Search Results (186)

The European flat oyster Ostrea edulis and the stony sea urchin Paracentrotus lividus play key ecological roles in Mediterranean coastal ecosystems, supporting biodiversity and ecosystem functions. The decline of their natural populations has prompted several restoration initiatives; however, the rapid spread of the invasive blue crab Callinectes sapidus raises concern about its potential impact on native species and the success of these efforts. This study investigated predation by male and female C. sapidus on different size classes of O. edulis and P. lividus under controlled laboratory conditions. Crabs were offered single or mixed prey species, and consumption was monitored over 24 h. Small (~33 mm shell length) oysters were heavily preyed upon (>62%), whereas medium (~60 mm) and large (~82 mm) individuals were not consumed, although attempted predation on medium individuals was observed. In contrast, P. lividus was only occasionally consumed (<32%) at the smallest size (~13.5 mm diameter). Predation differed slightly between sexes at shorter exposure times but converged after 24 h. These results indicate that C. sapidus poses a serious threat to O. edulis restoration, particularly for small individuals, while its impact on P. lividus appears limited. Incorporating predator assessment and mitigation strategies into restoration planning should become standard practice to strengthen the resilience and effectiveness of Mediterranean restoration efforts. Full article

This work investigates the penetration behavior of SiC particles into Digital Light Processing (DLP)-printed thermoset substrates under low-pressure cold-spray conditions, aiming to enhance surface hardness and wear resistance. A coupled simulation framework was established in which particle acceleration was obtained from CFD using ANSYS Fluent, and high-speed impact and embedding were modeled through ANSYS Explicit Dynamics. Two particle diameters (25 μm and 60 μm) were examined across inlet pressures from 2 to 5 bar to evaluate both the continuous influence of pressure and the two-level effect of particle size. Mesh convergence was achieved at a resolution of d_p/20, ensuring numerical stability and computational efficiency. The results showed a strong dependence of penetration depth on pressure and particle size: for 25 μm particles, penetration increased from 0.76 d_p at 2 bar to 1.53 d_p at 5 bar, while 60 μm particles exhibited deeper absolute embedding due to their significantly higher kinetic energy. Response-surface analysis further revealed nonlinear pressure effects and a predominantly linear size-dependent shift. Experimental validation at 3 bar confirmed a penetration depth of approximately 1 d_p, demonstrating good agreement between simulation and physical observation. Overall, the validated workflow provides quantitative insight into particle–substrate interaction in thermoset polymers and offers a practical basis for controlled particle embedding as a surface-strengthening strategy in additive manufacturing. Full article

This study evaluated the effects of late pruning and late apical leaf removal on grapevine phenology, fruit composition, yield parameters, xylem sap carbohydrate content, and grape skin polyphenol levels over two consecutive vintages (2022 and 2023). As expected, delayed pruning shifted the phenological stages, with more pronounced delays observed in 2022 than in 2023. However, by August, all the treatments had reached the berry-softening stage, indicating a convergence in ripening. The grape juice composition showed no significant differences in sugar content in 2022; however, in 2023, the °Brix was notably reduced in control vines subjected to late apical defoliation. The titratable acidity and pH remained stable across treatments and years, while the malic acid concentrations were consistently higher in the late-pruned treatments, particularly LP2 (late pruning 2 was performed when the control vines had reached the eight-leaves-folded development stage). Late pruning significantly reduced the yield and bunch size, especially for the 2023 LP2 treatment. In contrast, late apical defoliation had minimal impact on the yield components. Vegetative growth, as assessed by cane diameter and weight, also declined under late pruning. Despite this, the xylem sap analysis revealed no significant changes in the glucose, fructose, or myo-inositol levels, suggesting that the carbohydrate reserves remained unaffected. Notably, LP2 consistently resulted in the highest total polyphenol content in the grape skins across both years, indicating enhanced phenolic maturity. Although the polyphenol concentrations were generally higher in 2023, the treatment effects varied more widely, likely due to the differing environmental conditions. These findings suggest that late pruning—particularly LP2—can be a valuable tool for improving grape phenolic quality, albeit at the cost of reduced yield and vine vigor. This study highlights the importance of site- and season-specific canopy management strategies in balancing fruit quality with productivity under variable climatic conditions. Full article

Nonlinear seepage behavior within rock fractures represents a critical and actively researched challenge in underground engineering, energy exploitation, and environmental sciences. Through the integration of nonlinear seepage theory with coupled numerical simulations of fracture flow and matrix flow, this study systematically investigates the synergistic mechanisms governing the influence of filling particles, tortuous fractures, and porous matrices on fluid transport within fracture–porous matrix seepage systems. Key findings reveal that: (1) Horizontal fractures continuously receive fluid influx from the surrounding porous matrix, where the flow field maintains remarkable symmetry, with a critical matrix height-to-fracture aperture ratio regulating streamline divergence and convergence at the fracture outlet; (2) The flow field within horizontal fractures undergoes substantial transformation when the Reynolds number exceeds a critical threshold, while maintaining stable flow patterns and -ΔP-Q relationships below this value, demonstrating a distinct inertial-controlled flow regime transition; (3) Tortuous fracture geometries induce localized vortex formation and significant velocity fluctuations, particularly in the front and rear dip-angle zones, substantially enhancing fluid exchange efficiency compared to horizontal configurations; (4) The volumetric flow rate exhibits a non-monotonic relationship with inclination angle, peaking at approximately 36°, while a synergistic effect between fracture inclination and infill particle diameter systematically modulates pressure-drop-flow-rate relationships, with a critical d/h = 0.5 threshold distinguishing fundamentally different flow behaviors. These findings provide quantitative criteria for predicting nonlinear seepage in practical engineering scenarios involving complex fracture networks and filling materials, offering significant implications for risk assessment and drainage design in deep underground projects. Full article

Accurate plant organ segmentation and efficient phenotypic parameter acquisition remain major challenges in plant phenomics. This study develops an automated phenotyping framework for maize that integrates deep learning with 3D point cloud analysis to overcome the inefficiency and subjectivity of traditional manual methods. A high-quality 3D maize point cloud dataset was constructed, and a segmentation model named PSCSO was proposed based on the PointNet++ architecture. The model incorporates an SCConv module to reduce feature redundancy and uses the Sophia optimizer to improve convergence efficiency. Experimental results show the model achieved segmentation accuracies of 0.926 on the training set and 0.861 on the testing set, with a MIoU of 0.843, while significantly reducing training time. Based on the segmentation results, the model automatically estimates seven key phenotypic parameters: plant height, crown diameter, stem height, stem diameter, leaf length, leaf width, and leaf area. This is achieved by integrating point cloud algorithms including linear regression, PCA, and Delaunay triangulation. The predictions showed excellent agreement with manual measurements, with all parameters achieving R² values exceeding 0.91. Overall, this automated framework provides a reliable and high-throughput solution for plant phenotypic analysis. Full article

Perforated cylindrical shaped metal plates are used with high efficiency in the manufacture of deflectors, components of cooling systems, wind tunnels, climatic chambers, filters, and cylindrical implants. This is particularly important for lightweight cylindrical structures, where even minor changes in stiffness can affect structural strength. One of the most important parameters determining the mechanical behavior of such structures is the effective elastic modulus of the perforated element which characterizes its resistance to deformation. The research involves plates made of stainless steel 304 alloy, where perforations were created using the laser-cutting method. The cylindrical shape of the samples with height 50 mm, thickness 1 mm, and diameter 48 mm of each specimen was obtained using metal rolling and welding techniques. To determine the effective elastic modulus, a non-destructive material property evaluation method was applied by solving an inverse problem. In this research, resonance frequencies were determined using a laser vibrometer and a full factorial experimental plan was developed. Physical samples were digitized into 3D models using 3D scanning technology. To evaluate the accuracy of the applied finite element numerical model, its convergence analysis was performed. Numerical results were approximated using the least-squares method, while the effective elastic modulus was calculated by formulating and minimizing the error functional between experimental and numerical eigenfrequencies. The results indicate that increasing the relative perforation area from 0% to 50.24% leads to a decrease in the effective elastic modulus from 184.76 GPa to 50.69 GPa, confirming that increasing the perforation area in a stainless steel 304 cylinder reduces its elastic properties. The observed reduction in resonance frequencies and elastic properties is primarily due to the stiffness decrease caused by the higher perforation volume. Full article

Three-dimensional surface topography offers a noninvasive alternative to radiographic imaging for evaluating trunk morphology and posture. This study aimed to establish normative values of trunk cross-sectional geometry and to examine their associations with spinal alignment in healthy young adults. A cross-sectional sample of 108 participants (62 women, 46 men; 19–23 years, normal BMI) underwent assessment using two complementary techniques: TorsoScan, which reconstructed 360° trunk cross-sections at thoracic levels T1, T4, T8, and T12, and DIERS Formetric 4D, which quantified spinal posture parameters. For each cross-section, sagittal and coronal diameters and cross-sectional areas were calculated; sex differences were analyzed using Welch’s t tests and effect sizes, and associations with posture were examined by Pearson correlations with false discovery rate correction and regression modeling. Trunk geometry followed a regular thoracic profile, with the largest coronal diameter at T4 and the maximal area at T8. Men showed consistently larger diameters and areas than women, with large effect sizes at T4–T12. Four associations remained significant: reduced mid-thoracic breadth and area were linked to greater lumbar lordosis, while upper thoracic depth correlated positively with thoracic kyphosis. Predictive models based on posture explained limited variance (R² ≤ 0.19). These findings provide sex-specific reference values and demonstrate that TorsoScan and DIERS Formetric yield complementary, partly convergent measures that may support radiation-free evaluation of trunk morphology in clinical and rehabilitative settings. Full article

Tunnel construction in weak and fractured strata often faces risks such as tunnel face instability and large deformation of surrounding rock, which are difficult to effectively control using conventional support methods. Based on the engineering background of the No. 8# TA Tunnel in the F3 section of Georgia’s E60 Highway, this study employed ADECO-RS and developed a 3D numerical model with finite difference software to simulate full-face tunnel excavation process. The influence of advanced reinforcement measures on the stability of the surrounding rock was systematically investigated. The control effectiveness of different advanced reinforcement schemes was evaluated by comparing the displacement field, stress field, and plastic zone distribution of the surrounding rock under three conditions: no support, advanced pipe roof support, and a combination of pipe roof and glass fiber bolts. A comprehensive quantitative analysis of the synergistic effect of the combined reinforcement was also performed. The results indicated that significant extrusion deformation of the tunnel face and vault settlement occurred after excavation. The pressure arch developed within a range of 17.5 to 22 m above the tunnel vault. The surrounding rock of this tunnel was classified as type B (short-term stable). Deformation primarily occurred within one tunnel diameter ahead of the face, with the deformation rate significantly reduced after support. Advanced pipe roof support effectively restrained surrounding rock deformation, while the combination of advanced pipe roof and glass fiber bolts delivered better performance: reducing final convergence by 73.10%, pre-convergence by 82.69%, and face extrusion by 87.66%. The combined support also contracted the pressure arch boundaries from 17.5 to 22 m to 6–12.5 m, reduced the extent of major principal stress deflection, and significantly shrinks the plastic zone. Glass fiber bolts played a key role in controlling plastic zone expansion and ensuring stability. This study provides theoretical and numerical references for safe construction and advanced support design in tunnels under complex geological conditions. Full article

By imposing finite order constraints on Fibonacci anyon braid relations, we construct the finite quotient $G={\mathbb{Z}}_5⋊2I$, where $2I$ is the binary icosahedral group. The Gröbner basis decomposition of its $SL(2,\mathbb{C})$ character variety yields elliptic curves whose L-function derivatives $L^{\prime }(E,1)$ remarkably match fundamental biological structural ratios. Specifically, we demonstrate that the Birch–Swinnerton-Dyer conjecture’s central quantity: the derivative $L^{\prime }(E,1)$ of the L-function at 1 encodes critical cellular geometries: the crystalline B-DNA pitch-to-diameter ratio ($L^{\prime }(E,1)=1.730$ matching $34\AA /20\AA =1.70$), the B-DNA pitch to major groove width ($L^{\prime }=1.58$) and, additionally, the fundamental cytoskeletal scaling relationship where $L^{\prime }(E,1)=3.570\approx 25/7$, precisely matching the microtubule-to-actin diameter ratio. This pattern extends across the hierarchy ${\mathbb{Z}}_5⋊2P$ with $2P\in \{2O,2T,2I\}$ (binary octahedral, tetrahedral, icosahedral groups), where character tables of $2O$ explain genetic code degeneracies while $2T$ yields microtubule ratios. The convergence of multiple independent mathematical pathways on identical biological values suggests that evolutionary optimization operates under deep arithmetic-geometric constraints encoded in elliptic curve L-functions. Our results position the BSD conjecture not merely as abstract number theory, but as encoding fundamental organizational principles governing cellular architecture. The correspondence reveals arithmetic geometry as the mathematical blueprint underlying major biological structural systems, with Gross–Zagier theory providing the theoretical framework connecting quantum topology to the helical geometries that are essential for life. Full article

This work is framed as an application of static and small exponential random graph models for complex networks in multiple layers. This document revisits the small network and exhibits its potential. Examining the bibliography reveals considerable interest in large and dynamic complex networks. This research examines the application of small networks (50,000 population) for analyzing global commerce, conducting a comparative graph structure of the tariffs, and importing multilayer networks. The authors created and described the scenario where the readers can compare the graph models visually, at a glance. The proposed methodology represents a significant contribution, providing detailed descriptions and instructions, thereby ensuring the operational effectiveness of the application. The method is organized into five distinct blocks (Bn) and an accompanying appendix containing reproduction notes. Each block encompasses a primary task and associated sub-tasks, articulated through a hierarchical series of steps. The most challenging mathematical aspects of a small network analysis pertain to modeling and sample selection (sel_p). This document describes several modeling tasks that confirm that sel_p = 10 is the best option, including modeling the edges and the convergence and covariance model parameters, modeling the node factor by vertex names, Pearson residual distributions, goodness of fit, and more. This method establishes a foundation for addressing the intricate questions derived from the established hypotheses. It provides eight model specifications and a detailed description. Given the scope of this investigation, a historical examination of the relationships between different network actors is deemed essential, providing context for the study of actors engaged in global trade. Various analytical perspectives (six), encompassing degree analyses, diameter and edges, hubs and authority, co-citation and cliques in mutual and collapse approaches, k-core, and clustering, facilitate the identification of the specific roles played by actors within the importation network in comparison to the tariff network. This study focuses on the Latin American and Caribbean region. Full article

In large optical mirror processing (LOMP), the robot is required to carry a computer-controlled optical surfacing (CCOS) polishing tool capable of both fully covering the required material removal profile and maintaining sufficient redundancy for process adaptability. The designed LOMP robot is a five-degree-of-freedom (5-DOF) hybrid robot, where the workspace of its parallel mechanism is constrained by dimensional parameters, including the moving platform radius, the fixed/moving platform radius ratio, and link lengths. This paper presents an optimization study of dimensional parameters for robotic systems, aimed at meeting the workspace requirements of 1250 mm-diameter large optical mirrors. First, analytical models of the robot’s effective workspace and driving torque under different dimensional parameters are derived. Subsequently, workspace requirements and driving torque are established as optimization constraints, and a differential evolution algorithm is implemented to determine the optimal dimensional parameters for the LOMP system. To improve computational efficiency, the conventional differential evolution algorithm is enhanced through the integration of adaptive mutation and crossover operators, resulting in a modified adaptive differential evolution algorithm (ADEA) that demonstrates accelerated convergence characteristics while maintaining solution accuracy. Finally, MATLAB simulations demonstrate that the proposed ADEA successfully obtains optimal dimensional parameter combinations while satisfying all specified constraints. Based on the optimal dimensional parameters, an engineering prototype was manufactured. Experimental results verified the accuracy of the optimized design, providing a valuable reference for optimization of dimensional and structural parameters in similar engineering equipment. Full article

Glioblastoma multiforme (GBM) remains the most aggressive primary brain tumor with median survival of 14.6 months, necessitating novel therapeutic approaches. Here, we report the biogenic synthesis of zinc oxide nanoparticles (ZnO NPs) using Bacillus licheniformis strain TT14s isolated from mining environments and demonstrate their selective anti-glioma efficacy. ZnO NPs exhibited hexagonal wurtzite structure (crystallite size: 15.48 nm) with spherical morphology (19.37 ± 5.28 nm diameter) as confirmed by XRD, HRTEM, and comprehensive physicochemical characterization. Colloidal stability analysis revealed an isoelectric point at pH 7.46, ensuring optimal dispersion in biological media. Cytotoxicity evaluation revealed remarkable selectivity: at 100 μg/mL, ZnO NPs reduced NG-108 glioblastoma cell viability to 36.07 ± 1.89% within 1 h while maintaining 78.9 ± 0.94% viability in primary retinal cells. The selective cytotoxicity was attributed to the interplay of convergent mechanisms acting under dark conditions, including defect-mediated ROS generation supported by photoluminescence analysis revealing a characteristic oxygen vacancy emission at 550 nm, pH-dependent dissolution enhanced in the acidic tumor microenvironment, and preferential cellular uptake by rapidly proliferating cancer cells with compromised antioxidant defenses. Time-course analysis demonstrated concentration-dependent effects with therapeutic windows favoring normal cell preservation. The intrinsic cytotoxic activity under dark laboratory conditions eliminates the need for external activation, providing practical advantages for therapeutic applications. These findings establish ZnO NPs as promising candidates for targeted glioblastoma therapy, warranting further in vivo validation and mechanistic elucidation for clinical translation. Full article

Leaf nutrient resorption efficiency (NuRE) is critical for plant nutrient conservation, yet its relationship with leaf and root economic traits remains poorly understood in mangroves. We quantified nitrogen (N) and phosphorus (P) resorption across ten mangrove species (five trees and five shrubs) in Hainan, China, and related NuRE to key leaf (leaf mass per area, LMA; leaf dry mass content, LDMC; and green leaf nitrogen and phosphorus contents, N_gr and P_gr, respectively) and root (specific root length, SRL; root tissue density, RTD; root diameter, RD; and root nitrogen content, N_root) traits. We found that species with a lower leaf structural investment (LMA = 103–173 g m⁻², LDMC = 19–27%) presented a 6–45% greater N and P resorption efficiency than those with a higher structural investment (LMA = 213–219 g m⁻², LDMC = 26–31%). Contrary to global meta-analyses, higher green leaf N and P contents also predicted a greater NuRE, implying enhanced internal recycling under chronic nutrient limitation. Root traits (SRL, RTD, RD, and N_root) had no significant influence on NuRE, indicating decoupled above- versus belowground strategies. Trees and shrubs diverged in size but converged in NuRE–leaf trait relationships. These findings refine plant economics theory and guide restoration by prioritizing species with acquisitive, high-NuRE foliage for nutrient-poor coasts. Full article

This study presented an autonomous shield-cutting end-effector for maize surrounding weeding (SEMSW), addressing the challenges of the low weed removal rate (WRR) and high seedling damage rate (SDR) in northern China’s 3–5 leaf stage maize. The SEMSW integrated seedling positioning, robotic arm control, and precision weeding functionalities: a seedling positioning sensor identified maize seedlings and weeds, guiding XYZ translational motions to align the robotic arm. The seedling-shielding anti-cutting mechanism (SAM) enclosed crop stems, while the contour-adaptive weeding mechanism (CWM) activated two-stage retractable blades (TRWBs) for inter/intra-row weeding operations. The following key design parameters were determined: 150 mm inner diameter for the seedling-shielding disc; 30 mm minimum inscribed-circle for retractable clamping units (RCUs); 40 mm ground clearance for SAM; 170 mm shielding height; and 100 mm minimum inscribed-circle diameter for the TRWB. Mathematical optimization defined the shape-following weeding cam (SWC) contour and TRWB dimensional chain. Kinematic/dynamic models were introduced alongside an adaptive sliding mode controller, ensuring lateral translation error convergence. A YOLOv8 model achieved 0.951 precision, 0.95 mAP50, and 0.819 mAP50-95, striking a balance between detection accuracy and localization precision. Field trials of the prototype showed 88.3% WRR and 2.2% SDR, meeting northern China’s agronomic standards. Full article

Recognizing the interrelationship among variables becomes critical in genetic breeding programs, where the goal is often to optimize selection for multiple traits. Conventional multi-trait models face challenges such as convergence issues, and they fail to account for cause-and-effect relationships. To address these challenges, we conducted a comprehensive analysis involving confirmatory factor analysis (CFA), Bayesian networks (BN), structural equation modeling (SEM), and genome-wide selection (GWS) using data from 195 arabica coffee plants. These plants were genotyped with 21,211 single nucleotide polymorphism markers as part of the Coffea arabica breeding program at UFV/EPAMIG/EMBRAPA. Traits included vegetative vigor (VV), canopy diameter (CD), number of vegetative nodes (NVN), number of reproductive nodes (NRN), leaf length (LL), and yield (Y). CFA established the following latent variables: vigor latent (VL) explaining VV and CD; nodes latent (NL) explaining NVN and NRN; leaf length latent (LLL) explaining LL; and yield latent (YL) explaining Y. These were integrated into the BN model, revealing the following key interrelationships: LLL → VL, LLL → NL, LLL → YL, VL → NL, and NL → YL. SEM estimated structural coefficients, highlighting the biological importance of VL → NL and NL → YL connections. Genomic predictions based on observed and latent variables showed that using VL to predict NVN and NRN traits resulted in similar gains to using NL. Predicting gains in Y using NL increased selection gains by 66.35% compared to YL. The SEM-GWS approach provided insights into selection strategies for traits linked with vegetative vigor, nodes, leaf length, and coffee yield, offering valuable guidance for advancing Arabica coffee breeding programs. Full article