Species of the genus Corynosoma (Acanthocephala: Polymorphida) mainly parasitize marine mammals and rarely marine birds, and are of veterinary and medical importance due to causing corynosomiasis in wildlife and humans. However, the current knowledge of the mitochondrial genomes and mitogenomic phylogeny of this group remains very insufficient. In the present study, the complete mitochondrial genomes of C. bullosum (von Linstow, 1892) and C. evae Zdzitowiecki, 1984 were sequenced and annotated for the first time. Both mitogenomes comprise 12 protein-coding genes (missing atp8), 22 tRNA genes, and 2 ribosomal RNAs (rrnS and rrnL), plus 2 non-coding regions (NCR1 and NCR2). Corynosoma bullosum has the largest mitogenome (14,879 bp) of any polymorphid species reported so far, while C. evae has the smallest (13,947 bp), except for Sphaerirostris lanceoides (Petrochenko, 1949). Comparative mitogenomic analysis also revealed the presence of distinct discrepancies in A + T content and gene rearrangement across the families Polymorphidae, Centrorhynchidae, and Plagiorhynchidae. Moreover, phylogenetic analyses based on the concatenated amino acid sequences of 12 protein-coding genes strongly supported the monophyly of the order Polymorphida and a close affinity between the families Polymorphidae and Centrorhynchidae in Polymorphida. The present mitogenomic phylogeny provides additional evidence for a sister relationship between the genera Corynosoma and Bolbosoma and demonstrated that C. evae has a closer relationship with C. villosum than C. bullosum in the genus Corynosoma.

Unmanned aerial vehicle (UAV) formations for bearing-only passive detection are increasingly important in modern military confrontations, and the array of the formation is one of the decisive factors affecting the detection accuracy of the system. How to plan the optimal geometric array in bearing-only detection is a complex nondeterministic polynomial problem, and this paper proposed the distributed stochastic subgradient projection algorithm (DSSPA) with layered constraints to solve this challenge. Firstly, based on the constraints of safe flight altitude and fixed baseline, the UAV formation is layered, and the system model for bearing-only cooperative localisation is constructed and analysed. Then, the calculation formula for geometric dilution of precision (GDOP) in the observation plane is provided, this nonlinear objective function is appropriately simplified to obtain its quadratic form, ensuring that it can be adapted and used efficiently in the system model. Finally, the proposed distributed stochastic subgradient projection algorithm (DSSPA) combines the idea of stochastic gradient descent with the projection method. By performing a projection operation on each feasible solution, it ensures that the updated parameters can satisfy the constraints while efficiently solving the convex optimisation problem of array planning. In addition to theoretical proof, this paper also conducts three simulation experiments of different scales, validating the effectiveness and superiority of the proposed method for bearing-only detection array planning in UAV formations. This research provides essential guidance and technical reference for the deployment of UAV formations and path planning of detection platforms.

As avionics systems become increasingly complex, traditional fault prediction methods are no longer sufficient to meet modern demands. This paper introduces four advanced fault prediction methods for avionics components, utilising a multi-step prediction strategy combined with a stacking regressor. By selecting various standard regression models as base regressors, these base regressors are first trained on the original data, and their predictions are subsequently used as input features for training a meta-regressor. Additionally, the Tree-structured Parzen Estimator (TPE) algorithm is employed for hyperparameter optimisation. The experimental results demonstrate that the proposed stacking regression methods exhibit superior accuracy in fault prediction compared to traditional single-model approaches.

Increasing evidence indicates that (poly)phenol consumption can have beneficial effects in human brain function(1). The overall aim of this project is to investigate whether consumption of a (poly)phenol rich cranberry drink improve mental health and cognitive function in university students via the gut-brain axis.

A parallel randomised controlled trial was conducted in 72 young healthy final year university students assigned to consume 236 mL cranberry drink (442 mg polyphenols) or nutrient matched placebo, daily for 12 weeks. The primary outcome was mood, measured as Total Mood Disturbance (TMD), using the Profile of Mood States (POMS) questionnaire. Secondary outcomes included stress, anxiety and depression levels, measured using the Perceived Stress Scale (PSS), and the Hospital Anxiety Depression Scale (HADS) questionnaire, salivary cortisol levels and cognitive function measured using the Online General Cognitive Assessment Battery (CogniFit). All of these questionnaires were measured every 4 weeks. Blood and urine samples were collected to measure inflammatory markers, gut-brain-axis metabolites, short chain fatty acids (SCFA), and cranberry polyphenols metabolites. Faecal samples were also collected for measuring gut microbiome diversity and composition and faecal metabolomics. Diet was assessed using food frequency questionnaires (FFQ), 7-day food diaries (EPIC) and 24 h online dietary recalls (intake 24) during the study. Linear mixed-effect model (LMM) and ANCOVA were used to investigate the relationship between the 2 trial arms.

No significant differences were found between treatments for mood, stress levels, anxiety, depression, circulating short chain fatty acids or inflammatory markers, however a significantly lower diurnal area under the curve of salivary cortisol (p = 0.010) and significantly higher short-term memory (Z-score: p = 0.024; Raw-score: p = 0.034) was found at 12 weeks in the cranberry group compared with the placebo. Besides, significant differences between interventions were found in plasma gut-brain axis metabolites, including the kynurenine and bile acids pathway which were kynurenine (p = 0.023), kynurenic (p = 0.032), quinolinic (p = 0.018), and glycolithocholic acid (p = 0.04) as well as several plasma and urinary cranberry (poly)phenol metabolites were modulated by cranberry consumption.

In conclusion, daily cranberry (poly)phenol supplementation for 12 week did not improve mood, stress, anxiety, and depression symptoms in healthy university students, however it may modulate cortisol levels and some aspects of cognitive function via the gut-brain axis.

(Poly)phenol intake has been associated with reduced risk of non-communicable diseases in epidemiological studies1,2. However, there are no dietary assessment tools that have been developed to estimate (poly)phenol intake in the UK. This study aimed to develop a novel food frequency questionnaire (FFQ) to capture dietary (poly)phenol intake in the UK and assess its relative validity by comparison with 7-day diet diaries (7DD) and plasma and urine (poly)phenols metabolites.

The (poly)phenol FFQ was developed based on the EPIC-Norfolk FFQ validated for energy and nutrient intake estimation in the UK population3,4. Food items added to the FFQ based on total (poly)phenols content (≥ 1 mg/serving) and UK consumption. Participants aged 18 ∼ 29 years (n=255) completed the EPIC-Norfolk FFQ and a (poly)phenol FFQ. In a subgroup (n=60), 7DD, spot urine, and fasting plasma samples were collected. An in-house (poly)phenol database was used to estimate (poly)phenol intake from FFQs and 7DD. Plasma and urinary (poly)phenol metabolite levels were analysed using a validated LC-MS method5. The agreements between (poly)phenol intake estimated using the (poly)phenol and EPIC-Norfolk FFQ and 7DDs, as well as plasma and urinary biomarkers, were evaluated by intraclass correlation coefficients (ICC), weighted kappa, quartile classification, and Spearman correlations, and the associations were investigated using linear regression models adjusting for energy intake and multiple testing (FDR < 0.05).

Strong agreements were observed between hydroxycinnamic acids estimated from (poly)phenol FFQ and 7DDs (kappa=0.75), fair agreements were found between 9 (poly)phenol groups, including total (poly)phenol intake (kappa=0.41), while the agreements for the rest of 17 classes and subclasses of (poly)phenols were poor (kappa: 0.07 ∼ 0.39). Strong positive associations were found in 9 (poly)phenols estimated from 7DDs, including dihydroflavonols, flavones, ellagitannins, hydroxyphenylacetic acids, total stilbenes, resveratrol, total other (poly)phenols, tyrosols, and alkylphenols with stdBeta from 0.62 (95% CI: 0.42 ∼ 0.82) to 0.95 (0.86 ∼ 1.00) (FDR p < 0.05). (Poly)phenol FFQs estimated (poly)phenol intake exhibited positive associations with 99 urinary metabolites (stdBeta: 0.27 (0.06 ∼ 0.48) to 0.88 (0.72 ∼ 1.03)) and 25 plasma metabolites (stdBeta:

0.39 (0.17 ∼ 0.62) to 0.83 (0.64 ∼ 1.02)) (FDR p < 0.05). The agreement between (poly)phenol FFQs and the EPIC-Norfolk FFQs was moderate (ICC 0.51 ∼ 0.69) for all (poly)phenol intake after adjusting for energy intake. Compared with the EPIC-Norfolk FFQs estimated (poly)phenol intake, stronger and more agreements and associations were found in (poly)phenol FFQs estimated (poly)phenol with 7DDs and biomarkers.

(Poly)phenols estimated from (poly)phenol FFQ exhibited fair agreements and moderate to strong associations with 7DDs and biomarkers in the target population, indicating the novel questionnaire may be a promising tool to assess (poly)phenol intake. A representative UK population group is warranted for to test the further usability.

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%.

To meet the development needs of aeroengines for high thrust-to-weight ratios and fuel-air ratios, a high temperature rise triple-swirler main combustor was designed with a total fuel-air ratio of 0.037, utilising advanced technologies including staged combustion, multi-point injection and multi-inclined hole cooling. Fluent software was used to conduct numerical simulations under both takeoff and idle conditions, thereby obtaining the distribution characteristics of the velocity and temperature fields within the combustor, as well as the generation of pollutants. The simulation results indicate that under takeoff conditions, the high temperature rise triple-swirler combustor achieves a total pressure loss coefficient of less than 6% and a combustion efficiency exceeding 99%. Under takeoff conditions, the OTDF and RTDF values are 0.144 and 0.0738, respectively. The mole fraction of NOx emissions is 3,700ppm, while the mole fraction of soot emissions is 2.55×10−5ppm. Under idle conditions, the triple-swirler combustor maintains a total pressure loss coefficient of less than 6% and a combustion efficiency greater than 99.9%. The OTDF and RTDF values are 0.131 and 0.0624, respectively. The mole fractions of CO and UHC emissions are both 0×10−32ppm at the calculation limit of Fluent software.

This paper develops a novel full-state-constrained intelligent adaptive control (FIAC) scheme for a class of uncertain nonlinear systems under full state constraints, unmodeled dynamics and external disturbances. The key point of the proposed scheme is to appropriately suppress and compensate for unmodeled dynamics that are coupled with other states of the system under the conditions of various disturbances and full state constraints. Firstly, to guarantee that the time-varying asymmetric full state constraints are obeyed, a simple and valid nonlinear error transformation method has been proposed, which can simplify the constrained control problem of the system states into a bounded control problem of the transformed states. Secondly, considering the coupling relationship between the unmodeled dynamics and other states of the controlled system such as system states and control inputs, a decoupling approach for coupling uncertainties is introduced. Thereafter, owing to the employed dynamic signal and bias radial basis function neural network (BIAS-RBFNN) improved on traditional RBFNN, the adverse effects of unmodeled dynamics on the controlled system can be suppressed appropriately. Furthermore, the matched and mismatched disturbances are reasonably estimated and circumvented by a mathematical inequality and a disturbance observer, respectively. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed FIAC strategy.

During the automatic docking assembly of aircraft wing-fuselage, using monocular camera or dual-camera to monitor the docking stage of the fork-ear will result in an incomplete identification of the fork-ear pose-position and an inaccurate description of the deviation in the intersection holes’ position coordinates. To address this, a quality inspection and error correction method is proposed for the fork-ear docking assembly based on multi-camera stereo vision. Initially, a multi-camera stereo vision detection system is established to inspect the quality of fork-ear docking assembly. Subsequently, a spatial position solution mathematical model of the fork-ear feature points is developed, and a spatial pose determination mathematical model of fork-ear is established by utilised the elliptical cone. Finally, an enhanced artificial fish swarm particle filter algorithm is proposed to track and estimate the coordinate of the fork-ear feature points. An adaptive weighted fusion algorithm is employed to fuse the detection data from the multi-camera and the laser tracker, and a wing pose-position fine-tuning error correction model is constructed. Experimental results demonstrate that the method enhances the effect of the assembly quality inspection and effectively improves the wing-fuselage docking assembly accuracy of the fork-ear type aircraft.

Placing an inertial measurement unit (IMU) at the 5th lumbar vertebra (L5) is a frequently employed method to assess the whole-body center of mass (CoM) motion during walking. However, such a fixed position approach does not account for instantaneous changes in body segment positions that change the CoM. Therefore, this study aimed to assess the congruence between CoM accelerations obtained from these two methods. The CoM positions were calculated based on trajectory data from 49 markers placed on bony landmarks, and its accelerations were computed using the finite-difference algorithm. Concurrently, accelerations were obtained with an IMU placed at L5, a proxy CoM position. Data were collected from 16 participants. Bland–Altman Limits of Agreement and Statistical Parametric Mapping approaches were used to examine the similarity and differences between accelerations directly obtained from the IMU and those derived from position data of the L5 marker (ML5) and whole-body CoM during a gait cycle. The correlation was moderate between IMU and CoM accelerations (r = 0.58) and was strong between IMU and ML5 or between CoM and ML5 accelerations (r = 0.76). There were significant differences in magnitudes between CoM and ML5 and between CoM and IMU accelerations along the anteroposterior and mediolateral directions during the early loading response, mid-stance, and terminal stance to pre-swing. Such comprehensive understanding of the similarity or discrepancy between CoM accelerations acquired by a single IMU and a camera-based motion capture system could further improve the development of wearable sensor technology for human movement analysis.

The cyst nematodes, subfamily Heteroderinae, are plant pathogens of worldwide economic significance. A new cyst nematode of the genus Cactodera within the Heteroderinae, Cactodera xinanensis n. sp., was isolated from rhizospheres of crops in the Guizhou and Sichuan provinces of southwest China. The new species was characterized by having the cyst with a length/width = 1.3 ± 0.1 (1.1–1.6), a fenestral diameter of 28.1 ± 4.3 (21.3–38.7) μm, vulval denticles present; second-stage juvenile with stylet 21.5 ± 0.5 (20.3–22.6) μm long, tail 59.4 ± 2.0 (55.9–63.8) μm long and hyaline region 28.7 ± 2.7 (25.0–36.3) μm long, lateral field with four incisures; the eggshell with punctations. The new species can be differentiated from other species of Cactodera by a longer tail and hyaline region of second-stage juveniles. Phylogenetic relationships within populations and species of Cactodera are given based on the analysis of the internal transcribed spacer (ITS-rRNA), the large subunit of the nuclear ribosomal RNA (28S-rRNA) D2-D3 region and the partial cytochrome oxidase subunit I (COI) gene sequences here. The ITS-rRNA, 28S-rRNA and COI gene sequences clearly differentiated Cactodera xinanensis n. sp. from other species of Cactodera. A key and a morphological identification characteristic table for the species of Cactodera are included in the study.

Soluble Intercellular Adhesion Molecule-1 (sICAM-1) has emerged as an inflammatory biomarker of many essential functions. We investigated the level of sICAM-1 influenced by Clonorchis sinensis (C. sinensis) co-infection in chronic hepatitis B (CHB) patients to explore the degree of liver tissue inflammation and liver function damage after co-infection. The study included data from patients with C. sinensis mono-infection (n=27), hepatitis B virus (HBV) mono-infection (n=32), C. sinensis and HBV co-infection (n=24), post-hepatitis B liver cirrhosis (n=18), post-hepatitis B liver cirrhosis co-infected with C. sinensis (n=16), and healthy controls (n=39). The level of sICAM-1 was measured with specific enzyme-linked immunosorbent assay method. Compared to the healthy control group, all the experimental groups had significantly higher serum sICAM-1 levels. The levels of sICAM-1 in co-infected groups were significantly higher compared to the mono-infection groups and were positively correlated with the levels of glutamate aminotransferase (ALT) and aspartate aminotransferase (AST). Our research findings confirmed that co-infection could exacerbate liver tissue inflammation and liver function damage in patients, could raise the sICAM-1 level, and may lead to the chronicity of HBV infection. These results provide clues for pathological mechanism study and formulating treatment plans.

This study presents observations of coherent modes (CMs) in a spherical tokamak using a microwave interferometer near the midplane. The CMs within the 30–60 kHz frequency range were observed during electron cyclotron resonance heating only, and the frequency of the CMs increased proportionally with the square root of the electron temperature near $R = 0.7m$. Generally, these modes displayed bursting and chirping signatures with strong density rise and fall. Their appearance indicated an increase in the intensity of hard x rays, suggesting a deterioration in energetic electron confinement. Furthermore, the effect of CMs on the intensity of energetic electron-driven whistler waves was observed. They decreased when CMs were present and gradually increased with the decrease in CM intensity. The CMs may influence the intensity of whistler waves by affecting the energetic electron confinement.

This study presents a direct numerical simulation that investigates the transport mechanisms of sand particles in the neutrally stable atmospheric Ekman boundary layer (AEBL). The simulation models the AEBL in a half-channel flow, taking into account the Earth's rotation and adding a Coriolis term to the Navier–Stokes equations. The Lagrangian point-particle method with one-way coupling is used to track the sand particles. The main objective is to examine the impact of gravity and particle Stokes number on the sand particle dynamics. The results indicate that gravity has a significant effect on large-size sand particles, as seen in the mean and root-mean-square sand velocity profiles. The slip velocity profiles of sand particles relative to the surrounding air show that larger particles experience higher drag forces in the viscous sublayer, which hinders their forward movement. This effect is also amplified by gravity. Furthermore, the mean profiles of the streamwise and spanwise slip velocities exhibit distinct demarcations of the viscous sublayer, buffer layer and log-law region. The spatial and temporal Voronoï analysis reveals that gravity increases the clustering level of sand particles in the entire turbulent Ekman layer and reduces the time for the change of the Voronoï volume, particularly for large-size particles.

We optimize jet mixing using large eddy simulations (LES) at a Reynolds number of $3000$. Key methodological enablers consist of Bayesian optimization, a surrogate model enhanced by deep learning and persistent data topology for physical interpretation. The mixing performance is characterized by an equivalent jet radius ($R_{eq}$) derived from the streamwise velocity in a plane located $8$ diameters downstream. The optimization is performed in a 22-dimensional actuation space that comprises most known excitations. This search space parameterizes the distributed actuation imposed on the bulk flow and at the periphery of the nozzle in the streamwise and radial directions. The momentum flux measures the energy input of the actuation. The optimization quadruples the jet radius $R_{eq}$ with a $7$-armed blooming jet after around $570$ evaluations. The control input requires $2\,\%$ momentum flux of the main flow, which is one order of magnitude lower than an ad hoc dual-mode excitation. Intriguingly, a pronounced suboptimum in the search space is associated with a double-helix jet, a new flow pattern. This jet pattern results in a mixing improvement comparable to the blooming jet. A state-of-the-art Bayesian optimization converges towards this double-helix solution. The learning is accelerated and converges to another better optimum by including a deep-learning-enhanced surrogate model trained along the optimization. Persistent data topology extracts the global and many local minima in the actuation space. These minima can be identified with flow patterns beneficial to the mixing.