Total Error Research Articles

Impact of the total measurement error on sampling plan for bulk materials – An optimal sampling plan under ToS framework

Acceptance sampling plans are used in quality control between a producer and a buyer. However, in the literature of acceptance sampling for bulk materials, the term “sampling” refers to obtaining simple random samples in the traditional statistical sense, which may lead to heavy financial losses. In contrast, the Theory of Sampling (ToS) defines sampling as the collection of physical samples based on a thorough understanding of the material concerned. The goal of this research is to investigate the influence of incorrect sampling procedures on acceptance sampling in a mining value chain. To achieve this, the representative sampling approach of ToS was utilized, total measurement error was measured, and possibilities for improving sampling procedures were identified. A variable Quick Switch Sampling plan (QSS) based on the Beta distribution is applied. A nonlinear optimization model is used to estimate the plan parameters, which minimizes the necessary average sample number (ASN) while meeting the producer and consumer quality levels and maximum allowable risks using real-life data. Finally, A collaborative quality program is suggested for a solid producer-buyer relationship by using a correct sampling facility of the producer.

The Category Fluency Test components and their association with cognition and symptoms in adolescents with schizophrenia

BackgroundThe category Fluency Test (CFT) is included in the MATRICS consensus cognitive battery for schizophrenia (MCCB). This test has several components that allow the exploration of other cognitive functions and could be related to different symptom profiles. Methodsa sample of 98 Mexican adolescents was evaluated with the CFT and the following components were analyzed: total words, errors, clusters, switching, related words, and categories. Demographic and clinical differences among them and correlations with other MCCB tests and with Positive and Negative Syndrome Scale (PANSS) factors were explored. ResultsThe CFT components showed significant correlations with each other and with other MCCB tests, particularly with those related to verbal learning, working memory, and speed of processing. In addition, they showed moderate correlations with the total PANSS score and with the negative, positive, and cognitive PANSS factors. ConclusionsThe analysis of CFT components allows clinicians and investigators to obtain information regarding other cognitive functions and symptom profiles in adolescents with schizophrenia.

Performance Evaluation of an Immersive Virtual Reality Application for Rehabilitation after Arthroscopic Rotator Cuff Repair.

Few studies have evaluated the effectiveness of shoulder rehabilitation in virtual environments. The objective of this study was to investigate the performance of a custom virtual reality application (VR app) with a stereophotogrammetric system considered the gold standard. A custom VR app was designed considering the recommended rehabilitation exercises following arthroscopic rotator cuff repair. Following the setting of the play space, the user's arm length, and height, five healthy volunteers performed four levels of rehabilitative exercises. Results for the first and second rounds of flexion and abduction displayed low total mean absolute error values and low numbers of unmet conditions. In internal and external rotation, the number of times conditions were not met was slightly higher; this was attributed to a lack of isolated shoulder movement. Data is promising, and volunteers were able to reach goal conditions more often than not. Despite positive results, more literature comparing VR applications with gold-standard clinical parameters is necessary. Nevertheless, results contribute to a body of literature that continues to encourage the application of VR to shoulder rehabilitation programs.

QREChem: quantum resource estimation software for chemistry applications

As quantum hardware continues to improve, more and more application scientists have entered the field of quantum computing. However, even with the rapid improvements in the last few years, quantum devices, especially for quantum chemistry applications, still struggle to perform calculations that classical computers could not calculate. In lieu of being able to perform specific calculations, it is important have a systematic way of estimating the resources necessary to tackle specific problems. Standard arguments about computational complexity provide hope that quantum computers will be useful for problems in quantum chemistry but obscure the true impact of many algorithmic overheads. These overheads will ultimately determine the precise point when quantum computers will perform better than classical computers. We have developed QREChem to provide logical resource estimates for ground state energy estimation in quantum chemistry through a Trotter-based quantum phase estimation approach. QREChem provides resource estimates which include the specific overheads inherent to problems in quantum chemistry by including heuristic estimates of the number of Trotter steps and number of necessary ancilla, allowing for more accurate estimates of the total number of gates. We utilize QREChem to provide logical resource estimates for a variety of small molecules in various basis sets, obtaining estimates in the range of 107–1015 for total number of T gates. We also determine estimates for the FeMoco molecule and compare all estimates to other resource estimation tools. Finally, we compare the total resources, including hardware and error correction overheads, demonstrating the need for fast error correction cycle times.

Determination of the Accuracy of Quantitative Measurement Methods in the Clinical Chemistry Laboratory at Machakos Level V Hospital, Kenya

Accuracy is the extent to which measurements are closely related to the actual values of the analyte. Accuracy is one of the key requirements in Laboratory method verification. Verification is a method of ensuring that tests are carried out per the manufacturer's specifications when tested by laboratory staff and patients at the facility. Other elements of verification include precision, linearity verified with reportable range, uncertainty of measurement, carry-over studies, reference ranges and limit of detection. This study aims to determine the accuracy of the quantitative measurement methods in the Clinical Chemistry laboratory in the month of January 2023. It also provides an alternative method for meeting accreditation requirements on accuracy by medical laboratories seeking accreditation in far-stretched laboratories where interlaboratory comparison may not be feasible. A systematic review and meta-analysis at Machakos County, Kenya. The procedure involved analysing commercially available internal quality control material five (5) times a day for eight (8) days bearing the same lot number. Roche Diagnostics, Mannheim, Germany supplied all the reagents, internal quality control materials and calibrators. Accuracy was determined using Roche Cobas® Intergra 400 Clinical Chemistry analyser to perform a comparative descriptive analysis of albumin, alanine aminotransferase (ALT), alkaline phosphatase (Alk Phos), aspartate aminotransferase (AST), chloride, creatinine, Direct Bilirubin (D. Bil), Gamma-glutamyl transferase (GGT), potassium, sodium, total bilirubin, total protein, and urea. The study followed guidelines issued by the Clinical Laboratory Improvements Amendment 1988 (CLIA) and Clinical Laboratory Standards Institute (2014). Data analysis was carried out using Excel Windows 10 MS Office 2021. Mean, standard deviation (SD), Z-score, bias %, coefficient of variation (CV) %, and total error allowable (Tea) were calculated from the results of measurement of the analytes. All the analytes achieved a mean which fell within the manufacturer verification interval and hence passed. The bias % score for the analytes was as follows 5 analytes level (19.2%) scored 0 - ±1, 10 analytes level (38.5%) scored ±1.1 - ±2, 7 analytes level (26.9%) ±2.1 - ±3 and 4 analytes level (15.4%) scored above ±3. All analytes were found to have an excellent Z-score performance between 0 to ± 1.96. The total error allowable was found to fall within CLIA and CLSI specifications limits except for chloride PCC1/ Normal control which failed at 5.82% (CLIA target being ± 5%). Analytes' mean was expected to fall within the given manufacturer's mean. The bias % for the analytes which was at zero or near ± zero was considered an excellent score the further away the score was, the poorer the performance. The Z-score was also calculated to establish how far the observed mean fell from the target mean. All analytes were found to have an excellent Z-score performance between 0 to ± 1.96. A Z-score of ± 1.96 to ± 2.57 indicates good performance while a Z-score of over ± 2.58 indicates failed performance. These good results are attributable to the fact that analysis was carried out within a very short duration by the same person, on the state-of-the-art instrument, in a well-controlled environment hence no room for analytical variation. Chloride being an electrolyte failed since it is physiologically controlled in a strict and narrow range.

A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images

Abstract. In combination with observations from visible satellite channels, near-infrared channels can provide valuable additional cloud information, e.g. on cloud phase and particle sizes, which is also complementary to the information content of thermal infrared channels. Exploiting near-infrared channels for operational data assimilation and model evaluation requires a sufficiently fast and accurate forward operator. This study presents an extension to the method for fast satellite image synthesis (MFASIS) that allows for simulating reflectances of the 1.6 µm near-infrared channel based on a computationally efficient neural network with the same accuracy that has already been achieved for visible channels. For this purpose, it is important to better represent vertical variations in effective cloud particle radii, as well as mixed-phase clouds and molecular absorption in the idealized profiles used to train the neural network. A new approach employing a two-layer model of water, ice and mixed-phase clouds is described, and the relative importance of the different input parameters characterizing the idealized profiles is analysed. A comprehensive data set sampled from Integrated Forecasting System (IFS) forecasts together with different parameterizations of the effective water and ice particle radii is used for the development and evaluation of the method. Further evaluation uses a month of ICOsahedral Non-hydrostatic development based on version 2.6.1 (ICON-D2) hindcasts with effective radii directly determined by the two-moment microphysics scheme of the model. In all cases, the mean absolute reflectance error achieved is about 0.01 or smaller, which is an order of magnitude smaller than typical differences between reflectance observations and corresponding model values. The errors related to the imperfect training of the neural networks present only a small contribution to the total error, and evaluating the networks takes less than a microsecond per column on standard CPUs. The method is also applicable for many other visible and near-infrared channels with weak water vapour sensitivity.

A synergistic chemometric combination for whiteness and greenness assessed HPLC-DAD assay of aqueous extracts of ivy and thyme and potassium sorbate in a syrup formula

A stability-indicating fit-for-purpose method has been developed for co-determination of hederacoside C (HC), thymol (TL), and potassium sorbate (PS) combined in a syrup dosage form. Carvacrol (CL) was separated from thyme extract (THY) with demonstrated identity at high confidence. This method was based on a reversed-phase (RP) high performance liquid chromatographic (HPLC) separation of the cited analytes. The HPLC separation was performed on a RP Zorbax Eclipse XDB-C18 analytical column [250 × 4.6 mm, 5 µm (80 Å)] and SecurityGuard C18 guard column (4 × 3 mm, 5 µm) with gradient elution system of HPLC far UV grade acetonitrile and 20 mM aqueous potassium dihydrogen phosphate buffer adjusted to pH 2.1 as the mobile phase at a flow rate of 2.5 mL min−1 and column oven was set at 50 °C. Quantitation was achieved with the photodiode array detection (DAD) with interferent peak suppression. Target Measurement Uncertainty (TMU) was determined by misclassification rates approach employing 100,000 Monte Carlo simulations (MCS). The bias and imprecision standard deviation were combined as a total analytical error (TAE) and compared via an enhanced approach linked to product specifications, process variation, and shelf-life variability for proving a fit-for-purpose analytical procedure. The robustness of the method was checked using supersaturated design constructed from Plackett-Burman design (PB-SSD) which was statistically analysed via the very recent approach of autovalidation using self-validated ensemble modelling (SVEM). The calibration graphs for each analyte were rectilinear in the range of 42.12–560.00, 3.37–47.15 and 0.51–7.07 µg mL−1 for PS, HC, and TL, respectively. The proposed HPLC-DAD method was successfully applied for the determination of the investigated analytes in a syrup formula. Moreover, the whiteness and greenness of the procedure were assessed via the most recently cited approaches.

An in-depth comparative analysis of data-driven and classic regression models for scour depth prediction around cylindrical bridge piers

The study focuses on the critical concern of designing secure and resilient bridge piers, especially regarding scour phenomena. Traditional equations for estimating scour depth are limited, often leading to inaccuracies. To address these shortcomings, modern data-driven models (DDMs) have emerged. This research conducts a comprehensive comparison involving DDMs, including support vector machine (SVM), gene expression programming (GEP), multilayer perceptron (MLP), gradient boosting trees (GBT) and multivariate adaptive regression spline (MARS) models, against two regression equations for predicting scour depth around cylindrical bridge piers. Evaluation employs statistical indices, such as root-mean-square error (RMSE), coefficient of determination (R2), mean average error (MAE) and normalized discrepancy ratio (S(DDRmax)), to assess their predictive performance. A total of 455 datasets from previous research papers are employed for assessment. Dimensionless parameters Froude number Fr=Ugy\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left( {Fr = \\frac{U}{{\\sqrt {gy} }}} \\right)$$\\end{document}, Pier Froude number FrP=Ug′D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Fr_{P} = \\frac{U}{{\\sqrt {g^{\\prime } D} }}$$\\end{document}, and the ratio of scour depth to pier diameter (yD)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\frac{\ ext{y}}{{\ ext{D}}})$$\\end{document} are carefully selected as influential model inputs through dimensional analysis and the gamma test. The results highlight the superior performance of the SVM model. In the training phase, it exhibits an RMSE of 0.1009, MAE of 0.0726, R2 of 0.9401, and SDDR of 2.9237. During testing, the SVM model shows an RMSE of 0.023, MAE of 0.017, R2 of 0.984, and SDDR of 5.301. Additionally, it has an average error of − 0.065 and a total error of − 20.642 in the training set and an average error of − 0.005 and a total error of − 0.707 in the testing set. Conversely, the M5 model exhibits the lowest accuracy. The statistical metrics unequivocally establish the SVM model as significantly outperforming the experimental models, placing it in a higher echelon of predictive accuracy.

Cognitive impairment in Chinese adult patients with type III spinal muscular atrophy without disease-modifying treatment.

Spinal muscular atrophy (SMA) is a neurodegenerative disorder characterized by the degeneration of motor neurons in the spinal cord. It remains uncertain whether the cognitive performance of adult patients with SMA is impaired. The objective of this study was to assess the cognitive profile of adult Chinese patients with SMA and the association between clinical features and cognitive ability, particularly executive function. This cross-sectional study included 22 untreated adult patients with type III SMA and 20 healthy subjects. The following variables were assessed: general intelligence, memory, attention, language, executive function, depression, anxiety, and other demographic and clinical parameters. In addition, physical function was evaluated using the Hammersmith Functional Motor Scale Expanded (HFMSE), the Revised Upper Limb Module (RULM), and the 6-Minute Walk Test (6MWT). SMA patients had lower scores than healthy subjects in the Verbal Fluency Test, Stroop effect, Total Errors, Perseverative Responses, Perseverative Errors, and Non-perseverative Errors in the Wisconsin Card Sorting Test, showing impaired abilities of SMA patients in executive function. In the Attention Network Test (ANT), the results indicated that the SMA patients also had selective deficits in their executive control networks. Ambulant patients had better executive function test performance than non-ambulant ones. Compromised executive abilities in patients with SMA were correlated with a younger age at onset, poorer motor function, and higher levels of anxiety and depression. Our study presented the distribution of cognitive impairment in a Chinese cohort with SMA. Patients with type III SMA showed selective deficits in executive function, which may be associated with disease severity, physical impairment, depression and anxiety. Future cognitive studies, accounting for motor and emotional impairment, are needed to evaluate if executive impairment is driven by specific brain changes or by those confounding factors.

16 Validation of the Pillbox Test in a Veteran Sample: A Replication Study.

Objective:Assessment of medication management, an instrumental activity of daily living (IADL), is particularly important among Veterans, who are prescribed an average of 2540 prescriptions per year (Nguyen et al., 2017). The Pillbox Test (PT) is a brief, performance-based measure that was designed as an ecologically valid measure of executive functioning (EF; Zartman, Hilsabeck, Guarnaccia, & Houtz, 2013), the cognitive domain most predictive of successful medication schedule management (Suchy, Ziemnik, Niermeyer, & Brothers, 2020). However, a validation study by Logue, Marceaux, Balldin, and Hilsabeck (2015) found that EF predicted performance on the PT more so than processing speed (PS), but not the language, attention, visuospatial, and memory domains combined. Thus, this project sought to increase generalizability of the latter study by replicating and extending their investigation utilizing a larger set of neuropsychological tests.Participants and Methods:Participants included 176 patients in a mixed clinical sample (5.1% female, 43.2% Black/African American, 55.7% white, Mage = 70.7 years, SDage = 9.3, Medu = 12.6 years, SDedu = 2.6) who completed a comprehensive neuropsychological evaluation in a VA medical center. All participants completed the PT where they had five minutes to organize five pill bottles using a seven-day pillbox according to standardized instructions on the labels. Participants also completed some combination of 26 neuropsychological tests (i.e., participants did not complete every test as evaluations were tailored to disparate referral questions). Correlations between completed tests and number of pillbox errors were evaluated. These tests were then combined into the following six domains: language, visuospatial, working memory (WM), psychomotor/PS, memory, and EF. Hierarchical multiple regression was completed using these domains to predict pillbox errors.Results:Spearman’s correlation coefficients indicated that 25 tests had a weak to moderate relationship with PT total errors (rs = 0.23 -0.51); forward digit span was not significantly related (rs = 0.13). A forced-entry multiple regression was run to predict PT total errors from the six domains. The model accounted for 29% of the variance in PT performance, F(6, 169) = 11.56, p < .001. Of the domains, psychomotor/PS made the greatest contribution, f(169) = 2.73, p = .007, followed by language, f(169) = 2.41, p = .017, and WM, f(169) = 2.15, p = .033. Visuospatial performance and EF did not make significant contributions (ps>.05). Next, two hierarchical multiple regressions were run. Results indicated that EF predicted performance on the PT beyond measures of PS, AR2 = .02, p = .044, but not beyond the combination of all cognitive domains, AR2 = .00, p = .863.Conclusions:Results of this study partially replicated the findings of Logue et al. (2015). Namely, EF predicted PT performance beyond PS, but not other cognitive domains. However, when all predictors were entered into the same model, visuospatial performance did not significantly contribute to the prediction of pillbox errors. These results suggest that providers may benefit from investigating medication management abilities when deficits in PS, WM, and/or language are identified. Further research is needed to better understand which domains best predict PT failure.

51 The Minimal Effect of Depression on Cognitive Functioning when Accounting for Performance Validity

Objective:While much research has demonstrated a relationship between depression and cognitive deficits, most studies have neglected to include measurements of performance validity. The very small number of studies that have examined this relationship when accounting for performance validity have found that the relationship between depression and cognition is small or nonsignificant. The current study examined the relationship between depression (assessed through both clinical interview and self-report symptom measures) and multiple domains of cognition after accounting for noncredible performance on neuropsychological testing.Participants and Methods:Participants were veterans referred for outpatient clinical evaluation. Among other tests that varied across patients, the neuropsychological battery included: California Verbal Learning Test - second edition (CVLT-II) total immediate recall across trials 1–5, short delay free recall, and long delay free recall; Trail Making Test; FAS and Animal Fluency; Rey–Osterrieth Complex Figure Test (ROCF) copy and 3-minute delay recall; and Wisconsin Card Sorting Test (WCST) categories completed, total errors, and percent perseverative errors. These tests represent domains that have previously been examined in relation to depression (e.g., memory, processing speed, executive functioning). Evaluations were conducted for clinical purposes, so that some individuals who were not administered certain tests have missing data. The first set of regression analyses (N=206) included age, sex, and education at Step 1, Beck Depression Inventory-2 (BDI-2) total score at Step 2, and pass or failure of Trial 1 of the Test of Memory Malingering (TOMM) at Step 3 as predictors of performance on the 12 test indices. The second set of regression analyses (N=559) mirrored the first but with Major Depressive Disorder (MDD) diagnosis at Step 2 instead.Results:In the first set of analyses, after including TOMM in the model, only the relationship between BDI-2 and verbal fluency remained significant, but did not survive Bonferroni correction (p<.004). In the second set of analyses, before including the TOMM, MDD diagnosis was significantly related only to worse performance on Trails A and CVLT-II Short and Long Delay Free Recall, with small effect sizes (rp=.06–.15). When TOMM Trial 1 was included in the model, MDD diagnosis became a nonsignificant predictor of CVLT-II Long Delay Free Recall but remained a significant predictor for Trails A and CVLT-II Short Delay Free Recall (p<.05). After Bonferroni correction (p<.004), with TOMM Trial 1 included in the model, MDD diagnosis remained a significant predictor only of CVLT-II Short Delay Free Recall, with a small effect size (rp=.17).Conclusions:After accounting for noncredible performance, there was little evidence for a relationship between depression diagnosis or symptoms and many cognitive domains. These results suggest that previously reported effects of depression on cognition are not mainly due to underlying neurological mechanisms, but rather to motivational factors. Future research could focus on the potential psychological mechanisms (e.g., negative attitudes, expectancy bias, low motivation, etc.) driving the relationship between depression and low effort on cognitive testing. If replicated, the current findings could be valuable to clinicians treating depressed individuals who have concerns about their cognitive functioning, by indicating psychoeducation and reassurance.

Stability of magnetohydrodynamics free convective micropolar thermal liquid movement over an exponentially extended curved surface

Micro polar fluids have a wide variety of applications in biomedical, manufacturing, and technical activities, such as nuclear structures, biosensors, electronic heating and cooling, etc. The aim of this study is to investigate the properties of heat transfer on a magnetohydrodynamic free convection movement of micro polar fluid over an exponentially stretchable curved surface. The flow is non-turbulent and steady. The effects of Joule heating, varying thermal conductivity, irregular heat reservoir, and non-linear radiation are anticipated. The modelled PDEs are converted to ODEs via transformation, and the integration problems are then addressed using ND-Solve method along with bvp4c package. It is observed that velocity is reduced and the micro rotation field is increased as the micro rotation parameter is increased. It is witnessed that the temperature of the fluid enhances as the Eckert number is augmented. The velocity is increasing function of the curvature parameter while the decreases with increasing magnetic factor. The distribution of temperature is improved by a rise in temperature-dependent thermal conductivity characteristic. It is investigated that as the values of temperature ratio, Prandtl number, and the Biot number are increased the temperature distribution is enhanced. For the stability of the numerical results, the mean square residue error (MSRE) and total mean square residue error (TMSRE) are computed. For the confirmation of the present analysis, a comparison is done with the published study and excellent settlement is found.

54 The Relationship between Error-Monitoring and Measures of Real-World Awareness and Everyday Function

Objective:Aging older adults and individuals with mild cognitive impairment (MCI) experience changes in ability to self-monitor errors. Difficulties with accurate self-monitoring of errors can negatively impact everyday functioning. Without proper error recognition, individuals will continue to make mistakes and not implement compensatory strategies to prevent future errors. A modified Sustained Attention to Response Task (SART; Robertson et al., 1997) has previously been used to assess self-monitoring by the number of errors individuals were able to recognize. The current study sought to examine the relationship of this laboratory-based error-awareness task with everyday functional abilities as assessed by informants and with real-world error-monitoring. We hypothesized that self-monitoring would be significantly related to real-world error-monitoring and everyday functional abilities.Participants and Methods:135 community-dwelling participants (110 healthy older adults (HOA) and 25 individuals with MCI) were included from a larger parent study (mean age = 67.73, SD = 8.89). A modified SART was used to measure error-monitoring and create a self-monitoring variable by dividing accurately recognized errors by the total number of errors. Participants also completed simple and complex everyday tasks of daily living (e.g., making lemonade, cooking oatmeal, cleaning, filling medication pillbox) in a university campus apartment. Examiners coded both number of errors committed and self-corrections that were made during task completion. To examine real world error awareness, total self-correct errors were divided by the total number of errors. Knowledgeable informants (KI) completed the Everyday Cognition (ECog) scale, where they rated the participant on domains of memory, language, spatial abilities, planning, organization, and divided attention, to capture changes in everyday function. Pearson correlations were used to examine the relationship between SART self-monitoring and real-world error-monitoring, and changes in everyday functions as rated by their informants.Results:As self-monitoring scores on the SART increased, so too did real-world error awareness scores, r(133) = .18, p = .04. Higher self-monitoring scores on the SART were also significantly positively associated with functional performance abilities on the Ecog total (r(96) = -.24, p = .02). Further, higher self-monitoring on the SART was related to better functional performance within the Ecog domains of everyday memory (r(96) = -.23, p = .02), everyday language (r(96) = -.24, p = .02), everyday spatial abilities (r(96) = -.23, p = .02), and everyday planning (r(96) = -.21, p = .04). SART self-monitoring was not significantly related to everyday organization or divided attention domains.Conclusions:The findings revealed that better error-monitoring performance on a laboratory-based task was related to better error-monitoring when completing real-world activities, and less overall impairment in everyday function as reported by informants. Results support the ecological validity of the SART error-monitoring score and suggest that error-monitoring performance on the modified SART may have important clinical implications in predicting real-world error-monitoring and everyday function. Future research should consider how SART error-monitoring may predict everyday functioning, over and above other clinical measures.

68 The Impact of Pain Catastrophizing on Neuropsychological Performance in Youth with Persistent Post Concussive Symptoms

Objective:Patients with persistent post-concussion symptoms (PPCS) experience prolonged recovery (e.g., headache, fatigue, or dizziness) lasting >2 months post injury. These symptoms are thought to be maintained by several biopsychosocial factors including dysregulated stress responses, such as pain catastrophizing, that may drive behavioral avoidance and contribute to mood symptoms and cognitive difficulties. Conditions with similar symptomatology to PPCS (e.g., anxiety disorders, somatosensory disorders, chronic pain, etc.) also exhibit maladaptive thought patterns like pain catastrophizing as well as decrements in certain aspects of cognitive performance; however little is known about how pain catastrophizing might relate to neuropsychological performance in youth with PPCS. Therefore, the purpose of this study was to examine the relationship between pain catastrophizing and neuropsychological performance in youth participants with PPCS.Participants and Methods:A prospective case-control study design was used to examine 29 participants between the ages of 13 to 23. Participants were divided into two groups: 1) patients with PPCS (2-16 months post-injury; n = 15) and 2) age-matched, non-injured controls (n = 14). Participants completed the Pain Catastrophizing Scale (PCS) to determine degree of catastrophic thinking related to pain experience and the Beck Depression Inventory (BDI). Neuropsychological performance was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and a modified version of the Paced Auditory Serial Addition Test (PASAT) where performance was evaluated by total correct and error type (i.e., commission and omission) across 5 trials. ANCOVA was used to compare group differences in pain catastrophizing and neuropsychological tests scores while controlling for age and linear regressions examined the relationship between PCS total score and each neuropsychological test score while controlling for level of depression.Results:Overall, the PPCS group reported significantly higher levels of pain catastrophizing on the PCS compared to the control group (p < 0.01). For neuropsychological performance, the PPCS group scored significantly lower than the control group on List Learning (p < 0.01), Semantic Fluency (p < 0.05), and List Recall (p < 0.01) on the RBANS and made significantly higher omission errors (but not commission) on the PASAT(p <.01). Higher pain catastrophizing was also associated with poorer neuropsychological performance on the exact same tasks the PPCS group performed worse than controls. There was no significant interaction by group in the impact of PCS scores on neurocognitive performance.Conclusions:Compared to controls, youth PPCS patients reported higher levels of pain catastrophizing. Additionally, pain catastrophizing was associated with poorer neuropsychological performance. These findings suggest that increased pain catastrophizing after head injury could contribute to poorer cognitive performance in youth. As such, interventions that target maladaptive cognitive coping styles like pain catastrophizing may be especially helpful for patients with PPCS.

Data-driven wake model parameter estimation to analyze effects of wake superposition

Low-fidelity wake models are used for wind farm design and control optimization. To generalize to a wind farm model, individually modeled wakes are commonly superimposed using approximate superposition models. Wake models parameterize atmospheric and wake turbulence, introducing unknown model parameters that historically are tuned with idealized simulation or experimental data and neglect uncertainty. We calibrate and estimate the uncertainty of the parameters in a Gaussian wake model using Markov chain Monte Carlo (MCMC) for various wake superposition methods. Posterior distributions of the uncertain parameters are generated using power production data from large eddy simulations and a utility-scale wake steering field experiment. The posteriors for the wake expansion coefficient are sensitive to the choice of superposition method, with relative differences in the means and standard deviations on the order of 100%. This sensitivity illustrates the role of superposition methods in wake modeling error. We compare these data-driven parameter estimates to estimates derived from a standard turbulence-intensity based model as a baseline. To assess predictive accuracy, we calibrate the data-driven parameter estimates with a training dataset for yaw-aligned operation. Using a Monte Carlo approach, we then generate predicted distributions of turbine power production and evaluate against a hold-out test dataset for yaw-misaligned operation. For the cases tested, the MCMC-calibrated parameters reduce the total error of the power predictions by roughly 50% compared to the deterministic empirical model predictions. An additional benefit of the data-driven parameter estimation is the quantification of uncertainty, which enables physically quantified confidence intervals of wake model predictions.

Handwriting for efficient text entry in industrial VR applications: influence of board orientation and sensory feedback on performance.

Text entry in Virtual Reality (VR) is becoming an increasingly important task as the availability of hardware increases and the range of VR applications widens. This is especially true for VR industrial applications where users need to input data frequently. Large-scale industrial adoption of VR is still hampered by the productivity gap between entering data via a physical keyboard and VR data entry methods. Data entry needs to be efficient, easy-to-use and to learn and not frustrating. In this paper, we present a new data entry method based on handwriting recognition (HWR). Users can input text by simply writing on a virtual surface. We conduct a user study to determine the best writing conditions when it comes to surface orientation and sensory feedback. This feedback consists of visual, haptic, and auditory cues. We find that using a slanted board with sensory feedback is best to maximize writing speeds and minimize physical demand. We also evaluate the performance of our method in terms of text entry speed, error rate, usability and workload. The results show that handwriting in VR has high entry speed, usability with little training compared to other controller-based virtual text entry techniques. The system could be further improved by reducing high error rates through the use of more efficient handwriting recognition tools. In fact, the total error rate is 9.28% in the best condition. After 40 phrases of training, participants reach an average of 14.5 WPM, while a group with high VR familiarity reach 16.16 WPM after the same training. The highest observed textual data entry speed is 21.11 WPM.

Lack of observed interference by therapeutic monoclonal antibodies in select commonly utilized immunoassays

BackgroundTherapeutic monoclonal antibodies (tmabs) have been hypothesized to interfere with immunoassay measurements, although studies investigating this potential new class of interference are lacking. This study evaluated the effects of tmabs used in cancers ipilimumab (Bristol Myers Squibb), nivolumab (Bristol Myers Squibb), pembrolizumab (Merck) and autoimmune disorders adalimumab (AbbVie), infliximab (Janssen) and vedolizumab (Takeda) in common immunoassays used in the clinical laboratory. MethodsResidual sera from 10 randomly chosen patients were split into two tubes and spiked with same volume (approximately 5 % final volume) of either saline (control) or 6 tmabs (final concentration of 100 μg/mL each). Concentrations from sixteen analytes in 19 different assays were assessed: TSH (Roche and Beckman), free thyroxine (Roche and Siemens), cortisol (Beckman), Cancer Antigens (CA): CA19-9 (Beckman), CA15-3 (Roche), CA125 (Roche), and CA27.29 (Siemens), carcinoembryonic antigen (Beckman), alpha-fetoprotein (Beckman), thyroglobulin (Beckman) and thyroglobulin antibodies (Beckman), thyroid peroxidase antibody (Beckman), beta-human chorionic gonadotropin (Roche and Beckman), total prostate-specific antigen (Roche), parathyroid hormone (Roche) and antinuclear antibodies IgG (Werfen). The tmab spiked residual sera were compared with matched saline spiked sera and percent error was assessed against allowable total error defined from biological variation or CLIA limits. ResultsNone of the tested immunoassays were affected by the presence of the tmabs, in samples within or outside assay reference intervals. The median % error among all immunoassays ranged between −2.0% (for TSH) to 2.7% (for TPO Ab assay). ConclusionThese findings demonstrate no detectable tmab interference for the assessed immunoassays using spiked preparations of the tmabs in residual human sera. The findings are limited to the tmabs and immunoassays studied here.

A Study to Analyze the Turnaround Time of Selected Lab Sample Test Conducted in A Multispeciality Hospital in Vadodara, Gujarat

Abstract: Quality refers to a product or service's ability to meet customer needs and expectations. Clinicians prioritize service quality, including total test error, availability, cost, relevance, and timeliness. They may sacrifice analytical quality for faster turnaround time, leading to the rise of point-of-care testing. The objective of the study was to analyze the turnaround time of selected lab sample test conducted in a multispecialty hospital. Materials and Methods: The study adopted an observational research design, analyzing data collected from a multispecialty hospital in Vadodara. A structured and validated checklist provided by the organization was used as the data collection instrument. Out of total 480 samples, 329 samples were taken for analysis. Results: Out of total 480 samples, 329 samples (78.88%) were taken for analysis. 100 (54.65%) samples were within TAT time and 83 (45.35 %) samples were delayed. 48 (57.83%) samples had TAT between 35 minutes to 40 minutes, 22 (26.51%) samples had TAT between 40 minutes to 50 minutes, 9 (10.84%) samples had TAT between 50 minutes to 55 minutes, and 4 (4.82%) samples had TAT over 60 minutes. Average time between sample collection and lab reach was observed to be 15 min. 38 sec. Transport delay was observed. ConclusionIn conclusion, the study emphasized the importance of timely and accurate laboratory services in ensuring patient satisfaction. By analyzing and addressing the factors contributing to delays in TAT, hospitals can improve their overall service quality and attract more patients.

Bubble transport during SGTR accident in lead-cooled fast reactor: A machine learning

Steam generator tube rupture (SGTR) is one of the safety issues for pool-type lead-cooled fast reactors (LFR). Accurately quantifying and predicting the bubble transport result is essential for evaluating the accident. This paper addresses tracking the bubble motion using an Eulerian-Lagrangian method in CFD based on the Europe Lead cooling System (ELSY) primary system model at 1/8 centrosymmetric structure. The steady and transient bubble distributions in the system under different leakage heights are obtained. Furthermore, the simulation results are predicted by machine learning, where Gaussian Process Regression (GPR) is employed for both steady conditions and transient conditions. The data-driven GPR model is established for the bubble transport prediction, which can capture complex non-linear relationships between input variables (i.e. bubble diameter, SG leakage height, final position, system contaminate) and output (reached bubble percentage) responses directly from data. For steady conditions, the prediction results by the kernel function of Automatic Relevance Determination (ARD) Rational Quadratic show the best accuracy in predicting the percentages of bubbles reaching the core, top of the steam generator, and staying in the system, with a total Root Mean Square Error (RMSE) of 3.22%. Four typical transient cases of bubble accumulation in the core are selected for prediction. All the cases are well predicted by the kernel function of ARD Matern 3/2 with low mean averaged error.

Neural network modeling of motivation for government agencies’ top management of regions as a classification problem

Subject. The study deals with modeling the motivation of top managers of government agencies in regions to align the interests of people and the State. Objectives. The purpose of the study is to create a neural network model of motivation for top management of regional government institutions for a classification problem. Methods. Using neural networks, we simulate criteria for non-financial and financial motivation of the said top management, and criteria for strategic potential of regions. Financial motivation is defined as the salary of a senior civil servant, and non-financial motivation as his or her career growth. At the same time, the target function is a coefficient of natural population growth in regions, its positive value is assessed positively, and negative value negatively. As a result, the problem of binary classification in the trained neural network is solved. Results. Comparing the accuracy of the model in the considered example with accuracy that was obtained earlier, using logistic regression, we note that in the previous model, the total error in verification by the functions of non-financial and financial motivation and strategic potential was 39%. In our case, this error was only 12%. This suggests that neural networks enable to achieve much more accurate forecasting. Conclusions. The findings could be useful for regional government agencies to develop a constructive system of non-financial and financial motivation for their top managers.