Acceptable Error Research Articles

Exploring the utilization and deficiencies of Generative Artificial Intelligence in students’ cognitive and emotional needs: a systematic mini-review

Despite advances in educational technology, the specific ways in which Generative Artificial Intelligence (GAI) and Large Language Models cater to learners’ nuanced cognitive and emotional needs are not fully understood. This mini-review methodically describes GAI’s practical implementations and limitations in meeting these needs. It included journal and conference papers from 2019 to 2024, focusing on empirical studies that employ GAI tools in educational contexts while addressing their practical utility and ethical considerations. The selection criteria excluded non-English studies, non-empirical research, and works published before 2019. From the dataset obtained from Scopus and Web of Science as of June 18, 2024, four significant studies were reviewed. These studies involved tools like ChatGPT and emphasized their effectiveness in boosting student engagement and emotional regulation through interactive learning environments with instant feedback. Nonetheless, the review reveals substantial deficiencies in GAI’s capacity to promote critical thinking and maintain response accuracy, potentially leading to learner confusion. Moreover, the ability of these tools to tailor learning experiences and offer emotional support remains limited, often not satisfying individual learner requirements. The findings from the included studies suggest limited generalizability beyond specific GAI versions, with studies being cross-sectional and involving small participant pools. Practical implications underscore the need to develop teaching strategies leveraging GAI to enhance critical thinking. There is also a need to improve the accuracy of GAI tools’ responses. Lastly, deep analysis of intervention approval is needed in cases where GAI does not meet acceptable error margins to mitigate potential negative impacts on learning experiences.

Resilience, job satisfaction, occupational stress, and occupational accidents among healthcare professionals: A Bayesian network analysis.

Occupational accidents remain a critical challenge for healthcare professionals. In the present study, using the Bayesian network (BN) approach association among resilience, job satisfaction, stress, and occupational accidents among healthcare professionals is examined. Data was gathered using several valid questionnaires. The BN approach was utilized to analyze the r5/31/2024ionships between the variables of the current study. The performance of BN analysis was evaluated using related indexes. In total, 300 healthcare professionals participated in this study. Results showed that almost 23% of healthcare professionals had experienced occupational accidents. Results of the sensitivity analysis demonstrated that job satisfaction had the most significant influence on occupational accidents in healthcare settings. The belief updating analysis results showed that by increasing job satisfaction and decreasing stress of healthcare professionals the occurrence of occupational accidents decreased 9.8% and 6.4%, respectively. Moreover, decreasing the stress of healthcare professionals can lead to an increase in the level of job satisfaction. Evaluation indexes showed that the performance of the developed BN was acceptable (error rate: 16.09). The Findings reveal that both job satisfaction and stress had a significant influence on occupational accidents in healthcare professionals. Moreover, by influencing job satisfaction and stress, resilience can indirectly affect occupational accidents.

Cross-cultural adaptation and measurement properties of the Brazilian Portuguese Version of the Parkinson’s Disease Sleep Scale (PDSS-2)

ObjectivesThe Parkinson’s Disease Sleep Scale-2 (PDSS-2) is an updated tool designed to identify specific sleep disturbances in individuals with Parkinson’s disease (PD). However, for its application in Brazil, a process of cross-cultural adaptation and validation of measurement properties was required. MethodsThis methodological study adapted the PDSS-2 to Brazilian Portuguese (PDSS-2-Br) and assessed its measurement properties (Internal consistency, test-retest reliability, measurement error, construct validity, and interpretability). The adapted scale was then tested on 2 occasions within an interval of 7 days, to 50 individuals with PD. Test–retest reliability was evaluated using kappa statistics (k) for the individual items, intraclass correlation coefficient (ICC) for the total scores, standard error of measurement (SEM), minimal detectable change (MDC) and Bland-Altman plot. Construct validity was assessed through Spearman correlation coefficients between the PDSS-2-Br and independent variables, while interpretability was evaluated by examining ceiling and floor effects. ResultsPDSS-2-Br showed satisfactory semantic, idiomatic, cultural, and conceptual equivalence. It also demonstrated good internal consistency (α=0.64), adequate test-retest reliability between items (κw 0.42-0.94, 95% CI=0.30-0.95) and total score (ICC=0.94; 95% CI=0.89-0.96), acceptable measurement error (SEM, 2.07 points; MDC, 5.74 points), absence of ceiling and floor effects, and 87,5% of pre-defined hypotheses were confirmed, indicating adequate construct validity (Spearman correlations coefficients for the rs=0.50, p<0.001) of the PDSS-2-Br. ConclusionThe measurement properties of the PDSS-2-Br are equivalent to those of the original version, and the scale has been validated and confirmed as reliable to measure sleep disorders among the Brazilian population with PD.

Test–retest Reliability and Responsiveness of the Machine Learning-based Short-form of the Berg Balance Scale in Persons with Stroke

ObjectiveTo examine the test–retest reliability, responsiveness, and clinical utility of the machine learning-based short-form of the Berg Balance Scale (BBS-ML) in persons with stroke. DesignRepeated measures design. SettingA department of rehabilitation in a medical center. ParticipantsThis study recruited two groups: 50 persons who were more than 6 months post-stroke to examine the test–retest reliability, and 52 persons who were within 3 months post-stroke to examine the responsiveness. Test–retest reliability was investigated by administering assessments twice at a 2-week interval. Responsiveness was investigated by gathering data at admission and discharge from hospital. InterventionsNot applicable. Main outcome measureBBS-ML. ResultsThe BBS-ML exhibited excellent test–retest reliability (intraclass correlation coefficient = 0.99), acceptable minimal random measurement error (minimal detectable change % = 13.6%), and good responsiveness (Kazis' effect size and standardized response mean values ≥ 1.34). On average, the participants completed the BBS-ML in around 6 minutes per administration. ConclusionsOur findings indicate that the BBS-ML appears an efficient measure with excellent test–retest reliability and responsiveness. Moreover, the BBS-ML may be utilized as a substitute for the original BBS to monitor the progress of balance function in persons with stroke.

Auto Aligning, Error-Compensated Broadband Collimated Transmission Spectroscopy.

Broadband spectral measurements of the ballistic transmission of scattering samples are challenging. The presented work shows an approach that includes a broadband system and an automated adjustment unit for compensation of angular distortions caused by non-plane-parallel samples. The limits of the system in terms of optimal transmission and detected forward scattering influenced by the scattering phase function are investigated. We built and validated a setup that measures the collimated transmission signal in a spectral range from 300 nm to 2150 nm. The system was validated using polystyrene spheres and Mie calculations. The limits of the system in terms of optimal transmission and detected forward scattering were researched. The optimal working parameters of the system, analyzed by simulations using the Monte Carlo method, show that the transmission should be larger than 10% and less than 90% to allow for a reliable measurement with acceptable errors caused by noise and systematic errors of the system. The optimal transmission range is between 25% and 50%. We show that the phase function is important when considering the accuracy of the measurement. For strongly forward-scattering samples, errors of up to 80% can be observed, even for a very small numerical aperture of 6.6·10-4, as used in our experimental system. We also show that errors increase with optical thickness as the ballistic transmission decreases and the multiscattered fraction increases. In addition, errors caused by multiple reflections in the sample layer were analyzed and also classified as relevant for classical absorption spectroscopy.

Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction.

The first line of treatment for urinary incontinence is pelvic floor muscle (PFM) training, aimed at reducing leakage episodes by strengthening these muscles. However, many women struggle with performing correct PFM contractions or have misconceptions about their contractions. To address this issue, we present a novel PFM contraction quality assessment system. This system combines a PFM contraction detector with a maximal PFM contraction performance classifier. The contraction detector first identifies whether or not a PFM contraction was performed. Then, the contraction classifier autonomously quantifies the quality of maximal PFM contractions across different features, which are also combined into an overall rating. Both algorithms are based on artificial intelligence (AI) methods. The detector relies on a convolutional neural network, while the contraction classifier uses a custom feature extractor followed by a random forest classifier to predict the strength rating based on the modified Oxford scale. The AI algorithms were trained and tested using datasets measured by vaginal dynamometry, combined in some cases with digital assessment results from expert physiotherapists. The contraction detector was trained on one dataset and then tested on two datasets measured with different dynamometers, achieving 97% accuracy on the first dataset and 100% accuracy on the second. For the contraction performance classifier, the results demonstrate that important clinical features can be extracted automatically with an acceptable error. Furthermore, the contraction classifier is able to predict the strength rating within a ±1 scale point with 97% accuracy. These results demonstrate the system's potential to enhance PFM training and rehabilitation by enabling women to monitor and improve their PFM contractions autonomously.

Considerations for digitalisation of nickel electroforming

ABSTRACT This paper is a ‘follow-on’ from a paper previously published in this journal dealing with the laboratory to pilot scaling up approach using Industry 4.0 manufacturing methods. In particular, the paper reports a strategy for developing a model for the electroforming of nickel from a sulphamate electrolyte at laboratory scale which could subsequently provide an educated approach for transferring the process to a larger scale. At the laboratory scale, a rotating disc electrode assembly was used, which is a standard instrument to determine electrochemical parameters. Thereafter, small scale nickel discs were electroplated using this equipment, and a model of this process was developed and validated against those experimental results. These parameters were then used to actually produce electroforms in a prototype, 18 L tank system. Cross-validation between practical experiments and simulations followed which allowed for fine-tuning the model until it was consistently predicting the real process results within an acceptable error. Overall, it was found that a secondary current distribution model could be used for reasonably accurate description for the electroforming process, and could provide a quick virtual tool at a production facility.

Dexterous Manipulation Based on Object Recognition and Accurate Pose Estimation Using RGB-D Data.

This study presents an integrated system for object recognition, six-degrees-of-freedom pose estimation, and dexterous manipulation using a JACO robotic arm with an Intel RealSense D435 camera. This system is designed to automate the manipulation of industrial valves by capturing point clouds (PCs) from multiple perspectives to improve the accuracy of pose estimation. The object recognition module includes scene segmentation, geometric primitives recognition, model recognition, and a color-based clustering and integration approach enhanced by a dynamic cluster merging algorithm. Pose estimation is achieved using the random sample consensus algorithm, which predicts position and orientation. The system was tested within a 60° field of view, which extended in all directions in front of the object. The experimental results show that the system performs reliably within acceptable error thresholds for both position and orientation when the objects are within a ±15° range of the camera's direct view. However, errors increased with more extreme object orientations and distances, particularly when estimating the orientation of ball valves. A zone-based dexterous manipulation strategy was developed to overcome these challenges, where the system adjusts the camera position for optimal conditions. This approach mitigates larger errors in difficult scenarios, enhancing overall system reliability. The key contributions of this research include a novel method for improving object recognition and pose estimation, a technique for increasing the accuracy of pose estimation, and the development of a robot motion model for dexterous manipulation in industrial settings.

Optimizing H2 production from biomass: A machine learning-enhanced model of supercritical water gasification dynamics

Supercritical water gasification (SCWG) is recognized as an efficient technology for biomass conversion, demonstrating substantial potential in the sustainable energy sector. This paper focuses on the H2 production by SCWG of biomass. The objective is to develop an accurate gasification kinetic model that can facilitate the optimization of industrial reactor designs. A series of SCWG experiments is conducted in a batch reactor system under temperature of 500–600 °C and residence time of 1–20 min. Based on the experimental results, a hybrid-driven model for SCWG reaction is established. Firstly, experimental data is utilized to delineate SCWG reaction mechanistic model and forecast gas yields with an acceptable error margin. Subsequently, a Wasserstein Generative Adversarial Network Gradient Boosting Regression Grid Search (WGAN-GBR-GRID) model is applied to acquire knowledge of the predictive errors from the mechanistic model and to develop a hybrid model. The hybrid-driven model significantly enhances the average relative prediction error from 15.56 % to 0.02 % when compared to the mechanistic model. This result underscores the potential of the hybrid model in optimizing SCWG technology and the Shapley Additive exPlanations (SHAP) values in feature analysis shows the possible shortcomings of mechanistic model, thereby providing a new perspective for SCWG reaction dynamics modeling.

Multiple Criteria-Based Intelligent Techniques for Efficient Handover in Next-Generation Networks

Background: Several wireless technologies are assumed to be operating in cooperation for next-generation networks. These networks offer various services to mobile users; however, efficient handover between different networks is the most challenging task in high mobility scenarios. The traditional signal strength-based handover algorithms are not able to cope with mobile users' high-quality requirements. Methods: In this paper, multiple criteria-based intelligent techniques are proposed to deal with inefficiencies related to handover. This technique makes use of artificial neural networks that take multiple parameters as inputs in order to predict the degradation of parameters. These predictions are further used to design rules for initiating handover procedures prior to service quality degradation. Results: Based on the prediction results obtained by deep neural networks, the handover decisions are recommended according to the type of application: conversational or streaming. Conclusion: The simulation results demonstrate the efficacy of the proposed method as there is an improvement of up to 40% and 25% in terms of handover rate and service disruption time, respectively, with an acceptable prediction error of 0.05.

Impact of Generalized Versus Individualized Load-Velocity Equations on Velocity-Loss Magnitude in Bench-Press Exercise: Mixed-Model and Equivalence Analysis.

This study analyzed the influence of 2 velocity-based training-load prescription strategies (general vs individual load-velocity equations) on the relationship between the magnitude of velocity loss (VL) and the percentage of repetitions completed in the bench-press exercise. Thirty-five subjects completed 6 sessions consisting of performing the maximum number of repetitions to failure against their 40%, 60%, and 80% of 1-repetition maximum (1RM) in the Smith machine bench-press exercise using generalized and individualized equations to adjust the training load. A close relationship and acceptable error were observed between percentage of repetitions completed and the percentage of VL reached for the 3 loading magnitudes and the 2 load-prescription strategies studied (R2 from .83 to .94; standard error of the estimate from 7% to 10%). A simple main effect was observed for load and VL thresholds but not for load-prescription strategies. No significant interaction effects were revealed. The 40% and 60% 1RM showed equivalence on data sets and the most regular variation, whereas the 80% 1-repetition maximum load showed no equivalence and more irregular variation. These results suggest that VL is a useful variable to predict percentage of repetitions completed in the bench-press exercise, regardless of the strategy selected to adjust the relative load. However, caution should be taken when using heavy loads.

Surface Roughness Evaluation of Blast-Cleaned Structural Steel Plates Considering Measurement Deviation

AbstractSurface roughness is an important characteristic of blast-cleaned structural steel plates. This is because the adhesion of the coating and the frictional grip of the bolted connections are related to surface roughness. Surface roughness is usually measured using a surface roughness meter. Because the roughness of blast-cleaned surfaces is not uniform, it varies from position to position. The surface roughness test results obtained from a broad area are more valuable than those obtained from a narrow area. Because the measured values contain deviations, they are measured multiple times and averaged to reduce the influence of deviations. However, to evaluate roughness, it is necessary to clarify the measurement conditions to obtain accurate results. Consider combining these sentences as follows: In this study, the surface roughness of the blast-cleaned structural steel sample was measured at multiple locations to obtain its distribution characteristics. During each sample measurement, the detection sensor of the surface roughness meter was lifted and placed on the steel sample to determine deviations from the actual results. The surface roughness distributions of five blast-cleaned samples were the same under the identical measurement conditions. This indicates that accurate results can be obtained by measuring representative samples, without the need for measuring all samples; actually, 17 measurements were adequate to obtain a mean surface roughness value with a 95% confidence level and a 5% acceptable error rate.

A Predictive Model for Sintering Ignition Temperature Based on a CNN-LSTM Neural Network with an Attention Mechanism

The sintering ignition process parameters fluctuate frequently and significantly, resulting in large variations in ignition temperature, which in severe cases can exceed 200 °C. This not only increases gas consumption but also affects the quality of the sinter. Because the intelligent control model based on feedback mechanisms struggles to deal with high-frequency fluctuation conditions over time, the prediction of sintering ignition temperature using feedforward regulation is attracting increasing attention. Given the multi-variable, time-sequential and strongly coupled characteristics of the sintering ignition process, a convolutional neural network (CNN) and a long short-term memory (LSTM) network are deeply integrated, with an attention mechanism incorporated to develop the sintering ignition temperature prediction model, enabling the accurate prediction of the ignition temperature. The research demonstrates that the combination of a CNN and the attention mechanism effectively addresses the challenges posed by the multi-variable and strongly coupled nature of sintering ignition data to predictive modeling. The LSTM network resolves the sequential data issues through its gating mechanism. As a result, the coefficient of determination (R2 ) of the CNN_LSTM-Attention model in predicting the sintering ignition temperature can reach 0.97, with a mean absolute error (MAE) as low as 10.23 °C. The predicted values closely match the actual values, achieving a hit rate of 93% within the acceptable error range. These performance metrics are significantly superior to those of the CNN-Attention and LSTM-Attention models, greatly enhancing the control accuracy of the ignition temperature.

Deep Conformal Supervision: Leveraging Intermediate Features for Robust Uncertainty Quantification.

Trustworthiness is crucial for artificial intelligence (AI) models in clinical settings, and a fundamental aspect of trustworthy AI is uncertainty quantification (UQ). Conformal prediction as a robust uncertainty quantification (UQ) framework has been receiving increasing attention as a valuable tool in improving model trustworthiness. An area of active research is the method of non-conformity score calculation for conformal prediction. We propose deep conformal supervision (DCS), which leverages the intermediate outputs of deep supervision for non-conformity score calculation, via weighted averaging based on the inverse of mean calibration error for each stage. We benchmarked our method on two publicly available datasets focused on medical image classification: a pneumonia chest radiography dataset and a preprocessed version of the 2019 RSNA Intracranial Hemorrhage dataset. Our method achieved mean coverage errors of 16e-4 (CI: 1e-4, 41e-4) and 5e-4 (CI: 1e-4, 10e-4) compared to baseline mean coverage errors of 28e-4 (CI: 2e-4, 64e-4) and 21e-4 (CI: 8e-4, 3e-4) on the two datasets, respectively (p < 0.001 on both datasets). Based on our findings, the baseline results of conformal prediction already exhibit small coverage errors. However, our method shows a significant improvement on coverage error, particularly noticeable in scenarios involving smaller datasets or when considering smaller acceptable error levels, which are crucial in developing UQ frameworks for healthcare AI applications.

The Impact of Hydrogen on Flame Characteristics and Pollutant Emissions in Natural Gas Industrial Combustion Systems

To improve energy savings and emission reduction in industrial heating furnaces, this study investigated the impact of various molar fractions of hydrogen on natural gas combustion and compared the results of the Non-Premixed Combustion Model with the Eddy Dissipation Combustion Model. Initially, natural gas combustion in an industrial heating furnace was investigated experimentally, and these results were used as boundary conditions for CFD simulations. The diffusion flame and combustion characteristics of natural gas were simulated using both the non-premixed combustion model and the Eddy Dissipation Combustion Model. The results indicated that the Non-Premixed Combustion Model provided simulations more consistent with experimental data, within acceptable error margins, thus validating the accuracy of the numerical simulations. Additionally, to analyze the impact of hydrogen doping on the performance of an industrial gas heater, four gas mixtures with varying hydrogen contents (15% H2, 30% H2, 45% H2, and 60% H2) were studied while maintaining constant fuel inlet temperature and flow rate. The results demonstrate that the Non-Premixed Combustion Model more accurately simulates complex flue gas flow and chemical reactions during combustion. Moreover, hydrogen-doped natural gas significantly reduces CO and CO2 emissions compared to pure natural gas combustion. Specifically, at 60% hydrogen content, CO and CO2 levels decrease by 70% and 37.5%, respectively, while NO emissions increase proportionally; at this hydrogen content, NO concentration in the furnace chamber rises by 155%.

A-203 A Disconnect Between Detectable Patient Error Rates and QC Understanding

Abstract Background ISO 14971 defines “risk evaluation as the process of comparing the estimated risk against given risk criteria to determine the acceptability of the risk. Effective QC processes would detect if a stable acceptable error rate became unacceptable. Methods A survey was circulated through LinkedIn and Labvine. Forty respondents who included their credentials were included in the final analysis. Questions included: 1. What is the best choice for acceptable error rate when an analytical process is stable and in control, 2. If an analytical process “fails,” how many errors are you willing to report before the lab should stop reporting, and 3. “If an analytical process has a 5% stable error, how high would the error rate after failure need to become for a 1-2s rule to detect failure with one QC sample in a single QC run?” This interactive poster will allow viewers to cast their vote for Question 4 and then reveal an LJ chart depicting a stable error rate of 5% with a shift to show the number of 1-2s failures with their chosen acceptable post-failure error rate. Results During stable operation, only 8% wanted zero errors while 38% voted that they would allow up to 5% errors; After failure, 66% wanted to report zero errors and only 10% would allow 5% failure. 69% believed they would see a 1-2s failure in one run if the error rate rose to less than 50%; 19% believed it required a 100% error rate. Conclusions There is a strange disconnect where most believed it was acceptable to report more errors during stable operation than if the method failed. There is a widespread misunderstanding of the logic that connects management of acceptable patient risk with the capabilities of statistical QC.

B-112 Regression Analysis Techniques for Assessing Calibration Reproducibility Within and Between Instruments. A Practical Example with Blood Ca²⁺ Assayed with Two cobas® b 221Analyzers

Abstract Background Reproducibility of the calibration curve within the analytical measurement range (AMR) is crucial for ensuring reliable patient values within and between laboratory instruments to detect shifts and trends of clinical significance. In this practical example for ionized calcium (Ca²⁺) assayed with two cobas b221® instruments, the equality of calibration lines and the verification with patient specimen comparison study was assessed with regression analysis techniques. Methods Instruments: Two cobas b 221 (Roche Diagnostics). Calibration material: COOX/MSS™ (Five levels, Roche Diagnostics). This was independently assayed either three or five times in parallel with both instruments. The patient specimens were obtained by venipuncture in Na/Li-heparin BD™ tubes (Becton-Dickinson). Upon receipt, specimens were assayed with both instruments within two minutes. The data were transferred to Minitab® (Version 21, Minitab Inc) statistical software and analyzed with polynomial ordinary least squares (POLS), orthogonal and locally weighted scatterplot smoother (lowess) regression analysis models. The aptness of the model was assessed with standardized residuals (std. res.) diagnostics. For acceptable total error the CLIA’s criterion target value ± 10% was adopted. Results While the POLS model did not show probabilistically significant differences between instruments (P=0.8), it showed probabilistically significant differences between years (P=0.003). However, this was due to a small MSE (= 0.003 mg/dL), which is not clinically significant. The test for linearity showed probabilistically significant lack-of-fit (P&amp;lt;0.0001) due to a small MS pure error (&amp;lt;0.0001 mg/dL). The lowess for the standardized deleted residuals by the fitted value showed a quasi-linear relationship. The multiple comparisons (P=0.3) and the Levene’s (P=0.4) tests did not detect probabilistically significant heteroscedasticity of the variance by reference value. The plot of the std. res. by the leverage showed two possible outliers (std. res.&amp;gt;|3|) and no influential observations (Hi&amp;lt;0.5). These diagnostics proved the aptness of the POLS model. The harmonization between the two instruments was verified with patient specimens. The orthogonal regression model showed in the interval 3-6 mg/dL a quasi-linear relationship between the values as obtained with the two instruments. The orthogonal regression line (y=0.1+1.02 n) and the OLS regression line (y=0.03+601 n) were very similar. There was no probabilistically significant lack-of-fit (P=0.5). The plot of the std. res. by the leverage showed one possible outlier (std.res.&amp;gt;|3|) and no influential observations. Additionally, the plot of the relative differences between the values as obtained with b221#9371 and b221#24796 by the values as obtained with b221#9371 showed only one value exceeding half of the total allowable error. Conclusions The POLS model clearly showed that the calibration lines for the two instruments were quasi-linear in the interval 2-10 mg/dL and reproducible within and between instruments. Additionally, this reliable performance was verified with patient specimens in the interval 3-6 mg/dL. The differences between values as obtained with each instrument were within half of the CLIA’s total allowable error. This clearly showed that equality of calibration lines ensured interchangeable patient values between instruments. Finally, electronic transfer of data and the availability of statistical software like Minitab was crucial for conducting this study.

A-202 Quality control design survey reveals 399 ways to do it wrong

Abstract Background The effectiveness of statistical quality control processes to detect unacceptable patient risk depends on the choices that laboratory professionals make for the design of quality control charts and acceptable patient risk. Recommended best practice consists of putting a meaningful recent mean and SD on the QC chart, selecting rules based on the method’s margin for error to unacceptable risk or Sigma value, and basing acceptable risk on clinical need. Methods A survey was circulated through LinkedIn and Labvine. Forty respondents who included their credentials were included in the final analysis. Respondents were asked to select the one most correct answer four questions regarding the parameters that govern the effectiveness of quality control processes. 1. The best source of the mean on the QC chart? (Four choices) 2. Best source of the SD on the QC chart? (Five choices) 3. Best way to select QC rules for each QC sample? (Six choices) 4. If an analytical process “fails,” we should stop reporting before [errors are reported.] (Five choices) A total of 400 potential patterns of QC selections were chosen. (4 * 5 * 5 * 4). Results When asked acceptable error rate if an analytical process “fails,” responses were More than 5% errors are reported - 11%. Any errors are reported - 66% It varies with clinical use of the test - 16% It varies with capabilities of our method - 8% While most respondents chose at least one of the recommended answers for QC chart design and rules, only three chose the best choice for all parameters. Conclusions Despite decades of recommendations of best practice for quality control, typical practice throughout the laboratory community in general does not reflect this. It is time to consider a new approach to teaching, validating and certifying competency in medical laboratory quality control.

Polytopic autoencoders with smooth clustering for reduced-order modeling of flows

With the advancement of neural networks, there has been a notable increase, both in terms of quantity and variety, in research publications concerning the application of autoencoders to reduced-order models. We propose a polytopic autoencoder architecture that includes a lightweight nonlinear encoder, a convex combination decoder, and a smooth clustering network. Supported by several proofs, the model architecture ensures that all reconstructed states lie within a polytope, accompanied by a metric indicating the quality of the constructed polytopes, referred to as polytope error. Additionally, it offers a minimal number of convex coordinates for polytopic linear-parameter varying systems while achieving acceptable reconstruction errors compared to proper orthogonal decomposition (POD). To validate our proposed model, we conduct simulations involving two flow scenarios with the incompressible Navier-Stokes equation. Numerical results demonstrate the guaranteed properties of the model, low reconstruction errors compared to POD, and the improvement in error using a clustering network.

Time resolution of wind speed data introduces errors in wind power density assessment

Wind energy is critical in the global shift towards renewable energy sources, necessitating accurate potential assessments for informed decision-making and strategic development in this transition. Despite the widespread use of wind speed data with varying time resolutions for estimating wind energy, the influence of this variability on the accuracy of wind energy assessment remains uncertain. In this study, we investigate the impact of wind speed data’s time resolution on wind power density (WPD) assessment, utilizing high-frequency, in-situ observations collected from eight anemometer towers in China. Through analysis of wind speed data at nine resolutions, ranging from 10-minute to monthly intervals, we uncover significant errors introduced by coarser time resolutions in WPD calculations. Specifically, it reveals a systematic underestimation of WPD with decreasing time resolution, highlighting the importance of time resolution in wind energy evaluation. While hourly wind speed data yield acceptable WPD errors, daily and monthly resolutions lead to substantial underestimation due to the smoothing effect of fluctuations. We also delve into the underlying mechanisms contributing to these errors, including variations in the power law exponent and changes in the Weibull distribution shape factor with time resolution. Furthermore, we propose a novel method to mitigate WPD calculation errors using coarse time resolution data by regressing Weibull distribution factors. The findings offer valuable insights into wind speed data selection and accurate wind energy assessment, with implications for renewable energy planning and policy-making in the context of the global energy transition.