Search Results (633)

Background: The fetal mechanical PR interval (mPR), measured using pulsed-wave Doppler, is a widely used parameter to assess atrioventricular conduction in fetuses, particularly in cases at risk of developing atrioventricular (AV) block. However, the physiological factors that influence mPR readings are not fully understood. This study aimed to identify determinants affecting the measurement of the mPR interval using the mitral valve/aorta (MV/Ao) Doppler method in a cohort of structurally normal fetuses. Methods: We retrospectively analyzed 925 fetuses with normal echocardiographic findings and no structural cardiac or extracardiac anomalies. Correlation analysis, group comparisons, trend testing, and multivariable modeling were performed to assess the impact of biometric and Doppler parameters on mPR interval measurements. Results: The median mPR interval across the cohort was 116 ms (interquartile range: 108–123 ms). Fetuses were categorized into four gestational age groups (≤19 weeks, 20–23 weeks, 24–27 weeks, and ≥28 weeks). Significant differences in mPR were observed between gestational age groups (p < 0.01), with a positive trend across increasing gestational age (p < 0.0001). The strongest correlation was an inverse relationship between mPR and fetal heart rate (FHR) (ρ = −0.256, p < 0.01). Multivariable regression identified five independent predictors of mPR: lower FHR, greater biparietal diameter (BPD), larger pulmonary valve diameter (PVD), increased fronto-occipital diameter (FOD), and lower umbilical artery pulsatility index (UA PI). The final model explained approximately 9.9% of the variance in mPR interval (R² = 0.099). Conclusions: The fetal mPR interval increases with gestational age and is primarily influenced by fetal heart rate, even after adjusting for other factors. Certain biometric and Doppler parameters also contribute modestly to mPR variation. These findings highlight the importance of accounting for physiological variability when interpreting mPR measurements in clinical fetal cardiology. Full article

Exploration for hydrocarbon reservoirs is currently focused on increasingly difficult targets and geological structures, thus stimulating a growing requirement for new measurement methods and techniques that can provide more detailed information about lithology and reservoir parameter distribution in the vicinity of the target zone. This publication presents a method for increasing the resolution of the recorded surface seismic wavefield in the vicinity of example borehole Well-1 (western Poland) for reservoir horizons of the Rotliegend and Zechstein formations. The main stage of the research was the introduction of frequencies from vertical seismic profiling (VSP) into seismic traces. The shape filter deconvolution procedure was applied based on the operator calculated from VSP data, which was applied to seismic profiles extracted from 3D data. The procedure applied allowed for the reconstruction of higher-frequency spectrum necessary for a detailed imaging of the geological framework of the analyzed reservoir formations. In the next stage, seismic inversion calculations were conducted, both on VSP data (corridor stack and VSP-CDP transformation) and on surface seismic time sections. The results obtained as an acoustic impedance distribution enabled a more comprehensive structural interpretation and detailed analysis of the variability of reservoir properties in the analyzed well area. Full article

The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) provides invaluable nighttime light (NTL) radiance data, widely employed for diverse applications including urban and socioeconomic studies. However, the inherent reliability of NTL data as a proxy for socioeconomic activities is significantly compromised by atmospheric conditions, particularly aerosols. This study analyzed the long-term spatiotemporal variations in NTL radiance with respect to atmospheric aerosol optical depth (AOD) in nine major Asian cities from January 2012 to May 2021. Our findings reveal a complex and heterogeneous interplay between NTL radiance and AOD, fundamentally influenced by a region’s unique atmospheric characteristics and developmental stages. While major East Asian cities (e.g., Beijing, Tokyo, Seoul) exhibited a statistically significant inverse correlation, indicating aerosol-induced NTL suppression, other regions showed different patterns. For instance, the rapidly urbanizing city of Dhaka displayed a statistically significant positive correlation, suggesting a concurrent increase in NTL and AOD due to intensified urban activities. This highlights that the NTL-AOD relationship is not solely a physical phenomenon but is also shaped by independent socioeconomic processes. These results underscore the critical importance of comprehensively understanding these regional discrepancies for the reliable interpretation and effective reconstruction of NTL radiance data. By providing nuanced insights into how atmospheric aerosols influence NTL measurements in diverse urban settings, this research aims to enhance the utility and robustness of satellite-derived NTL data for effective socioeconomic analyses. Full article

Aging water pipelines are increasingly prone to structural failure, leakage, and ground subsidence, creating critical risks to urban infrastructure. Closed-circuit television endoscopy is widely used for internal assessment, but it depends on manual interpretation and lacks reliable quantitative defect information. Traditional vanishing point detection techniques, such as the Hough Transform, often fail under practical conditions due to irregular lighting, debris, and deformed pipe surfaces, especially when pipes are water-filled. To overcome these challenges, this study introduces a deep learning-based method that estimates inverse projection parameters from monocular endoscopic images. The proposed approach reconstructs a spatially accurate two-dimensional projection of the pipe interior from a single frame, enabling defect quantification for cracks, scaling, and delamination. This eliminates the need for stereo cameras or additional sensors, providing a robust and cost-effective solution compatible with existing inspection systems. By integrating convolutional neural networks with geometric projection estimation, the framework advances computational intelligence applications in pipeline condition monitoring. Experimental validation demonstrates high accuracy in pose estimation and defect size recovery, confirming the potential of the system for automated, non-disruptive pipeline health evaluation. Full article

In deep excavation dewatering engineering, fully enclosed cutoff walls are widely implemented to improve the efficiency of dewatering in the pit and prevent adverse environmental impacts such as land subsidence and damage to adjacent infrastructure. However, the presence of such impermeable barriers fundamentally alters flow dynamics, rendering conventional aquifer test interpretation methods inadequate. This study presents a novel closed-form analytical solution for transient drawdown in a leaky confined aquifer bounded by a rectangular, fully enclosed cutoff wall under constant-rate pumping. The solution is rigorously derived by applying the mirror image method within a superposition framework, explicitly accounting for the barrier effect of the curtain. A type-curve matching methodology is developed to inversely estimate key aquifer parameters—transmissivity, storativity, and vertical leakage coefficient—while incorporating the geometric and boundary effects of the curtain. The approach is validated against field data from a pumping test conducted at a deep excavation site in Wuhan, China. Excellent agreement is observed between predicted and measured drawdowns across multiple observation points, confirming the model’s fidelity. The proposed solution and parameter estimation technique provide a physically consistent, analytically tractable, and computationally efficient framework for interpreting pumping tests in constrained aquifer systems, thereby improving predictive reliability in dewatering design and supporting sustainable groundwater management in urban underground construction. Full article

Biodegradable zinc alloys have shown great potential in the biomedical field, but are limited by their poor mechanical properties. Alloying is essential for improving mechanical properties, yet designing multicomponent zinc alloys remains challenging due to complex elemental interactions. Notably, while data-driven active learning approaches offer new strategies for zinc alloy design, data quality issues such as redundancy, outliers, and inconsistencies in multi-source heterogeneous data hinder modeling accuracy and interpretability. In this work, we proposed a data quality management strategy based on recursive screening, targeting three key data problems, namely, redundant data (RD), outlier data (OD), and inconsistent target data (ID). Case studies on hydrogen embrittlement, phase-change refrigeration materials, and matbench_expt_gap datasets showed that, in the aforementioned data-driven research, RD optimized data distribution but risked precision loss in high-performance regions; OD enhanced minority alloy features but risked overfitting; and ID preserved high-performance data, boosting extrapolation but risking underfitting. Six multicomponent zinc alloys were designed and fabricated using these strategies. Experiments showed ID-optimized datasets achieving 482 MPa—near state-of-the-art performance. The highest tensile strength of 482 MPa was obtained in the alloy Zn-1.2Al-0.8Mg-0.45Li-0.3Mn (at%), designed via the ID-optimized dataset. The study revealed that in inverse design, predictive accuracy in high-performance regions outweighs data volume or density, underscoring the value of data quality management for multi-source materials development. Full article

This paper presents a rule-based negation and litotes detection system for Modern Standard Arabic. Unlike purely statistical approaches, the proposed pipeline leverages linguistic structures, lexical resources, and dependency parsing to identify negated expressions, exception clauses, and instances of litotic inversion, where rhetorical negation conveys an implicit positive meaning. The system was applied to a large-scale subset of the Arabic OSCAR corpus, filtered by sentence length and syntactic structure. The results show the successful detection of 5193 negated expressions and 1953 litotic expressions through antonym matching. Additionally, 200 instances involving exception prepositions were identified, reflecting their syntactic specificity and rarity in Arabic. The system is fully interpretable, reproducible, and well-suited to low-resource environments where machine learning approaches may not be viable. Its ability to scale across heterogeneous data while preserving linguistic sensitivity demonstrates the relevance of rule-based systems for morphologically rich and structurally complex languages. This work contributes a practical framework for analyzing negation phenomena and offers insight into rhetorical inversion in Arabic discourse. Although coverage of rarer structures is limited, the pipeline provides a solid foundation for future refinement and domain-specific applications in figurative language processing. Full article

This work develops an inverse data envelopment analysis (Inverse DEA) framework for portfolio optimization, treating return as a desirable output and volatility as an undesirable output. Using 20 industry-level portfolios from the Taiwan Stock Exchange (1365 stocks; FY-2020), we first evaluate efficiency with a directional-distance DEA model and identify 7 inefficient industries. We then formulate an Inverse DEA model that holds inputs and desirable outputs fixed and estimates the maximum feasible reduction in volatility. Estimated reductions range from 0.000827 to 0.007610, and substituting these targets into the base model drives each portfolio’s inefficiency score to zero $(\varphi =0)$, thereby making them efficient. To test robustness, we extend the analysis to a calm pre-crisis year (2019) and a recovery year (2021), which confirm that inefficiency and volatility-reduction targets behave logically across regimes, smaller cuts in stable markets, larger cuts in stressed conditions, and intermediate adjustments during recovery. We interpret these targets as theoretical envelopes that inform risk-reduction priorities rather than investable guarantees. The approach adds a forward-planning layer to DEA-based performance evaluation and provides portfolio managers with quantitative, regime-sensitive volatility-reduction targets at the industry level. Full article

Beginning with Mandelbrot’s insight that Fisher information may admit a thermodynamic interpretation, a growing body of work has connected this information-theoretic measure to fluctuation–dissipation relations, thermodynamic geometry, and phase transitions. Yet, these connections have largely remained at the level of formal analogies. In this work, we provide what is, to our knowledge, the first explicit realization of the epistemic-to-physical transition of Fisher information within a finite interacting quantum system. Specifically, we analyze a model of N fermions occupying two degenerate levels and coupled by a spin-flip interaction of strength V, treated in the grand canonical ensemble at inverse temperature $\beta$. We compute the Fisher information $F_N\left(V\right)$ associated with the sensitivity of the thermal state to changes in V, and show that it becomes an observer-independent, experimentally meaningful quantity: it encodes fluctuations, tracks entropy variations, and reveals structural transitions induced by interactions. Our findings thus demonstrate that Fisher information, originally conceived as an inferential and epistemic measure, can operate as a bona fide thermodynamic observable in quantum many-body physics, bridging the gap between information-theoretic foundations and measurable physical law. Full article

Hydrothermal conditions are a key indicator influencing the evolution of aquatic ecosystems, closely affecting the physical, chemical, and biological properties of water bodies. The construction of cascaded dams on the main stem of the Yangtze River has altered the natural water temperature regime, impacting the hydrothermal status of the water. Utilizing multi-source remote sensing data from Google Earth Engine to invert river surface water temperatures, a parameter-optimized CNN-LSTM-Attention hybrid interpretable water temperature prediction model was constructed. The model demonstrated credible accuracy. Based on the inversion results, the study revealed the spatiotemporal evolution characteristics of water temperature in the main stem of the Yangtze River before and after cascaded dam construction in the lower Jinsha River region and the Three Gorges Reservoir area. The results found that after the construction of the Three Gorges Dam, the annual average water temperature increased significantly by 0.813 °C. The “cold water stagnation effect” induced by cascaded development caused the water temperature amplitude to increase from 8.96 °C to 10.6 °C. Furthermore, the regulating effect of tributary confluence exhibited significant differences. For instance, colder tributaries like the Yalong River reduced the main stem water temperature, while warmer tributaries like the Jialing River, conversely, increased the main stem temperature. The construction of cascaded dams led to distinct variation characteristics in the areas downstream of the dams within the reservoir regions, where tributary inflows caused corresponding changes in the main stem water temperature. This study elucidates the long-term spatiotemporal variation characteristics of water temperature in the main stem of the Yangtze River. The model prediction results can assist in constructing an early warning indicator system for water temperature changes, providing reliable data support for promoting water environment sustainability and ecological civilization construction in the river basin. Full article

Quercetin, a key flavonoid in Anoectochilus roxburghii (Wall.) Lindl., plays an important role in determining the pharmacological value of this medicinal herb. However, traditional methods for quercetin quantification are destructive and time-consuming, limiting their application in real-time quality monitoring. This study investigates the hyperspectral response characteristics of quercetin using near-infrared hyperspectral imaging and establishes a feature-based model to explore its detectability in A. roxburghii leaves. We scanned standard quercetin solutions of known concentration under the same imaging conditions as the leaves to produce a dilution series. Feature-selection methods used included the successive projections algorithm (SPA), Pearson correlation, and competitive adaptive reweighted sampling (CARS). A 1D convolutional neural network (1D-CNN) trained on SPA-selected wavelengths yielded the best prediction performance. These key wavelengths—particularly the 923 nm band—showed strong theoretical and statistical relevance to quercetin’s molecular absorption. When applied to plant leaf spectra, the standard-trained model produced continuous predicted quercetin values that effectively distinguished cultivars with varying flavonoid contents. PCA visualization and ROC-based classification confirmed spectral transferability and potential for functional evaluation. This study demonstrates a non-destructive, spatially resolved, and biochemically interpretable strategy for identifying bioactive markers in plant tissues, offering a methodological basis for future hyperspectral inversion studies and intelligent quality assessment in herbal medicine. Full article

In the Alberta Oil Sands Region (AOSR), environmental stressors linked to oil sands industrial activity may have significant and species-specific impacts on local wildlife. This study evaluated the kynurenine–tryptophan ratio (KTR) as a potential biomarker for environmental exposure in longnose suckers (Catostomus catostomus) and white suckers (Catostomus commersonii) collected from various locations within the AOSR. The relationship between KTR and CYP1 enzyme activity (ethoxyresorufin-O-deethylase; EROD) was assessed alongside biometric indices, including gonadosomatic index (GSI), hepatic somatic index (HSI), and fat content. Both species exhibited increased EROD activity when exposed to oil sands natural deposits and potential industrial activity, indicating significant polycyclic aromatic compound (PAC) exposure. However, KTR changes were species-dependent: longnose suckers showed an inversely proportional relationship between KTR and EROD, while white suckers displayed a directly proportional correlation. Longnose suckers downstream of both municipal waste and industrial activity exhibited significant increases in GSI and fat content, with KTR varying more consistently by location rather than sex, suggesting that KTR may be a more reliable marker for location-based exposure. Species-specific differences in KTR and EROD relationships may be influenced by the distinct environmental requirements of each species, and their differing sensitivities to environmental conditions, including temperature, turbidity and flow conditions, during sampling periods. These findings illustrate the complexity of interpreting environmental biomarkers in wildlife and emphasize the need to consider ecological requirements and environmental conditions. Further research is necessary to validate this biomarker across different years and conditions and enhance its application in environmental monitoring and conservation efforts. Full article

Soil organic carbon (SOC) is a crucial indicator of soil quality and carbon cycling. While remote sensing and machine learning enable regional scale SOC prediction, most studies rely on vegetation indices (VIs) derived from bare-soil periods, potentially neglecting vegetation–soil interactions during crop growth. Given the bidirectional relationship between SOC and crop growth, we hypothesized that using crop-lush period VIs (VIs_lush) instead of bare-soil period VIs (VIs_bare) would increase the inversion accuracy. To test this hypothesis, we chose the cropland area in Liaoning Province as the study area and developed three modeling strategies (MS-1: VIs_lush + other features; MS-2: VIs_bare + other features; and MS-3: without VIs) using Landsat 8 imagery, topographic and precipitation data, and ensemble learning models (XGBoost, RF, and AdaBoost), with SHapley Additive exPlanations (SHAP) analysis for variable interpretation. We found that (1) all models achieved their highest performance under MS-1, with XGBoost outperforming the others across all modeling strategies; (2) for XGBoost, MS-1 yielded the highest inversion accuracy (R² = 0.84, RMSE = 2.22 g·kg⁻¹, RPD = 2.49, and RPIQ = 3.25); compared with MS-2, MS-1 reduced the RMSE by 0.31 g·kg⁻¹, increased R² from 0.77 to 0.84, and reduced the RPD by 0.31 and the RPIQ by 0.40, and compared with MS-3, MS-1 reduced the RMSE by 0.41 g·kg⁻¹, increased R² from 0.79 to 0.84, and reduced the RPD by 0.39 and the RPIQ by 0.51; (3) based on the SHAP analysis of the three modeling strategies, it is considered that precipitation, terrain and terrain analysis results are important indicators for SOC content inversion, and it is confirmed that VIs_lush contributed more than VIs_bare, supporting the rationale of using lush-period imagery; and (4) Liaoning Province exhibited distinct SOC spatial patterns (mean: 13.08 g·kg⁻¹), with values ranging from 2.19 g·kg⁻¹ (sandy central–western area) to 33.86 g·kg⁻¹ (eastern mountains/coast). This study demonstrates that integrating growth stage-specific VIs with ensemble learning can significantly enhance regional-scale SOC prediction. Full article

Accurate assessment of coalbed methane (CBM) content is essential for characterizing subsurface reservoir distribution, guiding well placement, and estimating reserves. Current methods for determining coal seam gas content mainly rely on direct laboratory measurements of core samples or indirect interpretations derived from well log data. However, conventional coring is costly, while log-based approaches often depend on linear empirical formulas and are restricted to near-wellbore regions. In practice, the relationships between elastic properties and gas content are highly complex and nonlinear, leading conventional linear models to produce substantial prediction errors and inadequate performance. This study introduces a novel method for predicting gas content in deep coal seams using a Conditional Generative Adversarial Network (CGAN). First, elastic parameters are obtained through pre-stack inversion. Next, sensitivity analysis and attribute optimization are applied to identify elastic attributes that are most sensitive to gas content. A CGAN is then employed to learn the nonlinear mapping between multiple fluid-sensitive seismic attributes and gas content distribution. By integrating multiple constraints to refine the discriminator and guide generator training, the model achieves accurate gas content prediction directly from seismic data. Applied to a real dataset from a CBM block in the Ordos Basin, China, the proposed CGAN-based method produces predictions that align closely with measured gas content trends at well locations. Validation at blind wells shows an average prediction error of 1.6 m³/t, with 83% of samples exhibiting errors less than 3 m³/t. This research presents an effective and innovative deep learning approach for predicting coalbed methane content. Full article

At a particular frequency, most materials and objects of interest exhibit a polarization signature, or Mueller matrix, of limited dimensionality, with many matrix elements either negligibly small or redundant due to symmetry. Robust design of a polarization sensor for a particular material or object of interest, or for an application with a limited set of materials or objects, will adapt to the signature subspace, as well as the available modulators, in order to avoid unnecessary measurements and hardware and their associated budgets, errors, and artifacts. At the same time, measured polarization features should be expressed in the Stokes–Mueller basis to allow use of known phenomenology for data interpretation and processing as well as instrument calibration and troubleshooting. This approach to partial Mueller-matrix polarimeter (pMMP) design begins by defining a vector space of reduced Mueller matrices and an instrument vector representing the polarization modulators and other components of the sensor. The reduced-Mueller vector space is proven to be identical to ${\mathbb{R}}^{15}$ and to provide a completely linear description constrained to the Mueller cone. The reduced irradiance, the inner product of the reduced instrument and target vectors, is then applied to construct classifiers and tune modulator parameters, for instance to maximize representation of a specific target in a fixed number of measured channels. This design method eliminates the use of pseudo-inverses and reveals the optimal channel compositions to capture a particular signature feature, or a limited set of features, under given hardware constraints. Examples are given for common optical division-of-amplitude (DoA) 2-channel passive and serial/DoT-DoA 4-channel active polarimeters with rotating crystal modulators for classification of targets with diattenuation and depolarization characteristics. Full article