Search Results (1,173)

In this work, a predefined-time formation output constraint control method is proposed for underactuated AUVs with input saturation. First, a coordinate transformation method is utilized to convert the underactuated AUV system into a fully actuated system form. A universal time-varying asymmetric barrier function is constructed to convert the system to an unconstrained form and construct the formation tracking error. Then, a predefined-time formation output constraint control law is designed based on the active disturbance rejection control framework and predefined-time control method, which can achieve the control objective without relying on the precise mathematical model of the system. In addition, to address the input saturation issue, a novel predefined-time auxiliary dynamic system (ADS) is proposed. The proposed method with ADS can ensure that the multi-AUV system with input saturation can complete the formation output constraint tracking control task within a predefined time. Finally, a simulation is designed to verify the effectiveness of the proposed method. Full article

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016–2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence. Full article

Fault-tolerant control for bionic robotic fish presents significant challenges due to the complex dynamics and asymmetric propulsion introduced by joint failures. To address this issue, this paper proposes a fault-tolerant following control framework for multi-joint bionic robotic fish by combining fuzzy control methodologies and dynamic model correction. Firstly, offline fault analysis is conducted based on the dynamic model under multi-variable parameter conditions, quantitatively deriving influence factor functions that characterize the effects of different joint faults on velocity and yaw performance of the robotic fish. Secondly, an adaptive-period yaw filtering algorithm combined with an improved line-of-sight navigation method is employed to accommodate the motion characteristics of bionic robotic fish. Thirdly, a dual-loop following control strategy based on fuzzy algorithms is designed, comprising coordinated velocity and yaw control loops, where velocity and yaw influence factors serve as fuzzy controller inputs with expert experience-based rule construction. Finally, extensive numerical simulations are conducted to verify the effectiveness of the proposed method. The obtained results indicate that the bionic robotic fish can achieve fault-tolerant following control under multiple fault types, offering a valuable solution for underwater operations in complex marine environments. Full article

Fully Homomorphic Encryption (FHE) allows a client to share their data with an external server without ever exposing their data. FHE serves as a potential solution for data breaches and the marketing of users’ private data. Unfortunately, FHE is much slower than conventional asymmetric cryptography, where data are encrypted only between endpoints. Within this work, we propose the Dynamic AcceleRaTor for Parallel Homomorphic pROGrams, DARTPHROG, as a potential tool for accelerating FHE. DARTPHROG is a superscalar architecture, allowing multiple homomorphic operations to be executed in parallel. Furthermore, DARTPHROG is the first to utilize the new Hardware Optimized Modular-Reduction (HOM-R) system, showcasing the uniquely efficient method compared to Barrett and Montgomery reduction. Coming in at 40.5 W, DARTPHROG is one of the smaller architectures for FHE acceleration. Our architecture offers speedups of up to 1860 times for primitive FHE operations such as ciphertext/plaintext and ciphertext/ciphertext addition, subtraction, and multiplication when operations are performed in parallel using the superscalar feature in DARTPHROG. The DARTPHROG system implements an assembler, a unique instruction set based on THUMB, and a homomorphic processor implemented on a Field Programmable Gate Array (FPGA). DARTPHROG is also the first superscalar evaluation of homomorphic operations when the Number Theoretic Transform (NTT) is excluded from the design. Our processor can therefore be used as a base case for evaluation when weighing the resource and execution impact of NTT implementations. Full article

We explore the spontaneous emission dynamics of a giant atom coupled to a photonic Creutz ladder, focusing on how flat-band frustration and synthetic gauge fields shape atom–photon interactions. The Creutz ladder exhibits perfectly flat bands, Aharonov–Bohm caging, and topological features arising from its nontrivial hopping structure. By embedding the giant atom at multiple spatially separated sites, we reveal interference-driven emission control and the formation of nonradiative bound states. Using both spectral and time-domain analyses, we uncover strong non-Markovian dynamics characterized by persistent oscillations, long-lived entanglement, and recoherence cycles. The emergence of bound-state poles in the spectral function is accompanied by spatially localized photonic profiles and directionally asymmetric emission, even in the absence of band dispersion. Calculations of von Neumann entropy and atomic purity confirm the formation of coherence-preserving dressed states in the flat-band regime. Furthermore, the spacetime structure of the emitted field displays robust zig-zag interference patterns and synthetic chirality, underscoring the role of geometry and topology in photon transport. Our results demonstrate how flat-band photonic lattices can be leveraged to engineer tunable atom–photon entanglement, suppress radiative losses, and create structured decoherence-free subspaces for quantum information applications. Full article

In this work, particle dynamics in a binary asteroid system is analyzed within the Circular Restricted Three-Body Problem (CRTBP) framework, assuming the largest body is treated as a mass point. The secondary body is modeled as a mass dipole in synchronous rotation with its orbital motion, which leads to the spin–orbit resonance. The third body is a point of negligible mass whose motion is restricted to the orbital plane of the primary bodies. We considered asymmetrical and symmetrical dipole cases. The number and positions of the equilibrium points are determined for the dynamical analysis, and the zero-velocity curves are studied. This model preserves the number and geometric arrangement of the equilibrium points compared to the CRTBP. The equilibrium points adjacent to the dipole are the most sensitive in position to the variations in physical parameters. Considering the solar radiation pressure on the third body, different initial conditions for its motion in the vicinity of the dipole are analyzed. As a result, the particle survival time in orbital motion is estimated before colliding or suffering gravitational ejection from the system. Full article

Tidal turbines represent a promising renewable energy source, generating power from ocean currents. However, due to tidal range variations, they sometimes become partially exposed to the free surface. When this occurs, the turbine experiences reduced power generation and unsteady torque caused by the asymmetric flow. Such conditions can lead to long-term degradation of turbine performance and reliability. From this perspective, a key question arises regarding how significantly power generation differs when turbines are exposed to the free surface. This study was conducted with the objective of quantitatively evaluating the differences in power generation and torque acting on the turbine due to free-surface exposure, in order to address this question. Numerical simulations considering free-surface exposure effects were developed to quantitatively assess these phenomena through Computational Fluid Dynamics (CFD). Additionally, this numerical model was validated by comparison against experimental data and verified by convergence tests. The results revealed that the tidal turbine exhibited power generation differences ranging from a maximum of 45% to a minimum of 0.44%, depending on the degree of free-surface exposure. These findings are expected to serve as valuable indicators for power generation when operating tidal turbines. Full article

The cytosine methylation status of symmetric and asymmetric sites of promoters of the genes encoding the membrane-bound subunits C and D of succinate dehydrogenase (SDH) was assessed during the germination of maize (Zea mays L.) seeds, when the stored lipids were utilized and the glyoxylate cycle produced succinate. The results of bisulfite sequencing of the promoters of Sdh genes in maize scutella allowed us to determine the cytosine methylation status in the CG, CNG, and CNN sites. The observed site-specific changes in the cytosine methylation status of the Sdh3-1 and Sdh3-2 genes encoding the SDH subunit C indicate an important role in controlling their transcriptional activity. In contrast, no marked changes were observed in the methylation of promoters of the Sdh4 gene, encoding SDH subunit D. The analysis of changes in the activity of the CG, CNG, and CNN DNA methyltransferases revealed the redistribution of activity between CG, CNG, and CNN DNA methyltransferases toward an increase in the proportion of CG DNA methyltransferases. The locus-specific methylation dynamics of SDH membrane subunit promoters during maize germination have been demonstrated. It is concluded that the changes in the cytosine methylation status may play a role in the regulation of the expression of the Sdh genes in the course of the conversion of succinate formed in the glyoxylate cycle. Full article

This study investigates the contributions of five key sectors—insurance, materials, utilities, real estate, and transport—to the financial markets of six Gulf Cooperation Council (GCC) countries from 2004 to 2023. Grounded in the Sectoral Linkage Theory and Endogenous Growth Theory, the study employs a Panel Autoregressive Distributed Lag (Panel ARDL) model to examine both short-term and long-term sectoral impacts on financial market resilience. The findings reveal that the insurance and transport sectors offer short-term market stimulation, but lack persistent effects. Conversely, the materials, utilities, and real estate sectors exhibit strong, long-run contributions to financial stability and economic diversification. These results highlight the asymmetric impact of sectoral dynamics on market performance in resource-rich contexts. This research contributes to the literature by providing empirical evidence on sectoral interdependence in oil-dependent economies and highlights the importance of structural diversification for sustainable financial resilience. The study provides actionable insights for policymakers and investors seeking to enhance market resilience and reduce reliance on hydrocarbon revenues through targeted sectoral development. Full article

Human–wildlife conflict (HWC) is intensifying in Japan, driven by complex socio-ecological changes. While the Social–Ecological Systems (SES) framework offers a valuable analytical tool, standard applications often fail to capture the crucial cultural specificities, demographic pressures, and institutional dynamics that define the Japanese context. This paper addresses these limitations by conducting a scoping review of academic and policy literature in order to synthesize the evidence needed to develop a culturally and institutionally attuned adaptation of the SES framework. The review’s findings confirm that profound demographic change (kaso and kōreika), unique institutional arrangements (the Ryōyūkai crisis), deep-seated cultural values, and asymmetric power relations are core systemic drivers of HWC, not external factors. Building on this evidence, we propose a theoretically grounded adapted framework that internalizes these factors as endogenous variables. The resulting framework serves as a more robust diagnostic tool for understanding and navigating HWC in Japan. It facilitates the identification of context-specific leverage points and offers a transferable methodological model for adapting SES analysis to other culturally distinct and rapidly changing societies. Full article

Altered precipitation regimes, both in intensity and duration, can profoundly influence the structure and function of soil microbial communities, yet the patterns and drivers of these responses remain unclear across ecosystem types. Here, using data exclusively from 101 field experiments conducted in China (yielding 695 observations), we investigated the impacts of altered precipitation on soil microbial biomass, diversity, and enzymatic activity in forest and grassland ecosystems. Soil microbial biomass carbon (MBC) and nitrogen (MBN) increased in response to precipitation addition, particularly in grasslands, but they decreased under reduced precipitation, with the decline being more pronounced in forests. The magnitude and duration of precipitation manipulation significantly influenced these effects, with moderate and long-term changes producing divergent responses. Bacterial diversity was largely unaffected by all precipitation treatments, whereas fungal diversity decreased significantly under intense and short-term reductions in precipitation. Enzyme activities exhibited the following element-specific patterns: carbon- and phosphorus-cycling enzymes and antioxidant enzymes were suppressed by precipitation reduction, especially in grasslands, while nitrogen-cycling enzymes showed no consistent response. Moreover, microbial responses were significantly shaped by environmental factors, including mean annual temperature (MAT), mean annual precipitation (MAP), and elevation. Our region-specific analysis highlights precipitation-driven microbial dynamics across China’s diverse climatic and ecological conditions. These findings demonstrate that soil microbial communities respond asymmetrically to precipitation changes, with responses shaped by both ecosystem type and climatic context, underscoring the need to account for environmental heterogeneity when predicting belowground feedback to climate change. Full article

This paper presents an empirical investigation into the role of Fintech risk, measured by the Fintech Financial Stress Indicator (FFSI), in shaping the dynamic behavior of bank performance by employing a panel vector autoregressive (PVAR) methodology on a dataset comprising 41 banks across 11 African economies over the semi-annual period from June 2004 to December 2020. The findings reveal that bank performance, measured by return on equity (ROE), exhibits a negative and short-lived response to FFSI shock, while the effects on bank stability, cost efficiency, and return on assets (ROA) are statistically insignificant. In addition, an increase in FFSI significantly enhances both ROA and ROE, with negligible impacts on cost efficiency and stability. In contrast, a decline in FFSI has a significant negative effect on ROE and stability but remains insignificant for ROA and cost efficiency. These results indicate that FFSI shocks have asymmetric effects on ROA, cost efficiency, and bank stability but a symmetric effect on ROE. The findings suggest that engagement in financial innovation initiatives may yield performance benefits for banks, provided such strategies are pursued within a sound regulatory framework to mitigate potential excessive risk-taking. Full article

Understanding asymmetric interactions among heterogeneous traffic participants is essential for managing congestion and enhancing safety at urban signalized intersections. This study proposes a cellular automaton modeling framework that captures the spatial and behavioral asymmetries among vehicles, bicycles, and pedestrians, with a particular focus on right-of-way hierarchies and conflict anticipation. Beyond simulation, the framework integrates a behavior pattern mining module that applies unsupervised trajectory clustering to identify recurrent interaction patterns emerging from mixed traffic flows. Simulation experiments are conducted under varying demand levels to investigate the propagation of congestion and the structural distribution of conflicts. The results reveal distinct asymmetric behavior patterns, such as right-turn vehicle blockage, non-lane-based bicycle overtaking, and pedestrian-induced disruptions. These patterns provide interpretable insights into the spatiotemporal dynamics of intersection performance and offer a data-driven foundation for optimizing signal control and multimodal traffic flow separation. The proposed framework demonstrates the value of combining microscopic modeling with data mining techniques to uncover latent structures in complex urban traffic systems. Full article

Accurate prediction of effluent quality indicators is essential for ensuring stable operation and regulatory compliance in wastewater treatment plants. However, the inherent spatial distribution and temporal fluctuations of wastewater processes present significant challenges for modeling. In this study, we propose a dynamic multi-scale spatio-temporal Transformer (DMST-Transformer) with a symmetric architecture to enhance prediction accuracy in complex wastewater systems. Unlike conventional asymmetric designs, the DMST-Transformer extracts spatial and temporal features in parallel using a spatial graph convolutional network and a multi-scale self-attention mechanism coupled with a dynamic self-tuning module. The model is evaluated on a full-process dataset collected from a municipal wastewater treatment plant, with biochemical oxygen demand selected as the target indicator. Experimental results on test data show that the DMST-Transformer achieves a coefficient of determination of 0.93, root mean square error of 1.40 mg/L, and mean absolute percentage error of 6.61%, outperforming classical models such as linear regression, partial least squares, and graph convolutional networks, as well as advanced deep learning baselines including Transformer and ST-Transformer. Ablation studies confirm the complementary effectiveness of the spatial and temporal modules, and computational time comparisons demonstrate the model’s suitability for real-time applications. These results validate the practical potential of the DMST-Transformer for robust effluent quality monitoring in wastewater treatment plants. Future research will focus on scaling the model to larger and more diverse datasets, extending it to predict additional water quality indicators, and deploying it in real-time environmental monitoring systems to support intelligent water resource management. Full article

Despite the growing relevance of subnational development strategies in emerging economies, the literature lacks empirical applications of classical trade models to territorial productive specialization. This study addresses this gap by adapting the Heckscher–Ohlin–Samuelson (HOS) model to identify optimal specialization patterns in intermediate municipalities with asymmetric factor endowments. Using data from 2017 to 2021 for Tunja and Chiquinquirá (Colombia), we estimate capital-to-labor ratios and sectoral factor intensities to detect specialization aligned with local comparative advantages. The results show that Tunja exhibits capital-abundant conditions favoring specialization in sectors such as real estate, construction, and financial services, while Chiquinquirá demonstrates labor-intensive dynamics suitable for tourism and service industries. Methodologically, the study extends the HOS model to subnational scales, offering a robust analytical tool for regional policy formulation. This article contributes to the academic debate by bridging international trade theory and regional development, and it provides empirical evidence to support place-based industrial policies. Our findings emphasize the importance of aligning productive strategies with structural endowments to foster inclusive and sustainable development in emerging territories. Full article