E-cigarettes on Instagram: Exploring vape content via an Australian vaping influencer.

INTRODUCTION: Mounting evidence suggests that electronic cigarettes (e-cigarettes) are extensively promoted and marketed using social media, including through user-generated content and social media influencers. This study explores how e-cigarettes are being promoted on Instagram, using a case-study approach, and the extent to which Meta's Restricted Goods and Services Policy (Meta's policy) is being applied and enforced. METHODS: We identified the accounts followed by an Australian Instagram influencer who primarily posts e-cigarette-related content. The main foci of these 855 accounts were coded and 369 vaping-focused accounts were identified. These vaping-focused accounts were then further coded by two trained coders. RESULTS: All (n=369; 100.0%) of the vape content posted by these accounts was positive in sentiment. One-third of the vape accounts (n=127; 34.4%) had a shared focus, indicating that vape content may permeate into other online communities through shared interests. A total of 64 accounts (17.3%) potentially violated Meta's policy by attempting to purchase, sell, raffle or gift e-cigarette products. CONCLUSIONS: The findings of this study suggest that pro-vaping information is available and accessible on Instagram. Much of the content identified in this study promoted the purchase or gifting of e-cigarette products and potentially violates Meta's policy. Greater regulation and/or stronger enforcement of e-cigarette content on social media platforms such as Instagram is necessary to prevent the ongoing promotion of these harmful products.

Development of a pharmaceutical science systematic review process using a semi-automated machine learning tool: Intravenous drug compatibility in the neonatal intensive care setting.

Our objective was to establish and test a machine learning-based screening process that would be applicable to systematic reviews in pharmaceutical sciences. We used the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) model, a broad search strategy, and a machine learning tool (Research Screener) to identify relevant references related to y-site compatibility of 95 intravenous drugs used in neonatal intensive care settings. Two independent reviewers conducted pilot studies, including manual screening and evaluation of Research Screener, and used the kappa-coefficient for inter-reviewer reliability. After initial deduplication of the search strategy results, 27 597 references were available for screening. Research Screener excluded 1735 references, including 451 duplicate titles and 1269 reports with no abstract/title, which were manually screened. The remainder (25 862) were subject to the machine learning screening process. All eligible articles for the systematic review were extracted from <10% of the references available for screening. Moderate inter-reviewer reliability was achieved, with kappa-coefficient ≥0.75. Overall, 324 references were subject to full-text reading and 118 were deemed relevant for the systematic review. Our study showed that a broad search strategy to optimize the literature captured for systematic reviews can be efficiently screened by the semi-automated machine learning tool, Research Screener.

Protocol for a systematic review of reviews on training primary care providers in dermoscopy to detect skin cancers.

INTRODUCTION: Globally, incidence, prevalence and mortality rates of skin cancers are escalating. Earlier detection by well-trained primary care providers in techniques such as dermoscopy could reduce unnecessary referrals and improve longer term outcomes. A review of reviews is planned to compare and contrast the conduct, quality, findings and conclusions of multiple systematic and scoping reviews addressing the effectiveness of training primary care providers in dermoscopy, which will provide a critique and synthesis of the current body of review evidence. METHODS AND ANALYSIS: Four databases (Cochrane, CINAHL, EMBASE and MEDLINE Complete) will be comprehensively searched from database inception to identify published, peer-reviewed English-language articles describing scoping and systematic reviews of the effectiveness of training primary care providers in the use of dermoscopy to detect skin cancers. Two researchers will independently conduct the searches and screen the results for potentially eligible studies using 'Research Screener' (a semi-automated machine learning tool). Backwards and forwards citation tracing will be conducted to supplement the search. A narrative summary of included reviews will be conducted. Study characteristics, for example, population; type of educational programme, including content, delivery method, duration and assessment; and outcomes for dermoscopy will be extracted into a standardised table. Data extraction will be checked by the second reviewer. Methodological quality will be evaluated by two reviewers independently using the Critical Appraisal Tool for Health Promotion and Prevention Reviews. Results of the assessments will be considered by the two reviewers and any discrepancies will be resolved by team consensus. ETHICS AND DISSEMINATION: Ethics approval is not required to conduct the planned systematic review of peer-reviewed, published articles because the research does not involve human participants. Findings will be published in a peer-reviewed journal, presented at leading public health, cancer and primary care conferences, and disseminated via website postings and social media channels. PROSPERO REGISTRATION NUMBER: CRD42023396276.

Critical Windows of Maternal Exposure to Biothermal Stress and Birth Weight for Gestational Age in Western Australia.

BACKGROUND: There is limited and inconsistent evidence on the risk of ambient temperature on small for gestational age (SGA) and there are no known related studies for large for gestational age (LGA). In addition, previous studies used temperature rather than a biothermal metric. OBJECTIVES: Our aim was to examine the associations and critical susceptible windows of maternal exposure to a biothermal metric [Universal Thermal Climate Index (UTCI)] and the hazards of SGA and LGA. METHODS: We linked 385,337 singleton term births between 1 January 2000 and 31 December 2015 in Western Australia to daily spatiotemporal UTCI. Distributed lag nonlinear models with Cox regression and multiple models were used to investigate maternal exposure to UTCI from 12 weeks preconception to birth and the adjusted hazard ratios (HRs) of SGA and LGA. RESULTS: Relative to the median exposure, weekly and monthly specific exposures showed potential critical windows of susceptibility for SGA and LGA at extreme exposures, especially during late gestational periods. Monthly exposure showed strong positive associations from the 6th to the 10th gestational months with the highest hazard of 13% for SGA (HR=1.13; 95% CI: 1.10, 1.14) and 7% for LGA (HR=1.07; 95% CI: 1.03, 1.11) at the 10th month for the 1st UTCI centile. Entire pregnancy exposures showed the strongest hazards of 11% for SGA (HR=1.11; 95% CI: 1.04, 1.18) and 3% for LGA (HR=1.03; 95% CI: 0.95, 1.11) at the 99th UTCI centile. By trimesters, the highest hazards were found during the second and first trimesters for SGA and LGA, respectively, at the 99th UTCI centile. Based on estimated interaction effects, male births, mothers who were non-Caucasian, smokers, ≥35 years of age, and rural residents were most vulnerable. CONCLUSIONS: Both weekly and monthly specific extreme biothermal stress exposures showed potential critical susceptible windows of SGA and LGA during late gestational periods with disproportionate sociodemographic vulnerabilities. https://doi.org/10.1289/EHP12660.

"Currently flying blind" Stakeholders' perceptions of implementing statewide population-based cancer staging at diagnosis into the Western Australian Cancer Registry: a rapid qualitative process evaluation of the WA Cancer Staging Project.

BACKGROUND: Cancer stage at diagnosis is essential for understanding cancer outcomes, guiding cancer control activities and healthcare services, and enabling benchmarking nationally and internationally. Yet, most cancer registries in Australia do not routinely collect this data. This study explored key stakeholders' perceptions of implementing cancer staging utilising Natural Language Processing and Machine Learning algorithms within the Western Australian Cancer Registry. METHODS: Perceptions of key breast and colorectal cancer stakeholders, including registry staff, clinicians, consumers, data scientists, biostatisticians, data management, healthcare staff, and health researchers, were collected. Prospective and retrospective qualitative proformas at two-time points of the Western Australian Cancer Staging Project were employed. The Consolidated Framework for Implementation Research was used to guide data collection, analysis and interpretation embedded in a Participatory Action Research approach. Data analysis also incorporated Framework Analysis and an adapted version of grading qualitative data using a visual traffic light labelling system to highlight the levels of positivity, negativity, and implementation concern. RESULTS: Twenty-nine pre-proformas and 18 post-proformas were completed online via REDCap. The grading and visual presentation of barriers and enablers aided interpretation and reviewing predicted intervention outcomes. Of the selected constructs, complexity (the perceived difficulty of the intervention) was the strongest barrier and tension for change (the situation needing change) was the strongest enabler. Implementing cancer staging into the Western Australian Cancer Registry was considered vital. Benefits included improved knowledge and understanding of various outcomes (e.g., treatment received as per Optimum Care Pathways) and benchmarking. Barriers included compatibility issues with current systems/workflows, departmental/higher managerial support, and future sustainment. CONCLUSIONS: The findings aid further review of data gaps, additional cancer streams, standardising cancer staging and future improvements. The study offers an adapted version of a rapid qualitative data collection and analytic approach for establishing barriers and enablers. The findings may also assist other population-based cancer registries considering collecting cancer stage at diagnosis.

Promotion of E-Cigarettes on TikTok and Regulatory Considerations.

E-cigarettes are promoted extensively on TikTok and other social media platforms. Platform policies to restrict e-cigarette promotion seem insufficient and are poorly enforced. This paper aims to understand how e-cigarettes are being promoted on TikTok and provide insights into the effectiveness of current TikTok policies. Seven popular hashtag-based keywords were used to identify TikTok accounts and associated videos related to e-cigarettes. Posts were independently coded by two trained coders. Collectively, the 264 videos received 2,470,373 views, 166,462 likes and 3426 comments. The overwhelming majority of videos (97.7%) portrayed e-cigarettes positively, and these posts received 98.7% of the total views and 98.2% of the total likes. A total of 69 posts (26.1%) clearly violated TikTok's own content policy. The findings of the current study suggest that a variety of predominantly pro-vaping content is available on TikTok. Current policies and moderation processes appear to be insufficient in restricting the spread of pro-e-cigarette content on TikTok, putting predominantly young users at potential risk of e-cigarette use.

A machine learning approach to predicting 30-day mortality following paediatric cardiac surgery: findings from the Australia New Zealand Congenital Outcomes Registry for Surgery (ANZCORS).

OBJECTIVES: We aim to develop the first risk prediction model for 30-day mortality for the Australian and New Zealand patient populations and examine whether machine learning (ML) algorithms outperform traditional statistical approaches. METHODS: Data from the Australia New Zealand Congenital Outcomes Registry for Surgery, which contains information on every paediatric cardiac surgical encounter in Australian and New Zealand for patients aged <18 years between January 2013 and December 2021, were analysed (n = 14 343). The outcome was mortality within the 30-day period following a surgical encounter, with ∼30% of the observations randomly selected to be used for validation of the final model. Three different ML methods were used, all of which employed five-fold cross-validation to prevent overfitting, with model performance judged primarily by the area under the receiver operating curve (AUC). RESULTS: Among the 14 343 30-day periods, there were 188 deaths (1.3%). In the validation data, the gradient-boosted tree obtained the best performance [AUC = 0.87, 95% confidence interval = (0.82, 0.92); calibration = 0.97, 95% confidence interval = (0.72, 1.27)], outperforming penalized logistic regression and artificial neural networks (AUC of 0.82 and 0.81, respectively). The strongest predictors of mortality in the gradient boosting trees were patient weight, STAT score, age and gender. CONCLUSIONS: Our risk prediction model outperformed logistic regression and achieved a level of discrimination comparable to the PRAiS2 and Society of Thoracic Surgery Congenital Heart Surgery Database mortality risk models (both which obtained AUC = 0.86). Non-linear ML methods can be used to construct accurate clinical risk prediction tools.

Determining cancer stage at diagnosis in population-based cancer registries: A rapid scoping review.

Introduction: Population-based cancer registries are the main source of data for population-level analysis of cancer stage at diagnosis. This data enables analysis of cancer burden by stage, evaluation of screening programs and provides insight into differences in cancer outcomes. The lack of standardised collection of cancer staging in Australia is well recognised and is not routinely collected within the Western Australia Cancer Registry. This review aimed to explore how cancer stage at diagnosis is determined in population-based cancer registries. Methods: This review was guided by the Joanna-Briggs Institute methodology. A systematic search of peer-reviewed research studies and grey literature from 2000 to 2021 was conducted in December 2021. Literature was included if peer-reviewed articles or grey literature sources used population-based cancer stage at diagnosis, and were published in English between 2000 and 2021. Literature was excluded if they were reviews or only the abstract was available. Database results were screened by title and abstract using Research Screener. Full-texts were screened using Rayyan. Included literature were analysed using thematic analysis and managed through NVivo. Results: The findings of the 23 included articles published between 2002 and 2021 consisted of two themes. (1) "Data sources and collection processes" outlines the data sources used, as well as the processes and timing of data collection utilised by population-based cancer registries. (2) "Staging classification systems" reveals the staging classification systems employed or developed for population-based cancer staging, including the American Joint Committee on Cancer's Tumour Node Metastasis and related systems; simplified systems classified into localised, regional, and distant categories; and miscellaneous systems. Conclusions: Differences in approaches used to determine population-based cancer stage at diagnosis challenge attempts to make interjurisdictional and international comparisons. Barriers to collecting population-based stage at diagnosis include resource availability, infrastructure differences, methodological complexity, interest variations, and differences in population-based roles and emphases. Even within countries, disparate funding sources and funder interests can challenge the uniformity of population-based cancer registry staging practices. International guidelines to guide cancer registries in collecting population-based cancer stage is needed. A tiered framework of standardising collection is recommended. The results will inform integrating population-based cancer staging into the Western Australian Cancer Registry.

Development and validation of a machine learning-based tool to predict autism among children.

Autism is a lifelong condition for which intervention must occur as early as possible to improve social functioning. Thus, there is great interest in improving our ability to diagnose autism as early as possible. We take a novel approach to this challenge by combining machine learning with maternal and infant health administrative data to construct a prediction model capable of predicting autism disorder (defined as ICD10 84.0) in the general population. The sample included all mother-offspring pairs from the Australian state of New South Wales (NSW) between January 2003 and December 2005 (n = 262,650 offspring), linked across three health administrative data sets including the NSW perinatal data collection (PDC); the NSW admitted patient data collection (APDC) and the NSW mental health ambulatory data collection (MHADC). Our most successful model was able to predict autism disorder with an area under the receiver operating curve of 0.73, with the strongest risk factors for diagnoses found to include offspring gender, maternal age at birth, delivery analgesia, maternal prenatal tobacco disorders, and low 5-min APGAR score. Our findings indicate that the combination of machine learning and routinely collected admin data, with further refinement and increased accuracy than achieved by us, may play a role in the early detection of autism disorders.

Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015.

Preterm birth is a global public health problem with a significant burden on the individuals affected. The study aimed to extend current research on preterm birth prognostic model development by developing and internally validating models using machine learning classification algorithms and population-based routinely collected data in Western Australia. The longitudinal retrospective cohort study involved all births in Western Australia between 1980 and 2015, and the analytic sample contains 81,974 (8.6%) preterm births (< 37 weeks of gestation). Prediction models for preterm birth were developed using regularised logistic regression, decision trees, Random Forests, extreme gradient boosting, and multi-layer perceptron (MLP). Predictors included maternal socio-demographics and medical conditions, current and past pregnancy complications, and family history. Class weight was applied to handle imbalanced outcomes and stratified tenfold cross-validation was used to reduce overfitting. Close to half of the preterm births (49.1% at 5% FPR, 95% CI 48.9%,49.5%) were correctly classified by the best performing classifier (MLP) for all women when current pregnancy information was available. The sensitivity was boosted to 52.7% (95% CI 52.1%,53.3%) after including past obstetric history in a sub-population of births from multiparous women. Around half of the preterm birth can be identified antenatally at high specificity using population-based routinely collected maternal and pregnancy data. The performance of the prediction models depends on the available predictor pool that is individual and time specific.

An impact review of a Western Australian research translation program.

The translation gap between knowledge production and implementation into clinical practice and policy is an ongoing challenge facing researchers, funders, clinicians and policy makers globally. Research generated close to practice and in collaboration with end users is an approach that is recognised as an effective strategy to facilitate an improvement in the relevance and use of health research as well as building research capacity amongst end users. The Research Translation Projects (RTP) program funded by the Western Australian (WA) Department of Health facilitates clinical and academic collaboration through competitive funding of short-term research projects. Its aim is to improve healthcare practice while also finding efficiencies that can be delivered to the WA health system. A mixed methods approach was adopted to evaluate the research impact of the RTP program, at completion of the two-year funding period, across a range of impact domains through the adaptation and application of the Canadian Academy of Health Sciences' (CAHS) framework for research impact. In addition, further analysis was undertaken to address specific objectives of the RTP program more closely, in particular research capacity building and collaboration and health system Inefficiencies targeted by the program. Social network analysis was applied to assess the extent and growth of collaboration across WA health organisations over time. Results indicated that the 'bottom up' approach to research translation has triggered modest, yet positive outcomes across impact domains including advancing knowledge, collaboration and capacity building as well as contributing to changes in policy and practice. Additionally, the projects identified opportunities by which inefficiencies in the health system can be addressed. Further work is required to better understand the pathways by which short-term outcomes can be translated into more long-term impacts and the mechanisms that trigger this process.

Development of a Machine Learning Model for the Estimation of Hip and Lumbar Angles in Ballet Dancers.

OBJECTIVE: Accurate field-based assessment of dance kinematics is important to understand the etiology, and thus prevention and management, of hip and back pain. The study objective was to develop a machine learning model to estimate thigh elevation and lumbar sagittal plane angles during ballet leg lifting tasks, using wearable sensor data. METHODS: Female dancers (n=30) performed ballet-specific leg lifting tasks to the front, side, and behind the body. Dancers wore six wearable sensors (100 Hz). Data were simultaneously collected using an 18-camera motion analysis system (250 Hz). Due to synchronization and hardware malfunction issues, only 23 dancers had usable data. Using leave-one-out cross-validation, machine learning models were compared with the optic motion capture system using root mean square error (RMSE) in degrees and correlation coefficients (r) over the complete movement profile of each leg lift and mean absolute error (MAE) and Bland Altman plots for peak angle accuracy. RESULTS: The average RMSE for model estimation was 6.8° for thigh elevation angle and 5.6° for lumbar spine sagittal plane angle, with respective MAE of 6.3°and 5.7°. There was a strong correlation between the machine learning model and optic motion capture for peak angle values (thigh r=0.86, lumbar r=0.96). CONCLUSION: The models developed demonstrated an acceptable degree of accuracy for the estimation of thigh elevation angle and lumbar spine sagittal plane angle during dance-specific leg lifting tasks. This provides potential for a near-real-time, field-based measurement system.

Mass transfer and flow characterization of novel algae-based nutrient removal system.

BACKGROUND: Recirculating aquaculture systems (RAS) are an essential component of sustainable inland seafood production. Still, nutrient removal from these systems can result in substantial environmental problems, or present a major cost factor with few added value options. In this study, an innovative and energy-efficient algae based nutrient removal system (NRS) was developed that has the potential to generate revenue through algal commercialization. We optimized mass transfer in our NRS design using novel aeration and mixing technology, using air lift pumps and developed an original membrane cartridge for the continuous operation of nutrient removal and algae production. Specifically, we designed, manufactured and tested a 60-L NRS prototype. Based on specific airlift mixing conditions as well as concentration gradients, we assessed NRS nutrient removal capacity. We then examined the effects of different internal bioreactor geometries and radial orientations on the mixing efficiency. RESULTS: Using the start-up dynamic method, the overall mass transfer coefficient was found to be in the range of 0.00164-0.0074 [Formula: see text], depending on flow parameters and we confirmed a scaling relationship of mass transfer across concentration gradients. We found the optimal Reynolds number to be 500 for optimal mass transfer, as higher Reynolds numbers resulted in a relatively reduced increase of mass transfer. This relationship between mass transfer and Reynolds number is critical to assess scalability of our system. Our results demonstrate an even distribution of dissolved oxygen levels across the reactor core, demonstrating adequate mixing by the airlift pump, a critical consideration for optimal algal growth. Distribution of dissolved gases in the reactor was further assessed using flow visualization in order to relate the bubble distribution to the mass transfer capabilities of the reactor. We run a successful proof of principle trial using the green alga Dunaliella tertiolecta to assess mass transfer of nutrients across the membrane and biomass production. CONCLUSIONS: Manipulation of the concentration gradient across the membrane demonstrates a more prominent role of airlift mixing at higher concentration gradients. Specifically, the mass transfer rate increased threefold when the concentration gradient was increased 2.5-fold. We found that we can grow algae in the reactor chamber at rates comparable to those of other production systems and that the membrane scaffolds effectively remove nutrients form the wastewater. Our findings provide support for scalability of the design and support the use of this novel NRS for nutrient removal in aquaculture and potentially other applications.

Research Screener: a machine learning tool to semi-automate abstract screening for systematic reviews.

BACKGROUND: Systematic reviews and meta-analyses provide the highest level of evidence to help inform policy and practice, yet their rigorous nature is associated with significant time and economic demands. The screening of titles and abstracts is the most time consuming part of the review process with analysts required review thousands of articles manually, taking on average 33 days. New technologies aimed at streamlining the screening process have provided initial promising findings, yet there are limitations with current approaches and barriers to the widespread use of these tools. In this paper, we introduce and report initial evidence on the utility of Research Screener, a semi-automated machine learning tool to facilitate abstract screening. METHODS: Three sets of analyses (simulation, interactive and sensitivity) were conducted to provide evidence of the utility of the tool through both simulated and real-world examples. RESULTS: Research Screener delivered a workload saving of between 60 and 96% across nine systematic reviews and two scoping reviews. Findings from the real-world interactive analysis demonstrated a time saving of 12.53 days compared to the manual screening, which equates to a financial saving of USD 2444. Conservatively, our results suggest that analysts who scan 50% of the total pool of articles identified via a systematic search are highly likely to have identified 100% of eligible papers. CONCLUSIONS: In light of these findings, Research Screener is able to reduce the burden for researchers wishing to conduct a comprehensive systematic review without reducing the scientific rigour for which they strive to achieve.

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015.

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression.

Development of a Human Activity Recognition System for Ballet Tasks.

BACKGROUND: Accurate and detailed measurement of a dancer's training volume is a key requirement to understanding the relationship between a dancer's pain and training volume. Currently, no system capable of quantifying a dancer's training volume, with respect to specific movement activities, exists. The application of machine learning models to wearable sensor data for human activity recognition in sport has previously been applied to cricket, tennis and rugby. Thus, the purpose of this study was to develop a human activity recognition system using wearable sensor data to accurately identify key ballet movements (jumping and lifting the leg). Our primary objective was to determine if machine learning can accurately identify key ballet movements during dance training. The secondary objective was to determine the influence of the location and number of sensors on accuracy. RESULTS: Convolutional neural networks were applied to develop two models for every combination of six sensors (6, 5, 4, 3, etc.) with and without the inclusion of transition movements. At the first level of classification, including data from all sensors, without transitions, the model performed with 97.8% accuracy. The degree of accuracy reduced at the second (83.0%) and third (75.1%) levels of classification. The degree of accuracy reduced with inclusion of transitions, reduction in the number of sensors and various sensor combinations. CONCLUSION: The models developed were robust enough to identify jumping and leg lifting tasks in real-world exposures in dancers. The system provides a novel method for measuring dancer training volume through quantification of specific movement tasks. Such a system can be used to further understand the relationship between dancers' pain and training volume and for athlete monitoring systems. Further, this provides a proof of concept which can be easily translated to other lower limb dominant sporting activities.

Puzzle-Solving Activity as an Indicator of Epistemic Confusion.

When students perform complex cognitive activities, such as solving a problem, epistemic emotions can occur and influence the completion of the task. Confusion is one of these emotions and it can produce either negative or positive outcomes, according to the situation. For this reason, considering confusion can be an important factor for educators to evaluate students' progression in cognitive activities. However, in digital learning environments, observing students' confusion, as well as other epistemic emotions, can be problematic because of the remoteness of students. The study reported in this article explored new methodologies to assess emotions in a problem-solving task. The experimental task consisted of the resolution of logic puzzles presented on a computer, before, and after watching an instructional video depicting a method to solve the puzzle. In parallel to collecting self-reported confusion ratings, human-computer interaction was captured to serve as non-intrusive measures of emotions. The results revealed that the level of self-reported confusion was negatively correlated with the performance on solving the puzzles. In addition, while comparing the pre- and post-video sequences, the experience of confusion tended to differ. Before watching the instructional video, the number of clicks on the puzzle was positively correlated with the level of confusion whereas the correlation was negatively after the video. Moreover, the main emotions reported before the video (e.g., confusion, frustration, curiosity) tended to differ from the emotions reported after the videos (e.g., engagement, delight, boredom). These results provide insights into the ambivalent impact of confusion in problem-solving task, illustrating the dual effect (i.e., positive or negative) of this emotion on activity and performance, as reported in the literature. Applications of this methodology to real-world settings are discussed.

Parasitic analysis and π-type Butterworth-Van Dyke model for complementary-metal-oxide-semiconductor Lamb wave resonator with accurate two-port Y-parameter characterizations.

The parasitic effects from electromechanical resonance, coupling, and substrate losses were collected to derive a new two-port equivalent-circuit model for Lamb wave resonators, especially for those fabricated on silicon technology. The proposed model is a hybrid π-type Butterworth-Van Dyke (PiBVD) model that accounts for the above mentioned parasitic effects which are commonly observed in Lamb-wave resonators. It is a combination of interdigital capacitor of both plate capacitance and fringe capacitance, interdigital resistance, Ohmic losses in substrate, and the acoustic motional behavior of typical Modified Butterworth-Van Dyke (MBVD) model. In the case studies presented in this paper using two-port Y-parameters, the PiBVD model fitted significantly better than the typical MBVD model, strengthening the capability on characterizing both magnitude and phase of either Y11 or Y21. The accurate modelling on two-port Y-parameters makes the PiBVD model beneficial in the characterization of Lamb-wave resonators, providing accurate simulation to Lamb-wave resonators and oscillators.

The electronic stethoscope.

Most heart diseases are associated with and reflected by the sounds that the heart produces. Heart auscultation, defined as listening to the heart sound, has been a very important method for the early diagnosis of cardiac dysfunction. Traditional auscultation requires substantial clinical experience and good listening skills. The emergence of the electronic stethoscope has paved the way for a new field of computer-aided auscultation. This article provides an in-depth study of (1) the electronic stethoscope technology, and (2) the methodology for diagnosis of cardiac disorders based on computer-aided auscultation. The paper is based on a comprehensive review of (1) literature articles, (2) market (state-of-the-art) products, and (3) smartphone stethoscope apps. It covers in depth every key component of the computer-aided system with electronic stethoscope, from sensor design, front-end circuitry, denoising algorithm, heart sound segmentation, to the final machine learning techniques. Our intent is to provide an informative and illustrative presentation of the electronic stethoscope, which is valuable and beneficial to academics, researchers and engineers in the technical field, as well as to medical professionals to facilitate its use clinically. The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.