Search Results (80)

This paper presents an enhanced pseudo-rigid body model (PRBM) integrated with the LuGre friction law to analyze the dynamic behavior of flexure-hinge-based piezoelectric stick–slip actuators (PSSAs). The PRBM captures flexure compliance through Lagrangian dynamics, while Newtonian mechanics describe the piezoelectric stack and slider motion. Non-linear contact effects, including stick–slip transitions, are modeled using the LuGre formulation. A mass–spring–damper model (MSDM) is also implemented as a baseline for comparison. The models are solved in MATLAB Simulink version R2021a and validated against experimental data from a published prototype. The enhanced PRBM achieves strong agreement with experiments, with a root mean square error of 20.19%, compared to 51.65% for the MSDM. By reformulating the equations into closed-form expressions, it removes symbolic evaluations required in the standard PRBM, resulting in one to two orders of magnitude faster simulation time while preserving accuracy. Stable transient simulations are achieved at fine time steps ($\Delta t={10}^{-8}$ s). A systematic parametric study highlights preload force, flexure stiffness, friction coefficients, and tangential stiffness as dominant factors in extending the linear frequency–velocity regime. Overall, the PRBM–LuGre framework bridges the gap between computationally intensive finite element analysis and oversimplified lumped models, providing an accurate and efficient tool for design-oriented optimization of compliant piezoelectric actuators. Full article

Accurate segmentation of fine roots in field rhizotron imagery is essential for high-throughput root system analysis but remains challenging due to limitations of traditional methods. Traditional methods for root quantification (e.g., soil coring, manual counting) are labor-intensive, subjective, and low-throughput. These limitations are exacerbated in in situ rhizotron imaging, where variable field conditions introduce noise and complex soil backgrounds. To address these challenges, this study develops an advanced deep learning framework for automated segmentation. We propose an improved U-shaped Convolutional Neural Network (U-Net) architecture optimized for segmenting larch (Larix olgensis) fine roots under heterogeneous field conditions, integrating both in situ rhizotron imagery and open-source multi-species minirhizotron datasets. Our approach integrates (1) a Convolutional Block Attention Module (CBAM) to enhance feature representation for fine-root detection; (2) an additive feature fusion strategy (UpAdd) during decoding to preserve morphological details, particularly in low-contrast regions; and (3) a transfer learning protocol to enable robust cross-species generalization. Our model achieves state-of-the-art performance with a mean intersection over union (mIoU) of 70.18%, mean Recall of 86.72%, and mean Precision of 75.89%—significantly outperforming PSPNet, SegNet, and DeepLabV3+ by 13.61%, 13.96%, and 13.27% in mIoU, respectively. Transfer learning further elevates root-specific metrics, yielding absolute gains of +0.47% IoU, +0.59% Precision, and +0.35% F1-score. The improved U-Net segmentation demonstrated strong agreement with the manual method for quantifying fine-root length, particularly for third-order roots, though optimization of lower-order root identification is required to enhance overall accuracy. This work provides a scalable approach for advancing automated root phenotyping and belowground ecological research. Full article

Understanding root decomposition dynamics is essential to address declining carbon sequestration and nutrient imbalances in monoculture plantations. This study elucidates how forest gaps regulate Pinus massoniana root decomposition through comparative methodological analysis, providing theoretical foundations for near-natural forest management and carbon–nitrogen cycle optimization in plantations. The results showed the following: (1) Root decomposition was significantly accelerated by the in situ soil litterbag method (ISLM) versus the traditional litterbag method (LM) (decomposition rate (k) = 0.459 vs. 0.188), reducing the 95% decomposition time (T_0.95) by nearly nine years (6.53 years vs. 15.95 years). ISLM concurrently elevated the root potassium concentration and reconfigured the relationships between root decomposition and soil nutrients. (2) Lower-order roots (orders 1–3) decomposed significantly faster than higher-order roots (orders 4–5) (k = 0.455 vs. 0.193). This disparity was amplified under ISLM (lower-/higher-order root k ratio = 4.1) but diminished or reversed under LM (lower-/higher-order root k ratio = 0.8). (3) Forest gaps regulated decomposition through temporal phase interactions, accelerating decomposition initially (0–360 days) while inhibiting it later (360–720 days), particularly for higher-order roots. Notably, forest gap effects fundamentally reversed between methodologies (slight promotion under LM vs. significant inhibition under ISLM). Our study reveals that conventional LM may obscure genuine ecological interactions during root decomposition, confirms lower-order roots as rapid nutrient-cycling pathways, provides crucial methodological corrections for plantation nutrient models, and advances theoretical foundations for precision management of P. massoniana plantations. Full article

The environmental heterogeneity caused by altitude can lead to trade-offs in nutrient utilization and allocation strategies among plant organs; however, there is still a lack of research on the nutrient variation in the “flower–leaf–branch–fine root–soil” systems of native shrubs along altitude gradients in China’s unique karst regions. Therefore, we analyzed the carbon (C), nitrogen (N), and phosphorus (P) contents and their ratios in flowers, leaves, branches, fine roots, and surface soil of Hypericum kouytchense shrubs across 2200–2700 m altitudinal range in southwestern China’s karst areas, where this species is widely distributed and grows well. The results show that H. kouytchense organs had higher N content than both global and Chinese plant averages. The order of C:N:P value across plant organs was branches > fine roots > flowers > leaves. Altitude significantly affected the nutrient dynamics in plant organs and soil. With increasing altitude, P content in plant organs exhibited a significant concave pattern, leading to unimodal trends in the C:P of plant organs, as well as the N:P of leaves and fine roots. Meanwhile, plant organs except branches displayed significant homeostasis coefficients in C:P and fine root P, indicating a shift in H. kouytchense’s P utilization strategy from acquisitive-type to conservative-type. Strong positive relationships between plant organs and soil P and available P revealed that P was the key driver of nutrient cycling in H. kouytchense shrubs, enhancing plant organ–soil coupling relationships. In conclusion, H. kouytchense demonstrates flexible adaptability, suggesting that future vegetation restoration and conservation management projects in karst ecosystems should consider the nutrient adaptation strategies of different species, paying particular attention to P utilization. Full article

Surface all-wave net radiation ($R_n$) is one of the essential parameters to describe surface radiative energy balance, and it is of great significance in scientific research and practical applications. Among various acquisition approaches, the estimation of $R_n$ from satellite data is gaining more and more attention. In order to obtain the daily $R_n$ ($R_{nd}$) from the instantaneous satellite observations, a parameter $C_d$, which is defined as the ratio between the $R_n$ at daily and at instantaneous under clear sky was proposed and has been widely applied. Inspired by the sinusoidal model, a new model for $C_d$ estimation, namely New Model, was proposed based on the comprehensive clear-sky $R_n$ measurements collected from 105 global sites in this study. Compared with existing models, New Model could estimate $C_d$ at any moment during 9:30~14:30 h, only depending on the length of daytime. Against the measurements, New Model was evaluated by validating and comparing it with two popular existing models. The results demonstrated that the $R_{nd}$ obtained by multiplying $C_d$ from New Model had the best accuracy, yielding an overall $R^2$ of 0.95, root mean square error (RMSE) of 14.07 Wm⁻², and Bias of −0.21 Wm⁻². Additionally, New Model performed relatively better over vegetated surfaces than over non- or less-vegetated surfaces with a relative RMSE (rRMSE) of 11.1% and 17.89%, respectively. Afterwards, the New Model $C_d$ estimate was applied with MODIS data to calculate $R_{nd}$. After validation, the $R_{nd}$ computed from $C_d$ was much better than that from the sinusoidal model, especially for the case MODIS transiting only once in a day, with $R_{nd}$-validated $R^2$ of 0.88 and 0.84, RMSEs of 19.60 and 27.70 Wm⁻², and Biases of −0.76 and 8.88 Wm⁻². Finally, more analysis on New Model further pointed out the robustness of this model under various conditions in terms of moments, land cover types, and geolocations, but the model is suggested to be applied at a time scale of 30 min. In summary, although the new $C_d$ model only works for clear-sky, it has the strong potential to be used in estimating $R_{nd}$ from satellite data, especially for those having fine spatial resolution but low temporal resolution. Full article

This article investigates predicted future developments in fine dining using a mixed-methods approach rooted in German gastronomic culture. By conducting an inductive media content analysis and ten semi-structured expert interviews with leading figures in Germany’s high-end food sector, we applied a qualitative mixed-methods approach. The study was based exclusively on data collected in 2018 and 2019, deliberately excluding pandemic-related developments in order to focus on long-term structural and cultural trends in fine dining. We identified two core thematic clusters: one related to sustainable food practices (ecology/sustainability, regionality, seasonality, from-farm-to-table, and vegetarianism/veganism) and the other to experiential dimensions of dining (experience, topic-based concept, and storytelling). Our findings contribute to the academic discussion on culinary futures and provide grounded insights into how fine dining is likely to evolve in response to broader societal, environmental, and cultural shifts. This study fills a significant research gap by systematically mapping emerging restaurant concepts based on non-COVID data, making it a valuable reference for scholars and practitioners alike. Full article

Accelerating the recovery of compacted soils caused by logging machinery using bioengineering techniques is a key goal of Sustainable Forest Management. This research was conducted on an abandoned skid trail with a uniform 15% slope and a history of heavy traffic, located in the Nav forest compartment of northern Iran. The main objectives were to assess (a) soil physical properties 35 years after skidding by a tracked bulldozer, (b) the impact of natural alder regeneration on soil recovery, and (c) the contribution of alder fine-root development to the restoration of compacted soils in beech stands. Soil physical properties and fine root biomass were analyzed across three depth classes (0–10 cm, 10–20 cm, 20–30 cm) and five locations (left wheel track (LT), between wheel tracks (BT), right wheel track (RT)) all with alder trees, and additionally control points inside the trail without alder trees (CPWA), as well as outside control points with alder trees (CPA). Sampling points near alder trees (RT, LT, BT) were compared to CPWA and CPA. CPA had the lowest soil bulk density, followed by LT, BT, RT, and CPWA. Bulk density was highest (1.35 ± 0.07 g cm⁻³) at the 0–10 cm depth and lowest (1.08 ± 0.4 g cm⁻³) at 20–30 cm. The fine root biomass at 0–10 cm depth (0.23 ± 0.21 g dm⁻³) was significantly higher than at deeper levels. Skid trail sampling points showed higher fine root biomass than CPWA but lower than CPA, by several orders of magnitude. Alder tree growth significantly reduced soil bulk density, aiding soil recovery in the study area. However, achieving optimal conditions will require additional time. Full article

Plant roots and soil properties play crucial roles in regulating soil hydrological processes, particularly in determining soil water infiltration capacity. However, the infiltration patterns and underlying mechanisms across different forest types in subtropical regions remain poorly understood. In this study, we measured the infiltration characteristics of three typical stands (pure Phyllostachys edulis forest, mixed Phyllostachys edulis-Cunninghamia lanceolata forest, and pure Cunninghamia lanceolata forest) using a double-ring infiltrometer. Stepwise multiple regression and structural equation modeling (SEM) were employed to analyze the effects of root traits and soil physicochemical properties on soil infiltration capacity. The results revealed the following: (1) The initial infiltration rate (IIR), stable infiltration rate (SIR), and average infiltration rate (AIR) followed the order pure Phyllostachys edulis stand > mixed stand > pure Cunninghamia lanceolata stand. (2) Compared to the pure Cunninghamia lanceolata stand, the IIR, SIR, and AIR in the pure Phyllostachys edulis stand increased by 6.66%, 35.63%, and 28.51%, respectively, while those in the mixed stand increased by 28.79%, 28.82%, and 33.51%. (3) Fine root biomass, root length density, non-capillary porosity, and soil bulk density were identified as key factors influencing soil infiltration capacity. (4) Root biomass and root length density affected infiltration capacity through both direct pathways and indirect pathways mediated by alterations in non-capillary porosity and soil bulk density. These findings provide theoretical insights into soil responses to forest types and inform sustainable water–soil management practices in Phyllostachys edulis plantations. Full article

Natural restoration of vegetation and plantation are effective land use measures to promote soil organic carbon (SOC) sequestration. How soil physicochemical properties, microorganisms, Glomalin-related soil proteins (GRSPs), and aggregates interact to regulate SOC accumulation and sequestration remains unclear. This study examined five land uses in the karst region of Southwest China: corn field (CF), corn intercropped with cabbage fields (CICF), orchard (OR), plantation (PL), and natural restoration of vegetation (NRV). The results revealed that SOC, total nitrogen (TN), total phosphorus (TP), total GRSP (T-GRSP), and easily extractable GRSP (EE-GRSP) contents were significantly higher under NRV and PL than in the CF, CICF, and OR, with increases ranging from 10.69% to 266.72%. Land use significantly influenced bacterial α-diversity, though fungal α-diversity remained unaffected. The stability of soil aggregates among the five land uses followed the order: PL > NRV > CF > OR > CICF. Partial least-squares path modeling (PLS-PM) identified land use as the most critical factor influencing SOC. SOC accumulation and stability were enhanced through improved soil properties, increased microbial diversity, and greater community abundance, promoting GRSP secretion and strengthening soil aggregate stability. In particular, soil microorganisms adhere to the aggregates of soil particles through the entanglement of fine roots and microbial hyphae and their secretions (GRSPs, etc.) to maintain the stability of the aggregates, thus protecting SOC from decomposition. Natural restoration of vegetation and plantation proved more effective for soil carbon sequestration in the karst region of Southwest China compared to sloping cropland and orchards. Full article

Biodegradable mulch films not only provide similar field benefits to conventional mulch films but also degrade naturally, rendering them an effective alternative to traditional polyethylene mulch films for mitigating “white pollution”. However, recent studies have focused on the material selection and soil ecological impacts of biodegradable mulch films, while their effects on soil water temperature regulation and root architecture in drip-irrigated rice cultivation remain unclear. To address this research gap, in this study, various treatments including no mulch (NM), conventional plastic mulch (PM), and four types of biodegradable mulch films (BM-W₁, BM-B₁, BM-B₂, and BM-B₃) were established, and their effects on the soil hydrothermal flux, root architecture, biomass accumulation, and resource use efficiency of drip-irrigated rice were analyzed at different growth stages. The results indicated the following: (1) Compared with the NM treatment, film mulching increased the soil hydrothermal fluxes and water retention capacity, thereby promoting root growth and biomass accumulation, ultimately increasing the effective panicle number and grain yield. (2) Among the biodegradable film treatments, BM-B₃ (with a degradation period of 105 days) maintained relatively higher soil temperature for a longer duration, which increased surface root distribution in the mid-to-late growth stages, further improving fine root growth and biomass accumulation, consequently enhancing both yield and water use efficiency. In contrast, BM-B₁ and BM-B₂ exhibited excessively rapid degradation rates, leading to significant fluctuations in soil moisture and temperature, thereby negatively affecting water supply and nutrient uptake and ultimately restricting root growth and development. (3) The entropy weight (EW) technique for order of preference by similarity to ideal solution (TOPSIS) model results revealed that although the PM treatment was more advantageous in terms of soil temperature, root dry weight, and soil moisture content, BM-B₃ provided a slightly higher yield than the PM treatment did and offered the advantage of biodegradability, making it a preferred alternative to conventional mulch film. In summary, this study revealed the mechanism by which biodegradable mulch films enhanced biomass accumulation and yield formation in drip-irrigated rice production by optimizing soil hydrothermal dynamics and root architecture, thereby exploring their potential as replacements for conventional mulch films. These findings provide a theoretical basis for the efficient and sustainable production of drip-irrigated rice in arid regions. Full article

Soil moisture is a critical factor influencing the growth and development of terrestrial ecosystems and vegetation. In this study, we utilized data on meteorology, soil moisture, soil texture, and the spatial distribution of planted and natural forests to examine the spatial distribution characteristics of soil moisture across soils with varying textures and depths. Geodetector models were constructed to analyze the driving mechanisms behind soil moisture dynamics. The key findings are as follows: (1) Soil moisture consumption in planted forests was significantly higher than in natural forests, with the magnitude of the difference taking the following order: coarse-textured soils > medium-textured soils > fine-textured soils. (2) The spatial differentiation of moisture content across soil layers was primarily determined by the 10–40 cm layer, while soil moisture in the 0–10 cm layer was more strongly influenced by wind speed. (3) The dominant plantation species in the watershed, Eucalyptus and Cunninghamia, have main roots extending to depths of 100–200 cm. The presence of these species in this soil layer contributes significantly to the spatial differentiation of soil moisture. This study reveals that planted forests planting consumes huge amount of soil moisture and affects the spatial differentiation of soil moisture, which provides theoretical guidance for the management of ecological restoration projects in this area. Full article

Bilinear pooling, as an aggregation approach that outputs second-order statistics of deep learning features, has demonstrated effectiveness in a wide range of visual recognition tasks. Among major improvements on the bilinear pooling, matrix square root normalization—applied to the bilinear representation matrix—is regarded as a crucial step for further boosting performance. However, most existing works leverage Newton’s iteration to perform normalization, which becomes computationally inefficient when dealing with high-dimensional features. To address this limitation, through a comprehensive analysis, we reveal that both the distribution and magnitude of eigenvalues in the bilinear representation matrix play an important role in the network performance. Building upon this insight, we propose a novel approach, namely RegCov, which regularizes the eigenvalues when the normalization is absent. Specifically, RegCov incorporates two regularization terms that encourage the network to align the current eigenvalues with the target ones in terms of their distribution and magnitude. We implement RegCov across different network architectures and run extensive experiments on the ImageNet1K and fine-grained image classification benchmarks. The results demonstrate that RegCov maintains robust recognition to diverse datasets and network architectures while achieving superior inference speed compared to previous works. Full article

In order to ensure the price stability of niche agricultural products and enhance farmers’ income, the study delves into the pattern of the ginger price fluctuation rule and its main influencing factors. By combining seasonal decomposition STL, long and short-term memory network LSTM, attention mechanism ATT and Kolmogorov-Arnold network, a combined STL-LSTM-ATT-KAN prediction model is developed, and the model parameters are finely tuned by using multi-population adaptive particle swarm optimisation algorithm (AMP-PSO). Based on an in-depth analysis of actual data on ginger prices over the past decade, the STL-LSTM-ATT-KAN model demonstrated excellent performance in terms of prediction accuracy: its mean absolute error (MAE) was 0.111, mean squared error (MSE) was 0.021, root mean squared error (RMSE) was 0.146, and the coefficient of determination (R²) was 0.998. This study provides the Ginger Industry, agricultural trade, farmers and policymakers with digitalised and intelligent aids, which are important for improving market monitoring, risk control, competitiveness and guaranteeing the stability of supply and price. Full article

Accurate and effective radar jamming effectiveness evaluation is the key to forming the radar jamming OODA (Observe, Orient, Decide, Act) loop. The current model-driven evaluation method has low confidence and the data-driven evaluation method lacks sufficient high-quality data; thus, the evaluations that are solely model-driven or data-driven are not effective. In order to solve this problem, we propose a radar jamming effectiveness hybrid model–data-driven evaluation method. Firstly, the mechanism of the model is constructed based on the jamming equations. Secondly, the quality of training data is improved by data cleaning and model correction, after which the hybrid model is realized by training with simulated data and fine-tuning it with real-world data. Finally, the validity of the method is proved by simulation experiments, which show that the method is capable of effectively improving the accuracy of prediction and evaluation, and has good practicability. Compared with the model-driven method, the RMSE (Root Mean Square Error) of the prediction results of this method is reduced by 88.26% and the MRE (Mean Relative Error) is reduced by 92.00%. Full article

Soil salinity is a major constraint to soil health and crop productivity, especially in arid and semi-arid regions. The most accurate measurement of soil salinity is considered to be the electrical conductivity of saturated soil extracts (EC_e). Because this method is labor-intensive, it is unsuitable for routine analysis in large soil sampling campaigns. This study aimed to identify the best models to estimate soil salinity based on EC_e in relation to a rapid electrical conductivity (EC) measurement in soil/water (referred to as S:W henceforward) extracts. We evaluated the relationship between EC_e and the EC_S:W extract ratios (1:1, 1:2, and 1:5) in salt-affected soils from the semi-arid Sehb El Masjoune region of Morocco. The soil salinity in this region is 0.5 to 235 dS/m, as determined by the EC_e method. A total of 125 soil samples, from topsoil (0–15 cm) and subsoil (15–30 cm) with mainly fine to medium textures, were analyzed using linear, logarithmic, and second-order polynomial regression models. The models included all samples or grouped samples according to soil texture (fine, medium) or specific textural classes. The mean EC_e values were 2.6, 3.1, and 7.9 times greater than the EC of 1:1, 1:2, and 1:5 S:W extracts, respectively. Polynomial regression models had the best predictive accuracy, R² = 0.98, and the lowest root mean square error of 10.6 to 10.7 dS/m for the EC_S:W extract ratios of 1:5 and 1:2. The polynomial models could represent the non-linear relationships between EC_e and salinity indicators, especially in the 80–170 dS/m salinity range, where other models typically underestimate the salinity. These results confirm that advanced regression techniques are suitable for predicting soil salinity in a salt-affected semi-arid region. The site-specific models outperformed previously published models, because they consider the spatial variability and heterogeneity of the salinity in the study area explicitly. This confirms the importance of calibrating soil salinity models according to the local soil and environmental conditions. Consequently, we can undertake soil salinity assessments in hundreds of samples by using the simple, rapid EC_S:W extraction method as a direct indicator of EC and extrapolate to EC_e with a polynomial regression model. Our approach enables the widespread soil salinity assessments that are needed for land-use planning, irrigation management, and crop selection in salt-affected landscapes. Full article