Identification Of Modes Research Articles

Performance monitoring and diagnosis of bridge structures based on time multiscale divide and conquer strategy

During the service life of bridge structures, they will be affected by various physical and environmental changes, which comprehensively manifest on different time scales. In other words, structural effects will have different variation patterns at different time scales, which will be reflected in the multi-scale characteristics of structural mechanical properties over time. However, most of the existing bridge performance indicators are defined on a time scale corresponding to a single factor. Therefore, it is necessary to analyze, process, and apply bridge monitoring signals differently at different time scales and components of different factors. This article proposes a signal decomposition and reconstruction method that extracts signals of different components from strain monitoring signals of bridge structures. On this basis, different feature extraction and indicator recognition are carried out for different extracted components. Among them, for component A related to bridge vibration, this paper proposes a strain mode identification method based on statistical stability. This method is based on the assumption that the strain mode under random noise has approximate time invariant characteristics, and noise is eliminated through statistical averaging. The component B of the vehicle induced effect is obtained through empirical mode decomposition (EMD) and bandpass filtering, and the lateral collaborative performance of the assembled beam bridge is characterized based on the correlation coefficient of component BA bridge hinge joint damage identification method based on temperature strain is proposed for component C of temperature effect. The effectiveness and feasibility of the above workflow were verified through real bridge data.

Playing with data differently: engaging with autism and gender through participatory arts/music and a performative framework for analysis.

There are increasing demands for Participatory Arts-Based (PAB) programs involved in health research to better evidence outcomes using robust quantitative evaluation methodologies taken from science, such as standardized questionnaires, to inform commissioning and scale-up decisions. However, for PAB researchers trying to do this, barriers arise from fundamental interdisciplinary differences in values and contexts. Researchers are required to navigate the tensions between the practice-based evidence produced by the arts and the evidence-based practice sought by psychologists. Consequently, there is a need for interdisciplinary arts-science collaborations to produce alternative methods of evaluation that are better aligned to PAB approaches, and which combine systematic rigor with a sensitivity to the values, contexts and strengths of this approach. The current article centers on the development of an alternative transdisciplinary analytic tool, the Participatory arts Play Framework (PP-Framework), undertaken as part of an arts-psychology collaboration for a UK AHRC-funded PAB research project: Playing A/Part: Investigating the identities and experiences of autistic girls. We present details of three stages in the development of the PP-Framework: 1. preliminary emergence of the framework from initial video analysis of observational data from participatory music and sound workshops run for 6 adolescent autistic girls (aged 11-16); 2. identification and application of modes of engagement; and 3. further testing of the framework as an evaluation tool for use in a real-world setting, involving professional musicians engaged in delivery of a creative music project at a center for homeless people. The PP-Framework maps types of participation in terms of performative behaviors and qualities of experience, understood as modes of play. It functions as a vehicle for analyzing participant engagement, providing a tool predicated on the processes of working in creative participatory contexts while also being sensitive to the esthetic qualities of what is produced and capable of capturing beneficial changes in engagement. It offers a conceptual approach for researchers to undertake observation of participatory arts practices, taking account of embodied engagement and interaction processes. It is informed by understandings of autistic performativity and masking in conjunction with an ecological understanding of sense making as being shaped by environments, social relations and sensing subjectivity. The framework has the potential to be a bi-directional tool, with application for both practitioners and participants.

Machine learning-based Shapley additive explanations approach for corroded pipeline failure mode identification

Rapid failure mode identification of oil and gas pipelines can prevent catastrophic consequences, improve fast intervention and enhance the design safety of these critical systems. This paper proposes explainable-based machine learning models using to determine the failure mode of corroded pipelines as a function of geometric configurations, material properties, and corrosion defect details. To determine the best identification model, this study examined eight machine learning models, including Nave Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Adaptive Boosting, Extreme Gradient Boosting, Light Gradient Boosting Machine, and Category Boosting, based on a comprehensive experimental database for steel pipelines with various corrosion/crack defect configurations. Furthermore, the Shapley additive explanations approach is utilized to rank the input variables for failure mode identification and explains the machine learning model predicting a specific failure mode for a given sample. In identifying the failure mode of corroded pipelines, the proposed Extreme Gradient Boosting model indicated the highest accuracy in term of performance evaluation compared to all other proposed models. In addition, the model-explanation findings show that the important parameter influencing the failure mechanism of corroded pipelines is the depth of corrosion defects followed by the pipeline wall thickness. The proposed framework is adaptable enough to allow further use of experimental results for having new insights.

Unveiling Regional Climate Patterns Through Global Subsurface Ocean Temperature Data: An AI Multi-Layer Analysis Framework

Regional-scale climate variability has far-reaching implications for both local and global climate systems, impacting factors like temperature, precipitation patterns, oceanic circulation, and the occurrence of extreme weather events. However, despite these influences, there is currently no universal methodology for the automated identification of regional-scale variability modes, including those less dominant globally, and for simultaneously exploring the influence of various ocean depth layers in characterizing these modes and diagnosing regional sea level variations. The presented innovative approach addresses these critical region-specific needs by assisting in the extraction of novel regional depth-layered variability modes and establishing their correlation with regional sea level fluctuations, employing tailored machine-learning techniques. This dual-purpose is achieved through the utilization of an optimized k-means clustering method for the automatic identification of regions with shared variability patterns across all global oceans, revealing previously unexplored regional variability modes. Additionally, guided by an EOF/PC analysis, the approach facilitates an automatic exploration of depth layers that significantly contribute to explaining sea level variability, providing insights into diverse climatic regions. Furthermore, the methodology is specifically designed for a multi-scale analysis, enabling the examination of climate variability spanning from months to several years. The results obtained through this approach have the potential to support informed decision-making regarding local climate-related changes.

Black-Pigmented Marine Pseudomonas aeruginosa Exhibiting Anti-Bacterial Activity against Multidrug-Resistant (MDR) Wound Infection Bacteria

The urgency of multidrug-resistant (MDR) bacterial infections in wounds is a significant concern due to the high prevalence of MDR in healthcare settings. Black pigmented marine bacteria, strain PS1C, were isolated from marine sediment on Awur Beach Jepara, Central Java, Indonesia, and cultured in the laboratory. The aim of this research includes molecular identification of strain PS1C, extracting black pigment from strain PS1C, isolation of MDR bacteria from wounds, and evaluating the antibacterial activity of black pigment from strain PS1C against MDR bacteria isolates of wound infections. We examined the 16S rRNA gene sequences strain PS1C to identify the species. Then, the black pigment from PS1C was extracted using methanol: acetone (7:3) solvent. Antibacterial activity was evaluated against MDR bacteria from wounds with the microdilution method. A black-pigmented bacterium was isolated and identified as Pseudomonas aeruginosa PS1C. We found that the black pigment from P. aeruginosa PS1C can be developed as an antibacterial agent against the MDR bacterial isolate of wounds with MIC and MBC values of 6.25-12.5 mg/ml and 6.25-25 mg/ml, respectively. In conclusion, the study's findings highlight the potential of the extract of black pigment from P. aeruginosa PS1C as an antibacterial agent against wound-causing MDR bacteria and reinforce previous research into P. aeruginosa can be isolated from marine sources. Additional in vivo investigations and the identification of the antibacterial activity's mode of action are required.

Transportation Simulation Modeling and Location-Based Services Data Completion Based on a Data and Model Dual-Driven Approach

The evolving economic and technological landscape has brought about significant changes in travel behaviors and traffic patterns. These changes have led to the emergence of complex, multi-modal travel demands that interact with transportation networks, posing new challenges in transportation analysis and control. The primary objective of this study is to address these challenges by improving transportation modeling and data completeness using advanced modeling tools and transportation big data. We propose a dual-driven simulation model that integrates transportation simulation and big data. The approach begins by utilizing initial Location-Based Services (LBS) data to establish a mesoscopic multi-modal simulation model, which is then calibrated. This calibrated model is then employed to complete the missing trajectories of the LBS data. The innovative aspect of this dual-driven simulation model lies in its novel approach to constructing transportation models and completing LBS data, thereby enhancing both the simulation accuracy and the results of missing path completion. We conduct tests using the urban area of Hangzhou as an example, and the results show that the Normalized Root Mean Square Error (NRMSE) between the average link speeds in the simulation model and in real world observation is reduced to 24.1%. In the LBS data completion process, our proposed method achieves a travel mode identification accuracy of 95.3% for private car travel. Compared to the two baseline methods, the average accuracy of completed trajectories increases by 6.31% and 2.46%, respectively.

A hybrid method for intercity transport mode identification based on mobility features and sequential relations mined from cellular signaling data

AbstractThe proliferation of mobile phones has generated vast quantities of cellular signaling data (CSD), covering extensive spatial areas and populations. These data, containing spatiotemporal information, can be employed to identify and analyze intercity transport modes, providing valuable insights for understanding travel distribution and behavior. However, CSD are primarily intended for communication purposes and are not directly suitable for transportation research due to issues such as low spatial precision, sparse sampling granularity, and lacking traffic semantic features. This article proposes a Hybrid model for identifying individual intercity transport modes based on CSD. Several multidimensional mobility features are proposed that extract interpretable motion characteristics from CSD. A preliminary transport mode probability judgment is made based on the mobility features. Then, the complete transport mode is confirmed considering the temporal continuity correlation of the entire trace. Experiments confirm the Hybrid model's superior precision in identifying transport modes over baseline models, with an average F1 score of 0.92, maintaining high accuracy across various trajectory lengths. This model would support further studying individual intercity travel behavior patterns, aiding transportation planning and operational management decisions using CSD.

Vigil AI Assemble Leveraging Ensemble Technique for Enhanced Detection of Multi-Converter Synchronous Active Islanding Mode in Hybrid Power Distribution Systems

The increasing complexity of contemporary power distribution networks and the necessity to incorporate renewable energy sources have led to the rise in popularity of hybrid power distribution systems. A significant challenge for such systems is the reliable identification of multi-converter synchronous islanding mode, wherein numerous converters operate in synchronous islanded situations. Using state-of-the-art artificial intelligence ensemble techniques, we introduce "Vigil AI Assemble," a novel approach to detecting multi-converter synchronous islanding modes in hybrid power distribution systems. The proposed method enhances the precision and robustness of islanding situation recognition by combining various AI algorithms, each with its own distinct properties. By combining several AI-based techniques, the ensemble method can analyze system properties, waveform data, and communication in real-time.

High-resolution frequency domain decomposition for modal analysis of bridges using train-induced free-vibrations

Modal parameters are structural inherent characteristics that can be applied for revealing performance of railway bridges. Free vibration signals generated by a passage of train are commonly utilized to estimate the modal parameters of railway bridges due to their higher signal-to-noise ratios compared to random vibrations caused by ambient loads. However, since free vibration signals rapidly decay over time, the available free-vibration data is typically short-time. When using the fast Fourier transform-based spectral estimation method for modal identification from short-time vibration data, a phenomenon known as spectral leakage occurs, leading to miss-identification of some structural modes. In this study, the classical frequency domain decomposition (FDD) is improved for modal identification of railway bridges, in which the higher resolution auto-power spectral density (PSD) and cross-PSD functions are calculated through the autoregressive (AR) model-based method. The AR model-based method improves both the smoothness and resolution of the PSD functions compared to the fast Fourier transform technique. These AR model-based PSD functions are then employed in the FDD process to facilitate frequency and mode shape identification while avoiding spurious noise modes. The proposed eigenvalue fitting technique is subsequently utilized to estimate damping ratios. Numerical simulation data as well as vibration data from an actual bridge are analyzed to validate the proposed method, with a comparison made to the Welch’s PSD-based method. The results demonstrate that the modified FDD approach enables more effective identification of structural modes, even in the presence of closely-spaced modes.

Use of a Modified Threshold Function in Fuzzy Cognitive Maps for Improved Failure Mode Identification

Fuzzy cognitive maps (FCMs) provide a rapid and efficient approach for system modeling and simulation. The literature demonstrates numerous successful applications of FCMs in identifying failure modes. The standard process of failure mode identification using FCMs involves monitoring crucial concept/node values for excesses. Threshold functions are used to limit the value of nodes within a pre-specified range, which is usually [0, 1] or [-1, + 1]. However, traditional FCMs using the tanh threshold function possess two crucial drawbacks for this particular.Purpose(i) a tendency to reduce the values of state vector components, and (ii) the potential inability to reach a limit state with clearly identifiable failure states. The reason for this is the inherent mathematical nature of the tanh function in being asymptotic to the horizontal line demarcating the edge of the specified range. To overcome these limitations, this paper introduces a novel modified tanh threshold function that effectively addresses both issues.

Delamination Initiation Identification of Mode II Based on AE Characterization of Composite Damage Mechanism

Delamination Initiation Identification of Mode II Based on AE Characterization of Composite Damage Mechanism

OVERVIEW OF STUDIES ON EXTERNAL CONFINEMENT OF R/C COLUMNS USING UNIAXIAL GEOGRID

It has been established in the literature that using uniaxial geogrid as internal confinement for reinforced concrete (R/C) columns would improve the strength and ductility characteristics of the column. However, the potential of using uniaxial geogrid as external confinement remains largely unexplored. The aim of the study is to assess the effectiveness of utilizing uniaxial geogrid as external confinement as compared to FRP external confinement, as well as various configurations of internal confinement, including uniaxial geogrid and stirrups. The assessment encompasses the analysis of axial load-displacement behavior, identification of failure modes, and evaluation of structural ductility through metrics such as fracture energy and energy dissipation. Test results indicate significant benefits associated with external confinement using uniaxial geogrid, including strength, ductility, and energy capacity. This study contributes to the progression of knowledge by illustrating the efficacy of uniaxial geogrid as an external confinement method for reinforced concrete columns. Additionally, it offers valuable comparative insights into different confinement techniques, informing the selection and design of optimal strategies for reinforced concrete structures.

OVERVIEW OF STUDIES ON EXTERNAL CONFINEMENT OF R/C COLUMNS USING UNIAXIAL GEOGRID

It has been established in the literature that using uniaxial geogrid as internal confinement for reinforced concrete (R/C) columns would improve the strength and ductility characteristics of the column. However, the potential of using uniaxial geogrid as external confinement remains largely unexplored. The aim of the study is to assess the effectiveness of utilizing uniaxial geogrid as external confinement as compared to FRP external confinement, as well as various configurations of internal confinement, including uniaxial geogrid and stirrups. The assessment encompasses the analysis of axial load-displacement behavior, identification of failure modes, and evaluation of structural ductility through metrics such as fracture energy and energy dissipation. Test results indicate significant benefits associated with external confinement using uniaxial geogrid, including strength, ductility, and energy capacity. This study contributes to the progression of knowledge by illustrating the efficacy of uniaxial geogrid as an external confinement method for reinforced concrete columns. Additionally, it offers valuable comparative insights into different confinement techniques, informing the selection and design of optimal strategies for reinforced concrete structures.

Wake mode identification of rotating triangle with machine learning approaches

The moving body usually leaves different forms of wake trails in fluids, and these wake fields contain physical information such as the state and geometry of the moving body. Recognizing and extracting these information from the wake can provide new insights for non-acoustic detection technology. Traditional methods do not easily extract the flow state, geometry, and other information directly from the wake structure. This work mainly uses convolutional neural network algorithms for intelligent recognition of the wake types of rotating triangles. Based on the flow field visualization technology of the soap film tunnel, the wake types of the flow around a structure controlled by external excitation of sinusoidal rotation are studied. The winding characteristics of the rotating triangle and the variation rule of the wake with control parameters are analyzed. At last, the recognition rate of the wake types on the test set is above 90%. The recognition rates of the experimental data not involved in the training conditions are all above 80%, demonstrating the generalizability of the model. This method provides a reference for further utilizing artificial intelligence in extracting physical information from wakes, playing a crucial role in advancing wake detection technology.

Intercity Traffic Travel Mode Identification Method Based on Mobile Signalling Data

With the popularity of mobile devices, the signalling data generated by them provides significant opportunities for studying intercity travel behaviour in terms of data scale and information continuity. However, due to the low quality of the data in spatial accuracy, temporal frequency, and traffic semantics, the accuracy of identifying individual travel modes is low and it is difficult to extend to complex traffic scenarios. In this paper, we propose a new framework for identifying individual intercity travel modes based on mobile signalling data. The framework includes components for data pre-processing, geo-information mapping, feature and attribute extraction, and travel mode recognition. We utilize a comprehensive detection model to identify users’ multimodal intercity transport behaviour. Using two modules, Random Forest Embedding (RFE) and Bidirectional Long Short-Term Memory (Bi-LSTM), the model can capture the spatiotemporal characteristics and complex multi-stage associations in intercity travel chains. A large-scale mobile phone dataset from Jiangsu Province, China, was used for verification. The results showed that, on average, the method was able to detect travel mode with 92% accuracy. This study provides valuable support for further research on individual travel behaviour and the enhancement of transportation planning.

Hierarchical Clustering-Based Collapse Mode Identification and Design Optimization of Energy-Dissipation Braces Inspired by the Triangular Resch Pattern

Hierarchical Clustering-Based Collapse Mode Identification and Design Optimization of Energy-Dissipation Braces Inspired by the Triangular Resch Pattern

Angle-resolved polarized Raman study of layered b-AsxPx-1 alloys: Identification of As-P vibrational modes

In this study, the polarization-resolved Raman spectra of b-AsxPx-1 flakes with varying arsenic concentration (x = 0, 0.4, and 0.8) are systematically investigated. A clear polarization dependence is observed in the Raman intensity of all the P-P, As-P, and As-As modes and confirmed by the first-principles calculations. The observed angle-dependence can be related to the structure of the Raman tensors of each peak. In particular, the Raman intensities corresponding to As-P vibration modes are studied in detail. The out of plane As-P mode (Ag1) is clearly identified, but the butterfly-shaped behavior observed in the polar plot around 350 cm−1 is interpreted as the combination of the two in-plane vibrations, one along the armchair and the other along the zigzag direction. This work allows unambiguous assignment of the Ag1, B2g, and Ag2 modes of the As-P vibrational modes and the importance of the consideration of the superposition of the in-plane B2g and Ag2 modes. Such study can be utilized to further characterize the anisotropic structural properties and analyze the local distribution of the As and P in b−AsxP1−x alloys which will play key roles in electronic and optical properties. This methodology can be further extended to alloyed two-dimensional material systems with in-plane anisotropy in general.

Stability analysis of a uniform viscous transonic flow past a rotating circular cylinder

This work analyzes the transonic flow around a rotating circular cylinder. The combination of a uniform freestream flow and the flow due to the rotation of the cylinder (assuming clockwise rotation) generates a lift force (Robins-Magnus effect) that, for transonic flows, is conditioned by the presence of a shock wave on the upper side of the cylinder.Previous studies have focused primarily on incompressible flows, compressible flows at low Mach numbers, and hypersonic flows with detached shock waves. These works have barely considered the effect of the presence of the shock wave, its interaction with the wake of the cylinder and its impact in the aerodynamic coefficients.Two-dimensional numerical simulations have been carried out using the SU2 code. From these simulations, the value of the aerodynamic coefficients as a function of time and a set of snapshots of the fluid field have been obtained and analyzed using the POD and DMD techniques. This has allowed the identification of the dominant modes that characterize the time variations in the lift and drag coefficients acting on the circular cylinder for different values of the characteristic parameters of the problem.

The risk-based operational safety strategy for Perkin-Elmer/Cetus DNA Thermal Cycler PCR systems

Introduction: this article was conducted in the field of polymerase chain reaction (PCR) test safety, where it is essential to understand the contribution and impact of risk analysis methods to improve test safety medical diagnosis at the institutional level. In this context, the current study focuses on failure modes and effects analysis (FMEA) applied to the Perkin-Elmer/Cetus deoxyribonucleic acid (DNA) thermocycler.Objective: to describe the application of FMEA to improve the operational safety of Perkin-Elmer/Cetus DNA thermal cyclers used in medical diagnostics in ataxic patients. Methods: to carry out this study, a methodology based on an exhaustive review of the scientific literature available in academic databases and relevant sources was used. Previous studies on FMEA analysis of DNA thermocyclers in common and expanded forms were collected and analyzed.Results: the results obtained demonstrated that FMEA is an effective tool to identify and evaluate possible failure modes of Perkin-Elmer/Cetus DNA thermal cyclers. Several defects were identified and their consequences, causes and classification based on their severity and frequency were determined. Likewise, corrective actions are proposed to reduce the identified risks.Conclusions: early identification of possible failure modes and implementation of corrective actions can reduce risk and guarantee the quality of the diagnoses carried out and therefore improve operational safety

Identification of key potential risk areas and key potential failure modes in hemodialysis rooms by the FMEA method following routine prevention and control of the COVID-19 pandemic.

Hemodialysis is an important part of nosocomial infection prevention and control (IPC). This study aimed to identify the key potential risk areas and failure modes in hemodialysis rooms in hospitals and put forward a series of improvement measures to prevent and control the spread of the coronavirus disease 2019 (COVID-19). Hemodialysis patients are highly susceptible to COVID-19 and usually have a high incidence of severe illness and mortality after infection with COVID-19. Therefore, IPC in hemodialysis patients is of crucial strategic significance. Based on 30 domain experts' interviews and careful analysis of prevention and control documents, we constructed a comprehensive failure system for a model that identifies the potential risks for nosocomial COVID-19 infection in the hemodialysis room. Subsequently, a thorough risk assessment of the potential failure factors identified in our model was conducted. The failure key factors corresponding to the human element in medical waste (garbage) disposal (C2) are verified to be the highest risk factors. They are as follows: The cleaning staff did not dispose of different types of medical waste (garbage) (C21), did not wear masks according to the regulations (C22), and lacked knowledge and norms of nosocomial IPC (C23). This study provides valuable insights for hospital decision-makers on the potential failure factors related to COVID-19 infections in hemodialysis rooms. By working with hospital infection specialists, the suggested improvement measures can help reduce the risk of virus exposure among hospital medical staff, patients, and cleaning staff.