Search Results (543)

The Electro-Hydraulic Servo Actuator for Active Suspensions (ASEHSA) plays a decisive role in shaping the holistic performance of vehicle suspension systems through its dynamic response speed and control precision. However, achieving high-performance control of ASEHSA still faces challenges. On one hand, existing model-based control methods are highly sensitive to parameter uncertainties and unmodeled nonlinear hydraulic dynamics, which can easily lead to reduced robustness in practical applications. On the other hand, traditional model-free strategies have limited time-delay compensation capabilities and often struggle to balance overshoot and settling time under delayed and disturbed conditions. To resolve this challenge, this study proposes an improved model-free adaptive control method that incorporates the differentiation of the tracking error (DE-IMFAC). Within the framework of traditional model-free adaptive control (MFAC), this approach reconfigures the time-delay term from an explicit form in the control law to implicit management, substantially mitigating the influence of time delays on system control performance. At the same time, by refining the performance criterion function and integrating a tracking error differentiation term together with dynamic weighting factors, the dynamic performance and adjustment flexibility of the controller are significantly enhanced. Additionally, by leveraging the characteristic equation of discrete autonomous systems and compression mapping theory, the BIBO stability of the DE-IMFAC control system and the monotonic convergence of the tracking error are rigorously established through theoretical analysis. Simulation and experimental results demonstrate that, compared with PID and traditional MFAC methods, DE-IMFAC significantly reduces integral absolute error, overshoot, settling time, and maximum position tracking error, while improving disturbance rejection capability. This approach does not depend on an accurate mathematical model of the ASEHSA system and maintains robust dynamic performance under complex operating environments characterized by time delays and data disturbances, providing a practical solution for ASEHSA and related industrial control systems. Full article

Differential wavefront sensing (DWS) is widely adopted for high-precision angular detection in interferometric systems, yet its measurement range is constrained by the nonlinear implicit phase–angle relationship. This paper proposes a strategy-enhanced differential evolution algorithm, termed Bi-inheritance and Tournament-Selection-based Differential Evolution (BiTsDE), to suppress nonlinear angular errors. The method introduces fitness-guided inheritance of mutation and crossover factors and tournament-based elite parent selection, enabling adaptive balance between global exploration and local exploitation. Unlike conventional DE variants that mainly tune control parameters, BiTsDE optimizes the evolutionary search strategy, enhancing early-stage diversity and late-stage convergence stability. Simulations demonstrate angular resolution better than 0.06 nrad within ±1 mrad. Experiments show that up to 600 μrad, BiTsDE reduces demodulation error by 99.9% compared with linear DWS, achieving 17.9 nrad precision and 42% faster convergence. These results validate BiTsDE as an effective solution for nonlinear error suppression in DWS-based high-precision optical metrology, particularly for space-based gravitational wave detection. Full article

Cross-domain sentiment analysis remains challenging due to distributional shifts and heterogeneous sentiment expressions across platforms. Existing domain adaptation approaches primarily focus on enforcing domain-invariant representations. However, such symmetry-preserving strategies often overlook directional and expression-level asymmetries. These asymmetries naturally arise in real-world sentiment data, particularly for context-inferred sentiment expressions. In this work, we propose a novel symmetry- and asymmetry-aware domain adaptation framework for cross-domain sentiment classification. The framework models symmetry through explicit multi-source distribution alignment, which captures transferable sentiment knowledge across domains. Additionally, aspect-level structural supervision organizes representations according to shared linguistic aspects. To address asymmetry, a directional divergence regularization is introduced. This component models expression-level and directional discrepancies between source and target domains. Importantly, the framework operates without requiring target-domain annotations. Experiments are conducted under a multi-source unsupervised domain adaptation setting using sentence-level hotel review datasets collected from multiple online platforms. Empirical results demonstrate strong performance for the proposed framework. It achieves an average Accuracy of 82.0% and Macro-F1 of 80.6%. The framework consistently and statistically significantly outperforms source-only, multi-source, and transformer-based adversarial adaptation baselines across all evaluated target domains (p < 0.05). Additional analyses confirm improved robustness to implicit sentiment expressions and platform-induced asymmetries. These findings highlight the importance of jointly modeling symmetry and asymmetry for robust cross-domain sentiment adaptation and provide a unified and deployable solution for sentiment analysis under realistic platform shifts. Full article

The rapid expansion of impervious surfaces in urban environments has significantly increased surface runoff and flood risk. Detention basins, implemented as part of Sustainable Urban Drainage Systems (SUDSs), are widely adopted worldwide to control peak discharges and mitigate recurrent flooding. In this study, an explicit flood routing model is applied to simulate the hydraulic behaviour of an urban detention reservoir, offering a computationally efficient alternative to traditional implicit numerical schemes by avoiding iterative solution procedures. In parallel, twenty-eight machine learning (ML) models are evaluated to estimate the percentage reduction in peak discharge required to comply with local regulatory constraints. The proposed framework integrates explicit hydrological routing with data-driven modelling to support decision-making during the design of detention systems. The methodology is applied to an urban catchment in Cartagena, Colombia, comparing an uncontrolled inflow hydrograph (without SUDSs) with an attenuated outflow hydrograph produced by the detention basin. The results demonstrate a substantial reduction in peak discharge and a delay in the time to peak, fully complying with Colombian regulations that require a minimum attenuation of 30%. Among the evaluated ML models, Squared Exponential Gaussian Process Regression achieved the best performance, yielding coefficient of determination (R²) values of 0.999 in both the validation and test sets. The findings confirm the potential of machine learning techniques to quantify peak-flow reduction requirements accurately and to support the planning and design of detention reservoirs in urban environments. The proposed approach constitutes a practical, efficient, and replicable tool for sustainable urban drainage design since the results of this research can be used to design detention pond systems employing ML tools. Full article

While traditional equations of state can determine thermophysical properties, they are computationally demanding, as most formulations are implicit and require iterative solutions. Dynamic simulation of complex energy systems involves various components defined by mathematical equations. Incorporating equations of state for refrigerant properties adds complexity, slowing down the computation. Moreover, studies have demonstrated that calculations of refrigerant thermophysical properties have the most significant impact on computational speed. Therefore, this work develops fast, accurate, and explicit thermodynamic formulations for thermophysical properties of propane, a widely used natural refrigerant for the new generation of heat pumps. The developed set of formulations yielded a mean absolute relative deviation of less than 1% for most of the formulations across the saturated lines and the different phase regions. The results show that using the explicit formulations for dynamic simulation of an air-source heat pump cycle achieves up to a 117× speedup compared to CoolProp, with a maximum relative error around 1% for the COP. This level of accuracy is suitable for applications such as vapor-compression cycle simulations, where accuracy is sacrificed in favor of computational speed. In addition, they offer greater flexibility for modelling and optimizing complex energy systems. Full article

One of the major challenges in autonomous path following for unmanned surface vehicles (USVs) is the impact of stochastic environmental forces—primarily wind, waves and currents—which introduce nonlinearities that affect control models. Conventional strategies often rely on minimizing cross-track error, resulting in a reactive system that corrects heading only after a disturbance has displaced the vessel, potentially leading to oscillatory behavior and reduced precision. Deep Reinforcement Learning (DRL) is successfully used for a wide range of nonlinear control tasks. It has already been shown that robust solutions that can handle disturbances such as sensor noise or changes in system dynamics can be obtained. This study investigates whether an agent, provided it can explicitly observe disturbances, can go beyond simply correcting deviations and autonomously learn the correlation between environmental conditions and necessary counter-forces. We show that integrating the wind vector directly into the agent’s observation space allows a Proximal Policy Optimization (PPO) policy to decouple the environmental cause from the kinematic effect, facilitating drift compensation before significant errors accumulate. By systematically comparing agents trained with randomized wind scenarios, we found that agents that can observe the wind can achieve goal reaching rates of up to 99.0% and reduce the spread of path deviation and velocity in our tested scenarios. Furthermore, our results quantify a distinct Pareto frontier between navigational velocity and tracking precision, demonstrating that explicit disturbance perception improves consistency, although robust implicit training already provides substantial resilience. These findings indicate that augmenting state observations with environmental data enhances the stability of learning-based controllers. Full article

Deep learning has become an important approach for automated software vulnerability detection. However, due to domain shift, existing models often suffer from significant performance degradation when applied to unseen projects. To address this issue, prior studies have widely adopted Domain Adaptation (DA) techniques to improve cross-project generalization. Nevertheless, these methods typically rely on the implicit “project-as-domain” assumption and require access to target project data during training, which limits their applicability in practice. To overcome these limitations, this paper proposes a vulnerability detection framework that combines semantic clustering with ensemble-based Domain Generalization (DG), termed Cluster-based Ensemble Learning for Vulnerability Detection (CEVD). CEVD first performs unsupervised clustering on code semantic embeddings to automatically automatically identify latent semantic structures that transcend project boundaries, constructing pseudo-domains with intra-domain homogeneity. A soft domain labeling strategy is further introduced to model the membership of samples in multiple pseudo-domains, preserving semantic overlap across boundaries. Building upon this, CEVD employs an ensemble learning framework that jointly trains multiple expert models and a domain classifier. The predictions of these experts are dynamically fused based on the weights generated by the domain classifier, enabling effective vulnerability detection on unseen projects without requiring access to target data. Extensive experiments on real-world datasets demonstrate that CEVD consistently outperforms state-of-the-art baselines across different pre-trained backbone models. This work demonstrates the effectiveness of semantic structure mining in capturing latent domains and offers a practical solution for improving generalization in cross-project vulnerability detection. Full article

KANECS is a 3D multigroup neutronics code based on the Simplified Spherical Harmonics (SP_N) approximation and the Continuous Galerkin Finite Element Method (CGFEM). In this work, the code is extended to solve the time-dependent neutron kinetics by implementing a fully implicit backward Euler scheme for the neutron transport equation and an implicit exponential integration for delayed neutron precursors. These schemes ensure unconditional stability and minimize temporal discretization errors, making the method suitable for fast transients. The new formulation transforms each time step into a transient fixed-source problem, which is solved efficiently using the GMRES solver with ILU preconditioning. The kinetics module is validated against established benchmark problems, including TWIGL, the C5G2 MOX benchmark, and both 2D and 3D mini-core rod-ejection transients. KANECS shows close agreement with the reference solutions from well-known neutron transport codes, with consistent accuracy in normalized power evolution, spatial power distributions, and steady-state eigenvalues. The results confirm that KANECS provides a reliable and accurate framework for solving neutron kinetics problems. Full article

This paper investigates a class of implicit neutral fractional integro-differential equations of Volterra–Fredholm type. The equations incorporate a tempered fractional derivative in the Caputo sense, along with both retarded (delay) and advanced arguments. The problem is formulated on a time domain segmented into past, present, and future intervals and includes nonlinear mixed integral operators. Using Banach’s contraction mapping principle and Schauder’s fixed point theorem, we establish sufficient conditions for the existence and uniqueness of solutions within the space of continuous functions. The study is then extended to general Banach spaces by employing Darbo’s fixed point theorem combined with the Kuratowski measure of noncompactness. Ulam–Hyers–Rassias stability is also analyzed under appropriate conditions. To demonstrate the practical applicability of the theoretical framework, explicit examples with specific nonlinear functions and integral kernels are provided. Furthermore, detailed numerical simulations are conducted using MATLAB-based specialized algorithms, illustrating solution convergence and behavior in both finite-dimensional and Banach space contexts. Full article

Path planning for multiple four-way shuttles in high-density warehousing is frequently hampered by efficiency-degrading conflicts, particularly head-on deadlocks. To address this challenge, this paper proposes a multi-agent reinforcement learning (MARL) framework based on Proximal Policy Optimization (PPO). The core of our approach is a novel Cooperative Avoidance Reward Mechanism (CARM), which employs a dual-component reward structure. This structure integrates a distance-guided reward to ensure efficient navigation towards targets and a cooperative avoidance reward that uses both immediate and delayed returns to incentivize implicit collaboration. This design effectively resolves conflicts and mitigates the policy instability often caused by traditional collision penalties. Experiments in a 20 × 20 grid simulation environment demonstrated that, compared to a rule-based A* and Conflict-Based Search (CBS) algorithms, the proposed method reduced the average travel distance and total time by 35.8% and 31.5%, respectively, while increasing system throughput by 49.7% and maintaining a task success rate of over 95%. Ablation studies further confirmed the critical role of CARM in achieving stable multi-agent collaboration. This work offers a scalable and efficient data-driven solution for real-time path planning in complex automated warehousing systems. Full article

Under sparse aperture (SA) conditions, inverse synthetic aperture radar (ISAR) imaging becomes a severely ill-posed inverse problem due to undersampled and noisy measurements, leading to pronounced degradation in azimuth resolution and image quality. Although deep learning approaches have demonstrated promising performance for SA-ISAR imaging, their practical deployment is often hindered by black-box behavior, fixed network depth, high computational cost, and limited robustness under extreme operating conditions. To address these challenges, this paper proposes an ADMM Denoising Deep Equilibrium Framework (ADnDEQ) for SA-ISAR imaging. The proposed method reformulates an ADMM-based unfolding process as an implicit deep equilibrium (DEQ) model, where ADMM provides an interpretable optimization structure and a lightweight DnCNN is embedded as a learned proximal operator to enhance robustness against noise and sparse sampling. By representing the reconstruction process as the equilibrium solution of a single-layer network with shared parameters, ADnDEQ decouples forward and backward propagation, achieves constant memory complexity, and enables flexible control of inference iterations. Experimental results demonstrate that the proposed ADnDEQ framework achieves superior reconstruction quality and robustness compared with conventional layer-stacked networks, particularly under low sampling ratios and low-SNR conditions, while maintaining significantly reduced computational cost. Full article

Quantum Key Distribution (QKD) is a process to establish a symmetric key between two parties using the principles of quantum mechanics. Currently, commercial QKD systems are still expensive, they require specific infrastructure, and they are impractical for deployment in portable or resource-constrained devices. In this article, we introduce the Butterfly Protocol (and its extended version) that enables QKD to be offered as a service to non-QKD-capable (portable or IoT) devices. Our key contributions include (1) resilience to the compromise of any single classical link, (2) protection against malicious QKD users, (3) implicit mutual authentication between users without relying on large post-quantum certificates, and (4) the Double Butterfly extension, which secures communication even when the underlying QKD network cannot be fully trusted. We also demonstrate how to integrate the Butterfly Protocol into TLS 1.3 and provide its initial security analysis. We present preliminary performance results and discuss the main bottlenecks in the Butterfly Protocol implementation. We believe that our solution represents a practical step toward integrating QKD into classical networks and extending its use to portable devices. Full article

The current paper retrieves implicit quiescent soliton solutions to optical metamaterials with nonlinear chromatic dispersion with generalized temporal evolution. Seven forms of self-phase modulation structures, as proposed by Kudryashov with time, are taken up. The implemented integration algorithm is Lie symmetry. A few of the solutions are in quadratures, while others are in terms of special functions. We also characterize the parameters that constrain the existence of such solutions. Full article

This paper investigates a modified one-dimensional Moore–Gibson–Thompson (MGT) thermoelasticity model that significantly extends the classical formulation by incorporating two key structural modifications: frictional damping and a novel cross-coupling structure. The system introduces a viscous frictional damping mechanism proportional to the velocity acting on the mechanical (elastic) field, enhancing dissipation, which is a common feature in models extending Green–Naghdi Type III thermoelasticity. The core novelty, however, lies in introducing an additional coupling structure that explicitly links the thermal relaxation effects with the mechanical dissipation effects. This modification moves beyond the standard MGT coupling and is rooted in an effort to model complex visco-thermal interactions, representing the primary contribution to the literature. The well posedness of this modified system is first established using semigroup theory. Through the construction of a new Lyapunov functional, sufficient conditions are then rigorously derived, ensuring the exponential stability of solutions under specific parameter regimes. Furthermore, a critical balance condition is identified between the thermal conductivity and the thermal relaxation time, beyond which the system’s energy decay ceases to be exponential. Finally, numerical experiments employing an explicit–implicit finite difference scheme validate the theoretical findings and illustrate the substantial influence of both the modified coupling and the frictional damping on the system’s long-term energy behavior. Full article

Objectives: To evaluate the changes in retinal function and neural conduction along the visual pathways after 12 months of treatment with Citicoline oral solution in patients with open-angle glaucoma (OAG). Methods: In this randomized, prospective, double-blind study, 29 OAG patients were enrolled. Fifteen patients (Citicoline Group, 15 eyes) received Citicoline oral solution (Neurotidine^®, 500 mg/day), and 14 patients (Placebo Group, 14 eyes) received placebo for 12 months. Visual field (VF), pattern electroretinogram (PERG), visual evoked potentials (VEP), and Retinocortical Time (RCT) were assessed at baseline and after 6 and 12 months. Brain Diffusion Tensor Imaging (DTI)-Magnetic Resonance Imaging (MRI) was performed at baseline and at 12 months. Results: PERG, VEP, and RCT baseline values were comparable between groups (p > 0.01) at baseline. After 12 months of Citicoline treatment, significant (p < 0.01) increases in PERG P50–N95 and VEP N75-P100 amplitudes, and significant shortening of PERG P50, VEP P100 implicit times and RCT were observed. VEP implicit times shortening significantly correlated with the changes in VF Mean Deviation, and RCT shortening was associated with changes in DTI-MRI metrics in the lateral geniculate nucleus and on optic radiations, without reaching the level of significance. No significant changes were found in the Placebo Group. Conclusions: In OAG, Citicoline oral solution enhances retinal function likely through neuromodulation processes and changes post-retinal visual pathway connectivity. This could explain the improvement of visual field defects. Full article