Search Results (10,968)

This study introduces a Caputo fractional-order version of the SEIRS-V model to investigate the spreading dynamics of worms within wireless sensor networks. Traditional integer-order worm propagation models describe the instantaneous evolution of network states; however, they do not adequately account for memory and hereditary characteristics that may influence the transmission dynamics. Consequently, their ability to represent realistic network behavior can be limited in systems where past states affect current propagation patterns. The framework divides sensor nodes into susceptible, exposed, infectious, recovered, and vaccinated classes, while explicitly incorporating worm transmission rates, temporary loss of immunity, and the impact of preventive security measures under limited resource conditions. A detailed theoretical examination is performed, covering the existence, boundedness, and uniqueness of solutions of the fractional-order system. The coupled nonlinear fractional system is solved semi-analytically by means of the Fractional Reduced Differential Transform (FRDT) technique. To confirm accuracy and robustness, the identical system is also discretized and solved using the finite difference scheme (FDS). Unlike previous studies on worm propagation models in wireless sensor networks, which are mainly limited to equilibrium point analysis and qualitative investigations without deriving explicit solutions, the present work develops an approximate semi-analytical solution for the fractional-order SEIRS-V system using the FRDTM. Comparisons between the two solution sets demonstrate excellent agreement and high precision. Numerical outcomes are presented through a series of 2D graphical profiles that illustrate the time-dependent behavior of each compartment and reveal the sensitivity of worm propagation and suppression to variations in the fractional order and key model parameters. The integrated theoretical and computational findings underscore the strong protective role of vaccination in mitigating worm outbreaks and offer valuable guidelines for strengthening cybersecurity measures in wireless sensor networks. Full article

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

The high dropout rate in massive open online courses (MOOCs) continues to limit their potential in promoting inclusive and sustainable learning. Although many prediction models have been used to identify potential dropouts, most studies view dropout as a static classification problem and fail to clearly reveal the dynamic trajectory of learner participation over time. Therefore, this study introduces a phased analysis perspective, treating MOOC dropout as a process that continuously evolves at different stages. On the basis of the KDDCUP2015 dataset, we constructed behavioral characteristics at three time points: the first week, the third week, and the fifth week. By combining robust feature analysis and interpretable models, we systematically examined the changing patterns of dropout modes. The results revealed significant differences across the different stages. In the early stage of the course, dropout was related mainly to the unstable interaction behaviors of learners, such as restricted access to resources and irregular participation rhythms. In the middle and late stages, task-oriented behaviors, especially those related to video-based learning activities, gradually became key factors. Notably, high-frequency video participation does not always reduce the risk of dropout; when video activity is high but the overall interaction rate is low, it is more likely to indicate an increase in the risk of dropout. These results indicate that the combination of behaviors is more crucial than mere activity levels. By revealing the changing characteristics of behaviors at different stages, this study helps support the design of more practical early warning methods. Full article

The health system is now more complex, uncertain, interdependent, and dynamically interconnected than ever, making traditional planning decisions based on static, reductionist models increasingly impracticable. Systems analytics approaches such as system dynamics, agent-based modeling, and network analysis are often deployed in isolation and fail to capture cross-level interactions and emergent system behavior. This study proposes an integrated multi-layer systems analytics framework that combines these analytical paradigms within a unified architecture to support adaptive health systems planning under uncertainty. The proposed framework introduces an Adaptive Policy Intelligence Layer (APIL), which enables continuous feedback-driven policy adaptation through dynamic monitoring, scenario evaluation, and real-time adjustment mechanisms. The model is evaluated under multiple simulation scenarios, including baseline conditions, demand shocks, resource constraints, and digital transformation environments. The findings provide strong quantitative and analytical evidence of improved system performance and resilience. More specifically, the digital transformation scenario achieved the lowest mean system pressure (0.128) and the highest resilience index (0.887), while the demand shock scenario produced the highest peak system pressure (0.306). The results demonstrate enhanced system resilience, more efficient resource deployment, and superior policy responsiveness compared with traditional single-method approaches. The originality of this study lies in integrating multi-level systems analytics with adaptive policy intelligence into a unified, feedback-driven decision-support framework for resilient health systems governance. The study contributes to systems analytics literature by advancing a synergistic and adaptive modeling paradigm capable of supporting policymakers in navigating complex and unstable healthcare environments. Full article

This paper proposes a generalized seasonal beta prime autoregressive moving average model with dynamic precision, denoted by BPSARMA, for modeling and forecasting positive-valued seasonal time series. The proposed framework extends the generalized BPARMA model by incorporating stochastic seasonal dynamics in the conditional mean through seasonal autoregressive and moving average components while allowing a flexible autoregressive structure for the conditional precision parameter, thereby accommodating time-varying uncertainty. The model also allows the inclusion of covariates and deterministic seasonal regressors. Parameter estimation is carried out by conditional maximum likelihood, and the main inferential and diagnostic tools are discussed. Monte Carlo simulations are conducted to examine the finite-sample behavior of the estimators and associated inference procedures. The practical usefulness of the proposed approach is illustrated through hydro-environmental time series applications, where its forecasting performance is evaluated using both in-sample and out-of-sample predictive measures. The empirical results indicate that the BPSARMA specification often provides competitive or superior forecasting accuracy relative to competing models, highlighting its usefulness for modeling and prediction in positive seasonal time series. Full article

Analytical solutions serve as primary benchmarks for verifying model test design, provide rapid predictive tools for preliminary design, and offer fundamental physical understanding of complex structure interaction problems of the geological body. It is essential for ensuring the reliability of experimental results. For the study on slope stability under earthquakes, the seismic behavior of key inclined cracks in the slope is a hot topic, which is a crucial issue in rock mechanics and engineering geomechanics. This paper studies the dynamic propagation of the inclined crack under seismic conditions, proposes the analytical solution of fracture mechanics, and conducts a centrifuge shaking table test accordingly for monitoring and validation. The analytical solution results have been validated experimentally by a centrifuge shaking table test on the seismic behavior of an inclined crack. Results indicate that the amplitude of seismic waves significantly affects crack propagation: the greater the amplitude, the faster the propagation rate. Analysis of crack propagation and maximum surface displacement reveals hysteresis and sudden jumps of surface deformation caused by rock mass structure and locked segments, both in indoor tests and in strong earthquake regions. This paper combines a theoretical and experimental monitoring study, providing a good example of integrating analytical solutions and modeling validation for research on earthquake-induced landslide disasters. Full article

Background: Agricultural logistics in Kazakhstan is critical for export-oriented supply chains, but its resilience is limited by infrastructure constraints, fluctuating export demand, and insufficient coordination between market and logistics processes. Methods: This study develops a conceptual multi-level model of the agricultural logistics system and a hybrid simulation model combining system dynamics and discrete-event simulation to analyze intermodal transportation under demand and capacity constraints. The model integrates demand formation, storage, transport, and export operations, as well as feedback mechanisms between fulfilled demand, repeat orders, and logistics performance. The model is implemented in AnyLogic 8.9. Results: The conceptual model structures the interaction of key participants, logistics facilities, and infrastructure levels within Kazakhstan’s agricultural logistics system. Simulation experiments reproduce cyclic logistics behavior and show that reduced logistics capacity increases the demand gap and system pressure, while stronger market signals intensify demand and infrastructure load. The results confirm that resilience depends on the balance between demand activation, logistics capacity, and replenishment policy. Conclusions: The proposed approach provides a tool for analyzing the resilience of agricultural intermodal logistics in Kazakhstan and supports scenario-based evaluation of infrastructure and market factors. The novelty lies in combining a conceptual multi-level logistics model with hybrid simulation of demand and logistics flows. Full article

Indoor Air Quality (IAQ) monitoring, especially in multi-zone smart buildings, is typically limited by the high computational and energy requirements of continuous sensor processing, which makes event-driven methods desirable for efficiency. Energy proportionality, in this context, refers to a system whose computational cost scales with the significance of detected environmental changes rather than with the fixed sampling rate. This paper presents a spike-driven neuromorphic sensing framework for decentralized IAQ monitoring that combines adaptive Kalman filter preprocessing, dynamic threshold-based asynchronous spike encoding, and a Leaky Integrate-and-Fire neural network with Spike-Timing-Dependent Plasticity (STDP) learning. Multiple-parameter IAQ data including PM₁, PM_2.5, PM₁₀, CO₂, CO, TVOCs, and O₃ were sampled from nine functionally differing zones of an educational building in Metro Manila, Philippines. The neuromorphic model yielded a mean Sparse Firing Ratio of 10.94%, a Mean Response Time of 10.62 timesteps, and an energy efficiency proxy score of 9.28. Neuron population scaling and parameter robustness analyses revealed that the four neurons per parameter were enough to saturate the performance, and FLOP-based estimation indicated an 8.9-fold computational reduction (approximately 89% fewer FLOPs) compared to LSTM inference. In addition, the revised Performance Efficiency Index and composite efficiency score corroborated the stable and energy-proportional nature of behavior in all zones. These results illustrate that spike-based neuromorphic computation is an energy-efficient and scalable way for decentralized smart-building IAQ monitoring, though hardware-level validation on dedicated neuromorphic processors remains necessary for absolute power saving verification. Multi-seed validation (five seeds) with expanded baselines including GRU, Temporal CNN, XGBoost, and Logistic Regression confirmed the robustness and repeatability of reported metrics. Full article

The blast furnace–basic oxygen furnace long process is the dominant steel production route in China. Increasing the scrap ratio is an effective way to reduce cost and carbon emissions, and scrap preheating is a key technology to achieve a high scrap ratio. To improve the low thermal efficiency and poor deep-bed melting performance of converter gas-based scrap preheating, an innovative process using oxygen-enriched combustion in a hot metal ladle is proposed. Numerical simulation is essential for capturing the complex multiphysics phenomena, as real-time monitoring of melting inside the packed scrap bed is extremely difficult. In this study, a novel multiphysics approach based on a User-Defined Function (UDF) is developed to dynamically track the progressive melting of the scrap skeleton, overcoming the key limitation of conventional enthalpy–porosity models that cannot capture the feedback between phase change and porous medium property evolution. A three-dimensional transient model was established, integrating turbulent combustion, gas–solid convective heat transfer in porous media, and solid–liquid phase change. The effects of impact pit depth, scrap porosity, and converter gas flow rate on temperature distribution, melting behavior, and thermal efficiency were systematically investigated. Results showed that porosity had the strongest influence; thermal efficiency increased from 33.92% to 65.59% as porosity rose from 0.6 to 0.8, due to a transition from conduction-dominated to coupled convection–conduction heat transfer. Converter gas flow rate exhibited a non-monotonic effect, peaking at 3688.14 m³·h⁻¹, highlighting a trade-off between energy input and gas residence time, while impact pit depth showed a limited effect with diminishing returns. A 600 s full-process simulation revealed stage-dependent melting, and the initial phase was crucial for process optimization. The optimal condition, with a pit depth of 64 cm, porosity of 0.8, and converter gas flow rate of 3688.14 m³·h⁻¹, achieved a 1.23% melting fraction and 65.59% thermal efficiency within 120 s. These findings clarify the combined roles of geometric confinement, permeability, and energy-residence time interactions, providing guidance for industrial scrap preheating design. Full article

To address the problems of communication delay and waste of communication resources in the formation process of UAVs, a dynamic event-triggered formation control method for second-order multi-leader UAV systems with communication delay is studied. On the basis of considering the communication delay, a dynamic triggering mechanism is designed. By adjusting the triggering time in real time, the system can be more effectively controlled based on its current state. According to the control method, the mathematical models for the extended state observer, controller, and dynamic event-triggering function of the system have been established. Its stability is demonstrated by Lyapunov stability theory and linear matrix inequality theory, and Zeno behavior is excluded. The simulation results show that compared with the existing methods, the proposed method can avoid the dependence on the global information of the network topology, reduce the communication frequency, and effectively save communication resources. Full article

As augmented reality head-up display (AR-HUD) becomes increasingly integrated into intelligent vehicles, inappropriate interface designs may increase drivers’ cognitive workload and delay hazard responses. This study investigates how cognitive style, driving task type, and AR-HUD navigation design jointly influence drivers’ behavioral performance and visual attention. A total of 55 participants were recruited and screened using the Group Embedded Figures Test, with 38 drivers finally selected for a 2 × 4 × 2 driving-simulation experiment comparing world-fixed (WF) and screen-fixed (SF) interfaces across goal-directed and stimulus-driven tasks. Reaction times and eye-tracking indicators were analyzed using generalized linear models. Results show that stimulus-driven tasks significantly increased reaction times, with rear-vehicle scenarios producing the longest responses (mean = 1.420). During lane-change tasks, WF displays significantly reduced fixation duration (p < 0.001) and fixation counts (p < 0.001), whereas SF displays improved attentional efficiency during pedestrian-warning tasks. In addition, field-dependent drivers exhibited significantly larger pupil diameters, indicating higher cognitive workload. These findings provide sensor-based evidence for AR-HUD systems that dynamically optimize interface presentation according to task context and workload conditions. Full article

A novel modified generalized Riccati equation mapping neural network-based approach is the basic theme of this study by exploring the nonlinear dynamical characteristics of the the strain wave model’s soliton solutions, which govern wave propagation in micro structured solids. Strain waves are particularly intriguing, since they preserve their form and speed throughout transmission. The nonlinear dynamical behaviors of strain waves may be modeled by partial differential equations in micro structured materials. In the realm of micro structured solids, there exists a class of phenomena that are referred to as micro strain waves. These waves arise in solids possessing intricate internal architectures, including periodic lattices, precisely engineered metamaterials Understanding these waves is key to designing more complex materials and new acoustic technologies. The activation function and the weight function of the neural network are assigned to each input layer, hidden layer and output layer and the neural network itself is a multi-layer computational network. Using the structure of the neural network, every neuron in the first hidden layer is given solutions to the Riccati equation, and the new highly expressive trial functions are generated in a systematic way. In this way, a large variety of exact soliton solutions are obtained, such as bright, dark, kink, and combined solitons as well as periodic and hyperbolic wave profiles. The influence of the essential physical and mathematical parameters is explored systematically using three-dimensional, two-dimensional and contour visualizations, which illustrate how parameter variations lead to changes in the amplitude, shape and stability of the wave structures. The solutions presented reveal the dynamic properties of micro strain solitons which leads to new avenues of investigation in the study of related nonlinear phenomena in micro structured solids. In a broader context, our results highlight the great potential of analytical techniques using neural networks as a powerful and versatile toolset to study complex nonlinear wave models within the applied sciences from acoustics to photonics to smart materials engineering. Full article

With the increase in operation time of DC traction systems due to the environment of tunnel and stress rupture, the insulation between the rail and ground inevitably decreases, causing increased stray current leakage. In view of this, we present an analytical and electrochemical study of stray current behavior and its corrosion impact arising from local rail-to-ground insulation damage in DC urban rail systems. A two-layer rail–earth continuous model of stray current distribution is developed (unilateral and bilateral supply cases) using Kirchhoff network formulations with insulation damage boundary conditions. Numerical simulations quantify the effects of damage location and grounding resistance on rail potential shifts, abrupt changes in rail and stray currents, and total leakage. To assess electrochemical consequences for nearby buried pipelines, the electrical model is proposed in this work with an impedance-informed corrosion model and Monte Carlo sampling of operational and electrical uncertainties to estimate dynamic corrosion rates and pitting evolution. The results show that single–point insulation faults shift the rail zero potential toward the fault, leading to instantaneous jumps in leakage and rail currents whose magnitude grows as damaged-point resistance decreases, markedly increasing pipeline corrosion risk. The integrated electrical-electrochemical framework provides a tool for detection, risk assessment, and mitigation planning for stray current-induced pipeline corrosion. Full article

Data-driven models can be used to understand basin-wide hydrological processes and generate predictions for future conditions, particularly in cases of scarce data availability related to basin characteristics. Although they have long been applied in hydrological modeling, there is still limited information regarding their ability to produce reliable long-term projections under climate change conditions. This study evaluates the long-term predictive performance of data-driven models by employing a hybrid deep learning architecture combining Wavelet Transform (WT) and Deep Neural Network (DNN). The dataset used in this study was obtained from the Yamula Reservoir Basin, a semi-arid agricultural basin in Türkiye. Monthly streamflow was simulated based on climate projection data from the HadGEM2-ES model under the RCP4.5 and RCP8.5 scenarios. Results showed that the WT–DNN framework was successful in learning the system dynamics and reproducing observed streamflow behavior. The model produced continuous projections for the future period; however, these projections should be interpreted with caution due to the increasing uncertainty associated with long-term climate forcing and the sensitivity of data-driven approaches to shifts in climatic and hydrological regimes. Full article

This article presents the synthesis, implementation, and experimental study of a PI-based adaptive actor–critic displacement volume controller of an axial-piston pump intended for open-loop circuit hydraulic drive systems. The proposed control structure combines a conventional PI actor with an adaptive critic that estimates the infinite-horizon cost through Bellman-error minimization. By using the tracking error and its integral as actor inputs, the controller avoids the need for an accurate plant model while retaining a compact and practically implementable structure. The adaptive laws are derived using gradient-based learning, and a Lyapunov-based analysis establishes closed-loop stability for sufficiently small adaptation gains. The controller is implemented in a fixed-step Simulink^® environment and deployed on a rapid prototyping platform with real-time communication to an industrial microcontroller and proportional valve amplifier. The experimental results obtained under four fixed loading conditions and dynamic load variations demonstrate a stable operation, bounded critic behavior, and a near-zero Bellman error during learning. Comparative tests against a classical PI controller, a Lyapunov-based model reference adaptive controller, and a generic actor–critic scheme show that the proposed PI-based actor–critic achieves the lowest performance index and the shortest settling times in most cases. Full article