The goal of this paper is to solve the problems of large volume, slow dynamic response and poor intelligent controllability of traditional gait rehabilitation training equipment by using the characteristic that the shear yield strength of magnetorheological fluid changes with the applied magnetic field strength. Based on the extended Bingham model, the main structural parameters of the magnetorheological fluid damper and its output force were simulated and optimized by using scientific computing software, and the three-dimensional modeling of the damper was carried out after the size was determined. On this basis and according to the design and use requirements of the damper, the finite element analysis software was used for force analysis, strength check and topology optimization of the main force components. Finally, a micro magnetorheological fluid damper suitable for wearable rehabilitation training system was designed, which has reference value for the design of lightweight, portable and intelligent rehabilitation training equipment.
Due to individual differences of the depth of anaesthesia (DOA) controlled objects, the drawbacks of monitoring index, the traditional PID controller of anesthesia depth could not meet the demands of nonlinear control. However, the adjustments of the rules of DOA fuzzy control often rely on personal experience and, therefore, it could not achieve the satisfactory control effects. The present research established a fuzzy closed-loop control system which takes the cerebral state index (CSI) value as a feedback controlled variable, and it also adopts the particle swarm optimization (PSO) to optimize the fuzzy control rule and membership functions between the change of CSI and propofol infusion rate. The system sets the CSI targets at 40 and 30 through the system simulation, and it also adds some Gaussian noise to imitate clinical disturbance. Experimental results indicated that this system could reach the set CSI point accurately, rapidly and stably, with no obvious perturbation in the presence of noise. The fuzzy controller based on CSI which has been optimized by PSO has better stability and robustness in the DOA closed loop control system.
ObjectiveTo investigate the effect of pulmonary ultrasound on pulmonary complications in ultra-fast-track anesthesia for congenital heart disease surgery.MethodsIn 2019, 60 patients with congenital heart diseases underwent ultra-fast-track anesthesia in Shenzhen Children's Hospital, including 34 males and 26 females with the age ranging from 1 month to 6 years. They were randomly divided into a normal group (group N, n=30) and a lung ultrasound optimization group (group L, n=30). Both groups were used the same anesthesia method and anesthetic compatibility. The group N was anesthetized by ultra-fast-track, the tracheal tube was removed after operation and then the patients were sent to the cardiac intensive care unit (CCU). After operation in the group L, according to the contrast of pre- and post-operational lung ultrasonic examination results, for the patients with fusion of B line, atelectasis and pulmonary bronchus inflating sign which caused the increase of lung ultrasound score (LUS), targeted optimization treatment was performed, including sputum suction in the tracheal tube, bronchoscopy alveolar lavage, manual lung inflation suction, ultrasound-guided lung recruitment and other optimization treatments, and then the patients were extubated after lung ultrasound assessment and sent to CCU. The occurrence of pulmonary complications, LUS, oxygenation index (OI), extubation time, etc were compared between the two groups.ResultsCompared with the induction of anesthesia and 1 hour after extubation of the two groups, the incidence of pulmonary complications in the group L (18 patients, 60.0%) was lower than that in the group N (26 patients, 86.7%, χ2= 4.17, P=0.040) and the rate of patients with LUS score reduction was higher in the group L (15 patients, 50.0%) than that in the group N (7 patients, 23.3%, χ2=4.59, P=0.032). The correlation analysis between the LUS and OI value of all patients at each time point showed a good negative correlation (P<0.05). Extubation time in the group L was longer than that in the group N (18.70±5.42 min vs. 13.47±4.73 min, P=0.001).ConclusionUltra-fast-track anesthesia for congenital heart disease can be optimized by pulmonary ultrasound examination before extubation, which can significantly reduce postoperative pulmonary complications, improve postoperative lung imaging performance, and help patients recover after surgery, and has clinical application value.
The vessels in the microcirculation keep adjusting their structure to meet the functional requirements of the different tissues. A previously developed theoretical model can reproduce the process of vascular structural adaptation to help the study of the microcirculatory physiology. However, until now, such model lacks the appropriate methods for its parameter settings with subsequent limitation of further applications. This study proposed an improved quantum-behaved particle swarm optimization (QPSO) algorithm for setting the parameter values in this model. The optimization was performed on a real mesenteric microvascular network of rat. The results showed that the improved QPSO was superior to the standard particle swarm optimization, the standard QPSO and the previously reported Downhill algorithm. We conclude that the improved QPSO leads to a better agreement between mathematical simulation and animal experiment, rendering the model more reliable in future physiological studies.
Objective To explore the application effect of process optimization in perioperative venous access management. Methods A total of 205 general surgery patients in the Operating Room of Cheng Du Shang Jin Nan Fu Hospital, West China Hospital of Sichuan University from April to May 2018 were selected as the control group, and 205 general surgery patients from June to August 2018 were selected as the observation group. The traditional management process was used in the control group, and the process optimization management was performed in the observation group. The establishment of venous access and related complications between the two groups of patients, as well as the satisfaction of patients and staff before and after the process optimization were compared. Results There was no significant difference in gender, age, education level, operation type, anesthesia method, operation duration, or intraoperative intravenous infusion channels between the two groups of patients (P>0.05). There was no statistically significant difference in gender, age, educational background, job title, job nature, or working years of the staff participating in the satisfaction survey before and after the process optimization (P>0.05). The rate of repetitive venous puncture (15.61% vs. 58.05%) and the idelness ratio of the intraoperative indwelling needle approach (10.73% vs. 52.20%) in the observation group were lower than those of the control group, and the differences were statistically significant (P<0.05). There was no statistically significant difference in the incidence of tube blockage, detubation, or phlebitis/exudation between the two groups (P>0.05). After process optimization, patient satisfaction (22.91±3.43 vs. 17.44±4.90) and staff satisfaction (28.17±2.56 vs. 20.65±3.71) were higher than before optimization, and the differences were statistically significant (P<0.05). Conclusions The process optimization of venous access management for perioperative patients can effectively reduce the rate of venous repeated venipuncture and the idelness ratio of the intraoperative indwelling needle approach, reduce invasive operations on patients, reduce the ineffective work of nurses, avoid the waste of medical resources such as manpower and materials, and improve the satisfaction of patients and staff. It is worthy of promotion and application.
The therapeutic effects of transcranial magnetic stimulation (TMS) are closely related to the structure of the stimulation coil. Based on this, this study designed an A-word coil and proposed a multi-strategy fusion multi-objective slime mould algorithm (MSSMA) aimed at optimizing the stimulation depth, focality, and intensity of the coil. MSSMA significantly improved the convergence and distribution of the algorithm by integrating a dual-elite guiding mechanism, a hyperbolic tangent control strategy, and a hybrid polynomial mutation strategy. Furthermore, compared with other stimulation coils, the novel coil optimized by the MSSMA demonstrates superior performance in terms of stimulation depth. To verify the optimization effects, a magnetic field measurement system was established, and a comparison of the measurement data with simulation data confirmed that the proposed algorithm could effectively optimize coil performance. In summary, this study provides a new approach for deep TMS, and the proposed algorithm holds significant reference value for multi-objective engineering optimization problems.
Thoracic trauma has the characteristics of complexity, specificity, urgency and severity. Therefore, the treatment is particularly important. Thoracic Traumatology Group, Trauma Medicine Branch of Zhejiang Medical Association organized the writing of the thoracic trauma and further optimization consensus of Zhejiang thoracic surgery industry Treatment and diagnosis of rib and sternum trauma: A consensus statement by Zhejiang Association for Thoracic Surgery (version 2021), compiled the popular science book Emergency Treatment and Risk Avoidance Strategy of Thoracic Trauma and Illustration of Real Scene Treatment of Trauma, actively prepared to build the trauma database of Zhejiang Province, and participated in the construction of trauma group in the Yangtze River Delta. Although Zhejiang Province has carried out many related works in the diagnosis and treatment of chest trauma, it is still inconsistent with the development requirements of the times. Standardization of chest trauma treatment, popularization of relevant knowledge, management of trauma big data, grass-roots radiation promotion tour and further optimization of industry consensus are the requirements and objectives of this era.
Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method. It outperformed existing methods, providing a superior performance and introducing a novel perspective for decoding EEG signals, thereby contributing to the field of emotion research from EEG signal.
Online hashing methods are receiving increasing attention in cross modal medical image retrieval research. However, existing online methods often lack the learning ability to maintain semantic correlation between new and existing data. To this end, we proposed online semantic similarity cross-modal hashing (OSCMH) learning framework to incrementally learn compact binary hash codes of medical stream data. Within it, a sparse representation of existing data based on online anchor datasets was designed to avoid semantic forgetting of the data and adaptively update hash codes, which effectively maintained semantic correlation between existing and arriving data and reduced information loss as well as improved training efficiency. Besides, an online discrete optimization method was proposed to solve the binary optimization problem of hash code by incrementally updating hash function and optimizing hash code on medical stream data. Compared with existing online or offline hashing methods, the proposed algorithm achieved average retrieval accuracy improvements of 12.5% and 14.3% on two datasets, respectively, effectively enhancing the retrieval efficiency in the field of medical images.
By analyzing the physiological structure and motion characteristics of human ankle joint, a four degree of freedom generalized spherical parallel mechanism is proposed to meet the needs of ankle rehabilitation. Using the spiral theory to analyze the motion characteristics of the mechanism and based on the method of describing the position with spherical coordinates and the posture with Euler Angle, the inverse solution of the closed vector equation of mechanism position is established. The workspace of mechanism is analyzed according to the constraint conditions of inverse solution. The workspace of the moving spherical center of the mechanism is used to match the movement space of the tibiotalar joint, and the workspace of the dynamic platform is used to match the movement space of subtalar joint. Genetic algorithm is used to optimize the key scale parameters of the mechanism. The results show that the workspace of the generalized spherical parallel mechanism can satisfy the actual movement space of human ankle joint rehabilitation. The results of this paper can provide theoretical basis and experimental reference for the design of ankle joint rehabilitation robot with high matching degree.
