Search Results (2,158)

Actuator placement optimization based on a reduced-order model is essential for controlling a high-dimensional system in real time. This paper discusses actuator placement in an unstable high-dimensional system based on a reduced-order model obtained by BPOD with input–output projection. Actuator locations in a linearized Ginzburg–Landau model are optimized with three objective functions based on a Riccati equation, a controllability Gramian, and an impulse response matrix. Further, the computation time for actuator selection and the resulting LQR performance are evaluated. The LQR performance is basically high when actuators are placed based on the Riccati equation or the impulse response matrix. The computation time of the method based on the impulse response matrix is much smaller than that of the other two methods. Thus, the method based on the impulse response matrix seems to have more advantages than the other two methods in terms of optimizing the actuator locations of the analyzed model. Moreover, it seems to be beneficial to place actuators with a low-dimensional model using this method. Full article

Harmonic Path Integral Diffusion (H-PID) provides an analytically tractable framework for sampling from a target density $p^{\left(\mathrm{tar}\right)}\left(x\right)\propto exp(-E\left(x\right))$. H-PID can be viewed as a diffusion bridge model solving a stochastic optimal transport problem from a $\delta$-density at $t=0$ to the target density at $t=1$. The dynamics are governed by a controlled stochastic differential equation, and the corresponding variational stochastic optimal transport objective combines a time-dependent quadratic potential, ${\beta }_t{\parallel x_t\parallel }^2/2$, with a kinetic control cost, $\parallel u(t;x_t){\parallel }^2/2$. The focus of this paper is the design of the temporal stiffness protocol ${\beta }_t$, which enables explicit control of intermediate sampling dynamics when the terminal density is fixed. We exploit the central advantage of H-PID—its integrability—which yields an explicit representation of the optimal control in terms of the target density and Green functions of the associated linear forward and backward diffusion-in-a-potential problems. Our main contribution is to convert this integrable structure into a practical methodology for protocol optimization. Specializing to piecewise-constant stiffness schedules and Gaussian-mixture targets, we develop two complementary optimization principles: The first is a deterministic one, relying on explicit evaluation of the dynamic marginals, and exemplified on a velocity-gradient-sensitivity objective, which provides a computationally controlled framework for optimizing transport regularity and stiffness. The second is a stochastic one, implemented via sampling, and exemplified on sharpness-based temporal-memory objective regularized to favor transitions within a prescribed time window that targets the temporal organization of the sampling path. These two objectives illuminate different aspects of the same protocol-design problem. The velocity-gradient-sensitivity objective serves as a clean methodological backbone and supports interpretable optimization and scaling studies. The sharpness-based objective reveals that schedule quality is target-dependent, and that the dependence on $\beta$ is not universal: different target geometries may favor different stiffness regimes and qualitatively different transient organizations. Examples with low- and moderate-dimensional Gaussian mixtures demonstrate that the proposed approach can control not only the terminal sampling accuracy but also the transient evolution of probability mass, while remaining computationally light and theoretically transparent. Full article

This study investigates how key milling parameters influence both cutting noise and surface quality during the machining of laminated bamboo lumber. Using a multifactorial optimal response surface methodology, the effects of fibre orientation (0–135°), spindle speed (7000–10,000 r/min), feed rate (0.5–2.0 m/min) and milling depth (0.5–2.0 mm) were quantified through 25 experimental runs. Cutting noise, measured as peak sound pressure level (SPL), ranged from 86.8 to 95.2 dB, increasing markedly with fibre angle, feed rate, and milling depth, but exhibiting a non-linear response to spindle speed. Surface roughness (S_a) varied from 2.6 to 11.7 µm and was most strongly governed by milling depth, followed by fibre orientation and feed rate, with a significant interaction between fibre orientation and spindle speed. Quadratic regression models demonstrated strong predictive performance (R² = 0.97 for SPL; R² = 0.85 for S_a). Based on the response surfaces, optimal low-noise, high-quality machining was achieved at moderate spindle speeds, low feed rates, and shallow milling depths. These findings provide a mechanistic basis for understanding noise–roughness coupling in bamboo machining and offer practical guidance for computer numerical control processing, tool selection, and industrial noise reduction strategies in bamboo manufacturing. Full article

Most existing DNN-based image watermarking methods adopt an “encoder–noise–decoder” paradigm, where the watermark is typically replicated and expanded in a straightforward manner and then directly fused with image features, which limits robustness under complex distortions. Although Transformers improve fusion via attention mechanisms, their quadratic computational complexity makes high-resolution processing prohibitively expensive. To address these issues, we propose CCViM, a robust watermarking framework built on Vision Mamba, which leverages the linear-complexity property of state-space models (SSMs) to enable efficient global interactions. We design a Watermark Representation Learning Module (WRLM) that performs hierarchical feature extraction and structured expansion of the watermark through cascaded VSS blocks, yielding semantically rich and perturbation-resistant watermark representations. In addition, we introduce an Interwoven Fusion Enhancement Module (IFEM), which employs a CCS6 structure to treat the watermark as a dynamic guidance signal. By combining contextual clustering with the Mamba mechanism, IFEM deeply interweaves the watermark into host features at both local and global levels. Experiments on COCO, DIV2K, and ImageNet demonstrate that CCViM consistently improves imperceptibility, robustness, and efficiency to varying degrees, and remains stable and high quality under attacks such as JPEG compression, cropping, and Gaussian blur. Full article

Background/Objectives: Sphingosine-1-phosphate (S1P) is implicated in glycemic control. However, its circulating levels and clinical significance in type 2 diabetes mellitus (T2DM) remain controversial. We assessed plasma S1P levels in T2DM patients, its associations with metabolic parameters and complications, and explored its biomarker potential and non-linear (U-/J-shaped) relationships. Methods: This cross-sectional study enrolled 140 patients with T2DM and 63 matching healthy controls. Plasma S1P was measured by competitive ELISA. Statistical analyses included comparisons, correlation, ROC analysis, multivariable logistic regression, and quadratic/spline regression for U-shaped relationships. Results: Plasma S1P was significantly elevated in T2DM patients [1256.7 (149.4–1510.0) ng/mL] compared to controls [1075.1 (202.0–1510.0) ng/mL; p < 0.001]. S1P correlated positively with age, disease duration, HbA1c, insulin resistance, TyG index, triglycerides, systolic blood pressure, and negatively with HDL-C. Patients with complications had higher S1P than those without (p = 0.001), with progressive increases from retinopathy to nephropathy to mixed complications. Insulin-treated patients exhibited the highest S1P levels (p < 0.001). ROC analysis showed moderate diagnostic accuracy (AUC = 0.724). S1P is an independent associated factor with complications (OR = 1.18 per 100 ng/mL, p = 0.003). Non-linear analysis revealed a U-shaped relationship with HDL-C (optimal S1P: 1100–1350 ng/mL) and a J-shaped relationship with complication risk (threshold ~1250 ng/mL). Conclusions: Plasma S1P is elevated in T2DM and correlates with disease severity, glycemic control, insulin resistance, and complications. S1P demonstrates moderate biomarker potential and exhibits non-linear U-/J-shaped relationships with metabolic parameters, suggesting an optimal therapeutic window of 1100–1280 ng/mL. These findings support S1P as a marker of cumulative disease burden and a potential therapeutic target. Full article

The planning of trajectories for Connected Autonomous Vehicles (CAVs) represents a pivotal aspect of autonomous driving technologies, enabling secure navigation within traffic environments. Traditional models for trajectory control primarily focus on the efficiency and safety of individual vehicles but often overlook the dynamics involved in vehicle-to-vehicle and vehicle-to-infrastructure interactions. This study introduces a novel concept, the “driving risk field,” which imposes constraints on vehicular movement within designated road spaces to enhance safety. A vehicle dynamics model is developed, employing a non-linear fifth-degree polynomial to approximate the trajectory curves, with optimization performed using the Sequential Quadratic Programming (SQP) method. The efficacy of the optimized model is validated through simulations on the Prescan/Simulink plat Full article

This study was conducted to investigate the effects of dietary supplementation with rumen-protected arginine (RP-Arg) on growth performance, rumen fermentation parameters, microbial diversity, and blood physiological and biochemical indices in fattening Altay sheep. A total of 24 healthy, 6-month-old Altay male lambs were randomly assigned to three groups, with eight replicates per group. The control group received a basal diet, while the experimental groups were supplemented with either 0.50% or 1.00% RP-Arg on a dry matter basis, respectively. The results indicated that RP-Arg supplementation had no significant effect on feed intake, growth performance, or slaughter performance of the lambs (p > 0.05), whereas backfat thickness decreased linearly (p < 0.05). With increasing RP-Arg levels, serum low-density lipoprotein cholesterol, leptin, and catalase activity increased linearly (p < 0.05), while blood ammonia, alanine aminotransferase, and Ig M exhibited a significant quadratic increase (p < 0.05). RP-Arg supplementation led to a linear decrease in ruminal propionate and valerate concentrations (p < 0.05). Analysis of bacterial diversity revealed that the class Vampirivibrionia and the order Gastranaerophilales were biomarkers for the 0.50% RP-Arg group, while several taxa within the phyla Proteobacteria and Thermoplasmatota served as biomarkers for the 1.00% RP-Arg group. In summary, although supplementation of a forage-based diet with RP-Arg partially modified rumen microbial composition and fermentation profile, and regulated several blood biochemical parameters, it did not translate into any beneficial effects on growth performance. Larger-scale studies are therefore warranted to further elucidate the role of RP-Arg in fattening lambs. Full article

To address the significant degradation of steering performance in dual-motor steer-by-wire (DMSBW) systems caused by the coupling of communication delays and motor faults, a robust fault-tolerant control strategy is proposed under the dual-motor collaborative driving mode. First, a matrix polytopic model is employed to describe the nonlinearities introduced by delays, establishing a delay-dependent DMSBW system dynamics model. Second, for electrical faults such as internal motor short circuits that cause a sudden drop in rotational speed, an adaptive motor-switching fault-tolerant mechanism is designed based on a smooth monitoring function to achieve rapid fault detection and steering function reconstruction. Furthermore, considering the coupled impact of delays and faults, a robust linear quadratic regulator (LQR) controller with feedforward compensation is designed to enhance system fault tolerance and robustness. Simulation results demonstrate that under steering wheel angle step input with delays, the proposed strategy achieves a rapid response without significant overshoot, and the steady-state tracking error is significantly reduced. In variable-speed single lane change maneuvers with coupled delays and severe motor faults, the peak and root mean square (RMS) errors of the front wheel angle are reduced to 0.0112 rad and 0.0031 rad, respectively. Compared to the delay-compensated nonlinear model predictive control (NMPC) and sliding mode control (SMC) strategies that do not account for delays, the peak error is reduced by 15.79% and 45.37%, while the RMS error decreases by 27.91% and 35.42%, respectively. Additionally, the peak and RMS errors of the sideslip angle and yaw rate are substantially reduced, validating the strategy’s excellent steering fault tolerance, robustness, and vehicle handling stability. Full article

Lipolysis is an interfacial reaction. Lecithins are natural emulsifiers containing a mixture of phospholipids (PL). Lecithin composition can be modified via enzymatic hydrolysis of PLs to produce lysophospholipids (LPL). The quantities of PL and LPL and the PL/LPL ratio are related to the emulsifying properties and interfacial activity of digestive lipases. This study aims to: (i) produce oil-in-water nanoemulsions of rapeseed oil (RSO) with soybean lecithin (SBL) and hydrolyzed lecithin (HL) at different concentrations and homogenization pressures and measure the mean droplet diameter (MDD) and polydispersity index (PdI) by dynamic light scattering; (ii) hydrolyze the emulsions in vitro with intestinal extracts of rainbow trout and estimate the degree of hydrolysis of lipids (DH) by the pH-stat method; and (iii) model the results on MDD, PdI, and DH through the response surface methodology (RSM). When HL was used as an emulsifier, DH, MDD, and PdI were fitted to polynomial quadratic, two-factor interaction, and linear models, respectively. MDD, PdI, and DH were fitted to polynomial quadratic SBL models. The optimal conditions were emulsifier concentrations of 0.45% and 0.76% w/w and homogenization pressures of 10,790 and 10,781 psi for HL and SBL, respectively. Under these conditions, DH = 34.9% and 33.08%, MDD = 241.9 and 543.6 nm, and PdI = 0.29 and 0.52 for HL and SBL, respectively. Full article

This study was conducted to evaluate the effect of varying dietary energy levels on milk production, feed intake, nutrient digestion and metabolism, and antioxidation function of lactating donkeys, and integrating 16S rRNA gene sequencing, metabolomics, and proteomics to comprehensively reveal the underlying regulatory networks. A single-factor, completely randomized design was used in this study. Twenty-four Dezhou donkeys with similar milk yield (3.25 ± 0.46 kg/d), lactation days (29 ± 4.34 d), parities (4.17 ± 1.17), and body weight (256 ± 34 kg) were randomly divided into three dietary treatments (n = 8), and either a fed high-energy diet (DE = 13.1 MJ/kg, HED), medium-energy diet (DE = 12.4 MJ/kg, MED), and low-energy diet (DE = 11.7 MJ/kg, LED). The experiment period included 2 weeks for adaptation and 8 weeks for data and sample collection. Orthogonal polynomial contrasts were used to evaluate the linear and quadratic effects of increasing dietary energy. There were no significant interaction effects between dietary energy level and lactation week on any milk production and quality variables (p > 0.05). Increasing dietary energy level increased DMI, milk production, milk production efficiency, and milk components (linear and quadratic; p < 0.05). Increasing dietary energy improved the digestibility of DM and neutral detergent fiber (linear; p < 0.05), and crude protein digestibility, energy digestibility and metabolism, and nitrogen metabolism (quadratic; p < 0.05). However, it decreased BHBA and NEFA concentrations (linear; p < 0.05). Furthermore, increasing dietary energy first increased then decreased the activities of GSH-PX, SOD, and T-AOC (linear and quadratic; p < 0.05), while increasing the MDA content (linear; p < 0.05). Compared with HED and MED, LED increased the relative abundance of the genera unclassified_f_Syntrophomonadaceae, Christensenellaceae_R-7_group and Treponema_2. Compared with HED, MED increased the relative abundance of the genera Ruminiclostridium_5, Ruminiclostridium_1, Family_XIII_UCG-001, unclassified_o__Clostridiales and norank_f__PL-11B10. Thyroid hormone synthesis, tyrosine metabolism, and glutathione metabolism pathways are critical metabolic routes; these pathways can enhance energy metabolism and antioxidant function, thereby improving the milk production performance of lactating donkeys. In conclusion, the digestible energy of 12.40 MJ/kg was optimal for the milk performance of lactating donkeys, whereas excessively high dietary energy (13.1 MJ/kg) may reduce milk performance. Full article

Background: Body mass index (BMI) is widely used as a simple anthropometric indicator, but its functional relevance to physical fitness in physically active populations, such as Physical Education students, remains debated. Aim: This study examined the association between BMI and selected components of physical fitness in Physical Education students, considering sex and country differences. Methods: A cross-sectional study was conducted among undergraduate Physical Education students from Poland and Romania (n = 515; mean age: 21.64 ± 1.34 years). BMI was calculated from measured height and body mass and analyzed as both a continuous and categorical variable. Physical fitness was assessed using three Eurofit tests evaluating upper-limb movement speed, trunk muscular endurance, and lower-limb explosive power. Analyses included correlation methods and multiple linear regression models with subgroup analyses, interaction terms, and quadratic BMI terms to assess nonlinearity. Results: Associations between BMI and fitness components were small in magnitude and inconsistent (r = −0.28 to 0.143; β = −1.614 to 0.005) and varied across tests and subgroups. No significant interaction effects by sex or country were observed, as interaction terms were not statistically significant, and no clear nonlinear relationships were identified. Sex and country were significantly associated with performance levels, whereas BMI contributed only marginally to explaining variability (ΔR² = 0.005–0.011). Conclusions: BMI showed limited and inconsistent associations with the assessed fitness components in this relatively homogeneous group of Physical Education students. It should be interpreted cautiously as a functional indicator and complemented with more precise measures of body composition and physical fitness. Full article

Deep learning has revolutionized medical image segmentation; however, the clinical deployment of state-of-the-art models is severely impeded by their quadratic computational complexity and substantial resource demands, particularly in multisensor and multimodal imaging scenarios. In response, the field is undergoing a paradigm shift towards efficiency, characterized by the rise of linear-complexity architectures and the optimization of foundation models. This paper presents a comprehensive survey of efficient medical image segmentation methodologies, systematically reviewing the evolution from heavy, accuracy-driven models to lightweight, deployment-ready paradigms. In particular, we highlight the growing importance of efficient segmentation in multisensor medical imaging, where heterogeneous data sources such as CT, MRI, ultrasound, and infrared imaging introduce additional challenges in scalability and computational cost. We propose a novel taxonomy that categorizes these advancements into four distinct streams: (1) Mamba and State Space Models, which leverage selective scanning mechanisms to achieve global receptive fields with linear complexity; (2) Efficient Adaptation of Foundation Models, focusing on parameter-efficient fine-tuning and knowledge distillation to tailor the Segment Anything Model (SAM) for medical domains; (3) Advanced Lightweight Architectures, covering the resurgence of large-kernel CNNs and the emergence of Kolmogorov–Arnold Networks (KANs); and (4) Data-Efficient Strategies, including semi-supervised and federated learning to address annotation scarcity. Furthermore, we conduct a rigorous comparative analysis of representative algorithms on mainstream benchmarks, providing a granular evaluation of the trade-offs between segmentation accuracy and computational overhead. The survey also discusses key challenges in multisensor and multimodal settings, including modality heterogeneity, data fusion complexity, and resource constraints. Finally, we identify critical challenges and outline future research directions, serving as a roadmap for the development of next-generation efficient and scalable medical image analysis systems. Full article

Seismic-induced torsional response remains a significant barrier to achieving resilient and sustainable building foundations, as traditional passive isolation systems often fail to regulate rotational motion effectively. This study examines an adaptive gyroscopic feedback-based foundation control system designed to provide automated torsional seismic mitigation. The proposed system integrates real-time angular velocity sensing using MEMS gyroscopes, Kalman filter state estimation, and an adaptive Linear Quadratic Regulator to modulate damping in response to changing ground motion. A single-degree-of-freedom torsional foundation model was developed and evaluated in GNU Octave 8.4.0/MATLAB R2024a Simulink using the recorded El Centro 1940 NS earthquake input. The adaptive controller achieved notable improvements, reducing total vibration energy by 69%, peak angular displacement by 47.6%, and RMS angular velocity by 39.5% relative to the uncontrolled case, while keeping control energy below 19% of the seismic input. These results demonstrate that gyroscopic feedback enhances damping, limits torsional resonance, and stabilises foundation behaviour under actual earthquake excitation. The system’s low energy requirement, compatibility with embedded hardware, and automated response characteristics underscore its potential for integration into sustainable and intelligent foundation designs. While results are demonstrated using the El Centro 1940 record as a benchmark, broader generalisation will be established through multi-record suites and uncertainty quantification in future work. The study highlights a feasible pathway for advancing automated seismic protection in buildings through active, sensor-driven torsional control. Full article

Reservoir computing (RC) is an efficient framework for processing time-series data. This work investigates the synchronization of two independently trained reservoir computers that, after training, operate without external input from the chaotic system and interact solely through symmetric linear coupling. This approach addresses a gap in existing reservoir computing-based synchronization studies, which predominantly rely on master–slave or system-driven configurations. In this work, we first build and train two reservoir computing models based on 3D nonlinear chaotic maps and hyperchaotic systems and then introduce a symmetric linear coupling mechanism between them. This study demonstrates that reservoir computing can accurately reproduce the short-term dynamics of chaotic systems and provides insight into the interactions between learned dynamical models, while also helping us understand how complex systems connect and operate collectively. We use this systematic approach to establish a framework for understanding how two trained reservoir computers interact under varying coupling strengths, enabling a detailed investigation of their synchronization behavior. To demonstrate the adaptability of the proposed framework to diverse dynamical behaviors, we systematically investigated three discrete chaotic and hyperchaotic systems: (1) discrete 3D sinusoidal map with discrete Lorenz attractor, (2) 3D sinusoidal map with conjoined Lorenz twin attractor, and (3) 3D quadratic hyperchaotic map. For performance evaluation, we trained coupled RCs and computed the synchronization error for different coupling strengths. We also present phase portraits and time-series plots of the attractors and RCs, along with the synchronization error as a function of the coupling strength, thereby demonstrating the possibility of synchronization of two linearly coupled RCs, which are independently trained on discrete, three-dimensional chaotic and hyperchaotic systems. Full article

Background/Objectives: Whole-blood viscosity (WBV) is increasingly used in cardiovascular risk assessment; however, inter-device comparability may depend on shear-rate definition. We performed a paired comparison of two scanning capillary viscometers to evaluate shear-dependent analytical agreement and its impact on clinical classification. Methods: In 300 identical blood samples, WBV was measured using Rheovis 2000A and Hemovister. Systolic WBV was defined at 300 s⁻¹ for both devices (shear-matched), whereas clinically defined diastolic WBV corresponded to 1 s⁻¹ for Rheovis 2000A and 5 s⁻¹ for Hemovister. Agreement was assessed using linear regression and Bland–Altman analysis. Hematocrit tertiles were examined as effect modifiers. Clinical agreement was evaluated using quadratic weighted Cohen’s κ. Results: Across matched shear rates (1000 to 1 s⁻¹), Hemovister yielded consistently higher WBV values than Rheovis 2000A, with statistically significant inter-device differences at all shear levels except 1000 s⁻¹. The magnitude of bias increased progressively as shear rate decreased, reaching −8.34 mPa·s at 1 s⁻¹. Under shear-matched systolic conditions (300 s⁻¹), the mean difference was −0.25 mPa·s (limits of agreement −1.72 to 1.22). In contrast, under clinically defined diastolic conditions (1 vs. 5 s⁻¹), the mean difference was 14.54 mPa·s (3.93 to 25.15), increasing across hematocrit tertiles. Clinical agreement was fair for systolic (κ = 0.31; 95% CI 0.24 to 0.39) and moderate for diastolic WBV (κ = 0.44; 95% CI 0.37 to 0.51). Notably, among samples classified as high by Hemovister, 72.8% (systolic) and 54.0% (diastolic) were reclassified as normal by Rheovis 2000A. Conclusions: Inter-device agreement in WBV measurement is strongly shear-dependent. Although numerical divergence increases at low shear, categorical concordance may remain moderate when device-specific reference thresholds are applied. Harmonization of shear definitions and reference frameworks may therefore be essential for consistent cross-platform interpretation. Full article