A Theoretical Model to Predict Both Horizontal Displacement and Vertical Displacement for Electromagnetic Induction-Based Deep Displacement Sensors

Deep displacement observation is one basic means of landslide dynamic study and early warning monitoring and a key part of engineering geological investigation. In our previous work, we proposed a novel electromagnetic induction-based deep displacement sensor (I-type) to predict deep horizontal displacement and a theoretical model called equation-based equivalent loop approach (EELA) to describe its sensing characters. However in many landslide and related geological engineering cases, both horizontal displacement and vertical displacement vary apparently and dynamically so both may require monitoring. In this study, a II-type deep displacement sensor is designed by revising our I-type sensor to simultaneously monitor the deep horizontal displacement and vertical displacement variations at different depths within a sliding mass. Meanwhile, a new theoretical modeling called the numerical integration-based equivalent loop approach (NIELA) has been proposed to quantitatively depict II-type sensors’ mutual inductance properties with respect to predicted horizontal displacements and vertical displacements. After detailed examinations and comparative studies between measured mutual inductance voltage, NIELA-based mutual inductance and EELA-based mutual inductance, NIELA has verified to be an effective and quite accurate analytic model for characterization of II-type sensors. The NIELA model is widely applicable for II-type sensors’ monitoring on all kinds of landslides and other related geohazards with satisfactory estimation accuracy and calculation efficiency

The Tumor Suppressive Role of eIF3f and Its Function in Translation Inhibition and rRNA Degradation

Deregulated translation plays an important role in human cancer. We previously reported decreased eukaryotic initiation factor 3 subunit f (eIF3f) expression in pancreatic cancer. Whether decreased eIF3f expression can transform normal epithelial cells is not known. In our current study, we found evidence that stable knockdown of eIF3f in normal human pancreatic ductal epithelial cells increased cell size, nuclear pleomorphism, cytokinesis defects, cell proliferation, clonogenicity, apoptotic resistance, migration, and formation of 3-dimensional irregular masses. Our findings support the tumor suppressive role of eIF3f in pancreatic cancer. Mechanistically, we found that eIF3f inhibited both cap-dependent and cap-independent translation. An increase in the ribosomal RNA (rRNA) level was suggested to promote the generation of cancer. The regulatory mechanism of rRNA degradation in mammals is not well understood. We demonstrated here that eIF3f promotes rRNA degradation through direct interaction with heterogeneous nuclear ribonucleoprotein (hnRNP) K. We showed that hnRNP K is required for maintaining rRNA stability: under stress conditions, eIF3f dissociates hnRNP K from rRNA, thereby preventing it from protecting rRNA from degradation. We also demonstrated that rRNA degradation occurred in non-P body, non-stress granule cytoplasmic foci that contain eIF3f. Our findings established a new mechanism of rRNA decay regulation mediated by hnRNP K/eIF3f and suggest that the tumor suppressive function of eIF3f may link to impaired rRNA degradation and translation

DA-Res2UNet: Explainable blood vessel segmentation from fundus images

Blood vessel segmentation in fundus images is necessary for the diagnosis of ophthalmic diseases. In recent years, deep learning has achieved eminent performance in blood vessel segmentation, and there still exist challenges to reduce misidentification and improve microvascular segmentation accuracy. One reason is that traditional Convolutional Neural Network (CNN) can not effectively extract multiscale information and discard the unnecessary information. Another reason is we can’t explain why some blood vessels fail to be identified. On the one hand, this paper proposes a Dual Attention Res2UNet (DA-Res2UNet) model. The DA-Res2UNet model uses Res2block rather than CNN to obtain more multiscale information and adds Dual Attention to help the model focus on important information and discard unnecessary information. On the other hand, the explainable method based on a pre-trained fundus image generator is adopted to explore how the model identifies blood vessels. We deduce several special situations that lead to the misidentification based on the model’s explanation and adjust the dataset for these special cases. The adjusted datasets significantly reduce the misidentification in the CHASE_DB1 dataset. Finally, the model trained by the adjusted datasets achieves the state-of-the-art F1-score of 81.88%, 82.77%, and 83.96% on the CHASE_DB1, DRIVE and STARE datasets, respectively

Investigation on interfacial defect criticality of FRP-bonded concrete beams

Bonding fiber reinforced polymer (FRP) has been proven to be an effective and efficient method to strengthen and/or retrofit deficient concrete components and structures. Interfacial defects may easily arise due to improper construction or environmental deterioration during the designed service life and they may cause an unfavorable effect on the local bond behavior and global performance of FRP-bonded concrete systems. However, the information on the interfacial defect effect and the guideline for distinguishing the criticality of interfacial defect is limited, making it difficult to assess the long term integrity. In this study, FRP-bonded concrete beams containing various interfacial defects are under four-point bending test to evaluate the defect effect and determine the interfacial defect criticality from location and size aspects. Meanwhile, finite element models representing different sizes of FRP-bonded concrete beams are built and simulated to study the size effect of beam. Both the experimental observation and numerical results indicate that the deep beam is more sensitive to interfacial defect than normal beam. The threshold for critical interfacial defect varies significantly depending on the beam type and defect location. The small, medium and large categories of interfacial defect can be classified according to the beam type, defect location and defect size sequentially. Different maintenance strategies should be adopted corresponding to small, medium and large interfacial defects. The interfacial defect criticality unveiled from this study can provide guidelines for maintenance when defect is detected during inspection and it can be beneficial to a more precise performance evaluation and service life prediction of FRP-bonded concrete structures

Dry Eye Disease following Refractive Surgery: A 12-Month Follow-Up of SMILE versus FS-LASIK in High Myopia

Purpose. To compare dry eye disease following SMILE versus FS-LASIK. Design. Prospective, nonrandomised, observational study. Patients. 90 patients undergoing refractive surgery for myopia were included. 47 eyes underwent SMILE and 43 eyes underwent FS-LASIK. Methods. Evaluation of dry eye disease was conducted preoperatively and at 1, 3, 6, and 12 months postoperatively, using the Salisbury Eye Evaluation Questionnaire (SEEQ) and TBUT. Results. TBUT reduced following SMILE at 1 and 3 months (p<0.001) and at 1, 3, and 6 months following FS-LASIK (p<0.001). TBUT was greater following SMILE than FS-LASIK at 3, 6, and 12 months (p<0.001, p<0.001, and p=0.009, resp.). SEEQ scores increased (greater symptoms) following SMILE at 1 month (p<0.001) and 3 months (p=0.003) and at 1, 3, and 6 months following FS-LASIK (p<0.001). SMILE produced lower SEEQ scores (fewer symptoms) than FS-LASIK at 1, 3, and 6 months (p<0.001). Conclusion. SMILE produces less dry eye disease than FS-LASIK at 6 months postoperatively but demonstrates similar degrees of dry eye disease at 12 months

Experimental Investigation on Interfacial Defect Criticality of FRP-Confined Concrete Columns

Defects between fiber reinforced polymer (FRP) and repaired concrete components may easily come out due to misoperation during manufacturing, environmental deterioration, or impact from external load during service life. The defects may cause a degraded structure performance and even the unexpected structural failure. Different non-destructive techniques (NDTs) and sensors have been developed to assess the defects in FRP bonded system. The information of linking up the detected defects by NDTs and repair schemes is needed by assessing the criticality of detected defects. In this study, FRP confined concrete columns with interfacial defects were experimentally tested to determine the interfacial defect criticality on structural performance. It is found that interfacial defect can reduce the FRP confinement effectiveness, and ultimate strength and its corresponding strain of column deteriorate significantly if the interfacial defect area is larger than 50% of total confinement area. Meanwhile, proposed analytical model considering the defect ratio is validated for the prediction of stress&ndash;strain behavior of FRP confined columns. The evaluation of defect criticality could be made by comparing predicted stress&ndash;strain behavior with the original design to determine corresponding maintenance strategies

The role of the disordered HfO2 network in the high- κ n-MOSFET shallow electron trapping

Current understanding of the bias temperature instability degradation usually comprises two parts: (1) shallow-level component that can recover within a short time and (2) deep level traps that the emission time of the trapped carrier is extremely long. Prevenient studies of the positive bias temperature instability degradation in the high-κ n-MOSFET indicate that oxygen vacancy (VO) is the dominant defect type that responds for the shallow electron trapping. However, recent experimental results reveal that the VO defect density required to accommodate the experimental measured recoverable threshold voltage degradation (ΔVth) is much higher than that of the reasonable atomic structure in the amorphous HfO2. On the other hand, investigations on the disordered Hf-O-Hf network in the amorphous HfO2 reveal their capabilities as charge trapping centers; therefore, in this work, atomic simulation work is performed, and our results show that the disordered Hf-O-Hf networks can act as effective electron capture centers with shallow levels near the Si conduction band. Moreover, the high density of the stretched Hf-O-Hf networks in the amorphous HfO2 also significantly enriches the shallow electron traps in the oxide.MOE (Min. of Education, S’pore)Published versio