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Underwater object detection is highly complex and requires a high speed and accuracy. In this paper, an underwater target detection model based on YOLOv8 (SPSM-YOLOv8) is proposed. It solves the problems of high computational complexities, slow detection speeds and low accuracies. Firstly, the SPDConv module is utilized in the backbone network to replace the standard convolutional module for feature extraction. This enhances computational efficiency and reduces redundant computations. Secondly, the PSA (Polarized Self-Attention) mechanism is added to filter and enhance the polarization of features in the channel and spatial dimensions to improve the accuracy of pixel-level prediction. The SCDown (spatial–channel decoupled downsampling) downsampling mechanism is then introduced to reduce the computational cost by decoupling the space and channel operations while retaining the information in the downsampling process. Finally, MPDIoU (Minimum Point Distance-based IoU) is used to replace the CIoU (Complete-IOU) loss function to accelerate the convergence speed of the bounding box and improve the bounding box regression accuracy. The experimental results show that compared with the YOLOv8n baseline model, the SPSM-YOLOv8 (SPDConv-PSA-SCDown-MPDIoU-YOLOv8) detection accuracy reaches 87.3% on the ROUD dataset and 76.4% on the UPRC2020 dataset, and the number of parameters and amount of computation decrease by 4.3% and 4.9%, respectively. The detection frame rate reaches 189 frames per second on the ROUD dataset, thus meeting the high accuracy requirements for underwater object detection algorithms and facilitating lightweight and fast edge deployment.

Autocollimators are widely used optical axis-measuring tools, but their measurement errors increase significantly when measuring under non-leveled conditions and they have a limited measurement range due to the limitations of the measurement principle. To realize axis measurement under non-leveled conditions, this paper proposes an autocollimator axis measurement method based on the strapdown inertial navigation system (SINS). First, the measurement model of the system was established. This model applies the SINS to measure the change in attitude of the autocollimator. The autocollimator was then applied to measure the angular relationship between the measured axis and its own axis, based on which the angular relationship of the axis was measured via computation through signal processing and data fusion in a multi-sensor system. After analyzing the measurement errors of the system model, the Monte Carlo method was applied to carry out a simulation analysis. This showed that the majority of the measurement errors were within ±0.002° and the overall measurement accuracy was within ±0.006°. Tests using equipment with the same parameters as those used in the simulation analysis showed that the majority of the measurement errors were within ±0.004° and the overall error was within ±0.006°, which is consistent with the simulation results. This analysis proves that this method solves the problem of the autocollimator being unable to measure the axis under non-leveled conditions and meets the needs of axis measurement with the application of autocollimators under a moving base.

To obtain a speaker’s pronunciation characteristics, a method is proposed based on an idea from bionics, which uses spectrogram statistics to achieve a characteristic spectrogram to give a stable representation of the speaker’s pronunciation from a linear superposition of short-time spectrograms. To deal with the issue of slow network training and recognition speed for speaker recognition systems on resource-constrained devices, based on a traditional SOM neural network, an adaptive clustering self-organizing feature map SOM (AC-SOM) algorithm is proposed. This algorithm automatically adjusts the number of neurons in the competition layer based on the number of speakers to be recognized until the number of clusters matches the number of speakers. A 100-speaker database of characteristic spectrogram samples was built and applied to the proposed AC-SOM model, yielding a maximum training time of only 304 s, with a maximum sample recognition time of less than 28 ms. Comparing to other approaches, the proposed method offers greatly improved training and recognition speed without sacrificing too much recognition accuracy. The promising results suggest that the proposed method satisfies real-time data processing and execution requirements for edge intelligence systems better than other speaker recognition methods.

A series of novel C-3-OH substituted gibberellin derivatives bearing an amide group were designed and synthesized from the natural product gibberellic acid (GA3). Their activities on the plant growth regulation of rice and Arabidopsis were evaluated in vivo. Among these compounds, 10d and 10f exhibited appreciable inhibitory activities on rice (48.6% at 100 μmol/L) and Arabidopsis (41.4% at 100 μmol/L), respectively. These results provide new insights into the design and synthesis of potential plant growth regulators.

In this paper, we propose a scheme for measuring the focal length of a collimating optical instrument. First, a mathematical model for measuring the focal length of a collimator with double gratings is derived based on the moiré fringe formula and the principles of geometric optics. Mathematical simulation shows that a slight difference in the focal length of two collimators has an important influence on the imaging law of moiré fringes. Our solution has a good resolution ability for focal length differences within 5‰, especially in the small angle range below 4°. Thus, the focal length of collimators can be measured by the amplification of the slight difference. Further, owing to the relative reference measurement, the measurement resolution at the symmetrical position of focal length is poor. Then, in the designed experiment, a corresponding moiré image at different angles is acquired using collimators with known focal length. The experimental results indicate that the root mean square error (RMSE) of the collimator corresponding to grating angles of 2°–4° is better than 4.7‰, indicating an ideal measurement accuracy of the proposed scheme. This work demonstrates that our proposed scheme can achieve an ideal accuracy in the measurement of a symmetrical optical path.

Vehicle platform vibration (VPV) directly affects the measurement accuracy of precise measuring instrument (PMI) fixed on it. In order to reduce the influences of VPV on measurement accuracy, it is necessary to perform vibration isolation between vehicle platform and PMI. Analysis of vibration characteristics is a prerequisite for vibration isolation. However, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) reveal that there is obvious mode mixing phenomenon in the collected VPV signals. In this paper, a noise stretch ensemble empirical mode decomposition (NSEEMD) method is proposed to suppress mode mixing, and the specific operation process of NSEEMD is expounded. By NSEEMD, mode mixing of the collected platform vibration data is well suppressed, and the principal component of platform vibration can be obtained.

Even when the Global Positioning System (GPS) signal is blocked, a rate gyroscope (gyro) north finder is capable of providing the required azimuth reference information to a certain extent. In order to measure the azimuth between the observer and the north direction very accurately, we propose a multi-position non-continuous rotation gyro north finding scheme. Our new generalized mathematical model analyzes the elements that affect the azimuth measurement precision and can thus provide high precision azimuth reference information. Based on the gyro’s principle of detecting a projection of the earth rotation rate on its sensitive axis and the proposed north finding scheme, we are able to deduct an accurate mathematical model of the gyro outputs against azimuth with the gyro and shaft misalignments. Combining the gyro outputs model and the theory of propagation of uncertainty, some approaches to optimize north finding are provided, including reducing the gyro bias error, constraining the gyro random error, increasing the number of rotation points, improving rotation angle measurement precision, decreasing the gyro and the shaft misalignment angles. According them, a north finder setup is built and the azimuth uncertainty of 18” is obtained. This paper provides systematic theory for analyzing the details of the gyro north finder scheme from simulation to implementation. The proposed theory can guide both applied researchers in academia and advanced practitioners in industry for designing high precision robust north finder based on different types of rate gyroscopes.

Background Osteoporosis (OP) is a condition featured by bone mass loss and bone tissue microarchitectural alterations due to impaired tissue homeostasis favoring excessive bone resorption versus deposition. The trigger of such an impairment and the downstream molecular pathways involved are yet to be clarified. Long non-coding RNA (lncRNA) plays a role in gene transcription, protein expression and epigenetic regulation; and altered expression results in immune or metabolism related desease development. To determine whether lncRNAs are involved in osteoporosis, we analyzed the expression profile of lncRNAs and mRNAs in osteoporosis. Method Three pairs of osteoporosis patients (OP group) and healthy people controls (NC group) were screened by microarray. Quantitative polymerase chain reaction (qRT-PCR) was performed to confirm dysregulated lncRNA expressions in 5 pairs of OP and NC group tissues samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to construct the lncRNA-mRNA co-expression network. Result Through co-expression analysis, differently expressed transcripts were divided into modules, and lncRNAs were functionally annotated. We further analyzed the clinical significance of crucial lncRNAs from modules in public data. Finally, the expression of five lncRNAs, CUST_44695_PI430048170-GeneSymbol:CTA-384D8.35;CUST_39447_PI430048170,CUST_73298_PI430048170,CUST_108340_PI430048170,CUST_118927_PI430048170,this four lncRNAs have not been annotation genes and have not found GeneSymbols, and by quantitative RT-PCR, which may be associated with osteoporosis patients’ overall survival. Conclusion Analysis of this study revealed that dysregulated lncRNAs and mRNAs in osteoporosis patients and health people controls could affect the immune or metabolism system and musculoskeletal cell differentiation. The biological functions of those lncRNAs need to be further validated.

[Purpose / Significance] In order to objectively and comprehensively reveal research progress and knowledge structure of brassinosteroid to guide researchers to better promote the follow-up study. [Method/Process] This paper systematically analyzed the big data of international literature on brassinosteroid from 1970 to 2019 based on Web of Science database by CiteSpace software. [Results / Conclusions] (1) Since the discovery of brassinosteroid, the total number of research has been increasing year by year, especially for recent ten years China, the United States and Japan have published the most number of papers, among which, the RIKEN (Japan) and Chinese Academy of Sciences are the main force. (2) There are five core author groups formed around Japanese researchers, and the internal cooperation is close and gradually spreads out. (3) The study of brassinosteroid can be divided into four stages: germination stage (1970-1990), formation stage (1991-2000), development period (2001-2010) and mature stage (2011

This paper describes a novel measurement method to accurately measure the rotating shaft tilt angle of rotating machine for alignment or compensation using a dual-axis inclinometer. A model of the rotating shaft tilt angle measurement is established using a dual-axis inclinometer based on the designed mechanical structure, and the calculation equation between the rotating shaft tilt angle and the inclinometer axes outputs is derived under the condition that the inclinometer axes are perpendicular to the rotating shaft. The reversal measurement method is applied to decrease the effect of inclinometer drifts caused by temperature, to eliminate inclinometer and rotating shaft mechanical error and inclinometer systematic error to attain high measurement accuracy. The uncertainty estimation shows that the accuracy of rotating shaft tilt angle measurement depends mainly on the inclinometer uncertainty and its uncertainty is almost the same as the inclinometer uncertainty in the simulation. The experimental results indicate that measurement time is 4 seconds; the range of rotating shaft tilt angle is 0.002° and its standard deviation is 0.0006° using NS-5/P2 inclinometer, whose precision and resolution are ±0.01° and 0.0005°, respectively.