Modelling hourly rates of evaporation from small lakes

The paper presents the results of a field study of open water evaporation carried out on three small lakes in Western and Northern Canada. In this case small lakes are defined as those for which the temperature above the water surface is governed by the upwind land surface conditions; that is, a continuous boundary layer exists over the lake, and large-scale atmospheric effects such as entrainment do not come into play. Lake evaporation was measured directly using eddy covariance equipment; profiles of wind speed, air temperature and humidity were also obtained over the water surfaces. Observations were made as well over the upwind land surface. <br><br> The major factors controlling open water evaporation were examined. The study showed that for time periods shorter than daily, the open water evaporation bears no relationship to the net radiation; the wind speed is the most significant factor governing the evaporation rates, followed by the land-water temperature contrast and the land-water vapour pressure contrast. The effect of the stability on the wind field was demonstrated; relationships were developed relating the land-water wind speed contrast to the land-water temperature contrast. The open water period can be separated into two distinct evaporative regimes: the warming period in the Spring, when the land is warmer than the water, the turbulent fluxes over water are suppressed; and the cooling period, when the water is warmer than the land, the turbulent fluxes over water are enhanced. <br><br> Relationships were developed between the hourly rates of lake evaporation and the following significant variables and parameters (wind speed, land-lake temperature and humidity contrasts, and the downwind distance from shore). The result is a relatively simple versatile model for estimating the hourly lake evaporation rates. The model was tested using two independent data sets. Results show that the modelled evaporation follows the observed values very well; the model follows the diurnal trends and responds to changes in environmental conditions

Comparison of sediment biomarker signatures generated using time-integrated and discrete suspended sediment samples

Sediment source fingerprinting using biomarker properties has led to new insights in our understanding of land use contributions to time-integrated suspended sediment samples at catchment scale. A time-integrated mass-flux sampler (TIMS; also known as the ‘Phillips’ sampler), a cost-effective approach for suspended sediment collection in situ. Such samplers are being used to collect sediment samples for source fingerprinting purposes, including by studies using biomarkers as opposed to more conventional tracer properties. Here, we assessed the performance of TIMS for collecting representative sediment samples for biomarkers during high discharge events in a small lowland agricultural catchment. Concentrations of long odd-chain n-alkanes (>C23) and both saturated free and bound fatty acids (C14-C32), as well as compound-specific 13C were compared between sediment collected by both TIMS and auto-samplers (ISCO). The results showed that concentrations of alkanes, free fatty acids and bound fatty acids are consistently comparable between TIMS and ISCO suspended sediment samples. Similarly, compound-specific 13C signals were not found to be significantly different in the suspended sediment samples collected using the different samplers. However, different magnitudes of resemblance in biomarker concentrations and compositions between the samples collected using the two sediment collection methods were confirmed by overlapping index and symmetric coordinates-based correlation analysis. Here, the difference is attributed to the contrasting temporal basis of TIMS (time-integrated) vs ISCO (discrete) samples, as well as potential differences in the particle sizes collected by these different sediment sampling methods. Nevertheless, our findings suggest that TIMS can be used to generate representative biomarker data for suspended sediment samples collected during high discharge events

Time series aggregation, disaggregation and long memory

We study the aggregation/disaggregation problem of random parameter AR(1) processes and its relation to the long memory phenomenon. We give a characterization of a subclass of aggregated processes which can be obtained from simpler, "elementary", cases. In particular cases of the mixture densities, the structure (moving average representation) of the aggregated process is investigated

Detecting intraday periodicities with application to high frequency exchange rates

Many recent papers have documented periodicities in returns, return volatility, bid–ask spreads and trading volume, in both equity and foreign exchange markets. We propose and employ a new test for detecting subtle periodicities in time series data based on a signal coherence function. The technique is applied to a set of seven half-hourly exchange rate series. Overall, we find the signal coherence to be maximal at the 8-h and 12-h frequencies. Retaining only the most coherent frequencies for each series, we implement a trading rule that is based on these observed periodicities. Our results demonstrate in all cases except one that, in gross terms, the rules can generate returns that are considerably greater than those of a buy-and-hold strategy, although they cannot retain their profitability net of transactions costs. We conjecture that this methodology could constitute an important tool for financial market researchers which will enable them to detect, quantify and rank the various periodic components in financial data better

Quantum Monte Carlo study of quasi-one-dimensional Bose gases

We study the behavior of quasi-one-dimensional (quasi-1d) Bose gases by Monte Carlo techniques, i.e., by the variational Monte Carlo, the diffusion Monte Carlo, and the fixed-node diffusion Monte Carlo technique. Our calculations confirm and extend our results of an earlier study [Astrakharchik et al., cond-mat/0308585]. We find that a quasi-1d Bose gas i) is well described by a 1d model Hamiltonian with contact interactions and renormalized coupling constant; ii) reaches the Tonks-Girardeau regime for a critical value of the 3d scattering length a_3d; iii) enters a unitary regime for |a_3d| -> infinity, where the properties of the gas are independent of a_3d and are similar to those of a 1d gas of hard-rods; and iv) becomes unstable against cluster formation for a critical value of the 1d gas parameter. The accuracy and implications of our results are discussed in detail.Comment: 15 pages, 9 figure

On the General Analytical Solution of the Kinematic Cosserat Equations

Based on a Lie symmetry analysis, we construct a closed form solution to the kinematic part of the (partial differential) Cosserat equations describing the mechanical behavior of elastic rods. The solution depends on two arbitrary analytical vector functions and is analytical everywhere except a certain domain of the independent variables in which one of the arbitrary vector functions satisfies a simple explicitly given algebraic relation. As our main theoretical result, in addition to the construction of the solution, we proof its generality. Based on this observation, a hybrid semi-analytical solver for highly viscous two-way coupled fluid-rod problems is developed which allows for the interactive high-fidelity simulations of flagellated microswimmers as a result of a substantial reduction of the numerical stiffness.Comment: 14 pages, 3 figure

On directed information theory and Granger causality graphs

Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.Comment: accepted for publications, Journal of Computational Neuroscienc

Hypertension in response to IL-6 during pregnancy: role of AT1-receptor activation

BACKGROUND: Increases in interleukin 6 (IL-6) and agonistic autoantibodies to the angiotensin II type 1 receptor (AT1-AA) are proposed to be important links between placental ischemia and hypertension in preeclampsia. METHODS: The purpose of this study was to determine whether IL-6 (5 ng/day), infused into normal pregnant (NP) rats, increased mean arterial pressure (MAP) and AT1-AA. MAP was analyzed in the presence and absence of an angiotensin type 1 receptor (AT1R) antagonist, losartan, L. RESULTS: MAP and AT1-AA increased from 102 ± 2 to 118 ± 4 mmHg and 0.7 ± 0.3 NP to 14.1 ± 1.4 chronotropic units with chronic IL-6 infusion. MAP responses to IL-6 were abolished in losartan pretreated rats (85 ± 4 in NP + L vs 85 ± 3 mmHg in IL-6 + L). CONCLUSION: These data indicate that IL-6 stimulates AT1-AA and that activation of the AT1R mediates IL-6 induced hypertension during pregnancy

Probing the wave function and dynamics of the quintet multiexciton state with coherent control in a singlet fission material

High-spin states play a key role in chemical reactions found in nature. In artificial molecular systems, singlet fission produces a correlated triplet-pair state, a spin-bearing excited state that can be harnessed for more efficient solar-energy conversion and photocatalysis. In particular, triplet-pair states with overall quintet character (total spin S=2) have been discovered, but many of the fundamental properties of these biexciton states remain unexplored. The net spin of these pair states makes spin-sensitive probes attractive for their characterization. Combined with their surprisingly long spin coherence (of order microseconds), this opens up techniques relying on coherent spin control. Here we apply coherent manipulation of triplet-pair states to (i) isolate their spectral signatures from coexisting free triplets and (ii) selectively couple quintet and triplet states to specific nuclear spins. Using this approach, we separate quintet and triplet transitions and extract the relaxation dynamics and hyperfine couplings of the fission-borne spin states. Our results highlight the distinct properties of correlated and free triplet excitons and demonstrate optically induced nuclear spin polarization by singlet fission

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach