxidation resistance, and creep resistance. In this paper, a semi-infinite lath structure model is constructed, and the expression of the surface temperature distribution of a Pt-Rh alloy plate with a circular through hole is obtained based on the non-Fourier heat conduction equation, complex function method and conformal mapping method. At the same time, the influence of the position of the circular through hole in the Pt-Rh bushing and the parameters of the incident light source (Non-diffusion incident wave number and relative thermal diffusion length) on the surface temperature distribution of the Pt-Rh bushing is studied by using this formula. It is found that: 1. heat concentration and fracture are occur easily at the through hole; 2. when the through hole is in the asymmetric center, the greater the asymmetry, the smaller the maximum temperature amplitude; 3. when the buried depth of the through hole increases, the maximum temperature amplitude decreases; 4. when the incident wave number and the relative thermal diffusion length of the incident light source are larger, the maximum temperature amplitude is smaller. The numerical results are almost consistent with those of ANSYS thermal simulation. The expression of the surface temperature distribution of the semi-infinite lath structure proposed in this paper can effectively reduce the loss of precious metal materials and the time of thermal simulation in the experimental process, as well as provide important significance for structural design, quality inspection, process optimization, and service life improvement of Pt-Rh bushings.

Active Disturbance Rejection Control in Magnetic Bearing Rotor Systems with Redundant Structures.

At present, magnetic bearings are a better energy-saving choice than mechanical bearings in industrial applications. However, there are strongly coupled characteristics in magnetic bearing-rotor systems with redundant structures, and uncertain disturbances in the electrical system as well as external disturbances, and these unfavorable factors degrade the performance of the system. To improve the anti-interference performance of magnetic bearing systems, this paper proposes the inverse of the current distribution matrix W-1 meaning that the active disturbance rejection control simulation model can be carried out without neglecting the current of each coil. Firstly, based on the working mechanism of magnetic bearings with redundant structures and the nonlinear electromagnetic force model, the current and displacement stiffness models of magnetic bearings are established, and a dynamic model of the rotor is constructed. Then, according to the dynamic model of the rotor and the mapping relationship between the current of each coil and the electromagnetic force of the magnetic bearing, we established the equivalent control loop of the magnetic bearing-rotor system with redundant structures. Finally, on the basis of the active disturbance rejection control (ADRC) strategy, we designed a linear active disturbance rejection controller (LADRC) for magnetic bearings with redundant structures under the condition of no coil failure, and a corresponding simulation was carried out. The results demonstrate that compared to PID+current distribution control strategy, the LADRC+current distribution control strategy proposed in this paper is able to effectively improve the anti-interference performance of the rotors supported by magnetic bearings with redundant structures.

Gradient Printing Alginate Herero Gel Microspheres for Three-Dimensional Cell Culture.

Hydrogel microspheres are widely used in tissue engineering, such as 3D cell culture and injection therapy, and among which, heterogeneous microspheres are drawing much attention as a promising tool to carry multiple cell types in separated phases. However, it is still a big challenge to fabricate heterogeneous gel microspheres with excellent resolution and different material components in limited sizes. Here, we developed a multi-channel dynamic micromixer, which can use active mechanical mixing to achieve rapid mixing with multi-component materials and extrude the homogenized material. By changing the flow rate ratio of the solutions of the two components and by rapidly mixing in the micromixer, real-time concentration change of the mixed material at the outlet could be monitored in a process so-called "gradient printing". By studying the mixing efficiency of the micromixer, its size and process parameters were optimized. Using the novel dynamic gradient printing method, the composition of the hydrogel microspheres can be distributed in any proportion and alginate heterogeneous gel microspheres with adjustable cell concentration were fabricated. The effects of cell concentration on cell viability and proliferation ability under three-dimensional culture conditions were also studied. The results showed that cells have very low death rate and can exchange substances within the microspheres. Due to the micromixing ability of the micromixers, the demand for biological reagents and materials such as cells, proteins, cytokines and other materials could be greatly reduced, which helps reduce the experimental cost and improve the feasibility of the method in practical use. The heterogeneous gel microsphere can be greatly valuable for research in various fields such as analytical chemistry, microarray, drug screening, and tissue culture.

Fault-Tolerant Control of Magnetically-Levitated Rotor with Redundant Structures Based on Improved Generalized Linearized EMFs Model.

Fault tolerance is one of the effective methods to improve the reliability of magnetic bearings, and the redundant magnetic bearing provides a feasible measure for fault-tolerant control. The linearization and accuracy of the electromagnetic force (EMF) from the redundant structures is crucial for designing fault-tolerant controllers. In the magnetic bearing with a redundant structure, the current distribution matrix W is an important factor that affects the accuracy of EMF. In this paper, we improved the accuracy of the EMF model and took the eight-pole symmetrical radial magnetic bearing as the research object. The corresponding displacement compensation matrices have been calculated for the different coils that fail in the magnetic bearing while the rotor is at the non-equilibrium position. Then, we propose a fault-tolerant control strategy that includes displacement compensation. The rigid body dynamics model of the rotor, supported by magnetic bearings with redundant structures, is established. Moreover, to verify the effectiveness of the proposed control strategy, we combined the rigid body dynamics model of the rotor with a fault-tolerant control strategy, and the corresponding simulation has been carried out. In the case of disturbance force and some coils fail in magnetic bearing and compared with the fault-tolerant control that absents the displacement compensation factors. The simulations demonstrate the disturbance rejection of magnetically levitated rotor system can be enhanced. The robustness of the rotor has been improved with the fault-tolerant control strategy proposed in this paper.

Strategies for multivariate analyses of imaging genetics study in Alzheimer's disease.

Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. Early diagnosis of AD is critical for the management of this disease. Imaging genetics examines the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on brain structure and function and many novel approaches of imaging genetics are proposed for studying AD. We review and synthesize the Alzheimer's Disease Neuroimaging Initiative (ADNI) genetic associations with quantitative disease endophenotypes including structural and functional neuroimaging, diffusion tensor imaging (DTI), positron emission tomography (PET), and fluid biomarker assays. In this review, we survey recent publications using neuroimaging and genetic data of AD, with a focus on methods capturing multivariate effects accommodating the large number variables from both imaging data and genetic data. We review methods focused on bridging the imaging and genetic data by establishing genotype-phenotype association, including sparse canonical correlation analysis, parallel independent component analysis, sparse reduced rank regression, sparse partial least squares, genome-wide association study, and so on. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future pharmaceutical therapy and biomarker development.

Identifying and characterizing different stages toward Alzheimer's disease using ordered core features and machine learning.

Based on the joint HCPMMP parcellation method we developed before, which divides the cortical brain into 360 regions, the concept of ordered core features (OCF) is first proposed to reveal the functional brain connectivity relationship among different cohorts of Alzheimer's disease (AD), late mild cognitive impairment (LMCI), early mild cognitive impairment (EMCI) and healthy controls (HC). A set of core network features that change significantly under the specifically progressive relationship were extracted and used as supervised machine learning classifiers. The network nodes in this set mainly locate in the frontal lobe and insular, forming a narrow band, which are responsible for cognitive impairment as suggested by previous finding. By using these features, the accuracy ranged from 86.0% to 95.5% in binary classification between any pair of cohorts, higher than 70.1%-91.0% when using all network features. In multi-group classification, the average accuracy was 75% or 78% for HC, EMCI, LMCI or EMCI, LMCI, AD against baseline of 33%, and 53.3% for HC, EMCI, LMCI and AD against baseline of 25%. In addition, the recognition rate was lower when combining EMCI and LMCI patients into one group of mild cognitive impairment (MCI) for classification, suggesting that there exists a big difference between early and late MCI patients. This finding supports the EMCI/LMCI inclusion criteria introduced by ADNI based on neuropsychological assessments.

Alzheimer's disease, mild cognitive impairment, and normal aging distinguished by multi-modal parcellation and machine learning.

A 360-area surface-based cortical parcellation is extended to study mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy control (HC) using the joint human connectome project multi-modal parcellation (JHCPMMP) proposed by us. We propose a novel classification method named as JMMP-LRR to accurately identify different stages toward AD by integrating the JHCPMMP with the logistic regression-recursive feature elimination (LR-RFE). In three-group classification, the average accuracy is 89.0% for HC, MCI, and AD compared to previous studies using other cortical separation with the best classification accuracy of 81.5%. By counting the number of brain regions whose feature is in the feature subset selected with JMMP-LRR, we find that five brain areas often appear in the selected features. The five core brain areas are Fusiform Face Complex (L-FFC), Area 10d (L-10d), Orbital Frontal Complex (R-OFC), Perirhinal Ectorhinal (L-PeEc) and Area TG dorsal (L-TGd, R-TGd). The features corresponding to the five core brain areas are used to form a new feature subset for three classifications with the average accuracy of 80.0%. Results demonstrate the importance of the five core brain regions in identifying different stages toward AD. Experiment results show that the proposed method has better accuracy for the classification of HC, MCI, AD, and it also proves that the division of brain regions using JHCPMMP is more scientific and effective than other methods.