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Articles 4741 - 4770 of 291657

Full-Text Articles in Physical Sciences and Mathematics

Cycle-Based Characterizations Of The Cycle Completable Graphs, Terry A. Mckee Mar 2026

Cycle-Based Characterizations Of The Cycle Completable Graphs, Terry A. Mckee

Theory & Applications of Graphs

Cycle completable graphs were originally defined to answer matrix completion problems and have since received diverse graph-theoretic descriptions, in spite of not directly mentioning cycles. This paper characterizes such graphs by their chordless cycles never having ``bridges'' (as defined in H.-J. Voss's 1991 monograph {\em Cycles and Bridges in Graphs\/}) that have more than two vertices of attachment in the cycle. This approach is then related to the well-studied, yet seemingly quite distinct, classes of chordal graphs and series-parallel graphs. This is done by allowing chords to be ``bridges'' that have exactly two vertices of attachment.

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Observational Diagnostics And Spectroscopic Detection Of Tropospheric Trace Gases And Oxidation Processes, Callum E. Flowerday Mar 2026

Observational Diagnostics And Spectroscopic Detection Of Tropospheric Trace Gases And Oxidation Processes, Callum E. Flowerday

Theses and Dissertations

The troposphere is governed by photochemical and radical-mediated processes that control atmospheric oxidation capacity, secondary pollutant formation, and the chemical lifetime of gases. Quantitative understanding of these processes requires both robust observational frameworks capable of resolving complex precursor–product relationships and instrumentation with sufficient sensitivity and selectivity to detect reactive and low-concentration species. This dissertation advances atmospheric chemistry through the parallel development of quantitative observational methodologies and refined spectroscopic measurement techniques. The first component of this work evaluates atmospheric oxidation systems using long-term monitoring data and empirical diagnostics. Multi-year analyses of ozone and particulate matter trends were conducted to characterize variability …

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Forest Fires Increase Vulnerability To Midwinter Rain-On-Snow Snowmelt In The Western Oregon Cascades, Sage C. Ebel, Kelly Gleason Mar 2026

Forest Fires Increase Vulnerability To Midwinter Rain-On-Snow Snowmelt In The Western Oregon Cascades, Sage C. Ebel, Kelly Gleason

Environmental Science and Management Faculty Publications and Presentations

Forest fires and rain-on-snow events in the seasonal snow zone of the Pacific Northwest are increasing in frequency and magnitude, yet the combined impacts of these events on snowpack and water resources remain poorly understood. We show that forest fires doubled midwinter snowmelt proportions in 2023 and 2024 compared to unburned reference sites in the western Oregon Cascades. Data from snow monitoring and micrometeorological stations installed across an elevational gradient revealed increased snowpack vulnerability during midwinter rain-on-snow events, where we observed more snowmelt per rain-on-snow event in the burned forest, indicating higher vulnerability of these snowpacks to rapid melt, increasing …

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Non-Hermitian Sl (3, C) Three-Mode Couplers, B. M. Rodríguez-Lara, Hamed Ghaemi-Dizicheh, Shahram Dehdashti, Andreas Hanke, Ahmed Touhami, J. Nötzel Mar 2026

Non-Hermitian Sl (3, C) Three-Mode Couplers, B. M. Rodríguez-Lara, Hamed Ghaemi-Dizicheh, Shahram Dehdashti, Andreas Hanke, Ahmed Touhami, J. Nötzel

Physics & Astronomy Faculty Publications

Photonic systems with exceptional points, where eigenvalues and corresponding eigenstates coalesce, have attracted interest due to their topological features and enhanced sensitivity to external perturbations. Non-Hermitian mode-coupling matrices provide a tractable analytic framework to model gain, loss, and chirality across optical, electronic, and mechanical platforms without the complexity of full open-system dynamics. Exceptional points define their spectral topology, and enable applications in mode control, amplification, and sensing. Yet N -mode couplers, the minimal setting for N th-order exceptional points, are often studied in specific designs that overlook their algebraic structure. We introduce a general sl(N , C) framework for arbitrary …

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Quantum Superpositions Of Conscious States In A Minimal Integrated Information Model, Kelvin J. Mcqueen, Ian T. Durham, Markus P. Müller Mar 2026

Quantum Superpositions Of Conscious States In A Minimal Integrated Information Model, Kelvin J. Mcqueen, Ian T. Durham, Markus P. Müller

Philosophy Faculty Articles and Research

Could there be quantum superpositions of conscious states, as suggested by the Wigner’s friend thought experiment? Mathematical theories of consciousness, notably integrated information theory (IIT), make this question more precise by associating physical systems with both quantitative amounts of consciousness and structural characterizations of conscious states. Motivated by a recent proposal that ties wave-function collapse to integrated information, we construct a simple quantum circuit that would, on that proposal, place a minimal system—a feedback dyad—into a superposition of states that differ in their associated conscious states. This “Schrödinger’s dyad” provides a controlled setting for evaluating a central desideratum of consciousness-based …

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Saturated Hierarchical Atomic Incremental Learning (Shail): A Behavioral Learning Perspective On Staged Mastery And Saturation, Ernest Fokoue Mar 2026

Saturated Hierarchical Atomic Incremental Learning (Shail): A Behavioral Learning Perspective On Staged Mastery And Saturation, Ernest Fokoue

Articles

We introduce Saturated Hierarchical Atomic Incremental Learning (sHAIL), a learning paradigm in which complex tasks are approached through a sequence of simpler atomic subtasks, each mastered to saturation before progression. The central mechanism is a saturation criterion that detects when learning dynamics enter a plateau region, triggering consolidation and subsequent ascent to a higher level of task complexity. We develop a theoretical framework for sHAIL and show that it naturally gives rise to \emph{staircased convergence}: alternating phases of rapid improvement and genuine plateau. Within each level, classical convergence guarantees apply under standard smoothness conditions, while the hierarchical transitions are driven …

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No Intelligence Without Statistics: The Invisible Backbone Of Artificial Intelligence, Ernest Fokoue Mar 2026

No Intelligence Without Statistics: The Invisible Backbone Of Artificial Intelligence, Ernest Fokoue

Articles

The rapid ascent of artificial intelligence (AI) is often portrayed as a revolution born from computer science and engineering. This narrative, however, obscures a fundamental truth: the theoretical and methodological core of AI is, and has always been, statistical. This paper systematically argues that the field of statistics provides the indispensable foundation for machine learning and modern AI. We deconstruct AI into nine foundational pillars—Inference, Density Estimation, Sequential Learning, Generalization, Representation Learning, Interpretability, Causality, Optimization, and Unification—demonstrating that each is built upon century-old statistical principles. From the inferential frameworks of hypothesis testing and estimation that underpin model evaluation, to the …

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Decorrelation, Diversity, And Emergent Intelligence: The Isomorphism Between Social Insect Colonies And Ensemble Machine Learning, Ernest Fokoue, Gregory Babbitt, Yuval Levental Mar 2026

Decorrelation, Diversity, And Emergent Intelligence: The Isomorphism Between Social Insect Colonies And Ensemble Machine Learning, Ernest Fokoue, Gregory Babbitt, Yuval Levental

Articles

Social insect colonies and ensemble machine learning methods represent two of the most successful examples of decentralized information processing in nature and computation respectively. Here we develop a rigorous mathematical framework demonstrating that ant colony decision-making and random forest learning are isomorphic under a common formalism of stochastic ensemble intelligence. We show that the mechanisms by which genetically identical ants achieve functional differentiation— through stochastic response to local cues and positive feedback—map precisely onto the bootstrap aggregation and random feature subsampling that decorrelate decision trees. Using tools from Bayesian inference, multi-armed bandit theory, and statistical learning theory, we prove that …

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A General Weighting Theory For Ensemble Learning: Beyond Variance Reduction Via Spectral And Geometric Structure, Ernest Fokoue Mar 2026

A General Weighting Theory For Ensemble Learning: Beyond Variance Reduction Via Spectral And Geometric Structure, Ernest Fokoue

Articles

Ensemble learning is traditionally justified as a variance-reduction strategy, explaining its strong performance for unstable predictors such as decision trees. This explanation, however, does not account for ensembles constructed from intrinsically stable estimators-including smoothing splines, kernel ridge regression, Gaussian process regression, and other regularized reproducing kernel Hilbert space (RKHS) methods whose variance is already tightly controlled by regularization and spectral shrinkage. This paper develops a general weighting theory for ensemble learning that moves beyond classical variance-reduction arguments. We formalize ensembles as linear operators acting on a hypothesis space and endow the space of weighting sequences with geometric and spectral constraints. …

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On The Scientific Stature Of Data Science: The Epistemological Unicorn, Ernest Fokoue Mar 2026

On The Scientific Stature Of Data Science: The Epistemological Unicorn, Ernest Fokoue

Articles

Data Science has ignited unprecedented academic, industrial, and pedagogical fervor, yet its status as a \textit{science} in the classical sense---comparable to physics or biology---remains profoundly unsettled. This article interrogates the epistemological foundations of Data Science by examining its hybrid theoretical lineage, from the Universal Approximation Theorem to the No-Free-Lunch Theorems, with special emphasis on the fundamental Bayesian optimality results for both regression and classification. We argue that Data Science is in a vigorous \textit{gestational period}, characterized not by an absence of principles but by a creative tension between empirical pragmatism and deep mathematical theory. The Cross-Validation score emerges as the …

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Projections Of The Characteristics Of Extreme Precipitation In East Africa Using Bias-Corrected Cmip6 Models, Exavery K. Makula Mar 2026

Projections Of The Characteristics Of Extreme Precipitation In East Africa Using Bias-Corrected Cmip6 Models, Exavery K. Makula

Tanzania Journal of Science

In the context of ongoing global climate change, the intensification of the hydrological cycle is expected to modify the frequency, magnitude, and spatial distribution of extreme precipitation events. Understanding how precipitation extremes will evolve under climate change is critical for East Africa (EA), a region highly vulnerable to hydroclimatic hazards and strongly dependent on rainfall-driven socioeconomic systems. The region experiences two main rainfall seasons, namely the long rains (March-May, MAM) and the short rains (October-December, OND). This study investigates future projections of the characteristics of extreme precipitation across the EA using an ensemble of biascorrected CMIP6 global climate models. Extreme …

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Shine: Multimodal Machine Learning Approaches For Solar Energetic Particles Events Event Prediction And Posthoc Analysis, Soukaina Filali Boubrahimi Mar 2026

Shine: Multimodal Machine Learning Approaches For Solar Energetic Particles Events Event Prediction And Posthoc Analysis, Soukaina Filali Boubrahimi

Funded Research Records

No abstract provided.

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Optimal Control And Bifurcation Analysis Of A Predator–Prey Model With Self-Limiting Growth And Predator Disease, Dipo Aldila, Muhammad Akmal Fasya, Bevina D. Handari, Chidozie Williams Chukwu, Olumuyiwa James Peter Mar 2026

Optimal Control And Bifurcation Analysis Of A Predator–Prey Model With Self-Limiting Growth And Predator Disease, Dipo Aldila, Muhammad Akmal Fasya, Bevina D. Handari, Chidozie Williams Chukwu, Olumuyiwa James Peter

Mathematical Modelling and Numerical Simulation with Applications

We propose and analyze an eco-epidemiological predator–prey model that incorporates self-limitation and disease transmission within the predator population. The model is formulated as a system of ordinary differential equations describing logistic prey growth under the interspecific interaction with the predator. On the other hand, the predator population is divided into susceptible and infected classes, whose growth is constrained by prey availability. Three biologically relevant equilibria are identified: predator extinction, disease-free coexistence, and coexistence with endemic disease. The existence and stability of these equilibria are determined by key ecological and epidemiological thresholds, including the basic reproduction number. Using numerical continuation methods, …

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Optimal Control Strategies For Infectious Diseases: A Numerical Simulation Approach, M. Abu Salek, Jannatun Nayeem, Romana Yesmin, M. Haider Ali Biswas, M. Humayun Kabir Mar 2026

Optimal Control Strategies For Infectious Diseases: A Numerical Simulation Approach, M. Abu Salek, Jannatun Nayeem, Romana Yesmin, M. Haider Ali Biswas, M. Humayun Kabir

Mathematical Modelling and Numerical Simulation with Applications

Various infectious diseases, such as Tuberculosis and Hepatitis B virus (HBV), caused by Mycobacterium bacteria and hepatitis B virus, respectively, pose serious threats worldwide. This paper examines the spread of these diseases using a mathematical model that incorporates control measures, including vaccination and awareness programs. We use a system of differential equations with control variables and apply Pontryagin's principle to determine optimal control strategies. The stability of the Disease Free Equilibrium (DFE) has been analysed and demonstrated to be both locally and globally asymptotically stable when the basic reproduction number remains below unity, thereby ensuring disease elimination. Furthermore, the Endemic …

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Measuring Market Risk Through Entropic Var, Dragomir Nedeltchev, Tsvetelin Zaevski Mar 2026

Measuring Market Risk Through Entropic Var, Dragomir Nedeltchev, Tsvetelin Zaevski

Mathematical Modelling and Numerical Simulation with Applications

The article aims to measure the market risk beyond the basic risk measures like the Value-at-Risk (VaR) and the Expected Shortfall (ES). The Entropic Value-at-Risk is selected among the available measures based on its advantages -- it is the coherent upper bound of the VaR and ES. This risk measure is applied to the classical Black-Scholes model as well as to some more realistic ones, such as the exponential tempered stable model (the log-returns are presented by a tempered stable L\'evy process), the stochastic volatility model of Heston, its jump extension of Bates, and another stochastic volatility model but with …

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Bifurcation Exploration Of Ion Acoustic Solitons Formation Of A Nonlinear Beta Fractional Kadomtsev-Petviashvili Burger Model In Plasma State, Mst. Razia Pervin, Alrazi Abdeljabbar, Fahad Sameer Alshammari, Mst. Shekha Khatun, Harun Or-Roshid Mar 2026

Bifurcation Exploration Of Ion Acoustic Solitons Formation Of A Nonlinear Beta Fractional Kadomtsev-Petviashvili Burger Model In Plasma State, Mst. Razia Pervin, Alrazi Abdeljabbar, Fahad Sameer Alshammari, Mst. Shekha Khatun, Harun Or-Roshid

Mathematical Modelling and Numerical Simulation with Applications

This research presents an extensive investigation of Ion acoustic soliton dynamics governed by a Beta-fractional Kadomtsev-Petviashvili-Burgurs (KPB) model. By engaging the planar dynamical system scheme in aggregation with the extended $(\phi, \psi)$ expansion, Kudryashov expansion, and the NMKM analytic schemes, we create a broad class of exact nonlinear pattern wave solutions. The local stability edifice of the fractional plasma model is explored through bifurcation theory, enabling the far-reaching classification of all admissible phase diagrams. Conforming Ion acoustic wave structures allied with every detour alignment are systematically assembled. Owing to the fractional and dissipative appearances of the model, an all-embracing assortment …

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Employing Effective Solution Methods For Caputo-Based Sequential Fractional Models, Eman A. A. Ziada, Mohamed F. Abouelenein, Hijaz Ahmad, Monica Botros Mar 2026

Employing Effective Solution Methods For Caputo-Based Sequential Fractional Models, Eman A. A. Ziada, Mohamed F. Abouelenein, Hijaz Ahmad, Monica Botros

Mathematical Modelling and Numerical Simulation with Applications

This paper investigates a class of nonlinear sequential singular fractional differential equations (FDEs) involving Caputo derivatives. This type of equation has several key advantages that enhance its value, such as capturing memory and hereditary effects. Viscoelastic materials and anomalous diffusion, as well as biological systems, can take advantage of this feature. In addition, fractional derivatives possess a sequential structure that enables the implementation of multiscale processes and hierarchical memory responses. Moreover, it provides an effective and flexible framework for solving differential equations compared to classical differential equations. It can therefore be used to model complex systems in physics, biology, and …

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Numerical Simulations And Hyers-Ulam Stability Of A Novel Nonlocal Anthropogenic Cutaneous Leishmaniasis Mathematical Model, Khalid Fanoukh Al Oweidi, Zakirullah -, Kamal Shah, Thabet Abdeljawad Mar 2026

Numerical Simulations And Hyers-Ulam Stability Of A Novel Nonlocal Anthropogenic Cutaneous Leishmaniasis Mathematical Model, Khalid Fanoukh Al Oweidi, Zakirullah -, Kamal Shah, Thabet Abdeljawad

Mathematical Modelling and Numerical Simulation with Applications

In this work, the fractal-fractional Atangana-Baleanu derivative with the Mittag-Leffler kernel is employed to capture the memory and hereditary effects inherent to anthropogenic cutaneous leishmaniasis transmission dynamics. The Banach fixed-point theorem and contraction mapping principle are used to prove the existence and uniqueness of solutions, while Hyers-Ulam stability of the system is analyzed to demonstrate the robustness of solutions with respect to small perturbations. Using a nonlinear least-squares approach, model parameters and fractional order are estimated using epidemiological data from the World Health Organization. The basic reproduction number $R_0 = 0.53$ indicates that the disease is under control after adding …

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Markov-Modulated Queueing Network For Mobile Traffic Aggregation With Threshold-Controlled Buffers, Anton A. Esin, Elmira Yu. Kalimulina Mar 2026

Markov-Modulated Queueing Network For Mobile Traffic Aggregation With Threshold-Controlled Buffers, Anton A. Esin, Elmira Yu. Kalimulina

Mathematical Modelling and Numerical Simulation with Applications

We study the problem of data transmission from mobile platforms operating in high-speed transit between cellular base stations, under conditions of unstable and intermittent connectivity. Conventional queueing and connectivity models often fail to capture the combined effects of rapidly changing signal conditions, finite buffer capacity, and dynamic topology. We aim to develop a tractable yet expressive model that integrates stochastic link availability, queue dynamics, and buffer control. We propose a mathematical framework based on queues whose service intensities are modulated by a continuous-time Markov chain (CTMC) representing signal conditions along a high-speed trajectory. The core subsystem is a two-stage (aggregation …

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Exact Soliton Solutions Of The Nonlinear Time-Fractional Schrödinger Equation Via Atangana–Baleanu And M-Truncated Operators, Bahadır Kopçasiz, Fatma Nur Kaya Sağlam, Mehmet Şenol Mar 2026

Exact Soliton Solutions Of The Nonlinear Time-Fractional Schrödinger Equation Via Atangana–Baleanu And M-Truncated Operators, Bahadır Kopçasiz, Fatma Nur Kaya Sağlam, Mehmet Şenol

Mathematical Modelling and Numerical Simulation with Applications

The main objective of this work is to obtain exact soliton solutions for a nonlinear time-fractional equation model describing wave profiles arising in various physical systems. To derive different wave structures associated with the considered model, two analytical techniques are employed: the extended G'\G^2-expansion method and the modified auxiliary equation (MAE) approach. A wave transformation is applied to reduce the nonlinear time-fractional equation to a nonlinear ordinary differential equation (NLODE) by means of the M-truncated and Atangana-Baleanu (AB) fractional operators. Several classes of solutions, including exponential, hyperbolic, and trigonometric wave forms, are obtained. Over and above the analytical results, graphical …

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Dynamical Analysis And Optimal Control Of The Mpox Transmission Model With Stratified Susceptibility, Tuhfatul Janan, Fatmawati Fatmawati, Cicik Alfiniyah, Santi Martini, Dipo Aldila, Agus Hasan Mar 2026

Dynamical Analysis And Optimal Control Of The Mpox Transmission Model With Stratified Susceptibility, Tuhfatul Janan, Fatmawati Fatmawati, Cicik Alfiniyah, Santi Martini, Dipo Aldila, Agus Hasan

Mathematical Modelling and Numerical Simulation with Applications

This study develops a mathematical model of Mpox transmission that combines stratified susceptibility (low-risk and high-risk groups) with vaccinated and asymptomatically infected compartments. The analysis of the model begins with examining the well-posedness, boundedness, and nonnegativity conditions for the solutions. The local stabilities of the equilibria are examined through the Jacobian matrix and phase plane analysis, while the global stabilities are provided through Lyapunov functions. The estimated parameters are verified using epidemiological data on Mpox cases in the United States, with an MAPE of 2.20% and R_0 > 1, indicating that Mpox remains endemic. Sensitivity analysis indicates that the contact rate …

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Toxicity, Chemistry, And Public Health Relevance Of Emerging Nicotine Analog Vapes, Pods, And Pouches., Rhea Raghu, Mohana Sengupta, Karen Lin, Felix Effah, Robert M. Strongin, Irfan Rahman Mar 2026

Toxicity, Chemistry, And Public Health Relevance Of Emerging Nicotine Analog Vapes, Pods, And Pouches., Rhea Raghu, Mohana Sengupta, Karen Lin, Felix Effah, Robert M. Strongin, Irfan Rahman

Chemistry Faculty Publications and Presentations

Electronic nicotine delivery system manufacturers, such as Charlie's Holdings Inc., ECBlend, Outlaw, and NicRiver, have recently introduced nicotine analogs, such as 6-methyl nicotine, 6-MN ("Metatine"), and nicotinamide, NA ("Nixamide," "Nixodine," or "Nixotin-free base and salt") in products to circumvent the U.S. FDA's premarket tobacco product application (PMTA) requirements. Marketed as "tobacco-free," "PMTA-exempt," or "FDA-approved," these compounds now appear in oral nicotine pouches and disposable bars/vapes from brands such as Outlaw Dip, Kumi-Six, SBX, Katchmi, and Spree Bar under proprietary labels including "NoNic6," "Metatine," or "NIC-SAFE." These products often mimic the appeal of conventional nicotine delivery systems, with extensive use of …

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Learning Ordinal Geometry: Semantic–Aware Kernels For Ordered Categorical Data, Ernest Fokoue Mar 2026

Learning Ordinal Geometry: Semantic–Aware Kernels For Ordered Categorical Data, Ernest Fokoue

Articles

Ordinal data arise ubiquitously in survey research, psychology, medicine, economics, and recommender systems, yet kernel methods for such data typically rely on either nominal encodings or arbitrary numeric codings. The former discards order information; the lat- ter imposes a fictitious metric structure. This paper develops a principled framework for kernel design on ordinal scales and introduces a new class of Semantic–Aware Ordinal Ker- nels (SAOK) that simultaneously capture ordinal order and semantic proximity between categories. We begin by formalizing order–preserving embeddings of finite chains and characterizing a broad family of chain distances that are conditionally negative definite. Through Schoen- berg …

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On Fibonacci Ensembles: An Alternative Approach To Ensemble Learning Inspired By The Timeless Architecture Of The Golden Ratio, Ernest Fokoue Mar 2026

On Fibonacci Ensembles: An Alternative Approach To Ensemble Learning Inspired By The Timeless Architecture Of The Golden Ratio, Ernest Fokoue

Articles

Nature rarely reveals her secrets bluntly, yet in the Fibonacci sequence she grants us a glimpse of her quiet architecture of growth, harmony, and recursive stability \citep{Koshy2001Fibonacci, Livio2002GoldenRatio}. From spiral galaxies to the unfolding of leaves, this humble sequence reflects a universal grammar of balance. In this work, we introduce \emph{Fibonacci Ensembles}, a mathematically principled yet philosophically inspired framework for ensemble learning that complements and extends classical aggregation schemes such as bagging, boosting, and random forests \citep{Breiman1996Bagging, Breiman2001RandomForests, Friedman2001GBM, Zhou2012Ensemble, HastieTibshiraniFriedman2009ESL}. Two intertwined formulations unfold: (1) the use of normalized Fibonacci weights -- tempered through orthogonalization and Rao--Blackwell optimization -- …

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Neutrosophic Sets In Neural Networks: Theory, Applications, And Challenges, Vladimir Simic, Dragan Pamucar, Hafiz Muhammad Athar Farid Mar 2026

Neutrosophic Sets In Neural Networks: Theory, Applications, And Challenges, Vladimir Simic, Dragan Pamucar, Hafiz Muhammad Athar Farid

Neutrosophic Systems with Applications

The integration of neutrosophic sets into neural networks presents a novel approach to handling uncertainty, indeterminacy, and falsity in data. Traditional neural networks typically operate under the assumption of precise and complete data, but real-world applications often involve noisy, incomplete, or ambiguous information. Neutrosophic sets extend fuzzy logic by incorporating three components: truth, indeterminacy, and falsity, allowing for a more nuanced representation of uncertain data. This paper explores the theoretical foundations of neutrosophic sets and their integration with neural networks, highlighting the challenges in computational complexity, training, and optimization. The paper also discusses the potential applications of neutrosophic neural networks …

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Neutrosophic Probability With Dynamic Temporal Uncertainty (Nptu), Bhimraj Basumatary, Ashoke Kumar Brahma Mar 2026

Neutrosophic Probability With Dynamic Temporal Uncertainty (Nptu), Bhimraj Basumatary, Ashoke Kumar Brahma

Neutrosophic Systems with Applications

This paper introduces Neutrosophic Probability with Dynamic Temporal Uncertainty (NPTU), an extension of classical neutrosophic probability that incorporates the dimension of time. In classical neutrosophic probability, the degrees of truth, indeterminacy, and falsity are considered static. However, real-world uncertainties evolve, and their degrees change as new information becomes available. NPTU models these uncertainties dynamically, allowing for more accurate decision-making in time-varying environments. The paper explores key mathematical properties of NPTU, including entropy, distance measures, similarity measures, and Kullback-Leibler (KL) divergence, to quantify and compare temporal uncertainty states. The proposed framework is demonstrated through a case study on stock price prediction, …

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Emergent Operator Logic: A Foundational Framework For Dynamic Reasoning And Generative Intelligence, Mona Gharib, Abduallah Gamal, Muhammad Nawaz, Basma Nasir Mar 2026

Emergent Operator Logic: A Foundational Framework For Dynamic Reasoning And Generative Intelligence, Mona Gharib, Abduallah Gamal, Muhammad Nawaz, Basma Nasir

Neutrosophic Systems with Applications

This paper introduces Emergent Operator Logic (EOL), a framework that treats propositions as continuous operators $F_p:X \rightarrow X$ on a complete metric state space $( X,d )$ and evaluates truth after action via a continuous valuation $V:X \rightarrow [ 0,1 ]$. Logical composition is realized by three operator-level connectives: sequential $p \circ q$(causal order), parallel $p\parallel q$(1-Lipschitz cooperative blend), and the emergent synthesis $E( p,q ) = \frac12( F_p \circ F_q + F_q \circ F_p )$, which symmetrizes non-commuting actions. We provide a Hilbert-style proof system (sound), an algebraic semantics via E-algebras, and show that the category of E-algebras is …

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Double-Valued Complex Neutrosophic Graphs, Suriyakumar G, V. J. Sudhakar, Takaaki Fujita Mar 2026

Double-Valued Complex Neutrosophic Graphs, Suriyakumar G, V. J. Sudhakar, Takaaki Fujita

Neutrosophic Systems with Applications

This paper introduces a novel graph-theoretic framework, called the double-valued complex neutrosophic graph, as an extension of double-valued neutrosophic set theory. Within this framework, we investigate several important classes of such graphs, including self-complementary, strong, and full double-valued complex neutrosophic graphs, and establish a number of their fundamental properties. To clarify the proposed concepts and demonstrate their structural behavior, several relevant illustrative examples are also provided.

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A Hybrid Multi-Criteria Decision-Making Approach For Sustainable Forest Fire Monitoring Using Unmanned Aerial Vehicles, Mohamed Eassa, Ahmed Abdelhafeez, Ahmad M. Nagm Mar 2026

A Hybrid Multi-Criteria Decision-Making Approach For Sustainable Forest Fire Monitoring Using Unmanned Aerial Vehicles, Mohamed Eassa, Ahmed Abdelhafeez, Ahmad M. Nagm

Neutrosophic Systems with Applications

Unmanned aerial vehicles (UAVs) have become an effective tool for forest fire monitoring. This study evaluates UAVs for forest fire management, addressing the challenges posed by ambiguous and uncertain factors. Single-valued neutrosophic sets (SVNSs) are employed to model complex uncertainties, as they incorporate three distinct membership values: false, true, and indeterminate. The evaluation of UAVs is a multifaceted task due to the variety of factors involved. To address this complexity, multi-criteria decision-making (MCDM) methods are used. Specifically, the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are integrated with SVNS to …

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On Links Between A Theorem Of Schoenberg, Rohlin Decompositions Of Measures, The Bochner-Minlos Theorem And The Fock Space, Daniel Alpay, Paula Cerejeiras, Palle Jorgensen, Uwe Kaehler Mar 2026

On Links Between A Theorem Of Schoenberg, Rohlin Decompositions Of Measures, The Bochner-Minlos Theorem And The Fock Space, Daniel Alpay, Paula Cerejeiras, Palle Jorgensen, Uwe Kaehler

Mathematics, Physics, and Computer Science Faculty Articles and Research

The main goal of this paper is to gain new results in stochastics by drawing on, and combining, different areas that are normally not considered to be related. Thus, in this paper we extend the previous class of Gaussian-like functions ML which will allow for future generalized stochastic processes in infinite dimensional analysis. We show that an approach similar to the one by the classical Bochner-Minlos theorem for the white-noise case can be achieved by using Gaussian-like functions belonging to a large family -the MLr classes (0 < r ≤∞). We show how Schoenberg’s theorem for positive definite functions on a Hilbert space allows to go beyond the classical setting of Bochner-Milnos theorem. Furthermore, we show that the application of the Rohlin’s disintegration theorem allows for a decomposition of the associated probability measures , see Theorems 3.2 and 4.3. We end this paper with several important examples of functions in these classes MLr and provide some interesting counterexamples, e.g. Theorem 7.4, to get a …

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