Search Results (735)

Fetal electroencephalogram (fEEG) recording could address the gap of functional fetal brain testing and enable direct assessment of fetal brain status during pregnancy and labor. However, the fetal EEG signal (~1 µV) is masked by roughly 80 dB (about 10,000×) of maternal ECG and other bioelectric interferences. This synthetic-data feasibility study presents whether the fetal EEG can be recovered from the maternal abdomen surface by using the following three sensing architectures on physiologically scaled synthetic data: single electrode, dual electrode, and a concentric 28-electrode array centered over the fetal head. We show that exploiting a known geometric attenuation steering vector with a minimum-variance distortionless-response (MVDR) beamformer raises recovery correlation from ~0 to ~0.28 and improves signal-to-noise ratio (SNR) by ~67 dB. A follow-up validation replaces the synthetic EEG generator with actual neonatal EEGs recorded from the scalp, obtained from OpenNeuro ds004577 and the Helsinki Zenodo corpus. Comparable recovery performance across both datasets confirms that the synthetic feasibility conclusion generalizes to real neonatal EEG composition and behavioral characteristics. The primary evaluation is based on waveform-level engineering metrics (correlation, SNR, and RMSE), while exploratory secondary analyses assess aEEG envelopes, band-power trends, and burst detection. These analyses are not intended as clinical validation. Independent per-electrode sensor noise remains the dominant residual limiter under the homogeneous geometric model. The reported r ≈ 0.28 should not be interpreted as expected in vivo performance at all gestational ages: an intact vernix layer can add approximately 35 dB of attenuation and makes the required noise floor substantially more stringent. This analysis adds a five-layer volume-conductor analysis, misalignment, depth and impedance-drift stress tests, recording-level statistics, comparisons with classical extraction families (PCA, ICA, adaptive cancelation, and multichannel Wiener filtering), an interference-alignment sensitivity analysis, and exploratory biomarker-level analyses, which together bound the idealizations of the forward model. Full article

A building façade normally keeps the weather out, lets in daylight, allows ventilation, and shapes how a building looks from outside. This study asks whether it can also help generate electricity without giving any of that up. Solar chimney power plants (SCPPs) generate clean electricity from solar heat, but they have mostly been studied for open, land-abundant rural sites. Mounting one onto a façade instead risks the very things a façade is meant to protect: thermal comfort, natural ventilation, and architectural freedom. No prior study has looked at energy output, thermal behaviour, double-skin façade (DSF) operability, and architectural freedom together for solar chimneys integrated into high-rise buildings, and this is the gap this work addresses. We coupled solar chimney power plants with double-skin façades across five configurations, evaluated using Computational Fluid Dynamics (CFD) validated against the Manzanares pilot plant to achieve 3.3% for velocity and 3.0% for temperature. Using the DSF as both collector and absorber (Model 1) pushes power to its highest point, 78.7 kW, but it drives inner-façade air to 327.1 K and shuts off ventilation entirely. Confining the collector to the roof and upper chimney instead (Model 5) settles for a more modest 31.1 kW, but it preserves DSF ventilation over most of the façade height and keeps inner air below 305.8 K through the majority of that range, with only the uppermost portion becoming thermally unsuitable for natural ventilation. Weighing power, thermal load, ventilation, and façade freedom together in a composite score, Model 5 comes out as the preferred configuration under the adopted equal-weight multi-criteria assessment. A parametric study of height, irradiance, and ambient temperature for Model 5 produced design equations that, paired with a Random Forest climate forecast, benchmarked against three alternative algorithms and evaluated on a chronological hold-out set (city-level test R² up to 0.83 for irradiance and 0.94 for temperature), power a predictive framework for hourly-to-annual energy output at any height and city, demonstrated here for seven cities across five continents. Full article

Electric pump-fed liquid rocket engines require coordinated propellant delivery under coupled hydraulic, electrical, actuator, and thermal constraints. This study develops an integrated reduced-order model of a dual-channel electric pump-fed propellant system, including the battery and DC bus, power converters, two independently driven motor–pump units, hydraulic feed lines, control valves, combustion chamber, and thermal states. A hierarchical constrained multi-criteria supervisory controller is formulated to regulate chamber pressure, oxidizer-to-fuel mixture ratio, feed-channel coordination, electrical loading, and thermal response. Performance is compared with a conventional PI controller and an enhanced PI configuration incorporating feedforward and disturbance compensation under nominal, degraded, long-duration, and constraint-active scenarios. Relative to the baseline PI controller, the proposed controller reduced the startup pressure peak from 2.64 to 2.32 MPa, pressure RMSE from 0.016 to 0.006 MPa, and mean branch synchronization error from 0.112 to 0.028 MPa. The minimum battery voltage increased from 87.0 to 90.4 V, while the peak motor current decreased from approximately 88 to 65 A. In the 1800 s thermal case, the maximum fuel-drive temperature decreased from approximately 104 to 74 °C. Numerical verification and comparison with published experimental benchmarks supported the physical plausibility and equilibrium-scale behavior of the reduced-order model, while differences in absolute transient time scales limit its use for quantitative prediction of hardware transient dynamics. The results indicate improved coordinated control within the investigated operating envelope and support the use of the framework for comparative system-level assessment and preliminary design of small-class electric-pump propulsion systems. Full article

The accelerated deployment of electric vehicles requires planning tools able to quantify how much charging infrastructure can be integrated into distribution systems without violating operational constraints. This paper proposes a multi-scenario optimization framework for the siting and sizing of electric vehicle charging stations in radial distribution networks. The problem is formulated as a mixed-integer nonlinear programming model in which candidate-station slots, binary siting decisions, integer EV assignments, hourly power-flow constraints, voltage limits, thermal limits, charger power factor, and charging strategy are coordinated. The objective function combines hosting capacity maximization with active energy losses and voltage deviation terms through a scalarized formulation. Unmanaged and managed charging strategies are evaluated under weekday and weekend operating scenarios. Four adaptive population-based optimizers are analyzed under identical computational conditions: particle swarm optimization, a population-based genetic algorithm, JAYA, and the multi-verse optimizer. Monte Carlo random sampling is included separately as a non-adaptive baseline without memory or learning. The methodology is tested on a modified 33-bus distribution system using Colombian demand profiles and line-current limits. The campaign includes 720 cases and 7200 independent runs. In the 720-case stochastic campaign, the largest feasible solution serves 765 EVs, equivalent to 5.508 MW, with a minimum voltage of 0.9084 p.u. and a maximum loading of 99.83%. Statistical validation shows no significant Holm-adjusted pairwise differences among the adaptive algorithms in hosting capacity, while PSO provides the most robust feasibility behavior. Supplementary robustness analyses quantify the influence of candidate-site definition, objective scaling, voltage limits, base charging-power scale, and native-load growth. A complementary deterministic 69-bus assessment under a normalized branch-current envelope preserves the qualitative managed-versus-unmanaged trend, with feasible sequential allocations of 779 and 225 equivalent EV charging units, respectively. The proposed framework provides a reproducible basis for identifying robust EVCS locations, estimating hosting capacity, and quantifying tradeoffs among charging capacity, network losses, voltage performance, and computational effort. Full article

High penetrations of wind and photovoltaic generation create simultaneous challenges for battery energy storage system (BESS) planning in distribution networks, including differences in nodal regulation value, power-energy configuration, and day-ahead operation under forecast uncertainty. This study develops a sequential planning-to-operation workflow comprising candidate-bus generation, siting and sizing within the candidate set, and finite-scenario day-ahead scheduling for a fixed configuration. First, nodal net-injection sensitivities, Jacobian-assisted pre-screening, and deterministic topology/support safeguards are used to generate the main candidate set, and alternating-current (AC) finite-difference refinement is performed only for the sensitivity-led fast set; in the IEEE-33 system, this refinement reduces the number of AC power-flow calls from 65 for full-node analysis to 17. Next, Sensitivity-Guided Envelope-Based Nonanticipative Adjustable Recourse Optimal Power Flow (SG-ENAR-OPF) is solved separately for each bus in the main candidate set; the BESS location and power/energy capacities are jointly determined subject to the P-Q LinDistFlow model, BESS duration constraints, a shared affine response, and finite-scenario constraints. The full-node audit serves only as an independent paper-level validation benchmark and is not part of the deployable workflow. After the configuration is fixed, interval forecasts for load, photovoltaic (PV) output, and wind-turbine (WT) output at $q^{05}$/$q^{50}$/$q^{95}$ are used to construct 25 static load-renewable disturbance points and seven temporal stress paths, over which a shared finite-scenario day-ahead policy is optimized. The IEEE-33 MAIN case selects Bus 30, with BESS capacities of approximately 10.66 MW/10.66 MWh. Using scaled public time-series data, the final policy is replayed over 46 consecutive 24 h execution windows, comprising 1104 h actual trajectories; under the 0.002 MW/MWh storage-engineering criterion, all 46/46 windows pass storage engineering validation, and energy continuity is maintained across all 45/45 interday boundaries. Further nonlinear AC post-validation converges at all 1104/1104 operating points, of which 1058/1104 satisfy the complete voltage and branch-capacity constraints. Supplementary results for IEEE-69 show that the workflow can be executed on a second radial test feeder. However, the conclusions are strictly limited to the tested feeders, finite scenarios, scaled public-data settings, and stated engineering tolerances and do not constitute a formal guarantee over a continuous uncertainty domain or of general cross-system applicability. Full article

To provide a unified description of the monotonic and cyclic compressive responses of concrete after freeze–thaw exposure, a one-dimensional phenomenological damage–plasticity constitutive framework is proposed. Freeze–thaw-induced pre-damage is quantified by the degradation of the initial static stiffness. A Mander-type equation is employed to describe the monotonic envelope, while residual strain is introduced to characterize plastic deformation. A signed stiffness variable is defined to distinguish pre-peak compaction from post-peak mechanical damage, and the unloading and reloading paths are represented by piecewise power-law functions for the pre-peak and post-peak regimes. The model is evaluated using 36 monotonic compression curves of recycled coarse aggregate self-compacting concrete subjected to sulfate freeze–thaw cycles and cyclic compression data for ordinary concrete subjected to seawater freeze–thaw cycles. After independent identification of the envelope-shape parameters, the monotonic responses yield an average R² of 0.978 and an average NRMSE of 0.041, whereas the complete cyclic responses yield an average R² of 0.941 and an average NRMSE of 0.066. Because the model parameters are identified separately using data from each freeze–thaw exposure level, these accuracy measures characterize parameter calibration and response reconstruction rather than independent prediction of untested exposure states. Freeze–thaw exposure causes substantially greater degradation of the initial static elastic modulus than reduction in peak stress, indicating that stiffness, strength, peak strain, and envelope shape should be treated as state variables at different hierarchical levels. The cyclic unloading stiffness exhibits both pre-peak enhancement and post-peak degradation, confirming the necessity of separately representing compaction and mechanical damage. The proposed model provides a unified representation of the monotonic envelope, residual deformation, and principal cyclic hysteretic characteristics of freeze–thaw-damaged concrete. Full article

During stratospheric airship station-keeping missions, the pronounced temperature-rise effect directly affects aerodynamic drag and power generation efficiency, yet most existing design optimization studies tend to neglect it, potentially leading to biased performance assessments. Thus, an aerodynamic–thermal multidisciplinary optimization framework was developed in this study for a stratospheric airship considering temperature-rise effects. High-fidelity simulations were employed to construct surrogate models for drag coefficient and daily power generation, and these models were subsequently integrated into a multidisciplinary optimization framework aimed at minimizing the total system mass. The optimization results show that, compared with the baseline optimal design, the globally optimized configuration is slenderer, the location of the maximum diameter shifts forward, and the nose and tail contraction becomes smoother. For the solar array layout, the optimized design is also shifted forward, with a shorter axial coverage range, and greater concentration in the fore and middle regions of the envelope. The optimized configuration reduces the combined mass of the propulsion, energy, and structural subsystems, resulting in an approximately 10% reduction in the total system mass. These results demonstrate that incorporating temperature-rise effects into multidisciplinary optimization can significantly affect the optimal design and provide a more realistic assessment of stratospheric airship performance during station-keeping operations. Both Bas and Opt use the temperature-coupled surrogate models; therefore, their comparison evaluates the benefit of full-variable co-design rather than a temperature-on/temperature-off ablation. Full article

Directional asymmetry and Dutch-roll are critical phenomena observed during powered parafoil vehicle (PPV) flight tests, complicating stable flight and leading to obvious lateral–directional biases in autopilot tracking. However, existing PPV models typically neglect propeller counter-torque (PCT), resulting in limited research on directional asymmetry. Moreover, conventional Newton–Euler formulations require explicit treatment of internal constraint forces, complicating analytical linearization and local stability analysis. To address these issues, first, this paper develops a nonlinear 9-degree-of-freedom (9-DOF) PPV model using Kane’s equations with quasi-velocities. This multibody dynamic model provides a compact and structurally consistent basis for linearization and stability analysis. Subsequently, the mechanism analysis shows that PCT shifts the coupled roll–yaw equilibrium and is the primary physical source of the observed directional asymmetry. In addition, the Dutch-roll mode is identified as the dominant oscillatory mode governing the lateral–directional stability of the PPV. The modal analysis further indicates that increasing thrust and directional control inputs reduce the Dutch-roll damping ratio. On this basis, a damping-ratio-based flight envelope is constructed. Furthermore, numerical simulations and flight-test comparisons demonstrate that the proposed model captures the principal PPV dynamic responses. The simulation results also support the mechanism analysis of directional asymmetry and the Dutch-roll. Full article

Industrial artificial intelligence (AI) fault-diagnostic systems can identify plausible causes under uncertainty, but their outputs alone do not determine how diagnostic support should be delivered during maintenance. This paper presents WrenchBuddy, a human-centered governance framework that manages three decisions during a fault episode: how much diagnostic structure to expose, what assistance posture to provide, and which first confirmatory question to prioritize. The framework is method-agnostic. In this illustration, its roles are instantiated using capped Bayesian-network views, a Comprehensive Operational Cost burden proxy, Fault-Situation Difficulty, a scenario-level physiological readiness input, Data Envelopment Analysis, a Help/Escalate rule, and Value of Information query ranking. A maintenance corpus from approximately 300 remotely monitored uninterruptible power supply machines provides 429 tagged incidents, while an independent physiological dataset provides the scenario-level readiness input. The reconstructed cause–alarm graph has 35 nodes and 34 edges. The Bounded view retains approximately 85% of Baseline coverage with approximately 57% of its interpretability burden. At the selected operating point, three alarms are escalated; bootstrap analysis shows that the two highest-difficulty decisions are stable, whereas the third is a borderline result based on seven incidents. Because the two datasets are not synchronized, the study evaluates governance-policy behavior rather than operational-outcome improvement. WrenchBuddy therefore provides an auditable framework for governing diagnostic exposure, assistance, and first-query selection, while synchronized human-subject validation remains future work. Full article

High wind-power penetration increases balancing requirements in integrated energy systems (IESs), while solvent-storage-assisted carbon capture power plants (CCPPs) and electric vehicle (EV) aggregators provide complementary flexibility at different timescales. This paper proposes an electric–carbon dual time-shift coordinated dispatch approach coupling carbon-energy shifting with vehicle-to-grid (V2G) electrical-energy shifting. First, a reduced-order model represents the dominant thermal inertia and short-term response of solvent regeneration. Second, EV availability uncertainty is characterized by Monte Carlo sampling, with quantile-based power and mobility-energy envelopes incorporated into aggregate SOC and mobility constraints together with a throughput-based battery-degradation cost. Finally, a 15-min mixed-integer linear programming model integrating power-to-gas, hydrogen-blended combined heat and power, thermal storage, and tiered carbon trading is solved using CPLEX. Compared with the baseline, the proposed coordinated dispatch strategy reduces operating cost from USD 77.19 × 10⁴ to 58.65 × 10⁴, net carbon emissions from 5841.71 to 2742.46 tCO₂, and the wind-curtailment rate from 42.98% to 1.15%. Specifically, relative to the same system without EV–V2G coordination, incorporating EV–V2G further reduces operating cost and net carbon emissions by 0.93% and 3.93%, respectively, while lowering the wind-curtailment rate from 4.23% to 1.15%, corresponding to a 72.8% relative reduction. Frequency-band analysis shows that the CCPP and electrolyzer provide the two largest contributions to low-frequency balancing, at 42.85% and 30.02%, respectively, whereas EV–V2G and CHP provide the two largest contributions to higher-frequency balancing, at 45.37% and 23.71%, respectively. The main limitations are the reduced-order regenerator model, fleet-level EV aggregation without distribution-network constraints, and fixed equipment capacities. Full article

Accurate modeling of photovoltaic (PV) cell behavior under varying operational conditions is essential for optimizing energy yield and system reliability. This study presents an extended simulation-based methodology for analyzing monocrystalline silicon PV cells using a double-diode model (DDM) with a physics-based, temperature- and irradiance-dependent parameterization. Building on a SPICE-equivalent circuit formulation, the governing implicit DDM equation is solved numerically to regenerate every current–voltage (I–V) and power–voltage (P–V) curve, and all circuit, block and flow diagrams are redrawn as vector-quality figures. Beyond the deterministic analysis, the manuscript introduces two extensions: (i) a quantitative statistical analysis of the influence of temperature (T), irradiance (G) and series resistance $\left(R_s\right)$ on open-circuit voltage, short-circuit current, maximum power and fill factor, using linear/log-linear regression, a multiple linear regression model and a Pearson correlation analysis; and (ii) a machine-learning (ML) validation study in which a random-forest surrogate model is trained on a 600-point physics-consistent synthetic dataset spanning the full $(T, G, R_s)$ operating envelope and evaluated with a held-out test split and 5-fold cross-validation. The surrogate reproduces the DDM outputs with cross-validated coefficients of determination above 0.98 for maximum power, open-circuit voltage, short-circuit current and fill factor, confirming that the DDM response surface is smooth, learnable and suitable for fast surrogate-based design optimization and maximum-power-point-tracking (MPPT) algorithm testing. Simulated outputs at standard test conditions (25 °C, 1000 W/m², AM 1.5) are compared against manufacturer datasheet values, and residual errors are analyzed and attributed to specific modeling assumptions. Full article

Nonsequential double ionization is a fundamental process in ultrafast strong-field physics, containing rich information about electron correlation dynamics. In few-cycle laser fields, the carrier-envelope phase becomes a key parameter for controlling electron behavior. However, this process involves nonlinear mechanisms such as multi-electron stochastic dynamics and complex Coulomb interactions, posing significant challenges to traditional analytical theories. To address this, this work develops a numerical experimental approach based on a Monte Carlo stochastic algorithm, transforming the quantum problem into a computable stochastic sampling task. Through statistical sampling and final-state analysis of tens of millions of quantum trajectories, the central regulatory role of the carrier-envelope phase is systematically revealed. The computational results show that this phase can not only independently regulate the yields of double ionization and frustrated double ionization but also control the branching ratio between them, with a peak-to-peak modulation amplitude of approximately 20%. Additionally, it enables fine-tuning of the electron momentum correlation distribution. The physical mechanisms underlying these regulatory effects are clearly elucidated through analysis of the quantum trajectories. This work not only clarifies the critical role of the carrier-envelope phase in few-cycle intense-field double ionization but also demonstrates the powerful capability of the Monte Carlo stochastic algorithm in revealing the intrinsic stochasticity of strong-field physics and achieving precise physical control, providing an example for the deep integration of intense-field physics and computational science. Full article

Vibration signals of planetary gearboxes under variable-speed conditions exhibit strong non-stationarity and modulation, so a single feature representation cannot comprehensively describe intricate fault patterns, and distribution discrepancies across rotating speeds degrade the cross-condition generalization of diagnostic models. To address these limitations, this paper proposes an adaptive domain-aligned multi-modal feature fusion network (ADAMFFN). Three parallel branches extract complementary features from dual-channel vibration signals: spatial coupling features from orbit images, time–frequency energy features from continuous wavelet transform (CWT) representations, and frequency-domain statistical (FreqStat) features from power and envelope spectra. Heterogeneous features are mapped into a shared latent subspace through a unified projection layer, deep cross-modal interaction is realized by a progressive fusion network, and a domain alignment mechanism based on a domain-adversarial neural network (DANN) is introduced to eliminate source–target distribution gaps via adversarial training. On eight leave-one-speed-out (LOSO) cross-speed tasks constructed on the public WT-Planetary Gearbox dataset, ADAMFFN achieves an average accuracy of 99.25%, outperforming the best single-branch and dual-branch schemes by 2.63 and 0.40 percentage points, respectively; ablation experiments verify the complementarity of the three modalities and the effectiveness of domain alignment. Cross-condition external validation on the Southeast University (SEU) gearbox dataset further demonstrates its generalization capability under a different test rig and acquisition conditions. Full article

Power boiler tubes are exposed to severe operating conditions that lead to wall thinning and material degradation. Reliable assessment of tube wall thickness is therefore essential for ensuring safe and efficient boiler operation. This exploratory laboratory study investigates the applicability of active thermography combined with analytical and machine learning (ML) approaches for non-contact wall thickness estimation in power boiler tubes. Experimental investigations were performed on a single boiler tube specimen with artificially introduced wall-thickness reductions. Thermal responses were recorded using an infrared camera under both heating and cooling excitation conditions. Based on the acquired thermographic data, analytical models and machine learning algorithms were developed to estimate tube wall thickness. The machine learning approach was implemented using Random Forest and Support Vector Regression models and compared with conventional analytical modeling techniques. For separately analyzed and relatively homogeneous measurement series, the machine learning models produced lower descriptive errors than the analytical models, with the estimated three-RMSE error envelope decreasing from 0.51 mm to 0.17 mm. However, when heating and cooling datasets were aggregated, the analytical models achieved lower root mean square error values and demonstrated greater stability than the machine learning methods. These findings indicate that model performance strongly depends on the size, characteristics, and homogeneity of the available training data. Owing to the limited number of independent measurement series, the reported results should be interpreted as a small-sample feasibility assessment rather than as evidence of the general superiority of machine learning modeling. The results support the potential of active thermography for non-contact assessment of boiler tube wall thickness under controlled laboratory conditions. Further validation using additional specimens, grouped cross-validation, and physics-based synthetic data is required before the methodology can be considered for industrial implementation. Full article

This paper presents an optimized control strategy that enables a grid-connected photovoltaic (PV) system to operate as a static synchronous compensator (PV-STATCOM) to damp power oscillations in the transmission system, using an arithmetic optimization algorithm (AOA). The key contribution of this work is a synchronized, AOA-optimized multi-mode switching approach that includes standard PV operation, Full STATCOM, and Partial STATCOM with ramp-rate recovery, rather than relying solely on PI-gain adjustment. This is accomplished across the complete pre-fault, fault, and post-fault cycle. Under the proposed strategy, the PV system temporarily curtails its real power output when power oscillations arise following a system disturbance, thereby releasing the full inverter capacity for STATCOM operation and, hence, for oscillation damping. Once the oscillations are damped, the PV system ramps its real power back to the pre-disturbance level; at night, the inverter’s full capacity remains available for damping oscillations. The control scheme is implemented with a set of proportional–integral (PI) controllers whose parameters are tuned with the AOA, and its performance is benchmarked against tuning with the cuckoo search (CS) algorithm. Simulation results demonstrate that the AOA-tuned PV-STATCOM significantly improves damping, reduces oscillation amplitudes, maintains the point-of-common-coupling voltage within the low-voltage ride-through envelope, and keeps the system frequency within grid-code limits, thereby ensuring stable grid operation. Compared to a CS-tuned benchmark, the AOA-tuned design keeps the frequency continuously within the grid code band, settles at nominal 50 Hz, and reduces the maximum voltage overshoot from 20% to 15%. Full article