Search Results (28,168)

Carbon dioxide (CO₂) enrichment is an important means of increasing crop productivity in protected cultivation. However, local canopy CO₂ concentration in Chinese solar greenhouses is jointly affected by gas release, pipeline transport, and ventilation disturbances, making fixed-parameter proportional–integral–derivative (PID) control unable to simultaneously achieve rapid tracking, low overshoot, and fast disturbance recovery. This study developed a CO₂ enrichment system comprising controlled thermal decomposition of ammonium bicarbonate, condensation and water scrubbing, near-canopy delivery, and programmable logic controller (PLC)-based closed-loop control, and proposed a dynamic-target fuzzy PID (DT-FPID) strategy. Step-response tests were used to establish a first-order-plus-dead-time model linking heater duty cycle to local canopy CO₂ concentration, followed by fixed-target tracking, rule-based dynamic-target execution, and short-term ventilation-disturbance recovery tests in a local validation zone of a Chinese solar greenhouse. Relative to fixed-parameter PID, DT-FPID showed approximately 68–79% lower maximum overshoot and approximately 35–70% shorter ±20 ppm precision settling time (T₂₀) in simulation. At 600 ppm, the ±5% settling time was approximately 71% shorter, whereas at 800 and 1000 ppm it was broadly comparable to PID. In the greenhouse experiments, each controller–target combination included three independent runs. Based on descriptive comparisons of group means, DT-FPID showed approximately 47–49% lower mean maximum overshoot, approximately 36–40% shorter mean settling time, and approximately 77–80% shorter mean ventilation-disturbance recovery time; its mean maximum overshoot and settling time were also lower than those of conventional fuzzy PID. All three dynamic-target field runs completed the prescribed switches among the 600, 800, and 1000 ppm target levels. These results support control performance only under the short-term local validation conditions of this study; they are not used to determine physiologically or economically optimal CO₂ concentrations or to extrapolate whole-greenhouse uniformity or long-term production effects. Full article

This work presents a compact square complementary split-ring resonator (CSRR) microwave sensor, combined with a machine-learning-based calibration strategy, to achieve superior texture-aware soil moisture quantification. Implemented on a Rogers RO3010 substrate with a 20 × 30 mm² footprint and operating near 1.3 GHz, the sensor exploits shifts in resonance/notch frequency and insertion loss (S21) to probe both the real and imaginary components of the soil’s complex permittivity. Full-wave 3D electromagnetic simulations guided optimisation of the CSRR topology and T-shaped microstrip feedline, yielding strong field confinement, high quality factor, and high Frequency Detection Resolution (FDR). Experiments on sand and loam across 0–30% and 0–40% moisture content ranges, respectively, demonstrate FDR values of 6.09 MHz (sand) and 6.86 MHz (loam), enabling discrimination of subtle permittivity changes. Several calibration strategies are developed and compared for complex permittivity extraction from measured S-parameters: linear and polynomial regression, a multivariable least-squares sensitivity-matrix model, and a delta-referenced multilayer perceptron (MLP) with z-score standardization. While polynomial and least-squares models significantly outperform linear regression (R² > 0.997), the MLP combined with the optimized CSRR architecture delivers the best performance, achieving near-ideal accuracy (R² ≈ 1, MAE < 0.001, RMSE < 0.001) for both soil types. These results demonstrate that the synergy between the novel CSRR sensor design and data-driven MLP calibration enables high-resolution, robust, and field-deployable soil moisture sensing, offering a compelling solution for next-generation agricultural and geotechnical monitoring systems. Full article

In this paper, we integrate liquidity risk into the generalized exchange option pricing model. Based on Esscher’s transformation theory, we calculate the expectation and variance of random variables to derive an explicit expression of the generalized exchange option pricing formula considering the liquidity risk of underlying assets, which avoids the complicated calculation of the optimal parameter vector ${\mathbf{h}}^{*}$. In addition, we plot time-series liquidity curves to screen underlying stocks that fit the model framework, and employ the simulated annealing algorithm to estimate model parameters based on historical data. Finally, numerical simulations are conducted with the estimated parameters and actual market data as inputs, confirming the internal consistency of the model. Relevant parameters are then adjusted to intuitively illustrate how liquidity risk affects option pricing and the associated price movements. Full article

Laser cladding is promising for use in repairing carburized gear steels, but parameter selection is challenging when clad geometry, substrate thermal disturbance, and morphological quality must be considered. Fifteen tracks of NHT.22.A01 iron-based powder were deposited on carburized-quenched 18CrNiMo7-6 steel with varying laser powers, scanning speeds, and powder feed rates. A physics-informed cascaded-learning framework predicted track width, height, Ac₁-boundary depth, and quality. Leave-one-out out-of-fold width predictions were transferred to height and depth models to prevent target leakage. An auxiliary continuous quality index enabled the bi-objective optimization of quality and high-hardness layer depth, while multi-indicator process maps with local sensitivity analysis supported rapid parameter adjustment. Leave-one-out (R²) values for three geometric responses ranged from 0.934 to 0.968, and the maximum relative error at an unseen boundary condition was 4.3%. The fitted HAZ-depth/track width scaling coefficient (0.211) lay within the Rosenthal theoretical range (0.15–0.25), confirming the physical consistency of the cascade relationship. The optimization revealed a trade-off between quality and high-hardness layer depth ; maximizing predicted high-hardness layer depth under selected a condition already represented in the training set. Repeatability was supported by an independent batch replicate. By integrating leakage-controlled cascading, physically interpretable validation, constrained optimization, and sensitivity-resolved process mapping, the framework provides a transparent and practically applicable approach to small-sample laser cladding process design. Full article

To decrease the environmental and health consequences associated with pollution, several cobalt oxides-decorated biochar substrates have been developed for effective catalytic reduction of 4-NP. The porous biochar was produced by pyrolysis of sewage sludge and acid demineralization, and subsequently, Co₃O₄ was loaded with varying amounts to synthesize Co₃O₄/biochar nanocomposites. The characterization of the materials included FTIR, XRD, SEM/EDX, TEM, BET, CHNS elemental analysis, TGA/DTA, and pH_pzc analysis. The catalytic activity of synthesized composites was monitored by UV-Vis spectroscopy to measure the transformation of a priority toxic pollutant, 4-NP, to a desirable pharmaceutical intermediate, 4-AP, by NaBH4, considering it a reducing agent. The influence of corresponding parameters such as catalyst dosage, NaBH₄ concentration, and Co₃O₄ content in the composite on the reduction time of 4-NP was modelled using central composite design (CCD) of RSM. The optimal conditions for the catalytic reduction of 0.3 mM 4-NP were found to be a catalyst dosage of 5 mg, a NaBH₄ concentration of 26 mM, and a Co₃O₄ loading of 13%, resulting in complete reduction within 5 min. The optimized catalyst displayed a high porosity, well dispersion of Co₃O₄ nanoparticles, good catalytic stability, and cost-effectiveness, revealing new insights into application of cobalt oxide and sewage sludge for pollutant reduction. Full article

This study introduces the Volterra–Gegenbauer model, based on convergent approximation theory, for effectively representing nonlinear dynamical systems from limited data. The model uses a second-order Volterra series, approximating its kernels (which capture the system’s nonlinearities) with an orthogonal finite basis of Gegenbauer polynomials. Furthermore, optimizing the Gegenbauer parameters allows for adjustable complexity, ensuring that the model captures low-frequency nonlinear variations while preventing overfitting. The model was applied to analyze the dynamics of global fisheries impacted by marine habitat degradation, using anthropogenic variables such as ocean acidification, CO₂ emissions, ocean heat content, and global temperature anomalies. Despite being based on a limited 74-year dataset, the model achieved a robust fit, with a mean relative error of 1.617%. This accuracy confirms the model’s ability to describe the nonlinear trend of ecosystem decline. Ultimately, this work provides a mathematically adaptable tool for modeling nonlinear systems in control applications with limited data. Finally, the study highlights the need to address the rapid ecological collapse of marine habitats. Full article

In recent years, numerous studies have reported on intelligent analytical applications for food quality and safety. To identify the underlying patterns and development trends in this field, this paper presents a comprehensive review from the perspectives of both data-driven and mechanism-driven paradigms. Under the data-driven paradigm, detection modalities may involve either single-modal or multimodal approaches. By integrating measured detection data with appropriate intelligent learning algorithms, specific tasks for food quality or safety assessment can be achieved. Research efforts in this area encompass the development of detection techniques, optimization of measurement parameters, construction of high-dimensional spectral features, design of feature extraction methods, selection and tuning of algorithms, and formulation of multimodal data fusion strategies. This paradigm is characterized by high computational speed and superior prediction or classification efficiency. Nevertheless, it is constrained by several limitations, including poor model interpretability, limited extrapolation and generalization capabilities, and a heavy reliance on high-quality annotated data. In contrast, the data–mechanism hybrid-driven paradigm integrates physical laws and other prior knowledge as constraints that are deeply embedded into neural network training. By combining data-driven mining capabilities with theoretical prior knowledge, this approach achieves improved predictive performance and decision-making reliability. This paradigm offers notable advantages, such as enhanced interpretability, greater trustworthiness, improved data efficiency, and reduced computational costs. It is particularly well-suited for small-sample or data-sparse scenarios, and thus represents a promising and important direction for future research in this domain. Full article

Single-pass across-track Synthetic Aperture Radar (SAR) interferometry is a well-established technique for the generation of high-quality digital elevation models (DEMs) that strongly benefits from formation flying. In this context, the TanDEM-X mission of the German Aerospace Center (DLR) has generated the most accurate global DEM by employing a formation of two satellites flying with a time-variant baseline. The achievable accuracy of the DEM, however, is intrinsically limited by phase unwrapping, which poses a limit to the maximum adoptable across-track baseline. Moreover, the time-variant baseline represents a trade-off between height accuracy and robustness and phase unwrapping errors and propellant consumption. To tackle these challenges, this study proposes an optimized single-pass multi-baseline approach, based on a nested helix configuration that overcomes such limitations. We determine the optimal orbital parameters to produce a high-quality DEM with uniform height of ambiguity for all latitudes, while also ensuring passive safety for the relative trajectories. Formations of three or four satellites allow at least two desired baselines to be achieved at all latitudes, the smallest of which aids phase unwrapping. The resulting deviations in the height of ambiguity are about 7–10% and 2–3.5% for the three- and four-satellite formations, respectively. The proposed approach therefore paves the way to multi-baseline interferometric SAR missions for the generation of DEMs with considerably improved quality. Full article

With the rapid development of the electric vehicle (EV) industry, large-scale integration of EVs into the power grid has led to increasingly prominent problems such as low charging efficiency, intensified load fluctuations, and reduced economic benefits for users. To address these issues, an optimization model is constructed with charging time, load fluctuation, and user charging cost as the objectives, comprehensively considering uncertainties including renewable energy output, user charging behavior, and electricity price fluctuations. An uncertainty-aware multi-objective scheduling strategy based on an improved chaotic Lévy flight multi-objective particle swarm optimization (CLM-MOPSO) algorithm is proposed. Specifically, Weibull and Beta distributions are adopted to generate scenarios for wind and photovoltaic power output, while Poisson and normal distributions are used to characterize the uncertainty of user charging behavior. In addition, a stochastic electricity price process and load uncertainty sets are introduced to establish a robust optimization framework based on multi-scenario stochastic programming. On this basis, an improved CLM-MOPSO algorithm is designed, in which Tent chaotic mapping is utilized for high-quality population initialization, Lévy flight mutation is introduced to enhance the global search capability, and adaptive parameter adjustment together with an external archive mechanism is incorporated to improve the search efficiency while maintaining good convergence and diversity of the Pareto solution set. Finally, simulation studies based on real road network and power grid operation data are conducted, and the results verify the effectiveness of the proposed method. The results demonstrate that the proposed method significantly reduces charging time, mitigates load fluctuations, and lowers user charging costs, while also exhibiting strong robustness and potential for practical engineering applications. Full article

We develop a PDE-constrained optimization framework for calibrating a regime-switching Heston–Merton model to S&P 500 index option prices. The model features two latent Markov regimes modulating stochastic volatility parameters and compound Poisson jumps, capturing the stylized fact that market volatility clusters differently during normal and crisis periods. Using real data from the Federal Reserve Economic Data (FRED) database covering July 2016 to July 2026 (2609 business days), we identify crisis regimes via VIX thresholds and estimate transition probabilities. Our empirical analysis reveals that crisis regimes exhibit 3.78 times higher long-run variance, 1.60 times higher vol-of-vol, and 113 times higher jump intensity compared to normal regimes. We derive the full adjoint system for the forward PIDE, including the previously undocumented jump operator adjoint and Markov-switching generator adjoint, and demonstrate that the adjoint method reduces per-iteration PDE solves from order-P to 2 regardless of parameter dimensionality. A panel calibration exercise demonstrates superior in-sample fit (${\mathrm{RMSE}}_{\mathrm{IV}}=1.24$ vol points) versus the nested Heston (2.87), Bates (2.31), and Black–Scholes (19.46) models. Out-of-sample Diebold–Mariano tests confirm statistically significant forecasting gains at the 1% level. The Feller condition is satisfied in both regimes. Full article

This paper proposes a parametric modeling and multi-objective optimization method based on optomechanical coupling analysis to address the design optimization of large-aperture primary mirrors in space-based solar extreme ultraviolet (EUV) imaging instruments. By establishing a parametric model of the primary mirror and combining topology optimization with dimensional optimization, the influence of key parameters—including mirror thickness, rib width, and lightweight hole dimensions—on the mirror surface shape accuracy (RMS) and structural fundamental frequency is systematically analyzed. The study employs finite element simulation and optomechanical coupling data processing algorithms to extract the rigid-body displacement and surface shape error of the primary mirror under gravitational loading and identifies high-impact parameters through sensitivity analysis. After optimization, the RMS values of the mirror’s surface shape in all three directions under a 1 g gravitational load are all better than 4.5 nm, meeting the requirements for spaceborne payloads. Experimental validation shows that the surface shape RMS of the assembled primary mirror is only 0.022λ (with λ = 632.8 nm), and the system wavefront error is less than 0.08λ, significantly surpassing the design specification requirement of 0.1λ. Full article

The clinical integration of artificial intelligence has outpaced the development of robust evaluative frameworks, raising critical safety concerns. This perspective establishes a clear taxonomy distinguishing probabilistic language models from deterministic classifiers and applies a multi-dimensional combinatorial model to calculate the requirements for complete diagnostic coverage. Our analysis demonstrates that comprehensive diagnostic coverage requires between 50,000 and 150,000 distinct, task-specific classifiers under subspecialty-level clinical granularity; conservative aggregated estimates (4500–18,750 binary classifiers) do not reflect the multiplicative expansion introduced by subtype differentiation, severity staging, temporal variants, demographic stratification, and equipment variation, whereas currently cleared devices cover less than one percent of this clinical space. More fundamentally, although population-trained models can generate conditional patient-specific risk estimates when predictors are informative and calibration is adequate, these statistical parameters optimized on population-scale data cannot provide the categorical certainty required for individual diagnostic decisions, which is a gap that clinical judgment must bridge. Because clinical AI tools are inherently statistical and perform reliably only on common, highly represented presentations while failing on rare, atypical cases rare in their training data, attempting to automate routine tasks leaves human clinicians with only the most challenging diagnostics. Furthermore, selective automation of these low-complexity cases introduces severe occupational hazards, including cognitive surrender, diagnostic complacency, and rapid expertise atrophy. Rather than pursuing the computationally and logistically unfeasible goal of complete diagnostic classification, developers should prioritize predictive, prognostic trajectory modeling. This paradigm shift aligns the probabilistic nature of machine learning with clinical utility, reinforcing clinical judgment as the irreplaceable diagnostic integrator. Full article

Heart rate variability (HRV) is a useful non-invasive tool for studying autonomic nervous system modulation under emotional stimulation; however, accurate estimation of dominant frequencies in HRV signals remains challenging due to their non-stationary nature and the sensitivity of some spectral methods to configuration parameters. This work presents a methodology for the spectral characterization of HRV signals derived from ECG recordings from the DREAMER database, with emphasis on optimizing the model order of the MUSIC algorithm to improve dominant frequency localization within the physiological low-frequency (LF) and high-frequency (HF) bands. The proposed methodology included ECG signal preprocessing, R-peak detection, RR interval extraction, HRV interpolation, and spectral analysis using MUSIC, while evaluating different model orders through a signal-driven composite criterion based on AIC, MDL, ESTER, eigengap, and model complexity. The criteria were normalized using min–max normalization and combined using equal predefined weights. The results showed that the signal-driven selection of the parameter p produced recording-dependent model order configurations and different dominant frequency estimates across the analyzed stimuli. The resulting LF/HF agreement was evaluated independently after model order selection and showed non-uniform correspondence across stimuli and spectral estimators. Overall, these findings indicate that model order selection can substantially influence the spectral characterization obtained with MUSIC and provide a signal-driven framework for examining this dependence in HRV recordings. Full article

Vibration damper detection in unmanned aerial vehicle (UAV)-based transmission line inspection presents distinctive task-specific challenges: the targets are not only small and weakly textured, but also characterized by slender structures. Their effective identification therefore depends on the preservation of local contour cues and the appropriate organization of deep contextual responses. To address the limitations of conventional lightweight detectors in structural feature representation, cross-scale semantic consistency, and bounding-box localization, this paper proposes CARE-Net (Cascaded Attention and Refinement Enhanced Network), a compact detection framework for vibration damper detection. CARE-Net adopts an asymmetric design consisting of front-end structural enhancement and back-end contextual refinement. Specifically, the Cascaded Residual Attention Block (CRAB) is deployed in the backbone to strengthen the representation of slender contours and local structural features of vibration damper targets. The Dynamic Context Refinement Network (DCRN) is introduced at the backbone–neck transition to improve the contextual organization of deep features and the quality of cross-scale feature fusion. Meanwhile, an Adaptive Focal Complete IoU Loss (AF-CIoU) is proposed to optimize bounding-box regression for difficult samples without altering the inference architecture. A UAV-based vibration damper dataset covering three condition categories, namely normal, rusted, and dilapidated, is constructed in this study. Experimental results show that CARE-Net achieves an mAP^@0.5 of 0.951 and an mAP^@0.5:0.95 of 0.628 with 2.44 M parameters and 6.2 GFLOPs. Further configuration experiments indicate that, compared with repeatedly introducing attention enhancement into high-level features, stage-specific feature modeling is better suited to the slender small-object detection task investigated in this study. The proposed method provides a solution for intelligent vibration damper inspection of transmission lines that balances detection accuracy, model compactness, and potential for terminal-side application. Full article

Active acoustic signaling can improve robot navigation in crowds, but methods based on a cooperative-response assumption may generate unnecessary signals near static pedestrians and misjudge signaling effectiveness when non-cooperative pedestrians approach. This study proposes heterogeneous interactive crowd navigation with acoustic signaling, a selective acoustic-signaling method for heterogeneous crowds. The proposed method models dynamic cooperative pedestrians, static non-responsive pedestrians, and approaching non-cooperative chasers. A differentiated reinforcement-learning social reward suppresses ineffective signaling in static-only neighborhoods, while a second training stage introduces non-cooperative chasers controlled by a standard optimal reciprocal collision avoidance policy with fixed parameters to improve motion avoidance when signaling cannot alter chaser motion. Experiments in corridor and square-hall environments used 500 test episodes per setting. The differentiated reward reduced total signaling rates by 41.2% and 47.0%, respectively, and reduced static-condition signaling rates to 0%, while largely preserving dynamic-condition signaling and navigation time. In complete heterogeneous scenarios, the proposed method achieved success rates of 0.94 and 0.91, collision rates of 0.02 and 0.04, and catch rates of 0.08 and 0.12. These results indicate improved signaling selectivity and navigation performance under the evaluated controlled simulation conditions involving predefined cooperative and non-responsive pedestrian behaviors. Full article