The present work aims to study short fatigue crack initiation and propagation induced by geometric microdefects. An empirical approach is used to evaluate the microstructural influences on crack ...growth behavior. Small fatigue crack test on titanium alloy Ti‐6Al‐4V is performed to investigate the crack induced from a geometric microdefect. Analyses on crack initiation and fracture are respectively performed to study the microstructural influences on the stages of initiation and fracture. Subsequently, an empirical multiscale model is employed to predict the short fatigue crack propagation of alloys underlining the transition features. Grain size variation is adopted to reflect the main microstructural contribution to the short crack propagation. An alpha factor is defined to demonstrate the transition interim from microstructurally short crack to physically short crack stages. Furthermore, a relationship between transition factor and grain size is correlated. The results indicate that the value of transition factor tends to decrease with the augment of average grain size. The empirical approach is validated through a variety of fatigue experimental data of alloys including Ti‐6Al‐4V, 2024‐T3, and GH4169.
Abstract Microstructural defects and inhomogeneity of titanium alloys fabricated by additive manufacturing technology make their fatigue performance much more complicated, especially reflected in the ...dispersion of fatigue life. This work employs crystal plasticity finite element method (CPFEM) to predict high cycle fatigue (HCF) life of bi-lamellar Ti-6Al-4V alloy. We first propose a modified VT technique to build representative volume element (RVE) models highlighting lamellar microstructure and micro-defects. Subsequently, fatigue indicator parameter (FIP) is adopted to analyse fatigue deformation under cyclic loading. Finally, HCF life determined by critical fatigue indicator parameter is compared with experimental data collected from published literatures. The results demonstrate that our approach is able to reflect the dispersion of fatigue life and to predict HCF life of bi-lamellar Ti-6Al-4V in a satisfactory manner.
Defect recognition is an important task in intelligent manufacturing. Due to the subjectivity of human annotation, the collected defect data usually contains a lot of noise and unpredictable ...uncertainties, which have a great negative influence on defect recognition. It is a significant challenge to discover an effective defect recognition model with satisfactory uncertainty processing ability. A natural way is to automatically search for an efficient deep model, which can be realized by neural architecture search (NAS). To achieve this, we propose an efficient fuzzy NAS framework for defect recognition, where the searched architecture can effectively handle uncertain information from the given datasets. Specifically, we first design a fuzzy search space and the related encoding strategy for fuzzy NAS. Then, we propose a comparator-based evolutionary search approach, where an online end-to-end comparator is learned to directly determine the selection of candidate architectures from the evolutionary population. The comparator works in an end-to-end way and it transforms the complex ranking problem of evaluating architectures into a simple classification task, which overcomes the rank disorder issue suffered from traditional performance predictors. A series of experimental results demonstrate that the architecture with fewer #Params (1.22 M) search by fuzzy neural architecture search framework for defect recognition method achieves higher accuracy (92.26%) compared to the state-of-the-art results (i.e., DARTS-PV) on the ELPV dataset, as well as competitive results (accuracy = 76.4%, #Params = 1.04 M) on the CODEBRIM dataset. Experimental results show the effectiveness and efficiency of our proposed method in handling uncertain problems.
•Complete process of crack network evolution is modelled through the proposed approach.•It covers multiple crack growth/coalescence, formation/propagation and the rupture.•Microcrack distribution is ...stochastic, which coincides with physical phenomenon.•Influence of aggregate volume fraction on concrete strength and ductility is studied.
Large quantities of microcracks existing in quasi-brittle materials like concrete can be graphically referred as crack network. Specifically, the network evolution includes microcrack growth and coalescence, macrocrack formation and propagation, and the final rupture. In this regard, the present work aims to formulate a numerical approach that is able to describe the whole process of crack network evolution for concrete materials. Particularly postulated is the stochastic distribution of microcracks in concrete, which coincides with the physical phenomenon in concrete. Highlighted are the growth and coalescence of microcracks, as well as the formation and propagation of macrocracks. A numerical case is carried out to study the concrete specimens that are subjected to tensile loading. The thorough process of crack network evolution is clearly simulated through the approach. Also investigated are the influence of aggregate volume fraction on concrete strength and ductility. The proposed approach provides a guide reference for concrete engineering practice, as well as modeling the complete process of concrete failure.
The analysis and discrimination of time series data has important practical significance. Currently, transforming the time series data into networks through visibility graph (VG) methods is an ...effective approach for classifying the series data through GNNs. However, there are two main obstacles to the VG method: (1) the tension between efficiency and complexity during weighted graph construction; (2) difficulty in assigning the different importance of nodes. To tackle these difficulties, we propose an improved weighted visibility graph algorithm (WLVG) in this paper. The proposed algorithm can first intelligently assign weights to the network according to the Euclidean distance among nodes, and then resample the network by the weight coefficients resulting in the removal of the unimportant edges. Finally, in order to effectively aggregate the information among neighbors, the graph isomorphism network (GIN) is utilized for identifying the objects. Experimental results show WLVG outperforms other baseline methods on several practical datasets and demonstrate its effectiveness.
•We propose a new weighting method, which can reduce the interference of remote noisy nodes.•We propose an improved visibility graph algorithm to actively drop the trivial edges in the network.•We combine the proposed weighting method with VG/HVG, which is then integrated with GIN.
Background
Hepatocellular carcinoma (HCC) is characterised by high malignancy, metastasis and recurrence, but the specific mechanism that drives these outcomes is unclear. Recent studies have shown ...that long noncoding RNAs (lncRNAs) can regulate the proliferation and apoptosis of hepatic cells.
Methods
We searched for lncRNAs and microRNAs (miRNAs), which can regulate IGF1 expression, through a bioinformatics website, and predicted that lncRNA taurine‐upregulated gene 1 (TUG1) would have multiple targets for miR‐1‐3p binding, meaning that lncRNA TUG1 played an adsorption role. A double luciferase assay was used to verify the targeting relationship between lncRNA TUG1 and miR‐1‐3p. Western blotting and qPCR were used to verify the targeting relationship between miR‐1‐3p and IGF1, and qPCR was used to verify the regulatory relationship between the lncRNA TUG1‐miR‐1‐3p‐IGF1 axis. CCK‐8 was used to detect the growth activity of miRNA‐transfected L‐O2 cells, and flow cytometry was used to detect cell cycle changes and apoptosis.
Result
The proliferation cycle of L‐O2 cells transfected with miR‐1‐3p mimics was significantly slowed. Flow cytometry showed that the proliferation of L‐O2 cells was slowed, and the apoptosis rate was increased. In contrast, when L‐O2 cells were transfected with miR‐1‐3p inhibitor, the expression of IGF1 was significantly upregulated, and the cell proliferation cycle was significantly accelerated. Flow cytometry showed that the cell proliferation rate was accelerated, and the apoptosis rate was reduced.
Conclusion
LncRNA TUG1 can adsorb miR‐1‐3p as a competitive endogenous RNA (ceRNA) to promote the expression of IGF1 and promote cell proliferation in hepatic carcinogenesis.
Biological function of lncRNA TUG1 in hepatoma cells. A, The growth activity of L‐O2 cells was detected by CCK‐8 after miRNA transfection. B, The proliferation of L‐O2 cells transfected with miRNA was detected by flow cytometry. C, The apoptosis rate of L‐O2 cells after transfection was determined by flow cytometry.
•A modeling method of Kirigami patterns based on chemical corrosion is proposed.•The proposed machine learning approach for prediction is efficient and accurate.•Multi-objective prediction including ...deformation fields and stress fields is realized using a general machine learning approach.
Kirigami-inspired designs hold great potential for the development of functional materials and devices, but predicting the morphological configuration of these structures under various loading conditions remains a challenge for traditional experimental and numerical methods. Here, we present a novel approach that utilizes machine learning algorithms to accurately predict the deformation and stress field of kirigami-inspired programmable active composites. To train our model, first, we used a chemical corrosion algorithm to generate a dataset of kirigami-inspired imaging model accompanied by utilizing finite element simulations to obtain their deformation and stress fields as the ground truth, and subsequently trained the machine learning model to offer robust predictions of the displacement and stress fields of the designated structures. The graphically preprocessing transformation between color space and deformation(stress) fields is used to match the fields prediction of mechanical problems with powerful machine learning approaches in image processing. Our results demonstrate the effectiveness of this approach in predicting the morphological and mechanical behaviors of kirigami-inspired active structures, paving the way for the development of advanced and functional composite designs that are programmable and active.
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Emotion-Cause Pair Extraction (ECPE) is a research objective focused on identifying and extracting all emotion-clause and cause-clause pairs from unannotated emotional text. Traditional methodologies ...have predominantly employed attention mechanisms or joint learning techniques for feature information interaction. However, these approaches often overlook the aggregation of features under the guidance of external knowledge. To enhance performance in addressing this ECPE challenge, we propose a novel knowledge-guided graph attention network, i.e., GAT-ECPE. This model chiefly relies on an interclause dependency graph as a guiding principle. By employing this knowledge-guided graph attention network, we can proficiently combine semantic and structural information between clauses. In addition, an interpair possibility graph, derived from the outcomes of subtasks, is integrated as an additional guiding principle. As such, we are able to aggregate features between clause pairs, thereby facilitating interaction between multiple tasks. Extensive experiments were conducted to validate our proposed model, and the obtained results demonstrate its superiority when compared to 12 considered baselines. In terms of performance metrics, our model achieves an F1 score F1 of 74.92% and a recall R of 77.52%. These values significantly outperform those achieved by state-of-the-art approaches, indicating the effectiveness and superiority of our GAT-ECPE.
Aim
Leptin is an important peptide hormone that regulates food intake and plays a crucial role in modulating olfactory function. Although a few previous studies have investigated the effect of leptin ...on odor perception and discrimination in rodents, research on the neural basis underlying the behavioral changes is lacking. Here we study how leptin affects behavioral performance during a go/no‐go task and how it modulates neural activity of mitral/tufted cells in the olfactory bulb, which plays an important role in odor information processing and representation.
Methods
A go/no‐go odor discrimination task was used in the behavioral test. For in vivo studies, single unit recordings, local field potential recordings and fiber photometry recordings were used. For in vitro studies, we performed patch clamp recordings in the slice of the olfactory bulb.
Results
Behaviorally, leptin affects performance and reaction time in a difficult odor‐discrimination task. Leptin decreases the spontaneous firing of single mitral/tufted cells, decreases the odor‐evoked beta and high gamma local field potential response, and has bidirectional effects on the odor‐evoked responses of single mitral/tufted cells. Leptin also inhibits the population calcium activity in genetically identified mitral/tufted cells and granule cells. Furthermore, in vitro slice recordings reveal that leptin inhibits mitral cell activity through direct modulation of the voltage‐sensitive potassium channel.
Conclusions
The behavioral reduction in odor discrimination observed after leptin administration is likely due to decreased neural activity in mitral/tufted cells, caused by modulation of potassium channels in these cells.
