Singular Model Research Articles

Generality-oriented optimization of enantioselective aminoxyl radical catalysis.

Catalytic enantioselective methods that are generally applicable to a broad range of substrates are rare. We report a strategy for the oxidative desymmetrization of meso-diols predicated on a nontraditional catalyst optimization protocol by using a panel of screening substrates rather than a singular model substrate. Critical to this approach was rational modulation of a peptide sequence in the catalyst incorporating a distinct aminoxyl-based active residue. A general catalyst emerged, providing high selectivity in the delivery of enantioenriched lactones across a broad range of diols, while also achieving up to ~100,000 turnovers.

Swarming procedures to solve the novel perturbed delay third order singular model

The current study shows a novel singular perturbed delay third order model (NSPD-TOM) with its two categories using the conventional Lane-Emden mathematical model. The comprehensive details of the perturbed, shape/delay and singular terms are also provided for both categories of the NSPD-TOM. The numerical results for solving the NSPD-TOM are provided by applying the procedures of artificial neural networks together with the optimizations performances of global swarming and local active set methods. The NSPD-TOM is performed based on the accuracy, substantiation, and authenticity using the achieved and exact results comparison. Moreover, the stochastic procedure is further authenticated by applying the statistical operators to solve the NSPD-TOM.

The Imitation of the Ovarian Fatty Acid Profile of Superfertile Dummerstorf Mouse Lines during IVM of Control Line Oocytes Could Influence Their Maturation Rates.

Declining human fertility worldwide is an attractive research target for the search for "high fertility" genes and pathways to counteract this problem. To study these genes and pathways for high fertility, the superfertile Dummerstorf mouse lines FL1 and FL2 are two unique model organisms representing an improved fertility phenotype. A direct reason for this remarkable characteristic of increased litter size, which reaches >20 pups/litter in both FLs, is the raised ovulation rate by approximately 100%, representing an impressive record in this field. Dummerstorf high-fertility lines incarnate extraordinary and singular models of high-fertility for other species, mostly farm animals, with the aim of improving production and reducing costs. Our main goal is to describe the genetic and molecular pathways to reach their phenotypical excellence, and to reproduce them using the control population. The large litter size and ovulation rate in Dummerstorf lines are mostly due to an increase in the quality of their oocytes, which receive a different intake of fat and are composed of different types and concentrations of fatty acids. As the follicular microenvironment plays a fundamental role during the oocytes development, in the present manuscript, we tried to improve the in vitro maturation technique by mimicking the fatty acid profile of FLs oocytes during the IVM of control oocytes. Currently, the optimization of the IVM system is fundamental mostly for prepubertal girls and oncological patients whose main source of gametes to restore fertility may be their maturation in vitro. Our data suggest that the specific fatty acid composition of FLs COCs can contribute to their high-fertility phenotype. Indeed, COCs from the control line matured in IVM-medium supplemented with C14:0 (high in FL2 COCs) or with C20:0, C21:0, C22:0, and C23:0 (high in FL1 COCs), but also control oocytes without cumulus, whose concentration in long-chain FAs are "naturally" higher, showing a slightly higher maturation rate. These findings represent an important starting point for the optimization of the IVM system using FA supplementation.

Lemon: LEns MOdelling with Neural networks – I. Automated modelling of strong gravitational lenses with Bayesian Neural Networks

ABSTRACT The unprecedented number of gravitational lenses expected from new-generation facilities such as the ESA Euclid telescope and the Vera Rubin Observatory makes it crucial to rethink our classical approach to lens-modelling. In this paper, we present lemon (Lens Modelling with Neural networks): a new machine-learning algorithm able to analyse hundreds of thousands of gravitational lenses in a reasonable amount of time. The algorithm is based on a Bayesian Neural Network: a new generation of neural networks able to associate a reliable confidence interval to each predicted parameter. We train the algorithm to predict the three main parameters of the singular isothermal ellipsoid model (the Einstein radius and the two components of the ellipticity) by employing two simulated data sets built to resemble the imaging capabilities of the Hubble Space Telescope and the forthcoming Euclid satellite. In this work, we assess the accuracy of the algorithm and the reliability of the estimated uncertainties by applying the network to several simulated data sets of 104 images each. We obtain accuracies comparable to previous studies present in the current literature and an average modelling time of just ∼0.5 s per lens. Finally, we apply the lemon algorithm to a pilot data set of real lenses observed with HST during the SLACS program, obtaining unbiased estimates of their SIE parameters. The code is publicly available on GitHub (https://github.com/fab-gentile/LEMON).

DFT assessments of BN, AlN, and GaN decorated carbon cage scaffolds for sensing the thiamazole drug

Sensing drug substances by nanostructures are very important in accordance with the management of targeted drug delivery processes and drug substances detections. Boron nitride (BN), aluminum nitride (AlN), and gallium nitride (GaN) decorated carbon cage (BN-C, AlN-C, and GaN-C) scaffolds were assessed towards sensing the thiamazole (TMZ) drug through the wB97XD/6–31 + G* level of density functional theory (DFT) computations. The singular models were optimized and their combinations to each other were stabilized to obtain the interacting [email protected] bimolecular complexes and their corresponding features. The results indicated the existence of non-covalent physical interactions between the substances and their electronic features indicated possibility of sensing function for the investigated scaffolds. Based on the variations of values of adsorption energy and energy gap, the features of recovery time and conductance rate were achieved to predict a sensing function for the models; [email protected] was found at the highest suitability in comparison with [email protected] and [email protected] models. The obtained thermochemistry results indicated a spontaneous process for the formation of [email protected] complexes. Based on all the obtained results, an order of [email protected] > [email protected] > [email protected] was found for describing stability, formation, and electronic features suitability by assigning specific features for each of the singular BN-C, AlN-C, and GaN-C scaffolds towards the TMZ drug. As a consequence, two purposes of detections and adsorptions were approached for the investigated scaffolds to develop sensing functions of BN-C, AlN-C, and GaN-C scaffolds for the TMZ drug.

Observability of Discrete-Time Two-Time-Scale Multi-Agent Systems with Heterogeneous Features under Leader-Based Architecture

This paper investigates the observability of discrete-time two-time-scale multi-agent systems with heterogeneous features under leader–follower architecture. First, a singular perturbation difference model for the discussed system is established based on consensus agreement. Second, to eliminate the numerical ill-posed problem that may arise from the singularly perturbed small parameter that distinguishes different time scales in the observability analysis, the order of the system model is reduced using the boundary layer theory of the singular perturbation system to obtain a slow-time-scale subsystem and a fast-time-scale subsystem. Then, based on the matrix theory, some algebraic and graphical features that guarantee the observability of the system are obtained. Finally, the validity of the theoretical results is verified by a numerical example.

Some disquisitions on cosmological 2-form dualities

In this work we study different aspect of self-interacting 2-form fields with special emphasis in their cosmological applications. We provide the explicit construction of how massless 2-forms are compatible with the cosmological principle without resorting to the dual scalar field formulation. In terms of the 2-form, the residual Euclidean group is non-trivially realised by means of a combination of external spatial translations and internal gauge transformations. After presenting the general discussion of the dualities in cosmological scenarios, we analyse particular examples for some singular models and discuss in some detail the dual descriptions of the DBI, the cuscuton and the ghost condensate as well as the role of the duality in the effective field theories of cosmological perturbations. We then proceed to analysing scenarios with several self-interacting massless 2-forms and we show that they naturally provide the dual description of a solid. We then show how the perfect fluid and superfluids can be obtained by taking the appropriate limits in the dual formulations. We finally consider the case of massive 2-forms and their duals and briefly discuss their potential signatures in gravitational waves astronomy.

Research on Singular Value Decomposition Recommendation Algorithm Based on Data Filling

In the era of big data, the problem of information overload has become increasingly prominent. Recommendation systems are widely studied due to the problem. Due to the sparseness of data, the recommendation effect is not always ideal. To alleviate the problem of data sparsity, a singular value decomposition recommendation algorithm based on data filling is proposed. First, an improved Tanimoto similarity coefficient calculation method is proposed to calculate the similarity, and effective interpolation data is generated for the singular value decomposition model according to the proposed prediction formula. The experimental results show that when using the same dataset MovieLens100K, compared with several commonly used recommendation algorithms, the improved algorithm improves the prediction accuracy of the model, In the best case, RMSE is 10.1% lower than KNNBasic, 7.8% lower than Slope One algorithm, 6.9% lower than SVD algorithm, and 4.8% lower than SVD++ algorithm, verifying that this method can improve the recommendation quality.

Comparison of different predictive models and their effectiveness in sunspot number prediction

Human activities and health are significantly influenced by solar activity. The sunspot number is one of the most commonly used measures of solar activity. The solar cycle’s quasi-periodic nature makes it an excellent choice for time series forecasting. Four models include three singular models, consisting of Long Short-Term Memory (LSTM), AutoRegressive Integrated Moving Average (ARIMA), and Seasonal AutoRegressive Integrated Moving Average (SARIMA), as well as a hybrid model were implemented to forecast maximum sunspot number of cycles 25 and 26. The hyperparameters of the singular models were optimized using Bayesian optimization. The LSTM-ARIMA hybrid model was able to achieve the best performance. The outstanding results of the LSTM-ARIMA model shows the potential of hybrid methods in improving the overall performance. Moreover, the LSTM model was able to outperform the ARIMA model, which demonstrates the ability of LSTM networks in learning from time-series data. The final model forecasts a peak sunspot number of 137.04 for Solar Cycle 25 in September 2024 and 164.3 for Solar Cycle 26 in December 2034, which is comparable to the National Aeronautics and Space Administration’s (NASA) prediction of 134.4 in October 2024 and 161.2 in December 2034.

Admissible transformation approach to Roesser state‐space model realization of singular multidimensional systems

AbstractThe problem of the singular Roesser (state‐space) model realization of non‐causal multivariate transfer (function) matrices is investigated. Specifically, the notion of so‐called admissible transformation is introduced, which allows to introduce and investigate a novel invariant matrix relationship on polynomial matrices. The singular multidimensional Roesser model (SNDRM) realization is transformed to the problem of how to reduce an initial polynomial matrix associated with the given transfer matrix into the objective polynomial matrix with a specific structure. Construction procedures are developed to obtain SNDRMs by applying admissible transformations on the initial polynomial matrix. Moreover, a separated standard form is introduced for an SNDRM so that the proposed approach can effectively treat both the left and right matrix fractional descriptions of a non‐causal multivariate transfer matrix in a unified manner. Non‐trivial examples are provided to illustrate the details and effectiveness of the proposed approach.

An Efficient Jacobi Spectral Collocation Method with Nonlocal Quadrature Rules for Multi-Dimensional Volume-Constrained Nonlocal Models

In this paper, an efficient Jacobi spectral collocation method is developed for multi-dimensional weakly singular volume-constrained nonlocal models including both nonlocal diffusion (ND) models and peridynamic (PD) models. The model equation contains a weakly singular integral operator with the singularity located at the center of the integral domain, and the numerical approximation of it becomes an essential difficulty in solving nonlocal models. To approximate such singular nonlocal integrals in an accurate way, a novel nonlocal quadrature rule is constructed to accurately compute these integrals for the numerical solutions produced by spectral methods. Numerical experiments are given to show that spectral accuracy can be obtained by using the proposed Jacobi spectral collocation methods for several different nonlocal models. Besides, we numerically verify that the numerical solution of our Jacobi spectral method can converge to its correct local limit as the nonlocal interactions vanish.

Feature relevance XAI in anomaly detection: Reviewing approaches and challenges.

With complexity of artificial intelligence systems increasing continuously in past years, studies to explain these complex systems have grown in popularity. While much work has focused on explaining artificial intelligence systems in popular domains such as classification and regression, explanations in the area of anomaly detection have only recently received increasing attention from researchers. In particular, explaining singular model decisions of a complex anomaly detector by highlighting which inputs were responsible for a decision, commonly referred to as local post-hoc feature relevance, has lately been studied by several authors. In this paper, we systematically structure these works based on their access to training data and the anomaly detection model, and provide a detailed overview of their operation in the anomaly detection domain. We demonstrate their performance and highlight their limitations in multiple experimental showcases, discussing current challenges and opportunities for future work in feature relevance XAI for anomaly detection.

Auraltypical acoustics? A critical review of key standards and practices

The concept of aural diversity was born out of awareness that there is a deeply embedded and intertwined, singular model of hearing at the core of the preponderance of acoustic standards and guidance, and hence practice (Drever 2015, 2017). This is exemplified with the prescription of the 'otologically normal' hearer derived equal loudness contours (ISO 226:2003), giving form to ubiquitous A-weighted decibel - the 'otologically normal' characterising the hearing of 18 to 25-year-olds, arguably the healthiest sub-group of society. This paper will critically review this assumption through reviewing the current set of acoustics standards in the UK. Crucially it will probe at some of the methods underpinning historic and foundational studies and concepts that are referenced in current standards, such as the reliance on WEIRD (Western, Educated, Industrialized, Rich, and Democratic societies) samples to stand in for the whole of the human population. (Henrich, Heine, Norenzayan 2010; Cobianchi & Rosas Pérez, 2021). Through interviews with acoustic professionals, the paper will then look at how this is carried through into practice and finally, discuss the consequences of slavishly following these models, which studies have shown (Drever 2017), results in distress, negative impact on health and well-being and societal exclusion for some.

Ricci flow on surfaces along the standard lightcone in the 3+1-Minkowski spacetime

Identifying any conformally round metric on the 2-sphere with a unique cross section of the standard lightcone in the 3+1-Minkowski spacetime, we gain a new perspective on 2d-Ricci flow on topological spheres. It turns out that in this setting, Ricci flow is equivalent to a null mean curvature flow first studied by Roesch–Scheuer along null hypersurfaces. Exploiting this equivalence, we can translate well-known results from 2d-Ricci flow first proven by Hamilton into a full classification of the singularity models for null mean curvature flow in the Minkowski lightcone. Conversely, we obtain a new proof of Hamilton’s classical result using only the maximum principle.

Singular spectrum analysis and modeling of the temporal variations of the global ionospheric F2-layer peak electron density retrieved from COSMIC radio occultation measurements

The singular spectrum analysis (SSA) is a relatively new method for decomposing time series into separable and interpretable series (i.e., trend, periodic, and irregular components). Here, the ionospheric variability in the peak electron density (NmF2) observations provided by FORMOSAT-3/Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation measurements during 2009–2019, which completely covers solar-cycle 24, was examined using the nonparametric method (SSA). This study identified different temporal patterns inherent in the COSMIC data, which can be employed to reconstruct/model noise-free time series. Based on a proper choice of SSA parameters, it was observed that SSA can be employed to extract various components of these datasets. Additionally, the results showed that the slowly varying component (trend) was largely influenced by solar activity and closely followed the behavior of the sunspot cycle. The SSA method demonstrated seasonal patterns (the semiannual component). Based on the patterns, the reconstructed series for each of the selected components were separately modeled with a corresponding fit function using the 10.7 cm solar proxy, which prior be smoothed by Spline fitting. It was observed that the polynomial function efficiently modeled the trend component with R2 = 0.95, while the harmonic function adequately represented the semiannual component with R2 = 0.97. To validate the proposed model, the data were estimated for the time interval from January 2020 to August 2022, which represented the ascending phase of the ongoing solar-cycle 25. The results revealed that the modeled data correlated with the observed COSMIC-2 data with a high correlation coefficient (0.95). These findings indicate that the underlying model can effectively describe the behavior of the electron density during the investigated period, and the SSA method is particularly powerful for analyzing nonstationary time series and capable of identifying sub-time series (trends along with seasonal periodic components).

Refined Admissible Analysis and Design Conditions for Discrete Fuzzy Singular Systems with Multiple Difference Matrices

ABSTRACT This study mainly discusses extended admissibility and fuzzy parallel distributed compensation (PDC) control issues for discrete singular fuzzy systems with multiple difference matrices existing in the rules. By the overall system associated with the discrete singular models with multiple difference matrices, we first propose an extended admissibility analysis criteria, where the new results not only involve some slack matrices but also have a less number of linear matrix inequalities’ (LMIs) constraints. Furthermore, by hiring the fuzzy PDC, explicit design criteria are further developed for the regarded system. Noticeably, the new design method can cope with controller synthesis of the admissibility and D-admissibility issues. Due to all the presented criteria are formed by the strict LMIs, we can readily evaluate them via some existing LMI solvers. Finally, two numerical examples are involved to demonstrate the applicability and the feasibility of the developed results.

Generalized Shifted Airfoil Polynomials of the Second Kind to Solve a Class of Singular Electrohydrodynamic Fluid Model of Fractional Order

In this manuscript, we find the numerical solutions of a class of fractional-order differential equations with singularity and strong nonlinearity pertaining to electrohydrodynamic flow in a circular cylindrical conduit. The nonlinearity of the underlying model is removed by the quasilinearization method (QLM) and we obtain a family of linearized equations. By making use of the generalized shifted airfoil polynomials of the second kind (SAPSK) together with some appropriate collocation points as the roots of SAPSK, we arrive at an algebraic system of linear equations to be solved in an iterative manner. The error analysis and convergence properties of the SAPSK are established in the L2 and L∞ norms. Through numerical simulations, it is shown that the proposed hybrid QLM-SAPSK approach is not only capable of tackling the inherit singularity at the origin, but also produces effective numerical solutions to the model problem with different nonlinearity parameters and two fractional order derivatives. The accuracy of the present technique is checked via the technique of residual error functions. The QLM-SAPSK technique is simple and efficient for solving the underlying electrohydrodynamic flow model. The computational outcomes are accurate in comparison with those of numerical values reported in the literature.

Singular cotangent models in fluids with dissipation

In this article we analyze several mathematical models with singularities where the classical cotangent model is replaced by a b-cotangent model. We provide physical interpretations of the singular symplectic geometry underlying in b-cotangent bundles featuring two models: the canonical (or non-twisted) model and the twisted one. The canonical one models systems on manifolds with boundary and the twisted one represents Hamiltonian systems with a singularity on the fiber. The twisted cotangent model includes (for linear potentials) the case of fluids with dissipation. We prove (non)-existence of cotangent lift dynamics and show the existence of an infinite number of escape orbits in this model. We also discuss more general physical interpretations of the twisted and non-twisted b-symplectic models. Twisted b-symplectic models yield in a natural way escape orbits that go to the critical set. Under compactness assumptions those escape orbits are continued as singular periodic orbits in the sense of Miranda and Oms (2021) and Miranda (2020). These models offer a Hamiltonian formulation for systems which are dissipative, extending the horizons of Hamiltonian dynamics and opening a new approach to study non-conservative systems.

Nonlinear stability of traveling waves to a parabolic-hyperbolic system modeling chemotaxis with periodic perturbations

We prove the nonlinear stability of large-amplitude traveling waves under space-periodic perturbations to a parabolic-hyperbolic system derived from a singular chemotaxis model describing the initiation of tumor angiogenesis. Compared with the previous results, we are able to prove the nonlinear stability of traveling waves even though the perturbation oscillates at the far fields.

Research on multiple transient modeling scheme for full-cycle design of the 12-pulse PSR rectifier

PurposeThis paper aims to propose a novel multiple transient modeling scheme for the 12-pulse phase-shifting reactor (PSR) rectifier to enhance the efficiency of full-cycle design evaluation.Design/methodology/approachThe detailed time-domain method is adopted to model the rectifier at the behavioral layer. The diode bridges/transformer model at the architecture layer is established by using the switch function and Park transformation. The frequency domain model at the functional layer is derived with the time-varying Fourier decomposition and frequency-shifting. At the component layer, the magneto-thermal characteristics of the rectifier are analyzed with field-circuit and magnetic-thermal coupling methods. A computer-aided design program integrating multiple modeling is also developed for industrial product design.FindingsThe function layer modeling is preferred in the initial design stage, making up for the lack of modeling accuracy at the architectural layer and the lack of modeling rapidity at the behavioral layer. The component modeling is irreplaceable for the detailed evaluation in the latter design stage. The multiple modeling scheme based on the four-layer modeling helps the designers achieve high-quality products with a short development cycle.Originality/valueThe singular transient modeling cannot cover the needs of different stages in the full-cycle design evaluation. This paper fills this gap with a novel multiple modeling scheme. Meanwhile, the proposed multiple modeling scheme and developed computer-aided design program provide a great convenience for full cycle design evaluation of the 12-pulse PSR rectifier.