A certain rubber shock absorber’s dynamic response research under impulse load

The complex impulse environment of artillery firing process brings very tough impulse resistance requirement for the artillery carrier equipment. The results of the vibration test of an electrical control box with a certain rubber shock absorber show that good shock absorb effect has not been received. Based on carefully theoretical analysis and simulation, it can be sure that the rigid collision when shock absorber reaches its limit is the reason. From the point of view of the theory and simulation of the experimental results, the analysis of the experiment results is given, which can provide the necessary theoretical basis of how to choose the appropriate shock absorber

Enhancing Building Semantic Segmentation Accuracy with Super Resolution and Deep Learning: Investigating the Impact of Spatial Resolution on Various Datasets

The development of remote sensing and deep learning techniques has enabled building semantic segmentation with high accuracy and efficiency. Despite their success in different tasks, the discussions on the impact of spatial resolution on deep learning based building semantic segmentation are quite inadequate, which makes choosing a higher cost-effective data source a big challenge. To address the issue mentioned above, in this study, we create remote sensing images among three study areas into multiple spatial resolutions by super-resolution and down-sampling. After that, two representative deep learning architectures: UNet and FPN, are selected for model training and testing. The experimental results obtained from three cities with two deep learning models indicate that the spatial resolution greatly influences building segmentation results, and with a better cost-effectiveness around 0.3m, which we believe will be an important insight for data selection and preparation

Text Classification via Large Language Models

Despite the remarkable success of large-scale Language Models (LLMs) such as GPT-3, their performances still significantly underperform fine-tuned models in the task of text classification. This is due to (1) the lack of reasoning ability in addressing complex linguistic phenomena (e.g., intensification, contrast, irony etc); (2) limited number of tokens allowed in in-context learning. In this paper, we introduce Clue And Reasoning Prompting (CARP). CARP adopts a progressive reasoning strategy tailored to addressing the complex linguistic phenomena involved in text classification: CARP first prompts LLMs to find superficial clues (e.g., keywords, tones, semantic relations, references, etc), based on which a diagnostic reasoning process is induced for final decisions. To further address the limited-token issue, CARP uses a fine-tuned model on the supervised dataset for $k$NN demonstration search in the in-context learning, allowing the model to take the advantage of both LLM's generalization ability and the task-specific evidence provided by the full labeled dataset. Remarkably, CARP yields new SOTA performances on 4 out of 5 widely-used text-classification benchmarks, 97.39 (+1.24) on SST-2, 96.40 (+0.72) on AGNews, 98.78 (+0.25) on R8 and 96.95 (+0.6) on R52, and a performance comparable to SOTA on MR (92.39 v.s. 93.3). More importantly, we find that CARP delivers impressive abilities on low-resource and domain-adaptation setups. Specifically, using 16 examples per class, CARP achieves comparable performances to supervised models with 1,024 examples per class.Comment: Pre-print Versio

Predicting coronary stenosis progression using plaque fatigue from IVUS-based thin-slice models: A machine learning random forest approach

Introduction: Coronary stenosis due to atherosclerosis restricts blood flow. Stenosis progression would lead to increased clinical risk such as heart attack. Although many risk factors were found to contribute to atherosclerosis progression, factors associated with fatigue is underemphasized. Our goal is to investigate the relationship between fatigue and stenosis progression based on in vivo intravascular ultrasound (IVUS) images and finite element models. Methods: Baseline and follow-up in vivo IVUS and angiography data were acquired from seven patients using Institutional Review Board approved protocols with informed consent obtained. Three hundred and five paired slices at baseline and follow-up were matched and used for plaque modeling and analysis. IVUS-based thin-slice models were constructed to obtain the coronary biomechanics and stress/strain amplitudes (stress/strain variations in one cardiac cycle) were used as the measurement of fatigue. The change of lumen area (DLA) from baseline to follow-up were calculated to measure stenosis progression. Nineteen morphological and biomechanical factors were extracted from 305 slices at baseline. Correlation analyses of these factors with DLA were performed. Random forest (RF) method was used to fit morphological and biomechanical factors at baseline to predict stenosis progression during follow-up. Results: Significant correlations were found between stenosis progression and maximum stress amplitude, average stress amplitude and average strain amplitude (p < 0.05). After factors selection implemented by random forest (RF) method, eight morphological and biomechanical factors were selected for classification prediction of stenosis progression. Using eight factors including fatigue, the overall classification accuracy, sensitivity and specificity of stenosis progression prediction with RF method were 83.61%, 86.25% and 80.69%, respectively. Conclusion: Fatigue correlated positively with stenosis progression. Factors associated with fatigue could contribute to better prediction for atherosclerosis progression

Combining IVUS + OCT data, biomechanical models and machine learning method for accurate coronary plaque morphology quantification and cap thickness and stress/strain index predictions

Assessment and prediction of vulnerable plaque progression and rupture risk are of utmost importance for diagnosis, management and treatment of cardiovascular diseases and possible prevention of acute cardiovascular events such as heart attack and stroke. However, accurate assessment of plaque vulnerability assessment and prediction of its future changes require accurate plaque cap thickness, tissue component and structure quantifications and mechanical stress/strain calculations. Multi-modality intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography image data with follow-up were acquired from ten patients to obtain accurate and reliable plaque morphology for model construction. Three-dimensional thin-slice finite element models were constructed for 228 matched IVUS + OCT slices to obtain plaque stress/strain data for analysis. Quantitative plaque cap thickness and stress/strain indices were introduced as substitute quantitative plaque vulnerability indices (PVIs) and a machine learning method (random forest) was employed to predict PVI changes with actual patient IVUS + OCT follow-up data as the gold standard. Our prediction results showed that optimal prediction accuracies for changes in cap-PVI (C-PVI), mean cap stress PVI (meanS-PVI) and mean cap strain PVI (meanSn-PVI) were 90.3% (AUC = 0.877), 85.6% (AUC = 0.867) and 83.3% (AUC = 0.809), respectively. The improvements in prediction accuracy by the best combination predictor over the best single predictor were 6.6% for C-PVI, 10.0% for mean S-PVI and 8.0% for mean Sn-PVI. Our results demonstrated the potential using multi-modality IVUS + OCT image to accurately and efficiently predict plaque cap thickness and stress/strain index changes. Combining mechanical and morphological predictors may lead to better prediction accuracies

Differential activation of the medial temporal lobe during item and associative memory across time

Studies have shown that the hippocampus plays a crucial role in associative memory. One central issue is whether the involvement of the hippocampus in associative memory remains stable or declines with the passage of time. In the majority of studies, memory performance declines with delay, confounding attempts at interpreting differences in hippocampal activation over time. To address this issue, we tried to equate behavioral performance as much as possible across time for memory of items and associations separately. After encoding words and word pairs, participants were tested for item and associative memories at four time intervals: 20-min, 1-day, 1-week, and 1-month. The results revealed that MTL activation differed over time for associative and item memories. For associative memory, the activation of the anterior hippocampus decreased from 20-min to 1-day then remained stable, whereas in the posterior hippocampus, the activation was comparable for different time intervals when old pairs were correctly retrieved. The hippocampal activation also remained stable when recombined pairs were correctly rejected. As this condition controls for familiarity of the individual items, correct performance depends only on associative memory. For item memory, hippocampal activation declined progressively from 20-min to 1-week and remained stable afterwards. By contrast, the activation in the perirhinal/entorhinal cortex increased over time irrespective of item and associative memories. Drawing on Tulving's distinction between recollection and familiarity, we interpret this pattern of results in accordance with Trace Transformation Theory, which states that as memories are transformed with time and experience, the neural structures mediating item and associative memories will vary according to the underlying representations to which the memories have been transformed

A systematic review of the mechanism of action and potential medicinal value of codonopsis pilosula in diseases

As a traditional Chinese medicinal herb with a long history, Codonopsis pilosula (CP) has attracted much attention from the medical community in recent years. This review summarizes the research progress of CP in the medical field in the past 5 years. By searching and analyzing the literature, and combining with Cytoscape software, we comprehensively examined the role and mechanism of action of CP in individual application, combination drug application, and the role and mechanism of action of codonopsis pilosula’s active ingredients in a variety of diseases. It also analyzes the medicinal use of CP and its application value in medicine. This review found that CP mainly manifests important roles in several diseases, such as cardiovascular system, nervous system, digestive system, immune system, etc., and regulates the development of many diseases mainly through the mechanisms of inflammation regulation, oxidative stress, immunomodulation and apoptosis. Its rich pharmacological activities and diverse medicinal effects endow CP with broad prospects and application values. This review provides valuable reference and guidance for the further development of CP in traditional Chinese medicine

Quantification of patient-specific coronary material properties and their correlations with plaque morphological characteristics: An in vivo IVUS study

BACKGROUND: A method using in vivo Cine IVUS and VH-IVUS data has been proposed to quantify material properties of coronary plaques. However, correlations between plaque morphological characteristics and mechanical properties have not been studied in vivo. METHOD: In vivo Cine IVUS and VH-IVUS data were acquired at 32 plaque cross-sections from 19 patients. Six morphological factors were extracted for each plaque. These samples were categorized into healthy vessel, fibrous plaque, lipid-rich plaque and calcified plaque for comparisons. Three-dimensional thin-slice models were constructed using VH-IVUS data to quantify in vivo plaque material properties following a finite element updating approach by matching Cine IVUS data. Effective Young\u27s moduli were calculated to represent plaque stiffness for easy comparison. Spearman\u27s rank correlation analysis was performed to identify correlations between plaque stiffness and morphological factor. Kruskal-Wallis test with Bonferroni correction was used to determine whether significant differences in plaque stiffness exist among four plaque groups. RESULT: Our results show that lumen circumference change has a significantly negative correlation with plaque stiffness (r = -0.7807, p = 0.0001). Plaque burden and calcification percent also had significant positive correlations with plaque stiffness (r = 0.5105, p \u3c 0.0272 and r = 0.5312, p \u3c 0.0193) respectively. Among the four categorized groups, calcified plaques had highest stiffness while healthy segments had the lowest. CONCLUSION: There is a close link between plaque morphological characteristics and mechanical properties in vivo. Plaque stiffness tends to be higher as coronary atherosclerosis advances, indicating the potential to assess plaque mechanical properties in vivo based on plaque compositions

Using optical coherence tomography and intravascular ultrasound imaging to quantify coronary plaque cap stress/strain and progression: A follow-up study using 3D thin-layer models

Accurate plaque cap thickness quantification and cap stress/strain calculations are of fundamental importance for vulnerable plaque research. To overcome uncertainties due to intravascular ultrasound (IVUS) resolution limitation, IVUS and optical coherence tomography (OCT) coronary plaque image data were combined together to obtain accurate and reliable cap thickness data, stress/strain calculations, and reliable plaque progression predictions. IVUS, OCT, and angiography baseline and follow-up data were collected from nine patients (mean age: 69; m: 5) at Cardiovascular Research Foundation with informed consent obtained. IVUS and OCT slices were coregistered and merged to form IVUS + OCT (IO) slices. A total of 114 matched slices (IVUS and OCT, baseline and follow-up) were obtained, and 3D thin-layer models were constructed to obtain stress and strain values. A generalized linear mixed model (GLMM) and least squares support vector machine (LSSVM) method were used to predict cap thickness change using nine morphological and mechanical risk factors. Prediction accuracies by all combinations (511) of those predictors with both IVUS and IO data were compared to identify optimal predictor(s) with their best accuracies. For the nine patients, the average of minimum cap thickness from IVUS was 0.17 mm, which was 26.08% lower than that from IO data (average = 0.23 mm). Patient variations of the individual errors ranged from ‒58.11 to 20.37%. For maximum cap stress between IO and IVUS, patient variations of the individual errors ranged from ‒30.40 to 46.17%. Patient variations of the individual errors of maximum cap strain values ranged from ‒19.90 to 17.65%. For the GLMM method, the optimal combination predictor using IO data had AUC (area under the ROC curve) = 0.926 and highest accuracy = 90.8%, vs. AUC = 0.783 and accuracy = 74.6% using IVUS data. For the LSSVM method, the best combination predictor using IO data had AUC = 0.838 and accuracy = 75.7%, vs. AUC = 0.780 and accuracy = 69.6% using IVUS data. This preliminary study demonstrated improved plaque cap progression prediction accuracy using accurate cap thickness data from IO slices and the differences in cap thickness, stress/strain values, and prediction results between IVUS and IO data. Large-scale studies are needed to verify our findings