Search Results (1,091)

Eutrophication has emerged as a critical threat to water quality degradation and ecosystem health on a global scale, calling for prompt management actions. Remote sensing enables the monitoring of eutrophication by detected changes in ocean color caused by fluctuations in chlorophyll a (chl a). Although chl a is a crucial indicator of phytoplankton biomass and nutrient overloading, it reflects the outcome of eutrophication rather than its cause. Nutrients, the primary “drivers” of eutrophication, are essential indicators for predicting the potential phytoplankton growth in water bodies, allowing adoption of effective preventive measures. Long-term monitoring of nutrients combined with multiple water quality indicators using remotely sensed data could lead to a more precise assessment of the trophic state. Retrieving non-optically active constituents, such as nutrients and DO, remains challenging due to their weak optical characteristics and low signal-to-noise ratios. This work is an attempt to review the current progress in the retrieval of un-ionized ammonia (NH₃), ammonium (NH₄⁺), ammoniacal nitrogen (AN), nitrite (NO₂⁻), nitrate (NO₃⁻), dissolved inorganic nitrogen (DIN), phosphate (PO₄³⁻), dissolved inorganic phosphorus (DIP), silicate (SiO₂) and dissolved oxygen (DO) using remotely sensed data. Most studies refer to Case II highly nutrient-enriched water bodies. The commonly used spaceborne and airborne sensors, along with the selected spectral bands and band indices, per study area, are presented. There are two main model categories for predicting nutrient and DO concentration: empirical and artificial intelligence (AI). Comparative studies conducted in the same study area have shown that ML and NNs achieve higher prediction accuracy than empirical models under the same sample size. ML models often outperform NNs when training data are limited, as they are less prone to overfitting under small-sample conditions. The incorporation of a wider range of conditions (e.g., different trophic state, seasonality) into model training needs to be tested for model transferability. Full article

The electric network frequency (ENF) serves as a vital criterion in geographical localization because its frequency fluctuations remain consistent within the same power grid. However, the performance of existing ENF-based audio geo-localization methods is limited when dealing with real-world scenarios, such as short audio durations and noisy environments. Moreover, the size of available ENF data is still small. To address these issues, we propose a novel audio inter-grid geo-localization method utilizing real-time online multimedia data. First, we construct the China-Online-Data dataset using online data, which integrates enhancement and harmonic selection to reduce noise and improve ENF estimation accuracy. Subsequently, we propose an ENF-based Dual-Channel Geo-Localization Network (DC-GLNet), which leverages both time and time-frequency domain information to improve feature extraction and classification performance. Experimental results demonstrate that the proposed method outperforms existing methods, particularly in short audio scenarios, achieving superior accuracy for inter-grid geo-localization. Full article

The dynamics of systems near tipping points attract considerable attention in the context of climate change, ecological regime shifts, disease spreading, and other complex systems undergoing transitions. In particular, the duration and cause of transitions between states remain subjects of ongoing debate. We address these questions by applying the large-fluctuation framework to analyse noise-induced transitions in a widely studied tipping model describing dynamics near a fold bifurcation. As complex systems are typically not in equilibrium, we include cyclic perturbations representing, for example, diurnal variations, seasonal cycles, solar activity oscillations, and Milankovitch cycles in the climate system. We investigate how the frequency and type of cyclic perturbation influence noise-induced transitions between states by examining the fluctuational force. Two types of periodic perturbations, additive and multiplicative, representing B- and R-tipping, are considered. We show, first, that depending on the type of cyclic perturbation, the fluctuations need to be synchronised with different perturbation phases to induce the transition. Secondly, we demonstrate that the transition duration depends on the perturbation frequency: when the periodic perturbation is slower than the system’s relaxation rate, the transition occurs within a single oscillatory cycle, whereas high-frequency perturbations can significantly prolong the transition time. Full article

Cavitation erosion is a major concern in hydraulic systems exposed to strong pressure fluctuations. Well-developed experimental techniques exist for detecting cavitation based on measuring induced noise or vibrations, but additional tools are needed to assess its aggressiveness under operating conditions. This study investigates the capability of acoustic emission (AE) to characterise cavitation aggressiveness during long-duration cloud cavitation. A 50 h erosion test was performed in a closed-loop cavitation tunnel using a Venturi equipped with an aluminium 7075-T6 specimen. Hydraulic conditions were controlled to maintain a constant cavity length, and AE signals were recorded every 10 min during two representative 4 h intervals at 34–38 h and 46–50 h. A new AE-derived power parameter was defined using the amplitude distribution of AE envelope peaks. Both the number of impacts and the power parameter increased markedly from the intermediate to the final interval, consistent with the growth of erosion and increasing surface roughness. Conversely, both quantities decreased systematically within each 2 h test as water temperature increased. Image analysis of selected areas confirmed the progression of pitting between 34 and 50 h. Overall, the findings demonstrate that AE can capture the combined influence of temperature and surface degradation on cavitation aggressiveness, highlighting its potential as a monitoring technique for hydraulic components. Full article

The safe and stable operation of distribution networks relies on the real-time monitoring, analysis, and feedback of power quality data. However, with the continuous advancement of distribution network construction, the number of distributed power electronic devices has increased significantly, leading to frequent power quality issues such as voltage fluctuations, harmonic pollution, and three-phase unbalance in distribution terminals. Therefore, the augmentation and processing of power quality data have become crucial for ensuring the stable operation of distribution networks. Traditional methods for augmenting and processing power quality data fail to consider the differentiated characteristics of burrs in signal sequences and neglect the comprehensive consideration of both time-domain and frequency-domain features in disturbance identification. This results in the distortion of high-frequency fault information, and insufficient robustness and accuracy in identifying Power Quality Disturbance (PQD) against the complex noise background of distribution networks. In response to these issues, we propose a power quality data augmentation and processing method for distribution terminals considering high-frequency sampling. Firstly, a burr removal method of the sampling waveform based on a high-frequency filter operator is proposed. By comprehensively considering the characteristics of concavity and convexity in both burr and normal waveforms, a high-frequency filtering operator is introduced. Additional constraints and parameters are applied to suppress sequences with burr characteristics, thereby accurately eliminating burrs while preserving the key features of valid information. This approach avoids distortion of high-frequency fault information after filtering, which supports subsequent PQD identification. Secondly, a PQD identification method based on a dual-channel time–frequency feature fusion network is proposed. The PQD signals undergo an S-transform and period reconfiguration to construct matrix image features in the time–frequency domain. Finally, these features are input into a Convolutional Neural Network (CNN) and a Transformer encoder to extract highly coupled global features, which are then fused through a cross-attention mechanism. The identification results of PQD are output through a classification layer, thereby enhancing the robustness and accuracy of disturbance identification against the complex noise background of distribution networks. Simulation results demonstrate that the proposed algorithm achieves optimal burr removal and disturbance identification accuracy. Full article

This study presents a novel softsign-function-based fractional-order proportional–integral–derivative (softsign-FOPID) controller optimized using the fungal growth optimizer (FGO) for the stabilization and precise position control of an unstable magnetic ball suspension system. The proposed controller introduces a smooth nonlinear softsign function into the conventional FOPID structure to limit abrupt control actions and improve transient smoothness while preserving the flexibility of fractional dynamics. The FGO, a recently developed bio-inspired metaheuristic, is employed to tune the seven controller parameters by minimizing a composite objective function that simultaneously penalizes overshoot and tracking error. This optimization ensures balanced transient and steady-state performance with enhanced convergence reliability. The performance of the proposed approach was extensively benchmarked against four modern metaheuristic algorithms (greater cane rat algorithm, catch fish optimization algorithm, RIME algorithm and artificial hummingbird algorithm) under identical conditions. Statistical analyses, including boxplot comparisons and the nonparametric Wilcoxon rank-sum test, demonstrated that the FGO consistently achieved the lowest objective function value with superior convergence stability and significantly better (p < 0.05) performance across multiple independent runs. In time-domain evaluations, the FGO-tuned softsign-FOPID exhibited the fastest rise time (0.0089 s), shortest settling time (0.0163 s), lowest overshoot (4.13%), and negligible steady-state error (0.0015%), surpassing the best-reported controllers in the literature, including the sine cosine algorithm-tuned PID, logarithmic spiral opposition-based learning augmented hunger games search algorithm-tuned FOPID, and manta ray foraging optimization-tuned real PIDD². Robustness assessments under fluctuating reference trajectories, actuator saturation, sensor noise, external disturbances, and parametric uncertainties (±10% variation in resistance and inductance) further confirmed the controller’s adaptability and stability under practical non-idealities. The smooth nonlinearity of the softsign function effectively prevented control signal saturation, while the fractional-order dynamics enhanced disturbance rejection and memory-based adaptability. Overall, the proposed FGO-optimized softsign-FOPID controller establishes a new benchmark in nonlinear magnetic levitation control by integrating smooth nonlinear mapping, fractional calculus, and adaptive metaheuristic optimization. Full article

Correlations in complex systems are often obscured by nonstationarity, long-range memory, and heavy-tailed fluctuations, which limit the usefulness of traditional covariance-based analyses. To address these challenges, we construct scale- and fluctuation-dependent correlation matrices using the multifractal detrended cross-correlation coefficient ${\rho }_r$ that selectively emphasizes fluctuations of different amplitudes. We examine the spectral properties of these detrended correlation matrices and compare them to the spectral properties of the matrices calculated in the same way from synthetic Gaussian and q-Gaussian signals. Our results show that detrending, heavy tails, and the fluctuation-order parameter r jointly produce spectra, which substantially depart from the random case even under the absence of cross-correlations in time series. Applying this framework to one-minute returns of 140 major cryptocurrencies from 2021 to 2024 reveals robust collective modes, including a dominant market factor and several sectoral components whose strength depends on the analyzed scale and fluctuation order. After filtering out the market mode, the empirical eigenvalue bulk aligns closely with the limit of random detrended cross-correlations, enabling clear identification of structurally significant outliers. Overall, the study provides a refined spectral baseline for detrended cross-correlations and offers a promising tool for distinguishing genuine interdependencies from noise in complex, nonstationary, heavy-tailed systems. Full article

Neuronal circuits arise as axons and dendrites extend, navigate, and connect to target cells. Axonal growth, in particular, integrates deterministic guidance from substrate mechanics and geometry with stochastic fluctuations generated by signaling, molecular detection, cytoskeletal assembly, and growth cone dynamics. A comprehensive quantitative description of this process remains incomplete. We review stochastic models in which Langevin dynamics and the associated Fokker–Planck equation capture axonal motion and turning under combined biases and noise. Paired with experiments, these models yield key parameters, including effective diffusion (motility) coefficients, speed and angle distributions, mean-square displacement, and mechanical measures of cell–substrate coupling, thereby linking single-cell biophysics and intercellular interactions to collective growth statistics and network formation. We further couple the Fokker–Planck description to a mechanochemical actin–myosin–clutch model and perform a linear stability analysis of the resulting dynamics. Routh–Hurwitz criteria identify regimes of steady extension, damped oscillations, and Hopf bifurcations that generate sustained limit cycles. Together, these results clarify the mechanisms that govern axonal guidance and connectivity and inform the design of engineered substrates and neuroprosthetic scaffolds aimed at enhancing nerve repair and regeneration. Full article

Deep-ultraviolet (DUV, 193 nm) tools for lithography and precision micromachining are often limited by beam-profile nonuniformity, which degrades critical-dimension control, line-edge roughness, and process windows. Conventional phase-dependent homogenizers can lose performance under realistic phase noise and pointing jitter. We investigate two complementary, energy–space-modulation routes to robust homogenization: (i) a metalens-based microlens array (MLA) that forms a flat-top via controlled beamlet overlap and (ii) a chromium-on-sapphire attenuator that equalizes intensity purely by amplitude shaping. Coupled FDTD and optical modeling guide a graded-transmittance Cr design (target transmittance 0.8–0.9) that converts a Gaussian input into a flat-top plateau. Experiments at 193 nm verify that both approaches achieve high static uniformity (Urms <3.5%). Under dynamic conditions, the MLA exhibits sensitivity to transverse-mode hops and phase fluctuations due to its reliance on coherent overlap, leading to reduced uniformity and fill factor. In contrast, the Cr attenuator remains phase-insensitive and maintains stable output under jitter, offering a power-robust, low-maintenance alternative for industrial DUV systems. We discuss design trade-offs and outline hybrid MLA + attenuation schemes that preserve MLA-level flatness while approaching the robustness of amplitude-shaping solutions. Full article

Accurate microclimate forecasting is essential for optimizing agricultural decision-making and resource management within Internet of Things (IoT)-enabled farming systems. This study proposes an Attention-Enhanced Dual-Branch Spatio-Temporal Deep Neural Network with Station Embeddings model designed for robust spatio-temporal multisensor temperature forecasting across heterogeneous environmental stations. The model integrates multisensor data parameters within a sliding-window temporal framework to capture both short-term fluctuations and long-term dependencies. Comprehensive experiments were conducted using data from two meteorological stations to evaluate model accuracy, generalization, and robustness against sensor noise. Results show that the proposed model outperforms both classical and persistence-based baselines, achieving an average RMSE of 1.65 °C and R² of 0.94 on test datasets. Feature correlation and importance analyses confirmed that the model learns physically meaningful relationships—particularly the influence of soil temperature and humidity on air temperature dynamics—while residual and convergence analyses verified its stability and unbiased learning behavior. Beyond algorithmic validation, this study highlights how the proposed model can be integrated into precision-agriculture systems for adaptive irrigation control, crop-growth forecasting, and microclimate-based disease-risk assessment. The model provides a scalable foundation for real-time IoT deployment on edge devices, enabling continuous environmental monitoring and intelligent actuation. These results demonstrate that data-driven deep learning models can bridge algorithmic forecasting and operational decision-making, contributing to sustainable and efficient agricultural management. Full article

The dissipative Kerr soliton (DKS) microcomb has been regarded as a highly promising multi-wavelength laser source for optical fiber communication, due to its excellent frequency and phase stability. However, in some specific application scenarios, such as direct modulation and direct detection (DM/DD), the relative intensity noise (RIN) performance of Kerr optical combs still fails to meet the requirements. Here, we systematically investigate the key factors that contribute to the power fluctuations in DKS combs. By exploiting the gain saturation effect of the semiconductor optical amplifier (SOA), the RIN of an on-chip DKS microcomb is effectively suppressed, achieving a maximum reduction of about 30 dB (@600 kHz offset frequency) for all comb lines. Moreover, such DKS comb RIN suppression technology based on an SOA chip can eliminate the need for additional complex feedback control circuits, showcasing the potential for further chip integration of the ultra-low-RIN DKS microcomb system. Full article

Against the backdrop of high-penetration distributed photovoltaic (DPV) integration into distribution networks, the limited measurability of small-scale DPV systems poses significant challenges to accurately estimating the baseline load of electric vehicle (EV) clusters. To address this issue, effective forecasting of DPV power output becomes essential. This paper proposes a domain-adversarial architecture for ultra-short-term DPV power prediction, designed to support baseline load estimation for EV clusters. The power output of DPV systems is influenced by scattered geographical distribution and abrupt weather changes, leading to complex spatiotemporal distribution shifts. These shifts result in a notable decline in the generalization capability of traditional models that rely on historical statistical patterns. To enhance the robustness of models in complex and dynamic environments, this paper proposes a domain-adversarial architecture for ultra-short-term DPV power forecasting, explicitly designed to address spatiotemporal distribution shifts by extracting spatiotemporal invariant features robust to distribution shifts. First, a Graph Attention Network (GAT) is utilized to capture spatial dependencies among PV stations, characterizing asynchronous power fluctuations caused by factors such as cloud movement. Next, the spatiotemporally fused features generated by the GAT are adaptively partitioned into multiple distribution domains using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), providing pseudo-supervised signals for subsequent adversarial learning. Finally, a Temporal Convolutional Network (TCN)-based domain-adversarial mechanism is introduced, where gradient reversal training forces the feature extractor to discard domain-specific characteristics, thereby effectively extracting spatiotemporal invariant features across domains. Experimental results on real-world distributed PV datasets validate the effectiveness of the proposed method in improving prediction accuracy and generalization capability under transitional weather conditions. Full article

Magnetic levitation (Maglev) technology has long garnered significant attention in the engineering community due to its inherent advantages, such as contactless operation, minimal friction losses, low noise, and high precision. Based on electromagnetic suspension (EMS) and electrodynamic principles, these systems are primarily developed for advanced transportation, while also inspiring emerging applications such as vibration isolation and flywheel energy storage. Despite progress, practical deployment faces critical challenges, including accurate modeling, robustness against nonlinear and uncertain dynamics, and control stability under complex conditions. Artificial intelligence (AI), particularly machine learning (ML) offers promising solutions. Studies show ML-based methods, i.e., improved particle swarm optimization (PSO) optimize proportional-integral-derivative (PID) to reduce controller output overshoot, deep reinforcement learning (DRL) reduces levitation gap fluctuation under complex conditions, ensemble learning achieves high fault diagnosis accuracy, and convolutional neural network-long short-term memory (CNN-LSTM) predictive maintenance cuts costs. This review summarizes recent AI-enabled advances in Maglev transportation system modeling, control, and optimization, highlighting representative algorithms, performance comparisons, technical challenges, and future directions toward intelligent, reliable, and energy-efficient transportation systems. Full article

Electric-field noise near ion-trap electrodes limits motional coherence and represents a key obstacle to scaling trapped-ion quantum systems. Here, we investigate how in situ Ar⁺ sputtering modifies motional heating and dephasing in multi-material surface-electrode traps. Trapped ions serve as local probes of electric-field fluctuations before and after controlled sputtering cycles. The data reveal a non-monotonic dependence of both the dephasing rate and the electric-field noise on the extent of Ar⁺ sputtering, with coherence initially improving while heating rates increase, followed by a reversal at longer exposures. This behavior highlights the intricate balance between beneficial surface cleaning and detrimental structural modification, driven by changes in surface morphology, redeposition of sputtered material, and diffusion on the surface, underscoring the complex interplay between surface composition and motional stability in multi-material electrode systems. Post-treatment scanning electron microscopy and energy-dispersive X-ray spectroscopy confirm significant modification of the multilayer structure. Technical noise was independently verified to be well below the observed levels. These findings indicate that in situ sputtering modifies surface properties in ways that can either mitigate or enhance electric-field noise, underscoring the need for precise control of material interfaces in next-generation ion-trap architectures. Full article

In this paper, the inductance of a sensorless PMSM (Permanent Magnet Synchronous Motor) drive system equipped with a periodic load torque compensator based on a wavelet denoising and least-order observer with time-delay compensation is estimated in real-time. In a sensorless PMSM system with constant load torque, the magnetically saturated inductance value remains constant. This constant inductance error causes minor performance degradation, such as a constant rotor position estimation error and non-optimal torque current, but it does not introduce a speed estimation error. Conversely, in a sensorless PMSM motor system subjected to periodic load torque, the magnetically saturated inductance error fluctuates periodically. This fluctuation leads to periodic variations in both the estimated position error and the speed error, ultimately degrading the load torque compensation performance. This paper applies the maximum energy-to-Shannon entropy criterion for the optimal selection of the mother wavelet in the wavelet transform to remove the motor signal noise and achieve more accurate inductance estimation. Additionally, the coherence and correlation theory is proposed to address the time delay in the least-order observer and improve the time delay. A self-saturation compensation method is also proposed to minimize periodic speed fluctuations and improve control accuracy through inductance parameter estimation. Finally, experiments were conducted on a sensorless PMSM drive system to verify the inductance estimation performance and validate the effectiveness of vibration reduction. Full article