Technical Note: Estimating fusion properties for polyacids

Abstract. Organic aerosol (OA) components are generally assumed to be liquid-like. Hence, to describe the partitioning of these components, the liquid vapor pressure of these components is desired. Polyacids and functionalized polyacids can be a significant part of OA. But often, measurements are available only for solid state vapor pressure, which can differ by orders of magnitude from their liquid counterparts. To convert such a sublimation pressure to a subcooled liquid vapor pressure, fusion properties (two out of these three quantities: fusion enthalpy, fusion entropy, fusion temperature) are required. Unfortunately, experimental knowledge of fusion properties is sometimes missing in part or totally, hence an estimation method is required. Several fusion data estimation methods are tested here against experimental data of polyacids. Next, we develop a simple estimation method, specifically for this kind of compounds, reducing significantly the estimation error.

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Estimating fusion properties for functionalised acids

Atmospheric Chemistry and Physics ◽

10.5194/acp-11-8385-2011 ◽

2011 ◽

Vol 11 (16) ◽

pp. 8385-8394 ◽

Cited By ~ 6

Author(s):

S. Compernolle ◽

K. Ceulemans ◽

J.-F. Müller

Keyword(s):

Vapour Pressure ◽

Estimation Error ◽

Estimation Method ◽

Organic Aerosol ◽

Estimation Methods ◽

Fusion Temperature ◽

Subcooled Liquid ◽

Fusion Data ◽

Simple Estimation ◽

Data Estimation

Abstract. Multicomponent organic aerosol (OA) is likely to be liquid, or partially liquid. Hence, to describe the partitioning of these components, their liquid vapour pressure is desired. Functionalised acids (e.g. diacids) can be a significant part of OA. But often measurements are available only for solid state vapour pressure, which can differ by orders of magnitude from their liquid counterparts. To convert such a sublimation pressure to a subcooled liquid vapour pressure, fusion properties (two out of these three quantities: fusion enthalpy, fusion entropy, fusion temperature) are required. Unfortunately, experimental knowledge of fusion properties is sometimes missing in part or completely, hence an estimation method is required. Several fusion data estimation methods are tested here against experimental data of functionalised acids, and a simple estimation method is developed, specifically for this family of compounds, with a significantly smaller estimation error than the literature methods.

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Technical Note: Vapor pressure estimation methods applied to secondary organic aerosol constituents from α-pinene oxidation: an intercomparison study

Atmospheric Chemistry and Physics ◽

10.5194/acp-10-6271-2010 ◽

2010 ◽

Vol 10 (13) ◽

pp. 6271-6282 ◽

Cited By ~ 32

Author(s):

S. Compernolle ◽

K. Ceulemans ◽

J.-F. Müller

Keyword(s):

Vapor Pressure ◽

Functional Groups ◽

State Of The Art ◽

Organic Aerosol ◽

Technical Note ◽

Estimation Methods ◽

Vapor Pressures ◽

Aerosol Formation ◽

Pressure Estimation ◽

Intercomparison Study

Abstract. We applied and compared seven vapor pressure estimation methods to the condensable compounds generated in the oxidation of α-pinene, as described by the state-of-the-art mechanism of the BOREAM model Capouet et al., 2008. Several of these methods had to be extended in order to treat functional groups such as hydroperoxides and peroxy acyl nitrates. Large differences in the estimated vapor pressures are reported, which will inevitably lead to large differences in aerosol formation simulations. Cautioning remarks are given for some vapor pressure estimation methods.

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Two-hop distance estimation in wireless sensor networks

International Journal of Distributed Sensor Networks ◽

10.1177/1550147716689683 ◽

2017 ◽

Vol 13 (2) ◽

pp. 155014771668968 ◽

Cited By ~ 2

Author(s):

Sunyong Kim ◽

Sun Young Park ◽

Daehoon Kwon ◽

Jaehyun Ham ◽

Young-Bae Ko ◽

...

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Estimation Error ◽

Estimation Method ◽

Distance Estimation ◽

Wireless Sensor ◽

Estimation Methods ◽

Accurate Estimation ◽

Distance Information ◽

Wide Range

In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.

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Lower Bound on the Accuracy of Parameter Estimation Methods for Linear Sensorimotor Synchronization Models

Timing & Time Perception ◽

10.1163/22134468-00002047 ◽

2015 ◽

Vol 3 (1-2) ◽

pp. 32-51 ◽

Cited By ~ 4

Author(s):

Nori Jacoby ◽

Peter E. Keller ◽

Bruno H. Repp ◽

Merav Ahissar ◽

Naftali Tishby

Keyword(s):

Parameter Estimation ◽

Lower Bound ◽

Computational Models ◽

Linear Models ◽

Estimation Error ◽

Explanatory Power ◽

Estimation Method ◽

Sensorimotor Synchronization ◽

Estimation Methods ◽

Unbiased Estimation

The mechanisms that support sensorimotor synchronization — that is, the temporal coordination of movement with an external rhythm — are often investigated using linear computational models. The main method used for estimating the parameters of this type of model was established in the seminal work of Vorberg and Schulze (2002), and is based on fitting the model to the observed auto-covariance function of asynchronies between movements and pacing events. Vorberg and Schulze also identified the problem of parameter interdependence, namely, that different sets of parameters might yield almost identical fits, and therefore the estimation method cannot determine the parameters uniquely. This problem results in a large estimation error and bias, thereby limiting the explanatory power of existing linear models of sensorimotor synchronization. We present a mathematical analysis of the parameter interdependence problem. By applying the Cramér–Rao lower bound, a general lower bound limiting the accuracy of any parameter estimation procedure, we prove that the mathematical structure of the linear models used in the literature determines that this problem cannot be resolved by any unbiased estimation method without adopting further assumptions. We then show that adding a simple and empirically justified constraint on the parameter space — assuming a relationship between the variances of the noise terms in the model — resolves the problem. In a follow-up paper in this volume, we present a novel estimation technique that uses this constraint in conjunction with matrix algebra to reliably estimate the parameters of almost all linear models used in the literature.

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Technical note: Vapor pressure estimation methods applied to secondary organic aerosol constituents from α-pinene oxidation: an intercomparison study

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-10-8487-2010 ◽

2010 ◽

Vol 10 (4) ◽

pp. 8487-8513 ◽

Cited By ~ 2

Author(s):

S. Compernolle ◽

K. Ceulemans ◽

J.-F. Müller

Keyword(s):

Vapor Pressure ◽

Functional Groups ◽

State Of The Art ◽

Organic Aerosol ◽

Technical Note ◽

Estimation Methods ◽

Vapor Pressures ◽

Aerosol Formation ◽

Pressure Estimation ◽

Intercomparison Study

Abstract. We applied and compared seven vapor pressure estimation methods to the condensable compounds generated in the oxidation of α-pinene, as described by the state-of-the-art mechanism of the BOREAM model (Capouet et al., 2008). Several of these methods had to be extended in order to treat functional groups such as hydroperoxides and peroxy acyl nitrates. Large differences in the estimated vapor pressures are reported, which will inevitably lead to large differences in aerosol formation simulations. Cautioning remarks are given for some vapor pressure estimation methods.

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Subcooled Liquid Vapor Pressure

IUPAC Standards Online ◽

10.1515/iupac.81.0878 ◽

2016 ◽

Author(s):

Monica Nordberg ◽

Douglas M. Templeton ◽

Ole Andersen ◽

John H. Duffus

Keyword(s):

Vapor Pressure ◽

Subcooled Liquid ◽

Liquid Vapor

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Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs

Sensors ◽

10.3390/s20226558 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6558

Author(s):

Yang Chen ◽

Chengcheng Hong ◽

Michael R. Pinsky ◽

Ting Ma ◽

Gilles Clermont

Keyword(s):

Blood Loss ◽

Early Stage ◽

Estimation Error ◽

Estimation Method ◽

Vital Signs ◽

Estimation Methods ◽

Clinical Implementation ◽

Arterial Blood ◽

Blood Loss Volume ◽

Estimation Models

Background: There are currently no effective and accurate blood loss volume (BLV) estimation methods that can be implemented in operating rooms. To improve the accuracy and reliability of BLV estimation and facilitate clinical implementation, we propose a novel estimation method using continuously monitored photoplethysmography (PPG) and invasive arterial blood pressure (ABP). Methods: Forty anesthetized York Pigs (31.82 ± 3.52 kg) underwent a controlled hemorrhage at 20 mL/min until shock development was included. Machine-learning-based BLV estimation models were proposed and tested on normalized features derived by vital signs. Results: The results showed that the mean ± standard deviation (SD) for estimating BLV against the reference BLV of our proposed random-forest-derived BLV estimation models using PPG and ABP features, as well as the combination of ABP and PPG features, were 11.9 ± 156.2, 6.5 ± 161.5, and 7.0 ± 139.4 mL, respectively. Compared with traditional hematocrit computation formulas (estimation error: 102.1 ± 313.5 mL), our proposed models outperformed by nearly 200 mL in SD. Conclusion: This is the first attempt at predicting quantitative BLV from noninvasive measurements. Normalized PPG features are superior to ABP in accurately estimating early-stage BLV, and normalized invasive ABP features could enhance model performance in the event of a massive BLV.

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A Sparse-Based Off-Grid DOA Estimation Method for Coprime Arrays

Sensors ◽

10.3390/s18093025 ◽

2018 ◽

Vol 18 (9) ◽

pp. 3025 ◽

Cited By ~ 5

Author(s):

Weijian Si ◽

Fuhong Zeng ◽

Changbo Hou ◽

Zhanli Peng

Keyword(s):

Covariance Matrix ◽

Estimation Error ◽

Estimation Method ◽

Doa Estimation ◽

Sample Covariance Matrix ◽

Estimation Methods ◽

Matrix Estimation ◽

Estimation Process ◽

Sample Covariance ◽

Coprime Arrays

Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from grid mismatch problem due to the discretization of the potential angle space, which will cause DOA estimation performance degradation when the target is off-grid. To this end, we proposed a sparse-based off-grid DOA estimation method for coprime arrays in this paper, which includes two parts: coarse estimation process and fine estimation process. In the coarse estimation process, the grid points closest to the true DOAs, named coarse DOAs, are derived by solving an optimization problem, which is constructed according to the statistical property of the vectorized covariance matrix estimation error. Meanwhile, we eliminate the unknown noise variance effectively through a linear transformation. Due to finite snapshots effect, some undesirable correlation terms between signal and noise vectors exist in the sample covariance matrix. In the fine estimation process, we therefore remove the undesirable correlation terms from the sample covariance matrix first, and then utilize a two-step iterative method to update the grid biases. Combining the coarse DOAs with the grid biases, the final DOAs can be obtained. In the end, simulation results verify the effectiveness of the proposed method.

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