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Spectrally Matched Fractional Sobolev Tikhonov Regularization for Periodic Deconvolution

Let T = R/Z and Aβ = (I−Δ)−β/2 with β > 0. From data gδ = Aβ f †+η, ∥η∥2 ≤ δ, consider the fractional Sobolev Tikhonov family f δ λ,s = argminf∈Hs(T) Aβ f −gδ 2 2 +λ ∥ f ∥2 Hs, s ≥ 0. Writing μk = (1+4π2k2)1/2 and ρ = s+β, the estimator is diagonal in the Fourier basis, bf δ λ,s(k) = μβ k 1+λ μ2ρ k bgδ (k). If f † ∈ Hr(T) and 0 < r ≤ 2ρ, then f δ λ,s− f † 2 ≤Caδλ−a+CbMrλb, a = β 2ρ , b = r 2ρ , Mr = f † Hr . where Cq = qq(1−q)1−q for 0 < q < 1 and C1 = 1. Direct minimization gives λ∗ ≍ δ2ρ (r+β) and f δ λ∗,s− f † 2 = O δr/(r+β). Thus the pre-saturation exponent is independent of the penalty order s. When r > 2ρ, the bias saturates and the rate becomes O(δ2ρ/(2ρ+β)); hence the least order avoiding saturation is smin = max{0, r/2−β}. The balanced half-power frequency satisfies μc ≍ δ−1/(r+β), whereas its logarithmic roll-off is −ρ/2. The same exponents persist for spectrally equivalent convolution operators c−μ−β k ≤ ak ≤ c+μ−β k . Fourier experiments on N = 2048 modes, using three source regularities, four penalty orders, five noise levels, and 30 perturbations per configuration, reproduce the predicted saturation ordering and transition boundary. For the H6 benchmark at δ = 10−3, the mean L2 error decreases from 1.4013×10−2 for s = 0 to 1.2710×10−3 for s = 2; the latter is the first tested order in the non-saturated regime.

groups
Takaaki Fujita mail -
Ajoy Kanti Das mail
link https://doi.org/10.54216/PAMDA.060101

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

A Symmetric Discrete-Gradient Finite-Volume Method for Positive Drift–Diffusion Flows

Consider the periodic gradient flow ∂tu = ∂x(m(u)∂xμ) , μ = h′(u)+V, E [u] = Z 1 0 {h(u)+Vu} dx, for strictly positive density u, convex entropy density h, and mobility m > 0. We couple a centered finite-volume flux with a symmetric two-state approximation of the chemical potential. Its entropy component is the divided difference Dh(a,b) = h(a)−h(b) a−b , Dh(a,a) = h′(a), which enforces the discrete chain rule exactly. With midpoint edge mobility and the logarithmic state un+1 i =expzn+1 i , the nonlinear update is self-adjoint and, for every solved algebraic step, satisfies E n+1 h −E n h = −ΔtΣi M n+1/2 i+1/2 (μn+1/2 i+1 −μn+1/2 i )2 Δx ≤ 0. The flux telescopes to conserve mass, the logarithmic parametrization confines finite roots to the positive cone, and states satisfying h′(ui)+Vi = const are fixed points. A midpoint expansion gives second-order consistency in time; the centered flux gives the same order in space. For h(u) = u(logu−1), a manufactured heat-flow calculation gives observed L2 orders 1.998 and 1.999 under coupled refinement. In a confining Fokker–Planck test, the maximum mass defect is 3.20×10−14 and the energy identity is satisfied within 1.42×10−14. Replacing Dh by the midpoint chemical potential in the same one-step problem produces an energy-balance defect 1.60×10−2, isolating the role of the temporal discrete gradient.

groups
Sergey Drominko mail -
Erina Kovachiskaya mail
link https://doi.org/10.54216/PAMDA.060103

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Heat-Semigroup Persistence on Data Graphs: A Multiscale Extension of Normalized Cut for Class-Separability Analysis

Let G = (V,W) be a weighted data graph with symmetric normalized Laplacian L = I−D−1/2WD−1/2, and let u denote the degree-balanced signal associated with a binary partition C∪ ¯C =V. Instead of reducing the partition geometry to the single Rayleigh quotient u⊤Lu/∥u∥22 , we study the heat-semigroup persistence Pu(t) = ∥e−tLu∥22 ∥u∥22 , HT (u) = 1 T Z T 0 Pu(t)dt. Writing Lφj = λjφj and ωj = |⟨u,φj⟩|2/∥u∥22 yields Pu(t) = Σj ωje−2tλj , so the complete curve is the Laplace transform of the label spectral measure νu = Σj ωjδλj . We prove four identities that give this construction a cut-theoretic interpretation. First, Pu is completely monotone. Second, −P′u(0)/2 = Ncut(C, ¯C). Third, for the instantaneous leakage rate κu(t) = −12 d logPu(t)/dt, one has κu(0) = Ncut and κ′u (t) = −2Varνu,t (λ) ≤ 0 under the exponentially tilted spectral measure. Fourth, when u ⊥ kerL, R ∞ 0 Pu(t)dt = u⊤L†u/(2∥u∥22). Hence normalized cut is only the zero-time slope of a multiscale diffusion object whose higher derivatives recover all spectral moments. A perturbation bound |HT (L)−HT (eL)| ≤ T∥L−eL∥2 is also established for a fixed partition signal. Numerical evaluation on a 1,797-sample, 64-variable handwritten-digit benchmark uses all 45 class pairs and ten repeated stratified train/test splits. With graphs formed exclusively from training observations, mean H1 has Spearman correlation −0.924 with held-out pairwise error (95% bootstrap interval [−0.957,−0.850]); normalized cut gives 0.927, and the second spectral central moment gives 0.930. The comparable predictive rankings are material: the proposed functional is not presented as a replacement for normalized cut, but as its multiscale completion, retaining spectral information that a first moment necessarily discards.

groups
Nader Taffach mail -
Mohammad Al-Shiekh mail
link https://doi.org/10.54216/PAMDA.060102

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Comparative Study of Two Optimization Algorithms for Solving Nonlinear Differential Equations: A Performance Analysis

The purpose of this work was to benchmark three population-based metaheuristic optimizers—Particle Swarm Optimization, Differential Evolution, and Grey Wolf Optimizer—when used to solve nonlinear ordinary differential equations within the Neural Network Trial Solution methodology. Problems used for testing were the Riccati initial value problem, the nonlinear pendulum IVP, the Bratu boundary value problem, and the Lane-Emden equation with index five. All problems were implemented such that their boundary/initial conditions were satisfied exactly through analytical construction while their residuals at collocation points were minimized through unconstrained optimization. Thirty Monte Carlo runs of each algorithm were performed with same underlying settings to facilitate statistical comparisons between algorithms. Metrics used for comparisons were mean absolute error (MAE), root mean square error (RMSE), maximum error at any point, and rate of convergence. All significant testing was performed with the Wilcoxon signed-rank test. PSO is shown to consistently provide the smallest mean absolute error across three of the four problems, with an MAE as small as 2.78×10−5 on the Bratu BVP, while GWO was shown to stagnate prematurely when solving boundary value problems.

groups
Qasim Tayyeh mail
link https://doi.org/10.54216/PMTCS.060201

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

A Mathematical Framework for Adaptive Rolling Conformal Quantile Boosting under Temporal Distribution Shift: Application to Hour-Ahead PM2.5 Forecast Intervals

Prediction intervals for temporally dependent data require both conditional quantile estimation and a calibration mechanism capable of responding to distribution shift. An adaptive rolling conformal quantile boosting (ARCQB) formulation is developed in which boosted quantile functions provide a nonlinear base interval and a sequential state variable controls the empirical conformal quantile. For target miscoverage 𝛼, the calibration state follows a projected stochastic recurrence, 𝛼𝑡+1 = ΠA{𝛼𝑡 + 𝛾(𝛼 − 𝑒𝑡 )}, where 𝑒𝑡 is the realized miss indicator. A telescoping identity links the time-averaged miss frequency to the state displacement and projection residuals; in the unprojected bounded case, the calibration error is 𝑂(𝑇−1). The interval width admits the exact decomposition 𝑤𝑡 = 𝑤(0) 𝑡 + 2𝑞𝑡 , separating predictive sharpness from conformal inflation. Numerical evaluation uses a strictly chronological one-hour-ahead design on hourly Beijing air-quality measurements. For nominal 90% coverage, raw boosted quantiles attain 83.18%, static conformal calibration 87.40%, and rolling conformal calibration 89.87%. ARCQB attains 90.05% with mean width 47.14 𝜇gm−3 and the lowest interval score, 72.86. Its maximum seasonal coverage deviation is 0.38 percentage points, compared with 7.99 points for the uncalibrated interval. The numerical behavior is therefore consistent with the feedback relation predicted by the calibration dynamics, while high-pollution regimes remain the principal source of conditional under-coverage.

groups
Aiyared Iampan mail -
Said Broumi mail
link https://doi.org/10.54216/PAMDA.050203

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new

Generating Neutrosophic Random Variables Based on Generalized Gamma Distribution

In practice, we encounter many systems that cannot be studied directly, either due to high costs or because some of these systems are not directly detectable. Therefore, we resort to simulation, which involves applying the study to systems similar to real-world systems and then projecting the results if they are suitable for the real system. The simulation process requires a thorough understanding of probability distributions and the methods used to transform random numbers following a regular distribution on [0,1] into random variables that follow it. This allows us to maximize the benefits of the simulation process and obtain more accurate results for all emerging conditions. The generalized gamma distribution is a family of three parameters characterized by high flexibility. It includes several important distributions as special cases, including the gamma, Weibull, exponential, and Rayleigh distributions, making it exceptionally valuable in engineering and reliability analysis. In previous research, we presented a neutrosophic view of the process of generating random numbers and some techniques used to generate random variables. In this research, we present a neutrosophic study for generating neutrosophic random variables following the generalized gamma distribution, a distribution widely used in engineering applications. The neutrosophic approach takes into account the uncertainty and indeterminacy of the parameters, resulting in random intervals for the variables rather than specific values, and thus provides more accurate simulation results that adapt to all the conditions that the system in operation may encounter.

groups
Khalifa AlShaqsi mail
link https://doi.org/10.54216/PAMDA.060104

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Exact Bias–Variance Decomposition and Degrees of Freedom in Ridge Regression: Theory, Verification, and a Disease-Progression Application

Ridge regression estimates β in the linear model y = Xβ +ε by βˆ (λ) = argminβ ∥y−Xβ∥2+λ∥β∥2, trading bias for variance as λ increases. This paper collects six results about βˆ (λ) into a single self-contained development, each proved and then checked numerically. The estimator is written in closed form through the singular value decomposition of X; its effective degrees of freedom, df(λ)=Σj d2j /(d2j +λ), are shown to be strictly decreasing and convex in λ; its exact bias and variance are derived in closed form; a strictly positive λ is shown always to exist that reduces mean squared estimation error below that of ordinary least squares whenever the noise variance is positive; the estimator is shown to coincide with the posterior mean under a Gaussian prior with precision proportional to λ; and the leave-one-out cross-validation error is shown to admit a closed-form shortcut that generalized cross-validation approximates by averaging its leverage terms. Every derived quantity is verified against data: the leave-one-out shortcut matches brute-force refitting exactly, and a calibrated Monte Carlo simulation confirms the closed-form bias and variance to within simulation error at every tested λ. Applied to a standard diabetes disease-progression dataset (n = 442, ten predictors), the theoretical construction correctly locates a strictly risk-reducing regularization region, and repeated cross-validation shows ridge, lasso, and elastic net all lying within one standard error of ordinary least squares in out-of-sample prediction error—consistent with the closed-form theory, which attributes the available gain to reduced parameter-estimation risk on a well-conditioned design rather than to prediction-error reduction.

groups
Dwi Retnowardani mail
link https://doi.org/10.54216/PAMDA.060105

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Neutrosophic–Fuzzy Evidence Fusion for Uncertainty-Aware Cultivar Identification from Viticultural Chemical Profiles

Cultivar identification from viticultural chemical profiles is a multiclass recognition problem in which a hard label alone does not reveal whether global chemometric evidence agrees with the local structure of previously observed samples. This paper proposes Neutrosophic–Fuzzy Viticultural Evidence Fusion (NFVEF), an uncertainty-aware classifier that combines a global discriminant probability vector G(x) ∈ ΔK−1 with a Gaussian fuzzy-neighborhood vector L(x) ∈ ΔK−1. For every cultivar k, the two evidence views are converted into Tk = p GkLk, Fk = p (1−Gk)(1−Lk), Ik = 1−Tk −Fk. where Ik is exactly the squared Hellinger disagreement between the Bernoulli support views Gk and Lk. A logarithmic fuzzy opinion pool Hk ∝ Gηk L1−η k is then attenuated by neutrosophic disagreement, Rk ∝ Hk exp(−κIk), before classification. The winning class is accompanied by an uncertainty score U = 1−Tˆk (1−Iˆk)(1−Fˆk ), enabling uncertain chemical profiles to be flagged rather than reported with unqualified confidence. The method is evaluated on the UCI Wine cultivar dataset using 60 repeated stratified splits at four synthetic analytical-perturbation levels δ ∈ {0,0.1,0.2,0.3} measured relative to training-feature standard deviations. NFVEF obtains mean accuracies of 0.9858, 0.9836, 0.9744, and 0.9728, respectively. At δ = 0.3, its paired accuracy advantage over linear discriminant analysis is 0.00278 with a 95% bootstrap interval [0.00123,0.00463], while RBF-SVM remains slightly better in raw accuracy. The uncertainty score detects NFVEF errors with mean AUC 0.9520 at the strongest perturbation, and retaining the lowest-uncertainty 90% of cases yields 0.9922 accuracy. The contribution is therefore not universal classifier dominance, but a mathematically interpretable fuzzy–neutrosophic evidence layer for cultivar identification from ambiguous chemical measurements.

groups
Amine Saddik mail -
Ika Agustin mail
link https://doi.org/10.54216/JNFS.110101

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

On Division of Symbolic n-Plithogenic Numbers

The main goal of this article is to study the division of symbolic n-plithogenic numbers using the identification method and n-plithogenic AH-isometry. In particular, we discuss the division of symbolic 2-plithogenic numbers and 3-plithogenic numbers, and we generalize these divisions. Additionally, we prove the validity of the formulas using AH-isometry and provide four worked examples to enhance understanding.

groups
P. Arulpandy mail -
S. Kalaiselvan mail -
M. Sundar mail -
G. Govindharaj mail -
P. Sugapriya mail
link https://doi.org/10.54216/IJNS.270228

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

Optimizing Navigation: Adaptive Map Reshaping and Shortest Path Analysis for Mobile Robots

To facilitate the practical deployment of robotics, efficient path planning is essential to ensure that robotic movement is accurate, safe, and goal-oriented. This study explores new approaches to map adaptation and path optimization for robot navigation between specified locations. The initial phase of the research involves designing an environment that enables the safe operation of robots. Subsequently, the collected data is processed to construct a graph using Dijkstra’s algorithm, which is employed to determine the shortest path between key points. When multiple paths are available, the algorithm selects the most efficient one, while ensuring safety in point-to-point transitions and when navigating around obstacles. In addition to this, a reinforced method is introduced to enhance the security of path planning. This approach expands the original trajectory to incorporate a safety buffer equal to half of the robot’s safety radius, thus maintaining a safe distance along the traveled route. The key contribution of this work lies in the development of novel maps featuring secure pathways, which can be utilized by optimization algorithms to improve navigation in unfamiliar terrains. Experimental results using PRM* and RRT* validate the accuracy of these maps, especially in complex, maze-like environments.

groups
Mohammed Rabeea Hashim Al-Dahhan mail -
Mahmood Abdulrazzaq Alsaadi mail -
Ruqayah R. Al-Dahhan mail -
Salah A. Aliesawi mail -
Omar Q. Mohsin mail
link https://doi.org/10.54216/FPA.210213

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new