This study investigated the improvement of the pozzolanic activity of pumice, perlite, and farin through mechanochemical activation (MCA). The properties of the materials were determined by performing XRF, XRD, and particle size and specific surface area analyses. The MCA of three different materials sourced from Türkiye was performed using a planetary ball mill, and their pozzolanic reactivity was systematically investigated. R3 test (bound water measurement) and strength activity index (SAI) test were used to evaluate pozzolanic activity. Based on the results, following MCA, the crystal structure was significantly disrupted, particularly in perlite and pumice, and the amount of amorphous phase increased more compared to farin, as confirmed by the decrease in XRD peak intensities. The amount of bound water tended to increase by increasing grinding time and grinding speed. The highest amount of bound water (7.5%) was obtained by grinding the pumice sample at 500 rpm, with ball-to-powder ratio (BPR) of 10 for 60 min. For the same material, the highest activity index (106%) was determined at 500 rpm, with a BPR of 15 and a grinding time of 60 min. In the perlite sample, the highest amount of bound water (7.07%) and the highest strength activity index (98%) were measured in the sample ground at 500 rpm for 60 min with a BPR of 15. In the farin sample, the highest amount of bound water (3.40%) was obtained at 500 rpm for 40 min with a BPR of 15, while the highest strength activity index (71.05%) was observed at 500 rpm for 40 min with a BPR of 10. The results show that the applied MCA process increases the activity of the materials.

Single-crystal 4H-SiC, as a wide-bandgap semiconductor material, has become a key substrate for high-power electronics and radio frequency devices due to its outstanding characteristics such as high-voltage tolerance, high-temperature stability, high-frequency efficiency and low loss. However, its inherent properties of high hardness and low fracture toughness also pose severe challenges to the ultra-precision processing of wafer substrates. In this study, through molecular dynamics methods, the influence of diamond abrasive grains with different sharpness on the processing of 4H-SiC at different grinding speeds was simulated, with a focus on analyzing its surface morphology, material removal behavior and subsurface damage characteristics. The structural evolution of 4H-SiC workpieces and diamond abrasive grains was identified through the radial distribution function, and the dynamic changes in temperature and stress during processing were further investigated to clarify the mechanism of abrasive wear and graphitization phenomena. The results show that regular octahedral abrasive grains with higher sharpness have better material removal efficiency, but they also cause more significant subsurface damage. Increasing the grinding speed helps to reduce the depth of subsurface damage. In addition, high temperature and high stress are the key factors leading to the transformation of diamond into graphite. Even under low-speed grinding conditions, the edges of the abrasive grains may still undergo graphitization due to stress concentration. The above findings have theoretical significance for an in-depth understanding of the material removal mechanism of 4H-SiC nano-grinding, and can also provide an important reference for the development of high-performance grinding wheels for SiC grinding.

Instream boulder placement is a widely adopted technique for restoring degraded streams to enhance instream complexity, aquatic habitat quality, fish passage, and overall ecological value. However, biological responses to these structures have been evaluated less frequently and with lower consistency as compared to physical/hydraulic habitat assessments. This experimental study examines the impact of instream boulder placement on the behavior of juvenile Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss), focusing on their movements upstream through a flume. The experimental setup featured seven boulder arrangements, including rock-ramp (areal densities from 0.0% to 8.3%) and cluster formations (V- and I-weirs), under two flow rates. Top-down and underwater cameras recordings were analyzed using AI-driven object-tracking algorithms document the movement behavior of the fish. We found that juvenile salmonids in fast flowing water have prolonged resting periods in favorable microhabitats created behind boulders. Atlantic salmon moved upstream most readily with the I-weirs, while rainbow trout did so with the V-upstream weirs. The results revealed strong predictive relationships between dimensionless ground speed and both Froude number and dimensionless swimming speed. Moreover, this study identified strong nonlinear relationships between habitat-scale hydrodynamic complexity metrics and key fish behavioral responses, including resting time, ground speed, passage time, and passage efficiency. The findings of this study enhance our understanding of fish behavior in complex hydraulic environments and offer empirically-based design guidance for more effective, species-specific fish passage and habitat restoration efforts. The study also demonstrates the utility of experiments using live fish in flumes to test fish passage structure designs.

Abstract Owing to its unique combination of properties, γ -TiAl alloy demonstrates remarkable advantages in high-temperature lightweight structural applications, establishing itself as a new critical material. However, the degradation mechanisms of material surface integrity at atomic-scale precision remain insufficiently investigated within existing machining systems. Based on molecular dynamics methodology, this research systematically investigates the influence of varying grinding parameters (depth and speed) on burr formation of γ -TiAl alloy during nanogrinding processes. The analysis elucidates the underlying correlations through multiple aspects: surface deformation, grinding force fluctuations, dislocation defects, and stress distribution. Simulation results demonstrate that employing higher grinding speeds in conjunction with reduced grinding depths enhances surface finish quality and minimizes material accumulation. However, due to complex dislocation dynamics, the evolution of exit burrs exhibits a non-monotonic behavior. A comprehensive analysis of surface deformation, forces, and defects indicates that, within the studied parameter range, a grinding speed of 400 m s −1 combined with a shallow grinding depth of 1 nm represents a favorable processing configuration. This work comprehensively reveals the formation mechanisms of surface burrs during nanogrinding of γ -TiAl alloys, establishing this mechanism as a fundamental basis in precision manufacturing applications of the alloy.

Aiming at the problems of low visualization level, inadequate data linkage, and delayed quality assessment under dynamic conditions in traditional grinding unit monitoring systems, this paper proposes a digital-twin-based real-time monitoring system for workpiece surface roughness. Integrating digital space modeling, virtual-reality interaction, and three-dimensional visualization techniques, the proposed system leverages the “five-dimensional model” concept to establish a comprehensive 3D visualization architecture tailored specifically for grinding unit. By capturing three key parameters: grinding speed (GS), robot feed multiplier (RFM) and emery wheel compression (EWC), an Particle Swarm Optimization-Backpropagation (PSO-BP) neural network model is developed to achieve accurate surface roughness (Ra) prediction and anomaly detection. Moreover, virtual-reality interactive simulation further strengthens monitoring effectiveness and responsiveness. Experimental results demonstrate that the proposed neural network model achieves excellent predictive performance in surface quality estimation, with a root-mean-square error (RMSE) of less than 0.003 μm. These findings verify the developed system’s high accuracy and practical feasibility, providing innovative insights and approaches for applying digital twin technology within traditional manufacturing environment.

Understanding how birds adjust their flight in response to biomechanical characteristics and environmental conditions can be useful for interpreting homing behavior. This study investigates homing pigeons’ (Columba livia) flight behavior using multi-sensor biologgers, integrating GPS, tri-axial accelerometer, pressure, and temperature sensors. Flight biomechanics were assessed by extracting: wingbeat frequency from the Short-Time Fourier Transform of the total acceleration signal and peak-to-peak acceleration from the dorso-ventral component. Landscape characteristics were provided by classifying land cover along the route using a geographic atlas and by computing flight altitude above ground level through the combination of pressure-derived altitude and a digital elevation model. The results reveal a progressive decrease in wingbeat frequency along the homing route, showing a linear relationship with traveled distance. To assess whether this pattern can be interpreted in terms of flight regulation, flight altitude was modeled as a function of biomechanical and environmental variables using a linear mixed-effect approach. The analysis indicates that flight altitude is significantly affected by wingbeat frequency as well as by temperature, ground speed, and land cover, with wingbeat frequency and temperature showing the strongest negative association.

Application of Peter Chew Method in Aerospace Engineering (Ground Speed)

ABSTRACTCentipedes locomote through complex obstacle‐rich environments by propagating waves of body bending and limb stepping. However, little is known about how collisions with obstacles influence locomotion. In terrestrial environments such as branches or leaf litter, obstacles can both cause drag and offer affordances for the animals to generate thrust. In laboratory experiments, we challenged Scolopendra polymorpha (∼9 cm long, ∼1 cm wide) to negotiate model heterogeneous terrains, hexagonal and square lattices composed of thin posts. The centipedes maintained rapid motion (∼0.2 body lengths per cycle, comparable to flat ground speed) across lattices of different spacings by altering their body and limb postures in response to collisions. Several behaviors minimized deleterious limb and head collisions: the first was “prolonged limb adduction,” in which consecutive limbs fold to the body after a leading limb collides with a post, while other limbs maintained a stepping pattern. The second, occurring in narrower lattices, was “body twisting,” in which the animal propagated local body twists to locomote on its side using the posts as footholds. In some cases, the animals used a peristaltic‐like gait, previously undocumented for this species. We propose that the principles discovered here can improve morphologies and control schemes for elongate robots tasked with navigating similar terradynamic scenarios.

Background. Integration of various navigation devices and systems by data combining is widely used for improving the reliability and accuracy. The classical combining method is that the greatest effect is achieved when combined systems measure the same parameters, and the frequency characteristics of the measurement errors differ significantly. This makes it possible to use the classical compensation and filtering scheme using a Kalman filter. Objective. This article presents the scheme, algorithm and program for combining flight speed data received from the on-board air data system and data received from the ground-based radio navigation angle-range measuring system. The result of such combining is used to correct the airspeed measurements. Methods. The peculiarity of the proposed integration procedure is that it consists of several stages. First, the ground speed is recovered by optimal estimation based on the angle-range measurement data. At the second stage, the estimated ground speed is integrated with the airspeed measurements using the compensation and filtering scheme, simultaneously correcting the airspeed measurements. Results. To verify the proposed integration scheme and algorithm, computer statistical modelling has been performed using a Kalman filter. The obtained results demonstrate the possibility of implementing the proposed scheme and algorithm for correcting the airspeed measurement system with increased accuracy. Conclusions. The advantages of the proposed design are that the process of ground speed recovery and the process of data combining are performed separately, which makes it possible to control the correction process.

This paper presents a research methodology for identifying the Pratt & Whitney Canada PW127G turboprop engine from simulation data using optimized feedforward neural networks (FNN). A set of measurable variables - ground speed, throttle lever angle, pressure altitude ​, high-pressure spool speed , and propeller speed ​ - is used to predict normalized engine torque, providing a surrogate engine model suitable for integration into flight simulators. The methodology follows a two-stage strategy. First, a baseline L-BFGS–trained FNN is combined with two architecture-search methods, Extended Great Deluge (EGD) and Bayesian Optimization (BO). On the turboprop dataset, BO achieves a lower test RMSE than EGD and is therefore selected as the preferred architecture optimization strategy. Second, BO is fixed and used to optimize two FNN configurations: Baseline FNN with inputs (ground speed, throttle lever angle, pressure altitude , propeller speed ​) and Core-Enhanced FNN additionally including high-pressure spool speed . The optimized Core-Enhanced FNN significantly reduces the root mean square error from 1.066 to 0.4834 on testing data, corresponding to an average error reduction of about 55% compared with Baseline FNN, and also decreases mean relative error and error variance, confirming the importance of core-speed information for high-fidelity torque prediction. The results demonstrate that L-BFGS–trained FNNs, combined with BO-based architecture search and simulation-derived data, provide an effective and computationally efficient surrogate engine model for turboprop torque (and indirectly thrust) estimation in advanced flight simulation and training applications.

Modelling of grinding load motion is important for determining the dynamic parameters of grinding bodies in grinding chambers, which directly affects the determination of energy consumption of rock destruction in mills of various types. The aim of the work is to create a digital physical model of the grinding load of a drum mill, taking into account the physical properties of the grinding rock with the possibility of optimising the grinding process using artificial intelligence. For physical simulation of the grinding load of the drum mill by the finite element method, the LS Dyna programme from the Ansys package was used. The finite element mesh was built in Altair Hypermesh, LS Prepost software was used to create the model and visualise the results of calculations, and ParaView was used for post-processing of the results. In the work the processes of rock pieces destruction at impact and abrasive impact of grinding bodies on the crushed material with consideration of physical properties of rock were investigated. As a result of combining several models of grinding load motion at different operating modes of the mill, a generalised model of the process of rock destruction was obtained, which takes into account physical properties of the material being ground, sizes of balls and pieces of rock, as well as the influence of fine fractions on the grinding process. It has been established that the criterion for optimising the mill design and operating parameters can be the rock grinding speed. The dynamic portraits of grinding load obtained in the study allow to optimise the grinding process by the criterion of energy consumption.

Continuous fiber reinforced metal matrix composites (CFMMCs) show great potential in aerospace industry due to their high specific strength, excellent high-temperature resistance, improved fatigue performance, and lightweight properties. Grinding plays a pivotal role in machining difficult-to-cut materials, including ceramic composites and metal matrix composites. However, the process often results in severe surface defects, such as matrix smearing, fiber fragmentation, and delamination, due to the high toughness of the metal matrix, the high hardness and brittleness of reinforcing fibers, and the inherent anisotropy and heterogeneity of composites. This study employs high-speed grinding (HSG) with single-grain abrasives to process SiCf/TC17 composites, aiming to improve the removal of matrix and fibers. Furthermore, this study investigates the coupled removal mechanisms of the matrix and fibers at various fiber grinding orientations, as well as the effects of grinding speed and maximum undeformed chip thickness (agmax) on material removal behaviors. Results show that fiber properties significantly influence the removal mechanism more than grinding direction. Cracks in fibers propagate perpendicularly to the tungsten core or radially. Increasing grinding speed from 30 m/s to 120 m/s while agmax=0.3 μm reduces matrix smearing and plastic flow traces, while HSG effectively mitigates large-scale fiber fragmentation. When vs=80 m/s, reducing agmax from 0.8 μm to 0.1 μm significantly enhances fiber removal quality by transitioning from large-scale fragmentation or fracture to micro-fragmentation, thereby substantially reducing matrix smearing defects on the machined surface.

Abstract A two-row upland seeder for cabbage was designed, developed, and tested to improve sowing efficiency and crop establishment in small-scale farming. It featured precision seeding and was evaluated with and without press wheels. Seed delivery rates decreased as ground speed increased, with the highest rate of 0.267 kg·ha −1 achieved at 0.6 m·s −1 ; faster speeds lowered accuracy. Slippage tests at 10 RPM showed stable traction with an average slippage of 3.92%. Field trials revealed that press wheels significantly enhanced seeding performance, providing more consistent sowing depths (mean 6.6 mm), higher germination rates (61%), and fewer missing hills than without press wheels. Statistical analysis (ANOVA, Tukey’s HSD) confirmed that press wheels had significant effects on sowing depth, germination rate, and seedling count ( p <0.05). Despite some issues like seed clogging and hopper damage, the seeder was user-friendly and effective. The study highlights the need for optimal speeds and press wheels to improve planting precision and crop establishment.

Abstract Timely planting and uniform stands are prerequisites for optimal corn ( Zea mays L.) production. However, frequent rainfall often limits corn acreage planted in the southeast region of the United States. Planting faster might offer a potential solution as new technology claims up to 19 km h −1 planting speeds without sacrificing seed singulation or yield. The objective of this study was to evaluate corn response to varying planting speeds in Mississippi. Trials were arranged as a randomized complete block design during the 2023 and 2024 cropping seasons. A precision planter (John Deere bar and MaxEmerge 2 row units retrofitted with Ag Leader SureSpeed and SureForce) was tested at 9.7, 14.5, and 17.7 km h −1 actual ground speeds. A mechanical planter (John Deere 1700 ground‐driven planter equipped with eSet meters) at 9.7 km h −1 was used as a standard check. Corn hybrid DKC 70‐27 was planted at 81,800 and 85,000 seeds ha −1 in 2023 and 2024, respectively. In both seasons, increased planting speed generally lowered plant population and quality of seed placement with increased skips and spacing variability. Planting at 14.5 km h −1 optimized precision and reduced multiples using the precision planter. Moreover, planting speed beyond 14.5 km h −1 did not affect corn yield. The precision planter at 17.7 km h −1 exhibited improved performance over the mechanical planter at 9.7 km h −1 , particularly in maintaining lower miss and multiple indices. Using this technology, Mississippi corn producers can plant more land within the critical planting window at higher speeds without affecting yield.

Two simple models—vaulting over stiff legs and rebounding over compliant legs—are employed to describe the mechanics of legged locomotion. It is agreed that compliant legs are necessary for describing running, and that leg compliance is also present during walking. Stiff legs continue to be employed to model walking under the assumption that the compliance of the leg during walking is low enough to be considered stiff. Here we study gait choice and walk-to-run transition in a biped with compliance and show that the principles underlying gait choice and transition are completely different from stiff legs. Two findings underpin our conclusions: First, at the same speed, step length and stance duration, multiple gaits that differ in the number of times the leg expands and contracts during a single stance are possible. Among them, humans and other animals choose the (normal) gait with M-shaped vertical ground reaction forces (vGRF) not just because of energy considerations but also constraints from forces. Second, the transition from walking to running occurs because of three factors: vGRF minimum at mid-stance characteristic of normal walking, synchronization of horizontal and vertical motions during single support, and velocity redirection during the double support. The insight above required an analytical approximation of the double spring-loaded pendulum (DSLIP) model describing the intricate oscillatory dynamics that relate single and double support phases. Additionally, we also examined DSLIP as a quantitative model for locomotion and conclude that DSLIP speed range is limited. However, insights gleaned from the analytical treatment of DSLIP are general and will inform the construction of more accurate models of walking.

Background/Objectives: Pharmaceutical preparation technologies can enhance the bioavailability of poorly water-soluble drugs. Ursolic acid (UA) has been found to possess anti-cancer and hepatoprotective properties, demonstrating its potential as a therapeutic agent; however, its hydrophobicity and low solubility present challenges in the development of drug formulations. This study investigates the preparation of a nano-UA suspension by wet grinding, researches the influence of process parameters on particle size, and explores the rules of particle breakage and agglomeration by combining model fitting. Methods: Wet grinding experiments were conducted using a laboratory-scale grinding machine. The particle size distributions (PSDs) of UA suspensions under different grinding conditions were measured using a laser particle size analyzer. A single-factor experimental design was employed to optimize operational conditions. Model parameters for a population balance model considering both breakage and agglomeration were determined by an evolutionary algorithm optimization method. By measuring the degree to which UA inhibits the colorimetric reaction between salicylic acid and hydroxyl radicals, its antioxidant capacity in scavenging hydroxyl radicals was indirectly evaluated. Results: Wet grinding process conditions for nano-UA particles were established, yielding a UA suspension with a D50 particle size of 122 nm. The scavenging rate of the final grinding product was improved to three times higher than that of the UA raw material (D50 = 14.2 μm). Conclusions: Preparing nano-UA suspensions via wet grinding technology can significantly enhance their antioxidant properties. Model regression analysis of PSD data reveals that increasing the grinding mill’s stirring speed leads to more uniform particle size distribution, indicating that grinding speed (power) is a critical factor in producing nanosuspensions.

Experimental study of surface quality and damage in grinding CFRP circular honeycomb cell with diamond saw blade

Influence of Hip Prosthesis Position on Postoperative Gait After Primary THA in Patients With Unilateral Femoral Head Necrosis.

Monocrystalline silicon is an excellent semiconductor material for integrated circuits. Its surface quality has an enormous effect on its service life. The surfaces are formed by ultra-precision machining using nano-grinding, one of the technologies that can achieve surface roughness at the nano- or sub-nano-scale. Therefore, subsurface damage of monocrystalline silicon in nano-grinding was studied by establishing a molecular dynamics simulation model, and the impact of machining parameters on the force–thermal behavior was analyzed. The results reveal that the mechanism of subsurface damage is mainly structural phase transformation and amorphization. In nano-grinding of monocrystalline silicon, the tangential grinding force has a relatively major role in material removal. With increasing grinding depth and grinding speed, the grinding heat rises, and a certain degree of high temperature strengthens the toughness of the material, improving the subsurface quality of monocrystalline silicon. Therefore, subsurface damage in monocrystalline silicon can be controlled by reducing the grinding depth and increasing the grinding speed.

Long landings can reduce runway utilization and increase the probability of runway incursions and excursions. Previous studies on long landings often lacked support from actual operational data and primarily relied on event-triggering logic established by airlines for parameter exceedance detection and retrospective analysis. In response, a comprehensive risk prediction framework for aircraft long landings, supported by Quick Access Recorder (QAR) data, was constructed. The framework includes a data analysis pipeline, a sequence prediction model, and performance evaluation metrics for accident warning efficiency. Specifically, approximately 3 million rows of real QAR data were collected, and reasonable landing intervals were extracted based on pilots’ correct landing sightlines, attention allocation, and actual visual scenarios at departure heights. Gradient Boosting Decision Trees (GBDT) were employed to develop a method for extracting landing interval feature data, based on monitored parameters and ranges of landing distance. Additionally, the GBDT-Informer long-sequence time series prediction model was developed to forecast landing distance, accompanied by the construction of effective metrics for evaluating prediction performance. The results indicate that the GBDT-Informer model effectively models the temporal dimensions of landing intervals, accurately predicting ground speed (GS), radio altitude (RALT), and landing distance sequences. Compared to other prediction models, the GBDT-Informer model consistently achieved the smallest RMSE, MAE, and MAPE values, demonstrating high prediction accuracy. This predictive framework allows for the analysis of the coupling relationships among multiple parameters in flight data and their interrelations with exceedance anomalies. The findings can be applied in actual flight landings to promptly assess whether landing distances exceed limits, providing quick references for flight crews during landing or go-around decisions, thereby enhancing operational safety margins during the landing phase.