[Evaluation of Different ISORROPIA-Ⅱ Modes and the Influencing Factors of Aerosol pH Based on Tianjin Online Data]

Aerosol acidity is closely related to particle properties and the explosive growth of secondary particles. Aerosol pH is difficult to measure directly but can be estimated indirectly by thermodynamic equilibrium modeling. ISORROPIA-Ⅱ is one of the most commonly used thermodynamic models and includes different modes (forward and reverse) and aerosol states (stable and metastable). Studies have shown that the calculated pH results vary with the selected mode and phase state. In addition to the selection of modes and phases, there are also other factors that influence the modeling results. In order to explore the appropriate mode and phase selection of ISORROPIA-Ⅱ as well as the factors influencing the model results under the air pollution characteristics of typical Chinese cities, the simulation results of different modes and aerosol states were analyzed by using online hourly data for Tianjin. The results showed that the pH calculations using the forward mode and metastable state were satisfactory at a higher RH. With increased temperature, the pH, aerosol water content, and concentration proportion in the aerosol phase of semi-volatile components all decreased. RH affected aerosol pH by influencing the aerosol water content and concentration of semi-volatile components. An increased cation concentration led to an increased pH and NH₃ concentration but a decreased HNO₃ concentration, whereas an increased anion concentration had the opposite effect. Ca²⁺, SO₄^2-, NO₃^-, and NH₄⁺ had a great influence on pH. Compared with SO₄^2-, NO₃^- had less effect on pH. Sensitive areas exist in the influence of NH₄⁺ on pH, and a high NH₄⁺ concentration did not cause a continuous pH increase. This study can improve the understanding of aerosol pH simulation using ISORROPIA-Ⅱ, and provides reference for research on the pH-related secondary generation mechanism, semi-volatile component gas-particle distribution, and pollution control measures.

Improving knowledge about permeability in membrane bioreactors through sensitivity analysis using artificial neural networks

Membrane bioreactor (MBR) has been widely employed for industrial effluent treatment, as its higher efficiency in removing pollutants makes effluent reuse more feasible. However, membrane fouling remains as a limiting factor for its greater diffusion. This work performed a sensitivity analysis study to investigate the effects of analytical and operating variables on membrane permeability. The case study is a MBR treating oil refinery effluents. After the identification and validation of a predictive neural model for permeability, sensitivity analysis methods based on both connection weights and variable disturbances were used to quantify and rank the variables influence. A comprehensive analysis showed that Suspended solids and Days between cleanings exerted greater effects on permeability, whereas sludge filterability and sludge temperature were less significant. In sequence, a specific analysis revealed distinct dynamics in MBR operation given different solids concentrations. For instance, from higher solids concentrations, among all the evaluated parameters, only COD presented low significance to the permeability. This evidence suggests that permeability is more susceptible to variations when operating with higher concentrations of Suspended solids. The global result of this study contributes to more efficient MBR operations since distinct relations with permeability imply different effects on membrane fouling.

Electrochemiluminescence Mechanisms Investigated with Smartphone-Based Sensor Data Modeling, Parameter Estimation and Sensitivity Analysis

The present study introduces a unified framework combining a mechanistic model with a genetic algorithm (GA) for the parameter estimation of electrochemiluminescence (ECL) kinetics of the Ru(bpy)32+/TPrA system occurring in a smartphone-based sensor. The framework allows a straightforward solution for simultaneous estimation of multiple parameters which can be, otherwise, time-consuming and lead to non-convergence. Model parameters are estimated by achieving a high correlation between the model prediction and the measured ECL intensity from the ECL sensor. The developed model is used to perform a sensitivity analysis (SA), which provides quantitative effects of the model parameters on the concentrations of chemical species involved in the system. The results demonstrate that the GA-based parameter estimation and the SA approaches are effective in analyzing the kinetics of the ECL mechanism. Therefore, these approaches can be incorporated as analysis tools in the ECL kinetics study with practical application in the calibration of mechanistic models for any required sensing condition.

Adolescent Haze-Related Knowledge Level Study: A Cross-Sectional Survey With Sensitivity Analysis

Objective: This study aimed to investigate the level of haze-related knowledge adolescents have and to explore relevant influencing factors. Methods: From June 2015 to January 2016, researchers randomly selected 2 districts from the 20 districts of Baoding, China. Then, researchers randomly selected two middle schools from two districts. By conducting a stratified cluster sampling and considering one class as a unit, researchers randomly selected, from the other middle school, five first-grade classes, five second-grade classes, five third-grade classes from the one middle school, and three first-grade classes, two second-grade classes, and two third-grade classes. Finally, 1,100 adolescents were investigated by using the demographic questionnaire and the Adolescent Haze-Related Knowledge Awareness Assessment Scale (AHRKAAS). Multiple linear regressions were conducted to explore factors affecting the adolescent haze-related knowledge. Sensitivity analysis was used to confirm associations between influencing factors and AHRKAAS scores. Results: The AHRKAAS score rate was 69.9%. The dimension of human factors of haze formation was the highest (score rate = 85.6%). The dimension of haze harms on the human body was the lowest (score rate = 57.1%). Compared with the group (monthly expenses <300 yuan), the group (monthly expenses ≥ 600 yuan) had a higher AHRKAAS score (β = 4.882, 95% CI: 0.979, 8.784). Compared with the group (Do not live with parents), the group (Live with parents) had a higher AHRKAAS score (β = 14.675, 95% CI: 9.494, 19.855). Compared with the group (Never undergo a physical examination), the group (Once a year) (β = 7.444, 95% CI: 2.922, 11.966) and the group (A few times a year) (β = 7.643, 95% CI: 2.367, 12.919) had a higher AHRKAAS score. Compared with the group (Know nothing), the group (Know most) (β = 9.623, 95% CI: 2.929, 16.316) and the group (Know very well) (β = 15.367, 95% CI: 7.220, 23.515) had a higher AHRKAAS score. These associations were still reliable and consistent in different sensitivity analysis models. Conclusion: The level of adolescent haze-related knowledge is low and is affected by monthly expenses, living condition, physical examination frequency, and knowledge of respiratory system diseases. Government bodies, schools, and research institutions should strengthen cooperation of health publicity and health education to improve adolescent haze-related knowledge.

Monte Carlo simulation-based probabilistic health risk assessment of metals in groundwater via ingestion pathway in the mining areas of Singhbhum copper belt, India

Probabilistic health risk assessment was conducted for metal exposure through groundwater in mining areas of Singhbhum Copper Belt, India. The concentrations of metals showed notable spatial variation exceeding drinking water standards at some of the locations. Hazard Quotient revealed that chronic risks to the local population were largely contributed by Mn, Co and As. The 95^th percentiles of Hazard Index (HI) calculated using Monte Carlo simulations showed that the HI for male, female and child populations was 2.87, 2.54 and 4.57 for pre-monsoon, 2.16, 1.88 and 3.49 for monsoon and 2.28, 2.02 and 3.75 for post-monsoon seasons, respectively. The Hazard Indices indicated that amongst the populations, risk was greater for child population and considering the seasons the risk was higher during the pre-monsoon season. The sensitivity analysis suggested that concentration of metals in groundwater and exposure duration were 2 most influential input variables that contributed to the total risk.

Uncertainty in Large-Signal Measurements Under Variable Load Conditions

We investigate the uncertainty of large-signal measurements of a microwave transistor due to variation in the load conditions at the fundamental frequency. In particular, we evaluate uncertainties in the complex frequency-domain traveling voltage waves. In our analysis, uncertainty sources typical for large-signal measurements are considered. Then, we discuss how the resultant uncertainty in the waves is dependent on a varying load reflection coefficient. For this investigation, we consider the total uncertainty of the waves and their magnitude and phase. We also show that these errors unavoidably affect the uncertainty of performance quantities, such as output power.

First expert elicitation of knowledge on drivers of emergence of the COVID-19 in pets

Infection with the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) induces the coronavirus infectious disease 19 (COVID‐19). Its pandemic form in human population and its probable animal origin, along with recent case reports in pets, make drivers of emergence crucial in domestic carnivore pets, especially cats, dogs and ferrets. Few data are available in these species; we first listed forty‐six possible drivers of emergence of COVID‐19 in pets, regrouped in eight domains (i.e. pathogen/disease characteristics, spatial‐temporal distance of outbreaks, ability to monitor, disease treatment and control, characteristics of pets, changes in climate conditions, wildlife interface, human activity, and economic and trade activities). Secondly, we developed a scoring system per driver, then elicited scientific experts (N = 33) to: (a) allocate a score to each driver, (b) weight the drivers scores within each domain and (c) weight the different domains between them. Thirdly, an overall weighted score per driver was calculated; drivers were ranked in decreasing order. Fourthly, a regression tree analysis was used to group drivers with comparable likelihood to play a role in the emergence of COVID‐19 in pets. Finally, the robustness of the expert elicitation was verified. Five drivers were ranked with the highest probability to play a key role in the emergence of COVID‐19 in pets: availability and quality of diagnostic tools, human density close to pets, ability of preventive/control measures to avoid the disease introduction or spread in a country (except treatment, vaccination and reservoir(s) control), current species specificity of the disease‐causing agent and current knowledge on the pathogen. As scientific knowledge on the topic is scarce and still uncertain, expert elicitation of knowledge, in addition with clustering and sensitivity analyses, is of prime importance to prioritize future studies, starting from the top five drivers. The present methodology is applicable to other emerging pet diseases.

Cost-utility analysis of Macitentan Vs. Bosentan in pulmonary atrial hypertension

Objective:

Endothelin (ET) receptor antagonists (ERAs) have considerable improvements in pulmonary arterial hypertension (PAH) patients’ symptoms. Macitentan, a novel ERA, has more significant positive effects like reduction of morbidity and mortality in PAH patients by 45% and decreases PAH hospitalization. Besides, macitentan was able to improve both the physical and mental aspects of patients’ lives. This study aimed to evaluate an incremental cost-utility analysis of macitentan compared with bosentan in PAH patients in the Iranian health care system.

Methods:

We developed a hybrid model consisting of a decision tree in which PAH patients would take and continue either macitentan or bosentan with different probabilities. Subsequently, each patient would enter one of the 4 Markov's, each consisting of 5 states, PAH fraction I, PAH fraction II, PAH fraction III, PAH fraction IV, and death. The cycles and time horizon were considered 3 months and lifetime, respectively. We assessed the impact of each medicine on patients’ quality-adjusted life-years (QALYs) and costs, consequently calculated the ICER (Incremental Cost-Effectiveness Ratio). The costs were measured in the dollar (1 dollar is equal to 42000 rials) with the perspective of the payer. The discount rates were assumed 3% for utility and 5% for costs. In addition, a sensitivity analysis was conducted.

Results:

The costs are about 14163 dollars for bosentan and 13876 dollars for macitentan for each patient in a lifetime. The QALY produced per patient by macitentan was 0.81 more than that of bosentan. The calculated ICER was -357.47 which means that for each incremental QALY, the payer is charged less.

Conclusion:

Macitentan is preferable to and dominant over bosentan in both effectiveness and expenditure. Thus, the therapeutic regimen containing macitentan is introduced as a favorable treatment strategy.

Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents

The chemical industry has made great contributions to the national economy, but frequent chemical plant explosion accidents (CPEAs) have also caused heavy property losses and casualties, as the CPEA is the result of interaction of many related risk factors, leading to uncertainty in the evolution of the accident. To systematically excavate and analyze the underlying causes of accidents, this paper first integrates emergency elements in the frame of orbit intersection theory and proposes 14 nodes to represent the evolution path of the accident. Then, combined with historical data and expert experience, a Bayesian network (BN) model of CPEAs was established. Through scenario analysis and sensitivity analysis, the interaction between factors and the impact of the factors on accident consequences was evaluated. It is found that the direct factors have the most obvious influence on the accident consequences, and the unsafe conditions contribute more than the unsafe behaviors. Furthermore, considering the factor chain, the management factors, especially safety education and training, are the key link of the accident that affects unsafe behaviors and unsafe conditions. Moreover, effective government emergency response has played a more prominent role in controlling environmental pollution. In addition, the complex network relationship between elements is presented in a sensitivity index matrix, and we extracted three important risk transmission paths from it. The research provides support for enterprises to formulate comprehensive safety production management strategies and control key factors in the risk transmission path to reduce CPEA risks.

Reference-based pattern-mixture models for analysis of longitudinal binary data

Pattern-mixture model (PMM)-based controlled imputations have become a popular tool to assess the sensitivity of primary analysis inference to different post-dropout assumptions or to estimate treatment effectiveness. The methodology is well established for continuous responses but less well established for binary responses. In this study, we formulate the copy-reference and jump-to-reference PMMs for longitudinal binary data using a multivariate probit model with latent variables. We discuss the maximum likelihood, Bayesian, and multiple imputation methods for estimating the treatment effect under the specified PMM. Simulation studies are conducted to evaluate the performance of these methods. These methods are also illustrated using data from a bipolar mania study.

Vibration characteristic analysis of single-cylinder two-stroke engine and mounting system optimization design

Compared with four-stroke engines, single-cylinder two-stroke engines have the characteristics of small inertia, high rotational speed, and wide excitation frequency range. However, the structural vibration and noise generated by the two-stroke engine are very violent. Hence, it is necessary to reduce the vibration and noise of the single-cylinder two-stroke engine. Based on the design theory of the engine mounting system, the excitation frequency, direction, and magnitude of a single-cylinder two-stroke engine are analyzed. The rubber isolator is selected as the new mount element, and the dynamic model of the engine powertrain mounting system is established based on ADAMS software. Based on the sensitivity analysis of the design variables of the mounting system, the natural frequency of the mounting system is used as an objective, and the three-directional stiffness of the mounting system is taken as design variables for the optimization problem. The optimization model is solved by the sequential quadratic programming method. The results show that the maximum frequency of the mounting system after optimization is less than 1/2 of the excitation frequency, and the isolation effect is achieved. The dynamic model and the optimization method presented in this article would provide a useful tool for the design and optimization of mounting system for the single-cylinder two-stroke engine to reduce vibration from the engine to the engine support.

Sensitivity Analysis and Extensions of Testing the Causal Direction of Dependence: A Rejoinder to Thoemmes (2019)

A commentary by Thoemmes on Wiedermann and Sebastian's introductory article on Direction Dependence Analysis (DDA) is responded to in the interest of elaborating and extending direction dependence principles to evaluate causal effect directionality. Considering Thoemmes' observation that some DDA principles are already well-established in machine learning, we argue that several other connections between DDA and research lines in theoretical statistics, econometrics, and quantitative psychology exist, suggesting that DDA is best conceptualized as a framework that summarizes and extends existing knowledge on properties of linear models under non-normality. Further, Thoemmes articulates concerns about assumptions of error distributions used in our main article and presents an artificial data example in which some DDA tests have suboptimal statistical power. We present extensions of DDA to entirely relax distributional assumptions about errors and describe two sensitivity analysis approaches to critically evaluate DDA results. Both sensitivity approaches are illustrated using Thoemmes' artificial data example. Incorporating DDA sensitivity results yields correct causal conclusions. Thus, overall, we stay with our initial conclusion that the use of higher moments in causal inference constitutes an exciting open research area.

Meta-analysis of several epidemic characteristics of COVID-19

As the COVID-19 pandemic has strongly disrupted people's daily work and life, a great amount of scientific research has been conducted to understand the key characteristics of this new epidemic. In this manuscript, we focus on four crucial epidemic metrics with regard to the COVID-19, namely the basic reproduction number, the incubation period, the serial interval and the epidemic doubling time. We collect relevant studies based on the COVID-19 data in China and conduct a meta-analysis to obtain pooled estimates on the four metrics. From the summary results, we conclude that the COVID-19 has stronger transmissibility than SARS, implying that stringent public health strategies are necessary.

Uncertainty quantification and sensitivity analysis of left ventricular function during the full cardiac cycle

Patient-specific computer simulations can be a powerful tool in clinical applications, helping in diagnostics and the development of new treatments. However, its practical use depends on the reliability of the models. The construction of cardiac simulations involves several steps with inherent uncertainties, including model parameters, the generation of personalized geometry and fibre orientation assignment, which are semi-manual processes subject to errors. Thus, it is important to quantify how these uncertainties impact model predictions. The present work performs uncertainty quantification and sensitivity analyses to assess the variability in important quantities of interest (QoI). Clinical quantities are analysed in terms of overall variability and to identify which parameters are the major contributors. The analyses are performed for simulations of the left ventricle function during the entire cardiac cycle. Uncertainties are incorporated in several model parameters, including regional wall thickness, fibre orientation, passive material parameters, active stress and the circulatory model. The results show that the QoI are very sensitive to active stress, wall thickness and fibre direction, where ejection fraction and ventricular torsion are the most impacted outputs. Thus, to improve the precision of models of cardiac mechanics, new methods should be considered to decrease uncertainties associated with geometrical reconstruction, estimation of active stress and of fibre orientation. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

[Fate Simulation of 2,4,4'-Trichlorobiphenyl in the Bohai Rim Using the Multimedia Model]

To study the multimedia transfer and fate of polychlorinated biphenyls (PCBs) in the Bohai Rim, we used the BETR-Urban-Rural model to simulate and calculate the concentration distribution, fate distribution, and transfer processes of 2,4,4'-trichlorobiphenyl (PCB28) in nine environmental compartments under a steady-state assumption. The input parameters and output results of the model underwent sensitivity analysis and uncertainty analysis, respectively. The results showed that the simulated concentrations of PCB28 in fresh water, rural soil, urban soil, and sediment could fit the measured concentrations well, thus verifying the reliability of the model. The concentration of PCB28 in urban soil was the highest, and the average concentration was 5.26×10^-6 mol·m^-3. In contrast, the concentration of PCB28 in rural air was the lowest, and the average concentration was 5.79×10^-14 mol·m^-3. When the environmental system reached equilibrium, the largest sink of PCB28 in the Bohai Rim was soil, accounting for approximately 96.45% of the total amount remaining in the system. The mutual transfer processes between air and other environmental compartments were the dominant pathways for PCB28 inter-media transport in the Bohai Rim. Most PCB28 entering the Bohai Sea was transferred by airflow, and the fluxes from rural air to coastal water accounted for approximately 97.22% of the total fluxes of PCB28 entering the sea. According to the result of sensitivity analysis, the emission rates, grid dimensions, and transport velocity were the key parameters affecting the model output. Uncertainty analysis showed that the distributions of PCB28 concentrations in rural air and urban air fitted well with lognormal distributions, and the coefficients of variances (CVs) were 0.44 and 0.41, respectively.

Meta-analysis of several epidemic characteristics of COVID-19

As the COVID-19 pandemic has strongly disrupted people's daily work and life, a great amount of scientific research has been conducted to understand the key characteristics of this new epidemic. In this manuscript, we focus on four crucial epidemic metrics with regard to the COVID-19, namely the basic reproduction number, the incubation period, the serial interval and the epidemic doubling time. We collect relevant studies based on the COVID-19 data in China and conduct a meta-analysis to obtain pooled estimates on the four metrics. From the summary results, we conclude that the COVID-19 has stronger transmissibility than SARS, implying that stringent public health strategies are necessary.

Simulation of Shape Memory Alloy (SMA)-Bias Spring Actuation for Self-Shaping Architecture: Investigation of Parametric Sensitivity

Parametric complexity of the thermomechanical shape memory alloy (SMA) model is one of the major barriers to advanced application of the SMA actuation in adaptive architecture. This article seeks to provide architectural practitioners with decision-making information about SMA actuator design parameters. Simulation-based global sensitivity analysis of an SMA-bias spring actuation model reveals that the SMA spring index (a spring’s outer diameter divided by its wire diameter) and stiffness of the bias spring are significant factors in both displacement and force exertion. Among all parameters, maximum output stroke and force largely depend on the temperature range at which the SMA spring operates. These findings also indicate a trade-off between the spring diameter and wire thickness, demonstrating that the output stroke and force tend to counter one another. Appropriate preloading and choice of an optimal spring index should be considered for desirable SMA motion.

Bayesian Regression Model for a Cost-Utility and Cost-Effectiveness Analysis Comparing Punch Grafting Versus Usual Care for the Treatment of Chronic Wounds

Punch grafting is a traditional technique used to promote epithelialization of hard-to-heal wounds. The main purpose of this observational study was to conduct a cost-utility analysis (CUA) and a cost-effectiveness analysis (CEA) comparing punch grafting (n = 46) with usual care (n = 34) for the treatment of chronic wounds in an outpatient specialized wound clinic from a public healthcare system perspective (Spanish National Health system) with a three-month time horizon. CUA outcome was quality-adjusted life years (QALYs) calculated from EuroQoL-5D, whereas CEA outcome was wound-free period. One-way sensitivity analyses, extreme scenario analysis, and re-analysis by subgroups were conducted to fight against uncertainty. Bayesian regression models were built to explore whether differences between groups in costs, wound-free period, and QALYs could be explained by other variables different to treatment. As main results, punch grafting was associated with a reduction of 37% in costs compared to usual care, whereas mean incremental utility (0.02 ± 0.03 QALYs) and mean incremental effectiveness (7.18 ± 5.30 days free of wound) were favorable to punch grafting. All sensitivity analyses proved the robustness of our models. To conclude, punch grafting is the dominant alternative over usual care because it is cheaper and its utility and effectiveness are greater.

Evaluating the research domain and achievement for a productive researcher who published 114 sole-author articles: A bibliometric analysis

BACKGROUND

Team science research includes authors from various fields collaborating to publish their work on certain topics. Despite the numerous papers that discussed the ordering of author names and the contributions of authors to an article, no paper evaluatedIn addition, few researchers publish academic articles without co-author collaboration. Whether the bibliometric indexes (eg, h-/x-index) of sole-author researchers are higher than those of other types of multiple authors is required for comparison. We aimed to evaluate a productive author who published 114 sole-author articles with exceptional RA and RD in academics.

METHODS

By searching the PubMed database (Pubmed.com), we used the keyword of (Taiwan[affiliation]) from 2016 to 2017 and downloaded 29,356 articles. One physician (Dr. Tseng from the field of Internal Medicine) who published 12 articles as a single author was selected. His articles and citations were searched in PubMed. A comparison of various types of author ordering placements was conducted using sensitivity analysis to inspect whether this sole author earns the highest metrics in RA. Social network analysis (SNA), Gini coefficient (GC), pyramid plot, and the Kano diagram were applied to gather the following data for visualization: RESULTS:: We observed that CONCLUSIONS:: The metrics on RA are high for the sole author studied. The author's RD can be denoted by the MeSH terms and measured by the GC. The author-weighted scheme is required for quantifying author credits in an article to evaluate the author's RA. Social network analysis incorporating the Kano diagrams provided insights into the relationships between actors (eg, coauthors, MeSH terms, or journals). The methods used in this study can be replicated to evaluate other productive studies on RA and RD in the future.

A Nested Ensemble Approach with ANNs to Investigate the Effect of Socioeconomic Attributes on Active Commuting of University Students

Analysis of travel mode choice is vital in policymaking and transportation planning to comprehend and forecast travel demands. Universities resemble major trip attraction hubs, with many students and faculty members living on campus or nearby. This study aims to investigate the effects of socioeconomic characteristics on the travel mode choice of university students. A nested ensemble approach with artificial neural networks (ANNs) was used to model the mode choice behavior. It was found that students generally prefer motorized modes (bus and car). A more detailed analysis revealed that teenage students (aged 17-19 years) had an approximately equal probability of selecting motorized and non-motorized modes. Graduate students revealed a higher tendency to select motorized modes compared with other students. The findings of this study demonstrate the need to promote non-motorized modes of transport among students, which is possible by providing favorable infrastructure for these modes.

Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters

Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descriptors of PN concentration. In this case, we resort to artificial neural networks in the forms of a feed-forward neural network (FFNN) and a time-delay neural network (TDNN) as modeling tools, and then, we attempt to find the best descriptors using all these combinations as model inputs. The best modeling tools are FFNN for daily averaged data (with R 2 = 0.77 ) and TDNN for hourly averaged data (with R 2 = 0.66 ) where the best combinations of meteorological parameters are found to be temperature, relative humidity, pressure, and wind speed. As the models follow the patterns of diurnal cycles well, the results are considered to be satisfactory. When PN measurements are not directly available or there are massive missing PN concentration data, PN models can be used to estimate PN concentration using available measured meteorological parameters.

Analysis of the Stochastic Population Model with Random Parameters

The population models allow for a better understanding of the dynamical interactions with the environment and hence can provide a way for understanding the population changes. They are helpful in studying the biological invasions, environmental conservation and many other applications. These models become more complicated when accounting for the stochastic and/or random variations due to different sources. In the current work, a spectral technique is suggested to analyze the stochastic population model with random parameters. The model contains mixed sources of uncertainties, noise and uncertain parameters. The suggested algorithm uses the spectral decompositions for both types of randomness. The spectral techniques have the advantages of high rates of convergence. A deterministic system is derived using the statistical properties of the random bases. The classical analytical and/or numerical techniques can be used to analyze the deterministic system and obtain the solution statistics. The technique presented in the current work is applicable to many complex systems with both stochastic and random parameters. It has the advantage of separating the contributions due to different sources of uncertainty. Hence, the sensitivity index of any uncertain parameter can be evaluated. This is a clear advantage compared with other techniques used in the literature.

Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide

Missing data due to loss to follow-up or intercurrent events are unintended, but unfortunately inevitable in clinical trials. Since the true values of missing data are never known, it is necessary to assess the impact of untestable and unavoidable assumptions about any unobserved data in sensitivity analysis. This tutorial provides an overview of controlled multiple imputation (MI) techniques and a practical guide to their use for sensitivity analysis of trials with missing continuous outcome data. These include δ- and reference-based MI procedures. In δ-based imputation, an offset term, δ, is typically added to the expected value of the missing data to assess the impact of unobserved participants having a worse or better response than those observed. Reference-based imputation draws imputed values with some reference to observed data in other groups of the trial, typically in other treatment arms. We illustrate the accessibility of these methods using data from a pediatric eczema trial and a chronic headache trial and provide Stata code to facilitate adoption. We discuss issues surrounding the choice of δ in δ-based sensitivity analysis. We also review the debate on variance estimation within reference-based analysis and justify the use of Rubin's variance estimator in this setting, since as we further elaborate on within, it provides information anchored inference.

Handling missing data in modelling quality of clinician-prescribed routine care: Sensitivity analysis of departure from missing at random assumption

Missing information is a major drawback in analyzing data collected in many routine health care settings. Multiple imputation assuming a missing at random mechanism is a popular method to handle missing data. The missing at random assumption cannot be confirmed from the observed data alone, hence the need for sensitivity analysis to assess robustness of inference. However, sensitivity analysis is rarely conducted and reported in practice. We analyzed routine paediatric data collected during a cluster randomized trial conducted in Kenyan hospitals. We imputed missing patient and clinician-level variables assuming the missing at random mechanism. We also imputed missing clinician-level variables assuming a missing not at random mechanism. We incorporated opinions from 15 clinical experts in the form of prior distributions and shift parameters in the delta adjustment method. An interaction between trial intervention arm and follow-up time, hospital, clinician and patient-level factors were included in a proportional odds random-effects analysis model. We performed these analyses using R functions derived from the jomo package. Parameter estimates from multiple imputation under the missing at random mechanism were similar to multiple imputation estimates assuming the missing not at random mechanism. Our inferences were insensitive to departures from the missing at random assumption using either the prior distributions or shift parameters sensitivity analysis approach.

Exhaustion of Skeletal Muscle Fibers Within Seconds: Incorporating Phosphate Kinetics Into a Hill-Type Model

Initiated by neural impulses and subsequent calcium release, skeletal muscle fibers contract (actively generate force) as a result of repetitive power strokes of acto-myosin cross-bridges. The energy required for performing these cross-bridge cycles is provided by the hydrolysis of adenosine triphosphate (ATP). The reaction products, adenosine diphosphate (ADP) and inorganic phosphate (P i ), are then used-among other reactants, such as creatine phosphate-to refuel the ATP energy storage. However, similar to yeasts that perish at the hands of their own waste, the hydrolysis reaction products diminish the chemical potential of ATP and thus inhibit the muscle's force generation as their concentration rises. We suggest to use the term "exhaustion" for force reduction (fatigue) that is caused by combined P i and ADP accumulation along with a possible reduction in ATP concentration. On the basis of bio-chemical kinetics, we present a model of muscle fiber exhaustion based on hydrolytic ATP-ADP-P i dynamics, which are assumed to be length- and calcium activity-dependent. Written in terms of differential-algebraic equations, the new sub-model allows to enhance existing Hill-type excitation-contraction models in a straightforward way. Measured time courses of force decay during isometric contractions of rabbit M. gastrocnemius and M. plantaris were employed for model verification, with the finding that our suggested model enhancement proved eminently promising. We discuss implications of our model approach for enhancing muscle models in general, as well as a few aspects regarding the significance of phosphate kinetics as one contributor to muscle fatigue.