The improvement of the accuracy and real-time performance of sector traffic flow prediction is of great significance to air traffic management decision-making. Sectors operate under complex spatial structures and time dimensions. Some neural network methods adopt sequence order to gradually transmit information, which makes it difficult to achieve complete parallel training. Not only does it take too long to train, resulting in low training efficiency, but it is also easy to lose the effective correlation information of long sequence data. To this end, a sector traffic flow prediction method based on attention-improved graph convolutional transformer (AGC-T) network is proposed to improve the current traffic prediction problem for sectors. First, the graph structure information and historical traffic data of the sector are input into the graph convolutional network improved based on the attention mechanism to fully capture the spatial relationship with sectors as nodes. Combined with the transformer’s multi-head self-attention mechanism, it can directly focus on the sequence data at any position without gradually transmitting information. Not only does it improve efficiency through parallel training, but the encoder-decoder structure can also mine the information features in the traffic data, focus on the traffic data features of key nodes and more accurately predict sector traffic. Finally, the operation traffic data of sectors in typical areas in central and southern China are taken as an example to analyse the model. The results show that compared with other prediction models, the AGC-T model $RSME$, $MAE$ and ${R^2}$ are 45.16%, 46.78% and 2.63% higher than the GCN model in the 15-min single-day traffic prediction task, and 41.74%, 35.27% and 1.20% higher than the GRU model. In the single-week traffic prediction task, $RSME$, $MAE$ and ${R^2}$ are 37.12%, 40.54% and 3.55% higher than the GCN model, and 35.15%, 35.17% and 0.65% higher than the GRU model, respectively, showing better prediction performance. This study will help air navigation service providers (ANSP) to make sector traffic predictions more accurately, thereby implementing more scientific and reasonable traffic management measures.

To investigate the stall mechanisms of a multi-stage axial compressor under different rotational speeds and identify the initial stall stages, this study focuses on a high-load nine-stage axial compressor, validated through experimental data. The results reveal that at 100% corrected rotational speed, flow instability is primarily triggered by corner separation in the front four stators (S1–S4). At 80% corrected rotational speed, the instability stems from the interaction between the first rotor (R1) tip leakage vortex and the main flow, coupled with the front four stators’ corner separation. Precise identification of initial stall locations in multi-stage axial compressors is imperative. The study first employs qualitative flow-field analysis to identify initial stall locations by comparing meridional mass flux variation contour maps and axial velocity iso-surfaces. The results show that the stall inception occurs at the S2 root under 100% corrected rotational speed, while at 80% corrected rotational speed, stall initiates simultaneously at both the S2 root and the R1 tip. Furthermore, an innovative three-dimensional flow blockage quantification method was established to systematically evaluate blockage severity within multi-stage blade passages. This approach utilises relative blockage variation metrics to quantitatively identify regions of rapid flow deterioration, achieving remarkable consistency with qualitative flow-field analysis. The qualitative and quantitative analysis results have been mutually corroborated. The proposed blockage quantification approach enables precise evaluation across stages without complex flow fields comparisons, allowing rapid identification of stall-initiating locations and supporting subsequent stability enhancement optimization.

The multi-UAV task allocation problem can be divided into two components: optimising UAV resource allocation and developing an optimal execution plan. Existing single-population algorithms often get trapped in local optima and require improved accuracy. Although multi-population algorithms perform better, they introduce higher complexity, significantly increasing running time. This paper proposes a Two-Stage Multi-Population Wolf Pack Algorithm (2SMPWPA) to address these issues. This algorithm innovatively splits the task allocation problem into two stages: the initial stage focuses on optimising UAV resource utilisation. In contrast, the subsequent stage focuses on optimising the execution plans for the existing UAV resources. Furthermore, the algorithm categorises the population into a leader group and two normal groups, where the leader group consists of elite individuals from the ordinary groups. To ensure the outstanding individuals in the normal groups have adequate computational resources, a population competition mechanism is introduced to dynamically adjust the size of each sub-population based on their average contribution to the optimal solution. To prevent the ‘big eats small’ scenario, the algorithm incorporates population protection and migration mechanisms to maintain diversity. Additionally, a population communication mechanism is implemented to preserve ‘vitality’ during the later iterations, preventing the algorithm from converging to local optima. Comparative experiments demonstrate that the 2SMPWPA significantly outperforms recent algorithms regarding solution accuracy, effectively addressing the trade-off between solution precision and running time.

The interaction of helminth infections with type 2 diabetes (T2D) has been a major area of research in the past few years. This paper, therefore, focuses on the systematic review of the effects of helminthic infections on metabolism and immune regulation related to T2D, with mechanisms through which both direct and indirect effects are mediated. Specifically, the possible therapeutic role of helminths in T2D management, probably mediated through the modulation of host metabolic pathways and immune responses, is of special interest. This paper discusses the current possibilities for translating helminth therapy from basic laboratory research to clinical application, as well as existing and future challenges. Although preliminary studies suggest the potential for helminth therapy for T2D patients, their safety and efficacy still need to be confirmed by larger-scale clinical studies.

A novel entomopathogenic nematode (EPN) species, Steinernema tarimense n. sp., was isolated from soil samples collected in a Populus euphratica forest located in Yuli County within the Tarim Basin of Xinjiang, China. Integrated morphological and molecular analyses consistently place S. tarimense n. sp. within the ‘kushidai-clade’. The infective juvenile (IJ) of new species is characterized by a body length of 674–1010 μm, excretory pore located 53–80 μm from anterior end, nerve ring positioned 85–131 μm from anterior end, pharynx base situated 111–162 μm from anterior end, a tail length of 41–56 μm, and the ratios D% = 42.0–66.6, E% = 116.2–184.4, and H% = 25.5–45.1. The first-generation male of the new species is characterized by a curved spicule length of 61–89 μm, gubernaculum length of 41–58 μm, and ratios D% = 36.8–66.2, SW% = 117.0–206.1, and GS% = 54.8–82.0. Additionally, the tail of first-generation female is conoid with a minute mucron. Phylogenetic analyses of ITS, 28S, and mt12S sequences demonstrated that the three isolates of S. tarimense n. sp. are conspecific and form a sister clade to members of the ‘kushidai-clade’ including S. akhursti, S. anantnagense, S. kushidai, and S. populi. Notably, the IJs of the new species exhibited faster development at 25°C compared to other Steinernema species. This represents the first described of an indigenous EPN species from Xinjiang, suggesting its potential as a novel biocontrol agent against local pests.

Haemonchus contortus is a parasitic nematode that causes significant economic losses in ruminant livestock worldwide. In this study, we assessed the global genetic diversity and population structure of H. contortus using mitochondrial COX1 and ribosomal ITS2 sequences retrieved from the NCBI GenBank database. In total, 324 haplotypes of the COX1 and 72 haplotypes of the ITS2 were identified. The haplotype diversity values were all higher than 0.5, and the nucleotide diversity values were higher than 0.005. The Tajima’s D value for COX1 (−1.65634) was higher than that for ITS2 (−2.60400). Fu’s Fs, Fu and Li’s D (FLD), and Fu and Li’s F (FLF) values also showed high negative values, indicating a high probability of future population growth. In addition, the high fixation index (FST) value suggests significant genetic differentiation among populations. The haplotype networks of H. contortus populations based on COX1 sequences revealed clear geographic clustering, whereas ITS2 sequences showed more haplotype admixture across regions. The results of phylogenetic analyses were consistent with the haplotype networks. These findings highlighted that H. contortus populations exhibit significant genetic variation and are undergoing rapid population expansion, with clear genetic differences across geographic regions. This study established critical baseline data for future molecular epidemiology studies, which could guide region-specific parasite surveillance and targeted control strategies, thus helping to mitigate the risk of cross-border parasite transmission and drug resistance.

This study explored mental workload recognition methods for carrier-based aircraft pilots utilising multiple sensor physiological signal fusion and portable devices. A simulation carrier-based aircraft flight experiment was designed, and subjective mental workload scores and electroencephalogram (EEG) and photoplethysmogram (PPG) signals from six pilot cadets were collected using NASA Task Load Index (NASA-TLX) and portable devices. The subjective scores of the pilots in three flight phases were used to label the data into three mental workload levels. Features from the physiological signals were extracted, and the interrelations between mental workload and physiological indicators were evaluated. Machine learning and deep learning algorithms were used to classify the pilots’ mental workload. The performances of the single-modal method and multimodal fusion methods were investigated. The results showed that the multimodal fusion methods outperformed the single-modal methods, achieving higher accuracy, precision, recall and F1 score. Among all the classifiers, the random forest classifier with feature-level fusion obtained the best results, with an accuracy of 97.69%, precision of 98.08%, recall of 96.98% and F1 score of 97.44%. The findings of this study demonstrate the effectiveness and feasibility of the proposed method, offering insights into mental workload management and the enhancement of flight safety for carrier-based aircraft pilots.

Interlaminar delamination damage is a common and typical defect in the context of structural damage in carbon fiber-reinforced resin matrix composites. The technology to identify such damage is crucial for improving the safety and reliability of structures. In this paper, we fabricated carbon fiber-reinforced composite laminates with different degrees of delamination damage, conducted static load experiments on them and used femtosecond fiber Bragg grating sensors (fsFBG) to determine their structural state to investigate the effects of delamination damage on their performance. We constructed a model to identify damage based on the deep residual shrinkage network, and used experimental data to enable it to identify varying degrees of delamination damage to carbon fiber-reinforced composites with an accuracy of 97.98%.

Aiming at the problem of fast and consensus obstacle avoidance of multiple unmanned aerial systems in undirected network, a multi-quadrotor unmanned aerial vehicles UAVs (QUAVs) finite-time consensus obstacle avoidance algorithm is proposed. In this paper, multi-QUAVs establish communication through the leader-following method, and the formation is led by the leader to fly to the target position automatically and avoid obstacles autonomously through the improved artificial potential field method. The finite-time consensus protocol controls multi-QUAVs to form a desired formation quickly, considering the existence of communication and input delay, and rigorously proves the convergence of the proposed protocol. A trajectory segmentation strategy is added to the improved artificial potential field method to reduce trajectory loss and improve the task execution efficiency. The simulation results show that multi-QUAVs can be assembled to form the desired formation quickly, and the QUAV formation can avoid obstacles and maintain the formation unchanged while avoiding obstacles.

During the investigation of parasitic pathogens of Mytilus coruscus, infection of a Perkinsus-like protozoan parasite was detected by alternative Ray's Fluid Thioglycolate Medium (ARFTM). The diameter of hypnospores or prezoosporangia was 8–27 (15.6 ± 4.0, n = 111) μm. The prevalence of the Perkinsus-like species in M. coruscus was 25 and 12.5% using ARFTM and PCR, respectively. The ITS1-5.8S-ITS2 fragments amplified by PCR assay had 100% homology to that of P. beihaiensis, suggesting that the protozoan parasite was P. beihaisensis and M. coruscus was its new host in East China Sea (ECS). Histological analysis showed the presence of trophozoites of P. beihaiensis in gill, mantle and visceral mass, and the schizonts only found in visceral mass. Perkinsus beihaiensis infection led to inflammatory reaction of hemocyte and the destruction of digestive tubules in visceral mass, which had negative effect on health of the farmed M. coruscus and it deserves more attention.

Three new species of Gyrodactylus were identified from the body surface of the Triplophysa species from the Qinghai-Tibet Plateau, Gyrodactylus triplorienchili n. sp. on Triplophysa orientalis in northern Tibet, G. yellochili n. sp. on T. sellaefer and T. scleroptera and G. triplsellachili n. sp. on T. sellaefer and T. robusta in Lanzhou Reach of the Yellow River. The three newly identified species share the nemachili group species’ characteristic of having inturning hamulus roots. Gyrodactylus triplorienchili n. sp. shared a quadrate sickle heel and a thin marginal hook sickle, two morphological traits that set them apart from G. yellochili n. sp. However, they may be identified by the distinct shapes of the sickle base and marginal hook sickle point. Gyrodactylus triplsellachili n. sp. had much larger opisthaptoral hard part size than the other two species. The three new species show relatively low interspecific differences of 2.9–5.3% p-distance for ITS1-5.85-ITS2 rDNA sequences. Phylogenetic analysis indicated that the three new species formed a well-supported monophyletic group (bp = 99) with the other nemachili group species.

Aircraft ground taxiing contributes significantly to carbon emissions and engine wear. The electric towing tractor (ETT) addresses these issues by towing the aircraft to the runway end, thereby minimising ground taxiing. As the complexity of ETT towing operations increases, both the towing distance and time increase significantly, and the original method for estimating the number of ETTs is no longer applicable. Due to the substantial acquisition cost of ETT and the need to reduce waste while ensuring operational efficiency, this paper introduces for the first time an ETT quantity estimation model that combines simulation and vehicle scheduling models. The simulation model simulates the impact of ETT on apron operations, taxiing on taxiways and takeoffs and landings on runways. Key timing points for ETT usage by each aircraft are identified through simulation, forming the basis for determining the minimum number of vehicles required for airport operations using a hard-time window vehicle scheduling model. To ensure the validity of the model, simulation model verification is conducted. Furthermore, the study explores the influence of vehicle speed and airport scale on the required number of ETTs. The results demonstrate the effective representation of real-airport operations by the simulation model. ETT speed, airport runway and taxiway configurations, takeoff and landing frequencies and imbalances during peak periods all impact the required quantity of ETTs. A comprehensive approach considering these factors is necessary to determine the optimal number of ETTs.

We present direct numerical simulation (DNS) and modelling of incompressible, turbulent, generalized Couette–Poiseuille flow. A particular example is specified by spherical coordinates $(Re,\theta,\phi )$, where $Re = 6000$ is a global Reynolds number, $\phi$ denotes the angle between the moving plate, velocity-difference vector and the volume-flow vector and $\tan \theta$ specifies the ratio of the mean volume-flow speed to the plate speed. The limits $\phi \to 0^\circ$ and $\phi \to 90^\circ$ give alignment and orthogonality, respectively, while $\theta \to 0^\circ,\ \theta \to 90^\circ$ correspond respectively to pure Couette flow in the $x$ direction and pure Poiseuille flow at angle $\phi$ to the $x$ axis. Competition between the Couette-flow shear and the forced volume flow produces a mean-velocity profile with directional twist between the confining walls. Resultant mean-speed profiles relative to each wall generally show a log-like region. An empirical flow model is constructed based on component log and log-wake velocity profiles relative to the two walls. This gives predictions of four independent components of shear stress and also mean-velocity profiles as functions of $(Re,\theta,\phi )$. The model captures DNS results including the mean-flow twist. Premultiplied energy spectra are obtained for symmetric flows with $\phi =90^\circ$. With increasing $\theta$, the energy peak gradually moves in the direction of increasing $k_x$ and decreasing $k_z$. Rotation of the energy spectrum produced by the faster moving velocity near the wall is also observed. Rapid weakening of a spike maxima in the Couette-type flow regime indicates attenuation of large-scale roll structures, which is also shown in the $Q$-criterion visualization of a three-dimensional time-averaged flow.

This essay reflects the journey of two business scholars, Stephen X. Zhang and Jiyao Chen, who ventured into mental health research during the COVID-19 pandemic. We experienced first-hand how health sciences have operated their publication systems in ways that uphold scientific standing while addressing real-world problems. In doing so, we found the publishing expectations and norms in health and medical sciences to be vastly different from those in management. This essay further discusses aspects such as the preference for evidence over theory, the relationship with basic sciences, diverse evaluation criteria, encouragement of exploration and replication, timeliness, and democratization and inclusivity of scholarship as concrete steps of responsible research.