Response Variance Research Articles

Sensitivities of subgrid-scale physics schemes, meteorological forcing, and topographic radiation in atmosphere-through-bedrock integrated process models: a case study in the Upper Colorado River basin

Abstract. Mountain hydrology is controlled by interacting processes extending from the atmosphere through the bedrock. Integrated process models (IPMs), one of the main tools needed to interpret observations and refine conceptual models of the mountainous water cycle, require meteorological forcing that simulates the atmospheric process to predict hydroclimate then subsequently impacts surface–subsurface hydrology. Complex terrain and extreme spatial heterogeneity in mountainous environments drive uncertainty in several key considerations in IPM configurations and require further quantification and sensitivity analyses. Here, we present an IPM using the Weather Research and Forecasting (WRF) model which forces an integrated hydrologic model, ParFlow-CLM, implemented over a domain centered over the East River watershed (ERW), located in the Upper Colorado River basin (UCRB). The ERW is a heavily instrumented 300 km2 region in the headwaters of the UCRB near Crested Butte, CO, with a growing atmosphere-through-bedrock observation network. Through a series of experiments in the water year 2019 (WY19), we use four meteorological forcings derived from commonly used reanalysis datasets, three subgrid-scale physics scheme configurations in WRF, and two terrain shading options within WRF to test the relative importance of these experimental design choices for key hydrometeorological metrics including precipitation and snowpack, as well as evapotranspiration, groundwater storage, and discharge simulated by the ParFlow-CLM. Our hypothesis is that uncertainty from synoptic-scale forcings produces a much larger spread in surface–subsurface hydrologic fields than subgrid-scale physics scheme choice. Results reveal that the WRF subgrid-scale physics configuration leads to larger spatiotemporal variance in simulated hydrometeorological conditions, whereas variance across meteorological forcing with common subgrid-scale physics configurations is more spatiotemporally constrained. Despite reasonably simulating precipitation, a delay in simulated discharge peak is due to a systematic cold bias across WRF simulations, suggesting the need for bias correction. Discharge shows greater variance in response to the WRF simulations across subgrid-scale physics schemes (26 %) rather than meteorological forcing (6 %). The topographic radiation option has minor effects on the watershed-average hydrometeorological processes but adds profound spatial heterogeneity to local energy budgets (±30 W m−2 in shortwave radiation and 1 K air temperature differences in late summer). This is the first presentation of sensitivity analyses that provide support to help guide the scientific community to develop observational constraints on atmosphere-through-bedrock processes and their interactions.

Combining Bayesian active learning and conditional Gaussian process simulation for propagating mixed uncertainties through expensive computer simulators

Resulted from the limited information on both parameters and excitation at the early design stage of aerospace structures, evaluating the reliability with high accuracy has been recognized as a challenging task. Imprecise probability models have been widely developed and accepted due to their flexibility in separating the aleatory and epistemic uncertainties, and then the potential of estimating the reliability with high confidence. However, the propagation of these models through expensive-to-evaluate simulators remains to be a challenge due to the hierarchical model structure. To fill this gap, a new Bayesian active learning method is devised for efficiently learning the functional behavior of the failure probability and response variance over the epistemic input parameters. This information is especially useful for evaluating the safety of structures and for managing the uncertainties during the design process. The proposed method is based on training/updating a Gaussian Process Regression (GPR) model in the augmented space of aleatory and epistemic parameters, with the training data actively produced using two well-designed acquisition functions. The induced posterior features of the quantities of interest are inferred numerically based on efficient simulation of the GPR model. Benefiting from the decoupling scheme and the Bayesian adaptive design strategy, the proposed method is extremely efficient and provides accuracy guarantee for the numerical results. The effectiveness and superiority of the proposed method are demonstrated with numerical and engineering benchmarks, including the dynamic reliability analysis of a satellite structure.

The (Ir)Relevance of Rule‐of‐Thumb Consumers for U.S. Business Cycle Fluctuations

AbstractWe estimate a medium‐scale model with and without rule‐of‐thumb consumers over the pre‐Volcker and the Great Moderation periods, allowing for indeterminacy. Passive monetary policy and sunspot fluctuations characterize the pre‐Volcker period for both models. In both subsamples, the estimated fraction of rule‐of‐thumb consumers is low, such that the two models are empirically almost equivalent; they yield very similar impulse response functions, variance, and historical decompositions. We conclude that rule‐of‐thumb consumers are irrelevant to explain aggregate U.S. business cycle fluctuations.

A wearable sensor based on multifunctional conductive hydrogel for simultaneous accurate pH and tyrosine monitoring in sweat

Flexible and wearable sweat sensors have drawn extensive attention by virtue of their continuous and real-time monitoring of molecular level information. However, current sweat-based sensors still pose several challenges, such as low accuracy for analytes detection, susceptibility to microorganism and poor mechanical performance. Herein, we demonstrated a noninvasive wearable sweat sensing patch composed of an electrochemical sensing system, and a pilocarpine-based iontophoretic system to stimulate sweat secretion. The electrochemical sensor based on tannic acid-Ag-carbon nanotube-polyaniline (TA-Ag-CNT-PANI) composite hydrogel was designed for on-body detection of pH and tyrosine (Tyr), a disease marker associated with multiple disorders, such as tyrosinemia and bulimia nervosa. The wearable sweat sensor can not only monitor the pH and Tyr in sweat simultaneously, but also further calibrate Tyr detection results with the measured pH value, so as to eliminate the effect of Tyr response variance at different pH and enhance the accuracy of the sensor. Furthermore, the presence of tannic acid chelated-Ag nanoparticles (TA-Ag NPs) and carbon nanotubes (CNTs) significantly improved the conductivity and flexibility of the hydrogel and endowed the composite hydrogel with antibacterial capability. Of note, the constructed wearable sensor was capable of monitoring Tyr with enhanced accuracy in various sweats.

Differential Activity of Orthosteric Agonists and Allosteric Modulators at Metabotropic Glutamate Receptor 7.

Metabotropic glutamate receptor 7 (mGlu7) is a G protein coupled receptor that has demonstrated promise as a therapeutic target across a number of neurologic and psychiatric diseases. Compounds that modulate the activity of mGlu7, such as positive and negative allosteric modulators, may represent new therapeutic strategies to modulate receptor activity. The endogenous neurotransmitter associated with the mGlu receptor family, glutamate, exhibits low efficacy and potency in activating mGlu7, and surrogate agonists, such as the compound L-(+)-2-Amino-4-phosphonobutyric acid (L-AP4), are often used for receptor activation and compound profiling. To understand the implications of the use of such agonists in the development of positive allosteric modulators (PAMs), we performed a systematic evaluation of receptor activation using a system in which mutations can be made in either protomer of the mGlu7 dimer; we employed mutations that prevent interaction with the orthosteric site as well as the G-protein coupling site of the receptor. We then measured increases in calcium levels downstream of a promiscuous G protein to assess the effects of mutations in one of the two protomers in the presence of two different agonists and three positive allosteric modulators. Our results reveal that distinct PAMs, for example N-[3-Chloro-4-[(5-chloro-2-pyridinyl)oxy]phenyl]-2-pyridinecarboxamide (VU0422288) and 3-(2,3-Difluoro-4-methoxyphenyl)-2,5-dimethyl-7-(trifluoromethyl)pyrazolo[1,5-a]pyrimidine (VU6005649), do exhibit different maximal levels of potentiation with L-AP4 versus glutamate, but there appear to be common stable receptor conformations that are shared among all of the compounds examined here. SIGNIFICANCE STATEMENT: This manuscript describes the systematic evaluation of the mGlu7 agonists glutamate and L-(+)-2-Amino-4-phosphonobutyric acid (L-AP4) in the presence and absence of three distinct potentiators examining possible mechanistic differences. These findings demonstrate that mGlu7 potentiators display subtle variances in response to glutamate versus L-AP4.

Research on Stock Price of Two Listed Ship Companies

The development of the manufacturing industries is crucial to the countrys economic growth and technological development. In this study, the stock price return rate of two listed companies (JIANIANHUA and JIALEIBI) are selected as data, and the VAR model is established to conduct impulse response analysis and variance decomposition analysis on the data. We may also find the economic relations by analysing the 2 ship companiess stock under covid. Through the analysis of the results, the following findings can be given: first, VAR model indicates that the companies shows significant impact on each other. Second, in the cruise ship industry, both JIANIANHUA and JIALEIBIs stock price have strong self-adjustment ability. Third, JIANIANHUAs rate of return is less influenced by the rate of return of JIALEBI, while JIALEBIs rate of return is much highly influenced by JIANIANHUA. The papers findings might be helpful to related investors interested in the cruise ship industry.

Rural Financial of Rural Revitalization Support on Farmers' Income: An Empirical Study from Anhui Province, China

Under the background of rural revitalization, rural finance is the driving force to promote rural economic development and the pillar to achieve the strategic goal of rural revitalization. The VAR model of the relationship between farmers' income growth rate and rural financial support, rural financial support and industrial structure was constructed, taking the rural financial data of Anhui Province from 1999 to 2017 as samples. And the impulse response function and variance decomposition analysis were conducted. The empirical results show that the growth rate of farmers' income is greatly affected by rural financial support policies.

Effects of COVID-19 and vaccination on the human immune system: cases of lymphopenia and autoimmunity

Effects of COVID-19 and vaccination on the human immune system: cases of lymphopenia and autoimmunity

Bitcoin Vs Gold: Which One is the Most Powerful in Boosting the Shariah Equity Index? Global Evidence

Abstract The study explores the most powerful between Bitcoin and Gold in boosting the Shariah Equity Index in Malaysia, the United Arab Emirates, China, Indonesia, The United States of America (USA), Japan, Oman, and Saudi Arabia in the short and long term. The study uses analysis of the first and second stages of the Granger Causality Test and Vector Error Correction Model (VECM), then Impulse Response Function (IRF) and Variance Decomposition (VDC) over the period 2013 to 2021. The finding proves that only Gold can affect the Islamic Equity Index in the short term, then Bitcoin and Gold proved to contribute equally to the Islamic Equity Index in the long term. However, Bitcoin has the potential to provide positively correlated shocks and dominate the value of Islamic equity indices in the long term. The results demonstrate that government intervention is decisive in maintaining the stability of the Shariah Equity Index from future Bitcoin threats. The study’s finding has practical implications for Islamic capital market Investors, Managers, and Authorities.

Forest conversion into pasture selects dung beetle traits at different biological scales depending on species pool composition.

The conversion of forests into open areas has large effects on the diversity and structure of native communities. The intensity of these effects may vary between regions, depending on the existence of native species adapted to open habitats in the regional pool or the time since habitat change.We assess the differences in species richness and functional diversity of dung beetle communities (Coleoptera: Scarabaeinae) between native forests and novel pasturelands of the Atlantic Forest and the Cerrado, two biomes with contrasting histories of human occupation in Brazil. We conducted standardized surveys in seven forest fragments and adjacent pastures in each region and measured 14 traits in individuals collected in each type of habitat at each particular site. We calculated functional richness, functional evenness, functional divergence, and community-weighted mean of traits for each area, and analyzed individual variation through nested variance decomposition and Trait Statistics.Communities were richer and more numerous at the Cerrado. We did not find any consistent relationship between functional diversity and forest conversion beyond the changes in species diversity. Although landscape changes were more recent at the Cerrado, the colonization of the new habitat by native species already adapted to open habitats lessens the functional loss in this biome. This indicates that habitat change's effects on trait diversity depend on the regional species pool rather than on time since land conversion.Forest conversion effects were primarily due to internal filtering. The effects of external filtering only appear at the intraspecific variance level, with contrasting differences between the Cerrado, where traits related to relocation behavior and size are selected, and the Atlantic Forest, where selection operates for traits related to relocation behavior and flight. These results evidence the importance of considering individual variance to address the responses of dung beetle communities to forest conversion.

The intrinsic variance of beauty judgment

Recall memory and sequential dependence threaten the independence of successive beauty ratings. Such independence is usually assumed when using repeated measures to estimate the intrinsic variance of a rating. We call “intrinsic” the variance of all possible responses that the participant could give on a trial. Variance arises within and across participants. In attributing the measured variance to sources, the first step is to assess how much is intrinsic. In seven experiments, we measure how much of the variability across beauty ratings can be attributed to recall memory and sequential dependence. With a set size of one, memory is a problem and contributes half the measured variance. However, we showed that for both beauty and ellipticity, with set size of nine or more, recall memory causes a mere 10% increase in the variance of repeated ratings. Moreover, we showed that as long as the stimuli are diverse (i.e., represent different object categories), sequential dependence does not affect the variance of beauty ratings. Lastly, the variance of beauty ratings increases in proportion to the 0.15 power of stimulus set size. We show that the beauty rating of a stimulus in a diverse set is affected by the stimulus set size and not the value of other stimuli. Overall, we conclude that the variance of repeated ratings is a good way to estimate the intrinsic variance of a beauty rating of a stimulus in a diverse set.

Pharmacogenomics-a New Frontier for Individualized Treatment of Parkinson's Disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease with a significant public health burden. It is characterized by the gradual degeneration of dopamine neurons in the central nervous system. Although symptomatic pharmacological management remains the primary therapeutic method for PD, clinical experience reveals significant inter-individual heterogeneity in treatment effectiveness and adverse medication responses. The mechanisms behind the observed interindividual variability may be elucidated by investigating the role of genetic variation in human-to-human variances in medication responses and adverse effects. This review aims to explore the impact of gene polymorphism on the efficacy of antiparkinsonian drugs. The identification of factors associated with treatment effectiveness variability might assist the creation of a more tailored pharmacological therapy with higher efficacy, fewer side outcomes, and cheaper costs. In this review, we conducted a thorough search in databases such as PubMed, Web of Science, and Google Scholar, and critically examined current discoveries on Parkinson's disease pharmacogenetics. The ethnicity of the individuals, research methodologies, and potential bias of these studies were thoroughly compared, with the primary focus on consistent conclusions. This review provides a summary of the existing data on PD pharmacogenetics, identifies its limitations, and offers insights that may be beneficial for future research. Previous studies have investigated the impact of gene polymorphism on the effectiveness and adverse effects of levodopa. The trendiest genes are the COMT gene, DAT gene, and DRD2 gene. However, limited study on other anti-Parkinson's drugs has been conducted. Therefore, In order to develop an individualized precision treatment for PD, it is an inevitable trend to carry out multi-center, prospective, randomized controlled clinical trials of PD pharmacogenomics covering common clinical anti-PD drugs in large, homogeneous cohorts.

Dynamic response analysis of nonlinear structures with hybrid uncertainties

The physical systems of nonlinear structural dynamics are usually expressed mathematically as ordinary differential equations. When hybrid uncertainties are in the system, it is necessary to understand the effects of uncertainties in the system response to accurately predict its behavior using mathematical models. To address the hybrid uncertainty of random and interval parameters often encountered in engineering applications, we propose the nonlinear structural dynamics analysis with hybrid uncertainty (NSDAHU) method. The expressions for calculating the statistical characteristics (i.e., mean and variance) of nonlinear structural dynamic responses with hybrid uncertainties are derived using random interval moment and random interval perturbation methods. The applicability, accuracy, and efficiency of the NSDAHU method are verified using Monte Carlo simulation method (MCSM). The NSDAHU method can not only solve hybrid random interval problems but also solve single interval, single random, and linear structural dynamics problems.

Variation in Global Policy Responses to COVID-19: A Bidirectional Analysis.

Against the unprecedented outbreaks of the COVID-19 variants, countries have introduced restrictive measures with discretion, ranging from lifting the closure thoroughly to implementing stringent policies, but all together guarding the global public health. Under the changing circumstances, we firstly apply the panel data vector autoregression (PVAR) model, using a sample of 176 countries/territories from 15 June 2021 to 15 April 2022, to estimate the potential associations among the policy responses, the progression of COVID-19 in deaths and vaccination, and medical resources possessed. Furthermore, we use the random effect method and the fixed effect speculation, to examine the determinants of policy variances across regions and over time. Our work has four main findings. Firstly, it showed the existence of a bidirectional relationship between the policy stringency and variables of interest including new daily deaths, the fully vaccinated percentage and health capacity. Secondly, conditional on the availability of vaccines, the sensitivity of policy responses to the death numbers tends to decline. Thirdly, the role of health capacity matters in coexisting with the virus mutation. Fourthly, regarding the variance in policy responses over time, the impact of new deaths tends to be seasonal. As to geographical differences in policy responses, we present the analysis for Asia, Europe, and Africa, and they show different levels of dependencies on the determinants. These findings suggest that bidirectional correlations exist in the complex context of wrestling with the COVID-19, as government interventions exert influence on the virus spread, the policy responses also progress alongside multiple factors evolving in the pandemic. This study will help policymakers, practitioners, and academia to formulate a comprehensive understanding of the interactions between policy responses and the contextualized implementation factors.

Deconvolution of clinical variance in CAR-T cell pharmacology and response.

Chimeric antigen receptor T cell (CAR-T) expansion and persistence vary widely among patients and predict both efficacy and toxicity. However, the mechanisms underlying clinical outcomes and patient variability are poorly defined. In this study, we developed a mathematical description of T cell responses wherein transitions among memory, effector and exhausted T cell states are coordinately regulated by tumor antigen engagement. The model is trained using clinical data from CAR-T products in different hematological malignancies and identifies cell-intrinsic differences in the turnover rate of memory cells and cytotoxic potency of effectors as the primary determinants of clinical response. Using a machine learning workflow, we demonstrate that product-intrinsic differences can accurately predict patient outcomes based on pre-infusion transcriptomes, and additional pharmacological variance arises from cellular interactions with patient tumors. We found that transcriptional signatures outperform T cell immunophenotyping as predictive of clinical response for two CD19-targeted CAR-T products in three indications, enabling a new phase of predictive CAR-T product development.

Static Subjective Visual Vertical (SVV) in Patients with Vestibular Migraine.

Vestibular migraine (VM) is one of the common causes of episodic dizziness, but it is underdiagnosed and poorly understood. Previous research suggests that otolith reflex pathway performance is often impaired in this patient group, leading to altered perception of roll plane stimuli. Clinically, this perception can be measured with subjective visual vertical (SVV) testing. The aim of this study is to compare static SVV performance (absolute mean SVV tilt, variance) in a cohort of patients diagnosed with VM to results obtained from clinically derived normative data. Retrospective case review. Ninety-four consecutive patients between 18 and 65 years of age diagnosed with VM were included in this comparison to clinically derived normative data. Retrospective chart review was completed. Demographic data, symptom report, and vestibular laboratory results were documented. SVV performance was documented in terms of absolute mean SVV tilt and response variance. Abnormal mean SVV tilt was described in 54% (n = 51) of patients with VM. Including abnormal response variance increased those identified with abnormal presentation to 67% (n = 63). Laboratory findings were insignificant for semicircular canal function, but of those with abnormal ocular vestibular myogenic potential results (n = 30), 77% (n = 23) demonstrated both abnormal SVV and utriculo-ocular reflex performance. There were no associations noted for SVV performance and demographic or other self-report variables. Absolute mean SVV tilt and response variance are often abnormal in patients diagnosed with VM. These findings support theories suggesting atypical intralabyrinthine integration within the vestibular nuclei and cerebellar nodular pathways.

Frequency-domain analysis of aperiodic control loops with identically distributed delays

Extending classical frequency domain analysis to systems with time-varying delays remains a challenge in the systems and control field. These systems are receiving a renewed interest due to emergent control applications in which the use of shared communication and computation resources induce severe time-varying delays in the loop. Here, an extension of frequency domain analysis is proposed for aperiodic control loops with time-varying delays, assumed to be independent and identically distributed and to follow an exponential distribution. In aperiodic control loops, the actuation is updated immediately after a delayed sensor measurement arrives at the controller. In the present framework, the amplitudes of expected values and variances of output responses to sinusoidal inputs are plotted as a function of the input frequency. This plot allows for inferring the behavior of the response to general input signals. The usefulness of the results is illustrated in the control of a double integrator with delayed measurements.

Exploratory Factor Analysis of the Romantic Relationship Stress Scale

Stress is inherent to romantic relationships. Current relationship scales measure individual stress, relationship conflict, and relationship happiness. However, no known empirically supported scale exists that comprehensively measures romantic relationship stress. We reviewed the extant theory, empirical findings, and measures within the broad category of relationship stress. Using this information, we created the Romantic Relationship Stress Scale (RRSS)—a three-part, 29-item measure. We administered the measure to married individuals ( N = 296) in either a proximal or long-distance relationship, in the context of a larger study. An exploratory factor analysis was performed on RRSS responses, using principal axis factoring with an oblique rotation. A three-factor solution was derived, representing the factors of relational negativity, stressful relationship events, and relational positivity. These factors explained 67.7% of the variance in RRSS responses, and were correlated in the expected directions. The overall scale alpha was 0.93, and subscale alphas ranged from 0.65–0.91. The RRSS demonstrated convergent validity; scores were positively correlated to scores on a widely used measure of individual stress. The RRSS discriminated between those in proximal versus long-distance relationships. Overall, the RRSS appears to be psychometrically sound, and can be considered for use in both research and clinical settings.

Understanding ecological response to physical characteristics in side channels of a large floodplain-river ecosystem

Side channels in large floodplain rivers serve a variety of important ecological roles, particularly in reaches where habitat conditions have been degraded or diminished. We developed hypotheses regarding side channel ecological structure whereby we expected species richness of young-of-year fishes to generally be higher in shallower, more physically heterogeneous side channels with lower velocities, with differences based on reproductive guild. We also hypothesized species richness of adult fishes to be higher in side channels with greater heterogeneity that could support diverse foraging resources and provide refugia during extreme flow conditions. To test these hypotheses, we used a 28-year fish community dataset from the Upper Mississippi and Illinois Rivers. Across six study reaches, we assessed metrics of side channel physical size, heterogeneity, and connectivity that were hypothesized to explain variance of fish community response, while accounting for site-level factors across 52 side channels using multilevel models. We then used these side channel-level characteristics in a K-means cluster analysis to classify 1126 side channels across 32 reaches of the river system. Our results indicated that the relative explanatory contributions of physical metrics varied by response variable, providing varying evidence in support of our hypotheses, and indicating that different forms of heterogeneity matter in different ways. Side channel-level factors were more explanatory of fish community responses in side channels of upstream reaches compared to downstream reaches and percent wet forest was the most explanatory side channel-level factor of fish community responses across all models. Our classification of side channels indicated strong spatial contrasts in the abundance and diversity of side channels across reaches. Scaling up to understand how the diversity and abundance of different types of side channels contributes to landscape-scale ecological functions and processes would be useful for establishing targets for reach-scale physical heterogeneity.

Stochastic Optimization of a Nonlinear Base Isolation System with LRB and EIMD for Building Structures

Base-isolated structures achieve superior seismic performance but with the cost of large deformation of the base floor, which possibly causes superstructure collapse during severe earthquakes. Hence, it is of great interest to develop hybrid base isolation systems that can reduce the base deformation and simultaneously improve the superstructure’s performance. Recently, several hybrid base isolation systems using inerter-based dampers have emerged; however, the nonlinear performance of the isolators has rarely been considered in these studies. In this paper, we conduct a nonlinear stochastic optimization of a novel hybrid base isolation system with lead-rubber bearings (LRB) and electromagnetic inertial mass dampers (EIMD). Based on the Bouc–Wen hysteretic model of the LRB, we derive the semianalytical solutions of the response variances of a simplified base-isolated model subjected to stationary seismic excitations. Then, based on the semianalytical solutions, we propose a procedure for optimizing the EIMD aimed at minimizing the superstructure interstory drift. In addition, we investigate the effect of site conditions and postyielding to preyielding stiffness ratios of the LRB on the optimal EIMD parameters and corresponding seismic performance. It is found that the hybrid base isolation system achieves better performance at a soft site than at a firm site. Results of a hybrid base-isolated building under artificial and real earthquake excitations illustrate that the EIMD can reduce both the base deformation and superstructure response, which outperforms the hybrid base-isolated building with conventional viscous dampers (VD).