Little is known about the association between iodine nutrition status and bone health. The present study aimed to explore the connection between iodine nutrition status, bone metabolism parameters, and bone disease (osteopenia and osteoporosis). A cross-sectional survey was conducted involving 391, 395, and 421 adults from iodine fortification areas (IFA), iodine adequate areas (IAA), and iodine excess areas (IEA) of China. Iodine nutrition status, bone metabolism parameters and BMD were measured. Our results showed that, in IEA, the urine iodine concentrations (UIC) and serum iodine concentrations (SIC) were significantly higher than in IAA. BMD and Ca2+ levels were significantly different under different iodine nutrition levels and the BMD were negatively correlated with UIC and SIC. Univariate linear regression showed that gender, age, BMI, menopausal status, smoking status, alcohol consumption, UIC, SIC, free thyroxine, TSH, and alkaline phosphatase were associated with BMD. The prevalence of osteopenia was significantly increased in IEA, UIC ≥ 300µg/L and SIC > 90µg/L groups. UIC ≥ 300µg/L and SIC > 90µg/L were risk factors for BMD T value < -1.0 SD. In conclusion, excess iodine can not only lead to changes in bone metabolism parameters and BMD, but is also a risk factor for osteopenia and osteoporosis.

In this paper, we propose a hybrid sparse array design utilizing Delaunay Triangulation algorithm for element positioning and Convex algorithm for element excitation optimization. This Delaunay Triangulation algorithm yields a radiation pattern devoid of grating lobes. Then Convex algorithm is used to optimize the element excitations to further decrease side-lobe-level. The minimum inter-element distance is as large as 8 times of wavelength. The peak-side-lobe-level can be −17.3 dB. Furthermore, beam steering can be achieved with good performance within 80° field-of-view range.

Sensory neuron membrane protein (SNMP) gene play a crucial role in insect chemosensory systems. However, the role of SNMP in the host searching behaviour of Rhopalosiphum padi (Hemiptera: Aphididae), a highly destructive pest of cereal crops, has not been clearly understood. Our previous research has shown that three wheat volatile organic compounds (VOCs) – (E)-2-hexenol, linalool, and octanal can attract R. padi, but the involvement of SNMP in the aphid’s olfactory response to these wheat VOCs has not to be elucidated. In this study, only one SNMP gene was cloned and characterised from R. padi. The results revealed that the SNMP belongs to the SNMP1 subfamily and was named RpadSNMP1. RpadSNMP11 was predominantly expressed in the antennae of the aphid, with significantly higher expression levels observed in winged forms, indicating that it is involved in olfactory responses of R. padi. RpadSNMP1 expression was significantly up-regulated following starvation, and the expression of this gene showed a decreasing trend after 24 h of aphid feeding. Functional analysis through RpadSNMP1 knockdown demonstrated a significant decrease in R. padi’s ability to search for host plants. The residence time of R. padi injected with dsRpadSNMP1 significantly shortened in response to (E)-2-hexenol, linalool and octanal according to the four-arm olfactometer, indicating the crucial role of RpadSNMP1 in mediating the aphid’s response to these wheat VOCs. Molecular docking suggested potential binding interactions between RpadSNMP1 and three wheat VOCs. Overall, these findings provided evidence for the involvement of RpadSNMP1 in host plant searching and lay a foundation for developing new methods to control this destructive pest.

This paper presents a notched ultra-wideband antenna designed to suppress interference from narrowband communication systems. The antenna features a defected ground structure and a stepped microstrip feedline for improved impedance matching and enhanced bandwidth. A bent slot structure is incorporated into the radiating patch to achieve the band-notched characteristic. It has a wide tunable frequency range which allows for flexible adjustment of the notch frequency. Traditional optimization methods, such as numerical analysis, are computationally expensive and inefficient, while heuristic algorithms are less precise. To address these challenges, an improved one-dimensional convolutional neural network (1DCNN-IPS) model is proposed for optimizing the bent slot design more efficiently. The trained 1DCNN-IPS model can accurately predict the antenna’s electromagnetic parameters, reducing mean squared error and training times compared to traditional methods. This provides an efficient and precise solution for antenna structural optimization.

Research findings based on the data of current automatic identification systems (AISs) can only be applied to some parts of navigation research owing to their insufficient mining depth. Previously, route planning research has been based on the waypoint and corresponding optimised algorithm without considering the actual navigation situation and sailing habits. The planned route considerably differs from the actual sailing route, and the application result is undesirable. A novel solution to support the route planning problem has been introduced owing to the large accumulation of AIS big data. In this study, the ship navigable route framework (SNRF) which is reflected by real data via mining AIS big data serves as the basic network for the planned maritime route. This study uses the concept of manifold distance based on AIS big data to build a maritime SNRF through high-density searching. It can provide basic theoretical support for actual navigation distance calculation, route planning and route accessibility inspection in the future.

Green water loads on prismatic obstacles (representing topside structures) mounted on the raised deck of a simplified vessel are investigated using computational fluid dynamics simulations and physical model testing with emphasis on examining different structure shapes, orientation angles and relative structure size. For each scenario investigated, several flow features are identified that characterize the green water interaction with the structure and influence loads, namely delayed flow diversion, formation of a vertical jet, scattered wave formation and the development of complex wake patterns. Comparing across structures, these interactions are more pronounced for blunt objects, and the associated force impulse is larger. For example, a cube with flow at normal incidence is found to experience approximately twice the force impulse of a circular cylinder of the same projected area. Equally, rotation of the cube leads to reduced run-up height and streamwise force on the structure. To explain these trends, a theoretical model based on Newtonian flow theory is adopted. This model provides an estimate of the streamwise force exerted on obstacles in high-Froude-number flows and shows good agreement with the numerical results when the flow is supercritical, shallow (small water depth relative to structure width) and the structure is tall (large structure height relative to water depth). Despite some limitations, the model should provide an efficient force prediction tool for practical use in design.

We incorporate the liquidity trap and private behavioral preferences into a New Keynesian dynamic stochastic general equilibrium model to analyze fiscal multipliers. The results indicate that the influence of the liquidity trap on fiscal policy is driven by a combination of the interest rate transmission effect and the precautionary savings effect, showing a notable amplification of multipliers based on estimates from U.S. data. Furthermore, we examine two types of private behavioral preferences: habit formation and investor confidence. Habit formation significantly boosts short-term government spending multipliers while exhibiting diverse impacts on different types of taxation. Compared to superficial habits, deep habits result in flatter multiplier curves. Investor confidence, being highly sensitive to output fluctuations, enhances both spending and tax multipliers over the medium to long term. Additionally, the investor confidence channel slightly amplifies the expansionary effect of the liquidity trap on multipliers, contrasting with the impact of habit formation.

The paper presents a novel control method aimed at enhancing the trajectory tracking accuracy of two-link mechanical systems, particularly nonlinear systems that incorporate uncertainties such as time-varying parameters and external disturbances. Leveraging the Udwadia–Kalaba equation, the algorithm employs the desired system trajectory as a servo constraint. First, the system’s constraints to construct its dynamic equation and apply generalized constraints from the constraint equation to an unconstrained system. Second, we design a robust approximate constraint tracking controller for manipulator control and establish its stability using Lyapunov’s law. Finally, we numerically simulate and experimentally validate the controller on a collaborative platform using model-based design methods.

Foodborne diseases are ongoing and significant public health concerns. This study analysed data obtained from the Foodborne Outbreaks Surveillance System of Wenzhou to comprehensively summarise the characteristics of foodborne outbreaks from 2012 to 2022. A total of 198 outbreaks were reported, resulting in 2,216 cases, 208 hospitalisations, and eight deaths over 11 years. The findings suggested that foodborne outbreaks were more prevalent in the third quarter, with most cases occurring in households (30.8%). Outbreaks were primarily associated with aquatic products (17.7%) as sources of contamination. The primary transmission pathways were accidental ingestion (20.2%) and multi-pathway transmission (12.1%). Microbiological aetiologies (46.0%), including Vibrio parahaemolyticus, Salmonella ssp., and Staphylococcus aureus, were identified as the main causes of foodborne outbreaks. Furthermore, mushroom toxins (75.0%), poisonous animals (12.5%), and poisonous plants (12.5%) were responsible for deaths from accidental ingestion. This study identified crucial settings and aetiologies that require the attention of both individuals and governments, thereby enabling the development of effective preventive measures to mitigate foodborne outbreaks, particularly in coastal cities.

The AIMTB rapid test assay is an emerging test, which adopted a fluorescence immunochromatographic assay to measure interferon-γ (IFN-γ) production following stimulation of effector memory T cells in whole blood by mycobacterial proteins. The aim of this article was to explore the ability of AIMTB rapid test assay in detecting Mycobacterium tuberculosis (MTB) infection compared with the widely applied QuantiFERON-TB Gold Plus (QFT-Plus) test among rural doctors in China. In total, 511 participants were included in the survey. The concordance between the QFT-Plus test and the AIMTB rapid test assay was 94.47% with a Cohen’s kappa coefficient (κ) of 0.84 (95% CI, 0.79–0.90). Improved concordance between the two tests was observed in males and in participants with 26 or more years of service as rural doctors. The quantitative values of the QFT-Plus test was higher in individuals with a result of QFT-Plus-/AIMTB+ as compared to those with a result of QFT-Plus-/AIMTB- (p < 0.001). Overall, our study found that there was an excellent consistency between the AIMTB rapid test assay and the QFT-Plus test in a Chinese population. As the AIMTB rapid test assay is fast and easy to operate, it has the potential to improve latent tuberculosis infection testing and treatment at the community level in resource-limited settings.

We report on an improved ytterbium-doped yttrium aluminum garnet thin-disk multi-pass amplifier for kilowatt-level ultrafast lasers, showcasing excellent beam quality. At a repetition rate of 800 kHz, the 6.8 ps, 276 W seed laser is amplified up to an average power of 1075 W, corresponding to a pulse energy of 1.34 mJ. The 36-pass amplifier is designed as a compact mirror array in which the beam alternately propagates between the mirrors and the disk by a quasi-collimated state. We adopted a quasi-collimated propagation to confine stray and diffracted light by the slight curvature of the disk, which enables us to achieve an outstanding extraction efficiency of up to 57% with excellent beam quality in stable laser operation at high power. The beam quality at 1075 W was measured to be M2 < 1.51. Furthermore, stability testing was demonstrated with a root-mean-square power fluctuation of less than 1.67% for 10 min.

To address the issues of low positioning accuracy and weak robustness of prior visual simultaneous localization and mapping (VSLAM) systems in dynamic environments, a semantic VSLAM (Sem-VSLAM) approach based on deep learning is proposed in this article. The proposed Sem-VSLAM algorithm adds semantic segmentation threads in parallel based on the open-source ORB-SLAM2’s visual odometry. First, while extracting the ORB features from an RGB-D image, the frame image is semantically segmented, and the segmented results are detected and repaired. Then, the feature points of dynamic objects are eliminated by using semantic information and motion consistency detection, and the poses are estimated by using the remaining feature points after the dynamic feature elimination. Finally, a 3D point cloud map is constructed by using tracking information and semantic information. The experiment uses Technical University of Munich public data to show the usefulness of the Sem-VSLAM algorithm. The experimental results show that the Sem-VSLAM algorithm can reduce the absolute trajectory error and relative attitude error of attitude estimation by about 95% compared to the ORB-SLAM2 algorithm and by about 14% compared to the VO-YOLOv5s in a highly dynamic environment and the average time consumption of tracking each frame image reaches 61 ms. It is verified that the Sem-VSLAM algorithm effectively improves the robustness and positioning accuracy in high dynamic environment and owning a satisfying real-time performance. Therefore, the Sem-VSLAM has a better mapping effect in a highly dynamic environment.

MicroRNAs (miRNAs) play important roles in regulating salt tolerance in Dongxiang wild rice (DXWR, Oryza rufipogon Griff.). The development of salt-responsive miRNA-simple sequence repeat (SSR) markers will significantly bolster research on DXWR, providing novel tools for exploring salt-tolerant genetic resources and advancing the development of salt-tolerant rice varieties. In the present study, a total of 137 miRNA-SSR markers were successfully developed, specifically derived from miRNAs responsive to salt stress in DXWR. Subsequently, a subset of 20 markers was randomly selected for validation across three distinct DXWR populations, along with 35 modern rice varieties. Notably, 13 of these markers exhibited remarkable polymorphism. The polymorphic markers collectively amplified 52 SSR loci, averaging four alleles per locus. The polymorphism information content values associated with these loci spanned from 0.23 to 0.70, with a mean value of 0.49. Particularly noteworthy is the miR162a-SSR marker, which demonstrated distinct allelic patterns and holds potential as a diagnostic marker for discriminating the salt-tolerant rice varieties from the non-tolerant varieties. This study provides a valuable tool for genetic analysis and precision breeding, facilitating the identification and utilization of valuable salt-tolerant genetic resources.

To compare the benefits and drawbacks of traditional and automated conservation assessments, we used a field-based study and automated conservation assessments using GeoCAT, red and ConR to assess four species of Buddleja (Scrophulariaceae), a cosmopolitan genus of flowering plants. Buddleja colvilei, Buddleja sessilifolia, Buddleja delavayi and Buddleja yunnanensis are endemic to the Himalayan region. They have not yet been assessed for the IUCN Red List of Threatened Species but are facing elevated risks of extinction because of various anthropogenic and environmental pressures. Buddleja sessilifolia and B. delavayi are listed as Plant Species with Extremely Small Populations in Yunnan, China, where they are known to be threatened. Although automated assessments evaluated B. delavayi and B. yunnanensis as Endangered and B. sessilifolia and B. colvilei as Vulnerable, our field studies indicated a different categorization for three of the species: B. delavayi and B. yunnanensis as Critically Endangered and B. sessilifolia as Endangered. Our findings indicate that the accuracy and reliability of assessment methods can differ and that field surveys remain important for conservation assessments. We recommend an integrated approach addressing these limitations, to safeguard the future of other species endemic to the Himalayan region.