System-reliability-based Disaster Resilience Evaluation of Cable-stayed Bridge under Fire Hazard Using Reliability-Redundancy Analysis

The 20th working conference of the IFIP Working Group 7.5 on Reliability and Optimization of Structural Systems (IFIP 2022) will be held at Kyoto University, Kyoto, Japan, September 19-20, 2022.The concept of disaster resilience recently emerged in efforts to gain holistic understanding of civil infrastructure systems exposed to various natural or human-made hazards. To effectively evaluate the resilience of complex infrastructure systems generally consisting of many interdependent structural components, Lim et al. (2022) proposed a system-reliability-based framework for disaster resilience. In the proposed framework, the disaster resilience of a civil infrastructure system is characterized by three criteria: reliability, redundancy, and recoverability. For comprehensive resilience analyses at the scale of individual structures, the reliability (β) and redundancy (π) indices were newly defined in the context of component- and system-level reliability analysis, respectively. Reliability-redundancy diagram, i.e., the scatter plot of the reliability and redundancy indices computed for each initial disruption scenario, was also proposed to help a decision-maker check whether the corresponding risk is acceptable for the society. In this paper, we demonstrate the framework through its application to a cable-stayed bridge in South Korea, the Seohae Grand Bridge under fire hazards. First, a probabilistic model is developed to describe the hazard of fire scenarios that may occur on the deck of the cable-stayed bridge. Next, finite element simulations are performed to compute the reliability and redundancy indices through component and system reliability analyses for the fire accident scenarios. An adaptive simulation method, AK-MCS (Echard et al. 2011), is employed to overcome the computational cost issue. The example successfully demonstrates that the reliability-redundancy analysis and diagram facilitate a comprehensive assessment of the disaster resilience of a complex civil infrastructure such as a cable-stayed bridge by using sophisticated computational simulations and advanced reliability methods

eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-

Contrary to other standard cameras, event cameras interpret the world in an entirely different manner; as a collection of asynchronous events. Despite event camera's unique data output, many event feature detection and tracking algorithms have shown significant progress by making detours to frame-based data representations. This paper questions the need to do so and proposes a novel event data-friendly method that achieve simultaneous feature detection and tracking, called event Clustering-based Detection and Tracking (eCDT). Our method employs a novel clustering method, named as k-NN Classifier-based Spatial Clustering and Applications with Noise (KCSCAN), to cluster adjacent polarity events to retrieve event trajectories.With the aid of a Head and Tail Descriptor Matching process, event clusters that reappear in a different polarity are continually tracked, elongating the feature tracks. Thanks to our clustering approach in spatio-temporal space, our method automatically solves feature detection and feature tracking simultaneously. Also, eCDT can extract feature tracks at any frequency with an adjustable time window, which does not corrupt the high temporal resolution of the original event data. Our method achieves 30% better feature tracking ages compared with the state-of-the-art approach while also having a low error approximately equal to it.Comment: IROS2022 accepted pape

(LC)2^2: LiDAR-Camera Loop Constraints For Cross-Modal Place Recognition

Localization has been a challenging task for autonomous navigation. A loop detection algorithm must overcome environmental changes for the place recognition and re-localization of robots. Therefore, deep learning has been extensively studied for the consistent transformation of measurements into localization descriptors. Street view images are easily accessible; however, images are vulnerable to appearance changes. LiDAR can robustly provide precise structural information. However, constructing a point cloud database is expensive, and point clouds exist only in limited places. Different from previous works that train networks to produce shared embedding directly between the 2D image and 3D point cloud, we transform both data into 2.5D depth images for matching. In this work, we propose a novel cross-matching method, called (LC)$^2$, for achieving LiDAR localization without a prior point cloud map. To this end, LiDAR measurements are expressed in the form of range images before matching them to reduce the modality discrepancy. Subsequently, the network is trained to extract localization descriptors from disparity and range images. Next, the best matches are employed as a loop factor in a pose graph. Using public datasets that include multiple sessions in significantly different lighting conditions, we demonstrated that LiDAR-based navigation systems could be optimized from image databases and vice versa.Comment: 8 pages, 11 figures, Accepted to IEEE Robotics and Automation Letters (RA-L

Modeling of Rf Interference Caused by Solid-State Drive Noise

In this paper, modeling of RFI problem caused by a solid-state drive (SSD) in a laptop is proposed. Two noise sources (one outside and one inside a cavity) in the SSD are reconstructed as dipole moments with magnitude-only near-field scanning data. The dipole moment inside a cavity is then replaced by a Huygens\u27 box covering four side surfaces of the cavity using a numerical simulation. The noise voltage at an RF antenna port is calculated by combining the two reconstructed noise sources with measured transfer functions. The model is successfully validated through a comparison of the calculation with measurement results

Survey of Public Attitudes toward the Secondary Use of Public Healthcare Data in Korea

Objectives Public healthcare data have become crucial to the advancement of medicine, and recent changes in legal structure on privacy protection have expanded access to these data with pseudonymization. Recent debates on public healthcare data use by private insurance companies have shown large discrepancies in perceptions among the general public, healthcare professionals, private companies, and lawmakers. This study examined public attitudes toward the secondary use of public data, focusing on differences between public and private entities. Methods An online survey was conducted from January 11 to 24, 2022, involving a random sample of adults between 19 and 65 of age in 17 provinces, guided by the August 2021 census. Results The final survey analysis included 1,370 participants. Most participants were aware of health data collection (72.5%) and recent changes in legal structures (61.4%) but were reluctant to share their pseudonymized raw data (51.8%). Overall, they were favorable toward data use by public agencies but disfavored use by private entities, notably marketing and private insurance companies. Concerns were frequently noted regarding commercial use of data and data breaches. Among the respondents, 50.9% were negative about the use of public healthcare data by private insurance companies, 22.9% favored this use, and 1.9% were “very positive.” Conclusions This survey revealed a low understanding among key stakeholders regarding digital health data use, which is hindering the realization of the full potential of public healthcare data. This survey provides a basis for future policy developments and advocacy for the secondary use of health data

RECIPE: How to Integrate ChatGPT into EFL Writing Education

The integration of generative AI in the field of education is actively being explored. In particular, ChatGPT has garnered significant interest, offering an opportunity to examine its effectiveness in English as a foreign language (EFL) education. To address this need, we present a novel learning platform called RECIPE (Revising an Essay with ChatGPT on an Interactive Platform for EFL learners). Our platform features two types of prompts that facilitate conversations between ChatGPT and students: (1) a hidden prompt for ChatGPT to take an EFL teacher role and (2) an open prompt for students to initiate a dialogue with a self-written summary of what they have learned. We deployed this platform for 213 undergraduate and graduate students enrolled in EFL writing courses and seven instructors. For this study, we collect students' interaction data from RECIPE, including students' perceptions and usage of the platform, and user scenarios are examined with the data. We also conduct a focus group interview with six students and an individual interview with one EFL instructor to explore design opportunities for leveraging generative AI models in the field of EFL education

Telomere maintenance through recruitment of internal genomic regions

Cells surviving crisis are often tumorigenic and their telomeres are commonly maintained through the reactivation of telomerase. However, surviving cells occasionally activate a recombination-based mechanism called alternative lengthening of telomeres (ALT). Here we establish stably maintained survivors in telomerase-deleted Caenorhabditis elegans that escape from sterility by activating ALT. ALT survivors trans-duplicate an internal genomic region, which is already cis-duplicated to chromosome ends, across the telomeres of all chromosomes. These 'Template for ALT' (TALT) regions consist of a block of genomic DNA flanked by telomere-like sequences, and are different between two genetic background. We establish a model that an ancestral duplication of a donor TALT region to a proximal telomere region forms a genomic reservoir ready to be incorporated into telomeres on ALT activation.

Modifications of T-Scores by Quantitative Ultrasonography for the Diagnosis of Osteoporosis in Koreans

To identify a proper T-score threshold for the diagnosis of osteoporosis in Koreans using quantitative ultrasonography (QUS), normative data from 240 females and 238 males (ages 20-29 yr) were newly generated. Then, the osteoporosis prevalence estimate for men and women over 50 yr of age was analyzed using previous World Health Organization (WHO) methods and heel QUS. T-scores were calculated from the normative data. There were definite negative correlations between age and all of the QUS parameters, such as speed of sound (SOS), broadband ultrasound attenuation (BUA), and estimated heel bone mineral density (BMD) (p<0.0001). After applying the recently determined prevalence of incident vertebral fracture in Koreans over 50 yr of age (11.6% and 9.1%, female vs male, respectively) to the diagnosis of osteoporosis by T-scores from heel BMD as measured by QUS, it was revealed that applicable T-scores for women and men were -2.25 and -1.85, respectively. These data suggest that simply using a T-score of -2.5, the classical WHO threshold for osteoporosis, underestimates the true prevalence when using peripheral QUS. Further prospective study of the power of QUS in predicting the absolute risk of fracture is needed

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √s = 13 TeV

Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂ t) and chromomagnetic (μ̂ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ¯ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ¯ final states. The values found for the parameters are AFB(1)=0.048−0.087+0.095(stat)−0.029+0.020(syst),μ̂t=−0.024−0.009+0.013(stat)−0.011+0.016(syst), and a limit is placed on the magnitude of | d̂ t| &lt; 0.03 at 95% confidence level. [Figure not available: see fulltext.