Search Results (505)

Downhole kick is one of the most severe safety hazards in deep and ultra-deep well drilling operations. Traditional monitoring methods, which rely on surface flow rate and fluid level changes, are limited by their delayed response and insufficient sensitivity, making them inadequate for early warning. This study proposes a real-time monitoring technique for gas content in drilling fluid based on the attenuation principle of Ba-133 γ-rays. By integrating laboratory static/dynamic experiments and Geant4-11.2 Monte Carlo simulations, the influence mechanism of gas–liquid two-phase media on γ-ray transmission characteristics is systematically elucidated. Firstly, through a comparative analysis of radioactive source parameters such as Am-241 and Cs-137, Ba-133 (main peak at 356 keV, half-life of 10.6 years) is identified as the optimal downhole nuclear measurement source based on a comparative analysis of penetration capability, detection efficiency, and regulatory compliance. Compared to alternative sources, Ba-133 provides an optimal energy range for detecting drilling fluid density variations, while also meeting exemption activity limits (1 × 10⁶ Bq) for field deployment. Subsequently, an experimental setup with drilling fluids of varying densities (1.2–1.8 g/cm³) is constructed to quantify the inverse square attenuation relationship between source-to-detector distance and counting rate, and to acquire counting data over the full gas content range (0–100%). The Monte Carlo simulation results exhibit a mean relative error of 5.01% compared to the experimental data, validating the physical correctness of the model. On this basis, a nonlinear inversion model coupling a first-order density term with a cubic gas content term is proposed, achieving a mean absolute percentage error of 2.3% across the full range and R² = 0.999. Geant4-based simulation validation demonstrates that this technique can achieve a measurement accuracy of ±2.5% for gas content within the range of 0–100% (at a 95% confidence interval). The anticipated field accuracy of ±5% is estimated by accounting for additional uncertainties due to temperature effects, vibration, and mud composition variations under downhole conditions, significantly outperforming current surface monitoring methods. This enables the high-frequency, high-precision early detection of kick events during the shut-in period. The present study provides both theoretical and technical support for the engineering application of nuclear measurement techniques in well control safety. Full article

The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration. Full article

In this work, we introduce IK-AUG, a unified algorithmic framework for kinematics-driven data augmentation tailored to sign language recognition (SLR). Departing from traditional augmentation techniques that operate at the pixel or feature level, our method integrates inverse kinematics (IK) and virtual simulation to synthesize anatomically valid gesture sequences within a structured 3D environment. The proposed system begins with sparse 3D keypoints extracted via a pose estimator and projects them into a virtual coordinate space. A differentiable IK solver based on forward-and-backward constrained optimization is then employed to reconstruct biomechanically plausible joint trajectories. To emulate natural signer variability and enhance data richness, we define a set of parametric perturbation operators spanning spatial displacement, depth modulation, and solver sensitivity control. These operators are embedded into a generative loop that transforms each original gesture sample into a diverse sequence cluster, forming a high-fidelity augmentation corpus. We benchmark our method across five deep sequence models (CNN3D, TCN, Transformer, Informer, and Sparse Transformer) and observe consistent improvements in accuracy and convergence. Notably, Informer achieves 94.1% validation accuracy with IK-AUG enhanced training, underscoring the framework’s efficacy. These results suggest that algorithmic augmentation via kinematic modeling offers a scalable, annotation free pathway for improving SLR systems and lays the foundation for future integration with multi-sensor inputs in hybrid recognition pipelines. Full article

Modern manufacturing demands real-time, scalable coordination that legacy manufacturing management systems cannot provide. Digital transformation encompasses the entire manufacturing infrastructure, which can be represented by digital twins for facilitating efficient monitoring, prediction, and optimization of factory operations. A Federated Digital Twin (FDT) emerges by combining heterogeneous digital twins, enabling real-time collaboration, data sharing, and collective decision-making. However, deploying FDTs introduces new concurrency control challenges, such as priority inversion and synchronization failures, which can potentially cause process delays, missed deadlines, and reduced customer satisfaction. Traditional concurrency control approaches in the computing domain, due to their reliance on static priority assignments and centralized control, are inadequate for managing dynamic, real-time conflicts effectively in real production lines. To address these challenges, this study proposes a novel concurrency control framework combining Deep Reinforcement Learning with the Priority Ceiling Protocol. Using SimPy-based discrete-event simulations, which accurately model the asynchronous nature of FDT interactions, the proposed approach adaptively optimizes resource allocation and effectively mitigates priority inversion. The results demonstrate that against the rule-based PCP controller, our hybrid DRLCC enhances completion time maximum of 24.27% to a minimum of 1.51%, urgent-job delay maximum of 6.65% and a minimum of 2.18%, while preserving lower-priority inversions. Full article

The two-stage proportional valve is a key control component in heavy-duty equipment, where its signal-flow characteristics critically influence operational performance. This study proposes an innovative flow characteristic regulation method using variable-gain feedback grooves. Unlike conventional throttling notch optimization, the core mechanism actively adjusts pilot–main valve mapping through feedback groove shape and area gain adjustments to achieve the desired flow curves. This approach avoids complex throttling notch issues while retaining the valve’s high dynamics and flow capacity. Mathematical modeling elucidated the underlying mechanism. Subsequently, trapezoidal and composite feedback grooves are designed and investigated via simulation. Finally, composite feedback groove spools tailored to construction machinery operating conditions are developed. Comparative experiments demonstrate the following: (1) Pilot–main mapping inversely correlates with area gain; increasing gain enhances micro-motion control, while decreasing gain boosts flow gain for rapid actuation. (2) This method does not significantly increase pressure loss or energy consumption (measured loss: 0.88 MPa). (3) The composite groove provides segmented characteristics; its micro-motion flow gain (2.04 L/min/0.1 V) is 61.9% lower than conventional valves, significantly improving fine control. (4) Adjusting groove area gain and transition point flexibly modifies flow gain and micro-motion zone length. This method offers a new approach for high-performance valve flow regulation. Full article

This paper investigates the well-posedness of the inverse problem for the time-fractional Burgers equation, which aims to reconstruct initial conditions from terminal observations. Such equations are crucial for the modeling of hydrodynamic phenomena with memory effects. The inverse problem involves inferring initial conditions from terminal observation data, and such problems are typically ill-posed. A framework based on optimal control theory is proposed, addressing the ill-posedness via $H^1$ regularization. Three substantial results are achieved: (1) a rigorous mathematical framework transforming the ill-posed inverse problem into a well-posed optimization problem with proven existence of solutions; (2) theoretical guarantee of solution uniqueness when the regularization parameter is $\alpha >0$ and the stability is of order O($\delta$) with respect to observation noise ($\delta$); and (3) the discovery of a “super-stability” phenomenon in numerical experiments, where the actual stability index (0.046) significantly outperforms theoretical expectations (1.0). Finally, the theoretical framework is validated through comprehensive numerical experiments, demonstrating the accuracy and practical effectiveness of the proposed optimal control approach for the reconstruction of hydrodynamic initial conditions. Full article

The urgent need for complex terrain adaptability in industrial automation and disaster relief has highlighted the great potential of octopedal wheel-legged robots. However, their design complexity and motion control challenges must be addressed. In this study, an innovative design approach is employed to construct a highly adaptive robot architecture capable of intelligently adjusting the wheel-leg configuration to cope with changing environments. An advanced kinematic analysis and simulation techniques are combined with inverse kinematic algorithms and dynamic planning to achieve a typical ‘Step-Wise Octopedal Dynamic Coordination Gait’ and different gait planning and optimization. The effectiveness of the design and control strategy is verified through the construction of an experimental platform and field tests, significantly improving the robot’s adaptability and mobility in complex terrain. Additionally, an optional integrated quadrotor module with a compact folding mechanism is incorporated, enabling the robot to overcome otherwise impassable obstacles via short-distance flight when ground locomotion is impaired. This achievement not only enriches the theory and methodology of the multi-legged robot design but also establishes a solid foundation for its widespread application in disaster rescue, exploration, and industrial automation. Full article

Diamond is an attractive substrate candidate for GaN high-electron-mobility transistors (HEMT) to enhance heat dissipation due to its exceptional thermal conductivity. However, the thermal boundary resistance (TBR) at the GaN–diamond interface poses a significant bottleneck to heat transport, exacerbating self-heating and limiting device performance. In this work, TCAD simulations were employed to systematically investigate the effects of thermal boundary layer (TBL) thickness (d_TBL) and thermal conductivity (κ_TBL) on the electrothermal behavior of GaN-on-diamond HEMTs. Results show that increasing the TBL thickness (5–20 nm) or decreasing its thermal conductivity (0.1–1.0 W/(m·K)) leads to elevated hotspot temperatures and degraded electron mobility, resulting in a notable deterioration of I–V characteristics. The nonlinear dependence of device performance on κ_TBL is attributed to Fourier’s law, where heat flux is inversely proportional to thermal resistance. Furthermore, the co-analysis of substrate thermal conductivity and interfacial quality reveals that interface TBR has a more dominant impact on device behavior than substrate conductivity. Remarkably, devices with low thermal conductivity substrates and optimized interfaces can outperform those with high-conductivity substrates but poor interfacial conditions. These findings underscore the critical importance of interface engineering in thermal management of GaN–diamond HEMTs and provide a theoretical foundation for future work on phonon transport and defect-controlled thermal interfaces. Full article

The increasing demand for sustainable disease management in aquaculture has intensified interest in plant-based therapeutics. This study evaluated the formulation and efficacy of andrographolide-loaded nanostructured lipid carriers (AND-NLCs) in Nile tilapia (Oreochromis niloticus) challenged with Streptococcus agalactiae ENC06. AND-NLCs were prepared by the phase-inversion technique and characterized by dynamic light scattering, transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), and in vitro release profiling. Antibacterial activity was assessed by measuring inhibition zone diameters, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC). Growth performance, feed utilization, hepatosomatic index (HSI), and disease resistance were evaluated over a 60-day feeding trial. The AND-NLCs exhibited an optimal particle size (189.6 nm), high encapsulation efficiency (90.58%), sustained release, and structural stability. Compared to the free AND and control group, AND-NLC supplementation significantly improved growth, feed efficiency, HSI, and positive allometric growth. It also enhanced survival (73.3%) and relative percent survival (RPS = 65.6%) following S. agalactiae ENC06 infection. Antibacterial efficacy and physiological responses showed positive correlations with nanoparticle characteristics. These findings suggest that AND-NLCs enhance bioavailability and therapeutic efficacy, supporting their potential as a functional dietary additive to promote growth and improve disease resistance in tilapia aquaculture. Full article

This work systematically addresses the dual challenges of non-inertial dynamic coupling and kinematic constraint redundancy encountered in dynamic modeling of serial–parallel–serial hybrid robotic mechanisms, and proposes an improved Newton–Euler modeling method with constraint compensation. Taking the Skiing Simulation Platform with 6-DOF as the research mechanism, the inverse kinematic model of the closed-chain mechanism is established through $G_F$ set theory, with explicit analytical expressions derived for the motion parameters of limb mass centers. Introducing a principal inertial coordinate system into the dynamics equations, a recursive algorithm incorporating force/moment coupling terms is developed. Numerical simulations reveal a 9.25% periodic deviation in joint moments using conventional methods. Through analysis of the mechanism’s intrinsic properties, it is identified that the lack of angular momentum conservation constraints on the end-effector in non-inertial frames leads to system controllability degradation. Accordingly, a constraint compensation strategy is proposed: establishing linearly independent differential algebraic equations supplemented with momentum/angular momentum balance equations for the end platform. Co-Simulation results demonstrate that the optimized model reduces the maximum relative error of actuator joint moments to 0.98%, and maintains numerical stability across the entire configuration space. The constraint compensation framework provides a universal solution for dynamics modeling of complex closed-chain mechanisms, validated through applications in flight simulators and automotive driving simulators. Full article

This study examined the regulatory mechanism of calcium (Ca) amendment on the dynamics of soil organic carbon (SOC) fractions and extracellular enzyme activities, elucidating the role of Ca in soil carbon cycling processes. A field experiment with maize was conducted, comparing treatments of low calcium (T1), high calcium (T2), and a calcium-free control (CK). Measurements included inter-root SOC fractions—soluble organic carbon (DOC), microbial biomass carbon (MBC), and readily oxidizable organic carbon (ROC)—and the activities of the following extracellular enzymes: β-xylanase, β-glucosidase (β-glu), phenol oxidase (Phox), peroxidase (Pero), phosphatase (Phos), acetylaminoglucosidase (NAG), and urease. The main findings indicated the following: (1) Calcium addition significantly increased SOC content (115.04% and 99.22% higher in T1 and T2, respectively, than CK during the entire reproductive period) and enhanced microbial activity (elevated DOC and MBC). However, SOC decreased by 8.44% (T1) and 16.38% (T2) relative to CK in the late reproductive stage (irrigation–ripening), potentially reflecting microbial utilization (supported by the inverse correlation between SOC and MBC/DOC), and maize carbon reallocation during grain filling. (2) Calcium activated β-glu, Phox, Phos, NAG, and urease (p < 0.05), with pronounced increases in Phox (241.13 IU·L⁻¹) and Phos (1126.65 U·L⁻¹), indicating enhanced organic matter mineralization and phosphorus availability. (3) Calcium-driven MBC and ROC accumulation was associated with the positive regulation of Phox (path coefficient > 0.8) and the negative regulation of Phos. SOC was co-regulated by β-glu and Phos (R² = 0.753). (4) Calcium dynamically optimized the short-term carbon distribution through enzyme activity while promoting long-term sequestration. Our study provides new evidence supporting multi-pathway interactions through which calcium mediates enzyme networks to influence the soil carbon cycle. The findings provide a theoretical foundation for calcium fertilizer management and soil carbon sequestration strategies in agriculture, advancing academic and practical goals for sustainable development and carbon neutrality. Full article

Background: The widespread use of smartphones and the internet has transformed communication, but excessive use has raised concerns about smartphone and internet addiction, which can lead to psychological, physical, and social issues. The objective of this literature review is to explore the relationship between smartphone and internet addiction and physical activity, particularly focusing on whether physical exercise, especially sports, can serve as a protective factor against addiction. The review aims to examine how physical activity can reduce the negative impacts of addiction and improve overall mental health. Methods: This review synthesizes empirical research on smartphone and internet addiction and its connection to physical activity. It examines studies exploring how addiction leads to physical inactivity and how participation in physical activities, especially sports, can counteract this effect. The review also evaluates research on psychological mechanisms, such as self-esteem, self-control, and emotional resilience, that mediate the relationship between physical activity and addiction. Additionally, it discusses how sociodemographic and contextual factors influence this relationship. Conclusions: The findings consistently show an inverse relationship between smartphone and internet use and physical activity, with physical activity acting as a protective factor against addiction. Sports and other physical activities have been linked to reduced addictive behaviors, enhanced psychological well-being, and improved emotional resilience. Promoting physical activity, particularly sports, along with psychological interventions, appears to be an effective strategy for preventing and treating smartphone and internet addiction. Future research should focus on developing tailored interventions and studying diverse populations to optimize addiction prevention. Full article

Inverse Laplace transforms (ILTs) are fundamental to a wide range of scientific and engineering applications—from diffusion NMR spectroscopy to medical imaging—yet their numerical inversion remains severely ill-posed, particularly in the presence of noise or sparse data. The primary objective of this study is to develop robust and efficient numerical methods that improve the stability and accuracy of ILT reconstructions under challenging conditions. In this work, we introduce a novel family of Kaczmarz-based ILT solvers that embed advanced regularization directly into the iterative projection framework. We propose three algorithmic variants—Tikhonov–Kaczmarz, total variation (TV)–Kaczmarz, and Wasserstein–Kaczmarz—each incorporating a distinct penalty to stabilize solutions and mitigate noise amplification. The Wasserstein–Kaczmarz method, in particular, leverages optimal transport theory to impose geometric priors, yielding enhanced robustness for multi-modal or highly overlapping distributions. We benchmark these methods against established ILT solvers—including CONTIN, maximum entropy (MaxEnt), TRAIn, ITAMeD, and PALMA—using synthetic single- and multi-modal diffusion distributions contaminated with 1% controlled noise. Quantitative evaluation via mean squared error (MSE), Wasserstein distance, total variation, peak signal-to-noise ratio (PSNR), and runtime demonstrates that Wasserstein–Kaczmarz attains an optimal balance of speed (0.53 s per inversion) and accuracy (MSE = $4.7\times {10}^{-8}$), while TRAIn achieves the highest fidelity (MSE = $1.5\times {10}^{-8}$) at a modest computational cost. These results elucidate the inherent trade-offs between computational efficiency and reconstruction precision and establish regularized Kaczmarz solvers as versatile, high-performance tools for ill-posed inverse problems. Full article

An active rotor with trailing edge flaps (TEFs) is an effective method for helicopter vibration elimination. The nonlinear hysteresis of piezoelectric actuators used to drive TEFs can adversely affect helicopter vibration control performance. In this paper, a hysteresis modeling and compensation study is performed for piezoelectric actuators used in TEFs. Firstly, the hysteresis characteristics of a rhombic frame actuator with input voltages at different frequencies are investigated by bench-top tests. Subsequently, the Bouc–Wen model is adopted to establish the hysteresis model of the piezoelectric actuator, with its parameters identified through the particle swarm optimization (PSO) algorithm. Experimental results demonstrate that the proposed model is capable of accurately capturing the hysteresis phenomenon of the piezoelectric actuator within the frequency range of 10–60 Hz. Finally, a compound control regime is established by integrating inverse Bouc–Wen model control with fuzzy PID feedback control. The experimental results indicate that the developed compound control regime can significantly suppress the piezoelectric actuator hysteresis of TEFs within the frequency bandwidth of 10–60 Hz, which lays the foundation for improving the vibration control performance of the active rotor with TEFs in the future. Full article

Hydrogel particles, renowned for their high water content and biocompatibility in drug delivery and tissue engineering, typically rely on complex, costly microfluidic systems to reach sub 5 µm dimensions. We present a vortex-based inverse-emulsion polymerization strategy in which UV crosslinking of polyethylene glycol diacrylate (PEGDA) dispersed in n-hexadecane and squalene yields tunable micro- and nanogels while delineating the parameters that govern particle size and uniformity. Systematic variation in surfactant concentration, vessel volume, continuous phase viscosity, vortex speed and duration, oil-to-polymer ratio, polymer molecular weight, and pulsed vortexing revealed that increases in surfactant level, vortex intensity/duration, vessel volume, and oil-to-polymer ratio each reduced mean diameter and PDI, whereas higher polymer molecular weight and continuous phase viscosity broadened the size distribution. We further investigated how these same parameters can be tuned to shift particle populations between nano- and microscale regimes. Under optimized conditions, microhydrogels achieved a coefficient of variation of 0.26 and a PDI of 0.07, with excellent reproducibility, and nanogels measured 161 nm (PDI = 0.05). This rapid, cost-effective method enables precise and scalable control over hydrogel dimensions using only standard laboratory equipment, without specialized training. Full article