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The Crankshaft Full-automatic Measuring Machine (CFMM) features high accuracy, high efficiency and complete measurement parameters, and represents the forefront of a geometric crankshaft accuracy measuring instrument. One of its core technologies is the high-precision radial following the crankshaft connecting rod journal measurement. In this paper, an independent probe design scheme combining the flexible dual-complex parallel four-bar guide mechanism and double displacement sensors based on the contact measurement method was proposed. It was suitable for the measurement of precision parts with eccentric characteristics such as crankshaft and camshaft measurement. Taking the spring as the flexible part, the probe prototype’s optimization design, processing and assembly were completed, the test device was built, and the system accuracy was calibrated under various positions and feed quantities of the probe. The results revealed that the expanded measurement uncertainty after double-sensor compensation was enhanced from 1.53 μm in single-sensor measurement to 0.44 μm, satisfying the high-precision requirements of engineering measurement accuracy and reducing the measurement cost.

In recent years,brain functional networks analysis using the resting state functional magnetic resonance imaging data has been widely used in computer-aided diagnosis tasks of various brain diseases.The graph convolutional network framework integrating clinical phenotypic measurements and brain functional networks improves the applicability of intelligent medical disease diagnosis models to the real world.However,the trustworthiness study is an important but still widely neglected component of disease diagnosis models based on brain functional networks.Adversarial attack techniques in medical machine learning can deceive models,which further leads to the security and trust issues of the model applied in clinical practice.Based on this,this paper proposes an adversarial attack method BFGCNattack on graph convolutional network for brain disease diagnosis,constructs a disease diagnosis model integrating clinical phenotypic measurements,and evaluates the robustness of brain functional networks-based disease diagnosis model in the face of adversarial attacks.Experimental results on the autism brain imaging data exchange dataset suggest that the models constructed using graph convolutional networks are vulnerable to the proposed adversarial attack.Even if only a small number(10%) of perturbations are performed,the model’s accuracy and classification margin significantly decrease,while the fooling rate significantly increases.

This study explored the prescription and medication rules of traditional Chinese medicine(TCM) in the prevention and treatment of diabetic microangiopathy based on literature mining. Relevant literature on TCM against diabetic microangiopathy was searched and prescriptions were collected. Microsoft Excel 2021 software was used to establish a prescription database, and an analysis was conducted on the frequency, properties, flavors, meridian tropism, and efficacy classifications of drugs. Association rule analysis, cluster analysis, and factor analysis were performed using SPSS Modeler 18.0 and SPSS Statistics 26.0 software. The characteristic active components and mechanisms of action of medium-high frequency drugs in the analysis of medication rules were explored through li-terature mining. A total of 1 327 prescriptions were included in this study, involving 411 drugs, with a total frequency reaching 19 154 times. The top five high-frequency drugs were Astragali Radix, Angelicae Sinensis Radix, Poria, Salviae Miltiorrhizae Radix et Rhizoma, and Rehmanniae Radix. The cold and warm drugs were used in combination. Drugs were mainly sweet, followed by bitter and pungent, and acted on the liver meridian. The majority of drugs were effective in tonifying deficiency, clearing heat, activating blood, and resolving stasis. Association rule analysis identified the highly supported drug pair of Astragali Radix-Angelicae Sinensis Radix and the highly confident drug combination of Poria-Alismatis Rhizoma-Corni Fructus. The strongest correlation was found among Astragali Radix, Angelicae Sinensis Radix, Poria, and Salviae Miltiorrhizae Radix et Rhizoma through the complex network analysis. Cluster analysis identified nine categories of drug combinations, while factor analysis identified 16 common factors. The analysis of active components in high-frequency drugs for the treatment of diabetic microangiopathy revealed that these effective components mainly exerted their effects by inhibiting oxidative stress and suppressing inflammatory reactions. The study found that the pathogenesis of diabetic microangiopathy was primarily characterized by deficiency in origin, with a combination of deficiency and excess. Deficiency was manifested as Qi deficiency and blood deficiency, while excess as phlegm-heat and blood stasis. The key organ involved in the pathological changes was the liver. The treatment mainly focused on supplementing Qi and nourishing blood, supplemented by clearing heat, coo-ling blood, activating blood, and dredging collaterals. Commonly used formulas included Danggui Buxue Decoction, Liuwei Dihuang Pills, Erzhi Pills, and Buyang Huanwu Decoction. The mechanisms of action of high-frequency drugs in the treatment of diabetic microangiopathy were often related to the inhibition of oxidative stress and suppression of inflammatory reactions. These findings can provide references for the clinical treatment of diabetic microangiopathy and the development of targeted drugs.