Search Results (8,508)

Aiming at the challenge of simultaneously optimizing ride comfort and wheel grounding performance for mining dump trucks under severe road conditions, this paper proposes a hydro-pneumatic suspension parameter design method based on an improved multi-objective particle swarm optimization (IMOPSO) algorithm. First, a dynamic model of the hydro-pneumatic suspension is established, incorporating the coupled nonlinear characteristics of the valve system and the gas chamber. The accuracy of the model is verified through bench tests. Subsequently, the influence of key parameters, including the damping orifice diameter, check valve seat hole diameter, and initial gas charging height, on the vertical dynamic performance of the vehicle, is systematically analyzed. On this basis, a multi-objective optimization model is constructed with the objective of minimizing the root mean square (RMS) values of both the sprung mass acceleration and the dynamic tire load. To enhance the global search capability and convergence performance of the MOPSO algorithm, adaptive inertia weighting, dynamic flight parameter update, and an enhanced mutation strategy are introduced. Simulation results demonstrate that the optimized suspension achieves significant improvements under various road conditions. On class-C roads, the RMS values of the sprung mass acceleration (SMA) and the dynamic tire load (DTL) are reduced by 37.6% and 15.8%, respectively, while the suspension rattle space (SRS) decreases by 10.2%. Under transient bump roads, the peak-to-peak (Pk-Pk) values of the same two indicators drop by 38.9% and 44.9%, respectively. Furthermore, compared to the NSGA-II algorithm, the proposed method demonstrates superior performance in terms of convergence stability and overall performance balance. These results indicate that the proposed design effectively balances ride comfort, wheel grounding performance, and driving safety. This study provides a theoretical foundation and an engineering-feasible method for the performance balancing and parameter co-design of suspension systems in heavy-duty engineering vehicles. Full article

Chrysopa pallens Stephens (Neuroptera: Chrysopidae) is a key predatory species in cotton agroecosystems. This study investigated the prey-mediated sublethal effects of imidacloprid and nitenpyram at low concentrations (LC₂₀), on C. pallens when exposed via consumption of contaminated prey, assessing impacts on its development and predatory function. C. pallens is a key predatory species in cotton agroecosystems. This study investigated the prey-mediated sublethal effects of imidacloprid and nitenpyram (LC₂₀) on the developmental performance and predatory capacity of C. pallens. Leaf-dipping bioassays were used to assess the toxicity of imidacloprid and nitenpyram to Aphis gossypii Glover (Hemiptera: Aphididae). Age-stage, two-sex life table analysis was conducted to evaluate their subsequent effects on the life history traits and predation performance of C. pallens. Imidacloprid was more toxic to A. gossypii than nitenpyram. Sublethal exposure marginally prolonged larval development, but the effect was not statistically significant. Both insecticides significantly extended the pupal stage, with nitenpyram inducing a greater delay. Imidacloprid markedly increased adult longevity, and both compounds significantly reduced female fecundity. Imidacloprid also suppress predatory behavior more potently, decreasing daily adult consumption and reducing first-instar attack rates by approximately 30%. Although all treatments followed a Holling type II functional response, both insecticides increased handling time and reduced searching efficiency. Overall, imidacloprid primarily inhibited predatory performance, whereas nitenpyram more strongly prolonged development and reduced critical population growth parameters. These findings provide essential evidence for ecological risk assessment and for refining the incorporation of natural enemies into cotton integrated pest management (IPM) strategies. Full article

For Co-N-C materials prepared under high-temperature calcination conditions, the formation of Co nanoparticles occurs when the metal loading exceeds 2%. Typically, CoNx is regarded as the primary active site of the catalyst, while Co nanoparticles are considered to possess limited catalytic activity. Consequently, within Co-N-C materials, Co nanoparticles are often likened to ‘ballast stone’ in a catalyst. In the model reaction of formic acid dehydrogenation, we incorporated boron into the precursor, thereby enhancing the electronic metal-support interactions (EMSI) between Co nanoparticles and carbon carriers. Consequently, this modification resulted in a catalytic performance of Co nanoparticles that was comparable to that of Co single-atom catalysts (SACs). Full article

In this study, we investigated the encapsulation of β-carotene (β-CE) within sunflower oil body (SFOB) emulsions and examined the role of xanthan gum (XG) in enhancing stability and digestion behavior. The optimal conditions were heating at 45 °C for 15 min, ultrasonic treatment at 270 W for 20 min, and homogenization at 80 MPa, achieving encapsulation efficiency (EE) up to 92%. Furthermore, XG was incorporated to improve structural, oxidative, thermal, and digestive stability. More than 1.5% XG enhanced absolute value of zeta potential (21.3 mV to 23.7 mV), reduced particle size (6.52 μm), and prevented phase separation. XG-coated emulsions exhibited improved stability under heating and oxidative conditions. Additionally, XG enhanced protein digestibility and lipid hydrolysis, as well as the bioaccessibility of β-CE during gastrointestinal digestion. The XG coating also improved photostability under sunlight and UV exposure, with 2% XG emulsions showing the least degradation of β-CE. Moreover, the 2% XG emulsion demonstrated the highest release of free fatty acids (85.75%) and β-CE utilization (80%). These results highlight the potential of SFOB-XG emulsions for the effective delivery of lipophilic bioactives. Full article

This study used a Systemic Modeling technique, based on the methodologies of Churchman and Ackoff, to integrate and assess the subsystems regulating the functionality of a Rocket Candy (R-Candy) motor. The nozzle and combustion chamber design was improved using a five-phase systemic architecture to assure the coherent interplay of essential factors, including pressure, temperature, and velocity fields. The principles of experimental rocketry are elucidated through the examination of impulse performance throughout class A to class C engines. A preliminary design was developed in SolidWorks 2024, incorporating the engine’s three main components: the igniter, the combustion chamber, and a convergent–divergent nozzle that enhances the acceleration of the exhaust gases. The system model was validated using simulations in FEATool and verified through experimentation. This allowed for the analysis of fluid behavior, as well as the geometry of the structures, initial parameters, and boundary conditions. The results demonstrate a strong correlation between the simulations and the experimental data, with discrepancies of less than 1.5%, confirming the reliability and feasibility of the nozzle design. The findings indicate that systemic modeling, in conjunction with CFD and experimentation, can provide a strategic framework for iterative refinement, optimization of key performance metrics, and the development of cost-effective, high-performance R-Candy engines for educational and experimental purposes. Full article

The detection of defects in key transmission-line equipment under complex environments often suffers from insufficient accuracy and reliability due to background interference and multi-scale feature variations. To address this issue, this paper proposes an improved defect detection model based on YOLOv11, named DCDW-YOLOv11. The model introduces deformable convolution C2f_DCNv3 in the backbone network to enhance adaptability to geometric deformations of targets, and incorporates the convolutional block attention module (CBAM) to highlight defect features while suppressing background interference. In the detection head, a dynamic head structure (DyHead) is adopted to achieve cross-layer multi-scale feature fusion and collaborative perception, along with the WIoU loss function to optimize bounding box regression and sample weight allocation. Experimental results demonstrate that on the transmission-line equipment defect dataset, DCDW-YOLOv11 achieves an accuracy, recall, and mAP of 94.4%, 92.8%, and 96.3%, respectively, representing improvements of 2.8%, 7.0%, and 4.4% over the original YOLOv11, and outperforming other mainstream detection models. The proposed method can provide high-precision and highly reliable defect detection support for intelligent inspection of transmission lines in complex scenarios. Full article

In this study, avocado seed (AS) waste was used as a feedstock for bacterial cellulose (BC) production. Global avocado consumption continues to rise due to its recognised health benefits, resulting in substantial amounts of waste generated by the avocado processing industry. This work proposes the efficient utilisation of avocado seed residues—rich in fermentable sugars—to enhance the economic viability of BC production while supporting responsible agro-industrial waste management. Hydrolysed avocado seeds were incorporated into a modified Hestrin–Schramm (MHS) medium for BC production using Komagataeibacter xylinus as the bacterial strain. The BC membranes obtained from the modified medium (BC-MHS) exhibited higher production (1.93 g/L) and productivity (0.19 g/L·day) compared with those produced in the standard HS medium (BC-HS). The morphology and nanofibre diameter (11–85 nm) of the resulting BC were not significantly affected; however, BC-MHS showed higher crystallinity (~78%) and a higher degradation temperature (~357 °C) than BC-HS. Conversely, the modified medium slightly reduced the mechanical performance of the BC in terms of elongation at break, tensile strength, and Young’s modulus. Overall, avocado seed waste was successfully transformed into a value-added material, demonstrating its potential for agro-industrial waste valorisation through scalable and sustainable biorefinery processes. Full article

As the threat from malicious UAVs continues to intensify, accurate identification of individual UAVs has become a critical challenge in regulatory and security domains. Existing single-signal analysis methods suffer from limited recognition accuracy. To address this issue, this paper proposes a high-precision individual identification method for UAVs based on FFS-SPWVD and DIR-YOLOv11. The proposed method first employs a frame-by-frame search strategy combined with the smoothing pseudo-Wigner–Ville distribution (SPWVD) algorithm to obtain effective time–frequency feature representations of flight control signals. Building on this foundation, the YOLOv11n network is adopted as the baseline architecture. To enhance the extraction of time–frequency texture features from UAV signals in complex environments, a Multi-Branch Auxiliary Multi-Scale Fusion Network is incorporated into the neck network. Meanwhile, partial space–frequency selective convolutions are introduced into selected C3k2 modules to alleviate the increased computational burden caused by architectural modifications and to reduce the overall number of model parameters. Experimental results on the public DroneRFb-DIR dataset demonstrate that the proposed method effectively extracts flight control frames and performs high-resolution time–frequency analysis. In individual UAV identification tasks, the proposed approach achieves 96.17% accuracy, 97.82% mAP50, and 95.29% recall, outperforming YOLOv11, YOLOv12, and YOLOv13. This study demonstrates that the proposed method achieves both high accuracy and computational efficiency in individual UAV recognition, providing a practical technical solution for whitelist identification and group size estimation in application scenarios such as border patrol, traffic control, and large-scale events. Full article

This study experimentally demonstrates that a bentonite–vermiculite composite (1:2 mass ratio) is the most effective formulation for the treatment of crude oil–contaminated wastewater. The sorbents were characterized using XRD, SEM/EDS, ζ-potential, DLS, and TGA/DSC to evaluate their structural, surface, and adsorption-related properties. Kinetic analysis showed that the adsorption process followed the pseudo-second-order (PSO) model (R² = 0.96–0.99), suggesting that surface interactions and intraparticle diffusion within the layered composite governed the overall adsorption rate. Thermodynamic analysis revealed negative Gibbs free energy values (ΔG < 0) and a moderately positive enthalpy change (ΔH ≈ 26 kJ·mol⁻¹), confirming that adsorption is spontaneous and endothermic, with contributions from physical interactions, ion exchange, and hydrophobic effects. After adsorption, the ζ-potential shifted toward less negative values, indicating partial surface charge neutralization by hydrocarbon species. TGA/DSC data further confirmed strong oil retention and preserved structural stability of the sorbents, while the DSC-derived enthalpy increased from 2.0 kJ·g⁻¹ to 141.6 kJ·g⁻¹ after hydrocarbon uptake, indicating pronounced energetic effects associated with sorbate incorporation. Techno-economic evaluation under industrially relevant conditions (Q = 120,000 L·h⁻¹; C₀ = 392 mg·L⁻¹) showed effective oil removal to residual concentrations below regulatory discharge limits at a low treatment cost. Full article

Reducing emissions of nitrogen compounds represents a significant challenge in environmental protection, and catalytic treatment is an effective approach. Carbon-based catalysts offer a promising alternative by exploiting the redox properties of carbon materials and eliminating the need for external reducing agents. In this study, nitrogen-free and nitrogen-doped activated carbons were used for NO reduction. The catalysts were developed by incorporating transition metals (Cu and Fe), alkali metals (K), and bimetallic Cu-K formulations. The addition of K to Cu and the presence of nitrogen functionalities improved the catalytic performance and an optimum Cu/K ratio was identified. The best-performing catalyst, AC_M_BM@5Cu5K, achieved 100% NO conversion at 410 °C, producing mainly N₂ and CO₂, while N₂O was detected as an intermediate and CO was not observed. The catalyst’s stability was evaluated in a 100 h continuous test at 376 °C, during which the catalyst maintained approximately 90% NO conversion for 40 h before deactivation. The deactivation mechanism is discussed in detail. Full article

This paper proposes a valorization approach for solid lavender residue, a by-product of the essential oil industry. The biomass residue was carbonized at atmospheric pressure and two temperatures (450 °C and 650 °C), followed by solvothermal modification with zinc ions (Zn²⁺, 3 and 5 mmol). The effects of temperature and Zn²⁺ incorporation on the elemental composition and morphology of the resulting biochar were examined using X-ray Fluorescence (XRF), Fourier Transform Infrared (FTIR) spectroscopy, and Scanning Electron Microscopy/Energy-Dispersive X-ray Spectroscopy (SEM/EDS) analyses. The applied Zn²⁺ modification was effective at both concentrations for the biochar obtained at both carbonization temperatures. However, a more uniform metal ion distribution was observed at 3 mmol, while at 5 mmol, a partial particle agglomeration occurred. Progressive degradation of the O–H, C=O, and C–O groups with increasing temperature and the presence of Zn–O-related interactions was observed. The results demonstrated consistent and reproducible trends, suggesting that controlled carbonization combined with Zn²⁺ incorporation can convert lavender residues into modified carbonaceous materials. Full article

Background/Objectives: Brucella is a major global One Health threat, causing an estimated 2.1 million human infections and substantial livestock losses annually, with no vaccine currently available for humans, underscoring the urgent need for a safe and effective vaccine. Methods: Employing a reverse vaccinology approach, a novel 175-mer multiepitope vaccine (Mvax) targeting Brucella FrpB was computationally designed in this study, incorporating two B-cell, two MHC class I (MHC-I), and three MHC class II (MHC-II) epitopes selected for their high predicted antigenicity, safety, and IFN-γ-inducing potential. Human β-defensin-3 (hBD3) was fused to the N-terminus as an adjuvant, followed by comprehensive in silico evaluation of the construct. Results: Population coverage analysis predicted 99.59% global MHC class I/II coverage for selected epitopes. In silico analyses predicted that Mvax has high solubility (Protein-SOL score: 0.808), a high antigenicity score (VaxiJen: 1.06), and a negative GRAVY index (−0.881), indicating favorable predicted physicochemical characteristics. iMODS, CABS-Flex 3, and molecular dynamics simulations suggested theoretical stability trends for the modeled vaccine complexes. C-ImmSim immune simulations further predicted elevated Th1 cell populations and associated cytokines (IL-12, IFN-γ, IL-2) following both single and multiple simulated Mvax exposures. Conclusions: The computational analyses described here provide a theoretical modeling basis for an antivirulence multi-epitope vaccine design against human brucellosis, with predicted metrics and simulated immune responses requiring empirical validation. Full article

Background: Superior cluneal nerve (SCN) entrapment is frequently underrecognized as a contributor to chronic Low Back Pain (cLBP) and gluteal pain. Musculoskeletal ultrasound may reveal surrogate markers indicative of a biomechanical entrapment environment. The primary objective was the prevalence of the ultrasound marker triad (Copeman Nod-ules-CN, thoracolumbar fascia-TLF thickening > 3 mm, and iliac enthesophytes. Secondary objectives included mean TLF thickness and its correlation with numeric pain rating scale (NPRS) and Douleur Neuropathique en 4 questions scores (DN4). Methods: In this single-center, cross-sectional observational pilot study, we enrolled 12 patients with cLBP (>12 weeks) localized to the SCN distribution and a healthy control group (12). Ultrasound measurements included TLF thickness in longitudinal and transverse planes, TLF convexity loss, iliac crest enthesophytes, and CN. Statistical analyses comprised Mann-Whitney U test, Fisher exact test, Spearman rank correlation, and multivariate logistic regression. Significance was set at p < 0.05. Results: The ultrasound marker triad (CN, iliac enthesophytes, and TLF thickening > 3 mm) demonstrated high diagnostic specificity: individually, CN were present in 91.7% of patients vs. 8.3% of controls (p < 0.001), iliac enthesophytes in 58.3% vs. 0% (p = 0.005), TLF thickening > 3 mm in 41.7% of patients vs. 0% of controls (p < 0.001)and TLF convexity loss in 100% vs. 75% (p = 0.03). Mean TLF thickness was significantly greater in patients—3.53 ± 0.46 mm longitudinal and 3.42 ± 0.39 mm transverse—compared with controls (2.61 ± 0.28 mm and 2.50 ± 0.32 mm; both p < 0.001). TLF thickness correlated strongly with NPRS (Spearman rho = 0.825; p = 0.001) but not with DN4. Logistic regression demonstrated that the marker triad accounted for 67% of NPRS variance (R² = 0.67). Conclusions: Ultrasound-detected fascial alterations and enthesopathic changes act as reliable surrogate markers for SCN entrapment and correlate strongly with nociceptive pain severity. The absence of correlation with neuropathic pain scores suggests a predominant fascial-muscular biomechanical mechanism rather than direct nerve damage. Incorporating this non-invasive protocol into clinical practice may enhance diagnostic precision and inform targeted rehabilitative strategies. Future multicenter, prospective studies with larger cohorts are warranted to validate these findings and establish standardized ultrasound criteria. Full article

In this paper, a wireless network architecture is considered that combines double intelligent reflecting surfaces (IRSs), energy harvesting (EH), and non-orthogonal multiple access (NOMA) with cooperative relaying (C-NOMA) to leverage the performance of non-line-of-sight (NLoS) communication mainly and incorporate energy efficiency in next-generation networks. To optimize the phase shifts of both IRSs, we employ a machine learning model that offers a low-complexity alternative to traditional optimization methods. This lightweight learning-based approach is introduced to predict effective IRS phase shift configurations without relying on solver-generated labels or repeated iterations. The model learns from channel behavior and system observations, which allows it to react rapidly under dynamic channel conditions. Numerical analysis demonstrates the validity of the proposed architecture in providing considerable improvements in spectral efficiency and service reliability through the integration of energy harvesting and relay-based communication compared with conventional systems, thereby facilitating green communication systems. Full article

As a pivotal component of the global carbon cycle, the spatial variation in soil respiration (Rs) is crucial for forecasting climate change trajectories. Despite extensive research on the spatial patterns of total Rs, the distinct drivers of its two components, heterotrophic respiration (Rh) and autotrophic respiration (Ra), are still not well defined. We compiled a global dataset from studies published between 2007 and 2023 to investigate the drivers of spatial variations in Rs, Ra, and Rh. This dataset comprises 308 annual flux measurements from 172 sites. The results showed that Rh contributed 63% and 60% to Rs in forest and grassland ecosystems, respectively. Further analyses using structural equation modelling (SEM) showed that the spatial variation in Rh and Ra exhibited divergent responses to climatic factors and plant community structure (mostly driven by gross primary production, GPP). Rh was more affected by mean annual temperature (MAT) than by mean annual precipitation (MAP), with standardized total effects of 0.17 (forests) and 0.57 (grasslands) for MAT versus 0.10 and 0.07 for MAP, respectively. In contrast, Ra exhibited greater sensitivity to MAP (0.08 and 0.18) than to MAT (−0.01 and 0.04). GPP exerted biome-specific effects: in forests, high GPP enhanced Rh (0.18) more substantially than Ra (0.08), while in grasslands, elevated GPP significantly increased Ra (0.34) but suppressed Rh (−0.30). Moreover, these variables incorporated into the SEMs accounted for a greater proportion of the variation in Rh and Ra in grasslands (R² = 0.73 for Rh, 0.48 for Ra) as compared to forests (R² = 0.21 for Rh, 0.22 for Ra), suggesting the greater complexity in forest soil C dynamics. By using the whole yearly measured soil respiration data around the world, this study highlights the differential environmental regulation of Rh and Ra, providing critical insights into the mechanisms governing Rs variations under climate change. Full article