Parametrized Research Articles

Cosmology in holographic non-minimal derivative coupling theory: Constraints from inflation and variation of gravitational constant

We consider a cosmological model of non-minimal derivative coupling (NMDC) to gravity with holographic effect from Bekenstein–Hawking entropy using Hubble horizon IR cutoff. Holographic parameter c is considered constant with value in a range, 0≤c<1. The NMDC effect is considered either as modification in kinetic scalar field or in gravitational constant. The NMDC effect allows gravitational constant to be time-varying. Since the NMDC effect is cosmological, definition of holographic density should include time-varying part of the gravitational constant. The NMDC part reduces strength of gravitational constant for κ>0 and opposite for κ<0. The holographic part enhances gravitational strength. Slow-roll parameters are derived. We use spectral index and tensor-to-scalar ratio to test the model against CMB constraint. Number of e-folding is chosen to be N≥60. Power-law scalar potentials, V=V0ϕn with n=2,4, and exponential potential, V=V0exp(−βϕ) are considered. Combined parametric plots of κ and ϕ show that the allowed regions of the power spectrum index and of the tensor-to-scalar ratio are not overlapping. NMDC inflation is ruled out and the holographic NMDC inflation is also ruled out for 0<c<1. NMDC significantly changes major anatomy of the dynamics, i.e. it gives new late-time attractor trajectories in acceleration regions. The holographic part clearly affects pattern of trajectories. However, for the holographic part to affect shape of the acceleration region, the NMDC field must be in presence. To constrain the model at late time, variation of gravitational constant is considered. Gravitational-wave standard sirens and supernovae data give a constraint, Ġ/G|t0≲3×10−12year−1 (Zhao et al., 2018) which, for this model, results in 10−12year−1≳−κϕ̇ϕ̈/MP2. Positive κ is favored and greater c2 results in lifting up lower bound of κ.

Non-lightlike Helices Associated with Helical Curves, Relatively Normal-Slant Helices and Isophote Curves in Minkowski 3-space

In this paper, we introduce a new type of non-lightlike general helix that we name non-lightlike associated helix which is associated with a non-lightlike special surface curve. By using the Darboux frame of a surface curve, we generate the position vector of a non-lightlike associated helix in parametric form. We investigate special cases when the non-lightlike surface curve is a helical curve, a relatively normal-slant helix or an isophote curve. In every case, we obtain the position vector of the non-lightlike associated helix by solving differential equations and examples are given for the achieved results.

Learning Vector Quantization for Robusta and Arabica Coffee Classification

ANN or artificial neural network is a way to solve various kinds of problems to make decisions based on training. One of the methods of JSt which contains competitive and supervised learning. Where this layer will automatically learn the classification of the closest input distances and will be distributed to the same class. there are 2 types of coffee beans that are famous in the world, namely arabica and robusta, for some people or the layman it will be very difficult to distinguish these 2 types of coffee beans apart from the fact that the shape is almost the same the color looks almost the same but there are a number of differences in the two coffee beans which we can see from the shape of the seed. Robusta has a shape that tends to be round and smaller in size, and has a rougher texture. Arabica, on the other hand, is slightly flatter and longer in shape. The size is slightly bigger than Robusta but the texture of Arabica is smoother than Robusta. This is the basis of this study where the images of the two coffee beans will be extracted using the first-order texture feature extraction method based on MU parameters, standard deviation, skewness, energy, entropy, and smoothness. The method for collecting data was in the form of a quantitative method using images from each coffee bean, both Arabica and Robusta, with a total of 130 images. The comparison between training_data and test_data is 80:20. Through research conducted in the form of performance parameters with the best accuracy, including: Learning rate 0.01, max epoch or maximum iteration of 10 and 30%, the amount of training data used is 39 training images and 26 test images resulting in an accuracy presentation of 71% for the training process and error with a percentage of 96% for the test process.

Understanding parametric architecture within the framework of architectural typology

The article deals with the issue of understanding parametric architecture within the framework of the traditional classification of architectural objects and considers the need to develop a new architectural system that can be based on the specifics of parametric architecture form generation and types of spatial structures. Examples of parametric architectural objects within the framework of traditional architectural typology, types of architectural composition and types of spatial structures are given. The principle of working with computer methods and the need to classify parametric objects within a new system based on the traditional one is described.

Response of Direct Seeded Rice to Iron and Zinc Fertilization Strategies under Middle Gujarat Conditions

Direct seeded rice (DSR) cultivation has received considerable attention due to its economic and labor-saving benefits. However, inadequate nutrient availability, particularly micronutrients, remains a significant constraint in achieving optimal crop growth and yield in direct seeded rice system. This research paper presents a comprehensive analysis of the impact of iron and zinc fertilization on yield, productivity and nutritional quality of direct seeded rice. This experiment was conducted at Experimental Farm, Regional Research Station, Anand Agricultural University, Anand, Gujarat, during kharif season of the year 2022 with twelve treatments of iron and zinc fertilization strategies viz. nutripriming, foliar application and soil drenching which were replicated thrice in randomized block design (RBD). In the treatments of nutripriming, seed were soaked in 0.5% solutions of iron sulphate and zinc sulphate individually and also in combination for 12 hrs. In other treatments, solutions of 0.5% concentration of iron sulphate and zinc sulphate were applied through foliar application at 30 and 50 DAS and soil drenching. Soil drenching was done by adding nutrient solutions (0.5% of iron sulphate and zinc sulphate each) directly on the rice sown lines of the plots. Findings demonstrate that targeted application of micronutrients (Fe and Zn) significantly enhanced the DSR performance in form of growth parameters, yield attributes, yield and nutrient content of grain and straw. Among all the treatments, (T5) nutripriming of direct seeded rice with 0.5% iron sulphate plus 0.5% zinc sulphate for 12 hours before sowing along with recommended dose of fertilizers has been found best in terms of plant height, panicles per m2, yield and Fe and Zn content in seed and straw of rice.

Study on magnetostriction of ferromagnetic particles–superconducting matrix based on multi-chain model

Based on the magnetic dipole theory and the elastic mechanical plane strain method, this paper establishes a multi-chain model of ferromagnetic particle inclusions–superconducting matrix and investigates the effects of the distribution form of ferromagnetic particle inclusions and elastic parameters on the effective magnetostriction of superconductors. A dimensionless parameter is introduced to describe the spatial distribution form of ferromagnetic particles in the matrix, and the calculation is carried out using the elastic mechanical plane strain method. The change of the effective magnetostriction following the elastic parameter is given for three kinds of distribution forms: single-chain, planar–laminar, and spatial three-dimensional multi-chain, respectively. The results show that the distribution of ferromagnetic particle inclusions and the change of elastic parameters have a notable influence on the displacement and magnetostrictive distribution of superconductors.

Gravitational redshift test of EEP with RadioAstron from near Earth to the distance of the Moon

The Einstein Equivalence Principle (EEP) is a cornerstone of general relativity and predicts the existence of gravitational redshift. We report on new results of measuring this shift with RadioAstron (RA), a space very long baseline interferometry (VLBI) spacecraft launched into an evolving high eccentricity orbit around Earth with geocentric distances reaching 353 000 km. The spacecraft and ground tracking stations at Pushchino, Russia, and Green Bank, USA, were each equipped with a hydrogen maser frequency standard allowing a possible violation of the predicted gravitational redshift, in the form of a violation parameter ɛ, to be measured. By alternating between RA’s frequency referencing modes during dedicated sessions between 2015 and 2017, the recorded downlink frequencies can essentially be corrected for the non-relativistic Doppler shift. We report on an analysis using the Doppler-tracking frequency measurements made during these sessions and find . We also discuss prospects for measuring ɛ with a significantly smaller uncertainty using instead the time-domain recordings of the spacecraft signals and envision how 10−7 might be possible for a future space VLBI mission.

Observational constraints on a logarithmic scalar field dark energy model and black hole mass evolution in the Universe

We propose a logarithmic parametrization form of energy density for the scalar field dark energy in the framework of the standard theory of gravity, which supports the necessary transition from the decelerated to the accelerated behavior of the Universe. The model under consideration is constrained by available observational data, including cosmic chronometers data-sets (CC), Baryonic Acoustic Oscillation (BAO) data-sets, and Supernovae (SN) data-sets, consisting of only two parameters alpha and beta . The combined CC+BAO+SN data-sets yields a transition redshift of z_{tr}=0.79^{+0.02}_{-0.02}, where the model exhibits signature-flipping and is consistent with recent observations. For the combined data-sets, the present value of the deceleration parameter is calculated to be q_{0}=-0.43^{+0.06}_{-0.06}. Furthermore, the analysis yields constraints on both the parameter density value for matter and the present value of the Hubble parameter, with values of Omega _{m0}=0.25849^{+0.00026}_{-0.00025} and H_{0}=67.79_{-0.59}^{+0.59} km/s/Mpc, respectively, consistent with the results obtained from Planck 2018. Finally, the study investigates how the mass of a black hole evolves over time in a Universe with both matter and dark energy. It reveals that the black hole mass increases initially but stops increasing as dark energy dominates.

Field-level Neural Network Emulator for Cosmological N-body Simulations

We build a field-level emulator for cosmic structure formation that is accurate in the nonlinear regime. Our emulator consists of two convolutional neural networks trained to output the nonlinear displacements and velocities of N-body simulation particles based on their linear inputs. Cosmology dependence is encoded in the form of style parameters at each layer of the neural network, enabling the emulator to effectively interpolate the outcomes of structure formation between different flat Lambda cold dark matter cosmologies over a wide range of background matter densities. The neural network architecture makes the model differentiable by construction, providing a powerful tool for fast field-level inference. We test the accuracy of our method by considering several summary statistics, including the density power spectrum with and without redshift space distortions, the displacement power spectrum, the momentum power spectrum, the density bispectrum, halo abundances, and halo profiles with and without redshift space distortions. We compare these statistics from our emulator with the full N-body results, the COmoving Lagrangian Acceleration (COLA) method, and a fiducial neural network with no cosmological dependence. We find that our emulator gives accurate results down to scales of k ∼ 1 Mpc−1 h, representing a considerable improvement over both COLA and the fiducial neural network. We also demonstrate that our emulator generalizes well to initial conditions containing primordial non-Gaussianity without the need for any additional style parameters or retraining.

Influence of pressure on elasticity, mechanical properties, and Li diffusion in battery electrode material LiCoO2: First-principles calculations

This study used first-principles calculations based on density functional theory with generalized gradient approximation (GGA) of the Perdew Burke and Ernzerhof (PBE) parameterized form to investigate the influence of pressure on the structural, elastic, and mechanical properties of rhombohedral LiCoO2. The results indicate that rhombohedral LiCoO2 remains stable up to 10 GPa. The calculated ground state properties agree well with experimental and other calculation data, validating the approach. Additionally, the calculated elastic constants, bulk modulus, Young's modulus, shear modulus, and Poisson's ratio at 0 GPa agree with previous results. The study found that all elastic constants continuously increase with increasing pressure. The deformation resistances along the axial direction are stronger than those in shape. The relationship between elasticity and length is such that C11 > C33, indicating that the incompressibility along the a axis is stronger than that along the c axis. The analysis of Poisson's ratio (ν) and Pugh's criterion B/G strongly suggests that rhombohedral LiCoO2 exhibits ductile behavior under pressure up to 10 GPa. The material is clearly anisotropic behavior, but the material exhibits improved isotropic behavior as pressure increases. Regarding Li ion diffusion in rhombohedral LiCoO2, the study found that the migration pathway for Li ions in rhombohedral LiCoO2 is a direct route that extends from the VLi site to the closest Li site. The diffusion process is hindered by a diffusion energy barrier of 0.87 eV, which is in good agreement with available theoretical data. Furthermore, the Li ion diffusion energy barrier increases with pressure because the decrease in dmean and polyhedral volume, along with changes in angle and torsional distortion factors of CoO6 octahedra, which can reduce the charging and discharging speeds of Li-ion batteries that use rhombohedral LiCoO2 as the cathode material.

Intelligent Identification of Trend Components in Singular Spectrum Analysis

Singular spectrum analysis (SSA) is a non-parametric adaptive technique used for time series analysis. It allows solving various problems related to time series without the need to define a model. In this study, we focus on the problem of trend extraction. To extract trends using SSA, a grouping of elementary components is required. However, automating this process is challenging due to the nonparametric nature of SSA. Although there are some known approaches to automated grouping in SSA, they do not work well when the signal components are mixed. In this paper, a novel approach that combines automated identification of trend components with separability improvement is proposed. We also consider a new method called EOSSA for separability improvement, along with other known methods. The automated modifications are numerically compared and applied to real-life time series. The proposed approach demonstrated its advantage in extracting trends when dealing with mixed signal components. The separability-improving method EOSSA proved to be the most accurate when the signal rank is properly detected or slightly exceeded. The automated SSA was very successfully applied to US Unemployment data to separate an annual trend from seasonal effects. The proposed approach has shown its capability to automatically extract trends without the need to determine their parametric form.

Hubble parameter reconstruction: A tool to explore the acceleration of the universe with observational constraints

This article introduces a novel parametric form of the Hubble parameter for exploring the accelerating cosmos in a model-independent manner. The model parameters are constrained using a Markov Chain Monte Carlo (MCMC) approach with a combined dataset spanning 31 cosmic chronometers (CC) data points, 26 non-correlated baryonic acoustic oscillations (BAO) points, and 1701 updated Pantheon+ supernovae type Ia (SNeIa) data points. We investigate the model’s kinematic behavior, including the transition from the deceleration phase to the acceleration phase, the jerk evolution, and the energy conditions. The results of the equation of state parameter suggest that our model behaves like the quintessence model and supports the current accelerating Universe. Additionally, we impose constraints on both the model parameters and the Hubble constant H0, and find that the values obtained for H0 align with other works obtained through reconstruction methods and observational data. Overall, this study provides valuable insights into the accelerating cosmos and highlights the importance of a model-independent approach in cosmological studies.

Analysis of interval-valued model for interaction between plankton-fish population in marine ecosystem

The crux of present investigation is to develop an interval-valued population model for the interaction between phytoplankton, zooplankton and fish population under cyrtoid type functional response. The present study is also concerned to analyze the mathematical model under impreciseness and the parametric functional form for an interval valued model parameters. The boundedness, existence of the solution, stability analysis and all possible feasible equilibrium points have been examined. The optimal harvesting strategy has been implemented and obtained the optimal solution with the help of the Pontryagin maximum principle. The significant impact of interval valued biological parameters has been analyzed and portrayed by means of graph for given different fit values, approximate to the model system in real scenario.

Quantifying noise limitations of neural network segmentations in high-resolution transmission electron microscopy

Motivated by the need for low electron dose transmission electron microscopy imaging, we report the optimal frame dose (i.e.e−/Å2) range for object detection and segmentation tasks with neural networks. The MSD-net architecture shows promising abilities over the industry standard U-net architecture in generalising to frame doses below the range included in the training set, for both simulated and experimental images. It also presents a heightened ability to learn from lower dose images. The MSD-net displays mild visibility of a Au nanoparticle at 20–30 e−/Å2, and converges at 200 e−/Å2 where a full segmentation of the nanoparticle is achieved. Between 30 and 200 e−/Å2 object detection applications are still possible. This work also highlights the importance of modelling the modulation transfer function when training with simulated images for applications on images acquired with scintillator based detectors such as the Gatan Oneview camera. A parametric form of the modulation transfer function is applied with varying ranges of parameters, and the effects on low electron dose segmentation is presented.

Detecting the complexity of a functional time series

ABSTRACT Consider a time series that takes values in a general topological space, and suppose that its Small-ball probability is factorised into two terms that play the role of a surrogate density and a volume term. The latter allows us to study the complexity of the underlying process. In some cases, the volume term can be analytically specified in a parametric form as a function of a complexity index. This work presents the study of an estimator for such an index whenever the volume term is monomial. Weak consistency and asymptotic Gaussianity are shown under an appropriate dependence structure, providing theoretical support for the construction of confidence intervals. A Monte Carlo simulation is performed to evaluate the performance of the approach under various conditions. Finally, the method is applied to identify the complexity of two real data sets.

Parameter estimation for empirical and semi-empirical models in a direct ethanol fuel cell

Experimental data from a Direct Ethanol Fuel Cell (DEFC) provides a general perspective about its performance; nevertheless, it does not provide information about the cell’s physical characteristics nor information to improve its performance. On the other hand, numerical simulation can be used to test the cell’s design and boost its performance but requires a set of physical parameters. In this proposal, we introduce a novel modification to an empirical model for a Direct Methanol Fuel Cell to make it suitable for DEFC simulations at different temperatures by a new semi-empirical mathematical model. In addition, we introduce temperature-depending parametric forms of several terms to reduce the number of possible parameters to estimate from the DEFC. Then, we combined the models with an estimation of distribution algorithm to find the numerical simulation that best reproduces the experimental polarization curve. The method is validated by estimating the parameters to reproduce the experimental data at different temperatures reported in the literature, and with data obtained in an in-house open-cathode DEFC, recorded at a scan rate of 10 mVs−1, using as fuel CH3CH2OH 1 M at 25 °C and 60 °C. From the estimation results at temperature set T1→=(T1a,T1c)∘C, the same parameters are used for a simulation at T2→=(T2a,T2c)∘C, demonstrating that it reproduces the two experimental polarization curves. Hence, the models and methods presented here can be used to reduce physical experimentation and to test different designs and operation settings.

A General Framework for Robust Monitoring of Multivariate Correlated Processes

Statistical process control (SPC) charts provide an important analytic tool for online monitoring of sequential processes. Conventional SPC charts are designed for cases when in-control (IC) process observations are independent and identically distributed at different observation times and the IC process distribution belongs to a parametric (e.g., normal) family. In practice, however, these model assumptions are rarely valid. To address this issue, there have been some existing discussions in the SPC literature for handling cases when the IC process distribution cannot be described well by a parametric form, and some nonparametric SPC charts have been developed based on data ranking and/or data categorization. However, both data ranking and data categorization would lose information in the original process observations. Consequently, the effectiveness of the nonparametric SPC charts would be compromised. In this article, we make another research effort to handle this problem by developing a general process monitoring framework that is robust to the IC process distribution and short-ranged serial correlation. The new method tries to preserve as much information in the original process observations as possible. Instead of using data ranking and/or data categorization, it is based on intensive data pre-processing, including data decorrelation, data transformation, and data integration. Because the distribution of the pre-processed data can be approximated well by a parametric distribution, the design and implementation of the new method is relatively simple. Numerical studies show that it is indeed robust to the IC process distribution and effective for online monitoring of multivariate processes with short-ranged serial correlation.

NOMA-based energy efficient resource allocation in wireless energy harvesting sensor networks

Non Orthogonal Multiple Access (NOMA) and energy harvesting are two widely used concepts to improve the performance of Wireless Sensor Networks. In this paper, we consider a NOMA-based Wireless Energy Harvesting Sensor Network (WEHSN), in which all sensors harvest their energy from a Hybrid Access Point (HAP), and then, simultaneously transmit their information in NOMA-based protocol to the HAP, if their harvested energy are sufficient and they have data for transmission. We formulate the resource allocation as an optimization problem to maximize the energy efficiency, defined as system throughput per total energy consumption in the network, in which the total energy consumption is summation of energy consumption in downlink and uplink phases. The optimization problem constraints are the time scheduling parameters and the sensors’ transmission powers. To solve this maximization problem, we use the Dinkelbach method to convert it to the parametric form and then, we apply the Karush–Kuhn–Tucker (KKT) conditions in the parametric optimization problem to derive the closed form expression for optimization problem. We evaluate the performance of proposed scheme in relation to energy efficiency, throughput and total energy consumption, and we compare them with OFDMA and TDMA-based WEHSN method. The numerical results show that, NOMA-based WEHSN has better performance in compared with OFDMA and TDMA-based WEHSN. Also, the effect of different amount of data at the sensors has been considered in numerical result which shows that when the sensors always have data for transmission, the system has better performance in terms of energy efficiency and throughput but it has more total energy consumption in comparison to the situation that the sensors occasionally have data for transmission.

What Is a Polygonal Impact Crater? A Proposed Framework Toward Quantifying Crater Shapes

AbstractImpact craters are used for a wide array of investigations of planetary surfaces. A crater form that is somewhat rare, forming only ∼10% of impact craters, is the polygonal impact crater (or PIC). These craters have been visually, manually identified as having at least two rim segments that are best represented as straight lines. Such straight lines or edges are most often used to infer details about the subsurface crust where faults control the structure of the crater cavity as it formed. The PIC literature is scant, but almost exclusively these craters are identified manually, and the potentially straight edges are classified and measured manually. The reliance on human subjectivity in both the identification and measurement motivated us to design a more objective algorithm to fit the crater rim shape, measure any straight edges, and measure joint angles between straight edges. The developed code uses a Monte Carlo approach from a user‐input number of edges to first find a reasonable shape from purely random possible shapes; it then uses an iterative Monte Carlo approach to improve the shape until a minimum difference between the shape and rim trace is found. It returns the result in a concise, parameterized form. This code is presented as a first step because, while we experimented with several different metrics, we could not find one that could consistently, objectively return an answer that stated which shape for a given crater was the best; this objective metric is an area for future improvement.

The Role of Fidelity in Goal-Oriented Semantic Communication: A Rate Distortion Approach

We study a variant of a robust description source coding framework, which is a relevant model for goal-oriented semantic information transmission, via its corresponding characterization. Considering two individual single-letter separable distortion constraints and input and output data acting as the intrinsic and extrinsic message, respectively, we first derive a lower bound on the optimal rates of the problem, as well as necessary and sufficient conditions for this bound to be tight. Subsequently, we prove a general result that provides in parametric form the optimal solution of the characterization of this problem. Capitalizing on these results, we examine the structure of the solution for one case study of general binary alphabets under Hamming distortions and solve in closed form a special case. We also solve another general binary alphabet case where a Hamming and an erasure distortion coexist, as a means to highlight the importance of selecting the type of the distortion constraint in goal-oriented semantic communication. Furthermore, we develop a goal-oriented Blahut-Arimoto (BA) algorithm, which can be used for the computation of any finite alphabet intrinsic or extrinsic message under individual distortion criteria. Finally, we revisit the problem for multidimensional independent and identically distributed (i.i.d.) jointly Gaussian processes with individual mean-square error (MSE) distortion constraints, providing new insights that have previously been overlooked. This work reveals the cardinal role of context-dependent fidelity criteria in goal-oriented semantic communication.