Search Results (2,717)

In this paper, we investigate the stability of a triangularly coupled triple-loop thermosyphon system with momentum and heat exchange at the coupling point as well as the existence of disturbances. The controller consists of a single, local-state feedback. From the stability analysis, we obtain explicit bounds on the feedback gains, which depend on the Rayleigh numbers and the momentum coupling parameter, but independent of the thermal coupling parameter. The existence of the stability bounds allows us to design decentralized adaptive controllers to automatically search for the feasible gains when the system parameters are unknown. In the case of existing disturbances in the system, we approximate the disturbances via an extended-state observer for the purpose of disturbance rejection. Numerical results are given to demonstrate the performance of the proposed decentralized disturbance rejection controller design. Full article

This paper presents a comprehensive analysis of the dynamic properties of low-power current transformers (LPCTs) in the context of their application in both power systems and electromechanical systems. Momentary changes in external loads occurring in the mechanical parts of systems, affecting their correct operation, cause the appropriate monitoring and control systems, including LPCTs, to operate in transient states where dynamic errors are significant. The issues discussed in this article are therefore important from both an electrical and mechanical engineering perspective. The study focuses on the evaluation of dynamic errors using two complementary performance criteria: the mean squared error and the absolute dynamic error. An equivalent circuit model of the LPCT is formulated and employed to investigate its response under transient conditions representative of modern energy networks as well as electromechanical devices, including drives, converters, and rotating machines operating under variable loads. A key contribution of this work is the determination of the upper bounds of dynamic errors, which establish the ultimate accuracy constraints of LPCTs when subjected to rapid current variations. The obtained results provide quantitative evidence of the impact of dynamic properties on the reliability of current measurements, thereby reinforcing the importance of the proposed error evaluation framework. In this context, the study demonstrates that a rigorous assessment of dynamic errors is essential for improving the functional performance of LPCTs, particularly in applications where steady-state accuracy must be complemented by a reliable transient response. Full article

This paper focuses on the issue of unmodeled dynamics and large-range parametric uncertainties in air-breathing hypersonic vehicles (AHV), proposing an adaptive dynamic surface control method based on radial basis function (RBF) neural networks. First, the hypersonic longitudinal model is transformed into a strict-feedback control system with model uncertainties. Then, based on backstepping control theory, adaptive dynamic surface controllers incorporating RBF neural networks are designed separately for the velocity and altitude channels. The proposed controller achieves three key functions: (1) preventing “differential explosion” through low-pass filter design; (2) approximating uncertain model components and unmodeled dynamics using RBF neural networks; (3) enabling real-time adjustment of controller parameters via adaptive methods to accomplish online estimation and compensation of system uncertainties. Finally, stability analysis proves that all closed-loop system signals are semi-globally uniformly bounded (SGUB), with tracking errors converging to an arbitrarily small residual set. The simulation results indicate that the proposed control method reduces steady-state error by approximately 20% compared to traditional controllers. Full article

We investigate the three-dimensional incompressible fractional Navier–Stokes system with directional fractional diffusion: a vertical dissipative operator of order $2\alpha \in (0,2]$ acting on the full velocity field together with a horizontal fractional operator of order $2\beta \in (0,2]$ applied to the vertical average of one horizontal component. This anisotropic, nonlocal structure captures media in which smoothing acts with unequal strength by direction. For small, divergence-free initial data in an anisotropic energy class, we establish global well-posedness and stability of the zero state, including uniqueness and continuous dependence on the data. The analysis crucially relies on an average oscillation decomposition in the vertical variable, a fractional Poincaré inequality aligned with the vertical direction, and sharp product/commutator bounds compatible with the anisotropic splitting. We provide explicit estimates for direction-dependent smoothing and algebraic decay governed by $(\alpha ,\beta )$, and we show that the conclusions persist under small perturbation of the dissipation parameters. Full article

A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem, a proportional-integral sliding mode surface is designed, and the control law is derived by minimizing a cost function that weights the predicted sliding mode surface and the control input. For the altitude subsystem, a backstepping control framework is adopted, with the SMPC strategy embedded in each step. Multi-source disturbances are modeled as composite additive disturbances, and an ELM-based neural network observer is constructed for their real-time estimation and compensation, thereby enhancing system robustness. The semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Simulation results demonstrate the comprehensive superiority of the proposed method: it achieves reductions in Root Mean Square Error (RMSE) of 99.60% and 99.22% for velocity and altitude tracking, respectively, compared to Prescribed Performance Control with Backstepping Control (PPCBSC), and reductions of 98.48% and 97.12% relative to Terminal Sliding Mode Control (TSMC). Under parameter uncertainties, the developed ELM observer outperforms RBF-based observer and Extended State Observer (ESO) by significantly reducing tracking errors. These findings validate the high precision and strong robustness of the proposed approach. Full article

Accurate trajectory tracking is crucial for fixed-wing unmanned aerial vehicles (UAVs) in executing diverse missions. However, the inherent strong nonlinearities, parametric uncertainties, and external disturbances in the UAV model present significant challenges for controller design. To address these challenges, this paper proposes a robust adaptive control strategy based on the backstepping technique. The proposed strategy effectively addresses a class of uncertainties with norm bounds that are unknown and state-dependent. An adaptive law is constructed to estimate the unknown parameters online, thereby enabling compensation for the effects of these uncertainties. Furthermore, to mitigate chattering, the controller is modified to generate smooth control inputs, ensuring that the steady-state tracking error is ultimately bounded and converges to an arbitrarily small neighborhood of zero. Simulation results demonstrate that, under realistic flight control sampling frequencies, the proposed controller achieves accurate trajectory tracking and eliminates the chattering phenomenon. Full article

This work presents and evaluates two threshold-based detection methods for the Six-State quantum key distribution protocol, considering a realistic scenario involving partial intercept–resend attack and channel noise. The statistical properties of the shared quantum bit error rate (QBER) are analyzed and used to estimate the attacker interception density from observed data. Building on this foundation, the work derives two optimal QBER detection thresholds designed to minimize both false positive and false negative rates, following, respectively, upper theoretical bounds and limit probability density function approach. A developed Qiskit simulation environment enables the evaluation and comparison of the two detection methods on simulated and real-inspired quantum systems with differing noise characteristics. This framework moves beyond theoretical analysis, allowing practical investigation of system noise effects on detection accuracy. Simulation results confirm that both methods are robust and effective, achieving high detection accuracy across all the tested configurations, thereby validating their applicability to real-world quantum communication systems. Full article

We investigate the thermal evolution of fermionic pairings in a finite-size SU(2) × SU(2) complex model, drawing an analogy to the BCS-BEC crossover in interacting quantum gases. Unlike the conventional crossover, which is driven by tuning the interaction strength, our study suggests that temperature alone can induce a smooth transition from weakly bound Cooper pairs (BCS-like state) to tightly bound dimers (BEC-like state). Using an exactly solvable model with a finite number of fermions, we analyze the structure of eigenstates, pairing correlations, and thermodynamic response functions. We demonstrate that different multiplet structures, characterized by distinct quasi-spin quantum numbers, become thermally accessible, effectively mimicking the crossover behavior seen in ultracold Fermi gases. Our results provide new insights into the role of thermal fluctuations in quantum pairing phenomena and suggest alternative routes for exploring crossover physics in mesoscopic and strongly correlated systems. Full article

Research on big data analytics (BDA) and supply chains often inventories “capabilities” but rarely explains how firms progress through adoption—or how governance over data and related resources shapes resilience outcomes. Drawing on 16 semi-structured interviews with senior managers in the manufacturing sector, we analyze organizational practices around data, analytics, and decision-making and synthesize a governed-adoption process framework. The framework specifies how five governance levers—ownership, standards, stewardship, access, lineage—operate differentially across four adoption gates (data plumbing—descriptive monitoring—predictive alerting—prescriptive decisioning). To move beyond staged descriptions, we make the underlying generative mechanisms explicit—Comparability, Explainability, Authorization, Fidelity, Executability—and link them to dynamic-capability micro foundations (sensing, seizing, reconfiguring) via decision-latency outcomes (“resilience timers”: Time-to-Detect, Time-to-Decide, Time-to-Reconfigure, Time-to-Recover). Brief deviant-case contrasts (e.g., notification without action; dashboards without owners) clarify boundary conditions under which governance enables or impedes resilient action. We also state concise, testable propositions (e.g., standards+lineage as a necessary condition for improving Time-to-Detect; ownership+access as necessary for improving Time-to-Decide) and provide gate exit-criteria to support evaluation and future comparative tests. Claims are bounded to analytic generalization from a single-country, manufacturing-sector qualitative sample; we make no assertion of statistical validation. Practically, the framework prioritizes governance work ahead of tool spend, helping organizations convert dashboards into repeatable decisions at speed. Full article

Object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging due to the prevalence of small targets, complex backgrounds, and the stringent requirement for real-time processing on computationally constrained platforms. Existing methods often struggle to balance detection accuracy, particularly for small objects, with operational efficiency. To address these challenges, this paper proposes YOLO-CAM, an enhanced object detector based on YOLOv5n. First, a novel Combined Attention Mechanism (CAM) is integrated to synergistically recalibrate features across both channel and spatial dimensions, enhancing the network’s focus on small targets while suppressing background clutter. Second, the detection head is strategically optimized by introducing a dedicated high-resolution head for tiny targets and removing a redundant head, thereby expanding the detectable size spectrum down to small pixels with reduced parameters. Finally, the CIoU loss is replaced with the inner-Focal-EIoU loss to improve bounding box regression accuracy, especially for low-quality examples and small objects. Extensive experiments on the challenging VisDrone2019 benchmark demonstrate the effectiveness of our method. YOLO-CAM achieves a mean Average Precision (mAP0.5) of 31.0%, which represents a significant 7.5% improvement over the baseline YOLOv5n, while maintaining a real-time inference speed of 128 frames per second. Comparative studies show that our approach achieves a superior balance between accuracy and efficiency compared to other state-of-the-art detectors. The results indicate that the proposed YOLO-CAM establishes a new way for accuracy–efficiency trade-offs in UAV-based detection. Due to its lightweight design and high performance, it is particularly suitable for deployment on resource-limited UAV platforms for applications requiring reliable real-time small object detection. Full article

Background/Objective: Voriconazole exhibits nonlinear pharmacokinetics and wide interindividual variability driven by CYP2C19 phenotype and clinical covariates, necessitating early therapeutic drug monitoring (TDM). This study aimed to assess how the choice of population pharmacokinetic (PopPK) models influences genotype-stratified voriconazole exposure under a standardized adult regimen, and to delineate model-specific implications for clinical prescribing. Methods: Five CYP2C19-informed PopPK models (Yun, Ling, Wang, Dolton, Friberg) were evaluated under one oral dosing scenario with an identical extensive metabolizers (EM)/intermediate metabolizer (IM)/poor metabolizers (PM) cohort; steady-state exposure metrics were compared across models, with sensitivity checks using model-specific cohorts. Results: Yun predicted the highest exposures with the steepest EM–IM–PM gradient, suggesting a need for caution against upper-tail exceedance when genotype effects are pronounced. Ling yielded intermediate exposures with a modest gradient, consistent with adult central tendencies, thus supporting its use for standard adult initial dosing. Wang primarily distinguished between EM and PM, proving useful for lower-bound checks where underexposure risk or limited genotype information is a concern. Friberg (and Dolton) demonstrated lower exposures with limited genotype separation, offering insights when persistent underexposure is suspected. Conclusions: These model-specific patterns indicate that PopPK model choice can influence initial dose-band selection and the timing of early TDM in routine adult care. Ling can serve as a baseline for standard adult initiation, whereas Yun is appropriate for safety-first scenarios when upper-tail risk from strong genotype effects is anticipated; Wang assists when IM data are lacking or when lower-bound checks are needed. Generalizability beyond standardized adult dosing (e.g., special populations) remains limited. Full article

Late-Archean seawater functioned as a vast, redox-tuned colloidal system for which its kinetics were largely governed by the surface chemistry of silica–iron nanoparticles. By reproducing Archean seawater (≈0.7 M ionic strength, 25 °C) in laboratory anoxic-to-mildly oxic reactors, the ζ potential (zeta-potential(ζ)) of silica–iron nanoparticles was investigated, and we tracked how transient O₂ pulses (≤9 mg L⁻¹) regulated it. The zeta (ζ) potential was applied as the key diagnostic parameter to quantify both the sign of the ζ potential and the colloidal stability of simulated silica–iron particles in dispersion. Under strictly anoxic conditions, silica colloids (SiO₂(aq)) exhibit a persistently negative ζ potential (ζ ≈ −25 mV) in the simulated seawater (pH 6.5), arising from deprotonated silanol groups (≡Si–O⁻). Upon the addition of Fe²⁺, the inner-sphere complexation of ferrous ions on SiO₂ colloids partially replaces ≡Si–O⁻ with ≡Si–O–Fe⁺/≡Si–O–Fe–OH sites; the net negative charge density at the outer Stern plane nevertheless increases, and the ζ potential shifts from −25 mV to −30 mV. As the simulated seawater was oxygenated, the dissolved and surface-bound Fe²⁺ ions were oxidized to Fe³⁺, causing the ζ potential to exceed −30 mV. This study demonstrates that Fe²⁺–silica interactions generate electrostatic destabilization, suspending micron-scale aggregates and thus modulating the solubility and speciation of SiO₂ in early oceans. Also, transient micro-oxic pulses are shown to shift silica–iron colloids between metastable aggregation and dispersion by modulating their ζ potential. Subsequently, AFM and TEM were used to characterize the morphological changes in the colloidal particles from the liquid state to the dry state. Furthermore, infrared and XPS analyses were conducted on the colloidal samples. These findings provide certain reference significance for reconstructing the chemical evolution process of seawater in the Late-Archean period and for understanding the factors influencing the silicon–iron cycle of seawater in the Late-Archean era. Full article

This paper presents a new robust control strategy for buck DC–DC converters that achieve fast and robust voltage regulation in the presence of load disturbances and model uncertainties. First, an adaptive state observer is designed to estimate the inductor current and capacitor voltage by utilizing the output measurement. The observer gains are tuned online via a Lyapunov-based adaptation law, ensuring that the estimation error remains uniformly bounded, even when the disturbances act on the system. Based on the state estimates, an integral sliding-mode controller is designed in order to eliminate the steady state error and ensure the finite time sliding. The detailed stability proofs for both the observer and the sliding-mode controller are derived showing the finite-time reaching of the sliding surface and exponential convergence of the voltage error. Simulation results under varying load profiles confirm that the proposed scheme outperforms traditional sliding-mode designs in terms of disturbance rejection and settling time, while avoiding excessive chattering. Full article

Deoxyribonucleic acid (DNA)-based biosensors are rapid, cost-effective, and portable devices for monitoring crop pathogens. However, their on-field operations rely on a laboratory-bound heating block, which controls temperature during sample preparation. This study aimed to develop a field-deployable heating block to assist in the DNA hybridization protocol of DNA-based biosensors. It should maintain $95 °\mathrm{C}$, $55 °\mathrm{C}$, and $20 °\mathrm{C}$ for 5, 10, and 5 min, respectively. It had aluminum bars, positive thermal coefficient ceramic heaters, a Peltier thermoelectric module, and DS18B20 thermistors, serving twelve 0.2 mL polymerase chain reaction (PCR) tubes. An Arduino microcontroller employing a proportional–integral–derivative (PID) algorithm with a solid-state relay was utilized. Machine performance for distilled water-filled PCR tubes showed a maximum $10 °\mathrm{C}$ thermal variation. The machine maintained $(96.00\pm 0.97) °\mathrm{C}$, $(55.15\pm 2.17) °\mathrm{C}$, and $(17.75\pm 0.71) °\mathrm{C}$ with root mean square errors (RMSEs) of $1.40 °\mathrm{C}$, $2.18 °\mathrm{C}$, and $2.36 °\mathrm{C}$, respectively. The average thermal rates were $(0.16\pm 0.11) °\mathrm{C}/\mathrm{s}$, $(0.29\pm 0.11) °\mathrm{C}/\mathrm{s}$, and $(0.14\pm 0.07) °\mathrm{C}/\mathrm{s}$ from ambient to $95 °\mathrm{C}$, $95 °\mathrm{C}$ to $55 °\mathrm{C}$, and $55 °\mathrm{C}$ to $20 °\mathrm{C}$, respectively. Overall, the low standard deviations and RMSEs demonstrate thermostable results and robust temperature control. Full article

Unmanned aerial vehicle (UAV) remote sensing is critical in assessing post-earthquake building damage. However, intelligent disaster assessment via remote sensing faces formidable challenges from complex backgrounds, substantial scale variations in targets, and diverse spatial disaster dynamics. To address these issues, we propose UEBNet, a high-precision post-earthquake building instance segmentation model that systematically enhances damage recognition by integrating three key modules. Firstly, the Depthwise Separable Convolutional Block Attention Module suppresses background noise that visually resembles damaged structures. This is achieved by expanding the receptive field using multi-scale pooling and dilated convolutions. Secondly, the Multi-feature Fusion Module generates scale-robust feature representations for damaged buildings with significant size differences by processing feature streams from different receptive fields in parallel. Finally, the Adaptive Multi-Scale Interaction Module accurately reconstructs the irregular contours of damaged buildings through an advanced feature alignment mechanism. Extensive experiments were conducted using UAV imagery collected after the Ms 6.8 earthquake in Tingri County, Tibet Autonomous Region, China, on 7 January 2025, and the Ms 6.2 earthquake in Jishishan County, Gansu Province, China, on 18 December 2023. Results indicate that UEBNet enhances segmentation mean Average Precision (${mAP}^{seg}$) and bounding box mean Average Precision (${mAP}^{box}$) by 3.09% and 2.20%, respectively, with equivalent improvements of 2.65% in F1-score and 1.54% in overall accuracy, outperforming state-of-the-art instance segmentation models. These results demonstrate the effectiveness and reliability of UEBNet in accurately segmenting earthquake-damaged buildings in complex post-disaster scenarios, offering valuable support for emergency response and disaster relief. Full article