Search Results (125)

Aided by quantum sources, quantum metrology helps enhance measurement precision. Here, we construct a theoretical model for quantum imaging based on squeezed states and present the corresponding numerical results. Through discretization and quantum Fisher information theory, we investigate the two-point resolution and spatial multi-parameter estimation of optical fields with unknown spatial distributions. We calculate and compare imaging results based on squeezed vacuum states, coherent states, and squeezed coherent states; our results show that squeezed coherent states yield greater quantum Fisher information, which can effectively improve imaging quality. In addition, we analyze the influence of imaging basis functions, degree of squeezing, quantum correlations, and other factors on imaging performance. The proposed quantum imaging model and computational method can be extended to more complex scenarios, such as multi-mode squeezed-state imaging schemes and incoherent imaging systems. In the future, this approach is expected to find applications in practical imaging systems, including Raman microscopy and stimulated Brillouin scattering imaging. Full article

Effective management of rye cover crops in cash-crop systems relies heavily on accurate biomass estimation. Low-altitude Unmanned Aerial Vehicle (UAV) imagery offers a promising high-resolution alternative, yet unlocking its full potential requires moving beyond basic estimation models to more integrative and explanatory models. This study obtains the measured height (MH), SPAD (Soil and Plant Analyzer Development) values, and measured dry biomass (MDB) and applies UAV remote sensing and machine learning to acquire the crop canopy height, vegetation indices (VIs), and vegetation fraction (VF) across growth stages. Among single-parameter biomass estimation models, the estimated height yields the best at the overall growth stage (R² = 0.935), whereas selected VIs perform the best at the non-seedling stage (R² = 0.851). For multi-parameters modeling, models combining height, VF, and VIs significantly outperform the single-parameter models, achieving better estimation results throughout each growth stage (Best R² = 0.951). Structural equation modeling clarifies the direct and indirect contributions of these parameters to biomass accumulation, revealing their synergistic effects. This study demonstrates the potential of UAV-based multi-parameter biomass estimation model to support more informed decisions in cover crop management and to advance broader precise agriculture practices. Additionally, the analytical framework developed here offers a transferable approach for high-resolution biomass monitoring in other crop systems. Full article

Addressing the challenge of tracing hidden and transient cross-connections in urban drainage networks, this study develops a SWMM–Bayesian coupled model based on the Py SWMM interface using the Daming Lake area in Jinan as a case study. By employing a Markov Chain Monte Carlo (MCMC) algorithm to drive the interaction between dynamic simulation and statistical inference, the model achieves multidimensional joint posterior estimation of pollution source location (J_x), discharge intensity (M), and discharge timing (T). The results indicate: (1) Model accuracy: The coupled model demonstrates strong source tracing capability, with mean absolute errors below 0.6% in single-parameter inversion. Under multi-parameter joint inversion, the true values of all parameters consistently fall within the 95% confidence intervals. (2) Parameter sensitivity: The influence of MCMC step size on the uncertainty of pollution tracing results is systematically clarified. Discrete source location estimates (J_x) exhibit high robustness to step size variation due to spatial heterogeneity in hydraulic responses, whereas continuous physical parameters (M and T) show strong dependence on the selected step size scale. (3) Practical application: The impact of spatial monitoring network configuration on pollution tracing performance is examined. By deploying a complementary monitoring system integrating trunk and branch pipelines, the inversion accuracy for mass (M) and time (T) parameters is significantly improved by 84.2% and 88.5%, respectively. Overall, the proposed pollution source tracing method for urban drainage networks effectively overcomes the multi-solution challenge in complex network inversion, providing critical technical support for refined urban water environment management. Full article

Background: In the population of patients with ischemic cardiomyopathy (IC) and reduced left ventricular ejection fraction, the benefits of prophylactic implantable cardioverter-defibrillator (ICD) therapy are not uniform. Identifying predictors of ventricular arrhythmias to estimate the risk of appropriate therapy is crucial. Methods: Patients with IC and an ICD for primary prevention implanted between 2006 and 2019 were retrospectively analyzed for appropriate therapy (ATh). The primary objective was to assess predictors of ATh development. The secondary objective was to assess the impact of ATh on survival. Results: Overall, 260 patients (age 67.3 ± 9.4 years, 15.4% female) were analyzed with a follow-up of 4.47 ± 3.02 years. ATh occurred in 79 patients (30.4% of the study group). Independent risk factors for ATh were as follows: non-sustained ventricular tachyarrhythmias (nsVTs) detected before ICD implantation, extensive area of ischemic left ventricular damage on echocardiographic assessment, left ventricular end-diastolic dimension (LVEDd) ≥ 68 mm, history of coronary artery bypass grafting (CABG), and presence of chronic total occlusion (CTO). A multiparameter logit model was created to estimate the probability of ATh. Patients with a score ≥ 0.6 had more than a six-fold higher risk of developing ATh compared with patients with a score < 0.6. Patients after ATh had significantly lower survival compared to patients without intervention (HR 1.69, p = 0.008). Conclusions: Patients with the independent risk factors listed above are at higher risk for ATh. A multiparameter logit model based on these risk factors is effective in estimating the risk of ATh. The occurrence of ATh was associated with a significantly higher risk of all-cause mortality. Full article

Background: Colorectal cancer (CRC) demonstrates substantial clinical and biological diversity across age groups, ancestral backgrounds, and treatment settings, alongside a rising incidence of early-onset disease (EOCRC). The mitogen-activated protein kinase (MAPK) pathway is a major driver of CRC development and therapy response; however, the distribution and prognostic value of MAPK alterations across distinct patient subgroups remain unclear. Methods: We analyzed 2515 CRC tumors with harmonized demographic, clinical, genomic, and treatment metadata. Patients were stratified by ancestry (Hispanic/Latino [H/L] vs. non-Hispanic White [NHW]), age at diagnosis (early-onset [EO] vs. late-onset [LO]), and FOLFOX chemotherapy exposure. MAPK pathway alterations were identified using a curated gene set encompassing canonical EGFR-RAS-RAF-MEK-ERK signaling components and regulatory nodes. Conversational artificial intelligence (AI-HOPE and AI-HOPE-MAPK) enabled natural language-driven cohort construction and exploratory analytics; findings were validated using Fisher’s exact testing, chi-square analyses, and Kaplan–Meier survival estimates. Results: MAPK pathway disruption demonstrated marked heterogeneity across ancestry and treatment contexts. Among EO H/L patients, FGFR3, NF1, and RPS6KA6 mutations were significantly enriched in tumors not receiving FOLFOX, whereas PDGFRB alterations were more frequent in FOLFOX-treated EO H/L tumors relative to EO NHW counterparts. In late-onset H/L disease, NTRK2 and PDGFRB mutations were more common in non-FOLFOX tumors. Distinct MAPK-associated alterations were also observed among NHW patients, particularly in non-FOLFOX settings, including AKT3, FGF4, RRAS2, CRKL, DUSP4, JUN, MAPK1, RRAS, and SOS1. Survival analyses provided borderline evidence that MAPK alterations may be linked to improved overall survival in treated EO NHW patients. Conversational AI markedly accelerated analytic throughput and multi-parameter discovery. Conclusions: Although MAPK alterations are pervasive in CRC, their distribution varies meaningfully by ancestry, age, and treatment exposure. These findings highlight NF1, MAPK3, RPS6KA4, and PDGFRB as potential biomarkers in EOCRC and H/L patients, supporting the need for ancestry-aware precision oncology approaches. Full article

Radar systems with exceptional anti-jamming performance are critical to meeting the high-performance requirements of future intelligent sensing systems. To address the deception jamming challenges encountered by intelligent sensing systems environments, a multi-parameter agile waveform is designed. The proposed waveform exhibits high flexibility across three dimensions—pulse width, pulse repetition interval, and carrier frequency. Compared to traditional single-parameter or two-parameter agile waveforms, which vary only one or two parameters, this multi-parameter approach significantly enhances anti-jamming performance by disrupting periodicity and providing higher flexibility in dynamic interference environments. To address the complex signal characteristics induced by multi-parameter agility, we further develop a low-complexity signal processing method based on a segmented multiple signal classification (MUSIC) algorithm, which accurately extracts Doppler information from pulse-compressed slow-time data to achieve high-precision velocity estimation. Both theoretical derivations and simulation results demonstrate that, compared with the conventional compressed sensing orthogonal matching pursuit method and the conventional MUSIC method that operate on the entire signal, our segmented approach divides the signal into smaller segments, reducing computational complexity and improving velocity estimation accuracy. Notably, even in high-intensity, densely jammed environments, the system reliably extracts target information. Full article

Predicting blast-induced vibrations in twin tunnels is challenging due to complex wave-cavity interactions, which render conventional scaled-distance (PPV-SD) models inadequate. This study introduces a hybrid empirical-probabilistic framework to quantify the probability of exceeding regulatory vibration thresholds. Field data from the Northern Marmara Highway project first quantitatively confirm the severe degradation of the classical scaled-distance (PPV-SD) method in twin-tunnel geometry, reducing a strong correlation (R = 0.82) to insignificance. A Random Forest sensitivity analysis, applied to 123 blast records, ranked the governing parameters, guiding the development of a deterministic multi-parameter regression model (R = 0.72). The core innovation of this framework is the embedding of this deterministic model within a Monte Carlo Simulation (MCS) to propagate documented input uncertainties, thereby generating a full probability distribution for PPV. This represents a fundamental advance beyond deterministic point-estimates, as it enables the direct calculation of exceedance probabilities for risk-informed decision-making. For instance, for a regulatory threshold of 10 mm/s, the framework quantified the exceedance probability as P (PPV > 10 mm/s) = 5.2%. The framework’s robustness was demonstrated via validation against 100 independent blast records, which showed strong calibration with 94% of observed PPV values captured within the model’s 90% confidence interval. This computationally efficient framework (<10,000 iterations) provides engineers with a practical tool for moving from deterministic safety factors to quantifiable, risk-informed decision-making. Full article

Driven by the urgent reduction in industrial energy consumption and nitrogen oxide (NO_x) emissions, numerical simulation becomes a significant tool to understand the internal working process and optimize the structure of the combustion chamber in coke oven. However, conventional numerical simulation is computationally expensive and impractical for real-time monitoring or multi-parameter optimization. To address this challenge, this study proposes a novel parameter fusion convolutional network (PFCN) to rapidly reconstruct the spatial temperature distribution in the combustion chamber of a coke oven. The key innovation of PFCN is its dual-stream encoding mechanism, which processes structural parameters (1 × 5 vector) and spatial coordinates (25 × 200 matrix) separately via dedicated encoders, followed by a cross-modal fusion to effectively integrate these heterogeneous inputs. Furthermore, a support vector machine (SVM) is coupled downstream of the PFCN to estimate the exhaust NO_x emissions based on the predicted physical information. This coupled PFCN–SVM framework allows universal applicability across different combustion chamber configurations. Based on this framework, parametric influence analysis and co-optimization of five key structural parameters are conducted for a three-stage air-supply coke oven. The results reveal that both the air staging ratio and staging height significantly affect combustion performance. Compared to the basecase, the optimized design simultaneously improves temperature homogeneity by 15.2% and reduces NO_x emissions by 8%, with negligible computational cost. This integrated data-driven approach demonstrates considerable potential for combustion chamber optimization, transient process predictions, multi-physics coupling analyses, and online control implementations. Full article

A major scientific challenge is understanding how precipitation systems will change under global warming. In particular, extreme precipitation events associated with hail and graupel are of significant concern. In this study, we evaluated the performance of the flagGraupelHail product from the Dual-Frequency Precipitation Radar (DPR) aboard the GPM Core Observatory using high-resolution dual-polarization observations from Multi-Parameter Phased Array Weather Radar (MP-PAWR). The analysis focused on a convective system that developed in a humid environment over the Tokyo region of Japan, providing a valuable assessment within a climatic regime that has been underrepresented in previous studies. A bias correction for MP-PAWR reflectivity, derived from XRAIN network comparisons, yielded good agreement with KuPR observations from the DPR. A new grid-matching method, suitable for comparing vertically varying hydrometeor particle types and available only for MP-PAWR, was also introduced. The comparison revealed that DPR flagGraupelHail detections generally corresponded to regions of graupel occurrence identified by the MP-PAWR GHratio, defined as the number of graupel/hail grids within a DPR observation volume, although DPR tended to detect fewer events. To improve detection performance, we introduced a new indicator, STH35-FH—the height difference between the 35 dBZ echo top and the 0 °C level—as a complementary parameter to the PTI value used to determine flagGraupelHail. Incorporating STH35-FH improved the consistency between DPR and MP-PAWR detections, reducing false positives and enhancing overall detection accuracy. These results demonstrate the value of combining ground-based and spaceborne radar observations to improve global precipitation retrievals, particularly in humid environments. This approach will contribute to more accurate global graupel/hail estimation by spaceborne precipitation radar and a better understanding of how global warming affects precipitation systems. Full article

The flotation stage is a critical segment of mineral processing production. In copper concentrate flotation, predicting the concentrate grade is essential for maintaining a stable flotation process, ensuring concentrate quality, and enhancing profits. To improve the prediction accuracy for the concentrate grade, we propose a prediction method based on an improved eXtreme Gradient Boosting (XGBoost) model using real copper concentrate flotation data in the paper. To address the issues of outliers and missing values in the collected dataset, we firstly present an outlier detection and imputation method using the Inter-Quartile Range (IQR) method and the MissForest (MF) algorithm. An XGBoost-based model is developed for predicting the copper concentrate grade. The model is trained using some key indicators, including feed grade, ore throughput, reagent concentration, pulp flow rate, air flow rate, level, and pH value, as the input features. Moreover, hyper-parameter tuning is optimized based on a Tree-Structured Parzen Estimator (TPE). Combining the IQR/MissForest with TPE-optimized XGBoost can enable an end-to-end prediction pipeline for the copper concentrate grade in the flotation process to address the issues of data anomalies and missing values in the flotation process, as well as the low efficiency of multi-parameter tuning, ensuring the accuracy of data processing and the effectiveness of model training. The experimental results demonstrate that compared with some traditional prediction methods, such as support vector machines, the proposed method achieves about a 25.3% reduction in the Root Mean Square Error (RMSE), indicating our method’s superior performance. Full article

This paper addresses the challenges of fault diagnosis in gas turbines (GTs) utilized in oil and gas pipeline systems by proposing a novel multiparameter analysis framework that integrates dynamic, quantile-based Statistical Process Control (SPC) with prior domain knowledge. The proposed approach initially employs a dynamic quantile SPC model to establish adaptive control limits, effectively handling the non-stationarity and non-normality of gas turbine operational data. By analyzing parameter variations under typical operating conditions and incorporating expert insights, a multiparameter fault analysis matrix and corresponding weighting factors are constructed to facilitate fault diagnosis with prior knowledge. Furthermore, a fault probability model based on parameter change rates and weighting factors is developed to quantify the likelihood of different fault modes. An operating condition clustering and correction mechanism enables the dynamic adjustment of control limits, thereby preventing misdiagnoses caused by varying operational states. The validity of the proposed method is demonstrated using real data from a domestic pipeline gas turbine, validated by real domestic pipeline GT data, outperforming existing models, with a fault accuracy up to 10%. The approach efficiently estimates fault probabilities and accurately detects both sudden and gradual faults, significantly enhancing intelligent fault diagnosis capabilities for gas turbines. Full article

Against the global backdrop of energy transition, the precise assessment of urban rooftop photovoltaic (PV) system capacity is recognized as crucial for optimizing the energy structure and enhancing the sustainable utilization efficiency of spatial resources. Publicly available aerial imagery is characterized by non-orthorectified issues; direct utilization is known to lead to geometric distortions in rooftop PV and errors in capacity prediction. To address this, a dual-optimization framework is proposed in this study, integrating monocular vision-based 3D reconstruction with a lightweight linear model. Leveraging the orthogonal characteristics of building structures, camera self-calibration and 3D reconstruction are achieved through geometric constraints imposed by vanishing points. Scale distortion is suppressed via the incorporation of a multi-dimensional geometric constraint error control strategy. Concurrently, a linear capacity-area model is constructed, thereby simplifying the complexity inherent in traditional multi-parameter fitting. Utilizing drone oblique photography and Google Earth public imagery, 3D reconstruction was performed for 20 PV-equipped buildings in Wuhan City. Two buildings possessing high-precision field survey data were selected as typical experimental subjects for validation. The results demonstrate that the 3D reconstruction method reduced the mean absolute percentage error (MAPE)—used here as an estimator of measurement uncertainty—of PV area identification from 10.58% (achieved by the 2D method) to 3.47%, while the coefficient of determination (R²) for the capacity model reached 0.9548. These results suggest that this methodology can provide effective technical support for low-cost, high-precision urban rooftop PV resource surveys. It has the potential to significantly enhance the reliability of energy planning data, thereby contributing to the efficient development of urban spatial resources and the achievement of sustainable energy transition goals. Full article

To address the limitations of low detection efficiency and poor spatial resolution of traditional cable insulation diagnosis methods, a novel cable insulation diagnosis method based on impedance spectroscopy has been proposed. An impedance spectroscopy analysis model of the frequency response of high-voltage single-core cables under different aging conditions has been established. The initial classification of insulation condition is achieved based on the impedance phase deviation between the test cable and the reference cable. Under localized aging conditions, the impedance phase spectroscopy is more than twice as sensitive to dielectric changes as the amplitude spectroscopy. Leveraging this advantage, a multi-parameter diagnostic framework is developed that integrates key spectral features such as the first phase angle zero-crossing frequency, initial phase, and resonance peak amplitude. The proposed method enables quantitative estimation of aging severity, spatial extent, and location. This technique offers a non-invasive, high-resolution solution for advanced cable health diagnostics and provides a foundation for practical deployment of power system asset management. Full article

Given the global rise in magnitude and frequency of extreme events due to climate change, accurately determining these values—typically through frequency analysis—is especially important. The article analyzes the particular aspects of three probability distributions of 4 and 5 parameters in flood frequency analysis (FFA) using the L-moments as a parameter estimation method. Aspects regarding the behavior of the five-parameter Wakeby, four-parameter generalized Pareto and four-parameter Burr distributions are highlighted in generating the maximum flow values in the area of low annual exceedance probabilities characteristic of rare and very rare events. After applying these distributions to four case studies, it was found that for the 10,000-year return period event, the relative error between multi-parameter distributions is under 20%—a more than acceptable margin given the extremely low exceedance probability. However, its importance depends on the use of the generated values, which in some cases can lead to excessive costs in establishing structural flood protection measures (urban planning), which can be avoided. It also highlights possible negative consequences (material and human lives) regarding the risk associated with these analyses that can lead to an under-dimensioning of this infrastructure. Full article

Traditional damage detection methods of prestressed concrete box girder bridges have low efficiency and cannot quantify the structure’s internal damage. We used an inversion method and a falling weight deflectometer to estimate the mechanical parameters of prestressed box girder bridges. A finite element model of the bridge dynamics under impact loading was established. A perturbation-based update was conducted, and a multi-parameter inversion algorithm was constructed. The measured data were used for the efficient identification of the bridge’s elasticity modulus and the prestressing tensile force. The theoretical validation indicated a high modeling accuracy and inversion efficiency, with a convergence accuracy within 1%. The initial value had a minimal influence on the inversion results. The engineering application showed that the maximum error of the elastic modulus between the inversion and the rebound methods was 1.55%. The loss rates of the deck slab’s elastic modulus and the prestressing force obtained from the inversion were 4.39% and 7.64%, respectively. The proposed method provides a new strategy for evaluating damage to prestressed box girder bridges. Full article