This paper focuses on the feature-based visual-inertial odometry (VIO) in dynamic illumination environments. While the performance of most existing feature-based VIO methods is degraded by the dynamic illumination, which leads to unstable feature association, we propose a tightly-coupled VIO algorithm termed RAFT-VINS, integrating a Lite-RAFT tracker into the visual inertial navigation system (VINS). The key module of this odometry algorithm is a lightweight optical flow network designed for accurate feature tracking with real-time operation. It guarantees robust feature association in dynamic illumination environments and thereby ensures the performance of the odometry. Besides, to further improve the accuracy of the pose estimation, a moving consistency check strategy is developed in RAFT-VINS to identify and remove the outlier feature points. Meanwhile, a tightly-coupled optimization-based framework is employed to fuse IMU and visual measurements in the sliding window for efficient and accurate pose estimation. Through comprehensive experiments in the public datasets and real-world scenarios, the proposed RAFT-VINS is validated for its capacity to provide trustable pose estimates in challenging dynamic illumination environments. Our codes are open-sourced on https://github.com/USTC-AIS-Lab/RAFT-VINS.

Connecting individual robots to form an inter-reconfigurable system with a flexible base size enhances the ability to access and cover areas for cleaning and maintenance tasks. Given that increased configuration complexity expands the search space dimension, an optimal routing solution ensuring efficiency is essential. In this paper, we present an inter-reconfigurable multi-robot system capable of adjusting the bases of its two units, along with an optimal path planning approach for confined spaces based on a modified informed rapidly-exploring random tree algorithm by a greedy set (RIRRT*). We validate the navigation of the proposed inter-reconfigurable platform using RIRRT* for four informed dimensional search spaces as a case study in both simulated and real-world environments. The proposed path planning method for the inter-reconfigurable system outperformed conventional strategies, achieving significant reduction in both execution time and energy utilization.

This cross-sectional study aimed to investigate the correlation between magnesium consumption and periodontitis in different body mass index (BMI) and waist circumference (WC) groups. 8385 adults who participated in the National Health and Nutrition Examination Survey during 2009–2014 were included. The correlation between dietary magnesium intake and periodontitis was first tested for statistical significance by descriptive statistics and weighted binary logistic regression. Subgroup analysis and interaction tests were performed to investigate whether the association was stable in different BMI and WC groups. There was a statistical difference in magnesium intake between periodontitis and non-periodontitis populations. In model 3, participants with the highest magnesium consumption had an odds ratio of 0.72 (0.57-0.92) for periodontitis compared to those with the lowest magnesium consumption. However, in subgroup analysis, the relationship between magnesium intake and periodontitis remained significant only in the non-general obese (BMI ≤ 30 kg/m2) and non-abdominal obese populations (WC ≤ 102 cm in men and ≤ 88 cm in women). Dietary magnesium intake might decrease the periodontitis prevalence in the American population, and this beneficial periodontal health role of magnesium consumption might only be evident in non-general obese and non-abdominal obese populations.

The oriental armyworm, Mythimna separata (Walker), is a highly migratory pest known for its sudden larval outbreaks, which result in severe crop losses. These unpredictable surges pose significant challenges for timely and accurate monitoring, as conventional methods are labour-intensive and prone to errors. To address these limitations, this study investigates the use of machine learning for automated and precise identification of M. separata larval instars. A total of 1577 larval images representing different instar were analysed for geometric, colour, and texture features. Additionally, larval weight was predicted using 13 regression models. Instar identification was conducted using Support Vector Classifier (SVC), Random Forest, and Multi-Layer Perceptron. Key feature contributing to classification accuracy were subsequently identified through permutation feature importance analysis. The results demonstrated the potential of machine learning for automating instar identification with high efficiency and accuracy. Predicted larval weight emerged as a key feature, significantly enhancing the performance of all identification models. Among the tested approaches, BaggingRegressor exhibited the best performance for larval weight prediction (R2 = 98.20%, RMSE = 0.2313), while SVC achieved the highest instar identification accuracy (94%). Overall, the integration of larval weight with other image-derived features proved to be a highly effective strategy. This study demonstrates the efficacy of machine learning in enhancing pest monitoring systems by providing a scalable and reliable framework for precise pest management. The proposed methodology significantly improves larval instar identification accuracy and efficiency, offering actionable insights for implementing targeted biological and chemical control strategies.

The cosmic 21 cm signal serves as a crucial probe for studying the evolutionary history of the Universe. However, detecting the 21 cm signal poses significant challenges due to its extremely faint nature. To mitigate the interference from the Earth’s radio frequency interference (RFI), the ground and the ionospheric effects, the Discovering the Sky at the Longest Wavelength (DSL) project will deploy a constellation of satellites in lunar orbit, with its high-frequency daughter satellite tasked with detecting the global 21 cm signal from cosmic dawn and reionization era (CD/EoR). We intend to employ the vari-zeroth-order polynomial (VZOP) for foreground fitting and subtracting. We have studied the effect of thermal noise, thermal radiation from the Moon, the lunar reflection, anisotropic frequency-dependent beam, inaccurate antenna beam pattern, and RFI contamination. We discovered that the RFI contamination can significantly affect the fitting process and thus prevent us from detecting the signal. Therefore, experimenting on the far side of the moon is crucial. We also discovered that using VZOP together with DSL, after 1080 orbits around the Moon, which takes about 103 days, we can successfully detect the CD/EoR 21 cm signal.

A vast amount of empirical and theoretical research on public good games indicates that the threat of punishment can curb free-riding in human groups engaged in joint enterprises. Since punishment is often costly, however, this raises an issue of second-order free-riding: indeed, the sanctioning system itself is a common good which can be exploited. Most investigations, so far, considered peer punishment: players could impose fines on those who exploited them, at a cost to themselves. Only a minority considered so-called pool punishment. In this scenario, players contribute to a punishment pool before engaging in the joint enterprise, and without knowing who the free-riders will be. Theoretical investigations (Sigmund et al., Nature 466:861–863, 2010) have shown that peer punishment is more efficient, but pool punishment more stable. Social learning, i.e., the preferential imitation of successful strategies, should lead to pool punishment if sanctions are also imposed on second-order free-riders, but to peer punishment if they are not. Here we describe an economic experiment (the Mutual Aid game) which tests this prediction. We find that pool punishment only emerges if second-order free riders are punished, but that peer punishment is more stable than expected. Basically, our experiment shows that social learning can lead to a spontaneously emerging social contract, based on a sanctioning institution to overcome the free rider problem.

China has dedicated significant efforts to preventing obesity, but the rising prevalence of overweight and obesity remains a pressing public health issue. Therefore, unique solutions are required to address this challenge in China. As a research priority, the food environment plays a pivotal role in addressing overweight and obesity. However, research on this topic in China lags behind that in other developed countries, and the conflicting global evidence on the association between the food environment and obesity cannot be directly applied to policymaking and intervention in China. In addition, the rapid advancement of digital technology has introduced complexities and uncertainties in the food environment. To address these challenges, we propose an alternative research framework through (a) dissecting the challenges associated with defining and measuring the food environment, (b) reorganising the relationship chains between the food environment and human diet/health and (c) taking into consideration digital platforms as crucial monitoring tools for studying the food environment. Our framework aims to unlock the potential of food environment research in the digital age, ultimately striving to tackle the overweight and obesity issues in China.

This study investigates the stability and instability of the language control network in bilinguals using longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) data. We compared the language control network of Chinese university students majoring in English with those not, using three other functional networks as controls. Results indicate that the English major group exhibits reduced stability and increased instability in the language control network compared with the non-English major group. This suggests that second language (L2) learning experience may induce adaptive neural changes. Moreover, the coexistence of stability and instability in the language control network appears less modular in the English major group, implying a more integrated response to language experience. Notably, these results were not observed in the control networks. Overall, these findings enhance the understanding of bilingual language control and the impact of L2 learning on neural plasticity.

Grounded in Hofstede cultural dimensions theory, we examine how informal institutional factors shape cross-country venture capital (VC) flows. Separating VC activity into flows, our method studies how an increment in inflows supports ventures, and an increment in outflows more investing activity. Results suggest that (1) uncertainty avoidance negatively affects investors and ventures (the last with a larger effect), (2) individualistic attitudes equally support both investors and ventures, and (3) a higher level of power distance contributes to a larger private investors sector, an effect that is greater under strong formal institutions (FIs). Effects of masculinity, long-term orientation, and indulgence are inconclusive. Results are robust to various specifications, use of instruments, and endogeneity treatments. The implication is that the optimal characteristics of informal institutions for fostering VC activity differ depending on the level of FIs, as both institutions interact to affect both investors and ventures.

Although nudibranchs are common and attractive animals, our understanding of these marine gastropods in Vietnam remains limited. Prior research has suggested that combining morphological examination with molecular analysis results in more accurate identification of nudibranchs. However, previous studies in Vietnam have typically relied solely on morphological methods for nudibranch identification. In this study, the nudibranch species Halgerda batangas was recorded in Vietnam for the first time based on both morphological and molecular approaches. Halgerda batangas was characterized by a network pattern consisting of orange lines, relatively low dorsal tubercles with red-orange caps and white basal rings, and an orange line along the foot margin. Molecular analysis corroborated the morphological findings. These results suggest that integrating morphological and molecular methods is an effective approach for identifying nudibranchs.

The TRiC/CCT complex assists in the folding of approximately 10% of cytosolic proteins through an ATP-driven conformational cycle, playing a crucial role in maintaining protein homeostasis. Despite our understanding of ATP-driven TRiC ring closing and substrate folding, the process and mechanisms underlying TRiC ring-opening and substrate release remain largely unexplored. In this study, by determining an ensemble of cryo-EM structures of yeast TRiC in the presence of ADP, including three intermediate transition states, we present a comprehensive picture of the TRiC ring-opening process. During this process, CCT3 detects the loss of γ-phosphate and initiates with the dynamics of its apical protrusion, and expands to the outward leaning of the consecutive CCT6/8/7/5 subunits. This is followed by significant movements of CCT2, CCT4, and especially CCT1 subunits, resulting in the opening of the TRiC rings. We also observed an unforeseen temporary separation between the two rings in the CCT2 side, coordinating the release of the originally locked CCT4 N-terminus, which potentially participates in the ring-opening process. Collectively, our study reveals a stepwise TRiC ring-opening mechanism, provides a comprehensive view of the TRiC conformational landscape, and sheds lights on its subunit specificity in sensing nucleotide status and substrate release. Our findings deepen our understanding of protein folding assisted by TRiC and may inspire new strategies for the diagnosis and treatment of related diseases.

Degrees, unlike entities or events, refer to comparative qualities and are closely tied to gradable adjectives such as “tall.” Degree expressions have been explored in second language (L2) research, covering areas such as learnability, first language (L1) transfer, contrastive analysis, and acquisition difficulty. However, a computational approach to the learning of degree expressions in L2 contexts, particularly for L1 Chinese learners of English, has not been thoroughly investigated. This study aims to fill this gap by utilizing natural language processing (NLP) methods, drawing insights from recent advancements in large language models (LLMs). This study extends Cong (2024)’s general-purpose assessment pipeline to specifically analyze degree expressions, predicting that surprisal metrics will correlate with proficiency levels and distinct developmental stages of L2 learners. Crucially, we address the limitations of surprisal metrics in capturing underuse or avoidance—common in L2 development—by integrating frequency-based analyses. Using an NLP pipeline developed with Stanza, we automatically identified and analyzed degree expressions, constructing linear mixed-effects models to track L2 development trajectories. Our findings reveal that as proficiency increases, learners use complex degree expressions more frequently, supporting theories linking difficulty and learnability. Higher surprisal values are associated with lower proficiency in using degree expressions, and these surprisals are more predictive of degree expressions proficiency than classic NLP measures. These results add further evidence that LLMs and NLP tools provide valuable insights into L2 development, specifically in the domain of degree expressions, expanding upon previous research and offering new approaches for understanding L2 learning processes.

The fingers known as bubbles (spikes) resulting from the penetration of light (heavy) fluids into heavy (light) fluids are significant large-scale features of Richtmyer–Meshkov instability (RMI). Through shock-tube experiments, we study finger collisions in light fluid layers under reshock conditions. Four unperturbed fluid layers with varying thicknesses are created to analyse the motion of waves and interfaces during finger collisions. The wave dynamics, sensitive to initial layer thicknesses, are characterized by a one-dimensional theory. Eight perturbed fluid layers, with four thicknesses and two interface phase combinations, are generated to explore the finger collision mechanism. It is shown that after reshock, the initial in-phase and anti-phase cases undergo spike–bubble rear-end collisions (SBCs) and spike–spike head-on collisions (SSCs), respectively. Compared with SBCs, SSCs significantly suppress spike growth, leading to the attenuation of perturbation growth, especially for larger thicknesses. As the initial thickness decreases, an SSC impedes the downstream interface from reversing its phase, resulting in abnormal RMI, thereby reducing the SSC's effectiveness in attenuating growth. The effects of rarefaction waves enhance both interfaces’ amplitudes and the whole layer's thickness, diminishing the intensity of finger collisions, while the second reshock exerts an opposing influence. Linear and nonlinear models, incorporating the influence of reshocks and rarefaction waves, are developed to predict the interface perturbation growth before and after finger collisions.

Fe2+-catalyzed transformation of poorly crystalline ferrihydrite into highly crystalline forms is critical in the biogeochemical cycles of Fe, nutrients, and trace elements. The co-existence of ferrihydrite and kaolinite is widespread in soils of tropical and subtropical regions. In this investigation, three associations of ferrihydrite–kaolinite with ratios of 10, 30, and 50% (10% Fhy–Kln, 30% Fhy–Kln, and 50% Fhy–Kln) were examined to study the impact of the initial Fe2+ concentration and pH on Fe2+-catalyzed transformation under anoxic conditions. The findings reveal that the ferrihydrite in the 10% Fhy–Kln associations has the smallest particle size and the largest number of surface hydroxyl groups. At 0.5 mM Fe2+ and pH 7.5, ferrihydrite underwent transformation into lepidocrocite, with the presence of kaolinite promoting the formation of goethite. Moreover, the presence of kaolinite influenced the morphology of the resulting transformation products. A decrease in pH hindered the transformation of ferrihydrite, while an increase in Fe2+ concentration resulted in the formation of magnetite. The impact of kaolinite in the association system on the transformations of ferrihydrite occurs primarily through alteration of the properties of ferrihydrite during its formation process.