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Abstract This paper proposes a novel approach for absolute velocity estimation of unmanned aerial vehicles in unknown and unmapped GNSS‐denied environments. The proposed method leverages the advantages of Fourier‐based image phase correlation and utilizes off‐the‐shelf onboard sensors, including a downward‐facing monocular camera, an inertial sensor, and a sonar. The non‐matching tracking approach is particularly appealing, offering accurate estimation while remaining robust against frequency‐dependent noise, significant intensity variations, and time‐varying illumination disturbances. In the proposed method, the first step involves computing global pixel motion from consecutive images using phase correlation, which utilizes the shift property of the Fourier transform. This pixel motion estimation serves as the basis for creating a closed‐loop solution for absolute velocity estimation. To further enhance accuracy, a Kalman filter is implemented to fuse all available data and provide a reliable velocity estimate. Validation of the proposed visual‐inertial technique is conducted through simulation experiments using AirSim and real‐world flight tests. The results demonstrate the practicality and effectiveness of the approach across a range of challenging scenarios.

In the existing pixel-level prediction methods of future remote sensing images, the impact of other factors on the outbreak of cyanobacteria blooms is often ignored, and only the single factor of chlorophyll a concentration is considered. This paper comprehensively considers the impact of chlorophyll a, temperature, and phycocyanin concentration on the outbreak of cyanobacteria blooms. Based on the Pix2Pix model, a 4D-Pix2Pix model is constructed. The Conv3D-LSTM network is used to extract the two-dimensional space, one-dimensional time, and one-dimensional multi-factor features of the multi-factor remote sensing image time series. The features are processed by the 3D-Unet network with a residual structure to achieve pixel-level prediction of future multi-factor remote sensing images. 3D-PatchGAN is used for adversarial training to optimize network performance. Finally, a cyanobacteria bloom outbreak level standard based on multi-factor remote sensing images is proposed to predict the spatiotemporal distribution of cyanobacteria blooms.

Food is the top priority for the people, and safety is the top priority for food. The quality and safety of food are related to the national economy and people's livelihood. With the development of Chinese economy and the improvement of people's quality of life, the scale of the food industry has also grown year by year, and the society and consumers have more stringent requirements on the quality of food production and its own safety. However, food quality and safety incidents occur frequently, making food quality and safety management an important task for improving people's livelihoods. Machine learning has been widely applied in the field of food quality and safety, with strong self-learning ability, good non-linear fitting ability, and fast modeling. Among them, neural network models and supervised learning methods can accurately and quickly detect and control the quality of food in the production process. This article focuses on the research progress of machine learning in the field of food quality and safety, and discusses it in three directions: Food quality inspection, food process traceability, and food safety warning. In order to clarify the focus, advantages and disadvantages, and future development direction of machine learning algorithms in food regulation, and provide theoretical support and technical guidance for the intelligent development of ensuring food quality and safety.

During shoulder arthroscopic surgery in the lateral decubitus position, effective and stable continuous traction is a basic requirement for the smooth progression of the surgery. Herein, we describe a safe, reliable, and cost-effective lateral decubitus traction assembly.

[Introduction] Since the power line has the characteristics of long transmission distance and a complex spatial environment, the UAV LiDAR point cloud technology can completely and efficiently obtain the geometric information of the power line and its surrounding spatial objects, and the existing supervised extraction and unsupervised extraction methods are deficient in point cloud data extraction in a large range of complex environments, according to the spatial environment characteristics of the main network and distribution network line point cloud data, a rapid extraction method of point cloud power line is proposed based on projection line characteristics and region growing algorithm. [Method] Firstly, in view of the characteristics that the overhead lines of the main network were usually higher than the surrounding spatial objects, the power lines were roughly extracted by the elevation histogram threshold method. Then, considering the characteristics that the vegetation canopy was higher than the distribution network line in the distribution network area, the KNN data points of the roughly extracted power line point cloud were obtained, and the point cloud was projected on the horizontal plane, and whether the point cloud was a power line point cloud was judged by the linear measurement of the point cloud. [Result] According to the existence of missing power line point clouds, all the power line point cloud clusters are obtained through a region growing mode, and on this basis, the catenary formula of each power line point cloud cluster is calculated through the catenary formula, and the point cloud with a fitting distance less than the threshold is merged as the same power line point cloud. [Conclusion] The proposed method aims at the problem of rapid power line extraction in inspection applications and overcomes the problem of power line point cloud missing and vegetation impact in the process of power line extraction, so this method can achieve power line point cloud extraction with high efficiency and accuracy.

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has had a significant impact on healthcare systems and economies worldwide. The continuous emergence of new viral strains presents a major challenge in the development of effective antiviral agents. Strategies that possess broad-spectrum antiviral activities are desirable to control SARS-CoV-2 infection. ACE2, an angiotensin-containing enzyme that prevents the overactivation of the renin angiotensin system, is the receptor for SARS-CoV-2. ACE2 interacts with the spike protein and facilitates viral attachment and entry into host cells. Yet, SARS-CoV-2 infection also promotes ACE2 degradation. Whether restoring ACE2 surface expression has an impact on SARS-CoV-2 infection is yet to be determined. Here, we show that the ACE2-spike complex is endocytosed and degraded via autophagy in a manner that depends on clathrin-mediated endocytosis and PAK1-mediated cytoskeleton rearrangement. In contrast, free cellular spike protein is selectively cleaved into S1 and S2 subunits in a lysosomal-dependent manner. Importantly, we show that the pan-PAK inhibitor FRAX-486 restores ACE2 surface expression and suppresses infection by different SARS-CoV-2 strains. FRAX-486-treated Syrian hamsters exhibit significantly decreased lung viral load and alleviated pulmonary inflammation compared with untreated hamsters. In summary, our findings have identified novel pathways regulating viral entry, as well as therapeutic targets and candidate compounds for controlling the emerging strains of SARS-CoV-2 infection.

Popliteal cysts, also termed Baker's cysts, are clinically common cystic lesions in the popliteal fossa. Typically, the contents of a ruptured cyst tend to spread into the myofascial interfaces in any direction, most commonly inferomedially or into a palpable superficial position. However, to our knowledge, reports of Baker's cysts dissecting into the deep intermuscular septum of the lower calf are extremely rare. We present here the details of the successful treatment through arthroscopy combined with lower calf incision of a patient who sustained hematoma of the knee and lower calf secondary to Baker's cyst rupture. Given the rarity of this disease in China, we present this case report to improve our understanding of the disease and avoid misdiagnosis and provide evidence for its clinical treatment, management, and prognosis.

Abstract Air pollution is a serious problem that affects economic development and people’s health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four cities. First, we use an ARIMA model to fit the linear part of the data and a CNN-LSTM model to fit the non-linear part of the data to avoid the problem of blinding in the CNN-LSTM hyperparameter setting. Then, to avoid the blinding dilemma in the CNN-LSTM hyperparameter setting, we use the Dung Beetle Optimizer algorithm to find the hyperparameters of the CNN-LSTM model, determine the optimal hyperparameters, and check the accuracy of the model. Finally, we compare the proposed model with nine other widely used models. The experimental results show that the model proposed in this paper outperforms the comparison models in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The RMSE values for the four cities were 7.594, 14.94, 7.841 and 5.496; the MAE values were 5.285, 10.839, 5.12 and 3.77; and the R2 values were 0.989, 0.962, 0.953 and 0.953 respectively.

Abstract Objectives Rare diseases are a global public health issue with a more pressing situation in China. Unfortunately, the relevant research and development in this country are still in its infancy, leading to limited drug accessibility. In view of this, the Chinese government has taken a series of countermeasures to promote orphan drug R&D in recent years, which has presented encouraging results. This paper aims to review incentive policies and funding initiatives formulated by the Chinese government and examine their implications on orphan drug R&D. Methods Policies targeting orphan drug R&D during 2012–2022 were retrieved from the relevant official websites, categorized into different themes and analyzed for the contents. Data on government funding, drug approval, clinical trial approval and orphan drug designation were collected through internet search to analyze the implications of those incentive policies and initiatives on orphan drug R&D in China. Results A total of 20 relevant policy documents were identified and five major themes were revealed through content analysis, including national strategy, expedited approval, safety and efficacy requirements, data protection and technical support. The government input in orphan drug R&D has witnessed a steady annual increase. Driven by those incentives, the numbers of orphan drugs approved for marketing and drug candidates entering clinical studies are increasing year by year, and more domestic pharmaceutical companies are actively involved in the R&D of orphan drugs. Conclusions Orphan drug development in China is growing rapidly under the stimulation of incentive regulatory policies and more investment in researches. China is working toward a more standardized and comprehensive rare disease ecosystem. However, there are still some challenges, such as the lack of sufficient financial support and the call for systematic legislation on rare diseases, to be addressed for future success.

To solve the problem of full-process path planning that includes multi-target path planning and automatic mooring, this paper proposes a hybrid D*Lite algorithm for an unmanned surface vessel with pose constraints (position and attitude) in an unknown environment. First, to solve the problem of multi-target path planning under the pose constraints, the D*Lite algorithm is combined with the Dubins search tree algorithm, and the corner at the sampling point is eliminated so that the path is smoothly connected. Second, to achieve the automatic mooring under the pose constraints and the avoidance of unknown obstacles, the D*Lite algorithm is combined with Reeds-Shepp curves to introduce the heading angle into the traditional two-dimensional search. This paper has conducted three groups of simulation experiments. In simulation experiment 1 and 2, compared with other algorithms in the multi-target points and automatic mooring environment for simulation experiments respectively, the D*Lite (Dubins) and D*Lite (RS) algorithm outperformed other algorithms in terms of planning time, planning distance, and smoothness. In simulation experiment 3, the effectiveness and superiority of the proposed method were verified on the grid map and compared with the traditional D*Lite algorithm. A visualization simulation was conducted, and the results revealed that the hybrid D*Lite algorithm enabled the unmanned surface vessel to efficiently achieve smooth movement with full-process throughout unknown environment.

Abstract The outbreak of the COVID-19 and the Russia Ukraine war has had a great impact on the rice supply chain. Compared with other grain supply chains, rice supply chain has more complex structure and data. Using digital means to realize the dynamic supervision of rice supply chain is helpful to ensure the quality and safety of rice. This study aimed to build a dynamic supervision model suited to the circulation characteristics of the rice supply chain and implement contractualization, analysis, and verification. First, based on an analysis of key information in the supervision of the rice supply chain, we built a dynamic supervision model framework based on blockchain and smart contracts. Second, under the logical framework of a regulatory model, we custom designed three types of smart contracts: initialization smart contract, model-verification smart contract, and credit-evaluation smart contract. To implement the model, we combined an asymmetric encryption algorithm, virtual regret minimization algorithm, and multisource heterogeneous fusion algorithm. We then analyzed the feasibility of the algorithm and the model operation process. Finally, based on the dynamic supervision model and smart contract, a prototype system is designed for example verification. The results showed that the dynamic supervision model and prototype system could achieve the real-time management of the rice supply chain in terms of business information, hazard information, and personnel information. It could also achieve dynamic and credible supervision of the rice supply chain’s entire life cycle at the information level. This new research is to apply information technology to the digital management of grain supply chain. It can strengthen the digital supervision of the agricultural product industry.

SARS- CoV-2 hijacks ACE2 for cell entry. Here, the authors report that dynamic SUMOylation modulates the TOLLIP-directed selective autophagic degradation of ACE2 and suggest SUMOylation inhibition as a potential intervention against SARS-CoV-2 infection.

As a testing technology for food, fluorescence spectrometry has the advantages of convenient, sensitive and efficient detection of complex and trace components in food. This article introduces the basic principles of fluorescence spectrum analysis technology, and systematically summarizes several fluorescence spectrum analysis technologies currently used in the field of food inspection, including atomic fluorescence spectrometry, two dimensional fluorescence correlation spectroscopy, excitation-emission matrices，front-face fluorescence spectroscopy，(total) synchronous fluorescence spectroscopy and fluorescence spectroscopy imaging technology, etc. The fluorescence spectrum analysis technology is used in the detection of different types of food such as grain and oil, livestock and poultry, aquatic products, dairy products, fruits and vegetables. The application methods and effects of fluorescence spectroscopy are summarized, and the existing problems and future development trends of fluorescence spectroscopy are analyzed.

OpenStreetMap has a large number of volunteers. There is a hypothesis that volunteers with different cultural backgrounds may have different editing behaviors when contributing to OSM. It may be strongly related to data quality and data reliability on OSM. As for the heterogeneity and the reliability of OSM data, previous research usually focuses on the geometric accuracy, spatial location accuracy and semantic integrity of OSM data, while few researchers have analyzed these problems from the perspective of editing behavior. On the grounds of relationship between mapping motivation and editing behavior, the dispersion of editing trajectory and clockwise direction index are proposed in the paper to explore whether the volunteers are sufficiently motivated and knowledgeable. In the experiments, the historical OSM data of four countries suggested that developed countries have lower trajectory dispersion. The lower degree of trajectory dispersion reflects the higher concentration and professionalism of volunteers. A high degree of drawing direction consistency shows volunteers who mapped French data were natives with local knowledge. From this point of view, this paper verifies that volunteer editing behavior is an effective method to analyze data quality heterogeneity and data reliability.

With the advancement of sensor technology, distributed processing technology, and wireless communication, Visual Sensor Networks (VSNs) are widely used. However, VSNs also have flaws such as poor data synchronization, limited node resources, and complicated node management. Thus, this paper proposes a sensor placement optimization method to save network resources and facilitate management. First, some necessary models are established, including the sensor model, the space model, the coverage model, and the reconstruction error model, and a dimensionality reduction search method is proposed. Next, following the creation of a multi-objective optimization function to balance reconstruction error and coverage, a clever optimization algorithm that combines the benefits of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) is applied. Finally, comparison studies validate the methodology presented in this paper, and the combined algorithm can enhance optimization effect while relatively reducing running time. In addition, a sensor coverage method for large-range target space with obstacles is discussed.

In this study, a novel planning and tracking approach is proposed for a mobile robotic arm to grab objects in an obstacle environment. First, we developed an improved APF-RRT* algorithm for the motion planning of a mobile robotic arm. This algorithm optimizes the selection of random tree nodes and smoothing the path. The invalid branch and the planning time are decreased by the artificial potential field, which is determined by the specific characteristics of obstacles. Second, a Fuzzy-DDPG-PID controller is established for the mobile robotic arm to track the planned path. The parameters of the PID controller are set using the new DDPG algorithm, which integrated FNN. The response speed and control accuracy of the controller are enhanced. The error and time of tracking of the mobile robotic arm are decreased. The experiment results verify that the proposed approach has good planning and tracking results, high speed and accuracy, and strong robustness. To avoid the occasionality of the experiments and fully illustrate the effectiveness and generality of the proposed approach, the experiments are repeated multiple times. The experiment results demonstrate the effectiveness of the proposed approach. It outperforms existing planning and tracking approaches.

Improper disposal of electronical waste (e-waste) causes harm to both public health and the environment, and how to effectively recycle and reduce electronical waste has become a common concern around the world. This study focuses on the design of the points system to encourage consumer participation in e-waste recycling programs. Based on the Theory of Planned Behavior (TPB) model, a semi-experimental design method was applied to influence consumer cognition and behavioral intention through information provision in survey design. Two surveys were conducted in two years apart to understand the temporal trend of consumer types and their preferences for the design of e-waste recycling points program. By comparing consumer types before and after the introduction of the points system, the research concludes that the points system has a positive impact on consumers' environmental consciousness and recycling intention. The results show that consumers generally have a strong sense of environmental protection after the introduction of the recycling points system, and that different consumer types differ significantly on subjective norm, perceived behavioral control, recycling motivation, points incentives, points redemption and recycling behavioral intentions. This suggests that the design of the points system can not only promote consumers' environmental awareness but also stimulate consumers to actively participate in e-waste recycling. Finally, several policy recommendations are discussed to help apply the points system to the empirical design of e-waste recycling programs.

In recent years, people’s quality of life has increased, and the requirements for fruits have also become higher; blueberries are particularly popular because of their rich nutrients. In the blueberry industry chain, sensory evaluation is an important link in determining the quality of blueberries. Therefore, to make a more objective scientific evaluation of blueberry quality and reduce the influence of human factors, on the basis of traditional sensory evaluation methods, machine learning is introduced to establish a support vector regression prediction model optimized by the particle swarm algorithm. Ten physical and chemical flavor indices of blueberries (such as catalase, flavonoids, and soluble solids) were used as input data, and sensory evaluation scores were used as output data. Three different predictive models were applied and compared: a particle swarm optimization support vector machine, a convolutional neural network, and a long short-term memory network model. To ensure reliability, the experiments with each of the three models were repeated 20 times, and the mean of each index was calculated. The experimental results showed that the root mean square error and mean absolute error of the particle swarm optimization support vector machine were 0.45 and 0.40, respectively; these values were lower than those of the convolutional neural network (0.96 and 0.78, respectively) and the long short-term memory network (1.22 and 0.97, respectively). Hence, these results highlighted the superiority of the proposed model when sample data are limited.

A single unmanned surface combatant (USV) has poor mission execution capability, so the cooperation of multiple unmanned surface ships is widely used. Cooperative hunting is an important aspect of multi USV collaborative research. Therefore, this paper proposed a cooperative hunting method for multi-USV based on the A* algorithm in an environment with obstacles. First, based on the traditional A* algorithm, a path smoothing method based on USV minimum turning radius is proposed. At the same time, the post order traversal recursive algorithm in the binary tree method is used to replace the enumeration algorithm to obtain the optimal path, which improves the efficiency of the A* algorithm. Second, a biomimetic multi USV swarm collaborative hunting method is proposed. Multiple USV clusters simulate the hunting strategy of lions to pre-form on the target’s path, so multiple USV clusters do not require manual formation. During the hunting process, the formation of multiple USV groups is adjusted to limit the movement and turning of the target, thereby reducing the range of activity of the target and improving the effectiveness of the algorithm. To verify the effectiveness of the algorithm, two sets of simulation experiments were conducted. The results show that the algorithm has good performance in path planning and target search.

In this paper, a new degradation model of the more electrical aircraft (MEA) actuation system is proposed. The bond graph model is used to build the dissimilar redundancy actuation system model, taking into account the degradation behavior (wear, fatigue, aging) of components in the system operation and the fault coupling phenomenon caused by the accumulation of dissipated energy in space and time, and a fault coupling degradation model of the actuation system is established with clear mechanism support. Through the fault mechanism analysis, various fault types are injected into the bond graph model of the actuation system, and the simulation results are compared. The influence of fault propagation on system output is analyzed, the system’s reliability is assessed, and the performance evaluation of the actuation system is realized while taking the fault coupling mechanism into account.

Abstract The ongoing 2019 novel coronavirus disease (COVID-19) caused by SARS-CoV-2 has posed a worldwide pandemic and a major global public health threat. The severity and mortality of COVID-19 are associated with virus-induced dysfunctional inflammatory responses and cytokine storms. However, the interplay between host inflammatory responses and SARS-CoV-2 infection remains largely unknown. Here, we demonstrate that SARS-CoV-2 nucleocapsid (N) protein, the major structural protein of the virion, promotes the virus-triggered activation of NF-κB signaling. After binding to viral RNA, N protein robustly undergoes liquid–liquid phase separation (LLPS), which recruits TAK1 and IKK complex, the key kinases of NF-κB signaling, to enhance NF-κB activation. Moreover, 1,6-hexanediol, the inhibitor of LLPS, can attenuate the phase separation of N protein and restrict its regulatory functions in NF-κB activation. These results suggest that LLPS of N protein provides a platform to induce NF-κB hyper-activation, which could be a potential therapeutic target against COVID-19 severe pneumonia.

In order to deal with the problem of low precision and ignoring the influence of some factors on the growth system of cyanobacteria, this paper considers the effects of light, water temperature and other influencing factors to analyze cyanobacteria growth system. The time stochastic differential equation of cyanobacteria growth with double nutrient cycling is established. The numerical analysis and the intelligent evolutionary algorithm are used to optimize the steady parameters in the time stochastic differential equation of cyanobacteria growth. The light, water temperature and other factors is used to build time series modeling. The time stochastic parameters and the prediction of cyanobacteria biomass is realized. The bifurcation theory and the central manifold theory were used to analyze the nonlinear dynamics of cyanobacteria growth with double nutrient cycling system, so that the conditions of cyanobacteria bloom and prediction of cyanobacteria blooms are achieved. Compared with the existing research, the method proposed in this paper not only determines the conditions of cyanobacteria bloom, but also improves the accuracy of water bloom prediction, and provides an effective reference for environmental protection departments.

As the main food source of the world’s population, grain quality safety is of great significance to the healthy development of human beings. The grain food supply chain is characterized by its long life cycle, numerous and complex business data, difficulty defining private information, and difficult managing and sharing. In order to strengthen the ability of information application processing and coordination of the grain food supply chain under many risk factors, an information management model suitable for the grain food supply chain is studied based on the blockchain multi-chain technology. First, the information on key links in the grain food supply chain is analyzed to obtain privacy data classifications. Second, a multi-chain network model of the grain food supply chain is constructed, and based on this model, the hierarchical encryption and storage mode of private data as well as the relay cross-chain communication mode, are designed. In addition, a complete consensus process, including CPBFT, ZKP, and KZKP algorithms, is designed for the global information collaborative consensus under the multi-chain architecture. Finally, the model is verified through performance simulation, theory analysis, and prototype system verification in terms of its correctness, security, scalability, and consensus efficiency. The results show that this research model effectively reduces the storage redundancy and deals with problems of data differential sharing in traditional single-chain research, as well as provides a secure data protection mechanism, a credible data interaction mechanism, and an efficient multi-chain collaborative consensus mechanism. By attempting to apply blockchain multi-chain technology to the grain food supply chain, this study provides new research ideas for the trusted protection of data and information collaborative consensus in this field.

The problem of cold-chain food safety is becoming increasingly prominent. Cold food chain risk assessment is an important way to ensure cold-chain food safety. Using CiteSpace, this study analyzes the knowledge map of research hotspots in the field of cold-chain food safety over the past 18 years, identifies the research keywords, presents the centrality statistics, and calculates the cluster values and average cluster contour values. Adopting a data-driven perspective, risk-assessment methods for cold food chains are summarized based on qualitative risk assessment, quantitative risk assessment, and comprehensive qualitative and quantitative risk assessment. The advantages and disadvantages of each are summarized. Finally, the problems and challenges in current cold food chain risk-assessment research are summarized in three aspects: the data credibility of cold food chain traceability systems, cold-chain food safety audit methods, and nontraditional cold food chain risk assessment. Suggestions are given for strengthening the cold food chain risk-assessment system to provide a decision-making reference to help regulatory authorities take risk prevention and control measures.

Rice is common in the human diet, making rice safety issues important. Moreover, rice processing safety is key for rice security, so rice processing chain risk assessment is critical. However, methods proposed to assess the rice processing chain risk have issues, such as the use of unreasonable thresholds for the rice processing chain and fixed weight. To solve these problems, we propose a risk assessment method for the rice processing chain based on a multidimensional trapezoidal cloud model. First, an evaluation model based on a multidimensional trapezoidal cloud model was established. Based on the historical evaluation results, Atanassov’s interval-valued intuition language numbers (AIVILNs) were introduced to determine the cloud model’s parameters. Second, the concept of dynamic weight was introduced to integrate the static and dynamic weights. An exponential function was used to construct dynamic weighting mechanisms, and the analytic hierarchy stage (AHP) was used to construct a static weight. The proposed method was validated by 104 sets of rice processing chain data, and the results show that the method could evaluate the risk level of the rice processing chain more accurately and reasonably than other methods, indicating that it can provide a sound decision-making basis for food safety supervision authorities.

In the traditional rapidly exploring random tree (RRT) algorithm, the planned path is not smooth, the distance is long, and the fault tolerance rate of the planned path is low. Disturbances in an environment can cause unmanned surface vessels (USVs) to deviate from their planned path during navigation. Therefore, this paper proposed a path-planning method considering environmental disturbance based on virtual potential field RRT* (VPF-RRT*). First, on the basis of the RRT* algorithm, a VPF-RRT* algorithm is proposed for planning the planning path. Second, an anti-environmental disturbance method based on a deep recurrent neural networks PI (DRNN-PI) controller is proposed to allow the USV to eliminate environmental disturbance and maintain its track along the planning path. Comparative simulation experiments between the proposed algorithm and the other algorithms were conducted within two different experimental scenes. In the path-planning simulation experiment, the VPF-RRT* algorithm had a shorter planning path and a smaller total turning angle when compared to the RRT* algorithm. In the path-tracking simulation experiment, when using the proposed algorithm, the USV could effectively compensate for the impact of environmental disturbance and maintain its navigation along the planning path. In order to avoid the contingency of the experiment and verify the effectiveness and generality of the proposed algorithm, three experiments were conducted. The simulation results verify the effectiveness of the proposed algorithm.

This paper solves the problem of cooperative path planning of multiple unmanned surface vehicles (USVs) for search and coverage tasks in water environments. Firstly, taking the search coverage problem of water surface pollutants as an example, the information concentration map is built to predict the diffusion of water surface pollutants. Secondly, we propose a region division method based on a Voronoi diagram, which divides the region and assigns it to each unmanned surface vehicle (USV). Then, on the basis of the traditional Model Predictive Control (MPC), the future reward index based on the regional centroid is introduced, and the Improved Salp Swarm Algorithm (ISSA) is used to solve MPC. Simulation results show the effectiveness of the proposed method.

In recent years, various food-safety issues have aroused public concern regarding safety in the food supply chain. Since grains are closely linked to human life and health, it is necessary to effectively manage information in the grain supply chain. The grain supply chain is characterized by a long life cycle, complex links, various hazards, and heterogeneous information sources. Problems with traditional traceability systems include easy data tampering, difficult hazardous-material information management, the “information isolated island” problem, and low traceability efficiency in the whole supply chain. Blockchain is a distributed computing paradigm characterized by decentralization, network-wide recording, security, and reliability. As such, it can reduce administrative costs and improve the efficiency of information management. Based on literature research and a field investigation of wheat-processing enterprises in Shandong Province, We analyze the operation process of grain supply chain. This study, therefore, proposed a new system architecture in the entire grain supply chain based on blockchain technology and designed a multimode storage mechanism that combines chain storage. This prototype system was tested and verified using actual cases and application scenarios. Compared to traditional systems, the proposed system is characterized by data security and reliability, information interconnection and intercommunication, real-time sharing of hazardous-material information, and dynamic and credible whole-process tracing. As such, this system is highly significant and has reference value for guaranteeing food quality and safety-process traceability.

Algal bloom is a nonlinear and time-varying process, which brings challenges for the accurate prediction. For the existing mechanism model of algae ignores the external key factors, we propose an algae growth model (AGM) optimized by action dependent heuristic dynamic programming (ADHDP). This model has the structure of information interaction with the outside, which can predict algal bloom with well adaptive ability. In this paper, chlorophyll-a concentration is used as the representative factor of algal bloom. We use ADHDP approach to map the external key factors to the time-varying parameters, so the AGM can be adjusted to realize the self-adaptive prediction with the changes in external environments. Compared with different prediction methods, the simulation result shows that the ADHDP-AGM prediction model can accurately predict the chlorophyll-a concentration under different data distributions. Moreover, the prediction process shows that the time-varying parameters in AGM conform to the evolution trend of chlorophyll-a concentration in fact, which further improves the interpretability of prediction model. It provides a new perspective for building a data-driven prediction model with clear physical significance, and makes the mechanism research and data science further fusion.

With the development of Agriculture 4.0, the requirements for sustainable agriculture and precision agriculture continue to grow. As one of the three major staple foods globally, the quality and safety of rice affect human health as well as social development. To ensure the quality and safety of rice and reduce the flow of problematic rice, a multi-layer blockchain-based rice supply chain refinement supervision model MBRRSM (Multi-blockchain Rice Refined Supervision Model) is proposed from the information level. First, the characteristics of information flow in the rice supply chain are analyzed, and a classification table of key information is constructed. Second, the MBRRSM framework is designed. Based on a multi-party hybrid encryption algorithm, secure multi-party computing algorithm, multi-mode storage mechanism, and SPOP (Supervision Proof of Peers) consensus algorithm, a set of mechanisms is designed for the transmission, use, storage, and consensus of rice data in MBRRSM. Subsequently, the security and performance capabilities of MBRRSM are analyzed. Meanwhile, the SPOP consensus algorithm is analyzed. Finally, a prototype system is built based on MBRRSM and verified through exemplary scenarios in different usage situations. The results show that research on the refined supervision of the rice supply chain based on multi-blockchain can finely supervise all types of data in the rice supply chain, and provide a guarantee for enterprise users to safely transmit and use data with different privacy levels. This study presents a unique research paradigm that introduces the theories and methods of the new information field generation into the field of agricultural research, and thus assists in the strategy implementation of “holding grain in the land and storing grain in technology”.

Multirotors play an important role in electric power inspection, border control, modern agriculture, forest fire fighting, flood control, disaster prevention, etc. Multirotor failures, such as a communication fault, a sensor failure, or a power system anomaly, may well lead to mission interruption, multirotor crashes, and even casualties. To ensure flight safety, a multirotor decision module should be established to prevent or reduce the adverse effects caused by failure. Therefore, this paper proposes a real-time safety decision-making method for multirotor flight strategies based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Firstly, the flight of the multirotor was simulated based on the Rflysim UAV flight simulation platform, and a fault-injection module was constructed to simulate different types of faults, so as to realize real-time monitoring of the flight status of the multirotor, and to collect flight data under various faults to establish condition assessment information sources. Then, based on the random forest algorithm, a failure level classification model of the multirotor was constructed, the model was trained and verified by inputting flight data of three types of safety level failures, and the model effectively classified the failure levels of the multirotor. Under this framework, a real-time safety decision-making model for the multirotor based on the TOPSIS model was constructed to realize the flight safety decision-making of the multirotor under different faults. This method can effectively realize the real-time decision-making for the flight strategy of a multirotor. By comparison with other models, the classification accuracy of the failure level classification model is higher, and the consideration of flight decision-making is more comprehensive and accurate, thus effectively ensuring the flight safety of the multirotor.

With the accelerated digital transformation, food security data is exponentially growing, making it difficult to process and analyze data as the primary challenge for food security risk regulation. The promotion of “big data + food” safety supervision can effectively reduce supervision costs and improve the efficiency of risk detection and response. In order to improve the utilization of testing data and achieve rapid risk assessment, this paper proposes a rice security risk assessment method based on the fusion of multiple machine learning models, and conducts experimental validation based on rice hazard detection data from 31 provinces in China excluding Hong Kong, Macao and Taiwan in 2018. The model comparison verifies that the risk assessment model shows better performance than other mainstream machine learning algorithms, and its evaluation accuracy is as high as 99.54%, which verifies that the model proposed in this paper is more stable and accurate, and can provide accurate and efficient decision-making basis for regulatory authorities.

Abstract Chromatin modifications, such as histone acetylation, ubiquitination, and methylation, play fundamental roles in maintaining chromatin architecture and regulating gene transcription. Although their crosstalk in chromatin remodeling has been gradually uncovered, the functional relationship between histone ubiquitination and methylation in regulating immunity and inflammation remains unclear. Here, it is reported that USP38 is a novel histone deubiquitinase that works together with the histone H3K4 modifier KDM5B to orchestrate inflammatory responses. USP38 specifically removes the monoubiquitin on H2B at lysine 120, which functions as a prerequisite for the subsequent recruitment of demethylase KDM5B to the promoters of proinflammatory cytokines Il6 and Il23a during LPS stimulation. KDM5B in turn inhibits the binding of NF‐κB transcription factors to the Il6 and Il23a promoters by reducing H3K4 trimethylation. Furthermore, USP38 can bind to KDM5B and prevent it from proteasomal degradation, which further enhances the function of KDM5B in the regulation of inflammation‐related genes. Loss of Usp38 in mice markedly enhances susceptibility to endotoxin shock and acute colitis, and these mice display a more severe inflammatory phenotype compared to wild‐type mice. The studies identify USP38‐KDM5B as a distinct chromatin modification complex that restrains inflammatory responses through manipulating the crosstalk of histone ubiquitination and methylation.

Active disturbance rejection control (ADRC) is a kind of effective tool in dealing with dynamic instability, simultaneous internal and external disturbances, nonlinearity, etc. However, the application of ADRC is limited because the input and output signals of the system are not synchronized due to the existence of time-delay impact in a time-delay system. Modified twice optimal control (MTOC) is a control algorithm proposed for the time-delay system. It can effectively compensate the time-delay impact, synchronize the input and output signals of the system, and provide control conditions for a better application of ADRC to system control. In this paper, a new control algorithm, namely active disturbance rejection modified twice optimal control (ADRMTOC), is proposed based on the suppression capability of ADRC to system total disturbance and the compensation ability of MTOC to time-delay impact. The simulation experiments show that the system under this control method has strong robustness and interference suppression ability, which makes ADRMTOC have certain industrial practical values.

Cruise ships are widely used in water quality monitoring but suffer from path planning problems in a surface water environment. Solutions to path planning problems have higher requirements of path planning distance and path planning time and the ability of real-time obstacles avoidance. This paper introduces a hybrid path planning method to solve the path planning problem when using unmanned cruise ships. First, a model of the surface water environment with unknown districts is established by the grid method. Then, the global path is planned by the A* algorithm based on such a model, and in unknown areas of the model, the artificial potential filed (APF) is employed for local path planning. The problem of obtaining unreachable targets and falling into a local minimum introduced by the APF is improved by using the optimized repulsion potential field function and adding the directional random escape strategy. The A* algorithm with self-learning ability is proposed for secondary planning and cases where a local path cannot be generated. Finally, the optimal path combined with the global path and the local path is smoothed. The simulation results show that the proposed algorithm has better performance than other algorithms from the aspects of distance cost and time cost.

The issue of food quality and safety is a major concern. Rice is considered one of the three staple foods. Rice quality and safety problems have occurred frequently, which seriously affect human health. The rice supply chain is characterized by complex links, discrete data, and numerous types of hazardous substances. Strengthening the information management and control capabilities of the rice supply chain is an important means to ensure the quality and safety of rice. Based on multi-chain collaboration, we have conducted research on information management and control of the rice supply chain. First, a multi-chain collaborative model of “blockchain + sub-chain” is designed. Based on this model, the following four mechanisms are designed: a trusted chain mechanism, a multi-level sub-chain encryption mechanism, a trusted supervision mechanism, and a hierarchical consensus mechanism. These mechanisms jointly serve the multi-chain collaborative management and control of the rice supply chain information. Secondly, smart contracts and operating procedures are designed, and a comparative analysis of them is executed. Finally, the design and implementation of the prototype system is carried out, and an example is verified and analyzed in a grain enterprise. Results show that this model serves the information supervision of the rice supply chain by studying the multi-chain collaboration. The study solves the real-time data interaction problem between each link of the rice supply chain. The credible management of information and control of the rice supply chain is accomplished. This study applies new information technology to the coordination and resource sharing of the food supply chain and provides ideas for the digital transformation of the food industry.

Rice is one of the three major staple foods in the world, and the quality and safety of rice are related to the development of human beings. The new crown epidemic, pesticide residues, insect pests, and heavy metal pollution have a certain security impact on the food supply chain. The rice supply chain is characterized by a long life cycle; complex roles in the main links; many types of hazards; and multidimensional, multisource, and heterogeneous information. To strengthen the rice supply chain’s supervision ability under the epidemic situation, a supervision cross-chain model suitable for the complicated data of the rice supply chain based on parallel blockchain theory and smart contract technology was built. Firstly, the data collected in the rice supply chain and different types of data stored in different parallel blockchains were analyzed. Secondly, based on data analysis, a collection/supervision cross-chain mechanism based on “hash lock + smart contract + relay chain”, a concurrency mechanism based on the K-means algorithm and a Bloom filter, and a consensus mechanism suitable for multichain consensus named the Supervision Practical Byzantine Fault Tolerance (SPBFT) were proposed. Furthermore, a cross-chain model of rice supply chain supervision was constructed. Finally, theoretical verification and simulation experiments were used to analyze the operation process, safety, cross-chain efficiency, and scalability of the model. The results showed that the application of parallel blockchains and smart contracts to supervision of rice supply chain information improved the convenience and security of information interaction between various links in the rice supply chain, the storage cost of supply chain data and the high latency of interaction was reduced, and the refined management of the rice supply chain data and personnel was realized. This research applied new information technology to the coordination and resource sharing of the food supply chain, and provides ideas for the digital transformation of the food industry.

To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.

A stochastic hybrid system (SHS) is a type of model describing real-world systems, who are both dynamic and hybrid. System performance evaluation is a key technology related to mission accomplishment and safety, where a rational selection of performance indicators is an important guarantee of truly and accurately evaluating system performance. This paper proposes a performance evaluation algorithm of SHS. First, the concept of fuzzy health degree is proposed and the SHS model studied in this paper is established. Then, the procedure of the algorithm is presented in detail, including hybrid state estimation based on a modified interacting-multiple-model algorithm, discretization of continuous variables, and quantitative calculation of fuzzy health degree. In order to validate the effectiveness of the proposed algorithm, an experiment of a quadrotor with sensor anomalies is made where the system performance is quantitatively described by using the fuzzy health degree. Comparative studies are also made and discussed.

In order to handle undesirable failures of a multicopter, which occurs in either the pre-flight process or the in-flight process, a failsafe mechanism design method based on supervisory control theory (SCT) is proposed for the semi-autonomous control mode. The failsafe mechanism is a control logic that guides what subsequent actions the multicopter should take, by taking account of real-time information from guidance, attitude control, diagnosis and other low-level subsystems. In order to design a failsafe mechanism for the multicopters, safety issues of the multicopters are introduced. Then, user requirements including functional requirements and safety requirements are textually described, where functional requirements guide the modelling of a general multicopter plant, and safety requirements cover the failsafe measures dealing with the presented safety issues. Based on these requirements, several multicopter modes and events are defined. On this basis, the multicopter plant and control specifications are modelled by automata. Then, a supervisor is synthesized by using SCT. In addition, the authors present three examples to demonstrate the potential conflicting phenomena due to the inappropriate design of control specifications. Finally, based on the obtained supervisor, an implementation method suitable for multicopters is presented, in which the supervisor is transformed into decision-making codes.

Abstract The noncanonical NF‐κB signaling pathway plays a critical role in a variety of biological functions including chronic inflammation and tumorigenesis. Activation of noncanonical NF‐κB signaling largely relies on the abundance as well as the processing of the NF‐κB family member p100/p52. Here, TRIM14 is identified as a novel positive regulator of the noncanonical NF‐κB signaling pathway. TRIM14 promotes noncanonical NF‐κB activation by targeting p100/p52 in vitro and in vivo. Furthermore, a mechanistic study shows that TRIM14 recruits deubiquitinase USP14 to cleave the K63‐linked ubiquitin chains of p100/p52 at multiple sites, thereby preventing p100/p52 from cargo receptor p62‐mediated autophagic degradation. TRIM14 deficiency in mice significantly impairs noncanonical NF‐κB‐mediated inflammatory responses as well as acute colitis and colitis‐associated colon cancer development. Taken together, these findings establish the TRIM14‐USP14 axis as a crucial checkpoint that controls noncanonical NF‐κB signaling and highlight the crosstalk between autophagy and innate immunity.

In spatial analysis applications, measuring the shape similarity of polygons is crucial for polygonal object retrieval and shape clustering. As a complex cognition process, measuring shape similarity should involve finding the difference between polygons, as objects in observation, in terms of visual perception and the differences of the regions, boundaries, and structures formed by the polygons from a mathematical point of view. In existing approaches, the shape similarity of polygons is calculated by only comparing their mathematical characteristics while not taking human perception into consideration. Aiming to solve this problem, we use the features of context and texture of polygons, since they are basic visual perception elements, to fit the cognition purpose. In this paper, we propose a contour diffusion method for the similarity measurement of polygons. By converting a polygon into a grid representation, the contour feature is represented as a multiscale statistic feature, and the region feature is transformed into condensed grid of context features. Instead of treating shape similarity as a distance between two representations of polygons, the proposed method observes similarity as a correlation between textures extracted by shape features. The experiments show that the accuracy of the proposed method is superior to that of the turning function and Fourier descriptor.

Quadcopters are widely used in a variety of military and civilian mission scenarios. Real-time online detection of the abnormal state of the quadcopter is vital to the safety of aircraft. Existing data-driven fault detection methods generally usually require numerous sensors to collect data. However, quadcopter airframe space is limited. A large number of sensors cannot be loaded, meaning that it is difficult to use additional sensors to capture fault signals for quadcopters. In this paper, without additional sensors, a Fault Detection and Identification (FDI) method for quadcopter blades based on airframe vibration signals is proposed using the airborne acceleration sensor. This method integrates multi-axis data information and effectively detects and identifies quadcopter blade faults through Long and Short-Term Memory (LSTM) network models. Through flight experiments, the quadcopter triaxial accelerometer data are collected for airframe vibration signals at first. Then, the wavelet packet decomposition method is employed to extract data features, and the standard deviations of the wavelet packet coefficients are employed to form the feature vector. Finally, the LSTM-based FDI model is constructed for quadcopter blade FDI. The results show that the method can effectively detect and identify quadcopter blade faults with a better FDI performance and a higher model accuracy compared with the Back Propagation (BP) neural network-based FDI model.

Water environment protection is of great significance for both economic development and improvement of people’s livelihood, where modeling of water environment evolution is indispensable in water quality analysis. However, many water quality indexes related to water quality model cannot be measured online, and some model parameters always vary among different water areas. Thus, this paper proposes a water quality soft-sensing method based on the water quality mechanism model to simulate evolution of water quality indexes online, where unscented Kalman filter is utilized to estimate model parameters. Furthermore, a modified fuzzy comprehensive evaluation method is presented to evaluate the level of water eutrophication condition. Finally, the water quality data collected from Taihu Lake and Beihai Lake are used to validate the effectiveness and generality of the proposed method. The results show that the proposed soft-sensing method is able to describe the variation of related water quality indexes, with better accuracy compared to nonlinear least squares based method and traditional trial-and-error based method. On this basis, the water eutrophication condition can be also accurately evaluated.