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The hoist cage is used to lift miners in a coal mine's auxiliary shaft. Monitoring miners' unsafe behaviors and their status in the hoist cage is crucial to production safety in coal mines. In this study, a visual detection model is proposed to estimate the number and categories of miners, and to identify whether the miners are wearing helmets and whether they have fallen in the hoist cage. A dataset with eight categories of miners' statuses in hoist cages was developed for training and validating the model. Using the dataset, the classical models were trained for comparison, from which the YOLOv5s model was selected to be the basic model. Due to small-sized targets, poor lighting conditions, and coal dust and shelter, the detection accuracy of the Yolov5s model was only 89.2%. To obtain better detection accuracy, k-means++ clustering algorithm, a BiFPN-based feature fusion network, the convolutional block attention module (CBAM), and a CIoU loss function were proposed to improve the YOLOv5s model, and an attentional multi-scale cascaded feature fusion-based YOLOv5s model (AMCFF-YOLOv5s) was subsequently developed. The training results on the self-built dataset indicate that its detection accuracy increased to 97.6%. Moreover, the AMCFF-YOLOv5s model was proven to be robust to noise and light. [ABSTRACT FROM AUTHOR]

Rolling bearings are crucial parts of primary mine fans. In order to guarantee the safety of coal mine production, primary mine fans commonly work during regular operation and are immediately shut down for repair in case of failure. This causes the sample imbalance phenomenon in fault diagnosis (FD), i.e., there are many more normal state samples than faulty ones, seriously affecting the precision of FD. Therefore, the current study presents an FD approach for the rolling bearings of primary mine fans under sample imbalance conditions via symmetrized dot pattern (SDP) images, denoising diffusion probabilistic models (DDPMs), the image generation method, and a convolutional neural network (CNN). First, the 1D bearing vibration signal was transformed into an SDP image with significant characteristics, and the DDPM was employed to create a generated image with similar feature distributions to the real fault image of the minority class. Then, the generated images were supplemented into the imbalanced dataset for data augmentation to balance the minority class samples with the majority ones. Finally, a CNN was utilized as a fault diagnosis model to identify and detect the rolling bearings' operating conditions. In order to assess the efficiency of the presented method, experiments were performed using the regular rolling bearing dataset and primary mine fan rolling bearing data under actual operating situations. The experimental results indicate that the presented method can more efficiently fit the real image samples' feature distribution and generate image samples with higher similarity than other commonly used methods. Moreover, the diagnostic precision of the FD model can be effectively enhanced by gradually expanding and enhancing the unbalanced dataset. [ABSTRACT FROM AUTHOR]

Healthy agroecosystems are dependent on a complex web of factors and inter-species interactions. Flowers are hubs for pathogen transmission, including the horizontal or vertical transmission of plant-viruses and the horizontal transmission of bee-viruses. Pollination by the European honey bee (Apis mellifera) is critical for industrial fruit production, but bees can also vector viruses and other pathogens between individuals. Here, we utilized commercial honey bee pollination services in blueberry (Vaccinium corymbosum) farms for a metagenomics-based bee and plant virus monitoring system. Following RNA sequencing, viruses were identified by mapping reads to a reference sequence database through the bioinformatics portal Virtool. In total, 29 unique plant viral species were found at two blueberry farms in British Columbia (BC). Nine viruses were identified at one site in Ontario (ON), five of which were not identified in BC. Ilarviruses blueberry shock virus (BlShV) and prune dwarf virus (PDV) were the most frequently detected viruses in BC but absent in ON, while nepoviruses tomato ringspot virus and tobacco ringspot virus were common in ON but absent in BC. BlShV coat protein (CP) nucleotide sequences were nearly identical in all samples, while PDV CP sequences were more diverse, suggesting multiple strains of PDV circulating at this site. Ten bee-infecting viruses were identified, with black queen cell virus frequently detected in ON and BC. Area-wide bee-mediated pathogen monitoring can provide new insights into the diversity of viruses present in, and the health of, bee-pollination ecosystems. This approach can be limited by a short sampling season, biased towards pollen-transmitted viruses, and the plant material collected by bees can be very diverse. This can obscure the origin of some viruses, but bee-mediated virus monitoring can be an effective preliminary monitoring approach. [ABSTRACT FROM AUTHOR]

Viruses infect all cellular life forms and cause various diseases and significant economic losses worldwide. The majority of viruses are positive-sense RNA viruses. A common feature of infection by diverse RNA viruses is to induce the formation of altered membrane structures in infected host cells. Indeed, upon entry into host cells, plant-infecting RNA viruses target preferred organelles of the cellular endomembrane system and remodel organellar membranes to form organelle-like structures for virus genome replication, termed as the viral replication organelle (VRO) or the viral replication complex (VRC). Different viruses may recruit different host factors for membrane modifications. These membrane-enclosed virus-induced replication factories provide an optimum, protective microenvironment to concentrate viral and host components for robust viral replication. Although different viruses prefer specific organelles to build VROs, at least some of them have the ability to exploit alternative organellar membranes for replication. Besides being responsible for viral replication, VROs of some viruses can be mobile to reach plasmodesmata (PD) via the endomembrane system, as well as the cytoskeleton machinery. Viral movement protein (MP) and/or MP-associated viral movement complexes also exploit the endomembrane-cytoskeleton network for trafficking to PD where progeny viruses pass through the cell-wall barrier to enter neighboring cells. [ABSTRACT FROM AUTHOR]

Rolling bearings are a vital component of mechanical equipment. It is crucial to implement rolling bearing fault diagnosis research to guarantee the stability of the long-term action of mechanical equipment. Conversion of rolling bearing vibration signals into images for fault diagnosis research has been a practical diagnostic approach. The current paper presents a rolling bearing fault diagnosis method using symmetrized dot pattern (SDP) images and a deep residual network with convolutional block attention module (CBAM-DRN). The rolling bearing vibration signal is first visualized and transformed into an SDP image with distinct fault characteristics. Then, CBAM-DRN is utilized to derive characteristics directly and detect faults from the input SDP images. In order to prevent conventional time-frequency images from being limited by their inherent flaws and avoid missing the fault features, the SDP technique is employed to convert vibration signals into images for visualization. DRN enables adequate extraction of rolling bearing fault characteristics and prevents training difficulties and gradient vanishing in deep level networks. CBAM assists the diagnostic model in concentrating on the image's more distinctive parts and preventing the interference of non-featured parts. Finally, the method's validity was tested with a composite fault dataset of motor bearings containing multiple loads and fault diameters. The experimental results reflect that the presented approach can attain a diagnostic precision of over 99% and good stability and generalization. [ABSTRACT FROM AUTHOR]

This review summarizes the history and current state of the known genetic basis for soybean resistance to Soybean mosaic virus (SMV), and examines how the integration of molecular markers has been utilized in breeding for crop improvement. SVM causes yield loss and seed quality reduction in soybean based on the SMV strain and the host genotype. Understanding the molecular underpinnings of SMV–soybean interactions and the genes conferring resistance to SMV has been a focus of intense research interest for decades. Soybean reactions are classified into three main responses: resistant, necrotic, or susceptible. Significant progress has been achieved that has greatly increased the understanding of soybean germplasm diversity, differential reactions to SMV strains, genotype–strain interactions, genes/alleles conferring specific reactions, and interactions among resistance genes and alleles. Many studies that aimed to uncover the physical position of resistance genes have been published in recent decades, collectively proposing different candidate genes. The studies on SMV resistance loci revealed that the resistance genes are mainly distributed on three chromosomes. Resistance has been pyramided in various combinations for durable resistance to SMV strains. The causative genes are still elusive despite early successes in identifying resistance alleles in soybean; however, a gene at the Rsv4 locus has been well validated. [ABSTRACT FROM AUTHOR]

Continuous rotor dynamics remains stagnant. In this paper, aim at multi-span and multi-disc rotor-bearing system, the continuous rotor dynamic analysis method (CRDAM) is proposed. The force acting on the shaft by the rotating eccentric disc is simulated as a point force. The counterforce of bearing is also considered as a point force. The shaft is considered free-ended. A continuous rotor dynamic model is obtained and an analytical solution is proposed to express the unbalance response as function of the position, unbalance, support stiffness and damping. The proposed method is validated by numerical experiments in which unbalance responses obtained by it are compared with that obtained by the two classical methods the finite element method (FEM) and Ricatti method. The results indicate that the proposed method is applicable to calculating unbalance response of multi-disc and multi-span rotor. Moreover, it is closer to FEM than Ricatti and can be applied to actual high speed rotors. Among the three methods, the calculating speed of Ricatti is the fastest, CRDAM is the second fastest and FEM is the slowest. The proposed method, which solves the forward problems of the continuous rotor dynamics for the multi-disc and multi-span rotors, can provide theoretical basis for further studies on inverse problems such as identification of rotor unbalance and bearing stiffness and damping coefficients without test runs and external excitations. [ABSTRACT FROM AUTHOR]

Identification of bearings' stiffness and damping coefficients, which strongly affects the dynamic characteristics of rotors, is another inverse problem of Rotor Dynamics. In this paper, aiming at multi-disc and multi-span rotors, two novel algorithms are proposed for identifying each bearing's coefficients based on the continuous rotor dynamic analysis method. A linear functional relationship between the main complex coefficients and the cross-coupled complex coefficients is obtained, which eliminates the coupling between the coefficients and the rotor unbalance in the forward problem. Then, Algorithm I is proposed. However, it is only suitable for rolling-bearing. To solve the problem, changing the rotating speed slightly is proposed to solve the difficulty that another set of equations cannot be developed because the slope of the proposed linear function is constant when the rotating speed is maintained at a fixed speed. Then, Algorithm II, which can be applied to both rolling-bearing and oil-journal bearing, is provided. Numerical investigations are conducted to study the two methods. It is indicated that there should be a measuring point, called an adjustment point, near each bearing, whose coefficients should be identified, to obtain high identification accuracy. Moreover, the identification accuracy of the two algorithms is strongly related to sensor resolution. When the measuring errors of all the required unbalance responses are zero or the same, the identification errors are almost equal to zero. In conclusion, the proposed algorithms provide a method for monitoring the stiffness and damping coefficients of all bearings in a multi-disc and multi-span rotor under operation conditions to predict rotor dynamic behavior for the safe and steady running of rotating machines. [ABSTRACT FROM AUTHOR]

Rotor unbalance identification plays a critical role in balancing rotors. In this paper, concerned with multi-disc and multi-span rotor-bearing systems, two novel algorithms called the Single Direction Algorithm (SDA) and the Two Orthogonal Direction Algorithm (TODA) are proposed for identifying rotor unbalance from unbalance responses. A matrix method is proposed to solve the problem of the equations being non-linear transcendental, there being too many unknown variables in the equations, and rotor unbalances and bearing coefficients being coupled together. The unbalance responses at all the eccentric discs are necessary for identifying their unbalances. Numerical simulations are conducted to validate the proposed methods. Moreover, an adjustment point is found, and a proper sensor resolution is suggested to achieve high identification accuracy by means of numerical studies. In addition, the identification accuracy of SDA is better than TODA, and SDA is more practical and suitable for medium-speed and high-speed rotors. The proposed algorithms have the flexibility to incorporate any number of bearings and discs and provide a technique for monitoring rotor unbalance without test runs or external exciters. [ABSTRACT FROM AUTHOR]

Spring failure is one of the critical causes of the structural damage and low screening efficiency of mining vibrating screens. Therefore, spring failure diagnosis is necessary to prompt maintenance for the safety and reliability of mining vibrating screens. In this paper, a spring failure diagnosis approach is developed. A finite element model of mining vibrating screens is established. Simulations are carried out and the spring failure influence rules of spring failure on the dynamic characteristics of mining vibrating screens are obtained. These influence rules indicate that the amplitude variation coefficients (AVCs) of the four spring seats in the x, y, and z directions can reveal two kinds of single spring failure and four kinds of double spring failure, which are useful for diagnosing spring failure. Furthermore, experiments are conducted. Comparison analyses of the experimental results and simulation results indicate that the proposed approach is capable of revealing various kinds of spring failure. Therefore, this approach provides useful information for diagnosing spring failure and guiding technical staff to routinely maintain mining vibrating screens. [ABSTRACT FROM AUTHOR]

Prune dwarf virus (PDV) is a member of ilarviruses that infects stone fruit species such as cherry, plum and peach, and ornamentally grown trees worldwide. The virus lacks an RNA silencing suppressor. Infection by PDV either alone, or its mixed infection with other viruses causes deteriorated fruit marketability and reduced fruit yields. Here, we report the molecular identification of PDV from sweet cherry in the prominent fruit growing region of Ontario, Canada known as the Niagara fruit belt using next generation sequencing of small interfering RNAs (siRNAs). We assessed its incidence in an experimental farm and determined the full genome sequence of this PDV isolate. We further constructed an infectious cDNA clone. Inoculation of the natural host cherry with this clone induced a dwarfing phenotype. We also examined its infectivity on several common experimental hosts. We found that it was infectious on cucurbits (cucumber and squash) with clear symptoms and Nicotiana benthamiana without causing noticeable symptoms, and it was unable to infect Arabidopsis thaliana. As generating infectious clones for woody plants is very challenging with limited success, the PDV infectious clone developed from this study will be a useful tool to facilitate molecular studies on PDV and related Prunus-infecting viruses. [ABSTRACT FROM AUTHOR]

Potyviruses represent the largest group of known plant RNA viruses and include many agriculturally important viruses, such as Plum pox virus, Soybean mosaic virus, Turnip mosaic virus, and Potato virus Y. Potyviruses adopt polyprotein processing as their genome expression strategy. Among the 11 known viral proteins, the nuclear inclusion protein b (NIb) is the RNA-dependent RNA polymerase responsible for viral genome replication. Beyond its principal role as an RNA replicase, NIb has been shown to play key roles in diverse virus–host interactions. NIb recruits several host proteins into the viral replication complexes (VRCs), which are essential for the formation of functional VRCs for virus multiplication, and interacts with the sumoylation pathway proteins to suppress NPR1-mediated immunity response. On the other hand, NIb serves as a target of selective autophagy as well as an elicitor of effector-triggered immunity, resulting in attenuated virus infection. These contrasting roles of NIb provide an excellent example of the complex co-evolutionary arms race between plant hosts and potyviruses. This review highlights the current knowledge about the multifunctional roles of NIb in potyvirus infection, and discusses future research directions. [ABSTRACT FROM AUTHOR]

This paper presents a method for calculating the health degree (HD) of a braking system of a mine hoist combined with three-level fuzzy comprehensive assessment (TLFCA) and a back-propagation neural network (BPNN). Firstly, the monitored values of a sensor are fused by multi-time fusion and the fuzzy comprehensive assessment values (FCAVs) of the health condition (HC) of the sensor are obtained. Secondly, the FCAVs of all sensors in a subsystem are fused by multi-sensor fusion, and FCAVs of the subsystem are obtained. Then the FCAVs of all subsystems are fused by multi-subsystem fusion and FCAVs of the system are obtained. All the FCAVs are fed into a pre-trained neural network, and the corresponding HD of the sensor, subsystem and system is obtained. Finally, the practicability, reliability and sensitivity of the proposed method are verified by the monitored values of the test rig. This paper presents a method to provide technical support for intelligent maintenance, and also provides necessary data for further prognostics health management (PHM) of the braking system. The method presented in this paper can also be used as a reference for the HD calculation of the whole hoist and other complicated equipment. [ABSTRACT FROM AUTHOR]