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.

Diet is an important modifiable lifestyle factor for human health, and plant-rich dietary patterns are associated with lower risk of non-communicable diseases in numerous studies. However, objective assessment of plant-rich dietary exposure in nutritional epidemiology studies remains challenging. This study aimed to develop and evaluate metabolic signatures of the most widely used plant-rich dietary patterns using a targeted metabolomics method comprising of 108 plant food metabolites.

A total of 218 healthy participants from the POLYNTAKE cohort were included, aged 51.5 ± 17.7 years, with 24h urine samples measured using ultra-high-performance liquid chromatography–mass spectrometry. The validation dataset employed three sample types to test the robustness of the signature, including 24h urine (ABP cohort, n = 88), plasma (POLYNTAKE cohort, n = 195), and spot urine (TwinsUK cohort, n = 198). Adherence to the plant-rich diet was assessed using a priori plant- rich dietary patterns. A combination of metabolites that evaluates the adherence and metabolic response to a specific diet was identified as metabolic signature. We applied linear regression analysis to select the metabolites significantly associated with dietary patterns (adjusting energy intake), and ridge regression to estimate penalized weights of each candidate metabolite. The correlation between metabolic signature and the dietary pattern was assessed by Spearman analysis (FDR < 0.05).

The metabolic signatures consisting of 42, 22, 35, 15, 33, and 33 predictive metabolites across different subclasses were found to be associated with adherence to Amended Mediterranean Score (A-MED), Original MED (O-MED), Dietary Approaches to Stop Hypertension (DASH), Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND), healthy Plant-based Diet Index (hPDI) and unhealthy PDI (uDPI), respectively. The overlapping and distinct predictive metabolites across six dietary patterns predominantly consisted of phenolic acids (n = 38), including 14 cinnamic acids, 14 hydroxybenzoic acids, seven phenylacetic acids, and three hippuric acids. Six metabolites were included in all signatures, including two lignans: enterolactone-glucuronide, enterolactone-sulfate, and four phenolic acids: cinnamic acid, cinnamic acid-4'-sulfate, 2'- hydroxycinnamic acid, and 4-methoxybenzoic acid-3-sulfate. The established signatures were robustly correlated with dietary patterns in validation dataset (r = 0.13 - 0.40, FDR < 0.05).

We developed and evaluated a set of metabolic signatures that robustly reflected the adherence and metabolic response to plant-rich dietary patterns, suggesting the potential of these signatures to serve as an objective assessment of free-living eating habits.

For the launch vehicle attitude control problem, traditional methods can seldom accurately identify the fault types, making the control method lack of pertinence, which largely affects the effect of attitude control. This paper proposes an active fault tolerant control strategy, which mainly includes fault diagnosis and fault tolerant control. In the fault diagnosis part, a small deviation attitude dynamics model of the launch vehicle is established, Kalman filters with different structures are designed to detect and isolate faults through residual changes, and the fault quantity of the actuator is further estimated. In the fault tolerant control part, the following control scheme is adopted according to the above diagnostic information: when the sensor fault is detected, the sensor measurement data is reconstructed; when the actuator fault is identified, the control allocation matrix is reconstructed. Simulation results show that the proposed method can effectively diagnose sensor fault and actuator faults, and significantly improve attitude tracking accuracy and control adjustment time.

Schistosoma japonicum and S. mansoni were tested for reactivity with an anti-inducible nitric oxide (iNOS) antibody and the distribution of iNOS was studied by immunofluorescent tests in different stages of the parasites. Reactivity was associated with the tegument in both larval schistosomes (sporocysts and cercariae) and eggs. With adult worms, the majority of the immunofluorescence was predominantly subtegumental in S. japonicum and parenchymal in S. mansoni. Fluorescence was also observed in host tissues (snails and mouse liver). In Western blots, the enzyme of S. japonicum had an apparent molecular weight of about 210 kDa. The possible role of worm and host iNOS in the parasite–host interrelation remains to be clarified.

OBJECTIVES/GOALS: Ambulatory methods are useful tools to study physical and mental health in everyday life. While many studies show daily activity improves mood, the effects of daily light exposure on mood remain unknown. This study evaluated the effects of daily natural light exposure and activity on daily mood and evaluate whether depression moderate effects. METHODS/STUDY POPULATION: 82 adults with lifetime major depression disorder (25 current) and 49 healthy controls were recruited from the greater Chicago community (N = 131, 62% female, age M = 30.15, SD = 9.94). At baseline, participants completed the Inventory of Depression and Anxiety Symptoms to measure depression symptoms of anhedonia, or loss of pleasure. Positive and negative affect were then measured 3x daily for 14-days via self-report using smartphones while light exposure and activity were continuously recorded from a wrist-worn actigraphy device. Following prior studies, daily natural light exposure was measured as the total number of white light samples greater than 1000 lux each day. Multilevel models were used to separate within-person (daily level) from between-person (subject level) effects. RESULTS/ANTICIPATED RESULTS: Results revealed daily within-person activity (p < .001) and natural light exposure duration (p = .035) were independently associated with increased positive affect. Effects were significantly moderated by baseline anhedonia symptoms (3-way interaction: p = .004). Natural light exposure duration only increased positive affect on lower activity days for high anhedonia and higher activity days for low anhedonia (ps < .018). Significant results remained controlling for between-person light and activity, time of year, age, sex, negative affect, and baseline general depression symptoms. Compared to one’s own daily averages, daily activity and natural light exposure may be independent pathways to boost positive affect, especially for individuals with high anhedonia symptoms. DISCUSSION/SIGNIFICANCE: Results suggest daily natural light exposure may be an accessible, low-cost alternative to independently increase positive affect in depression on days when activity is low. Translational applications are discussed focusing on transdiagnostic implications for physical and mental health conditions that disrupt mood and limit activity.

Polarized electron beam production via laser wakefield acceleration in pre-polarized plasma is investigated by particle-in-cell simulations. The evolution of the electron beam polarization is studied based on the Thomas–Bargmann–Michel–Telegdi equation for the transverse and longitudinal self-injection, and the depolarization process is found to be influenced by the injection schemes. In the case of transverse self-injection, as found typically in the bubble regime, the spin precession of the accelerated electrons is mainly influenced by the wakefield. However, in the case of longitudinal injection in the quasi-1D regime (for example, F. Y. Li et al., Phys. Rev. Lett. 110, 135002 (2013)), the direction of electron spin oscillates in the laser field. Since the electrons move around the laser axis, the net influence of the laser field is nearly zero and the contribution of the wakefield can be ignored. Finally, an ultra-short electron beam with polarization of $99\%$ can be obtained using longitudinal self-injection.

With the wide application of quadrotor unmanned aerial vehicles (UAVs), the requirements for their safety and reliability are becoming increasingly stringent. In this paper, based on the feedback of airframe performance health perception information and the predictive function control strategy, the autonomous maintenance of a quadrotor UAV with multi-actuator degradation is realised. Autonomous maintenance architecture is constructed by the predictive maintenance (PdM) idea and the Laguerre function model predictive pontrol (LF-MPC) strategy. Using the two-stage Kalman filter (TSKF) method, based on the established UAV degradation model, the aircraft state and actuator degradation state are predicted simultaneously. For the predictive perception of system health, on the one hand, the system health degree (HD) based on Mahalanobis distance is defined by the degree of airframe state deviation from the expected state, and then the failure threshold of the UAV is obtained. On the other hand, according to the degradation state of each actuator, a comprehensive degradation variable fused with different weight coefficients of multiple actuators degradation is used to obtain the probability density function (PDF) of remaining useful life (RUL) prediction. For the autonomous maintenance of system health, the LF-MPC weight matrixes are adjusted adaptively in real-time based on the HD evaluation, to achieve a compromise balance between UAV performance and control effect, and greatly extend the working time of UAV. Simulation results verified the effectiveness of the proposed method.

Echinococcosis poses a significant threat to public health. The Chinese government has implemented prevention and control measures to mitigate the impact of the disease. By analyzing data from the Chinese Center for Disease Control and Prevention and the State Council of the People’s Republic of China, we found that implementation of these measures has reduced the infection rate by nearly 50% between 2004 to 2022 (from 0.3975 to 0.1944 per 100,000 person-years). Nonetheless, some regions still bear a significant disease burden, and lack of detailed information limites further evaluation of the effects on both alveolar and cystic echinococcosis. Our analysis supports the continuing implementation of these measures and suggests that enhanced wildlife management, case-based strategies, and surveillance systems will facilitate disease control.