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With the rapid development of the Industrial Internet of Things in rotating machinery, the amount of data sampled by mechanical vibration wireless sensor networks (MvWSNs) has increased significantly, straining bandwidth capacity. Concurrently, the safety requirements for rotating machinery have escalated, necessitating enhanced real-time data processing capabilities. Conventional methods, reliant on experiential approaches, have proven inefficient in meeting these evolving challenges. To this end, a fault detection method for rotating machinery based on mobileNet in MvWSNs is proposed to address these intractable issues. The small and light deep learning model is helpful to realize nearly real-time sensing and fault detection, lightening the communication pressure of MvWSNs. The well-trained deep learning is implanted on the MvWSNs sensor node, an edge computing platform developed via embedded STM32 microcontrollers (STMicroelectronics International NV, Geneva, Switzerland). Data acquisition, data processing, and data classification are all executed on the computing- and energy-constrained sensor node. The experimental results demonstrate that the proposed fault detection method can achieve about 0.99 for the DDS dataset and an accuracy of 0.98 in the MvWSNs sensor node. Furthermore, the final transmission data size is only 0.1% compared to the original data size. It is also a time-saving method that can be accomplished within 135 ms while the raw data will take about 1000 ms to transmit to the monitoring center when there are four sensor nodes in the network. Thus, the proposed edge computing method shows good application prospects in fault detection and control of rotating machinery with high time sensitivity.

In the context of agricultural modernization and intelligentization, automated fruit recognition is of significance for improving harvest efficiency and reducing labor costs. The variety of fruits commonly planted in orchards and the fluctuations in market prices require farmers to adjust the types of crops they plant flexibly. However, the differences in size, shape, and color among different types of fruits make fruit recognition quite challenging. If each type of fruit requires a separate visual model, it becomes time-consuming and labor intensive to train and deploy these models, as well as increasing system complexity and maintenance costs. Therefore, developing a general visual model capable of recognizing multiple types of fruits has great application potential. Existing multi-fruit recognition methods mainly include traditional image processing techniques and deep learning models. Traditional methods perform poorly in dealing with complex backgrounds and diverse fruit morphologies, while current deep learning models may struggle to effectively capture and recognize targets of different scales. To address these challenges, this paper proposes a general fruit recognition model based on the Multi-Scale Attention Network (MSA-Net) and a Hough Transform localization compensation mechanism. By generating multi-scale feature maps through a multi-scale attention mechanism, the model enhances feature learning for fruits of different sizes. In addition, the Hough Transform ellipse detection compensation mechanism uses the shape features of fruits and combines them with MSA-Net recognition results to correct the initial positioning of spherical fruits and improve positioning accuracy. Experimental results show that the MSA-Net model achieves a precision of 97.56, a recall of 92.21, and an mAP@0.5 of 94.81 on a comprehensive dataset containing blueberries, lychees, strawberries, and tomatoes, demonstrating the ability to accurately recognize multiple types of fruits. Moreover, the introduction of the Hough Transform mechanism reduces the average localization error by 8.8 pixels and 3.5 pixels for fruit images at different distances, effectively improving the accuracy of fruit localization.

Periodicity, self-excitation, and time ratio asymmetry are the fundamental characteristics of the human gait. In order to imitate these mentioned characteristics, a pattern generator with four degrees of freedom is proposed based on cardioid oscillators developed by the authors. The proposed pattern generator is composed of four coupled cardioid oscillators, which are self-excited and have asymmetric time ratios. These oscillators are connected with other oscillators through coupled factors. The dynamic behaviors of the proposed oscillators, such as phase locking, time ratio, and self-excitation, are analyzed via simulations by employing the harmonic balance method. Moreover, for comparison, the simulated trajectories are compared with the natural joint trajectories measured in experiments. Simulation and experimental results show that the behaviors of the proposed pattern generator are similar to those of the natural lower limb. It means the simulated trajectories from the generator are self-excited without any additional inputs and have asymmetric time ratios. Their phases are locked with others. Moreover, the proposed pattern generator can be applied as the reference model for the lower limb exoskeleton controlling algorithm to produce self-adjusted reference trajectories.

Large quantities of stone cutting are generally done using robots. The current stone cutting robot drives the cutting machine to move and complete the cutting through the manipulator, but its moving range is limited. In response to this problem, a large heavy-duty stone cutting robot was designed in this subject. According to the design requirements and the functions to be realized by the equipment, the detailed structural design of each component of the stone cutting robot was carried out, and the static analysis of the robot arm and arm was carried out using ANSYS to ensure the safe operation of the equipment. The D-H parameter method was used to establish the kinematics model of the stone cutting robot, the positive kinematics equation was obtained by using the homogeneous transformation matrix, and the inverse solution of the kinematics equation was combined with the algebraic method. The proposed design was reasonable through experiments.

Anisotropic materials are of considerable interest because of their unique combination of polarization- or direction-dependent electrical, optical, and thermoelectric properties. Low-symmetry two-dimensional （2D） materials formed by van der Waals stacking of covalently bonded atomic layers are inherently anisotropic. Layered SnSe exhibits a low degree of lattice symmetry, with a distorted NaC1 structure and an in-plane anisotropy. Here we report a systematic study of the in-plane anisotropic properties in layered SnSe, using angle-resolved Raman scattering, optical absorption, and electrical transport studies. The optical and electrical characterization was direction-dependent, and successfully identified the crystalline orientation in the layered SnSe. Furthermore, the dependence of Raman-intensity anisotropy on the SnSe flake thickness and the excitation wavelength were investigated by both experiments and theoretical calculations. Finally, the electrical transport studies demonstrated that few-layer SnSe field- effect transistors （FETs） have a large anisotropic ratio of carrier mobility （N 5.8） bet- ween the armchair and zigzag directions, which is a record high value reported for 2D anisotropic materials. The highly-anisotropic properties of layered SnSe indicate considerable promise for anisotropic optics, electronics, and optoelectronics.

Feng Qin and colleagues perform a genome-wide association study for drought tolerance in maize seedlings and find 42 candidate genes. They characterize a promoter insertion in the ZmVPP1 gene containing MYB-binding sites, which enables the drought-inducible expression of ZmVPP1 , leading to drought tolerance, a phenotype confirmed through transgenic experiments. Maize production is threatened by drought stress worldwide. Identification of the genetic components underlying drought tolerance in maize is of great importance. Here we report a genome-wide association study (GWAS) of maize drought tolerance at the seedling stage that identified 83 genetic variants, which were resolved to 42 candidate genes. The peak GWAS signal showed that the natural variation in ZmVPP1 , encoding a vacuolar-type H + pyrophosphatase, contributes most significantly to the trait. Further analysis showed that a 366-bp insertion in the promoter, containing three MYB cis elements, confers drought-inducible expression of ZmVPP1 in drought-tolerant genotypes. Transgenic maize with enhanced ZmVPP1 expression exhibits improved drought tolerance that is most likely due to enhanced photosynthetic efficiency and root development. Taken together, this information provides important genetic insights into the natural variation of maize drought tolerance. The identified loci or genes can serve as direct targets for both genetic engineering and selection for maize trait improvement.

In the context of climate change, drought is one of the most limiting factors that influence crop production. Maize, as a major crop, is highly vulnerable to water deficit, which causes significant yield loss. Thus, identification and utilization of drought-resistant germplasm are crucial for the genetic improvement of the trait. Here we report on a high-quality genome assembly of a prominent drought-resistant genotype, CIMBL55. Genomic and genetic variation analyses revealed that 65 favorable alleles of 108 previously identified drought-resistant candidate genes were found in CIMBL55, which may constitute the genetic basis for its excellent drought resistance. Notably, ZmRtn16 , encoding a reticulon-like protein, was found to contribute to drought resistance by facilitating the vacuole H + -ATPase activity, which highlights the role of vacuole proton pumps in maize drought resistance. The assembled CIMBL55 genome provided a basis for genetic dissection and improvement of plant drought resistance, in support of global food security. High-quality genome assembly of a prominent drought-resistant maize germplasm CIMBL55 and genetic variation analyses provide a resource for genetic dissection and result in the improvement of maize drought resistance.

Background Gene expression is a key determinant of cellular response. Natural variation in gene expression bridges genetic variation to phenotypic alteration. Identification of the regulatory variants controlling the gene expression in response to drought, a major environmental threat of crop production worldwide, is of great value for drought-tolerant gene identification. Results A total of 627 RNA-seq analyses are performed for 224 maize accessions which represent a wide genetic diversity under three water regimes; 73,573 eQTLs are detected for about 30,000 expressing genes with high-density genome-wide single nucleotide polymorphisms, reflecting a comprehensive and dynamic genetic architecture of gene expression in response to drought. The regulatory variants controlling the gene expression constitutively or drought-dynamically are unraveled. Focusing on dynamic regulatory variants resolved to genes encoding transcription factors, a drought-responsive network reflecting a hierarchy of transcription factors and their target genes is built. Moreover, 97 genes are prioritized to associate with drought tolerance due to their expression variations through the Mendelian randomization analysis. One of the candidate genes, Abscisic acid 8′-hydroxylase , is verified to play a negative role in plant drought tolerance. Conclusions This study unravels the effects of genetic variants on gene expression dynamics in drought response which allows us to better understand the role of distal and proximal genetic effects on gene expression and phenotypic plasticity. The prioritized drought-associated genes may serve as direct targets for functional investigation or allelic mining.

The worldwide production of maize (Zea mays L.) is frequently impacted by water scarcity and as a result, increased drought tolerance is a priority target in maize breeding programs. While DREB transcription factors have been demonstrated to play a central role in desiccation tolerance, whether or not natural sequence variations in these genes are associated with the phenotypic variability of this trait is largely unknown. In the present study, eighteen ZmDREB genes present in the maize B73 genome were cloned and systematically analyzed to determine their phylogenetic relationship, synteny with rice, maize and sorghum genomes; pattern of drought-responsive gene expression, and protein transactivation activity. Importantly, the association between the nucleic acid variation of each ZmDREB gene with drought tolerance was evaluated using a diverse population of maize consisting of 368 varieties from tropical and temperate regions. A significant association between the genetic variation of ZmDREB2.7 and drought tolerance at seedling stage was identified. Further analysis found that the DNA polymorphisms in the promoter region of ZmDREB2.7, but not the protein coding region itself, was associated with different levels of drought tolerance among maize varieties, likely due to distinct patterns of gene expression in response to drought stress. In vitro, protein-DNA binding assay demonstrated that ZmDREB2.7 protein could specifically interact with the target DNA sequences. The transgenic Arabidopsis overexpressing ZmDREB2.7 displayed enhanced tolerance to drought stress. Moreover, a favorable allele of ZmDREB2.7, identified in the drought-tolerant maize varieties, was effective in imparting plant tolerance to drought stress. Based upon these findings, we conclude that natural variation in the promoter of ZmDREB2.7 contributes to maize drought tolerance, and that the gene and its favorable allele may be an important genetic resource for the genetic improvement of drought tolerance in maize.

Drought stress seriously affects tomato growth, yield and quality. Previous reports have pointed out that melatonin (MT) can alleviate drought stress damage to tomato. To better understand the possible physiological and molecular mechanisms, chlorophyll fluorescence parameters and leaf transcriptome profiles were analyzed in the “Micro Tom” tomato cultivar with or without melatonin irrigation under normal and drought conditions. Polyethylene glycol 6000 (PEG6000) simulated continuous drought treatment reduced plant height, but melatonin treatment improved plant growth rate. Physiological parameter measurements revealed that the drought-induced decreases in maximum efficiency of photosystem II (PSII) photochemistry, the effective quantum yield of PSII, electron transfer rate, and photochemical quenching value caused by PEG6000 treatment were alleviated by melatonin treatment, which suggests a protective effect of melatonin on PSII. Comparative transcriptome analysis identified 447, 3982, 4526 and 3258 differentially expressed genes (DEGs) in the comparative groups plus-melatonin vs . minus-melatonin (no drought), drought vs . no drought (minus-melatonin), drought vs . no drought (melatonin) and plus-melatonin vs . minus-melatonin (drought), respectively. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis revealed that DEGs in the four comparative groups were involved in multiple metabolic processes and closely related to hormone signal transduction and transcription factors. Transcriptome data revealed that melatonin changed the expression pattern of most hormone signal transduction related DEGs induced by drought, and improved plant drought resistance by down-regulating the expression of linoleic acid catabolic enzyme genes. These results provide new insights into a probable mechanism of the melatonin-induced protection of photosynthesis and enhancement of drought tolerance in tomato plants.