Values Of Parameters Research Articles

Application of a machine learning method to model-based library approach for critical dimension measurements using CD-SEM

A model-based library (MBL) method has already been established for the accurate measurement of the critical dimension (CD) of semiconductor linewidth using critical dimension scanning electron microscope (CD-SEM) images. In this work, the MBL method is further investigated by combining CD-SEM image simulation and a neural network algorithm. The secondary electron linescan profiles were first calculated using a Monte Carlo simulation method, enabling us to obtain the dependence of linescan profiles on the selected values of various geometrical parameters (e.g. top CD, sidewall angle and height) for Si and Au trapezoidal line structures. Machine learning methods have then been applied to predict the linescan profiles from a randomly selected training set of the calculated profiles. The predicted results agree very well with the calculated profiles with the standard deviations of 0.1% and 6% for the relative error distributions of Si and Au line structures, respectively. The findings show that the machine learning methods can be practically applied to the MBL method for reducing the library size, accelerating the construction of the MBL database and enriching the content of the available MBL database.

Synthesis and Characterization of Lithium-doped BaTiO3/Si(100) Thin Films with Variations of 0 %, 0.5 %, 1 % and 1.5 % as Potential Forerunners of Solar Cells

Thin films of BaTiO3/Si(100) in Lithium with doping variations of 0%, 0.5%, 1%, and 1.5% were successfully grown on a p-type Si substrate using Chemical Solution Deposition method. The films were annealed at 850 °C for 15 hours. The crystal structure was characterized using X-Ray Diffraction (XRD) with Material Analysis Using Diffraction (MAUD), Scanning Electron Microscope- Energy Dispersive X-Ray (SEM-EDX), optical absorption, and current – voltage (I-V) as potential solar cells. The results showed that the addition of Lithium doping affected the value of the lattice parameters and formed tetragonal crystals. The characterization results show that the bandgap energy value of the thin film due to lithium doping reduces the bandgap energy value because the donor atom added to a semiconductor causes the allowable energy level to be slightly below the conduction band. The presence of this new band causes the thin film bandgap energy to decrease with a five-valent tantalum dip. The morphological properties showed that the BaTiO3/Si(100) thin film particles in the deposited Lithium had a reasonably homogeneous grain. With the addition of lithium acetate as a binder into barium titanate, the grain size is getting smaller because it is suspected that the lithium-ion radius is smaller than the barium-ion radius. Measurement of I-V on the thin film shows that the output voltage value increases with more light intensity hitting the surface of the thin film. The greater the light intensity, the greater the energy of the photons, so the electrons are easier to jump. The three things above (both electrical and morphological properties) conclude that the thin films grown have the potential for solar cells.

Structural and practical identifiability analysis in bioengineering: a beginner’s guide

Advancements in digital technology have brought modelling to the forefront in many disciplines from healthcare to architecture. Mathematical models, often represented using parametrised sets of ordinary differential equations, can be used to characterise different processes. To infer possible estimates for the unknown parameters, these models are usually calibrated using associated experimental data. Structural and practical identifiability analyses are a key component that should be assessed prior to parameter estimation. This is because identifiability analyses can provide insights as to whether or not a parameter can take on single, multiple, or even infinitely or countably many values which will ultimately have an impact on the reliability of the parameter estimates. Also, identifiability analyses can help to determine whether the data collected are sufficient or of good enough quality to truly estimate the parameters or if more data or even reparameterization of the model is necessary to proceed with the parameter estimation process. Thus, such analyses also provide an important role in terms of model design (structural identifiability analysis) and the collection of experimental data (practical identifiability analysis). Despite the popularity of using data to estimate the values of unknown parameters, structural and practical identifiability analyses of these models are often overlooked. Possible reasons for non-consideration of application of such analyses may be lack of awareness, accessibility, and usability issues, especially for more complicated models and methods of analysis. The aim of this study is to introduce and perform both structural and practical identifiability analyses in an accessible and informative manner via application to well established and commonly accepted bioengineering models. This will help to improve awareness of the importance of this stage of the modelling process and provide bioengineering researchers with an understanding of how to utilise the insights gained from such analyses in future model development.

Extrinsic calibration for large FOV based on inverse depth parameterized bundle adjustment

To overcome the reliance on high-precision calibration plates in camera calibration, this paper proposes an extrinsic calibration method suitable for three-dimensional digital image correlation (3D-DIC) in large field of view (FOV). The method combines image feature algorithms with DIC techniques to extract matching point pairs (MPPs) from the left and right images of binocular cameras. These MPPs are then homogenized within the FOV. Next, initial values of the extrinsic parameters are solved based on epipolar constraint theory. Finally, the calibration parameters are nonlinearly optimized using the bundle adjustment method. To achieve stable and reliable numerical optimization in large FOV applications, the calibration control points (CCPs)’ spatial coordinates are represented using inverse depth parameterization. In scenarios where there might be a lack of sufficient CCPs, speckle patterns are artificially introduced to supplement the scene features. However, there is a lack of reliable experimental basis on how to add CCPs within the FOV. Therefore, through simulations, the factors affecting calibration accuracy are analyzed to guide the actual calibration process. The proposed method’s reliability and accuracy in large FOV 3D measurement are verified through experiments.

Comment on “Comparison of the Efficiencies of the Prognostic Generalized Complementary Functions on Evaporation Estimation” by Wang, L., et al., Published in Journal of Geophysical Research: Atmospheres

AbstractSurprising significant underperformance of the polynomial complementary relationship (PCR) of evaporation (Szilagyi et al., 2017, https://doi.org/10.1002/2016jd025611) by Wang et al. (2023, https://doi.org/10.1029/2023jd038683) is caused by the (a) station‐by‐station application of a grid‐based estimation procedure of the Priestley‐Taylor parameter (α) value, and (b) choice of the wind function. Application of the Rome wind function in the Penman equation together with either a well‐chosen single (constant) α or α as a function of the wet‐environment air temperature, should result in much improved evaporation estimates by the PCR in line with previous studies.

Testing Fraternal Birth Order Effects and Antagonistic Effects for Homosexual Men: Power Comparison of Various Methods.

Research on the biological determinants of male homosexual preference has long realized that the older brother effect (FBOE, i.e., a higher fraternal birth rank of homosexuals) and the antagonist effect (AE, i.e., more fertile women have a higher chance of having a homosexual son) can both generate family data where homosexual men have more siblings and more older siblings than heterosexual men. Various statistical approaches were proposed in the recent literature to evaluate whether the action of FBOE or AE could be discriminated from empirical data, by controlling for the other effect. Here, we used simulated data to formally compare all the approaches that we could find in the relevant literature for their ability to reject the null hypothesis in the presence of a specified alternative hypothesis (tests based on regression, Bayesian modeling, or contingency tables). When testing for the FBOE, the relative performance of the different tests was different depending on the specific function generating the older brother effect. Even if no tests were found to always perform better than the others, some tests performed systematically poorly, and some tests displayed a systematic high rate of type-I error. For testing the AE, the relative performance of the tests was generally not changed across all parameter values assayed, providing a clear ranking of the various proposed approaches. Pros and cons for each candidate test are discussed, taking into consideration power and the rate of type-I error but also practicability, the possibility to control for confounding variables, and to consider alternative hypotheses.

Preliminary study of urine analysis in pregnant cows: insights for improved herd management

ABSTRACT This study aimed to establish a standard urine profile for pregnant cows, commonly seen patients in veterinary clinics. Urine samples from 15 pregnant cows (6 Belgian Blue and 9 Holstein) of various ages were analysed, all showing no clinical signs of urinary tract disease. Samples were collected during preventive check-ups, either by spontaneous micturition or catheterisation. Macroscopic evaluation and urine dipstick tests revealed a mean pH of 8.17 ± 0.49. Moderate glycosuria and proteinuria were detected in two animals. No urobilinogen, ketone bodies, haemoglobin, or traces of blood were found. Laboratory urinalysis provided values (with 95% confidence intervals): osmolarity 493–1110.00 mOsm/l, sodium 52.00–122.00 mmol/l, calcium 1.28–3.98 mmol/l, potassium 76–144.00 mmol/l, magnesium 5.00–8.00 mmol/l, chlorine 40.00–80.00 mmol/l, protein 35.00–104.00 mg/l, glucose 0.40–1.27 mmol/l, creatinine 34.00–76.00 mmol/l, and urea 681.00–2086 mmol/l. These findings present the initial descriptive values of urine biochemical parameters in pregnant cows, emphasising their significance in assessing animal health. This research underscores its relevance by providing fundamental insights into animal health, offering a pathway to enhance agricultural sustainability and address food security, echoing the United Nations’ Sustainable Development Goals 1, 2, 8 and 12.

The construction of exact solution and explicit propagating optical soliton waves of Kuralay equation by the new extended direct algebraic and Nucci’s reduction techniques

ABSTRACT The aim of this paper is to investigate the integrable motion of induced curves using the Kuralay equation, which is a complex integrable coupled system. The soliton solutions derived from Kuralay equation are supposed to represent the most advanced research in several significant phenomena, including optical fibers, nonlinear optics, and ferromagnetic materials. Analytical methods are used to obtain traveling wave solutions for this model as the Cauchy problem cannot be addressed by the inverse scattering transform. In order to find the solitary wave solutions, the new extended direct algebraic and Nucci’s reduction approaches are taken over. As a result, the new extended direct algebraic method provides singular, mixed singular, periodic, mixed trigonometric, complex combo, trigonometric, mixed hyperbolic, plane, and combined bright-dark soliton solutions. The Nucci’s reduction technique develops the first integral of differential equation to discuss the conservation and exact solutions. To ensure the sensitivity of the study, the effect of waves on the propagation of solitons and the sensitivity of the model is examined. To illustrate how the fitting values of the system parameters may be utilized to anticipate the behavioral reactions to pulse propagation, the resulting solutions are visually shown in 2D and 3D charts.

A coupled DEM-CFD analysis of asphaltene particles agglomeration in turbulent pipe flow

We performed numerical investigations of the agglomeration behaviour of asphaltene particles in a turbulent pipe flow by using a commercial discrete element modelling (DEM) software, EDEM, coupled with a computational fluid dynamics (CFD) software package, FLUENT, in four-way coupling. We describe the particle–particle and particle–wall interactions to the Hertz–Mindlin approach extended with the Johnson–Kendall–Roberts contact force model. The very small size of asphaltene particles, however, requires minor time steps resulting in excessively long computing times. To reduce the latter, we developed a novel scaling method in which (a) the value of the shear modulus is reduced while the non-dimensional Cohesion number is kept constant by also reducing the value of the interfacial energy, and (b) the values of the friction parameters are adapted. We calibrated our scaling method by means of two tests, viz. the stop distance of a single particle sliding and rolling over a flat surface, and a common angle of repose test, both for particles 100 and 60 µm in size. As a result, we were able to increase both the Rayleigh time step and the DEM time step by a factor of 25. The eventual DEM/CFD simulations for 5 µm particles in a turbulent pipe flow still took almost 4 months on a 48 cores workstation. They resulted in visualisations and in quantitative data on, among other things, number and size (gyration radius) of agglomerates, their fractal dimension, and a coordination number, all as a function of time.

Thermo-mechanical finite element analysis of the solid-state metal deposition via lateral friction surfacing

Lateral friction surfacing (LFS) is a novel variation of the friction surfacing (FS) process for solid-state fabrication of ultra-thin and smooth metal coatings. In the LFS technique, pressing the lateral side of a rotating rod against the substrate's surface generates frictional heat and plastic deformation, leading to the deposition of consumable material from the lateral surface of the rod onto the substrate. This study presents numerical analysis for such a complex coupled thermo-mechanical process to investigate the distribution of various variables such as temperatures, local pressure, stress, and strain throughout the consumable rod and substrate. A finite element model of the process was developed through ABAQUS/Explicit method to investigate the thermo-mechanical response of rod and substrate materials by incorporating various features such as process parameters, mechanical behaviors of materials, temperature-dependent material properties, and failure criteria. The finite element model was validated by conducting experimental analysis using the same materials and values of process parameters. The finite element simulation results were consistent with previous experiments and affirmed that process temperature is lower than in conventional FS and localized in a small area, which can reduce thermal impacts on the consumable rod and substrate materials. Although the majority of the frictional heat was transferred to the Al6061-T6 rod due to its higher thermal conductivity, it was revealed that the maximum process temperature occurred on the mild steel substrate. The results of the study demonstrate that selecting an appropriate dwell-phase duration not only enhances the material's temperature to facilitate improved deposition but also ensures more consistent contact across all points along the rod's side, resulting in a uniform deposition.

Transparent windows in a layered medium with mosaic layers.Windows distribution by area

The work considers a layered system, the layers of which consist of transparent and opaque elements. At the places where parts of the transparent elements are opposite each other in all layers, a transparent through window is formed. The work is devoted to the case when transparent and opaque elements are the cells of a Voronoi mosaic. The distributions of windows by area were obtained for different values of parameters – the number of layers, the average size of cells, the part of transparent cells from their total number. It is shown that the numerically obtained distributions for the case under consideration are very well described by the approximation found analytically for a simplified system. The conditions for its applicability are found. The distributions over the area of the entire “transparent through” region in the sample were obtained.

Radiative dynamics of laser-driven Li@C n embedded in quantum plasma

This work considers a guest Lithium atom (Li@C n ) in an endohedral fullerene, embedded in a quantum plasma modeled by the more general exponential cosine screened Coulomb (MGECSC) potential, under the influence of a spherical confinement and laser radiation field. The system is examined in nonrelativistic form and the related wave equation is solved using the tridiagonal matrix method (TMM), thus obtaining the discrete-continuum spectrum and related wave functions. The numerical values of the relevant parameters in this process are physically accessible values. The effects of the plasma, laser field and endohedral cavity on the photoionization cross section (PCS) are analysed in detail. The shielding effect of the plasma medium and the pulsating effect of the laser field modify the effective potential energy of the system, affecting the localizations of the 2s and continuum states, causing various overlapping cases. Considering different values of the endohedral encompassement parameters, which means that different types of fullerenes are taken into account, overlapping cases occur for different spectra and wave functions. Scrutinising these overlappings, the confinement and Cooper resonances of the PCSs are analysed. This analysis provides many details for the radiative dynamics of an artificial system Li@C n . The relevant ranges and critical values of plasma, laser field, and endohedral encapsulation parameters in the formation process of these resonances and PCSs are explained, as well as the cross-section curves, resonance positions, effective photoelectron energy range, and general PCS behavior, which can be important for potential experiments in addition to other theoretical investigations.

The local electronic interaction and strain effects on transverse dynamical spin susceptibility of [formula omitted]-graphyne

We present the behaviors of dynamical transverse spin susceptibilities of undoped β-graphyne monolayer using the Green’s function approach in the context of Hubbard model Hamiltonian. Such dynamical spin susceptibility is proportional to inelastic cross section of neutron beam from the layer. Specially, the effects of magnetic field, on-site coulomb repulsion strength and strain parameter on the spin excitation modes of β-graphyne monolayer are investigated via calculating correlation function of transverse components of spin density operators. Our results show the increase of absolute value of compressive strain parameter leads to move the magnetic excitation modes to higher energies. Also the intensity of peaks in inelastic cross section reduces with absolute value of compressive strain parameter. We also show that applying biaxial tensile strain causes to decrease the intensity of peaks in dynamical transverse spin susceptibility. Finally the effects of magnetic field and on-site coulomb repulsion interaction strength on frequency dependence of dynamical spin structure factor of β-graphyne layer are studied. Moreover the frequency positions of spin excitation mode of β-graphyne monolayer have been investigated due to the effects of biaxial strains, applied magnetic field and Hubbard parameter strength in details.

A cost-efficient relocation model minimizing economic and social disruption of the disaster-affected population of the Kedarnath considering uncertainty

In 2013, Kedarnath, India, faced a devastating landslide-induced flood, causing substantial damage, loss of life, and displacement. This event highlights a gap in post-disaster relocation models, hindering efficient transportation to preferred locations. The challenge intensifies as coordinating relocation with individual preferences, coupled with post-disaster data uncertainty, complicates efforts and triggers a series of effects on community acceptance, psychological well-being, and overall resilience. The study addresses post-disaster relocation gaps by introducing a multi-objective transportation model (MOTM) with psychological support, optimizing resettlement to minimize budget impact, distance, and economic and social disruption (ESD). The proposed interval-valued pentagonal neutrosophic number (IVPN) and defuzzification technique advances disaster management theory and offers a practical solution for uncertainty in data, bridging theory, and practical application in the disaster response phase. The MOTM efficiently minimizes transportation, psychological intervention, and rental costs through fuzzy goal programming (FGP), global criterion method (GCM), and neutrosophic compromise approach (NCA), implemented with LINGO optimization software. The values of parameters considered in the MOTM are captured in IVPN format and converted to crisp values, availing the proposed defuzzification technique. The performance evaluation of the defuzzification technique is carried out through a comparative analysis of past literature. The proposed MOTM and IVPN were implemented in the 2013 Kedarnath flood case study to generate diverse relocation options. GCM provides relocation options at a higher cost and distance but with minimal ESD by providing a preferred location to a family count as high as 3324 out of 3491 families. The FGP technique provides results similar to NCP that reduce the costs and distance of the relocation process by 6.95% and 0.88%, respectively; however, it increases ESD by allocating preferred locations to only 2623 out of 3491 families. The study provides Pareto optimal solutions for the smooth application of MOTM for diverse post-disaster instances.

Magnetized effects of double diffusion model on mixed convective Casson nanofluid subject to generalized perspective of Fourier and Fick’s laws

Understanding the flow behavior of non-Newtonian fluids from an industrial standpoint is crucial. Many industrial and technical activities, such as the extrusion of polymer sheets, the manufacturing of paper, and the development of photographic films, require non-Newtonian fluids. Heat and mass transport have various manufacturing uses. However, classical heat and mass transfer theories (Fourier and Fick laws) cannot anticipate thermal and solute relaxation time occurrences. The purpose of this investigation is to apply the modified Ohm law to the heat and mass transportation systems, which are established by generalized Fourier and Fick’s equations, respectively. A three-dimensional Darcy–Forchheimer flow through a porous medium integrating Hall and ion slip effects is studied for a non-Newtonian fluid known as a “Casson nanofluid” with mixed convection across a stretched surface. To investigate heat transfer augmentation, the modified Buongiorno model for nanofluids is used. It covers practical nanofluid properties as well as the mechanics of random motion and thermo-migration in nanoparticles. These groups of Partial Differential Equations (PDEs) that represent the mathematical model are combined with the proper similarity transformations to create an ordinary differential equations system, which is then resolved using the power of the Lobatto IIIA method. Examples of numerical and graphical data are given to show how various physical constraints affect the variation for velocities, temperatures, mass transfer, dimensionless shear stress, as well as Nusselt and Sherwood numbers. It turns out that lowering the Casson fluid parameters’ values reduces the velocity in the spatial coordinates (x, y). A rise in the Hall parameter's values ultimately leads to an improvement in the fluid. This paper sheds light on useful applications including power generation, conservation of energy, friction elimination, and nanofluidics. Nonetheless, the work highlights an important point: by carefully adjusting the Casson parameter, thermophoresis parameter, and Brownian motion parameter, the flow of a Casson fluid, including nanoparticles, may be controlled.

Non-Markovian processes on heteroclinic networks.

Sets of saddle equilibria connected by trajectories are known as heteroclinic networks. Trajectories near a heteroclinic network typically spend a long period of time near one of the saddles before rapidly transitioning to the neighborhood of a different saddle. The sequence of saddles visited by a trajectory can be considered a stochastic sequence of states. In the presence of small-amplitude noise, this sequence may be either Markovian or non-Markovian, depending on the appearance of a phenomenon called lift-off at one or more saddles of the network. In this paper, we investigate how lift-off occurring at one saddle affects the dynamics near the next saddle visited, how we might determine the order of the associated Markov chain of states, and how we might calculate the transition probabilities of that Markov chain. We first review methods developed by Bakhtin to determine the map describing the dynamics near a linear saddle in the presence of noise and extend the results to include three different initial probability distributions. Using Bakhtin's map, we determine conditions under which the effect of lift-off persists as the trajectory moves past a subsequent saddle. We then propose a method for finding a lower bound for the order of this Markov chain. Many of the theoretical results in this paper are only valid in the limit of small noise, and we numerically investigate how close simulated results get to the theoretical predictions over a range of noise amplitudes and parameter values.

A radiotherapy community data-driven approach to determine which complexity metrics best predict the impact of atypical TPS beam modeling on clinical dose calculation accuracy.

To quantify the impact of treatment planning system beam model parameters, based on the actual spread in radiotherapy community data, on clinical treatment plans and determine which complexity metrics best describe the impact beam modeling errors have on dose accuracy. Ten beam modeling parameters for a Varian accelerator were modified in RayStation to match radiotherapy community data at the 2.5, 25, 50, 75, and 97.5 percentile levels. These modifications were evaluated on 25 patient cases, including prostate, non-small cell lung, H&N, brain, and mesothelioma, generating 1,000 plan perturbations. Differences in the mean planned dose to clinical target volumes (CTV) and organs at risk (OAR) were evaluated with respect to the planned dose using the reference (50th-percentile) parameter values. Correlation between CTV dose differences, and 18 different complexity metrics were evaluated using linear regression; R-squared values were used to determine the best metric. Perturbations to MLC offset and transmission parameters demonstrated the greatest changes in dose: up to 5.7% in CTVs and 16.7% for OARs. More complex clinical plans showed greater dose perturbation with atypical beam model parameters. The mean MLC Gap and Tongue & Groove index (TGi) complexity metrics best described the impact of TPS beam modeling variations on clinical dose delivery across all anatomical sites; similar, though not identical, trends between complexity and dose perturbation were observed among all sites. Extreme values for MLC offset and MLC transmission beam modeling parameters were found to most substantially impact the dose distribution of clinical plans and careful attention should be given to these beam modeling parameters. The mean MLC Gap and TGi complexity metrics were best suited to identifying clinical plans most sensitive to beam modeling errors; this could help provide focus for clinical QA in identifying unacceptable plans.

Optimization of suspended particulate transport parameters from measured concentration profiles with a new analytical model

The water body’s suspended concentration reflects many coastal environmental indicators, which is important for predicting ecological hazards. The modeling of any concentration in water requires solving the settling-diffusion equation (SDE), and the values of several key input parameters therein (settling velocity ws, eddy diffusivity Ds, and erosion rates p(t)) directly determine the prediction performance. The time-consuming large-scale simulations would benefit if the parameter values could be estimated through available observations in the target sea area. The present work proposes a new optimization method for synchronously estimating the three parameters from limited concentration observations. First, an analytical solution to the one-dimensional vertical (1DV) SDE for suspended concentrations in an unsteady scenario is derived. Second, the near bottom suspended sediment concentration (SSC) profiles are measured with high-resolution observation. Third, the key parameters are optimized through the best fit of the measured SSC profiles and those modeled with the unsteady solution. Nonlinear least square fitting (NLSF) is introduced to judge the best fits automatically. The high-resolution concentration measurements in a specially-designed cylindrical tank experiment using the Yellow River Delta sediments test the proposed method. The method performs well in the initial period of turbulence generation when sediment resuspension is significant. It optimizes p(t), ws, and Ds with reasonable values and uniqueness of their combination. The proposed theory is a practical tool for quickly estimating key substance transport parameters from limited observations; it also has the potential to construct local parametric models to benefit the 3D modeling of coastal substance transport. Although the present work takes SSC as an example, it can be extended to any suspended particulate concentration in the water.

In vitro PK/PD modeling of tyrosine kinase inhibitors in non-small cell lung cancer cell lines.

Tyrosine kinase inhibitors (TKIs) are routinely prescribed for the treatment of non-small cell lung cancer (NSCLC). As with all medications, patients can experience adverse events due to TKIs. Unfortunately, the relationship between many TKIs and the occurrence of certain adverse events remains unclear. There are limited invivo studies which focus on TKIs and their effects on different regulation pathways. Many invitro studies, however, that investigate the effects of TKIs observe additional changes, such as changes in gene activations or protein expressions. These studies could potentially help to gain greater understanding of the mechanisms for TKI induced adverse events. However, in order to utilize these pathways in a pharmacokinetic/pharmacodynamic (PK/PD) framework, an invitro PK/PD model needs to be developed, in order to characterize the effects of TKIs in NSCLC cell lines. Through the use of ordinary differential equations, cell viability data and nonlinear mixed effects modeling, an invitro TKI PK/PD model was developed with estimated PK and PD parameter values for the TKIs alectinib, crizotinib, erlotinib, and gefitinib. The relative standard errors for the population parameters are all less than 25%. The inclusion of random effects enabled the model to predict individual parameter values which provided a closer fit to the observed response. It is hoped that this model can be extended to include invitro data of certain pathways that may potentially be linked with adverse events and provide a better understanding of TKI-induced adverse events.

Aczel–Alsina T-norm based group decision-making technique for the evaluation of electric cars using generalized orthopair fuzzy aggregation information with unknown weights

Data management and finding precise outcomes from large amounts of information are among the biggest challenges for scientists. The technique of multi-attribute group decision-making (MAGDM) is a valuable tool for investigating fuzzy data precisely. The key objective of the paper is to redefine the q-rung orthopair (q-RO) fuzzy set (FS) (q-ROFS) in the term of interval-valued and proposed new aggregation operators (AOs) based on the Aczel-Alsina (AA) t-norm (TN) and t-conorm (TCN) operations. The AA operational laws are a generalized form of existing TNs and TCNs and give more reliable results because they can fluctuate in their parametric values. The concept of interval-valued enlarges the space of membership degree (MD) and non-membership degree (NMD) for decision-makers. By taking qth power, the interval-valued q-ROFS (IV-q-ROFS) structure. The IV-q-ROFS can handle the uncertainty and vagueness in data, then interval-valued intuitionistic FS (IV-IFS) and interval-valued Pythagorean FS (PyFS) (IV-PyFS) and provide accurate results. The thought of power AOs (PAOs) makes the relationship between weight vectors and reduces the chances of uncertainty in aggregated results. By taking advantage of PAOs, this article is devoted to introducing the interval-valued q-ROF Aczel-Alsina power-weighted averaging (IV-q-ROFAAPWA) and interval-valued q-ROF Aczel-Alsina power-weighted geometric (IV-q-ROFAAPWG) operators. The fundamental axioms of AOs, idempotency, boundedness, and monotonicity, are also discussed. To illustrate the importance of suggested AOs, the real-life problem of electric car selection was solved by applying the MAGDM method using the proposed IV-q-ROFAAPWA and IV-q-ROFAAPWG operators. The comparison of proposed AOs with currently present AOs is also part of the article. We finally constructed solid conclusions.