Search Results (132)

Minimizing the impacts of coupling, randomness, time variation and uncertain nonlinearity to enhance real-time performance is critical for controlling complex industrial systems. This paper proposes an optimized adaptive filtering method (LAF-MTNF) for time-varying uncertain nonlinear systems with multiple-input multiple-output (MIMO) measurement noise, which integrates the multi-dimensional Taylor network (MTN) with Lyapunov stability theory (LST). Leveraging MTN’s inherent advantages—simple structure, linear parameterization, and low computational complexity—LAF-MTNF achieves efficient real-time filtering while avoiding the exponential computation burden of neural networks. The contributions of this work are threefold: (1) A novel integration of LST and MTN is proposed for MIMO filtering, in which an energy space is constructed with a unique global minimum to eliminate local optimization traps, addressing the stability deficit of traditional MTN filters using LMS/RLS algorithms. (2) Convergence performance is systematically quantified by deriving explicit expressions for the error convergence rate (regulated by a positive constant) and convergence region (a sphere centered at the origin) while modifying adaptive gain to avoid singularity, filling the gap of incomplete performance analysis in existing Lyapunov-based filters. (3) The design is disturbance-independent, relying only on input/output measurements and requiring no prior knowledge of noise statistics, thus enhancing robustness to unknown industrial disturbances. We systematically analyze the Lyapunov stability of LAF-MTNF, and simulations on a complex MIMO system verify that it outperforms existing methods in filtering precision (mean error 0.0227 vs. 0.0674 of RBFNN) and dynamic response speed, while ensuring asymptotic stability and real-time applicability. The proposed LAF-MTNF method achieves significant advantages over traditional adaptive filtering methods in filtering accuracy, convergence speed and anti-cross-coupling capability. This method has broad application prospects in high-precision industrial servo motion control, power system state monitoring and other multi-variable nonlinear industrial scenarios with complex noise environments. Full article

With the acceleration of the global energy transition, the photovoltaic industry has become a significant force in the promotion of green development, and photovoltaic raw materials play a crucial role in this process. In this paper, 177 countries during the period of 2001 to 2024 were taken as the research subjects, with a focus on polysilicon and silicon wafers as components of upstream photovoltaic raw materials. Through a combination of the evolutionary analysis of nodes, the overall structure, and the three-dimensional structure with an exponential random graph model, the evolution and dynamic mechanisms of the global photovoltaic raw material trade network are explored. The study reveals the following: (1) The global PV raw material trade volume tended to increase from 2001 to 2024. (2) The global photovoltaic raw material trade network showed a tendency towards the “enhanced dominance of core countries and denser trade connections,” with the trade volume between core countries continuously expanding and the network density, average clustering coefficient, and connection efficiency increasing annually, which is a reflection of the globalization and regional cooperation of the global photovoltaic industry. (3) From the weighted out-degree and in-degree ranking evolution of the global photovoltaic raw materials trade network, it can be seen that China consolidated its core position, while Southeast Asian countries tended to transfer their processing and manufacturing links. The status of the United States and traditional industrial powers gradually declined, which is a reflection of the restructuring of the global industrial chain along with regional geopolitical agglomeration effects. (4) Internal attributes such as the national economic level, population size, and urbanization rate, as well as external network effects such as common language and geographical proximity, significantly influence the formation path of the photovoltaic raw material trade network. Moreover, the network exhibits distinct heterogeneous complementarity mechanisms and path dependence characteristics, with a structural evolution that tends toward stability and cooperative relationships showing significant time inertia. Overall, the global trade volume of photovoltaic raw materials continues to grow, and the core positions of major countries such as China, the United States, and Germany remain prominent but show a transitional trend towards Southeast Asian countries. The strengthening of the level of coordination and cooperation among global photovoltaic raw material producers to ensure supply chain stability, promote resource sharing and technological progress, and achieve the sustainable development of green energy policies is necessary. Full article

This paper investigates the stability of nonlinear systems with truncated $\mathcal{V}$-fractional-order derivatives. Initially, based on the fundamental properties of $\mathcal{V}$-fractional calculus, the Bellman–Gronwall inequality for $\mathcal{V}$-fractional $\alpha$-differentiable functions is derived. Subsequently, several sufficient conditions for the stability of the considered systems are established via the Lyapunov direct method. For practical applications, multiple synchronization criteria for drive-response systems are further deduced by leveraging the aforementioned stability results. Finally, numerical examples are presented to verify the effectiveness and feasibility of the main theoretical findings. Full article

Underwater micro-bubble detection entails multiple challenges, including diminutive target sizes, sparse pixel information, pronounced specular highlights and water scattering, indistinct bubble boundaries, and adhesion or overlap between instances. To address these issues, we propose HSMD-YOLO, an improved detector tailored for high-resolution micro-bubble detection and built upon YOLOv11. The model incorporates three novel components: the Scale Switch Block (SSB), a scale-transformation module that suppresses artifacts and background noise, thereby stabilizing edges in thin-walled bubble regions and enhancing sensitivity to geometric contours; the Global Local Refine Block (GLRB), which achieves efficient global relationship modeling with an asymptotic linear complexity ($O\left(N\right)$) in spatial dimensions while further refining local features, thereby strengthening boundary perception and improving bubble–background separability; and the Bidirectional Exponential Moving Attention Fusion (BEMAF), which accommodates the multi-scale nature of bubbles by employing a parallel multi-kernel architecture to extract spatial features across scales, coupled with a multi-stage EMA based attention mechanism to enhance detection robustness under weak boundaries and complex backgrounds. Experiments conducted on an Side-Illuminated Light Field Bubble Database (SILB-DB) and a public gas–liquid two-phase flow dataset (GTFD) demonstrate that HSMD-YOLO achieves mAP@50 scores of 0.911 and 0.854, respectively, surpassing mainstream detection methods. Ablation studies indicate that SSB, GLRB, and BEMAF contribute performance gains of 1.3%, 2.0%, and 0.4%, respectively, thereby corroborating the effectiveness of each module for micro-scale object detection. Full article

This paper proposes an impulsive SIRQ model for the analysis of computer network resilience against malware propagation and distributed denial-of-service (DDoS) attacks. The model extends classical epidemic frameworks by combining the continuous-time dynamics of malicious object spreading with discrete control actions corresponding to mass updates, node isolation, and access control policies. A qualitative analysis of the resulting system of impulsive differential equations is performed. The basic reproduction number ${\mathcal{R}}_0$, identified as a threshold parameter characterizing the intensity of attack propagation, and sufficient conditions for the global asymptotic stability of the infection-free state are established. It is shown that, under periodic impulsive control, the infection-free state can be stabilized with respect to the target population coordinates even when ${\mathcal{R}}_0>1$. An exponential decay estimate for the total active threat is derived, guaranteeing the asymptotic extinction of the infected and quarantined node populations. The proposed approach provides quantitative criteria for the effectiveness of impulsive cyber defense strategies and offers a theoretical foundation for the design of adaptive multi-layer protection systems for critical information infrastructures. Practical interpretation of the results illustrates the dependence of the critical impulsive control period on the model parameters and demonstrates the applicability of the approach to cybersecurity strategy design. Full article

Background: Revision anterior cruciate ligament reconstruction (ACLR) presents an increasing clinical challenge with higher failure rates than primary reconstruction. However, the evolving research landscape and clinical evidence regarding key biomechanical risk factors remain incompletely synthesized. Methods: A sequential dual methodological approach was applied. First, a bibliometric analysis of the Web of Science Core Collection was performed to map global research trends and identify emerging hotspots. Based on the identified hotspot, a PRISMA-compliant meta-analysis of studies retrieved from PubMed, Embase, the Cochrane Library, and Web of Science was subsequently conducted. Clinical and radiographic outcomes were synthesized using random-effects models in R. Results: The bibliometric analysis included 4213 publications and demonstrated exponential growth in revision ACLR research, identifying posterior tibial slope (PTS) as the dominant research hotspot. A meta-analysis of 11 studies involving 299 patients showed significant postoperative improvements in patient-reported outcomes and objective knee stability measures, along with a mean PTS reduction of 8.72° (95% CI 7.84–9.60; p < 0.001), while no significant change in patellar height was observed. The pooled return-to-sport rate was 74% (95% CI 64–82%), and the most common complications were symptomatic hardware and postoperative recurvatum. Conclusions: PTS has emerged as a key focus in revision ACLR research, and addressing this biomechanical factor may be associated with improved functional and radiographic outcomes. However, current evidence is mainly derived from retrospective studies, and further prospective research is needed to confirm long-term efficacy and refine surgical indications. Full article

This study constructs a global transportation carbon emission spatial correlation network via a modified gravity model and explores its evolutionary characteristics and dynamic mechanisms by integrating three-dimensional evolutionary analysis (node, overall, structural) and temporal exponential random graph model (TERGM). The main findings are as follows: (1) Global transportation carbon emission spatial correlation intensity keeps rising, with improved connectivity and integration, forming three regionally agglomerated correlation poles centered on the United States (America), China (Asia) and major European countries (Europe). (2) Network centrality distributes asymmetrically: Switzerland, Norway and the United States remain core nodes, while China, Japan and other Asian economies with strong direct correlation radiation are not in the core tier. (3) Third, evolutionary dynamics stem from the synergistic interaction of multidimensional attributes. ① Economic level positively drives bidirectional connection emission and attraction; economic scale and openness curb emission but boost attraction, while tertiary industry structure inhibits both. ② Only economic level and government efficiency exert significant positive effects on absdiff, fostering network heterophilic attraction. ③ Spatial and institutional proximity in edgecov effectively facilitate connection formation. ④ Endogenous network variables present a collaborative mechanism of reciprocity and transmission, constrained by network density. ⑤ Temporal effects show early connection structure forms path dependence, resulting in low dynamic variability and overall network stability. Full article

Timing skew is a critical bottleneck in high-speed Time-Interleaved (TI) Analog-to-Digital Converters (ADCs) that severely degrades dynamic range. This paper presents a mathematically rigorous, data-driven synchronization framework for calibrating effective sampling timing in TI-ADCs based on the Kuramoto oscillator model. Conventional clock-alignment methods often fail to capture signal-path mismatches, such as sampling switch aperture delay, while correlation-based techniques suffer from signal-dependent “blind-spot” regions. Overcoming this fundamental limitation without analog complexity is achieved via a fully digital feedback loop where each sub-ADC channel is modeled as a coupled oscillator following discrete-time Kuramoto dynamics. Unlike traditional approaches that rely on auxiliary analog phase detectors, the proposed scheme utilizes the ADC outputs to estimate and correct the effective sampling instants directly. A Lyapunov-based stability analysis proves that global phase synchronization is guaranteed when the adaptive coupling strength exceeds a critical value $K_c$. Theoretical results show that the system ensures exponential convergence of phase alignment, driving the total inter-channel timing error toward zero without relying on input-signal statistics. Behavioral MATLAB R2025a simulations of a 12-bit, 4-channel, 10 GS/s TI ADC confirm the analytical predictions. The proposed Kuramoto-based calibration achieves a residual skew reduction of over 99% and an SFDR improvement of 55.12 dB compared to correlation-based methods, even at blind-spot input frequencies, while adaptively reducing digital control power through dynamic coupling adjustment. The study demonstrates that data-driven, synchronization-based calibration provides an input-independent, energy-efficient, and mathematically verifiable solution for system-level timing correction in TI ADCs. Full article

The global energy transition faces the critical challenge of intermittency in renewable sources, which causes grid imbalances and estimated annual losses of USD 42 billion. Within the framework of circular economy and sustainability, mechanical energy storage (MES) systems—such as compressed air energy storage (CAES) and flywheels—emerge as scalable, long-lived solutions (over 30 years), reducing dependence on fossil fuels by up to 94%. To provide a comprehensive assessment, this study applies a Technology–Economy–Policy (TEP) framework to differentiate the maturity and iteration rates of MES sub-technologies (CAES, flywheels, pumped hydro). Furthermore, it integrates core circular economy indicators—lifespan extension, material efficiency, and multi-vector synergy—to evaluate the sustainability impact of these systems. To assess their impact and evolution, a quantitative bibliometric methodology was applied, analyzing 706 documents from the Scopus database (2010–2025). The study employed tools such as R Studio (Bibliometrix), VOSviewer, and Plotly for co-occurrence mapping, cluster density analysis, and keyword burst detection. Results reveal exponential growth in research, fitted to a logistic model (R² = 0.969), with a projected productivity peak in 2032. A technological shift toward high-efficiency solutions, such as adiabatic CAES (75%) and flywheels (95%), is evident, with grid stability prioritized. Furthermore, artificial intelligence is already applied in 40% of new management models to optimize these hybrid systems. The analysis, which quantitatively identifies underexplored areas such as socio-technical integration and standardized testing protocols, concludes that integrating MES is essential for the sustainability and circularity of the power system, enabling synergy with other vectors such as green hydrogen and fostering scalable business models that strengthen the circular economy in the energy sector. Full article

Global climate change demands modeling approaches that are both computationally efficient and physically faithful to the system’s long-term dynamics. Classical Energy Balance Models (EBMs), while valuable, are fundamentally limited by their memoryless exponential response, which fails to represent the prolonged thermal inertia of the climate system—particularly that associated with deep-ocean heat uptake. In this study, we introduce a fractional Energy Balance Model (fEBM) by replacing the classical integer-order time derivative with a Caputo fractional derivative of order $\alpha (0<\alpha \le 1)$, thereby embedding long-range memory directly into the model structure. We establish a rigorous mathematical foundation for the fEBM, including proofs of existence, uniqueness, and asymptotic stability, ensuring theoretical well-posedness and numerical reliability. The model is calibrated and validated against historical global mean surface temperature data from NASA GISTEMP and radiative forcing estimates from IPCC AR6. Relative to the classical EBM, the fEBM achieves a substantially improved representation of observed temperatures, reducing the root mean square error by approximately 29% during calibration (1880–2010) and by 47% in out-of-sample forecasting (2011–2023). The optimized fractional order $\alpha =0.75\pm 0.03$ emerges as a physically interpretable measure of aggregate climate memory, consistent with multi-decadal ocean heat uptake and observed persistence in temperature anomalies. Residual diagnostics and robustness analyses further demonstrate that the fractional formulation captures dominant temporal dependencies without overfitting. By integrating mathematical rigor, uncertainty quantification, and physical interpretability, this work positions fractional calculus as a powerful and responsible framework for reduced-order climate modeling and long-term projection analysis. Full article

Traditional path planning methods primarily optimize distance or time, without fully considering the impact of slope gradients in park road networks, variations in vehicle load capacity, and braking energy recovery characteristics on the energy consumption of pure electric commercial vehicles. To address these issues, this paper proposes a globally optimized path planning method based on energy consumption minimization. The proposed method introduces a multi-factor coupled energy consumption model for pure electric commercial vehicles, integrating slope gradients, load capacity, motor efficiency, and energy recovery. Using this vehicle energy consumption model and the park road network topology map, an energy consumption topology map representing energy consumption between any two nodes is constructed. An energy-optimized improved ant colony optimization algorithm (E-IACO) is proposed. By introducing an exponential energy consumption heuristic factor and an enhanced pheromone update mechanism, it prioritizes energy-saving path exploration, thereby effectively identifying the optimal energy consumption path within the constructed energy consumption topology map. Simulation results demonstrate that in typical three-dimensional industrial park scenarios, the proposed energy-optimized path planning method achieves maximum reductions of 10.57% and 4.90% compared to the A* algorithm and ant colony optimization (ACO), respectively, with average reductions of 5.14% and 1.97%. It exhibits excellent stability and effectiveness across varying load capacities. This research provides a reliable theoretical framework and technical support for reducing logistics operational costs in industrial parks and enhancing the economic efficiency of pure electric commercial vehicles. Full article

The focus of this research is the qualitative behavior of a reaction–diffusion neural network with distributed delays and conformable derivatives under impulsive perturbations. In particular, the almost periodic behavior of the proposed model is studied using a Lyapunov-based approach. By constructing an appropriate Lyapunov-type function, criteria that guarantee the existence and uniqueness of an almost periodic state are provided. The established criteria extend a few existing results on the almost periodicity of conformable models and contribute to the development of the field. In addition, the notion of global conformable exponential stability is introduced and analyzed for the developed model. A suitable example is discussed. Full article

This paper investigates the existence and global exponential stability of almost periodic solutions in a class of impulsive Cohen–Grossberg-type bidirectional associative memory (BAM) neural networks with time-varying delays. Real neural systems often experience sudden perturbations and nonuniform temporal interactions, leading to complex oscillatory behaviors. To capture these effects, a new impulsive Cohen–Grossberg BAM model is developed that integrates both delays and impulsive influences within a unified framework. Using the theory of almost periodic functions, fixed point methods, and impulsive differential inequalities, new sufficient conditions are derived for the existence and stability of almost periodic solutions. A Lyapunov functional combined with a generalized Gronwall-type inequality provides rigorous global exponential stability criteria. Numerical simulations confirm the theoretical analysis. The results extend existing studies and offer new insights into how delay and impulsive factors jointly shape the stability and dynamics of hybrid neural systems, contributing to the design of robust and delay-tolerant neural architectures. Full article

The system of full compressible magneto-micropolar flows is discussed in 3D bounded domains with slip boundary conditions. Based on the energy method, after establishing some key a priori exponential decay-in-times rates of the strong solutions, we obtain both the global existence and exponential stability of strong solutions. In particular, it should be pointed out that the estimates of ${\parallel (\mathrm{curl}\mathbf{u},\mathrm{curl}\mathbf{w})\parallel }_{L^2}$ and ${\parallel (\mathrm{div}\mathbf{u},\mathrm{div}\mathbf{w})\parallel }_{L^2}$ are established separately, which implies that the growth rate of $(\mathrm{div}\mathbf{u},\mathrm{div}\mathbf{w})$ in $L^2$ are faster than that of $(\mathrm{curl}\mathbf{u},\mathrm{curl}\mathbf{w})$ under the condition that the diameter of the domain is suitably large. Compared with previous works, we no longer consider the pressure P as $\rho \theta$, but as variable in $(x,t)$, and directly deal with ${\parallel \nabla P\parallel }_{L^2}$. Based on slip boundary conditions, we established the $L^p$-norm for the gradient of effective viscous flux, and the term ${\parallel \nabla P\parallel }_{L^2}$ can be controlled by $\parallel (\nabla {\mathbf{u}}_t,\nabla {\mathbf{w}}_t,\nabla {\mathbf{b}}_t){\parallel }_{L^2}$. Through precise calculations, we found that $\parallel (\nabla {\mathbf{u}}_t,\nabla {\mathbf{w}}_t,\nabla {\mathbf{b}}_t){\parallel }_{L^2}$ is dependent on ${\parallel \nabla P\parallel }_{L^2}$. Therefore, the smallness condition we propose does not depend on the $L^r$-norm of the density gradient, which means that density can contain large oscillations. Full article

The stability of energy demand–supply systems is often affected by delayed feedback caused by regulatory inertia, communication lags, and heterogeneous agent responses. Conventional models typically assume discrete delays, which may oversimplify real dynamics and reduce controller effectiveness. This work addresses this limitation by introducing a novel class of nonlinear energy models with distributed delay feedback governed by gamma-distributed memory kernels. Specifically, we consider both weak (exponential) and strong (Erlang-type) kernels to capture a spectrum of memory effects. Using the linear chain trick, we reformulate the resulting integro-differential model into a higher-dimensional system of ordinary differential equations. Analytical conditions for local asymptotic stability and Hopf bifurcation are derived, complemented by Lyapunov-based global stability criteria. The related numerical analysis confirms the theoretical findings and reveals a distinct stabilization regime. Compared to fixed-delay approaches, the proposed framework offers improved flexibility and robustness, with implications for delay-aware energy control and infrastructure design. Full article