Search Results (301)

Primordial black holes (PBHs) in the asteroid-mass range, with typical masses ${10}^{17}\mathrm{g}\lesssim M\lesssim {10}^{23}\mathrm{g}$, have drawn significant recent attention as viable dark matter candidates. The peak frequencies of photons emitted via Hawking radiation from asteroid-mass PBHs range from infrared to $\gamma$-ray bands. We calculate expected local transit rates for extended PBH mass distributions that could comprise all dark matter. We evaluate prospects for detecting Hawking-radiated photons from local PBH transits through the inner Solar System and from PBH explosions in the far outer edges of the Solar System. We consider several existing and proposed ground-based and space-based instruments sensitive to photons from the radio band to ultrahigh-energy $\gamma$-rays. We find that the proposed instruments, such as the All-sky Medium Energy Gamma-ray Observatory eXplorer (AMEGO-X) satellite, can reliably detect PBH transits within $\mathcal{O}\left(0.1\mathrm{AU}\right)$ of the Earth, while the High Altitude Water Cherenkov (HAWC) observatory and Large High Altitude Air Shower Observatory (LHAASO) are both sensitive to PBH explosions out to $\mathcal{O}\left(0.1\mathrm{pc}\right)$ and $\mathcal{O}\left(0.5\mathrm{pc}\right)$, respectively. We conclude by specifically considering potential companion electromagnetic signatures in the case of a PBH explosion about ${10}^3\mathrm{AU}$ from Earth, which has been suggested as a potential source for the ∼220 PeV ultrahigh-energy KM3-230213A neutrino event observed by the KM3NeT collaboration in 2023. Whereas we find that the recent KM3NeT event would not have yielded detectable electromagnetic signals—due to its location on the sky, proposed distance from Earth, and the offline status of the HAWC observatory at that time—we demonstrate that future PBH explosions at comparable distances could yield electromagnetic signals measurable from Earth, depending on the alignment of the PBH burst with detector fields of view. Full article

This study proposes a stability-aware load-shaping energy management system (EMS) for a hybrid electric shipboard power system. The proposed EMS uses the energy storage system (ESS) as a dynamic load-shaping buffer to reduce active diesel-generator (DG) low-load exposure and electrical power fluctuations. A supervisory reference-generation procedure integrating low-pass filtering, ESS state-of-charge (SOC) compensation, DG ramp-rate limiting, residual-power calculation, and explicit power and SOC constraints was implemented on a real-time controller. Comparative experiments were conducted on an MW-class platform comprising one active 600 kW DG, a 400 kW/400 kWh ESS, two 450 kW propulsion-load channels, and a 100 kW service-load channel connected to a 750 V DC bus. The second installed DG remained offline during all comparative experiments. Under a common one-hour ship-load profile, the proposed EMS reduced the low-load exposure ratio from 0.1320 to 0.00139, the DG power variance from 3.06 × 10⁴ to 1.37 × 10⁴ kW², and the mean DG ramp rate from 13.8 to 0.776 kW/s relative to the rule-based EMS. These values correspond to reductions of approximately 98.9%, 55.2%, and 94.4%, respectively. After terminal-SOC correction, the BSFC-map-estimated equivalent fuel consumption decreased from 90.4 to 88.2 kg. Experimental parameter-sensitivity tests demonstrated the trade-offs among DG power smoothing, low-load exposure, SOC regulation, and ESS participation. A supplementary offline Monte Carlo analysis further indicated that the principal comparative benefits were maintained under bounded variations in load magnitude and fluctuation amplitude. The results demonstrate that the proposed EMS improves supervisory DG loading quality while maintaining the ESS within its prescribed power and SOC limits. Full article

This paper presents a hybrid physics–AI framework for real-time emission monitoring in industrial boiler systems within an IIoT-enabled Industry 4.0 environment. The proposed framework integrates physics-based emission estimation with AI-based anomaly detection within a unified operational technology and information technology (OT–IT) architecture to support continuous environmental monitoring. Process data, including fuel oil consumption, oxygen concentration, temperature, and pressure, are acquired from an industrial boiler through a Siemens programmable logic controller (PLC) using an Open Platform Communications Unified Architecture (OPC UA) communication layer. The acquired measurements are processed at the edge analytics level to estimate the emission rates of carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM) using stoichiometric combustion models based on fuel composition and flue gas characteristics. An autoencoder-based anomaly detection model is employed to identify abnormal operating conditions by monitoring the reconstruction error against a predefined threshold. The framework is validated using a PLC-based quasi-real-time prototype that replays one year of historical industrial boiler operating data. The emission estimation results show close agreement with reference engineering calculations, with relative errors below 0.1% across the evaluated operating conditions. The anomaly detection model achieved an F1-score of 96.14% and an AUC of 0.981. An edge monitoring dashboard provides real-time visualization of process variables, estimated emissions, and alarm status, while cloud connectivity supports remote monitoring and long-term data analytics. Overall, the proposed framework demonstrates how existing industrial process data can be utilized to transform conventional offline emission estimation into a continuous OT–IT monitoring service for legacy industrial environments. Full article

Contemporary cyber threats increasingly employ multi-stage and behaviourally adaptive strategies that challenge static intrusion detection and non-adaptive deception mechanisms. Existing approaches typically treat threat detection, deception deployment, and adversarial attribution as separate functions, limiting timely response and underusing the behavioural evidence generated during attacker interaction. This study develops and evaluates a theory-informed computational and operational framework for autonomous cyber deception. The principal research artefact is a reusable closed-loop architecture rather than a single predictive model: it specifies the interacting components, interfaces, data and control flows, decision rules, and feedback mechanisms that connect detection, deception, telemetry, and attribution. Methodologically, the study follows an engineering design-and-evaluation approach comprising problem and requirement identification from the literature, architectural synthesis, component-level mathematical modelling, prototype implementation, and controlled cyber-range evaluation. In this context, modelling refers to the distinct computational models embedded within the framework: a hybrid detection model combining supervised classification, anomaly detection, and temporal sequence analysis; a Markov Decision Process and reinforcement-learning policy model for selecting and reconfiguring deception actions under engagement, intelligence-gain, resource, and containment objectives; and similarity-based and Bayesian attribution models for estimating MITRE ATT&CK techniques from incomplete behavioural evidence. The component models were developed offline using the NSL-KDD, CICIDS2017, UNSW-NB15, and ToN-IoT datasets, while the integrated prototype was evaluated separately in a controlled enterprise-like cyber range using reconnaissance, brute-force, exploitation, and multi-stage attack scenarios. The reported classification metrics were calculated from the labelled cyber-range evaluation events, not by pooling the four benchmark datasets. On this integrated cyber-range evaluation set, the system achieved 95.4% detection accuracy, 93.6% precision, 94.7% recall, and a 94.1% F1-score, with a mean detection latency of 85 ms. It also achieved 100% honeypot deployment reliability, 92% dynamic reconfiguration success, 88% fingerprinting resistance, and attacker engagement durations of up to 280 s. The attribution component demonstrated end-to-end generation of ATT&CK-aligned technique hypotheses from deception-derived telemetry; however, the present archived evaluation does not support per-technique or baseline-comparative performance claims. These findings show that specialised models and operational services can be coordinated within a unified adaptive defence process, while also identifying the additional class-level and ablation evidence required for rigorous attribution validation. Full article

Continuous sensing of group feeding demand is needed to support feeding decisions on large-scale pig farms. In dense group-housing environments, crowding, occlusion, and variation in posture around the trough can reduce the stability of visual feeding-status recognition. Feeding schedules also commonly rely on fixed periods or historical patterns, limiting their ability to assess whether daily feeding-behavior signals are aligned with group demand. Here, we propose a video-based workflow for sensing group feeding activity and conducting an offline replay evaluation of feeding-schedule allocation rules in group-housed pigs. Using synchronized feeding videos and feeder records, we developed a workflow that integrates rotated object detection, visual feeding intensity calculation, time-period-level construction of video-derived feeding demand, and feeding strategy evaluation. Rotated bounding-box detection achieved an mAP50-95 of 0.908 for feeding recognition, supporting its feasibility for feeding/non-feeding recognition in this dense-pen scene. Visual feeding intensity derived from model outputs was most strongly correlated with actual feed intake increment under a 20 min aggregation window with no time lag (r = 0.80). In the evaluation based on video-derived feeding demand, the video-perceived hybrid strategy reduced the peak time difference to 14.51 min, achieved a synchronization correlation of 0.611, and reduced the mismatch rate to 5.13%. These findings suggest that video-based behavioral signals can serve as a group-level proxy for feeding demand and can support offline evaluation of behavior-aware feeding schedules. However, the demand and strategy analyses were based on a single pen observed for 72 h, and validation across additional pens, batches, age stages, seasons, and closed-loop feeding conditions is required before practical deployment. Full article

The empirical descriptors of high-entropy alloys and oxides, from the mixing entropy and atomic-size mismatch to the Miedema enthalpies and the $\mathsf{\Omega }$, $\mathrm{\Phi }$, and $\phi$ stability parameters, are quoted in nearly every design study, yet they are reimplemented ad hoc by individual groups, by closed web calculators, and now inside language-model agent frameworks, where fabrication of property values is a documented failure mode. The resulting numbers disagree and cannot be traced or reproduced. We present HEA-Bench, an open calculator in which every descriptor is a closed-form expression over a curated, literature-cited element-property table, with the six canonical phase-prediction rules reported alongside their thresholds and sources rather than as predictions. One calculation core is delivered as a dependency-free Python (version 3.10 or later) library, a zero-install browser application, an offline desktop executable, and a Model Context Protocol server that exposes it to AI agents as deterministic tools, returning every value with its unit, citation key, and version so an agent’s reasoning trace can be audited. The implementation reproduces published per-alloy and per-oxide anchor values to their printed precision and extends to high-entropy oxides in four structure families. The numerical instability of $\mathsf{\Omega }$ near zero mixing enthalpy is quantified and exposed as a callable check. Full article

Modern electrical distribution systems face increasingly complex protection coordination challenges due to variations in power flows under different operating conditions. In this context, this work proposes an adaptive coordination scheme based on deep reinforcement learning (DRL) to reduce the operating times of overcurrent relays while maintaining sensitivity, selectivity, and speed requirements. The methodology was implemented in Python 3.13 using the IEEE 33-bus distribution network with distributed generation (DG), and the coordination problem was addressed using the Deep Deterministic Policy Gradient (DDPG) algorithm, which adjusts the protection settings from previously calculated fault currents. The results show that the DDPG-based approach reduces fault-clearing times compared with the conventional methodology, achieving reductions between 17.01% and 77.5% for three-phase faults and between 18.5% and 74.1% for single-phase-to-ground faults across the analyzed scenarios, without compromising coordination between primary and backup relays. In addition, a comparison with a PSO-based offline optimization approach was included as an additional benchmark, showing that the proposed method provides competitive operating times while preserving its adaptive learning-based nature. These findings show that the proposed methodology is a viable option for adaptive protection coordination in modern distribution networks. Full article

The classical Fortescue symmetrical-component transform remains fully invertible as a similarity transform when the complete sequence-domain matrix is retained. In practice, however, untransposed three-phase overhead lines are often summarized by the diagonal sequence quantities $Z_0$ and $Z_1$, while the asymmetry appears through coupled off-diagonal terms that are less convenient for compact parameterization and measurement-based interpretation. This paper presents an Extended Sequence Transform that reorganizes the six independent entries of the phase-domain impedance matrix into six structured parameters by means of a lossless, invertible $6\times 6$ linear mapping. The first two parameters are identical to the classical zero- and positive-sequence impedances, which preserves backward compatibility. The remaining four parameters isolate asymmetry information in a form from which all entries of the classical sequence matrix can be recovered exactly, and from which the full phase-domain matrix is reconstructed to machine precision. The proposed representation does not diagonalize an untransposed line; rather, it provides a compact and explicit six-scalar parameterization that separates the classical sequence pair from four asymmetry descriptors. Numerical validation on a 50 km untransposed overhead line confirms exact round-trip reconstruction and exact agreement of the unbalance factors obtained from the classical and extended representations. A stochastic perturbation study further shows that round-trip reconstruction remains at numerical precision and that, within the tested perturbation grid, the $M_2$ factor is more sensitive than $M_0$. Line-constant calculations performed in OpenDSS for two typical 400 kV tower geometries link the four asymmetry parameters to specific geometric features, a worked offline measurement example recovers them from simulated three-phase terminal tests, and a distributed-model study confirms that the lumped-parameter description of the asymmetry remains accurate to within about 0.6% up to 150 km. Full article

While data fitting is extensively used in polymer processing to extract fundamental rheological properties, its application for direct macroscopic geometric transfer between complex operational dies remains largely unexplored. Optimizing extrusion dies for corrugated plastic pipes traditionally requires time-consuming offline laboratory rheology, creating a major development bottleneck when dealing with proprietary, undocumented blends. To address this gap, this study introduces a novel, data-driven protocol for predicting the die pressure drop that eliminates the need for independent laboratory rheometry. Unlike traditional in situ methods that seek pure material properties, our approach back-calculates lumped, effective Power Law parameters directly from macroscopic pressure drops of existing converging dies. This uniquely embeds both material and geometric flow characteristics under actual processing conditions. Experimental validation demonstrates that this workflow, supported by an iterative refinement strategy, yields prediction errors typically within 10%. Ultimately, this lightweight computational tool provides engineers with a rapid-iteration framework to significantly accelerate early-stage die design. Full article

To reveal the controlling mechanism of cooling rate on the continuous cooling transformation, microstructure evolution and mechanical performances of Q580R low-temperature pressure vessel steel, this study took industrial-scale Q580R steel as the research object. The JMatPro thermodynamic software was utilized for simulating and calculating its equilibrium phase diagram, TTT diagram, CCT diagram and mechanical property evolution. Continuous cooling experiments with a wide range of cooling rates between 0.1 and 50 °C/s were executed on a Gleeble-3500 thermal simulator. Combined with optical microscopy, scanning electron microscopy and Vickers hardness tester for microstructure characterization and property testing, the measured CCT diagram was constructed and contrasted with the simulation results for verification. Experimentally, the phase composition of Q580R steel evolves at regular intervals with cooling rate. As the cooling rate rises, the ferrite content constantly decreases, the bainite content first increases and subsequently decreases, and the martensite content constantly increases. When the cooling rate reaches 30 °C/s, the martensite proportion can exceed 90%, and the microstructure is significantly refined. The hardness of the material first increases rapidly and subsequently trends to be steady as the cooling rate rises, reaching 308 HV₁₀ at 50 °C/s. The measured transformation law, microstructure evolution and hardness change exceedingly corresponds to the JMatPro simulation results. This validates the credibility of the simulation prediction. This study clarifies the quantitative relationship among “cooling rate-microstructure-properties” of Q580R steel, which can provide theoretical basis and data support for the precise design of heat treatment process and the optimization of strength and toughness. The established relationship can directly guide the formulation of controlled cooling parameters during hot rolling and off-line quenching and tempering production of Q580R pressure vessel plates, helping manufacturers optimize industrial heat-treatment procedures to satisfy low-temperature toughness requirements for petrochemical and cryogenic pressure vessel service. Full article

As renewable energy sources become increasingly integrated into interconnected power networks, system operating points (OPs) undergo frequent and unpredictable shifts. However, conventional delays in updating equivalent model parameters during these OP transitions often compromise modeling accuracy. To address this challenge, this study proposes an online dynamic OP clustering method for interconnected grids featuring wind and photovoltaic generation. First, an equivalent model for renewable-integrated interconnected grids is established. Subsequently, a dynamic OP clustering strategy is developed; this strategy combines an offline construction phase utilizing joint probability distributions and data clustering with an online update mechanism that dynamically adjusts cluster boundaries via membership calculations. This approach enables real-time clustering, effectively minimizing equivalence errors and adapting swiftly to ongoing network variations. Simulation results based on the China–Mongolia interconnected power grid demonstrate that the proposed method significantly outperforms traditional static approaches in both equivalence accuracy and computational adaptability. By delivering precise, real-time network equivalents, this approach provides robust support for practical grid operations, including online security assessment, optimal power dispatching, and transient stability analysis, thereby contributing to the long-term stability and sustainability of modern power systems. Full article

With the expansion of transmission networks and the increasing penetration of inverter-based resources (IBRs), fixed offline distance-protection settings face increasing difficulty in balancing selectivity, sensitivity, and operating speed. This problem is particularly evident in Zone III remote-backup protection, where conservative load-avoidance settings may create blind zones. This paper proposes an adaptive three-zone distance-protection setting method based on explicit sensitivity constraints and a disturbance-domain model. The method has two main features. First, online recalculation is restricted to the local disturbance domain affected by topology changes, thereby avoiding network-wide recomputation. Second, Zone II and Zone III settings are determined by a constrained model that incorporates real-time branch coefficients, load impedance, sensitivity requirements, and downstream coordination limits. A fallback mechanism is also included to maintain security under data loss or abnormal measurements. In a 220 kV case study, the proposed method increases the Zone II sensitivity coefficient from 1.92 to 1.95 and the Zone III remote-backup sensitivity coefficient from 0.83 to 1.35. Additional tests under high-resistance faults, measurement errors, volatile load, and inverter-based resource integration show that the method preserves selectivity while reducing backup protection blind zones. The disturbance-domain strategy also reduces the average recalculation time from 820 ms to 18 ms in the tested regional setting-calculation scenario. Full article

Aiming at the challenging issue of nonlinear coupling control between cooling intensity and solidification rate in the secondary cooling zone of round billet continuous casting, this study proposes an efficient 3D temperature field modeling method that integrates hybrid coordinate systems with equal-area meshing. The model is applicable to the temperature range of 800–1520 °C during the continuous casting process. With the modeling strategies of constructing an r-θ-z hybrid coordinate system and designing a dynamic equal-area meshing method, and combined with a topological structure optimization algorithm, the geometric adaptability and numerical stability of the model are significantly improved. Based on this, an explicit-semi-implicit dual-mode finite difference solution model is developed, where the explicit scheme meets real-time online calculation requirements, and the semi-implicit scheme combined with preconditioned Gauss–Seidel iteration enables high-precision offline simulation. Furthermore, a boundary condition model incorporating adaptive mold heat flux correction and multi-mechanism heat transfer in the secondary cooling zone is established. Based on Microsoft Visual Studio 2019 (Version 16.11) C++ development, SIMD vectorization and temperature gradient threshold optimization technologies are employed, resulting in a 35% improvement in computational efficiency. Industrial validation results show that, taking 42CrMo steel with a casting speed of 0.24 m/min and a cross-section of φ600 mm as an example, the deviation between the calculated surface temperature (887 °C) and the measured value (876 °C) of the round billet in the straightening zone is only 11 °C, and the calculation error of the cold billet diameter is only 0.325% (with a calculated value of 597.548 mm and a measured average value of 599.5 mm), both meeting the accuracy requirements for engineering applications. The model breaks through the limitations of traditional empirical formulas and provides theoretical support for digital control of continuous casting processes and quality optimization of high-alloy steels. Full article

Low-frequency seismic disturbances significantly limit the performance of precision engineering systems and active vibration isolation platforms. Model predictive control (MPC) is widely applied in such systems due to its ability to handle multi-variable dynamics and constraints. However, its performance strongly depends on model accuracy. To address this issue, this paper proposes a multilayer perceptron-enhanced model predictive control (MLP-MPC) framework for active vibration isolation. In the proposed approach, a multilayer perceptron (MLP) is trained offline to learn the mapping between the current system state and the free-response term in the MPC prediction equation. During online implementation, the trained MLP replaces the model-based free-response calculation while preserving the original quadratic programming structure of conventional MPC. The proposed method is evaluated on a single-degree-of-freedom active vibration isolation system under low-frequency sinusoidal excitation and measured seismic disturbances. The simulation results show that MLP-MPC achieves reduced running RMS tracking error and lower moving-window RMS error compared with conventional MPC and Proportional–Integral–Derivative (PID) control. The results suggest that integrating data-driven free-response estimation into predictive control provides a practical approach to enhancing the performance of low-frequency vibration suppression while maintaining computational feasibility. Full article

The adsorption of rare-earth Y-based inclusions (Y, Y₂O₃, Y₂O₂S) on Al₂O₃, YAlO₃, and Y₂O₃ refractory surfaces is a primary cause of nozzle clogging during the continuous casting of rare-earth steels. Conventional anti-clogging strategies, being passive and offline, lack real-time adjustability. This study aims to elucidate the mechanism by which external positive charge modulates interfacial adsorption. Using first-principles calculations combined with partial density of states, charge density difference, and thermodynamic analyses, we investigated the adsorption behavior of Y, Y₂O₃, and Y₂O₂S on Al₂O₃ (001), YAlO₃ (001), and Y₂O₃ (001) surfaces under neutral and positively charged states (+2, +4). A triple inhibition mechanism is revealed: electronically, external charge disrupts O-p and Y-d orbital hybridization, attenuating interfacial covalent bonding; electrostatically, the net positive charge shifts the interfacial interaction from attraction to repulsion, creating a physical barrier; and thermodynamically, the Gibbs free energy change ΔG increases under charged conditions, indicating a quantifiable reduction in adsorption spontaneity. These findings provide a theoretical basis for the development of active anti-clogging strategies in rare-earth steel production. Full article