Search Results (87)

Accurate modeling of stem taper is essential for forest management decisions, including the definition of cutting cycles, the feasibility of annual harvesting, assortment classification, size and volume estimation, and ensuring sustainable production continuity. This study modeled the stem taper of Araucaria angustifolia (Bertol.) Kuntze stands in southern Brazil using Kozak’s variable-exponent model fitted with nonlinear mixed-effects techniques. Both fixed- and mixed-effects models showed high predictive performance, regardless of calibration. An unstructured (UN) covariance structure was required to reduce autocorrelation. The mixed-effects model improved predictive accuracy by up to 22%, achieved R² values above 0.99 with RMSE < 0.74 cm, and significantly reduced residual autocorrelation in diameter estimates. The most effective calibration of random effects was achieved using diameter measurements taken at heights between 0.3 and 6.3 m above ground (approximately between 1.3% and 28.3% of the total height, considering the tallest tree as a reference). This research improves the accuracy of volume estimation and the definition of timber assortments for A. angustifolia, thereby supporting forest management decision-making in southern Brazil. Full article

High-speed silicon (Si) photonic transmitters operating in the O-band require higher on-chip optical power to support advanced modulation formats and ever-increasing line rates. A straightforward approach is to operate laser diodes at higher output power or employ more specialized sources, but this raises cost and exacerbates nonlinear effects such as self-phase modulation, two-photon absorption, and free-carrier generation in high-index-contrast Si waveguides. This paper proposes a low-cost 4 × 1 tree-cascade multimode interference (MMI) power combiner on a Si-on-insulator platform at 1310 nm wavelength that enables coherent power scaling while remaining fully compatible with standard commercial O-band lasers. The device employs adiabatic tapers and low-loss S-bends to ensure uniform field evolution, suppress local field enhancement, and mitigate nonlinear phase accumulation. The optimized layout occupies a compact footprint of 12 µm × 772 µm and achieves a simulated normalized power transmission of 0.975 with an insertion loss of 0.1 dB. Spectral analysis shows a 3 dB bandwidth of 15.8 nm around 1310 nm, across the O-band operating window. Thermal analysis shows that wavelength drift associated with ±50 °C temperature variation remains within the device bandwidth, ensuring stable operation under realistic laser self-heating and environmental changes. Owing to its broadband response, fabrication tolerance, and compatibility with off-the-shelf laser diodes, the proposed combiner is a promising building block for O-band transmitters and photonic neural-network architectures based on cascaded splitter and combiner meshes, while preserving linear transmission and enabling dense, large-scale photonic integration. Full article

Climate volatility and market uncertainty pose significant challenges to agricultural stability. We assess whether and how agricultural industrial agglomeration shapes China’s agricultural economic resilience using province-level panel data for 2003–2023 and a transparent, entropy-weighted index spanning resistance, recovery, and adaptability. Four results stand out. First, in a two-way fixed-effects model, agglomeration is associated with higher resilience on average, and this finding remains robust across multiple robustness tests and after addressing endogeneity concerns. Second, regional subgroup analyses reveal pronounced heterogeneity, providing evidence for geographically targeted policy design. Third, mechanism analysis reveals that the agricultural research intensity serves as a partial mediator between agglomeration and resilience. Fourth, the agglomeration-resilience relationship is nonlinear—N-shaped in the aggregate, while panel quantile regressions reveal an inverted-U among low-resilience provinces and an N-shaped pattern at the median and upper end of the distribution. In an extension, global Moran’s I statistics for three alternative resilience indices reveal significant positive spatial autocorrelation, indicating that agricultural economic resilience tends to cluster geographically and that spatial spillovers are likely to be present. In conclusion, agglomeration is a net enhancer of agricultural economic resilience, but its payoffs are agglomeration- and distribution-dependent: gains taper or reverse around the mid-range for low-resilience provinces, while the median and upper segments benefit again as specialization deepens, in a setting where resilience itself is spatially clustered. Reinforcing the research channel and tailoring actions to local resilience levels are therefore pivotal. Full article

Overweight has become a major public health concern in China’s rapidly urbanizing cities. Patterns of environmental exposure differ notably between men and women, both before and after marriage. This study examines how built environment characteristics influence the risk of overweight, with particular attention to nonlinear associations as well as variations by marital status and gender. Drawing on survey data from 2634 Shanghai residents, we applied extreme gradient boosting to model complex environment–health relationships. The results indicate that greenness, park accessibility, population density, and transit conditions are associated with overweight through nonlinear pathways, with threshold and plateau effects suggesting that benefits taper off, or risks escalate, beyond certain levels. These optimal ranges differ across gender–marriage groups: moderate density and green exposure were generally protective, but the effective ranges were narrower for women and unmarried individuals. Married men benefited more consistently, likely supported by healthier routines reinforced through spousal support, whereas married women showed weaker or even adverse effects, potentially due to greater family responsibilities. Overall, the findings reveal that overweight is shaped by socially differentiated nonlinearities in environmental exposures. Urban planning and public health policies should therefore optimize built environment attributes within effective ranges and tailor interventions to diverse demographic groups. Full article

As ESG sustainability uncertainty intensifies and globalisation deepens, the energy trilemma—security, equity, and sustainability—emerges as the defining calculus of modern energy policy. Therefore, this investigation explores the influence of ESG sustainability uncertainty intensification and globalisation on the energy trilemma, while controlling education, urbanization and economic growth, using data from 2001 to 2022. The energy trilemma offers an all-inclusive gauge for understanding the effect of ESG sustainability uncertainty on energy trilemma. The study employed Lewbel’s Two Stage Least Squares method to examine the connection. The results disclose that ESG sustainability uncertainty is negatively associated with all three trilemma pillars. Globalisation displays a nonlinear influence: its squared terms are negative and statistically significant, implying diminishing marginal benefits at high levels of openness. This paper’s significance lies in evidence that ESG sustainability uncertainty erodes all three pillars of the energy trilemma, while globalization’s benefits taper at high openness—strengthening the mandate for a clean, just, secure, and sustainable transition. Full article

As high-speed railway systems continue to develop toward intelligent operation, axle box bearings integrated with sensors have become key components for real-time condition monitoring. However, introducing sensor-embedded slots disrupts the structural continuity and thermal conduction paths of traditional bearing rings. This results in localized stress concentrations and thermal distortion, which compromise the bearing’s overall performance and service life. This study focuses on a double-row tapered roller bearing used in axle boxes and develops a multi-physics finite element model incorporating the effects of sensor-embedded grooves, based on Hertzian contact theory and the Palmgren frictional heat model. Both contact load verification and thermo-mechanical coupling analysis were performed to evaluate the influence of two key design parameters—groove depth and arc length—on equivalent stress, temperature distribution, and thermo-mechanical coupling deformation. The results show that the embedded slot structure significantly alters the local thermodynamic response. Especially when the slot depth reaches a certain value, both stress and deformation due to thermo-mechanical effects exhibit obvious nonlinear escalation. During the design process, the length and depth of the arc-shaped embedded slot, among other parameters, should be strictly controlled. The study of the stress and temperature characteristics under the thermos-mechanical coupling effect of the axle box bearing is of crucial importance for the design of the intelligent bearing body structure and safety assessment. Full article

The size and flexibility of offshore turbine blades manufactured from composite materials have continuously increased in recent years. In this context, accurate and efficient aeroelastic analyses are important for designing and safely assessing long, flexible blades. Existing linear beam models need to be revised to offer accurate estimates of the geometric nonlinear effects triggered by large displacements. Nonlinear, geometrically exact beam models that have already been extensively used for the above purpose are generally difficult to converge and inefficient. We propose a novel co-rotational beam model for the nonlinear analysis of wind turbine blades. The method adopts vector complement to resolve rotation vector singularity problems. A complete anisotropic cross-sectional stiffness matrix and Timoshenko beam elements are introduced to capture full coupling effects. The method also considers the anisotropy and taper effects caused by the non-uniformity of chord length and material distributions. We established the nonlinear aeroelastic model of the DTU 10 MW turbine, and the results showed that the taper effect dramatically reduced the blade torsion angle by up to 31.44% under rated wind speed. Meanwhile, static beam experiments demonstrate that the accuracy error of the current method is only 1.78%, which is significantly lower than the 17.8% error of the conventional finite element beam method. Full article

Most physical systems exhibit nonlinear behavior while in motion, making their resolution challenging due to nonlinearity, dynamic effects, and sensitivity to parameters such as frequency and amplitude. Traditional analytical and numerical approaches can address these challenges but offer high computational costs, particularly in solving the system of free vibrations produced by the tapered beam. Predicting the behavior of this model is complicated, due to its high sensitivity and nonlinearity. Previously, standard neural network models have been used to solve dynamical systems, but they lack efficiency in handling nonlinearity. In this paper, we propose a novel deep learning model that predicts the amplitude of vibrations of a tapered beam. The primary focus of this study is to address the nonlinearity of the model and accurately predict the amplitude of vibrations. To solve this issue, we introduce a deep neural network designed to manage both nonlinearity and dynamical effects, including amplitude. The approach is significant in terms of computational and time efficiency compared to traditional numerical methods. The proposed work provides comparative results generated by the deep neural network, the backward difference formula as an analytical technique, and the Adams–Bashforth–Moulton predictor–corrector method as a numerical approach. The results demonstrate that our model outperforms existing numerical and analytical techniques. With the help of mean square error, Thiel’s inequality coefficient, and mean absolute error, the accuracy of our model can be verified; the lower these values, the more accurate our model will be. In our proposed model, the values are $8.389\times {10}^{-9}$ for mean square error, $5.563\times {10}^{-4}$ for Thiel’s inequality coefficient, and 0.347 for mean absolute error; all these values are close to zero, signifying the accuracy of our model. The conclusion confirms that our proposed approach, even with changeable hyperparameters, is more suitable and accurate than numerical and analytical methods. Full article

Accurate estimation of individual tree stem volume is essential for forest resource assessment and the implementation of sustainable forest management. In South Korea, traditional regression models based on non-destructive and easily measurable field variables such as diameter at breast height (DBH) and total height (TH) have been widely used to construct stem volume tables. However, these models often fail to adequately capture the nonlinear taper of tree stems. In this study, we evaluated and compared the predictive performance of traditional regression models and two machine learning algorithms—Random Forest (RF) and Extreme Gradient Boosting (XGBoost)—using stem profile data from 1000 destructively sampled Chamaecyparis obtusa trees collected across 318 sites nationwide. To ensure compatibility with existing national stem volume tables, all models used only DBH and TH as input variables. The results showed that all three models achieved high predictive accuracy (R² > 0.997), with XGBoost yielding the lowest RMSE (0.0164 m³) and MAE (0.0126 m³). Although differences in performance among the models were marginal, the machine learning approaches demonstrated flexible and generalizable alternatives to conventional models, providing a practical foundation for large-scale forest inventory and the advancement of digital forest management systems. Full article

This article presents a comprehensive overview of recent advancements in photonic crystal fiber (PCF)-based sensors, with a particular focus on the surface plasmon resonance (SPR) phenomenon for biosensing. With their ability to modify core and cladding structures, PCFs offer exceptional control over light guidance, dispersion management, and light confinement, making them highly suitable for applications in refractive index (RI) sensing, biomedical imaging, and nonlinear optical phenomena such as fiber tapering and supercontinuum generation. SPR is a highly sensitive optical phenomenon, which is widely integrated with PCFs to enhance detection performance through strong plasmonic interactions at metal–dielectric interfaces. The combination of PCF and SPR technologies has led to the development of innovative sensor geometries, including D-shaped fibers, slotted-air-hole structures, and internal external metal coatings, each optimized for specific sensing goals. These PCF-SPR-based sensors have shown promising results in detecting biomolecular targets such as excess cholesterol, glucose, cancer cells, DNA, and proteins. Furthermore, this review provides an in-depth analysis of key design parameters, plasmonic materials, and sensor models used in PCF-SPR configurations, highlighting their comparative performance metrics and application prospects in medical diagnostics, environmental monitoring, and chemical analysis. Thus, an exhaustive analysis of various sensing parameters, plasmonic materials, and sensor models used in PCF-SPR sensors is presented and explored in this article. Full article

This paper proposes a high-sensitivity-integrated temperature sensor with low complexity based on a silicon waveguide. The waveguide layout is optimized through the finite-difference time-domain (FDTD) simulations, and a compressed taper structure improves the efficiency of speckle data collection while reducing the system complexity and cost. To achieve precise temperature demodulation, this paper employed a convolutional neural network (CNN) for nonlinear fitting. Experimental results demonstrate the sensor’s ability to perform temperature measurement in the range of −20 °C to 100 °C, with a best resolution of 0.00287 °C (2.87 mK). The resolution and reliability of the measurements are validated by comparison with the theoretical values. This study introduces a novel approach to silicon waveguide-based temperature sensing. Full article

This study innovatively explores vibrational control with reference to elliptical thickness variation. Traditionally, plate vibrations have been analysed by incorporating circular, linear, parabolic, and exponential thickness variations. However, these variations often fall short in optimizing vibrational characteristics. So, we develop a new formula specifically for orthotropic as well as isotropic plates with elliptical thickness profiles and employ the Rayleigh–Ritz method to calculate the vibrational frequencies of the plate. This research demonstrates that elliptical variation significantly reduces vibrational frequencies compared to conventional thickness profiles. The findings indicate that this unique configuration enhances vibrational control, offering potential applications in engineering fields where vibration reduction is essential. The results provide a foundation for further exploration of non-standard thickness variations in the design of advanced structural components. The study reveals that the elliptical variation in tapering parameter is a much better choice than other variation parameters studied in the literature for the purpose of optimizing the vibrational frequency of plates. Full article

The stem taper function is essential in predicting diameter outside bark (DOB) variations along the tree height, contributing to volume estimation, harvest planning, and precision forestry. Traditional taper models, such as the Kozak function, offer interpretability but often fail to capture nonlinear growth dynamics and regional variability, particularly in the upper stem segments. This study aimed to evaluate and compare the prediction accuracy of conventional and machine learning-based taper models using Pinus densiflora, a representative conifer species in Korea. Field data from two ecologically distinct regions (Gangwon and Central Korea) were used to build and test four models: the Kozak taper function, random forest, extreme gradient boosting, and an artificial neural network (ANN). Model performance was assessed using the RMSE, R², and MAE, along with stem profile visualizations for representative trees. The results showed that the ANN consistently achieved the highest prediction accuracy across both regions, particularly at an upper crown zone relative height (RH) > 0.8, while maintaining smooth and stable taper curves. In contrast, the Kozak model tended to underestimate the diameter of the upper stem. This study demonstrates that machine learning models, particularly ANNs, can effectively enhance the taper prediction precision and serve as practical tools for data-driven forest management and the implementation of precision forestry in Korea. Full article

The intelligent bearing with an embedded sensor is a key technology to realize the running state monitoring of railway locomotive axle box bearings at the source end. To investigate the mechanical properties of axle box bearings with embedded sensor slots, based on nonlinear Hertzian contact theory and the bearing fatigue life theory, a mechanical equivalent analysis model with a virtual mandrel is established for double-row tapered roller bearings used in axle boxes with sensor-embedded slots, which integrally considers the effects of external forces. After verifying the mesh independence before and after embedding the sensor slots, the contact load of tapered rollers calculated by the mechanical model is compared with the theoretical solution based on Hertz contact which verifies the validity of the model from the perspective of contact load. The results show that adjusting the grooving depth and axial position has a significant effect on the local stress peak, and an excessive grooving depth or inappropriate axial position will trigger fatigue damage. This study provides a theoretical basis for analyzing the mechanical characteristics of sensor-embedded slots used in railway locomotive axle box bearings. Full article

We demonstrate mid-infrared ultraflat broadband supercontinuum (SC) generation in a 40 cm long tapered fluorotellurite fiber pumped by a Raman soliton source. By tapering the end of the large-core-diameter fluorotellurite fiber, the dispersion is regulated and the nonlinear effect is enhanced, which effectively extends the mid-infrared SC spectral range and increases the spectral flatness. Finally, we obtained an SC light source with a spectral range from 1.8 to 4.7 μm; the 10 dB bandwidth of the source completely covers 1.88–4.22 μm, which has the farthest flat spectral edge in fluorotellurite fibers. The output power of the SC laser is about 1.04 W, and the power ratio of those above 3 μm in the spectrum to the total SC is ~24%. The optical-to-optical conversion efficiency is about 75%. Our results show that tapering of fluorotellurite fiber is an effective method to further extend and flatten the mid-infrared SC. Full article