Constraining Dark Energy and Cosmological Transition Redshift with Type Ia Supernovae

The property of dark energy and the physical reason for acceleration of the present universe are two of the most difficult problems in modern cosmology. The dark energy contributes about two-thirds of the critical density of the present universe from the observations of type-Ia supernova (SNe Ia) and anisotropy of cosmic microwave background (CMB).The SN Ia observations also suggest that the universe expanded from a deceleration to an acceleration phase at some redshift, implying the existence of a nearly uniform component of dark energy with negative pressure. We use the ``gold'' sample containing 157 SNe Ia and two recent well-measured additions, SNe Ia 1994ae and 1998aq to explore the properties of dark energy and the transition redshift. For a flat universe with the cosmological constant, we measure $\Omega_{M}=0.28_{-0.05}^{+0.04}$, which is consistent with Riess et al. The transition redshift is $z_{T}=0.60_{-0.08}^{+0.06}$. We also discuss several dark energy models that define the $w(z)$ of the parameterized equation of state of dark energy including one parameter and two parameters ($w(z)$ being the ratio of the pressure to energy density). Our calculations show that the accurately calculated transition redshift varies from $z_{T}=0.29_{-0.06}^{+0.07}$ to $z_{T}=0.60_{-0.08}^{+0.06}$ across these models. We also calculate the minimum redshift $z_{c}$ at which the current observations need the universe to accelerate.Comment: 16 pages, 5 figures, 1 tabl

Cosmological Dynamics of Phantom Field

We study the general features of the dynamics of the phantom field in the cosmological context. In the case of inverse coshyperbolic potential, we demonstrate that the phantom field can successfully drive the observed current accelerated expansion of the universe with the equation of state parameter $w_{\phi} < -1$. The de-Sitter universe turns out to be the late time attractor of the model. The main features of the dynamics are independent of the initial conditions and the parameters of the model. The model fits the supernova data very well, allowing for $-2.4 < w_{\phi} < -1$ at 95 % confidence level.Comment: Typos corrected. Some clarifications and references added. To appear in Physical Review

Dilatonic ghost condensate as dark energy

We explore a dark energy model with a ghost scalar field in the context of the runaway dilaton scenario in low-energy effective string theory. We address the problem of vacuum stability by implementing higher-order derivative terms and show that a cosmologically viable model of ``phantomized'' dark energy can be constructed without violating the stability of quantum fluctuations. We also analytically derive the condition under which cosmological scaling solutions exist starting from a general Lagrangian including the phantom type scalar field. We apply this method to the case where the dilaton is coupled to non-relativistic dark matter and find that the system tends to become quantum mechanically unstable when a constant coupling is always present. Nevertheless, it is possible to obtain a viable cosmological solution in which the energy density of the dilaton eventually approaches the present value of dark energy provided that the coupling rapidly grows during the transition to the scalar field dominated era.Comment: 26 pages, 6 figure

Vector field as a quintessence partner

We derive generic equations for a vector field driving the evolution of flat homogeneous isotropic universe and give a comparison with a scalar filed dynamics in the cosmology. Two exact solutions are shown as examples, which can serve to describe an inflation and a slow falling down of dynamical ``cosmological constant'' like it is given by the scalar quintessence. An attractive feature of vector field description is a generation of ``induced mass'' proportional to a Hubble constant, which results in a dynamical suppression of actual cosmological constant during the evolution.Comment: 14 pages, LaTeX file, iopart class, discussion extended, reference adde

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients

Coupled dark energy: Towards a general description of the dynamics

In dark energy models of scalar-field coupled to a barotropic perfect fluid, the existence of cosmological scaling solutions restricts the Lagrangian of the field \vp to p=X g(Xe^{\lambda \vp}), where X=-g^{\mu\nu} \partial_\mu \vp \partial_\nu \vp /2, $\lambda$ is a constant and $g$ is an arbitrary function. We derive general evolution equations in an autonomous form for this Lagrangian and investigate the stability of fixed points for several different dark energy models--(i) ordinary (phantom) field, (ii) dilatonic ghost condensate, and (iii) (phantom) tachyon. We find the existence of scalar-field dominant fixed points (\Omega_\vp=1) with an accelerated expansion in all models irrespective of the presence of the coupling $Q$ between dark energy and dark matter. These fixed points are always classically stable for a phantom field, implying that the universe is eventually dominated by the energy density of a scalar field if phantom is responsible for dark energy. When the equation of state w_\vp for the field \vp is larger than -1, we find that scaling solutions are stable if the scalar-field dominant solution is unstable, and vice versa. Therefore in this case the final attractor is either a scaling solution with constant \Omega_\vp satisfying 0<\Omega_\vp<1 or a scalar-field dominant solution with \Omega_\vp=1.Comment: 21 pages, 5 figures; minor clarifications added, typos corrected and references updated; final version to appear in JCA

Stability analysis of agegraphic dark energy in Brans-Dicke cosmology

Stability analysis of agegraphic dark energy in Brans-Dicke theory is presented in this paper. We constrain the model parameters with the observational data and thus the results become broadly consistent with those expected from experiment. Stability analysis of the model without best fitting shows that universe may begin from an unstable state passing a saddle point and finally become stable in future. However, with the best fitted model, There is no saddle intermediate state. The agegraphic dark energy in the model by itself exhibits a phantom behavior. However, contribution of cold dark matter on the effective energy density modifies the state of teh universe from phantom phase to quintessence one. The statefinder diagnosis also indicates that the universe leaves an unstable state in the past, passes the LCDM state and finally approaches the sable state in future.Comment: 15 pages, 12 figure

Vector field and rotational curves in dark galactic halos

We study equations of a non-gauge vector field in a spherically symmetric static metric. The constant vector field with a scale arrangement of components: the time component about the Planck mass m_{Pl} and the radial component about M suppressed with respect to the Planck mass, serves as a source of metric reproducing flat rotation curves in dark halos of spiral galaxies, so that the velocity of rotation v_0 is determined by the hierarchy of scales: \sqrt{2} v_0^2= M/m_{Pl}, and M\sim 10^{12} GeV. A natural estimate of Milgrom's acceleration about the Hubble rate is obtained.Comment: 17 pages, iopart style, misprint remove

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction—A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients

COVLIAS 1.0: Lung segmentation in COVID-19 computed tomography scans using hybrid deep learning artificial intelligence models

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi-or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value &lt; 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet &gt; VGG-SegNet &gt; NIH &gt; SegNet. The HDL runs in &lt;1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings