High Speed Research Articles

Design and test of a novel converging groove-guided seed tube for precision seeding of maize

In precision seeding operations, the irregular shapes of maize seed particles and the suboptimal geometric structure of the seed tube contribute to the rolling and bouncing of seeds upon falling into the furrow, ultimately compromising seeding uniformity. To address this issue, a convergent groove-guided (CGG) seed tube was proposed based on the principle of brachistochrone curve considering friction (BCCF). The discrete element method (DEM) was employed to conduct a comprehensive phenomenological analysis and numerical investigation of particle guide characteristics. Bench tests and field experiments were designed to verify the simulation results. Through response surface analysis, the optimal seeding performance was achieved at a groove angle of 111.34°, tube incline angle of 1.56°, and ground speed of 7.2 km h−1, resulting in a lateral dispersion landing range of 11.10 mm, angular velocity of 17.38 rad s−1, seed landing speed variation coefficient of 2.29%, and plant spacing variation coefficient of 6.04%. Bench test results unveiled that, under a ground speed of 7.2 km h−1, the variation coefficient of plant spacing for CGG seed tubes was measured at 7.63%, with a deviation of 1.59% from the simulation results. At high ground speeds of 9.0–14.4 km h−1, the seed guiding performance was also better than traditional seed tubes. Consistent results were also verified through field experiments. Therefore, the CGG seed tube manifested superior seed guidance efficacy when juxtaposed with its conventional counterpart, and this design can provide technical support for the realization of seed-to-ground positioning technology.

Exploring torque characteristics of an integrated ultra conducting magnetic coupling incorporating axial magnetic field through magnetic equivalent circuit modelling

Ultra-conductors are a recent class of materials that display superconducting attributes at room temperature and even at higher temperatures. In this paper, we introduce a novel hybrid ultra-conducting coupling (NHUC) where the secondary component includes copper and an ultra-conductor (considered as a room temperature superconductor in much research). We suggest a model based on magnetic equivalent circuit (MEC) to evaluate the distribution of magnetic fields and characteristics related to torque. The 3D model accounts for boundary impacts during mating, and formulation for magnetic flux and torque are derived using Kirchhoff’s and Ampere’s loop laws, respectively. A 3D finite element (FEM) model is constructed, and simulations are performed using iodine-doped double-walled carbon nanotube (IDWCT) as the ultra-conductor for a wider operating range. At a high slip speed of 1200 rpm, a maximum torque of 309 Nm and stable torque of 113 Nm are observed. Adjusting air gap thickness or permanent magnet dimensions results in torque variations. Additionally, due to carbon nanotube (CNT) characteristics, a reduction in losses from the Joule effect is noted. The maximum error between torque results from simulation and the suggested model is 7.68%, confirming accordance. Comparison alongside the slotted eddy current coupling (SEC) reveals that the torque of the NHUC exceeds that of the SEC, diminishing as the air gap widens.

A novel approach integrating CFBG and optical filters to mitigate CD and PMD effects in high speed DWDM fiber optic links

A novel approach integrating CFBG and optical filters to mitigate CD and PMD effects in high speed DWDM fiber optic links

Hybrid neural network-aided strong wind speed prediction along rail network

With the advent of networked rail operations and increasing risks caused by extreme weather conditions, there is an urgent need to enhance the safety early warning and emergency protection for rail under extreme wind environments. By deploying anemometers along the railway and monitoring the surrounding wind speeds, dispatchers can proactively take protective measures to mitigate the impact of severe weather on rail transportation. In particular, accurate time-series forecasting during periods when wind speeds exceed the threshold is critical for railway anomaly monitoring. This study employs Extreme Value Theory (EVT) to determine the threshold for strong winds. In order to identify high wind speeds, memory networks are used to memorize strong wind events in historical data. By combining the Gated Recurrent Unit (GRU) module with the attention mechanism, this study ultimately develops a hybrid neural network model in the prediction of wind speed. Based on real-world datasets of wind speeds along the Hangzhou-Haining intercity railway and along the Norway rail network via meteorological stations, the effectiveness of the hybrid neural network model has been verified by comparing base models. The proposed strong wind speed prediction has practical applications in enhancing early warning and rail safety under complex wind climate.

Traffic Feature Selection and Distributed Denial of Service Attack Detection in Software-Defined Networks Based on Machine Learning.

As 5G technology becomes more widespread, the significant improvement in network speed and connection density has introduced more challenges to network security. In particular, distributed denial of service (DDoS) attacks have become more frequent and complex in software-defined network (SDN) environments. The complexity and diversity of 5G networks result in a great deal of unnecessary features, which may introduce noise into the detection process of an intrusion detection system (IDS) and reduce the generalization ability of the model. This paper aims to improve the performance of the IDS in 5G networks, especially in terms of detection speed and accuracy. It proposes an innovative feature selection (FS) method to filter out the most representative and distinguishing features from network traffic data to improve the robustness and detection efficiency of the IDS. To confirm the suggested method's efficacy, this paper uses four common machine learning (ML) models to evaluate the InSDN, CICIDS2017, and CICIDS2018 datasets and conducts real-time DDoS attack detection on the simulation platform. According to experimental results, the suggested FS technique may match 5G network requirements for high speed and high reliability of the IDS while also drastically cutting down on detection time and preserving or improving DDoS detection accuracy.

Evaluating the Ventilation Performance of Single-Span Plastic Greenhouses with Continuous Screened Side Openings

Natural ventilation is the most cost-effective environmental control method for protected horticulture. To overcome the issues associated with high temperatures in greenhouses, analyzing their ventilation characteristics and maximizing their natural ventilation performance are essential. Therefore, in this study, the natural ventilation performance of arched single-span plastic greenhouses with screened side openings was empirically investigated. Three identical single-span plastic greenhouses were used in this study, each with different side-opening heights. Temperature and wind-speed data were collected, and the ventilation-volume flow rate was calculated considering both buoyancy and wind forces. The natural ventilation performance of the greenhouses was strongly and positively correlated with the ventilation-area ratio and outdoor wind speed. The ventilation rate increased linearly with an increase in the ventilation-area ratio and high outdoor wind speeds. However, the association between ventilation performance and indoor–outdoor temperature differences was not strong. When the ratios of ventilation areas in the greenhouse were changed to 0.08, 0.19, and 0.29, the average indoor–outdoor temperature differences were 14.0, 10.1, and 7.7 °C, respectively, and the ventilation rates were 0.0081, 0.0196, and 0.0315 m3 s−1 m−2, respectively. The proportion of wind in the total ventilation performance was high with a low ratio of ventilation openings and high outdoor wind speeds. However, the proportion of the buoyancy was high, with a high ratio of ventilation openings and a large indoor–outdoor temperature difference. Overall, this study provides foundational insights for optimizing the design and evaluation of ventilation openings in greenhouses, considering the outdoor wind speed.

A Novel Joint Motion Compensation Algorithm for ISAR Imaging Based on Entropy Minimization.

Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging. Therefore, this paper proposes a joint ISAR motion compensation algorithm based on entropy minimization under low-SNR conditions. Firstly, the motion of the space target is analyzed, and the echo signal model is obtained. Then, the motion of the space target is modeled as a high-order polynomial, and a parameterized joint compensation model of high-speed motion and translational motion is established. Finally, taking the image entropy after joint motion compensation as the objective function, the red-tailed hawk-Nelder-Mead (RTH-NM) algorithm is used to estimate the target motion parameters, and the joint compensation is carried out. The experimental results of simulation data and real data verify the effectiveness and robustness of the proposed algorithm.

How to catch a shear band and explain plasticity of metallic glasses with continuum mechanics

Capturing a shear band in a metallic glass during its propagation experimentally is very challenging. Shear bands are very narrow but extend rapidly over macroscopic distances, therefore, characterization of large areas at high magnification and high speed is required. Here we show how to control the shear bands in a pre-structured thin film metallic glass in order to directly measure local strains during initiation, propagation, or arrest events. Based on the experimental observations, a model describing the shear banding phenomenon purely within the frameworks of continuum mechanics is formulated. We claim that metallic glasses exhibit an elastic limit of about 5% which must be exceeded locally either at a stress concentrator to initiate a shear banding event, or at the tip of a shear band during its propagation. The model can successfully connect micro- and macroscopic plasticity of metallic glasses and suggests an alternative interpretation of controversial experimental observations.

Discrete Adjoint Optimization Method for Low-Boom Aircraft Design Using Equivalent Area Distribution

This paper introduces a low-boom aircraft optimization design method guided by equivalent area distribution, which effectively improves the intuitiveness and refinement of inverse design. A gradient optimization method based on discrete adjoint equations is proposed to achieve the fast solution of the gradient information of target equivalent area distribution relative to design variables and to drive the aerodynamic shape update to the optimal solution. An optimization experiment is carried out based on a self-developed supersonic civil aircraft configuration with engines. The results show that the equivalent area distribution adjoint equation can accurately solve the gradient information. After optimization, the sonic boom level of the aircraft was reduced by 13.2 PLdB, and the drag coefficient was reduced by 60.75 counts. Moreover, the equivalent area distribution adjoint optimization method has outstanding advantages, such as high sensitivity and fast convergence speed, and can take both the low sonic boom and the low drag force of the aircraft into account, providing a powerful tool for the comprehensive optimization design of supersonic civil aircraft by considering sonic boom and aerodynamic force.

Gapr for large-scale collaborative single-neuron reconstruction.

Whole-brain analysis of single-neuron morphology is crucial for unraveling the complex structure of the brain. However, large-scale neuron reconstruction from terabyte and even petabyte data of mammalian brains generated by state-of-the-art light microscopy is a daunting task. Here, we developed 'Gapr' (Gapr accelerates projectome reconstruction) that streamlines deep learning-based automatic reconstruction, 'automatic proofreading' that reduces human workloads at high-confidence sites, and high-throughput collaborative proofreading by crowd users through the Internet. Furthermore, Gapr offers a seamless user interface that ensures high proofreading speed per annotator, on-demand conversion for handling large datasets, flexible workflows tailored to diverse datasets and rigorous error tracking for quality control. Finally, we demonstrated Gapr's efficacy by reconstructing over 4,000 neurons in mouse brains, revealing the morphological diversity in cortical interneurons and hypothalamic neurons. Here, we present Gapr as a solution for large-scale single-neuron reconstruction projects.

Single-modulator, dual comb serrodyne spectroscopy.

Dual optical frequency comb spectroscopy allows for high speed, broadband measurements without any moving parts. Here, we combine differential chirp downconversion to probe large spectral bandwidths and serrodyne modulation to separate the positive and negative sidebands in a single modulator. As an initial demonstration, we apply this approach to measure a sharp cavity resonance to illustrate the system performance. We then measure methane transitions in the near-infrared and compare the resulting spectra to models based upon the current spectroscopic databases. The serrodyne method has lower hardware requirements compared to many existing approaches, and its simplicity enables a high degree of mutual coherence between the two combs. Further, this method is readily amenable to chip-scale photonic integration.

P-Type Schottky Contacts for Graphene Adjustable-Barrier Phototransistors.

The graphene adjustable-barriers phototransistor is an attractive novel device for potential high speed and high responsivity dual-band photodetection. In this device, graphene is embedded between the semiconductors silicon and germanium. Both n-type and p-type Schottky contacts between graphene and the semiconductors are required for this device. While n-type Schottky contacts are widely investigated, reports about p-type Schottky contacts between graphene and the two involved semiconductors are scarce. In this study, we demonstrate a p-type Schottky contact between graphene and p-germanium. A clear rectification with on-off ratios of close to 103 (±5 V) and a distinct photoresponse at telecommunication wavelengths in the infrared are achieved. Further, p-type silicon is transferred to or deposited on graphene, and we also observe rectification and photoresponse in the visible range for some of these p-type Schottky junctions. These results are an important step toward the realization of functional graphene adjustable-barrier phototransistors.

Energy Management Strategy for Distributed Photovoltaic 5G Base Station DC Microgrid Integrated with the CF-P&O-INC MPPT Algorithm

With its technical advantages of high speed, low latency, and broad connectivity, fifth-generation mobile communication technology has brought about unprecedented development in numerous vertical application scenarios. However, the high energy consumption and expansion difficulties of 5G infrastructure have become the main obstacles restricting its widespread application. Therefore, aiming to optimize the energy utilization efficiency of 5G base stations, a novel distributed photovoltaic 5G base station DC microgrid structure and an energy management strategy based on the Curve Fitting–Perturb and Observe–Incremental Conductance (CF-P&O-INC) Maximum Power Point Tracking (MPPT) algorithm from the perspectives of energy and information flows are proposed. Simulation results show that the proposed MPPT algorithm can increase the efficiency to 99.95% and 99.82% under uniform irradiation and partial shading, respectively. Under the proposed strategy, when the base station load changes drastically, the voltage fluctuation of the DC bus is less than 1.875%, and returns to a steady state within 0.07s, alleviating the high energy consumption of 5G base stations effectively and achieving coordinated optimization management of various types of energy in multi-source power supply systems.

Effects of Acute Sleep Deprivation on Sporting Performance in Athletes: A Comprehensive Systematic Review and Meta-Analysis.

Using meta-analysis to comprehensively and quantitatively evaluate the impact of acute sleep deprivation on different sports performance of athletes, this study aims to provide scientific guidance for coaches in optimizing and adjusting training and competition arrangements. Establishing literature inclusion and exclusion criteria, we conducted searches in both Chinese and English databases. Using stata 14.0, we analyzed 75 indicators from 27 included literature, focusing on three aspects: the impact of acute sleep deprivation on overall athletic performance, the impact on sporting performance across various athletic abilities, and the disparities in athletic performance between morning and afternoon following acute sleep deprivation. The effect size of acute sleep deprivation on overall athletic performance was -0.56 (P<0.05). Sub-analyses revealed effect sizes of -0.23 (P<0.05) for whole night sleep deprivation, -1.17 (P<0.05) for partial sleep deprivation at the end of the night, and -0.25 (P>0.05) for partial sleep deprivation in the beginning of the night. The effect sizes of acute sleep deprivation on high intensity intermittent exercise, skill control, speed, aerobic endurance, and explosive power indicators were -1.57, -1.06, -0.67, -0.54, and -0.39 respectively (P<0.05). The effect sizes of acute sleep deprivation on the overall athletic performance in the morning and afternoon were -0.30, and -1.11, respectively (P<0.05). Acute sleep deprivation significantly impairs the overall athletic performance of athletes, with a more pronounced negative impact observed with partial sleep deprivation at the end of the night. Various types of exercise performance are adversely affected by acute sleep deprivation, with magnitude of impact ranking high intensity intermittent, skill control, speed, aerobic endurance, and explosive power. Following acute sleep deprivation, athletes' overall sporting performance in the afternoon is inferior to that in the morning.

A grader for fresh jujube with electromagnetic synchronous actuator assembly

AbstractIn recent years, the post‐harvest commercialization of fresh jujube sorting technology is backward, the efficiency of manual grading is low, and the application of machine vision technology in fruit grading has been a research hotspot. To improve the grading accuracy and speed of the fresh jujube machine vision grading machine, this study designs a method of fresh jujube machine vision grading using an electromagnetic synchronous actuator. The grading machine consists of an automatic distribution and arrangement system, an image acquisition and processing system, and a synchronous actuator assembly, which uses a sensitive electromagnet to adsorb a silicon steel switching buckling plate to execute the action. The image processing process includes an image color component, Sobel operator filtering, Otsu method binarization, and ellipse fitting to extract the color and size eigenvalues. The experimental results show that the date images can be acquired quickly and processed accurately with a processing speed of 50 ms per frame. In addition, the proposed actuator can be sensitively triggered with a response time of 40 ms. The grader successfully classified jujube into five size classes and one color class. The grading accuracy and non‐destructive rate reached 93.7% and 100%, respectively. The grading efficiency is 20 jujubes per second, with a maximum production rate of 1400 kilograms per hour. This grading machine realizes the requirements of high accuracy, high speed, and non‐destructive grading of fresh jujube and provides an important technical and theoretical basis for the development of machine vision jujube rapid grading technology.Practical applicationsIn recent years, sorting technology in the post‐harvest commercialization of fruits has been outdated, and manual grading has been inefficient. The application of machine vision technology in fruit grading has been a research hotspot in recent years. This study designs a machine vision grading of fresh dates with an electromagnetic synchronous actuator. The grader was composed of a roller conveyor, synchronous actuator assembly, image main processor, and actuator controlling co‐processor. Experimental results indicated that jujube images could be acquired rapidly and processed accurately, with a processing speed of 50 ms per frame. In addition, the proposed actuator could be sensitively triggered with a response time of 40 ms. Finally, the grader successfully sorted jujubes into five size classes and one color class. The grading accuracy rate and non‐destructive rate reached 93.7% and 100%, respectively. The grading efficiency was 20 jujubes per second, and the maximum productivity was 1400 kg/h.

Thermography and deep learning for detection of ground beef adulteration

The aim of this study was to evaluate the use of thermal imaging as a rapid and non-destructive method for the detection of adulteration in minced beef. The adulterant materials investigated included avian offal (chicken gizzard) and ovine offal (sheep lung), which have been observed to be used as adulterants in the market. The weight ratios (w/w) of these impurities ranged from 0 to 30 % at 5 % intervals along with the pure samples. Since induction of a temperature gradient helps in thermographic diagnosis, heating and cooling pretreatments were utilized and compared. Convolutional neural network models were developed to identify the adulteration levels in ground beef. The models trained using data of both thermal processes (heating and cooling) were able to detect 8 levels of sheep lung adulteration in ground beef with 99 % accuracy. It was also able to detect the adulteration level of chicken gizzards in the warming method with 99 % accuracy and in the cooling method with 84 % accuracy. Since the method was capable of detecting impurities as low as 5 %, it points to a promising technique with high speed and accuracy for the detection of various types of adulteration in minced meat.

Applications of high performance liquid chromatography-mass spectrometry in proteomics

Proteomics profiling plays an important role in biomedical studies. Proteomics studies are much more complicated than genome research, mainly because of the complexity and diversity of proteomic samples. High performance liquid chromatography-mass spectrometry (HPLC-MS) is a fundamental tool in proteomics research owing to its high speed, resolution, and sensitivity. Proteomics research targets from the peptides and individual proteins to larger protein complexes, the molecular weight of which gradually increases, leading to sustained increases in structural and compositional complexity and alterations in molecular properties. Therefore, the selection of various separation strategies and stationary-phase parameters is crucial when dealing with the different targets in proteomics research for in-depth proteomics analysis. This article provides an overview of commonly used chromatographic-separation strategies in the laboratory, including reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), hydrophobic interaction chromatography (HIC), ion-exchange chromatography (IEC), and size-exclusion chromatography (SEC), as well as their applications and selectivity in the context of various biomacromolecules. At present, no single chromatographic or electrophoretic technology features the peak capacity required to resolve such complex mixtures into individual components. Multidimensional liquid chromatography (MDLC), which combines different orthogonal separation modes with MS, plays an important role in proteomics research. In the MDLC strategy, IEC, together with RPLC, remains the most widely used separation mode in proteomics analysis; other chromatographic methods are also frequently used for peptide/protein fractionation. MDLC technologies and their applications in a variety of proteomics analyses have undergone great development. Two strategies in MDLC separation systems are mainly used in proteomics profiling: the "bottom-up" approach and the "top-down" approach. The "shotgun" method is a typical "bottom-up" strategy that is based on the RPLC or MDLC separation of whole-protein-sample digests coupled with MS; it is an excellent technique for identifying a large number of proteins. "Top-down" analysis is based on the separation of intact proteins and provides their detailed molecular information; thus, this technique may be advantageous for analyzing the post-translational modifications (PTMs) of proteins. In this paper, the "bottom-up" "top-down" and protein-protein interaction (PPI) analyses of proteome samples are briefly reviewed. The diverse combinations of different chromatographic modes used to set up MDLC systems are described, and compatibility issues between mobile phases and analytes, between mobile phases and MS, and between mobile phases in different separation modes in multidimensional chromatography are analyzed. Novel developments in MDLC techniques, such as high-abundance protein depletion and chromatography arrays, are further discussed. In this review, the solutions proposed by researchers when encountering compatibility issues are emphasized. Moreover, the applications of HPLC-MS combined with various sample pretreatment methods in the study of exosomal and single-cell proteomics are examined. During exosome isolation, the combined use of ultracentrifugation and SEC can yield exosomes of higher purity. The use of SEC with ultra-large-pore-size packing materials (200 nm) enables the isolation of exosomal subgroups, and proteomics studies have revealed significant differences in protein composition and function between these subgroups. In the field of single-cell proteomics, researchers have addressed challenges related to reducing sample processing volumes, preventing sample loss, and avoiding contamination during sample preparation. Innovative methods and improvements, such as the utilization of capillaries for sample processing and microchips as platforms to minimize the contact area of the droplets, have been proposed. The integration of these techniques with HPLC-MS shows some progress. In summary, this article focuses on the recent advances in HPLC-MS technology for proteomics analysis and provides a comprehensive reference for future research in the field of proteomics.

Design and Application of an S-band Fast Radio Bursts Search Pipeline for the Nanshan 26 m Radio Telescope

Fast radio bursts (FRBs) are among the most studied radio transients in astrophysics, but their origin and radiation mechanism are still unknown. It is a challenge to search for FRB events in a huge amount of observational data with high speed and high accuracy. With the rapid advancement of the FRB research process, FRB searching has changed from archive data mining to either long-term monitoring of the repeating FRBs or all-sky surveys with specialized equipments. Therefore, establishing a highly efficient and high quality FRB search pipeline is the primary task in FRB research. Deep learning techniques provide new ideas for FRB search processing. We have detected radio bursts from FRB 20201124A in the L-band observational data of the Nanshan 26 m radio telescope (NSRT-26m) using the constructed deep learning based search pipeline named dispersed dynamic spectra search (DDSS). Afterwards, we further retrained the deep learning model and applied the DDSS framework to S-band observations. In this paper, we present the FRB observation system and search pipeline using the S-band receiver. We carried out search experiments, and successfully detected the radio bursts from the magnetar SGR J1935+2145 and FRB 20220912A. The experimental results show that the search pipeline can complete the search efficiently and output the search results with high accuracy.

Exploration of lead-free novel double perovskite halides Na2TlBiX6 (X = Cl, Br, I) for flexible memory devices: Using DFT approach

Flexible resistive switching random access memory (RRAM) devices have drawn significant attention due to their low power consumption, high speed and simple structure. Halide perovskites with excellent optoelectronic properties such as high charge carrier mobility and tunable bandgap emerge as the best material for flexible RRAM devices. In this study, for the first time, the structural, mechanical, optoelectronic and thermoelectric properties of Na2TlBiX6 (X = Cl, Br, I) were explored using the Wien2k package for flexible RRAM devices. Elastic parameters show that studied perovskites display mechanical stability, ductile nature and anisotropic behaviour with lower values of the bulk modulus. In comparison, Na2TlBiI6 has a smaller bulk modulus which makes it a suitable material for flexible RRAM devices. Furthermore, the large machinability index of Na2TlBiI6 among the studied perovskites highlights its feasibility for industrial applications. The semiconductor nature of Na2TlBiX6 (X = Cl, Br, I) is confirmed from band structure, total density of states (TDOS) and partial density of states (PDOS) calculation. The optical study shows that Na2TlBiI6 can absorb a wide range of ultraviolet and visible electromagnetic incident radiations making it an excellent material for flexible RRAM devices. Lastly, in terms of transport properties at room temperature, Na2TlBiI6 reveals a larger figure of merit (ZT) than other studied perovskites making it a suitable candidate for thermoelectric applications. The outcome of this study suggests that Na2TlBiI6 is the perfect candidate for flexible RRAM devices at room temperature.

Flight behaviour diverges more between seasonal forms than between species in Pieris butterflies.

In flying animals, wing morphology is typically assumed to influence flight behaviours. Whether seasonal polymorphism in butterfly morphology is linked to adaptive flight behaviour remains unresolved. Here, we compare the flight behaviours and wing morphologies of the spring and summer forms of two closely related butterfly species, Pieris napi and P. rapae. We first quantify three-dimensional flight behaviour by reconstructing individual flight trajectories using stereoscopic high-speed videography in an experimental outdoor cage. We then measure wing size and shape, which are characteristics assumed to influence flight behaviours in butterflies. We show that seasonal, but not interspecific, differences in flight behaviour might be associated with divergent forewing shapes. During spring, Pieris individuals are small and have elongated forewings, and generally fly at low speed and acceleration, while having a high flight curvature. On the contrary, summer individuals are larger and exhibit rounded forewings. They fly at high speed and acceleration, while having high turning acceleration and advance ratio. Our study provides one of the first quantitative pieces of evidence of different flight behaviours between seasonal forms of two Pieris butterfly species. We discuss the possibility that this co-divergence in flight behaviour and morphology is an adaptation to distinct seasonal environments. Properly identifying the mechanisms underpinning such divergence, nonetheless, requires further investigations to disentangle the interacting effects of microhabitats, predator community, parasitoid pressure and behavioural differences between sexes.