Search Results (2,745)

This paper investigates a robust optimal reinsurance and investment problem for an insurance company operating in a Markov-modulated financial market. The insurer’s surplus process is modeled by a diffusion process with jumps, which is correlated with financial risky assets through a common shock structure. The economic regime switches according to a continuous-time Markov chain. To address model uncertainty concerning both diffusion and jump components, we formulate the problem within a robust optimal control framework. By applying the Girsanov theorem for semimartingales, we derive the dynamics of the wealth process under an equivalent martingale measure. We then establish the associated Hamilton–Jacobi–Bellman (HJB) equation, which constitutes a coupled system of nonlinear second-order integro-differential equations. An explicit form of the relative entropy penalty function is provided to quantify the cost of deviating from the reference model. The theoretical results furnish a foundation for numerical solutions using actor–critic reinforcement learning algorithms. Full article

The transition to a circular economy (CE) in plastic packaging faces persistent barriers, including regulatory fragmentation, technological limitations, and supply chain disconnection. This study examines how multinational companies address these challenges by leveraging enablers such as advanced policies, technological innovation, and cross-sectoral collaboration. Based on a PRISMA-guided systematic review and a descriptive–explanatory case study, semi-structured interviews with senior managers were analyzed through thematic coding and data triangulation. Findings reveal that regulatory measures like virgin plastic taxation and post-consumer recycled material (PCR) incentives are effective only when synchronized with technical capacities. Investments in recycling infrastructure and circular design, such as resin standardization, enhance the quality of secondary materials, while local supply contracts and digital traceability platforms reduce volatility. Nevertheless, negative consumer perceptions and inconsistent PCR quality remain major obstacles. Unlike prior studies that examine barriers and enablers separately, this research develops an integrative framework where their interaction is conceptualized as a systemic and non-linear process. The study contributes to CE theory by reframing barriers as potential drivers of innovation and provides practical strategies, combining policy instruments, Industry 4.0 technologies, and collaborative governance to guide multinational firms in accelerating circular transitions across diverse regulatory contexts. Full article

In this paper, we investigate the integrability and solution structure of the semi-discrete KP equation. A bilinear Bäcklund transformation, Lax pair, and nonlinear superposition formula are systematically derived, establishing the integrability of the system. Furthermore, one- and two-periodic wave solutions are constructed using Hirota’s method combined with Riemann theta functions. By means of a rigorous limiting procedure, the asymptotic behavior of the periodic wave solutions is analyzed, and the connection between periodic wave and soliton solutions is established. These results not only enrich the solution structure of the semi-discrete KP equation but also provide new perspectives on periodic phenomena in discrete integrable systems. Full article

The problem of nonlinear elastic transverse oscillations of a beam moving along its axis and subjected to an axial compressive or tensile force is considered. A theoretical study is carried out using the asymptotic method of nonlinear mechanics KBM (Krylov–Bogolyubov–Mitropolsky). Using this methods, differential equations were obtained in a standard form, determining the law of variation in amplitude and frequency as functions of kinematic, force, and physico-mechanical parameters in both resonant and non-resonant regimes. The fourth-order Runge–Kutta method was applied for the oscillatory system numerical analysis. The computation of complex mathematical expressions and graphical representation of the results were implemented in the mathematical software Maple 15. The results obtained can be applied for engineering calculations of structures containing moving beams subjected to compressive or tensile forces. Full article

The objective of this study is to analyze spatial entropy flows that reveal the directional dynamics of climate change—patterns that remain obscured in traditional statistical analyses. This approach enables the identification of pathways for “climate information transport”, highlights associations with atmospheric circulation types, and allows for the localization of both sources and “informational voids”—regions where entropy is dissipated. The analytical framework is grounded in a quantitative assessment of long-term climate variability across Europe over the period 1901–2010, utilizing Shannon entropy as a measure of atmospheric system uncertainty and variability. The underlying assumption is that the variability of temperature and precipitation reflects the inherently dynamic character of climate as a nonlinear system prone to fluctuations. The study focuses on calculating entropy estimated within a 70-year moving window for each calendar month, using bivariate distributions of temperature and precipitation modeled with copula functions. Marginal distributions were selected based on the Akaike Information Criterion (AIC). To improve the accuracy of the estimation, a block bootstrap resampling technique was applied, along with numerical integration to compute the Shannon entropy values at each of the 4165 grid points with a spatial resolution of 0.5° × 0.5°. The results indicate that entropy and its derivative are complementary indicators of atmospheric system instability—entropy proving effective in long-term diagnostics, while its derivative provides insight into the short-term forecasting of abrupt changes. A lag analysis and Spearman rank correlation between entropy values and their potential supported the investigation of how circulation variability influences the occurrence of extreme precipitation events. Particularly noteworthy is the temporal derivative of entropy, which revealed strong nonlinear relationships between local dynamic conditions and climatic extremes. A spatial analysis of the information entropy field was also conducted, revealing distinct structures with varying degrees of climatic complexity on a continental scale. This field appears to be clearly structured, reflecting not only the directional patterns of change but also the potential sources of meteorological fluctuations. A field-theory-based spatial classification allows for the identification of transitional regions—areas with heightened susceptibility to shifts in local dynamics—as well as entropy source and sink regions. The study is embedded within the Fokker–Planck formalism, wherein the change in the stochastic distribution characterizes the rate of entropy production. In this context, regions of positive divergence are interpreted as active generators of variability, while sink regions function as stabilizing zones that dampen fluctuations. Full article

The development of automated systems for real-time material evaluation is becoming increasingly critical for structural engineering applications, infrastructure diagnostics and advanced material research. This work introduces a novel, fully automated nonlinear acoustics monitoring platform that employs Bulk Wave excitation in combination with non-contact Laser Doppler Vibrometry (LDV) detection to continuously assess the microstructural evolution of cement-based composites. Unlike conventional approaches—such as ultrasonic velocity measurements or compressive strength tests—which lack sensitivity to early-stage changes and also require manual operation, the proposed system enables unsupervised, high-precision monitoring of the material by leveraging the second and third harmonic generation (β₂, β₃) as nonlinear indicators of internal material changes. A specialized LabVIEW-based software manages excitation control, signal acquisition, frequency-domain analysis, and real-time feedback. As an initial step, the system’s stability, linearity, and measurement reliability were validated on metallic samples, and verified through long-duration experiments. Subsequently, the system was used to monitor hydration in cement-based specimens with varying water-to-cement and carbon nanotube (CNT) reinforcement ratios, thereby demonstrating its capability to resolve subtle nonlinear responses. The results highlight the system’s enhanced sensitivity, repeatability, and scalability, demonstrating that it as a powerful tool for structural health monitoring, smart infrastructure, and predictive maintenance applications. Full article

Vibrations induced by marine environmental loads can compromise the operational performance of offshore platforms and, in severe cases, result in structural instability or overturning. This study proposes a biomimetic nonlinear X-shaped vibration isolation system (NXVIS) to suppress earthquake-induced vibration response in offshore platforms. Compared with traditional passive vibration isolators, the key innovations of the NXVIS include: (1) the proposed NXVIS can be tailored to different load requirements and resonant frequencies to accommodate diverse offshore platforms and environmental loads; (2) By adjusting isolator parameters (e.g., link length and spring stiffness, etc.), the anti-vibration system can achieve different types of nonlinear stiffness and a large-stroke quasi-zero stiffness (QZS) range, enabling ultra-low frequency (ULF) vibration control without compromising load capacity. To evaluate the effectiveness of the designed NXVIS for vibration suppression of jacket offshore platforms under seismic loads, numerical analysis was performed on a real offshore platform subjected to seismic loads. The results show that the proposed nonlinear vibration isolation solution significantly reduces the dynamic response of deck displacement and acceleration under seismic loads, demonstrating effective low-frequency vibration control. This proposed NXVIS provides a novel and effective method for manipulating beneficial nonlinearities to achieve improved anti-vibration performance. Full article

Electricity tariffs in emerging economies are often influenced by macroeconomic volatility and regulatory design, affecting both affordability and system stability. Understanding these interactions is crucial for anticipating price fluctuations and ensuring sustainable energy policy. This paper examines the influence of macroeconomic conditions on electricity tariff dynamics in Colombia by integrating econometric and machine learning approaches. Using monthly data from 2009 to 2024 and a set of 153 macroeconomic indicators condensed via principal component analysis (PCA), we assess the predictive performance of vector autoregressive (VAR), SARIMAX, and XGBoost models, as well as a hybrid VAR–XGBoost specification. Impulse-response analysis reveals that tariff components exhibit limited sensitivity to macroeconomic shocks, underscoring the buffering role of regulation and sector-specific drivers. However, forecasting exercises demonstrate that accuracy is highly component-specific: SARIMAX performs best for transmission and restrictions, and VAR dominates for distribution and losses, while the hybrid model outperforms for generation and commercialization. These findings highlight that although macroeconomic pass-through into tariffs is weak, hybrid approaches that combine structural econometric dynamics with nonlinear learning can deliver tangible forecasting gains. The study contributes to the literature on electricity pricing in emerging economies and offers practical insights for regulators and policymakers concerned with tariff predictability and energy affordability. Full article

This study investigates the dynamics of optical solitons for the variable-coefficient coupled higher-order nonlinear Schrödinger equation (VCHNLSE) enriched with $\beta$-derivatives. By employing an extended direct algebraic method (EDAM), we successfully derive explicit soliton solutions that illustrate the intricate interplay between nonlinearities and variable coefficients. Our approach facilitates the transformation of the complex NLS into a more manageable form, allowing for the systematic exploration of diverse solitonic structures, including bright, dark, and singular solitons, as well as exponential, polynomial, hyperbolic, rational, and Jacobi elliptic solutions. This diverse family of solutions substantially expands beyond the limited soliton interactions studied in conventional approaches, demonstrating the superior capability of our method in unraveling new wave phenomena. Furthermore, we rigorously demonstrate the robustness of these soliton solutions against various perturbations through comprehensive stability analysis and numerical simulations under parameter variations. The practical significance of this work lies in its potential applications in advanced optical communication systems. The derived soliton solutions and the analysis of their dynamics provide crucial insights for designing robust signal carriers in nonlinear optical media. Specifically, the management of variable coefficients and fractional-order effects can be leveraged to model and engineer sophisticated dispersion-managed optical fibers, tunable photonic devices, and ultrafast laser systems, where controlling pulse propagation and stability is paramount. The presence of $\beta$-fractional derivatives introduces additional complexity to the wave propagation behaviors, leading to novel dynamics that we analyze through numerical simulations and graphical representations. The findings highlight the potential of the proposed methodology to uncover rich patterns in soliton dynamics, offering insights into their robustness and stability under varying conditions. This work not only contributes to the theoretical foundation of nonlinear optics but also provides a framework for practical applications in optical fiber communications and other fields involving nonlinear wave phenomena. Full article

To investigate the bending performance and damage characteristics of segmental joints with double sealing gaskets in large-diameter shield tunnels under high water pressure, this study established a three-dimensional high-fidelity numerical model of the segment-joint system based on the Pearl River Estuary Tunnel project. A comprehensive analysis was conducted on the mechanical and deformation behavior of large-diameter shield tunnel segmental joints under combined compressive/flexural loading. The research systematically examined the evolving relationships between bending moments, vertical displacements, and joint opening at the double-sealed gasketed joints under varying axial compression conditions, thereby elucidating the phased characteristics of joint deformation. The results indicate that the deformation patterns of double-sealed gasketed segmental joints under compressive/flexural loading exhibit pronounced nonlinearity and stage-dependent features. Both positive and negative bending moment scenarios demonstrate four distinct failure phases. Under high-water-pressure conditions, structural damage initiation consistently occurs at waterproof sealing grooves and bolt holes, regardless of bending moment direction. As loading intensifies, cracks propagate symmetrically at 45° angles from the joint interface, generating extended fracture networks, which creates additional water infiltration pathways, significantly compromising the joint’s waterproofing integrity. Full article

In recent years, with the development of novel hardware such as memristors, integrating chaotic systems with hardware implementations has enabled efficient and controllable generation of chaotic signals, providing new avenues for both theoretical research and engineering applications. In this work, we propose a novel memristor-based chaotic system, named the three-dimensional discrete echo-memristor map (3D-DEMM). The 3D-DEMM is capable of generating complex dynamic behaviors with self-similar attractor structures; specifically, under different parameters and initial conditions, the system produces similar attractor shapes at different amplitudes, which we refer to as an echo chaotic map. By incorporating the discrete nonlinear characteristics of memristors, the 3D-DEMM is systematically designed. We first conduct a thorough dynamic analysis of the 3D-DEMM, including attractor visualization, Lyapunov exponents, and NIST tests, to verify its chaoticity and self-similarity. Subsequently, the attractors of the 3D-DEMM are captured on a DSP platform, demonstrating discrete-time hardware simulation and real-time operation. Experimental results show that the proposed system not only exhibits highly controllable chaotic behavior but also demonstrates strong robustness in maintaining amplitude-invariant attractor shapes, providing a new theoretical and practical approach for memristor-based chaotic signal generation and applications in information security. Full article

One of the challenges in inertia sensor applications is the need for a class of devices that operate at one of the ring resonant frequencies to achieve large amplitudes of vibration. However, large amplitudes tend to produce undesirable nonlinear effects due to geometrical nonlinearities. Hence, a rigorous experimental dynamic analysis of rotating thin circular ring-type structures is considered important to gain a deeper understanding of the device’s nonlinear behavior as well as the potential performance improvements. This study aims to experimentally investigate the nonlinear dynamic behavior of rotating thin circular rings and the effects of angular rate as well as mass mismatch variations on the system natural frequency. A prototype made of a macroscale thin cylindrical structure is employed to study the nonlinear dynamic behavior of rotating thin circular rings. Using a precision rate table equipped with a slip ring as well as non-contact sensors/actuators, experiments that closely represent the actual physical operating conditions of angular rate sensors are developed. Natural frequency variations due to the input angular rate changes are measured in time and frequency domains. Useful experimental observations on the frequency split and mass mismatch effects have been performed. Typical nonlinear behavior, such as jump phenomena of a rotating thin circular cylinder, is noted. The nonlinear dynamic behavior of a ring-type resonator system, which is subjected to external excitations, is experimentally investigated. Results from the present experimental study on the mechanics of the ring structure are expected to provide further insight into the design and operation of ring-type resonators for angular rate sensing applications. Full article

This study explores how higher mathematics education can be reoriented towards greater sustainability, thereby better preparing students to meet the challenges of the future and supporting their sustainable employability. An interpretative phenomenological analysis was conducted to explore the lived experiences of university mathematics teachers (N = 6) integrating sustainability principles into their teaching practice. Data were collected through interviews, which revealed five thematic areas: responsibility for contributing to a sustainable future, pedagogical contradictions, ways of promoting sustainability, finding community and transdisciplinarity. These themes formed the basis of strategic principles including multi-level integration, methodological and content support, professional community development and transdisciplinarity embedded in a non-linear, cyclical implementation model. Results show that effective integration requires a combination of individual motivation with systemic institutional support, access to structured resources, and collaboration across institutions and disciplines. The proposed framework not only aligns mathematics education with the Sustainable Development Goals (SDGs) but also enhances students’ ability to apply mathematical tools to solve complex real-world problems, contributing to their long-term professional sustainability and adaptation to different educational contexts. Full article

Considering the effects of complex correlations between variables and uncertainty of degradation processes in multivariate degradation systems, a system reliability assessment method that integrated Chatterjee correlation coefficient and stochastic process theory is proposed. First, due to temporal uncertainty and measurement error in the univariate degradation process, a general Wiener-process-based state space model is constructed to determine the marginal distributions. Secondly, the nonlinear and asymmetric correlation between variables is analyzed by the nonparametric Chatterjee correlation coefficient. The multivariate joint degradation model is constructed by combining the Vine copula technique. The copula structure selection is optimized based on the goodness-of-fit criterion for modeling the degradation dependency network. In order to verify the validity of the method, comparative experiments based on the C-MAPSS aero-engine degradation dataset are conducted. Compared with the independent model ignoring the correlation of the variables, Vine copula with Chatterjee coefficient shows the rationality of the system reliability assessment. The system reliability curve lies between the cases of complete independence and complete dependence of variables. Compared to the traditional Vine copula model with Kendall coefficient, the method in this paper shows a significant improvement in asymmetric correlation characterization, with a Vuong test value of 6.37. The assessment method given in this paper provided an improved paradigm for reliability assessments of complex systems. Full article

The safety and reliability of modern power systems are increasingly challenged by adverse environmental conditions. (1) Background: Ice accumulation on power transmission lines is recognized as a severe threat to grid stability, as tower collapse, conductor breakage, and large-scale outages may be caused, thereby making accurate monitoring essential. (2) Methods: A rule-driven and interpretable stereo vision framework is proposed for three-dimensional (3D) detection and quantitative measurement of transmission line icing. The framework consists of three stages. First, adaptive preprocessing and segmentation are applied using multiscale Retinex with nonlinear color restoration, graph-based segmentation with structural constraints, and hybrid edge detection. Second, stereo feature extraction and matching are performed through entropy-based adaptive cropping, self-adaptive keypoint thresholding with circular descriptor analysis, and multi-level geometric validation. Third, 3D reconstruction is realized by fusing segmentation and stereo correspondences through triangulation with shape-constrained refinement, reaching millimeter-level accuracy. (3) Result: An accuracy of 98.35%, sensitivity of 91.63%, specificity of 99.42%, and precision of 96.03% were achieved in contour extraction, while a precision of 90%, recall of 82%, and an F1-score of 0.8594 with real-time efficiency (0.014–0.037 s) were obtained in stereo matching. Millimeter-level accuracy (Mean Absolute Error: 1.26 mm, Root Mean Square Error: 1.53 mm, Coefficient of Determination = 0.99) was further achieved in 3D reconstruction. (4) Conclusions: Superior accuracy, efficiency, and interpretability are demonstrated compared with two existing rule-based stereo vision methods (Method A: ROI Tracking and Geometric Validation Method and Method B: Rule-Based Segmentation with Adaptive Thresholding) that perform line icing identification and 3D reconstruction, highlighting the framework’s advantages under limited data conditions. The interpretability of the framework is ensured through rule-based operations and stepwise visual outputs, allowing each processing result, from segmentation to three-dimensional reconstruction, to be directly understood and verified by operators and engineers. This transparency facilitates practical deployment and informed decision making in real world grid monitoring systems. Full article