Experimental model updating of slope considering spatially varying soil properties and dynamic loading

AbstractThe widespread threat posed by slope structure failures to human lives and property safety is widely acknowledged. Additionally, natural soil often displays spatial variability due to geological deposition and other factors. Therefore, predicting the seismic response of slopes subjected to ground motions and inversely analyzing the spatial distribution of soils remains an unresolved issue. In the present work, a shaking table experimental test is first designed and carried out, in which a soft‐soil slope dynamic system is established. To capture the seismic response of the soft‐soil slope, specifically the experimental characteristic of acceleration and soil pressure response in both spatial domain and time domain, a series of sensors were pre‐embedded in the slope. Subsequently, a model updating approach is proposed for slope seismic analysis that incorporates spatial variability of soil. In addition, to enhance computational efficiency, the dimensionality reduction of Karhunen–Loève expansion method is introduced to reduce inverse analysis parameters. On the basis of 34 samples collected from experimental data, it is shown that near‐fault pulse‐like ground motions deliver greater concentrated energy, causing more severe damage to slope structures, especially the topsoil layer. Furthermore, using data obtained from a shaking table test subjected to ground motion Recorded Sequence Number 158H1 from the Pacific Earthquake Engineering Research Center NGA‐West2 database as an example, it is also shown that the proposed approach demonstrates high accuracy in predicting the spatial distribution of the maximum shear modulus in soil slope dynamic systems. The present work not only addresses the challenges posed by mainshock–aftershock effects but also highlights the potential of model updating approaches to enhance the understanding of slope behavior under seismic loading conditions.</jats:p

An On-demand Photonic Ising Machine with Simplified Hamiltonian Calculation by Phase-encoding and Intensity Detection

Photonic Ising machine is a new paradigm of optical computing, which is based on the characteristics of light wave propagation, parallel processing and low loss transmission. Thus, the process of solving the combinatorial optimization problems can be accelerated through photonic/optoelectronic devices. In this work, we have proposed and demonstrated the so-called Phase-Encoding and Intensity Detection Ising Annealer (PEIDIA) to solve arbitrary Ising problems on demand. The PEIDIA is based on the simulated annealing algorithm and requires only one step of optical linear transformation with simplified Hamiltonian calculation. With PEIDIA, the Ising spins are encoded on the phase term of the optical field and only intensity detection is required during the solving process. As a proof of principle, several 20 and 30-dimensional Ising problems have been solved with high ground state probability

Comparison of Nasopharyngeal MR, 18 F-FDG PET/CT, and 18 F-FDG PET/MR for Local Detection of Natural Killer/T-Cell Lymphoma, Nasal Type.

Objectives The present study aims to compare the diagnostic efficacy of MR, 18F-FDG PET/CT, and 18F-FDG PET/MR for the local detection of early-stage extranodal natural killer/T-cell lymphoma, nasal type (ENKTL). Patients and Methods Thirty-six patients with histologically proven early-stage ENKTL were enrolled from a phase 2 study (Cohort A). Eight nasopharyngeal anatomical regions from each patient were imaged using 18F-FDG PET/CT and MR. A further nine patients were prospectively enrolled from a multicenter, phase 3 study; these patients underwent 18F-FDG PET/CT and PET/MR after a single 18F-FDG injection (Cohort B). Region-based sensitivity and specificity were calculated. The standardized uptake values (SUV) obtained from PET/CT and PET/MR were compared, and the relationship between the SUV and apparent diffusion coefficients (ADC) of PET/MR were analyzed. Results In Cohort A, of the 288 anatomic regions, 86 demonstrated lymphoma involvement. All lesions were detected by 18F-FDG PET/CT, while only 70 were detected by MR. 18F-FDG PET/CT exhibited a higher sensitivity than MR (100% vs. 81.4%, χ2 = 17.641, P < 0.001) for local detection of malignancies. The specificity of 18F-FDG PET/CT and MR were 98.5 and 97.5%, respectively (χ2 = 0.510, P = 0.475). The accuracy of 18F-FDG PET/CT was 99.0% and the accuracy of MR was 92.7% (χ2 = 14.087, P < 0.001). In Cohort B, 72 anatomical regions were analyzed. PET/CT and PET/MR have a sensitivity of 100% and a specificity of 92.5%. The two methods were consistent (κ = 0.833, P < 0.001). There was a significant correlation between PET/MR SUVmax and PET/CT SUVmax (r = 0.711, P < 0.001), and SUVmean (r = 0.685, P < 0.001). No correlation was observed between the SUV and the ADC. Conclusion In early-stage ENKTL, nasopharyngeal MR showed a lower sensitivity and a similar specificity when compared with 18F-FDG PET/CT. PET/MR showed similar performance compared with PET/CT

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Highly stretchable and kirigami-structured strain sensors with long silver nanowires of high aspect ratio

10.3390/machines9090186Machines9918

From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits

<div><p>Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, continuous simulation is applied to evaluate the performance of these topologies. We demonstrate the usefulness of this method by designing an example biological function: the SOS response of <i>E</i>. <i>coli</i>. Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions. Possible circuits are ranked according to their robustness against perturbations in parameter and gene expressions. The biological network is among the candidate networks, yet novel designs can be generated. Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks.</p></div

Performances of minimal networks and candidate networks.

<p>The distribution of Q value and basin size for minimal networks and candidate networks. Minimal networks are represented in red and candidate networks in black. The large blue star represents the biological network.</p

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization