Intelligent calibration of static FEA computations based on terrestrial laser scanning reference

The demand for efficient and accurate finite element analysis (FEA) is becoming more prevalent with the increase in advanced calibration technologies and sensor-based monitoring methods. The current research explores a deep learning-based methodology to calibrate FEA results. The utilization of monitoring reference results from measurements, e.g., terrestrial laser scanning, can help to capture the actual features in the static loading process. We learn the deviation sequence results between the standard FEA computations with the simplified geometry and refined reference values by the long short-term memory method. The complex changing principles in different deviations are trained and captured effectively in the training process of deep learning. Hence, we generate the FEA sequence results corresponding to next adjacent loading steps. The final FEA computations are calibrated by the threshold control. The calibration reduces the mean square errors of the FEA future sequence results significantly. This strengthens the calibration depth. Consequently, the calibration of FEA computations with deep learning can play a helpful role in the prediction and monitoring problems regarding the future structural behaviors. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge

Can large language models (LLMs) express their uncertainty in situations where they lack sufficient parametric knowledge to generate reasonable responses? This work aims to systematically investigate LLMs' behaviors in such situations, emphasizing the trade-off between honesty and helpfulness. To tackle the challenge of precisely determining LLMs' knowledge gaps, we diagnostically create unanswerable questions containing non-existent concepts or false premises, ensuring that they are outside the LLMs' vast training data. By compiling a benchmark, UnknownBench, which consists of both unanswerable and answerable questions, we quantitatively evaluate the LLMs' performance in maintaining honesty while being helpful. Using a model-agnostic unified confidence elicitation approach, we observe that most LLMs fail to consistently refuse or express uncertainty towards questions outside their parametric knowledge, although instruction fine-tuning and alignment techniques can provide marginal enhancements. Moreover, LLMs' uncertainty expression does not always stay consistent with the perceived confidence of their textual outputs.Comment: 20 page

MicroRNA-197 Promotes Metastasis of Hepatocellular Carcinoma by Activating Wnt/β-Catenin Signaling

Background/Aims: MicroRNA-197 (miR-197) has been shown to play roles in epithelialmesenchymal transition (EMT) and metastasis. The Wnt/β-catenin pathway is associated with EMT, but whether miR-197 regulatesWnt/β-catenin remains unclear. This study was to demonstrate the role of miR-197 on the Wnt/β-catenin pathway in hepatocellular carcinoma (HCC). Methods: Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to detect the expression of miR-197 in 105 HCC specimens and 15 HCC cell lines. We tested the predicted target gene of miR-197 using a genetic report system. The role of miR-197 in HCC cell invasion and migration (wound healingand cell invasion and migrationby Transwell assays) and in an HCC xenograft modelwas analyzed. Results: Using a miRNA microarray analysis of HCC specimens and compared with non-metastatic HCC, miR-197 was identified as one of the most upregulated miRNAs in metastatic HCC. miR-197 expression was positively associated with the invasiveness of HCC cell lines. Metastatic HCC cells with high miR-197 expression had Wnt/β-catenin signaling activation. High levels of miR-197 expression also promoted EMT and invasionHCC cells in vitro and in vivo. miR-197 directly targeted Axin-2, Naked cuticle 1 (NKD1), and Dickkopf-related protein 2 (DKK2), leading to inhibition of Wnt/β-catenin signaling. High miR-197 expression was found in HCC specimens from patients with portal vein metastasis;high miR-197 expression correlated to the expression of Axin2, NKD1, and DKK2. Conclusion: miR-197 promotes HCC invasion and metastasis by activating Wnt/β-catenin signaling. miR-197 could possibly be used as a prognostic marker and therapeutic target for HCC

Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

The demand for efficient and accurate finite element analysis (FEA) is becoming more prevalent with the increase in advanced calibration technologies and sensor-based monitoring methods. The current research explores a deep learning-based methodology to calibrate FEA results. The utilization of monitoring reference results from measurements, e.g., terrestrial laser scanning, can help to capture the actual features in the static loading process. We learn the deviation sequence results between the standard FEA computations with the simplified geometry and refined reference values by the long short-term memory method. The complex changing principles in different deviations are trained and captured effectively in the training process of deep learning. Hence, we generate the FEA sequence results corresponding to next adjacent loading steps. The final FEA computations are calibrated by the threshold control. The calibration reduces the mean square errors of the FEA future sequence results significantly. This strengthens the calibration depth. Consequently, the calibration of FEA computations with deep learning can play a helpful role in the prediction and monitoring problems regarding the future structural behaviors

Energy-Saving Performance of Flap-Adjustment-Based Centrifugal Fan

The current paper mainly focuses on finding a more appropriate way to enhance the fan performance at off-design conditions. The centrifugal fan (CF) based on flap-adjustment (FA) has been investigated through theoretical, experimental, and finite element methods. To obtain a more predominant performance of CF from the different adjustments, we carried out a comparative analysis on FA and leading-adjustment (LA) in aerodynamic performances, which included the adjusted angle of blades, total pressure, efficiency, system-efficiency, adjustment-efficiency, and energy-saving rate. The contribution of this paper is the integrated performance curve of the CF. Finally, the results showed that the effects of FA and LA on economic performance and energy savings of the fan varied with the blade angles. Furthermore, FA was feasible, which is more sensitive than LA. Moreover, the CF with FA offered a more extended flow-range of high economic characteristic in comparison with LA. Finally, when the operation flow-range extends, energy-saving rate of the fan with FA would have improvement

Peptide Mimic Isolated by Autoantibody Reveals Human Arrest Defective 1 Overexpression Is Associated with Poor Prognosis for Colon Cancer Patients

Tumor-associated antigens, which induce the generation of autoantibodies, are useful as cancer biomarkers in early detection and prognostic prediction of cancer. To isolate a novel cancer marker, we used serum antibodies from colon cancer patients to screen a phage display peptide library. A positive peptide 249C (VPLYSNTLRYGF) that could specifically react with serum from colon cancer patients was isolated, and the corresponding antigen–human arrest defective 1 (ARD1A), which shares an identical LYSNTL motif with 249C, was identified. Both immunological assays and three-dimensional structure analysis showed that the LYSNTL region is an epitope of ARD1A. Using ELISA and immunohistochemistry, we found anti-ARD1A antibody levels in serum from patients with colon cancer were significantly higher than those in healthy volunteers (P < 0.001), and ARD1A expression was detected in 84.1% (227/270) of colon cancer tissues compared with 22.7% (55/242) of matched noncancerous tissues (P < 0.001) and 4.8% (2/42) of benign lesions (P < 0.001). Furthermore, multivariate analysis with Cox proportional hazards regression models revealed that ARD1A-positive patients had significantly shortened overall survival (OS) (HR, 1.91, P = 0.039) and borderline significantly shortened disease-free survival (DFS) (HR, 1.70; P = 0.068). Kaplan–Meier survival curves also showed that ARD1A expression was associated significantly with shortened DFS (P = 0.037) and OS (P = 0.019). These results indicate that ARD1A is a novel tumor-associated antigen and a potential prognostic factor for colon cancer