2,245 research outputs found
Atrial cellular electrophysiological changes in patients with ventricular dysfunction may predispose to AF
<b>Background:</b>
Left ventricular systolic dysfunction (LVSD) is a risk factor for atrial fibrillation (AF), but the atrial cellular electrophysiological mechanisms in humans are unclear.
Objective
This study sought to investigate whether LVSD in patients who are in sinus rhythm (SR) is associated with atrial cellular electrophysiological changes that could predispose to AF.
<b>Methods:</b>
Right atrial myocytes were obtained from 214 consenting patients in SR who were undergoing cardiac surgery. Action potentials or ion currents were measured using the whole-cell-patch clamp technique.
<b>Results:</b>
The presence of moderate or severe LVSD was associated with a shortened atrial cellular effective refractory period (ERP) (209 ± 8 ms; 52 cells, 18 patients vs 233 ± 7 ms; 134 cells, 49 patients; P <0.05); confirmed by multiple linear regression analysis. The left ventricular ejection fraction (LVEF) was markedly lower in patients with moderate or severe LVSD (36% ± 4%, n = 15) than in those without LVSD (62% ± 2%, n = 31; P <0.05). In cells from patients with LVEF ≤ 45%, the ERP and action potential duration at 90% repolarization were shorter than in those from patients with LVEF > 45%, by 24% and 18%, respectively. The LVEF and ERP were positively correlated (r = 0.65, P <0.05). The L-type calcium ion current, inward rectifier potassium ion current, and sustained outward ion current were unaffected by LVSD. The transient outward potassium ion current was decreased by 34%, with a positive shift in its activation voltage, and no change in its decay kinetics.
<b>Conclusion:</b>
LVSD in patients in SR is independently associated with a shortening of the atrial cellular ERP, which may be expected to contribute to a predisposition to AF
Functional models and extending strategies for ecological networks
Complex network analysis is rising as an essential tool to understand properties of ecological landscape networks, and as an aid to land management. The most common methods to build graph models of ecological networks are based on representing functional connectivity with respect to a target species. This has provided good results, but the lack of a model able to capture general properties of the network may be seen as a shortcoming when the activity involves the proposal for modifications in land use. Similarity scores, calculated between nature protection areas, may act as a building block for a graph model intended to carry a higher degree of generality. The present work compares several design choices for similarity-based graphs, in order to determine which is most suitable for use in land management
Automated Targeting for Property Integration
Resource conservation is an effective way for reducing operation cost and to maintain business sustainability. Most previous works have been restricted to "chemo-centric" or concentration-based systems where the characterisation of the streams and constraints on the process sinks are described in terms of the concentration of pollutants. However, there are many applications in which stream quality is characterised by physical or chemical properties rather than pollutant concentration. In this work, the automated targeting approach originally developed for the synthesis of composition-based resource conservation network is extended for property-based network. Based on the concept of insight-based targeting approach, the automated targeting technique is formulated as a linear programming (LP) model for which the global optimum is guaranteed. Two literature examples are solved to illustrate the proposed approach
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High performance Carnot Batteries based on hybrid cycles
Pumped thermal energy storage (PTES) has seen a rapid increase in research interest and private investment during the last few years. A range of different concepts has been proposed, based on different thermodynamic cycles, and the most promising ones are already being turned into demonstration projects or small-scale storage plants. These include PTES systems based on the Joule-Brayton cycle, the Rankine cycle and the Liquid Air cycle, among others. This presentation will explore how hybridising some of these concepts can result in systems that are more flexible, cheaper, or have superior performance compared with the original cycles. More specifically, two examples will be shown where the Joule-Brayton cycle can be effectively used to support a Rankine battery and a Liquid Air battery. One general advantage of Brayton-PTES systems is that they can use molten salts as liquid storage media. Molten salts are cheap, safe and abundant, and have been used for concentrated solar power (CSP) applications in a large number of commercial plants. Employing the same storage material at similar temperature levels opens the possibility of hybrid “solar-PTES” systems that would require less capital investment than two separate plants. Such a hybrid system could charge the same hot stores using either solar energy or off-peak electricity, becoming both a power plant and an energy storage plant, therefore increasing the capacity factor while employing a single heat engine during discharge. A numerical model has been implemented to study a solar-PTES system where an existing CSP plant (based on the Rankine cycle) is retrofitted with a Brayton heat pump, and several strategies are explored to boost the overall performance. Similar configurations could be employed to transform other kinds of thermal power plant (such as coal power plants) into Brayton-Rankine batteries. In contrast to most PTES systems, liquid air energy storage (LAES) stores most of the available energy cryogenically, by liquefying atmospheric air and storing it at very low temperatures. This is advantageous because liquid air has a very high energy density - and is free. However, the difficulties in reaching full liquefaction during the charge process have a significant impact on the round-trip efficiency of the cycle. It has been found that these difficulties can be greatly minimised by employing the support of a Brayton cycle. A hybrid system was designed where a Brayton-PTES plant operates as a topping cycle and an LAES plant operates as a bottoming cycle. The cooling provided by the Brayton cycle allows the LAES side to achieve full air liquefaction, which translates into a significant boost in performance. Furthermore, the cold thermal reservoirs that would be required by the two separate cycles are replaced by a single heat exchanger that acts between them, therefore saving significant amounts of storage media per unit of energy stored. Results from a numerical study indicate that the hybrid cycle can increase round-trip efficiency by 5-10 percent points compared with the separate cycles, and achieve an even larger increase in terms of energy density
Field oriented control dataset of a 3-phase permanent magnet synchronous motor
This paper presents a dataset of a 3-phase Permanent Magnet Synchronous Motor (PMSM) controlled by a Field Oriented Control (FOC) scheme. The data set was generated from a simulated FOC motor control environment developed in Simulink; the model is available in the public GitHub repository1. The dataset includes the motor response to various input signal shapes that are fed to the control scheme to verify the control capabilities when the motor is subjected to real life scenarios and corner conditions. Motor control is one of the most widespread fields in control engineering as it is widely used in machine tools and robots, the FOC scheme is one of the most used control approaches thanks to its performance in speed and torque control, with the drawback of having to handcraft the Proportional-Integrative-Derivative (PID) regulators using Look Up Tables (LUT). The test conditions are designed by setting a motor desired speed. Different input speed variations shapes are proposed as well as extreme scenarios where the linear behaviour of the PID regulator is challenged by applying fast and high magnitude speed variations so that the PID controller is not able to correctly follow the reference. The measured data includes both the outer and inner-loop signals of the FOC, which opens the possibility to develop non-linear control approaches such as Machine Learning (ML) and Neural Networks (NN) with different topologies to replace the linear controllers in the FOC scheme
An Evaluation of Feature Selection Robustness on Class Noisy Data
With the increasing growth of data dimensionality, feature selection has become a crucial step in a variety of machine learning and data mining applications. In fact, it allows identifying the most important attributes of the task at hand, improving the efficiency, interpretability, and final performance of the induced models. In recent literature, several studies have examined the strengths and weaknesses of the available feature selection methods from different points of view. Still, little work has been performed to investigate how sensitive they are to the presence of noisy instances in the input data. This is the specific field in which our work wants to make a contribution. Indeed, since noise is arguably inevitable in several application scenarios, it would be important to understand the extent to which the different selection heuristics can be affected by noise, in particular class noise (which is more harmful in supervised learning tasks). Such an evaluation may be especially important in the context of class-imbalanced problems, where any perturbation in the set of training records can strongly affect the final selection outcome. In this regard, we provide here a two-fold contribution by presenting (i) a general methodology to evaluate feature selection robustness on class noisy data and (ii) an experimental study that involves different selection methods, both univariate and multivariate. The experiments have been conducted on eight high-dimensional datasets chosen to be representative of different real-world domains, with interesting insights into the intrinsic degree of robustness of the considered selection approaches
A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari
In the last years, the smart city paradigm has been deeply studied to support sustainable mobility and to improve human living conditions. In this context, a new smart city based on Social Internet of Things paradigm is presented in this article. Starting from the tracking of all vehicles (that is, private and public) and pedestrians, integrated with air quality measurements (that is, in real time by mobile and fixed sensors), the system aims to improve the viability of the city, both for pedestrian and vehicular users. A monitoring network based on sensors and devices hosted on board in local public transport allows real time monitoring of the most sensitive areas both from traffic congestion and from an environmental point of view. The proposed solution is equipped with an appropriate intelligence that takes into account instantaneous speed, type of traffic, and instantaneous pollution data, allowing to evaluate the congestion and pollution condition in a specific moment. Moreover, specific tools support the decisions of public administration facilitating the identification of the most appropriate actions for the implementation of effective policies relating to mobility. All collected data are elaborated in real time to improve traffic viability suggesting new directions and information to citizens to better organize how to live in the city
A new knock event definition for knock detection and control optimization
[EN] In this paper, the knock phenomenon is studied and characterized in the time-frequency domain. From the analysis results, a new knock event definition is proposed, which compares the excitation of the cylinder resonance produced by the autoignition of the end gas to that associated with the combustion. The new definition permits a more consistent differentiation between knocking and not knocking cycles than the classical approach in the literature, thus allowing the improvement of the knock control strategies.
The new knock index proposed analyses the frequency spectrum of the pressure signal in two locations, i.e. near the maximum heat release and near the end of combustion, by using the fast Fourier transform and a window function, and it is compared with the classical MAPO definition, which consists on finding the maximum pressure oscillation in the time domain. Both indices have been implemented online in a four-stroke SI engine and its performance is illustrated by using a classical knock control strategy. Results obtained under different operating conditions demonstrate that the improved knock index definition can substantially reduce the variability of the spark advance angle control, avoiding strong knocking events and reducing engine vibration.Bares-Moreno, P.; Selmanaj, D.; Guardiola, C.; Onder, C. (2018). A new knock event definition for knock detection and control optimization. Applied Thermal Engineering. 131:80-88. https://doi.org/10.1016/j.applthermaleng.2017.11.138S808813
Knock probability estimation through an in-cylinder temperature model with exogenous noise
[EN] This paper presents a new knock model which combines a deterministic knock model based on the in-cylinder temperature and an exogenous noise disturbing this temperature. The autoignition of the end-gas is modelled by an Arrhenius-like function and the knock probability is estimated by propagating a virtual error probability distribution. Results show that the random nature of knock can be explained by uncertainties at the in cylinder temperature estimation. The model only has one parameter for calibration and thus can be easily adapted online.
In order to reduce the measurement uncertainties associated with the air mass flow sensor, the trapped mass is derived from the in-cylinder pressure resonance, which improves the knock probability estimation and reduces the number of sensors needed for the model.
A four stroke SI engine was used for model validation. By varying the intake temperature, the engine speed, the injected fuel mass, and the spark advance, specific tests were conducted, which furnished data with various knock intensities and probabilities. The new model is able to predict the knock probability within a sufficient range at various operating conditions. The trapped mass obtained by the acoustical model was compared in steady conditions by using a fuel balance and a lambda sensor and differences below 1% were found. (C) 2017 Elsevier Ltd. All rights reserved.Bares-Moreno, P.; Selmanaj, D.; Guardiola, C.; Onder, C. (2018). Knock probability estimation through an in-cylinder temperature model with exogenous noise. Mechanical Systems and Signal Processing. 98:756-769. https://doi.org/10.1016/j.ymssp.2017.05.033S7567699
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