Damage Detection of Structures Identified with Deterministic-Stochastic Models Using Seismic Data

A deterministic-stochastic subspace identification method is adopted and experimentally verified in this study to identify the equivalent single-input-multiple-output system parameters of the discrete-time state equation. The method of damage locating vector (DLV) is then considered for damage detection. A series of shaking table tests using a five-storey steel frame has been conducted. Both single and multiple damage conditions at various locations have been considered. In the system identification analysis, either full or partial observation conditions have been taken into account. It has been shown that the damaged stories can be identified from global responses of the structure to earthquakes if sufficiently observed. In addition to detecting damage(s) with respect to the intact structure, identification of new or extended damages of the as-damaged counterpart has also been studied. This study gives further insights into the scheme in terms of effectiveness, robustness, and limitation for damage localization of frame systems

Personal Exposure to Submicrometer Particles and Heart Rate Variability in Human Subjects

We conducted a study on two panels of human subjects—9 young adults and 10 elderly patients with lung function impairments—to evaluate whether submicrometer particulate air pollution was associated with heart rate variability (HRV). We measured these subjects’ electrocardiography and personal exposure to number concentrations of submicrometer particles with a size range of 0.02–1 μm (NC(0.02–1)) continuously during daytime periods. We used linear mixed-effects models to estimate the relationship between NC(0.02–1) and log(10)-transformed HRV, including standard deviation of all normal-to-normal intervals (SDNN), square root of the mean of the sum of the squares of differences between adjacent NN intervals (r-MSSD), low frequency (LF, 0.04–0.15 Hz), and high frequency (HF, 0.15–0.40 Hz), adjusted for age, sex, body mass index, tobacco exposure, and temperature. For the young panel, a 10,000-particle/cm(3) increase in NC(0.02–1) with 1–4 hr moving average exposure was associated with 0.68–1.35% decreases in SDNN, 1.85–2.58% decreases in r-MSSD, 1.32–1.61% decreases in LF, and 1.57–2.60% decreases in HF. For the elderly panel, a 10,000-particle/cm(3) increase in NC(0.02–1) with 1–3 hr moving average exposure was associated with 1.72–3.00% decreases in SDNN, 2.72–4.65% decreases in r-MSSD, 3.34–5.04% decreases in LF, and 3.61–5.61% decreases in HF. In conclusion, exposure to NC(0.02–1) was associated with decreases in both time-domain and frequency-domain HRV indices in human subjects

Improving the Performance of R17 Type-II Codebook with Deep Learning

The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain partial reciprocity between uplink and downlink channels to select part of angular-delay-domain ports for measuring and feeding back the downlink channel state information (CSI), where the performance of existing deep learning enhanced CSI feedback methods is limited due to the deficiency of sparse structures. To address this issue, we propose two new perspectives of adopting deep learning to improve the R17 Type-II codebook. Firstly, considering the low signal-to-noise ratio of uplink channels, deep learning is utilized to accurately select the dominant angular-delay-domain ports, where the focal loss is harnessed to solve the class imbalance problem. Secondly, we propose to adopt deep learning to reconstruct the downlink CSI based on the feedback of the R17 Type-II codebook at the base station, where the information of sparse structures can be effectively leveraged. Besides, a weighted shortcut module is designed to facilitate the accurate reconstruction. Simulation results demonstrate that our proposed methods could improve the sum rate performance compared with its traditional R17 Type-II codebook and deep learning benchmarks.Comment: Accepted by IEEE GLOBECOM 2023, conference version of Arxiv:2305.0808

S-Petasin, the Main Sesquiterpene of Petasites formosanus, Inhibits Phosphodiesterase Activity and Suppresses Ovalbumin-Induced Airway Hyperresponsiveness

S-Petasin is the main sesquiterpene of Petasites formosanus, a traditional folk medicine used to treat hypertension, tumors and asthma in Taiwan. The aim of the present study was to investigate its inhibitory effects on phosphodiesterase (PDE) 1–5, and on ovalbumin (OVA)-induced airway hyperresponsiveness (AHR) in a murine model of allergic asthma. S-Petasin concentration-dependently inhibited PDE3 and PDE4 activities with 50% inhibitory concentrations (IC50) of 25.5, and 17.5 μM, respectively. According to the Lineweaver-Burk analysis, S-petasin competitively inhibited PDE3 and PDE4 activities with respective dissociation constants for inhibitor binding (Ki) of 25.3 and 18.1 μM, respectively. Both IC50 and Ki values for PDE3 were significantly greater than those for PDE4. S-Petasin (10–30 μmol/kg, administered subcutaneously (s.c.)) dose-dependently and significantly attenuated the enhanced pause (Penh) value induced by methacholine (MCh) in sensitized and challenged mice. It also significantly suppressed the increases in total inflammatory cells, lymphocytes, neutrophils, eosinophils and levels of cytokines, including interleukin (IL)-2, IL-4 and IL-5, tumor necrosis factor (TNF)-α and interferon (IFN)-γ in bronchoalveolar lavage fluid (BALF) of these mice. In addition, S-petasin (10–30 μmol/kg, s.c.) dose-dependently and significantly attenuated total and OVA-specific immunoglobulin E (IgE) levels in the serum and BALF, and enhanced the IgG2a level in serum of these mice. The PDE4H value of S-petasin was >300 μM; therefore, its PDE4H/PDE4L value was calculated to be >17. In conclusion, the present results for S-petasin at least partially explain why Petasites formosanus is used as a folk medicine to treat asthma in Taiwan

The Higgs boson inclusive decay channels H→bbˉH \to b\bar{b} and H→ggH \to gg up to four-loop level

The principle of maximum conformality (PMC) has been suggested to eliminate the renormalization scheme and renormalization scale uncertainties, which are unavoidable for the conventional scale setting and are usually important errors for theoretical estimations. In this paper, by applying PMC scale setting, we analyze two important inclusive Standard Model Higgs decay channels, $H\rightarrow b\bar{b}$ and $H\rightarrow gg$, up to four-loop and three-loop levels accordingly. After PMC scale setting, it is found that the conventional scale uncertainty for these two channels can be eliminated to a high degree. There is small residual initial scale dependence for the Higgs decay widths due to unknown higher-order $\{\beta_i\}$-terms. Up to four-loop level, we obtain $\Gamma(H\rightarrow b\bar{b}) = 2.389\pm0.073 \pm0.041$ MeV and up to three-loop level, we obtain $\Gamma(H\rightarrow gg) = 0.373\pm0.030$ MeV, where the first error is caused by varying $M_H=126\pm4$ GeV and the second error for $H\to b\bar{b}$ is caused by varying the $\overline{\rm MS}$-running mass $m_b(m_b)=4.18\pm0.03$ GeV. Taking $H\to b\bar{b}$ as an example, we present a comparison of three BLM-based scale setting approaches, e.g. the PMC-I approach based on the PMC-BLM correspondence, the $R_\delta$-scheme and the seBLM approach, all of which are designed to provide effective ways to identify non-conformal $\{\beta_i\}$-series at each perturbative order. At four-loop level, all those approaches lead to good pQCD convergence, they have almost the same pQCD series, and their predictions are almost independent on the initial renormalization scale. In this sense, those approaches are equivalent to each other.Comment: 14 pages, 7 figures. References updated and discussions improved. To be published in Eur.Phys.J.