Variable Lag Research Articles

Temperature and exudativory as drivers of the marmoset (Callithrixspp.) daily activity period

Primates are affected by fluctuations in ambient temperatures, mostly through thermoregulatory costs and changes in the availability of food. In the present study, we investigate whether the ambient temperature and proxies of food availability affect the activity period of marmosets (Callithrix spp.). We predicted that: (i) at colder sites, marmosets would spend more time at sleeping sites; (ii) midday resting bouts would be longer at hotter sites; (iii) the onset/cessation of activity and resting behavior at midday would be more closely related to temperature than food availability, and (iv) highly exudativorous groups would have higher total levels of resting. We compiled data on the onset and cessation of activity and the time spent resting at midday from seven marmoset studies from sites with a wide range of temperatures. We used generalized linear mixed models to verify the relationship between the dependent variables (lag between dawn and the onset of activities, lag between cessation of activities and dusk, and proportion of resting during midday) and the minimum and maximum temperatures at the respective study sites, together with proxies of food availability (exudativory rates, the amount of habitat available per individual, and net primary productivity) using each sample month as a sampling unit and the identity of the study as a categorical random factor. At colder sites and during colder months, the marmosets left sleeping trees later in the morning and ceased their activities earlier, while at hotter sites and during hotter months, they spent more time resting during midday. More exudativorous groups become active later in the morning, but also ceased their activities later. The abundance of food did not affect the timing of activities. We provide evidence that both low and high temperatures affect marmosets' activities, and that their activity period appears to be more influenced by the thermal environment than food availability.

A Process Monitoring Method Based on Dynamic Autoregressive Latent Variable Model and Its Application in the Sintering Process of Ternary Cathode Materials

Due to the ubiquitous dynamics of industrial processes, the variable time lag raises great challenge to the high-precision industrial process monitoring. To this end, a process monitoring method based on the dynamic autoregressive latent variable model is proposed in this paper. First, from the perspective of process data, a dynamic autoregressive latent variable model (DALM) with process variables as input and quality variables as output is constructed to adapt to the variable time lag characteristic. In addition, a fusion Bayesian filtering, smoothing and expectation maximization algorithm is used to identify model parameters. Then, the process monitoring method based on DALM is constructed, in which the process data are filtered online to obtain the latent space distribution of the current state, and T2 statistics are constructed. Finally, by comparing with an existing method, the feasibility and effectiveness of the proposed method is tested on the sintering process of ternary cathode materials. Detailed comparisons show the superiority of the proposed method.

DOES TOURISM SECTOR MATTER IN REGIONAL ECONOMIC DEVELOPMENT

The regional autonomy policy is expected to promote the government in carrying out fiscal management independently. However, local own-source revenue was the only source of revenue and measurement of independence and it has not been achieved optimally. Many provinces failed to fully support regional needs, even though it has the highest tourism sector revenue contributors. The purpose of this study is to analyze the influence of the tourism sector in terms of supply and demand on regional independence. To analyze the regional income model seen from the contribution of the tourism sector by including the lag variable, this study used a Dynamic Panel method which is also known as Partial Adjusted in Central Java Province, Indonesia from 2013-2018. The result showed that the increase in local own-source revenue from the demand side is more elastic than the supply contribution. The addition of new tourist objects without proper management tends not to have a significant impact on regional income. Also, the attraction of people visiting the tourist in Central Java Province is still high because of a significant positive number of tourist arrivals. The government needs to focus on managerial improvements, development, and innovation of existing tourist objects

A spatiotemporal analysis of the driving forces behind the energy interactions of the Chinese economy: Evidence from static and dynamic perspectives

China is the world's biggest energy consumer and carbon emitter; this is a great challenge to both global and national environmental security. To achieve energy conservation and emission reduction goals, it is essential to identify the driving factors that affect the energy interaction patterns of the Chinese economy. For this purpose, the spatial independent variable lag model (SLX) and structural decomposition analysis (SDA) methods were integrated to detect province-level consumption-based energy changes induced by five driving factors, and due consideration was also given to interregional spillover effects from both static and dynamic perspectives. The results of this study uncovered that energy intensity acted as a major driver for energy conservation both in the short- and long-terms, whilst the role of energy intensity in curbing energy consumption was weakened. In contrast, optimizing production structure offers the potential for big energy reductions and it was further noted that spillover effects contributed to both national and regional energy conservation. Consumption per capita and population size presented similar static effects on energy increases. The spillover effects of the former had a greater impact on energy consumption growth during the investigated period, whilst both static and dynamic processes of consumption structure failed to form fixed direct and spillover effects on energy use at the regional level. The findings of this study contribute to understanding the regional heterogeneity of driving forces for energy consumption and provide a solid foundation for making effective energy conservation regulations.

Research on Environmental Regulation, Technological Innovation and Green Transformation of Manufacturing Industry in the Yangtze River Economic Belt

The green transformation of the manufacturing industry is crucial for high-quality development of the Yangtze River Economic Belt, and environmental regulation and technological innovation may play key roles. Considering the undesirable output of the manufacturing industry, this paper adopted the undesirable-SE-SBM Model to measure the green transformation efficiency, which can reflect the core transformation performance. On this basis, this paper respectively adopted system generalized method of moments (SYS-GMM) and differential generalized method of moments (DIF-GMM) to explore the driving factors of green transformation, which fully considered the lag variable of transformation efficiency. The estimated results of green transformation showed that the efficiency of the Yangtze River Economic Belt has maintained an overall growth trend, while that of the eastern regions was higher than that of the central and western regions. The regional difference of transformation efficiencies showed a trend of convergence first and then expansion, however, a few regions such as Chongqing have achieved leapfrog development. The estimated results of driving factors showed the first-stage lag affected the green transformation positively, while the second-stage lag had a significantly negative effect. The ratchet effect and cumulative effect led to the continued efforts on green transformation, however, the timeliness of policy might cause a rebound in practice. As mentioned in green paradox, the environmental regulation had a negative effect, which might bring compliance costs. The technology innovation level indeed promoted the green transformation of manufacturing, but the scientific research investment did not exert the expected positive effect, while the utilization of many research funds lacked market orientation. Economic development level had a negative effect on green transformation, and it would play a positive effect only if it reached a certain stage. The industrialization and urbanization affected the efficiency positively, and the external dependence degree had a significant negative effect. It was not clear whether foreign direct investment (FDI) brought a pollution haven or pollution halo effect. In view of these conclusions, local governments should strictly enforce environmental regulations, build the regional green innovation system, improve marketization of research funds, optimize the export structure, and promote new urbanization and new industrialization.

Dynamics of the accommodative response after smoking cannabis.

Cannabis is the most widely consumed illicit drug worldwide. It has been suggested that cannabis could generate blurred vision during reading tasks. The goal of this study was to objectively assess the acute effects of smoking cannabis on the dynamics of ocular accommodation. The influence of other factors, including target distance and the direction of accommodation, as well as personal characteristics, were also analysed. Nineteen young people who were occasional cannabis users participated in the study (mean age 22.53 [3.12] years). Their usage profiles were evaluated by means of the Cannabis Use Disorders Identification Test-revised (CUDIT-r). The dynamics of the accommodative response were evaluated using an open-field auto refractor (Grand Seiko WAM-5500). The participants completed two different experimental sessions, one week apart, and in random order (baseline session and after smoking cannabis). During these sessions, the amplitude of the response (D), mean velocity (D/s), peak velocity (D/s), response time (s), accommodative lag (D) and accommodation variability (D) were measured. The results indicated that cannabis use had a significant main effect on the mean accommodation/disaccommodation velocity (F1,13 =7.21; p=0.02; =0.396). Cannabis consumption also interacted significantly with other factors. Response time showed a significant two-way interaction between condition×target distance (F1,13 =11.71; p=0.005; =0.474) and condition×accommodation direction (F1,13 =8.71; p=0.01; =0.401). For mean velocity, two-way interactions were found between condition×age (F1,13 =6.03; p=0.03; =0.354), condition×CUDIT-r score (F1,13 =6.03; p=0.03; =0.356) and condition×target distance (F1,13 =7.20; p=0.02; =0.396). These findings suggest that cannabis use can alter the accommodation process, although further studies should be carried out to explore the role of attention deficits. According to these results, certain daily activities that depend on an accurate accommodative function may be affected by cannabis use.

FORECASTING ELECTRICITY SUPPLIED IN TURKEY USING HOLT-WINTERS’ MULTIPLICATIVE METHOD AND ARTIFICIAL NEURAL NETWORK (ANN) MODELS

Electricity is one of the most essential necessities in today’s world and has an important role for the development of societies and economics. The need for electricity is expanding continuously due to increasing population, urbanization and industrialization. Hence, the purpose of this study was to develop the best model for forecasting electricity supplied in Turkey by applying the multiplicative Holt-Winters method and multilayer feed-forward neural network model. The monthly electricity supplied in Turkey from January 2000 until December 2019 were obtained from monthly electricity statistics report presented by the International Energy Agency (EIA). The data were divided into two sets comprising in-sample data from January 2000 until December 2015 and out-sample data from January 2016 to December 2019. The multiplicative Holt-Winters was used since the electricity supplied in Turkey exhibit trend and seasonal gave the out-sample forecast of 3.6990. The best multilayer feed forward neural network (MFFNN) model with three input lag variable, one hidden node, one output node, sigmoid transfer function in hidden layer and linear transfer function in output layer gave the out-sample forecast of 2.1483. Hence it can be concluded that, the multilayer feed-forward neural network model is more accurate than multiplicative Holt-Winters method to forecast the electricity supplied in Turkey.

Education and Crime across America: Inequity’s Cost

Much of current research on crime and education has focused on the effect of minimum dropout age on rates of crime. Combining the FBI’s uniform crime reporting database and district finance data, we study the longitudinal relationship between crime in every town/city (whose police department has reported crime statistics) and its school district spending in years 2003 to 2018. We combine over 213 datasets to control for population, density, wealth, education, employment, cost-of-living, race, law enforcement, and voting history. Additionally, we also look at teacher salary, teacher engagement, and student chronic absenteeism. Using linear mixed-effect modeling, we find an overall average of 2.35% percent decrease in property crime for every $1000 more a school district spends per pupil on education. Moreover, a $1000 increase in education spending decreased property crime nearly four times as much as a 10 percent increase in per capita income. We also looked at the range in district spending in towns/cities and counties whose students attend multiple districts. We find that for every $1000 difference in district spending within a city, property crime increases by an average of 3%; interestingly, violent crime decreases by 3%. When we lag variables of education quality, allowing these effects to playout, we also find that for every 10 percentage-point increase in chronic absenteeism among students, violent crime increases by 4%. Importantly, we find no such effect for property crime, suggesting a distinct mechanism of education on violent crime. Additionally, both law enforcement and unemployment explain little variance in crime. Our results demonstrate a robust relationship between education funding and reduced crime across America with regard to amount spent per student as well as equity in spending.

On the Exponential Stability of Neutral Linear Systems with Variable Delays

We investigate the exponential stability of a linear system of neutral-type with variable time lags. By using the Newton–Leibniz formula and a Lyapunov–Krasovskii functional, we prove two results on the exponential stability of solutions. The stability criteria are stated in the form of linear matrix inequalities. By using the MATLAB-Simulink software, we present two numerical examples illustrating the applicability of our assumptions. The obtained results extend and generalize the already known results available from the related literature.

Contagion Effect in ASEAN-5 Currency Markets During COVID-19

The aim of this study is to examine an existence of contagion in ASEAN-5 currency markets during COVID-19 period, the type of contagion whether it is pure contagion that happens in the short run only or fundamentals-based contagion in the long run and the country source of this contagion effect. The study starts by imposing structure to the regional exchange rates guided by variable lag Granger causality and variable lag Transfer Entropy, followed by co-integration and Error Correction Model (VECM) within the Structural VAR framework to capture the short run, long run and error correction term (ect) in currency market of ASEAN-5. The next empirical framework used is wavelet analysis specifically wavelet power spectrum and wavelet coherency to capture the multi scale time and frequency domain of currency market volatility and covariance respectively. The VECM findings show there exists long run cointegration in the ASEAN-5 currency markets during COVID-19 and any disequilibrium will be adjusted by Indonesian Rupiah, Malaysia Ringgit and Singapore Dollar at daily rate of adjustments of 6.58%, 1.47% and 2.45% respectively. The wavelet power spectrum provides evidence of short run and long run contagion effect in Indonesia, Malaysia and Singapore currency market, while Philippines and Thailand experience minimal contagion effect in the short run and heightened volatility in the long run for Thailand. No long run contagion effect for Philippines. The wavelet coherency shows contagion effect emanating from Indonesia Rupiah to its neighboring countries in the short and only to Thailand and Malaysia in the long run.

Corporate risk-taking with long-term, short-term and subordinate credit ratings

PurposeThis study empirically examines, from the first quarter of 1981 until the fourth quarter of 2017, the relations across customary domestic issuer credit ratings (long-term, short-term and subordinate) and three popular corporate risk-taking measurements (the variability of operating profitability, net profitability, and research and development expenses).Design/methodology/approachThe author deploys categorical regressions and robustness tests with control variables, interaction terms, fixed effect variables, lag variables and delta variables.FindingsThe author documents that both short-term and subordinate domestic credit ratings are key determinants of the volatility of operating profitability. The author also identifies long-term credit ratings as secondary factors, yet they do affect broader corporate risk-taking behavioral features (along all three measurements). Furthermore, the author finds that the higher (lower) the credit ratings assigned, i.e. the superior (inferior) the credit quality externally judged, the more (less) overall risk firms tend to undertake.Originality/valueIt is the first research to examine both the inclusive influence and the granular effects of credit ratings on corporate risk-taking (CRT) behavior. It is also the only enquiry to inspect the specific relationships along three types of domestic issuer credit ratings: long-term, short-term and subordinate ratings.

Geometrical Framework Application Directions in Identification Systems. Review

The approaches review of the framework application in identification problems is fulfilled. It is showed that this concept can have different interpretations of identification problems. In particular, the framework is understood as a frame, structure, system, platform, concept, and basis. Two directions of this concept application are allocated: 1) the framework integrating the number of methods, approaches or procedures; b) the mapping describing in the generalized view processes and properties in a system. We give the review of approaches that are the basis of the second direction. They are based on the analysis of virtual geometric structures. These mappings (frameworks) differ in the theory of chaos, accidents, and the qualitative theory of dynamic systems. Introduced mappings (frameworks) are not set a priori, and they are determined based of the experimental data processing. The main directions analysis of geometrical frameworks application is fulfilled in structural identification problems of systems. The review includes following directions: i) structural identification of nonlinear systems; ii) an estimation of Lyapunov exponents; iii) structural identifiability of nonlinear systems; iv) the system structure choice with lag variables; v) system attractor reconstruction.

Connected Vehicle Data Time Series Dependence for Machine Learning Model Selection and Specification

<div class="section abstract"><div class="htmlview paragraph">Connected vehicle data unlock compelling solutions for vehicle owners and fleet managers. In selecting machine learning algorithms for use in predicting a connected vehicle signal value, time series dependency is critical to understand. With little to no time series dependency, conventional machine learning models may be used with a feature set that has few or no lag variables. If there is a lot of time series dependency including long-term dependencies, deep learning architectures like variants of recurrent neural networks (RNN) may be a better approach. Further, at any time step, RNN features may be specified to use some number of past time steps to predict the latest value. This paper seeks to identify time series dependency of connected vehicle signals, and selection of the number of time steps to look back in the features set to minimize error.</div></div>

A Precipitation forecasting in central ecotone region in Brazil using artificial neural networks and public climatic data

Precipitation forecasting may be of great value for farming, helping to reduce crop losses and irrigation costs, besides leveraging crop yield estimates. In Brazil, the state of Tocantins has a great part of its economy based on agriculture, where precipitation forecasting may help improve local production. Besides, rainfall forecasting can also contribute to urban planning through the management of water resources, among other applications Artificial Neural Networks (ANNs) have been used with relative success in precipitation forecasting for different locations and climates. With that, this work presents a method for weekly precipitation forecasting in six locations in the Brazilian state of Tocantins using ANNs and public climatic data. For that, MultiLayer Perceptron (MLP) networks were trained with data from local weather stations and El Niño Southern Oscillation (ENSO) related indices. First, input variables were selected using the forward selection algorithm. After that, ANN hyperparameters and input variables lag were optimized. The average Root Means Square Error (RMSE) of the final models was of 31.35 mm/week for the training dataset and 33.38 mm/week for the test dataset. Respectively, these values represent 9,83% and 10,46% of the maximum weekly precipitation found in the work dataset, which was of 319.1 mm. The results suggest that the created models are capable of reasonably good weekly precipitation forecasts, providing valuable information for farming, water resources management, urban planning and other related activies. Although there is possibly room for model improvement.

Nontarget emotional stimuli must be highly conspicuous to modulate the attentional blink.

The attentional blink (AB) is often considered a top-down phenomenon because it is triggered by matching an initial target (T1) in a rapid serial visual presentation (RSVP) stream to a search template. However, the AB is modulated when targets are emotional, and is evoked when a task-irrelevant, emotional critical distractor (CDI) replaces T1. Neither manipulation fully captures the interplay between bottom-up and top-down attention in the AB: Valenced targets intrinsically conflate top-down and bottom-up attention. The CDI approach cannot manipulate second target (T2) valence, which is critical because valenced T2s can “break through” the AB (in the target-manipulation approach). The present research resolves this methodological challenge by indirectly measuring whether a purely bottom-up CDI can modulate report of a subsequent T2. This novel approach adds a valenced CDI to the “classic,” two-target AB. Participants viewed RSVP streams containing a T1–CDI pair preceding a variable lag to T2. If the CDI’s valence is sufficient to survive the AB, it should modulate T2 performance, indirectly signaling bottom-up capture by an emotional stimulus. Contrary to this prediction, CDI valence only affected the AB when CDIs were also extremely visually conspicuous. Thus, emotional valence alone is insufficient to modulate the AB.

Saudi Arabia's sustainable tourism development model: New empirical insights

AbstractThe study extended the conventional Autoregressive Distributed Lag (ARDL) model into the lead model, supported with a partial adjustment model and adaptive expectation model. The ARDL‐LEAD modelling is applied to Saudi Arabia's tourism industry to assess the country's environmental sustainability agenda (ESA) by using monthly time series data from 1995M01‐2018M12. The short‐run results confirmed the Environmental Kuznets Curve (EKC) hypothesis with the lag variables, while the U‐shaped relationship was found between economic growth (EG) and CO2 emissions with the leads variable. The inbound tourism and trade openness (TOP) decrease CO2 emissions at the lead factor in the short‐run. The long‐run relationship verified the U‐shaped relationship between EG and CO2 emissions, while TOP openness increases CO2 emissions to substantiate 'pollution haven hypothesis (PHH)' in a country. The Wald Granger causality shows the different variations at the current period and forecasted estimates. The Granger estimates at the current level confirmed the growth‐led emissions (GLE), tourism‐led emissions (TLE), and tourism‐led growth (TLG) hypotheses, while at forecast estimates, the results substantiate the TLE and emissions‐led growth (ELD) hypotheses in a country.

Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future

Accurate runoff forecasting plays a considerable role in the appropriate water resource planning and management. The spatial and temporal evaluation of the flood susceptibility was explored in the Quebec basin, Canada. This study provides a new strategy for runoff modelling as one of the complicated variables by developing new machine learning techniques along with remote sensing. A novel scheme of the Group Method of Data Handling (GMDH) known as the generalized structure of GMDH (GSGGMDH) is developed to overcome this classical approach's limitation. A simple time series based scenario with exogenous variables including precipitation and Normalized Difference Vegetation Index (NDVI) was introduced for runoff forecasting. MODIS data included MOD13Q1 product was employed and a JavaScript code was developed to preprocess collected data in the Google Earth Engine (GEE) environment. Using different seasonal and non-seasonal lags of all input variables, the developed GSGMDH found the most optimum input combination for each station in terms of simplicity and accuracy, simultaneously (average values; SI = 0.554, RMSRE = 1.55, MAE = 5.076). The precipitation values are modelled with the CanEsm2 climate change model. To apply NDVI for runoff forecasting, a simple spatial-temporal GSGMDH based model was developed (average values; SI = 0.27; RMSRE = 8.27, MAE = 0.08). The forecasting results indicated that the months in which the maximum runoff occurred have changed, and these months have increased compared to the historic period. In the historical period, the frequency of maximum runoff was in April and March. Still, for the two forecasting periods (i.e. 2020–2039 and 2040–2059), the months in which the maximum runoff has occurred have changed, and their amount has been reduced and added to other months, especially February and August.

COVID-TRACK: world and USA SARS-COV-2 testing and COVID-19 tracking

BackgroundThe COVID-19 pandemic has caused major health and socio-economic disruptions worldwide. Accurate investigation of emerging data is crucial to inform policy makers as they construct viral mitigation strategies. Complications such as variable testing rates and time lags in counting cases, hospitalizations and deaths make it challenging to accurately track and identify true infectious surges from available data, and requires a multi-modal approach that simultaneously considers testing, incidence, hospitalizations, and deaths. Although many websites and applications report a subset of these data, none of them provide graphical displays capable of comparing different states or countries on all these measures as well as various useful quantities derived from them. Here we introduce a freely available dynamic representation tool, COVID-TRACK, that allows the user to simultaneously assess time trends in these measures and compare various states or countries, equipping them with a tool to investigate the potential effects of the different mitigation strategies and timelines used by various jurisdictions.FindingsCOVID-TRACK is a Python based web-application that provides a platform for tracking testing, incidence, hospitalizations, and deaths related to COVID-19 along with various derived quantities. Our application makes the comparison across states in the USA and countries in the world easy to explore, with useful transformation options including per capita, log scale, and/or moving averages. We illustrate its use by assessing various viral trends in the USA and Europe.ConclusionThe COVID-TRACK web-application is a user-friendly analytical tool to compare data and trends related to the COVID-19 pandemic across areas in the United States and worldwide. Our tracking tool provides a unique platform where trends can be monitored across geographical areas in the coming months to watch how the pandemic waxes and wanes over time at different locations around the USA and the globe.

Impact of Public Debt on Economic Growth: Evidence from Nigeria

The rising debt profile of developing economies like Nigeria has remained a concern for the policymakers and other agents of the economy. Thus, this study examines the impact of public debt on economic growth in Nigeria. The study used the Johansen cointegration test, Ordinary Least Square technique and Vector Error Correction Model to analyze data sourced from the Central Bank of Nigeria, Debt Management Office, International Mone-tary Fund and the World Bank for the period 1981-2019. The study demon-strates the presence of a long-run equilibrium relationship between public debt and economic growth in Nigeria. The analysis reveals evidence of an adverse impact of public debt on economic performance for the study peri-od in Nigeria. However, the impact of the relationship is only significant with the lag variable. The study demonstrates that inflation, interest rates, oil price and investment exert influence on economic growth in Nigeria. Follow-ing the results of this study and the level of debt in Nigeria, policymakers must support private sector led investment drive for improved economic performance. Also, the government must make sure that borrowing is based on project specific needs that guarantee repayment of borrowed funds. Finally, the government must enforce good governance and institutional structures to discourage the misappropriation of resources and encourage economic growth.

The relationship between barchan size and barchan morphology: a case study from Northern Namibia

ABSTRACT Landform allometry has been a topic of inquiry since at least the 1970s. In this study, the presence of allometry is investigated for a barchan dunefield in northern Namibia. Using a combination of traditional morphometric parameters and techniques borrowed from geometric morphometrics it is shown that barchan allometry is present. This allometry is a combination of positive and negative allometry. Barchans show a definite change in mean shape as the size of the dune increases becoming more asymmetric. Differences in horn length, along with dune width, show positive allometry indicating that it changes faster than the shape changes. Barchan bilateral asymmetry and stoss length show negative allometry indicating that changes in these variables lag behind changes in size. Together, these results hint at the possible presence of threshold size differences beyond which distinct shape changes can be observed.