Impact Of Time Lags Research Articles

RBF neural network dynamic sliding mode control based on lambert W function for piezoelectric stick-slip actuator.

This paper presents a novel approach for increasing the precision of high-precision positioning control experiments for a piezoelectric stick-slip actuator system. This is achieved through dynamic sliding mode control with a radial basis function neural network (RBFNN) based on the Lambert W function. The proposed control strategy is divided into two parts: scanning mode control and stepping mode control. For scanning control, a dynamic sliding mode controller was designed to solve the jitter problem in traditional sliding mode control. The introduction of the RBFNN avoids the effects of uncertainty terms and unknown disturbances in the model; reduces the controller gain, which must be adjusted; and improves the robustness of the system to disturbances. The stability of the dynamic sliding mode controller based on the RBFNN was verified through a Lyapunov analysis, and the Lambert W function was introduced to optimize the controller parameters responsible for the time lag in the closed-loop control system. This optimization improved the system's robustness against time delays, which can adversely affect its performance. Simulation and experimental results indicated that the proposed control strategy achieved a positioning control accuracy of <40nm during the scanning phase and was robust in the presence of a load. In long-distance positioning control experiments, the control strategy achieved a control target of 40 μm while maintaining the positioning control accuracy and reducing the impact of time lag on the system.

Time lag effect on malaria transmission dynamics in an Amazonian Colombian municipality and importance for early warning systems

Malaria remains a significant public health problem worldwide, particularly in low-income regions with limited access to healthcare. Despite the use of antimalarial drugs, transmission remains an issue in Colombia, especially among indigenous populations in remote areas. In this study, we used an SIR Ross MacDonald model that considered land use change, temperature, and precipitation to analyze eco epidemiological parameters and the impact of time lags on malaria transmission in La Pedrera—Amazonas municipality. We found changes in land use between 2007 and 2020, with increases in forested areas, urban infrastructure and water edges resulting in a constant increase in mosquito carrying capacity. Temperature and precipitation variables exhibited a fluctuating pattern that corresponded to rainy and dry seasons, respectively and a marked influence of the El Niño climatic phenomenon. Our findings suggest that elevated precipitation and temperature increase malaria infection risk in the following 2 months. The risk is influenced by the secondary vegetation and urban infrastructure near primary forest formation or water body edges. These results may help public health officials and policymakers develop effective malaria control strategies by monitoring precipitation, temperature, and land use variables to flag high-risk areas and critical periods, considering the time lag effect.

Reliable numerical treatment with Adams and BDF methods for plant virus propagation model by vector with impact of time lag and density

Plant disease incidence rate and impacts can be influenced by viral interactions amongst plant hosts. However, very few mathematical models aim to understand the viral dynamics within plants. In this study, we will analyze the dynamics of two models of virus transmission in plants to incorporate either a time lag or an exposed plant density into the system governed by ODEs. Plant virus propagation model by vector (PVPMV) divided the population into four classes: susceptible plants [S(t)], infectious plants [I(t)], susceptible vectors [X(t)], and infectious vectors [Y(t)]. The approximate solutions for classes S(t), I(t), X(t), and Y(t) are determined by the implementation of exhaustive scenarios with variation in the infection ratio of a susceptible plant by an infected vector, infection ratio of vectors by infected plants, plants' natural fatality rate, plants' increased fatality rate owing to illness, vectors' natural fatality rate, vector replenishment rate, and plants' proliferation rate, numerically by exploiting the knacks of the Adams method (ADM) and backward differentiation formula (BDF). Numerical results and graphical interpretations are portrayed for the analysis of the dynamical behavior of disease by means of variation in physical parameters utilized in the plant virus models.

A machine learning framework to improve effluent quality control in wastewater treatment plants

Due to the intrinsic complexity of wastewater treatment plant (WWTP) processes, it is always challenging to respond promptly and appropriately to the dynamic process conditions in order to ensure the quality of the effluent, especially when operational cost is a major concern. Machine Learning (ML) methods have therefore been used to model WWTP processes in order to avoid various shortcomings of conventional mechanistic models. However, to the best of the authors' knowledge, no ML applications have focused on investigating how operational factors can affect effluent quality. Additionally, the time lags between process steps have always been neglected, making it difficult to explain the relationships between operational factors and effluent quality. Therefore, this paper presents a novel ML-based framework designed to improve effluent quality control in WWTPs by clarifying the relationships between operational variables and effluent parameters. The framework consists of Random Forest (RF) models, Deep Neural Network (DNN) models, Variable Importance Measure (VIM) analyses, and Partial Dependence Plot (PDP) analyses, and uses a novel approach to account for the impact of time lags between processes. Details of the framework are provided along with a demonstration of its practical applicability based on a case study of the Umeå WWTP in Sweden involving a large number of samples (105763) representing the full scale of the plant's operations. Two effluent parameters, Total Suspended Solids in effluent (TSSe) and Phosphate in effluent (PO4e), and thirty-two operational variables are studied. RF models are developed, validated using DNN models as references, and shown to be suitable for VIM and PDP analyses. VIM identifies the variables that most strongly influence TSSe and PO4e, while PDP elucidates their specific effects on TSSe and PO4e. The major findings are: (1) Influent temperature is the most influential variable for both TSSe and PO4e, but it affects them in different ways; (2) PO4e depends strongly on the TSS in aeration basins – higher TSS concentrations in aeration basins generally promote PO4 removal, but excess TSS can have negative effects; (3) In general, the impact of TSS in aeration basins on TSSe and PO4e increases with the distances of the basin from the merging outlet, so more attention should be paid to the TSS concentration in the third or fourth aeration basins than the first and second ones; (4) Returning excessive amounts of sludge through the second return sludge pipe should be avoided because of its adverse impact on TSSe removal. These results could support the development of more advanced control strategies to increase control precision and reduce running costs in the Umeå WWTP and other similarly configured WWTPs. The framework could also be applied to other parameters in WWTPs and industrial processes in general if sufficient high-resolution data are available.

Temporal scale analysis of shale gas dynamic coupling flow

Shale gas is one of the world’s strategic energy reserves. The microscopic pores and the fracture structure of the shale rocks play an important role in shale gas flow. An effective reservoir simulation must account for the heterogeneity and anisotropy of the microscopic pore structure as well as the dynamics between the gas flow and the gas diffusion processes. In this study, the time lag effect, the dynamic coupling of gas flow and diffusion as well as the heterogeneity and anisotropy of the pores are taken into account in order to properly model shale gas production on a large scale. The effect of these variables on the temporal scale analysis of the wellbore gas pressure is presented, as the reliable estimate of wellbore pressure would allow better prediction of the duration of well shut-in periods. Gas production under the intermittent operation scheme at different temporal scales is discussed. The results showed that the well startup is composed of five stages, while the well shut-in is composed of only three stages. The crossflow stage is prolonged by the dynamic coupling. The heterogeneity, anisotropy and dynamic coupling phenomena are accurately presented on the temporal scale diagram. Moreover, the impact of time lag on the gas pressure is better captured by the dynamic coupling between the gas flow and the gas diffusion. The proposed model better simulates actual shale reservoirs and, hence, would be helpful in selecting suitable production rates.

Impact of time lags on diurnal estimates of canopy transpiration and canopy conductance from sap-flow measurements of Populus cathayana in the Qinghai–Tibetan Plateau

Recently, canopy transpiration (Ec) has been often estimated by xylem sap-flow measurements. However, there is a significant time lag between sap flow measured at the base of the stem and canopy transpiration due to the capacitive exchange between the transpiration stream and stem water storage. Significant errors will be introduced in canopy conductance (gc) and canopy transpiration estimation if the time lag is neglected. In this study, a cross-correlation analysis was used to quantify the time lag, and the sap flow-based transpiration was measured to parameterize Jarvis-type models of gc and thus to simulate Ec of Populus cathayana using the Penman–Monteith equation. The results indicate that solar radiation (Rs) and vapor pressure deficit (VPD) are not fully coincident with sap flow and have an obvious lag effect; the sap flow lags behind Rs and precedes VPD, and there is a 1-h time shift between Ec and sap flow in the 30-min interval data set. A parameterized Jarvis-type gc model is suitable to predict P. cathayana transpiration and explains more than 80% of the variation observed in gc, and the relative error was less than 25%, which shows a preferable simulation effect. The root mean square error (RMSEs) between the predicted and measured Ec were 1.91 × 10−3 (with the time lag) and 3.12 × 10−3 cm h−1 (without the time lag). More importantly, Ec simulation precision that incorporates time lag is improved by 6% compared to the results without the time lag, with the mean relative error (MRE) of only 8.32% and the mean absolute error (MAE) of 1.48 × 10−3 cm h−1.

Evolutionary dynamics of collective action when individual fitness derives from group decisions taken in the past

Many actions take some time to have an impact – their effects only appear at some point in the future. Such time lags turn out to be ubiquitous among living organisms. Here we study the impact of time lags in the evolutionary dynamics of cooperative collective action. We consider a population in which individuals interact via a N-Person Stag Hunt dilemma and must opt to cooperate or defect. In the absence of any delay, the replicator dynamics reveals the existence of regimes in which two internal fixed points appear simultaneously. We show that the presence of time delay in the fitness of individuals leads to a delayed replicator equation exhibiting new evolutionary profiles, each profile being separated by critical values of the delay that we determine explicitly. When we break the symmetry in the time lags, we show that, generally, defectors take more advantage from delay than cooperators. Finally, when we take into consideration, approximately, effects associated with the finite population size, we find that counter-intuitive evolutionary outcomes may occur, resulting from the interplay between delay and the basins of attraction in the neighborhood of the internal fixed-points, and which may lead to full cooperation in conditions under which the outcome would be Full Defection in infinite populations.

Microarray gene expression analysis of porcine skeletal muscle sampled at several post mortem time points

A pilot study using Affymetrix Gene Chip ® Porcine Genome Arrays was set up to evaluate the impact of time lags from death on gene expression profiling of porcine skeletal muscle at four post mortem times (up to 24 h) during the routine processing of fresh thighs. All post chip parameters and data analyses (Average background, Scale Factors, Percent Presence, 3′/5′ ratios of β-actin and glyceraldehyde-3-phosphase dehydrogenase control genes, RNA degradation diagnostics, principal component analysis, hierarchical clustering, mixed regression models with time effects) did not show any effect of post mortem time. Therefore, microarray data obtained from muscle specimens collected in a processing plan over a quite long period have the potential to identify treatments or pre mortem conditions without any potential bias derived from subtle RNA degradation. These results open new perspectives to develop and analyse gene expression biomarkers for pig production and product authentication.

Optimising Skill Acquisition for Laparoendoscopic Single-Site Surgery using The Time-Lag Model

Introduction: Acquiring skills for Laparoendoscopic Single-Site Surgery (LESS) is complex and shown to have a very steep learning curve. We aimed to develop and test the novel concept of Time-Lag for LESS skill acquisition using an ex-vivo training module. Methods: We used a Time-Lag Camera-Monitor System that allows for a 2.0 second delay between real-time hand movement and the perception of the movement on the monitor. We tested the impact of time-lag on the time taken to learn to perform a simulated Vesicourethral Anastomosis (VUA) by taking two groups of 3 novices and training the first group to perform the procedure with the time-lag system, and the second without the time-lag. Once each novice could perform the procedure satisfactorily, each group was timed in performing the VUA in a real-time setting. Results: The group who had initially trained with the time-lag system performed the final VUA 1.8 times faster than the second group. Video analysis showed that novices who trained using the time-lag system had reduced intention anxiety compared to the second group. Conclusions: The Time-Lag model has potential application in acquiring skills for LESS. It could be used for preliminary skill acquisition in other laparoscopic procedures during training.

Multistability and gluing bifurcation to butterflies in coupled networks with non-monotonic feedback

Neural networks with a non-monotonic activation function have been proposed to increase their capacity for memory storage and retrieval, but there is still a lack of rigorous mathematical analysis and detailed discussions of the impact of time lag. Here we consider a two-neuron recurrent network. We first show how supercritical pitchfork bifurcations and a saddle-node bifurcation lead to the coexistence of multiple stable equilibria (multistability) in the instantaneous updating network. We then study the effect of time delay on the local stability of these equilibria and show that four equilibria lose their stability at a certain critical value of time delay, and Hopf bifurcations of these equilibria occur simultaneously, leading to multiple coexisting periodic orbits. We apply centre manifold theory and normal form theory to determine the direction of these Hopf bifurcations and the stability of bifurcated periodic orbits. Numerical simulations show very interesting global patterns of periodic solutions as the time delay is varied. In particular, we observe that these four periodic solutions are glued together along the stable and unstable manifolds of saddle points to develop a butterfly structure through a complicated process of gluing bifurcations of periodic solutions.

The Effects of Air Pollution on Acute Mortality in Moscow

ISEE-417 Objective: The objective of the study was to estimate the relationship between outdoor air pollution levels and daily nonaccidental mortality in Moscow. Material and Methods: Moscow is the only city in Russia with continuous air pollution monitoring. Twenty-four-hour average concentrations of PM10 were available in the period between January 2003 and December 2005. NOx, SO2, and ozone 24-hour average concentrations were available also. As confounding factors we considered humidity, air temperature, and atmospheric pressure. Daily mortality from all natural causes, cardiovascular and respiratory mortality among all ages was obtained from the state statistical service for the same period of time. The relationship between daily mortality and air pollution was investigated with a Poisson model including distributed lag model approach. Results: Concentration-response factor in 95% CI was estimated in a range of 0.3% to 0.7% increase of daily mortality per 10 μg/m3 of PM10 with z = 4.7. Impact of time lag was statistically insignificant. Concentration-response factor for daily mortality in summer was 0.6% to 1.5% in 95% CI. Results for 1-day lag were statistically significant with z = 2.05. Concentration-response factor for daily mortality in winter was lower, equal to 0.1% to 0.8% in 95% CI. Central estimate for total acute mortality attributed to PM10 air pollution was 2300 cases annually, or 1.7% of total annual mortality in Moscow. Conclusions: The estimates of concentration-response factor are within the range of results for European cities, but higher than results of most recent studies in United States. One of the explanations could be the difference in air conditioning practice, and indoor/outdoor activities. About 2% of mortality attributed to air pollution is about twice higher than transport accident fatalities in Moscow.

Time lags in fund of funds

The purpose of this paper is to analyse the impact upon tracking errors (TEs) of time lags in the calculation of fund of funds (FoF) net asset value (NAV). We examine how microstructure effects produce noise in the NAV of FoF and therefore noise in the TE. For that, we use simulations to calculate FoF NAVs at different closing dates. We then compare series of TEs to analyse the impact of time lags and formalise a relation adjusting the TE including error terms in the ratio.

The usefulness of the NHS Economic Evaluation Database to researchers undertaking technology assessment reviews.

Health-care technology reviews now increasingly include outcome costs as well as clinical effects. This study reports the findings and implications of a survey to explore the usefulness of the National Health Service Economic Evaluation Database (NHS EED) within this process. Postal survey of lead authors of Technology Assessment Reviews (TARs) commissioned by the United Kingdom's National Institute for Clinical Excellence (NICE). The questionnaire investigated the usefulness of NHS EED in terms of (a) search strategy, (b) data extraction, (c) quality assessment, and (d) determining requirements for new modeling studies. Qualitative data were requested, including opinions regarding NHS EED. NHS EED was used in 90 percent of all identified reviews (n = 46). The questionnaire response rate was 63 percent. The percentage of scores 3 or above (most useful), 2 or below (least useful), or N/A were, respectively, (a) search strategy= 62 percent, 23 percent, 15 percent; (b) data extraction = 23 percent, 27 percent, 50 percent, (c) quality assessment= 38 percent, 19 percent, 42 percent; (d) determining requirements for new modeling studies = 27 percent, 23 percent, 50 percent. The results were expanded further in the qualitative data from the respondents. NHS EED is a useful tool for a variety of tasks in the NICE/TAR process but not, unsurprisingly, as a replacement for understanding primary studies. There is, however, a need to reduce the impact of time lags between the publication of economic evaluations and the appearance of abstracts relating to them on NHS EED. The results will inform future developments of the NHS EED database, which should increase its usefulness to researchers.

The Impact of Time Lags in Competitor Response on Competition

AbstractStrategic management theory suggests that when time lags occur between one firm's action and another firm's reaction, the firm initiating a strategic move may gain a significant advantage over its rival. As a result, competitive activity is stimulated as rival firms apply tactics that have the potential of creating lag effects. Implicit in this conception of lag effects is the belief that first‐mover advantages may enable the initiating firm to establish a competitive advantage by creating long‐lived advantages, in branding or distribution, for example. However, time lags may matter in and of themselves, that is, absent first‐mover advantages, time lags may stimulate competition. Time lags increase the cognitive complexity of the task of tracking and sorting out competitive actions/signals, which inhibits tacit collusion. The results of two experiments using business executives as subjects support the notion that even absent first‐mover advantages, time lags stimulate competition. They also suggest that when a time‐lagged variable (for example, a nonprice variable) is studied in conjunction with a nonlagged variable (price), competition is stimulated on both variables.RésuméSelon la théorie des stratégies de la gestion, le lapse de temps possible entre l'action d'une entreprise et la réaction d'une autre peut dormer à la firme qui lance une manoeuvre stratégique un avantage important sur l'entreprise concurrente. Par conséquent, l'activité compétitive croi&amp;#x030c;t au fur et à mesure que les entreprises rivales adoptent des stratégies qui favorisent des effets de décalage temporel entre action et réaction. Cette conception des effets de décalage temporel comporte l'idée que les avantages dus au simple fait de prendre l'initiative permettent à l'entreprise de réaliser un avantage concurrentiel par la création d'avantages à long terme, notamment dans les domaines de la distribution ou de l'enregistrement des marques. Par ailleurs, il peut arriv‐er que les décalages aient des conséquences intrin‐séques, c'est‐à‐dire que, měme en l'bsence d'un avantage réel d'initiateur, le décalage augmente la compétition. Les décalages entravent le dépistage et l'interprétation des signaux et actions, ce qui entrave la complic‐ité tacite. Les résultats de deux enquětes, menées auprès d'informateurs cadres, semblent confirmer l'hypothèse que le principe du décalage stimule la compétition, měme en l'absence des avantages d'initiateur. Les résultats de ces expériences suggèrent aussi que la conjonc‐tion d'une variable décalée (par exemple une variable autre que le prix) et une variable non‐décalée (le prix) augmente la concurrence sur les deux variables.