Search Results (1,205)

This paper presents a novel algebraic saturation-based Proportional–Integral–Derivative (ASB-PID) controller for achieving ultra-fast and well-damped dynamic response in automatic voltage regulator (AVR) systems. The proposed controller incorporates an algebraic saturation-based nonlinear transformation applied to both the error signal and its derivative, enabling adaptive control sensitivity across different operating regions. This formulation preserves high sensitivity near the equilibrium point while effectively limiting excessive control action under large transient deviations, thereby overcoming the inherent trade-off between response speed and overshoot observed in conventional PID-based controllers. To address the highly nonlinear and multimodal tuning problem, the controller parameters are optimally determined using the Animated Oat Optimization Algorithm (AOOA), which provides strong global exploration capability and stable convergence behavior. The effectiveness of AOOA is first validated through comparative analysis with widely used metaheuristic algorithms, including Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). Furthermore, the proposed controller is benchmarked against recently developed high-performance AVR control strategies, including Gudermannian-PID (G-PID), fractional-order PID (FOPID), and higher-order PID-based controllers. Simulation results demonstrate that the proposed AOOA-optimized ASB-PID controller achieves a rise time of 0.0215 s and a settling time of 0.0383 s with zero overshoot and negligible steady-state error, significantly outperforming both competing optimization algorithms and state-of-the-art control designs. Comprehensive benchmarking further confirms that the proposed method consistently delivers superior performance in terms of speed, stability, and robustness, indicating that it provides an effective, computationally efficient, and scalable solution for high-performance AVR systems and broader nonlinear control applications. Unlike conventional nonlinear PID designs based on hyperbolic or sigmoid mappings, the proposed algebraic formulation provides a more explicit and effective saturation mechanism, enabling a superior balance between transient speed and overshoot suppression without increasing controller complexity. Full article

In poplar, the protracted stable genetic transformation procedure constrains rapid gene functional analyses. To address this limitation, we optimized a protocol for the high-yield isolation and efficient transient transformation of protoplasts from leaves of tissue-cultured poplar 84K (Populus alba × Populus glandulosa). Through systematic refinement, we determined that an enzymatic digestion solution containing 3% cellulase R-10, 0.3% macerozyme R-10, 0.8% pectolyase R-10, and 0.4 M mannitol was optimal. This formulation, applied over a 3 h digestion period, yielded 12.9 × 10⁶ protoplasts per gram fresh weight, with 93.45% viability. Furthermore, we optimized the parameters for polyethylene glycol -mediated transformation. Using 60 µg of plasmid DNA, 40% polyethylene glycol 4000, and a 20 min incubation, we achieved a high transfection efficiency of 68.67%. The established transient expression system thus provides a reliable, rapid, and effective platform for functional characterization-related studies, such as subcellular localization, protein–protein interactions, and gene expression analyses in poplar, thereby supporting molecular breeding applications. Full article

In arid inland watersheds, the compounding impacts of climate change and intensive human activities have severely altered hydrological regimes and accelerated landscape degradation. However, conventional spatial planning often overlooks the critical coupling between subsurface hydrological processes and surface landscape dynamics. Taking the Manas River Watershed in northwestern China as a representative case, this research investigates the multi-scale dynamics of landscape patterns and their underlying spatial determinants. Integrating multi-period land-use data (2000–2020), landscape metrics, and the GeoDetector model, we diverge from conventional uniform buffer approaches by redefining riparian boundaries utilizing four distinct River–Groundwater Transformation (RGT) patterns. This methodological shift reveals critical eco-hydrological heterogeneities previously masked by fixed-width approaches. Our multi-scale analyses demonstrate that watershed-level landscapes exhibited a trajectory of declining diversity, transient recovery, and ultimately, intensified fragmentation, while riparian patches concurrently expanded and became increasingly homogenized. GeoDetector assessments indicate a fundamental shift in driving forces: early-stage variations were constrained by natural factors, whereas post-2010 dynamics became overwhelmingly dominated by socio-economic determinants, particularly agricultural expansion and GDP growth. Crucially, our RGT-coupled spatial analysis reveals a strong spatial association between agricultural sprawl and landscape risk hotspots concentrated within groundwater overflow zones—a pattern consistent with, but not directly demonstrating, disrupted vertical hydrological connectivity. Direct verification of subsurface mechanisms would require continuous piezometric monitoring beyond the scope of this study. Consequently, rather than generic zoning, we propose a multi-scale “hydro-spatial” governance framework featuring targeted interventions. By establishing strict agricultural redlines in vulnerable overflow zones and implementing eco-hydrological restoration tailored to specific RGT regimes, this paradigm delivers robust methodological insights for advancing precision spatial planning in fragile arid ecosystems. Full article

Lithium-ion battery thermal runaway has become a key safety hazard restricting the development of electric vehicles. Early precursor signals of thermal runaway are characterized by multi-scale features, weak signal strength and spatial coupling, posing significant challenges for traditional methods in achieving accurate early warning. To solve this problem, a multi-scale spatiotemporal attention network (MSTA-Net) is proposed for battery thermal runaway early warning. First, a systematic feature engineering process is designed, including signal denoising, normalization processing and multi-level feature construction, to fully extract discriminative information from voltage and temperature signals. Then, the MSTA-Net architecture is constructed, which includes three parallel feature extraction branches: local fine perception branch based on 1D depthwise separable convolution to capture transient anomalies, a temporal evolution modeling branch based on bidirectional gated recurrent units to learn long-term trends, and a global spatial dependence branch based on a graph attention network to model the spatial propagation of thermal runaway. Finally, an adaptive fusion gate is designed to dynamically fuse the features of each branch according to the input context. The experimental results on the self-built battery thermal runaway dataset show that the proposed MSTA-Net achieves a recall rate of 98.7%, an average early warning time of 115 s and a false alarm rate of 0 times/h. Compared with traditional machine learning and deep learning models such as Random Forest, LSTM and Transformer, the model has significant advantages in early warning accuracy, timeliness and robustness. Ablation experiments verify the effectiveness of each component of the MSTA-Net. The proposed method can provide reliable early warning of thermal runaway only by using the existing voltage and temperature sensors of the battery management system, which has important engineering application value. Full article

In this paper, a novel anti-windup prescribed performance terminal sliding mode control method is proposed for the fixed-time tracking problem of a nonholonomic constrained mobile deicing manipulator system with model uncertainty and external disturbance. Firstly, a fixed-time preset performance function related to the initial error is proposed to constrain and transform the tracking error, so as to ensure that the tracking error of the system converges in a fixed time and has good transient and steady-state performance. Secondly, in order to accelerate the convergence to the equilibrium state, a fast terminal sliding mode surface with preset performance tracking error and a new fixed time reaching rate are constructed. By using Lyapunov analysis, the global fixed-time convergence of the scheme is theoretically verified. The control method is compared with the FTSMC method through simulation experiments, and the effectiveness of the designed control method is further verified. Full article

Pharmaceuticals and personal care products (PPCPs) are widely recognized as persistent contaminants in urban aquatic systems, yet their behavior is typically interpreted under steady-state assumptions driven by continuous discharge of treated wastewater. This paradigm overlooks the dominant role of episodic pollution pulses associated with combined sewer overflow (CSO) events. This review advances a new conceptual framework in which PPCP contamination is understood as a manifestation of complex phenomenon, arising from the interaction of intense precipitation, hydraulic exceedance of sewer systems, and mobilization of accumulated contaminants. We critically synthesize current knowledge on the occurrence, transport, transformation, and removal of PPCPs across wastewater effluents and CSO discharges, integrating insights from degradation kinetics, environmental monitoring, and treatment technologies. Comparative analysis reveals strong matrix-dependent variability in PPCP attenuation, with enhanced degradation in estuarine and marine systems driven by complex photochemical and biogeochemical interactions. However, under CSO-driven pulse conditions, these processes become transient and non-linear, challenging conventional assumptions of steady-state degradation and risk assessment. The findings highlight that CSO events can generate short-duration but high-intensity contamination peaks, often exceeding baseline concentrations and potentially amplifying ecological risks and antimicrobial resistance selection. We propose a matrix-reactivity and pulse-driven framework to better capture the dynamic fate of PPCPs under real-world conditions. Future research should prioritize event-based monitoring, real-time sensing, and time-resolved risk assessment models to address the limitations of current approaches. This work redefines PPCP pollution as a dynamic, episodic, extreme-event-driven process, with important implications for urban water management under increasing climatic variability. Full article

This paper presents a systematic design of a predefined-time sliding mode controller with integrated prescribed performance strategies, tailored to the longitudinal dynamics of hypersonic vehicles (HSVs) operating under strict real-time and high-precision requirements. A prescribed error transformation function is introduced to simultaneously constrain both transient and steady-state behaviors. This transformation converts the original constrained tracking problem into an equivalent unconstrained stabilization problem, thereby simplifying the controller synthesis. Based on the decomposed control-oriented state-space equations, separate sliding mode controllers are designed for the velocity and attitude subsystems. The proposed strategy guarantees that the tracking errors converge to zero within a user-predefined time, while strictly satisfying the prescribed performance bounds at every stage of the closed-loop response. The efficacy of the method is validated through numerical simulations. Full article

Complex human activities consist of sequential, simple limb movements, acting as impulse responses from the motor system. In short-term human activity recognition (ST-HAR), the inherently brief observation window results in non-stationary signals and “information starvation,” breaking the time-translational symmetry of kinetic signals. Moreover, traditional Variational Mode Decomposition (VMD) and Hilbert Transform (HT) suffer from suboptimal decomposition levels (K) and spectral asymmetry. This paper proposes an improved VMD-HT framework to enhance feature extraction from short-term Inertial Measurement Unit (IMU) signals. First, an instantaneous-frequency-driven adaptive VMD method is developed to mitigate mode mixing by automatically determining the optimal K. Second, an information-enhanced instantaneous energy density (IEIE) feature is introduced. By fusing kinetic energy from both positive and negative frequency domains, this feature restores the spectral symmetry of the energy representation, precisely quantifying fine motion variations and compensating for information loss caused by the limited temporal span. Experimental results on PAMAP2, WARD, and a self-collected dataset, NOITOM, demonstrate the method’s effectiveness. With a 0.5 s window, the proposed model achieves outstanding recognition accuracies of 93.60%, 96.41%, and 97.22%, respectively, outperforming state-of-the-art approaches in capturing transient short-term information. Full article

Inter-well interference in multi-well pad development is a critical factor influencing the recovery efficiency of shale gas reservoirs. This study presents a comprehensive semi-analytical model to characterize the transient pressure behavior and interference mechanisms of multi-well multi-stage fractured horizontal wells (MFHWs). Utilizing point source functions and the principle of superposition, the model accounts for complex shale gas transport mechanisms, including gas desorption, diffusion, and real-gas compressibility via pseudo-pressure transformation. The proposed model is validated against the industrial standard numerical simulator KAPPA-Saphir, showing an excellent match across most flow regimes, with a maximum relative error of 3.2% and an average relative error of less than 1% across the entire production period. The results identify five distinct flow stages: fracture linear flow, fracture radial flow, compound linear flow, compound radial flow, and boundary-dominated flow. Sensitivity analysis reveals that decreasing the inter-well spacing significantly shortens the fracture radial flow duration, while longitudinal staggering of wellbore centers effectively mitigates early-time interference and promotes more uniform reservoir drainage. Furthermore, it is observed that in multi-well systems, inner wells suffer from more severe energy competition and faster pressure depletion than peripheral wells. Based on these findings, it is proposed that the inter-well spacing should exceed four times the fracture half-length, and a staggered fracture arrangement (the relative positions in the x-direction of the fractures between the wells are not the same) should be prioritized. This work provides a robust theoretical framework and practical guidelines for optimizing well spacing and infill drilling strategies in shale gas reservoirs. Full article

Time–frequency analysis (TFA) facilitates the extraction of instantaneous frequency (IF) features from non-stationary signals. Transient Extraction Transform (TET) produces highly concentrated time–frequency representations (TFRs) for pulse-like fault vibration signals. Nevertheless, in noisy environments characterized by strong impulsive-like infinite-variance processes with a low characteristic exponent α, the performance of TET degrades significantly. First, an adaptive fractional-order low-order statistical function (AFLOF) is defined, which adaptively determines optimal parameter values based on noise impulse level. Subsequently, a robust transient extraction transform (RTET) based on AFLOF is proposed to accommodate various α-stable distribution environments defined by characteristic metrics, thereby facilitating the efficient acquisition of time–frequency features from rapidly varying signals. To mitigate the blurring effects faced by conventional TET algorithms in linear TFA-like scenarios, a robust generalized instantaneous extraction transform (RGTET) is further constructed. The computational framework of RGTET is rigorously derived, along with its corresponding inverse transform expression. Compared with existing methods, RGTET demonstrates superior adaptive performance in parameter adaptation. Finally, the proposed methods are applied to analyze pulse-like mechanical fault vibration signals and conduct time–frequency analysis of continuous signals under complex background noise conditions. The results demonstrate the robustness and adaptability of the proposed RTET and RGTET methods. Full article

A Controllable Line-Commutated Converter (CLCC) is a novel piece of equipment for enhancing the commutation failure resistance of High-Voltage Direct Current (HVDC) transmission systems. Traditional lumped parameter models ignore the high-frequency coupling effects of internal distributed stray capacitances, resulting in insufficient transient simulation accuracy and restricting refined engineering design. Taking the CLCC in the HVDC transformation project as the research object, this paper analyzes the distribution characteristics of stray parameters in a press-pack Insulated Gate Bipolar Transistor (IGBT) under stacked structures. By integrating distributed stray parameter networks with the nonlinear characteristics of the devices, an improved IGBT equivalent circuit model is established, with key parameters identified based on field-measured data. Furthermore, an LCC-CLCC simulation model is built and used to replace the improved IGBT model to conduct short-circuit fault simulation verification. The results demonstrate that the high-fidelity model accurately reproduces transient waveforms under Alternating Current (AC) voltage disturbance and faithfully reflects the actual operating characteristics of a surge arrester and IGBT, thereby effectively compensating for the idealized errors inherent in traditional models. This modeling methodology provides a robust theoretical and simulation foundation for parameter optimization, valve control system design, and the secure operation of a CLCC. Full article

Condition monitoring in engineering systems requires analytical frameworks that connect physically meaningful signal representations with statistically consistent decision mechanisms. Although spectral analysis, time–frequency methods, and machine learning have each advanced significantly, they are often treated as separate methodological domains. This work presents a unified structural framework that integrates classical spectral techniques, time–frequency representations, and supervised learning within a coherent monitoring architecture. Rather than providing a systematic survey, the study adopts a conceptual perspective to explicitly describe the analytical linkage between signal transformation, feature construction, and statistical inference. The discussion begins with Fourier-based descriptors and power spectral density formulations, and extends to short-time Fourier transform and continuous wavelet transform frameworks, highlighting their resolution characteristics for non-stationary signals. These representations are then connected to feature-space construction and learning-based decision models through an explicit mapping between physical signal properties and statistical inference mechanisms. An illustrative synthetic analysis is included to demonstrate how representation fidelity influences feature-space structure and downstream classification behaviour under transient conditions. These results are intended to provide conceptual insight rather than generalizable performance claims. Applications across multiple engineering domains are discussed to highlight the generality of the proposed framework. Finally, key research challenges, including dynamic operating regimes, data imbalance, interpretability, and computational constraints, are outlined. The proposed framework emphasises the complementary roles of transform-based representation and learning-based inference, providing a structured foundation for scalable and interpretable condition monitoring systems. Full article

Bolt loosening is a critical precursor to structural failure in major industrial and transportation equipment. Although acoustic non-destructive testing (NDT) offers a cost-effective diagnostic solution, its practical deployment is often hindered by low signal-to-noise ratios (SNRs) and the limited ability of conventional models to isolate fine-grained transient acoustic signatures from complex background interference. To address these challenges, this paper proposes PCT-Net, a multi-scenario noise-adaptive fusion network for bolt-state recognition. First, an Adaptive Spectral Masking mechanism is introduced as a data augmentation strategy. Instead of rigid zero-padding, it dynamically blends local spectral energies to encourage the learning of more robust and noise-invariant representations. Furthermore, rather than simply concatenating multiple modules, PCT-Net adopts a synergistic feature extraction framework to decouple complex acoustic signatures. A perceptual frontend is used to establish acoustically meaningful representation priors. To handle the highly dispersed characteristics of loosening signals, cascaded convolutional modules progressively suppress redundant environmental interference while capturing high-frequency local transient impacts. Meanwhile, to overcome the limited receptive field of convolutional operations, an embedded Transformer mechanism is introduced to model long-range temporal dependencies and low-frequency structural variations throughout the tapping cycle. By integrating local fine-grained transient modeling with global structural dependency modeling, the proposed network can better distinguish subtle decision boundaries among different loosening states. Extensive experiments show that PCT-Net achieves a classification accuracy of 97.12% under standard conditions and maintains stable performance under severe noise scenarios. These results demonstrate the effectiveness of the proposed method and highlight its potential for intelligent industrial safety monitoring. Full article

Extracting evoked potentials (EPs) from single trials in electroencephalography (EEG) remains a major challenge due to a characteristically low signal-to-noise ratio (SNR). This paper presents an enhanced denoising framework that combines multiresolution wavelet transform (MWT) with a statistical resampling technique. A key contribution is the introduction of an SNR-based preprocessing step that assesses individual trials and discards those with an SNR below 0 dB to prevent heavily corrupted data from degrading the analysis. Unlike traditional methods that rely on Gaussian noise assumptions, our approach utilizes empirical resampling to estimate optimal wavelet coefficient thresholds in a fully data-driven manner. Hard thresholding is subsequently applied to isolate transient neural events from background fluctuations. The method was validated using synthetic signals and real EEG recordings from ten subjects (aged 20–31 years) performing an Eriksen flanker task. Results from simulations demonstrated a significant mean SNR improvement of 13 dB. In real data applications, the error-monitoring components (Ne and Pe) were clearly identified at the single-trial level, with peak latencies observed at approximately 180 ms and 220 ms, respectively. This approach enables reliable single-trial EP analysis without the need for templates or multichannel recordings, offering a robust tool for brain–computer interfaces and clinical diagnostics. Full article

Background/Objectives: Early transient endocrine dysfunction after simultaneous pancreas-kidney transplantation (SPK) frequently triggers urgent investigations to exclude thrombosis, pancreatitis, or rejection, yet many recipients recover during the index admission. We tested whether pre-transplant day zero (D0) serum Fourier-transform infrared spectroscopy (FTIRS) captures a biochemical fingerprint associated with a Start&Stop trajectory (initial insulin independence followed by transient dysfunction with recovery). Methods: In a single-center retrospective case-control study nested within 104 consecutive SPK recipients with available D0 serum, 12 Start&Stop cases were matched 1:1 to 12 No-Stop controls. Serum FTIR spectra went through structured quality control and standardized preprocessing. A Naïve Bayes classifier with Fast Correlation-Based Filter (FCBF) feature selection was evaluated using leave-one-out cross-validation (LOOCV) and label-permutation analysis. Results: Under LOOCV, the primary FTIRS model (Savitzky-Golay second derivative; 600–900 and 2800–3400 cm⁻¹) achieved excellent discrimination (ROC-AUC 1.00) with accuracy 0.958 and F1 score 0.958. Discrimination collapsed under label permutation (ROC-AUC 0.461), supporting a non-random label-spectrum association. Discriminant information mapped mainly to carbohydrate/glycoprotein-associated bands (~946–1161 cm⁻¹), protein structural contributions near the amide III region (~1300 cm⁻¹), and lipid/protein stretching modes (~2865–3163 cm⁻¹), consistent with a multicomponent systemic biochemical state. Conclusions: In this exploratory matched case-control cohort, pre-transplant D0 serum FTIRS signatures were associated with the subsequent Start&Stop phenotype after SPK. These findings should be interpreted as recipient-side exploratory risk-stratification signals rather than clinically actionable decision tools. Larger multicenter validation in unselected cohorts, with standardized endpoint adjudication, preanalytical control, fully nested model development and inter-instrument harmonization, is required before clinical implementation or population-level risk calibration. Full article