Evaluation of Bridge Maintenance Priorities in Megacities

Bridges are key social overhead capital facilities with a direct impact on citizen safety and the socioeconomic development of a country. Many bridges in South Korea were completed during the nation’s period of rapid economic growth in the 1970s and 1980s, meaning that bridge aging is now accelerating and should be of real concern. Based on records of other highly developed countries not having adequate maintenance systems for old bridges and consequently experiencing a number of tragic bridge collapses, Korea is at risk of following suit. Being able to measure the efficiency of bridge management considering economic and social values is therefore critical. In this work, a qualitative bridge maintenance priority evaluation model using open-source bridge information was developed. The model was evaluated by applying it to eight actual bridges located in Daegu Metropolitan City, Korea. It is concluded that aging bridge maintenance and management can be performed more efficiently and reliably if the model proposed in this work is used

Parametric Method and Building Information Modeling-Based Cost Estimation Model for Construction Cost Prediction in Architectural Planning

Economic feasibility and cost analysis in the preliminary planning stage of early-phase construction projects have a significant impact on project management and implementation. However, while estimating the construction cost per unit area, the existing approaches do not account for factors other than the area-related information, causing estimation error. Therefore, a construction cost estimation model that can be utilized in the early phase of a construction project is developed in this study based on BIM in the architectural planning stage. Moreover, goodness of fit and accuracy of the model were verified through a validation method considering the BIM design process. The proposed model showed higher accuracy than the conventional models in terms of the floor area. Furthermore, it was possible to confirm the model performance based on the cost estimation accuracy range presented by the AACE. In addition, the developed model can generate estimation results corresponding to Class 1–3, a subsequent construction project stage. The findings of this study emphasize the importance of jointly considering the processes related to construction cost estimation and indicate that parameters other than the floor area need to be considered for construction cost estimation in the early phase of a construction project

Segmental Dynamics of an Isolated Component Polymer Chain in Polymer Blends Near the Glass Transition

The segmental dynamics of a component chain isolated in its blending partner chains is examined using the reorientation of polymer-tethered fluorescent probes near the glass transition. It is found that the temperature dependence of the dynamics of an isolated component follows that of the other component, with a horizontal shift corresponding to the glass transition temperature modification, which may result from a local composition of ≈10% isolated component. On the contrary, the dynamic heterogeneity, another key dynamic feature near the glass transition, shows that the local dynamic environment of an isolated component becomes either as heterogeneous as a more inherently heterogeneous component or more heterogeneous than either. These observations emphasize that not only the chain connectivity but also the dynamic modulation of a component by the other component needs to be addressed in order to understand the segmental dynamics of an isolated component in polymer blends

In-sensor reservoir computing for language learning via two-dimensional memristors

© 2021 The Authors.The dynamic processing of optoelectronic signals carrying temporal and sequential information is critical to various machine learning applications including language processing and computer vision. Despite extensive efforts to emulate the visual cortex of human brain, large energy/time overhead and extra hardware costs are incurred by the physically separated sensing, memory, and processing units. The challenge is further intensified by the tedious training of conventional recurrent neural networks for edge deployment. Here, we report in-sensor reservoir computing for language learning. High dimensionality, nonlinearity, and fading memory for the in-sensor reservoir were achieved via two-dimensional memristors based on tin sulfide (SnS), uniquely having dual-type defect states associated with Sn and S vacancies. Our in-sensor reservoir computing demonstrates an accuracy of 91% to classify short sentences of language, thus shedding light on a low training cost and the real-time solution for processing temporal and sequential signals for machine learning applications at the edge.11Nsciescopu

Induction of Fatty Acid Oxidation Underlies DNA Damage‐Induced Cell Death and Ameliorates Obesity‐Driven Chemoresistance

Abstract The DNA damage response is essential for preserving genome integrity and eliminating damaged cells. Although cellular metabolism plays a central role in cell fate decision between proliferation, survival, or death, the metabolic response to DNA damage remains largely obscure. Here, this work shows that DNA damage induces fatty acid oxidation (FAO), which is required for DNA damage‐induced cell death. Mechanistically, FAO induction increases cellular acetyl‐CoA levels and promotes N‐alpha‐acetylation of caspase‐2, leading to cell death. Whereas chemotherapy increases FAO related genes through peroxisome proliferator‐activated receptor α (PPARα), accelerated hypoxia‐inducible factor‐1α stabilization by tumor cells in obese mice impedes the upregulation of FAO, which contributes to its chemoresistance. Finally, this work finds that improving FAO by PPARα activation ameliorates obesity‐driven chemoresistance and enhances the outcomes of chemotherapy in obese mice. These findings reveal the shift toward FAO induction is an important metabolic response to DNA damage and may provide effective therapeutic strategies for cancer patients with obesity

Clinical Characteristics of Asymptomatic and Symptomatic Pediatric Coronavirus Disease 2019 (COVID-19): A Systematic Review

Background and objectives: Characterization of pediatric coronavirus disease 2019 (COVID-19) is necessary to control the pandemic, as asymptomatic or mildly infected children may act as carriers. To date, there are limited reports describing differences in clinical, laboratory, and radiological characteristics between asymptomatic and symptomatic infection, and between younger and older pediatric patients. The objective of this study is to compare characteristics among: (1) asymptomatic versus symptomatic and (2) less than 10 versus greater or equal to 10 years old pediatric COVID-19 patients. Materials and Methods: We searched for all terms related to pediatric COVID-19 in electronic databases (Embase, Medline, PubMed, and Web of Science) for articles from January 2020. This protocol followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Results: Eligible study designs included case reports and series, while we excluded comments/letters, reviews, and literature not written in English. Initially, 817 articles were identified. Forty-three articles encompassing 158 confirmed pediatric COVID-19 cases were included in the final analyses. Lymphocytosis and high CRP were associated with symptomatic infection. Abnormal chest CT more accurately detected asymptomatic COVID-19 in older patients than in younger ones, but clinical characteristics were similar between older and younger patients. Conclusions: Chest CT scan findings are untrustworthy in younger children with COVID-19 as compared with clinical findings, or significant differences in findings between asymptomatic to symptomatic children. Further studies evaluating pediatric COVID-19 could contribute to potential therapeutic interventions and preventive strategies to limit spreading