Search Results (29)

Multi-station sensing telemetry typically arrives out-of-order at the Open RAN (O-RAN) Near-RT RIC due to non-deterministic jitter in cloud-native protocol stacks, inducing a “temporal scrambling” effect that invalidates traditional spatial fusion. To bridge this gap, we introduce Age-of-Sensing (AoS) as a dynamic reliability metric for asynchronous sensing reports and establish an AoS-aware graph neural network (GNN) paradigm for asynchronous sensing fusion. This paradigm shifts the focus from conventional spatial-only aggregation to time-aware inference by explicitly incorporating sensing freshness into graph-based fusion. As a physics-informed realization of this paradigm, we present Time-Aware Fusion (TA-Fusion), which introduces a TA-Gate mechanism to recalibrate node trust prior to graph aggregation. Unlike passive feature concatenation, the TA-Gate serves as an active gating signal to prioritize fresh telemetry while adaptively suppressing stale outliers. On a standardized O-RAN benchmark, TA-Fusion achieves a root mean square error (RMSE) of $12.22$ m, delivering a $21.7\%$ reduction in Mean absolute error (MAE) over the AoS-aware GNN baseline and maintaining robustness in extreme jitter scenarios where traditional linear methods suffer from severe accuracy degradation due to their static weighting logic. Extensive Monte Carlo simulations confirm that the framework preserves consistent error bounds across diverse base station geometries without manual recalibration. These findings support the real-time feasibility of the proposed paradigm for delay-critical Integrated Sensing and Communication (ISAC) services, providing a resilient spatial foundation for 6G orchestration under substantial network-layer jitter. Full article

This paper proposes a novel four-dimensional strongly dissipative nonlinearly coupled hyperchaotic system, investigates its dynamical characteristics, and demonstrates its applicability through Deoxyribonucleic Acid (DNA)-encoded RGB image encryption. First, a four-dimensional nonlinearly coupled hyperchaotic system with strong dissipativity is constructed. Nonlinear dynamics analysis methods, including phase trajectory diagrams, Lyapunov exponent spectra, and bifurcation diagrams, are employed to thoroughly reveal the system’s complex dynamical evolution mechanisms. The analysis indicates that the system not only possesses a wide range of chaotic parameters but also exhibits rich phenomena of multiple coexisting attractors, demonstrating a high degree of multistability. This characteristic offers potential advantages for image encryption, as it increases the diversity of dynamical behaviors and enhances sensitivity to initial conditions. The physical realizability of the chaotic behavior is further verified through an analog circuit implementation. Consequently, the system supports the design of encryption algorithms with larger key spaces, stronger resistance to phase space reconstruction, and improved pseudo-randomness, making it particularly suitable for applications with extremely high security requirements. Subsequently, leveraging the highly random chaotic sequences generated by this system, combined with various DNA coding rules and operations, the RGB image components are scrambled and diffused for encryption. Security analysis demonstrates that the algorithm effectively passes examinations across multiple dimensions, including histogram analysis, information entropy, adjacent pixel correlation, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and The Peak Signal-to-noise Ratio (PSNR). It achieves favorable encryption results, significantly enhances image resistance against attacks, and provides a reliable technical solution for the secure transmission of remote sensing and military images. Full article

This study develops a reproducible and auditable workflow to prepare regulatory air-quality monitoring time series for subsequent temporal analysis, including observational PRE/POST applications around coal-fired power plant closures in northwestern Spain. The dataset comprises daily concentrations from 28 monitoring stations (2006–2023) for PM₁₀, PM_2.5, NO, NO₂, NO_x, O₃, SO₂, and CO, affected by missingness, structural inconsistencies, and extreme values. The contribution of this study lies in integrating standardized data ingestion and QA/QC chained-equation imputation with Bayesian Ridge regression, hold-out validation, physicochemical consistency checks, and robust extreme-value handling within a traceable processing workflow. Missing values are reconstructed per pollutant using plant-level multi-station pooling to improve stability. Performance is evaluated using a 5% masked hold-out and summarized with MAE, RMSE, R², and bias, complemented by an operational fit-quality label. Post-imputation controls enforce NO–NO₂–NO_x consistency and the physical constraint PM_2.5 ≤ PM₁₀, while extreme values are screened through a hierarchical robustness framework combining a Hampel filter, winsorization, and a Tukey IQR criterion. The workflow outputs documented diagnostics and robust daily series while preserving the traceability of observed values, flags, edits, and final decisions. Full article

Detecting precipitation patterns remains a central challenge in hydrological sciences due to the non-linear nature of atmospheric dynamics and the growing influence of climatic variability. This study investigates the evolution of a 64-year daily precipitation series (1961–2024) at the Tulcea meteorological station (Dobrogea, Romania) and introduces a novel adaptation of the Most Probable Precipitation Method (AMPPM), shifting its application from a regional spatial framework to a temporal one. Shannon Entropy is used as a measure of “climatic disorder.” Model evaluation incorporates Mean Error (ME), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), which here measure structural divergence rather than predictive accuracy. Results demonstrate that the Synthetic Representative Series (SRS) isolates the stable climatic signal, reducing the global coefficient of variation (cv (%)) to 70.96% and mitigating extreme skewness typical of coastal convective activity. Seasonal entropy analysis reveals divergence: winter entropy decreases through signal stabilization (minimum 2.00 bits in March), whereas July–October entropy increases, highlighting previously hidden high-frequency daily oscillations. The aggregated Tot_64 series achieves a final entropy of 2.75 bits, confirming a complex, multi-state daily precipitation process. MAE and RMSE values for the SRS (e.g., October: MAE = 1.20, RMSE = 4.53; Tot_64: MAE = 1.40, RMSE = 4.58) indicate that the SRS captures dominant precipitation patterns with minimal deviation, comparable to or better than the moving average approaches. Full article

Social acceleration and recurrent structural shocks increase habitus–field mismatch, yet similar exposure does not produce uniform trajectories of daily well-being or suicidal distress. This paper asks how comparable structural strain can generate divergent, path-dependent outcomes and why suicidal vulnerability may persist after objective conditions improve. We develop a theory-building, concept-driven framework that integrates Bourdieu’s practice theory with social and behavioural scholarship on stress, anomie, and despair, and conceptualises these dynamics as social hysteresis. The regime-based model specifies two ideal-typical response orientations through which mismatch can stabilise: an anomic regime marked by shame, withdrawal, and inwardly directed harm, and a radicalising regime marked by grievance framing, moral indignation, and organised participation, without implying violent extremism. Represented through hysteresis loops, the framework implies multistability, asymmetric switching thresholds, and scarring, providing a mechanism for persistence and non-linearity in distress trajectories. The model derives testable expectations for longitudinal panel and experience-sampling designs and suggests that prevention and intervention design should combine reductions in mismatch with relational and institutional infrastructures that facilitate regime shifts and reopen the space of possibles. Full article

Background: In the era of longevity, many low- and middle-income countries (LMICs) still lack a comprehensive understanding of health deficits among older adults and the care burden associated with “unhealthy longevity”. This study aims to reveal future changes in care needs and pressure in China from 2030 to 2100. Method: This study develops a multistate demographic forecasting framework by integrating a Markov-based health state transition model with the conceptual logic of an age-shift algorithm. Transition probability matrices by age and gender are estimated using nationally representative microdata from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Baseline population data from the National Bureau of Statistics and WPP 2024 are then used to simulate the evolution of health status among older adults in China from 2030 to 2100. Finally, person-years with disability (PYD) are calculated to evaluate the projected magnitude, structure, and gender disparities of long-term care needs over time. Results: Between 2030 and 2100, the number of disabled older adults in China is projected to follow an inverted U-shaped trend—peaking at 160 million in 2070 and remaining above 115 million by 2100. The share of disabled individuals among older adults rises steadily, from 39.75% to 45.28%. Person-years with disability (PYD) show sustained growth, especially among the oldest-old and women. By 2100, adults aged 95 and older contribute over 20 million PYD—eight times the 2030 level. Gender disparities widen: in 2100, women aged 85–94 account for 53.94 million severe-disability PYD, exceeding men by 8.22 million. These trends reflect mounting structural pressures on China’s long-term care system, increasingly driven by age- and gender-specific disability burdens. Conclusions: If the current disability trend continues unchecked, health risks for older adults will grow over time. In the near future, China will face an extremely heavy care burden and pressure, which will severely impact its economic and social systems. Seizing this critical window for policy action and system improvement is crucial to reducing risks in the longevity era. Full article

The geometric content of chaos in nonlinear systems with multiple stabilities of high order is a challenge to computation. We introduce a single algorithmic framework to overcome this difficulty in the present study, where a parametrically forced oscillator with cubic–quintic nonlinearities is considered as an example. The framework starts with the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, which is a self-learned algorithm that extracts an interpretable and correct model by simply analyzing time-series data. The resulting parsimonious model is well-validated, and besides being highly predictive, it also offers a solid base on which one can conduct further investigations. Based on this tested paradigm, we propose a unified diagnostic pathway that includes bifurcation analysis, computation of the Lyapunov exponent, power spectral analysis, and recurrence mapping to formally describe the dynamical features of the system. The main characteristic of the framework is an effective algorithm of computational basin analysis, which is able to display attractor basins and expose the fine scale riddled structures and fractal structures that are the indicators of extreme sensitivity to initial conditions. The primary contribution of this work is a comprehensive dynamical analysis of the DM-CQDO, revealing the intricate structure of its stability landscape and multi-stability. This integrated workflow identifies the period-doubling cascade as the primary route to chaos and quantifies the stabilizing effects of key system parameters. This study demonstrates a systematic methodology for applying a combination of data-driven discovery and classical analysis to investigate the complex dynamics of parametrically forced, high-order nonlinear systems. Full article

Traditional Hopfield neural networks (HNNs) suffer from limitations in generating controllable chaotic dynamics, which are essential for applications in neuromorphic computing and secure communications. Memristors, with their memory-dependent nonlinear characteristics, provide a promising approach to regulate neuronal activities, yet systematic studies on attractor offset behaviors remain scarce. In this study, we propose a fully memristive electromagnetic radiation neural network by incorporating three distinct memristors as external electromagnetic stimuli into an HNN. The parameters of the memristors were tuned to modulate chaotic oscillations, while variations in initial conditions were employed to explore multistability through bifurcation and basin stability analyses. The results demonstrate that the system enables large-scale amplitude control of chaotic signals via memristor parameter adjustments, allowing arbitrary scaling of attractor amplitudes. Various offset behaviors emerge, including parameter-driven symmetric double-scroll relocations in phase space and initial-condition-induced offset boosting that leads to extreme multistability. These dynamics were experimentally validated using an STM32-based electronic circuit, confirming precise amplitude and offset control. Furthermore, a multi-channel pseudo-random number generator (PRNG) was implemented, leveraging the initial-boosted offset to enhance security entropy. This offers a hardware-efficient chaotic solution for encrypted communication systems, demonstrating strong application potential. Full article

Fractional-order chaotic systems have emerged as powerful tools in secure communications and multimedia protection owing to their memory-dependent dynamics, large key spaces, and high sensitivity to initial conditions. However, most existing fractional-order image encryption schemes rely on fixed-order chaos and conventional solvers, which limit their complexity and reduce unpredictability, while also neglecting the potential of variable fractional-order (VFO) dynamics. Although similar phenomena have been reported in some fractional-order systems, the coexistence of hidden attractors and stable equilibria has not been extensively investigated within VFO frameworks. To address these gaps, this paper introduces a novel discrete variable fractional-order dark matter–dark energy (VFODM-DE) chaotic system. The system is discretized using the piecewise constant argument discretization (PWCAD) method, enabling chaos to emerge at significantly lower fractional orders than previously reported. A comprehensive dynamic analysis is performed, revealing rich behaviors such as multistability, symmetry properties, and hidden attractors coexisting with stable equilibria. Leveraging these enhanced chaotic features, a pseudorandom number generator (PRNG) is constructed from the VFODM-DE system and applied to grayscale image encryption through permutation–diffusion operations. Security evaluations demonstrate that the proposed scheme offers a substantially large key space (approximately $2^{249}$) and exceptional key sensitivity. The scheme generates ciphertexts with nearly uniform histograms, extremely low pixel correlation coefficients (less than 0.04), and high information entropy values (close to 8 bits). Moreover, it demonstrates strong resilience against differential attacks, achieving average NPCR and UACI values of about 99.6% and 33.46%, respectively, while maintaining robustness under data loss conditions. In addition, the proposed framework achieves a high encryption throughput, reaching an average speed of 647.56 Mbps. These results confirm that combining VFO dynamics with PWCAD enriches the chaotic complexity and provides a powerful framework for developing efficient and robust chaos-based image encryption algorithms. Full article

Ocean circulation plays an important role in the formation and occurrence of extreme climate events. The study shows that the periodic variation of ocean circulation under strong wind stress is closely related to climate oscillation. Ocean circulation is a nonlinear dynamic system, which shows complex nonlinear characteristics, so the essence behind ocean circulation has not been clearly explained. Therefore, the response and evolution of the circulation system to wind stress are studied based on the bifurcation and catastrophe theories in nonlinear dynamics. First, the quasi-geostrophic gyre equation and the normalized gravity model are introduced and developed to study ocean circulation driven by wind stress, and solved using the Galerkin method. Then, the bifurcation and catastrophe behaviors of the system governed by the low-order ocean circulation model during the change in wind stress intensity and the coexistence of multiple equilibria in the circulation system are studied in detail. The results show that saddle and unstable nodes appear in the system after a cusp catastrophe. With the change in model parameters, the unstable node becomes the unstable focus, and then the subcritical Hopf bifurcation occurs. The system forms a bistable interval when the system undergoes a catastrophe twice, and the system shows hysteresis. In addition, multiple equilibrium states are coexisting in the circulating system, and the unstable equilibrium state always changes into a stable equilibrium state through vortex movement. Therefore, there is a route for the system to induce short-term climate oscillation, that is, in the multi-stable equilibrium state of the system, the vortex oscillates after being subject to small disturbances, and then triggers climate oscillation. Full article

This paper proposes a novel five-dimensional (5D) memristor-based chaotic system by introducing a flux-controlled memristor into a 3D chaotic system with two stable equilibrium points, and increases the dimensionality utilizing the state feedback control method. The newly proposed memristor-based chaotic system has line equilibrium points, so the corresponding attractor belongs to a hidden attractor. By using typical nonlinear analysis tools, the complicated dynamical behaviors of the new system are explored, which reveals many interesting phenomena, including extreme homogeneous and heterogeneous multistabilities, hidden transient state and state transition behavior, and offset-boosting control. Meanwhile, the unstable periodic orbits embedded in the hidden chaotic attractor were calculated by the variational method, and the corresponding pruning rules were summarized. Furthermore, the analog and DSP circuit implementation illustrates the flexibility of the proposed memristic system. Finally, the active synchronization of the memristor-based chaotic system was investigated, demonstrating the important engineering application values of the new system. Full article

In this work, a novel conservative memristive chaotic system is constructed based on a smooth memristor. In addition to generating multiple types of quasi-periodic trajectories within a small range of a single parameter, the amplitude of the system can be controlled by changing the initial values. Moreover, the proposed system exhibits nonlinear dynamic characteristics, involving extreme multistability behavior of isomorphic and isomeric attractors. Finally, the proposed system is implemented using STMicroelectronics 32 and applied to image encryption. The excellent encryption performance of the conservative chaotic system is proven by an average correlation coefficient of 0.0083 and an information entropy of 7.9993, which provides a reference for further research on conservative memristive chaotic systems in the field of image encryption. Full article

Background: The vast majority of populations are facing growth in the proportion of older persons. Hence, there is an interest in identifying factors associated with longer and healthier life in older ages. Lifestyle, including diet, is crucial for healthy life expectancy, but evidence to support more specific dietary guidelines easily implemented in real life is lacking. Whole grains are specific dietary components with unexplored potential in healthy ageing. Methods: Using an illness-death multistate model approach with a priori chosen confounder control, the association between whole-grain intake and expected time as “healthy” and “with disease” during 20 years of follow-up was assessed. Healthy ageing was defined as the absence of cancer, ischemic heart disease, stroke, type 2 diabetes, asthma, chronic obstructive pulmonary disease, and dementia. Results: Based on data from 22,606 men and 25,468 women from the Diet, Cancer and Health cohort withmean follow-up times of 14 to 17 years, respectively, a doubling in whole-grain intake was associated with 0.43 (95% CI: 0.33–0.52) and 0.15 (0.06–0.24) years more lived without disease, for men and women. When comparing extreme quartiles, men with the highest whole-grain intake lived on average one year more without disease than those consuming the least. Furthermore, whole-grain intake was inversely associated with life expectancy with disease. Conclusions: This study suggests that whole grains are associated with healthy ageing and inversely associated with life expectancy with disease after age 50. These findings should encourage guidelines for increased whole-grain intake, especially among those with low intake, to support disease-free good health in the last part of life. Full article

Pouring is an important process in the production of solid propellant rocket engines, and usually, the cost of a solid propellant rocket engine is extremely high. Therefore, pouring production with high reliability is very important. The pouring of three engines of solid propellant rocket engines simultaneously can greatly improve its production efficiency. However, it makes the system more complex and redundant. For a multi-state system, it is difficult to make an accurate evaluation of system reliability. Aiming at the redundancy of multiple engines and acousto-optic combined control in the three-engine simultaneously slurry pouring alarm control system with dissimilar redundant alarm units, a reliability analysis method is proposed based on the combination of Failure Mode Effect Analysis (FMEA) and Fault Tree Analysis (FTA). The control system is divided into several redundant states according to the alarm function, and then the Bayesian Networks method is used for reliability evaluation and calculation. Finally, the reliabilities of systems with dissimilar redundancy degrees are obtained. The tangible results of this research work are as follows: (1) The research results obtained by applying the FMEA method laid a foundation for the establishment of a fault trees model for analyzing the reliability of the control system using the FTA method, in addition, which can guide the maintenance and fault identification of the control system during engineering application. (2) The calculated value of the reliability of the control system is 0.999989, and the mean time between failures is MTBF is 5 × 10⁴ by using the fault tree analysis method, which proves that the designed three-engine simultaneous pouring system is very reliable. (3) Based on the calculation and comparison of the Bayesian Networks of redundant three-engine pouring control systems, the circuit diagram of the improved control system is identified. Full article

The impact of climate change has led to significant changes in hydroclimatic patterns and continuous stress on water resources through frequent wet and dry spells. Hence, understanding and effectively addressing the escalating impact of climate change on hydroclimatic patterns, especially in the context of meteorological drought, necessitates precise modeling of these phenomena. This study focuses on assessing the accuracy of drought modeling using the well-established Standard Precipitation Index (SPI) in the Aegean region of Türkiye. The study utilizes monthly precipitation data from six stations in Cesme, Kusadasi, Manisa, Seferihisar, Selcuk and Izmir at Kucuk Menderes Basin covering the period from 1973 to 2020. The dataset is divided into three sets, training (60%), validation (20%), and testing (20%) sets. The study aims to determine the SPI-3, SPI-6 and SPI-12 using a multi-station prediction technique. Three boosting regression models (BRMs), namely Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting (GradBoost), were employed and optimized with the help of the Weighted Mean of Vectors (INFO) technique. Model performances were then evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R²) and the Willmott Index (WI). Results demonstrated a distinct superiority of the XgBoost model over AdaBoost and GradBoost in terms of accuracy. During the test phase, the XgBoost model achieved RMSEs of 0.496, 0.429 and 0.389 for SPI-3, SPI-6 and SPI-12, respectively. The WIs were 0.899, 0.901 and 0.825 for SPI-3, SPI-6 and SPI-12, respectively. These are considerably lower than the corresponding values obtained by the other models. Yet, the comparative statistical analysis further underscores the effectiveness of XgBoost in modeling extended periods of drought in the Aegean region of Türkiye. Full article