Response Data Research Articles

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 >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.

Triptolide and its prodrug Minnelide target high-risk MYC-amplified medulloblastoma in preclinical models.

Most children with medulloblastoma (MB) achieve remission, but some face very aggressive metastatic tumors. Their dismal outcome highlights the critical need to advance therapeutic approaches that benefit such high-risk patients. Minnelide, a clinically relevant analog of the natural product triptolide, has oncostatic activity in both preclinical and early clinical settings. Despite its efficacy and tolerable toxicity, this compound has not been evaluated in MB. Utilizing a bioinformatic data set that integrates cellular drug response data with gene expression, we predicted that Group 3 (G3) MB, which has a poor 5-year survival, would be sensitive to triptolide/Minnelide. We subsequently showed that both triptolide and Minnelide attenuate the viability of G3 MB cells ex vivo. Transcriptomic analyses identified MYC signaling, a pathologically relevant driver of G3 MB, as a downstream target of this class of drugs. We validated this MYC dependency in G3 MB cells and showed that triptolide exerts its efficacy by reducing both MYC transcription and MYC protein stability. Importantly, Minnelide acted on MYC to reduce tumor growth and leptomeningeal spread, which resulted in improved survival of G3 MB animal models. Moreover, Minnelide improved the efficacy of adjuvant chemotherapy, further highlighting its potential for the treatment of MYC-driven G3 MB.

Generalized single index modeling of longitudinal data with multiple binary responses.

In health and clinical research, medical indices (eg, BMI) are commonly used for monitoring and/or predicting health outcomes of interest. While single-index modeling can be used to construct such indices, methods to use single-index models for analyzing longitudinal data with multiple correlated binary responses are underdeveloped, although there are abundant applications with such data (eg, prediction of multiple medical conditions based on longitudinally observed disease risk factors). This article aims to fill the gap by proposing a generalized single-index model that can incorporate multiple single indices and mixed effects for describing observed longitudinal data of multiple binary responses. Compared to the existing methods focusing on constructing marginal models for each response, the proposed method can make use of the correlation information in the observed data about different responses when estimating different single indices for predicting response variables. Estimation of the proposed model is achieved by using a local linear kernel smoothing procedure, together with methods designed specifically for estimating single-index models and traditional methods for estimating generalized linear mixed models. Numerical studies show that the proposed method is effective in various cases considered. It is also demonstrated using a dataset from the English Longitudinal Study of Aging project.

A novel framework for quantitative resilience assessment in complex engineering systems during early and late design stages

Recently, resilience assessment has evolved and grown in prominence, yet most studies are carried out on operational stage when sufficient knowledge on the processes is available, overlooking the design stage, a time frame that is more suitable for making a resilient system. To this end, this work aims at developing a novel quantitative resilience assessment framework for engineering systems with two different approaches that practically analyse resilience at the early and late design stages when detailed information on the system’s safety and resilience capabilities may be deficient. In the early design stage, system resilience attributes are identified, and expert judgment is used to assess their quality. In the late design stage, attributes are derived from revealed information such as detailed emergency response and safety barrier data. In both stages, dynamic Bayesian Network (DBN) is used to quantify resilience based on the acquired information. Since the green hydrogen technology is relatively novice, the application of the proposed framework is demonstrated in a resilience assessment of a green hydrogen plant undergoing hydrogen release scenarios. The proposed framework can be used as an effective tool for early design improvements as well as enhancing process safety in the late design stage of hydrogen plants or any other complex engineering system.

An Exploration of the Development of Computerized Data Mining Techniques and Their Application

The process of data mining involves extracting valuable information and knowledge from vast amounts of data, encompassing various fields such as statistics, machine learning, and database theory. It transcends mere data processing tools, employing intelligent methods for data analysis and stands as a pivotal technology in the era of big data. Its applications span across numerous domains including consumer behavior analysis, market marketing strategies, risk management, medical diagnosis, and fraud detection, with notable prominence in the realm of commerce. In e-commerce platforms, data mining techniques adeptly analyze user purchase histories and browsing records to precisely identify user preferences, recommend personalized products, thus enhancing user satisfaction and sales revenue. In devising marketing strategies, analyzing market data and consumer feedback optimizes product promotion strategies, bolstering market competitiveness. In the medical domain, data mining exhibits robust vitality. By analyzing patient medical records, genetic data, drug response data, predictions of disease trends, optimization of treatment plans, and even early disease warnings are made possible, thereby improving disease cure rates. In the realm of financial security, data mining plays a pivotal role by analyzing transaction data and user behavior to timely detect abnormal transactions and identify fraudulent activities, thereby safeguarding the security and stability of the financial system. In the Internet of Things (IoT) domain, data mining techniques analyze massive sensor data, enabling real-time monitoring and predictive maintenance of devices, thus enhancing operational efficiency and longevity. Data mining extends beyond the aforementioned domains, finding significant applications in social governance, scientific research, intelligent manufacturing, and more. Not only does data mining enhance the scientific rigor and precision of decision-making, but it also empowers various industries to improve efficiency, realize intelligent transformations, thereby further advancing societal progress.

Activity of osimeRTInib in non-small-cell lung Cancer with UNcommon epidermal growth factor receptor mutations: retrospective Observational multicenter study (ARTICUNO)

Osimertinib represents the standard of care for the treatment of advanced non-small-cell lung cancer (NSCLC) harboring classical epidermal growth factor receptor (EGFR) mutations, constituting 80%-90% of all EGFR alterations. In the remaining cases, an assorted group of uncommon alterations of EGFR (uEGFR) can be detected, which confer variable sensitivity to previous generations of EGFR inhibitors, overall with lower therapeutic activity. Data on osimertinib in this setting are limited and strongly warranted. The ARTICUNO study retrospectively evaluated data on osimertinib activity from patients with advanced NSCLC harboring uEGFR treated in 21 clinical centers between August 2017 and March 2023. Data analysis was carried out with a descriptive aim. Investigators collected response data according to RECIST version 1.1 criteria. The median duration of response, progression-free survival (mPFS), and overall survival were estimated by the Kaplan-Meier method. Eighty-six patients harboring uEGFR and treated with osimertinib were identified. Patients with 'major' uEGFR, that is, G719X, L861X, and S768I mutations (n= 51), had an overall response rate (ORR) and mPFS of 50% and 9 months, respectively. Variable outcomes were registered in cases with rarer 'minor' mutations (n= 27), with ORR and mPFS of 31% and 4 months, respectively. Among seven patients with exon 20 insertions, ORR was 14%, while the best outcome was registered among patients with compound mutations including at least one classical EGFR mutation (n= 13). Thirty patients presented brain metastases (BMs) and intracranial ORR and mPFS were 58% and 9 months, respectively. Amplification of EGFR or MET, TP53 mutations, and EGFR E709K emerged after osimertinib failure in a dataset of 18 patients with available rebiopsy. The ARTICUNO study confirms the activity of osimertinib in patients with uEGFR, especially in those with compound uncommon-common mutations, or major uEGFR, even in the presence of BMs. Alterations at the E709 residue of EGFR are associated with resistance to osimertinib.

Digital Therapy for Negative Consumption Experiences: The Impact of Emotional and Rational Reviews on Review Writers

Abstract This research tests a solution for consumers to recover faster from negative experiences. We identify this solution by examining how the manner in which review writers express their emotions and rational thoughts in their reviews causally influences review writers. The results of five studies (field data and experiments) show that, similar to writing about traumatic life events, when review writers express both emotional and rational aspects in reviews about negative consumption experiences, they feel better afterwards (i.e., they recover affectively), and are more likely to purchase again (i.e., they recover cognitively). We further examine why writing integrated reviews has positive effects on review writers by collecting biophysiological response data, which provide support for an account related to affective recovery, and by analyzing thought listing data, which provide support for an account related to cognitive recovery. This research shows that writing online reviews can serve as a digital therapy tool that helps consumers recover from negative experiences.

Measurement properties of the Swedish version of the anterior cruciate ligament return to sport after injury scale (ACL-RSI): A Rasch analysis.

This study aimed to investigate the measurement properties, according to the Rasch Measurement Theory, of the anterior cruciate ligament return to sport after injury scale (ACL-RSI) in patients treated with ACL reconstruction in Sweden. ACL-RSI responses from 1065 patients treated with ACL reconstruction were extracted from a rehabilitation-specific registry. Rasch analyses were performed on ACL-RSI item response data using the RUMM2030plus software. The analyses focused on targeting (person-item threshold distribution), item hierarchy, response category thresholds, overall and individual item and person fit, differential item functioning (DIF), unidimensionality and reliability. The ACL-RSI had compromised fit to the Rasch model, including DIF and malfunctioning response categories. Several items correlate with each other and the presumptions to aggregate item responses into one single score were not met. When accounting for local dependency, the measurement properties of the ACL-RSI improved in terms of model fit and DIF and unidimensionality were supported. The ACL-RSI was found to have signs of multidimensionality and local dependency, that is, the answers to one item are influenced by the answers to other items. As such, researchers should be careful when using the ACL-RSI as one single score to evaluate patients treated with ACL reconstruction, unless local dependency is accounted for in the scoring process. Level III.

Entry-Level Occupational Therapy Student Volunteer Activities During COVID-19 and Beliefs about Practitioner Roles in Crisis Management

Background: Due to the global novel coronavirus disease 2019 (COVID-19) pandemic, many students in entry-level occupational therapy programs faced changes to their daily roles, habits, and routines. To understand beliefs about OT practitioner roles in a public health crisis, a cross-sectional study was designed to understand occupational therapy students (OTS) and occupational therapy assistant students (OTAS) volunteer activities during the COVID-19 pandemic. Methods: A survey was developed and distributed in April 2020, and closed after five weeks. Quantitative data was analyzed in Statistical Package for Social Sciences (Version 27) and open response data was categorized in Microsoft Excel. Results: Respondents included students from entry-level OT/OTA accredited programs such as occupational therapy doctorate, entry level master’s, bachelor’s to master’s and occupational therapy assistant (n=853, 2.8% of OTS/OTAS population). Nearly all respondents endorsed that OT practitioners have at least one role during a crisis (n=851, 99.8%) and after a crisis (n=848, 99.4%) like COVID-19. A minority of students volunteered in response to COVID-19 (n=60, 7.0%). Volunteer activities addressed clinical work, food insecurity, loneliness, social justice, healthcare workers, COVID-19 transmission, and personal protective equipment. Inspirations for volunteering included altruism, justice, personal responsibility, changes to routines, calls to action, and fulfilling obligations. Discussion: It is hoped that this study serves as a springboard for future research related to the OTS/OTAS response to COVID-19. Educators can prepare students for future public health emergencies by exposing them to information regarding OT’s role in disaster management.

Performance Comparison of Convolutional Neural Network-Based Hearing Loss Classification Model Using Auditory Brainstem Response Data.

This study evaluates the efficacy of several Convolutional Neural Network (CNN) models for the classification of hearing loss in patients using preprocessed auditory brainstem response (ABR) image data. Specifically, we employed six CNN architectures-VGG16, VGG19, DenseNet121, DenseNet-201, AlexNet, and InceptionV3-to differentiate between patients with hearing loss and those with normal hearing. A dataset comprising 7990 preprocessed ABR images was utilized to assess the performance and accuracy of these models. Each model was systematically tested to determine its capability to accurately classify hearing loss. A comparative analysis of the models focused on metrics of accuracy and computational efficiency. The results indicated that the AlexNet model exhibited superior performance, achieving an accuracy of 95.93%. The findings from this research suggest that deep learning models, particularly AlexNet in this instance, hold significant potential for automating the diagnosis of hearing loss using ABR graph data. Future work will aim to refine these models to enhance their diagnostic accuracy and efficiency, fostering their practical application in clinical settings.