Response Data Research Articles

Perceived Barriers to Physical Activity Among Youth Living in Rural and Urban Canadian Communities: A Nationally Representative Cross-Sectional Study.

We used nationally representative data to explore associations among location of residence (rural/urban) and perceived barriers to physical activity (PA) in Canadian youth. We analyzed the 2017 Canadian Community Health Survey, Barriers to Physical Activity Rapid Response data for 12- to 17-year-old youth. Nine items from the survey assessing perceived barriers to PA were combined into 3 barrier domains: resources, motivational, and socioenvironmental. The likelihood of reporting barriers to PA based on rural-urban location was examined using survey-weighted binary logistic regression following a model fitting approach. Sociodemographic factors were modeled as covariates and tested in interaction with location. For each barrier domain, we derived the best-fitting model with fewest terms. There were no location-specific effects related to reporting any barrier or motivation-related PA barriers. We found a sex by location interaction predicting the likelihood of reporting resource-related barriers. Rural boys were less likely to report resource-related barriers compared with urban boys (odds ratio [OR] = 0.42 [0.20, 0.88]). Rural girls were more likely to report resource-related barriers compared with boys (OR = 3.72 [1.66, 8.30]). Regarding socioenvironmental barriers, we observed a significant body mass index by location interaction demonstrating that rural youth with body mass index outside the "normal range" showed a higher likelihood of reporting socioenvironmental barriers compared with urban youth (OR = 2.38 [1.32, 4.30]). For urban youth, body mass index was unrelated to reporting socioenvironmental barriers (OR = 1.07 [0.67, 1.71]). PA barriers are not universal among Canadian youth. Our analyses highlight the importance of testing interactions in similar studies as well as considering key sociodemographic characteristics when designing interventions.

Physics-informed surrogate modeling for a damaged rotating shaft

In the online structural health monitoring framework, surrogate modeling aims to approximate the predictions of the underlying high-fidelity model to facilitate fast simulations. Generally, conventional machine learning techniques based on purely data-driven approaches often behave as black boxes, producing predictions that may lack physical consistency. In an effort to overcome these limitations, we proposed a neural network mapping constrained by the known physical relationships of an engineering model, which can be considered as a physics-informed inductive bias. This approach aims to enhance computational efficiency while reducing the required number of training examples. The physics-informed term guides the training process to yield predictions that exhibit greater physical consistency while maintaining high performance. We here propose a surrogate modeling approach relying on a Physics-Informed Neural Network (PINN) scheme, which is designed to enable robust and real-time monitoring of ballistic impact damage on a rotor shaft. At first, a high fidelity finite element model is developed to simulate the response of the rotor shaft under various operational conditions. As a first reduction step, substructuring is used to decrease the dimensionality of the system's matrices, thereby reducing the computational cost. For comparison and validation, an alternative model based on the Timoshenko beam theory is utilized. Both models examine the effects of impact-induced vibration loads due to ballistic occurrences. The PINN model receives the external load as input and predicts the system's response. The system matrices extracted from both models constitute the physics-informed term. This term is incorporated into the PINN architecture in order to steer the learning process towards more physically consistent and reliable predictions regarding the state and the response of the structure. The algorithm's performance is evaluated using response data derived from both models.

Validation of a model-based dual-band modal decomposition and expansion approach for fatigue monitoring of offshore wind turbine foundations

The fatigue limit state strongly drives the lifetime of offshore wind turbine support structures. Despite steady advancements in their design, the accumulation of fatigue damage over the lifetime of the structure remains uncertain; one way to improve certainty is to directly monitor structural loads at fatigue-critical locations, e.g., near or below the mudline. However, directly monitoring the loads at these locations is often not possible, since these locations do not allow for easy installation or retrofitting of sensors. To this end, model-based virtual sensing techniques can be employed to estimate the structural response at unmeasured locations based on vibration response data and a finite element model which can accurately describe the modal and operational deflection shapes of a specific turbine [1,2,3]. For this contribution, a modal decomposition and expansion (MDE) algorithm [4] will be used to predict strain time series at fatigue-critical locations, based on acceleration and strain data obtained from the tower of an operational OWT. Emphasis will be put on sparse data situations in which a realistic amount of sensor data is used as input. Fibre-optical strain gauges close to and below the mudline level (Fig 1) will be used for direct validation of the extrapolation capabilities. The required finite element model is built using an in-house developed and verified integrated FE model class [5,6], in combination with a unique database which contains all available geotechnical and structural data of offshore wind turbines (owimetadatabase) [7,8]. This combination allows us to generate fleet-wide, turbine-specific, finite elements models for several wind farms. The results will be presented in terms of time series (e.g., Fig. 2) and fatigue damage-related metrics. Preliminary results show that strain time series estimated with the MDE method show good agreement with the validation data.

Serotonin receptor-mediated vasorelaxation occurs primarily through 5-HT4 activation in bovine lateral saphenous vein.

To better understand mechanisms of serotonin- (5-HT) mediated vasorelaxation, isolated lateral saphenous veins from cattle were assessed for vasoactivity using myography in response to increasing concentrations of 5-HT or selective 5-HT receptor agonists. Vessels were pre-contracted with 1 × 10-4 M phenylephrine and exposed to increasing concentrations of 5-HT or 5-HT receptor agonists that were selective for 5-HT1B, 5-HT2B, 5-HT4, and 5-HT7. Vasoactive response data were normalized as a percentage of the maximum contractile response induced by the phenylephrine pre-contraction. At 1 × 10-7 M 5-HT, a relaxation was observed with an 88.7% decrease (p < 0.01) from the phenylephrine maximum. At 1 × 10-4 M 5-HT, a contraction was observed with a 165% increase (p < 0.01) from the phenylephrine maximum. Increasing concentrations of agonists selective for 5-HT2B, 5-HT4, or 5-HT7 resulted in a 27%, 92%, or 44% (p < 0.01) decrease from the phenylephrine maximum, respectively. Of these 5-HT receptor agonists, the selective 5-HT4 receptor agonist resulted in the greatest potency (-log EC50) value (6.30) compared with 5-HT2B and 5-HT7 receptor agonists (4.21 and 4.66, respectively). To confirm the involvement of 5-HT4 in 5-HT-mediated vasorelaxation, blood vessels were exposed to either DMSO (solvent control) or a selective 5-HT4 antagonist (1 × 10-5 M) for 5-min prior to the phenylephrine pre-contraction and 5-HT additions. Antagonism of the 5-HT4 receptor attenuated the vasorelaxation caused by 5-HT. Approximately 94% of the vasorelaxation occurring in response to 5-HT could be accounted for through 5-HT4, providing strong evidence that 5-HT-mediated vasorelaxation occurs through 5-HT4 activation in bovine peripheral vasculature.

유아교육전공 대학생의 그릿과 자기효능감이 행복감에 미치는 영향

The purpose of this study is to examine the effect of grit and self-efficacy of college students majoring in early childhood education on happiness. For the study, a survey was conducted on college students majoring in early childhood education located in D city, and the response data of 241 people were analyzed with the SPSS 25.0 statistical program. Looking at the results of the study, first, it was found that there was a significant positive correlation between grit, self-efficacy, and happiness of college students majoring in early childhood education. Second, it was confirmed that the grit of college students majoring in early childhood education had a positive effect on happiness. In addition, it was confirmed that the self-efficacy of college students majoring in early childhood education had a positive effect on happiness. The results of this study suggest that the improvement of grit and self-efficacy must be preceded in order to come up with practical measures that can help improve the happiness of college students.

Optimizing and Predicting Antidepressant Efficacy in Patients with Major Depressive Disorder Using Multi-Omics Analysis and the Opade AI Prediction Tools.

According to the World Health Organization (WHO), major depressive disorder (MDD) is the fourth leading cause of disability worldwide and the second most common disease after cardiovascular events. Approximately 280 million people live with MDD, with incidence varying by age and gender (female to male ratio of approximately 2:1). Although a variety of antidepressants are available for the different forms of MDD, there is still a high degree of individual variability in response and tolerability. Given the complexity and clinical heterogeneity of these disorders, a shift from "canonical treatment" to personalized medicine with improved patient stratification is needed. OPADE is a non-profit study that researches biomarkers in MDD to tailor personalized drug treatments, integrating genetics, epigenetics, microbiome, immune response, and clinical data for analysis. A total of 350 patients between 14 and 50 years will be recruited in 6 Countries (Italy, Colombia, Spain, The Netherlands, Turkey) for 24 months. Real-time electroencephalogram (EEG) and patient cognitive assessment will be correlated with biological sample analysis. A patient empowerment tool will be deployed to ensure patient commitment and to translate patient stories into data. The resulting data will be used to train the artificial intelligence/machine learning (AI/ML) predictive tool.

Exact optimisation of spatiotemporal monitoring networks by p‐splines with applications in groundwater assessment

AbstractThis paper develops methods to optimise the sampling strategy for monitoring networks which have fixed locations with regular sampling but with only a proportion of these locations to be used on each sampling occasion. This creates the need for a dynamic spatiotemporal sampling design which makes optimal choices of the locations to be sampled on each occasion. This is a commonly occurring scenario in many environmental settings where there is an existing network of monitoring stations and sampling can be expensive. The particular context of optimisation of an existing groundwater monitoring network is discussed in the paper. The standard design criteria of integrated variance (IV) and variance of the integral (VI) are adapted to the spatiotemporal setting. p‐spline models are shown to allow exact computation of IV and VI, in the case of the additive errors, and a very good approximation of IV in the case of multiplicative errors. The speed of these exact computations allows the globally optimal sampling design to be identified efficiently. In the standard case of additive errors, the design criteria are able to exploit information across time. The only information needed is the location of sampling points, not the values sampled. This contrasts with the case of multiplicative errors where the design criteria are also influenced by the observed response data. Simulated and real examples are used to illustrate the results throughout.

Bayesian time domain approach for damping identification and uncertainty quantification in stay cables using free vibration response

The dynamic characteristics, particularly damping, play crucial roles in the design, monitoring, and vibration control of stay cables. The free vibration test, commonly used for damping identification of stay cables, demonstrates notable technical advantages in specific mode excitation and improving the signal-to-noise ratio. Nevertheless, challenges arise from uncertainties associated with ineluctable ambient vibration influences and noise contamination in measured responses. In recent years, there has been a growing interest in Bayesian modal identification methods due to their remarkable ability to quantify uncertainties. However, further advancements are needed in the time domain, particularly when addressing the relatively short duration of free vibration response data. This is essential to minimize the impact of background ambient vibration and enhance the accuracy of modal identification. This paper proposes a novel Bayesian modal identification approach specifically crafted to address free vibration data affected by ambient vibrations, aiming to achieve high-precision modal identification and effective uncertainty quantification from free vibration response of stay cables. Additionally, a speedup algorithm is used for determining the most probable values (MPVs) to enhance computational efficiency. The efficacy of the proposed approach is validated through simulated scenarios, encompassing both a single-degree-of-freedom structure and a finite element model of a stay cable. Furthermore, the effectiveness of the proposed method is validated through its application in a laboratory test involving a scale model of the stay cable, as well as in a field test of actual stay cables of Sutong Bridge. The results demonstrate that the proposed method achieves high accuracy in identifying modal damping ratios, and effectively quantifies uncertainties in the identification results.

Automatic damping estimation via bootstrap technique and Bayesian analysis for mechanical system condition monitoring

Monitoring mechanical systems that are exposed to dynamic loads during operation requires a technical indicator that provides reliable information on degradation. In this paper, the damping ratio is used as an indicator and can be obtained from monitoring data such as acceleration. Its change from the initial value highly correlates with the system change that can lead to failure. The damping ratio can be relatively easily estimated from the free response data. Such events occur frequently for systems that are subjected to environmental loads such as wind gusts on structures, or sudden stops and changes in the operational cycle.The problem addressed by this research is the fully autonomous detection of these free-response events and the subsequent estimation of the damping ratio, including uncertainty. Therefore, a novel monitoring approach is proposed, utilising the Bootstrap technique and Bayesian analysis. Bootstrap provides automatic free response detection without monitoring the excitation force. Once the event is detected as a valid free response, Bayesian analysis in the form of a gamma-exponential conjugate prior is applied to estimate the damping ratio. As damping estimation is in the form of distribution, uncertainty is also quantified, which is a required feature when monitoring the degradation process and remaining useful life prediction.The monitoring approach is demonstrated and verified numerically and experimentally on a single degree of freedom air-bearing testbed as well as on multiple degrees of freedom beam system. As shown, the combination of the Bootstrap technique and Bayesian analysis provided satisfactory results, demonstrating that the degradation process can be reliably monitored by the proposed approach.

Output-only response migration method of single-layer reticulated shells based on generative adversarial network

As an important form of important public building roofs, single-layer latticed shells (SLRSs) are equipped with health monitoring systems (SHMs) to collect extensive data to study their complex response characteristics. For SLRSs without structural SHMs, limited response data can only be obtained through on-site measurements, which limits the further application of data-driven machine learning methods on SLRSs. Based on the measured actual response, a traditional generative network can generate fake responses similar to the actual responses to expand the existing response dataset. However, this method is not subject to enough constraints, leading to a small portion of the generated fake responses do not match the actual situation. This paper proposes a response migration method to expand the response dataset of SLRS. Firstly, the output-only response migration method for SLRS is proposed, utilizing frequency domain responses from two different response domains to expand the responses of the actual SLRS through response migration. Then, more constraints are applied to the response migration process to reduce the occurrence of abnormal fake responses through cycle consistency generation adversarial network (GAN). The adaptive response migration generation adversarial network (ARMGAN) is proposed based on cycle consistency GAN, which can automatically select networks with better migration performance. Finally, two numerical spherical SLRS examples and an engineering example are used to verify the proposed method. The responses migrated through ARMGAN have characteristics similar to the actual responses, and ARMGAN can significantly reduce abnormal fake responses.

Evaluating a National Traditional Chinese Medicine Examination via Cognitive Diagnostic Approaches

The current research utilized diagnostic classification models (DCMs), an advanced psychometric theory, to evaluate the examination’s quality using psychometric methods for a more precise and comprehensive understanding of health professionals’ competence. Data was gathered from 16,310 fourth-year Traditional Chinese Medicine undergraduates who completed the Standardized Competence Test for Traditional Chinese Medicine Undergraduates (SCTTCMU) comprising 300 multiple-choice items. The study examined the fundamental assumptions, model-data fit, and cognitive diagnostic theory models’ item and test properties. The generalized deterministic input, noisy, “and” gate model applied in this research demonstrated a strong alignment with the real response data, meeting all the necessary assumptions. Cognitive diagnostic analysis indicated that all items exhibited satisfactory psychometric characteristics, and the reported scores offered insights into candidates’ proficiency in cognitive skills. It is expected that the advent of modern psychometric technology will contribute to the improvement of refined diagnostic information for health professional candidates. Furthermore, this research holds the potential to significantly enhance sustainability in healthcare practices, knowledge, economics, resource use, and community resilience.

A quick response data collection and management system for digital twin production line based on cloud-fog-edge computing collaboration

Background A production line is the basic unit of smart factories and smart manufacturing. However, owing to the development of the industrial Internet of Things, sensors, and other technologies, more data are being collected, leading to a data explosion, and the heterogeneous nature of multiple sources makes it difficult to manage data in a unified manner. Methods A production line data collection, storage, and management system based on cloud-fog-edge computing collaboration and a digital twin was designed. Multi-source heterogeneous data were collected and transmitted based on the OPC UA, and an information model of the production line was established. Modules for data mapping, publishing, and receiving were developed to achieve unified data collection and transmission. The data storage and management platform was constructed by front-end and back-end separation technologies. Results The developed data collection and management system was verified for functionality and performance on a digital twin production line. Functional tests show that the system has the functions of data acquisition and transmission, device addition and viewing, device data querying and downloading, data and model visualization, and user rights setting. The average time for edge data collection and transmission is 183.6ms. The average response time of the cloud for fog requests is less than 1s. This shows that the system can satisfy the real-time requirements of a digital twin production line. Conclusions The proposed system is real-time and stable, providing support for big data and virtual-reality interaction in digital twins.

Exact solution for the electrical response of a constant phase element with a series resistance to linear voltage sweep

A resistance Rs in series with a constant phase element (CPE) of frequency-dependent impedance given by the power law function Zc(s)=1/(Cαsα) is commonly used for the analysis of steady-state frequency response data exhibiting non-purely capacitive behavior. This is the case in most (bio)(electro)chemical systems including dielectrics, batteries, supercapacitors, capacitive deionization units, biological tissues and bioelectrodes. Passing to the time domain, the current, voltage and charge of the system are governed by differential equations with non-integer, fractional-order operators. The purpose of this study is to provide the exact analytical expressions for the electrical response of an Rs-CPE model under linear sweep voltammetry with the use of the Laplace transform method. The electrical variables are expressed in terms of special functions regularly encountered with fractional calculus such as the Fox’s H-function and the Mittag-Leffler function, and can be used for modeling non-ideal devices as well as extracting their characteristic parameters.

Analysis of sampled-data hybrid integrator-gain systems: A discrete-time approach

Hybrid integrator-gain systems (HIGS) are hybrid control elements used to overcome fundamental performance limitations of linear time-invariant feedback control, and have enjoyed recent successes in engineering applications such as high-precision motion systems. However, despite the relevance of digital implementations, the creation of sampled-data versions of HIGS and their formal analysis have not been addressed in the literature so far, and will form the topic of the present paper. Thereto, we present discrete-time HIGS elements, which preserve the main philosophy behind the operation of HIGS in continuous time. Moreover, stability criteria are presented that can be used to certify input-to-state stability of discrete-time and sampled-data HIGS-controlled systems based on both (i) (measured) frequency response data, and (ii) linear matrix inequalities (LMIs). A comparison between these stability criteria is presented as well. A numerical case study is provided to illustrate the application of the main results.

A detailed quartz and feldspar luminescence chronology for the Khonako II loess section (Southern Tajikistan)

Parts of Central Asia have been dominated by high dust flux since the start of the Quaternary. The resulting loess-palaeosol deposits are important archives for landscape and climate changes, but there is a lack of chronological data for the subaerial deposits of Southern Tajikistan. Our study presents a high-resolution luminescence chronology for the Khonako II section in the Khovaling region. Thirty-seven samples were collected from the first ∼8 m of the section, corresponding to the Holocene soil and the upper part of the Loess 1 (L1) unit. We applied quartz OSL dating to the coarse-silt fraction (40–63 μm) and polymineral pIRIR200,290 dating to the very fine sand fraction (63–90 μm). The polymineral pIRIR200,290 signal has satisfactory characteristics. The quartz dose response curve displays a non-single exponential growth above ∼100 Gy, thus, the De values were estimated by fitting a single exponential function plus linear component to the dose response data. To assess the pIRIR200,290 residual dose at deposition, we used the offset between our quartz and polymineral chronologies from Khonako II and equivalent doses measured on modern dust samples from Tajikistan and Uzbekistan. The resulting 9.2 ± 0.4 Gy dose was then subtracted from the pIRIR200,290 doses for final age calculation. The quartz OSL and pIRIR ages are then generally consistent. The obtained high-resolution luminescence chronology shows that the upper part of the L1 unit accumulated during the end of marine isotope stage (MIS) 3 and through MIS 2 (39–20 ka). The average dust accumulation rates (DAR) increased from ∼31 cm ka−1 to 41 cm ka−1 between MIS 3 and MIS 2. The DAR reached a peak of ∼71 cm ka−1 at 21 ka. A hiatus of ∼7–9 ka was then identified between the end of the last glacial maximum (LGM) and the beginning of the Holocene, and the average DAR decreased during the Holocene. Our results indicate a continuous high dust flux in the Khovaling plateau over the last 39 ka, but reworking processes and potential deflation events mark the LGM-Holocene transition. High-resolution luminescence dating studies in Tajikistan are therefore important to detect potential discontinuities in the sequences and provide an independent timescale for interpreting the climate proxies and calculating dust accumulation rates.

Large-scale evaluation of cold-start mitigation in adaptive fact learning: Knowing “what” matters more than knowing “who”

Adaptive learning systems offer a personalised digital environment that continually adjusts to the learner and the material, with the goal of maximising learning gains. Whenever such a system encounters a new learner, or when a returning learner starts studying new material, the system first has to determine the difficulty of the material for that specific learner. Failing to address this “cold-start” problem leads to suboptimal learning and potential disengagement from the system, as the system may present problems of an inappropriate difficulty or provide unhelpful feedback. In a simulation study conducted on a large educational data set from an adaptive fact learning system (about 100 million trials from almost 140 thousand learners), we predicted individual learning parameters from response data. Using these predicted parameters as starting estimates for the adaptive learning system yielded a more accurate model of learners’ memory performance than using default values. We found that predictions based on the difficulty of the fact (“what”) generally outperformed predictions based on the ability of the learner (“who”), though both contributed to better model estimates. This work extends a previous smaller-scale laboratory-based experiment in which using fact-specific predictions in a cold-start scenario improved learning outcomes. The current findings suggest that similar cold-start alleviation may be possible in real-world educational settings. The improved predictions can be harnessed to increase the efficiency of the learning system, mitigate the negative effects of a cold start, and potentially improve learning outcomes.

Reducing flight upset risk and startle response: A study of the wake vortex alert with licensed commercial pilots

The study aimed at investigating the impact of an innovative Wake Vortex Alert (WVA) avionics on pilots' operation and mental states, intending to improve aviation safety by mitigating the risks associated with wake vortex encounters (WVEs). Wake vortices, generated by jet aircraft, pose a significant hazard to trailing or crossing aircrafts. Despite existing separation rules, incidents involving WVEs continue to occur, especially affecting smaller aircrafts like business jets, resulting in aircraft upsets and occasional cabin injuries. To address these challenges, the study focused on developing and validating an alert system that can be presented to air traffic controllers, enabling them to warn flight crews. This empowers the flight crews to either avoid the wake vortex or secure the cabin to prevent injuries. The research employed a multidimensional approach including an analysis of human performance and human factors (HF) issues to determine the potential impact of the alert on pilots' roles, tasks, and mental states. It also utilizes Human Assurance Levels (HALs) to evaluate the necessary human factors support based on the safety criticality of the new system. Realistic flight simulations were conducted to collect data of pilots' behavioural, subjective and neurophysiological responses during WVEs. The data allowed for an objective evaluation of the WVA impact on pilots' operation, behaviour and mental states (mental workload, stress levels and arousal). In particular, the results highlighted the effectiveness of the alert system in facilitating pilots' preparation, awareness and crew resource management (CRM). The results also highlighted the importance of avionics able to enhance aviation safety and reducing risks associated with wake vortex encounters. In particular, we demonstrated how providing timely information and improving situational awareness, the WVA will minimize the occurrence of WVEs and contribute to safer aviation operations.

Exploring the accessibility of primary health care data in Europe's COVID-19 response: developing key indicators for managing future pandemics (Eurodata study)

BackgroundPrimary Health Care (PHC) plays a crucial role in managing the COVID-19 pandemic, with only 8% of cases requiring hospitalization. However, PHC COVID-19 data often goes unnoticed on European government dashboards and in media discussions. This project aims to examine official information on PHC patient care during the COVID-19 pandemic in Europe, with specific objectives: (1) Describe PHC’s clinical pathways for acute COVID-19 cases, including long-term care facilities, (2) Describe PHC COVID-19 pandemic indicators, (3) Develop COVID-19 PHC activity indicators, (4) Explain PHC’s role in vaccination strategies, and (5) Create a PHC contingency plan for future pandemics.MethodsA mixed-method study will employ two online questionnaires to gather retrospective PHC data on COVID-19 management and PHC involvement in vaccination strategies. Validation will occur through focus group discussions with medical and public health (PH) experts. A two-wave Delphi survey will establish a European PHC indicators dashboard for future pandemics. Additionally, a coordinated health system action plan involving PHC, secondary care, and PH will be devised to address future pandemic scenarios. Analysis: Quantitative data will be analysed using STATA v16.0 for descriptive and multivariate analyses. Qualitative data will be collected through peer-reviewed questionnaires and content analysis of focus group discussions. A Delphi survey and multiple focus groups will be employed to achieve consensus on PHC indicators and a common European health system response plan for future pandemics. The Eurodata research group involving researchers from 28 European countries support the development.DiscussionWhile PHC manages most COVID-19 acute cases, data remains limited in many European countries. This study collects data from numerous countries, offering a comprehensive perspective on PHC’s role during the pandemic in Europe. It pioneers the development of a PHC dashboard and health system plan for pandemics in Europe. These results may prove invaluable in future pandemics. However, data may have biases due to key informants’ involvement and may not fully represent all European GP practices. PHC has a significant role in the management of the COVID-19 pandemic, as most of the cases are mild or moderate and only 8% needed hospitalization. However, PHC COVID-19 activity data is invisible on governments’ daily dashboards in Europe, often overlooked in media and public debates.

Unsupervised quantitative structural damage identification method based on BiLSTM networks and probability distribution model

Current unsupervised structural damage identification methods mainly focus on unsupervised damage detection, i.e., detecting whether damage occurs, but rarely involve damage localization and quantification in practical civil engineering structures. Thus, based on bidirectional long short-term memory (BiLSTM) networks and a probability distribution model, i.e., generalized extreme value distribution (GEVD), this paper proposes an unsupervised structural damage detection, localization, and quantification method. The response correlations model among sensors under healthy conditions was built by employing the BiLSTM networks for reconstructing response data. Then, reconstruction error between reconstructed and true data is used as damage-sensitive features and thus is applied to detect and localized damage. In addition, a stable damage quantification result can be obtained based on the probability distribution model of damage-sensitive features. The proposed method is validated by using the data obtained from a numerical steel beam model and a practical long-span cable-stayed bridge. The research results demonstrate that the proposed method can effectively detect, localize, and quantify damage in an unsupervised manner, even with a limited number of sensors and low-order modal information in acceleration data. The proposed method possesses the capability to autonomously furnish alerts regarding structural damage location and severity under Environmental and Operational Conditions.

ETMR-23. CNS SARCOMAS WITHCIC- ORATXN1-ALTERATIONS — A META-ANALYSIS OF CLINICAL AND MOLECULAR FEATURES IN A LARGE SERIES OF PATIENTS

Abstract BACKGROUND In 2021, the 5th WHO classification of central nervous system (CNS) tumors acknowledged CNS sarcomas with CIC alterations as a distinct tumor type, which may also expand to non-CIC alterations including ATXN1. To tackle the lack of treatment protocols for these highly aggressive tumors, we conducted a meta-analysis to provide an overview of clinical and molecular characteristics, define clinically relevant subtypes, and ultimately advise on treatment guidelines. METHODS We collected molecular and clinical data for &amp;gt;200 patients with CIC- or ATXN1-altered CNS sarcomas by screening our in-house DNA methylation database (581 potential patients initially identified), publication databases (50 patients), and contacting centers worldwide (60 patients). Tumor tissue (FFPE and fresh frozen) for further molecular characterization was obtained from a subset of patients. DNA methylation-based clustering analyses (t-SNE, UMAP, consensus partitioning) were performed, integrating gene fusion details (RNAseq, FISH), copy-number variations, and clinical patient data to identify associations between (epi-)genetic and clinical parameters. RESULTS Most prominent fusion partners for CIC (n=47) and ATXN1 (n=9) were NUTM1 (n=18), LEUTX (n=17) and DUX4 (n=15). Clustering analyses of DNA methylation patterns indicated several epigenetic subtypes associated with different fusion types. Potential clinical implications are still under evaluation. Age distribution of patients at diagnosis in this cohort ranged from two months to 71 years (median=7.5 years). We observed no gender bias. Survival analyses are currently being performed, first results on survival and follow-up studies will be shown at the symposium. CONCLUSION We show first-of-its-kind data describing a large cohort of CNS sarcomas with CIC or ATXN1 alterations, outlining molecular and clinical characteristics, which may facilitate the development of guidelines for optimal treatment of these tumors based on retrospective response data. New molecular insights may also open up an avenue for targeted therapeutic approaches.