916 resultados para Conception of time


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Amyloid fibrils resulting from uncontrolled peptide aggregation are associated with several neurodegenerative diseases. Their polymorphism depends on a number of factors including pH, ionic strength, electrostatic interactions, hydrophobic interactions, hydrogen bonding, aromatic stacking interactions, and chirality. Understanding the mechanism of amyloid fibril formation can improve strategies towards the prevention of fibrillation processes and enable a wide range of potential applications in nanotemplating and nanotechnology.

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PV only generates electricity during daylight hours and primarily generates over summer. In the UK, the carbon intensity of grid electricity is higher during the daytime and over winter. This work investigates whether the grid electricity displaced by PV is high or low carbon compared to the annual mean carbon intensity using carbon factors at higher temporal resolutions (half-hourly and daily). UK policy for carbon reporting requires savings to be calculated using the annual mean carbon intensity of grid electricity. This work offers an insight into whether this technique is appropriate. Using half hourly data on the generating plant supplying the grid from November 2008 to May 2010, carbon factors for grid electricity at half-hourly and daily resolution have been derived using technology specific generation emission factors. Applying these factors to generation data from PV systems installed on schools, it is possible to assess the variation in the carbon savings from displacing grid electricity with PV generation using carbon factors with different time resolutions. The data has been analyzed for a period of 363 to 370 days and so cannot account for inter-year variations in the relationship between PV generation and carbon intensity of the electricity grid. This analysis suggests that PV displaces more carbon intensive electricity using half-hourly carbon factors than using daily factors but less compared with annual ones. A similar methodology could provide useful insights on other variable renewable and demand-side technologies and in other countries where PV performance and grid behavior are different.

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The time-course of metabolic events following response to a model hepatotoxin ethionine (800 mg/kg) was investigated over a 7 day period in rats using high-resolution (1)H NMR spectroscopic analysis of urine and multivariate statistics. Complementary information was obtained by multivariate analysis of (1)H MAS NMR spectra of intact liver and by conventional histopathology and clinical chemistry of blood plasma. (1)H MAS NMR spectra of liver showed toxin-induced lipidosis 24 h postdose consistent with the steatosis observed by histopathology, while hypertaurinuria was suggestive of liver injury. Early biochemical changes in urine included elevation of guanidinoacetate, suggesting impaired methylation reactions. Urinary increases in 5-oxoproline and glycine suggested disruption of the gamma-glutamyl cycle. Signs of ATP depletion together with impairment of the energy metabolism were given from the decreased levels in tricarboxylic acid cycle intermediates, the appearance of ketone bodies in urine, the depletion of hepatic glucose and glycogen, and also hypoglycemia. The observed increase in nicotinuric acid in urine could be an indication of an increase in NAD catabolism, a possible consequence of ATP depletion. Effects on the gut microbiota were suggested by the observed urinary reductions in the microbial metabolites 3-/4-hydroxyphenyl propionic acid, dimethylamine, and tryptamine. At later stages of toxicity, there was evidence of kidney damage, as indicated by the tubular damage observed by histopathology, supported by increased urinary excretion of lactic acid, amino acids, and glucose. These studies have given new insights into mechanisms of ethionine-induced toxicity and show the value of multisystem level data integration in the understanding of experimental models of toxicity or disease.

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Dynamic multi-user interactions in a single networked virtual environment suffer from abrupt state transition problems due to communication delays arising from network latency--an action by one user only becoming apparent to another user after the communication delay. This results in a temporal suspension of the environment for the duration of the delay--the virtual world `hangs'--followed by an abrupt jump to make up for the time lost due to the delay so that the current state of the virtual world is displayed. These discontinuities appear unnatural and disconcerting to the users. This paper proposes a novel method of warping times associated with users to ensure that each user views a continuous version of the virtual world, such that no hangs or jumps occur despite other user interactions. Objects passed between users within the environment are parameterized, not by real time, but by a virtual local time, generated by continuously warping real time. This virtual time periodically realigns itself with real time as the virtual environment evolves. The concept of a local user dynamically warping the local time is also introduced. As a result, the users are shielded from viewing discontinuities within their virtual worlds, consequently enhancing the realism of the virtual environment.

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We study linear variable coefficient control problems in descriptor form. Based on a behaviour approach and the general theory for linear differential algebraic systems we give the theoretical analysis and describe numerically stable methods to determine the structural properties of the system.

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One of the most common Demand Side Management programs consists of Time-of-Use (TOU) tariffs, where consumers are charged differently depending on the time of the day when they make use of energy services. This paper assesses the impacts of TOU tariffs on a dataset of residential users from the Province of Trento in Northern Italy in terms of changes in electricity demand, price savings, peak load shifting and peak electricity demand at substation level. Findings highlight that TOU tariffs bring about higher average electricity consumption and lower payments by consumers. A significant level of load shifting takes place for morning peaks. However, issues with evening peaks are not resolved. Finally, TOU tariffs lead to increases in electricity demand for substations at peak time.

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Several methods are examined which allow to produce forecasts for time series in the form of probability assignments. The necessary concepts are presented, addressing questions such as how to assess the performance of a probabilistic forecast. A particular class of models, cluster weighted models (CWMs), is given particular attention. CWMs, originally proposed for deterministic forecasts, can be employed for probabilistic forecasting with little modification. Two examples are presented. The first involves estimating the state of (numerically simulated) dynamical systems from noise corrupted measurements, a problem also known as filtering. There is an optimal solution to this problem, called the optimal filter, to which the considered time series models are compared. (The optimal filter requires the dynamical equations to be known.) In the second example, we aim at forecasting the chaotic oscillations of an experimental bronze spring system. Both examples demonstrate that the considered time series models, and especially the CWMs, provide useful probabilistic information about the underlying dynamical relations. In particular, they provide more than just an approximation to the conditional mean.

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Residential electricity demand in most European countries accounts for a major proportion of overall electricity consumption. The timing of residential electricity demand has significant impacts on carbon emissions and system costs. This paper reviews the data and methods used in time use studies in the context of residential electricity demand modelling. It highlights key issues which are likely to become more topical for research on the timing of electricity demand following the roll-out of smart metres.

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This large-scale study examined the development of time-based prospective memory (PM) across childhood and the roles that working memory updating and time monitoring play in driving age effects in PM performance. One hundred and ninety-seven children aged 5 to 14 years completed a time-based PM task where working memory updating load was manipulated within individuals using a dual task design. Results revealed age-related increases in PM performance across childhood. Working memory updating load had a negative impact on PM performance and monitoring behavior in older children, but this effect was smaller in younger children. Moreover, the frequency as well as the pattern of time monitoring predicted children’s PM performance. Our interpretation of these results is that processes involved in children’s PM may show a qualitative shift over development from simple, nonstrategic monitoring behavior to more strategic monitoring based on internal temporal models that rely specifically on working memory updating resources. We discuss this interpretation with regard to possible trade-off effects in younger children as well as alternative accounts.

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Using a numerical implementation of the Cowley and Lockwood (1992) model of flow excitation in the magnetosphere–ionosphere (MI) system, we show that both an expanding (on a _12-min timescale) and a quasiinstantaneous response in ionospheric convection to the onset of magnetopause reconnection can be accommodated by the Cowley–Lockwood conceptual framework. This model has a key feature of time dependence, necessarily considering the history of the coupled MI system. We show that a residual flow, driven by prior magnetopause reconnection, can produce a quasi-instantaneous global ionospheric convection response; perturbations from an equilibrium state may also be present from tail reconnection, which will superpose constructively to give a similar effect. On the other hand, when the MI system is relatively free of pre-existing flow, we can most clearly see the expanding nature of the response. As the open-closed field line boundary will frequently be in motion from such prior reconnection (both at the dayside magnetopause and in the cross-tail current sheet), it is expected that there will usually be some level of combined response to dayside reconnection.

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A numerical model embodying the concepts of the Cowley-Lockwood (Cowley and Lockwood, 1992, 1997) paradigm has been used to produce a simple Cowley– Lockwood type expanding flow pattern and to calculate the resulting change in ion temperature. Cross-correlation, fixed threshold analysis and threshold relative to peak are used to determine the phase speed of the change in convection pattern, in response to a change in applied reconnection. Each of these methods fails to fully recover the expansion of the onset of the convection response that is inherent in the simulations. The results of this study indicate that any expansion of the convection pattern will be best observed in time-series data using a threshold which is a fixed fraction of the peak response. We show that these methods used to determine the expansion velocity can be used to discriminate between the two main models for the convection response to a change in reconnection.

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This paper presents a numerical model for predicting the evolution of the pattern of ionospheric convection in response to general time-dependent magnetic reconnection at the dayside magnetopause and in the cross-tail current sheet of the geomagnetic tail. The model quantifies the concepts of ionospheric flow excitation by Cowley and Lockwood (1992), assuming a uniform spatial distribution of ionospheric conductivity. The model is demonstrated using an example in which travelling reconnection pulses commence near noon and then move across the dayside magnetopause towards both dawn and dusk. Two such pulses, 8 min apart, are used and each causes the reconnection to be active for 1 min at every MLT that they pass over. This example demonstrates how the convection response to a given change in the interplanetary magnetic field (via the reconnection rate) depends on the previous reconnection history. The causes of this effect are explained. The inherent assumptions and the potential applications of the model are discussed.