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The world is facing an unprecedented socio-economic crisis caused by the novel coronavirus infection (COVID-19). The virus is also spreading through the import and export food supply chains. The Chinese authorities have discovered the COVID-19 virus in various imported frozen meat packages. Traceability plays a vital role in food quality and food safety. The Internet of Things (IoT) provides solutions to overseeing environmental conditions, product quality, and product traceability. These solutions are traditionally based on a centralized architecture, which does not guarantee tamper-proof data sharing. The blockchain is an emerging technology that provides tamper-proof data sharing in real-time. This article presents a blockchain-enabled supply chain architecture to ensure the availability of a tamper-proof audit trail. This tamper-proof audit trail helps to make sure that all safety measures are undertaken to minimize the risk of COVID-19 and other bacteria, fungi, and parasites being present in the frozen meat supply chain.

Objective: To explore the mechanisms underlying endothelin-1 (ET-1) elevations induced by excessive fluoride exposure. Methods: We measured serum and bone fluoride ion content and plasma ET-1 levels and compared these parameters among different groups in an animal model. We also observed morphological changes in the aorta and endothelium of rabbits. In cell experiments, human umbilical vein endothelial cells (HUVECs) were treated with varying concentrations of NaF for 24h, with or without 10 µM U0126 pretreatment for 1 h. ET-1 levels in culture fluid and intracellular reactive oxygen species (ROS) levels, as well as ET1 gene, endothelin-converting enzyme-1 (ECE-1), extracellular signal-regulating kinase 1/2 (ERK1/2), pERK1/2 expression levels and RAS activation were measured and compared among the groups. Results: Plasma ET-1 levels of rabbits increased significantly in fluorinated groups compared with those in the control group. The rabbit thoracic aortas became slightly hardened in fluorinated groups compared with those in the control group, and some vacuoles were present in the endothelial cell cytoplasm of the rabbits in fluorinated groups. In our cell experiments, ET1 gene and ECE-1 expression levels in HUVECs and ET-1 expression levels in the cell culture supernatants increased significantly in some experimental groups compared with those in the control group. These trends paralleled the changes in intracellular ROS levels, RAS activation, and the pERK1/2-to-ERK1/2 ratio. After U0126 was added, ECE-1 expression and ET-1 levels decreased significantly. Conclusion: Excessive fluoride exposure leads to characteristic endothelial damage (vacuoles), thoracic aorta hardening, and plasma ET-1 level elevations in rabbits. In addition, the ROS-RAS-MEK1/2-pERK1/2/ERK1/2 pathway plays a crucial—and at least partial—role in ET-1 over-expression, which is promoted by excessive fluoride exposure.

Forecasting the prices of hogs has always been a popular field of research. Such information has played an essential role in decision-making for farmers, consumers, corporations, and governments. It is hard to predict hog prices because too many factors can influence them. Some of the factors are easy to quantify, but some are not. Capturing the characteristics behind the price data is also tricky considering their non-linear and non-stationary nature. To address these difficulties, we propose Heterogeneous Graph-enhanced LSTM (HGLTSM), which is a method that predicts weekly hog price. In this paper, we first extract the historical prices of necessary agricultural products in recent years. Then, we utilize discussions from the online professional community to build heterogeneous graphs. These graphs have rich information of both discussions and the engaged users. Finally, we construct HGLSTM to make the prediction. The experimental results demonstrate that forum discussions are beneficial to hog price prediction. Moreover, our method exhibits a better performance than existing methods.

A collective action that considerably affects government management and public security, e.g., a mass demonstration, usually experiences a long development period, originating from small and uncertain variations called weak signals on social media. Researchers generally identify collective action by small changes in communication frequency, emerging key words, and sentiment. However, most studies only consider the present environment, which may not evolve into a collective action, or conduct a short-term prediction in which significant damage is already done when the collective action is identified. This paper proposes a predictive framework to identify potential collective actions, considering the future evolution as well as the present situation, and providing a reference for early decision making. In the framework, a future sign to describe events is improved and the enhanced gray system theory is used to predict the evolution of a future sign. Mentions of events surrounding the Arab Spring-using over 300000 different open-content Web sources crawled from social media in seven different languages-are analyzed, which suggests that the predictive framework can more precisely identify the weak signals of collective actions.

Fundus blood vessel image segmentation plays an important role in the diagnosis and treatment of diseases and is the basis of computer-aided diagnosis. Feature information from the retinal blood vessel image is relatively complicated, and the existing algorithms are sometimes difficult to perform effective segmentation with. Aiming at the problems of low accuracy and low sensitivity of the existing segmentation methods, an improved U-shaped neural network (MRU-NET) segmentation method for retinal vessels was proposed. Firstly, the image enhancement algorithm and random segmentation method are used to solve the problems of low contrast and insufficient image data of the original image. Moreover, smaller image blocks after random segmentation are helpful to reduce the complexity of the U-shaped neural network model; secondly, the residual learning is introduced into the encoder and decoder to improve the efficiency of feature use and to reduce information loss, and a feature fusion module is introduced between the encoder and decoder to extract image features with different granularities; and finally, a feature balancing module is added to the skip connections to resolve the semantic gap between low-dimensional features in the encoder and high-dimensional features in decoder. Experimental results show that our method has better accuracy and sensitivity on the DRIVE and STARE datasets (accuracy (ACC) = 0.9611, sensitivity (SE) = 0.8613; STARE: ACC = 0.9662, SE = 0.7887) than some of the state-of-the-art methods.

This study proposes an electromagnetic-assisted stamping (EMAS) method with magnetic-force loading at the sheet end in order to control the springback phenomenon. The new method does not change the structure of the mold and does not generate a magnetic force at the sheet corner compared to traditional EMAS. Thus, the new approach could greatly extend the mold lifespan and could be readily adopted in commercial production environments. The effects of technological parameters, such as the distance between the blank holder and die, discharge voltage, and sheet thickness on the springback phenomenon were analyzed. Our results suggest that tangential stress and elastic strain energy both decrease with the increase of discharge voltage. The simulation method accurately predicted the deformation of the sheet during the quasi-static stamping and dynamic magnetic forming processes. The simulation and experimental results both show that as the discharge voltage increases, the bent angle after springback decreases.

The coil is the most important component in electromagnetic forming. Two important questions in electromagnetic forming are how to obtain the desired magnetic force distribution on the sheet and increase the service life of the coil. A uniform pressure coil is widely used in sheet embossing, bulging, and welding. However, the coil is easy to break, and the manufacturing process is complex. In this paper, a new uniform-pressure coil with a planar structure was designed. A three-dimensional (3D) finite element model was established to analyze the effect of the main process parameters on magnetic force distribution. By comparing the experimental results, it was found that the simulation results have a higher analysis precision. Based on the simulation results, the resistivity of the die, spacing between the left and right parts of the coil, relative position between coil and sheet, and sheet width significantly affect the distribution of magnetic force. Compared with the structure and magnetic force on a traditional uniform pressure coil, the planar uniform pressure coil can produce a uniform magnetic force distribution on the sheet, reduce the manufacturing difficulty, reduce manufacturing cost, and enhance the service life for the coil.

A three-dimensional (3D) finite-element model (FEM), including quasi-static stamping, sequential coupling for electromagnetic forming (EMF) and springback, was established to analyze the springback calibration by electromagnetic force. Results show that the tangential stress at the sheet bending region is reduced, and even the direction of tangential stress at the bending region is changed after EMF. The springback can be significantly reduced with a higher discharge voltage. The simulation results are in good agreement with the experiment results, and the simulation method has a high accuracy in predicting the springback of quasi-static stamping and electromagnetic forming.

To compare with quasi-static uniaxial tensioning, researchers designed an electromagnetic uniaxial tension method using a runway coil. However, the requirements to obtain a uniformly deformed sample and the ways the stress changes on the sample using a runway coil have not been studied in the past. In this study, a three-dimensional (3D) sequential coupling method was developed to analyze the factors affecting on-sheet deformation inhomogeneity under electromagnetic uniaxial tension. Two main process parameters, comprising the die type and the relative position of the coil and sheet, were evaluated. Under the optimal parameters, the experiment and simulation both obtained uniformly deformed samples with different discharge conditions, and the simulation method had a high accuracy in modeling the deformation process. The stress state of the sample is approximately unidirectional tensile stress before 240 μs. After 240 μs, the three main stresses showed significant oscillations.

Abstract The quorum‐sensing (QS) response of Vibrio fischeri involves a rapid switch between low and high induction states of the lux operon over a narrow concentration range of the autoinducer (AI) 3‐oxo‐hexanoyl‐L‐homoserine lactone. In this system, LuxR is an AI‐dependent positive regulator of the lux operon, which encodes the AI synthase. This creates a positive feedback loop common in many bacterial species that exhibit QS‐controlled gene expression. Applying a combination of modeling and experimental analyses, we provide evidence for a LuxR autoregulatory feedback loop that allows LuxR to increase its concentration in the cell during the switch to full lux activation. Using synthetic lux gene fragments, with or without the AI synthase gene, we show that the buildup of LuxR provides more sensitivity to increasing AI, and promotes the induction process. Elevated LuxR levels buffer against spurious variations in AI levels ensuring a robust response that endows the system with enhanced hysteresis. LuxR autoregulation also allows for two distinct responses within the same cell population.

Prediction of aerosol optical thickness (AOT) is important to study worldwide climate changes. Researchers have built multiple AOT prediction models. However, few researches were focused on the validation of input attributes for AOT regression. In this paper, we proposed a support vector regression (SVR) model-based sensitivity analysis approach to order 35 MODIS input attributes according to their sensitivity to prediction outputs. Next, the attribute sensitivity orders are used for feature selection in the context of regression by removing insensitive attribute one at a time or by removing attributes whose sensitive orders are larger than number k . The experimental results based on the collocated data between MODIS and AERONET from 2009 to 2011 showed that the top 10 insensitive attributes can be screened to speed up prediction model computation with very little loss of accuracy. The results also suggested that the top sensitive attributes are the most informative attributes, requiring the highest precision for accurate AOT prediction. Thereby, our approach will be valuable for remote sensing scientists or atmospheric scientists to optimize the design precision of top sensitive attributes in scanning equipment like MODIS and therefore improve AOT retrieval accuracy.

Pathfinding is a fundamental task in many applications including robotics, computer games, and vehicle navigation. Waypoint graph is often used for pathfinding due to its advantage in specifying the space and obstacles in a region. Currently the waypoint graph based pathfinding suffers from large computation overhead and hence long latency in dynamic environment, where the location of obstacles may change. In this paper, we propose a fast approach for waypoint graph based pathfinding in such scenario. We eliminate unnecessary waypoints and edges to make the graph sparse. And then we design a prediction-based local method to handle the dynamic change in the environment. Extensive simulation has been done and the results show that the proposed approach outperforms existing approaches.

The ecotropic virus integration site 1 (EVI1) transcription factor is associated with human myeloid malignancy of poor prognosis and is overexpressed in 8-10% of adult AML and strikingly up to 27% of pediatric MLL-rearranged leukemias. For the first time, we report comprehensive genomewide EVI1 binding and whole transcriptome gene deregulation in leukemic cells using a combination of ChIP-Seq and RNA-Seq expression profiling. We found disruption of terminal myeloid differentiation and cell cycle regulation to be prominent in EVI-induced leukemogenesis. Specifically, we identified EVI1 directly binds to and downregulates the master myeloid differentiation gene Cebpe and several of its downstream gene targets critical for terminal myeloid differentiation. We also found EVI1 binds to and downregulates Serpinb2 as well as numerous genes involved in the Jak-Stat signaling pathway. Finally, we identified decreased expression of several ATP-dependent P2X purinoreceptors genes involved in apoptosis mechanisms. These findings provide a foundation for future study of potential therapeutic gene targets for EVI1-induced leukemia.

In recent years, sensor technology has been widely used in the defense and control of sensitive areas in cities, or in various scenarios such as early warning of forest fires, monitoring of forest pests and diseases, and protection of endangered animals. Deploying sensors to collect data and then utilizing unmanned aerial vehicle (UAV) to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method. The current strategies for efficient data collection in above scenarios are still imperfect, and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection. With regards this, this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering, multi-UAV mission area allocation, and sub-area data acquisition scheme optimization. The improved α-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors, and the K-Dimensional method is used to form a multi-UAV cooperative workspace, and then, the genetic algorithm is used to trade-off the speed with the age of information (AoI) of the collected information and the energy consumption to form the multi-UAV data collection operation scheme. The combined optimization scheme in paper improves the performance by 95.56% and 58.21%, respectively, compared to the traditional baseline model. In order to verify the excellent generalization and applicability of the proposed method in real scenarios, the simulation test is conducted by introducing the digital elevation model data of the real terrain, and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of ±10%. Then, the advantages and disadvantages of the present method with the existing mainstream schemes are tested, and the results show that the present method has a huge advantage in terms of space and time complexity, and at the same time, the accuracy for data extraction is relatively improved by 10.46% and 12.71%. Finally, by eliminating the clustering process and the subtask assignment process, the AoI performance decreases by 3.46× and 4.45×, and the energy performance decreases by 3.52× and 4.47×. This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations. [ABSTRACT FROM AUTHOR]

Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer (RoBGP). Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors. It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information, effectively addressing the issue of long-distance dependencies. Furthermore, the Global Pointer model is employed to comprehensively recognize all nested entities in the text. We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models. This research confirms the effectiveness of RoBGP in Chinese biomedical NER, providing reliable technical support for biomedical information extraction and knowledge base construction. [ABSTRACT FROM AUTHOR]