Search Results (1,748)

The adoption of artificial intelligence (AI) in industrial manufacturing lags behind research progress, partly due to smaller, imbalanced datasets derived from real processes. In non-destructive aerospace testing, this challenge is amplified by the low defect rates of high-quality manufacturing. This study evaluates the use of synthetic data, generated via multiresolution stochastic texture synthesis, to mitigate class imbalance in material defect classification for the superalloy Inconel 718. Multiple datasets with increasing imbalance were sampled, and an image classification model was tested under three conditions: native data, data augmentation, and synthetic data inclusion. Additionally, round robin tests with experts assessed the realism and quality of synthetic samples. Results show that synthetic data significantly improved model performance on highly imbalanced datasets. Expert evaluations provided insights into identifiable artificial properties and class-specific accuracy. Finally, a quality assessment model was implemented to filter low-quality synthetic samples, further boosting classification performance to near the balanced reference level. These findings demonstrate that synthetic data generation, combined with quality control, is an effective strategy for addressing class imbalance in industrial AI applications. Full article

Obligate intracellular parasites must efficiently invade host cells to complete their life cycle and facilitate transmission. For the malaria-causing parasite Plasmodium falciparum, the invasion of an erythrocyte is a critical process, and thereby a key target for intervention strategies. In this study, we investigate the role of the ApiAP2 family transcription factor PfAP2-06B (PF3D7_0613800) in the intraerythrocytic developmental cycle of P. falciparum and focus on its regulation of genes involved in erythrocyte invasion. Conditional knockdown of PfAP2-06B resulted in a defect in asexual growth and impaired erythrocyte invasion. Bulk RNA sequencing (RNA-seq) analysis revealed that PfAP2-06B modulates the expression of invasion-related genes during the schizont stage. Single-cell RNA sequencing indicated that PfAP2-06B influences invasion gene expression and contributes to stochastic variations in expression of cell-to-cell genes. These results underscore the critical function of PfAP2-06B in the process of erythrocyte invasion and suggest its potential as a target for novel malaria control strategies. Importance: Understanding gene regulation in Plasmodium falciparum is essential for uncovering mechanisms of parasite development and pathogenicity. The research underscores the pivotal role of PfAP2-06B in regulating critical aspects of Plasmodium intraerythrocytic development and host cell invasion, demonstrating that PfAP2-06B plays a key role in orchestrating stage-specific gene expression. These findings provide new insights into the transcriptional networks of P. falciparum and highlight PfAP2-06B as a potential target for therapeutic intervention. This work advances our understanding of malaria pathogenesis and developing effective interventions. Full article

This paper proposes a method for assessing societal risk along a traffic link by integrating a stochastic formulation of the fundamental diagram. The approach accounts for uncertainty in vehicle speed due to user heterogeneity, vehicle characteristics, and environmental conditions. The risk index is decomposed into occurrence, vulnerability, and exposure components, with the occurrence probability modeled as a function of stochastic speed. The inverse gamma distribution is adopted to represent speed variability, enabling analytical tractability and control over dispersion. Numerical results show that urban and suburban environments exhibit distinct sensitivity to model parameters, particularly the gamma shape parameter η and the composite parameter c = β · v₀ obtained by the product of the occurrence parameter β and the free speed flow v₀. Graphical representations illustrate the impact of uncertainty on risk estimation. The proposed framework enhances existing deterministic methods by incorporating probabilistic elements, offering a foundation for future applications in traffic safety management and infrastructure design. Full article

In this study, a new method was applied to systematically combine the two controllers, which can help overcome the limitations of non-systematic combinations such as rule-based methods. For the model-based process, the bicycle model was used. Then, the model probability was calculated through the interactive multiple model filtering algorithm, which stochastically determines the most appropriate model that fits the current dynamic situation of the vehicle well. Based on this result, a hybrid path tracking controller was developed using the model probability of each method. The superiority of the proposed method was validated using the MORAI Drive simulator, which reflects the real road environment well enough. The results showed that the RMS tracking performance error was reduced by 6.0–8.8% in quarter-circle path and 3.3% in general path compared to single methods. Full article

This study explores a stochastic guarantee cost control (GCC) for time-varying systems with random parameters and asymmetric saturation actuators by employing the integral reinforcement learning (IRL) method in the dynamic event-triggered (DET) mode. Firstly, a modified Hamilton–Jacobi–Isaac (HJI) equation is formulated, and then the worst-case disturbance policy and the asymmetric saturation optimal control signal can be obtained. Secondly, the multivariate probabilistic collocation method (MPCM) is used to evaluate the value function at designated sampling points. The purpose of introducing the MPCM is to simplify the computational complexity of stochastic dynamic programming (SDP) methods. Furthermore, the DET mode is utilized to solve the SDP problem to reduce the computational burden on communication resources. Finally, the Lyapunov stability theorem is applied to analyze the stability of time-varying systems, and the simulation shows the feasibility of the designed method. Full article

This paper addresses the critical multiple-uncertainty challenge in laser link acquisition for space gravitational wave detection missions—a key bottleneck where spacecraft attitude alignment for laser link establishment is perturbed by inherent random disturbances in such missions, while also needing to balance ultra-high attitude precision, fuel efficiency, and compliance with engineering constraints. To tackle this, a convex optimization-based attitude control strategy integrating covariance control and free terminal time optimization is proposed. Specifically, a stochastic attitude dynamics model is first established to explicitly incorporate the aforementioned random disturbances. Subsequently, an objective function is designed to simultaneously minimize terminal state error and fuel consumption, with three key constraints (covariance constraints, pointing constraints, and torque saturation constraints) integrated into the convex optimization framework. Furthermore, to resolve non-convex terms in chance constraints, this study employs a hierarchical convexification method that combines Schur’s complementary theorem, second-order cone relaxation, and Taylor expansion techniques. This approach ensures lossless relaxation, renders the optimization problem computationally tractable without sacrificing solution accuracy, and overcomes the shortcomings of traditional convexification methods in handling chance constraints. Finally, numerical simulations demonstrate that the proposed method adheres to engineering constraints while maintaining spacecraft attitude errors below 1 μrad under environmental uncertainties. This study provides a convex optimization solution for laser link acquisition in space gravitational wave detection missions considering uncertainty conditions, and its framework can be extended to the optimal design of other stochastically uncertain systems. Full article

The challenge of optimally controlling energy storage systems under uncertainty conditions, whether due to uncertain storage device dynamics or load signal variability, is well established. Recent research works tackle this problem using two primary approaches: optimal control methods, such as stochastic dynamic programming, and data-driven techniques. This work’s objective is to quantify the inherent trade-offs between these methodologies and identify their respective strengths and weaknesses across different scenarios. We evaluate the degradation of performance, measured by increased operational costs, when a reinforcement learning policy is adopted instead of an optimal control policy, such as dynamic programming, Pontryagin’s minimum principle, or the Shortest-Path method. Our study examines three increasingly intricate use cases: ideal storage units, storage units with losses, and lossy storage units integrated with transmission line losses. For each scenario, we compare the performance of a representative optimal control technique against a reinforcement learning approach, seeking to establish broader comparative insights. Full article

This work examines the mechanical behaviour of 3D-printed stochastic lattice structures fabricated using a semi-controlled design. A primary goal is to predict and optimize the mechanical response of these Acrylic Styrene Acrylonitrile (ASA) filament structures when subjected to compressive stress. By transitioning from a purely stochastic method to a semi-controlled tessellation approach within Rhinoceros 7 software, we effectively generated the proposed design models. This methodology results in mechanical responses that are both predictable and reliable. The design parameters, including nodal formation, strut thickness, and lattice generation based on a predefined geometric routine, are associated with the regulation of the relative density. This approach aims to minimize the effect of relative density on the actual stiffness and strength evaluation. Our findings are cantered on the compressive testing of structures, which were generated using a Voronoi population distributed along a parabolic curve. We analyzed their mechanical response to the point of failure by examining stress–strain fluctuations. Three distinct behaviour stages are observed: elastic range, plastic range, and collapse without densification. The influence of crosslink geometry on the elastic responses was highlighted, with parabolic configurations affecting the peak stresses and elastic line slopes. The structures exhibited purely brittle behaviour, characterized by abrupt local cracking and oscillatory plateau formation in the plastic stage. Full article

We study stochastic production planning in capacity-constrained manufacturing systems, where feasible operating states are restricted to a convex safe-operating region. The objective is to minimize the total cost that combines a quadratic production effort with an inventory holding cost, while automatically halting production when the state leaves the safe region. We derive the associated Hamilton–Jacobi–Bellman (HJB) equation, establish the existence and uniqueness of the value function under broad conditions, and prove a concavity property of the transformed value function that yields a robust gradient-based optimal feedback policy. From an operations perspective, the stopping mechanism encodes hard capacity and safety limits, ensuring bounded risk and finite expected costs. We complement the analysis with numerical methods based on finite differences and illustrate how the resulting policies inform real-time decisions through two application-inspired examples: a single-product case calibrated with typical process-industry parameters and a two-dimensional example motivated by semiconductor fabrication, where interacting production variables must satisfy joint safety constraints. The results bridge rigorous stochastic control with practical production planning and provide actionable guidance for operating under uncertainty and capacity limits. Full article

In real-world applications, finite time convergence to a desired Lyapunov stable equilibrium is often necessary. This notion of stability is known as finite time stability and refers to systems in which the state trajectory reaches an equilibrium in finite time. This paper explores the notion of finite time stability in probability within the context of nonlinear stochastic dynamical systems. Specifically, we introduce sufficient conditions based on Lyapunov methods, utilizing Lyapunov functions that satisfy scalar differential inequalities involving fractional powers for guaranteeing finite time stability in probability. Then, we address the finite time optimal control problem by developing a framework for designing optimal feedback control laws that achieve finite time stochastic stability of the closed-loop system using a Lyapunov function that also serves as the solution to the steady-state stochastic Hamilton–Jacobi–Bellman equation. Full article

Dynamic state estimation of floating offshore wind turbines (FOWTs) in complex marine environments is a core challenge for digital twin systems. This study proposes a joint estimation framework that integrates windowed dynamic mode decomposition (W-DMD) and an adaptive strong tracking Kalman filter (ASTKF). W-DMD extracts dominant modes under stochastic excitations through a sliding-window strategy and constructs an interpretable reduced-order state-space model. ASTKF is then employed to enhance estimation robustness against environmental uncertainties and noise. The framework is validated through numerical simulations under turbulent wind and wave conditions, demonstrating high estimation accuracy and strong robustness against sudden environmental disturbances. The results indicate that the proposed method provides a computationally efficient and interpretable tool for FOWT digital twins, laying the foundation for predictive maintenance and optimal control. Full article

Soil bacterial communities are crucial drivers of nutrient cycling and ecosystem functioning; however, their temporal dynamics under arbuscular mycorrhizal (AM) fungi colonization remain insufficiently characterized. In this study, we used a non-destructive continuous sampling method and undertook a 90-day pot experiment to examine the process of shaping the bacterial community of hyphosphere soil. Following inoculation with AM fungi, we found an increase in the α-diversity index of the hyphosphere bacterial community. The community diversity and richness and the key bacterial taxa in the hyphosphere both gradually increased from 30 to 60 days and stabilized thereafter. Principal coordinated (PCoA) analysis and network analysis further confirmed these findings. Stabilized by 60 days post-inoculation, with deterministic processes dominating assembly in inoculated AM fungi soils, while stochastic processes prevailed in non-inoculated controls. Inoculation strengthened bacterial associations with available phosphorus, while making the key bacterial communities more responsive to multiple soil physicochemical properties (available P, CEC, N, and TOC). These findings provide critical insights into AM fungi mediation of soil microbiome dynamics, with the identified 60-day stabilization period offering a key temporal framework for understanding tripartite soil–AM fungi-bacteria interactions. Full article

Achieving carbon peaking and neutrality is critical for global sustainability efforts and addressing climate change, yet improving land use carbon emission efficiency (LUCE) remains a challenge, especially in resource-dependent regions like Shanxi Province. Existing studies often overlook the spatial heterogeneity of LUCE and the mechanisms behind its driving factors. This study assesses LUCE disparities and explores low-carbon land use pathways in Shanxi to support its sustainable transition. Based on county-level land use data from 1990 to 2022, carbon emissions were estimated, and LUCE was measured using a three-stage super-efficient SBM-DEA model, with stochastic frontier analysis (SFA) to control for external noise. eXtreme Gradient Boosting (XGBoost) with SHAP values was used to identify key socio-economic and environmental drivers. The results show the following: (1) emissions rose 2.46-fold, mainly due to expanding construction land and shrinking cultivated land, with hotspots in Taiyuan, Jinzhong, and Linfen; (2) LUCE improved due to gains in technical and scale efficiency, while pure technical efficiency stayed stable; (3) urbanization and government intervention promoted LUCE, whereas higher per capita GDP constrained it; and (4) population density, economic growth, urbanization, and green technology were the dominant, interacting drivers of land use carbon emissions. This study integrates LUCE assessment with interpretable machine learning, demonstrating a framework that links efficiency evaluation with driver analysis. The findings provide critical insights for formulating regionally adaptive low-carbon land use policies, which are essential for achieving ecological sustainability and supporting the sustainable development of resource-based regions. Full article

In this study, a nonlinear dynamic model is developed to examine the stability and vibration behavior of a self-balancing electric Segway operating over irregular stochastic terrains. The Segway is treated as a three-degrees-of-freedom cart–inverted pendulum system, incorporating elastic and damping effects at the wheel–ground interface. Road irregularities are generated in accordance with international standard using high-order filtered noise, allowing for representation of surface classes from smooth to highly degraded. The governing equations, formulated via Lagrange’s method, are transformed into a Lorenz-like state-space form for nonlinear analysis. Numerical simulations employ the fourth-order Runge–Kutta scheme to compute translational and angular responses under varying speeds and terrain conditions. Frequency-domain analysis using Fast Fourier Transform (FFT) identifies resonant excitation bands linked to road spectral content, while Kernel Density Estimation (KDE) maps the probability distribution of displacement states to distinguish stable from variable regimes. The Lyapunov stability assessment and bifurcation analysis reveal critical velocity thresholds and parameter regions marking transitions from stable operation to chaotic motion. The study quantifies the influence of the gravity–damping ratio, mass–damping coupling, control torque ratio, and vertical excitation on dynamic stability. The results provide a methodology for designing stability control systems that ensure safe and comfortable Segway operation across diverse terrains. Full article

Accurate prediction of component content in the rare-earth extraction and separation process is crucial for control system design, product quality control, and optimization of energy consumption. To improve prediction accuracy and modeling efficiency, this paper proposes a model for predicting component content based on an Improved Black-winged Kite Algorithm-Optimized Stochastic Configuration Network (IBKA-SCN). First, we develop an Improved Black-winged Kite Algorithm (IBKA), incorporating good point set initialization and Lévy random-walk strategies to enhance global optimization capability. Theoretical convergence analysis is provided to ensure the stability and effectiveness of the algorithm. Second, to address the issue that constraint parameters and weight-scaling factors in Stochastic Configuration Network (SCN) rely on manual experience and struggle to balance accuracy and efficiency, IBKA is employed to adaptively search for the optimal hyperparameter combination. The applicability of IBKA-SCN is corroborated through four real-world regression tasks. Finally, the effectiveness of the proposed method is validated through an engineering case study on predicting component content. The results show that IBKA-SCN significantly outperforms existing mainstream methods in both prediction accuracy and modeling speed. Full article