Search Results (127)

High-resolution CTD profiles, with SVP cross-validation of the sound speed field, were recorded at a single station in the outer roadstead of the Port of Constanța (northwest Black Sea; 44°07′41″ N, 28°53′15″ E; depth ≈ 25 m; June 2024), revealing a strongly stratified, five-layer water column driven by three combined forcing mechanisms: seasonal thermal stratification with an abnormally shallow Cold Intermediate Water layer (7.3–15.6 m), Danube-sourced freshwater input, and anthropogenic disturbances consistent with port and anchorage activity. A contextual hypothesis is proposed that conflict-related marine traffic intensification may contribute to observed signals, but physical measurements cannot establish causation. At the main pycnocline (7.31–15.62 m), a density difference of Δρ = 4.02 kg m⁻³ yields a maximum Brunt–Väisälä frequency of N² = 2.37 × 10⁻³ s⁻², reducing vertical eddy diffusivity by two orders of magnitude (Kz ≈ 10⁻⁶ m² s⁻¹). Physical conditions—a shallow mixed layer (~0.7–1.2 m) and strong pycnocline—support the theoretical expectation of surface-layer contaminant accumulation; however, no chemical measurements were carried out to confirm contaminant presence. All contamination inferences rely exclusively on physical proxies (turbidity, dissolved oxygen, and density gradients), and contaminant retention remains untested for lack of direct chemical evidence. A dimensional Stratification-Controlled Retention Index (SCRI = N²/Kz; units: m⁻² s⁻¹) is introduced, and its consistency with the observed hydrographic structure is demonstrated. Full article

Efficient task assignment for mobile robots is a crucial challenge in modern intralogistics. This paper presents an integrated cyber-physical framework combining predictive tree search on switching max-plus linear systems with a physical IoT-based dispatch interface. The scheduling problem is modelled as a discrete event system, where standard max-plus algebra captures robot synchronization, and a switching mechanism represents alternative resource assignments. To address real-world operational disturbances, the predictive model is enhanced with a fault-tolerant control (FTC) mechanism that dynamically estimates and adapts to non-stationary transport delays. The resulting decision space, which grows exponentially with the prediction horizon, is explored via a predictive tree search algorithm utilizing a quadratic cost function to penalize excessive and uneven transport times. The physical dispatch layer is realized using KIS.BOX IoT devices acting as operator-controlled stations, communicating with the central controller via a WebSocket/STOMP event stream and a lightweight REST API. Simulation results obtained in a Blender 3D environment demonstrate that the proposed FTC predictive strategy significantly reduces the variance of task completion times under fault conditions compared to a baseline First-In-First-Out approach. Furthermore, the IoT integration successfully simulates and validates the feasibility of human-in-the-loop task injection within a realistic, stochastic scenario. Full article

Multirotor unmanned aerial vehicles (UAVs) rely on electronic speed controllers (ESCs) that decode motor commands from pulse-width modulation (PWM) signals, making the flight-controller-to-ESC command path a physical-layer attack surface for intentional electromagnetic interference (IEMI). This paper presents a mechanism-based analysis of IEMI attacks that induce motor stoppage in UAV brushless DC motor controllers. We develop a timing-error model in which a sinusoidal disturbance on the PWM line shifts the detected edge instants and drives the decoded pulse width into stop-equivalent regimes, and we show that the disturbance reaching the ESC’s thresholding node is shaped by a frequency-selective cascade of the PWM cable’s coupling response and the ESC’s input-path transfer function. We experimentally characterize this model on five commercial ESCs through conducted and radiated injection. The measured thresholds differ by more than an order of magnitude across ESCs and are reordered between frequency bands and injection modes; comparing conducted and radiated results allows us to attribute these differences primarily to the cable coupling response and reveals cases where it either hides or amplifies an ESC’s susceptibility. The susceptible frequency also shifts with PWM cable length in qualitative agreement with transmission-line resonance, confirming that observed radiated susceptibility reflects the joint design of ESC and cable rather than a single intrinsic property. The cable lengths examined here (45–125 cm) are longer than those of compact multirotors and were chosen to place resonances within our antenna’s band; we discuss the implications of this choice and identify shorter, deployment-realistic cables as a priority for future work. Full article

Liquid storage tanks are widely used in sectors such as water treatment, oil and gas, food processing, and chemical manufacturing. Knowing the exact amount of liquid in a tank is essential for ensuring safety, preventing spills, and optimizing process control; therefore, the liquid level in a tank must be maintained at a precise reference point. This is where liquid level control for tanks becomes crucial and constitutes a fundamental problem in the industrial sector due to nonlinearities, multivariable coupling, and stochastic disturbances. Given the drawbacks of available control methods, such as classical Model Predictive Control (MPC), which are highly dependent on model accuracy and struggle to reject complex stochastic noise, predicting random disturbances represents a major technological challenge. A new approach is proposed to specifically address the problem and challenge of the four-tank system, where water levels in two lower tanks must be controlled by two pumps, often with varying delays and significant parameter disturbances. To establish a relationship between expected performance and MPC parameters, this approach uses a novel hybrid nonlinear MPC, Extended State Observer, and Physics-Informed Neural State Estimation (NMPC-ESO-PINSE) architecture. A Physics-Informed Neural State Estimation (PINSE) layer, chosen for its learning capacity, is designed to filter sensor noise by applying Bernoulli’s physical laws, while an Extended State Observer (ESO) is integrated to capture and compensate for unmodeled uncertainties in the process. Finally, a proposed hybrid (NMPC-ESO-PINSE) strategy leverages these clean, physically consistent state estimations to solve a non-convex optimization problem via Sequential Quadratic Programming (SQP), computing optimal pump voltages. Extensive numerical simulations demonstrate the superior resilience of this decoupled framework against parametric drifts and continuous noise sequences, yielding a $+27.36\%$ reduction in global Root Mean Square Error (RMSE) compared to standard NMPC, accelerating the closed-loop settling time to $15.2 \mathrm{s}$, and restricting transient overshoot to just $0.18\%$. Full article

Microplastics and nanoplastics (MNPLs) are increasingly recognized as dynamic vectors capable of transporting a wide range of environmental contaminants, as well as acting as physical particulates. Their small size, high surface reactivity and strong sorption capacity allow them to carry metals, pesticides, pharmaceuticals and endocrine-active compounds into biological systems. This narrative review examines how these particle-contaminant complexes influence oxidative stress, inflammatory signaling and endocrine function during early development. Relevant literature was identified through structured searches of PubMed, Scopus, Web of Science and Google Scholar, with a focus on the physicochemical properties of plastics, sorption mechanisms, gut barrier physiology and developmental toxicology. Early developmental stages are particularly sensitive, as immature mucus layers, permeable epithelial junctions and underdeveloped detoxification pathways facilitate the uptake and systemic distribution of MNPLs. Once internalized, these particles and their chemical cargo promote the generation of reactive oxygen species through redox-active contaminants, surface-catalysed reactions and mitochondrial dysfunction. The resulting oxidative imbalance activates stress-responsive pathways, including Nrf2–Keap1 signaling, and promotes lipid peroxidation, DNA damage and cellular dysfunction. MNPLs also stimulate inflammatory cascades by activating pattern-recognition receptors, altering cytokine profiles and disrupting epithelial homeostasis. These responses are intensified in the presence of sorbed pollutants, leading to sustained inflammatory states that can be particularly detrimental during organogenesis and immune maturation. Endocrine function is likewise affected, as MNPLs transport hormonally active chemicals and can interfere with hormone-responsive pathways through oxidative and inflammatory mechanisms. These interactions may disrupt thyroid signaling, metabolic regulation and the development of the reproductive axis, with potential long-term physiological consequences. Integrating evidence from polymer chemistry, contaminant behavior and developmental physiology, this review shows that MNPLs act as biologically active vectors that may increase oxidative, inflammatory and endocrine disturbances during early development. These findings highlight the importance of considering particle–contaminant interactions as a critical component of early-life risk assessment. Full article

High penetration of distributed photovoltaic (PV) generation has intensified voltage violations and stochastic voltage fluctuations in distribution networks, while existing voltage–var control methods still have limitations in terms of communication dependence, scalability, and edge deployment. To address these issues, this paper proposes a stability-constrained voltage–var self-optimizing control method for distribution networks based on the Bandit-Guided Online Self-Tuning Group Relative Policy Optimization (BOST-GRPO) algorithm. First, based on the LinDistFlow linearized power-flow model, a communication-free, decentralized, and locally observable reinforcement learning control environment is constructed, enabling each node to independently generate reactive power regulation commands using only local voltage measurements. Second, a contraction-mapping-based stability constraint is embedded into the policy output layer, theoretically guaranteeing the local exponential convergence of nodal voltage deviations around the equilibrium point and reducing the risk of voltage instability caused by overly aggressive policy actions. Meanwhile, device capacity constraints are incorporated into the policy output through a tanh-based action mapping, ensuring the physical feasibility of control commands. On this basis, BOST-GRPO realizes the online self-tuning of key hyperparameters within a single training process through a Bandit-guided mechanism, thereby avoiding the repeated training overhead caused by traditional offline hyperparameter tuning. Simulation results on the IEEE 33-bus system show that the proposed method outperforms benchmark reinforcement learning algorithms in final test cost, voltage deviation suppression, steady-state error, and regulation speed. Further tests under sensitivity matrix mismatch, different initial voltage disturbance intensities, and the extended IEEE 69-bus system demonstrate that the proposed method achieves good robustness and scalability. Full article

Nonlinear Model Predictive Control (NMPC) has garnered significant attention in autonomous systems due to its ability to predict future states and manage complex vehicle dynamics. However, the adaptability of existing NMPC methods is constrained by having to manually set the weight coefficients in the NMPC cost function. This study aims to explore a novel approach that integrates NMPC with Reinforcement Learning (RL), specifically employing Proximal Policy Optimization (PPO), to dynamically adjust NMPC weight matrices. The investigation begins by establishing a physics-based model for a two wheeled differential drive vehicle. A PPO model is then trained and deployed in real time to adapt to the NMPC weight matrices, achieving a 71% reduction in tracking error compared with the NMPC baseline. Importantly, the performance gain arises from PPO’s ability to reshape the NMPC cost function in real time, amplifying both orientation and lateral penalties in curves while relaxing them on straights, thereby enabling adaptive trade-offs between accuracy and control effort that static-weight NMPC cannot achieve. To enhance safety, the controller is integrated with a Control Barrier Function (CBF) layer for real-time obstacle avoidance, while PPO’s real-time weight adaptation contributes to improved tracking performance relative to NMPC+CBF. Finally, robustness evaluations under friction uncertainty, sensor noise, and path disturbances demonstrate that the PPO+NMPC+CBF method maintains reliable tracking accuracy and safety margins. Full article

This work investigates a hardware-aware, circuit-level emulation of BB84-like circuit primitives on noisy intermediate-scale quantum (NISQ) processors. The motivation is to evaluate whether BB84-like basis sifting and intercept–resend-induced QBER behavior remain observable when selected BB84 operations are mapped to parallel single-qubit circuits on gate-based devices. The proposed mapping represents Alice’s preparation, optional Eve intercept–resend emulation, and Bob’s measurement as processor-internal circuit layers; it is therefore an on-chip emulation and not an end-to-end optical QKD implementation. Experiments combine real IBM superconducting processors with Qiskit, Cirq, and Azure/Q# simulator-based or noise-modeled evaluations. Baseline QBER was first calibrated for each backend, and intercept–resend experiments then produced a clear QBER separation from the no-eavesdropper condition. The observed sifted-bit utilization was close to the expected 50% BB84 basis-matching reference, while the constant-depth circuit structure supported scalable raw/sifted-bit generation before any classical post-processing. These observations are treated as implementation-level consistency checks and backend-dependent experimental metrics, rather than as new BB84 protocol-level results. Finite-shot uncertainty, calibration drift, and backend-specific noise are treated as limitations of the proposed QBER-based evaluation rule rather than as deployment-level security guarantees. Because the study does not implement a physical quantum channel, authenticated classical communication, error correction, privacy amplification, finite-key security analysis, or general QKD attack models, the reported metrics should be interpreted as raw/sifted-bit experimental metrics and QBER-based disturbance evaluation for BB84-like NISQ emulation, not as secure key rates, secure throughput, or practical QKD deployment results. Full article

Accurate liquid level regulation is critical for ensuring printing quality and process stability in stereolithography (SLA) 3D printing. However, traditional liquid level control methods often suffer from insufficient prediction accuracy, poor disturbance rejection capability, and limited adaptability under dynamic printing conditions. To address these challenges, this paper proposes an enhanced Transformer-based time series prediction model integrated with an xLSTM module for SLA liquid level prediction and adaptive control. By embedding the xLSTM structure into the Transformer encoder, the proposed model combines the global dependency modeling capability of self-attention mechanisms with the local temporal feature extraction capability of recurrent memory units, thereby improving the prediction accuracy and robustness of liquid level sequences. Experimental datasets were collected from an actual SLA printing platform, including multiple process-related features such as layer height, laser power, platform position, and vacuum pressure. Comparative experiments were conducted against conventional Transformer, LSTM, xLSTM, GRU, TCN, and PID-based methods. The results demonstrate that the proposed model achieves the best prediction performance, with an MAE of 0.174, RMSE of 0.222, and R² of 0.9903. Compared with the original Transformer model, the proposed approach significantly reduces prediction error and improves fitting stability. In disturbance rejection experiments, the proposed strategy effectively suppresses liquid level fluctuations under sudden pulse interference conditions, exhibiting superior robustness and dynamic response capability compared with traditional PID control. Furthermore, physical printing experiments verify that the proposed method can improve surface quality, contour accuracy, and structural stability of printed parts. Overall, the proposed Transformer–xLSTM framework provides an effective intelligent prediction and control solution for SLA liquid level regulation, offering significant potential for high-precision and intelligent additive manufacturing applications. Full article

This paper presents a capability-aware frequency control strategy for microgrids comprising a ramp-rate-limited synchronous generator (SG) and a bounded inverter-based resource (IBR). In contrast to conventional droop and virtual inertia methods, the proposed design activates IBR support according to whether the required power-rate exceeds the ramp-rate capability of synchronous generation. A smooth activation mechanism detects when the required power-ramp demand exceeds the SG ramp-rate limit. The IBR is then engaged to supply the excess ramping requirement while providing additional damping through frequency-deviation feedback. A two-timescale model is formulated, where the IBR power-tracking dynamics evolve on a fast boundary-layer timescale. In contrast, the SG regulation loop evolves on a slow electromechanical timescale. Using singular perturbation theory combined with Lyapunov and input-to-state stability (ISS) analysis, local practical stability of the closed-loop system is established for sufficiently fast IBR dynamics. The proposed framework yields a physically interpretable coordination mechanism that exploits the fast response of IBR without introducing artificial inertia or frequency-domain disturbance splitting. Full article

This study presents a novel hierarchical hybrid control architecture for the attitude stabilization of a 3-Degree-of-Freedom (3-DOF) hover system. Operating on a linearized state-space model, a Linear Quadratic Regulator (LQR) is deployed as the optimal inner-loop core to guarantee baseline multi-variable stability. To dramatically improve transient performance and suppress high-frequency oscillations, Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC) are incorporated not as conventional additive inputs, but as dynamic reference-reshaping supervisory mechanisms in the outer loop. This structural decoupling preserves the optimal characteristics of the LQR while effectively attenuating chattering, thereby preventing physical actuator fatigue. Furthermore, the Big Bang–Big Crunch (BB-BC) metaheuristic algorithm is employed to systematically optimize the design parameters of the supervisory layers, enabling effective steady-state error reduction with a remarkably low computational cost. Comparative evaluations demonstrate that the proposed LQR-STSMC framework significantly accelerates system responsiveness, reducing rise times by approximately 80% to 90% and consistently lowering settling times across all operational axes while achieving a reduction of up to two orders of magnitude in overall tracking errors (ITAE) relative to the baseline LQR. Although evaluations involving Model Predictive Control (MPC) demonstrate improvements in transient response and a reduction in total error compared to the standard LQR, the proposed LQR-STSMC architecture exhibits significantly better overall performance and superior disturbance rejection capabilities. Simulation results under continuous aerodynamic perturbations (wind disturbances) confirm that the proposed hierarchical methodology effectively eliminates steady-state offsets, fundamentally outperforming both classical LQR and MPC in terms of robustness, precision, and ultra-fast transient performance. Full article

The Water-Sediment Regulation Scheme (WSRS) rapidly delivers large amounts of water, sediment, and nutrients to the Yellow River Estuary (YRE) in summer (wet season). However, how these abrupt environmental changes affect phytoplankton distribution through bottom-up versus top-down control mechanisms remains poorly understood. In this study, we examined the spatiotemporal distribution of environmental drivers, grazing pressure, and phytoplankton communities in surface and bottom layers of the YRE during WSRS. Our results indicate that the WSRS transitioned phytoplankton distribution from a relatively uniform pattern pre-WSRS to a highly heterogeneous one during the sediment regulation stage. Before WSRS, phytoplankton abundance peaked near the river mouth and was co-dominated by chlorophytes, cryptophytes, and diatoms in both layers. During the water regulation stage, abundance decreased across layers, with the surface community incorporating more dinoflagellates and the bottom layer transitioning toward higher diatom and lower chlorophyte proportions. Subsequently, vertical stratification intensified during the sediment regulation stage, characterized by a chlorophytes-dominated surface hotspot (with abundance 6.8-fold higher than pre-WSRS levels) in contrast to a depauperate bottom layer. Regression tree and redundancy analysis results showed that WSRS shifts phytoplankton regulation from a bottom-up state in the pre-stage to top-down dominance during the water regulation stage, and finally to a vertically stratified regulatory state in the SR stage, with top-down control in the surface layer and bottom-up control in the bottom layer. Our findings highlight that trophic interactions and physical processes play more critical roles than previously recognized in regulating phytoplankton distribution in estuaries subjected to high-intensity hydrological disturbances. Full article

High-speed train aerodynamics have mainly been improved by passive design methods, such as streamlined noses, local fairings, and surface smoothing. These methods have achieved clear benefits, but several important aerodynamic problems remain difficult to solve by geometry optimization alone. Open-air drag is still affected by tail flow separation, base-pressure recovery, and disturbances around bogies and the underbody; crosswind safety is influenced by unsteady leeward-side separation and wake asymmetry; slipstream behavior depends on wake vortices, boundary-layer development, and complex near-ground underbody flow; and tunnel-related pressure transients arise from compression-wave generation, propagation, and reflection. These coupled effects mean that one fixed train shape cannot perform optimally in all operating conditions. For this reason, this review proposes that active flow control (AFC) should not be regarded only as a drag-reduction or stability-improvement technique for high-speed trains. Instead, it should be understood as a mission-adaptive aerodynamic control framework, in which different control actions are used for different operating scenarios. This paper first clarifies that passive optimization is increasingly subject to diminishing returns under multi-objective and engineering constraints. It then reviews AFC studies on drag reduction, base-pressure recovery, wake and slipstream control, underbody flow conditioning, crosswind mitigation, and tunnel pressure-wave suppression. Related AFC studies on bluff bodies, road vehicles, and other separated flows are included only when their physical relevance to trains is clear. The review further distinguishes gross aerodynamic improvement from net energy gain and identifies actuator power, durability, maintainability, acoustic impact, validation level, and full-scale transferability as decisive feasibility factors. Current research is still dominated by open-loop numerical studies with simplified actuation. Future work should therefore move toward multi-objective, closed-loop, energy-aware, sensor–actuator-integrated, and explainable machine-learning-assisted AFC. The main message is that the next step in train aerodynamics is not simply a better fixed shape, but a control-enabled train that can selectively redistribute aerodynamic authority across its mission profile. Full article

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

Decentralised manufacturing is expanding as digitally controlled fabrication tools become accessible to SMEs, independent operators, and community workshops outside traditional factory settings, but the resulting heterogeneous, autonomously operated network introduces systemic uncertainty that no central authority governs. This paper proposes a systems-theoretic framework in which Free and Open-Source Software (FOSS) governance acts as the structural interoperability layer of a distributed cyber–physical manufacturing system (CPS), and node-local digital twins—each hosting a machine learning (ML) disturbance estimator—provide local adaptive compensation without centralised data aggregation. A defining property of the architecture is automatic improvement propagation: learned corrections distribute via federated learning to structurally similar nodes without operator intervention, and the open, observable FOSS ecosystem enables advances in one fabrication modality to transfer to others through shared interface standards. The framework is applied analytically to three disturbance classes: regulatory restriction, technical process variability, and supply chain disruption. Across cases, the analysis shows how open modular interfaces and local adaptation preserve functional continuity under perturbations that would more strongly affect centralised architectures. The contribution is a unified mathematical basis for robustness analysis in decentralised manufacturing CPS and a foundation for future simulation and empirical validation. Full article