Two-photon spectroscopy of trapped HD+^+ ions in the Lamb-Dicke regime

We study the feasibility of nearly-degenerate two-photon rovibrational spectroscopy in ensembles of trapped, sympathetically cooled hydrogen molecular ions using a resonance-enhanced multiphoton dissociation (REMPD) scheme. Taking advantage of quasi-coincidences in the rovibrational spectrum, the excitation lasers are tuned close to an intermediate level to resonantly enhance two-photon absorption. Realistic simulations of the REMPD signal are obtained using a four-level model that takes into account saturation effects, ion trajectories, laser frequency noise and redistribution of population by blackbody radiation. We show that the use of counterpropagating laser beams enables optical excitation in an effective Lamb-Dicke regime. Sub-Doppler lines having widths in the 100 Hz range can be observed with good signal-to-noise ratio for an optimal choice of laser detunings. Our results indicate the feasibility of molecular spectroscopy at the $10^{-14}$ accuracy level for improved tests of molecular QED, a new determination of the proton-to-electron mass ratio, and studies of the time (in)dependence of the latter.Comment: 16 pages, 17 figure

Research and application of indirect monitoring methods for transport infrastructures to monitor and evaluate structural health

Currently, bridge constructions in Vietnam, as well as in the world, are regularly monitored and evaluated to ensure safety in the exploitation process and to prevent damage. The traditional method of monitoring by geodetic tools through monitoring cycles often brings results with significant errors, thus not really representing the performance of the structure and potential damages on it. Recently, to overcome the factors observed by geodetic methods, sensors are directly located on the construction to monitor the change of parameters, such as stress, deformation and vibrations. From that monitoring it is possible to assess the mining safety level of the structure through the data collected continuously from the sensors. However, the funding needs for each monitoring system and for each specific project may be very large, not to mention the need to spend a large amount of resources to maintain the monitoring system for many projects, including high prices from experts and exclusive distributors. Instead of using sensors and machines on constructions, the research and application of sensors placed on a vehicle that often passes on a traffic structure may present several benefits. In this case, the structures are indirectly monitored through equipment placed on vehicles moving along the structure. In this work, focus is given on researching and application of indirect monitoring methods by installing sensors on vehicles to identify frequency and evaluate bridge structures’ performance.The authors acknowledge the financial support of the project research “B2021-GHA04” of the Ministry of Education and Training Vietnam. This work is financed by national funds through FCT - Foundation for Science and Technology, under grant agreement [POCI-01-0247-FEDER-039555-UM.2.19] attributed to the 2nd author. This work was partly financed by FCT / MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020

Fluorescence quenching studies of structure and dynamics in calmodulin-eNOS complexes

This is the peer reviewed version of the following article: Arnett David C.,Persechini Anthony,Tran Quang-Kim,Black D.J. and Johnson Carey K.(2015), Fluorescence quenching studies of structure and dynamics in calmodulin–eNOS complexes, FEBS Letters, 589, doi: 10.1016/j.febslet.2015.03.035, which has been published in final form at http://doi.org/10.1016/j.febslet.2015.03.035. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Activation of endothelial nitric oxide synthase (eNOS) by calmodulin (CaM) facilitates formation of a sequence of conformational states that is not well understood. Fluorescence decays of fluorescently labeled CaM bound to eNOS reveal four distinct conformational states and single-molecule fluorescence trajectories show multiple fluorescence states with transitions between states occurring on time scales of milliseconds to seconds. A model is proposed relating fluorescence quenching states to enzyme conformations. Specifically, we propose that the most highly quenched state corresponds to CaM docked to an oxygenase domain of the enzyme. In single-molecule trajectories, this state occurs with time lags consistent with the oxygenase activity of the enzyme

Fluorescence quenching studies of structure and dynamics in calmodulin-eNOS complexes

This is the peer reviewed version of the following article: Arnett David C.,Persechini Anthony,Tran Quang-Kim,Black D.J. and Johnson Carey K.(2015), Fluorescence quenching studies of structure and dynamics in calmodulin–eNOS complexes, FEBS Letters, 589, doi: 10.1016/j.febslet.2015.03.035, which has been published in final form at http://doi.org/10.1016/j.febslet.2015.03.035. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Activation of endothelial nitric oxide synthase (eNOS) by calmodulin (CaM) facilitates formation of a sequence of conformational states that is not well understood. Fluorescence decays of fluorescently labeled CaM bound to eNOS reveal four distinct conformational states and single-molecule fluorescence trajectories show multiple fluorescence states with transitions between states occurring on time scales of milliseconds to seconds. A model is proposed relating fluorescence quenching states to enzyme conformations. Specifically, we propose that the most highly quenched state corresponds to CaM docked to an oxygenase domain of the enzyme. In single-molecule trajectories, this state occurs with time lags consistent with the oxygenase activity of the enzyme

Opportunities and challenges of digital twins in structural health monitoring

Digital twin (DT) is one of the most modern and promising technologies in realizing smart manufacturing and implementing Industry 4.0. DT offers opportunity to integrate the physical world with digital world with a seamless data source. Civil engineering industry, in general, is facing many challenges in the process of digital transformation to improve efficiency and technology to meet the current growth rate of the economy. DT technology has the potential to transform and improve the exploitation and management of infrastructure in civil engineering, especially in the service phase. Based on DT model, managers and maintenance operators can test different scenarios, improve efficiency, and make accurate decisions in maintenance of the structure, leading to reduction of management and other regular monitoring costs, as well as accurate prediction of risks during the lifespan of the infrastructure. This study presents advances in digital twin implementations in structural health monitoring. This presents the opportunities and challenges of the digital twin in structural health monitoring with current technologies and future directions.The authors acknowledge the financial support of the project research “B2022-GHA-03” of the Ministry of Education and Training. This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020. The first author acknowledges the funding by MIT Portugal Program (MPP) through the MPP2030-FCT Research Grants. The second author acknowledges the funding by FCT through the Scientific Employment Stimulus—4th Edition. Nguyen Huu Quyet was funded by the Master, Ph.D. Scholarship Programme of Vingroup Innovation Foundation (VINIF), code VINIF.2022.ThS.075

Damage detection of structural based on indirect vibration measurement results combined with Artificial Neural Network

In Structural Health Monitoring (SHM), damage detection and maintenance are among the most critical factors. For surface damage, damage detection is simple and easy to perform. However, detecting and repairing is difficult for damage hidden deep in the structure. Using the structure's dynamic features, damage can be detected and repaired in time. With the development of sensor technology, indirect vibration measurement solutions give accurate results, minimizing errors by infinitely increasing the number of measurements. This solution offers a great opportunity to reduce the cost of structural health monitoring. Based on the large amount of data obtained from indirect monitoring, artificial intelligence technologies can be used to obtain a more comprehensive model of SHM. In this paper, the dynamic responses of the structure will be extracted and determined through a vehicle crossing the bridge. Based on the results of structural dynamic response, a finite element model is built and updated so that this model can represent the real structure. Damage cases will be analyzed and evaluated as input to train the Artificial neural network. The trained network can detect damage through regular health monitoring by indirect methods

Damage detection of structural based on indirect vibration measurement results combined with Artificial Neural Network

In Structural Health Monitoring (SHM), damage detection and maintenance are among the most critical factors. For surface damage, damage detection is simple and easy to perform. However, detecting and repairing is difficult for damage hidden deep in the structure. Using the structure's dynamic features, damage can be detected and repaired in time. With the development of sensor technology, indirect vibration measurement solutions give accurate results, minimizing errors by infinitely increasing the number of measurements. This solution offers a great opportunity to reduce the cost of structural health monitoring. Based on the large amount of data obtained from indirect monitoring, artificial intelligence technologies can be used to obtain a more comprehensive model of SHM. In this paper, the dynamic responses of the structure will be extracted and determined through a vehicle crossing the bridge. Based on the results of structural dynamic response, a finite element model is built and updated so that this model can represent the real structure. Damage cases will be analyzed and evaluated as input to train the Artificial neural network. The trained network can detect damage through regular health monitoring by indirect methods

Study on the model to determine riverbed scour and the influence of bridge construction on riverbed deformation

River geomorphic features can dramatically change over time. It is undoubtedly the most dynamic geomorphic system that engineers have to manage in the design and maintenance of bridges. In the event of major floods, significant changes can take place within a short period of time. In contrast to rivers which are dynamic, bridges generally do not move except in accordance with planned structural deflections caused by anticipated static and dynamic loading. The stability and safety of bridges can be jeopardized in several ways as a result of riverbed channel changes, being the removal of bed material in the vicinity of bridge foundations (as a result of local scouring phenomenon) the most common cause of several bridge collapses worldwide. Due to the complex nature of the fluid flow, there are still many uncertainties that affect the design process of bridge piers. Therefore, different approaches have been used comprising empirical formulations, experimental studies, sophisticated numerical simulations, and, whenever possible, underwater survey activities as a manner of monitoring the scouring process at bridge piers founded in alluvial/movable riverbeds. Therefore, the present study analyses the local scour phenomenon around the bridge piers of an intervened Vietnam bridge – Doan Hung bridge, by using different monitoring inspection surveys over a period of time. Research results will clarify the influence of bridge piers’ design and construction on movable beds, indicate future predictions to monitor and control the severity of the scouring process, and propose management measures for the safety of bridge infrastructures.This work was supported by the Project PTDC-ECI-EGC-5177-2020 (POSEIDON project), funded by national funds through the FCT – Portuguese Foundation for Science and Technology. This work was partly financed by FCT / MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020 and Vingroup and supported by Innovation Foundation (VINIF) under project code VINIF.2021.DA00192. Tran Quang Minh was supported by the doctoral Grant reference PRT/BD/154268/2022 financed by Portuguese Foundation for Science and Technology (FCT), under MIT Portugal Program (2022 MPP2030-FCT). Hélder S. Sousa acknowledges the funding by FCT through the Scientific Employment Stimulus - 4th Edition

Developing a comprehensive quality control framework for roadway bridge management: a case study approach using key performance indicators

Transportation infrastructures, especially roadway bridges, play a pivotal role in socioeconomic development. Recently, bridge engineers are increasingly facing the challenge in terms of shifting their strategy from building new facilities to maintaining the existing aging infrastructures, to preserve their service performance during the operational stage. In fact, the infrastructure administrators lack a quality control (QC) strategy for the existing roadway bridges, which leads to the decision-making application and tool being still minor. To overcome those challenging issues, this paper proposes a quality control framework for roadway bridge management using key performance indicators (KPIs). The case study methodology is suggested to be used and then conducted for several bridges, mostly in European countries. In which the performance indicators (PIs) and goals (PGs) are defined, after assessing the bridges and vulnerable zones, the derivation KPIs from those PIs are introduced and developed considering time functions and different maintenance scenarios. Eventually, a two-stage quality control framework will be proposed in which the static stage includes preparatory works, inspection responsibilities, and a quick assessment of KPIs; while the dynamic stage helps the decision maker in estimating the time remaining of the bridge service life, managing the evolution of KPIs as well as planning the best possible maintenance strategy. The selected two case studies are present and curated, which show the excellent potential to develop a long-term strategy for roadway bridge management on a lifecycle level.This research was funded by FCT/MCTES through national funds (PIDDAC) from the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under the reference UIDB/04029/2020, and from the Associate Laboratory Advanced Production and Intelligent Systems ARISE, under the reference LA/P/0112/2020, as well as financial support of the project re-search “B2022-GHA-03” from the Ministry of Education and Training. And The APC was funded by ANI (“Agência Nacional de Inovação”) through the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference PO-CI-01-0247- FEDER-069642, which was cofinanced by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalisation Program (POCI).Minh Q. Tran acknowledges the support by the doctoral grant reference PRT/BD/154268/2022, financed by Portuguese Foundation for Science and Technology (FCT), under the MIT Portugal Program (2022 MPP2030-FCT)

Finite element model updating for composite plate structures using particle swarm optimization algorithm

In the Architecture, Engineering, and Construction (AEC) industry, particularly civil engineering, the Finite Element Method (FEM) is a widely applied method for computational designs. In this regard, computational simulation has increasingly become challenging due to uncertain parameters, significantly affecting structural analysis and evaluation results, especially for composite and complex structures. Therefore, determining the exact computational parameters is crucial since the structures involve many components with different material properties, even removing some additional components affects the calculation results. This study presents a solution to increase the accuracy of the finite element (FE) model using a swarm intelligence-based approach called the particle swarm optimization (PSO) algorithm. The FE model is created based on the structure’s easily observable characteristics, in which uncertainty parameters are assumed empirically and will be updated via PSO using dynamic experimental results. The results show that the finite element model achieves high accuracy, significantly improved after updating (shown by the evaluation parameters presented in the article). In this way, a precise and reliable model can be applied to reliability analysis and structural design optimization tasks. During this research project, the FE model considering the PSO algorithm was integrated into an actual bridge’s structural health monitoring (SHM) system, which was the premise for creating the initial digital twin model for the advanced digital twinning technologyThis work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020. The authors also acknowledge ANI (“Agência Nacional de Inovação”) for the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference POCI-01-0247-FEDER-069642, cofinanced by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalization Program (POCI).Minh Q. Tran was supported by the doctoral grant reference PRT/BD/154268/2022 financed by the Portuguese Foundation for Science and Technology (FCT), under the MIT Portugal Program (2022 MPP2030-FCT). Minh Q. Tran acknowledges Huan X. Nguyen (Faculty of Science and Technology, Middlesex University, London NW4 4BT, UK) and Thuc V. Ngo (Mien Tay Construction University, Institute of Science and International Cooperation, 85100 Vĩnh Long, Vietnam) for their support as cosupervisors as well as specific suggestions in terms of the “conceptualization” and “methodology” of this paper. Helder S. Sousa acknowledges the funding by FCT through the Scientific Employment Stimulus—4th Editio