Parameter Constraints Research Articles

Solitons in magneto-optic waveguides with generalized Kudryashov’s form of self-phase modulation structure

AbstractThe current paper recovers soliton solutions in magneto-optic waveguides having generalized form of Kudryashov’s self-phase modulation structure. The retrieval of soliton solutions is achieved by the well-known and primitive $$G^{\prime }/G$$ G ′ / G -expansion scheme. The intermediary functions that made this retrieval possible are Jacobi’s elliptic functions and Weierstrass’ elliptic functions. The parameter constraints for the solitons to exist are also presented.

Constraining parameters for the accelerating universe in [formula omitted] gravity

In the paper, we present an accelerating cosmological model in f(R,Lm) gravity with the parameter constrained through the cosmological data sets. At the beginning, we have employed a functional form of f(R,Lm)=R2+αR2+Lmβ, where α and β are model parameters. This model is well motivated from the Starobinsky model in f(R) gravity and the power law form of f(Lm). The Hubble parameter has been derived with some algebraic manipulation and constrained by Hubble data and Pantheon+ data. With the constraint parameters, present value of deceleration parameter has been obtained to as q0≈−0.63 with the transition at zt≈0.7. It shows the early deceleration and late time acceleration behaviour. The present value of other geometric parameters such as the jerk and snap parameter are obtained to be j0≈0.78 and s0≈0.1 respectively. The state finder diagnostic test gives the quintessence behaviour at present and converging to ΛCDM at late times. Moreover the Om(z) diagnostics gives negative slope which shows that the model favours the state finder diagnostic result. Also the current age of Universe has been obtained as, t0=13.64Gyrs. The equation of state parameter also shows the quintessence behaviour. Based on the present analysis, it indicates that the f(R,Lm) gravitational theory may be another alternative to study the dark energy models.

Compatibility of Gravitational Baryogenesis with theoretical framework of [formula omitted] Gravity

The primary goal of this article is to analyze the disproportion between matter and antimatter in the Universe using the phenomenon of gravitational baryogenesis under the framework of f(R,G,T) gravity, where R, G and T are Ricci Scalar, Gauss–Bonnet invariant and the trace of energy momentum tensor. This phenomenon based on charge parity violation interactions and in this article we generate it through the coupling between the baryon matter current (Jν) and ∂ν(R+G+T) as well as for generalized case with (Jν) and ∂νf(R+G+T). We evaluate the ratio ηBS (baryon to entropy) of the propose model of f(R,G,T) gravity for gravitational baryogenesis and generalized gravitational baryogenesis with power-law form consideration for the scale factor in different eras of the Universe. We notice that the results of (ηBS) are compatible with its observational bound under the optimal choice of model parameters and hence we say that this gravity is good for gravitational baryogenesis under certain constraints of model parameters.

The Simons Observatory: impact of bandpass, polarization angle and calibration uncertainties on small-scale power spectrum analysis

We study the effects due to mismatches in passbands, polarization angles, and temperature and polarization calibrations in the context of the upcoming cosmic microwave background experiment Simons Observatory (SO). Using the SO multi-frequency likelihood, we estimate the bias and the degradation of constraining power in cosmological and astrophysical foreground parameters assuming different levels of knowledge of the instrumental effects. We find that incorrect but reasonable assumptions about the values ofall the systematics examined here can have significant effects on cosmologicalanalyses, hence requiring marginalization approaches at the likelihood level.When doing so, we find that the most relevant effect is due to bandpass shifts. When marginalizing over them, the posteriors of parameters describing astrophysical microwave foregrounds (such as radio point sources or dust) get degraded, while cosmological parameters constraints are not significantly affected.Marginalization over polarization angles with up to 0.25° uncertainty causes an irrelevant bias ≲ 0.05 σ in all parameters.Marginalization over calibration factors in polarization broadens the constraints on the effective number of relativistic degrees of freedom Neff by a factor 1.2, interpreted here as a proxy parameter for non standard model physics targeted by high-resolution CMB measurements.

A novel design method of a nonlinear elastic mechanism for series elastic actuators

Series elastic actuators with increasing nonlinear stiffness(NlSEA) have promising applications in human-machine interactions for the existence of a nonlinear elastic mechanism that supplies inherent safety and can implement high torque resolution under low load and quick dynamic response under high load. In this study, a novel design method for a nonlinear elastic mechanism that can be applied in NlSEA is proposed. By combining the S-shaped cantilever beam and specialized cam surface, this study proposed an analytical approach to design a nonlinear elastic mechanism satisfying a given stiffening torque-deformation relationship. The geometric parameters constraints were discussed to support parameter determination. Two kinds of cases of nonlinear elastic mechanisms with increasing and constant stiffness characteristics were conducted to verify the effectiveness and generalizability of the proposed design method by simulations. Moreover, a nonlinear elastic mechanism with an increasing stiffness characteristic was designed and a prototype was fabricated. A torque-deformation verification experiment, regulation response experiments, and sinusoidal tracking experiments were conducted to verify the accuracy, response performance, and tracking performance of the prototype. The designed nonlinear elastic mechanism has a large torque-to-mass ratio in a compact and lightweight structure.

ThunderBoltz: an open-source direct simulation Monte Carlo Boltzmann solver for plasma transport, chemical kinetics, and 0D modeling

Abstract Plasma-neutral interactions, including reactive kinetics, are often either studied in 0D using ODE-based descriptions, or in multi-dimensional fluid or particle-based plasma codes. The latter case involves a complex assembly of procedures that are not always necessary to test effects of underlying physical models and mechanisms for particle-based descriptions. Here we present ThunderBoltz, a lightweight, publicly available 0D direct simulation Monte Carlo code designed to accommodate a generalized combination of species and arbitrary cross sections without the overhead of expensive field solves. It can produce electron, ion, and neutral velocity distributions in applied AC/DC E-field and/or static B-field scenarios. The code is built in the C++ standard library and includes a convenient Python interface that allows for input file generation (including compatibility with cross section data from the LXCat database), electron transport and reaction rate constant post-processing, input parameter constraint satisfaction, calculation scheduling, and diagnostic plotting. These codes can be accessed at the repository: https://github.com/lanl/ThunderBoltz. In this work we compare ThunderBoltz transport calculations against Bolsig+ calculations, benchmark test problems, and swarm experiment data, finding good agreement with all three in the appropriate field regimes. In addition, we present example use cases where the electron, ion, and background neutral particle species are self-consistently evolved to model the background kinetics, a feature that is absent in fixed-background Monte Carlo and n-term Boltzmann solvers. The latter functionality allows for the possibility of particle-based chemical kinetics simulations of the plasma and neutral species as a new alternative to ODE-based approaches.

Development and application of an intelligent nitrogen removal diagnosis and optimization framework for WWTPs: Low-carbon and stable operation

Optimizing nitrogen removal is crucial for ensuring the efficient operation of wastewater treatment plants (WWTPs), but it is susceptible to variations in influent conditions and operational parameter constraints, and conflicts with the energy-saving and carbon emission reduction goals. To address these issues, this study proposes a hybrid framework integrating process simulation, machine learning, and multi-objective genetic algorithms for nitrogen removal diagnosis and optimization, aiming to predict the total nitrogen in effluent, diagnose nitrogen over-limit risks, and optimize the control strategies. Taking a full-scale WWTP as a case study, a process time-lag simulation-enhanced machine learning model (PTLS-ML) was developed, achieving R2 values of 0.94 and 0.79 for the training and testing sets, respectively. The proposed model successfully identified the potential reasons of nitrogen over-limit risks under different influent conditions and operational parameters, and accordingly provided optimization suggestions. In addition, the multi-objective optimization (MOO) algorithms analysis further demonstrated that maintaining 4–6 mg/L total nitrogen concentration in effluent by adjusting process operational parameters can effectively balance multiple objectives (i.e., effluent water quality, operating costs, and greenhouse gas emissions), achieving coordinated optimization. This framework can serve as a reference for stable operation, energy-saving, and emission reduction in the nitrogen removal of WWTPs.

Renewable energy-powered water desalination and treatment network under wind power and water demand uncertainty: A possibilistic chance-constrained programming

Given the scarcity of freshwater resources, the growing significance of desalination is undeniable. It holds immense potential, particularly in regions grappling with severe water shortages. However, desalination's Achilles heel lies in its voracious energy appetite, requiring roughly ten times more energy than wastewater treatment. Moreover, the prevalent use of fossil fuels in desalination plants poses concerning issues like environmental pollution, fossil fuel depletion, and rising costs. The present study has designed an integrated Water desalination and treatment Network that includes a number of desalination facilities, storage centers, wind farms, and wastewater treatment facilities. The water desalination and treatment network has been structured using a Mixed-Integer Linear Programming (MILP) model, considering uncertainties in wind power and water demand. Employing a chance constraint probabilistic programming approach, this model ensures robustness and balances conservatism with investment attractiveness. It aims to enhance resilience against fluctuations in wind energy and water demand within the water and energy supply chain network. The study applied this model to optimize the locations of desalination plants, treatment centers, and storage facilities. This integrated model ensures autonomy, eliminating the need for external water and energy sources while reliably meeting regional demands. In the context of the Makran coasts case study, our comprehensive mathematical model demonstrates an optimal allocation with 96.67 % attributed to fixed costs and only 3.33 % to variable costs. Moreover, this model precisely optimizes the locations of two desalination centers, two storage facilities, and ten water treatment centers, effectively managing the need for external water resources. Ultimately, through a rigorous sensitivity analysis, we unveiled that the chance constraint parameters have a significant impact on the variable costs.

High-efficiency adaptive temperature control for thermoelectric system based on the OBPPID strategy

Thermoelectric cooler (TEC) is widely used for temperature control in optoelectronics, machinery, biomedicine, and other fields. However, the unstable temperature control over wide ranges and different gradients restricts the efficiency of TEC. To highlight the neural network’s effectiveness in controlling TEC systems, the optimized BPPID (OBPPID) strategy is proposed. In the OBPPID strategy, the factors s̃ and G are decided through formula derivation and the particle swarm optimization (PSO) algorithm, respectively contributing to the backward adjustment and forward propagation of BPPID. The OBPPID solves three issues that the BPPID faces: gradient vanishing of network weights, over-reliance of initial network weights and range constraint of control parameters. The OBPPID and BPPID have been simulated 1000 times to evaluate control performance. The results show that, with random initial network weights, the OBPPID strategy reduces the cost variability of the BPPID strategy from 92 to 17, achieving an 81.52% reduction. Physical tests have confirmed that in the temperature control over wide range and the temperature gradients of 5°C, 20°C, and 80°C, the OBPPID strategy can adaptively adjust the control parameters and provide higher efficiency than PID, BPPID and fuzzy PID strategies for TEC system.

Optimal Investment Strategy for DC Pension Plan with Stochastic Salary and Value at Risk Constraint in Stochastic Volatility Model

This paper studies the optimal asset allocation problem of a defined contribution (DC) pension plan with a stochastic salary and value under a constraint within a stochastic volatility model. It is assumed that the financial market contains a risk-free asset and a risky asset whose price process satisfies the Stein–Stein stochastic volatility model. To comply with regulatory standards and offer a risk management tool, we integrate the dynamic versions of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and worst-case CVaR (wcCVaR) constraints into the DC pension fund management model. The salary is assumed to be stochastic and characterized by geometric Brownian motion. In the dynamic setting, a CVaR/wcCVaR constraint is equivalent to a VaR constraint under a higher confidence level. By using the Lagrange multiplier method and the dynamic programming method to maximize the constant absolute risk aversion (CARA) utility of terminal wealth, we obtain closed-form expressions of optimal investment strategies with and without a VaR constraint. Several numerical examples are provided to illustrate the impact of a dynamic VaR/CVaR/wcCVaR constraint and other parameters on the optimal strategy.

A comparative study of cosmological constraints from weak lensing using Convolutional Neural Networks

Weak Lensing (WL) surveys are reaching unprecedented depths, enabling the investigation of very small angular scales. At these scales, nonlinear gravitational effects lead to higher-order correlations making the matter distribution highly non-Gaussian. Extracting this information using traditional statistics has proven difficult, and Machine Learning based summary statistics have emerged as a powerful alternative. We explore the capabilities of a discriminative, Convolutional Neural Networks (CNN) based approach, focusing on parameter constraints in the (Ω m , σ8) cosmological parameter space. Leveraging novel training loss functions and network representations on WL mock datasets without baryons, we show that our models achieve ~ 5 times higher figure of merit in the σ8-Ω m plane than the power spectrum, ~ 3 times higher than peak counts, and ~ 2 times higher than previous CNN-learned summary statistics and scattering transforms, for noise levels relevant to Rubin or Euclid. For WL convergence maps with baryonic physics, our models achieve ~ 2.3 times stronger constraining power than the power spectrum at these noise levels, also outperforming previous summary statistics. To further explore the possibilities of CNNs for this task, we also discuss transfer learning where we adapt pre-trained models, trained on different tasks or datasets, for cosmological inference, finding that these do not improve the performance.

Gravitational lensing of dark energy models and ΛCDM using observational data in loop quantum cosmology

This paper investigates the accelerated cosmic expansion in the late Universe by examining two dark energy models, viscous modified Chaplygin gas (VsMCG) and variable modified Chaplygin gas (VMCG), within loop quantum cosmology alongside the ΛCDM model. The objective is to constrain cosmic parameters using the ΛCDM model and 30 of the latest H(z) measurements from cosmic chronometers (CC), including Type Ia Supernovae, Gamma-Ray Bursts (GRB), Quasars, and 24 uncorrelated baryon acoustic oscillations (BAO) measurements across a redshift range from 0.106 to 2.33. The latest Hubble constant measurement from Riess in 2022 is included to enhance constraints. In the ΛCDM, VsMCG, and VMCG frameworks, best-fit parameters for the Hubble parameter (H0) and sound horizon (rd) are obtained. The results highlight significant disparities between H0 and rd values from late-time observational measurements, reflecting the known H0 and rd tensions. The gravitational lensing optical depth of the two dark energy models is studied by plotting log⁡(τ(zl)/H0−3τN) vs zl. The probability of finding gravitational lenses (optical depth) in both models increases with lens redshift zl. The change in optical depth behavior for different parameter constraints is graphically analyzed. A joint analysis of VsMCG and VMCG with ΛCDM is conducted. While the models diverge in the early Universe, they are indistinguishable at low redshift. Using the Akaike information criteria, the analysis indicates that neither dark energy model can be dismissed based on the latest observations.

Optimised Implementation of Adaptive Rns Using Power-Aware CRT

In order to get an efficient comprehensive analysis on Doppler estimation in RADAR; need an enhanced arithmetic formulation procedure for density, power and latency optimisations. Modular adders and multipliers are very crucial components in the performance of residue number system-based applications. The Residue Number System (RNS) is a non-positional number system that allows parallel computations without transfers between digits. However, some operations in RNS require knowledge of the positional characteristic of a number. Among these operations is the conversion from RNS to the positional number system. The methods of reverse conversion for general form moduli based on the Chinese remainder theorem and the mixed-radix conversion are considered, as well as the optimized methods for special form moduli. A modified New CRT-I & New CRT-II with conjugate moduli set is considered to implement adder, multipliers and subtractions with optimised algorithms. This paper mainly deals with the conversion of numbers from binary to RNS as well RNS to binary with the specific modulo {2^n±k} which proves this new method. Modified Radix16 booth encoding algorithm and square carry bypass adder are used in implementation of RNS system to reduce parameter constraints.

Constraints on the parameters of modified Chaplygin–Jacobi and modified Chaplygin–Abel gases in f(T) gravity

In this work, we explore the parameter constraints of two dark energy models, namely the Modified Chaplygin–Jacobi gas (MCJG) and Modified Chaplygin–Abel gas (MCAG), within the context of [Formula: see text] gravity model in a non-flat Friedmann–Lemaître–Robertson–Walker (FLRW) universe. Our investigation involves comparing the equation of state for the MCJG and MCAG dark energy models with the equation of state derived from the [Formula: see text] gravity model. To derive constraints for the dark energy and [Formula: see text] gravity models, we use recent astronomical datasets, including [Formula: see text] data, type Ia supernovae observations, Gamma Ray Bursts data, quasar data, and Baryon Acoustic Oscillation (BAO) measurements. We present the reduced Hubble parameter in terms of observable parameters such as [Formula: see text] (density parameter of radiation), [Formula: see text] (density parameter of dark matter), [Formula: see text] (density parameter associated with spatial curvature), [Formula: see text] (density parameter of Modified Chaplygin–Jacobi gas), [Formula: see text] (density parameter of Modified Chaplygin–Abel gas), and [Formula: see text] (present value of the Hubble parameter). We explore the cosmological evolution through various cosmic diagnostic parameters, including the deceleration parameter, [Formula: see text] diagnostic, and statefinder diagnostic pair [Formula: see text]. These diagnostic parameters offer valuable insights into the expansion dynamics and the nature of dark energy in the universe. We have also assessed the viability of the models using the information criteria. Our aim is to shed light on the nature of dark energy and its connection to the [Formula: see text] gravity model, and ultimately gain a deeper understanding of the underlying mechanisms driving the accelerated expansion of our universe.

Modelling the redshift-space cluster–galaxy correlation function on Mpc scales with emulation of the pairwise velocity distribution

ABSTRACT We present a method for modelling the cluster–galaxy correlation function in redshift space, down to $\sim$ Mpc scales. The method builds upon the so-called galaxy infall kinematics (GIK) model, a parametric model for the pairwise velocities of galaxies with respect to nearby galaxy clusters. We fit the parameters of the GIK model to a suite of simulations run with different cosmologies, and use Gaussian processes to emulate how the GIK parameters depend upon cosmology. This emulator can then be combined with knowledge of the real-space clustering of clusters and galaxies, to predict the cluster–galaxy correlation function in redshift space, $\xi _\mathrm{cg}^s$. Fitting this model to an observed $\xi _\mathrm{cg}^s$ enables the extraction of cosmological parameter constraints, and we present forecasts for a survey like that currently being done by the Dark Energy Spectroscopic Instrument (DESI). We also perform tests of the robustness of our constraints from fitting to mock data extracted from N-body simulations, finding that fitting to scales $\lesssim 3 \, h^{-1}\, \mathrm{Mpc}$ leads to a biased inference on cosmology, due to model mis-specification on these scales. Finally, we discuss what steps will need to be taken in order to apply our method to real data.

Trade-offs in biomethane production by substrate mixture optimization under German market conditions

BackgroundNew regulations and market conditions in Germany affect the profitability of biomethane upgrading as a repowering option for existing biogas plants following on-site CHP utilization. These conditions present trade-off challenges between higher sustainability requirements, maintaining production capacity and new revenue opportunities. Optimization methods, such as linear programming (LP), are essential for determining the ideal substrate mixture and profitable solutions amidst multiple market conditions, plant-specific process constraints, and substrate properties.MethodsWe updated a substrate mixture optimization model within an assessment framework for the repowering of existing biogas plants (BGPs), which focuses on the operator’s perspective. By integrating multiple German biomethane markets for various BGPs, we assessed changes in the substrate mixture, GHG emissions, contribution margins, and constraint parameters to derive conclusions for operators and future framework design.ResultsIntegrating market revenues and constraints can increase contribution margins by 12–55%. Additional gains can be achieved by considering multiple markets simultaneously but limited to a few BGPs. The plant-specific LP solution space and used benchmark market are decisive. The former limits the potential of high substrate-specific contribution margins, which has a significantly higher impact than the relation between plant-specific characteristics and process constraints. The advanced fuel market is currently the lead market for biomethane, incentivizing GHG-emission extensive substrates, decreasing gas production and GHG emissions but increasing levelized cost of energy (LCOE) and partially CO2 abatement costs.ConclusionsThe key to improve profitability and to supply an increasing biomethane demand while fulfilling new requirements is a large LP solution space. Increasing market options, substrate availability, and digestion system capacity achieve this on the operator’s side. Policy makers could reduce normative requirements such as the maize cap or double counting of advanced fuels and favor high but uniform GHG requirements. Operators can prepare robustly for the future substrate mixture by adding digester volume and pre-treatment tech, ensuring long-term and diverse substrate availability, and contracts with flexible components. Although current market conditions can improve specific GHG emissions, they do not necessarily increase manure usage when other options, such as straw, are viable. Other regulatory support systems will be required to do so.

Solutions of crack constraint parameter in 3D models −− II: Through T-stress

To more accurately analyze the crack front fields, especially under the low constraint conditions which are more common in practical engineering structures, two-parameter approaches (e.g. J-A(A2), J-Q, etc.) are proposed. Currently, no theoretical solutions of the constraint parameters (e.g. A(A2), Q, etc.) in two-parameter approaches are available. Meanwhile, numerical methods for constraint parameter determination are not practical because of significant resource and time consumption, especially for 3D solid models or engineering structures. Therefore, theory and formulae for the estimation of constraint parameter A in 3D solid models are developed in the current work based on T-stress, so as to make it possible to apply two-parameter approaches in practical engineering structures under normal working condition. Two actual structures are used to validate the engineering application of the developed estimate method and formulae.

Chemically reactive non-Newtonian fluid flow through a vertical microchannel with activation energy impacts: A numerical investigation

This work examines the second law analysis of an electrically conducting reactive third-grade fluid flow embedded with the porous medium in a microchannel with the influence of variable thermal conductivity, activation energy, viscous dissipation, joule heating, and radiative heat flux. A suitable non-dimensional variable is included into the governing equations to transform them into an ensemble of equations that are devoid of dimensions. The acquired equations are then tackled using the Runge Kutta Felhberg 4th and 5th order (RKF-45) approach in conjunction with the shooting methodology. Through comparison with the current results, the numerical results are verified, which provides a good agreement. From the present outcomes, it is established that the entropy generation is supreme for the viscous heating constraint, variable thermal conductivity, Frank Kameneski, heat source ratio parameter and third-grade fluid material constraint. The Bejan number boosts up with larger values of activation energy, and Frank Kameneski constraint and the decreasing nature is noticed for increasing third-grade material parameter, viscous heating parameter. With magnetism, the fluid’s velocity slows down because of a resistive force. A similar impact in the channel on velocity is noticed for larger third-grade fluid, activation energy parameter, and Frank-Kameniski parameters and increasing behavior is noticed for variable thermal conductivity, and permeability parameter. Further, it is cleared that the variable thermal conductivity assumption in the channel plate leads to a significant under prediction of the irreversibility rate.

Cosmological constraints from the eBOSS Lyman-α forest using the PRIYA simulations

We present new cosmological parameter constraints from the eBOSS Lyman-α forest survey. We use a new theoretical model and likelihood based on the PRIYA simulation suite. PRIYA is the first suite to resolve the Lyman-α forest in a (120 Mpc/h)3 volume, using a multi-fidelity emulation technique. We use PRIYA to predict Lyman-α forest observables with ≲ 1% interpolation error over an 11 dimensional (9 simulated, 2 in post-processing) parameter space. We identify an internal tension within the flux power spectrum data. Once the discrepant data is removed, we find the primeval scalar spectral index measured at a pivot scale of k 0 = 0.78 Mpc-1 to be nP = 1.009+0.027 -0.018 at 68% confidence. This measurement from the Lyman-α forest flux power spectrum alone is in reasonable agreement with Planck, and in tension with earlier eBOSS analyses. The amplitude of matter fluctuations is σ 8 = 0.733+0.026 -0.029 at 68% confidence, in agreement with Dark Energy Survey weak lensing measurements and other small-scale structure probes and in tension with CMB measurements from Planck and ACT. The effective optical depth to Lyman-α photons from our pipeline is in good agreement with earlier high resolution measurements. We find a linear power at z = 3 and k = 0.009 s/km of Δ2 L = 0.302+0.024 -0.027 with a slope n eff = -2.264+0.026 -0.018. Our flux power spectrum only chains prefer a low level of heating during helium reionization. When we add IGM temperature data we find nP = 0.983 ± 0.020 and σ 8 = 0.703+0.023 -0.027. Our chains prefer an early and long helium reionization event, as suggested by measurements from the helium Lyman-α forest. In the near future we will use our pipeline to infer cosmological parameters from the DESI Lyman-α data.

توصيف حل البرمجة غير الخطية متعددة الأهداف ذات دوال هدف استقرابية و معلمات ضبابية في القيود

This paper presents a solution to multiple objective non-linear programming (MONLP) problems with rough objective functions and constraints with fuzzy parameters, we offer an algorithm to solve this type of problem, where we deal with multiple objective functions first, then with the fuzziness of the constraint parameter Characterizing, second and third, we deal with the roughness of the objective function. So, the problem finally turns into a non-linear programming problem with crisp objective functions and constraints. Finally, an illustrative example is given to show the application of the algorithm. Keywords: Multiobjective non-linear programming (MONLP), rough function, Fuzzy number, Ranking function.