Short-term Component Research Articles

Nonlinear modeling to describe the pattern of 15 milk protein and nonprotein compounds over lactation in dairy cows

The protein profile of milk includes several caseins, whey proteins, and nonprotein nitrogen compounds, which influence milk's value for human nutrition and its cheesemaking properties for the dairy industry. To fill in the gap in current knowledge of the patterns of these individual nitrogenous compounds throughout lactation, we tested the ability of a parametric nonlinear lactation model to describe the pattern of each N compound expressed qualitatively (as % of total milk N), quantitatively (in g/L milk), and as daily yield (in g/d). The lactation model was tested on a data set of detailed milk nitrogenous compound profiles (15 fractions-12 protein traits and 3 nonproteins-for each expression mode: 45 traits) obtained from 1,342 cows reared in 41 multibreed herds. Our model was a modified version of Wilmink's model, often used for describing milk yield during lactation because of its reliability and ease of parameter interpretation from a biological point of view. We allowed the sign of the persistency coefficient (parameter c) that explained the variation in the long-term milk component (parameter a) to be positive or negative. We also allowed the short-term milk component (parameter b) to be positive or negative, and we estimated a specific speed of adaptation parameter (parameter k) for each trait rather than assumed a value a priori, as in the original model (k = 0.05). These 4 parameters were included in a nonlinear mixed model with cow breed and parity order as fixed effects, and herd-date as random. Combinations of the positive and negative signs of the b and c parameters allowed us to identify 4 differently shaped lactation curves, all found among the patterns exhibited by the nitrogenous fractions as follows: the "zenith" curve (with a maximum peak; for milk yield and 10 other N traits), the "nadir" curve (with a minimum point; for 20 traits, including almost all those expressed in g/L of milk), the "downward" curve (continuously decreasing; for 14 traits, including almost all those in g/d), and the "upward" curve (continuously increasing; only for κ-casein, in % N). Direct estimation of the k parameters specific to each trait showed the large variability in the adaptation speed of fresh cows and greatly increased the model's flexibility. The results indicated that nonlinear parametric mathematical models can effectively describe the different and complex patterns exhibited by individual nitrogenous fractions during lactation; therefore, they could be useful tools for interpreting milk composition variations during lactation.

Long-term guided wave structural health monitoring in an uncontrolled environment through long short-term principal component analysis

Environmental effects are a significant challenge in guided wave structural health monitoring systems. These effects distort signals and increase the likelihood of false alarms. Many research papers have studied mitigation strategies for common variations in guided wave datasets reproducible in a lab, such as temperature and stress. There are fewer studies and strategies for detecting damage under more unpredictable outdoor conditions. This article proposes a long short-term principal component analysis reconstruction method to detect synthetic damage under highly variational environments, like precipitation, freeze, and other conditions. The method does not require any temperature or other compensation methods and is tested by approximately seven million guided wave measurements collected over 2 years. Results show that our method achieves an area under curve score of near 0.95 when detecting synthetic damage under highly variable environmental conditions.

Primary Care of Adult Patients After Stroke: A Scientific Statement From the American Heart Association/American Stroke Association.

Primary care teams provide the majority of poststroke care. When optimally configured, these teams provide patient-centered care to prevent recurrent stroke, maximize function, prevent late complications, and optimize quality of life. Patient-centered primary care after stroke begins with establishing the foundation for poststroke management while engaging caregivers and family members in support of the patient. Screening for complications (eg, depression, cognitive impairment, and fall risk) and unmet needs is both a short-term and long-term component of poststroke care. Patients with ongoing functional impairments may benefit from referral to appropriate services. Ongoing care consists of managing risk factors such as high blood pressure, atrial fibrillation, diabetes, carotid stenosis, and dyslipidemia. Recommendations to reduce risk of recurrent stroke also include lifestyle modifications such as healthy diet and exercise. At the system level, primary care practices can use quality improvement strategies and available resources to enhance the delivery of evidence-based care and optimize outcomes.

Structural priming is supported by different components of nondeclarative memory: Evidence from priming across the lifespan.

Structural priming is the tendency to repeat syntactic structure across sentences and can be divided into short-term (prime to immediately following target) and long-term (across an experimental session) components. This study investigates how nondeclarative memory could support both the transient, short-term and the persistent, long-term structural priming effects commonly seen in the literature. We propose that these characteristics are supported by different subcomponents of nondeclarative memory: Perceptual and conceptual nondeclarative memory respectively. Previous studies have suggested that these subcomponents age differently, with only conceptual memory showing age-related decline. By investigating how different components of structural priming vary across the life span, we aim to elucidate how nondeclarative memory supports 2 seemingly different components of structural priming. In 167 participants ranging between 20 and 85 years old, we find no change in short-term priming magnitude and performance on perceptual tasks, whereas both long-term priming and conceptual memory vary with age. We suggest therefore that the 2 seemingly different components of structural priming are supported by different components of nondeclarative memory. These findings have important implications for theoretical accounts of structural priming. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Short-Time/-Angle Spectral Analysis for Vibration Monitoring of Bearing Failures under Variable Speed

Vibration-condition monitoring aims to detect bearing damages of rotating machinery’s incipient failures mainly through time–frequency methods because of their efficient analysis of nonstationary signals. However, by having failures with impulse behavior, short-term events have a tendency to be diluted under variable-speed conditions, while information on frequency changes tends to be lost. Here, we introduce an approach to highlighting bearing impulsive failures by measuring short-term spectral components to deal with variable-speed vibrations. The short-term estimator employs two sliding windows: a small one that measures the instantaneous amplitude level and tracks impulsive components and a large interval that evaluates the average background amplitude. Aiming to characterize cyclo-non-stationary processes with impulsive behavior, the emphasizing high-order-based estimator based on the principle of spectral entropy is introduced. For evaluation, both visual inspection and classifier performance are assessed, contrasting the spectral-entropy estimator with the widely used spectral-kurtosis approach for dealing with impulsive signals. The validation of short-time/-angle spectral analysis performed on three datasets at variable speed showed that the proposed spectral-entropy estimator is a promising indicator for emphasizing bearing failures with impulse behavior.

Statistical methods for composite endpoints.

Composite endpoints are commonly used in clinical trials, and time-to-first-event analysis has been the usual standard. Time-to-first-event analysis treats all components of the composite endpoint as having equal severity and is heavily influenced by short-term components. Over the last decade, novel statistical approaches have been introduced to overcome these limitations. We reviewed win ratio analysis, competing risk regression, negative binomial regression, Andersen-Gill regression, and weighted composite endpoint (WCE) analysis. Each method has both advantages and limitations. The advantage of win ratio and WCE analyses is that they take event severity into account by assigning weights to each component of the composite endpoint. These weights should be pre-specified because they strongly influence treatment effect estimates. Negative binomial regression and Andersen-Gill analyses consider all events for each patient -rather than only the first event - and tend to have more statistical power than time-to-first-event analysis. Pre-specified novel statistical methods may enhance our understanding of novel therapy when components vary substantially in severity and timing. These methods consider the specific types of patients, drugs, devices, events, and follow-up duration.

Mortality Forecasting with an Age-Coherent Sparse VAR Model

This paper proposes an age-coherent sparse Vector Autoregression mortality model, which combines the appealing features of existing VAR-based mortality models, to forecast future mortality rates. In particular, the proposed model utilizes a data-driven method to determine the autoregressive coefficient matrix, and then employs a rotation algorithm in the projection phase to generate age-coherent mortality forecasts. In the estimation phase, the age-specific mortality improvement rates are fitted to a VAR model with dimension reduction algorithms such as the elastic net. In the projection phase, the projected mortality improvement rates are assumed to follow a short-term fluctuation component and a long-term force of decay, and will eventually converge to an age-invariant mean in expectation. The age-invariance of the long-term mean guarantees age-coherent mortality projections. The proposed model is generalized to multi-population context in a computationally efficient manner. Using single-age, uni-sex mortality data of the UK and France, we show that the proposed model is able to generate more reasonable long-term projections, as well as more accurate short-term out-of-sample forecasts than popular existing mortality models under various settings. Therefore, the proposed model is expected to be an appealing alternative to existing mortality models in insurance and demographic analyses.

The entangled state: How state-business relations shaped the German corporate tax regime

Contemporary tax research is split into two camps: comparative approaches emphasize continuity and cross-country differences, while the globalization literature stresses similar changes across countries. Counter the continuity thesis, this paper finds that neoliberal dynamics were at play in a case considered largely resilient to such dynamics: German governments implemented a series of corporate tax reforms which radically curbed business taxes and added a short-term and cost-cutting component to investments and corporate finance. While these changes point towards neoliberal change, they were distinct from the trends we see in other economies: crucially, the German reforms did not follow the common trend of reducing taxes for individuals and entailed a particular emphasis on enhancing multinational’s access to international capital – but did not liberate financial incomes from tax in general. Based on archival documents from the Bundestagsarchiv, this paper traces the process of German tax reforms and finds that neoliberal dynamics were at play but received a local (export-oriented) colour through processes specific to the German polity. Because consensual institutions granted power to a specific business coalition, radical change was long blocked. Reforms could only be implemented once the state forged a new coalition. Making sense of the mediation of neoliberal dynamics through state institutions can contribute to a better understanding of the variegated nature of neoliberalism.

Involvement of Pannexin-1 in the Mechanism of Deprivation Potentiation of Population Spikes of Neurons in Rat Hippocampal Field CA1

Testing of the connections of Schaffer collaterals with field CA1 neurons in living slices of rat hippocampus was used to study the mechanism of deprivation potentiation (DeP) of population spikes developing as a result of a 60-min pause in stimulation (deprivation). Previous studies have shown that DeP has the property of input specificity and the ability to persist for prolonged periods; it consists of two components with independent mechanisms of induction: an initial short-term “peak” of presynaptic origin and a long-lasting “plateau” with a Ca2+-dependent postsynaptic mechanism mediated by P2 purine receptors. Studies of the properties of the input specificity of DeP were run using stimulation of two different populations of Schaffer collaterals and recording of pop-spikes in the general population of neurons in field CA1. These experiments showed that changes in synaptic efficiency in the deprived input depend only on a presynaptic mechanism responsible for the development of the short-term component of DeP. Studies of the postsynaptic mechanism of induction of the long-term component of DeP demonstrated that the function of the adenosine triphosphate (ATP) source required for activation of P2 purine receptors is mediated by pannexin-1, which forms ATP-conducting channels on the postsynapse. A working model of the cyclic mechanism of induction of the long-term component of DeP is presented.

Meteorological Detrending of Ozone at Three Sites in the Dallas-Fort Worth Area: Application of KZ Filter Method

The Dallas-Fort Worth (DFW) area that experiences high temperature and intense solar radiation falls into the moderate nonattainment classification. The variation in meteorological parameters plays an important role in ambient ozone levels variation. Meteorological influences need to be decoupled from ozone data for long-term trend analysis. Temporal separation of maximum daily average 8-h ozone (MDA8 ozone), maximum daily temperature (TMAX), daily average solar radiation (DASR), and daily average wind speed (DAWS) were conducted using Kolmogorov-Zurbenko (KZ) filter for ozone records at Keller (C17), Arlington (C61), Red Bird (C402) monitoring stations in the DFW area from 2003 to 2017. Temporal separation, regression analysis, and meteorological detrending were performed. The long-term component had a clear and stable trend. The contribution of the long-term component to total variation was negligible, which is less than 2%. This is due to the removal of the data noise from the original time series data. The seasonal component had a major contribution (55% to 72%) in the total variation of the maximum temperature and solar radiation. However, the short-term component was dominant in the total variation of the MDA8 ozone (41–54%) and wind speed (68–79%). Regression analysis showed the baseline component bears the highest correlation than the short-term and raw. Solar radiation had the highest correlation to the MDA8 ozone, followed by temperature data in all three stations. Meteorological detrending showed the detrended long-term ozone had an increasing trend. The increasing trend was significant at C402 with a trend of 0.19 ± 0.006 ppb/y (0.398 R2), whereas slight increasing trends were found at C17 (0.072 ± 0.006 (0.107 R2)) and at C61 (0.019 ± 0.007 (0.005 R2)). The increasing trend of long-term components of MDA8 ozone was justified by the increasing level of NOx and VOCs from the mobile sources in the DFW area.

Modeling The Relationships Between Export, Import, Inflation, Interest Rate, and Rupiah Exchange

This research was a modification of research by Catalbas (2016) and Pratikto (2012). The model that can separate long-term and short-term components are the Vector Error Correction Model (VECM). This study aimed to model export, import, inflation, interest rates, and the rupiah exchange rate using VECM and to test the causality between variables using the Granger Causality test. The inter-variable model obtained in this study was VECM with lag 2 using a deterministic trend with the assumption of none intercept no trend and two cointegrations. In export and import, there was an adjustment mechanism from the short-term to the long-term. This research model was appropriate to forecast the export and import where VECM with export and import as the target variables, the cointegration equation (long-run model) for cointegration equation (long-run model) for Based on the Granger Causality test, it was found that there was a one-way relationship between exchange rates and inflation, export and interest rates, export and import, inflation and export, and import and the interest rate at the significance level of 5%.

How to remove the seasonal atmospheric effect: An application on a surface volcanic temperature at Nisyros active volcano, Hellenic Volcanic Arc

Surface temperature at a volcanic system is the result of a mixture of two heat sources originating one from above and another from below the ground surface. The atmosphere significantly affects the surface's temperature of the volcano, and removal of the atmospheric effect is required for meaningful interpretations prior to any characterization of anomalous volcanic activity. Time series of meteorological data were acquired from the closest weather station to Nisyros volcano, and surface volcanic temperature from the southern part of Nisyros caldera were analyzed. Time series analysis took place at different time scales through a decomposition method. The decomposition of the time series into long-, seasonal-, and short-term components was performed by the application of the Kolmogorov-Zurbenko (KZ) filter to separate the noise that appears on the obtained data. The Time Series Regression (TSR) model was applied in all time scales. The long- and the short-term components of the surface volcanic temperature show no significant correlation with the atmospheric variables, while the seasonal term indicates a very strong association with an R2 equal with 99%. After the removal of the atmospheric effect, our model indicates that there is a normal activity of the seasonal heat emission in Nisyros with no anomalous signals in the temperature residuals during our survey. This methodology may facilitate the process of filtering a seasonal atmospheric effect and better comprehend the heat emission of a volcano-hydrothermal system from temperature surface readings.

Determinants of the capital structure of large companies: Evidence from Serbia

The subject of this study is the capital structure of the largest Serbian companies in the period after the global economic crisis in 2008. The research sample comprises the 141 largest non-financial (mostly private) companies in Serbia over the period 2009–2017. In order to identify the key determinants of the capital structure of the sampled companies, three models of financial leverage were tested (total, short-term, and long-term) using panel data fixed effects models. The main result of the analysis indicates that these companies, mostly financed by short-term debt, predominantly belong to the ‘pecking order’ theory. When the total leverage is split into its short-term and long-term components, the results show that short-term leverage behaviour aligns with the ‘pecking order’ theory, whereas long-term leverage is fully consistent with the expectations of the trade-off theory. Also, this study shows that the country-specific determinants, such as inflation and development of the banking sector, have a significant impact on the capital structure of the largest companies in Serbia.

Modeling the Spread of COVID-19 in Lebanon: A Bayesian Perspective

This article investigates the problem of modelling the trend of the current Coronavirus disease 2019 pandemic in Lebanon along time. Two different models were developed using Bayesian Markov chain Monte Carlo simulation methods. The models fitted included Poisson autoregressive as a function of a short-term dependence only and Poisson autoregressive as a function of both a short-term dependence and a long-term dependence. The two models are compared in terms of their predictive ability using mean predictions, root mean squared error and deviance information criterion. The Poisson autoregressive model that allows capturing both short-term and long-term components performs best under all criterions. The use of such a model can greatly improve the estimation of number of new infections, and can indicate whether disease has an upward/downward trend, and where about every country is on that trend, so that containment measures can be applied and/or relaxed. The Bayesian model is flexible in characterizing the uncertainty in the model outputs. The model is also applicable to other countries and more time periods as data becomes available. Further research is encouraged.

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Recently, traffic flow prediction has drawn significant attention because it is a prerequisite in intelligent transportation management in urban informatics. The massively available traffic data collected from various sensors in transportation cyber-physical systems brings the opportunities in accurately forecasting traffic trend. Recent advances in deep learning shows the effectiveness on traffic flow prediction though most of them only demonstrate the superior performance on traffic data from a single type of vehicular carriers (e.g., cars) and does not perform well in other types of vehicles. To fill this gap, in this article, we propose a wide-attention and deep-composite (WADC) model, consisting of a wide-attention module and a deep-composite module, in this article. In particular, the wide-attention module can extract global key features from traffic flows via a linear model with self-attention mechanism. The deep-composite module can generalize local key features via convolutional neural network component and long short-term memory network component. We also perform extensive experiments on different types of traffic flow datasets to investigate the performance of WADC model. Our experimental results exhibit that WADC model outperforms other existing approaches.

Market-Wide Illiquidity and the Volatility of a Model-Free Stochastic Discount Factor

We show that illiquidity risk matters for asset pricing independently of the specific functional form of the liquidity-based asset pricing model. Employing a non-parametric model-free stochastic discount factor (SDF), estimated using different sets of portfolio returns coming from both the stock and the corporate bond markets, we show that market-wide illiquidity affects positive and significantly the volatility of the SDF. This finding is robust to the use of alternative market-wide illiquidity metrics and persists after we control for GARCH effects on the short-term component of the SDF’s volatility. Overall, our analysis shows that market-wide illiquidity is a relevant driver of the time-varying behavior of the assets’ risk premium.

Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection

Abstract We consider a GARCH-MIDAS model with short-term and long-term volatility components, in which the long-term volatility component depends on many macroeconomic and financial variables. We select the variables that exhibit the strongest effects on the long-term stock market volatility via maximizing the penalized log-likelihood function with an Adaptive-Lasso penalty. The GARCH-MIDAS model with variable selection enables us to incorporate many variables in a single model without estimating a large number of parameters. In the empirical analysis, three variables (namely, housing starts, default spread and realized volatility) are selected from a large set of macroeconomic and financial variables. The recursive out-of-sample forecasting evaluation shows that variable selection significantly improves the predictive ability of the GARCH-MIDAS model for the long-term stock market volatility.

Analyzing the liabilities and stockholders' equity of the world's leading public oil and gas corporations

Subject. The article investigates liabilities and equity of leading public oil and gas companies from 2006 to 2018. Objectives. The focus is on determining the current values of the main components of liabilities and equity of leading public oil and gas companies, identifying the key trends over the studied period and factors that led to the changes. Methods. The paper employs methods of comparative, financial, and economic analysis, generalization of official annual reports on financial and business operations of the said corporations. Results. Based on the results of the comprehensive analysis of balance sheets of 25 oil and gas companies, I established movements in the size and structure of liabilities and equity in the public sector of the industry and the main factors that contributed to the changes. Conclusions. Over the studied period, I revealed an increase in the balance sheet valuation of liabilities and equity of most of leading public oil and gas companies, notwithstanding a noticeable decrease in their value after the industry crisis. The components of liabilities and equity remained approximately equal, but the transformation in the ratio between the components occurred within the indicators themselves. The long-term component started prevailing in the structure of liabilities. It was driven by growing total debt, which almost equaled the value of accounts payable. As a result, the total debt became the dominant component of long-term liabilities, and accounts payable retained key positions only in the short-term component.

Trading Strategies: Forecasting Index Futures Prices with Short-Term Investor Sentiment

ABSTRACT Behavior Finance Theory explains the short-term deviations of futures price. However, the previous studies generally view sentiment as one-time dimension. This article, on a larger basis, captures both long-term and short-term investor sentiment. In such case, short-term predictive power of investor sentiment on index futures returns can be analyzed in two prospects. On the one hand, the spot market sentiment and futures market sentiment have more predictive power on short-term components of returns than long-term components of returns. On the other hand, short-term sentiment components of spot market and futures market are more statistically significant on returns than long-term components. To further explain that, out-of-sample evidence of short-term sentiment trading strategies is presented, which proves a statistically significant return with an annualized return of 40% and annualized Sharpe ratio of 2.4.

Dynamic Spillovers Between International Crude Oil Market and China's Commodity Sectors: Evidence From Time-Frequency Perspective of Stochastic Volatility

We study the time-frequency dynamics of stochastic volatility spillovers between international crude oil markets and China’s commodity sectors in the spectral representation framework of generalized forecast error variance decomposition (GFEVD). We find evidence that international crude oil markets has significant volatility spillover effects on China's bulk commodity markets, and the volatility spillovers are sensitive to extreme geopolitical or financial events. The net spillovers of international oil markets are almost positive and driven mainly by short-term components (within a week). However, uncertain financial factors from China such as the market-oriented reform in 2013 and the stock disaster in 2015 adversely affect the net oil-commodity volatility spillovers through the medium-term components (week to a month) and long-term components (month to a year). Moreover, the volatility spillover effects of crude oil prices on different commodity sectors in China are heterogeneous. Metal, coal coke and steel ore and energy commodity sectors are more affected by crude oil prices, while non-metal building materials and agricultural commodities are less affected. These outcomes implement necessary implications for investors and policymakers.