Model Building Procedure Research Articles

A physiologically based pharmacokinetic model of cefepime to predict its pharmacokinetics in healthy, pediatric and disease populations

The physiologically based pharmacokinetic modeling (PBPK) approach can predict drug pharmacokinetics (PK) by combining changes in blood flow and pathophysiological alterations for developing drug-disease models. Cefepime hydrochloride is a parenteral cephalosporin that is used to treat pneumonia, sepsis, and febrile neutropenia, among other things. The current study sought to identify the factors that impact cefepime pharmacokinetics (PK) following dosing in healthy, diseased (CKD and obese), and pediatric populations. For model construction and simulation, the modeling tool PK-SIM was utilized. Estimating cefepime PK following intravenous (IV) application in healthy subjects served as the primary step in the model-building procedure. The prediction of cefepime PK in chronic kidney disease (CKD) and obese populations were performed after the integration of the relevant pathophysiological changes. Visual predictive checks and a comparison of the observed and predicted values of the PK parameters were used to verify the developed model. The results of the PK parameters were consistent with the reported clinical data in healthy subjects. The developed PBPK model successfully predicted cefepime PK as observed from the ratio of the observed and predicted PK parameters as they were within a two-fold error range.

Time Management Behavior Structural Equation Model Predicts Global Sleep Quality in Traditional Entry University Students

ABSTRACT Background Poor sleep is commonplace among traditional entry university students. Lifestyle modifications, such as time management behaviors, may improve sleep quality by allocating sufficient time for sleep and mitigating stress-associated sleep latency inefficiencies. Purpose The purpose of our study was to evaluate time management behaviors as predictors of sleep quality in traditional entry university students. We hypothesized that time management behaviors would predict perceived control of time, which in turn, would predict global sleep quality. Methods A cross-sectional convenience sample of university students (n = 302) completed a 73-item instrument comprised the Pittsburgh Sleep Quality Index and the Time Management Behavior scale. Model building procedures included exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. Results Our specified model identified significant paths between setting goals and priorities (β = .261; p = .012), mechanics of time management (β = .210; p = .043) and preference for organization (β = .532; p < .001) for perceived control of time (R2 = .300 p < .001). We further identified a significant path between perceived control of time and global sleep quality (R2 = .196, p = .022). Discussion Our study suggests that time management behaviors are associated with global sleep quality. Translation to Health Education Practice Health education interventions addressing sleep quality of traditional entry university students should consider incorporating time management behaviors.

Forecasting Peak Electricity Consumption Demand in Luzon by utilizing ARIMA Model

This research study focuses on forecasting the future values of peak demand in electricity consumption for Luzon, Philippines based on monthly historical data spanning from 2001 to 2020. The data was obtained from the official website of the Philippines Department of Energy (DOE). The primary objective of this study is to employ the ARIMA (Autoregressive Integrated Moving Average) model-building procedure developed by Box and Jenkins to accomplish accurate peak demand forecasting. The methodology involved conducting various tests and evaluations to identify the ARIMA model with the least Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). After careful analysis, the best-fitting ARIMA model was determined to be ARIMA (11, 1, 12). The findings of this study indicate that, according to the ARIMA (11, 1, 12) model, Luzon's peak demand is projected to reach 10,497.65 megawatts by December 2021. Furthermore, the model predicts that by the end of 2022, 2023, and 2024, Luzon's peak demand will be approximately 10,738.34 MW, 10,953.98 MW, and 11,148.43 MW per electrical grid, respectively. The accuracy of the ARIMA (11, 1, 12) model is found to be satisfactory, with a low MAPE value of 3.639% and the most negligible RMSE value of 517.132. The implications of these forecasted peak demand values are significant for decision-makers in the energy and utilities sector. The accurate predictions provided by the ARIMA model can aid in resource allocation, infrastructure planning, and overall operational strategies to effectively meet the anticipated high-demand periods. In conclusion, this study successfully forecasts Luzon's future values of peak demand in electricity consumption using the ARIMA (11, 1, 12) model. The findings highlight the importance of accurate peak demand forecasting and provide valuable insights for energy industry professionals.

The reporting of prognostic prediction models for obstetric care was poor: a cross-sectional survey of 10-year publications

BackgroundTo investigate the reporting of prognostic prediction model studies in obstetric care through a cross-sectional survey design.MethodsPubMed was searched to identify prognostic prediction model studies in obstetric care published from January 2011 to December 2020. The quality of reporting was assessed by the TRIPOD checklist. The overall adherence by study and the adherence by item were calculated separately, and linear regression analysis was conducted to explore the association between overall adherence and prespecified study characteristics.ResultsA total of 121 studies were included, while no study completely adhered to the TRIPOD. The results showed that the overall adherence was poor (median 46.4%), and no significant improvement was observed after the release of the TRIPOD (43.9 to 46.7%). Studies including both model development and external validation had higher reporting quality versus those including model development only (68.1% vs. 44.8%). Among the 37 items required by the TRIPOD, 10 items were reported adequately with an adherence rate over of 80%, and the remaining 27 items had an adherence rate ranging from 2.5 to 79.3%. In addition, 11 items had a report rate lower than 25.0% and even covered key methodological aspects, including blinding assessment of predictors (2.5%), methods for model-building procedures (4.5%) and predictor handling (13.5%), how to use the model (13.5%), and presentation of model performance (14.4%).ConclusionsIn a 10-year span, prognostic prediction studies in obstetric care continued to be poorly reported and did not improve even after the release of the TRIPOD checklist. Substantial efforts are warranted to improve the reporting of obstetric prognostic prediction models, particularly those that adhere to the TRIPOD checklist are highly desirable.

Predictive Modeling of MEMS via Generic Meminductors: The Multiport Inductor Approach

It is shown for the first time that a large class of generic meminductors with a specific form of the state equation can be constructed from elements of Chua’s periodical table and a multiport inductor. The electrical port of this inductor is identical to the physical port of the meminductor. The other ports, which may be of general, i.e. not electrical nature, interconnect the processes that make up the overall dynamics of the element. This result allows creating predictive models of a wide range of existing MEMS (Micro-Electro-Mechanical Systems) exhibiting the behavior of generic meminductors. A model building procedure is described using the example of an elastic meminductive system. The model is then implemented in SPICE and verified by computer simulations.

Anticoagulant Effects of Dabigatran on Coagulation Laboratory Parameters in Pediatric Patients: Combined Data from Five Pediatric Clinical Trials.

Background Dabigatran etexilate, a direct oral thrombin inhibitor, is approved to treat venous thromboembolism (VTE) in both adults and children. Objectives This population analysis characterized relationships between dabigatran total plasma concentrations and coagulation laboratory parameters (activated partial thromboplastin time [aPTT]; diluted thrombin time [dTT]; ecarin clotting time [ECT]). Methods Data from three phase 2a and one single-arm and one randomized, comparative phase 2b/3 pediatric studies (measurements: aPTT 2,925 [ N = 358]; dTT 2,348 [ N = 324]; ECT 2,929 [ N = 357]) were compared with adult data (5,740 aPTT, 3,472 dTT, 3,817 ECT measurements; N = 1,978). Population models were fitted using nonlinear mixed-effects modeling. Covariates (e.g., sex, age) were assessed on baseline and drug-effect parameters, using a stepwise covariate model-building procedure. Results Overall, relationships between dabigatran, aPTT, dTT, and ECT were similar in children and adults. For children aged <6 months, a higher proportion of baseline samples were outside or close to the upper aPTT and ECT adult ranges. No age-related differences were detected for dTT. With increasing dabigatran concentration, aPTT rose nonlinearly (half the maximum effect at 368 ng/mL dabigatran) while dTT and ECT increased linearly (0.37 and 0.73% change per ng/mL dabigatran, respectively). Mean baseline aPTT (45 vs. 36 seconds) and ECT (40 vs. 36 seconds) were slightly increased for those aged <6 months versus older children. Conclusion The similar relationships of laboratory parameters observed across pediatric age groups suggests that developmental changes in the hemostatic system may have little effect on response to dabigatran.

Efficient and relevant stepwise covariate model building for pharmacometrics.

Covariate modeling is an important opportunity for pharmacometrics to influence decision making in drug development. The stepwise covariate model (SCM) building procedure is the most common method for covariate model development. Despite its advantages, the traditional SCM method is known to have long runtimes and the suboptimal ability to select relevant covariates, especially in more complex phase III settings. In this work, two alternative approaches are presented: SCM+, which introduces the “adaptive scope reduction” and changes to general estimation settings, and “stage‐wise filtering,” which groups covariates into categories based on their importance (mechanistic, structural, and exploratory). The three methods (SCM, SCM+, and SCM+ with stage‐wise filtering) are applied to data from a simulated phase III population pharmacokinetic study and are compared in terms of efficiency and relevance. The two SCM+ methods were considerably more efficient than the traditional SCM: the number of function evaluations was reduced by 70% for SCM+ and by 76% for SCM+ with stage‐wise filtering compared to SCM; the corresponding number of executed models was reduced by 44% for SCM+ and 70% for SCM+ with stage‐wise filtering. In addition, among the three methods, SCM+ with stage‐wise filtering selected the highest number of relevant covariates. Given the improved efficiency and ability to select relevant covariates shown in this work, the use of SCM+ and stage‐wise filtering can greatly increase the efficiency of covariate modeling in drug development, which will ultimately facilitate more timely support for decision making.

PROGNOSTIC SIGNIFICANCE OF BLOOD PRESSURE PARAMETERS FOR PREDICTING CARDIOVASCULAR DEATH

Objective: The nycterohemeral rhythm of blood pressure (BP) and heart rate has been associated with cardiovascular outcomes. However, long-term prognostic significance of BP at different levels of heart rates has rarely been studied. We therefore conducted the present analysis to investigate the prognostic values for cardiovascular outcome by the ambulatory BP parameters during different time windows Design and method: We enrolled 5 large cohorts from whole world, including CARDIA, IDACO, JHS, SPRINT to find out independent prognostic factors for cardiovascular disease mortality, feature engineering and feature transformation were used. Several statistical and machine learning methods including the stepwise procedure, lasso penalty, and random survival forest with bootstrap technique were used to screen important candidates of risk factors. Models building are based on systolic BP replacement into the established Framingham risk score. Directly one to one replacement and indirectly two stage replacement methods are considered with variable diversity. C-statistics, NRI are used for model performance evaluation. Results: In model building procedure, nighttime BP combined with pulse pressure, morning-evening difference of SBP and weighted SBP consistently had a higher C-statistics than had office systolic BP. In validation datasets, equations incorporating mean BP and pulse pressure had best performance in C-index (0.754). In contrast, the C-index (higher is better), of the usual Framingham risk score with office systolic BP are 0.744. Furthermore, the best model had acceptable predicted accuracy based on model calibration statistics (p &gt; 0.05). Conclusions: Our study demonstrated that mean BP at night combined with pulse pressure, morning-evening difference of SBP and weighted SBP, throughout 24 hours had the largest predictive power of 20-year cardiovascular death in population with ABPM recordings. The transportability of the proposed cardiovascular risk function should be evaluated in future studies.

ParMA: Parallelized Bayesian Model Averaging for Generalized Linear Models

This paper describes the gretl function package ParMA, which provides Bayesian model averaging (BMA) in generalized linear models. In order to overcome the lack of analytical specification for many of the models covered, the package features an implementation of the reversible jump Markov chain Monte Carlo technique, following the original idea by Green (1995), as a flexible tool to model several specifications. Particular attention is devoted to computational aspects such as the automatization of the model building procedure and the parallelization of the sampling scheme.

Correlates of COVID-19 Vaccine Acceptance, Hesitancy and Refusal among Employees of a Safety Net California County Health System with an Early and Aggressive Vaccination Program: Results from a Cross-Sectional Survey.

Since health professionals provide frontline care to COVID-19 patients, information on vaccine acceptance among healthcare workers is needed. We developed and implemented an anonymous internet-based cross-sectional survey with direct solicitation among employees of a safety net health system. Items queried demographic and health-related characteristics, experience with and knowledge of COVID-19, and determinants of decisions to vaccinate. COVID-19 vaccine acceptance groups (acceptors, hesitant, refusers) were defined; an adapted version of the WHO vaccine hesitancy scale was included. The survey demonstrated good reliability (Cronbach’s alpha = 0.92 for vaccine hesitancy scale; 0.93 for determinants). General linear and logistic regression methods examined factors which were univariately associated with vaccine hesitancy and vaccine acceptance, respectively. Multivariable models were constructed with stepwise model-building procedures. Race/ethnicity, marital status, job classification, immunocompromised status, flu vaccination and childhood vaccination opinions independently predicted hesitancy scale scores. Gender, education, job classification and BMI independently predicted acceptance, hesitancy, and refusal groups. Among hesitant employees, uncertainty was reflected in reports of motivating factors influencing their indecision. Despite a strong employee-support environment and job protection, respondents reported physical and mental health effects. The appreciation of varied reasons for refusing vaccination should lead to culturally sensitive interventions to increase vaccination rates amongst healthcare workers.

Dying within dyads: Stress, sense of security and support during palliative home care.

ObjectivesTo examine similarities and dissimilarities in patient and family caregiver dyads in their experience of stress, support, and sense of security.Methods144 patients and their family caregivers participated. Patients were admitted to six Swedish specialist palliative home care units and diagnosed with a non-curable disease with an expected short survival. We analysed similarity patterns of answers within dyads (correlations) as well as dissimilarities, expressed as the difference between within-dyad responses. The latter were subjected to a model-building procedure using GLM, with 13 sociodemographic and clinical characteristics as independent variables.ResultsWithin dyads, patients and family caregivers scored similar in their perception of support and sense of security with care. There was also dissimilarity within dyad responses in their perception of stress and support that could be attributed to sociodemographic or clinical characteristics. When patients scored higher levels of stress than family caregivers, the family caregiver was more likely to be male. Also family caregiver attachment style (attachment anxiety), patient age and the relationship of the family caregiver to the patient explained dissimilarities within the dyads.ConclusionsPatients and family caregivers within the dyads often, but not always, had similar scores. We suggest that it is important that the healthcare staff identify situations in which perceptions within the dyads regarding stress and perception of support differ, such that they can recognise patients’ and family caregivers’ unique needs in different situations, to be able to provide adequate support and facilitate dyadic coping.

Statistical Analysis of Landslide Susceptibility, Macerata Province (Central Italy)

Every year, institutions spend a large amount of resources to solve emergencies generated by hydrogeological instability. The identification of areas potentially subject to hydrogeological risks could allow for more effective prevention. Therefore, the main aim of this research was to assess the susceptibility of territories where no instability phenomena have ever been detected. In order to obtain this type of result, statistical assessments of the problem cannot be ignored. In this case, it was chosen to analyse the susceptibility to landslide using a flexible method that is attracting great interest in the international scientific community, namely the Weight of Evidence (WoE). This model-building procedure, for calculating landslide susceptibility, used Geographic Information Systems (GIS) software by means of mathematical operations between rasters and took into account parameters such as geology, acclivity, land use, average annual precipitation and extreme precipitation events. Thus, this innovative research links landslide susceptibility with triggering factors such as extreme precipitation. The resulting map showed a low weight of precipitation in identifying the areas most susceptible to landslides, although all the parameters included contributed to a more accurate estimate, which is necessary to preserve human life, buildings, heritage and any productive activity.

The development and external validation of an overall survival nomogram in medically inoperable centrally located early-stage non-small cell lung carcinoma

Background and purposeCurrent nomograms predicting survival prognosis after stereotactic body radiation therapy (SBRT) in non-small cell lung cancer (NSCLC) are based on peripherally located tumors. However, patients with a central lung tumor tend to be older, the tumor is often larger and fraction-schedules are risk-adapted. Therefore, we developed and externally validated a nomogram to predict overall survival (OS) in patients having centrally located early-stage NSCLC treated with SBRT. Materials and methodsPatients who underwent SBRT for centrally located NSCLC were identified and baseline characteristics were obtained. A nomogram was built to predict 6-month, 1-, 2- and 3-year OS using Cox proportional hazards model. The model building procedure was validated using bootstrap sampling. To determine generalizability, external validation was performed on a cohort of patients with central NSCLC treated with SBRT from another center. Discriminatory ability was measured with the concordance index (C-index) and calibration plots were used to compare Kaplan–Meier-estimated and nomogram-predicted OS. ResultsThe nomogram was built on data of 220 patients and consisted of the following variables: PTV, age, WHO performance status, tumor lobe location and ultracentral location. The C-index of the nomogram (corrected for optimism) was moderate at 0.64 (95% confidence interval (CI) 0.59–0.69). Calibration plots showed favorable predictive accuracy. The external validation showed acceptable validity with a C-index of 0.62 (95% CI 0.61–0.64). DiscussionWe developed and externally validated the first nomogram to estimate the OS-probability in patients with centrally located NSCLC treated with SBRT. This nomogram is based on 5 patient and tumor characteristics and gives an individualized survival prediction.

Exploding operators for Majorana neutrino masses and beyond

Building UV completions of lepton-number-violating effective operators has proved to be a useful way of studying and classifying models of Majorana neutrino mass. In this paper we describe and implement an algorithm that systematises this model-building procedure. We use the algorithm to generate computational representations of all of the tree-level completions of the operators up to and including mass-dimension 11. Almost all of these correspond to models of radiative neutrino mass. Our work includes operators involving derivatives, updated estimates for the bounds on the new-physics scale associated with each operator, an analysis of various features of the models, and a look at some examples. We find that a number of operators do not admit any completions not also generating lower-dimensional operators or larger contributions to the neutrino mass, ruling them out as playing a dominant role in the neutrino-mass generation. Additionally, we show that there are at most five models containing three or fewer exotic multiplets that predict new physics that must lie below 100 TeV. Accompanying this work we also make available a searchable database containing all of our results and the code used to find the completions. We emphasise that our methods extend beyond the study of neutrino-mass models, and may be useful for generating completions of high-dimensional operators in other effective field theories. Example code: ref. [37].

In silico design, building and gas adsorption of nano-porous graphene scaffolds

Graphene-based nano-porous materials (GNM) are potentially useful for all those applications needing a large specific surface area (SSA), typical of the bidimensional graphene, yet realized in the bulk dimensionality. Such applications include for instance gas storage and sorting, catalysis and electrochemical energy storage. While a reasonable control of the structure is achieved in micro-porous materials by using nano-micro particles as templates, the controlled production or even characterization of GNMs with porosity strictly at the nano-scale still raises issues. These are usually produced using dispersion of nano-flakes as precursors resulting in little control on the final structure, which in turn reflects in problems in the structural model building for computer simulations. In this work, we describe a strategy to build models for these materials with predetermined structural properties (SSA, density, porosity), which exploits molecular dynamics simulations, Monte Carlo methods and machine learning algorithms. Our strategy is inspired by the real synthesis process: starting from randomly distributed flakes, we include defects, perforation, structure deformation and edge saturation on the fly, and, after structural refinement, we obtain realistic models, with given structural features. We find relationships between the structural characteristics and size distributions of the starting flake suspension and the final structure, which can give indications for more efficient synthesis routes. We subsequently give a full characterization of the models versus H2 adsorption, from which we extract quantitative relationship between the structural parameters and the gravimetric density. Our results quantitatively clarify the role of surfaces and edges relative amount in determining the H2 adsorption, and suggest strategies to overcome the inherent physical limitations of these materials as adsorbers. We implemented the model building and analysis procedures in software tools, freely available upon request.

Periodic autoregressive models for time series with integrated seasonality

A comprehensive seasonally integrated periodic autoregressive model is suggested which is shown to be flexible enough to include both the stochastic seasonal integrated and random trigonometric polynomial-based models. The demonstration of the equivalence between the two approaches is the objective of two theorems that are stated and proved in some details. A nice advantage of our model building procedure is that it is able to provide the user not only with a detailed model for data description and forecasting purpose but in addition with a hint at the presence of seasonal unit roots. The model which is illustrated in the present paper may be considered parsimonious, i.e. the number of estimated parameters, for a given goodness-of-fit criterion, is taken as low as possible, in two ways. First, by imposing unit roots and seasonal unit roots so that some estimated parameters are replaced by a differencing operator with fixed coefficients, and, second, by adopting a subset periodic autoregressive model, so that some parameters do not need to be estimated as they are constrained to equal zero. The effectiveness of our model is highlighted by an extensive simulation experiment that supports our claim that the model building procedure described here may be of good use as well for checking the existence of seasonal unit roots. Applications to real-world time series data sets are reported, and promising results are obtained that allow us to suggest that the seasonally integrated periodic autoregressive model may be safely used for modelling a wide range of seasonal time series data. In addition, well-known widely used tests, such as HEGY and the Taylor variance ratio test, are shown to provide us with results that generally agree with our findings.

Modeling the Impact of Cardiopulmonary Irradiation on Overall Survival in NRG Oncology Trial RTOG 0617

To quantitatively predict the impact of cardiopulmonary dose on overall survival (OS) after radiotherapy for locally advanced non-small cell lung cancer. We used the NRG Oncology/RTOG 0617 dataset. The model building procedure was preregistered on a public website. Patients were split between a training and a set-aside validation subset (N = 306/131). The 191 candidate variables covered disease, patient, treatment, and dose-volume characteristics from multiple cardiopulmonary substructures (atria, lung, pericardium, and ventricles), including the minimum dose to the hottest x% volume (Dx%[Gy]), mean dose of the hottest x% (MOHx%[Gy]), and minimum, mean (Mean[Gy]), and maximum dose. The model building was based on Cox regression and given 191 candidate variables; a Bonferroni-corrected P value threshold of 0.0003 was used to identify predictors. To reduce overreliance on the most highly correlated variables, stepwise multivariable analysis (MVA) was repeated on 1000 bootstrapped replicates. Multivariate sets selected in ≥10% of replicates were fit to the training subset and then averaged to generate a final model. In the validation subset, discrimination was assessed using Harrell c-index, and calibration was tested using risk group stratification. Four MVA models were identified on bootstrap. The averaged model included atria D45%[Gy], lung Mean[Gy], pericardium MOH55%[Gy], and ventricles MOH5%[Gy]. This model had excellent performance predicting OS in the validation subset (c = 0.89). The risk of death due to cardiopulmonary irradiation was accurately modeled, as demonstrated by predictions on the validation subset, and provides guidance on the delivery of safe thoracic radiotherapy.

Change in the Prevalence and Determinants of Consanguineous Marriages in India between National Family and Health Surveys of 1992–1993 and 2015–2016

This study aimed to determine the changing prevalence of consanguineous marriage in India between two national-level surveys. The primary hypothesis was whether region of residence and religious affiliation continue to play a significant role in determining consanguineous marriage even after controlling other potentially significant confounding variables. Data from the 81,781 and 85,851 ever-married women during the National Family and Health Surveys (NFHS) survey periods 1992-1993 (NFHS-1) and 2015-2016 (NFHS-4), respectively, were used in the analysis. Multinomial and binary logistic regression analyses examined determinants of consanguineous marriage types and of paternal and maternal first-cousin marriages, respectively. In both analyses a systematic model-building procedure was adopted. Altogether, four models were estimated. In the final model (model 4) of both the analyses, all respondent background characteristics (region of residence, religious affiliation, sociodemographic, household wealth) and years of survey were included. Although the overall prevalence of consanguineous marriage in India declined significantly (16%), it was not uniform across respondent background characteristics. The northern region of India (154%) showed a significant increase in consanguineous marriage, whereas eastern (31%), central (2.3%), northeastern (40%), and southern (8%) regions showed a significant decline. Significant declines in consanguineous marriage were found for Hindus (16%) and Muslims (29%); for Muslims of eastern (48%), central (29%), western (31%), and southern (27%) regions; and for Hindus in the western region (37%). Relative risk ratios estimated using multinomial logistic regression models suggest those living in the southern region show 9.55 (p < 0.001), 5.96 (p < 0.001), and 38.16 (p < 0.001) times more likelihood in the prevalence of first-cousin, second-cousin, and uncle-niece marriages, respectively, compared to the northern region after controlling all other confounding variables. Muslims also showed 3.76 (p < 0.001) and 2.91 (p < 0.001) times more likelihood in first-cousin and second-cousin marriages, respectively, compared to Hindus. Adjusted odds ratios (AORs) estimated using binary logistic regression models suggest those living in southern and northeastern regions were 1.25 (p < 0.001) and 1.36 (p < 0.05) times more likely, respectively, to marry a maternal first cousin compared to the northern region. The AOR estimates also show that Muslims were 1.11 (p < 0.01) times more likely to marry a maternal first cousin compared to Hindus. The authors conclude that, despite significant development in the socioeconomic condition of India during the postglobalization era (beginning in 1992-1993), region of residence and religious affiliation continue to play significant role in determining consanguineous marriage.

A model-based motion capture marker location refinement approach using inverse kinematics from dynamic trials.

Marker-based motion capture techniques are commonly used to measure human body kinematics. These techniques require an accurate mapping from physical marker position to model marker position. Traditional methods utilize a manual process to achieve marker positions that result in accurate tracking. In this work, we present an optimization algorithm for model marker placement to minimize marker tracking error during inverse kinematics analysis of dynamic human motion. The algorithm sequentially adjusts model marker locations in 3-D relative to the underlying rigid segment. Inverse kinematics is performed for a dynamic motion capture trial to calculate the tracking error each time a marker position is changed. The increase or decrease of the tracking error determines the search direction and number of increments for each marker coordinate. A final marker placement for the model is reached when the total search interval size for every coordinate falls below a user-defined threshold. Individual marker coordinates can be locked in place to prevent the algorithm from overcorrecting for data artifacts such as soft tissue artifact. This approach was used to refine model marker placements for eight able-bodied subjects performing walking trials at three stride frequencies. Across all subjects and stride frequencies, root mean square (RMS) tracking error decreased by 38.4% and RMS tracking error variance decreased by 53.7% on average. The resulting joint kinematics were in agreement with expected values from the literature. This approach results in realistic kinematics with marker tracking errors well below accepted thresholds while removing variance in the model-building procedure introduced by individual human tendencies.

Effects of air cleaners and school characteristics on classroom concentrations of particulate matter in 34 elementary schools in Korea

Exposure to particulate matter (PM) in school environments has been associated with respiratory illnesses among children. Although using air cleaners was reported to reduce PM exposure and improve residents' health in homes, their effects in classrooms are not well understood. We examined how the use of air cleaners in classrooms and school/classroom characteristics affect the levels of indoor PM. Our environmental study included 102 classrooms from 34 elementary schools located on the mainland peninsula and an island in Korea. Indoor and outdoor PM were monitored simultaneously with portable aerosol spectrometers, and indoor gravimetric PM levels were measured with low volume, size-selective samplers during the class hours. Correlations among PM measurements were computed and final multiple regression models for indoor PM were constructed with a model building procedure. Correlation between indoor and outdoor PM2.5 (PM < 2.5 μm in aerodynamic diameter) was higher (r = 0.78, p < 0.01) than that of PM10 (PM < 10 μm) (r = 0.49, p < 0.01). School location, classroom occupant density, and ambient PM levels significantly (p-values<0.05) affected classroom PM concentrations. The adjusted PM levels in classrooms using air cleaners were significantly (p-values<0.01) lower by approximately 35% than in classrooms not using them. However, air cleaners appeared to remove PM2.5 more effectively than PM10, perhaps because coarse particles settle more rapidly than fine particles on surfaces, or their resuspension and generation rate by occupants exceeds the removal rate by air cleaners. Our study suggests that routine cleaning to remove surface dust along with the use of air cleaners might be required to effectively reduce occupants’ exposure in classrooms.