Search Results (421)

(1) The importance of social structure and dominance hierarchies in cooperative-breeding species is well-documented, yet the inclusion of these processes in conservation translocation planning remains limited. Here, we empirically measured the social networks of three populations of the endangered Floreana Mockingbird, then used model simulations of different translocation scenarios to test the effects of social disruption on the social networks. (2) We used social network analysis and Exponential Random Graph Models (ERGMs) to characterise dominance hierarchies, group structure, and the consequences of selectively removing individuals from family groups. (3) Dominance hierarchies were strongly transitive, with age emerging as the primary determinant of dominance relationships. Simulated removals demonstrated that the loss of individuals occupying different network positions produced variable levels of social disruption. (4) Although age is the principal driver of antagonistic interactions, network properties such as high betweenness centrality and the presence of a broker (individuals that occupy a strategic position) are also critical considerations for translocation design. Incorporating social network structure into management strategies can minimise group disruption and enhance the success of conservation translocations for endangered cooperative breeders. Full article

From an information geometric perspective, this study considers a natural foliation of dualistic structures associated with escort distributions of exponential families. We propose an extended divergence on this foliation by continuous-type escort distributions. Specifically, we investigate the foliation formed by escort distributions to analyze the transition of q-parameters, rather than relying on a fixed parameter. Within this foliation, distinct q-parameters and their corresponding dualistic $\alpha$-parameters were defined on each leaf. Finally, we present a decomposition of the extended divergence on this foliation, providing an analog to the method previously established for discrete escort distributions. Full article

This paper introduces a new family of extended degenerate Bell-based Appell polynomials by applying Euler’s integral as a fractional operator to the Appell-type degenerate Bell polynomials. Beginning with the exponential operational rule and employing the fractional operator framework, this paper derives the operational connection, generating function, explicit summation formula, determinant representation, complete four-step proof via Cramer’s rule, recurrence relations, and the monomiality principle with raising and lowering operators and rigorously verifies the commutation relation. Applications to the extended degenerate Bell–Bernoulli, Bell–Euler, and Bell–Genocchi polynomials are presented as three numbered examples, each with generating functions; operational connections; determinant representations; numerical first values; and graphical illustrations including surface plots, zero diagrams, and stacked zero figures. Full article

This paper addresses the data-driven identification of open-chain robot morphology from finite windows of heterogeneous signals, including commanded joint references, measured joint states, and end-effector pose observations. Unlike conventional calibration procedures that assume a known kinematic topology, the proposed formulation estimates both discrete structural quantities and continuous kinematic coordinates: the number of active joints, the revolute/prismatic token sequence, Product-of-Exponentials (POE) screw axes, and the home pose of the end effector. A temporal transformer encoder is used as the main estimator and compared with a gated recurrent unit (GRU) baseline on the same dataset, with the same output heads and a multitask physics-aware objective. The continuous target is expressed in POE coordinates rather than as a Denavit–Hartenberg table because POE directly represents spatial joint axes and avoids several frame-assignment ambiguities. Simulated results on a noisy benchmark of 48 serial-robot families show that both sequence models recover the discrete structure on the tested in-library trajectories, while their continuous reconstruction errors reveal different trade-offs in screw-axis, home-pose, and trajectory reconstruction accuracy. The study also discusses inactive-slot masking, out-of-library behavior, synthetic-to-real limitations, persistent excitation, and the role of the learned model as an initialization for subsequent calibration refinement. Full article

This article develops a unified asymptotic theory for conditional U-statistics based on delta-sequence smoothing, thereby extending, in a substantial and conceptually coherent manner, the classical kernel-based framework for localized nonlinear conditional functionals. The proposed methodology is formulated in a highly general nonparametric setting and includes, as particular cases, the estimator of Stute, histogram-type procedures, orthogonal series methods, and a broad family of approximation schemes generated by positive delta sequences. In contrast with the existing literature, the present work explicitly incorporates response missingness under a Missing-at-Random mechanism, a setting of considerable methodological importance in modern statistical inference. Within this incomplete-data framework, we introduce a complete-case conditional U-statistic estimator and establish its asymptotic properties under general smoothness, integrability, and positivity conditions. Our first main contribution is the derivation of non-asymptotic exponential concentration inequalities for the proposed estimator, both in the bounded-kernel case and in the more delicate unbounded regime, with the latter being handled through a conditional Bernstein-type moment assumption. These inequalities provide a sharp probabilistic control of the stochastic fluctuations and constitute a fundamental technical device for the subsequent asymptotic analysis. Our second contribution is the establishment of strong consistency with explicit convergence rates, together with asymptotic normality of the localized estimator. In particular, the analysis makes precise the manner in which smoothing, dimensionality, interaction order, and missingness jointly determine the asymptotic bias and variance structure. The missing-data mechanism enters the limiting theory in a nontrivial yet fully quantifiable way through the observation probabilities, thereby yielding a refined description of the effective loss of information induced by incomplete responses. The scope of the theory is sufficiently broad to cover a wide class of nonlinear statistical functionals arising in discrimination, metric learning, multipartite ranking, conditional dependence analysis, generalized multi-sample U-statistics, and set-indexed conditional inference. To complement the theoretical developments, we conduct an extensive simulation study under several data-generating schemes, smoothing configurations, and missingness intensities. The numerical results corroborate the asymptotic theory, illustrate the finite-sample bias–variance trade-off inherent in delta-sequence localization, and demonstrate the stability and practical accuracy of the proposed estimator over a wide range of relevant regimes. Taken together, these results show that delta-sequence conditional U-statistics provide a flexible, mathematically rigorous, and broadly applicable framework for higher-order nonparametric inference with incomplete data. Full article

This study investigates the ($3+1$)-dimensional extended Kadomtsev–Petviashvili equation via traveling-wave phase-space geometry. The equation is reduced to a planar Hamiltonian system with cubic nonlinearity, whose conserved energy partitions the phase space into periodic orbits, separatrices, and unbounded trajectories. Closed-form profiles for the gradient variable $\phi =U_{\xi }$ are obtained through separation of variables; the corresponding field U is recovered by quadrature and must satisfy a zero-mean condition for periodic reconstruction. In particular, for $h_1>0$, the reconstructed field exhibits kink/antikink-type rather than localized-pulse behavior. Under weak periodic forcing, an explicit Melnikov amplitude factor is derived. Its exponential decay with the forcing frequency implies that the leading-order separatrix splitting distance $\mu A\left(\omega \right)$ becomes exponentially small at high frequency, while the simple-zero condition still predicts transverse intersections of stable and unstable manifolds and the onset of horseshoe chaos. Applying the complete discriminant method yields eight distinct solution families—hyperbolic, trigonometric, rational, and Jacobi elliptic—each associated with a unique orbital topology. These results enrich both the dynamical theory and the exact solution framework of higher-dimensional nonlinear evolution equations. Full article

In this paper, we introduce a new extension of the Topp–Leone-G family, called the generalized Marshall–Olkin Topp–Leone-G (GMOTL-G) family of distributions. The proposed family is obtained by combining the generalized Marshall–Olkin and Topp–Leone-G generators, leading to a more flexible class of models for lifetime data. We study several of its mathematical and statistical properties and focus in particular on the generalized Marshall–Olkin Topp–Leone exponential (GMOTL-E) distribution as an important special case. For this model, we derive and discuss a number of useful characteristics, including the moment generating function, moments, order statistics, residual and reversed residual life functions, mean deviations, asymptotic behavior, and stochastic ordering. We also develop maximum likelihood estimation for the model parameters under both complete and right-censored samples. In addition, we construct a goodness-of-fit test for the proposed model under independent right censoring using a chi-square type approach. The performance of the estimation and testing procedures is investigated through simulation, and the results show good behavior of the estimators and satisfactory agreement between empirical and theoretical significance levels. Finally, two real data applications, one with complete data and one with right-censored data, are presented to illustrate the flexibility and practical usefulness of the proposed model. These results show that the new family provides an effective tool for modeling lifetime data and for assessing model adequacy in the presence of right censoring. Full article

Narrative extraction algorithms construct storylines by finding coherent paths through document collections. The Narrative Trails algorithm frames this as maximum-capacity path optimization, where path quality depends on a coherence function measuring document relationships. We introduce quantum kernels as coherence functions for narrative extraction—to the best of our knowledge, the first systematic characterisation of quantum kernel methods for storyline extraction—and compare them against classical baselines on two corpora using a multi-seed protocol. The sweep covers 93 method evaluations (54 quantum kernels across three encoder families—R_Y+CNOT-ring, IQP/ZZ-feature-map, and a projected quantum kernel—and 39 classical kernels—cosine, RBF, and the cluster-aware Narrative Trails baseline). On 11,215 human navigation paths from Wikispeedia, evaluation metrics divide into two clusters that disagree with each other: alignment-based metrics (length-normalised DTW and per-step DTW similarity) favour methods that produce long alignment-rich paths, while set-overlap metrics (Jaccard and F1) favour methods that produce shorter paths with higher article overlap. On LLM-judged coherence for Cuban news storylines, evaluated under a 12-method × 5-seed × 30-endpoint-pair × 2-judge design (Claude Sonnet 4.5 and GPT-4o, both at $T=0$ via structured tool calling), the cluster-aware classical baseline is the top method in terms of mean overall coherence; the 5-method quantum-kernel pool and the 7-method classical-kernel pool on matched projection input show no significant differences after Holm correction. Cross-task analysis reveals that LLM coherence rank correlates with alignment-cluster Wikispeedia metrics (Spearman $\rho \approx +0.70$) and anti-correlates with overlap-cluster metrics ($\rho \approx -0.62$). A closed-form theoretical analysis shows that the depth-1 R_Y+CNOT-ring kernel reduces to a classical product-of-cosines kernel order equivalent to RBF, explaining the absence of empirical separation at low depth; deeper encoders break the cancellation but exponentially concentrate kernel values, eroding inter-pair distinguishability. Our results characterise quantum coherence kernels as competitive with classical kernels on the same projected input rather than decisively superior, with the cluster-aware classical baseline retaining a modest advantage attributable to its explicit topical structure. Full article

This article discusses the role of Kaupapa Māori in transforming Māori educational experiences within Aotearoa (New Zealand) over the past forty years. Since the initial articulation of Kaupapa Māori from the mid-1980s, there has been an exponential growth in its development and application across Aotearoa (New Zealand). There has been extensive documentation that it was within the education sector that Kaupapa Māori initiatives were developed and initiated by Māori in response to the failure of mainstream conventional education to provide for Māori children. That response was formalized through the establishment of Te Kōhanga Reo (Māori Language Nests) and Kura Kaupapa Māori (Māori Immersion Schools), which were led by Māori. Since then, there has been an increased utilization of Kaupapa Māori theory as a foundation for understanding, explaining and critiquing key issues facing Māori and Aotearoa more broadly. In the research project “Kaupapa Māori: Creating an Indigenous Model for Systems Change”, we undertook a series of interviews (n = 80) with Māori people involved in a range of sites who utilize Kaupapa Māori as the foundation in their lives, both personally, as whānau (extended family), and in their work. A key question posed was: What are the success factors within Kaupapa Māori that can inform innovative models for systems change that will transform inequities experienced by Māori? This was asked to gain insights into how Kaupapa Māori have created transformative and meaningful change across a range of sectors and sites. Where the wider project included participation from across a broad range of social contexts, this article looks at key themes that arose from how kaikōrero (participants) saw transformative change occurring through being a part of Kaupapa Māori educational developments. Kaikōrero shared multiple ways in which transformation occurred for individuals, within their whānau (extended families), through intergenerational changes and impacts at community and systems levels. Full article

Discrete memristive chaotic maps are promising for secure communications due to their digital compatibility, yet existing designs face limitations, including narrow hyperchaotic ranges and a single type of chaotic attractor. This paper proposes a family of 2D hyperchaotic maps by coupling a discrete exponential memristor with four 1D seed maps. Theoretical analysis reveals that the exponential memristor induces non-hyperbolic fixed points and periodicity with respect to the memristor’s initial charge, enabling controlled coexistence of both homogeneous and heterogeneous multistable attractors. Numerical simulations show two positive Lyapunov exponents (LEs) and broad hyperchaotic regions; the memristor-coupled Sine map achieves a maximum LE of 0.4963 and spectral entropy (SE) of 0.8915, outperforming representative cosine- and quadratic-based benchmarks. A pseudorandom number generator (PRNG) passes all National Institute of Standards and Technology (NIST) SP 800-22 tests. STM32F407-based hardware experiments confirm physical realizability, and an image encryption application demonstrates near-ideal entropy (7.9883) and strong differential attack resistance. These results establish the discrete exponential memristor as an effective nonlinearity for enriching chaos complexity and hardware-oriented security primitives. Full article

This work presents a new family of distributions (FoDs) called the exponentiated half logistic-generalized-Topp-Leone-G (EHL-GEN-TL-G) family. This family can be expressed as an infinite linear combination of exponentiated-G densities, which facilitates the derivation of its important statistical properties. The shapes of the density and hazard rate functions were investigated for special cases. The model parameters were estimated using six different methods, with the maximum likelihood technique emerging as the best approach. The consistency of the parameter estimates was then validated through Monte Carlo simulations. The exponentiated half logistic-generalized-Topp-Leone-Weibull (EHL-GEN-TL-W) distribution, a sub-model of the EHL-GEN-TL-G family, was applied to three sets of engineering failure time data. The results indicated that, based on in-sample goodness-of-fit criteria, the EHL-GEN-TL-W model provided the best fit among the several established models considered. Additionally, the EHL-GEN-TL-W regression model was developed, and its practical utility in modeling failure data was demonstrated. Full article

We study binary hypothesis testing for i.i.d. observations under a multiplicative context weight. For the optimal weighted total loss, defined as the sum of weighted type-I and type-II losses, we prove the logarithmic asymptotic $L_n^{\ast }=exp\{-n{D}_C^{\mathrm{w}}(\mathbb{P},\mathbb{Q})+o\left(n\right)\},n\to \infty$, where $D_C^{\mathrm{w}}$ is the weighted Chernoff information. The single-letter form of the exponent relies on a structural assumption that the weight factorises across observations, $\phi \left(x_1^n\right)={\displaystyle \prod _{i=1}^n}\phi \left(x_i\right)$; this restriction is essential for the single-letter representation and should be distinguished from the weaker qualitative description “multiplicative context weight”. The proof embeds the weighted geometric mixtures $\phi p^{\alpha }{q}^{1-\alpha }$ into a likelihood-ratio exponential family and identifies the rate through its log-normaliser. We also derive concentration bounds for the tilted weighted log-likelihood, obtain closed forms for Gaussian, Poisson, and exponential models, and extend the exponent characterisation to finitely many hypotheses. Full article

Pollution by persistent organic pollutants (POPs) constitutes an environmental and public health crisis of planetary scale due to their toxicity, persistence, and capacity for bioaccumulation in ecosystems. Given the limitations of conventional methods, which are often costly or generate hazardous byproducts, advanced oxidation processes (AOPs) have emerged as critical alternatives for the terminal destruction of these compounds. However, a persistent gap remains between laboratory-scale innovations and their real industrial application. To address this issue, the study employs a systematic and quantitative bibliometric analysis of the scientific literature produced between 2000 and 2026. A total of 5911 documents indexed in Scopus were analyzed using specialized tools such as R Studio (bibliometrix) 2026.04.0+526 and VOSviewer (1.6.20) to map productivity, impact, and the intellectual structure of the field through co-occurrence networks and international collaboration. The results demonstrate exponential growth in research, with an annual rate exceeding 18%. China leads scientific production with 109 publications, while Spain and France record the highest impact per article, with averages of 217.5 and 213.5 citations respectively, underscoring the influence of their researchers as theoretical and methodological benchmarks. Authors such as Malato (Spain) and Oturan (France) act as central nodes of international collaboration, accumulating thousands of citations in areas such as solar photocatalysis and electro-Fenton processes. The analysis confirms that solar photocatalysis and electrochemical processes are the most effective AOP families, consistently reporting degradation efficiencies above 85–90%. Wastewater treatment is identified as the primary research driver, while advanced catalyst design has evolved into a niche technical specialization. Journals such as Chemosphere and Science of the Total Environment have consolidated as the main dissemination channels for this research. Full article

Although the standard cosmological model successfully describes most current observational data, it faces several theoretical and observational challenges that motivate the exploration of alternative frameworks. In this work, we investigate a class of geometric cosmology models (GC) obtained by adding an infinite tower of higher-order curvature invariants to the Einstein–Hilbert action. Focusing on an exponential ansatz for the characteristic function entering the modified Friedmann equations, we derive the late-time background evolution for three families of solutions within this framework, named as (i) GILA, (ii) GR-deformation, and (iii) non-GR contribution. These models are confronted with recent Cosmic Chronometer and Type Ia supernova data, as well as age estimates of the oldest globular clusters—a constraint frequently overlooked in the literature. The stiffness of the equations in certain regions of parameter space, together with technical difficulties arising from the inclusion of the globular cluster bound, motivates the development of a dedicated methodology as an alternative to standard Markov Chain Monte Carlo techniques. Our results show that two entire families of GC models (non-GR contribution and GR-deformation) are ruled out by the data, whereas some families within the GILA model can successfully account for all data sets. For these models, meaningful constraints on their free parameters can be derived from the statistical analysis. Nevertheless, model comparison criteria reveal a preference in the data for $\Lambda$CDM over the GILA models examined here. Although none of the proposed models provides a preferred alternative to $\Lambda$CDM given the specific characteristic function considered here, this work establishes a clear methodology for testing alternative cosmological models, including the globular cluster constraint, and indicates the way for future research of GILA models with alternative choices of the characteristic function. Full article

Reliability studies frequently employ progressive censoring schemes that remove surviving units during testing, yet statistical inference under such designs remains vulnerable to parametric model misspecification. When distributional assumptions fail, conventional maximum likelihood estimators converge to systematically biased limits, producing confidence intervals with severely degraded coverage. We develop a flexible inferential framework that models the hazard function through a neural network architecture, avoiding commitment to a parametric family. To quantify uncertainty, we introduce a stratified weighted bootstrap procedure that preserves the dependency structure induced by progressive removals. We establish that the proposed estimator achieves the minimax optimal nonparametric rate $n^{-\alpha /(2\alpha +1)}$ for $\alpha$-smooth hazard functions and prove that the bootstrap consistently approximates the sampling distribution, yielding asymptotically valid pointwise confidence intervals for the survival function. A local asymptotic analysis precisely characterizes the efficiency–robustness tradeoff. Comprehensive simulations comparing against parametric methods, penalized splines, piecewise exponential models, and kernel estimators demonstrate that our method maintains 92–94% coverage under misspecification, whereas parametric alternatives collapse to 40–45% and simpler nonparametric methods achieve only 85–91%. The neural network architecture provides 23–29% lower integrated mean squared error than penalized splines using the same bootstrap, confirming that both components of our framework contribute to performance. Computational requirements remain practical: parallelized bootstrap inference completes in under 25 s on an 8-core processor for typical sample sizes. Application to electronic component lifetime data illustrates how the methodology yields materially different reliability assessments with direct implications for warranty planning. Full article