Standard Parameter Estimation Research Articles

Resiliency of Apache Trout habitats in a warmer and drier climate future

AbstractObjectiveThe Southwest has the hottest and driest climate in the United States, and projections show that it will only get hotter and drier into the 2100s. The Apache Trout Oncorhynchus apache is native to the Southwest and is currently listed as threatened under the U.S. Endangered Species Act. Our goals were to understand how climate factors influence the distribution of juvenile Apache Trout (<125 mm TL) and how climate change will influence the suitability of Apache Trout habitat into the 2080s.MethodsWe used a species distribution model to evaluate how climatic and other factors influence the distribution of juvenile Apache Trout. We used predictions from the model to evaluate how climate change might impact the suitability of streams designated for recovery of the species into the 2080s.ResultJuvenile Apache Trout occurrence was predicted well by mean July stream temperature (°C), mean annual precipitation (dm), stream slope (%), and the presence of nonnative trout (area under the receiver operating characteristic curve = 0.85). Standardized parameter estimates showed that Rainbow Trout O. mykiss presence and annual precipitation influenced occupancy the most. Model predictions for the 2080s showed suitable habitat (occurrence probability ≥ 0.25) to increase for 11 (of 45) Apache Trout streams in the increased temperature (+3°C) only scenario (scenario 1), as headwater reaches that are currently too cold warmed to become more suitable. When we also included projected declines in annual precipitation (−5%) for the 2080s (scenario 2), the amount of suitable habitat decreased for eight Apache Trout streams and remained unchanged in all other streams.ConclusionMost Apache Trout populations are isolated upstream of barriers to nonnative trout in stream reaches that are currently thermally suitable with respect to mean July temperatures and would remain suitable into the 2080s. Cold headwater reaches are projected to warm, becoming more suitable in the 2080s. Thus, intentional isolation and the resultant truncated downstream distributions of Apache Trout populations in headwater streams explain the nominal effect of projected temperature increases due to climate change on this cold‐adapted salmonid. Standardized model parameters suggest that future declines in precipitation, manifested through reduced snowpack and its influence on streamflows, will play a larger role than temperature in the suitability—and, thus, resiliency—of Apache Trout habitats at least into the 2080s.

Using the (Iterative) Ensemble Kalman Smoother to Estimate the Time Correlation in Model Error

Numerical weather prediction systems contain model errors related to missing and simplified physical processes, and limited model resolution. While it has been widely recognized that these model errors need to be included in the data assimilation formulation, providing prior estimates of their spatio-temporal characteristics is a hard problem. We follow a systematic path to estimate parameters in the model error formulation, specifically related to time-correlated model errors. This problem is more difficult than the standard parameter estimation problem because the model error parameters are only visible through the random model error realisations. By concentrating on linear and nonlinear low-dimensional systems, we are able to highlight the many aspects of this problem, using state augmentation in an ensemble Kalman smoother (EnKS) and its iterative variant (IEnKS). It is not possible to estimate the model error parameters in one assimilation window because enough information has to be gathered to see the parameters through the random errors, even when every time step is observed. If only one parameter is estimated in a linear one-dimensional system the EnKS works well, but when we try to estimate two parameters the method fails. An IEnKS is able to find the correct parameter values for the linear system. For the highly nonlinear logistic map the IEnKS can get stuck in local minima, but with careful tuning of the step length in the iterations and careful transformation of the solution space the correct parameter values can be found. The main conclusion is that estimating model error parameters –even in low-dimensional systems– is a difficult problem, but via careful reformulation of the problem practical solutions can be found.

Does the Sleman Chili Auction Market Affect the Local, Regional and National Market Prices in Indonesia?

Promoting the horticultural auction market as an alternative outlet is becoming more popular. The horticultural auction market has some benefits, such as reducing price volatility and controlling inflation. This research used a case study in Sleman Regency, which established a horticultural auction market to manage volatile chili prices. The daily price data with ECM-GARCH analysis was used to investigate market integration between the Sleman chili auction market and other markets. The result showed that the Sleman auction market had a beneficial impact on the price decline in the local markets. This impact indicated that the auction market controls inflation at the regional level. However, only 5% to 13% of daily variations of the long-term equilibrium of the auction market to other markets were adjusted. There was information efficiency of the chili prices in Indonesia based on the GARCH model. This study proves that auction markets promote control of macro inflation. This study was the first to manage horticultural product price risk from a chili auction market perspective. Moreover, the model applied in this study expands the ECM-GARCH model to improve the validity of an ECM's significance tests and the efficiency of standard ECM parameter estimation.

Quijote-PNG: Quasi-maximum Likelihood Estimation of Primordial Non-Gaussianity in the Nonlinear Dark Matter Density Field

Future large-scale structure surveys are expected to improve current bounds on primordial non-Gaussianity (PNG), with a significant impact on our understanding of early universe physics. The level of such improvements will however strongly depend on the extent to which late-time nonlinearities erase the PNG signal on small scales. In this work, we show how much primordial information remains in the bispectrum of the nonlinear dark matter density field by implementing a new, simulation-based methodology for joint estimation of PNG amplitudes (f NL) and standard Lambda cold dark matter parameters. The estimator is based on optimally compressed statistics, which, for a given input density field, combine power spectrum and modal bispectrum measurements, and numerically evaluate their covariance and their response to changes in cosmological parameters. In this first analysis, we focus on the matter density field, and we train and validate the estimator using a large suite of N-body simulations (Quijote-png), including different types of PNG (local, equilateral, orthogonal). We explicitly test the estimator’s unbiasedness, optimality, and stability with respect to changes in the total number of input realizations. While the dark matter power spectrum itself contains negligible PNG information, as expected, including it as an ancillary statistic increases the PNG information content extracted from the bispectrum by a factor of order 2. As a result, we prove the capability of our approach to optimally extract PNG information on nonlinear scales beyond the perturbative regime, up to . At the same time, we discuss the significant information on cosmological parameters contained on these scales.

How foreign cultural identity affects franchise business in overseas markets

This study aimed to explore which factors influence franchise restaurant consumers’ loyalty by developing a theoretical framework consisting of cultural identity, cultural marketing, food and beverages quality, employee service quality, flow experience, satisfaction, and loyalty. Differences between non-Korean style and Korean style franchise were also considered. Standardized parameter estimates of the PLS was used for assessment of the conceptual framework and test of research hypotheses. Data of 496 Chinese consumers who had visited franchised restaurants were analyzed using multiple group analysis with SmartPLS 3. The findings suggest that foreign cultural identity positively affects perceived cultural marketing, which in turn affects food and beverages quality, service quality, and flow experience. Detailed findings and implications can be found in the main content.

High prevalence of rheumatoid arthritis and its risk factors among Tibetan highlanders living in Tsarang, Mustang district of Nepal

BackgroundIn Tsarang (at 3560 m), which is located in Mustang, 62.7% of the residents answered that they had a subjective medical history of arthritis, and 41.1% of the residents answered that their families had a subjective medical history of arthritis on a survey conducted in 2017. The expression of hypoxia-inducible factor (HIF) and its effects are deeply involved in hypoxic adaptation in Tibetan highlanders. At the same time, HIF is also related to the onset of rheumatoid arthritis. Therefore, the adaptive mechanism acquired by Tibetan highlanders may promote the development of rheumatoid arthritis. The prevalence of rheumatoid arthritis is estimated to be approximately 0.5–1.0% worldwide. The objective of this study was to estimate the prevalence of rheumatoid arthritis in Tsarang residents using existing diagnostic criteria and to explore its risk factors.MethodsAn epidemiological survey was conducted in Tsarang in 2019. Data obtained from anthropometry and questionnaires were statistically analyzed. Biochemical measurements using blood samples were also performed, and the results were used to assess arthritis status. Residents’ joint status was scored, and arthritis was assessed based on the clinical disease activity index and ACR/EULAR 2010 criteria.ResultsTwenty-seven males and 50 females participated in this survey. In Tsarang, ACR/EULAR 2010 classified 4.3% of males and 7.1% of females as having rheumatoid arthritis, indicating a very high estimated prevalence. We also performed a multivariate analysis to explore its risk factors, and two factors, older age (standardized parameter estimate = 4.84E−01, 95% CI = [9.19E−02, 8.76E−01], p = 0.0170) and a history of living in urban areas (standardized parameter estimate = − 5.49E−01, 95% CI = [− 9.21E−01, 1.77E−01], p = 0.0050), significantly contributed to the higher ACR/EULAR 2010 score in females. In addition, three factors, having no spouse (standardized parameter estimate = 3.17E−01, 95% CI = [5.74E−02, 5.77E−01], p = 0.0179), having a smoking habit (standardized parameter estimate = 2.88E−01, 95% CI = [1.71E−02, 5.59E−01], p = 0.0377), and a history of living in urban areas (standardized parameter estimate = − 3.69E−01, 95% CI = [− 6.83E−01, − 5.60E−02], p = 0.0219), resulted in significantly higher clinical disease activity index scores in females. Furthermore, smoking habits were found to significantly increase blood hyaluronic acid in both males (standardized parameter estimate = 6.03E−01, 95% CI = [3.06E−01, 9.01E−01], p = 0.0020) and females (standardized parameter estimate = 4.87E−01, 95% CI = [5.63E−02, 9.18E−01], p = 0.0291).ConclusionsIn this study, we evaluated the symptoms of arthritis and estimated the prevalence of rheumatoid arthritis using classification criteria for Tibetan highlanders who have adapted to the hypoxic environment and fostered their own culture. The high prevalence of rheumatoid arthritis among Tsarang residents suggests that the hypoxic adaptation mechanism involving HIF in Tibetan highlanders may promote the onset or exacerbation of rheumatoid arthritis. The high prevalence of rheumatoid arthritis among Tibetan highlanders may be related not only to the environmental factors analyzed in this study but also to hypoxic adaptation genes. Further investigation is needed to clarify the genetic factors involved.

The fear of COVID-19 and job insecurity impact on depression and anxiety: An empirical study in China in the COVID-19 pandemic aftermath.

The employees' psychological health and resilience in times of emergency and general uncertainty was chosen due to the immense implications for economics, entrepreneurs, psychologists and psychiatrists, and policymakers. This study aims to provide an insight into uncertainty-induced anxiety and depression among Chinese employees in the aftermath of the COVID-19 outbreak. Analysis performed in the context of China in the COVID-19 pandemic aftermath is significant due to the universal nature of external shock impact on psychological welfare, applicable across nations and business sectors and in similar contexts. The statistical analysis was performed with SEM software AMOS version 23. The research model consisting of fear of COVID-19, job insecurity, anxiety, depression, was empirically tested. A purposive sampling technique was applied with the online questionnaire shared with employees in companies located in China. Respondents were working in educational services, information technology, engineering, electronics, and other sectors on white-collar jobs. The data collection was conducted from May to August 2020, in the aftermath of the COVID-19 pandemic in China. The research sample consisting of 283 respondents was used for analysis. Path analysis was performed, and standardized parameter estimates, standard errors, and p-values were calculated. The results indicate a positive and significant impact of job insecurity on depression and anxiety. Furthermore, results indicate that the fear of COVID-19 significantly impacts anxiety and depression but does not impact job insecurity. The findings can be used in a multidisciplinary effort to mitigate the psychological damage. Furthermore, they complement the ongoing epidemiological and scientific discourse on people's personal health and choice of coping.

Effect of Subclinical Hypothyroidism on the Association between Hemoglobin A1c and Reduced Renal Function: A Prospective Study.

Subclinical hypothyroidism (SCH) was reported to be associated with accelerating endothelial dysfunction, which is recognized as one of the upstream mechanisms that leads to glomerular injury (lower glomerular filtration rate (GFR)). SCH was also reported to be associated with hyperglycemia, which is associated with higher hemoglobin A1c (HbA1c) levels and induces endothelial dysfunction. Therefore, SCH status could influence the association between HbA1c and reduced eGFR. To clarify those associations, we conducted a prospective study of 1580 Japanese individuals who participated in an annual health check-up in 2014 with 2.8 years of follow-up. All participants had free triiodothyronine (T3) and free thyroxine (T4) levels in the normal range. Among study participants, 88 were diagnosed as having SCH. Even though no significant correlation was observed between HbA1c and annual change in estimated GFR among participants without SCH (multi-adjusted standardized parameter estimate (β) = 0.03, p = 0.250), a significant inverse association was observed among participants with SCH (β = −0.26, p = 0.014). When those analyses were performed among participants who were not taking glucose lowering medication, the observed associations were essentially the same: β = 0.03, p = 0.266 for participants without SCH and β = −0.32, p = 0.006 for participants with SCH, respectively. Therefore, SCH status could influence the association between HbA1c and renal function.

Postamputation Cognitive Impairment Is Related to Worse Perceived Physical Function Among Middle-Aged and Older Prosthesis Users

ObjectiveTo compare characteristics between middle-aged and older prosthesis users with and without cognitive impairment and determine whether cognitive impairment contributes to variability in perceived physical function. DesignCross-sectional, observational study SettingGeneral community. ParticipantsAdults 45 years or older, at least 1-year post lower limb amputation (LLA) who were walking independently with a prosthesis (N=119). InterventionNot applicable. Main Outcome MeasuresWe identified cognitive impairment using an education-adjusted Telephone Interview for Cognitive Status-modified score. Perceived physical function was measured using the Prosthesis Mobility Questionnaire. ResultsOf 119 participants (mean age, 62.6±8.2 years; male: 89.1%; vascular etiology: 82.4%; years since amputation: 4.9±4.7 years), 28 (23.5%) had cognitive impairment. Compared with participants without cognitive impairment, those with cognitive impairment were more likely to use an assistive device (60.7% vs 25.3%, P=.002); were older (66.3±7.3 vs 61.5±8.1 years, P=.006) and had more chronic conditions (7.1±3.4 vs 5.4±2.5, P=.004), more depressive symptoms (6.6±5.1 vs 4.2±3.8, P=.008), and worse perceived physical function (2.0±0.6 vs 2.6±0.7, P<.001). Using backward stepwise linear regression, we found that participants with cognitive impairment had worse perceived physical function (standardized parameter estimate [β]=−0.15, P=.02), even after adjusting for depressive symptoms (β=−0.31, P<.001), prosthesis satisfaction (β=0.34, P<.001), number of chronic conditions (β=−0.19, P=.006), and assistive device use (βcane=0.01, P=.93; βother=−0.20, P=.003). Together, these variables explained 59% of perceived physical function variability. ConclusionsCognitive impairment is common and associated with worse perceived physical function post LLA, even after controlling for physical and mental health differences. Tailored rehabilitation interventions may be needed to improve perceived physical function in prosthesis users with cognitive impairment.

Impact of Empowering Leadership, Innovative Work, and Organizational Learning Readiness on Sustainable Economic Performance: An Empirical Study of Companies in Russia during the COVID-19 Pandemic

The COVID-19 pandemic shocked the global economy, with numerous companies suffering losses and shutting down. However, some companies proved to be resilient, being able to sustain their economic performance despite the pandemic. The study aims to explain the sustainable economic performance of companies during the COVID-19 pandemic. The relationships between empowering leadership, innovative work behavior, organizational readiness to change, and sustainable economic performance were assessed. The data were collected via an online questionnaire from January 2021 to March 2021, during the height of the COVID-19 pandemic in Russia. The respondents were Russian companies’ employees holding management positions, competent to objectively assess organizational circumstances during the COVID-19 pandemic. A sample of 337 was used in the analysis. Confirmatory factor analysis (CFA) with maximum likelihood estimation was conducted using SPSS AMOS. The structural model was tested with standardized parameter estimates, standard errors, and p-values calculated. The findings of the study suggest that innovative work behavior and organizational readiness to learn have a direct influence on sustainable economic performance. The findings also suggest that empowering leadership impacts innovative work behavior but not sustainable economic performance. The mediation analysis indicates that innovative work behavior is a mediator between empowering leadership and sustainable economic performance, whereas organizational readiness to learn is not a moderator. The study adds to the leadership and sustainability body of knowledge and contributes to the research on the COVID-19 pandemic in the organizational context.

Python-based reduced order quadrature building code for fast gravitational wave inference

The next generation of gravitational wave observatories will reach low frequency limits on the orders of a few Hz, thus enabling the detection of gravitational wave signals of very long duration. The run time of standard parameter estimation techniques with these long waveforms can be months or even years, making it impractical with existing Bayesian inference pipelines. Reduced order modeling and the reduced order quadrature integration rule have recently been exploited as promising techniques that can greatly reduce parameter estimation computational costs. We describe a Python-based reduced order quadrature building code, pyroq, which builds the reduced order quadrature data needed to accelerate parameter estimation of gravitational waves. We present the first bases for the imrphenomxphm waveform model of binary-black-hole coalescences, including subdominant harmonic modes and precessing spins effects.

An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data.

Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches only yield reliable parameter estimates in specific situations (c.f. fixed drift rates vs drift rates varying over trials). In addition, they become computationally unfeasible when more general DDM variants are considered (e.g., with collapsing bounds). In this note, we propose a fast and efficient approach to parameter estimation that relies on fitting a “self-consistency” equation that RT fulfill under the DDM. This effectively bypasses the computational bottleneck of standard DDM parameter estimation approaches, at the cost of estimating the trial-specific neural noise variables that perturb the underlying evidence accumulation process. For the purpose of behavioral data analysis, these act as nuisance variables and render the model “overcomplete,” which is finessed using a variational Bayesian system identification scheme. However, for the purpose of neural data analysis, estimates of neural noise perturbation terms are a desirable (and unique) feature of the approach. Using numerical simulations, we show that this “overcomplete” approach matches the performance of current parameter estimation approaches for simple DDM variants, and outperforms them for more complex DDM variants. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

On the importance of lensing for galaxy clustering in photometric and spectroscopic surveys

We study the importance of gravitational lensing in modelling the number counts of galaxies for the first time in spectroscopic surveys. We confirm previous results for photometric surveys, showing that lensing cannot be neglected in a survey like LSST since it would infer a significant shift of standard cosmological parameters. For a spectroscopic survey like SKA2, we find that neglecting lensing in the monopole, quadrupole and hexadecapole of the correlation function can also induce an important shift of cosmological parameters. For ΛCDM parameters, the shift is moderate, of the order of 0.6σ or less. However, for a model-independent analysis, that measures the growth rate of structure in each redshift bins, neglecting lensing introduces a shift of up to 2.3σ at high redshift. Since the growth rate is directly used to test the theory of gravity, such a strong shift would wrongly be interpreted as the breakdown of General Relativity. This shows the importance of including lensing in the analysis of future surveys. For a survey like DESI, we find on the other hand that lensing is not important, mainly due to the value of the magnification bias parameter of DESI, s(z), which strongly reduces the lensing contribution at high redshift. This result relies on our theoretical modelling of s(z) in DESI and should therefore be confirmed with measurements of s(z) in simulations.We also propose a way of improving the analysis of spectroscopic surveys, by including the cross-correlations between different redshift bins (which is neglected in spectroscopic surveys) from the spectroscopic survey or from a different photometric sample. We show that including the cross-correlations in the SKA2 analysis does not improve the constraints. On the other hand replacing the cross-correlations from SKA2 by cross-correlations measured with LSST improves the constraints by 10% to 20%.The marginal improvement is mainly due to the density correlations between nearby bins and, therefore, does notstrongly depend on our knowledge of the magnification bias. Interestingly, for standard cosmological parameter estimation, the photometric survey LSST in its 12 redshift bin configurationand the spectroscopic SKA2 survey are highly complementary, since they are affected by different degeneracies between parameters:LSST yields the tightest constraints on Ωcdm, h and n s, while SKA2 better constrains Ωbaryon, A s and the bias.

Bayesian Inverse Regression for Vascular Magnetic Resonance Fingerprinting.

Standard parameter estimation from vascular magnetic resonance fingerprinting (MRF) data is based on matching the MRF signals to their best counterparts in a grid of coupled simulated signals and parameters, referred to as a dictionary. To reach a good accuracy, the matching requires an informative dictionary whose cost, in terms of design, storage and exploration, is rapidly prohibitive for even moderate numbers of parameters. In this work, we propose an alternative dictionary-based statistical learning (DB-SL) approach made of three steps: 1) a quasi-random sampling strategy to produce efficiently an informative dictionary, 2) an inverse statistical regression model to learn from the dictionary a correspondence between fingerprints and parameters, and 3) the use of this mapping to provide both parameter estimates and their confidence indices. The proposed DB-SL approach is compared to both the standard dictionary-based matching (DBM) method and to a dictionary-based deep learning (DB-DL) method. Performance is illustrated first on synthetic signals including scalable and standard MRF signals with spatial undersampling noise. Then, vascular MRF signals are considered both through simulations and real data acquired in tumor bearing rats. Overall, the two learning methods yield more accurate parameter estimates than matching and to a range not limited to the dictionary boundaries. DB-SL in particular resists to higher noise levels and provides in addition confidence indices on the estimates at no additional cost. DB-SL appears as a promising method to reduce simulation needs and computational requirements, while modeling sources of uncertainty and providing both accurate and interpretable results.

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

Algorithm for identification of DC motor parameters by method of dynamic expansion of regressor and mixing

Subject of Research. The paper considers the problem of identifying the parameters of various robotic objects. A DC motor is used as an example. Existing methods for identification of parameters require either a large amount of time for accurate determination of the required values or give an estimate with a large error. We propose to expand the application area of the identification algorithm by the method of Dynamic Regressor Extension and Mixing for control problems of robotic objects with a DC motor. Method. The first stage of Dynamic Regressor Extension and Mixing method generates to this procedure. Main Results. A new algorithm is proposed for identifying the parameters of DC motor models. It is shown that, when using the new approach, the fluctuations in parameter estimates are significantly lower, while the response time is much shorter. When using the gradient method, the transient time to estimate the signal parameters is 350 seconds, while for the Dynamic Regressor Extension and Mixing method this time does not exceed six seconds. Besides, Dynamic Regressor Extension and Mixing method has not got overshoot. Practical Relevance. The results of the work can be applied to the design of automatic control systems in control problems of electromechanical objects, including DC motors.new regression forms by applying a dynamic operator to the original regression data. Then, the required combination of new data is selected to obtain the final desired regression form. Standard parameter estimation methods are applied.

Association between serum sodium level within normal range and handgrip strength in relation to hypertension status: a cross-sectional study

Serum sodium concentration within the normal range could act as an indicator of age-related changes such as decrease in muscle strength and impairment of capillary function. Since endothelial injury stimulates endothelial repair by enhancing CD34-positive cell production, the level of serum sodium may be inversely associated with that of circulating CD34-positive cells, thus indicating the degree of age-related endothelial injury. We conducted a cross-sectional study of 246 elderly Japanese men aged 60–69 years. Subjects were stratified by hypertension status because hypertension should act as a strong confounding factor for the analyses performed in this study. Serum sodium concentration was positively associated with handgrip strength in non-hypertensive subjects [standardized parameter estimate (β) = 0.29; p = 0.003], but not for hypertensive subjects (β = 0.01; p = 0.878), while it was inversely associated with circulating CD34-positive cell levels in non-hypertensive subjects [simple correlation coefficient (r) = − 0.28; p = 0.002] but not for hypertensive subjects (r = − 0.07; p = 0.454). For non-hypertensive elderly subjects, serum sodium concentration within the normal range is positively associated with handgrip strength and inversely associated with CD34-positive cells, thus partly indicating the degree of age-related endothelium injury. These associations could prove to be an efficient tool for clarifying the background mechanism governing the decrease in age-related muscle strength.

Population Pharmacokinetics of Levetiracetam in Patients with Traumatic Brain Injury and Subarachnoid Hemorrhage Exhibiting Augmented Renal Clearance.

Patients with severe trauma exhibit augmented renal clearance, which can alter the dosing requirement of renally eliminated drugs. This study aimed to develop a population pharmacokinetic model for levetiracetam in patients with severe traumatic brain injury and aneurysmal subarachnoid hemorrhage, and use it to describe optimal dosing regimens. This was a prospective open-label observational study. Critically ill adult patients with severe traumatic brain injury or aneurysmal subarachnoid hemorrhage without renal dysfunction and receiving levetiracetam were eligible. Serial levetiracetam plasma concentrations were analyzed to develop a population pharmacokinetic model and perform dosing simulations. A two-compartment model best described the concentration-time data from 30 patients. The mean ± standard deviation parameter estimates were bioavailability (F) of 0.8 ± 0.2, absorption rate constant of 2.4 ± 2 h-1, clearance 2.5 ± 1.1 L/h, central volume of distribution 8.9 ± 3.0 L/h, and transfer rate constraints of 1.8 ± 1.1 h-1 from central to peripheral compartments and 0.7 ± 0.3 h-1 from peripheral to central compartments. For the simulated intermittent dosing regimens, on average, the median trough concentration reduced by 50% for every 40-mL/min/1.73m2 increase in urinary creatinine clearance. Simulated doses of at least 6 g/day were required for some levels of augmented renal clearance. Patients with severe traumatic brain injury and aneurysmal subarachnoid hemorrhage with augmented renal clearance are at risk of not achieving target levetiracetam plasma concentrations. We suggest dose titration guided by measured creatinine clearance, and/or, therapeutic drug monitoring if available, to minimize the risk of seizures.

Impact of dividend policy determinants of listed companies on indian capital market

The core purpose of financial management is the maximization of shareholders’ wealth with three core decisions namely, dividend, financing and investment decisions. In simple words dividend is that percentage of net profit of a company which is distributed among the shareholders. The research gap we identified for this study is that, a comparison of different industries dividend policy impact on the Indian Capital Market and the samples drawn from the National stock Exchange (NSE) India. The main objective of this project is to study the influence of dividend policy on the market price of selected company's scrip in NSE. The researcher has chosen Empirical study to prove the hypothesis and 15 companies balance sheet for 10 years (2007–2016) were collected from the respected companies websites and the following ratios have been calculated to test the hypothesis, net profit ratio, gross profit ratio, earning per share, dividend yield, dividend payout ratio, return on equity and the market price of share. The analyses conducted, the parameter estimates are then viewed within AMOS graphics and it displays the standardized parameter estimates. The regression analysis model revealed that the significant impact of the above said independent variables on the market price of share. The result shows that there is significant impact of dividend policy variables on the outcome variable market price.

An Unscented Kalman Filter Based on-line Diagnostic Approach For PEM Fuel Cell Flooding

Poor water management usually leads to various degrees of flooding in the hydrogen type fuel cell, affecting both the instantaneous performance and the long-term durability of the system adversely. While a lot of fuel cell diagnostic tools exist that could be utilized for the flooding diagnostics, most of these approaches are intrusive, requiring special modification to the fuel cell that affects its integrity, or special equipment (e.g. AC spectrometer) that adds to the complexity and cost of the system, and therefore are not considered to be a viable solution for the on-board integration of the diagnostic scheme.This paper proposes a model based approach for the fuel cell flooding diagnostics problem, utilizing only the cell current and voltage, and the inlet pressures of the fuel cell as the input signals of the diagnostic scheme. A diagnostic-oriented fuel cell system dynamic model is developed to incorporate the effects of the fault, i.e. the flooding, on the system dynamics. For simplicity, only the cathode channel flooding, the cathode gas diffusion layer (GDL) flooding, and the anode channel flooding are considered while we neglect the mass transport loss through the anode GDL. The cathode channel flooding and the GDL flooding diagnostic problems are decoupled and formulated as standard joint state and parameter estimation problems, with the amounts of the liquid water treated as varying system parameters to be identified. The unscented Kalman Filter technique has been applied to solve these problems. Simulation results validate the applicability of the cascading unscented Kalman filter design for flooding diagnostics.