Search Results (278)

Efficient froth flotation of fine smithsonite from slime-containing zinc oxide ores remains challenging due to low particle–bubble collision efficiency and strong surface hydration. Conventional agglomeration methods suffer from high reagent costs, non-selective agglomeration, or reduced surface hydrophobicity. Herein, non-hydrolyzable alkali metal salts, exemplified by NaCl, were introduced as novel and efficient coagulants to enhance the flotation of fine smithsonite, and the underlying mechanisms were systematically elucidated. In the sodium oleate flotation system, alkali metal ions promoted the formation and agglomeration of oleate micelles. Meanwhile, they significantly facilitated collector adsorption onto the smithsonite surface and improved the hydrophobicity of the mineral particles. At high ionic strengths, compression of the electrical double layer reduced the Zeta potential and interparticle electrostatic repulsion. These synergistic mechanisms promoted the growth and stability of hydrophobic aggregates, increasing their collision and attachment efficiency with bubbles. By employing non-hydrolyzable salts, the loss of surface hydrophobicity typically induced by conventional hydrolyzable coagulants was avoided. Validation tests on an industrial zinc oxide ore confirmed the feasibility of this approach, offering a promising pathway to mitigate zinc resource losses and associated environmental hazards. Full article

Fluid–structure interaction (FSI) research is crucial for applications in fields such as naval engineering, geological hazards, and biomechanics. Traditional grid-based methods (such as CFD) often face challenges in simulating large-deformation flow fields and complex boundary conditions, where mesh distortion can compromise simulation accuracy. Building upon the DualSPHysics5.2 framework, this study leverages the strengths of weakly compressible SPH (WCSPH) in modeling free surface flows and large-deformation fluids, as well as the discrete element method (DEM), for accurately describing particle collisions and fragmentation behaviors. We propose an improved MSPH-DEM coupling algorithm that incorporates moving least squares (MLS) correction for kernel function gradient optimization. This algorithm utilizes MLS-based gradient correction to achieve smoother fluid surfaces as well as bidirectional coupling between fluids and particles. Experimental validation demonstrates that in dam break simulations, this method reduces pressure errors. In the dam break impacting a cube experiment, it enhances accuracy, while in the dam break impacting a baffle experiment, the horizontal displacement of marker points closely aligns with the experimental values from Liao et al. This approach effectively improves the accuracy of the simulations of FSI problems, offering a more reliable numerical simulation methodology for engineering applications such as geological hazard prevention. Full article

The modelling of kilonova spectra, particularly in the late-time nebular phase, relies heavily on accurate atomic data. While significant progress has been made regarding energy levels and radiative transition rates for r-process elements, data for collisional processes remains scarce. Current models often rely on the Van Regemorter and Axelrod approximations for effective collision strengths. In this work we present the calculation of electron-impact excitation (EIE) collision strengths for relevant r-process elements. We employ the Independent-Process, Isolated-Resonance Distorted-Wave (IPIRDW) approximation to account for the contribution of resonant excitation, which is crucial at the low temperatures characteristic of kilonovae. We demonstrate the validity of our method by benchmarking against R-Matrix calculations for Te iii, finding good agreement while maintaining a significantly lower computational cost. We focus on the relevant 2.1 μm feature and estimate a mass of 2.6× 10 − 3 M ⊙ using our IPIRDW data for EIE effective collision strengths, compatible with other recent estimations using R-Matrix data. Full article

This paper presents a software application developed in Python (v3.9) for obstacle avoidance trajectory planning in the RoboDK (v6.0) virtual environment. The proposed method automatically scans the virtual station, identifies obstacles, discretizes the workspace into a three-dimensional free-space-graph (FSG) and searches for candidate routes between start and finish points. Each route is then verified at the robot level by inverse kinematics and collision control, and the validated solutions can be transformed into preview curves, intermediate points and motion programs executable by the robot. The study includes an initial test scenario performed with the ABB IRB 6650-125/3.2 robot and randomly generated obstacles, followed by a series of benchmark tests performed in different virtual scenarios and with four different robot models. In the comparative tests, the proposed method was evaluated together with a rapidly exploring random tree (RRT) reference planner and the native probabilistic roadmap (PRM) planner, embedded RoboDK. The final scenario included a robotic cell with realistic objects. The results show that the application can identify valid executable routes and, in some cases, several alternative variants for the same pair of target points. Overall, the benchmark suggests that the analyzed methods have different strengths and should be viewed as complementary solutions. In the tested scenarios, RRT was the method with the lowest computational times, while the proposed method offered the possibility of generating several alternative routes. At the current stage, the application can thus be used as an offline programming tool but also as a research and analysis tool for planning robotic trajectories in the presence of static obstacles. Full article

Tactical athletes, including military service members, are exposed to occupational demands that increase their risk of mild traumatic brain injury (mTBI), particularly through blast exposure, falls, collisions, and repeated sub-concussive events. Although clinical tools and progressive return-to-activity protocols support acute management, recovery may remain fragmented when physical, cognitive, psychological, and performance domains are not integrated. Military personnel require recovery models which extend beyond symptom resolution and return-to-duty clearance. Holistic performance programming offers a multidimensional framework which incorporates subject matter experts across strength and conditioning, rehabilitation, nutrition, behavioural health, cognitive performance, and human performance optimisation. This narrative review examines the role of holistic performance programming in optimising recovery from mTBI among tactical athletes, with emphasis on interdisciplinary care, structured assessment, recovery periodisation, monitoring technologies, and return-to-duty readiness. The role of embedded subject matter experts in identifying and monitoring mTBI; interdisciplinary care models which integrate clinical and performance expertise; structured recovery pathways from assessment to reintegration; and the importance of flexibility, communication, and service member engagement are examined. In addition, the review assesses the potential use of biomarkers, wearable technologies, and multi-domain assessment tools to guide individualised recovery. Holistic performance programming may bridge the gap between clinical recovery and operational readiness following mTBI. By integrating physical, cognitive, psychological, nutritional, and sleep-related strategies, this approach may reduce fragmented care and better address the complex nature of mTBI recovery. Interdisciplinary performance teams may improve early recognition, individualised rehabilitation, safer return-to-duty decisions, and long-term readiness. Future practice should prioritise standardised assessment, real-time monitoring, education, and stigma reduction. Full article

Anti-collision detection technologies primarily rely on optical, radar, or laser sensors; however, their performance often deteriorates severely under adverse weather conditions (e.g., rain, snow, fog) or in scenarios involving visual occlusion. By contrast, magnetic anomaly detection leverages perturbations in the geomagnetic field induced by target objects (e.g., vehicles, metallic obstacles), exhibiting intrinsic all-weather operability and strong anti-interference capability. Nevertheless, conventional magnetic anomaly detection methods suffer from the limited applicability of the magnetic dipole model, which only affords coarse positioning accuracy and is predominantly suited for long-range targets. To address this limitation, this paper proposes an Extended N-th-Pole Magnetic Dipole (E-NMD) model that improves accuracy by analyzing the Lagrangian cosine term and rigorously constraining truncation errors under specific operational conditions. Experimental results demonstrate that, for steel with a relative permeability of 200, the model achieves a fitting variance of 99.87%. Furthermore, to overcome the inversion difficulties arising when the strength of short-range magnetic anomalies is comparable to sensor noise, an Adaptive Iterative Extended Kalman Filter (AI-EKF) is developed to enable robust noise suppression and precise distance estimation. Results indicate that E-NMD outperforms the traditional N-th-Pole Magnetic Dipole (NMD) model in proximal state estimation, achieving a 39.62% reduction in Root Mean Square Error (RMSE). Finally, in light of parameter uncertainty in magnetic anomaly targets under real-world conditions, a Dual-Mode Pairwise Iterative Extended Kalman Filter (DI-EKF) is introduced to jointly estimate parameters and system states, yielding an 89% reduction in RMSE compared to AI-EKF. Full article

Air–ground collaborative systems leverage the complementary strengths of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) and hold significant potential for logistics in complex, unstructured environments. However, trajectory planning in infrastructure-free mountainous regions remains challenging owing to the need for continuous tight coupling, obstacle avoidance, and reliable communication-link maintenance. To address these challenges, this study proposes a cooperative trajectory planning framework that enforces strict inter-vehicle distance constraints to maintain communication connectivity. By formulating the coordination problem in terms of relative configurations between air and ground vehicles, the proposed framework exhibits translational invariance, reflecting an underlying symmetry with respect to global position shifts. This symmetry-aware formulation reduces reliance on absolute coordinates and promotes consistent cooperative behavior under environmental variability. The trajectory planning problem is mathematically formulated as a constrained multi-objective nonlinear programming (MONLP) model that balances energy consumption and trajectory smoothness. An adaptive inertia weight particle swarm optimization (AIWPSO) algorithm is developed to efficiently solve the resulting optimization problem. Simulation results demonstrate that the proposed approach generates smooth, collision-free trajectories while maintaining stable air–ground coordination, demonstrating improved feasibility and robustness over conventional planning methods in unstructured mountainous environments. Full article

Hot-filament chemical vapor deposition (HFCVD) facilitates the realization of industrial mass production owing to its simple synthesis device, facile control of process conditions, and low preparation cost. Reactive pressure is one of the deposition parameters that exert a profound influence on the growth of HFCVD diamond films on polycrystalline diamond (PCD) substrates, primarily affecting the growth rate and grain size of the deposited diamond coating. A univariate experimental approach was employed to investigate the effects of reactive pressure (2 kPa, 3 kPa, 4 kPa, 5 kPa) on the properties of as-deposited diamond films. The results show that with the increase in reactive pressure, the growth rate increased first and then decreased, peaking at 5.366 μm/h at 3 kPa. The fractal dimension and grain size follow a similar variation trend, both decreasing first and then increasing. The grain size drops to 15.8 nm when the reactive pressure is 3 kPa, at which point the adhesive strength of the film is maximized. This phenomenon can be attributed to the fact that excessively low reactive pressure extends the mean free path of particles and active species, endowing them with higher kinetic energy and reducing collision-induced energy loss. This in turn significantly promotes diamond nucleation, secondary nucleation and grain refinement, thus facilitating the growth of nanocrystalline diamond. In contrast, an excessively high pressure yields the opposite effect, inhibiting nucleation and promoting grain coarsening. Full article

Low-Power Wide-Area Networks (LPWANs) like LoRaWAN enable IoT applications with low-power and long-range characteristics. While LoRaWAN class B mode is server-initiated downlink communication-oriented, its uplink communication, especially in mobile scenarios, remains underexplored. This paper proposes two novel systems, Edge-based SmartSync and Gateway-based SmartSync, aiming to enhance uplink by leveraging class B synchronization. Edge-based SmartSync enables end devices to dynamically adjust the Spreading Factor (SF) based on real-time Received Signal Strength Indicator (RSSI) from beacons, achieving a significant improvement in terms of packet delivery and energy consumption. Gateway-based SmartSync ensures the fair distribution of end devices across a lower SF to further enhance the efficiency of the system. The beacon is reengineered to convey sensitivity limits to end devices. The systems were implemented in the OMNeT++ simulator over a 25 km² area with 100–1000 mobile devices and evaluated against a baseline using metrics like the Packet Delivery Ratio, collisions, and energy consumption. The obtained results show that both systems are capable of improving the delivery ratio by over 40% and reducing collisions by 80% compared to the baseline, with energy savings exceeding 35%. Proposed systems offer cost-effective, adaptable solutions, paving the way for more reliable IoT deployments. Full article

This study investigates the influence of H13 steel substrate surface roughness on the corrosion behavior of CrAlN coatings in a 3.5 wt.% NaCl solution. The interfacial structure of the coatings and the evolution of corrosion products were characterized using electrochemical techniques, X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM). Results indicate that reducing the substrate surface roughness from 0.235 μm to 0.167 μm resulted in a proportional decrease in the coating’s critical load (Lc1), from 23.3 N to 17.3 N. Concurrently, the corrosion potential (Ecorr) shifted positively, the charge transfer resistance (Rct) increased significantly, and the corrosion current density (Icorr) decreased markedly. After 14 days of immersion, the most substantial positive shift in Ecorr was observed, moving from −1.038 V to −0.803 V (ΔE = 0.235 V). Rct increased dramatically from 2360 Ω·cm² to 2.772 × 10⁶ Ω·cm², representing an enhancement of two orders of magnitude. Icorr decreased from 7.003 × 10⁻⁵ A·cm⁻² to 1.182 × 10⁻⁶ A·cm⁻², corresponding to a reduction of 98%. Following 20 days of immersion, the sample with a substrate roughness of 0.214 μm exhibited corrosion damage to the underlying substrate. In contrast, the coating on the sample with a lower roughness (0.167 μm) remained relatively intact. Surface roughness directly governs collision, adsorption, and diffusion processes during coating deposition. While higher roughness enhances coating-substrate interfacial adhesion, it concomitantly increases surface porosity, ultimately compromising corrosion resistance. Therefore, practical applications necessitate a comprehensive optimization of coating adhesion strength and corrosion resistance, tailored to specific service environments. Full article

To address the lack of contact and breakage parameters in the discrete element method (DEM) simulation of potato seed cutting and sorting processes, this study took the ‘Atlantic’ potato seed as the research object and constructed a crushable potato model using EDEM. Through physical experiments, the mean average diameter, moisture content, density, Poisson’s ratio, and elastic modulus were measured. The coefficients of collision restitution, static friction, and rolling friction between the potato seed and the Q235 steel plate were determined as 0.576, 0.559, and 0.073, respectively. Using the actual repose angle of 22.89° as the response target, and combining the steepest ascent test with the Box–Behnken design, the non-cohesive contact parameters between potato seed particles were calibrated. The resulting coefficients of collision restitution, static friction, and rolling friction between particles were determined as 0.404, 0.412, and 0.0156, respectively. Finally, based on physical shear tests (maximum shear force 23.56 N), significant influencing factors were identified through Plackett–Burman screening as the bonding radius ratio r and the normal stiffness per unit area Kn. Using the steepest ascent test and the Box–Behnken response surface method, the key bonding parameters of the Bonding V2 model were calibrated as follows: r = 1.098, K_n = 8.597 × 10⁷ N·mm⁻³, tangential stiffness per unit area K_t = 3.250 × 10⁶ N·mm⁻³, critical compressive stress σ_n = 5.500 × 10⁵ Pa, and shear strength τ_t = 3.000 × 10⁴ Pa. The relative error between the simulated and actual maximum shear forces was 0.89%, which is small. The calibrated flexible model accurately represents the physical characteristics of potato seeds and provides a reliable reference for the design of mechanized potato seed cutting and sorting equipment. Full article

Knee biomechanical demands vary across different sports due to sport- and position-specific patterns of muscle recruitment. To return to performance, athletes must adequately restore knee kinematics to regain control over the same sport mechanics that led to the initial anterior cruciate ligament (ACL) injury. ACL graft selection should therefore minimize donor site morbidity and support sport-specific demands. This study aims to address the available evidence and guide surgical graft choice in athletes. A literature search of PubMed, MEDLINE, Scopus, and Web of Science (up to September 2025) assessed BPTB, hamstring, and quadriceps tendon autografts. Outcomes included revision, graft survival, return to sport, time to return, PROMs, anterior knee pain, donor site morbidity, and prognostic factors (age, sex). Sports were classified as pivoting, contact/collision, or endurance/non-pivoting. The results were synthesized narratively. In pivoting and cutting sports, bone–patellar tendon–bone (BPTB) autografts offer high survival rates but are associated with a high incidence of anterior knee pain, which is a substantial drawback in kneeling or flexion-intensive sports. Hamstring tendon (HT) grafts carry higher revision rates in female and younger patients, though they have low donor site morbidity that does not appear to affect long-term athletic performance. Quadriceps tendon (QT) grafts are emerging as a promising option for pivoting athletes. However, conflicting results indicate that the revision risk is comparable to that of HT grafts and possible long-standing extensor mechanism weakness. Contact and collision sports demonstrate similar trends, but kneeling and contact injuries are more common in this group. Thus, while prioritizing powerful hamstring strength, anterior knee pain symptoms should still be carefully considered. The diameter of the HT autograft should exceed 7.5 mm to ensure comparable revision outcomes with BPTB. QT grafts remain a limited-evidence attractive option. Endurance and non-pivoting athletes require fewer pivoting mechanics but rely heavily on muscle symmetry and repetitive motion. BPTB grafts are less suitable in this category due to alterations in sprint mechanics, muscle asymmetry, and repetitive patellofemoral joint loading. HT grafts provide favorable rates of return to sport, whereas evidence regarding QT graft use in non-pivoting athletes remains limited. Full article

The utilization of biopolymers as raw materials for the development of sustainable materials has become one of the most promising strategies to minimize the negative impact of plastic pollution. Tubers such as purple yam are rich in starch, which serves as the main component for producing strong and durable bioplastics with properties comparable to conventional plastics. In this study, purple yam flour was used as a raw material to develop a biodegradable film through the casting method. Additionally, Flour Nanoparticles (FN) extracted via the Aqueous Counter Collision technique were incorporated to enhance the mechanical, morphological, and barrier properties of the films. The nanoparticles exhibited sizes below 100 nm, as determined by DLS analysis. The casting process was carried out using film solutions containing 2 wt% flour and 15 wt% glycerol, with FN concentrations of 5 wt%, 15 wt%, and 25 wt%. The main results showed that the films with 25 wt% FN displayed improved mechanical strength, increasing from 2.2 MPa (control) to 7.3 MPa, as well as enhanced thermal resistance, rising from 68 °C (control) to 102 °C. The films also exhibited a smoother morphology, indicating improved water vapor transmission (WVT). The incorporation of FN thus contributed to the development of films with reduced hydrophobicity. Full article

Non-crimp fabric (NCF) composites are increasingly adopted for structural components due to their high mechanical performance and processability. Like other fibre-reinforced plastics, NCFs remain vulnerable to in-service damage from tool drops or unintended collisions, which can substantially reduce load-bearing capacity. This study aimed to develop a validated numerical model capable of simulating damage initiation and post-impact behaviour through an integrated experimental–numerical approach. The mechanical properties of a representative unidirectional NCF composite were first experimentally established. Then, tubular NCF subcomponents were fabricated and tested under a two-phase loading protocol. In the first phase, damage was introduced using quasi-static indentation or controlled low-velocity impact. In the second phase, the residual load-bearing capacity of the damaged subcomponents was assessed under four-point bending. To support the research objective, a finite element model was developed in LS-DYNA to simulate both phases, using the MAT_ENHANCED_COMPOSITE_DAMAGE (MAT54) material formulation. Non-measurable input parameters, including stress limit factors and erosion strain thresholds, were calibrated via parameter estimation, sensitivity analysis, and iterative refinement. The final model showed close agreement with experiments in predicted damage location, deformation mode, and residual strength. X-ray computed tomography was used to validate delamination predictions. The findings support the development of reliable and cost-effective numerical tools for damage assessment in advanced composite structures. Full article

Harmful algal blooms pose significant risks to water resource management and aquatic ecosystem health, rendering early detection of algal bloom proliferation essential. In this study, we present an electrochemical strategy for the real-time detection of individual Chlorella cells using the single-particle collision method at an ultramicroelectrode (UME). The detection principle relies on monitoring changes in the redox probe flux at the UME induced by attachment of the target. Both diffusional and migrational transport were considered to promote particle collision at the UME. Detection sensitivity for negatively charged microalgae was enhanced by exploiting migration effects. To control migration strength, neutral and charged redox probes were selected, and the ionic strength was adjusted to tune electrostatic attraction, yielding microalgae capture on the UME with a collision frequency that depended on the solution composition. Conversely, migration was suppressed by increasing the ionic strength, and inverse migration was implemented, and resulting collision responses were compared. Furthermore, COMSOL Multiphysics simulations were used to estimate the size of detected Chlorella cells. The collision frequencies expected from diffusion and migration were compared with the experimental values, and a calibration curve relating collision frequency to Chlorella concentration was established. Consequently, this methodology provides a promising platform for the early monitoring of algal blooms by simultaneously determining microalgal size and concentration. Full article