Search Results (4,378)

This paper presents an absolute distance measurement system based on three-wavelength synchronous phase-shifting interferometry. A synthetic wavelength chain is established using three semiconductor lasers in an all-fiber Fizeau interferometer. By integrating a piezoelectric transducer (PZT)-driven sinusoidal phase modulation with multi-channel synchronous sampling for phase demodulation, and further combining it with a fractional multiplication method, the proposed system achieves high-precision absolute distance measurement over an extended range. Experimental results demonstrate an unambiguous measurement range of 240 μm, a static measurement precision better than 0.6 nm, and a dynamic displacement measurement accuracy superior to 2 nm in comparison with the reference device. The main error sources of the system, including synthetic wavelength uncertainty, phase measurement uncertainty, and air refractive index uncertainty, are systematically modeled and analyzed. In addition, the influence of dynamic factors, such as PZT nonlinearity, is discussed and compensated. The proposed method provides a robust and high-precision solution for absolute ranging and shows strong potential for applications in industrial precision inspection and optical sensing. Full article

To improve solar irradiance simulation accuracy for precise photovoltaic power forecasting, we developed a hybrid framework combining WRF-Solar numerical simulation and random forest (RF) machine learning for a PV plant in Guangdong, China. Weather conditions were objectively classified into clear, intermittent cloudy, and overcast using the Daily Variability Index (DVI) and Daily Clear-sky Index (DCI). We calibrated the WRF-Solar model’s microphysics and radiative transfer schemes via sensitivity tests to optimize overcast-sky performance, then applied RF correction to the simulated irradiance. Results show that RF correction significantly reduces simulation errors for intermittent and overcast conditions, while the original WRF-Solar outperforms the corrected results under clear skies due to RF overfitting. Full article

Extrusion-based bioprinting faces significant challenges in achieving the shape fidelity and internal porosity necessary for cell viability, often hindered by subjective assessment methods. This study investigated the relationship between rheological properties and print quality using a natural polymer biomaterial ink composed of 12% gelatin, 5% alginate, and 1% carboxymethylcellulose. We conducted a comparative analysis between traditional pneumatic systems and screw-driven volumetric extrusion, utilizing a suite of quantitative metrics: Spreading Ratio (SR), Printability Index (P_r), Uniformity Ratio (UF), Collapse Angle (θ), and evaluated porosity. Our results demonstrate that the screw-driven system’s positive displacement mechanism provides superior control over filament morphology by enabling precise volumetric modulation. While the pneumatic system exhibited a high SR of 1.82 and the lowest porosity at 59.92%, the screw-driven system allowed for “under-extrusion” to compensate for viscoelastic die swell. Reducing the flow rate to 50% in the screw system lowered the SR to 1.09, nearly matching the nozzle diameter, and increased porosity to 76.46%. Furthermore, the screw-driven system achieved an ideal P_r of 1.0, whereas the pneumatic system produced distorted, rounded pores with a P_r of 1.57. The findings indicate that screw-driven extruders can decouple line complex rheology from the printing process, allowing for finer spatial resolution and better pore interconnectivity. Full article

Relaxor ferroelectric single crystals, such as Pb(In₁/₂Nb₂/₃)O₃–Pb(Mg₁/₂Nb₂/₃)O₃–PbTiO₃, possess extraordinary electro-optic (EO) coefficients, offering immense potential for next-generation integrated modulators. However, the application of PIN-PMN-PT in fiber-optic gyroscopes (FOGs) is hindered by the challenge of fabricating high-quality optical waveguides with strict mode selectivity, as conventional diffusion typically excites multi-mode propagation. Here, the fabrication of high-quality, mode-selective waveguides is achieved in rhombohedral PIN-PMN-PT via a nickel in-diffusion technique. The resulting graded-index structures exhibit a Gaussian profile with a maximum refractive index change (∆n) of 1.53% while preserving the single crystal structure. Under specific processing conditions, we achieve precise mode selectivity, enabling exclusive transverse electric (TE) mode transmission. This mode selectivity fulfills the requirements for single-mode Y-branch geometries, establishing a robust platform for ultra-compact, low driving voltage modulators and advancing the miniaturization of inertial navigation and integrated photonic systems. Full article

This study aimed to detect growth stress at the individual-tree level in chestnut (Castanea crenata Sieb. et Zucc.) plantations using UAV-based RGB orthomosaic and multispectral imagery and to determine an optimal NDVI threshold for stress classification. UAV surveys were conducted over a 21 ha chestnut orchard located in Gongju, Chungcheongnam-do, Republic of Korea. NDVI was calculated and analyzed at the individual-tree level using multispectral imagery. Based on field observations, 100 healthy trees and 23 stressed trees were selected for statistical analysis. The mean NDVI value was 0.900 ± 0.012 for healthy trees and 0.816 ± 0.013 for stressed trees, showing a highly significant difference (p < 0.001). ROC analysis showed excellent classification performance with an AUC of 1.00. The optimal NDVI threshold determined using Youden’s index was 0.855. Independent validation in another chestnut plantation approximately 1 km away achieved high classification accuracy using the same threshold. These results indicate that UAV-based multispectral imagery combined with NDVI analysis provides an effective approach for early detection of growth stress and precision monitoring at the individual-tree level in chestnut plantations. This study provides a practical and efficient approach for the early detection of growth stress at the individual-tree level, enabling early intervention against potential declines in tree vitality and proactive management in chestnut orchards. The proposed NDVI threshold-based method offers a simple yet robust tool that can be readily applied in precision forestry and smart agriculture to support large-scale monitoring and informed management decisions for maintaining orchard productivity, enabling cost-effective early intervention at the individual-tree level, which is difficult to achieve using conventional ground-based surveys in complex mountainous orchards. Full article

Frequency response analysis (FRA) is recognized as an effective method in power transformer winding fault diagnosis. However, the traditional numerical index methods focus on the overall features of FRA curves, making it difficult to capture subtle deformations in transformer windings. Similarly, existing digital image processing methods rely on a single feature or a simple feature combination without adaptive fusion. These methods ignore differences in the data distributions of features, leading to feature mismatch, the loss of sensitive fault information, and lower diagnostic accuracy. To solve this problem, a novel adaptive multiple-image-feature fusion method for transformer winding fault diagnosis is proposed. First, a multi-dimensional feature space combining image pixel matrix similarity, morphological features, and image texture features is built to decode the difference in fault of FRA images. Second, the multiple kernel learning (MKL) framework is used to dynamically adjust the fusion weights of different kernels to make features compatible and remove redundant information. Finally, comparative and ablation experiments show that the proposed method outperforms the traditional methods in identifying different types and levels of faults. The method achieves over 99% accuracy in fault type identification across SVM, KNN, and RF classifiers. For radial deformation (RD) severity prediction, the accuracy of the proposed model is 93.37% with SVM and 94.85% with KNN, outperforming the full-feature concatenation method. These results confirm the method’s robustness and diagnostic precision. Full article

The rapid growth of hiking tourism presents a critical challenge for balancing visitor safety with the sustainable management of ecologically fragile mountain environments. Traditional models developed in urban settings struggle to capture the highly non-linear, heterogeneous, and zero-inflated characteristics of wilderness trekking behavior. In order to quantify the nonlinear and threshold-based effects of environmental variables on hikers’ spatial decisions in unstructured wilderness and to identify distinct behavioral regimes for segmented management, this study introduces an explainable machine learning framework to reconstruct hikers’ spatial decision-making in a complex mountainous system in Inner Mongolia, China. Random Forest (RF), XGBoost, and LightGBM were compared in predicting trail density and the Euclidean distance to the nearest trail. Results show that transforming behavioral traces into continuous proximity surfaces dramatically improves model performance, with XGBoost achieving the highest predictive accuracy for Trail_Dist. By integrating the SHapley Additive exPlanations framework, this study moves beyond black-box prediction to reveal the nonlinear mechanisms driving hiker behavior. Key findings include: (1) Nighttime light range exhibits a U-shaped threshold effect as the primary anthropogenic attractor. (2) Elevation shows an exponential inhibitory trend above 1238 m. (3) Strong spatial coupling exists between elevation and slope, alongside a landscape compensation effect where high Normalized Difference Vegetation Index (NDVI) areas attract off-trail movements. This research provides a robust methodological pathway for predicting behavior in unstructured outdoor environments. It offers a scientific foundation for smart scenic area management, including optimized route planning, precise ecological protection zoning, and targeted emergency rescue preparedness. Full article

In the parallel operation of islanded microgrids, line impedance mismatches and random load fluctuations, along with the dynamic response lag and difficulty in multidimensional parameter tuning of traditional control strategies, lead to power sharing imbalances and instability in frequency and voltage. To address these issues, this paper proposes a hierarchical cooperative control strategy based on consistency that integrates load-observation-based dynamic reference feedforward (LODRF) and adaptive particle swarm optimization (APSO). First, an improved adaptive virtual impedance (IAVI) strategy based on consistency is introduced into the virtual synchronous generator control framework. Second, an LODRF mechanism is applied at the secondary control layer to actively reconstruct the power baseline by observing the load status at the point of common coupling (PCC) in real time. Furthermore, an APSO algorithm utilizing the integral of time-weighted absolute error (ITAE) as a global performance index is constructed to optimize key proportional–integral controller parameters cooperatively. Simulation results from a four-unit heterogeneous parallel system in MATLAB/Simulink demonstrate that the IAVI strategy enables stable convergence of frequency and voltage and proportional power sharing. Compared with the system without LODRF, the proposed strategy reduces maximum frequency and voltage dynamic deviations under load disturbances by 78.5% and 53.3%, respectively, and shortens effective recovery times by 0.01 s and 0.09 s, respectively. Moreover, compared with the standard PSO algorithm, the APSO-optimized system reduces maximum frequency and voltage deviations by 3.1% and 36.4%, respectively. Additionally, average active and reactive power sharing errors in the steady state are kept below 0.9%, verifying the significant advantages of the strategy in improving dynamic disturbance rejection and steady-state precision. Full article

Frequent extreme heatwaves (HWs) have significantly exacerbated urban thermal risks, yet the regulatory mechanisms of urban morphology remain poorly understood. This study focuses on the core urban areas of Beijing and develops a Local Climate Zone (LCZ)-constrained spatiotemporal data fusion model (LCZ-FSDAF) to generate high-resolution Land Surface Temperature (LST) datasets from 2015 to 2024. By integrating urban–rural gradient analysis with the XGBoost-SHAP model, this study quantitatively resolves the spatiotemporal evolution of land surface temperature during heatwaves and the nonlinear threshold effects of urban morphological parameters, using a representative extreme heatwave event in July 2023 as a case study. The results indicate that the LCZ-FSDAF model achieves high precision across complex urban underlying surfaces (up to 0.946, RMSE as low as 0.762 K), effectively capturing the spatial heterogeneity of the urban thermal environment. Over the past decade, heatwave events in Beijing have exhibited a significant trend of increasing frequency, duration, and intensity. During these events, LST displays a concentric core-high, periphery-low structure; however, the peak temperature shifts toward high-density built-up areas in the sub-core, manifesting a distinct heat island core shift phenomenon. Furthermore, the impact of urban morphology on LST is characterized by significant nonlinearity, with the Normalized Difference Vegetation Index (NDVI) and Mean Building Height (MBH) identified as dominant factors. Notably, Building Coverage (BC) and Sky View Factor (SVF) exhibit pronounced threshold effects across different thermal indicators. Findings of this study are useful for guiding urban planning, optimizing spatial configurations, formulating urban heat island mitigation policies under heatwaves, and promoting the Sustainable Development Goals (SDGs) of cities and communities. Full article

Aiming to address the problems of low accuracy in coal–rock identification during coal mining, which lead to energy waste and safety hazards, a high-precision coal–rock medium identification method combining terahertz time-domain spectroscopy technology and multiple machine learning algorithms is proposed. By preparing coal–rock samples with a gradient change in coal content, terahertz time-domain spectroscopy data of coal–rock mixed media are collected, and optical parameters such as the refractive index and absorption coefficient are extracted. Principal component analysis is used to reduce the dimensionality of the terahertz data, and machine learning algorithms such as support vector machine, least squares support vector machine, artificial neural networks, and random forests are adopted for classification and identification. The study found that terahertz waves are more sensitive to coal–rock media in the 0.7–1.3 THz frequency band, and that the refractive index and absorption coefficient of coal–rock mixed media are significantly positively correlated with coal content within the range of 0–30%. After feature extraction and K-fold cross-validation, the random forest model achieved a coal–rock classification accuracy of over 96% on the test set, significantly outperforming other comparison algorithms. The research verifies the efficiency and practicality of terahertz technology combined with multiple machine learning algorithms in coal–rock identification, providing a new method for fields such as mineral separation. This method has, to a certain extent, broken through the accuracy bottleneck of traditional coal–rock identification technologies within its applicable range, providing a new solution for real-time detection of coal–rock interfaces and is expected to further reduce the risks of ineffective mining and roof accidents in the future. Full article

Background: Body mass index (BMI) is widely used as a simple anthropometric indicator, but its functional relevance to physical fitness in physically active populations, such as Physical Education students, remains debated. Aim: This study examined the association between BMI and selected components of physical fitness in Physical Education students, considering sex and country differences. Methods: A cross-sectional study was conducted among undergraduate Physical Education students from Poland and Romania (n = 515; mean age: 21.64 ± 1.34 years). BMI was calculated from measured height and body mass and analyzed as both a continuous and categorical variable. Physical fitness was assessed using three Eurofit tests evaluating upper-limb movement speed, trunk muscular endurance, and lower-limb explosive power. Analyses included correlation methods and multiple linear regression models with subgroup analyses, interaction terms, and quadratic BMI terms to assess nonlinearity. Results: Associations between BMI and fitness components were small in magnitude and inconsistent (r = −0.28 to 0.143; β = −1.614 to 0.005) and varied across tests and subgroups. No significant interaction effects by sex or country were observed, as interaction terms were not statistically significant, and no clear nonlinear relationships were identified. Sex and country were significantly associated with performance levels, whereas BMI contributed only marginally to explaining variability (ΔR² = 0.005–0.011). Conclusions: BMI showed limited and inconsistent associations with the assessed fitness components in this relatively homogeneous group of Physical Education students. It should be interpreted cautiously as a functional indicator and complemented with more precise measures of body composition and physical fitness. Full article

Cancer remains a primary global health challenge, accounting for millions of new cases and significant mortality annually. Although doxorubicin (DOX) is a fundamental anthracycline used for various malignancies, its therapeutic index is severely limited by poor selectivity, systemic toxicity, and dose-dependent cardiotoxicity. To address these issues, Janus nanoparticles (JNPs) have emerged as a promising bifunctional platform characterized by a structural asymmetry that allows for the independent functionalization of each hemisphere. This review examines primary fabrication routes—such as masking, microfluidics, self-assembly, and phase separation—and their specific applications in DOX delivery. The anisotropic architecture of JNPs enables a “separate rooms” concept, allowing for the co-delivery of incompatible drugs while facilitating multi-stimuli-responsive release mechanisms triggered by pH, enzymes, or NIR light. Furthermore, JNPs have demonstrated enhanced tumor accumulation and reduced systemic toxicity compared to conventional isotropic carriers. Recent developments even highlight the use of autonomous nanomotors to improve therapeutic delivery while minimizing premature leakage. However, clinical translation is currently hindered by manufacturing complexity, high equipment costs, scalability issues, and a lack of standardized reporting in the literature. Ultimately, JNPs represent a sophisticated frontier in precision oncology, though robust manufacturing processes and characterization protocols are required for future medical adoption. Full article

Background: Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition for which non-pharmacological interventions remain the primary therapeutic approach. Although research output in this field has increased substantially, a comprehensive synthesis of its developmental trajectory and emerging directions is still lacking. Methods: We conducted a bibliometric analysis of publications on non-pharmacological interventions for ASD indexed in the Web of Science Core Collection between 2001 and 2025. Knowledge structures, research hotspots, and temporal trends were visualized and analyzed using CiteSpace. Results: The field has transitioned from an early focus on behavioral interventions in children to a diversified and interdisciplinary research ecosystem spanning the lifespan. Recent growth has been driven by the integration of neuroscience-based approaches, particularly neuromodulation techniques, alongside continued refinement of behavioral, sensorimotor, and complementary therapies. Increasing attention has been paid to individual heterogeneity, methodological rigor, and mechanism-oriented research. Current frontiers emphasize multimodal intervention strategies, neural plasticity-based mechanisms, and the development of personalized precision intervention frameworks. Conclusions: This bibliometric analysis delineates the intellectual evolution of non-pharmacological intervention research for ASD and identifies key research gaps, particularly the need for longitudinal and pragmatic studies targeting individualized treatment response. The findings provide an evidence-informed overview of current concepts and emerging research directions in non-pharmacological care for ASD, with important implications for future clinical research, intervention design, and strategic research planning. Full article

Background: Treatment options for vulvar lichen sclerosus (VLS) remain limited; therefore, therapies that improve quality of life and reduce neoplastic risk are needed. Photodynamic therapy (PDT) is a potential option. This study aimed to evaluate quality of life and sexual function in patients treated according to the protocol used at our institution. Methods: Forty patients with refractory VLS underwent PDT using a 10% 5-aminolevulinic acid nanoemulsion (Ameluz^®) applied to lesions under an occlusive aluminum foil dressing. Patients received 1–6 sessions of 10 min illumination (LED: 37 J/cm², ~77 mW/cm²) at 4–6-week intervals. The Dermatology Life Quality Index (DLQI) and Female Sexual Function Index (FSFI) were used for assessment. Results: Thirty-seven participants answered DLQI, while 20 declared themselves to be sexually active and were included in the analysis. Greater number of PDT sessions was associated with a lower DLQI score (τ = −0.583; adjusted p < 0.001). The number of PDT sessions and the total FSFI score (p = 0.014), as well as desire (p = 0.016), arousal (p = 0.020), orgasm (p = 0.020), and satisfaction (p = 0.016) domains were significantly correlated. Age correlated positively with DLQI scores (p = 0.016), indicating greater disease burden in older patients. Longer disease duration was also associated with poorer quality of life (p = 0.020). Conclusions: PDT can be considered an effective treatment for patients with VLS refractory to standard topical corticosteroid and calcineurin inhibitor therapies when delivered using a refined, patient-centered protocol. This optimized approach used in our institution is based on short irradiation time and precise light delivery, providing a favorable balance between therapeutic efficacy, patient comfort, and treatment feasibility. Our findings also suggest that the cumulative number of PDT sessions is a key factor for clinical response. Further studies should address long-term outcomes. Full article

From the 1987 Brundtland Report to the present day, sustainable development has been a fundamental principle influencing the global environmental and economic agenda. The Environmental Performance Index (EPI) serves as a comprehensive and multidimensional framework for objectively assessing countries’ progress in sustainable development through measurable indicators of national environmental outcomes. The 2024 EPI Framework classifies 58 indicators into 11 issue categories and three policy objectives. This study utilized 13 indicators pertaining to environmental health policy as input features, with income level serving as the output feature. The income level was predicted utilizing the stacking classifier algorithm. The stacking classifier algorithm is an ensemble learning method that integrates various base estimators to enhance predictive accuracy. This study employed extreme gradient boosting, light gradient boosting machine, and Extra Trees classifiers as the base estimators for the stacking classifier, while the Random Forest classifier served as the final estimator. It was observed that the income level could be predicted with an overall precision of 88.4% and recall of 89.5%, with class-level F1 scores ranging from 0.796 to 0.991. Full article