Vibration based damage identification of a scale-model steel frame structure subjected to bolt connection failures

Large-span steel frame structures prove to be an ideal choice for their speed of construction, relatively low cost, strength, durability and structural design flexibility. For this type of structure, the beam-column connections are critical for its structural integrity and overall stability. This is because a steel frame generally fails first at its connectors, due to the change in stress redistribution with adjacent members and material related failures, caused by various factors such as fire, seismic activity or material deterioration. Since particular attention is required at a steel frame’s connection points, this study explores the applicability of a comprehensive structural health monitoring (SHM) method to identify early damage and prolong the lifespan of connection points of steel frames. An impact hammer test was performed on a scale-model steel frame structure, recording its dynamic response to the hammer strike via an accelerometer. The testing procedure included an intact scenario and two damage scenarios by unfastening four bolt connections in an accumulating order. Based entirely on time-domain experimental data for its calibration, an Auto Regressive Average Exogenous (ARMAX) model is used to create a simple and accurate model for vibration simulation. The calibrated ARMAX model is then used to identify various bolt-connection related damage scenarios via R2 value. The findings in this study suggest that the proposed time-domain approach is capable of identifying structural damage in a parsimonious manner and can be used as a quick or initial solution

BTS: Bifold Teacher-Student in Semi-Supervised Learning for Indoor Two-Room Presence Detection Under Time-Varying CSI

In recent years, indoor human presence detection based on supervised learning (SL) and channel state information (CSI) has attracted much attention. However, the existing studies that rely on spatial information of CSI are susceptible to environmental changes, such as object movement, atmospheric factors, and machine rebooting, which degrade prediction accuracy. Moreover, SL-based methods require time-consuming labeling for retraining models. Therefore, it is imperative to design a continuously monitored model life-cycle using a semi-supervised learning (SSL) based scheme. In this paper, we conceive a bifold teacher-student (BTS) learning approach for presence detection systems that combines SSL by utilizing partially labeled and unlabeled datasets. The proposed primal-dual teacher-student network intelligently learns spatial and temporal features from labeled and unlabeled CSI. Additionally, the enhanced penalized loss function leverages entropy and distance measures to distinguish drifted data, i.e., features of new datasets affected by time-varying effects and altered from the original distribution. The experimental results demonstrate that the proposed BTS system sustains asymptotic accuracy after retraining the model with unlabeled data. Furthermore, the label-free BTS outperforms existing SSL-based models in terms of the highest detection accuracy while achieving the asymptotic performance of SL-based methods

New Insights on 30 Dor B Revealed by High-Quality Multi-wavelength Observations

The supernova remnant (SNR) 30 Dor B is associated with the \ion{H}{2} region ionized by the OB association LH99. The complex interstellar environment has made it difficult to study the physical structure of this SNR. We have used Hubble Space Telescope H$\alpha$ images to identify SNR shocks and deep Chandra X-ray observations to detect faint diffuse emission. We find that 30 Dor B hosts three zones with very different X-ray surface brightnesses and nebular kinematics that are characteristic of SNRs in different interstellar environments and/or evolutionary stages. The ASKAP 888 MHz map of 30 Dor B shows counterparts to all X-ray emission features except the faint halo. The ASKAP 888 MHz and 1420 MHz observations are used to produce a spectral index map, but its interpretation is complicated by the background thermal emission and the pulsar PSR J0537$-$6910's flat spectral index. The stellar population in the vicinity of 30 Dor B indicates a continuous star formation in the past 8--10 Myr. The observed very massive stars in LH99 cannot be coeval with the progenitor of 30 Dor B's pulsar. Adopting the pulsar's spin-down timescale, 5000 yr, as the age of the SNR, the X-ray shell would be expanding at $\sim$4000 km\,s$^{-1}$ and the post-shock temperature would be 1--2 orders of magnitude higher than that indicated by the X-ray spectra. Thus, the bright central region of 30 Dor B and the X-ray shell requires two separate SN events, and the faint diffuse X-ray halo perhaps other older SN events

PGT-Net: Progressive Guided Multi-task Neural Network for Small-area Wet Fingerprint Denoising and Recognition

Fingerprint recognition on mobile devices is an important method for identity verification. However, real fingerprints usually contain sweat and moisture which leads to poor recognition performance. In addition, for rolling out slimmer and thinner phones, technology companies reduce the size of recognition sensors by embedding them with the power button. Therefore, the limited size of fingerprint data also increases the difficulty of recognition. Denoising the small-area wet fingerprint images to clean ones becomes crucial to improve recognition performance. In this paper, we propose an end-to-end trainable progressive guided multi-task neural network (PGT-Net). The PGT-Net includes a shared stage and specific multi-task stages, enabling the network to train binary and non-binary fingerprints sequentially. The binary information is regarded as guidance for output enhancement which is enriched with the ridge and valley details. Moreover, a novel residual scaling mechanism is introduced to stabilize the training process. Experiment results on the FW9395 and FT-lightnoised dataset provided by FocalTech shows that PGT-Net has promising performance on the wet-fingerprint denoising and significantly improves the fingerprint recognition rate (FRR). On the FT-lightnoised dataset, the FRR of fingerprint recognition can be declined from 17.75% to 4.47%. On the FW9395 dataset, the FRR of fingerprint recognition can be declined from 9.45% to 1.09%

Recombinant VP1, an Akt Inhibitor, Suppresses Progression of Hepatocellular Carcinoma by Inducing Apoptosis and Modulation of CCL2 Production

BACKGROUND: The application of viral elements in tumor therapy is one facet of cancer research. Recombinant capsid protein VP1 (rVP1) of foot-and-mouth disease virus has previously been demonstrated to induce apoptosis in cancer cell lines. Here, we aim to further investigate its apoptotic mechanism and possible anti-metastatic effect in murine models of hepatocellular carcinoma (HCC), one of the most common human cancers worldwide. METHODOLOGY/PRINCIPAL FINDINGS: Treatment with rVP1 inhibited cell proliferation in two murine HCC cell lines, BNL and Hepa1-6, with IC₅₀ values in the range of 0.1-0.2 µM. rVP1 also induced apoptosis in these cells, which was mediated by Akt deactivation and dissociation of Ku70-Bax, and resulted in conformational changes and mitochondrial translocation of Bax, leading to the activation of caspases-9, -3 and -7. Treatment with 0.025 µM rVP1, which did not affect the viability of normal hepatocytes, suppressed cell migration and invasion via attenuating CCL2 production. The production of CCL2 was modulated by Akt-dependent NF-κB activation that was decreased after rVP1 treatment. The in vivo antitumor effects of rVP1 were assessed in both subcutaneous and orthotopic mouse models of HCC in immune-competent BALB/c mice. Intratumoral delivery of rVP1 inhibited subcutaneous tumor growth as a result of increased apoptosis. Intravenous administration of rVP1 in an orthotopic HCC model suppressed tumor growth, inhibited intra-hepatic metastasis, and prolonged survival. Furthermore, a decrease in the serum level of CCL2 was observed in rVP1-treated mice. CONCLUSIONS/SIGNIFICANCE: The data presented herein suggest that, via inhibiting Akt phosphorylation, rVP1 suppresses the growth, migration, and invasion of murine HCC cells by inducing apoptosis and attenuating CCL2 production both in vitro and in vivo. Recombinant protein VP1 thus has the potential to be developed as a new therapeutic agent for HCC

Taiwan Oscillation Network

The Taiwan Oscillation Network (TON) is a ground-based network to measure solar intensity oscillations to study the internal structure of the Sun. K-line full-disk images of 1000 pixels diameter are taken at a rate of one image per minute. Such data would provide information onp-modes withl as high as 1000. The TON will consist of six identical telescope systems at proper longitudes around the world. Three telescope systems have been installed at Teide Observatory (Tenerife), Huairou Solar Observing Station (near Beijing), and Big Bear Solar Observatory (California). The telescopes at these three sites have been taking data simultaneously since October of 1994. Anl – v diagram derived from 512 images is included to show the quality of the data

Senescence rewires microenvironment sensing to facilitate anti-tumor immunity

Cellular senescence involves a stable cell cycle arrest coupled to a secretory program that, in some instances, stimulates the immune clearance of senescent cells. Using an immune competent liver cancer model in which senescence triggers CD8 T cell-mediated tumor rejection, we show that senescence also remodels the cell surface proteome to alter how tumor cells sense environmental factors, as exemplified by Type II interferon (IFN-y). Compared to proliferating cells, senescent cells upregulate the IFN-y receptor, become hypersensitized to microenvironmental IFN-y, and more robustly induce the antigen presenting machinery--effects also recapitulated in human tumor cells undergoing therapy-induced senescence. Disruption of IFN-y sensing in senescent cells blunts their immune-mediated clearance without disabling the senescence state or its characteristic secretory program. Our results demonstrate that senescent cells have an enhanced ability to both send and receive environmental signals, and imply that each process is required for their effective immune surveillance