Parametrized Research Articles

Minimum-time interception of a moving target by a material point in a viscous medium

In this paper we investigated a model that describes the motion of a material point in a viscous medium under a force that is arbitrary in direction, but limited in magnitude. This model was named ”the isotropic rocket” in the early work of Rufus Isaacs. We obtained a parametric description of a reachable set for the isotropic rocket and solved a group of reachability problems for a final configuration that varies in a known time-dependent manner (moving target). To describe the reachable set, we obtained an explicit parametric form of its boundary and all its projections onto all subspaces of the state space. Convergent algorithms have been proposed for computing minimum-time interception in position and velocity spaces. Finally, we numerically investigated particular cases of minimum-time interception, validating the development of this study.

A novel lifetime analysis of repairable systems via Daubechies wavelets

AbstractIt is well-known that minimally repaired systems are characterized by non-homogeneous Poisson processes. When the underlying lifetime distribution function or its associated intensity function is completely known, the statistical inference of the cumulative number of failures/repairs is made through some standard techniques. On the other hand, when the parametric form of the lifetime distribution or the intensity function is not known, non-parametric inference techniques such as non-parametric maximum likelihood estimate (NPMLE) and kernel-based estimates are employed. Since these techniques are based on the underlying lifetime data, the resulting estimates of the cumulative number of failures/repairs with respect to the system operation time are discontinuous and troublesome in assessing the reliability measures such as reliability function and mean time to failure, etc. In this paper, we propose a novel lifetime analysis of the repairable systems via Daubechies wavelets. More specifically, we generalize the seminal Daubechies wavelet estimate by Kuhl and Bhairgond (Winter Simul Conf Proc 1:562–571, 2000) by combining the ideas on NPMLE and KMBs. In both simulation experiments and real data analyses, we investigate applicability of our Daubechies wavelet-based approaches and compare them with the conventional non-parametric methods.

The Fav-Jerry Distribution: Another Member in the Lindley Class with Applications

In this paper, we designed another one-parameter distribution using a mixture of exponential and gamma distributions. This new distribution is unique among other members of the Lindley class because the qunatile function has a closed-functional form hence lending itself to analytical study. This distribution is named Fav-Jerry after the names of the authors. The statistical properties and point estimation using some non-Bayesian methods were studied. We deploy tow real datasets to demonstrate the usefulness of the new model. The real data applications using data sets on mortality rate and failure rate in a particular airplane showed that the proposed model fits well compared to its competitors, therefore, the Fav-Jerry distribution is superior to Two parameter Chris-Jerry(TPCJ), Chris-Jerry, Exponentiated Inverted Exponential distribution, and Weibull distributions and then parametric plots showing the histogram, CDF, survival and TTT plots gotten from both data sets are displayed.

Study of pressure parametric dark energy model in the framework of f(Q) gravity

In this work, we have proposed a simple parametrization for the pressure component [Formula: see text] of the dark energy model and have studied the cosmological implications of this model in the framework of [Formula: see text] modified gravity theory, aka, the symmetric teleparallel gravity theory, where Q is known as the nonmetricity scalar. By considering a particular parametric form of [Formula: see text], we obtained the Hubble solution for the [Formula: see text] modified gravity model. In order to see whether this model is consistent with or challenges the [Formula: see text]CDM limits, we tried to put constraints on the model parameters using the recent observational datasets like Hubble data, Cosmic Chronometer data, Type Ia Supernovae (SNIa) data, baryonic acoustic oscillations (BAO) data. We have employed the [Formula: see text] minimization technique and have carried out the Markov Chain Monte Carlo (MCMC) analysis using emcee package. We have found that the deceleration parameter shows a smooth transition from positive to negative value in recent past which is essential for the structure formation of the Universe. It has been found that the parametric form of the dark energy pressure parameter is consistent with current cosmological scenario.

Adaptive Takagi–Sugeno (T–S) fuzzy observer based fault tolerant control for DC–DC converters with robustness against uncertainties

AbstractThis paper proposes a Takagi–Sugeno fuzzy system model based fault tolerant control scheme for DC–DC converters, which is robust against parameter uncertainties and achieves the output voltage of an ideal converter. The control involves estimating the duty cycle change in the form of a fault parameter required to track the output voltage, in the presence of several uncertain conditions including converter losses, variation in input voltage, and unknown and changing output load. An adaptive law is designed to estimate the fault parameter that guarantees state and parameter error convergence. The adaptive law is derived using the Lyapunov stability theorem and the required parameters are evaluated by solving a linear matrix inequalities optimization problem. The load resistance is estimated in parallel by using a Kalman filter and fed to the fault parameter estimation scheme. Furthermore, a fast and robust method to detect short and open circuit switch faults is also presented. The proposed technique offers a simple, yet effective method to regulate the output voltage under several faulty and uncertain conditions. The proposed technique is tested on a DC–DC boost converter simulation model and the demonstrated MATLAB/Simulink results show the effectiveness of the proposed algorithm.

Nonparametric Machine Learning for Stochastic Frontier Analysis: A Bayesian Additive Regression Tree Approach

The stochastic frontier model is widely used in economics, finance, and management to estimate the production function and efficiency of a firm or industry. In the current literature of stochastic frontier analysis, parametric forms of production functions, such as Cobb-Douglas and Translog, are often assumed a priori without validation, which may suffer from model misspecification and lead to biased estimates of efficiency. To address this issue, a new set of stochastic frontier models is proposed, namely, 1) monBART-SFM that constructs a monotone-constrained nonparametric production function via an extension of the monotone Bayesian Additive Regression Tree (monBART) framework; 2) softBART-SFM that is built upon softBART, a smooth version of BART, where both models offer greater flexibility in modeling complex and nonlinear production functions that may have high dimensional inputs. The performance of the proposed method is illustrated through simulation studies and a real data application.

Assortment Optimization: An Annotated Reading Assortment

Which varieties or brands of a product should a retailer stock on its shelf? Carrying a large variety caters to more customers' needs, but could cannibalize the sales of high-end brands and also cause an inventory nightmare. Assortment optimization aims to formalize these tradeoffs, with the basic problem being as follows. There is a universe of brands j ∈ U , each with a market-accepted price r j . For any S ⊆ U , a function ϕ ( j, S ) indicates the probability that a representative customer from the population would purchase j when given the choice from assortment S , under the market prices. The optimization problem is to maximize the average revenue per customer, i.e. [EQUATION] (1) possibly with constraints on S due to shelf size. Assortment optimization started out by showing how to efficiently find the optimal S from the exponentially many possibilities, under well-established parametric forms for the function ϕ that are called ( discrete) choice models. Since then, the literature has developed choice models of its own that are specialized for assortment optimization. The basic problem has also been extended, and connected with topics such as online algorithms, machine learning, and mechanism design that are mainstream in the Economics and Computation community, with a vast horizon for future directions. This is an annotated reading list about assortment optimization, that aims to provide broad coverage while facing a "cardinality constraint" on the number of papers in the assortment.

IoT-Based Vaname Shrimp Pond Water Quality Monitoring Using the Quamonitor Tool

Pond water pollution is the main problem in vannamei shrimp cultivation, so to overcome this problem, The decline in water quality is caused by a decline in the quality of pH, salinity and temperature in pond water. Researchers created the Quamonitor tool to monitor IoT-based vaname shrimp pond water. The research was conducted using the Research and Development (R&D) method with the Analyze, Design, Develop, Implement and Evaluate (ADDIE) model, assisted by WiFi, MQTT and Node Red networks to monitor water in order to display monitoring results on Android. This study aims to monitor the condition of vaname shrimp pond water quality in the form of pH, temperature, and salinity parameters using pH E-4052C sensor, DS18B20 temperature sensor, TDS/Conductivity DFRobot V1.0 sensor and NodeMCU ESP32 as a data manager and system controller that runs in real time. The test results of PH sensors, temperature sensors and TDS/conductivity sensors obtained a sensor error percentage of 0.29% for pH parameters, 1.7% for temperature parameters and 0.41% for salinity parameters. Based on the sensor error value, Thingspeak graph display, real time monitor display on Android, it can be said that the manufacture of the Quamonitor tool for IoT-based vaname shrimp pond water quality monitoring is successful and can function well

A new analytical model of the cosmic-ray energy flux for Galactic diffuse radio emission

Low-frequency radio observations of diffuse synchrotron radiation offer a unique vantage point from which to investigate the intricate relationship between gas and magnetic fields in the formation of structures within the Galaxy, spanning from the diffuse interstellar medium (ISM) to star-forming regions. Achieving this pivotal objective hinges on a comprehensive understanding of cosmic-ray properties; these dictate the effective energy distribution of relativistic electrons, which are primarily responsible for the observable synchrotron radiation. Notably, cosmic-ray electrons (CRe) with energies of between 100 MeV and 10 GeV play a crucial role in determining the majority of the sky brightness below the GHz range. However, their energy flux (je) remains elusive because of solar modulation. We propose a way to derive observational constraints on this energy gap of interstellar CRe through the brightness temperature spectral index of low-frequency radio emission, here denoted βobs. We introduce a new parametric analytical model that fits available data for je in accordance with the βobs values measured in the literature between 50 MHz and 1 GHz for diffuse emission in the Milky Way. Our model accounts for multiple observations considering magnetic-field strengths consistent with existing measurements below 10 μG. We present a first all-sky map of the average component of the magnetic field perpendicular to the line of sight and validate our methodology against state-of-the art numerical simulations of the diffuse ISM. This research makes headway in modeling Galactic diffuse emission with a practical, parametric form. It provides essential insights that will help preparations for the imminent arrival of the Square Kilometre Array.

HETEROSKEDASTICITY ROBUST SPECIFICATION TESTING IN SPATIAL AUTOREGRESSION

Spatial autoregressive (SAR) and related models offer flexible yet parsimonious ways to model spatial and network interactions. SAR specifications typically rely on a particular parametric functional form and an exogenous choice of the so-called spatial weight matrix with only limited guidance from theory in making these specifications. Also, the choice of a SAR model over other alternatives, such as spatial Durbin (SD) or spatial lagged X (SLX) models, is often arbitrary, raising issues of potential specification error. To address such issues, this paper develops a new specification test within the SAR framework that can detect general forms of misspecification including that of the spatial weight matrix, the functional form and the model itself. The test is robust to the presence of heteroskedasticity of unknown form in the disturbances and the approach relates to the conditional moment test framework of Bierens ([1982, Journal of Econometrics 20, 105–134], [1990, Econometrica 58, 1443–1458]). The Bierens test is shown to be inconsistent in general against spatial alternatives and the new test introduces modifications to achieve test consistency in the spatial setting. A central element is the infinite-dimensional endogeneity induced by spatial linkages. This complexity is addressed by introducing a new component to the omnibus test that captures the effects of potential spatial matrix misspecification. With this modification, the approach leads to a simple pivotal test procedure with standard critical values that is the first test in the literature to have power against misspecifications in the spatial linkages. We derive the asymptotic distribution of the test under the null hypothesis of correct SAR specification and prove consistency. A Monte Carlo study is conducted to study its finite sample performance. An empirical illustration on the performance of the test in modeling tax competition in Finland is included.

Time-Variant Reliability for Systems with Non-monotonic Limit-State Functions

Most engineering time-variant reliability problems are the result of component degradation and stochastic loading. The resultant failure modes, and their resultant limit-state functions, produce limit-state surfaces with unpredictable temporal trajectories that may exhibit a combination of increasing and decreasing failure probabilities. In many cases the trajectories are monotonic so that failure increases predictably: in other cases, this is not so. In this paper we present the discrete-time set theory derivation for non-monotonic situations wherein the limit-state surface may recede to provide, what only appears to be, ever decreasing failure probability. The presence of both monotonic and non-monotonic limit-state functions can be easily detected by a parametric polar plot of the most-likely failure points in standard normal space. The polar plot reveals the temporal limit-state surfaces that need to be retained to represent the system limit-state surfaces at any time instant. The minimum set herein is called the extreme limit-state surface. The impact of the work is that the cumulative distribution function (cdf) can be provided with a minimum of failure and safe events. This in turn gives rise to several solution options such as the multi-normal integral method or a special Monte Carlo simulation that obviates the tedious marching-out routine. A series system and a parallel system show the efficacy of the theory.

Sub-grid Variability and its Impact on Exposure in Regional Scale Air Quality and Integrated Assessment Models: Application of the uEMEP Downscaling Model

Regional scale air quality and integrated assessment models are necessarily limited in their spatial resolution, particularly when applied to larger regions such as Europe or for global applications. The EMEP MSC-W chemical transport model and the GAINS integrated assessment model, which makes use of source receptor information precalculated by the EMEP MSC-W model, use resolutions of 0.1°×0.1° and 0.3°×0.2°. These resolutions cannot account for variability at finer scales. Variability within grids, both concentration and population distributions, can be significant. To improve exposure calculations that take into account the sub-grid variability, the uEMEP model has been applied in this study to provide suitable parameterisations of the sub-grid variability within an EMEP MSC-W grid. The uEMEP model is applied on a European domain at 250 × 250 m2. This provides roughly 7000 sub-grids within each EMEP MSC-W 0.3°×0.2° grid to analyse the sub-grid variability in exposure. The analysis derives an exposure correction factor (ECF) for each source sector in each EMEP MSC-W grid. This factor provides information on the impact of sub-grid resolution on the population weighted concentrations within each grid. The population weighted standard deviation is also calculated, providing information on sub-grid variability to derive frequency distributions and parameterised forms of the sub-grid variability suitable for health risk assessments. The impact of sub-grid variability is assessed for the pollutants PM2.5, NOX and NO2. For NO2, additional chemistry calculations are carried out on the NOX frequency distributions. We show that the parameterised form of the sub-grid variability provides close to equivalent results as the original uEMEP calculations. Exposure to PM2.5 increases by around 13% when the sub-grid variability is taken into account for a 2030 emission scenario. For NO2, this increase is around 31% for the same scenario. A similar increase is found in the health risk indicator ’population attributable fraction’ when no lower concentration threshold is used in the concentration–response function. When applying the World Health Organizations recommended threshold value we find that including the sub-grid variability increases the calculated population attributable fraction by 32% and 46% for PM2.5 and NO2 respectively. We conclude that sub-grid variability should be taken into account in future exposure calculations for regional scale modelling and impact assessments.

An overlooked source of uncertainty in the mass of the Milky Way

ABSTRACT In the conventional approach to decomposing a rotation curve into a set of contributions from mass model components, the measurements of the rotation curve at different radii are taken to be independent. It is clear, however, that radial correlations are present in such data, for instance (but not only) because the orbital speed depends on the mass distribution at all (or, minimally, inner) radii. We adopt a very simple parametric form for a covariance matrix and constrain its parameters using Gaussian process regression. Applied to the rotation curve of the Milky Way, this suggests the presence of correlations between neighbouring rotation curve points with amplitudes of $\lt 10\, \mathrm{km}\, \mathrm{s}^{-1}$ over length scales of 1.5–$2.5\, \mathrm{kpc}$ regardless of the assumed dark halo component. We show that accounting for such covariance can result in a ∼50 per cent lower total mass estimate for the Milky Way than when it is neglected, and that the uncertainty in model parameters increases such that it seems more representative of the uncertainty in the rotation curve measurement. The statistical uncertainty associated with the covariance is comparable to or exceeds the total systematic uncertainty budget. Our findings motivate including more detailed treatment of rotation curve covariance in future analyses.

Fractional Quadratic Deceleration Parameter (FQDP): Observational and Theoretical Perspectives

This study explore the dynamics of cosmic expansion through a detailed exploration of a novel form of the deceleration parameter, denoted as q = α(t 2−1) t 2+1 , within the framework of f(R,T) gravity theory. This study employs both observational and theoretical approaches to unveil the intricate interplay of cosmic forces and phenomena that have a profound influence on the ever-evolving universe. The primary objective of this investigation is to gain a deeper understanding of the expanding dynamics, particularly the transition from deceleration to acceleration. The findings of this paper shed light on the presence of a phase transition and an initial singularity, while remaining consistent with the ΛCDM model. Moreover, this study serves as a foundation for further exploration within the f(R,T) gravity framework, providing fresh insights into the cosmos and offering a profound comprehension of the universe’s dynamic evolution.

The Pitfalls of Reverse Engineering Topology Optimised Load-Carrying Structural Parts into Parametric Models

In this paper, we will show the topological optimization of a load-carrying structural parts and the errors that can occur when the topological solution in the form of a triangulated mesh of the surface of the optimized part is reconstructed into a parametric CAD form. Such a reconstruction always introduces geometric errors in the resulting optimal structure of the structural element. A numerical example will be used to show how quickly stress concentrations are reproduced on surfaces where they should not be. The numerical results are also supported by experiment. In the conclusion, it is suggested how, with a specific topological model, the obtained stress concentrations at the edges of the structure can be reduced.

Production study of Fr, Ra and Ac radioactive ion beams at ISOLDE, CERN

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Analysis of modeled 3D solar magnetic field during 30 X/M-class solar flares

Using non-linear force free field (NLFFF) extrapolation, 3D magnetic fields were modeled from the 12-min cadence Solar Dynamics Observatory Helioseismic and Magnetic Imager (HMI) photospheric vector magnetograms, spanning a time period of 1 hour before through 1 hour after the start of 18 X-class and 12 M-class solar flares. Several magnetic field parameters were calculated from the modeled fields directly, as well as from the power spectrum of surface maps generated by summing the fields along the vertical axis, for two different regions: areas with photospheric |Bz|≥ 300 G (active region—AR) and areas above the photosphere with the magnitude of the non-potential field (BNP) greater than three standard deviations above |BNP|̄ of the AR field and either the unsigned twist number |Tw| ≥ 1 turn or the shear angle Ψ ≥ 80° (non-potential region—NPR). Superposed epoch (SPE) plots of the magnetic field parameters were analyzed to investigate the evolution of the 3D solar field during the solar flare events and discern consistent trends across all solar flare events in the dataset, as well as across subsets of flare events categorized by their magnetic and sunspot classifications. The relationship between different flare properties and the magnetic field parameters was quantitatively described by the Spearman ranking correlation coefficient, rs. The parameters that showed the most consistent and discernable trends among the flare events, particularly for the hour leading up to the eruption, were the total unsigned flux ϕ), free magnetic energy (EFree), total unsigned magnetic twist (τTot), and total unsigned free magnetic twist (ρTot). Strong (|rs| ∈ [0.6, 0.8)) to very strong (|rs| ∈ [0.8, 1.0]) correlations were found between the magnetic field parameters and the following flare properties: peak X-ray flux, duration, rise time, decay time, impulsiveness, and integrated flux; the strongest correlation coefficient calculated for each flare property was 0.62, 0.85, 0.73, 0.82, −0.81, and 0.82, respectively.

Sandwich boosting for accurate estimation in partially linear models for grouped data

Abstract We study partially linear models in settings where observations are arranged in independent groups but may exhibit within-group dependence. Existing approaches estimate linear model parameters through weighted least squares, with optimal weights (given by the inverse covariance of the response, conditional on the covariates) typically estimated by maximizing a (restricted) likelihood from random effects modelling or by using generalized estimating equations. We introduce a new ‘sandwich loss’ whose population minimizer coincides with the weights of these approaches when the parametric forms for the conditional covariance are well-specified, but can yield arbitrarily large improvements in linear parameter estimation accuracy when they are not. Under relatively mild conditions, our estimated coefficients are asymptotically Gaussian and enjoy minimal variance among estimators with weights restricted to a given class of functions, when user-chosen regression methods are used to estimate nuisance functions. We further expand the class of functional forms for the weights that may be fitted beyond parametric models by leveraging the flexibility of modern machine learning methods within a new gradient boosting scheme for minimizing the sandwich loss. We demonstrate the effectiveness of both the sandwich loss and what we call ‘sandwich boosting’ in a variety of settings with simulated and real-world data.

Energy Supply Security and Trade Routes Competition between European and Asian Countries: The Geoeconomic Importance of Azerbaijan

Azerbaijan's abundant energy resources and its role as a key transit hub for pipelines have made it a focal point in the geoeconomic policies of its energy-rich neighbors and European countries with significant energy demand. This study aims to conduct a geoeconomic evaluation of the energy sector and trade routes, which have been central to the Europe-Asia competition for centuries, focusıng on regional economic policies centered around Azerbaijan. It has analyzed the existing and planned potential route projects for trade and energy supply between the two continents, providing an assessment of possible gains and losses on regional and global levels. This analysis has been presented in the form of geoeconomic parameters derived from numerical and descriptive content analysis.

Vector-Algebra Algorithms to Draw the Curve of Alignment, the Great Ellipse, the Normal Section, and the Loxodrome

This paper recasts four geodetic curves—the great ellipse, the normal section, the loxodrome, and the curve of alignment—into a parametric form of vector-algebra formula. These formulas allow these curves to be drawn using simple, efficient, and robust algorithms. The curve of alignment, which seems to be quite obscure, ought not to be. Like the great ellipse and the loxodrome, and unlike the normal section, the curve of alignment from point A to point B (both on the same ellipsoid) is the same as the curve of alignment from point B to point A. The algorithm used to draw the curve of alignment is much simpler than any of the others and its shape is quite similar to that of the geodesic, which suggests it would be a practical surrogate when drawing these curves.