Dark matter with chiral symmetry admixed hadronic matter in compact stars

With the two-fluid TOV equation, the properties of dark matter (DM) admixed NSs (DANSs) have been studied. Different from previous studies, we found that increase of the maximum mass and decrease of the radius of 1.4 $M_\odot$ can occur simultaneously in DANS. This stems from the fact that the equation of state (EOS) of DM can be very soft at low density but very stiff at high density. It is well known that the IU-FSU and XS models can not reproduce the neutron star (NS) with a maximum mass greater than 2.0 $M_\odot$. However, considering IU-FSU and XS models in DANS, there are always mass and interactions of DM that can reproduce a maximum mass greater than 2.0 $M_\odot$ and the radius of 1.4 $M_\odot$ below 13.7km. The difference of DANS between the DM with chiral symmetry (DMC) and the DM with meson exchange (DMM) becomes obvious when the central energy density ratio of the DM is greater than one of the NM. When the central energy density ratio of the DM is greater than one of the NM, the DMC model with the DM mass of 1000 MeV still can reproduce a maximum mass greater than 2.0 $M_\odot$ and the radius of 1.4 $M_\odot$ below 13.7km. In the same case, although the maximum mass of DANS with the DMM model is greater than 2.0 $M_\odot$ , the radius of 1.4 $M_\odot$ with the DMM model will surpass 13.7km obviously. \com{In two-fluid system, it is worth noting that the maximum mass of DANS can be larger than 3.0 $M_\odot$. As a consequence, the dimensionless tidal deformability $\Lambda_{CP}$ of DANS with 1.4 $M_\odot$, which increase with increasing the maximum mass of DANS, could be larger than 800 when the radius of DANS with 1.4 $M_\odot$ is about 13.0km.}

Central Energy Storage System to Reduce the Harmful Effects of PV Systems under a High Penetration Scenario

Photovoltaic (PV) systems are used to generate electricity and they are considered less aggressive in their environmental impact. These systems require no further maintenance once they are installed, they do not pollute the environment and their average lifespan is high. These are the reasons why they are considered a good alternative for electricity generation. However, PV systems have different challenges to solve when they are connected to low voltage distribution grids and one of them is related to the power quality when there is a high penetration of PV systems, when voltage variations usually appear. In this paper, a central energy storage system (ESS) is considered to alleviate grid voltage variations under a high penetration scenario of PV systems instead of using multiples ESS. The proposal controls the energy injected into the grid to maintain the voltage variation within the standard, no limitations are imposed on the PV systems, and then as an advantage of the proposal, the PVs’ available energy is fully delivered to the grid. In addition, in the absence of sunlight the ESS may provide power to the grid. An analysis and experimental setup were built to validate the proposed scheme.

Development of the Technical Structure of the “Cow Energy” Concept

Regional energy supply is an important topic in the context of the energy transition in Germany. The “Cow Energy” project aims to combine the production of energy and milk for the farmer. In order to take the different needs into account, a central energy management system (EMS) is being established. This system records and simulates how much electricity is generated from renewable sources (biogas, solar, wind, etc.) on the farm. This is compared with the consumption of the barn technology (milking robot, feeding robot, etc.). This energy management is regulated according to the needs of the cows. In order to balance the fluctuations between energy production and energy consumption, the EMS regulates various battery systems. One goal is to network this energy system with the region and to establish regional energy networks.

A comparison of shear- and compression-induced mechanotransduction in SW1353 chondrocytes

Mechanotransduction is a biological phenomenon where mechanical stimuli are converted to biochemical responses. A model system for studying mechanotransduction are the chondrocytes of articular cartilage. Breakdown of this tissue results in decreased mobility, increased pain, and reduced quality of life. Either disuse or overloading can disrupt cartilage homeostasis, but physiological cyclical loading promotes cartilage homeostasis. To model this, we exposed SW1353 cells to cyclical mechanical stimuli, shear and compression, for different durations of time (15 and 30 min). By utilizing liquid chromatography-mass spectroscopy (LC-MS), metabolomic profiles were generated detailing metabolite features and biological pathways that are altered in response to mechanical stimulation. In total, 1,457 metabolite features were detected. Statistical analyses identified several pathways of interest. Taken together, differences between experimental groups were associated with inflammatory pathways, lipid metabolism, beta-oxidation, central energy metabolism, and amino acid production. These findings expand our understanding of chondrocyte mechanotransduction under varying loading conditions and time periods.

Parametric time-frequency analysis and its applications in biomedical and multimedia

Analysis of non-stationary signals is a challenging task. The purpose of this thesis is to explore an efficient and powerful technique to analyze and classify two types of non-stationary signals, that is, multimedia signals in higher frequency range (44.1 kHz) and biomedical signals in lower frequency range (2 kHz). An adaptive true non-stationary time -frequency signal analysis tool - matching pursuit, is introduced and applied to decompose the sample signals into time-frequency functions or atoms. Atom parameters are analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using matching pursuit, several additional features, such as central energy and octave activeness ratio, are also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. In the 6-group classification of 96 pieces of 5-second music signals, such as, christmas choir, country, greek music, jazz, rock and scottish music, the accuracy reaches 89.6%, when the feature set includes standard deviation of octave (the scale factor which controls the width of the window function), median of octave, standard deviation of innerProdI (imaginary part of the inner-product between the signal and the atom), standard deviation of realGG (real part of the inner-product between the complex atom and its conjugate), and central energy. For the database of 112 pieces of 10-second music sugnals, the 2-group classification (rock-like and classical-like) accuracy achieves 100%, having a standard deviation of octaves in the first 2,000 atoms as the discriminant feature. An accuracy of 74.2% is obtained for the 2-group knee sound signal classification, and optimum feature set comprises octave activeness ratio, central energy and standard deviation of innerProdI. From our experiments, it is evident that the matching pursuit algorithm with the Gabor dictionary decomposes non-stationary signals, including multimedia signals in higher frequency and biomedical signals in lower frequency ranges, into atoms whose parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.

Parametric time-frequency analysis and its applications in biomedical and multimedia

Analysis of non-stationary signals is a challenging task. The purpose of this thesis is to explore an efficient and powerful technique to analyze and classify two types of non-stationary signals, that is, multimedia signals in higher frequency range (44.1 kHz) and biomedical signals in lower frequency range (2 kHz). An adaptive true non-stationary time -frequency signal analysis tool - matching pursuit, is introduced and applied to decompose the sample signals into time-frequency functions or atoms. Atom parameters are analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using matching pursuit, several additional features, such as central energy and octave activeness ratio, are also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. In the 6-group classification of 96 pieces of 5-second music signals, such as, christmas choir, country, greek music, jazz, rock and scottish music, the accuracy reaches 89.6%, when the feature set includes standard deviation of octave (the scale factor which controls the width of the window function), median of octave, standard deviation of innerProdI (imaginary part of the inner-product between the signal and the atom), standard deviation of realGG (real part of the inner-product between the complex atom and its conjugate), and central energy. For the database of 112 pieces of 10-second music sugnals, the 2-group classification (rock-like and classical-like) accuracy achieves 100%, having a standard deviation of octaves in the first 2,000 atoms as the discriminant feature. An accuracy of 74.2% is obtained for the 2-group knee sound signal classification, and optimum feature set comprises octave activeness ratio, central energy and standard deviation of innerProdI. From our experiments, it is evident that the matching pursuit algorithm with the Gabor dictionary decomposes non-stationary signals, including multimedia signals in higher frequency and biomedical signals in lower frequency ranges, into atoms whose parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.

Virtual Energy Storage in RES-Powered Smart Grids with Nonlinear Model Predictive Control

The integration of a variety of heterogeneous energy sources and different energy storage systems has led to complex infrastructures and made apparent the urgent need for efficient energy control and management. This work presents a non-linear model predictive controller (NMPC) that aims to coordinate the operation of interconnected multi-node microgrids with energy storage capabilities. This control strategy creates a superstructure of a smart-grid consisting of distributed interconnected microgrids, and has the ability to distribute energy among a pool of energy storage means in an optimal way, formulating a virtual central energy storage platform. The goal of this work is the optimal exploitation of energy produced and stored in multi-node microgrids, and the reduction of auxiliary energy sources. A small-scale multi-node microgrid was used as a basis for the mathematical modelling and real data were used for the model validation. A number of operation scenarios under different weather conditions and load requests, demonstrates the ability of the NMPC to supervise the multi-node microgrid resulting to optimal energy management and reduction of the auxiliary power devices operation.

Application of Carbon-13 Isotopomer Analysis to Assess Perinatal Myocardial Glucose Metabolism in Sheep

Ovine models of pregnancy have been used extensively to study maternal–fetal interactions and have provided considerable insight into nutrient transfer to the fetus. Ovine models have also been utilized to study congenital heart diseases. In this work, we demonstrate a comprehensive assessment of heart function and metabolism using a perinatal model of heart function with the addition of a [U-13C]glucose as tracer to study central energy metabolism. Using nuclear magnetic resonance spectroscopy, and metabolic modelling, we estimate myocardial citric acid cycle turnover (normalized for oxygen consumption), substrate selection, and anaplerotic fluxes. This methodology can be applied to studying acute and chronic effects of hormonal signaling in future studies.