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Transmissible gastroenteritis virus (TGEV) is an etiological agent of enteric disease that results in high mortality rates in piglets. The economic impact of the virus is considerable, causing significant losses to the pig industry. The development of an efficacious subunit vaccine to provide promising protection against TGEV is of the utmost importance. The viral antigen, spike glycoprotein (S), is widely regarded as one of the most effective antigenic components for vaccine research. In this study, we employed immunoinformatics and molecular dynamics approaches to develop an ‘ideal’ multi-epitope vaccine. Firstly, the dominant, non-toxic, highly antigenic T (Th, CTL) and B cell epitopes predicted from the TGEV S protein were artificially engineered in tandem to design candidate subunit vaccines. Molecular docking and dynamic simulation results demonstrate that it exhibits robust interactions with toll-like receptor 4 (TLR4). Of particular significance was the finding that the vaccine was capable of triggering an immune response in mammals, as evidenced by the immune simulation results. The humoral aspect is typified by elevated levels of IgG and IgM, whereas the cellular immune aspect is capable of eliciting the robust production of interleukins and cytokines (IFN-γ and IL-2). Furthermore, the adoption of E. coli expression systems for the preparation of vaccines will also result in cost savings. This study offers logical guidelines for the development of a secure and efficacious subunit vaccine against TGEV, in addition to providing a novel theoretical foundation and strategy to prevent associated CoV infections.

Porcine parvovirus 7 (PPV7) was first discovered in swine in 2016, and PPV7 infection has been detected in aborted pig fetuses and in sows that experienced reproductive failure. The objective of this study was to report the prevalence and genetic characterization of PPV7 in Hunan, China. Seventy of the four hundred and twenty-two (16.6%) serum, semen, and tissue samples collected from pigs were positive for PPV7. One complete PPV7 strain and eighteen complete cap gene sequences were obtained; nucleotide and amino acid identity among the nineteen Cap sequences were 88.1–99.4% and 88.1–100%, respectively. They shared identity with previously discovered sequences ranging from 86.6 to 98.9% and 83.7 to 99.8% at the nucleotide- and amino acid-level, respectively. The phylogenetic tree analysis exhibited that PPV7 strains had two major groups based on the presence or absence of five amino acid (181–185) insertions on the Cap protein. Analysis of the Cap protein demonstrated that PPV7 Cap had significant variability, implying that PPV7 evolved at high substitution rates. Substantial variations of that PPV7 Cap may enable the emergence of newly mutated capsid profiles due to its viral adaptation to host responses. Furthermore, antigenic alteration owing to PPV7 Cap protein amino acid mutations at immune epitopes may enable viruses to escape from the host’s immune system. This study determined the prevalence and genetic characteristics of PPV7 circulating in swine in Hunan, China, and provided the impetus and basis to further investigate the pathogenicity and epidemiology of PPV7.

To tackle the complexity and limited applicability of high-precision segmentation models for damaged road markings, this study proposes a Multi-level Adaptive Lightweight Network (MALNet) based on knowledge distillation. By incorporating multi-scale dilated convolution and adaptive spatial channel attention fusion modules, the MALNet model significantly enhances the precision, integrity, and robustness of its segmentation branch. Furthermore, it employs an intricate knowledge distillation strategy, channeling rich, layered insights from a teacher model to a student model, thus elevating the latter’s segmentation ability. Concurrently, it streamlines the student model by markedly reducing its parameter count and computational demands, culminating in a segmentation network that is both high-performing and pragmatic. Rigorous testing on three distinct data sets for damaged road marking detection—CDM_P (Collective Damaged road Marking—Public), CDM_H (Collective Damaged road Marking—Highways), and CDM_C (Collective Damaged road Marking—Cityroad)—underscores the MALNet model’s superior segmentation abilities across all damage types, outperforming competing models in accuracy and completeness. Notably, the MALNet model excels in parameter efficiency, computational economy, and throughput. After distillation, the student model’s parameters and computational load decrease to only 31.78% and 27.40% of the teacher model’s, respectively, while processing speeds increase to 1.9 times, demonstrating a significant improvement in lightweight design.

Iron–chalcogenide superconductors continue to captivate researchers due to their diverse crystalline structures and intriguing superconducting properties, positioning them as both a valuable platform for theoretical investigations and promising candidates for practical applications. This review begins with a comprehensive overview of the fabrication techniques employed for various iron–chalcogenide superconductors, accompanied by a summary of their phase diagrams. Subsequently, it delves into the upper critical field, anisotropy, and critical current density. Furthermore, it discusses the successful fabrication of meters-long coated conductors and explores their applications in superconducting radio-frequency cavities and coils. Finally, several prospective avenues for future research are proposed.

The visual servoing of manipulators is challenged by two main problems: the singularity of the inverse Jacobian and the physical constraints of a manipulator. In order to overcome the singularity issue, this paper presents a novel approach for image-based visual servoing (IBVS), which converts the propagation of errors in the image plane into the conduction of virtual forces using the principle of virtual work. This approach eliminates the need for Jacobian inversion computations and prevents matrix inversion singularity. To tackle physical constraints, reverse thinking is adopted to derive the function of the upper and lower bounds of the joint velocity on the joint angle. This enables the proposed method to configure the physical constraints of the robot in a more intuitive manner. To validate the effectiveness of the proposed method, an eye-in-hand system based on UR5 in VREP, as well as a physical robot, were established.

This article proposes a hierarchical control strategy to address semi-ABS control as well as the precise clamping force control problems for an integrated electric parking brake (iEPB) system. To this end, a detailed system model, including modeling of the motor, transmission mechanism, friction and braking torque, is constructed for controller and observer design, and a sliding-mode-based observer (SMO) is proposed to estimate the load torque by using the motor rotational speed without installing a force sensor. In addition, a stable and reliable tire–road friction coefficient (TRFC) estimation method is adopted, and the desired slip ratio (DSR) is observed as the target that the rear wheels cycle around. At the upper level of the hierarchical control structure, the desired clamping forces of the rear wheels are generated using a sliding mode control (SMC) technique, and the control objective is to track the DSR to make full use of the road condition. At the lower level, the motor is controlled to track the desired clamping force generated from the upper controller. The hardware-in-the-loop (HIL) experimental results demonstrate the effectiveness and high tracking precision of the proposed strategy under different road conditions, and the estimation parameters based on the proposed observers are timely and accurate to satisfy the control requirements.

Multipactor is a common discharge phenomenon occurring in space microwave systems. The surface microstructure has been verified to be effective in mitigating multipactor. In this work, we design a square coaxial low-pass filter (SCLPF) with dielectric sheets loaded to check the multipactor dependence on the structure parameters of the loaded dielectric sheets, and further model groove structures on the sensitive area surface to lower the risk of multipactor. Simulation results indicate that the SCLPF loaded with alumina and PTFE exhibits favorable operational characteristics, and the multipactor threshold is significantly improved after introducing the surface grooves. Then, we investigate the effects of three typical groove parameters, groove depth, groove number, and aspect ratio, on the multipactor threshold of the SCLPF device. The results show that the multipactor threshold rises at first as the groove number and groove depth increase, and then the threshold reaches a saturation status. For a deeper analysis of multipactor, we discuss how the grooves shelter the secondary electrons, and further mitigate the electron avalanche. Furthermore, the mechanisms of threshold saturation under the effect of groove parameters are analyzed in detail, and a contour map for the multipactor threshold of PTFE-loaded SCLPFs is given, which makes significant sense for predicting the multipactor threshold of the devices with its sensitive surface being grooved. The regularity of modulating the multipactor threshold by the groove structures obtained in this study is of engineering significance for suppressing multipactor in microwave devices in practical applications.

Fixed-wing unmanned aerial vehicles (UAVs) and multi-rotor UAVs are widely utilized in large-area (>1 km2) environmental monitoring and small-area (2) fine vegetation surveys, respectively, having different characteristics in terms of flight cost, operational efficiency, and landing and take-off methods. However, large-area fine mapping in complex forest environments is still a challenge in UAV remote sensing. Here, we developed a method that combines a multi-rotor UAV and a fixed-wing UAV to solve this challenge at a low cost. Firstly, we acquired small-scale, multi-season ultra-high-resolution red-green-blue (RGB) images and large-area RGB images by a multi-rotor UAV and a fixed-wing UAV, respectively. Secondly, we combined the reference data of visual interpretation with the multi-rotor UAV images to construct a semantic segmentation model and used the model to expand the reference data. Finally, we classified fixed-wing UAV images using the large-area reference data combined with the semantic segmentation model and discuss the effects of different sizes. Our results show that combining multi-rotor and fixed-wing UAV imagery provides an accurate prediction of tree species. The model for fixed-wing images had an average F1 of 92.93%, with 92.00% for Quercus wutaishanica and 93.86% for Juglans mandshurica. The accuracy of the semantic segmentation model that uses a larger size shows a slight improvement, and the model has a greater impact on the accuracy of Quercus liaotungensis. The new method exploits the complementary characteristics of multi-rotor and fixed-wing UAVs to achieve fine mapping of large areas in complex environments. These results also highlight the potential of exploiting this synergy between multi-rotor UAVs and fixed-wing UAVs.

The spawning grounds are important areas for the survival and reproduction of aquatic organisms and play an important role in the replenishment of fishery resources. The density of fish larvae in the Pearl River Estuary (PRE) was analyzed to establish Habitat Suitability Index (HSI) based on marine environmental factors. Survey data and satellite remote sensing data, including sea surface temperature, sea surface salinity and chlorophyll a concentration, from 2014 to 2017 during April–September were analyzed. Results showed that the accuracy of the HSI model based on the larval density and environmental factors was more than 60%, and the distribution trend of HSI was consistent with the distribution trend of larval density. The HSI models constructed based on Arithmetic Mean Model (AMM), Geometric Mean Model (GMM) and Minimum Model (MINM) methods can better predict the spatial–temporal distribution of larvae in the PRE. Among them, the accuracy of the HSI model constructed by the AMM and GMM methods was the highest in April (71%) and September (93%); the accuracy of the HSI model constructed by the MINM method was the highest in June (70%), July (84%) and August (64%). In general, the areas with high HSI values are mainly distributed in the offshore waters of the PRE. The spatial–temporal distribution of larvae in the PRE was influenced by monsoon, Pearl River runoff, Guangdong coastal currents and the invasion of high-salinity seawater from the outer sea.

Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under different months. For a case study area in Wanglang Nature Reserve, Southwest China, we acquired monthly RGB and multispectral images on six occasions over the growing season. We found that the accuracy of RGB imagery varied considerably (the highest intersection over union (IoU), 83.72%, was in April and the lowest, 76.81%, was in June), while the accuracy of multispectral imagery was consistently high (IoU > 81%). In April and October, the accuracy of the RGB imagery was slightly higher than that of multispectral imagery, but for the other months, multispectral imagery was more accurate (IoU was nearly 6% higher than those of the RGB imagery for June). Adding vegetation indices (VIs) improved the accuracy of the RGB models during summer, but there was still a gap to the multispectral model. Hence, our results indicate that the optimized time of the year for identifying Faxon fir using UAV imagery is during the peak of the growing season when using a multispectral imagery. During the non-growing season, RGB imagery was no worse or even slightly better than multispectral imagery for Faxon fir identification. Our study can provide guidance for optimizing observation plans regarding data collection time and UAV loads and could further help enhance the utility of UAVs in forestry and ecological research.

This paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically, the YOLOX algorithm is employed to track and recognize the target object, which is then combined with the improved KF model for precise tracking and recognition. In the LSTM-KF model, three different LSTM networks (f, Q, and R) are adopted to model a nonlinear transfer function to enable the model to learn rich and dynamic Kalman components from the data. The experimental results disclose that the improved LSTM-KF model exhibits higher recognition accuracy than the standard LSTM and the independent KF model. It verifies the robustness, effectiveness, and reliability of the autonomous UAV tracking system based on the improved LSTM-KF model in object recognition and tracking and 3D attitude estimation.

Unregulated livestock breeding and grazing can degrade grasslands and damage the ecological environment. The combination of remote sensing and artificial intelligence techniques is a more convenient and powerful means to acquire livestock information in a large area than traditional manual ground investigation. As a mainstream remote sensing platform, unmanned aerial vehicles (UAVs) can obtain high-resolution optical images to detect grazing livestock in grassland. However, grazing livestock objects in UAV images usually occupy very few pixels and tend to gather together, which makes them difficult to detect and count automatically. This paper proposes the GLDM (grazing livestock detection model), a lightweight and high-accuracy deep-learning model, for detecting grazing livestock in UAV images. The enhanced CSPDarknet (ECSP) and weighted aggregate feature re-extraction pyramid modules (WAFR) are constructed to improve the performance based on the YOLOX-nano network scheme. The dataset of different grazing livestock (12,901 instances) for deep learning was made from UAV images in the Hadatu Pasture of Hulunbuir, Inner Mongolia, China. The results show that the proposed method achieves a higher comprehensive detection precision than mainstream object detection models and has an advantage in model size. The mAP of the proposed method is 86.47%, with the model parameter 5.7 M. The average recall and average precision can be above 85% at the same time. The counting accuracy of grazing livestock in the testing dataset, when converted to a unified sheep unit, reached 99%. The scale applicability of the model is also discussed, and the GLDM could perform well with the image resolution varying from 2.5 to 10 cm. The proposed method, the GLDM, was better for detecting grassland grazing livestock in UAV images, combining remote sensing, AI, and grassland ecological applications with broad application prospects.

The aim of this study is to investigate the repressive effects of enzyme-digested edible bird’s nest (EBND) on the combination of arid environment and UV-induced intracellular oxidative stress, cell death, DNA double-strand breaks (DSBs) and inflammatory responses in human HaCaT keratinocytes and three-dimensional (3D) epithelium equivalents. An oxygen radical antioxidant capacity assay showed that EBND exhibited excellent peroxyl radical scavenging activity and significantly increased cellular antioxidant capacity in HaCaT cells. When EBND was administered to HaCaT cells and 3D epitheliums, it exhibited significant preventive effects on air-drying and UVA (Dry-UVA)-induced cell death and apoptosis. Dry-UVA markedly induced intracellular reactive oxygen species (ROS) generation in HaCaT cells and 3D epitheliums as quantified by CellROX® Green/Orange reagents. Once HaCaT cells and 3D epitheliums were pretreated with EBND, Dry-UVA-induced intracellular ROS were significantly reduced. The results from anti-γ-H2A.X antibody-based immunostaining showed that EBND significantly inhibited Dry-UVA-induced DSBs in HaCaT keratinocytes. Compared with sialic acid, EBND showed significantly better protection for both keratinocytes and 3D epitheliums against Dry-UVA-induced injuries. ELISA showed that EBND significantly suppressed UVB-induced IL-6 and TNF-α secretion. In conclusion, EBND could decrease arid environments and UV-induced harmful effects and inflammatory responses in human keratinocytes and 3D epithelium equivalents partially through its antioxidant capacity.

Fabrication of high-performance superconducting wires and tapes is essential for large-scale applications of superconducting materials. The powder-in-tube (PIT) method involves a series of cold processes and heat treatments and has been widely used for fabricating BSCCO, MgB2, and iron-based superconducting wires. The densification of the superconducting core is limited by traditional heat treatment under atmospheric pressure. The low density of the superconducting core and a large number of pores and cracks are the main factors limiting the current-carrying performance of PIT wires. Therefore, to improve the transport critical current density of the wires, it is essential to densify the superconducting core and eliminate pores and cracks to enhance grain connectivity. Hot isostatic pressing (HIP) sintering was employed to improve the mass density of superconducting wires and tapes. In this paper, we review the development and application of the HIP process in the manufacturing of BSCCO, MgB2, and iron-based superconducting wires and tapes. The development of HIP parameters and the performance of different wires and tapes are reviewed. Finally, we discuss the advantages and prospects of the HIP process for the fabrication of superconducting wires and tapes.

Brussels chicory, a typical vegetable in Mediterranean diets, has been recently reported to stabilize advanced atherosclerotic plaques in the brachiocephalic artery of apoE-deficient (Apoe−/−) mice. Herein, we investigated whether Brussels chicory can stabilize advanced plaques in the aorta via improving oxidative stress. Thirty week old Apoe−/− mice were fed the AIN-93G diet or supplemented with 0.5% freeze-dried Brussels chicory for twenty weeks. Aortic plaque size and stability, aortic relaxation, monocyte adhesion to aortic endothelium, free radicals, and enzymatic and non-enzymatic factors involved in free radical production and elimination in aorta and serum were measured. Brussels chicory consumption did not alter aortic plaque size, however, it stabilized aortic plaques, promoted aortic relaxation, and also inhibited monocyte adhesion to aortic endothelium. Moreover, this administration reduced oxidized LDL (ox-LDL) and 4-hydroxynonenal (4-HNE) content in aortic plaques, associated with inhibited aortic NADPH oxidase (NOX) and uncoupled endothelial nitric oxide synthase (eNOS)-mediated free radical production. However, Brussels chicory consumption did not appreciably alter aortic and serum superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) activities, aortic glutathione (GSH), as well as serum non-enzymatic antioxidants, such as bilirubin, uric acid, and GSH. Collectively, improved oxidative stress might contribute to the atheroprotective effect of Brussels chicory, supporting the prospect of the antioxidant therapy in advanced atherosclerosis progression.

Doublesex and Mab-3 related Transcription Factor 3 (DMRT3) is associated with the prognosis of some tumors. It is possible to explore the role of DMRT3 in the cancer process using bioinformatic approaches and experimental validation. We comprehensively explored the clinical and immunological characteristics of DMRT3. The DMRT3 expression is abnormal in human cancers and correlates with clinical staging. A high DMRT3 expression is significantly associated with poor overall survival (OS) in KIRC, KIRP, LUAD, and UCEC. Amplification was the greatest frequency of the DMRT3 alterations in pan-cancer. The OS was significantly lower in the DMRT3 altered group than in the DMRT3 unaltered group (P = 0.0276). The DMRT3 expression was significantly associated with MSI in three cancer types and TMB in six cancer types. The DMRT3 expression was significantly correlated with the level of the immune cell infiltration and the immune checkpoint genes. The DMRT3 was involved in some pathways in pan-cancer. DMRT3 may play a role in chemotherapy and may be associated with chemoresistance. A ceRNA network of KCNQ1OT1/miR-335-5p/DMRT3 was constructed in LUAD. DMRT3 was significantly upregulated in the LUAD cell lines. DMRT3 was aberrantly expressed in pan-cancer and may promote tumorigenesis and progression via different mechanisms. DMRT3 can be used as a therapeutic target to treat cancer in humans.

Plant density is a significant variable in crop growth. Plant density estimation by combining unmanned aerial vehicles (UAVs) and deep learning algorithms is a well-established procedure. However, flight companies for wheat density estimation are typically executed at early development stages. Further exploration is required to estimate the wheat plant density after the tillering stage, which is crucial to the following growth stages. This study proposed a plant density estimation model, DeNet, for highly accurate wheat plant density estimation after tillering. The validation results presented that (1) the DeNet with global-scale attention is superior in plant density estimation, outperforming the typical deep learning models of SegNet and U-Net; (2) the sigma value at 16 is optimal to generate heatmaps for the plant density estimation model; (3) the normalized inverse distance weighted technique is robust to assembling heatmaps. The model test on field-sampled datasets revealed that the model was feasible to estimate the plant density in the field, wherein a higher density level or lower zenith angle would degrade the model performance. This study demonstrates the potential of deep learning algorithms to capture plant density from high-resolution UAV imageries for wheat plants including tillers.

The rapid boom of the global population is causing more severe food supply problems. To deal with these problems, the agricultural greenhouse is an effective way to increase agricultural production within a limited space. To better guide agricultural activities and respond to future food crises, it is important to obtain both the agricultural greenhouse area and quantity distribution. In this study, a novel dual-task algorithm called Pixel-based and Object-based Dual-task Detection (PODD) that combines object detection and semantic segmentation is proposed to estimate the quantity and extract the area of agricultural greenhouses based on RGB remote sensing images. This algorithm obtains the quantity of agricultural greenhouses based on the improved You Only Look Once X (YOLOX) network structure, which is embedded with Convolutional Block Attention Module (CBAM) and Adaptive Spatial Feature Fusion (ASFF). The introduction of CBAM can make up for the lack of expression ability of its feature extraction layer to retain more important feature information. Adding the ASFF module can make full use of the features in different scales to increase the precision. This algorithm obtains the area of agricultural greenhouses based on the DeeplabV3+ neural network using ResNet-101 as a feature extraction network, which not only effectively reduces hole and plaque issues but also extracts edge details. Experimental results show that the mAP and F1-score of the improved YOLOX network reach 97.65% and 97.50%, 1.50% and 2.59% higher than the original YOLOX solution. At the same time, the accuracy and mIoU of the DeeplabV3+ network reach 99.2% and 95.8%, 0.5% and 2.5% higher than the UNet solution. All of the metrics in the dual-task algorithm reach 95% and even higher. Proving that the PODD algorithm could be useful for agricultural greenhouse automatic extraction (both quantity and area) in large areas to guide agricultural policymaking.

Since it is difficult to accurately track reference trajectories under the condition of stride constraints for an unmanned hexapod robot moving with rhythmic gait, an omnidirectional tracking strategy based on model predictive control and real-time replanning is proposed in this paper. Firstly, according to the characteristic that the stride dominates the rhythmic motion of an unmanned multi-legged robot, a body-level omnidirectional tracking model is established. Secondly, a quantification method of limb’s stretch and yaw constraints described by motion stride relying on a tripod gait is proposed, and then, a body-level accurate tracking controller based on constrained predictive control is designed. Then, in view of the low tracking efficiency of the robot under the guidance of common reference stride, a solution strategy of variable stride period and a real-time replanning scheme of reference stride are proposed based on the limb constraints and the integral mean, which effectively avoid the tracking deviation caused by the guidance of constant reference strides. Finally, the effectiveness and practicability of the proposed control strategy are demonstrated through the comparative analysis and simulation test of a hexapod robot WelCH with omnidirectional movement ability to continuously track the directed curve and the undirected polyline trajectory.

A series of NiAlO composite oxide catalysts with high surface areas and high Ni dispersion were prepared through an improved co-precipitation method. The new preparation method effectively improved the specific surface area and pore volume of the catalyst, promoted the dispersion of nickel species, alleviated the agglomeration of the catalyst, and improved the stability of the catalyst by strengthening the interaction between Ni and Al. The typical catalyst Ni20Al had a specific surface area of 359 m2/g and a NiAl2O4 phase. In the dehydrogenation of methylcyclohexane over the Ni20Al catalyst, the conversion of methylcyclohexane could reach 77.4%, with toluene selectivity of 85.6%, and a hydrogen release rate of 63.94 mmol g−1 h−1, and did not show any significant inactivation during the stability test over 29 h under the reaction conditions of reaction temperature 450 °C and LHSV = 4 mL g−1 h−1. However, the conversion of methylcyclohexane with the IM-NiAl catalyst prepared through the traditional impregnation method was only 50.75%, with toluene selectivity of 70.5%, and with a hydrogen release rate of 35.84 mmol g−1 h−1, and the lifetime of the catalyst was only 15 h.

The existing precision grazing technology helps to improve the utilization rate of livestock to pasture, but it is still at the level of “collectivization” and cannot provide more accurate grazing management and control. (1) Background: In recent years, with the rapid development of agent-related technologies such as deep learning, visual navigation and tracking, more and more lightweight edge computing cell target detection algorithms have been proposed. (2) Methods: In this study, the improved YOLOv5 detector combined with the extended dataset realized the accurate identification and location of domestic cattle; with the help of the kernel correlation filter (KCF) automatic tracking framework, the long-term cyclic convolution network (LRCN) was used to analyze the texture characteristics of animal fur and effectively distinguish the individual cattle. (3) Results: The intelligent UAV equipped with an AGX Xavier high-performance computing unit ran the above algorithm through edge computing and effectively realized the individual identification and positioning of cattle during the actual flight. (4) Conclusion: The UAV platform based on airborne depth reasoning is expected to help the development of smart ecological animal husbandry and provide better precision services for herdsmen.

Inspired by the behaviour of animal populations in nature, we propose a novel exploration algorithm based on Lévy flight (LF) and artificial potential field (APF). The agent is extended to the swarm level using the APF method through the LF search environment. Virtual leaders generate moving steps to explore the environment through the LF mechanism. To achieve collision-free movement in an unknown constrained environment, a swarm-following mechanism is established, which requires the agents to follow the virtual leader to carry out the LF. The proposed method, combining the advantages of LF and APF which achieve the effect of flocking in an exploration environment, does not rely on complex sensors for environment labelling, memorising, or huge computing power. Agents simply perform elegant and efficient search behaviours as natural creatures adapt to the environment and change formations. The method is especially suitable for the camouflaged flocking exploration environment of bionic robots such as flapping drones. Simulation experiments and real-world experiments on E-puck2 robots were conducted to evaluate the effectiveness of the proposed LF-APF algorithm.

To achieve Sustainable Development Goal 7 (SDG7), it is essential to detect the spatiotemporal patterns of electricity consumption, particularly the spatiotemporal heterogeneity of consumers. This is also crucial for rational energy planning and management. However, studies investigating heterogeneous users are lacking. Moreover, existing works focuses on mathematic models to identify and predict electricity consumption. Additionally, owing to the complex non-linear interrelationships, interactive visualizations are more effective in detecting patterns. Therefore, by combining geospatial dashboard knowledge and interactive visualization technology, a Smart Electricity dashboard (SmartEle) was designed and developed to interactively visualize big electrical data and interrelated factors. A university campus as the study area. The SmartEle system addressed three challenges. First, it permitted user group-oriented monitoring of electricity consumption patterns, which has seldom been considered in existing studies. Second, a visualization-driven data mining model was proposed, and an interactive visualization dashboard was designed to facilitate the perception of electricity usage patterns at different granularities and from different perspectives. Finally, to deal with the non-linear features of electricity consumption, the ATT-LSTM machine learning model to support multivariate collaborative predicting was proposed to improve the accuracy of short-term electricity consumption predictions. The results demonstrated that the SmartEle system is usable for electricity planning and management.

Recently, unmanned aerial vehicle (UAV) remote sensing has been widely used in emergency scenarios; the operating mode has transitioned from one UAV to multiple UAVs. However, the current multiple-UAV remote sensing mode is characterized by high labor costs and limited operational capabilities; meanwhile, there is no suitable UAV swarm scheduling method that can be applied to remote sensing in emergency scenarios. To solve these problems, this study proposes a UAV swarm scheduling method. Firstly, the tasks were formulated and decomposed according to the data requirements and the maximum flight range of a UAV; then, the task sets were decomposed according to the maximum flight range of the UAV swarm to form task subsets; finally, aiming at the shortest total flight range of the task subsets and to balance the flight ranges of each UAV, taking the complete execution of the tasks as the constraint, the task allocation model was constructed, and the model was solved via a particle swarm optimization algorithm to obtain the UAV swarm scheduling scheme. Compared with the direct allocation method and the manual scheduling methods, the results show that the proposed method has high usability and efficiency.

S-adenosylhomocysteine (SAH) is a risk factor of cardiovascular diseases and atherosclerosis. However, the causal association between SAH and atherosclerosis is still uncertain. In the present study, heterozygous SAH hydrolase (SAHH+/−) knockout mice were bred with apolipoprotein E-deficient mice to produce ApoE−/−/SAHH+/− mice. At 8 weeks of age, these mice were fed on AIN-93G diets added with or without betaine (4 g betaine/100 g diet) for 8 weeks. Compared with ApoE−/−/SAHHWT mice, SAHH deficiency caused an accumulation of plasma SAH concentration and a decrease in S-adenosylmethionine (SAM)/SAH ratio as well as plasma homocysteine levels. Betaine supplementation lowered SAH levels and increased SAM/SAH ratio and homocysteine levels in ApoE−/−/SAHH+/− mice. Furthermore, SAHH deficiency promoted the development of atherosclerosis, which was reduced by betaine supplementation. The atheroprotective effects of betaine on SAHH-deficiency-promoted atherosclerosis were associated with inhibition of NFκB inflammation signaling pathway and inhibition of proliferation and migration of smooth muscle cells. In conclusion, our results suggest that betaine supplementation lowered plasma SAH levels and protected against SAHH-deficiency-promoted atherosclerosis through repressing inflammation and proliferation and migration of smooth muscle cells.

In this study, a combination of example-based feature extraction and visual interpretation was applied to analyze the coastline variations in the Guangdong–Hong Kong–Macao Greater Bay Area (GHMGBA) from the past four decades based on the Landsat satellite remote sensing image data from 1987–2018, using ENVI and ArcGIS software. The results showed that the total length of the coastline of the GHMGBA increased in the past four decades, rising from 1291 km in 1987 to 1411 km in 2018. Among these, artificial coastline increased by 450 km, while the other coastline types decreased. The type of coastline that decreased the most was bedrock coastline, by a total of 172 km. The silty coastline disappeared, and almost all of it was converted to artificial coastline. Variations in the coastline of the GHMGBA were mainly connected to human activities and showed an overall trend of advancing towards the ocean. Dynamic monitoring of coastline variations can provide a reference for the protection of natural resources, sustainable marine development and rational planning of the coastal zone.

Spawning grounds are important areas for fish survival and reproduction, and play a key role in the supplement of fishery resources. This study investigated environmental effects on the spatiotemporal variability of spawning ground in the Pearl River Estuary (PRE), China, using the generalized additive model (GAM), based on satellite remote sensing (sea surface temperature (SST), chlorophyll-a concentration (Chl-a), sea surface salinity (SSS), depth), and in situ observations. Results showed that 39.8% of the total variation in fish egg density was explained by these factors. Among them, the most important factor was SST, accounting for 14.3%, followed by Depth, SSS, and Chl-a, with contributions of 9.7%, 8.5%, and 7.3%, respectively. Spawning grounds in the PRE were mainly distributed in the waters with SST of 22 °C, depth of 30–50 m, SSS of 16–35 ‰, and Chl-a of 6–15 mg/m3. From spring to summer, the spawning ground moved from the outlet of the PRE to the east. The distribution of the spawning ground in the PRE was mainly affected by the Pearl River Plume (PRP), Guangdong Coastal Current (GCC), and monsoons in this area.

The accurate estimation of grassland vegetation parameters at a high spatial resolution is important for the sustainable management of grassland areas. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) sensors with a single laser beam emission capability can rapidly detect grassland vegetation parameters, such as canopy height, fractional vegetation coverage (FVC) and aboveground biomass (AGB). However, there have been few reports on the ability to detect grassland vegetation parameters based on RIEGL VUX-1 UAV LiDAR (Riegl VUX-1) systems. In this paper, we investigated the ability of Riegl VUX-1 to model the AGB at a 0.1 m pixel resolution in the Hulun Buir grazing platform under different grazing intensities. The LiDAR-derived minimum, mean, and maximum canopy heights and FVC were used to estimate the AGB across the entire grazing platform. The flight height of the LiDAR-derived vegetation parameters was also analyzed. The following results were determined: (1) The Riegl VUX-1-derived AGB was predicted to range from 29 g/m2 to 563 g/m2 under different grazing conditions. (2) The LiDAR-derived maximum canopy height and FVC were the best predictors of grassland AGB (R2 = 0.54, root-mean-square error (RMSE) = 64.76 g/m2). (3) For different UAV flight altitudes from 40 m to 110 m, different flight heights showed no major effect on the derived canopy height. The LiDAR-derived canopy height decreased from 9.19 cm to 8.17 cm, and the standard deviation of the LiDAR-derived canopy height decreased from 3.31 cm to 2.35 cm with increasing UAV flight altitudes. These conclusions could be useful for estimating grasslands in smaller areas and serving as references for other remote sensing datasets for estimating grasslands in larger areas.

The Pearl River Estuary (PRE) is one of the major fishing grounds for the squid Uroteuthis chinensis. Taking that into consideration, this study analyzes the environmental effects on the spatiotemporal variability of U. chinensis in the PRE, on the basis of the Generalized Additive Model (GAM) and Clustering Fishing Tactics (CFT), using satellite and in situ observations. Results show that 63.1% of the total variation in U. chinensis Catch Per Unit Effort (CPUE) in the PRE could be explained by looking into outside factors. The most important one was the interaction of sea surface temperature (SST) and month, with a contribution of 26.7%, followed by the interaction effect of depth and month, fishermen’s fishing tactics, sea surface salinity (SSS), chlorophyll a concentration (Chl a), and year, with contributions of 12.8%, 8.5%, 7.7%, 4.0%, and 3.1%, respectively. In summary, U. chinensis in the PRE was mainly distributed over areas with an SST of 22–29 °C, SSS of 32.5–34‰, Chl a of 0–0.3 mg × m−3, and water depth of 40–140 m. The distribution of U. chinensis in the PRE was affected by the western Guangdong coastal current, distribution of marine primary productivity, and variation of habitat conditions. Lower stock of U. chinensis in the PRE was connected with La Niña in 2008.

Free and glycosylated sesquiterpene lactones (SLs), which are abundant in leafy vegetables including Brussels/witloof chicory, possess health-promoting effects in vivo. However, the pharmacokinetics of dietary source of SLs remain largely unknown. In this open-label and single-dose trial, sixteen healthy volunteers consumed 150 g of Brussels/witloof chicory juice containing 48.77 μmol SLs in 5 min. Blood, urine, and fecal samples were collected before and after chicory consumption in 24 h. No SLs were detected in the serum, urine, and fecal samples before chicory consumption in all of the participants. Chicory consumption increased lactucin, 11β,13-dihydrolactucin, and their glucuronide/sulfate conjugates, rather than lactucopicrin and 11β,13-dihydrolactucopicrin, as well as glycosylated SLs in biological samples. The peak concentration of total SLs in serum reached 284.46 nmol/L at 1 h, while, in urine, this peak was 220.3 nmol between 2 and 6 h. The recovery of total SLs in blood, urine, and feces was 7.03%, 1.13%, and 43.76% of the ingested dose, respectively. Human fecal suspensions with intestinal microbiota degraded glycosylated SLs in chicory, and converted lactucopicrin and 11β,13-dihydrolactucopicrin to lactucin and 11β,13-dihydrolactucin, respectively. Collectively, Brussels/witloof chicory SLs are poorly bioavailable and they undergo partial gut microbial and phase II metabolism in humans.

Currently, there is limited knowledge about the immunological profiles of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). We used computer-based immunoinformatic analysis and the newly resolved 3-dimensional (3D) structures of the SARS-CoV-2 S trimeric protein, together with analyses of the immunogenic profiles of SARS-CoV, to anticipate potential B-cell and T-cell epitopes of the SARS-CoV-2 S protein for vaccine design, particularly for peptide-driven vaccine design and serological diagnosis. Nine conserved linear B-cell epitopes and multiple discontinuous B-cell epitopes composed of 69 residues on the surface of the SARS-CoV-2 trimeric S protein were predicted to be highly antigenic. We found that the SARS-CoV-2 S protein has a different antigenic profile than that of the SARS-CoV S protein due to the variations in their primary and 3D structures. Importantly, SARS-CoV-2 may exploit an immune evasion mechanism through two point mutations in the critical and conserved linear neutralization epitope (overlap with fusion peptide) around a sparsely glycosylated area. These mutations lead to a significant decrease in the antigenicity of this epitope in the SARS-CoV-2 S protein. In addition, 62 T-cell epitopes in the SARS-CoV-2 S protein were predicted in our study. The structure-based immunoinformatic analysis for the SARS-CoV-2 S protein in this study may improve vaccine design, diagnosis, and immunotherapy against the pandemic of COVID-19.

Porcine parvovirus 7 (PPV7) belonging to the genus Chapparvovirus in the family Parvoviridae, has been identified in the USA, Sweden, Poland, China, South Korea and Brazil. Our objective was to determine the phylogeny, estimate the time of origin and evolutionary dynamics of PPV7, and use computer-based immune-informatics to assess potential epitopes of its Cap, the main antigenic viral protein, for vaccines or serology. Regarding evolutionary dynamics, PPV7 had 2 major clades, both of which possibly had a common ancestor in 2004. Furthermore, PPV7 strains from China were the most likely ancestral strains. The nucleotide substitution rates of NS1 and Cap genes were 8.01 × 10−4 and 2.19 × 10−3 per site per year, respectively, which were higher than those reported for PPV1-4. The antigenic profiles of PPV7 Cap were revealed and there were indications that PPV7 used antigenic shift to escape from the host’s immune surveillance. Linear B cell epitopes and CD8 T cell epitopes of Cap with good antigenic potential were identified in silico; these conserved B cell epitopes may be candidates for the PPV7 vaccine or for the development of serological diagnostic methods.

The leaf area index (LAI) is a crucial parameter of vegetation structure. It provides key information for earth surface process simulations and climate change research on the global and regional scales. Focusing on the meadow steppe in Hulunber, Inner Mongolia, China, the present study assessed the accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product in the study area. First, seven field campaigns collecting ground-based measurements were conducted during the growing season in 2013, and 252 pairs of LAIs and spectra were collected. Then, seven scenes of high-resolution LAI maps were obtained from the corresponding 30 m Chinese HJ-1A/1B charge-coupled diode (CCD) images by employing a regression approach. Finally, comparisons between the MODIS LAI product and the high resolution LAI maps were made to determine the accuracy of the MODIS LAI product. Moreover, the corresponding 500 m MODIS LAI maps were derived from the daily MODIS surface reflectance product to support the findings using the 1 km HJ LAI product and the ground-based comparison. The results showed that, compared to the ground data, the MODIS LAI product followed a reasonable seasonal trajectory during the growing season. However, an anomaly existed at the beginning of the growing season. Also, a slight overestimation was found for the MODIS LAI product compared to the HJ-retrieved LAI maps. The average overestimation for the LAI was approximately 0.4 m2/m2, and the relative absolute errors of the product ranged from 10%–50%. The overestimation at the beginning and end of the growing season was higher due to the interference of soil background and grass variation. The results of this study provide a comprehensive understanding of the accuracy of the regional MODIS LAI product for the Hulunber meadow steppe. This research is important for improving regional modeling and prediction of vegetation biogeochemical processes and earth system productivity.

This article reviews studies regarding wild animal surveys based on multiple platforms, including satellites, manned aircraft, and unmanned aircraft systems (UASs), and focuses on the data used, animal detection methods, and their accuracies. We also discuss the advantages and limitations of each type of remote sensing data and highlight some new research opportunities and challenges. Submeter very-high-resolution (VHR) spaceborne imagery has potential in modeling the population dynamics of large (>0.6 m) wild animals at large spatial and temporal scales, but has difficulty discerning small (

Urban flood control requires real-time and spatially detailed information regarding the waterlogging depth over large areas, but such information cannot be effectively obtained by the existing methods. Video supervision equipment, which is readily available in most cities, can record urban waterlogging processes in video form. These video data could be a valuable data source for waterlogging depth extraction. The present paper is aimed at demonstrating a new approach to extract urban waterlogging depths from video images based on transfer learning and lasso regression. First, a transfer learning model is used to extract feature vectors from a video image set of urban waterlogging. Second, a lasso regression model is trained with these feature vectors and employed to calculate the waterlogging depth. Two case studies in China were used to evaluate the proposed method, and the experimental results illustrate the effectiveness of the method. This method can be applied to video images from widespread cameras in cities, so that a powerful urban waterlogging monitoring network can be formed.

We used unmanned aerial vehicles (UAVs) to carry out a relatively complete population census of large wild herbivores in Maduo County on the Tibetan Plateau in the spring of 2017. The effective area covered by aerial surveys was 326.6 km2, and 23,784 images were acquired. Interpretation tag libraries for UAV images were created for wild animals, including Kiang (Equus kiang), Tibetan gazelle (Procapra picticaudata), and blue sheep (Pseudois nayaur), as well as livestock, including yaks and Tibetan sheep. Large wild herbivores in the survey transect were identified through manual imagery interpretation. Densities ranged from 1.15/km2 for Kiang, 0.61/km2 for Tibetan gazelle, 0.62/km2 for blue sheep, 4.12/km2 for domestic yak, and 7.34/km2 for domestic sheep. A method based on meadows in the cold and warm seasons was used for estimating the densities and numbers of large wild herbivores and livestock, and was verified against records of livestock numbers. Population estimates for Kiang, Tibetan gazelle, blue sheep, domestic yak, and domestic sheep were 17,109, 15,961, 9324, 70,846, and 102,194, respectively. Based on published consumption estimates, the results suggest that domestic stock consume 4.5 times the amount of vegetation of large wild herbivores. Compared with traditional ground survey methods, performance of UAV remote sensing surveys of large wild herbivore populations was fast, economical and reliable, providing an effective future method for surveying wild animals.

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass (R2 = 0.340, root-mean-square error (RMSE) = 81.89 g·m−2, and relative error of 14.1%). The improvement of multiple regressions to the R2 and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (